diff options
Diffstat (limited to 'runtime/onert')
656 files changed, 0 insertions, 91959 deletions
diff --git a/runtime/onert/CMakeLists.txt b/runtime/onert/CMakeLists.txt deleted file mode 100644 index 88d52a5bd..000000000 --- a/runtime/onert/CMakeLists.txt +++ /dev/null @@ -1,15 +0,0 @@ -if(NOT BUILD_ONERT) - return() -endif(NOT BUILD_ONERT) - -add_subdirectory(backend) -add_subdirectory(frontend) -add_subdirectory(core) -add_subdirectory(api) -add_subdirectory(sample) - -if(NOT ENABLE_TEST) - return() -endif(NOT ENABLE_TEST) - -add_subdirectory(test) diff --git a/runtime/onert/api/CMakeLists.txt b/runtime/onert/api/CMakeLists.txt deleted file mode 100644 index 9c6dd90cc..000000000 --- a/runtime/onert/api/CMakeLists.txt +++ /dev/null @@ -1,27 +0,0 @@ -file(GLOB_RECURSE API_SRC "*.cc") - -set(ONERT_DEV nnfw-dev) -add_library(${ONERT_DEV} SHARED ${API_SRC}) - -# Public headers to publish -# nnfw_internal.h is header for runtime developer, so it will not be installed -# But runtime developer can use nnfw_internal.h by linking nnfw-dev -set(NNFW_API_HEADERS include/nnfw.h include/nnfw_experimental.h) - -target_link_libraries(${ONERT_DEV} PUBLIC nnfw-nnapi-header) -target_link_libraries(${ONERT_DEV} PRIVATE onert_core) -target_link_libraries(${ONERT_DEV} PRIVATE jsoncpp tflite_loader circle_loader ${LIB_PTHREAD}) -target_link_libraries(${ONERT_DEV} PRIVATE nnfw_common) -target_link_libraries(${ONERT_DEV} PRIVATE nnfw_coverage) -# NOTE Below line is added to remove warning for android build -# It will be removed after android build uses gold linker -if (ANDROID) - target_link_libraries(${ONERT_DEV} INTERFACE log) -endif (ANDROID) - -target_include_directories(${ONERT_DEV} PUBLIC include) -set_target_properties(${ONERT_DEV} PROPERTIES PUBLIC_HEADER "${NNFW_API_HEADERS}") - -install(TARGETS ${ONERT_DEV} - LIBRARY DESTINATION lib - PUBLIC_HEADER DESTINATION include/nnfw) diff --git a/runtime/onert/api/include/nnfw.h b/runtime/onert/api/include/nnfw.h deleted file mode 100644 index 9348df6ae..000000000 --- a/runtime/onert/api/include/nnfw.h +++ /dev/null @@ -1,490 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file nnfw.h - * @brief This file describes runtime API - */ -#ifndef __NNFW_H__ -#define __NNFW_H__ - -#include <stddef.h> -#include <stdint.h> - -#ifdef __cplusplus -extern "C" { -#endif - -/** - * @brief Session to query with runtime - * - * <p>nnfw_session is started and passed by calling {@link nnfw_create_session}. - * Each session has its own inference environment, such as model to inference, backend usage, etc. - * - * <p>Load model by calling {@link nnfw_load_model_from_file} - * - * <p>After loading, prepare inference by calling {@link nnfw_prepare}. - * Application can set runtime environment before prepare by calling - * {@link nnfw_set_available_backends} and {@link nnfw_set_op_backend}, and it is optional. - * - * <p>Application can inference by calling {@link nnfw_run}. - * Before inference, application has responsibility to set input tensor to set input data by calling - * {@link nnfw_set_output}, and output tensor to get output by calling {@link nnfw_set_input} - * - * <p>To support input and output setting, application can get - * input and output tensor information by calling<ul> - * <li>{@link nnfw_input_size}</li> - * <li>{@link nnfw_output_size}</li> - * <li>{@link nnfw_input_tensorinfo}</li> - * <li>{@link nnfw_output_tensorinfo}</li> - * </ul> - * - * <p>Application can inference many times using one session, - * but next inference can do after prior inference end - * - * <p>Application cannot use muitiple model using one session - */ -typedef struct nnfw_session nnfw_session; - -/** - * @brief Tensor types - * - * The type of tensor represented in {@link nnfw_tensorinfo} - */ -typedef enum { - /** A tensor of 32 bit floating point */ - NNFW_TYPE_TENSOR_FLOAT32 = 0, - /** A tensor of 32 bit signed integer */ - NNFW_TYPE_TENSOR_INT32 = 1, - /** - * A tensor of 8 bit integers that represent real numbers. - * - * real_value = (integer_value - zeroPoint) * scale. - */ - NNFW_TYPE_TENSOR_QUANT8_ASYMM = 2, - /** A tensor of boolean */ - NNFW_TYPE_TENSOR_BOOL = 3, - - /** A tensor of 8 bit unsigned integer */ - NNFW_TYPE_TENSOR_UINT8 = 4, - - /** A tensor of 64 bit signed integer */ - NNFW_TYPE_TENSOR_INT64 = 5, - -} NNFW_TYPE; - -/** - * @brief Result values returned from a call to an API function - */ -typedef enum { - /** Successful */ - NNFW_STATUS_NO_ERROR = 0, - /** - * An error code for general use. - * Mostly used when there is no specific value for that certain situation. - */ - NNFW_STATUS_ERROR = 1, - /** Unexpected null argument is given. */ - NNFW_STATUS_UNEXPECTED_NULL = 2, - /** When a function was called but it is not valid for the current session state. */ - NNFW_STATUS_INVALID_STATE = 3, - /** When it is out of memory */ - NNFW_STATUS_OUT_OF_MEMORY = 4, - /** When it was given an insufficient output buffer */ - NNFW_STATUS_INSUFFICIENT_OUTPUT_SIZE = 5, -} NNFW_STATUS; - -/** - * @brief Data format of a tensor - */ -typedef enum { - /** Don't care layout */ - NNFW_LAYOUT_NONE = 0, - /** - * Channel last layout - * If rank is 4, layout is NHWC - */ - NNFW_LAYOUT_CHANNELS_LAST = 1, - /** - * Channel first layout - * If rank is 4, layout is NCHW - */ - NNFW_LAYOUT_CHANNELS_FIRST = 2, -} NNFW_LAYOUT; - -/** - * @brief Information ID for retrieving information on nnfw (e.g. version) - */ -typedef enum { - /** nnfw runtime version - * Its value is uint32 in 0xMMmmmmPP, where MM = major, mmmm = minor, PP = patch. - */ - NNFW_INFO_ID_VERSION = 0, -} NNFW_INFO_ID; - -/** - * @brief Maximum rank expressible with nnfw - */ -#define NNFW_MAX_RANK (6) - -/** - * @brief tensor info describes the type and shape of tensors - * - * <p>This structure is used to describe input and output tensors. - * Application can get input and output tensor type and shape described in model by using - * {@link nnfw_input_tensorinfo} and {@link nnfw_output_tensorinfo} - * - * <p>Maximum rank is 6 (NNFW_MAX_RANK). And tensor's dimension value is filled in 'dims' field from - * index 0. - * For example, if tensor's rank is 4, - * application can get dimension value from dims[0], dims[1], dims[2], and dims[3] - */ -typedef struct nnfw_tensorinfo -{ - /** The data type */ - NNFW_TYPE dtype; - /** The number of dimensions (rank) */ - int32_t rank; - /** - * The dimension of tensor. - * Maximum rank is 6 (NNFW_MAX_RANK). - */ - int32_t dims[NNFW_MAX_RANK]; -} nnfw_tensorinfo; - -/** - * @brief Create a new session instance. - * - * <p>This only creates a session. - * Model is loaded after {@link nnfw_load_model_from_file} is invoked. - * And inference is performed after {@link nnfw_run} is invoked. - * - * <p>{@link nnfw_close_session} should be called once - * if session is no longer need - * - * @param[out] session The session to be created - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_create_session(nnfw_session **session); - -/** - * @brief Close a session instance - * - * After called, access to closed session by application will be invalid - * - * @param[in] session The session to be closed - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_close_session(nnfw_session *session); - -/** - * @brief Load model from nnpackage file or directory - * - * The length of \p package_file_path must not execeed 1024 bytes including zero at the end. - * - * @param[in] session nnfw_session loading the given nnpackage file/dir - * @param[in] package_file_path Path to the nnpackage file or unzipped directory to be loaded - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_load_model_from_file(nnfw_session *session, const char *package_file_path); - -/** - * @brief Apply i-th input's tensor info to resize input tensor - * - * This function should be called before {@link nnfw_prepare} is invoked, and - * should be called after {@link nnfw_load_model_from_file} is invoked - * See {@link nnfw_prepare} for information applying updated tensor info - * If this function is called many times for same index, tensor info is overwritten - * - * @deprecated Deprecated since 1.7.0. Use {@link nnfw_set_input_tensorinfo} instead. - * - * @param[in] session Session to the input tensor info is to be set - * @param[in] index Index of input to be applied (0-indexed) - * @param[in] tensor_info Tensor info to be applied - * @return @c NNFW_STATUS_NO_ERROR if successful, otherwise return @c NNFW_STATUS_ERROR - */ -NNFW_STATUS nnfw_apply_tensorinfo(nnfw_session *session, uint32_t index, - nnfw_tensorinfo tensor_info); - -/** - * @brief Set input model's tensor info for resizing - * - * This function can be called at any time after calling {@link nnfw_model_load_from_file}. Changing - * input tensor's shape will cause shape inference for the model. There are two different types of - * shape inference - static and dynamic. Which one to use is depend on the current state of the - * session. - * When it is called after calling {@link nnfw_model_load_from_file} and before calling {@link - * nnfw_prepare}, this info will be used when {@link nnfw_prepare}. And it will perform static shape - * inference for all tensors. - * When it is called after calling {@link nnfw_prepare} or even after {@link nnfw_run}, this info - * will be used when {@link nnfw_run}. And the shapes of the tensors are determined on the fly. - * If this function is called many times for the same index, it is overwritten. - * - * @param[in] session Session to the input tensor info is to be set - * @param[in] index Index of input to be set (0-indexed) - * @param[in] tensor_info Tensor info to be set - * @return @c NNFW_STATUS_NO_ERROR if successful, otherwise return @c NNFW_STATUS_ERROR - */ -NNFW_STATUS nnfw_set_input_tensorinfo(nnfw_session *session, uint32_t index, - const nnfw_tensorinfo *tensor_info); - -/** - * @brief Prepare session to be ready for inference - * - * This phase may finalize model compilation, scheduling, and additional settings. - * If {@link nnfw_apply_tensor} is called to apply input tensor info different with model - * before this function, tries to resize all tensors. - * - * @param[in] session the session to be prepared - * @return @c NNFW_STATUS_NO_ERROR if successful, otherwise return @c NNFW_STATUS_ERROR - */ -NNFW_STATUS nnfw_prepare(nnfw_session *session); - -/** - * @brief Run inference - * - * <p>This function should be called after model is loaded by {@link nnfw_load_model_from_file}, - * session is prepared for inference by {@link nnfw_prepare}, set input and output buffers - * by {@link nnfw_set_input} and {@link nnfw_set_output}.</p> - * - * <p>This function return after inference is finished.</p> - * - * @param[in] session The session to run inference - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_run(nnfw_session *session); - -/** - * @brief Run inference asynchronously - * - * <p>This function must be called after model is loaded by {@link nnfw_load_model_from_file}, - * session is prepared for inference by {@link nnfw_prepare}, set input and output buffers - * by {@link nnfw_set_input} and {@link nnfw_set_output}.</p> - * - * <p>This function returns immediately after starting a thread to run the inference. - * To get the result of it or to do the next inference with {@link nnfw_run} or - * {@link nnfw_run_async}, {@link nnfw_await} must be called to ensure the current asynchronous - * inference has finished. Only one asynchronous inference is allowed at a time for a session. - * If this function is called while the previous one is still running, it returns an error.</p> - * - * @param[in] session The session to run inference - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_run_async(nnfw_session *session); - -/** - * @brief Wait for asynchronous run to finish - * - * <p>This function must be called after calling {@link nnfw_run_asnyc}, and can be called only once - * for a {@link nnfw_run_async} call. - * - * <p>When this function returns, it means that this session has finished the asynchronous run. Then - * the user can safely use the output data.</p> - * - * <p>This function returns after the asynchronous inference is finished.</p> - * - * @param[in] session The session to run inference - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_await(nnfw_session *session); - -/** - * @brief Set input buffer - * - * This function must be called after {@link nnfw_prepare}, \p buffer given to this function can be - * reused for many inferences. \p length must be greater or equal than the operand requires. To - * specify an optional input, you can either not call this for that input or call this with \p - * buffer of NULL and \p length of 0. - * - * @param[in] session Session to the input is to be set - * @param[in] index Index of input to be set (0-indexed) - * @param[in] type Type of the input - * @param[in] buffer Raw buffer for input - * @param[in] length Size of bytes of input buffer - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_input(nnfw_session *session, uint32_t index, NNFW_TYPE type, - const void *buffer, size_t length); - -/** - * @brief Set output buffer - * - * This function must be called after {@link nnfw_prepare}, \p buffer given to this function can be - * reused for many inferences. \p length must be greater or equal than the operand requires. An - * output operand can have unspecified shape and deduced dynamically during the execution. You must - * provide \p buffer large enough. - * - * @param[in] session Session from inference output is to be extracted - * @param[in] index Index of output to be set (0-indexed) - * @param[in] type Type of the output - * @param[out] buffer Raw buffer for output - * @param[in] length Size of bytes of output buffer - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_output(nnfw_session *session, uint32_t index, NNFW_TYPE type, void *buffer, - size_t length); - -/** - * @brief Get the number of inputs - * - * Application can call this function to get number of inputs defined in loaded model. - * This function should be called after {@link nnfw_load_model_from_file} is invoked to load model - * - * @param[in] session Session from input information is to be extracted - * @param[out] number Variable which the number of inputs is put into - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_input_size(nnfw_session *session, uint32_t *number); - -/** - * @brief Get the number of outputs - * - * Application can call this function to get number of outputs defined in loaded model. - * This function should be called after {@link nnfw_load_model_from_file} is invoked to load model - * - * @param[in] session Session from output information is to be extracted - * @param[out] number Variable which the number of outputs is put into - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_output_size(nnfw_session *session, uint32_t *number); - -/** - * @brief Set the layout of an input - * - * The input that does not call this has NNFW_LAYOUT_NHWC layout - * - * @param[in] session session from inference input is to be extracted - * @param[in] index index of input to be set (0-indexed) - * @param[in] layout layout to set to target input - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_input_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout); - -/** - * @brief Set the layout of an output - * - * The output that does not call this has NNFW_LAYOUT_NHWC layout - * - * @param[in] session session from inference output is to be extracted - * @param[in] index index of output to be set (0-indexed) - * @param[in] layout layout to set to target output - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_output_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout); - -/** - * @brief Get i-th input tensor info - * - * <p>Before {@link nnfw_prepare} is invoked, this function return tensor info in model, - * so updated tensor info by {@link nnfw_apply_tensorinfo} is not returned.</p> - * - * <p>After {@link nnfw_prepare} is invoked, this function return updated tensor info - * if tensor info is updated by {@link nnfw_apply_tensorinfo}.</p> - * - * @param[in] session Session from input information is to be extracted - * @param[in] index Index of input - * @param[out] tensor_info Tensor info (shape, type, etc) - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_input_tensorinfo(nnfw_session *session, uint32_t index, - nnfw_tensorinfo *tensor_info); - -/** - * @brief Get i-th output tensor info - * - * <p>After {@link nnfw_load_model_from_file} and before {@link nnfw_prepare} is invoked, it returns - * tensor info in the model.</p> - * - * <p>After {@link nnfw_prepare} and before {@link nnfw_run} is invoked, this function returns - * updated tensor info if tensor info is updated by {@link nnfw_set_input_tensorinfo}.</p> - * - * <p>After {@link nnfw_run} is invoked(at least once), it returns the updated tensor info during - * the latest execution.</p> - * - * @param[in] session Session from output information is to be extracted - * @param[in] index Index of output - * @param[out] tensor_info Tensor info (shape, type, etc) - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_output_tensorinfo(nnfw_session *session, uint32_t index, - nnfw_tensorinfo *tensor_info); - -/** - * @brief Set available backends - * - * This function should be called before {@link nnfw_prepare} is invoked. - * - * <p>Supported backends differs on each platforms. - * For example, `x86_64` supports "cpu" only. - * Multiple backends can be set and they must be separated by a semicolon (ex: "acl_cl;cpu"). - * For each backend string, `libbackend_{backend}.so` will be dynamically loaded during - * {@link nnfw_prepare}. - * Among the multiple backends, the 1st element is used as the default backend.</p> - * - * @param[in] session session to which avilable backends are set - * @param[in] backends available backends on which nnfw uses - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_available_backends(nnfw_session *session, const char *backends); - -/** - * @brief Set the operation's backend - * - * This function should be called before {@link nnfw_prepare} is invoked. - * - * <p>The backend for op has higher priority than available backends specified by - * {@link nnfw_set_available_backends}.</p> - * - * @deprecated Deprecated since 1.8.0. - * - * @param[in] session session to be modified - * @param[in] op operation to be set - * @param[in] backend bakcend on which operation run - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_op_backend(nnfw_session *session, const char *op, const char *backend); - -/** - * @brief Retrieve uint32 type of nnfw information for given information ID. - * - * <p>Retrieves the information of property given by information id </p> - * - * @note: The input session could be null for global information (e.g. runtime version).* - * - * @param[in] session session to be queried on. - * @param[in] information ID to be queried - * @param[out] val uint32 value to be returned. - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_query_info_u32(nnfw_session *session, NNFW_INFO_ID id, uint32_t *val); - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/runtime/onert/api/include/nnfw_experimental.h b/runtime/onert/api/include/nnfw_experimental.h deleted file mode 100644 index 94f781988..000000000 --- a/runtime/onert/api/include/nnfw_experimental.h +++ /dev/null @@ -1,99 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_EXPERIMENTAL_H__ -#define __NNFW_EXPERIMENTAL_H__ - -#include "nnfw.h" - -// Used for custom kernel development - -/* - * operand type, used only for custom operations - */ -typedef struct -{ - nnfw_tensorinfo type; - void *allocation; -} nnfw_operand; - -/* - * Used as input to custom operation eval function - */ -typedef struct -{ - size_t ninputs; - nnfw_operand *inputs; - - size_t noutputs; - nnfw_operand *outputs; -} nnfw_custom_kernel_params; - -/* - * Custom kernel evaluation function - * - * param[in] params custom operation parameters - * param[in] userdata pointer to user-specified buffer( kernel instance specific ) - */ -typedef void (*nnfw_custom_eval)(nnfw_custom_kernel_params *params, char *userdata, - size_t userdata_size); - -/* - * custom operation registration info - */ -typedef struct -{ - nnfw_custom_eval eval_function; -} custom_kernel_registration_info; - -NNFW_STATUS nnfw_register_custom_op_info(nnfw_session *session, const char *id, - custom_kernel_registration_info *info); - -/** - * @brief Get the input tensor index by name - * - * This function finds an input tensor of the given name. - * If found, the index value is set to the address that @c index points to, and returns - * @c NNFW_STATUS_NO_ERROR. Otherwise, @c index is unchanged and returns @c NNFW_STATUS_ERROR . - * - * @note If two or more input tensors are of the same name, the one with the lowest index is always - * returned. - * - * @param[in] session the session object - * @param[in] tensorname the name of the tensor to find, a null terminated char pointer string - * @param[out] index the index to be ret - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_input_tensorindex(nnfw_session *session, const char *tensorname, uint32_t *index); - -/** - * @brief Get the input tensor index by name - * - * This function finds an input tensor of the given name. - * If found, the index value is set to the address that @c index points to, and returns - * @c NNFW_STATUS_NO_ERROR. Otherwise, @c index is unchanged and returns @c NNFW_STATUS_ERROR . - * - * @note If two or more input tensors are of the same name, the one with the lowest index is always - * returned. - * - * @param[in] session the session object - * @param[in] tensorname the name of the tensor to find, a null terminated char pointer string - * @param[out] index the index to be ret - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_output_tensorindex(nnfw_session *session, const char *tensorname, uint32_t *index); - -#endif // __NNFW_EXPERIMENTAL_H__ diff --git a/runtime/onert/api/include/nnfw_internal.h b/runtime/onert/api/include/nnfw_internal.h deleted file mode 100644 index eb4b6d629..000000000 --- a/runtime/onert/api/include/nnfw_internal.h +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_INTERNAL_H__ -#define __NNFW_INTERNAL_H__ - -#include "nnfw.h" - -NNFW_STATUS nnfw_set_config(nnfw_session *session, const char *key, const char *value); - -NNFW_STATUS nnfw_get_config(nnfw_session *session, const char *key, char *value, size_t value_size); - -/** - * @brief Load a circle model from buffer. - * - * The buffer must outlive the session. - * - * @param[in] session session - * @param[in] buffer Pointer to the buffer - * @param[in] size Buffer size - * @return NNFW_STATUS - */ -NNFW_STATUS nnfw_load_circle_from_buffer(nnfw_session *session, uint8_t *buffer, size_t size); - -#endif // __NNFW_INTERNAL_H__ diff --git a/runtime/onert/api/include/nnfw_version.h b/runtime/onert/api/include/nnfw_version.h deleted file mode 100644 index 8c6ea3994..000000000 --- a/runtime/onert/api/include/nnfw_version.h +++ /dev/null @@ -1,26 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_VERSION_H__ -#define __NNFW_VERSION_H__ - -/** - * NNFW_VERSION is a uint32 value representing nnfw runtime version - * in 0xMMmmmmPP, where MM = major, mmmm = minor, PP = patch - */ -#define NNFW_VERSION 0x01000a00 - -#endif // __NNFW_VERSION_H__ diff --git a/runtime/onert/api/src/CustomKernel.cc b/runtime/onert/api/src/CustomKernel.cc deleted file mode 100644 index 3f3a5d81e..000000000 --- a/runtime/onert/api/src/CustomKernel.cc +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "CustomKernel.h" - -namespace onert -{ -namespace frontend -{ -namespace custom -{ - -using namespace backend::custom; - -class APIConverter -{ -public: - static nnfw_operand convertOperand(void *alloc, const TypeInfo &type) - { - nnfw_operand api_operand; - api_operand.allocation = alloc; - api_operand.type = convertType(type); - return api_operand; - } - - static nnfw_tensorinfo convertType(const TypeInfo &type) - { - nnfw_tensorinfo api_type; - api_type.rank = type.shape.rank(); - assert(type.shape.rank() <= 6); - std::copy(type.shape.dims().begin(), type.shape.dims().end(), std::begin(api_type.dims)); - - switch (type.dtype) - { - case ir::DataType::FLOAT32: - api_type.dtype = NNFW_TYPE_TENSOR_FLOAT32; - break; - case ir::DataType::INT32: - api_type.dtype = NNFW_TYPE_TENSOR_INT32; - break; - case ir::DataType::QUANT_UINT8_ASYMM: - api_type.dtype = NNFW_TYPE_TENSOR_QUANT8_ASYMM; - break; - case ir::DataType::BOOL8: - api_type.dtype = NNFW_TYPE_TENSOR_BOOL; - break; - default: - throw std::runtime_error("Unsupported tensor datatype"); - } - return api_type; - } -}; - -Kernel::Kernel(const nnfw_custom_eval evalFunction) - : _in_params(), _userdata(nullptr), _userdata_size(0), _evalFunction(evalFunction) -{ -} - -void Kernel::configure(CustomKernelConfigParams &&inParams) -{ - _userdata = inParams.userdata; - _userdata_size = inParams.userdata_size; - - _in_params = std::move(inParams); -} - -void Kernel::run() -{ - nnfw_custom_kernel_params params; - - // set input tensor buffer and types - params.ninputs = _in_params.input_tensors.size(); - params.inputs = new nnfw_operand[params.ninputs]; - - for (size_t i = 0; i < params.ninputs; ++i) - { - auto *buf = _in_params.input_tensors[i]->buffer(); - assert(buf); - params.inputs[i] = APIConverter::convertOperand(buf, _in_params.input_types[i]); - } - - // set output tensor buffer and types - params.noutputs = _in_params.output_tensors.size(); - params.outputs = new nnfw_operand[params.noutputs]; - - for (size_t i = 0; i < params.noutputs; ++i) - { - auto *buf = _in_params.output_tensors[i]->buffer(); - assert(buf); - params.outputs[i] = APIConverter::convertOperand(buf, _in_params.output_types[i]); - } - - _evalFunction(¶ms, _userdata, _userdata_size); - - delete[] params.inputs; - delete[] params.outputs; -} - -} // namespace custom -} // namespace frontend -} // namespace onert diff --git a/runtime/onert/api/src/CustomKernel.h b/runtime/onert/api/src/CustomKernel.h deleted file mode 100644 index a42f7a639..000000000 --- a/runtime/onert/api/src/CustomKernel.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CUSTOM_KERNEL_H__ -#define __ONERT_BACKEND_CUSTOM_KERNEL_H__ - -#include "nnfw_experimental.h" - -#include "backend/CustomKernelBuilder.h" -#include "exec/IFunction.h" - -#include <vector> - -namespace onert -{ -namespace frontend -{ -namespace custom -{ - -class Kernel : public ::onert::exec::IFunction -{ -public: - explicit Kernel(nnfw_custom_eval evalFunction); - - backend::custom::CustomKernelConfigParams _in_params; - - char *_userdata; - size_t _userdata_size; - - nnfw_custom_eval _evalFunction; - // nnfw_custom_type_infer _type_infer_function; //Unused for now - - /** - * Fills _params field used later by user specified eval function - * @param inParams custom kernel parameters - */ - virtual void configure(backend::custom::CustomKernelConfigParams &&inParams); - - void run() override; -}; - -} // namespace custom -} // namespace frontend -} // namespace onert - -#endif // __ONERT_BACKEND_CUSTOM_KERNEL_H__ diff --git a/runtime/onert/api/src/CustomKernelRegistry.cc b/runtime/onert/api/src/CustomKernelRegistry.cc deleted file mode 100644 index 7812609d1..000000000 --- a/runtime/onert/api/src/CustomKernelRegistry.cc +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "CustomKernelRegistry.h" - -#include <memory> - -namespace onert -{ -namespace frontend -{ -namespace custom -{ - -void KernelRegistry::registerKernel(const std::string &id, nnfw_custom_eval evalFunction) -{ - _storage.emplace(id, evalFunction); -} - -std::shared_ptr<backend::custom::IKernelBuilder> KernelRegistry::getBuilder() -{ - return std::make_unique<KernelBuilder>(this); -} - -std::unique_ptr<Kernel> KernelRegistry::buildKernelForOp(const std::string &id) -{ - auto it = _storage.find(id); - if (it == _storage.end()) - { - throw std::runtime_error("Unable to find associated kernel for op"); - } - - return std::make_unique<Kernel>(it->second); -} - -// Kernel builder -std::unique_ptr<exec::IFunction> -KernelBuilder::buildKernel(const std::string &id, - backend::custom::CustomKernelConfigParams &¶ms) const -{ - auto kernel = _registry->buildKernelForOp(id); - kernel->configure(std::move(params)); - - return kernel; -} - -KernelBuilder::KernelBuilder(KernelRegistry *registry) : _registry(registry) {} - -} // namespace custom -} // namespace frontend -} // namespace onert diff --git a/runtime/onert/api/src/CustomKernelRegistry.h b/runtime/onert/api/src/CustomKernelRegistry.h deleted file mode 100644 index fe60d5bcc..000000000 --- a/runtime/onert/api/src/CustomKernelRegistry.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CUSTOM_KERNEL_REGISTRY_H__ -#define __ONERT_BACKEND_CUSTOM_KERNEL_REGISTRY_H__ - -#include "CustomKernel.h" - -#include <unordered_map> -#include <functional> -#include <memory> - -#include <iostream> - -namespace onert -{ -namespace frontend -{ -namespace custom -{ - -class KernelRegistry -{ -public: - void registerKernel(const std::string &id, nnfw_custom_eval evalFunction); - - std::shared_ptr<backend::custom::IKernelBuilder> getBuilder(); - std::unique_ptr<Kernel> buildKernelForOp(const std::string &id); - -private: - std::unordered_map<std::string, nnfw_custom_eval> _storage; -}; - -class KernelBuilder : public backend::custom::IKernelBuilder -{ -public: - KernelBuilder(KernelRegistry *registry); - - std::unique_ptr<exec::IFunction> - buildKernel(const std::string &id, - backend::custom::CustomKernelConfigParams &¶ms) const override; - -private: - KernelRegistry *_registry; -}; - -} // namespace custom -} // namespace frontend -} // namespace onert - -#endif // __ONERT_BACKEND_CUSTOM_KERNEL_REGISTRY_H__ diff --git a/runtime/onert/api/src/OpMap.lst b/runtime/onert/api/src/OpMap.lst deleted file mode 100644 index fa8afae5e..000000000 --- a/runtime/onert/api/src/OpMap.lst +++ /dev/null @@ -1,152 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MAP_MACRO -#error Define MAP_MACRO before including this file -#endif - -// Table: circle operation ID order -// circle operation | onert internal operation -MAP_MACRO(ADD , Add) -MAP_MACRO(AVERAGE_POOL_2D , AvgPool2D) -MAP_MACRO(CONCATENATION , Concat) -MAP_MACRO(CONV_2D , Conv2D) -MAP_MACRO(DEPTHWISE_CONV_2D , DepthwiseConv2D) -// DEPTH_TO_SPACE -MAP_MACRO(DEQUANTIZE , Dequantize) -MAP_MACRO(EMBEDDING_LOOKUP , EmbeddingLookup) -MAP_MACRO(FLOOR , Floor) -MAP_MACRO(FULLY_CONNECTED , FullyConnected) -MAP_MACRO(HASHTABLE_LOOKUP , HashtableLookup) -MAP_MACRO(L2_NORMALIZATION , L2Normalization) -MAP_MACRO(L2_POOL_2D , L2Pool2D) -MAP_MACRO(LOCAL_RESPONSE_NORMALIZATION , LocalResponseNormalization) -MAP_MACRO(LOGISTIC , Logistic) -// LSH_PROJECTION -MAP_MACRO(LSTM , LSTM) -MAP_MACRO(MAX_POOL_2D , MaxPool2D) -MAP_MACRO(MUL , Mul) -MAP_MACRO(RELU , ReLU) -MAP_MACRO(RELU_N1_TO_1 , ReLU1) -MAP_MACRO(RELU6 , ReLU6) -MAP_MACRO(RESHAPE , Reshape) -MAP_MACRO(RESIZE_BILINEAR , ResizeBilinear) -MAP_MACRO(RNN , RNN) -MAP_MACRO(SOFTMAX , Softmax) -MAP_MACRO(SPACE_TO_DEPTH , SpaceToDepth) -// SVDF -MAP_MACRO(TANH , Tanh) -// CONCAT_EMBEDDINGS -// SKIP_GRAM -// CALL -MAP_MACRO(CUSTOM , Custom) -// EMBEDDING_LOOKUP_SPARSE -MAP_MACRO(PAD , Pad) -// UNIDIRECTIONAL_SEQUENCE_RNN -MAP_MACRO(GATHER , Gather) -MAP_MACRO(BATCH_TO_SPACE_ND , BatchToSpaceND) -MAP_MACRO(SPACE_TO_BATCH_ND , SpaceToBatchND) -MAP_MACRO(TRANSPOSE , Transpose) -MAP_MACRO(MEAN , Mean) -MAP_MACRO(SUB , Sub) -MAP_MACRO(DIV , Div) -MAP_MACRO(SQUEEZE , Squeeze) -// UNIDIRECTIONAL_SEQUENCE_LSTM -MAP_MACRO(STRIDED_SLICE , StridedSlice) -// BIDIRECTIONAL_SEQUENCE_RNN -MAP_MACRO(EXP , Exp) -MAP_MACRO(TOPK_V2 , TopKV2) -MAP_MACRO(SPLIT , Split) -// LOG_SOFTMAX -// DELEGATE -// BIDIRECTIONAL_SEQUENCE_LSTM -MAP_MACRO(CAST , Cast) -MAP_MACRO(PRELU , PReLU) -MAP_MACRO(MAXIMUM , Max) -MAP_MACRO(ARG_MAX , ArgMax) -MAP_MACRO(MINIMUM , Min) -// LESS (Comparison) ? -MAP_MACRO(NEG , Neg) -// PADV2 -// GREATER (Comparison) ? -// GREATER_EQUAL (Comparison) ? -// LESS_EQUAL (Comparison) ? -MAP_MACRO(SELECT , Select) -MAP_MACRO(SLICE , Slice) -MAP_MACRO(SIN , Sin) -MAP_MACRO(TRANSPOSE_CONV , TransposeConv) -// SPARSE_TO_DENSE -MAP_MACRO(TILE , Tile) -MAP_MACRO(EXPAND_DIMS , ExpandDims) -// EQUAL (Comparison) ? -// NOT_EQUAL (Comparison) ? -MAP_MACRO(LOG , Log) -MAP_MACRO(SUM , ReduceSum) -MAP_MACRO(SQRT , SQRT) -MAP_MACRO(RSQRT , RSQRT) -MAP_MACRO(SHAPE , Shape) -MAP_MACRO(POW , Pow) -// ARG_MIN -// FAKE_QUANT -MAP_MACRO(REDUCE_PROD , ReduceProd) -MAP_MACRO(REDUCE_MAX , ReduceMax) -MAP_MACRO(PACK , Pack) -MAP_MACRO(LOGICAL_OR , LogicalOr) -MAP_MACRO(ONE_HOT , OneHot) -MAP_MACRO(LOGICAL_AND , LogicalAnd) -MAP_MACRO(LOGICAL_NOT , LogicalNot) -MAP_MACRO(UNPACK , Unpack) -MAP_MACRO(REDUCE_MIN , ReduceMin) -// FLOOR_DIV -MAP_MACRO(REDUCE_ANY , ReduceAny) -// SQUARE -MAP_MACRO(ZEROS_LIKE , ZerosLike) -MAP_MACRO(FILL , Fill) -// FLOOR_MOD -MAP_MACRO(RANGE , Range) -// RESIZE_NEAREST_NEIGHBOR -// LEAKY_RELU -MAP_MACRO(SQUARED_DIFFERENCE , SquaredDifference) -// MIRROR_PAD -MAP_MACRO(ABS , Abs) -// SPLIT_V -// UNIQUE -// CEIL -MAP_MACRO(REVERSE_V2 , Reverse) -// ADD_N -// GATHER_ND -MAP_MACRO(COS , Cos) -// WHERE -// RANK -// ELU -// REVERSE_SEQUENCE -// MATRIX_DIAG -// QUANTIZE -// MATRIX_SET_DIAG -MAP_MACRO(ROUND , Round) -// HARD_SWISH -MAP_MACRO(IF , If) -MAP_MACRO(WHILE , While) -// NON_MAX_SUPPRESSION_V4 -// NON_MAX_SUPPRESSION_V5 -// SCATTER_ND -// SELECT_V2 (Select) ? -// DENSIFY -// SEGMENT_SUM -MAP_MACRO(BATCH_MATMUL , BatchMatMul) -MAP_MACRO(BCQ_GATHER , BCQGather) -MAP_MACRO(BCQ_FULLY_CONNECTED , BCQFullyConnected) -MAP_MACRO(INSTANCE_NORM , InstanceNorm) diff --git a/runtime/onert/api/src/nnfw_api.cc b/runtime/onert/api/src/nnfw_api.cc deleted file mode 100644 index ff5e679da..000000000 --- a/runtime/onert/api/src/nnfw_api.cc +++ /dev/null @@ -1,362 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "nnfw_api_internal.h" -#include "nnfw_version.h" - -// Double-check enum value changes - -#define STATIC_ASSERT_ENUM_CHECK(ENUM, VAL) static_assert((ENUM) == (VAL), #ENUM " has changed") - -STATIC_ASSERT_ENUM_CHECK(NNFW_TYPE_TENSOR_FLOAT32, 0); -STATIC_ASSERT_ENUM_CHECK(NNFW_TYPE_TENSOR_INT32, 1); -STATIC_ASSERT_ENUM_CHECK(NNFW_TYPE_TENSOR_QUANT8_ASYMM, 2); -STATIC_ASSERT_ENUM_CHECK(NNFW_TYPE_TENSOR_BOOL, 3); -STATIC_ASSERT_ENUM_CHECK(NNFW_TYPE_TENSOR_UINT8, 4); -STATIC_ASSERT_ENUM_CHECK(NNFW_TYPE_TENSOR_INT64, 5); - -STATIC_ASSERT_ENUM_CHECK(NNFW_STATUS_NO_ERROR, 0); -STATIC_ASSERT_ENUM_CHECK(NNFW_STATUS_ERROR, 1); -STATIC_ASSERT_ENUM_CHECK(NNFW_STATUS_UNEXPECTED_NULL, 2); -STATIC_ASSERT_ENUM_CHECK(NNFW_STATUS_INVALID_STATE, 3); -STATIC_ASSERT_ENUM_CHECK(NNFW_STATUS_OUT_OF_MEMORY, 4); -STATIC_ASSERT_ENUM_CHECK(NNFW_STATUS_INSUFFICIENT_OUTPUT_SIZE, 5); - -STATIC_ASSERT_ENUM_CHECK(NNFW_LAYOUT_NONE, 0); -STATIC_ASSERT_ENUM_CHECK(NNFW_LAYOUT_CHANNELS_LAST, 1); -STATIC_ASSERT_ENUM_CHECK(NNFW_LAYOUT_CHANNELS_FIRST, 2); - -STATIC_ASSERT_ENUM_CHECK(NNFW_INFO_ID_VERSION, 0); - -#undef STATIC_ASSERT_ENUM_CHECK - -#define NNFW_RETURN_ERROR_IF_NULL(p) \ - do \ - { \ - if ((p) == NULL) \ - return NNFW_STATUS_UNEXPECTED_NULL; \ - } while (0) - -/* - * Create a new session instance - * - * @param session the session to be created - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_create_session(nnfw_session **session) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - - *session = new (std::nothrow) nnfw_session(); - if (*session == nullptr) - return NNFW_STATUS_OUT_OF_MEMORY; - return NNFW_STATUS_NO_ERROR; -} - -/* - * Close a session instance - * - * @param session the session to be closed - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_close_session(nnfw_session *session) -{ - delete session; - return NNFW_STATUS_NO_ERROR; -} - -/* - * Load model from nnpackage file or directory - * - * @param session nnfw_session loading the given nnpackage file/dir - * @param package_file_path path to the nnpackage file or unzipped directory to be loaded - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_load_model_from_file(nnfw_session *session, const char *pacakge_file_path) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->load_model_from_file(pacakge_file_path); -} - -/* - * Prepare session to be ready for inference - * This phase may finalize model compilation, scheduling, and additional settings. - * - * @param session the session to be prepared - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_prepare(nnfw_session *session) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->prepare(); -} - -/* - * Run inference - * - * @param session the session to run inference - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_run(nnfw_session *session) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->run(); -} - -NNFW_STATUS nnfw_run_async(nnfw_session *session) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->run_async(); -} - -NNFW_STATUS nnfw_await(nnfw_session *session) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->await(); -} - -/* - * Set input - * - * @param session session to the input is to be set - * @param index index of input to be set (0-indexed) - * @param type type of the input - * @param buffer raw buffer for input - * @param length size of bytes of input - * - * @return NNFW_STATUS_NO_ERROR if successful - */ - -NNFW_STATUS nnfw_set_input(nnfw_session *session, uint32_t index, NNFW_TYPE type, - const void *buffer, size_t length) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->set_input(index, type, buffer, length); -} - -/* - * Set output - * - * @param session session from inference output is to be extracted - * @param index index of output to be set (0-indexed) - * @param type type of the output - * @param buffer raw buffer for output - * @param length size of bytes of output - * - * @return NNFW_STATUS_NO_ERROR if successful - */ - -NNFW_STATUS nnfw_set_output(nnfw_session *session, uint32_t index, NNFW_TYPE type, void *buffer, - size_t length) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->set_output(index, type, buffer, length); -} - -/* - * Get the number of inputs - * - * @param[in] session session from input information is to be extracted - * @param[out] number variable which the number of inputs is put into - * - * @return NNFW_STATUS_NO_ERROR if successful - */ - -NNFW_STATUS nnfw_input_size(nnfw_session *session, uint32_t *number) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->input_size(number); -} - -/* - * Get the number of outputs - * - * @param[in] session session from output information is to be extracted - * @param[out] number variable which the number of outputs is put into - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_output_size(nnfw_session *session, uint32_t *number) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->output_size(number); -} - -/* - * Set the layout of an input - * @note The input that does not call this has NNFW_LAYOUT_CHANNELS_LAST layout - * - * @param[in] session session from inference input is to be extracted - * @param[in] index index of input to be set (0-indexed) - * @param[in] layout layout to set to target input - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_input_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->set_input_layout(index, layout); -} - -/* - * Set the layout of an output - * @note The output that does not call this has NNFW_LAYOUT_CHANNELS_LAST layout - * - * @param[in] session session from inference output is to be extracted - * @param[in] index index of output to be set (0-indexed) - * @param[in] layout layout to set to target output - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_output_layout(nnfw_session *session, uint32_t index, NNFW_LAYOUT layout) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->set_output_layout(index, layout); -} - -/* - * Get i-th input tensor info - * - * @param[in] session session from input information is to be extracted - * @param[in] index index of input - * @param[out] tensor_info nnfw_tensor_info - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_input_tensorinfo(nnfw_session *session, uint32_t index, - nnfw_tensorinfo *tensor_info) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->input_tensorinfo(index, tensor_info); -} - -/* - * Get i-th output tensor info - * - * @param[in] session session from output information is to be extracted - * @param[in] index index of output - * @param[out] tensor_info nnfw_tensor_info - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_output_tensorinfo(nnfw_session *session, uint32_t index, - nnfw_tensorinfo *tensor_info) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->output_tensorinfo(index, tensor_info); -} - -/* - * Register custom operation - * @param session session to register this operation - * @param id operation id - * @param info registration info ( eval function, etc. ) - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_register_custom_op_info(nnfw_session *session, const char *id, - custom_kernel_registration_info *info) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->register_custom_operation(id, info->eval_function); -} - -NNFW_STATUS nnfw_apply_tensorinfo(nnfw_session *session, uint32_t index, - nnfw_tensorinfo tensor_info) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->apply_tensorinfo(index, tensor_info); -} - -NNFW_STATUS nnfw_set_input_tensorinfo(nnfw_session *session, uint32_t index, - const nnfw_tensorinfo *tensor_info) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->set_input_tensorinfo(index, tensor_info); -} - -/* - * Set available backends - * - * @param[in] session session to which a avilable backends are set - * @param[in] backends available backends on which nnfw uses - */ -NNFW_STATUS nnfw_set_available_backends(nnfw_session *session, const char *backends) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->set_available_backends(backends); -} - -/* - * Set the operation's backend - * - * @param[in] session session to be modified - * @param[in] op operation to be set - * @param[in] backend bakcend on which operation run - * - * @return NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_set_op_backend(nnfw_session *session, const char *op, const char *backend) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->set_op_backend(op, backend); -} - -/* - * Retrieve uint32 type of nnfw information for given information ID. - * - * @param[in] session session to be queried on - * @param[in] information ID to be queried - * @param[out] val uint32 value to be returned - * - * @return @c NNFW_STATUS_NO_ERROR if successful - */ -NNFW_STATUS nnfw_query_info_u32(nnfw_session *session, NNFW_INFO_ID id, uint32_t *val) -{ - (void)session; - switch (id) - { - case NNFW_INFO_ID_VERSION: - if (val) - { - *val = NNFW_VERSION; - return NNFW_STATUS_NO_ERROR; - } - break; - default: - return NNFW_STATUS_ERROR; - } - // It should not be reached. - return NNFW_STATUS_ERROR; -} - -NNFW_STATUS nnfw_load_circle_from_buffer(nnfw_session *session, uint8_t *buffer, size_t size) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->load_circle_from_buffer(buffer, size); -} - -NNFW_STATUS nnfw_input_tensorindex(nnfw_session *session, const char *tensorname, uint32_t *index) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->input_tensorindex(tensorname, index); -} - -NNFW_STATUS nnfw_output_tensorindex(nnfw_session *session, const char *tensorname, uint32_t *index) -{ - NNFW_RETURN_ERROR_IF_NULL(session); - return session->output_tensorindex(tensorname, index); -} diff --git a/runtime/onert/api/src/nnfw_api_internal.cc b/runtime/onert/api/src/nnfw_api_internal.cc deleted file mode 100644 index aa066e190..000000000 --- a/runtime/onert/api/src/nnfw_api_internal.cc +++ /dev/null @@ -1,896 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "nnfw_api_internal.h" -#include "CustomKernelRegistry.h" -#include "compiler/Compiler.h" -#include "util/ConfigSource.h" -#include "util/Exceptions.h" -#include "exec/Execution.h" -#include "circle_loader.h" -#include "tflite_loader.h" -#include "json/json.h" -#include "ir/OpCode.h" -#include <fstream> -#include <iostream> -#include <string> -#include <vector> -#include <dirent.h> -#include <util/ConfigSource.h> -#include <misc/string_helpers.h> - -/* - * API does not accept string argument longer than max length below - */ -#define MAX_BACKEND_NAME_LENGTH 32 -#define MAX_OP_NAME_LENGTH 64 -#define MAX_PATH_LENGTH 1024 -#define MAX_TENSOR_NAME_LENGTH 64 - -// Is null-terminating in length ? -static bool null_terminating(const char *str, uint32_t length) -{ - for (uint32_t i = 0; i < length; i++) - { - if (*(str + i) == '\0') - { - return true; - } - } - return false; -} - -static onert::ir::Layout convertLayout(NNFW_LAYOUT layout) -{ - if (layout == NNFW_LAYOUT_CHANNELS_LAST) - { - return onert::ir::Layout::NHWC; - } - else if (layout == NNFW_LAYOUT_CHANNELS_FIRST) - { - return onert::ir::Layout::NCHW; - } - return onert::ir::Layout::UNKNOWN; -} - -NNFW_STATUS getTensorIndexImpl(const onert::ir::Graph &graph, const char *tensorname, - uint32_t *index, bool is_input) -{ - if (!tensorname || !index) - return NNFW_STATUS_UNEXPECTED_NULL; - - if (!null_terminating(tensorname, MAX_TENSOR_NAME_LENGTH)) - { - std::cerr << "nnpackage path is too long" << std::endl; - return NNFW_STATUS_ERROR; - } - - auto ind_found = is_input ? graph.getInputIndex(tensorname) : graph.getOutputIndex(tensorname); - - if (ind_found.undefined()) - { - // Not found - return NNFW_STATUS_ERROR; - } - else - { - *index = ind_found.value(); - return NNFW_STATUS_NO_ERROR; - } -} - -nnfw_session::nnfw_session() - : _subgraphs{nullptr}, _execution{nullptr}, - _kernel_registry{std::make_shared<onert::frontend::custom::KernelRegistry>()} -{ - // DO NOTHING -} - -nnfw_session::~nnfw_session() = default; - -NNFW_STATUS nnfw_session::load_circle_from_buffer(uint8_t *buffer, size_t size) -{ - if (!isStateInitialized()) - return NNFW_STATUS_INVALID_STATE; - - if (!buffer) - return NNFW_STATUS_UNEXPECTED_NULL; - - if (size == 0) - return NNFW_STATUS_ERROR; - - try - { - _subgraphs = onert::circle_loader::loadModel(buffer, size); - } - catch (const std::exception &e) - { - std::cerr << "Error during model loading : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - - _compiler = std::make_unique<onert::compiler::Compiler>(_subgraphs); - - _state = State::MODEL_LOADED; - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::load_model_from_file(const char *package_dir) -{ - if (!isStateInitialized()) - return NNFW_STATUS_INVALID_STATE; - - if (!package_dir) - { - std::cerr << "package_dir is null." << std::endl; - return NNFW_STATUS_UNEXPECTED_NULL; - } - - if (!null_terminating(package_dir, MAX_PATH_LENGTH)) - { - std::cerr << "nnpackage path is too long" << std::endl; - return NNFW_STATUS_ERROR; - } - - // TODO : add support for zipped package file load - DIR *dir; - if (!(dir = opendir(package_dir))) - { - std::cerr << "invalid nnpackge directory: " << package_dir << std::endl; - return NNFW_STATUS_ERROR; - } - closedir(dir); - - try - { - std::string manifest_file_name(package_dir); - manifest_file_name += "/metadata/MANIFEST"; - std::ifstream mfs(manifest_file_name); - - // extract the filename of the first(index 0) model - // e.g. In MANIFEST file, { "models" : [ "firstmodel.tflite", "2nd.tflite" ] } - Json::Value root; - mfs >> root; - const Json::Value &models = root["models"]; - const Json::Value &model_types = root["model-types"]; - - auto model_file_path = package_dir + std::string("/") + models[0].asString(); // first model - auto model_type = model_types[0].asString(); // first model's type - if (model_type == "tflite") - { - _subgraphs = onert::tflite_loader::loadModel(model_file_path.c_str()); - } - else if (model_type == "circle") - { - _subgraphs = onert::circle_loader::loadModel(model_file_path.c_str()); - } - else - { - std::cerr << "Unsupported model type in MANIFEST" << std::endl; - return NNFW_STATUS_ERROR; - } - _subgraphs->primary()->bindKernelBuilder(_kernel_registry->getBuilder()); - } - catch (const std::exception &e) - { - std::cerr << "Error during model loading : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - - _compiler = std::make_unique<onert::compiler::Compiler>(_subgraphs); - - _state = State::MODEL_LOADED; - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::prepare() -{ - // NOTE. If users want to run prepare() more than one time, this could be removed. - if (!isStateModelLoaded()) - { - std::cerr << "Error during model prepare : "; - if (isStateInitialized()) - { - std::cerr << "prepare should be run once"; - } - else - { - std::cerr << "invalid state"; - } - std::cerr << std::endl; - return NNFW_STATUS_INVALID_STATE; - } - - if (!_subgraphs || !primary_subgraph() || primary_subgraph()->isBuildingPhase()) - { - std::cerr << "Error during model prepare : " - << "prepare should be run after load_model" << std::endl; - return NNFW_STATUS_ERROR; - } - - try - { - _subgraphs.reset(); - std::shared_ptr<onert::exec::ExecutorMap> executors = _compiler->compile(); - _execution = std::make_shared<onert::exec::Execution>(executors); - } - catch (const std::exception &e) - { - std::cerr << "Error during model prepare : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - - _state = State::PREPARED; - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::run() -{ - if (!isStatePreparedOrFinishedRun()) - { - std::cerr << "Error during nnfw_session::run : " - << "run should be run after prepare" << std::endl; - return NNFW_STATUS_INVALID_STATE; - } - - try - { - _execution->execute(); - } - catch (const onert::InsufficientBufferSizeException &e) - { - // Currently insufficient buffer always means output buffer. - std::cerr << "Error during nnfw_session::run : " << e.what() << std::endl; - return NNFW_STATUS_INSUFFICIENT_OUTPUT_SIZE; - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::run : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - - _state = State::FINISHED_RUN; - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::run_async() -{ - if (!isStatePreparedOrFinishedRun()) - { - std::cerr << "Error during nnfw_session::run_async : " - << "run_async should be run after prepare" << std::endl; - return NNFW_STATUS_INVALID_STATE; - } - - _execution->startExecute(); - - _state = State::RUNNING; - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::await() -{ - if (!isStateRunning()) - { - std::cerr << "Error during nnfw_session::run_await : " - << "run_await should be run after run_async" << std::endl; - return NNFW_STATUS_ERROR; - } - - _execution->waitFinish(); - - _state = State::FINISHED_RUN; - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::set_input(uint32_t index, NNFW_TYPE /*type*/, const void *buffer, - size_t length) -{ - if (!isStatePreparedOrFinishedRun()) - { - std::cerr << "Error during nnfw_session::set_input : invalid state" << std::endl; - return NNFW_STATUS_INVALID_STATE; - } - - if (!buffer && length != 0) - { - std::cerr - << "Error during nnfw_session::set_input : given buffer is NULL but the length is not 0" - << std::endl; - return NNFW_STATUS_ERROR; - } - - try - { - _execution->setInput(onert::ir::IOIndex(index), buffer, length); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::set_input : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::set_output(uint32_t index, NNFW_TYPE /*type*/, void *buffer, - size_t length) -{ - if (!isStatePreparedOrFinishedRun()) - { - std::cerr << "Error during nnfw_session::set_output : invalid state" << std::endl; - return NNFW_STATUS_INVALID_STATE; - } - - if (!buffer && length != 0) - { - std::cerr - << "Error during nnfw_session::set_output : given buffer is NULL but the length is not 0" - << std::endl; - return NNFW_STATUS_ERROR; - } - - try - { - _execution->setOutput(onert::ir::IOIndex(index), buffer, length); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::set_output : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::input_size(uint32_t *number) -{ - if (isStateInitialized()) // Model is not loaded - return NNFW_STATUS_INVALID_STATE; - - try - { - if (number == nullptr) - { - std::cerr << "Error during nnfw_session::input_size, number is null pointer." << std::endl; - return NNFW_STATUS_UNEXPECTED_NULL; - } - *number = primary_subgraph()->getInputs().size(); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::input_size : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::output_size(uint32_t *number) -{ - if (isStateInitialized()) // Model is not loaded - return NNFW_STATUS_INVALID_STATE; - - try - { - if (number == nullptr) - { - std::cerr << "Error during nnfw_session::output_size, number is null pointer." << std::endl; - return NNFW_STATUS_UNEXPECTED_NULL; - } - *number = primary_subgraph()->getOutputs().size(); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::output_size" << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::set_input_layout(uint32_t index, NNFW_LAYOUT layout) -{ - try - { - if (layout != NNFW_LAYOUT_NONE && layout != NNFW_LAYOUT_CHANNELS_FIRST && - layout != NNFW_LAYOUT_CHANNELS_LAST) - { - std::cerr << "Error during nnfw_session::set_input_layout, not supported layout" << std::endl; - return NNFW_STATUS_ERROR; - } - _execution->setInputLayout(onert::ir::IOIndex(index), convertLayout(layout)); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::set_input_layout : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::set_output_layout(uint32_t index, NNFW_LAYOUT layout) -{ - try - { - if (layout != NNFW_LAYOUT_NONE && layout != NNFW_LAYOUT_CHANNELS_FIRST && - layout != NNFW_LAYOUT_CHANNELS_LAST) - { - std::cerr << "Error during nnfw_session::set_output_layout, not supported layout" - << std::endl; - return NNFW_STATUS_ERROR; - } - _execution->setOutputLayout(onert::ir::IOIndex(index), convertLayout(layout)); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::set_output_layout : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -static NNFW_TYPE datatype_to_nnfw_dtype(onert::ir::DataType dt) -{ - using onert::ir::DataType; - switch (dt) - { - case DataType::FLOAT32: - return NNFW_TYPE_TENSOR_FLOAT32; - case DataType::INT32: - return NNFW_TYPE_TENSOR_INT32; - case DataType::QUANT_UINT8_ASYMM: - return NNFW_TYPE_TENSOR_QUANT8_ASYMM; - case DataType::BOOL8: - return NNFW_TYPE_TENSOR_BOOL; - case DataType::UINT8: - return NNFW_TYPE_TENSOR_UINT8; - case DataType::INT64: - return NNFW_TYPE_TENSOR_INT64; - case DataType::UINT32: - case DataType::QUANT_INT8_SYMM: - default: - throw std::runtime_error("Error: Model has type that runtime API does not support."); - } -} - -NNFW_STATUS nnfw_session::apply_tensorinfo(uint32_t index, nnfw_tensorinfo ti) -{ - // sanity check - { - if (isStateInitialized()) - { - std::cerr << "Error during set_input_tensorinfo : should be run after load_model" - << std::endl; - return NNFW_STATUS_INVALID_STATE; - } - - if (ti.rank <= 0 || ti.rank > NNFW_MAX_RANK) - { - std::cerr << "unsupported rank: " << ti.rank << std::endl; - return NNFW_STATUS_ERROR; - } - - for (int32_t i = 0; i < ti.rank; ++i) - { - if (ti.dims[i] <= 0) - { - std::cerr << "dim must be positive integer but was " << ti.dims[i] << std::endl; - return NNFW_STATUS_ERROR; - } - } - } - - auto ind = primary_subgraph()->getInputs().at(index); - auto &input = primary_subgraph()->operands().at(ind); - - onert::ir::Shape new_shape(ti.rank); - for (int32_t i = 0; i < ti.rank; i++) - new_shape.dim(i) = ti.dims[i]; - - // if passed shape is same with the shape of model, do nothing - if (input.info().shape() == new_shape) - return NNFW_STATUS_NO_ERROR; - - if (!isStatePreparedOrFinishedRun()) - { - // In this case, if we apply input shape in primary_subgraph, it will propagate after - // compilation and excution - - // overwrite input shape with the shape from ti - input.info().shape(new_shape); - } - else // when called after nnfw_session::prepare() - { - _execution->changeInputShape(onert::ir::IOIndex(index), new_shape); - } - - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::set_input_tensorinfo(uint32_t index, const nnfw_tensorinfo *ti) -{ - nnfw_tensorinfo ti_copy = *ti; - return apply_tensorinfo(index, ti_copy); -} - -NNFW_STATUS nnfw_session::input_tensorinfo(uint32_t index, nnfw_tensorinfo *ti) -{ - if (isStateInitialized()) - return NNFW_STATUS_INVALID_STATE; - - try - { - if (ti == nullptr) - { - std::cerr << "Error during nnfw_session::input_tensorinfo, tensorinfo is null pointer." - << std::endl; - return NNFW_STATUS_UNEXPECTED_NULL; - } - if (index >= primary_subgraph()->getInputs().size()) - { - std::cerr << "Error during nnfw_session::input_tensorinfo, index is out of range." - << std::endl; - return NNFW_STATUS_ERROR; - } - auto opidx = primary_subgraph()->getInputs().at(index); - auto shape = primary_subgraph()->operands().at(opidx).shape(); - if (isStatePreparedOrFinishedRun()) - shape = _execution->getInputShape(onert::ir::IOIndex{index}); - ti->rank = shape.rank(); - for (int j = 0; j < ti->rank; ++j) - { - ti->dims[j] = shape.dim(j); - } - ti->dtype = datatype_to_nnfw_dtype(primary_subgraph()->operands().at(opidx).typeInfo().type()); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::input_tensorinfo : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::output_tensorinfo(uint32_t index, nnfw_tensorinfo *ti) -{ - if (isStateInitialized()) - return NNFW_STATUS_INVALID_STATE; - - if (ti == nullptr) - { - std::cerr << "Error during nnfw_session::output_tensorinfo, tensorinfo is null pointer." - << std::endl; - return NNFW_STATUS_UNEXPECTED_NULL; - } - - if (index >= primary_subgraph()->getOutputs().size()) - { - std::cerr << "Error during nnfw_session::output_tensorinfo, index is out of range." - << std::endl; - return NNFW_STATUS_ERROR; - } - - try - { - auto opidx = primary_subgraph()->getOutputs().at(index); - auto shape = primary_subgraph()->operands().at(opidx).shape(); - // If it is called after `nnfw_run` then get the shape from Execution, not from the graph - if (isStateFinishedRun()) - shape = _execution->getOutputShape(onert::ir::IOIndex{index}); - ti->rank = shape.rank(); - for (int j = 0; j < ti->rank; ++j) - { - ti->dims[j] = shape.dim(j); - } - ti->dtype = datatype_to_nnfw_dtype(primary_subgraph()->operands().at(opidx).typeInfo().type()); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::output_tensorinfo : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - - return NNFW_STATUS_NO_ERROR; -} -NNFW_STATUS nnfw_session::register_custom_operation(const std::string &id, - nnfw_custom_eval eval_func) -{ - _kernel_registry->registerKernel(id, eval_func); - return NNFW_STATUS_NO_ERROR; -} - -static std::string get_op_backend_string(std::string op) -{ -#define MAP_MACRO(CircleName, OneRTName) {#CircleName, #OneRTName}, - - static std::unordered_map<std::string, std::string> operation_map = { -#include "OpMap.lst" - }; - -#undef MAP_MACRO - - auto n = operation_map.find(op); - - if (n == operation_map.end()) - { - // this return value is handled by a caller to return error code - return std::string(""); - } - else - { - return n->second; - } -} - -NNFW_STATUS nnfw_session::set_available_backends(const char *backends) -{ - if (!isStateModelLoaded()) - return NNFW_STATUS_INVALID_STATE; - - try - { - if (!backends) - return NNFW_STATUS_UNEXPECTED_NULL; - if (null_terminating(backends, MAX_BACKEND_NAME_LENGTH) == false) - return NNFW_STATUS_ERROR; - - auto &options = _compiler->options(); - - using namespace onert::util; - - options.backend_list = nnfw::misc::split(std::string{backends}, ';'); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::set_available_backends : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::set_op_backend(const char *op, const char *backend) -{ - if (!isStateModelLoaded()) - return NNFW_STATUS_INVALID_STATE; - - try - { - if (!op || !backend) - return NNFW_STATUS_UNEXPECTED_NULL; - if (!null_terminating(op, MAX_OP_NAME_LENGTH) || - !null_terminating(backend, MAX_BACKEND_NAME_LENGTH)) - return NNFW_STATUS_ERROR; - - auto key = get_op_backend_string(op); - - if (key.empty()) - { - return NNFW_STATUS_ERROR; - } - - auto &opcode_to_backend = _compiler->options().manual_scheduler_options.opcode_to_backend; - opcode_to_backend.emplace(onert::ir::toOpCode(key), backend); - } - catch (const std::exception &e) - { - std::cerr << "Error during nnfw_session::set_op_backend : " << e.what() << std::endl; - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -NNFW_STATUS nnfw_session::set_config(const char *key, const char *value) -{ - if (!isStateModelLoaded()) - return NNFW_STATUS_INVALID_STATE; - - if (!key || !value) - return NNFW_STATUS_UNEXPECTED_NULL; - - auto &options = _compiler->options(); - - using namespace onert::util; - - const std::string skey = key; - - if (skey == config::TRACE_FILEPATH) - { - options.trace_filepath = value; - } - else if (skey == config::GRAPH_DOT_DUMP) - { - options.graph_dump_level = toInt(value); - } - else if (skey == config::OP_SEQ_MAX_NODE) - { - options.op_seq_max_node = toInt(value); - } - else if (skey == config::EXECUTOR) - { - options.executor = value; - } - else if (skey == config::OP_BACKEND_ALLOPS) - { - options.manual_scheduler_options.backend_for_all = value; - } - else if (skey == config::USE_SCHEDULER) - { - options.he_scheduler = toBool(value); - } - else if (skey == config::PROFILING_MODE) - { - options.he_profiling_mode = toBool(value); - } - else if (skey == config::DISABLE_COMPILE) - { - options.disable_compile = toBool(value); - } - else - { - return NNFW_STATUS_ERROR; - } - return NNFW_STATUS_NO_ERROR; -} - -onert::ir::Graph *nnfw_session::primary_subgraph() -{ - if (_subgraphs) - { - assert(!_execution); - return _subgraphs->primary().get(); - } - else - { - assert(_execution); - // TODO Remove const_cast - // We assumed the graph will not change after compilation, but shape could change - return const_cast<onert::ir::Graph *>(&_execution->primary_subgraph()); - } -} - -NNFW_STATUS nnfw_session::get_config(const char *key, char *value, size_t value_size) -{ - if (!isStateModelLoaded()) - return NNFW_STATUS_INVALID_STATE; - - if (!key || !value) - return NNFW_STATUS_UNEXPECTED_NULL; - - auto &options = _compiler->options(); - - auto check_boundary = [](size_t dest_size, std::string &src) { - if (dest_size < src.length() + 1 /* for '\0' */) - { - std::cerr << "buffer is small to copy config value." << std::endl; - return false; - } - return true; - }; - - if (key == onert::util::config::BACKENDS) - { - if (options.backend_list.size() == 0) - return NNFW_STATUS_NO_ERROR; // no setting backend is not an error of get_config_str() - - auto str = nnfw::misc::join(options.backend_list.begin(), options.backend_list.end(), ";"); - - if (!check_boundary(value_size, str)) - return NNFW_STATUS_ERROR; - - strncpy(value, str.c_str(), value_size); - } - else if (key == onert::util::config::EXECUTOR) - { - if (!check_boundary(value_size, options.executor)) - return NNFW_STATUS_ERROR; - - strncpy(value, options.executor.c_str(), options.executor.length()); - } - else - { - return NNFW_STATUS_ERROR; - } - - return NNFW_STATUS_NO_ERROR; -} - -bool nnfw_session::isStateInitialized() -{ - if (_state == State::INITIALIZED) - { - assert(!_subgraphs); - assert(!_compiler); - assert(!_execution); - return true; - } - else - { - return false; - } -} - -bool nnfw_session::isStateModelLoaded() -{ - if (_state == State::MODEL_LOADED) - { - assert(_subgraphs); - assert(_compiler); - assert(!_execution); - assert(!primary_subgraph()->isBuildingPhase()); - return true; - } - else - { - return false; - } -} - -bool nnfw_session::isStatePrepared() -{ - if (_state == State::PREPARED) - { - assert(!_subgraphs); - assert(_compiler); - assert(_execution); - assert(!primary_subgraph()->isBuildingPhase()); - return true; - } - else - { - return false; - } -} - -bool nnfw_session::isStateRunning() -{ - if (_state == State::RUNNING) - { - assert(!_subgraphs); - assert(_compiler); - assert(_execution); - assert(!primary_subgraph()->isBuildingPhase()); - return true; - } - return false; -} - -bool nnfw_session::isStateFinishedRun() -{ - if (_state == State::FINISHED_RUN) - { - assert(!_subgraphs); - assert(_compiler); - assert(_execution); - assert(!primary_subgraph()->isBuildingPhase()); - return true; - } - else - { - return false; - } -} - -bool nnfw_session::isStatePreparedOrFinishedRun() -{ - return isStatePrepared() || isStateFinishedRun(); -} - -NNFW_STATUS nnfw_session::input_tensorindex(const char *tensorname, uint32_t *index) -{ - return getTensorIndexImpl(*primary_subgraph(), tensorname, index, true); -} - -NNFW_STATUS nnfw_session::output_tensorindex(const char *tensorname, uint32_t *index) -{ - return getTensorIndexImpl(*primary_subgraph(), tensorname, index, false); -} diff --git a/runtime/onert/api/src/nnfw_api_internal.h b/runtime/onert/api/src/nnfw_api_internal.h deleted file mode 100644 index 604ba38b4..000000000 --- a/runtime/onert/api/src/nnfw_api_internal.h +++ /dev/null @@ -1,161 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __API_NNFW_API_INTERNAL_H__ -#define __API_NNFW_API_INTERNAL_H__ - -#include "nnfw.h" -#include "nnfw_experimental.h" - -#include <util/GeneralConfigSource.h> - -#include <string> -#include <memory> - -namespace onert -{ -namespace frontend -{ -namespace custom -{ -class KernelRegistry; -} -} // namespace frontend -namespace exec -{ -class Execution; -} // namespace exec -namespace ir -{ -class Graph; -class Subgraphs; -} // namespace ir -namespace compiler -{ -class Compiler; -} // namespace compiler -} // namespace onert - -struct nnfw_session -{ -private: - /** - * @brief Enum class to express the session's state - * - * State transition diagram: - * - * +--------------+ - * | INITIALIZED | - * +--------------+ - * | - * | load_model - * v - * +--------------+ - * | MODEL_LOADED | - * +--------------+ - * | - * | prepare - * v - * +--------------+ - * | PREPARED | --------+ - * +--------------+ | - * | | - * | run | - * v | - * +--------------+ run | - * | | -----+ | - * +-----> | FINISHED_RUN | | | run_async - * | | | <----+ | - * | +--------------+ | - * | | | - * | await | run_async | - * | v | - * | +--------------+ | - * +------ | RUNNING | <-------+ - * +--------------+ - */ - enum class State - { - INITIALIZED, //< Session is initialized and nothing has done to it - MODEL_LOADED, //< Model is loaded - PREPARED, //< Prepared(compiled) for execution - RUNNING, //< Execution is in progress (only for asynchronous execution) - FINISHED_RUN //< Executed at least once - }; - -public: - nnfw_session(); - ~nnfw_session(); - - NNFW_STATUS load_model_from_file(const char *package_file_path); - NNFW_STATUS prepare(); - NNFW_STATUS run(); - - NNFW_STATUS run_async(); - NNFW_STATUS await(); - - NNFW_STATUS set_input(uint32_t index, NNFW_TYPE type, const void *buffer, size_t length); - NNFW_STATUS set_output(uint32_t index, NNFW_TYPE type, void *buffer, size_t length); - - NNFW_STATUS input_size(uint32_t *number); - NNFW_STATUS output_size(uint32_t *number); - - NNFW_STATUS set_input_layout(uint32_t index, NNFW_LAYOUT layout); - NNFW_STATUS set_output_layout(uint32_t index, NNFW_LAYOUT layout); - - NNFW_STATUS apply_tensorinfo(uint32_t index, nnfw_tensorinfo ti); // Will be deprecated - NNFW_STATUS set_input_tensorinfo(uint32_t index, const nnfw_tensorinfo *ti); - - NNFW_STATUS input_tensorinfo(uint32_t index, nnfw_tensorinfo *ti); - NNFW_STATUS output_tensorinfo(uint32_t index, nnfw_tensorinfo *ti); - - NNFW_STATUS set_available_backends(const char *backends); - NNFW_STATUS set_op_backend(const char *op, const char *backend); - - // - // Internal-only API - // - - NNFW_STATUS set_config(const char *key, const char *value); - NNFW_STATUS get_config(const char *key, char *value, size_t value_size); - NNFW_STATUS load_circle_from_buffer(uint8_t *buffer, size_t size); - - // - // Experimental API - // - - NNFW_STATUS register_custom_operation(const std::string &id, nnfw_custom_eval eval_func); - NNFW_STATUS input_tensorindex(const char *tensorname, uint32_t *index); - NNFW_STATUS output_tensorindex(const char *tensorname, uint32_t *index); - -private: - onert::ir::Graph *primary_subgraph(); - bool isStateInitialized(); - bool isStateModelLoaded(); - bool isStatePrepared(); - bool isStateRunning(); - bool isStateFinishedRun(); - bool isStatePreparedOrFinishedRun(); - -private: - State _state{State::INITIALIZED}; - std::shared_ptr<onert::ir::Subgraphs> _subgraphs; - std::unique_ptr<onert::compiler::Compiler> _compiler; - std::shared_ptr<onert::exec::Execution> _execution; - std::shared_ptr<onert::frontend::custom::KernelRegistry> _kernel_registry; -}; - -#endif // __API_NNFW_API_INTERNAL_H__ diff --git a/runtime/onert/api/src/nnfw_debug.cc b/runtime/onert/api/src/nnfw_debug.cc deleted file mode 100644 index b9f110390..000000000 --- a/runtime/onert/api/src/nnfw_debug.cc +++ /dev/null @@ -1,29 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "nnfw_api_internal.h" - -#include <util/ConfigSource.h> - -NNFW_STATUS nnfw_set_config(nnfw_session *session, const char *key, const char *value) -{ - return session->set_config(key, value); -} - -NNFW_STATUS nnfw_get_config(nnfw_session *session, const char *key, char *value, size_t value_size) -{ - return session->get_config(key, value, value_size); -} diff --git a/runtime/onert/backend/CMakeLists.txt b/runtime/onert/backend/CMakeLists.txt deleted file mode 100644 index 42d622aa8..000000000 --- a/runtime/onert/backend/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -set(LIB_ONERT_BACKEND_ACL_COMMON onert_backend_acl_common) - -add_subdirectory(cpu) -add_subdirectory(acl_cl) -add_subdirectory(acl_neon) -add_subdirectory(acl_common) diff --git a/runtime/onert/backend/acl_cl/Backend.h b/runtime/onert/backend/acl_cl/Backend.h deleted file mode 100644 index 5c5041378..000000000 --- a/runtime/onert/backend/acl_cl/Backend.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_BACKEND_H__ -#define __ONERT_BACKEND_ACL_CL_BACKEND_H__ - -#include <memory> -#include <backend/Backend.h> - -#include "Config.h" -#include "ConstantInitializer.h" -#include "KernelGenerator.h" -#include "TensorManager.h" -#include "Optimizer.h" -#include "AclTensorRegistry.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -class Backend : public ::onert::backend::Backend -{ -public: - Backend() : _config{std::make_shared<Config>()} {} - - std::shared_ptr<IConfig> config() const override { return _config; } - - std::unique_ptr<BackendContext> newContext(const ir::Graph &graph, - const std::shared_ptr<custom::IKernelBuilder> &, - bool is_linear_executor) const override - { - const auto &operands = graph.operands(); - const auto &operations = graph.operations(); - auto context = std::make_unique<BackendContext>(this, &graph); - auto tm = createTensorManager(is_linear_executor); - auto tr = std::make_shared<acl_common::AclTensorRegistry<TensorManager>>(tm); - auto tb = std::make_shared<TensorBuilder>(operands, tm, tr); - context->tensor_registry = tr; - context->tensor_builder = tb; - context->constant_initializer = std::make_shared<ConstantInitializer>(operands, tr); - context->kernel_gen = std::make_shared<KernelGenerator>(operands, operations, tb, tr); - context->tensor_register = nullptr; - context->optimizer = std::make_shared<Optimizer>(context.get()); - return context; - } - -private: - std::shared_ptr<IConfig> _config; -}; - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_BACKEND_H__ diff --git a/runtime/onert/backend/acl_cl/CLTimer.h b/runtime/onert/backend/acl_cl/CLTimer.h deleted file mode 100644 index 722dc68ef..000000000 --- a/runtime/onert/backend/acl_cl/CLTimer.h +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_CLTIMER_H__ -#define __ONERT_BACKEND_ACL_CL_CLTIMER_H__ - -#include <util/ITimer.h> -#include <arm_compute/core/CL/OpenCL.h> -#include <arm_compute/runtime/CL/CLScheduler.h> -#include <chrono> -#include <list> -#include <sstream> - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -/** - * @brief Class to measure CL kernels execution time - */ -class CLTimer : public util::ITimer -{ -public: - /** - * @brief This function replaces CL function, which enqueues a command to execute a kernel - * with a wrapper which remembers enqueued kernels - */ - void handleBegin() override - { - _measured_events.clear(); - - _origin_enqueue_function = arm_compute::CLSymbols::get().clEnqueueNDRangeKernel_ptr; - - auto _timer_enqueue_function = [this](cl_command_queue command_queue, cl_kernel kernel, - cl_uint work_dim, const size_t *gwo, const size_t *gws, - const size_t *lws, cl_uint num_events_in_wait_list, - const cl_event *event_wait_list, cl_event *usr_event) { - cl_event event; - cl_int enqueue_res = - this->_origin_enqueue_function(command_queue, kernel, work_dim, gwo, gws, lws, - num_events_in_wait_list, event_wait_list, &event); - this->_measured_events.emplace_back(event); - - // According to spec, if NULL was provided in usr_event - event shouldn't be returned - if (usr_event != nullptr) - { - clRetainEvent(event); - *usr_event = event; - } - return enqueue_res; - }; - arm_compute::CLSymbols::get().clEnqueueNDRangeKernel_ptr = _timer_enqueue_function; - - // Set CL_QUEUE_PROFILING_ENABLE flag for the CL command-queue, if it isn't already set - auto &cl_scheduler = arm_compute::CLScheduler::get(); - auto props = cl_scheduler.queue().getInfo<CL_QUEUE_PROPERTIES>(); - if ((props & CL_QUEUE_PROFILING_ENABLE) == 0) - { - cl_scheduler.set_queue( - cl::CommandQueue(cl_scheduler.context(), props | CL_QUEUE_PROFILING_ENABLE)); - } - }; - - /** - * @brief Get timer result by addition executed CL kernels durations - */ - void handleEnd() override - { - _timer_res = 0; - for (auto const &event : _measured_events) - { - cl_ulong start; - cl_ulong end; - event.getProfilingInfo(CL_PROFILING_COMMAND_START, &start); - event.getProfilingInfo(CL_PROFILING_COMMAND_END, &end); - _timer_res += (end - start) / 1000.f; // nanoseconds -> microseconds - } - - // Restore origin CL enqueue function - arm_compute::CLSymbols::get().clEnqueueNDRangeKernel_ptr = _origin_enqueue_function; - }; - -private: - std::function<decltype(clEnqueueNDRangeKernel)> _origin_enqueue_function; - std::list<::cl::Event> _measured_events; -}; - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_CLTIMER_H__ diff --git a/runtime/onert/backend/acl_cl/CMakeLists.txt b/runtime/onert/backend/acl_cl/CMakeLists.txt deleted file mode 100644 index 6f91d9691..000000000 --- a/runtime/onert/backend/acl_cl/CMakeLists.txt +++ /dev/null @@ -1,19 +0,0 @@ -# Unsupported architecture -nnfw_find_package(ARMCompute QUIET) -if(NOT ARMCompute_FOUND) - return() -endif(NOT ARMCompute_FOUND) - -set(LIB_ONERT_BACKEND_ACL_CL onert_backend_acl_cl) - -file(GLOB_RECURSE SOURCES "*.cc") - -add_library(${LIB_ONERT_BACKEND_ACL_CL} SHARED ${SOURCES}) - -target_link_libraries(${LIB_ONERT_BACKEND_ACL_CL} PRIVATE ${LIB_ONERT_BACKEND_ACL_COMMON}) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_CL} PRIVATE nnfw_common) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_CL} PRIVATE nnfw_coverage) - -set_target_properties(${LIB_ONERT_BACKEND_ACL_CL} PROPERTIES OUTPUT_NAME backend_acl_cl) - -install(TARGETS ${LIB_ONERT_BACKEND_ACL_CL} DESTINATION lib) diff --git a/runtime/onert/backend/acl_cl/Config.cc b/runtime/onert/backend/acl_cl/Config.cc deleted file mode 100644 index 8017bdb0b..000000000 --- a/runtime/onert/backend/acl_cl/Config.cc +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// For CLKernelLibraryEx initialization -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/CLKernelLibraryEx.h" - -#include <util/ConfigSource.h> - -#include <arm_compute/runtime/CL/CLScheduler.h> - -#include "Config.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -bool Config::initialize() -{ - if (!arm_compute::opencl_is_available()) - { - return false; - } - arm_compute::CLScheduler::get().default_init(); - // NOTE CLKernelLibraryEx must use the same context as CLScheduler - // It did not check whether another device is available. - arm_compute::CLKernelLibraryEx::get().init( - "./cl_kernels/", arm_compute::CLScheduler::get().context(), cl::Device::getDefault()); - - return true; -} - -ir::Layout Config::supportLayout(const ir::Operation &, ir::Layout frontend_layout) -{ - const std::string acl_layout_str = util::getConfigString(util::config::ACL_LAYOUT); - if (acl_layout_str == "NHWC") - { - return ir::Layout::NHWC; - } - else if (acl_layout_str == "NCHW") - { - return ir::Layout::NCHW; - } - - return frontend_layout; -} - -} // namespace acl_cl -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_cl/Config.h b/runtime/onert/backend/acl_cl/Config.h deleted file mode 100644 index f71e81b6a..000000000 --- a/runtime/onert/backend/acl_cl/Config.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_CONFIG_H__ -#define __ONERT_BACKEND_ACL_CL_CONFIG_H__ - -#include "CLTimer.h" -#include <memory> -#include <backend/IConfig.h> -#include <arm_compute/runtime/CL/CLScheduler.h> - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -class Config : public IConfig -{ -public: - std::string id() override { return "acl_cl"; } - bool initialize() override; - bool supportPermutation() override { return true; } - ir::Layout supportLayout(const ir::Operation &node, ir::Layout frontend_layout) override; - bool supportDynamicTensor() override { return false; } - bool supportFP16() override { return true; } - void sync() const override { arm_compute::CLScheduler::get().sync(); } - - std::unique_ptr<util::ITimer> timer() override { return std::make_unique<CLTimer>(); } -}; - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_CONFIG_H__ diff --git a/runtime/onert/backend/acl_cl/ConstantInitializer.cc b/runtime/onert/backend/acl_cl/ConstantInitializer.cc deleted file mode 100644 index b45b91058..000000000 --- a/runtime/onert/backend/acl_cl/ConstantInitializer.cc +++ /dev/null @@ -1,146 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <AclActivationBuilder.h> -#include <AclFunction.h> -#include <Convert.h> -#include <Swizzle.h> - -#include "ConstantInitializer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -ConstantInitializer::ConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg) - : acl_common::AclConstantInitializer{operands, tensor_reg} -{ - // DO NOTHING -} - -void ConstantInitializer::visit(const ir::operation::EmbeddingLookup &node) -{ - copyInputInitialize(node, ir::operation::EmbeddingLookup::LOOKUPS); -} - -void ConstantInitializer::visit(const ir::operation::Gather &node) -{ - copyInputInitialize(node, ir::operation::Gather::INDICES); -} - -void ConstantInitializer::visit(const ir::operation::HashtableLookup &node) -{ - copyInputInitialize(node, ir::operation::HashtableLookup::LOOKUPS); - copyInputInitialize(node, ir::operation::HashtableLookup::KEYS); -} - -void ConstantInitializer::visit(const ir::operation::SpaceToBatchND &node) -{ - const auto &block_size_index = node.getInputs().at(ir::operation::SpaceToBatchND::BLOCK_SIZE); - const auto &block_size_obj = _operands.at(block_size_index); - - if (block_size_obj.isConstant()) - { - _init_map[block_size_index] = [](const ir::Operand &model_obj, backend::ITensor &obj) { - assert(model_obj.data()); - const auto &shape = model_obj.shape(); - const auto base = reinterpret_cast<const int32_t *>(model_obj.data()->base()); - assert(model_obj.shape().rank() == 1); - obj.access([&](ITensor &tensor) { - for (size_t i = 0; i < shape.num_elements(); ++i) - { - const int32_t value = base[shape.num_elements() - i - 1]; - int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer() + - tensor.calcOffset({static_cast<int32_t>(i)})); - *into = value; - } - }); - }; - } - - const auto &paddings_index = node.getInputs().at(ir::operation::SpaceToBatchND::PADDINGS); - const auto &paddings_obj = _operands.at(paddings_index); - if (paddings_obj.isConstant()) - { - _init_map[paddings_index] = [](const ir::Operand &model_obj, backend::ITensor &obj) { - assert(model_obj.data()); - const auto &shape = model_obj.shape(); - const auto base = reinterpret_cast<const int32_t *>(model_obj.data()->base()); - assert(model_obj.shape().rank() == 2); - assert(obj.dimension(0) == 2); - obj.access([&](ITensor &tensor) { - for (auto i = 0; i < shape.dim(0); ++i) - { - for (auto j = 0; j < shape.dim(1); ++j) - { - const int32_t value = base[i * 2 + j]; - int32_t *into = reinterpret_cast<int32_t *>( - tensor.buffer() + tensor.calcOffset({shape.dim(0) - i - 1, j})); - *into = value; - } - } - }); - }; - } -} - -void ConstantInitializer::visit(const ir::operation::Reverse &node) -{ - const auto &output_index = node.getOutputs().at(0); - - const auto &input_index = node.getInputs().at(ir::operation::Reverse::Input::INPUT); - const auto &input_obj = _operands.at(input_index); - - const auto &axis_index = node.getInputs().at(ir::operation::Reverse::Input::AXIS); - const auto &axis_obj = _operands.at(axis_index); - - const auto ifm_rank = input_obj.shape().rank(); - const auto frontend_layout = this->_current_op_seq_layout; - - auto output_tensor = this->_tensor_reg->getITensor(output_index); - const auto backend_layout = output_tensor->layout(); - - if (axis_obj.isConstant()) - { - _init_map[axis_index] = [ifm_rank, frontend_layout, backend_layout](const ir::Operand &operand, - backend::ITensor &obj) { - assert(operand.data()); - - const auto axis_value = *(reinterpret_cast<const int32_t *>(operand.data()->base())); - int32_t axis_tmp = axis_value; - if (axis_tmp < 0) - { - axis_tmp = axis_tmp + ifm_rank; - } - - auto axis = - acl_common::ToARMComputeAxis(ifm_rank, axis_tmp, frontend_layout, backend_layout).value(); - - obj.access([&](ITensor &tensor) { - int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer()); - *into = (int32_t)axis; - }); - }; - } -} - -} // namespace acl_cl -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_cl/ConstantInitializer.h b/runtime/onert/backend/acl_cl/ConstantInitializer.h deleted file mode 100644 index 9f3acb461..000000000 --- a/runtime/onert/backend/acl_cl/ConstantInitializer.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_ACL_CL_CONSTANT_INITIALIZER_H__ -#define __ONERT_COMPILER_ACL_CL_CONSTANT_INITIALIZER_H__ - -#include "AclConstantInitializer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -class ConstantInitializer : public acl_common::AclConstantInitializer -{ -public: - ConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg); - -public: - using acl_common::AclConstantInitializer::visit; - void visit(const ir::operation::EmbeddingLookup &) final; - void visit(const ir::operation::Gather &) final; - void visit(const ir::operation::HashtableLookup &) final; - void visit(const ir::operation::SpaceToBatchND &) final; - void visit(const ir::operation::Reverse &) final; -}; - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_COMPILER_ACL_CL_CONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/backend/acl_cl/KernelGenerator.cc b/runtime/onert/backend/acl_cl/KernelGenerator.cc deleted file mode 100644 index cc9afcaeb..000000000 --- a/runtime/onert/backend/acl_cl/KernelGenerator.cc +++ /dev/null @@ -1,1618 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "KernelGenerator.h" - -#include <arm_compute/runtime/CL/CLFunctions.h> // Include all ARM Compute CL functions -#include <arm_compute/runtime/CL/CLFunctionsEx.h> // Include all ARM Compute EX CL functions - -#include <AclActivationBuilder.h> -#include <AclFunction.h> -#include <Convert.h> -#include <Swizzle.h> - -#include "ir/Index.h" -#include "ir/DataType.h" -#include "ir/InternalType.h" -#include "exec/NopFunction.h" -#include "exec/FunctionSequence.h" -#include "util/logging.h" -#include "util/Utils.h" -#include "AclKernelGen.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -using ::onert::backend::acl_common::asAclFunction; -using ActivationBuilder = ::onert::backend::acl_common::AclActivationBuilder< - ::arm_compute::ICLTensor, ::arm_compute::CLActivationLayer, acl_common::AclFunction>; - -KernelGenerator::KernelGenerator( - const ir::Operands &operands_ctx, const ir::Operations &operations_ctx, - const std::shared_ptr<TensorBuilder> &tensor_builder, - const std::shared_ptr<acl_common::AclTensorRegistry<TensorManager>> &tensor_reg) - : _ctx(operands_ctx), _operations_ctx(operations_ctx), _tensor_builder(tensor_builder), - _tensor_reg(tensor_reg), _current_op_seq_layout(ir::Layout::UNKNOWN) -{ - // DO NOTHING -} - -void KernelGenerator::visit(const ir::OpSequence &op_seq) -{ - // TODO Move this to IKernelGenerator - // (all derivatives have the same implementation for this) - assert(!_return_fn_seq); - _return_fn_seq = std::make_unique<exec::FunctionSequence>(); - _return_fn_seq->enableDynamicShapeInferer(false); - - _current_op_seq_layout = op_seq.getLayout(); - for (const auto &operation_idx : op_seq.operations()) - { - const auto &node = _operations_ctx.at(operation_idx); - node.accept(*this); - _return_fn_seq->append(releaseFunction()); - } -} - -void KernelGenerator::visit(const ir::operation::BatchToSpaceND &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)}; - const auto block_size_index{ - node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto block_size_tensor = _tensor_reg->getAclTensor(block_size_index); - - assert(_ctx.at(block_size_index).data()); - - auto fn = acl_common::generateLayer<arm_compute::CLBatchToSpaceLayer>( - ifm_tensor->handle(), block_size_tensor->handle(), ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::BinaryArithmetic &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)}; - - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index); - - const auto act_info = acl_common::asActivationLayerInfo(activation); - - std::unique_ptr<arm_compute::IFunction> fn; - switch (node.param().arithmetic_type) - { - case ir::operation::BinaryArithmetic::ArithmeticType::ADD: - { - fn = acl_common::generateLayer<arm_compute::CLArithmeticAddition>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), - arm_compute::ConvertPolicy::SATURATE, act_info); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::SUB: - { - fn = acl_common::generateLayer<arm_compute::CLArithmeticSubtraction>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), - arm_compute::ConvertPolicy::SATURATE, act_info); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::MUL: - { - fn = acl_common::generateLayer<arm_compute::CLPixelWiseMultiplication>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), 1.0, // scale - arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_NEAREST_EVEN, - act_info); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::DIV: - { - fn = acl_common::generateLayer<arm_compute::CLArithmeticDivision>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), act_info); - break; - } - default: - assert(false && "The BinaryArithmetic operation supports only binary arithmetic operations"); - break; - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Conv2D &node) -{ - using ir::operation::Conv2D; - - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)}; - const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)}; - const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)}; - - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]. - const auto &ker_shape = _ctx.at(ker_index).shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - - const auto stride = node.param().stride; - const auto padding = ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, - ker_width, ker_height); - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto ker_tensor = _tensor_reg->getAclTensor(ker_index); - auto bias_tensor = _tensor_reg->getAclTensor(bias_index); - - const auto conv_info = acl_common::asPadStrideInfo(padding, stride); - const auto act_info = acl_common::asActivationLayerInfo(activation); - - auto fn = acl_common::generateLayer<arm_compute::CLConvolutionLayer>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), ifm_tensor->handle(), - ker_tensor->handle(), bias_tensor->handle(), ofm_tensor->handle(), conv_info, - ::arm_compute::WeightsInfo(), ::arm_compute::Size2D(1U, 1U), act_info); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node) -{ - using ir::operation::DepthwiseConv2D; - - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)}; - const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)}; - const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)}; - - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - // Kernel format is [1, kernel_height, kernel_width, depth_out]. - const auto &ker_shape = _ctx.at(ker_index).shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - - const auto stride = node.param().stride; - const auto padding = ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, - ker_width, ker_height); - const auto multiplier = node.param().multiplier; - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto ker_tensor = _tensor_reg->getAclTensor(ker_index); - auto bias_tensor = _tensor_reg->getAclTensor(bias_index); - - const auto conv_info = acl_common::asPadStrideInfo(padding, stride); - const auto act_info = acl_common::asActivationLayerInfo(activation); - - { - auto fn = acl_common::generateLayer<arm_compute::CLDepthwiseConvolutionLayer>( - ifm_tensor->handle(), ker_tensor->handle(), bias_tensor->handle(), ofm_tensor->handle(), - conv_info, multiplier, act_info); - - _return_fn = asAclFunction(std::move(fn)); - } -} - -void KernelGenerator::visit(const ir::operation::Concat &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - std::vector<ir::OperandIndex> input_indexes; - - for (const auto &input : node.getInputs()) - input_indexes.emplace_back(input); - - const auto axis = node.param().axis; - - // Concat elimination check - bool eliminated = _tensor_builder->areSubTensorsOf(ofm_index, node.getInputs()); - if (eliminated) - { - // If concat eliminated, return a NOP IFunction - VERBOSE(acl_cl_KernelGenerator_Concat) << "Concat eliminated" << std::endl; - _return_fn = std::make_unique<exec::NopFunction>(); - return; - } - - auto output_tensor = _tensor_reg->getAclTensor(ofm_index); - std::vector<::arm_compute::ICLTensor *> input_tensors; - for (auto &ifm_ind : input_indexes) - input_tensors.emplace_back(_tensor_reg->getAclTensor(ifm_ind)->handle()); - - std::unique_ptr<::arm_compute::IFunction> fn; - if (input_indexes.size() < 2) - { - fn = acl_common::generateLayer<arm_compute::CLCopy>(input_tensors.at(0), - output_tensor->handle()); - } - else - { - const auto rank = _ctx.at(ofm_index).shape().rank(); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = output_tensor->layout(); - const auto fixed_axis = - acl_common::ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value(); - fn = acl_common::generateLayer<::arm_compute::CLConcatenateLayer>( - input_tensors, output_tensor->handle(), fixed_axis); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::FullyConnected &node) -{ - const auto output_index{node.getOutputs().at(0)}; - auto output_tensor = _tensor_reg->getAclTensor(output_index); - const auto activation = node.param().activation; - - auto fn = acl_common::kernelGenFullyConnected<acl_common::AclFunction, ::arm_compute::ICLTensor, - ::arm_compute::CLFullyConnectedReshapingLayer>( - node, _ctx, _tensor_builder, _tensor_reg, _current_op_seq_layout); - _return_fn = std::make_unique<exec::FunctionSequence>( - std::move(fn), ActivationBuilder::generate(activation, output_tensor->handle())); -} - -void KernelGenerator::visit(const ir::operation::Reduce &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reduce::Input::INPUT)}; - const auto axes_index{node.getInputs().at(ir::operation::Reduce::Input::AXES)}; - const auto keep_dims{node.param().keep_dims}; - const auto reduce_type = node.param().reduce_type; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - // Convert to ACL axes taking into account negative values and possible duplicates. - const auto &axes = _ctx.at(axes_index); - const auto input_rank = _ctx.at(input_index).shape().rank(); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = input_tensor->layout(); - - std::unique_ptr<arm_compute::IFunction> fn; - if (reduce_type == ir::operation::Reduce::ReduceType::MEAN) - { - const auto acl_axes = - acl_common::asCoordinates(axes, input_rank, frontend_layout, backend_layout); - fn = acl_common::generateLayer<arm_compute::CLReduceMean>(input_tensor->handle(), acl_axes, - keep_dims, output_tensor->handle()); - } - else - { - const auto acl_axes = acl_common::asSet(axes, input_rank, frontend_layout, backend_layout); - - fn = acl_common::generateLayer<arm_compute::CLReduceOperation>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(), - output_tensor->handle(), acl_axes, keep_dims, acl_common::convertReduceType(reduce_type)); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Reshape &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - // NOTE This operation must not be changed the layout from frontend to backend - // So, PermutationOperationPass makes layouts of frontend and backend the same. - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = output_tensor->layout(); - assert((_ctx.at(input_index).shape().rank() < 4 && _ctx.at(output_index).shape().rank() < 4) || - frontend_layout == backend_layout); - UNUSED_RELEASE(frontend_layout); - UNUSED_RELEASE(backend_layout); - - auto fn = acl_common::generateLayer<arm_compute::CLReshapeLayer>(input_tensor->handle(), - output_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Squeeze &node) -{ - // Squeeze is identical to reshape except that it has an optional dimensions input. - // In addition, optional dims_index is ignored since output tensor already has squeezed shape - // by freezer and toco - // TODO Support multi-layout for frontend and backend - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)}; - const auto dims{node.param().dims}; - const auto ndim{node.param().ndim}; - (void)dims; - (void)ndim; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - auto fn = acl_common::generateLayer<arm_compute::CLReshapeLayer>(input_tensor->handle(), - output_tensor->handle()); - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Softmax &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)}; - - const auto beta = node.param().beta; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - auto fn = acl_common::generateLayer<arm_compute::CLSoftmaxLayer>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(), - output_tensor->handle(), beta); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Slice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Slice::Input::INPUT)}; - const auto begins_index{node.getInputs().at(ir::operation::Slice::Input::BEGINS)}; - const auto sizes_index{node.getInputs().at(ir::operation::Slice::Input::SIZES)}; - - auto outputData_tensor = _tensor_reg->getAclTensor(output_index); - auto inputData_tensor = _tensor_reg->getAclTensor(input_index); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = inputData_tensor->layout(); - - // Set initializers for indices data such as order of inputData - int input_rank = _ctx.at(input_index).shape().rank(); - std::vector<int32_t> starts; - std::vector<int32_t> ends; - starts.resize(input_rank, 0); - ends.resize(input_rank, 0); - { - assert(_ctx.at(begins_index).data()); - assert(_ctx.at(sizes_index).data()); - auto beginData_base = _ctx.at(begins_index).data()->base(); - auto sizeData_base = _ctx.at(sizes_index).data()->base(); - const int beginData_size = _ctx.at(begins_index).shape().num_elements(); - const int sizeData_size = _ctx.at(sizes_index).shape().num_elements(); - - using ir::DataType; - - UNUSED_RELEASE(beginData_size); - UNUSED_RELEASE(sizeData_size); - - assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32); - assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32); - assert(beginData_size == input_rank); - assert(sizeData_size == input_rank); - - assert(beginData_base != nullptr); - for (int n = 0; n < input_rank; ++n) - { - auto axis = ::onert::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout, - backend_layout) - .value(); - - int32_t begin_value = *(reinterpret_cast<const int32_t *>(beginData_base) + n); - starts[axis] = begin_value; - - int32_t size_value = *(reinterpret_cast<const int32_t *>(sizeData_base) + n); - ends[axis] = begin_value + size_value; - } - } - - ::arm_compute::Coordinates starts_set; - ::arm_compute::Coordinates ends_set; - - for (size_t i = 0; i < starts.size(); ++i) - { - starts_set.set(i, starts[i]); - ends_set.set(i, ends[i]); - } - - auto fn = acl_common::generateLayer<arm_compute::CLSlice>( - inputData_tensor->handle(), outputData_tensor->handle(), starts_set, ends_set); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::StridedSlice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)}; - const auto starts_index{node.getInputs().at(ir::operation::StridedSlice::Input::STARTS)}; - const auto ends_index{node.getInputs().at(ir::operation::StridedSlice::Input::ENDS)}; - const auto strides_index{node.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)}; - - auto outputData_tensor = _tensor_reg->getAclTensor(output_index); - auto inputData_tensor = _tensor_reg->getAclTensor(input_index); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = inputData_tensor->layout(); - - // Set initializers for indices data such as order of inputData - int input_rank = _ctx.at(input_index).shape().rank(); - std::vector<int32_t> starts; - std::vector<int32_t> ends; - std::vector<int32_t> strides; - starts.resize(input_rank, 0); - ends.resize(input_rank, 0); - strides.resize(input_rank, 0); - { - assert(_ctx.at(starts_index).data()); - assert(_ctx.at(ends_index).data()); - assert(_ctx.at(strides_index).data()); - auto startData_base = _ctx.at(starts_index).data()->base(); - auto endData_base = _ctx.at(ends_index).data()->base(); - auto stridesData_base = _ctx.at(strides_index).data()->base(); - const int startData_size = _ctx.at(starts_index).shape().num_elements(); - const int endData_size = _ctx.at(ends_index).shape().num_elements(); - const int stridesData_size = _ctx.at(strides_index).shape().num_elements(); - - using ir::DataType; - - UNUSED_RELEASE(startData_size); - UNUSED_RELEASE(endData_size); - UNUSED_RELEASE(stridesData_size); - - assert(_ctx.at(starts_index).typeInfo().type() == DataType::INT32); - assert(_ctx.at(ends_index).typeInfo().type() == DataType::INT32); - assert(_ctx.at(strides_index).typeInfo().type() == DataType::INT32); - assert(startData_size == input_rank); - assert(endData_size == input_rank); - assert(stridesData_size == input_rank); - - assert(startData_base != nullptr); - for (int n = 0; n < input_rank; ++n) - { - auto axis = ::onert::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout, - backend_layout) - .value(); - - int32_t start_value = *(reinterpret_cast<const int32_t *>(startData_base) + n); - starts[axis] = start_value; - - int32_t end_value = *(reinterpret_cast<const int32_t *>(endData_base) + n); - ends[axis] = end_value; - - int32_t strides_value = *(reinterpret_cast<const int32_t *>(stridesData_base) + n); - strides[axis] = strides_value; - } - } - - // Set mask bits such as order of inputData - const auto begin_mask = acl_common::ReorderBits<int32_t>(node.param().begin_mask, input_rank, - frontend_layout, backend_layout); - const auto end_mask = acl_common::ReorderBits<int32_t>(node.param().end_mask, input_rank, - frontend_layout, backend_layout); - const auto shrink_axis_mask = acl_common::ReorderBits<int32_t>( - node.param().shrink_axis_mask, input_rank, frontend_layout, backend_layout); - - ::arm_compute::Coordinates starts_set; - ::arm_compute::Coordinates ends_set; - ::arm_compute::BiStrides strides_set; - - for (size_t i = 0; i < starts.size(); ++i) - { - starts_set.set(i, starts[i]); - ends_set.set(i, ends[i]); - strides_set.set(i, strides[i]); - } - - // Disable applied dim_correction - if (inputData_tensor->num_dimensions() != inputData_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(inputData_tensor); - } - - auto fn = acl_common::generateLayer<arm_compute::CLStridedSlice>( - inputData_tensor->handle(), outputData_tensor->handle(), starts_set, ends_set, strides_set, - begin_mask, end_mask, shrink_axis_mask); - - // Revert disabling applied dim_correction - if (inputData_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(inputData_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Transpose &node) -{ - const auto ofm_idx{node.getOutputs().at(0)}; - const auto ifm_idx{node.getInputs().at(ir::operation::Transpose::Input::INPUT)}; - const auto perm_idx{node.getInputs().at(ir::operation::Transpose::Input::PERMUTATION)}; - - const auto rank = _ctx.at(ifm_idx).shape().rank(); - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_idx); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_idx); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = ifm_tensor->layout(); - - const auto &perms = _ctx.at(perm_idx); - std::vector<int32_t> pv; - if (perms.shape() == ir::Shape{0}) - { - pv.resize(rank); - std::iota(pv.begin(), pv.end(), 0); - std::reverse(pv.begin(), pv.end()); - } - else - { - pv = _ctx.at(perm_idx).asVector<int32_t>(); - } - - std::unique_ptr<arm_compute::IFunction> fn; - if (rank == 1) - { - fn = acl_common::generateLayer<arm_compute::CLCopy>(ifm_tensor->handle(), ofm_tensor->handle()); - } - else if (rank == 2) - { - assert(pv.size() == 2 && pv.at(0) == 1 && pv.at(1) == 0); - fn = acl_common::generateLayer<arm_compute::CLTranspose>(ifm_tensor->handle(), - ofm_tensor->handle()); - } - else - { - auto backend_pv = - acl_common::getARMComputePermutationVector(rank, pv, frontend_layout, backend_layout); - - fn = acl_common::generateLayer<arm_compute::CLPermute>(ifm_tensor->handle(), - ofm_tensor->handle(), backend_pv); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseActivation &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ElementwiseActivation::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - const ::arm_compute::ActivationLayerInfo act_info = acl_common::asActivationLayerInfo( - node.param().op_type, node.param().alpha, node.param().beta); - - auto fn = acl_common::generateLayer<arm_compute::CLActivationLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), act_info); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseBinary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::RHS)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index); - - std::unique_ptr<arm_compute::IFunction> fn; - switch (node.param().op_type) - { - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_AND: - { - fn = acl_common::generateLayer<arm_compute::CLBinaryLogicalOp>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle(), - arm_compute::BinaryLogicalOperation::AND); - break; - } - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR: - { - fn = acl_common::generateLayer<arm_compute::CLBitwiseOr>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle()); - break; - } - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MAX: - { - fn = acl_common::generateLayer<arm_compute::CLElementwiseMax>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle()); - break; - } - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MIN: - { - fn = acl_common::generateLayer<arm_compute::CLElementwiseMin>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle()); - break; - } - default: - { - std::string err_msg("acl_cl KernelGenerator : " + node.name() + - "is not elementwise-binary operations"); - assert(false && err_msg.c_str()); - break; - } - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseUnary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - std::unique_ptr<arm_compute::IFunction> fn; - switch (node.param().op_type) - { - case ir::operation::ElementwiseUnary::Type::ABS: - { - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::ABS}; - - fn = acl_common::generateLayer<arm_compute::CLActivationLayer>( - input_tensor->handle(), output_tensor->handle(), act_info); - break; - } - case ir::operation::ElementwiseUnary::Type::CAST: - { - if (input_tensor->data_type() == output_tensor->data_type()) - { - fn = acl_common::generateLayer<arm_compute::CLCopy>(input_tensor->handle(), - output_tensor->handle()); - } - else if (_ctx.at(input_index).typeInfo().type() == ir::DataType::BOOL8) - { - fn = acl_common::generateLayer<arm_compute::CLCastBool>(input_tensor->handle(), - output_tensor->handle()); - } - else - { - // TODO Support converting float to int32 as round down - fn = acl_common::generateLayer<arm_compute::CLCast>( - input_tensor->handle(), output_tensor->handle(), arm_compute::ConvertPolicy::SATURATE); - } - break; - } - case ir::operation::ElementwiseUnary::Type::DEQUANTIZE: - { - fn = acl_common::generateLayer<arm_compute::CLDequantizationLayer>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::EXP: - { - fn = acl_common::generateLayer<arm_compute::CLExpLayer>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::FLOOR: - { - fn = acl_common::generateLayer<arm_compute::CLFloor>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::LOGICAL_NOT: - { - fn = acl_common::generateLayer<arm_compute::CLBitwiseNot>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::NEG: - { - fn = acl_common::generateLayer<arm_compute::CLNeg>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::RSQRT: - { - fn = acl_common::generateLayer<arm_compute::CLRsqrtLayer>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::SQRT: - { - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::SQRT}; - - fn = acl_common::generateLayer<arm_compute::CLActivationLayer>( - input_tensor->handle(), output_tensor->handle(), act_info); - break; - } - default: - { - throw std::runtime_error("acl_cl KernelGenerator : " + node.name() + "is not supported yet"); - break; - } - } - - auto acl_fn = asAclFunction(std::move(fn)); - - _return_fn = std::move(acl_fn); -} - -void KernelGenerator::visit(const ir::operation::ExpandDims &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ExpandDims::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - auto fn = acl_common::generateLayer<arm_compute::CLReshapeLayer>(input_tensor->handle(), - output_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::InstanceNorm &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::InstanceNorm::Input::INPUT)}; - const auto gamma_index{node.getInputs().at(ir::operation::InstanceNorm::Input::GAMMA)}; - const auto beta_index{node.getInputs().at(ir::operation::InstanceNorm::Input::BETA)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto gamma_tensor = _tensor_reg->getAclTensor(gamma_index); - auto beta_tensor = _tensor_reg->getAclTensor(beta_index); - auto epsilon = node.param().epsilon; - auto activation = node.param().activation; - - auto fn = acl_common::generateLayer<arm_compute::CLInstanceNormalizationLayerEx>( - ifm_tensor->handle(), ofm_tensor->handle(), gamma_tensor->handle(), beta_tensor->handle(), - epsilon); - - _return_fn = std::make_unique<exec::FunctionSequence>( - asAclFunction(std::move(fn)), ActivationBuilder::generate(activation, ofm_tensor->handle())); -} - -void KernelGenerator::visit(const ir::operation::LSTM &node) -{ - _return_fn = acl_common::kernelGenLSTM<acl_common::AclFunction, ::arm_compute::ICLTensor, - ::arm_compute::CLLSTMLayer>(node, _ctx, _tensor_reg); -} - -void KernelGenerator::visit(const ir::operation::Comparison &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input0_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)}; - const auto input1_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)}; - - const auto comparison_type = node.param().comparison_type; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input0_tensor = _tensor_reg->getAclTensor(input0_index); - auto input1_tensor = _tensor_reg->getAclTensor(input1_index); - - auto fn = acl_common::generateLayer<arm_compute::CLComparison>( - input0_tensor->handle(), input1_tensor->handle(), output_tensor->handle(), - (arm_compute::ComparisonOperation)comparison_type); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::OneHot &node) -{ - const auto output_idx{node.getOutputs().at(0)}; - const auto indices_idx{node.getInputs().at(ir::operation::OneHot::Input::INDICES)}; - const auto depth_idx{node.getInputs().at(ir::operation::OneHot::Input::DEPTH)}; - const auto onvalue_idx{node.getInputs().at(ir::operation::OneHot::Input::ON_VALUE)}; - const auto offvalue_idx{node.getInputs().at(ir::operation::OneHot::Input::OFF_VALUE)}; - const auto depth = _ctx.at(depth_idx).asScalar<int32_t>(); - assert(depth > 0); - - auto output_tensor = _tensor_reg->getAclTensor(output_idx); - auto indices_tensor = _tensor_reg->getAclTensor(indices_idx); - auto onvalue_tensor = _tensor_reg->getAclTensor(onvalue_idx); - - const size_t output_rank = _ctx.at(output_idx).shape().rank(); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = output_tensor->layout(); - int32_t axis = node.param().axis == -1 ? output_rank - 1 : node.param().axis; - axis = acl_common::ToARMComputeAxis(output_rank, axis, frontend_layout, backend_layout).value(); - - if (output_tensor->num_dimensions() != output_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and output_tensor is applied dim_correction - acl_common::disableDimCorrection(output_tensor); - } - - std::unique_ptr<::arm_compute::IFunction> fn; - const auto &offvalue = _ctx.at(offvalue_idx); - if (offvalue.isConstant()) - { - fn = acl_common::generateLayer<arm_compute::CLOneHot>( - indices_tensor->handle(), onvalue_tensor->handle(), output_tensor->handle(), - acl_common::asPixelValue(offvalue), static_cast<uint32_t>(depth), axis); - } - else - { - auto offvalue_tensor = _tensor_reg->getAclTensor(offvalue_idx); - fn = acl_common::generateLayer<arm_compute::CLOneHot>( - indices_tensor->handle(), onvalue_tensor->handle(), offvalue_tensor->handle(), - output_tensor->handle(), static_cast<uint32_t>(depth), axis); - } - - if (output_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(output_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Pack &node) -{ - const auto output_index{node.getOutputs().at(0)}; - auto axis{node.param().axis}; - - const auto output_rank = _ctx.at(output_index).shape().rank(); - - std::vector<ir::OperandIndex> input_indexes; - for (const auto &input_index : node.getInputs()) - input_indexes.emplace_back(input_index); - - auto output = _tensor_reg->getAclTensor(output_index)->handle(); - std::vector<arm_compute::ICLTensor *> inputs; - for (const auto &input_index : input_indexes) - inputs.emplace_back(_tensor_reg->getAclTensor(input_index)->handle()); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = _tensor_reg->getAclTensor(output_index)->layout(); - - if (axis < 0) - axis += output_rank; - axis = acl_common::ToARMComputeAxis(output_rank, axis, frontend_layout, backend_layout).value(); - - // Disable applied dim_correction - for (const auto &input_index : input_indexes) - { - const auto &input_tensor = _tensor_reg->getAclTensor(input_index); - if (input_tensor->num_dimensions() != input_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(input_tensor); - } - } - - auto fn = acl_common::generateLayer<arm_compute::CLStackLayer>(inputs, axis, output); - - // Revert disabling applied dim_correction - for (const auto &input_index : input_indexes) - { - const auto &input_tensor = _tensor_reg->getAclTensor(input_index); - if (input_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(input_tensor); - } - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Pool2D &node) -{ - auto raw_fn = acl_common::kernelGenPool2D<::arm_compute::CLPoolingLayer>( - node, _ctx, _tensor_reg, _current_op_seq_layout, - acl_common::convertPoolType(node.param().op_type)); - - const auto ofm_index{node.getOutputs().at(0)}; - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - const auto activation = node.param().activation; - _return_fn = std::make_unique<exec::FunctionSequence>( - asAclFunction(std::move(raw_fn)), - ActivationBuilder::generate(activation, ofm_tensor->handle())); -} - -void KernelGenerator::visit(const ir::operation::Permute &node) -{ - const auto ofm_idx{node.getOutputs().at(0)}; - const auto ifm_idx{node.getInputs().at(0)}; - const auto permute_type = node.getPermuteType(); - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_idx); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_idx); - const auto rank = _ctx.at(ofm_idx).shape().rank(); - assert(_ctx.at(ifm_idx).shape().rank() == _ctx.at(ofm_idx).shape().rank()); - - std::unique_ptr<::arm_compute::IFunction> fn; - arm_compute::PermutationVector pv; - if (permute_type == ir::operation::Permute::Type::NCHW_TO_NHWC && rank == 4) - { - // WHCN -> CWHN - pv = arm_compute::PermutationVector{2, 0, 1}; - - fn = acl_common::generateLayer<arm_compute::CLPermute>(ifm_tensor->handle(), - ofm_tensor->handle(), pv); - } - else if (permute_type == ir::operation::Permute::Type::NHWC_TO_NCHW && rank == 4) - { - // CWHN -> WHCN - pv = arm_compute::PermutationVector{1, 2, 0}; - - fn = acl_common::generateLayer<::arm_compute::CLPermute>(ifm_tensor->handle(), - ofm_tensor->handle(), pv); - } - else - { - fn = acl_common::generateLayer<arm_compute::CLCopy>(ifm_tensor->handle(), ofm_tensor->handle()); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ResizeBilinear &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ResizeBilinear::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - auto fn = acl_common::generateLayer<arm_compute::CLScale>( - ifm_tensor->handle(), ofm_tensor->handle(), ::arm_compute::InterpolationPolicy::BILINEAR, - ::arm_compute::BorderMode::REPLICATE, ::arm_compute::PixelValue(0.f), - ::arm_compute::SamplingPolicy::TOP_LEFT); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ResizeNearestNeighbor &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ResizeNearestNeighbor::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - auto fn = acl_common::generateLayer<arm_compute::CLScale>( - ifm_tensor->handle(), ofm_tensor->handle(), - ::arm_compute::InterpolationPolicy::NEAREST_NEIGHBOR, ::arm_compute::BorderMode::REPLICATE, - ::arm_compute::PixelValue(0.f), ::arm_compute::SamplingPolicy::TOP_LEFT); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::RNN &node) -{ - const auto output_index{node.getOutputs().at(ir::operation::RNN::Output::OUTPUT)}; - const auto hidden_state_out_index{ - node.getOutputs().at(ir::operation::RNN::Output::HIDDEN_STATE_OUT)}; - - const auto input_index{node.getInputs().at(ir::operation::RNN::Input::INPUT)}; - const auto weights_index{node.getInputs().at(ir::operation::RNN::Input::WEIGHTS)}; - const auto recurrent_weights_index{ - node.getInputs().at(ir::operation::RNN::Input::RECURRENT_WEIGHTS)}; - const auto bias_index{node.getInputs().at(ir::operation::RNN::Input::BIAS)}; - const auto hidden_state_in_index{node.getInputs().at(ir::operation::RNN::Input::HIDDEN_STATE_IN)}; - - const auto activation = node.param().activation; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto hidden_state_out_tensor = _tensor_reg->getAclTensor(hidden_state_out_index); - - auto input_tensor = _tensor_reg->getAclTensor(input_index); - auto weights_tensor = _tensor_reg->getAclTensor(weights_index); - auto recurrent_weights_tensor = _tensor_reg->getAclTensor(recurrent_weights_index); - auto bias_tensor = _tensor_reg->getAclTensor(bias_index); - auto hidden_state_in_tensor = _tensor_reg->getAclTensor(hidden_state_in_index); - auto act_info = ::onert::backend::acl_common::asActivationLayerInfo(activation); - - auto copy_layer = acl_common::generateLayer<arm_compute::CLCopy>( - hidden_state_in_tensor->handle(), hidden_state_out_tensor->handle()); - _return_fn = asAclFunction(std::move(copy_layer)); - - auto fn = acl_common::generateLayer<arm_compute::CLRNNLayer>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(), - weights_tensor->handle(), recurrent_weights_tensor->handle(), bias_tensor->handle(), - hidden_state_out_tensor->handle(), output_tensor->handle(), act_info); - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::SpaceToBatchND &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)}; - const auto block_size_index{ - node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)}; - const auto paddings_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto block_size_tensor = _tensor_reg->getAclTensor(block_size_index); - auto paddings_tensor = _tensor_reg->getAclTensor(paddings_index); - - assert(_ctx.at(block_size_index).data()); - assert(_ctx.at(paddings_index).data()); - - auto fn = acl_common::generateLayer<arm_compute::CLSpaceToBatchLayer>( - ifm_tensor->handle(), block_size_tensor->handle(), paddings_tensor->handle(), - ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::SpaceToDepth &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)}; - - auto block_size = node.param().block_size; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - auto fn = acl_common::generateLayer<arm_compute::CLSpaceToDepthLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), block_size); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::EmbeddingLookup &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lookups_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::LOOKUPS)}; - const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto lookups_tensor = _tensor_reg->getAclTensor(lookups_index); - auto values_tensor = _tensor_reg->getAclTensor(values_index); - - auto fn = acl_common::generateLayer<arm_compute::CLEmbeddingLookup>( - values_tensor->handle(), output_tensor->handle(), lookups_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::L2Normalization &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::L2Normalization::Input::INPUT)}; - - // {CL|Neon}L2Normalization performs the reduction only along dimension 0 - // L2 Normalization always performs the reduction along the depth axis - // Thus, we repurpose {CL|Neon}NormalizationLayers to act as depthwise L2 normalizations by - // choosing normalization parameters as below - - const auto &ifm_shape = _ctx.at(ifm_index).shape(); - // TODO Support optional constant dimension that normalization would be performed on - const auto normalization_axis = _ctx.at(ifm_index).shape().rank() - 1; - int32_t radius = - 2 * ifm_shape.dim(normalization_axis) + 1; // normSize = depth(last dimension) * 2 + 1 - float alpha = 1.0f; // In the implementation to make alpha_ become 1 - float beta = 0.5f; // pow(reduction, -0.5) = 1 / sqrt(reduction) - float bias = 0.0f; // Don't offset the reduction. - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - const auto norm_info = ::arm_compute::NormalizationLayerInfo(::arm_compute::NormType::CROSS_MAP, - radius, alpha, beta, bias, false); - - auto fn = acl_common::generateLayer<arm_compute::CLNormalizationLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), norm_info); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::HashtableLookup &node) -{ - const auto output_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::OUTPUT)}; - const auto hits_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::HITS)}; - - const auto lookups_index{node.getInputs().at(ir::operation::HashtableLookup::Input::LOOKUPS)}; - const auto keys_index{node.getInputs().at(ir::operation::HashtableLookup::Input::KEYS)}; - const auto values_index{node.getInputs().at(ir::operation::HashtableLookup::Input::VALUES)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto hits_tensor = _tensor_reg->getAclTensor(hits_index); - - auto lookups_tensor = _tensor_reg->getAclTensor(lookups_index); - auto keys_tensor = _tensor_reg->getAclTensor(keys_index); - auto values_tensor = _tensor_reg->getAclTensor(values_index); - - auto fn = acl_common::generateLayer<arm_compute::CLHashtableLookup>( - lookups_tensor->handle(), keys_tensor->handle(), values_tensor->handle(), - output_tensor->handle(), hits_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::PReLU &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::PReLU::Input::INPUT)}; - const auto alpha_index{node.getInputs().at(ir::operation::PReLU::Input::ALPHA)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto alpha_tensor = _tensor_reg->getAclTensor(alpha_index); - - auto fn = acl_common::generateLayer<arm_compute::CLPReluLayer>( - ifm_tensor->handle(), alpha_tensor->handle(), ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::TransposeConv &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ker_index{node.getInputs().at(ir::operation::TransposeConv::Input::KERNEL)}; - const auto ifm_index{node.getInputs().at(ir::operation::TransposeConv::Input::INPUT)}; - - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ker_shape = _ctx.at(ker_index).shape().asFeature(_current_op_seq_layout); - - const auto stride = node.param().stride; - - assert((node.param().padding.type == ir::PaddingType::SAME) || - (node.param().padding.type == ir::PaddingType::VALID)); - auto padding = ir::calculatePadding(node.param().padding, ofm_shape, ifm_shape, stride, - ker_shape.W, ker_shape.H); - uint32_t invalid_horizontal = 0; - uint32_t invalid_vertical = 0; - if (node.param().padding.type == ir::PaddingType::VALID) - { - invalid_horizontal = - ofm_shape.W - (1 + (ifm_shape.W - 1) * stride.horizontal) - (ker_shape.W - 1); - invalid_vertical = ofm_shape.H - (1 + (ifm_shape.H - 1) * stride.vertical) - (ker_shape.H - 1); - } - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto ker_tensor = _tensor_reg->getAclTensor(ker_index); - - const auto tconv_info = acl_common::asPadStrideInfo(padding, stride); - - auto fn = acl_common::generateLayer<arm_compute::CLTransposeConvLayer>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), ifm_tensor->handle(), - ker_tensor->handle(), nullptr, ofm_tensor->handle(), tconv_info, invalid_horizontal, - invalid_vertical); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::SquaredDifference &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index); - - auto fn = acl_common::generateLayer<arm_compute::CLElementwiseSquaredDiff>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::TopKV2 &node) -{ - const auto outputValues_index{node.getOutputs().at(ir::operation::TopKV2::Output::OUTPUT_VALUES)}; - const auto outputIndices_index{ - node.getOutputs().at(ir::operation::TopKV2::Output::OUTPUT_INDICES)}; - - const auto inputData_index{node.getInputs().at(ir::operation::TopKV2::Input::INPUT)}; - - // Currently, we only support the vector input. - assert(_ctx.at(inputData_index).shape().rank() == 1 || - _ctx.at(inputData_index).shape().rank() == 2); - - const auto k = node.param().k; - - auto values_tensor = _tensor_reg->getAclTensor(outputValues_index); - auto indices_tensor = _tensor_reg->getAclTensor(outputIndices_index); - auto input_tensor = _tensor_reg->getAclTensor(inputData_index); - - auto fn = acl_common::generateLayer<arm_compute::CLTopKV2>( - input_tensor->handle(), k, values_tensor->handle(), indices_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Gather &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)}; - const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)}; - - const auto ifm_rank = _ctx.at(ifm_index).shape().rank(); - const auto axis_raw = node.param().axis; - const auto axis_value = (axis_raw < 0 ? (ifm_rank + axis_raw) : axis_raw); - const int axis = ::onert::backend::acl_common::ToARMComputeAxis(ifm_rank, axis_value).value(); - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto indices_tensor = _tensor_reg->getAclTensor(indices_index); - - // NOTE The frontend layout and backend layout must be the same for this operation. - // If not the same, we have to add a stage(?) to perform permutation of output tensor. It - // is not not efficient even if it works well. If so, it would be better to set the - // layout of these backend tensors to the same layout. - // There is also one thing we have to think about. This operation depends on the layout of - // a model. For example, if a model in NHWC has this operation as output rank == 4, indices - // rank == 2 and axis == 2, this operation should work as the axis W and C, but the axis W - // and C are not sequential in NCHW. So the backend in NCHW cannot handle this case. - const auto backend_layout = ofm_tensor->layout(); - UNUSED_RELEASE(backend_layout); - assert(backend_layout == ifm_tensor->layout()); - assert(backend_layout == indices_tensor->layout()); - assert(ifm_rank < 4 || _current_op_seq_layout == backend_layout); - - // input is n-D, indices k-D, output is (n + k - 1)-D - size_t n = ifm_rank; - assert(n == ifm_tensor->num_dimensions()); - size_t k = _ctx.at(indices_index).shape().rank(); - assert(k == indices_tensor->num_dimensions()); - - // Disable applied dim_correction - if (n != ifm_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and ifm tensor is applied dim_correction - acl_common::disableDimCorrection(ifm_tensor); - } - if (k != indices_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and indices tensor is applied dim_correction - acl_common::disableDimCorrection(indices_tensor); - } - - auto fn = acl_common::generateLayer<arm_compute::CLGatherEx>( - ifm_tensor->handle(), indices_tensor->handle(), ofm_tensor->handle(), axis); - - // Revert disabling applied dim_correction - if (ifm_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(ifm_tensor); - } - if (indices_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(indices_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ArgMax &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ArgMax::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::ArgMax::Input::AXIS)}; - - auto ifm_shape = _ctx.at(ifm_index).shape(); - auto ofm_shape = _ctx.at(ofm_index).shape(); - - assert((ifm_shape.rank() - 1) == ofm_shape.rank()); - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - const auto ifm_rank = _ctx.at(ifm_index).shape().rank(); - auto frontend_layout = _current_op_seq_layout; - auto backend_layout = ifm_tensor->layout(); - - int axis_value = _ctx.at(axis_index).asScalar<int32_t>(); - if (axis_value < 0) - { - axis_value += ifm_rank; - } - - auto acl_axis = - acl_common::ToARMComputeAxis(ifm_rank, axis_value, frontend_layout, backend_layout).value(); - - auto fn = acl_common::generateLayer<arm_compute::CLArgMinMaxLayerEx>( - ifm_tensor->handle(), acl_axis, ofm_tensor->handle(), - ::arm_compute::ReductionOperation::ARG_IDX_MAX); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::LocalResponseNormalization &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{ - node.getInputs().at(ir::operation::LocalResponseNormalization::Input::INPUT)}; - - auto radius = node.param().radius; - auto alpha = node.param().alpha; - auto beta = node.param().beta; - auto bias = node.param().bias; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - const auto norm_info = ::arm_compute::NormalizationLayerInfo( - ::arm_compute::NormType::CROSS_MAP, radius * 2 + 1, alpha, beta, bias, false); - - auto fn = acl_common::generateLayer<arm_compute::CLNormalizationLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), norm_info); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::DepthToSpace &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::DepthToSpace::Input::INPUT)}; - - auto block_size = node.param().block_size; - assert(block_size > 0); - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - auto fn = acl_common::generateLayer<arm_compute::CLDepthToSpaceLayer>( - input_tensor->handle(), output_tensor->handle(), block_size); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Split &node) -{ - const auto ifm_index{node.getInputs().at(ir::operation::Split::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::Split::Input::AXIS)}; - - assert(node.param().num_splits == static_cast<int>(node.getOutputs().size())); - if (!_ctx.at(axis_index).isConstant()) - { - throw std::runtime_error("Non-constant axis_index NYI for acl_cl backend"); - } - - const auto ifm_rank = _ctx.at(ifm_index).shape().rank(); - std::vector<ir::OperandIndex> output_indexes; - for (const auto &output : node.getOutputs()) - output_indexes.emplace_back(output); - - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - std::vector<arm_compute::ICLTensor *> output_tensors; - for (const auto &ofm_ind : output_indexes) - output_tensors.emplace_back(_tensor_reg->getAclTensor(ofm_ind)->handle()); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = ifm_tensor->layout(); - auto axis = _ctx.at(axis_index).asScalar<int32_t>(); - if (axis < 0) - axis += ifm_rank; - axis = acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value(); - - auto fn = - acl_common::generateLayer<arm_compute::CLSplit>(ifm_tensor->handle(), output_tensors, axis); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::SplitV &node) -{ - const auto ifm_index{node.getInputs().at(ir::operation::SplitV::Input::INPUT)}; - const auto size_split_index{node.getInputs().at(ir::operation::SplitV::Input::SIZE_SPLITS)}; - const auto split_dim_index{node.getInputs().at(ir::operation::SplitV::Input::SPLIT_DIM)}; - - assert(node.param().num_splits == static_cast<int>(node.getOutputs().size())); - - const size_t ifm_rank = _ctx.at(ifm_index).shape().rank(); - std::vector<ir::OperandIndex> output_indexes; - for (const auto &output : node.getOutputs()) - output_indexes.emplace_back(output); - - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto size_split_tensor = _tensor_reg->getAclTensor(size_split_index); - - std::vector<arm_compute::ICLTensor *> output_tensors; - for (const auto &ofm_ind : output_indexes) - output_tensors.emplace_back(_tensor_reg->getAclTensor(ofm_ind)->handle()); - - auto fn = std::make_unique<arm_compute::CLSplitVEx>(); - const auto &split_dim_op = _ctx.at(split_dim_index); - if (split_dim_op.isConstant()) - { - int32_t split_dim = split_dim_op.asScalar<int32_t>(); - uint32_t split_dim_revised = (split_dim < 0) ? (split_dim + ifm_rank) : split_dim; - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = ifm_tensor->layout(); - - if (ifm_tensor->num_dimensions() != ifm_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and ifm tensor is applied dim_correction - acl_common::disableDimCorrection(ifm_tensor); - } - - split_dim_revised = - acl_common::ToARMComputeAxis(ifm_rank, split_dim_revised, frontend_layout, backend_layout) - .value(); - fn->configure(ifm_tensor->handle(), size_split_tensor->handle(), split_dim_revised, - output_tensors, node.param().num_splits); - - if (ifm_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(ifm_tensor); - } - } - else - { - throw std::runtime_error("Non-constant split_dim NYI for acl_cl backend"); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Unpack &node) -{ - const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)}; - auto axis{node.param().axis}; - - const auto input_rank = _ctx.at(input_index).shape().rank(); - - std::vector<ir::OperandIndex> output_indexes; - for (const auto &output_index : node.getOutputs()) - output_indexes.emplace_back(output_index); - - auto input_tensor = _tensor_reg->getAclTensor(input_index); - std::vector<arm_compute::ICLTensor *> outputs; - for (const auto &output_index : output_indexes) - outputs.emplace_back(_tensor_reg->getAclTensor(output_index)->handle()); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = _tensor_reg->getAclTensor(input_index)->layout(); - if (axis < 0) - axis += input_rank; - axis = acl_common::ToARMComputeAxis(input_rank, axis, frontend_layout, backend_layout).value(); - - // Disable applied dim_correction - if (input_tensor->num_dimensions() != input_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(input_tensor); - } - - auto fn = - acl_common::generateLayer<arm_compute::CLUnstack>(input_tensor->handle(), outputs, axis); - - // Revert disabling applied dim_correction - if (input_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(input_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Pad &node) -{ - const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)}; - const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)}; - const auto output_index{node.getOutputs().at(0)}; - assert(_ctx.at(pad_index).data()); - - auto rank = _ctx.at(input_index).shape().rank(); - auto pad_base = _ctx.at(pad_index).data()->base(); - - auto input_type = _ctx.at(input_index).typeInfo(); - auto data_type = acl_common::asDataType(input_type.type()); - auto quant_info = ::arm_compute::QuantizationInfo(input_type.scale(), input_type.offset()); - const auto pixel_value = ::arm_compute::PixelValue(0, data_type, quant_info); - - auto input = _tensor_reg->getAclTensor(input_index)->handle(); - auto output = _tensor_reg->getAclTensor(output_index)->handle(); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = _tensor_reg->getAclTensor(input_index)->layout(); - - ::arm_compute::PaddingList padding_list; - padding_list.resize(rank); - for (int32_t n = 0; n < rank; ++n) - { - const int32_t *from = reinterpret_cast<const int32_t *>(pad_base) + (n * 2); - - const auto axis = - acl_common::ToARMComputeAxis(rank, n, frontend_layout, backend_layout).value(); - padding_list[axis] = ::arm_compute::PaddingInfo{from[0], from[1]}; - } - - // Disable applied dim_correction - const auto &input_tensor = _tensor_reg->getAclTensor(input_index); - if (input_tensor->num_dimensions() != input_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(input_tensor); - } - - auto fn = - acl_common::generateLayer<arm_compute::CLPadLayer>(input, output, padding_list, pixel_value); - - // NOTE Do not revert disabling applied dim_correction for 4D. - // It would produce a mistach of result by incorrect offset_first_element in - // ICLKernel::add_tensor_argument<3>(). - // We have to disable applied dim_correction and not to revert enabling for the kernel that slices - // 4D to 3D because slicing arm_compute::Window can causes incorrect offset_first_element if the - // used tensor is 4D and the tensor's high dimention is 1 - if (input_tensor->num_dimensions() < 4 && input_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(input_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ConvertFp32ToFp16 &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ConvertFp32ToFp16::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - auto fn = acl_common::generateLayer<arm_compute::CLDepthConvertLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), ::arm_compute::ConvertPolicy::SATURATE, 0); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ConvertFp16ToFp32 &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ConvertFp16ToFp32::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - auto fn = acl_common::generateLayer<arm_compute::CLDepthConvertLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), ::arm_compute::ConvertPolicy::SATURATE, 0); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Reverse &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::Reverse::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::Reverse::Input::AXIS)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto axis_tensor = _tensor_reg->getAclTensor(axis_index); - - // WORKAROUND: acl-cl backend only allow U32 type for axis - // ConstantInitializer will resolve S32 type to U32 type - if (_ctx.at(axis_index).isConstant() && - (axis_tensor->handle()->info()->data_type() == arm_compute::DataType::S32)) - { - axis_tensor->handle()->info()->set_data_type(arm_compute::DataType::U32); - } - - auto fn = acl_common::generateLayer<arm_compute::CLReverse>( - ifm_tensor->handle(), ofm_tensor->handle(), axis_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -} // namespace acl_cl -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_cl/KernelGenerator.h b/runtime/onert/backend/acl_cl/KernelGenerator.h deleted file mode 100644 index e8a922677..000000000 --- a/runtime/onert/backend/acl_cl/KernelGenerator.h +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_KERNEL_GENERATOR_H__ -#define __ONERT_BACKEND_ACL_CL_KERNEL_GENERATOR_H__ - -#include <backend/IKernelGenerator.h> - -#include "ir/Operands.h" -#include "TensorBuilder.h" -#include "AclTensorRegistry.h" -#include "TensorManager.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -class KernelGenerator : public IKernelGenerator -{ -public: - KernelGenerator(const ir::Operands &operands_ctx, const ir::Operations &operations_ctx, - const std::shared_ptr<TensorBuilder> &tensor_builder, - const std::shared_ptr<acl_common::AclTensorRegistry<TensorManager>> &_tensor_reg); - - void visit(const ir::OpSequence &) override; - void visit(const ir::operation::BatchToSpaceND &) override; - void visit(const ir::operation::BinaryArithmetic &) override; - void visit(const ir::operation::Conv2D &) override; - void visit(const ir::operation::DepthwiseConv2D &) override; - void visit(const ir::operation::Concat &) override; - void visit(const ir::operation::FullyConnected &) override; - void visit(const ir::operation::Reduce &) override; - void visit(const ir::operation::Reshape &) override; - void visit(const ir::operation::Squeeze &) override; - void visit(const ir::operation::Softmax &) override; - void visit(const ir::operation::Slice &) override; - void visit(const ir::operation::StridedSlice &) override; - void visit(const ir::operation::Transpose &) override; - void visit(const ir::operation::ElementwiseActivation &) override; - void visit(const ir::operation::ElementwiseBinary &) override; - void visit(const ir::operation::ElementwiseUnary &) override; - void visit(const ir::operation::ExpandDims &) override; - void visit(const ir::operation::InstanceNorm &) override; - void visit(const ir::operation::Comparison &) override; - void visit(const ir::operation::LSTM &) override; - void visit(const ir::operation::OneHot &) override; - void visit(const ir::operation::Pack &) override; - void visit(const ir::operation::Pool2D &) override; - void visit(const ir::operation::Permute &) override; - void visit(const ir::operation::ResizeBilinear &) override; - void visit(const ir::operation::ResizeNearestNeighbor &) override; - void visit(const ir::operation::RNN &) override; - void visit(const ir::operation::SpaceToBatchND &) override; - void visit(const ir::operation::SpaceToDepth &) override; - void visit(const ir::operation::EmbeddingLookup &) override; - void visit(const ir::operation::L2Normalization &) override; - void visit(const ir::operation::HashtableLookup &) override; - void visit(const ir::operation::PReLU &) override; - void visit(const ir::operation::TransposeConv &) override; - void visit(const ir::operation::SquaredDifference &) override; - void visit(const ir::operation::TopKV2 &) override; - void visit(const ir::operation::Gather &) override; - void visit(const ir::operation::ArgMax &) override; - void visit(const ir::operation::LocalResponseNormalization &) override; - void visit(const ir::operation::DepthToSpace &) override; - void visit(const ir::operation::Split &) override; - void visit(const ir::operation::SplitV &) override; - void visit(const ir::operation::Unpack &) override; - void visit(const ir::operation::Pad &) override; - void visit(const ir::operation::ConvertFp32ToFp16 &) override; - void visit(const ir::operation::ConvertFp16ToFp32 &) override; - void visit(const ir::operation::Reverse &) override; - -private: - const ir::Operands &_ctx; - const ir::Operations &_operations_ctx; - std::shared_ptr<TensorBuilder> _tensor_builder; - std::shared_ptr<acl_common::AclTensorRegistry<TensorManager>> _tensor_reg; - ir::Layout _current_op_seq_layout; -}; - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_KERNEL_GENERATOR_H__ diff --git a/runtime/onert/backend/acl_cl/Optimizer.cc b/runtime/onert/backend/acl_cl/Optimizer.cc deleted file mode 100644 index 9134d3fb8..000000000 --- a/runtime/onert/backend/acl_cl/Optimizer.cc +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Optimizer.h" - -#include "ParentInfo.h" - -#include <cassert> -#include <compiler/LoweredGraph.h> -#include <util/logging.h> -#include "AclSubTensorAnalyzer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -Optimizer::Optimizer(BackendContext *context) - : _context{context}, - _tensor_builder{std::dynamic_pointer_cast<TensorBuilder>(context->tensor_builder)} -{ - assert(context); -} - -void Optimizer::optimize() -{ - // Concat elimination (build subtensor info) - { - acl_common::AclSubTensorAnalyzer sa{*_context->graph()}; - for (auto op_info : _context->operation_list()) - { - auto &op = _context->graph()->operations().at(op_info.index); - sa.setLayout(op_info.layout); - op.accept(sa); - } - - _tensor_builder->parent_map(sa.releaseParentMap()); - } -} - -} // namespace acl_cl -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_cl/Optimizer.h b/runtime/onert/backend/acl_cl/Optimizer.h deleted file mode 100644 index 18d38ec1b..000000000 --- a/runtime/onert/backend/acl_cl/Optimizer.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_OPTIMIZER_H__ -#define __ONERT_BACKEND_ACL_CL_OPTIMIZER_H__ - -#include <backend/IOptimizer.h> -#include <backend/BackendContext.h> -#include "TensorBuilder.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -class Optimizer : public IOptimizer -{ -public: - Optimizer(BackendContext *context); - - void optimize() override; - -private: - BackendContext *_context; - std::shared_ptr<TensorBuilder> _tensor_builder; -}; - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_OPTIMIZER_H__ diff --git a/runtime/onert/backend/acl_cl/TensorBuilder.h b/runtime/onert/backend/acl_cl/TensorBuilder.h deleted file mode 100644 index 91502d39a..000000000 --- a/runtime/onert/backend/acl_cl/TensorBuilder.h +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_TENSOR_BUILDER_H__ -#define __ONERT_BACKEND_ACL_CL_TENSOR_BUILDER_H__ - -#include <AclTensorBuilder.h> - -#include "operand/CLTensor.h" -#include "operand/CLSubTensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -using TensorBuilder = - acl_common::AclTensorBuilder<operand::ICLTensor, operand::CLTensor, operand::CLSubTensor>; - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_TENSOR_BUILDER_H__ diff --git a/runtime/onert/backend/acl_cl/TensorManager.h b/runtime/onert/backend/acl_cl/TensorManager.h deleted file mode 100644 index ab295dbec..000000000 --- a/runtime/onert/backend/acl_cl/TensorManager.h +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_TENSOR_MANAGER_H__ -#define __ONERT_BACKEND_ACL_CL_TENSOR_MANAGER_H__ - -#include <arm_compute/runtime/CL/CLBufferAllocator.h> -#include <arm_compute/runtime/PoolManager.h> -#include <arm_compute/runtime/BlobLifetimeManager.h> -#include <arm_compute/runtime/MemoryManagerOnDemand.h> -#include <arm_compute/runtime/MemoryGroup.h> - -#include <AclMemoryManager.h> -#include <AclLinearMemoryManager.h> -#include <AclInternalBufferManager.h> -#include <AclTensorManager.h> - -#include "operand/CLTensor.h" -#include "operand/CLSubTensor.h" - -#include "util/logging.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ - -using MemoryManager = - acl_common::AclMemoryManager<operand::ICLTensor, operand::CLTensor, operand::CLSubTensor>; - -using LinearMemoryManager = acl_common::AclLinearMemoryManager< - operand::ICLTensor, operand::CLTensor, operand::CLSubTensor, - ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager, - ::arm_compute::BlobLifetimeManager, ::arm_compute::CLBufferAllocator, - ::arm_compute::MemoryGroup>; - -using InternalBufferManager = acl_common::AclInternalBufferManager< - ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager, - ::arm_compute::BlobLifetimeManager, ::arm_compute::CLBufferAllocator>; - -using TensorManager = - acl_common::AclTensorManager<operand::ICLTensor, operand::CLTensor, operand::CLSubTensor>; - -inline TensorManager *createTensorManager(bool is_linear_executor) -{ - if (is_linear_executor) - { - VERBOSE(acl_cl_createTensorManager) << "AclTensorManager as Linear" << std::endl; - return new TensorManager(new MemoryManager(), new LinearMemoryManager(), - new InternalBufferManager()); - } - else - { - VERBOSE(acl_cl_createTensorManager) << "AclTensorManager" << std::endl; - return new TensorManager(new MemoryManager(), new MemoryManager(), new InternalBufferManager()); - } -} - -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_TENSOR_MANAGER_H__ diff --git a/runtime/onert/backend/acl_cl/acl_cl.cc b/runtime/onert/backend/acl_cl/acl_cl.cc deleted file mode 100644 index 88378b13a..000000000 --- a/runtime/onert/backend/acl_cl/acl_cl.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <util/logging.h> - -#include "Backend.h" - -extern "C" { -onert::backend::Backend *onert_backend_create() -{ - VERBOSE(onert_backend_create) << "'acl_cl' loaded\n"; - return new onert::backend::acl_cl::Backend; -} - -void onert_backend_destroy(onert::backend::Backend *backend) -{ - VERBOSE(onert_backend_create) << "'acl_cl' unloaded\n"; - delete backend; -} -} diff --git a/runtime/onert/backend/acl_cl/operand/CLSubTensor.cc b/runtime/onert/backend/acl_cl/operand/CLSubTensor.cc deleted file mode 100644 index 234229787..000000000 --- a/runtime/onert/backend/acl_cl/operand/CLSubTensor.cc +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "CLSubTensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ -namespace operand -{ - -CLSubTensor::CLSubTensor(ICLTensor *parent, const arm_compute::TensorShape &tensor_shape, - const arm_compute::Coordinates &coords, size_t rank, bool extend_parent) - : _cl_sub_tensor(std::make_shared<arm_compute::CLSubTensor>(parent->handle(), tensor_shape, - coords, extend_parent)), - _rank{rank} -{ - // DO NOTHING -} - -const arm_compute::CLSubTensor *CLSubTensor::handle() const { return _cl_sub_tensor.get(); } - -arm_compute::CLSubTensor *CLSubTensor::handle() { return _cl_sub_tensor.get(); } - -} // namespace operand -} // namespace acl_cl -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_cl/operand/CLSubTensor.h b/runtime/onert/backend/acl_cl/operand/CLSubTensor.h deleted file mode 100644 index fedc17fc2..000000000 --- a/runtime/onert/backend/acl_cl/operand/CLSubTensor.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__ -#define __ONERT_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__ - -#include <arm_compute/runtime/CL/CLSubTensor.h> -#include "ICLTensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ -namespace operand -{ - -class CLSubTensor : public ICLTensor -{ -public: - CLSubTensor() = delete; - -public: - CLSubTensor(ICLTensor *parent, const arm_compute::TensorShape &tensor_shape, - const arm_compute::Coordinates &coords, size_t rank, bool extend_parent = false); - -public: - size_t num_dimensions() const final { return _rank; } - -public: - const arm_compute::CLSubTensor *handle() const override; - arm_compute::CLSubTensor *handle() override; - -public: - // This method is used to prevent the use of memcpy for SubTensor - bool has_padding() const override { return true; } - -private: - std::shared_ptr<arm_compute::CLSubTensor> _cl_sub_tensor; - size_t _rank; -}; - -} // namespace operand -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_OPERAND_CL_SUB_TENSOR_H__ diff --git a/runtime/onert/backend/acl_cl/operand/CLTensor.cc b/runtime/onert/backend/acl_cl/operand/CLTensor.cc deleted file mode 100644 index f37edff51..000000000 --- a/runtime/onert/backend/acl_cl/operand/CLTensor.cc +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "CLTensor.h" - -#include <arm_compute/runtime/CL/CLScheduler.h> -#include <arm_compute/runtime/CL/CLMemory.h> -#include <arm_compute/runtime/CL/CLMemoryRegion.h> - -#include <Convert.h> - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ -namespace operand -{ - -CLTensor::CLTensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses) - : _cl_tensor(std::make_shared<arm_compute::CLTensor>()), _rank{rank}, _num_uses{num_uses} -{ - allocator()->init(info); -} - -const arm_compute::CLTensor *CLTensor::handle() const { return _cl_tensor.get(); } - -arm_compute::CLTensor *CLTensor::handle() { return _cl_tensor.get(); } - -arm_compute::CLTensorAllocator *CLTensor::allocator() { return _cl_tensor->allocator(); } - -void CLTensor::setBuffer(void *host_ptr) -{ - // Constructs a Buffer on a user-supplied memory - auto buffer = cl::Buffer(arm_compute::CLScheduler::get().context(), - CL_MEM_USE_HOST_PTR | CL_MEM_READ_WRITE, info()->total_size(), host_ptr); - // import memory - allocator()->import_memory(buffer); -} - -} // namespace operand -} // namespace acl_cl -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_cl/operand/CLTensor.h b/runtime/onert/backend/acl_cl/operand/CLTensor.h deleted file mode 100644 index c92208803..000000000 --- a/runtime/onert/backend/acl_cl/operand/CLTensor.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__ -#define __ONERT_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__ - -#include <arm_compute/core/TensorInfo.h> -#include <arm_compute/runtime/CL/CLTensor.h> -#include <arm_compute/runtime/CL/CLScheduler.h> -#include "arm_compute/runtime/CL/CLTensorAllocator.h" -#include "ICLTensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ -namespace operand -{ - -class CLTensor : public ICLTensor -{ -public: - CLTensor() = delete; - -public: - CLTensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses); - -public: - size_t num_dimensions() const final { return _rank; } - -public: - const arm_compute::CLTensor *handle() const override; - arm_compute::CLTensor *handle() override; - size_t num_uses() const { return _num_uses; } - -public: - arm_compute::CLTensorAllocator *allocator(); - /** Set given buffer as the buffer of the tensor - * - * @note Ownership of the memory is not transferred to this object. - * Thus management (allocate/free) should be done by the client. - * - * @param[in] host_ptr Storage to be used. - */ - void setBuffer(void *host_ptr); - -private: - std::shared_ptr<arm_compute::CLTensor> _cl_tensor; - size_t _rank; - size_t _num_uses; -}; - -} // namespace operand -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_OPERAND_CL_TENSOR_H__ diff --git a/runtime/onert/backend/acl_cl/operand/ICLTensor.cc b/runtime/onert/backend/acl_cl/operand/ICLTensor.cc deleted file mode 100644 index b400ef9cf..000000000 --- a/runtime/onert/backend/acl_cl/operand/ICLTensor.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ICLTensor.h" - -#include <arm_compute/runtime/CL/CLScheduler.h> - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ -namespace operand -{ - -void ICLTensor::access(const std::function<void(ITensor &tensor)> &fn) -{ - auto &queue = ::arm_compute::CLScheduler::get().queue(); - - // This is an optional input - if (total_size() == 0) - return; - - map(queue); - fn(*this); - unmap(queue); -} -} // namespace operand -} // namespace acl_cl -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_cl/operand/ICLTensor.h b/runtime/onert/backend/acl_cl/operand/ICLTensor.h deleted file mode 100644 index 5427000f9..000000000 --- a/runtime/onert/backend/acl_cl/operand/ICLTensor.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__ -#define __ONERT_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__ - -#include <arm_compute/core/CL/ICLTensor.h> - -#include <IACLTensor.h> - -namespace onert -{ -namespace backend -{ -namespace acl_cl -{ -namespace operand -{ - -class ICLTensor : public acl_common::IACLTensor -{ -public: - const arm_compute::ICLTensor *handle() const override = 0; - arm_compute::ICLTensor *handle() override = 0; - -public: - void access(const std::function<void(ITensor &tensor)> &fn) final; - -private: - void map(cl::CommandQueue &q, bool blocking = true) { return handle()->map(q, blocking); } - void unmap(cl::CommandQueue &q) { return handle()->unmap(q); } -}; - -} // namespace operand -} // namespace acl_cl -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_CL_OPERAND_I_CL_TENSOR_H__ diff --git a/runtime/onert/backend/acl_common/AclActivationBuilder.h b/runtime/onert/backend/acl_common/AclActivationBuilder.h deleted file mode 100644 index bfdea6ea0..000000000 --- a/runtime/onert/backend/acl_common/AclActivationBuilder.h +++ /dev/null @@ -1,125 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_ACL_ACTIVATION_BUILDER_H__ -#define __ONERT_BACKEND_ACL_COMMON_ACL_ACTIVATION_BUILDER_H__ - -#include <memory> - -#include <ir/InternalType.h> -#include <exec/IFunction.h> -#include <exec/NopFunction.h> - -#include "Convert.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -template <typename T_Tensor, typename T_ActivationLayer, typename T_ExecFunction> -class AclActivationBuilder -{ -private: - static std::unique_ptr<exec::IFunction> generateReLU(T_Tensor *ifm_alloc); - static std::unique_ptr<exec::IFunction> generateReLU1(T_Tensor *ifm_alloc); - static std::unique_ptr<exec::IFunction> generateReLU6(T_Tensor *ifm_alloc); - -public: - static std::unique_ptr<exec::IFunction> generate(ir::Activation code, T_Tensor *ifm_alloc); -}; - -template <typename T_Tensor, typename T_ActivationLayer, typename T_ExecFunction> -std::unique_ptr<exec::IFunction> -AclActivationBuilder<T_Tensor, T_ActivationLayer, T_ExecFunction>::generateReLU(T_Tensor *ifm_alloc) -{ - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU}; - - auto fn = std::make_unique<T_ActivationLayer>(); - - fn->configure(ifm_alloc, nullptr, act_info); - - return asFunction<T_ExecFunction>(std::move(fn)); -} - -template <typename T_Tensor, typename T_ActivationLayer, typename T_ExecFunction> -std::unique_ptr<exec::IFunction> -AclActivationBuilder<T_Tensor, T_ActivationLayer, T_ExecFunction>::generateReLU1( - T_Tensor *ifm_alloc) -{ - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f}; - - auto fn = std::make_unique<T_ActivationLayer>(); - - fn->configure(ifm_alloc, nullptr, act_info); - - return asFunction<T_ExecFunction>(std::move(fn)); -} - -template <typename T_Tensor, typename T_ActivationLayer, typename T_ExecFunction> -std::unique_ptr<exec::IFunction> -AclActivationBuilder<T_Tensor, T_ActivationLayer, T_ExecFunction>::generateReLU6( - T_Tensor *ifm_alloc) -{ - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f}; - - auto fn = std::make_unique<T_ActivationLayer>(); - - fn->configure(ifm_alloc, nullptr, act_info); - - return asFunction<T_ExecFunction>(std::move(fn)); -} - -template <typename T_Tensor, typename T_ActivationLayer, typename T_ExecFunction> -std::unique_ptr<exec::IFunction> -AclActivationBuilder<T_Tensor, T_ActivationLayer, T_ExecFunction>::generate(ir::Activation code, - T_Tensor *ifm_alloc) -{ - switch (code) - { - case ir::Activation::NONE: - { - return std::make_unique<exec::NopFunction>(); - } - case ir::Activation::RELU: - { - return generateReLU(ifm_alloc); - } - case ir::Activation::RELU1: - { - return generateReLU1(ifm_alloc); - } - case ir::Activation::RELU6: - { - return generateReLU6(ifm_alloc); - } - default: - { - throw std::runtime_error("Not supported, yet"); - } - } -} - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_ACL_ACTIVATION_BUILDER_H__ diff --git a/runtime/onert/backend/acl_common/AclConstantInitializer.cc b/runtime/onert/backend/acl_common/AclConstantInitializer.cc deleted file mode 100644 index 6ad5b7b69..000000000 --- a/runtime/onert/backend/acl_common/AclConstantInitializer.cc +++ /dev/null @@ -1,128 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "AclConstantInitializer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -AclConstantInitializer::AclConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg) - : IConstantInitializer{operands}, _tensor_reg{tensor_reg} -{ - // DO NOTHING -} - -void AclConstantInitializer::copyInputInitialize(const ir::Operation &node, uint32_t index) -{ - assert(node.getInputs().size() > index); - - const auto &input_index = node.getInputs().at(index); - const auto &input_obj = _operands.at(input_index); - registerCopyInitializer(input_index, input_obj); -} - -void AclConstantInitializer::permuteInputInitialize(const ir::Operation &node, uint32_t index) -{ - assert(node.getInputs().size() > index); - - const auto &input_index = node.getInputs().at(index); - const auto &input_obj = _operands.at(input_index); - registerPermuteInitializer(input_index, input_obj); -} - -void AclConstantInitializer::visit(const ir::operation::BatchToSpaceND &node) -{ - const auto &block_size_index = node.getInputs().at(ir::operation::BatchToSpaceND::BLOCK_SIZE); - const auto &block_size_obj = _operands.at(block_size_index); - - if (block_size_obj.isConstant()) - { - _init_map[block_size_index] = [](const ir::Operand &model_obj, backend::ITensor &obj) { - assert(model_obj.data()); - const auto &shape = model_obj.shape(); - const auto base = reinterpret_cast<const int32_t *>(model_obj.data()->base()); - assert(model_obj.shape().rank() == 1); - obj.access([&](ITensor &tensor) { - for (size_t i = 0; i < shape.num_elements(); ++i) - { - const int32_t value = base[shape.num_elements() - i - 1]; - int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer() + - tensor.calcOffset({static_cast<int32_t>(i)})); - *into = value; - } - }); - }; - } -} - -void AclConstantInitializer::visit(const ir::operation::Conv2D &node) -{ - permuteInputInitialize(node, ir::operation::Conv2D::KERNEL); - copyInputInitialize(node, ir::operation::Conv2D::BIAS); -} - -void AclConstantInitializer::visit(const ir::operation::DepthwiseConv2D &node) -{ - permuteInputInitialize(node, ir::operation::DepthwiseConv2D::KERNEL); - copyInputInitialize(node, ir::operation::DepthwiseConv2D::BIAS); -} - -void AclConstantInitializer::visit(const ir::operation::FullyConnected &node) -{ - copyInputInitialize(node, ir::operation::FullyConnected::WEIGHT); - copyInputInitialize(node, ir::operation::FullyConnected::BIAS); -} - -void AclConstantInitializer::visit(const ir::operation::LSTM &node) -{ - copyInputInitialize(node, ir::operation::LSTM::INPUT_TO_INPUT_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::INPUT_TO_FORGET_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::INPUT_TO_CELL_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::INPUT_TO_OUTPUT_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::RECURRENT_TO_INPUT_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::RECURRENT_TO_FORGET_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::RECURRENT_TO_CELL_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::RECURRENT_TO_OUTPUT_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::CELL_TO_INPUT_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::CELL_TO_FORGET_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::CELL_TO_OUTPUT_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::INPUT_GATE_BIAS); - copyInputInitialize(node, ir::operation::LSTM::FORGET_GATE_BIAS); - copyInputInitialize(node, ir::operation::LSTM::OUTPUT_GATE_BIAS); - copyInputInitialize(node, ir::operation::LSTM::PROJECTION_WEIGHTS); - copyInputInitialize(node, ir::operation::LSTM::PROJECTION_BIAS); -} - -void AclConstantInitializer::visit(const ir::operation::RNN &node) -{ - copyInputInitialize(node, ir::operation::RNN::WEIGHTS); - copyInputInitialize(node, ir::operation::RNN::RECURRENT_WEIGHTS); - copyInputInitialize(node, ir::operation::RNN::BIAS); -} - -void AclConstantInitializer::visit(const ir::operation::TransposeConv &node) -{ - permuteInputInitialize(node, ir::operation::TransposeConv::KERNEL); -} - -} // namespace acl_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_common/AclConstantInitializer.h b/runtime/onert/backend/acl_common/AclConstantInitializer.h deleted file mode 100644 index 52f4c54cf..000000000 --- a/runtime/onert/backend/acl_common/AclConstantInitializer.h +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_ACL_COMMON_ACLCONSTANT_INITIALIZER_H__ -#define __ONERT_COMPILER_ACL_COMMON_ACLCONSTANT_INITIALIZER_H__ - -#include <backend/IConstantInitializer.h> -#include <ir/Operands.h> -#include "AclTensorRegistry.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -class AclConstantInitializer : public IConstantInitializer -{ -public: - AclConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg); - -public: - void visit(const ir::operation::BatchToSpaceND &) override; - void visit(const ir::operation::Conv2D &) override; - void visit(const ir::operation::DepthwiseConv2D &) override; - void visit(const ir::operation::FullyConnected &) override; - void visit(const ir::operation::LSTM &) override; - void visit(const ir::operation::RNN &) override; - void visit(const ir::operation::TransposeConv &) override; - -protected: - void copyInputInitialize(const ir::Operation &node, uint32_t index); - void permuteInputInitialize(const ir::Operation &node, uint32_t index); - -private: - std::shared_ptr<ITensorRegistry> tensor_registry() const final { return _tensor_reg; } - -protected: - std::shared_ptr<ITensorRegistry> _tensor_reg; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_COMPILER_ACL_COMMON_ACLCONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/backend/acl_common/AclFunction.h b/runtime/onert/backend/acl_common/AclFunction.h deleted file mode 100644 index 94b65863a..000000000 --- a/runtime/onert/backend/acl_common/AclFunction.h +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_KERNEL_ACL_FUNCTION_H__ -#define __ONERT_BACKEND_ACL_COMMON_KERNEL_ACL_FUNCTION_H__ - -#include <exec/IFunction.h> -#include <arm_compute/runtime/IFunction.h> -#include <memory> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -class AclFunction : public ::onert::exec::IFunction -{ -public: - AclFunction() = delete; - -public: - AclFunction(std::unique_ptr<::arm_compute::IFunction> &&func) : _func(std::move(func)) - { - // DO NOTHING - } - -public: - void run() override { _func->run(); } - void prepare() override { _func->prepare(); } - -private: - std::unique_ptr<::arm_compute::IFunction> _func; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_KERNEL_ACL_FUNCTION_H__ diff --git a/runtime/onert/backend/acl_common/AclInternalBufferManager.h b/runtime/onert/backend/acl_common/AclInternalBufferManager.h deleted file mode 100644 index f893bb44b..000000000 --- a/runtime/onert/backend/acl_common/AclInternalBufferManager.h +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_INTERNAL_BUFFER_MANAGER_H__ -#define __ONERT_BACKEND_ACL_COMMON_INTERNAL_BUFFER_MANAGER_H__ - -#include <arm_compute/runtime/IMemoryManager.h> -#include <cassert> -#include <memory> -#include <backend/IMemoryManager.h> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -// NOTE. If any backend can use something like InternalBufferManager, -// this interface can be moved to core/include/backend/ -/** - * @brief Interface for InternalBufferManager which has ::arm_compute::IMemoryManager pointer - */ -struct IInternalBufferManager : public backend::IMemoryManager -{ - virtual ~IInternalBufferManager() = default; - - /** - * @brief Get shared_ptr of ::arm_compute::IMemoryManager - */ - virtual std::shared_ptr<::arm_compute::IMemoryManager> internal_buffer_manager(void) = 0; -}; - -/** - * @brief class for InternalBufferManager which has ::arm_compute::IMemoryManager pointer - */ -template <typename T_MemoryManager, typename T_PoolManager, typename T_LifetimeManager, - typename T_Allocator> -class AclInternalBufferManager : public IInternalBufferManager -{ -public: - AclInternalBufferManager() : _allocator{nullptr} - { - std::shared_ptr<T_LifetimeManager> lifetime_mgr = std::make_shared<T_LifetimeManager>(); - std::shared_ptr<T_PoolManager> pool_mgr = std::make_shared<T_PoolManager>(); - - _internal_manager = std::make_shared<T_MemoryManager>(lifetime_mgr, pool_mgr); - assert(_internal_manager); - } - - virtual ~AclInternalBufferManager() = default; - - /** - * @brief Allocate the internal buffer manager on acl - */ - void allocate(void) override - { - _allocator = std::make_shared<T_Allocator>(); - _internal_manager->populate(*_allocator, 1); - } - - /** - * @brief Deallocate the internal buffer manager on acl - */ - void deallocate(void) override { _internal_manager->clear(); } - - /** - * @brief Get shared_ptr of ::arm_compute::IMemoryManager - */ - std::shared_ptr<::arm_compute::IMemoryManager> internal_buffer_manager(void) override - { - return _internal_manager; - } - -private: - std::shared_ptr<T_Allocator> _allocator; - std::shared_ptr<T_MemoryManager> _internal_manager; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_INTERNAL_BUFFER_MANAGER_H__ diff --git a/runtime/onert/backend/acl_common/AclKernelGen.h b/runtime/onert/backend/acl_common/AclKernelGen.h deleted file mode 100644 index 257bbd3b4..000000000 --- a/runtime/onert/backend/acl_common/AclKernelGen.h +++ /dev/null @@ -1,342 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_ACL_KERNEL_GEN_H_ -#define __ONERT_BACKEND_ACL_COMMON_ACL_KERNEL_GEN_H_ - -#include <exec/IFunction.h> -#include <ir/Operands.h> - -#include <ir/operation/LSTM.h> -#include <arm_compute/runtime/CL/CLFunctions.h> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -void enableDimCorrection(IACLTensor *tensor) -{ - size_t input_rank = tensor->num_dimensions(); - const_cast<arm_compute::TensorShape &>(tensor->info()->tensor_shape()) - .set(input_rank - 1, tensor->info()->dimension(input_rank - 1), true); -} - -void disableDimCorrection(IACLTensor *tensor) -{ - size_t input_rank = tensor->num_dimensions(); - const_cast<arm_compute::TensorShape &>(tensor->info()->tensor_shape()) - .set(input_rank - 1, tensor->info()->dimension(input_rank - 1), false); -} - -template <typename Layer, typename... Args> -std::unique_ptr<arm_compute::IFunction> generateLayer(Args &&... args) -{ - auto l = std::make_unique<Layer>(); - - l->configure(std::forward<Args>(args)...); - - return l; -} - -template <typename Layer, typename... Args> -std::unique_ptr<arm_compute::IFunction> -generateLayer(std::shared_ptr<arm_compute::IMemoryManager> memory_manager, Args &&... args) -{ - auto l = std::make_unique<Layer>(memory_manager); - - l->configure(std::forward<Args>(args)...); - - return l; -} - -template <typename T_FunctionWrapper, typename T_Tensor, typename T_ACLLayer, - typename T_TensorRegistry> -std::unique_ptr<exec::IFunction> kernelGenLSTM(const ir::operation::LSTM &node, - const ir::Operands &operands, - const std::shared_ptr<T_TensorRegistry> &tensor_reg) -{ - // TODO Support dynamic rnn - // TODO Fix subtle error in the case of non-CIFG, non-peephole and No Projection. - const auto scratch_buffer_index{ - node.getOutputs().at(ir::operation::LSTM::Output::SCRATCH_BUFFER)}; - const auto output_state_out_index{ - node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT_STATE_OUT)}; - const auto cell_state_out_index{ - node.getOutputs().at(ir::operation::LSTM::Output::CELL_STATE_OUT)}; - const auto output_index{node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT)}; - - const auto input_index{node.getInputs().at(ir::operation::LSTM::Input::INPUT)}; - const auto input_to_input_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_INPUT_WEIGHTS)}; // optional - const auto input_to_forget_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_FORGET_WEIGHTS)}; - const auto input_to_cell_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_CELL_WEIGHTS)}; - const auto input_to_output_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS)}; - const auto recurrent_to_input_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS)}; // optional - const auto recurrent_to_forget_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_FORGET_WEIGHTS)}; - const auto recurrent_to_cell_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_CELL_WEIGHTS)}; - const auto recurrent_to_output_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS)}; - const auto cell_to_input_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_INPUT_WEIGHTS)}; // optional - const auto cell_to_forget_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_FORGET_WEIGHTS)}; // optional - const auto cell_to_output_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_OUTPUT_WEIGHTS)}; // optional - const auto input_gate_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_GATE_BIAS)}; - const auto forget_gate_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::FORGET_GATE_BIAS)}; - const auto cell_bias_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_BIAS)}; - const auto output_gate_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_GATE_BIAS)}; - const auto projection_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_WEIGHTS)}; // optional - const auto projection_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_BIAS)}; // optional - const auto output_state_in_index{ - node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_STATE_IN)}; - const auto cell_state_in_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_STATE_IN)}; - const auto cell_threshold = node.param().cell_threshold; - const auto projection_threshold = node.param().projection_threshold; - - bool has_input_to_input_weights = operands.at(input_to_input_weights_index).shape().dim(0) != 0 && - operands.at(input_to_input_weights_index).shape().dim(1) != 0; - bool has_recurrent_to_input_weights = - operands.at(recurrent_to_input_weights_index).shape().dim(0) != 0 && - operands.at(recurrent_to_input_weights_index).shape().dim(1) != 0; - bool has_cell_to_forget_weights = operands.at(cell_to_forget_weights_index).shape().dim(0) != 0; - bool has_cell_to_output_weights = operands.at(cell_to_output_weights_index).shape().dim(0) != 0; - bool has_projection_weights = operands.at(projection_weights_index).shape().dim(0) != 0 && - operands.at(projection_weights_index).shape().dim(1) != 0; - bool has_projection_bias = operands.at(projection_bias_index).shape().dim(0); - - // NOTE The input_to_input_weights and the recurrent_to_input_weights do not exist in CIFG. - // true: no CIFG - // false: CIFG - // NOTE The cell_to_input_weights does not exist in non-peephole although regular LSTM(non-CIFG). - bool has_cifg_param = has_input_to_input_weights && has_recurrent_to_input_weights; - - // NOTE The cell_to_forget_weights and the cell_to_output_weights exist in peephole. - // But the cell_to_input_weights does not exist in regular CIFG although peephole. - // true: peephole - // false: no peephole - bool has_peephole_param = has_cell_to_forget_weights && has_cell_to_output_weights; - - // NOTE Although the projection weights has data the projection bias may not have data. - bool has_projection_param = has_projection_weights; - - const auto activation = node.param().activation; - const auto cell_clip = cell_threshold; - const auto projection_clip = projection_threshold; - assert(cell_clip >= 0.f && projection_clip >= 0.f); - - auto scratch_buffer_tensor = tensor_reg->getAclTensor(scratch_buffer_index); - auto output_state_out_tensor = tensor_reg->getAclTensor(output_state_out_index); - auto cell_state_out_tensor = tensor_reg->getAclTensor(cell_state_out_index); - auto output_tensor = tensor_reg->getAclTensor(output_index); - - auto input_tensor = tensor_reg->getAclTensor(input_index); - - auto input_to_forget_weights_tensor = tensor_reg->getAclTensor(input_to_forget_weights_index); - auto input_to_cell_weights_tensor = tensor_reg->getAclTensor(input_to_cell_weights_index); - auto input_to_output_weights_tensor = tensor_reg->getAclTensor(input_to_output_weights_index); - auto recurrent_to_forget_weights_tensor = - tensor_reg->getAclTensor(recurrent_to_forget_weights_index); - auto recurrent_to_cell_weights_tensor = tensor_reg->getAclTensor(recurrent_to_cell_weights_index); - auto recurrent_to_output_weights_tensor = - tensor_reg->getAclTensor(recurrent_to_output_weights_index); - - auto forget_gate_bias_tensor = tensor_reg->getAclTensor(forget_gate_bias_index); - auto cell_bias_tensor = tensor_reg->getAclTensor(cell_bias_index); - auto output_gate_bias_tensor = tensor_reg->getAclTensor(output_gate_bias_index); - auto output_state_in_tensor = tensor_reg->getAclTensor(output_state_in_index); - auto cell_state_in_tensor = tensor_reg->getAclTensor(cell_state_in_index); - - auto act_info = asActivationLayerInfo(activation); - - ::arm_compute::LSTMParams<T_Tensor> lstm_params{}; - if (has_cifg_param) - { - auto input_to_input_weights_tensor = - tensor_reg->getAclTensor(input_to_input_weights_index); // optional - auto recurrent_to_input_weights_tensor = - tensor_reg->getAclTensor(recurrent_to_input_weights_index); // optional - auto cell_to_input_weights_handle = - has_peephole_param ? tensor_reg->getAclTensor(cell_to_input_weights_index)->handle() - : nullptr; // optional (non-cifg && peephole) - auto input_gate_bias_tensor = tensor_reg->getAclTensor(input_gate_bias_index); // optional - lstm_params.set_cifg_params(input_to_input_weights_tensor->handle(), - recurrent_to_input_weights_tensor->handle(), - cell_to_input_weights_handle, input_gate_bias_tensor->handle()); - } - if (has_peephole_param) - { - auto cell_to_forget_weights_tensor = - tensor_reg->getAclTensor(cell_to_forget_weights_index); // optional - auto cell_to_output_weights_tensor = - tensor_reg->getAclTensor(cell_to_output_weights_index); // optional - lstm_params.set_peephole_params(cell_to_forget_weights_tensor->handle(), - cell_to_output_weights_tensor->handle()); - } - if (has_projection_param) - { - auto projection_weights_tensor = tensor_reg->getAclTensor(projection_weights_index); // optional - auto projection_bias_handle = has_projection_bias - ? tensor_reg->getAclTensor(projection_bias_index)->handle() - : nullptr; // optional - lstm_params.set_projection_params(projection_weights_tensor->handle(), projection_bias_handle); - } - - auto fn = generateLayer<T_ACLLayer>( - input_tensor->handle(), input_to_forget_weights_tensor->handle(), - input_to_cell_weights_tensor->handle(), input_to_output_weights_tensor->handle(), - recurrent_to_forget_weights_tensor->handle(), recurrent_to_cell_weights_tensor->handle(), - recurrent_to_output_weights_tensor->handle(), forget_gate_bias_tensor->handle(), - cell_bias_tensor->handle(), output_gate_bias_tensor->handle(), - output_state_in_tensor->handle(), cell_state_in_tensor->handle(), - scratch_buffer_tensor->handle(), output_state_out_tensor->handle(), - cell_state_out_tensor->handle(), output_tensor->handle(), lstm_params, act_info, cell_clip, - projection_clip); - - return std::make_unique<T_FunctionWrapper>(std::move(fn)); -} - -template <typename T_FunctionWrapper, typename T_Tensor, typename T_ACLLayer, - typename T_TensorBuilder, typename T_TensorRegistry> -std::unique_ptr<exec::IFunction> -kernelGenFullyConnected(const ir::operation::FullyConnected &node, const ir::Operands &operands, - const std::shared_ptr<T_TensorBuilder> &tensor_builder, - const std::shared_ptr<T_TensorRegistry> &tensor_reg, ir::Layout layout) -{ - using ir::operation::FullyConnected; - - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)}; - const auto weight_index{node.getInputs().at(FullyConnected::Input::WEIGHT)}; - const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)}; - - const auto input_rank = operands.at(input_index).shape().rank(); - - const auto output_size = - operands.at(output_index).shape().dim(operands.at(output_index).shape().rank() - 1); - UNUSED_RELEASE(output_size); - assert(operands.at(bias_index).shape().dim(0) == output_size); - assert(operands.at(weight_index).shape().dim(0) == output_size); - const auto batch_size = - operands.at(output_index).shape().dim(operands.at(output_index).shape().rank() - 2); - const auto input_size = - operands.at(weight_index).shape().dim(operands.at(weight_index).shape().rank() - 1); - - // Check for reshaping input's shape into rank-2 - bool needs_reshape = false; - ir::Shape reshape(2); - if (input_rank == 3 || input_rank == 4) - { - const auto &ifm_shape = operands.at(input_index).shape(); - auto feature_size = 1; - for (int i = 0; i < ifm_shape.rank(); ++i) - { - feature_size *= ifm_shape.dim(i); - } - - UNUSED_RELEASE(feature_size); - assert(feature_size == batch_size * input_size); - - // for reshaping - needs_reshape = true; - reshape.dim(0) = batch_size; /* H */ - reshape.dim(1) = input_size; /* W */ - } - - auto output_tensor = tensor_reg->getAclTensor(output_index); - const auto input_tensor = tensor_reg->getAclTensor(input_index); - const auto weight_tensor = tensor_reg->getAclTensor(weight_index); - const auto bias_tensor = tensor_reg->getAclTensor(bias_index); - const auto frontend_layout = layout; - const auto acl_layout = output_tensor->handle()->info()->data_layout(); - - typename T_ACLLayer::KernelType kernel_type = T_ACLLayer::KernelType::GENERAL; - if (operands.at(weight_index).isConstant()) - { - kernel_type = T_ACLLayer::KernelType::PREPROCESSED_WEIGHTS; - assert(operands.at(weight_index).data()); - } - - auto fn = generateLayer<T_ACLLayer>( - tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(), - weight_tensor->handle(), bias_tensor->handle(), output_tensor->handle(), needs_reshape, - asTensorShape(reshape, frontend_layout, asRuntimeLayout(acl_layout)), kernel_type); - - return std::make_unique<T_FunctionWrapper>(std::move(fn)); -} - -template <typename T_ACLLayer, typename T_PoolOp, typename T_AclTensorRegistry> -std::unique_ptr<::arm_compute::IFunction> -kernelGenPool2D(const T_PoolOp &node, const ir::Operands &operands, - const std::shared_ptr<T_AclTensorRegistry> &tensor_reg, ir::Layout layout, - ::arm_compute::PoolingType pooling_type) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(0)}; - - const auto ofm_shape = operands.at(ofm_index).shape().asFeature(layout); - const auto ifm_shape = operands.at(ifm_index).shape().asFeature(layout); - - const auto kh = node.param().kh; - const auto kw = node.param().kw; - const auto stride = node.param().stride; - const auto padding = - ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh); - - VERBOSE(Pool2DParam) << "IFM_H: " << ifm_shape.H << std::endl; - VERBOSE(Pool2DParam) << "IFM_W: " << ifm_shape.W << std::endl; - VERBOSE(Pool2DParam) << "OFM_H: " << ofm_shape.H << std::endl; - VERBOSE(Pool2DParam) << "OFM_W: " << ofm_shape.W << std::endl; - VERBOSE(Pool2DParam) << "KER_H: " << kh << std::endl; - VERBOSE(Pool2DParam) << "KER_W: " << kw << std::endl; - VERBOSE(Pool2DParam) << "STRIDE_H: " << stride.vertical << std::endl; - VERBOSE(Pool2DParam) << "STRIDE_W: " << stride.horizontal << std::endl; - VERBOSE(Pool2DParam) << "PAD(T): " << padding.top << std::endl; - VERBOSE(Pool2DParam) << "PAD(B): " << padding.bottom << std::endl; - VERBOSE(Pool2DParam) << "PAD(L): " << padding.left << std::endl; - VERBOSE(Pool2DParam) << "PAD(R): " << padding.right << std::endl; - - auto ofm_tensor = tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = tensor_reg->getAclTensor(ifm_index); - - ::arm_compute::PoolingLayerInfo info{ - pooling_type, ::arm_compute::Size2D{kw, kh}, ifm_tensor->info()->data_layout(), - asPadStrideInfo(padding, stride), true /* exclude_padding */}; - - auto fn = generateLayer<T_ACLLayer>(ifm_tensor->handle(), ofm_tensor->handle(), info); - - return fn; -} - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_ACL_KERNEL_GEN_H_ diff --git a/runtime/onert/backend/acl_common/AclLinearMemoryManager.h b/runtime/onert/backend/acl_common/AclLinearMemoryManager.h deleted file mode 100644 index 09f25e7a8..000000000 --- a/runtime/onert/backend/acl_common/AclLinearMemoryManager.h +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_LINEAR_MEMORY_MANAGER_H__ -#define __ONERT_BACKEND_ACL_COMMON_LINEAR_MEMORY_MANAGER_H__ - -#include <cassert> - -#include "AclMemoryManager.h" -#include "ir/OperandIndexMap.h" -#include "util/logging.h" - -namespace -{ - -template <typename T_MemoryManager, typename T_PoolManager, typename T_LifetimeManager> -std::shared_ptr<T_MemoryManager> createMemoryManager() -{ - std::shared_ptr<T_LifetimeManager> lifetime_mgr = std::make_shared<T_LifetimeManager>(); - std::shared_ptr<T_PoolManager> pool_mgr = std::make_shared<T_PoolManager>(); - - std::shared_ptr<T_MemoryManager> mem_mgr = - std::make_shared<T_MemoryManager>(lifetime_mgr, pool_mgr); - return mem_mgr; -} - -} // namespace - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor, typename T_MemoryManager, - typename T_PoolManager, typename T_LifetimeManager, typename T_Allocator, - typename T_MemoryGroup> -class AclLinearMemoryManager : public AclMemoryManager<T_ITensor, T_Tensor, T_SubTensor> -{ -public: - AclLinearMemoryManager() - : _allocator{nullptr}, - _io_manager{createMemoryManager<T_MemoryManager, T_PoolManager, T_LifetimeManager>()}, - _io_group{std::make_shared<T_MemoryGroup>(_io_manager)} - { - // DO NOTHING - } - - virtual ~AclLinearMemoryManager() = default; - - void allocate(void) override - { - _allocator = std::make_shared<T_Allocator>(); - _io_manager->populate(*_allocator, 1); - _io_group->acquire(); - } - - void deallocate(void) override - { - _io_group->release(); - _io_manager->clear(); - } - - void startLifetime(const ir::OperandIndex &ind) override - { - auto &tensors = this->tensors(); - assert(tensors.find(ind) != tensors.end()); - - auto tensor = tensors[ind]; - assert(tensor->handle()); - - _io_group->manage(tensor->handle()); - } - - void finishLifetime(const ir::OperandIndex &ind) override - { - auto &tensors = this->tensors(); - assert(tensors.find(ind) != tensors.end()); - - auto tensor = tensors[ind]; - assert(tensor->allocator()); - - tensor->allocator()->allocate(); - } - -private: - std::shared_ptr<T_Allocator> _allocator; - std::shared_ptr<T_MemoryManager> _io_manager; - std::shared_ptr<T_MemoryGroup> _io_group; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_LINEAR_MEMORY_MANAGER_H__ diff --git a/runtime/onert/backend/acl_common/AclMemoryManager.h b/runtime/onert/backend/acl_common/AclMemoryManager.h deleted file mode 100644 index eefcec130..000000000 --- a/runtime/onert/backend/acl_common/AclMemoryManager.h +++ /dev/null @@ -1,98 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__ -#define __ONERT_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__ - -#include <arm_compute/core/Types.h> -#include <arm_compute/runtime/IMemoryManager.h> -#include <cassert> - -#include "backend/IMemoryManager.h" -#include "ir/OperandIndexMap.h" -#include "Convert.h" -#include "util/logging.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -class AclMemoryManager : public backend::IMemoryManager -{ -public: - AclMemoryManager() - { - // DO NOTHING - } - - virtual ~AclMemoryManager() = default; - - void allocate(void) override - { - for (const auto &tensor_entry : _tensors) - { - auto tensor = tensor_entry.second; - tensor->allocator()->allocate(); - } - } - - void deallocate(void) override - { - for (const auto &tensor_entry : _tensors) - { - auto tensor = tensor_entry.second; - tensor->allocator()->free(); - } - } - - virtual void startLifetime(const ir::OperandIndex &) { /* DO NOTHING */} - virtual void finishLifetime(const ir::OperandIndex &) { /* DO NOTHING */} - - void buildTensor(const ir::OperandIndex &ind, const ::arm_compute::TensorInfo &info, size_t rank, - size_t num_uses) - { - auto tensor = std::make_shared<T_Tensor>(info, rank, num_uses); - _tensors[ind] = tensor; - } - - void buildSubtensor(std::shared_ptr<T_ITensor> parent_tensor, const ir::OperandIndex &child_ind, - const ::arm_compute::TensorShape &shape, - const ::arm_compute::Coordinates &coordinates, size_t rank, - bool extent_parent) - { - auto subtensor = - std::make_shared<T_SubTensor>(parent_tensor.get(), shape, coordinates, rank, extent_parent); - _subtensors[child_ind] = subtensor; - } - - ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &tensors(void) { return _tensors; } - - ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> &subtensors(void) { return _subtensors; } - -private: - ir::OperandIndexMap<std::shared_ptr<T_Tensor>> _tensors; - ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> _subtensors; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__ diff --git a/runtime/onert/backend/acl_common/AclSubTensorAnalyzer.h b/runtime/onert/backend/acl_common/AclSubTensorAnalyzer.h deleted file mode 100644 index beec95718..000000000 --- a/runtime/onert/backend/acl_common/AclSubTensorAnalyzer.h +++ /dev/null @@ -1,111 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_ACL_SUB_TENSOR_ANALYZER_H__ -#define __ONERT_BACKEND_ACL_COMMON_ACL_SUB_TENSOR_ANALYZER_H__ - -#include <ir/OperationVisitor.h> -#include <ir/Graph.h> -#include "ParentInfo.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -/** - * @brief Class to analyze tensor subsumption - */ -class AclSubTensorAnalyzer : public ir::OperationVisitor -{ -public: - /** - * @brief Construct a new SubTensorAnalyzer object - * @param[in] ctx Graph operand set - */ - AclSubTensorAnalyzer(const ir::Graph &graph) : _graph{graph} - { - // DO NOTHING - } - -public: - void setLayout(ir::Layout layout) { _current_op_layout = layout; } - - void visit(const ir::operation::Concat &node) override - { - // If operator is concat, fill subsumption info - int32_t axis_raw = node.param().axis; - - const auto &output_index = node.getOutputs().at(0); - const auto &inputs = node.getInputs(); - - int32_t axis_point = 0; - const auto rank = _graph.operands().at(output_index).shape().rank(); - int32_t axis = axis_raw < 0 ? (axis_raw + rank) : axis_raw; - assert(rank > axis); - - for (const auto &ind : inputs) - { - /** - * NOTE Not support below cases. - * 1. concat's input is a constant. - * 2. concat's input is a input of model. - * 3. concat's input already becomes a subtensor of another concat. - */ - if (_graph.operands().at(ind).isConstant() || _graph.getInputs().contains(ind) || - _parent_map.find(ind) != _parent_map.end()) - { - return; - } - } - - for (const auto &input_index : inputs) - { - auto input_shape = _graph.operands().at(input_index).shape(); - assert(rank == input_shape.rank()); - - ir::Coordinates coordinate_info{}; - for (int i = 0; i < rank; i++) - { - coordinate_info.set(i, 0); - } - coordinate_info.set(axis, axis_point); - - _parent_map.emplace( - input_index, acl_common::ParentInfo{output_index, _current_op_layout, coordinate_info}); - - axis_point += input_shape.dim(axis); - } - } - - std::unordered_map<ir::OperandIndex, ParentInfo> &&releaseParentMap() - { - return std::move(_parent_map); - } - -private: - const ir::Graph &_graph; - std::unordered_map<ir::OperandIndex, ParentInfo> _parent_map; - ir::Layout _current_op_layout{ir::Layout::UNKNOWN}; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_ACL_SUB_TENSOR_ANALYZER_H__ diff --git a/runtime/onert/backend/acl_common/AclTensorBuilder.h b/runtime/onert/backend/acl_common/AclTensorBuilder.h deleted file mode 100644 index bb7abc95d..000000000 --- a/runtime/onert/backend/acl_common/AclTensorBuilder.h +++ /dev/null @@ -1,438 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_TEMPL_TENSOR_BUILDER_H__ -#define __ONERT_BACKEND_ACL_COMMON_TEMPL_TENSOR_BUILDER_H__ - -#include <memory> -#include <queue> - -#include <arm_compute/core/Types.h> -#include <backend/ITensorBuilder.h> -#include "ir/OperandIndexMap.h" -#include <ir/Operands.h> -#include "AclTensorManager.h" -#include "AclTensorRegistry.h" -#include <memory> -#include "ParentInfo.h" -#include <util/Utils.h> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -enum class UsesType -{ - FIRST, - LAST -}; - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -class AclTensorBuilder : public ITensorBuilder -{ -public: - using T_AclTensorManager = AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>; - - AclTensorBuilder(const ir::Operands &operands, T_AclTensorManager *tensor_mgr, - const std::shared_ptr<AclTensorRegistry<T_AclTensorManager>> &tensor_reg); - - /** - * @brief Register tensor information to allocate on ACL-CL backend - * @param[in] ind Operand index - * @param[in] info Tensor information - * @param[in] layout Tensor data layout - */ - void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info, - ir::Layout backend_layout) override; - - void notifyFirstUse(const ir::OperandIndex &) override; - void notifyLastUse(const ir::OperandIndex &) override; - - bool isRegistered(const ir::OperandIndex &) const override; - - void prepare(void) override; - void allocate() override; - void postFunctionPrepare() override; - - T_AclTensorManager *acl_tensor_manager(void) { return _tensor_mgr.get(); } - - void setUsesCount(const ir::OperandIndex &index, size_t num_uses) - { - assert(_uses_count_map.find(index) != _uses_count_map.end() ? _uses_count_map[index] == num_uses - : true); - _uses_count_map[index] = num_uses; - } - - void parent_map(std::unordered_map<ir::OperandIndex, ParentInfo> &&parent_map) - { - _parent_map = std::move(parent_map); - } - - bool areSubTensorsOf(const ir::OperandIndex &parent, const ir::OperandIndexSequence &seq); - - /** - * @brief Check child tensor is allocated as subtensor of parent tensor - * @param[in] parent Index of parent - * @param[in] child Index of child - * @return @c true if child is allocated as subtensor of parent, otherwise @c false - */ - bool isSubTensorOf(const ir::OperandIndex &parent, const ir::OperandIndex &child); - -private: - void buildTensors(void); - ir::OperandIndex findRootParent(ir::OperandIndex index); - -private: - const ir::Operands &_operands; - ir::OperandIndexMap<ir::OperandInfo> _tensor_info_map; - ir::OperandIndexMap<ir::Layout> _tensor_layout_map; - ir::OperandIndexMap<size_t> _uses_count_map; - - std::unique_ptr<T_AclTensorManager> _tensor_mgr; - std::shared_ptr<AclTensorRegistry<T_AclTensorManager>> _tensor_reg; - - // for linear executor - std::vector<std::pair<UsesType, ir::OperandIndex>> _lifetime_seq; - - // Extra info for concat elimination - ir::OperandIndexMap<ParentInfo> _parent_map; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#include <cassert> -#include <stack> - -#include "Convert.h" - -#include "util/logging.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::AclTensorBuilder( - const ir::Operands &operands, T_AclTensorManager *tensor_mgr, - const std::shared_ptr<AclTensorRegistry<T_AclTensorManager>> &tensor_reg) - : _operands{operands}, _tensor_mgr{tensor_mgr}, _tensor_reg{tensor_reg} -{ - assert(_tensor_mgr); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::registerTensorInfo( - const ir::OperandIndex &ind, const ir::OperandInfo &info, ir::Layout backend_layout) -{ - assert(_tensor_mgr->constTensors().size() == 0); - assert(_tensor_mgr->nonconstTensors().size() == 0); - - _uses_count_map[ind] = _operands.at(ind).getUses().size(); - - if (_parent_map.count(ind) == 0) - { - // Normal Tensors - _tensor_info_map.emplace(ind, info); - _tensor_layout_map.insert({ind, backend_layout}); - } - else - { - // SubTensors - assert(!info.isConstant() && "Subtensors of constants are not supported yet."); - - // Update offset info and emplace - auto &parent_info = _parent_map[ind]; - const auto &obj = _operands.at(ind); - auto parent_index = parent_info.parent; - auto &offset = parent_info.coordinates; - auto frontend_layout = parent_info.frontend_layout; - - assert(obj.shape().rank() <= ir::Shape::MAX_RANK); - auto shape = obj.shape(); - if (_operands.at(parent_index).shape().rank() >= 4 && frontend_layout == ir::Layout::NHWC && - backend_layout == ir::Layout::NCHW) - { - // Permutation changing layout beyond 4-D is not supported yet - const auto parent_rank = _operands.at(parent_index).shape().rank(); - assert(parent_rank == 4); - shape.extendRank(parent_rank); - offset = {offset[0], offset[3], offset[1], offset[2]}; - } - else if (_operands.at(parent_index).shape().rank() >= 4 && - frontend_layout == ir::Layout::NHWC && backend_layout == ir::Layout::NCHW) - { - // Permutation changing layout beyond 4-D is not supported yet - const auto parent_rank = _operands.at(parent_index).shape().rank(); - assert(parent_rank == 4); - shape.extendRank(parent_rank); - offset = {offset[0], offset[2], offset[3], offset[1]}; - } - auto new_shape = permuteShape(shape, frontend_layout, backend_layout); - auto oi = ir::OperandInfo::createStaticInfo(new_shape, obj.typeInfo()); - _tensor_info_map.emplace(ind, oi); - } -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::notifyFirstUse(const ir::OperandIndex &ind) -{ - _lifetime_seq.emplace_back(UsesType::FIRST, ind); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::notifyLastUse(const ir::OperandIndex &ind) -{ - _lifetime_seq.emplace_back(UsesType::LAST, ind); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -bool AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::isRegistered( - const ir::OperandIndex &ind) const -{ - return _tensor_info_map.find(ind) != _tensor_info_map.end(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::prepare(void) -{ - buildTensors(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::allocate(void) -{ - // Update lifetime sequence to apply subtensor optimization - - std::unordered_map<ir::OperandIndex, ir::OperandIndex> root_map; - std::function<ir::OperandIndex &(ir::OperandIndex)> find_root = - [&](ir::OperandIndex ind) -> ir::OperandIndex & { - ir::OperandIndex &ret = root_map[ind]; - - // We know the root parent value already - if (ret.valid()) - return ret; - - auto itr = _parent_map.find(ind); - if (itr == _parent_map.end()) - { - // If there is no parent, let's store the value of itself - return ret = ind; - } - else - { - return ret = find_root(itr->second.parent); - } - }; - - ir::OperandIndexMap<bool> first_use_check; - ir::OperandIndexMap<bool> last_use_check; - std::map<size_t, std::pair<UsesType, ir::OperandIndex>> lifetime_map; - for (size_t i = 0; i < _lifetime_seq.size(); i++) - { - auto &entry = _lifetime_seq[i]; - if (entry.first != UsesType::FIRST) - continue; - auto root_ind = find_root(entry.second); - if (first_use_check[root_ind]) - continue; - first_use_check[root_ind] = true; - lifetime_map[i] = {UsesType::FIRST, root_ind}; - } - - for (int i = _lifetime_seq.size() - 1; i >= 0; i--) - { - auto &entry = _lifetime_seq[i]; - if (entry.first != UsesType::LAST) - continue; - auto root_ind = find_root(entry.second); - if (last_use_check[root_ind]) - continue; - last_use_check[root_ind] = true; - lifetime_map[i] = {UsesType::LAST, root_ind}; - } - - for (auto &entry : lifetime_map) - { - auto &use = entry.second; - auto use_type = use.first; - auto use_index = use.second; - assert(use_index.valid()); - if (use_type == UsesType::FIRST) - _tensor_mgr->startLifetime(use_index); - else - _tensor_mgr->finishLifetime(use_index); - } - - _tensor_mgr->allocateConsts(); - - // TODO Since `_parent_map` is filled for all Concat nodes even if the node this backend uses - // After refactoring BackendContext we can uncomment this - // assert(_tensor_info_map.size() == - // _tensor_mgr->nonconstTensors().size() + num of constants of _tensor_info_map + - // _parent_map.size()); - _tensor_mgr->allocateNonconsts(); - - _tensor_mgr->allocateInternalBufferManager(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::postFunctionPrepare(void) -{ - _tensor_mgr->tryDeallocConstants(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::buildTensors(void) -{ - assert(_tensor_mgr->constTensors().size() == 0); - assert(_tensor_mgr->nonconstTensors().size() == 0); - - // Normal tensors - for (auto &entry : _tensor_info_map) - { - auto ind = entry.first; - if (_parent_map.count(ind) > 0) - continue; - - const auto &info = entry.second; - const auto &backend_layout = _tensor_layout_map[ind]; - auto tensor_info = - asTensorInfo(info.shape(), info.typeInfo(), ir::Layout::UNKNOWN, backend_layout, true); - _tensor_mgr->buildTensor(ind, tensor_info, info.shape().rank(), info.isConstant(), - _uses_count_map[ind]); - } - - // Subtensors - assert(_tensor_mgr->nonconstSubtensors().size() == 0); - // TODO Iterate `_parent_map` instead, once the optimizer bug is fixed - // `Optimizer` iterates the entire OpSequences, so there is a bug if iterating _parent_map - for (auto &entry : _tensor_info_map) - { - auto ind = entry.first; - if (_parent_map.count(ind) == 0) - continue; - - // To make subtensor, parent tensor must be made first - // For this condition, use stack - // 1) Push one subtensor index to stack (iterate subtensors) - // 2) If tensor at stack top is already made, pop and go to 4) - // 3) If tensor pushed at 1) is not made, check parent tensor - // 3-1) If parent tensor is already made, we can make child tensor - // Make child tensor and pop, go to 4) - // 3-2) If parent tensor is not made, we can't make child tensor yet - // Push parent tensor index to stack and return to 4) - // 4) If stack is empty, return to 1), else return to 2) - auto &subtensors = _tensor_mgr->nonconstSubtensors(); - - std::stack<ir::OperandIndex> stack; - stack.push(ind); - - while (!stack.empty()) - { - const auto current = stack.top(); - const auto &tensor_info = _tensor_info_map.at(current); - const auto &parent_info = _parent_map.at(current); - - // Already generated SubTensor - if (subtensors.find(current) != subtensors.end()) - { - stack.pop(); - continue; - } - - auto parent = parent_info.parent; - std::shared_ptr<T_ITensor> parent_tensor = _tensor_mgr->findTensorAsParent(parent); - if (!parent_tensor) - { - // Cannot find allocated parent tensor: allocate parent first - assert(_parent_map.count(parent) > 0); - stack.push(parent); - continue; - } - assert(parent_tensor != nullptr); - - // Child's type should be same with parent - assert(tensor_info.typeInfo().offset() == - parent_tensor->info()->quantization_info().uniform().offset); - assert(tensor_info.typeInfo().scale() == - parent_tensor->info()->quantization_info().uniform().scale); - assert(tensor_info.typeInfo().type() == parent_tensor->data_type()); - - // NOTE SubTensor's layout must be the same with layout of parent tensor - const auto &root_parent = findRootParent(parent); - const auto &backend_layout = _tensor_layout_map[root_parent]; - - auto shape = asTensorShape(tensor_info.shape(), ir::Layout::UNKNOWN, backend_layout, true); - ::arm_compute::Coordinates coordinates = - asTensorCoordinate(parent_info.coordinates, ir::Layout::UNKNOWN, backend_layout); - _tensor_mgr->buildSubtensor(parent, current, shape, coordinates, tensor_info.shape().rank(), - true); - stack.pop(); - } - } -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -bool AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::areSubTensorsOf( - const ir::OperandIndex &parent, const ir::OperandIndexSequence &seq) -{ - for (auto &cand : seq) - { - if (!isSubTensorOf(parent, cand)) - { - return false; - } - } - return true; -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -bool AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::isSubTensorOf( - const ir::OperandIndex &parent, const ir::OperandIndex &child) -{ - auto itr = _parent_map.find(child); - if (itr == _parent_map.end()) - { - return false; - } - - return itr->second.parent == parent; -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -ir::OperandIndex -AclTensorBuilder<T_ITensor, T_Tensor, T_SubTensor>::findRootParent(ir::OperandIndex ind) -{ - if (_parent_map.find(ind) == _parent_map.end()) - return ind; - - auto parent_ind = _parent_map.at(ind).parent; - return findRootParent(parent_ind); -} - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_TEMPL_TENSOR_BUILDER_H__ diff --git a/runtime/onert/backend/acl_common/AclTensorManager.h b/runtime/onert/backend/acl_common/AclTensorManager.h deleted file mode 100644 index b999a39a9..000000000 --- a/runtime/onert/backend/acl_common/AclTensorManager.h +++ /dev/null @@ -1,301 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__ -#define __ONERT_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__ - -#include <arm_compute/runtime/IMemoryManager.h> - -#include "backend/ITensorManager.h" -#include "AclMemoryManager.h" -#include "AclInternalBufferManager.h" -#include "ir/OperandIndexMap.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -class AclTensorManager : public backend::ITensorManager -{ -public: - using T_AclMemoryManager = AclMemoryManager<T_ITensor, T_Tensor, T_SubTensor>; - - AclTensorManager(T_AclMemoryManager *const_mgr, T_AclMemoryManager *nonconst_mgr, - IInternalBufferManager *inter_mgr); - - virtual ~AclTensorManager() = default; - - void allocateConsts(void); - void allocateNonconsts(void); - void deallocateConsts(void); - void deallocateNonconsts(void); - - void allocateInternalBufferManager(void); - void deallocateInternalBufferManager(void); - - void buildTensor(const ir::OperandIndex &ind, const ::arm_compute::TensorInfo &info, size_t rank, - bool as_const, size_t num_uses); - void buildSubtensor(const ir::OperandIndex &parent, const ir::OperandIndex &child, - const ::arm_compute::TensorShape &shape, - const ::arm_compute::Coordinates &coordinates, size_t rank, - bool extent_parent); - - std::shared_ptr<T_ITensor> findTensorAsParent(const ir::OperandIndex &ind); - - void startLifetime(const ir::OperandIndex &ind); - void finishLifetime(const ir::OperandIndex &ind); - - std::shared_ptr<T_ITensor> at(const ir::OperandIndex &ind); - - ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &constTensors(void); - ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &nonconstTensors(void); - ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> &nonconstSubtensors(void); - - std::shared_ptr<::arm_compute::IMemoryManager> internal_buffer_manager(void); - - void iterate(const std::function<void(const ir::OperandIndex &)> &fn); - - void tryDeallocConstants(void); - -private: - std::unique_ptr<T_AclMemoryManager> _const_mgr; - std::unique_ptr<T_AclMemoryManager> _nonconst_mgr; - std::unique_ptr<IInternalBufferManager> _inter_mgr; - ir::OperandIndexMap<T_AclMemoryManager &> _ind_to_mgr; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#include <cassert> -#include "util/logging.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::AclTensorManager( - T_AclMemoryManager *const_mgr, T_AclMemoryManager *nonconst_mgr, - IInternalBufferManager *inter_mgr) - : _const_mgr{const_mgr}, _nonconst_mgr{nonconst_mgr}, _inter_mgr{inter_mgr} -{ - // DO NOTHING -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::allocateConsts(void) -{ - _const_mgr->allocate(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::allocateNonconsts(void) -{ - _nonconst_mgr->allocate(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::deallocateConsts(void) -{ - _const_mgr->deallocate(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::deallocateNonconsts(void) -{ - _nonconst_mgr->deallocate(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::allocateInternalBufferManager(void) -{ - _inter_mgr->allocate(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::deallocateInternalBufferManager(void) -{ - _inter_mgr->deallocate(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::buildTensor( - const ir::OperandIndex &ind, const ::arm_compute::TensorInfo &info, size_t rank, bool as_const, - size_t num_uses) -{ - assert(_ind_to_mgr.find(ind) == _ind_to_mgr.end()); - if (as_const) - { - _const_mgr->buildTensor(ind, info, rank, num_uses); - _ind_to_mgr.insert({ind, *_const_mgr}); - } - else - { - _nonconst_mgr->buildTensor(ind, info, rank, num_uses); - _ind_to_mgr.insert({ind, *_nonconst_mgr}); - } -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::buildSubtensor( - const ir::OperandIndex &parent, const ir::OperandIndex &child, - const ::arm_compute::TensorShape &shape, const ::arm_compute::Coordinates &coordinates, - size_t rank, bool extent_parent) -{ - assert(_ind_to_mgr.find(child) == _ind_to_mgr.end()); - std::shared_ptr<T_ITensor> parent_tensor = findTensorAsParent(parent); - assert(parent_tensor); - _nonconst_mgr->buildSubtensor(parent_tensor, child, shape, coordinates, rank, extent_parent); - _ind_to_mgr.insert({child, *_nonconst_mgr}); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -std::shared_ptr<T_ITensor> -AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::findTensorAsParent(const ir::OperandIndex &ind) -{ - - auto &tensors = _nonconst_mgr->tensors(); - auto &subtensors = _nonconst_mgr->subtensors(); - if (tensors.find(ind) != tensors.end()) - { - // Parent is allocated as tensor - return tensors[ind]; - } - else if (subtensors.find(ind) != subtensors.end()) - { - // Parent is allocated as subtensor - return subtensors[ind]; - } - else - { - return nullptr; - } -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::startLifetime(const ir::OperandIndex &ind) -{ - assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end()); - _ind_to_mgr.at(ind).startLifetime(ind); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::finishLifetime(const ir::OperandIndex &ind) -{ - assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end()); - _ind_to_mgr.at(ind).finishLifetime(ind); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -std::shared_ptr<T_ITensor> -AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::at(const ir::OperandIndex &ind) -{ - if (_ind_to_mgr.find(ind) == _ind_to_mgr.end()) - return nullptr; - - auto &tensors = _ind_to_mgr.at(ind).tensors(); - if (tensors.find(ind) != tensors.end()) - { - return tensors.at(ind); - } - else - { - return _ind_to_mgr.at(ind).subtensors().at(ind); - } -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -ir::OperandIndexMap<std::shared_ptr<T_Tensor>> & -AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::constTensors(void) -{ - return _const_mgr->tensors(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -ir::OperandIndexMap<std::shared_ptr<T_Tensor>> & -AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::nonconstTensors(void) -{ - return _nonconst_mgr->tensors(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -ir::OperandIndexMap<std::shared_ptr<T_SubTensor>> & -AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::nonconstSubtensors(void) -{ - return _nonconst_mgr->subtensors(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -std::shared_ptr<::arm_compute::IMemoryManager> -AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::internal_buffer_manager(void) -{ - return _inter_mgr->internal_buffer_manager(); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::iterate( - const std::function<void(const ir::OperandIndex &)> &fn) -{ - for (auto it : _nonconst_mgr->tensors()) - fn(it.first); - - for (auto it : _nonconst_mgr->subtensors()) - fn(it.first); - - for (auto it : _const_mgr->tensors()) - fn(it.first); -} - -template <typename T_ITensor, typename T_Tensor, typename T_SubTensor> -void AclTensorManager<T_ITensor, T_Tensor, T_SubTensor>::tryDeallocConstants(void) -{ - auto &tensors = _const_mgr->tensors(); - - for (auto it = tensors.begin(); it != tensors.end();) - { - const auto &ind = it->first; - auto tensor = it->second; - // NOTE The condition "tensor->num_uses() < 2" is used to prevent deallocating a constant tensor - // used in several nodes. - if (tensor->handle() && !tensor->handle()->is_used() && tensor->num_uses() < 2) - { - VERBOSE(AclTensorManager) << "Tensor #" << ind.value() - << " will be deallocated as an unused constant tensor" << std::endl; - tensor->allocator()->free(); - tensor.reset(); - it = tensors.erase(it); - } - else - { - ++it; - } - } -} - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__ diff --git a/runtime/onert/backend/acl_common/AclTensorRegistry.h b/runtime/onert/backend/acl_common/AclTensorRegistry.h deleted file mode 100644 index 02d66db99..000000000 --- a/runtime/onert/backend/acl_common/AclTensorRegistry.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_ACL_TENSOR_REGISTRY_H__ -#define __ONERT_BACKEND_ACL_COMMON_ACL_TENSOR_REGISTRY_H__ - -#include "backend/ITensorRegistry.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -/** - * @brief Tensor registry class for acl backends - * - * This is implemented as a wrapper of AclTensorManager. - */ -template <typename T_AclTensorManager> class AclTensorRegistry : public ITensorRegistry -{ -public: - AclTensorRegistry(T_AclTensorManager *tensor_mgr) : _tensor_mgr{tensor_mgr} {} - - ITensor *getITensor(const ir::OperandIndex &ind) override { return _tensor_mgr->at(ind).get(); } - - ITensor *getNativeITensor(const ir::OperandIndex &ind) override { return getITensor(ind); } - - auto getAclTensor(const ir::OperandIndex &ind) { return _tensor_mgr->at(ind).get(); } - -private: - T_AclTensorManager *_tensor_mgr; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_ACL_TENSOR_REGISTRY_H__ diff --git a/runtime/onert/backend/acl_common/CMakeLists.txt b/runtime/onert/backend/acl_common/CMakeLists.txt deleted file mode 100644 index d3ae5acf7..000000000 --- a/runtime/onert/backend/acl_common/CMakeLists.txt +++ /dev/null @@ -1,19 +0,0 @@ -# Unsupported architecture -nnfw_find_package(ARMCompute QUIET) -if(NOT ARMCompute_FOUND) - return() -endif(NOT ARMCompute_FOUND) - -file(GLOB SOURCES "*.cc") - -add_library(${LIB_ONERT_BACKEND_ACL_COMMON} STATIC ${SOURCES}) - -target_include_directories(${LIB_ONERT_BACKEND_ACL_COMMON} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_COMMON} PUBLIC onert_core) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_COMMON} PUBLIC arm_compute arm_compute_ex) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_COMMON} PUBLIC nnfw_lib_misc) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_COMMON} PRIVATE nnfw_common) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_COMMON} PRIVATE nnfw_coverage) - -set_target_properties(${LIB_ONERT_BACKEND_ACL_COMMON} PROPERTIES POSITION_INDEPENDENT_CODE ON) -set_target_properties(${LIB_ONERT_BACKEND_ACL_COMMON} PROPERTIES OUTPUT_NAME backend_acl_common) diff --git a/runtime/onert/backend/acl_common/Convert.cc b/runtime/onert/backend/acl_common/Convert.cc deleted file mode 100644 index 6ef6a2dc3..000000000 --- a/runtime/onert/backend/acl_common/Convert.cc +++ /dev/null @@ -1,365 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Convert.h" - -#include "Swizzle.h" -#include "ir/DataType.h" -#include "ir/operation/ElementwiseActivation.h" -#include <memory> - -namespace -{ - -::arm_compute::DataLayout asDataLayout(onert::ir::Layout layout) -{ - switch (layout) - { - case onert::ir::Layout::NHWC: - return ::arm_compute::DataLayout::NHWC; - case onert::ir::Layout::NCHW: - return ::arm_compute::DataLayout::NCHW; - default: - return ::arm_compute::DataLayout::UNKNOWN; - } -} - -} // namespace - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -::arm_compute::TensorShape asTensorShape(const ir::Shape &shape, ir::Layout frontend_layout, - ir::Layout backend_layout, bool apply_dim_correction) -{ - // If shape's rank is 0, the tensor is a scalar - // Sometimes, some ACL kernel can use a scalar as tensor. But ACL does not allocate buffer for - // tensor having rank as 0. - const auto tensor_shape = shape.rank() == 0 ? ir::Shape{1} : shape; - - const uint32_t rank = tensor_shape.rank(); - - ::arm_compute::TensorShape res{}; - - res.set_num_dimensions(rank); - - for (uint32_t axis = 0; axis < rank; ++axis) - { - // NOTE In some cases, in incorrect dimensions is required. - // For example, intput_size is 1 in LSTM. The input-to-input weights([num_units, input_size]) of - // LSTM is used as the weight of the FullyConnected. - // The FullyConnected's weight must be greater or equal than 2-dimensions. - // However, if the dimension correction is applied to input_to_input_weights with input_size - // equal to 1, it will be changed to 1-D. - // So input_to_input_weights is not used by the weight of FullyConnected. - res.set(ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value(), - tensor_shape.dim(axis), apply_dim_correction); - } - - return res; -} - -::arm_compute::Coordinates asTensorCoordinate(const ir::Coordinates &coord, - ir::Layout frontend_layout, ir::Layout backend_layout) -{ - const uint32_t rank = coord.size(); - - ::arm_compute::Coordinates res{}; - - res.set_num_dimensions(rank); - - for (uint32_t axis = 0; axis < rank; ++axis) - { - res.set(ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value(), coord[axis]); - } - - return res; -} - -::arm_compute::DataType asDataType(const ir::DataType type) -{ - switch (type) - { - case ir::DataType::FLOAT32: - return ::arm_compute::DataType::F32; - case ir::DataType::INT32: - return ::arm_compute::DataType::S32; - case ir::DataType::UINT32: - return ::arm_compute::DataType::U32; - case ir::DataType::QUANT_UINT8_ASYMM: - return ::arm_compute::DataType::QASYMM8; - case ir::DataType::BOOL8: - case ir::DataType::UINT8: - return ::arm_compute::DataType::U8; - case ir::DataType::QUANT_INT8_SYMM: - return ::arm_compute::DataType::S8; - case ir::DataType::FLOAT16: - return ::arm_compute::DataType::F16; - case ir::DataType::INT64: - return ::arm_compute::DataType::S64; - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} - -::arm_compute::QuantizationInfo asQuantizationInfo(const float scale, const int32_t offset) -{ - return ::arm_compute::QuantizationInfo(scale, offset); -} - -::arm_compute::TensorInfo asTensorInfo(const ir::Shape &shape, const ir::TypeInfo &typeInfo, - ir::Layout frontend_layout, ir::Layout backend_layout, - bool apply_dim_correction) -{ - ::arm_compute::TensorInfo info( - asTensorShape(shape, frontend_layout, backend_layout, apply_dim_correction), 1, - asDataType(typeInfo.type()), asQuantizationInfo(typeInfo.scale(), typeInfo.offset())); - info.set_data_layout(asDataLayout(backend_layout)); - return info; -} - -::arm_compute::PadStrideInfo asPadStrideInfo(const ir::ExplicitPadding &padding, - const ir::Stride &stride) -{ - return ::arm_compute::PadStrideInfo{stride.horizontal, - stride.vertical, - padding.left, - padding.right, - padding.top, - padding.bottom, - ::arm_compute::DimensionRoundingType::FLOOR}; -} - -::arm_compute::ActivationLayerInfo asActivationLayerInfo(const ir::Activation act_code) -{ - switch (act_code) - { - case ir::Activation::NONE: - return ::arm_compute::ActivationLayerInfo{}; - case ir::Activation::RELU: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU}; - case ir::Activation::RELU1: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 1.0f, -1.0f}; - case ir::Activation::RELU6: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.0f, 0.0f}; - // Cases for activation of LSTM. - case ir::Activation::TANH: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0f, 1.0f}; - case ir::Activation::SIGMOID: - // NOTE The sigmoid function is a special case of the Logistic function when L=1, k=1, x0=0. - // TODO In ACL and nnapi sepc, currently, Logistic's L always is 1, k always is 1, x0 always - // 0(always sigmoid) regardless of values of the parameter. - // If ACL support non-sigmoid logistic, should fix param values. - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.0f, 0.0f}; - default: - throw std::runtime_error{"Not supported, yet"}; - break; - } -} - -::arm_compute::ActivationLayerInfo -asActivationLayerInfo(const ir::operation::ElementwiseActivation::Type op_type, float alpha, - float beta) -{ - switch (op_type) - { - case ir::operation::ElementwiseActivation::Type::RELU: - if (beta == 0.f) - { - if (alpha == ir::operation::ElementwiseActivation::infinity) - { - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::RELU}; - } - else - { - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, alpha}; - } - } - else - { - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, alpha, beta}; - } - case ir::operation::ElementwiseActivation::Type::TANH: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::TANH, alpha, beta}; - case ir::operation::ElementwiseActivation::Type::LOGISTIC: - // NOTE The sigmoid function is a special case of the Logistic function when L=1, k=1, x0=0. - // TODO In ACL and nnapi sepc, currently, Logistic's L always is 1, k always is 1, x0 always - // 0(always sigmoid) regardless of values of the parameter. - // If ACL support non-sigmoid logistic, should fix param values. - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC}; - case ir::operation::ElementwiseActivation::Type::LEAKY_RELU: - return ::arm_compute::ActivationLayerInfo{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::LEAKY_RELU, alpha}; - default: - throw std::runtime_error{"Not supported, yet"}; - break; - } -} - -arm_compute::Coordinates asCoordinates(const ir::Operand &operand, int32_t rank, - ir::Layout frontend_layout, ir::Layout backend_layout) -{ - std::set<uint32_t> axes = asSet(operand, rank, frontend_layout, backend_layout); - - arm_compute::Coordinates reduce_axes; - for (const int32_t axis : axes) - { - reduce_axes.set(reduce_axes.num_dimensions(), axis); - } - - return reduce_axes; -} - -std::set<uint32_t> asSet(const ir::Operand &operand, int32_t rank, ir::Layout frontend_layout, - ir::Layout backend_layout) -{ - std::set<std::uint32_t> axes; - - for (size_t i = 0; i < operand.shape().num_elements(); ++i) - { - int32_t axis = 0; - switch (operand.typeInfo().type()) - { - case ir::DataType::INT32: - axis = reinterpret_cast<const int32_t *>(operand.data()->base())[i]; - break; - case ir::DataType::INT64: - axis = reinterpret_cast<const int64_t *>(operand.data()->base())[i]; - break; - default: - throw std::runtime_error("acl_common::asSet: Not supported data type"); - } - if (axis < 0) - axis += rank; - axes.insert(ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value()); - } - - return axes; -} - -std::unique_ptr<AclFunction> asAclFunction(std::unique_ptr<::arm_compute::IFunction> &&layer) -{ - return std::make_unique<AclFunction>(std::move(layer)); -} - -ir::Layout asRuntimeLayout(::arm_compute::DataLayout data_layout) -{ - switch (data_layout) - { - case ::arm_compute::DataLayout::NHWC: - return ir::Layout::NHWC; - case ::arm_compute::DataLayout::NCHW: - return ir::Layout::NCHW; - default: - return ir::Layout::UNKNOWN; - } -} - -ir::DataType asRuntimeDataType(::arm_compute::DataType data_type) -{ - switch (data_type) - { - case ::arm_compute::DataType::F32: - return ir::DataType::FLOAT32; - case ::arm_compute::DataType::S32: - return ir::DataType::INT32; - case ::arm_compute::DataType::U32: - return ir::DataType::UINT32; - case ::arm_compute::DataType::QASYMM8: - return ir::DataType::QUANT_UINT8_ASYMM; - case ::arm_compute::DataType::U8: - return ir::DataType::UINT8; - case ::arm_compute::DataType::QSYMM8: - return ir::DataType::QUANT_INT8_SYMM; - case ::arm_compute::DataType::F16: - return ir::DataType::FLOAT16; - case ::arm_compute::DataType::S64: - return ir::DataType::INT64; - default: - throw std::runtime_error{"Not supported, yet"}; - break; - } -} - -arm_compute::PoolingType convertPoolType(ir::operation::Pool2D::PoolType pool_type_ir) -{ - switch (pool_type_ir) - { - case ir::operation::Pool2D::PoolType::AVG: - return arm_compute::PoolingType::AVG; - case ir::operation::Pool2D::PoolType::L2: - return arm_compute::PoolingType::L2; - case ir::operation::Pool2D::PoolType::MAX: - return arm_compute::PoolingType::MAX; - default: - throw std::runtime_error("convertPoolType: Not supported operation yet"); - } -} - -arm_compute::ReduceOperation convertReduceType(ir::operation::Reduce::ReduceType reduce_type_ir) -{ - switch (reduce_type_ir) - { - case ir::operation::Reduce::ReduceType::MAX: - return arm_compute::ReduceOperation::MAX; - case ir::operation::Reduce::ReduceType::MIN: - return arm_compute::ReduceOperation::MIN; - case ir::operation::Reduce::ReduceType::SUM: - return arm_compute::ReduceOperation::SUM; - default: - throw std::runtime_error("convertReduceType: Not supported operation yet"); - } -} - -arm_compute::PixelValue asPixelValue(const ir::Operand &operand) -{ - assert(operand.isConstant()); - assert(operand.shape().num_elements() == 1); - switch (operand.typeInfo().type()) - { - case ir::DataType::INT32: - return arm_compute::PixelValue(operand.asScalar<int32_t>()); - case ir::DataType::INT64: - return arm_compute::PixelValue(operand.asScalar<int64_t>()); - case ir::DataType::UINT32: - return arm_compute::PixelValue(operand.asScalar<uint64_t>()); - case ir::DataType::UINT8: - return arm_compute::PixelValue(operand.asScalar<uint8_t>()); - case ir::DataType::FLOAT32: - return arm_compute::PixelValue(operand.asScalar<float>()); - default: - throw std::runtime_error("asPixelValue : Not supported datatype yet"); - } -} - -} // namespace acl_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_common/Convert.h b/runtime/onert/backend/acl_common/Convert.h deleted file mode 100644 index 0b36df102..000000000 --- a/runtime/onert/backend/acl_common/Convert.h +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_CONVERT_H__ -#define __ONERT_BACKEND_ACL_COMMON_CONVERT_H__ - -#include <arm_compute/core/PixelValue.h> -#include <arm_compute/core/TensorInfo.h> -#include <arm_compute/core/SubTensorInfo.h> -#include <arm_compute/core/TensorShape.h> -#include <arm_compute/core/TypesEx.h> - -#include "ir/Layout.h" -#include "ir/InternalType.h" -#include "ir/Operand.h" -#include "ir/operation/Pool2D.h" -#include "ir/operation/Reduce.h" -#include "ir/operation/ElementwiseActivation.h" -#include "ir/Shape.h" -#include "ir/TypeInfo.h" -#include "ir/Coordinates.h" -#include "ir/Padding.h" -#include "misc/feature/Shape.h" -#include "misc/kernel/Shape.h" - -#include "AclFunction.h" - -#include <set> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -::arm_compute::TensorShape asTensorShape(const ir::Shape &shape, ir::Layout frontend_layout, - ir::Layout backend_layout, - bool apply_dim_correction = true); -::arm_compute::Coordinates asTensorCoordinate(const ir::Coordinates &coord, - ir::Layout frontend_layout, - ir::Layout backend_layout); -::arm_compute::DataType asDataType(ir::DataType type); -::arm_compute::TensorInfo asTensorInfo(const ir::Shape &shape, const ir::TypeInfo &typeInfo, - ir::Layout frontend_layout, ir::Layout backend_layout, - bool apply_dim_correction = true); - -::arm_compute::PadStrideInfo asPadStrideInfo(const ir::ExplicitPadding &padding, - const ir::Stride &stride); - -::arm_compute::ActivationLayerInfo asActivationLayerInfo(ir::Activation act_code); -::arm_compute::ActivationLayerInfo -asActivationLayerInfo(const ir::operation::ElementwiseActivation::Type op_type, float alpha, - float beta); - -arm_compute::Coordinates asCoordinates(const ir::Operand &operand, int32_t rank, - ir::Layout frontend_layout, ir::Layout backend_layout); - -std::set<uint32_t> asSet(const ir::Operand &operand, int32_t rank, ir::Layout frontend_layout, - ir::Layout backend_layout); - -std::unique_ptr<AclFunction> asAclFunction(std::unique_ptr<::arm_compute::IFunction> &&layer); - -template <typename T_Function> -std::unique_ptr<T_Function> asFunction(std::unique_ptr<::arm_compute::IFunction> &&fn) -{ - return std::make_unique<T_Function>(std::move(fn)); -} - -ir::Layout asRuntimeLayout(::arm_compute::DataLayout data_layout); -ir::DataType asRuntimeDataType(::arm_compute::DataType data_type); - -arm_compute::PoolingType convertPoolType(ir::operation::Pool2D::PoolType pool_type_ir); -arm_compute::ReduceOperation convertReduceType(ir::operation::Reduce::ReduceType reduce_type_ir); - -arm_compute::PixelValue asPixelValue(const ir::Operand &operand); - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_CONVERT_H__ diff --git a/runtime/onert/backend/acl_common/IACLTensor.cc b/runtime/onert/backend/acl_common/IACLTensor.cc deleted file mode 100644 index 70988bd11..000000000 --- a/runtime/onert/backend/acl_common/IACLTensor.cc +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "IACLTensor.h" -#include "Convert.h" -#include "Swizzle.h" - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -size_t IACLTensor::dimension(size_t index) const -{ - // Assume that the front is higher dimensional. - // i.g. N: 0, C: 1, H: 2, W: 3 for NCHW layout - // NOTE This tensor must not be applied dim correction - auto rank = num_dimensions(); - rank = rank == 0 ? 1 : rank; - assert(rank > index); - const ARMComputeAxis reversed{(static_cast<uint32_t>(rank - index) - 1)}; - return info()->dimension(reversed.value()); -} - -size_t IACLTensor::calcOffset(const ir::Coordinates &coords) const -{ - auto rank = num_dimensions(); - rank = rank == 0 ? 1 : rank; - assert(rank == coords.size()); - - ::arm_compute::Coordinates acl_coords; - for (uint32_t i = 0; i < rank; ++i) - { - const ARMComputeAxis reversed{static_cast<uint32_t>((rank - i) - 1)}; - acl_coords.set(reversed.value(), coords[i]); - } - - return info()->offset_element_in_bytes(acl_coords); -} - -ir::Layout IACLTensor::layout() const { return acl_common::asRuntimeLayout(info()->data_layout()); } - -ir::DataType IACLTensor::data_type() const -{ - return acl_common::asRuntimeDataType(info()->data_type()); -} - -float IACLTensor::data_scale() const -{ - // FIXME What if quantization info is non-uniform? - return info()->quantization_info().uniform().scale; -} - -int32_t IACLTensor::data_offset() const -{ - // FIXME What if quantization info is non-uniform? - return info()->quantization_info().uniform().offset; -} - -} // namespace acl_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_common/IACLTensor.h b/runtime/onert/backend/acl_common/IACLTensor.h deleted file mode 100644 index 3d1268940..000000000 --- a/runtime/onert/backend/acl_common/IACLTensor.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_I_ACL_TENSOR_H__ -#define __ONERT_BACKEND_ACL_COMMON_I_ACL_TENSOR_H__ - -#include <backend/ITensor.h> -#include <arm_compute/core/ITensor.h> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -/** - * @brief Class representing Tensor for ACL - * @todo Override is_dynamic() method. We don't support dynamic tensor for ACL yet as of Apr, 2020. - * FYI, ACL ITensorInfo has is_dynamic() method, which seems currently not used. - * Maybe for ACL, this method can be implemented using ITensorInfo::is_dynamic() in future. - */ -class IACLTensor : public ITensor -{ -public: - IACLTensor() = default; - IACLTensor(const IACLTensor &) = delete; - IACLTensor &operator=(const IACLTensor &) = delete; - IACLTensor(IACLTensor &&) = default; - IACLTensor &operator=(IACLTensor &&) = default; - -public: - uint8_t *buffer() const final { return handle()->buffer(); } - size_t total_size() const final { return info()->total_size(); } - size_t dimension(size_t index) const final; - size_t calcOffset(const ir::Coordinates &coords) const final; - ir::Layout layout() const final; - ir::DataType data_type() const final; - float data_scale() const override; - int32_t data_offset() const override; - bool has_padding() const override { return info()->has_padding(); } - bool is_dynamic() const override { return false; } - -public: - virtual const arm_compute::ITensor *handle() const = 0; - virtual arm_compute::ITensor *handle() = 0; - - const arm_compute::ITensorInfo *info() const { return handle()->info(); } - arm_compute::ITensorInfo *info() { return handle()->info(); } -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif //__ONERT_BACKEND_ACL_COMMON_I_ACL_TENSOR_H__ diff --git a/runtime/onert/backend/acl_common/ParentInfo.h b/runtime/onert/backend/acl_common/ParentInfo.h deleted file mode 100644 index 708436327..000000000 --- a/runtime/onert/backend/acl_common/ParentInfo.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_PARENT_INFO_H__ -#define __ONERT_BACKEND_ACL_COMMON_PARENT_INFO_H__ - -#include <ir/Index.h> -#include <ir/Coordinates.h> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -/** - * @brief Struct to represent parent operand in child operand - */ -struct ParentInfo -{ - ir::OperandIndex parent; - ir::Layout frontend_layout; - ir::Coordinates coordinates; -}; - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_PARENT_INFO_H__ diff --git a/runtime/onert/backend/acl_common/Swizzle.h b/runtime/onert/backend/acl_common/Swizzle.h deleted file mode 100644 index e1c7f8041..000000000 --- a/runtime/onert/backend/acl_common/Swizzle.h +++ /dev/null @@ -1,160 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_COMMON_SWIZZLE_H__ -#define __ONERT_BACKEND_ACL_COMMON_SWIZZLE_H__ - -#include <cassert> -#include <ir/Layout.h> - -namespace onert -{ -namespace backend -{ -namespace acl_common -{ - -class ARMComputeAxis -{ -public: - ARMComputeAxis() = default; - -public: - explicit ARMComputeAxis(uint32_t value) : _value{value} - { - // DO NOTHING - } - -public: - uint32_t value(void) const { return _value; } - -private: - uint32_t _value; -}; - -// Convert axis in acl order -inline ARMComputeAxis ToARMComputeAxis(uint32_t rank, uint32_t axis, - const ir::Layout org_layout = ir::Layout::UNKNOWN, - const ir::Layout acl_layout = ir::Layout::UNKNOWN) -{ - assert(rank > axis); - - const ARMComputeAxis reversed{(rank - axis) - 1}; - - if (rank >= 4 && org_layout == ir::Layout::NHWC && acl_layout == ir::Layout::NCHW) - { - // NHWC -> WHCN - // DEPTH - if (0 == reversed.value()) - { - return ARMComputeAxis{2}; - } - // WIDTH - if (1 == reversed.value()) - { - return ARMComputeAxis{0}; - } - // HEIGHT - if (2 == reversed.value()) - { - return ARMComputeAxis{1}; - } - } - if (rank >= 4 && org_layout == ir::Layout::NCHW && acl_layout == ir::Layout::NHWC) - { - // NCHW -> CWHN - // WIDTH - if (0 == reversed.value()) - { - return ARMComputeAxis{1}; - } - // HEIGHT - if (1 == reversed.value()) - { - return ARMComputeAxis{2}; - } - // DEPTH - if (2 == reversed.value()) - { - return ARMComputeAxis{0}; - } - } - - return reversed; -} - -inline ::arm_compute::Coordinates -getARMComputeAxises(uint32_t rank, const ir::Layout org_layout = ir::Layout::UNKNOWN, - const ir::Layout acl_layout = ir::Layout::UNKNOWN) -{ - ::arm_compute::Coordinates res{}; - - res.set_num_dimensions(rank); - - for (uint32_t axis = 0; axis < rank; ++axis) - { - res.set(axis, ToARMComputeAxis(rank, axis, org_layout, acl_layout).value()); - } - - return res; -} - -// Restructure runtime_permutationVector to ACL_permutationVector -inline ::arm_compute::PermutationVector -getARMComputePermutationVector(uint32_t rank, const std::vector<int32_t> runtime_pv, - const ir::Layout org_layout = ir::Layout::UNKNOWN, - const ir::Layout acl_layout = ir::Layout::UNKNOWN) -{ - // rank upto 4 is supported - assert(rank <= 4); - assert(runtime_pv.size() > 0); - - int new_pv[4] = {0}; - ::arm_compute::Coordinates axises = getARMComputeAxises(rank, org_layout, acl_layout); - - for (uint32_t i = 0; i < rank; ++i) - { - new_pv[axises[i]] = ToARMComputeAxis(rank, runtime_pv[i], org_layout, acl_layout).value(); - } - - ::arm_compute::PermutationVector ACL_PV = - ::arm_compute::PermutationVector{new_pv[0], new_pv[1], new_pv[2], new_pv[3]}; - ACL_PV.set_num_dimensions(rank); - - return ACL_PV; -} - -template <typename T> -inline T ReorderBits(T in, size_t numOfBits, const ir::Layout org_layout = ir::Layout::UNKNOWN, - const ir::Layout acl_layout = ir::Layout::UNKNOWN) -{ - assert(numOfBits > 0); - T out = 0; - for (int32_t i = numOfBits - 1; i >= 0; --i) - { - const uint32_t toShift = - numOfBits - ToARMComputeAxis(numOfBits, i, org_layout, acl_layout).value() - 1; - out += ((in & 1) << toShift); - in >>= 1; - } - return out; -} - -} // namespace acl_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_COMMON_SWIZZLE_H__ diff --git a/runtime/onert/backend/acl_neon/Backend.h b/runtime/onert/backend/acl_neon/Backend.h deleted file mode 100644 index 35d6e4e8e..000000000 --- a/runtime/onert/backend/acl_neon/Backend.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_BACKEND_H__ -#define __ONERT_BACKEND_ACL_NEON_BACKEND_H__ - -#include <memory> -#include <backend/Backend.h> -#include <ir/Operands.h> - -#include "Config.h" -#include "ConstantInitializer.h" -#include "KernelGenerator.h" -#include "TensorManager.h" -#include "Optimizer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -class Backend : public ::onert::backend::Backend -{ -public: - Backend() : _config{std::make_shared<Config>()} {} - - std::shared_ptr<IConfig> config() const override { return _config; } - - std::unique_ptr<BackendContext> newContext(const ir::Graph &graph, - const std::shared_ptr<custom::IKernelBuilder> &, - bool is_linear_executor) const override - { - const auto &operands = graph.operands(); - const auto &operations = graph.operations(); - auto context = std::make_unique<BackendContext>(this, &graph); - auto tm = createTensorManager(is_linear_executor); - auto tr = std::make_shared<acl_common::AclTensorRegistry<TensorManager>>(tm); - auto tb = std::make_shared<TensorBuilder>(operands, tm, tr); - context->tensor_registry = tr; - context->tensor_builder = tb; - context->constant_initializer = std::make_shared<ConstantInitializer>(operands, tr); - context->kernel_gen = std::make_shared<KernelGenerator>(operands, operations, tb, tr); - context->tensor_register = nullptr; - context->optimizer = std::make_shared<Optimizer>(context.get()); - return context; - } - -private: - std::shared_ptr<IConfig> _config; -}; - -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_BACKEND_H__ diff --git a/runtime/onert/backend/acl_neon/CMakeLists.txt b/runtime/onert/backend/acl_neon/CMakeLists.txt deleted file mode 100644 index 03d4946e0..000000000 --- a/runtime/onert/backend/acl_neon/CMakeLists.txt +++ /dev/null @@ -1,19 +0,0 @@ -# Unsupported architecture -nnfw_find_package(ARMCompute QUIET) -if(NOT ARMCompute_FOUND) - return() -endif(NOT ARMCompute_FOUND) - -set(LIB_ONERT_BACKEND_ACL_NEON onert_backend_acl_neon) - -file(GLOB_RECURSE SOURCES "*.cc") - -add_library(${LIB_ONERT_BACKEND_ACL_NEON} SHARED ${SOURCES}) - -target_link_libraries(${LIB_ONERT_BACKEND_ACL_NEON} PRIVATE ${LIB_ONERT_BACKEND_ACL_COMMON}) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_NEON} PRIVATE nnfw_common) -target_link_libraries(${LIB_ONERT_BACKEND_ACL_NEON} PRIVATE nnfw_coverage) - -set_target_properties(${LIB_ONERT_BACKEND_ACL_NEON} PROPERTIES OUTPUT_NAME backend_acl_neon) - -install(TARGETS ${LIB_ONERT_BACKEND_ACL_NEON} DESTINATION lib) diff --git a/runtime/onert/backend/acl_neon/Config.cc b/runtime/onert/backend/acl_neon/Config.cc deleted file mode 100644 index 4e78efd2d..000000000 --- a/runtime/onert/backend/acl_neon/Config.cc +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Config.h" - -#include <util/ConfigSource.h> - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -bool Config::initialize() { return true; } - -ir::Layout Config::supportLayout(const ir::Operation &, ir::Layout frontend_layout) -{ - const std::string acl_layout_str = util::getConfigString(util::config::ACL_LAYOUT); - if (acl_layout_str == "NHWC") - { - return ir::Layout::NHWC; - } - else if (acl_layout_str == "NCHW") - { - return ir::Layout::NCHW; - } - - return frontend_layout; -} - -} // namespace acl_neon -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_neon/Config.h b/runtime/onert/backend/acl_neon/Config.h deleted file mode 100644 index 089d9479a..000000000 --- a/runtime/onert/backend/acl_neon/Config.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_CONFIG_H__ -#define __ONERT_BACKEND_ACL_NEON_CONFIG_H__ - -#include <backend/IConfig.h> -#include <memory> -#include <util/ITimer.h> - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -class Config : public IConfig -{ -public: - std::string id() override { return "acl_neon"; } - bool initialize() override; - ir::Layout supportLayout(const ir::Operation &node, ir::Layout frontend_layout) override; - bool supportPermutation() override { return true; } - bool supportDynamicTensor() override { return false; } - bool supportFP16() override { return false; } - - std::unique_ptr<util::ITimer> timer() override { return std::make_unique<util::CPUTimer>(); } -}; - -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_CONFIG_H__ diff --git a/runtime/onert/backend/acl_neon/ConstantInitializer.cc b/runtime/onert/backend/acl_neon/ConstantInitializer.cc deleted file mode 100644 index 79edb9ded..000000000 --- a/runtime/onert/backend/acl_neon/ConstantInitializer.cc +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConstantInitializer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -ConstantInitializer::ConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg) - : acl_common::AclConstantInitializer{operands, tensor_reg} -{ - // DO NOTHING -} - -void ConstantInitializer::visit(const ir::operation::SpaceToBatchND &node) -{ - const auto &block_size_index = node.getInputs().at(ir::operation::SpaceToBatchND::BLOCK_SIZE); - const auto &block_size_obj = _operands.at(block_size_index); - - if (block_size_obj.isConstant()) - { - _init_map[block_size_index] = [](const ir::Operand &model_obj, backend::ITensor &obj) { - assert(model_obj.data()); - const auto &shape = model_obj.shape(); - const auto base = reinterpret_cast<const int32_t *>(model_obj.data()->base()); - assert(model_obj.shape().rank() == 1); - obj.access([&](ITensor &tensor) { - for (size_t i = 0; i < shape.num_elements(); ++i) - { - const int32_t value = base[shape.num_elements() - i - 1]; - int32_t *into = reinterpret_cast<int32_t *>(tensor.buffer() + - tensor.calcOffset({static_cast<int32_t>(i)})); - *into = value; - } - }); - }; - } - - const auto &paddings_index = node.getInputs().at(ir::operation::SpaceToBatchND::PADDINGS); - const auto &paddings_obj = _operands.at(paddings_index); - if (paddings_obj.isConstant()) - { - _init_map[paddings_index] = [](const ir::Operand &model_obj, backend::ITensor &obj) { - assert(model_obj.data()); - const auto &shape = model_obj.shape(); - const auto base = reinterpret_cast<const int32_t *>(model_obj.data()->base()); - assert(model_obj.shape().rank() == 2); - assert(shape.dim(0) == 2); - assert(shape.dim(1) == 2); - obj.access([&](ITensor &tensor) { - for (auto i = 0; i < shape.dim(0); ++i) - { - for (auto j = 0; j < shape.dim(1); ++j) - { - const int32_t value = base[i * 2 + j]; - int32_t *into = reinterpret_cast<int32_t *>( - // The coordinates of NETensor are different from the coordiantes of CLTensor in - // this operand. - // NEON : {j, reversed i} - // CL : {reversed i, j} - tensor.buffer() + tensor.calcOffset({j, shape.dim(0) - i - 1})); - *into = value; - } - } - }); - }; - } -} - -} // namespace acl_neon -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_neon/ConstantInitializer.h b/runtime/onert/backend/acl_neon/ConstantInitializer.h deleted file mode 100644 index c7d71cdcf..000000000 --- a/runtime/onert/backend/acl_neon/ConstantInitializer.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_ACL_NEON_CONSTANT_INITIALIZER_H__ -#define __ONERT_COMPILER_ACL_NEON_CONSTANT_INITIALIZER_H__ - -#include "AclConstantInitializer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -class ConstantInitializer : public acl_common::AclConstantInitializer -{ -public: - ConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg); - -public: - using acl_common::AclConstantInitializer::visit; - void visit(const ir::operation::SpaceToBatchND &node) final; -}; - -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_COMPILER_ACL_NEON_CONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/backend/acl_neon/KernelGenerator.cc b/runtime/onert/backend/acl_neon/KernelGenerator.cc deleted file mode 100644 index 598d043e7..000000000 --- a/runtime/onert/backend/acl_neon/KernelGenerator.cc +++ /dev/null @@ -1,1437 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "KernelGenerator.h" - -#include <arm_compute/runtime/NEON/NEFunctions.h> // Include all ARM Compute NEON functions -#include <arm_compute/runtime/NEON/NEFunctionsEx.h> // Include all ARM Compute EX NEON functions - -#include <AclActivationBuilder.h> -#include <AclFunction.h> -#include <Convert.h> -#include <Swizzle.h> - -#include "ir/Index.h" -#include "ir/DataType.h" -#include "ir/InternalType.h" -#include "exec/NopFunction.h" -#include "util/logging.h" -#include "util/Utils.h" -#include "AclKernelGen.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -using ::onert::backend::acl_common::asAclFunction; -using ActivationBuilder = ::onert::backend::acl_common::AclActivationBuilder< - ::arm_compute::ITensor, ::arm_compute::NEActivationLayer, acl_common::AclFunction>; - -KernelGenerator::KernelGenerator( - const ir::Operands &operands_ctx, const ir::Operations &operations_ctx, - const std::shared_ptr<TensorBuilder> &tensor_builder, - const std::shared_ptr<acl_common::AclTensorRegistry<TensorManager>> &tensor_reg) - : _ctx(operands_ctx), _operations_ctx(operations_ctx), _tensor_builder(tensor_builder), - _tensor_reg(tensor_reg), _current_op_seq_layout(ir::Layout::UNKNOWN) -{ - // DO NOTHING -} - -void KernelGenerator::visit(const ir::OpSequence &op_seq) -{ - // TODO Move this to IKernelGenerator - // (all derivatives have the same implementation for this) - assert(!_return_fn_seq); - _return_fn_seq = std::make_unique<exec::FunctionSequence>(); - _return_fn_seq->enableDynamicShapeInferer(false); - - _current_op_seq_layout = op_seq.getLayout(); - for (const auto &operation_idx : op_seq.operations()) - { - const auto &node = _operations_ctx.at(operation_idx); - node.accept(*this); - _return_fn_seq->append(releaseFunction()); - } -} - -void KernelGenerator::visit(const ir::operation::ArgMax &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ArgMax::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::ArgMax::Input::AXIS)}; - - const auto ifm_rank = _ctx.at(ifm_index).shape().rank(); - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto frontend_layout = _current_op_seq_layout; - auto backend_layout = ifm_tensor->layout(); - - int axis_value = _ctx.at(axis_index).asScalar<int32_t>(); - if (axis_value < 0) - { - axis_value += ifm_rank; - } - assert(axis_value >= 0 && axis_value < ifm_rank); - const auto fixed_axis = - acl_common::ToARMComputeAxis(ifm_rank, axis_value, frontend_layout, backend_layout).value(); - - auto fn = acl_common::generateLayer<arm_compute::NEArgMinMaxLayer>( - ifm_tensor->handle(), fixed_axis, ofm_tensor->handle(), - arm_compute::ReductionOperation::ARG_IDX_MAX); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::BatchToSpaceND &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)}; - const auto block_size_index{ - node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto block_size_tensor = _tensor_reg->getAclTensor(block_size_index); - - assert(_ctx.at(block_size_index).data()); - - auto fn = acl_common::generateLayer<arm_compute::NEBatchToSpaceLayer>( - ifm_tensor->handle(), block_size_tensor->handle(), ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::BinaryArithmetic &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)}; - - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index); - - std::unique_ptr<arm_compute::IFunction> fn; - switch (node.param().arithmetic_type) - { - case ir::operation::BinaryArithmetic::ArithmeticType::ADD: - { - fn = acl_common::generateLayer<arm_compute::NEArithmeticAddition>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), - arm_compute::ConvertPolicy::SATURATE); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::SUB: - { - fn = acl_common::generateLayer<arm_compute::NEArithmeticSubtraction>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), - arm_compute::ConvertPolicy::SATURATE); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::MUL: - { - // RoundingPolicy for scale:1.0 is only allowed RoundingPolicy::TO_ZERO - fn = acl_common::generateLayer<arm_compute::NEPixelWiseMultiplication>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle(), 1.0, // scale - arm_compute::ConvertPolicy::SATURATE, arm_compute::RoundingPolicy::TO_ZERO); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::DIV: - { - fn = acl_common::generateLayer<arm_compute::NEElementwiseDivision>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle()); - break; - } - default: - assert(false && "The BinaryArithmetic operation supports only binary arithmetic operations"); - break; - } - _return_fn = std::make_unique<exec::FunctionSequence>( - asAclFunction(std::move(fn)), ActivationBuilder::generate(activation, ofm_tensor->handle())); -} - -void KernelGenerator::visit(const ir::operation::Conv2D &node) -{ - using ir::operation::Conv2D; - - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)}; - const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)}; - const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)}; - - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]. - const auto &ker_shape = _ctx.at(ker_index).shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - - const auto stride = node.param().stride; - const auto padding = ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, - ker_width, ker_height); - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto ker_tensor = _tensor_reg->getAclTensor(ker_index); - auto bias_tensor = _tensor_reg->getAclTensor(bias_index); - - const auto conv_info = acl_common::asPadStrideInfo(padding, stride); - const auto act_info = acl_common::asActivationLayerInfo(activation); - - auto fn = acl_common::generateLayer<arm_compute::NEConvolutionLayer>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), ifm_tensor->handle(), - ker_tensor->handle(), bias_tensor->handle(), ofm_tensor->handle(), conv_info, - ::arm_compute::WeightsInfo(), ::arm_compute::Size2D(1U, 1U), act_info); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::DepthToSpace &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::DepthToSpace::Input::INPUT)}; - - auto block_size = node.param().block_size; - assert(block_size > 0); - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - auto fn = acl_common::generateLayer<arm_compute::NEDepthToSpaceLayer>( - input_tensor->handle(), output_tensor->handle(), block_size); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node) -{ - using ir::operation::DepthwiseConv2D; - - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)}; - const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)}; - const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)}; - - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - // Kernel format is [1, kernel_height, kernel_width, depth_out]. - const auto &ker_shape = _ctx.at(ker_index).shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - - const auto stride = node.param().stride; - const auto padding = ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, - ker_width, ker_height); - const auto multiplier = node.param().multiplier; - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto ker_tensor = _tensor_reg->getAclTensor(ker_index); - auto bias_tensor = _tensor_reg->getAclTensor(bias_index); - - const auto conv_info = acl_common::asPadStrideInfo(padding, stride); - const auto act_info = acl_common::asActivationLayerInfo(activation); - - { - auto fn = acl_common::generateLayer<arm_compute::NEDepthwiseConvolutionLayer>( - ifm_tensor->handle(), ker_tensor->handle(), bias_tensor->handle(), ofm_tensor->handle(), - conv_info, multiplier, act_info); - - _return_fn = asAclFunction(std::move(fn)); - } -} - -void KernelGenerator::visit(const ir::operation::Concat &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - std::vector<ir::OperandIndex> input_indexes; - for (const auto &input : node.getInputs()) - input_indexes.emplace_back(input); - - const auto axis = node.param().axis; - - // Concat elimination check - bool eliminated = _tensor_builder->areSubTensorsOf(ofm_index, node.getInputs()); - if (eliminated) - { - // If concat eliminated, return a NOP IFunction - VERBOSE(acl_neon_KernelGenerator_Concat) << "Concat eliminated" << std::endl; - _return_fn = std::make_unique<exec::NopFunction>(); - return; - } - - auto output_tensor = _tensor_reg->getAclTensor(ofm_index); - std::vector<::arm_compute::ITensor *> input_tensors; - for (const auto &ifm_ind : input_indexes) - input_tensors.emplace_back(_tensor_reg->getAclTensor(ifm_ind)->handle()); - - std::unique_ptr<::arm_compute::IFunction> fn; - if (input_indexes.size() < 2) - { - fn = acl_common::generateLayer<arm_compute::NECopy>(input_tensors.at(0), - output_tensor->handle()); - } - else - { - const auto rank = _ctx.at(ofm_index).shape().rank(); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = output_tensor->layout(); - const auto fixed_axis = - acl_common::ToARMComputeAxis(rank, axis, frontend_layout, backend_layout).value(); - fn = acl_common::generateLayer<arm_compute::NEConcatenateLayer>( - input_tensors, output_tensor->handle(), fixed_axis); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseActivation &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ElementwiseActivation::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - const ::arm_compute::ActivationLayerInfo act_info = acl_common::asActivationLayerInfo( - node.param().op_type, node.param().alpha, node.param().beta); - - std::unique_ptr<arm_compute::IFunction> fn; - if (node.param().op_type == ir::operation::ElementwiseActivation::Type::LOGISTIC) - { - // NOTE NEActivationLayer can generate produce erroneous results. it were caused by - // 'vexpq_f32()'. - // The neon function returns a value outside of the limit of representation in float as 'NaN' - // instead of 'INF', and then the result of this op will be errors due to the 'NaN'. - fn = acl_common::generateLayer<arm_compute::NEActivationLayerEx>( - ifm_tensor->handle(), ofm_tensor->handle(), act_info); - } - else - { - fn = acl_common::generateLayer<arm_compute::NEActivationLayer>(ifm_tensor->handle(), - ofm_tensor->handle(), act_info); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseBinary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::RHS)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index); - - std::unique_ptr<arm_compute::IFunction> fn; - switch (node.param().op_type) - { - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_AND: - { - fn = acl_common::generateLayer<arm_compute::NELogicalAnd>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle()); - break; - } - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR: - { - fn = acl_common::generateLayer<arm_compute::NELogicalOr>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle()); - break; - } - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MAX: - { - fn = acl_common::generateLayer<arm_compute::NEElementwiseMax>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle()); - break; - } - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MIN: - { - fn = acl_common::generateLayer<arm_compute::NEElementwiseMin>( - lhs_tensor->handle(), rhs_tensor->handle(), output_tensor->handle()); - break; - } - default: - { - std::string err_msg("acl_neon KernelGenerator : " + node.name() + - "is not elementwise-binary operations"); - assert(false && err_msg.c_str()); - break; - } - } - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseUnary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - std::unique_ptr<arm_compute::IFunction> fn; - switch (node.param().op_type) - { - case ir::operation::ElementwiseUnary::Type::ABS: - { - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::ABS}; - - fn = acl_common::generateLayer<arm_compute::NEActivationLayer>( - input_tensor->handle(), output_tensor->handle(), act_info); - break; - } - case ir::operation::ElementwiseUnary::Type::CAST: - { - if (input_tensor->data_type() == output_tensor->data_type()) - { - fn = acl_common::generateLayer<arm_compute::NECopy>(input_tensor->handle(), - output_tensor->handle()); - } - else if (_ctx.at(input_index).typeInfo().type() == ir::DataType::BOOL8) - { - fn = acl_common::generateLayer<arm_compute::NECastBool>(input_tensor->handle(), - output_tensor->handle()); - } - else - { - fn = acl_common::generateLayer<arm_compute::NECast>( - input_tensor->handle(), output_tensor->handle(), arm_compute::ConvertPolicy::SATURATE); - } - break; - } - case ir::operation::ElementwiseUnary::Type::DEQUANTIZE: - { - fn = acl_common::generateLayer<arm_compute::NEDequantizationLayer>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::EXP: - { - fn = acl_common::generateLayer<arm_compute::NEExpLayer>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::FLOOR: - { - fn = acl_common::generateLayer<arm_compute::NEFloor>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::LOGICAL_NOT: - { - fn = acl_common::generateLayer<arm_compute::NEBitwiseNot>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::NEG: - { - fn = acl_common::generateLayer<arm_compute::NENegLayer>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::RSQRT: - { - fn = acl_common::generateLayer<arm_compute::NERsqrtLayer>(input_tensor->handle(), - output_tensor->handle()); - break; - } - case ir::operation::ElementwiseUnary::Type::SQRT: - { - const ::arm_compute::ActivationLayerInfo act_info{ - ::arm_compute::ActivationLayerInfo::ActivationFunction::SQRT}; - - fn = acl_common::generateLayer<arm_compute::NEActivationLayer>( - input_tensor->handle(), output_tensor->handle(), act_info); - break; - } - default: - { - throw std::runtime_error("acl_neon KernelGenerator : " + node.name() + - "is not supported yet"); - break; - } - } - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::EmbeddingLookup &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lookups_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::LOOKUPS)}; - const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto lookups_tensor = _tensor_reg->getAclTensor(lookups_index); - auto values_tensor = _tensor_reg->getAclTensor(values_index); - - auto fn = acl_common::generateLayer<arm_compute::NEEmbeddingLookup>( - values_tensor->handle(), output_tensor->handle(), lookups_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::FullyConnected &node) -{ - const auto output_index{node.getOutputs().at(0)}; - auto output_tensor = _tensor_reg->getAclTensor(output_index); - const auto activation = node.param().activation; - - auto fn = acl_common::kernelGenFullyConnected<acl_common::AclFunction, ::arm_compute::ITensor, - ::arm_compute::NEFullyConnectedReshapingLayer>( - node, _ctx, _tensor_builder, _tensor_reg, _current_op_seq_layout); - _return_fn = std::make_unique<exec::FunctionSequence>( - std::move(fn), ActivationBuilder::generate(activation, output_tensor->handle())); -} - -void KernelGenerator::visit(const ir::operation::HashtableLookup &node) -{ - const auto output_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::OUTPUT)}; - const auto hits_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::HITS)}; - - const auto lookups_index{node.getInputs().at(ir::operation::HashtableLookup::Input::LOOKUPS)}; - const auto keys_index{node.getInputs().at(ir::operation::HashtableLookup::Input::KEYS)}; - const auto values_index{node.getInputs().at(ir::operation::HashtableLookup::Input::VALUES)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto hits_tensor = _tensor_reg->getAclTensor(hits_index); - - auto lookups_tensor = _tensor_reg->getAclTensor(lookups_index); - auto keys_tensor = _tensor_reg->getAclTensor(keys_index); - auto values_tensor = _tensor_reg->getAclTensor(values_index); - - auto fn = acl_common::generateLayer<arm_compute::NEHashtableLookup>( - lookups_tensor->handle(), keys_tensor->handle(), values_tensor->handle(), - output_tensor->handle(), hits_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Gather &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)}; - const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)}; - - const auto ifm_rank = _ctx.at(ifm_index).shape().rank(); - const auto axis_raw = node.param().axis; - const auto axis_value = (axis_raw < 0 ? (ifm_rank + axis_raw) : axis_raw); - // Converting in reverse order - const int axis = ::onert::backend::acl_common::ToARMComputeAxis(ifm_rank, axis_value).value(); - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto indices_tensor = _tensor_reg->getAclTensor(indices_index); - const auto backend_layout = ofm_tensor->layout(); - UNUSED_RELEASE(backend_layout); - - // NOTE The frontend layout and backend layout must be the same for this operation. - // If not the same, we have to add a stage(?) to perform permutation of output tensor. It - // is not not efficient even if it works well. If so, it would be better to set the - // layout of these backend tensors to the same layout. - // There is also one thing we have to think about. This operation depends on the layout of - // a model. For example, if a model in NHWC has this operation as output rank == 4, indices - // rank == 2 and axis == 2, this operation should work as the axis W and C, but the axis W - // and C are not sequential in NCHW. So the backend in NCHW cannot handle this case. - assert(backend_layout == ifm_tensor->layout()); - assert(backend_layout == indices_tensor->layout()); - assert(ifm_rank < 4 || _current_op_seq_layout == backend_layout); - - // input is n-D, indices k-D, output is (n + k - 1)-D - size_t n = ifm_rank; - assert(n == ifm_tensor->num_dimensions()); - size_t k = _ctx.at(indices_index).shape().rank(); - assert(k == indices_tensor->num_dimensions()); - - // Disable applied dim_correction - if (n != ifm_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and ifm tensor is applied dim_correction - acl_common::disableDimCorrection(ifm_tensor); - } - if (k != indices_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and indices tensor is applied dim_correction - acl_common::disableDimCorrection(indices_tensor); - } - - auto fn = acl_common::generateLayer<arm_compute::NEGatherEx>( - ifm_tensor->handle(), indices_tensor->handle(), ofm_tensor->handle(), axis); - - // Revert disabling applied dim_correction - if (ifm_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(ifm_tensor); - } - if (indices_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(indices_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::InstanceNorm &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::InstanceNorm::Input::INPUT)}; - const auto gamma_index{node.getInputs().at(ir::operation::InstanceNorm::Input::GAMMA)}; - const auto beta_index{node.getInputs().at(ir::operation::InstanceNorm::Input::BETA)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto gamma_tensor = _tensor_reg->getAclTensor(gamma_index); - auto beta_tensor = _tensor_reg->getAclTensor(beta_index); - auto epsilon = node.param().epsilon; - auto activation = node.param().activation; - - auto fn = acl_common::generateLayer<arm_compute::NEInstanceNormalizationLayerEx>( - ifm_tensor->handle(), ofm_tensor->handle(), gamma_tensor->handle(), beta_tensor->handle(), - epsilon); - - _return_fn = std::make_unique<exec::FunctionSequence>( - asAclFunction(std::move(fn)), ActivationBuilder::generate(activation, ofm_tensor->handle())); -} - -void KernelGenerator::visit(const ir::operation::L2Normalization &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::L2Normalization::Input::INPUT)}; - - // {CL|Neon}L2Normalization performs the reduction only along dimension 0 - // L2 Normalization always performs the reduction along the depth axis - // Thus, we repurpose {CL|Neon}NormalizationLayers to act as depthwise L2 normalizations by - // choosing normalization parameters as below - - const auto &ifm_shape = _ctx.at(ifm_index).shape(); - // TODO Support optional constant dimension that normalization would be performed on - const auto normalization_axis = _ctx.at(ifm_index).shape().rank() - 1; - int32_t radius = - 2 * ifm_shape.dim(normalization_axis) + 1; // normSize = depth(last dimension) * 2 + 1 - float alpha = 1.0f; // In the implementation to make alpha_ become 1 - float beta = 0.5f; // pow(reduction, -0.5) = 1 / sqrt(reduction) - float bias = 0.0f; // Don't offset the reduction. - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - const auto norm_info = ::arm_compute::NormalizationLayerInfo(::arm_compute::NormType::CROSS_MAP, - radius, alpha, beta, bias, false); - - auto fn = acl_common::generateLayer<arm_compute::NENormalizationLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), norm_info); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::LocalResponseNormalization &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{ - node.getInputs().at(ir::operation::LocalResponseNormalization::Input::INPUT)}; - - auto radius = node.param().radius; - auto alpha = node.param().alpha; - auto beta = node.param().beta; - auto bias = node.param().bias; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - const auto norm_info = ::arm_compute::NormalizationLayerInfo( - ::arm_compute::NormType::CROSS_MAP, radius * 2 + 1, alpha, beta, bias, false); - - auto fn = acl_common::generateLayer<arm_compute::NENormalizationLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), norm_info); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::LSTM &node) -{ - _return_fn = acl_common::kernelGenLSTM<acl_common::AclFunction, ::arm_compute::ITensor, - ::arm_compute::NELSTMLayer>(node, _ctx, _tensor_reg); -} - -void KernelGenerator::visit(const ir::operation::Pack &node) -{ - const auto output_index{node.getOutputs().at(0)}; - auto axis{node.param().axis}; - - const auto output_rank = _ctx.at(output_index).shape().rank(); - - std::vector<ir::OperandIndex> input_indexes; - for (const auto &input_index : node.getInputs()) - input_indexes.emplace_back(input_index); - - auto output = _tensor_reg->getAclTensor(output_index)->handle(); - std::vector<arm_compute::ITensor *> inputs; - for (const auto &input_index : input_indexes) - inputs.emplace_back(_tensor_reg->getAclTensor(input_index)->handle()); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = _tensor_reg->getAclTensor(output_index)->layout(); - - if (axis < 0) - axis += output_rank; - axis = acl_common::ToARMComputeAxis(output_rank, axis, frontend_layout, backend_layout).value(); - - // Disable applied dim_correction - for (const auto &input_index : input_indexes) - { - const auto &input_tensor = _tensor_reg->getAclTensor(input_index); - if (input_tensor->num_dimensions() != input_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(input_tensor); - } - } - - auto fn = acl_common::generateLayer<arm_compute::NEStackLayer>(inputs, axis, output); - - // Revert disabling applied dim_correction - for (const auto &input_index : input_indexes) - { - const auto &input_tensor = _tensor_reg->getAclTensor(input_index); - if (input_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(input_tensor); - } - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Pad &node) -{ - const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)}; - const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)}; - const auto output_index{node.getOutputs().at(0)}; - assert(_ctx.at(pad_index).data()); - - auto rank = _ctx.at(input_index).shape().rank(); - auto pad_base = _ctx.at(pad_index).data()->base(); - - auto input = _tensor_reg->getAclTensor(input_index)->handle(); - auto output = _tensor_reg->getAclTensor(output_index)->handle(); - - ::arm_compute::PaddingList padding_list; - padding_list.resize(rank); - for (int32_t n = 0; n < rank; ++n) - { - const int32_t *from = reinterpret_cast<const int32_t *>(pad_base) + (n * 2); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = _tensor_reg->getAclTensor(input_index)->layout(); - const auto axis = - acl_common::ToARMComputeAxis(rank, n, frontend_layout, backend_layout).value(); - padding_list[axis] = ::arm_compute::PaddingInfo{from[0], from[1]}; - } - - const auto input_type = _ctx.at(input_index).typeInfo(); - UNUSED_RELEASE(input_type); - assert(input->info()->data_type() == acl_common::asDataType(input_type.type())); - assert(input->info()->quantization_info() == - ::arm_compute::QuantizationInfo(input_type.scale(), input_type.offset())); - const auto pixel_value = - ::arm_compute::PixelValue(0, input->info()->data_type(), input->info()->quantization_info()); - - auto fn = - acl_common::generateLayer<arm_compute::NEPadLayer>(input, output, padding_list, pixel_value); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Pool2D &node) -{ - auto raw_fn = acl_common::kernelGenPool2D<::arm_compute::NEPoolingLayer>( - node, _ctx, _tensor_reg, _current_op_seq_layout, - acl_common::convertPoolType(node.param().op_type)); - - const auto ofm_index{node.getOutputs().at(0)}; - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - const auto activation = node.param().activation; - _return_fn = std::make_unique<exec::FunctionSequence>( - asAclFunction(std::move(raw_fn)), - ActivationBuilder::generate(activation, ofm_tensor->handle())); -} - -void KernelGenerator::visit(const ir::operation::Permute &node) -{ - const auto ofm_idx{node.getOutputs().at(0)}; - const auto ifm_idx{node.getInputs().at(0)}; - const auto permute_type = node.getPermuteType(); - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_idx); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_idx); - const auto rank = _ctx.at(ofm_idx).shape().rank(); - assert(_ctx.at(ifm_idx).shape().rank() == _ctx.at(ofm_idx).shape().rank()); - - std::unique_ptr<::arm_compute::IFunction> fn; - arm_compute::PermutationVector pv; - if (permute_type == ir::operation::Permute::Type::NCHW_TO_NHWC && rank == 4) - { - // WHCN -> CWHN - pv = arm_compute::PermutationVector{2, 0, 1}; - - fn = acl_common::generateLayer<arm_compute::NEPermute>(ifm_tensor->handle(), - ofm_tensor->handle(), pv); - } - else if (permute_type == ir::operation::Permute::Type::NHWC_TO_NCHW && rank == 4) - { - // CWHN -> WHCN - pv = arm_compute::PermutationVector{1, 2, 0}; - - fn = acl_common::generateLayer<arm_compute::NEPermute>(ifm_tensor->handle(), - ofm_tensor->handle(), pv); - } - else - { - fn = acl_common::generateLayer<arm_compute::NECopy>(ifm_tensor->handle(), ofm_tensor->handle()); - } - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::PReLU &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::PReLU::Input::INPUT)}; - const auto alpha_index{node.getInputs().at(ir::operation::PReLU::Input::ALPHA)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto alpha_tensor = _tensor_reg->getAclTensor(alpha_index); - - auto fn = acl_common::generateLayer<arm_compute::NEPReluLayer>( - ifm_tensor->handle(), alpha_tensor->handle(), ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Reduce &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reduce::Input::INPUT)}; - const auto axes_index{node.getInputs().at(ir::operation::Reduce::Input::AXES)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - // Convert to ACL axes taking into account negative values and possible duplicates. - const auto &axes = _ctx.at(axes_index); - const auto input_rank = _ctx.at(input_index).shape().rank(); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = input_tensor->layout(); - const auto reduce_axes = - acl_common::asCoordinates(axes, input_rank, frontend_layout, backend_layout); - const auto reduce_type = node.param().reduce_type; - const auto keep_dims = node.param().keep_dims; - - std::unique_ptr<::arm_compute::IFunction> fn; - if (reduce_type == ir::operation::Reduce::ReduceType::MEAN) - { - fn = acl_common::generateLayer<arm_compute::NEReduceMean>(input_tensor->handle(), reduce_axes, - keep_dims, output_tensor->handle()); - } - else if (reduce_type == ir::operation::Reduce::ReduceType::SUM) - { - fn = acl_common::generateLayer<arm_compute::NEReduceSum>(input_tensor->handle(), reduce_axes, - keep_dims, output_tensor->handle()); - } - else - { - fn = acl_common::generateLayer<arm_compute::NEReduceOperation>( - input_tensor->handle(), reduce_axes, keep_dims, output_tensor->handle(), - acl_common::convertReduceType(reduce_type)); - } - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Reshape &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - // NOTE This operation must not be changed the layout from frontend to backend - // So, PermutationOperationPass makes layouts of frontend and backend the same. - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = output_tensor->layout(); - assert((_ctx.at(input_index).shape().rank() < 4 && _ctx.at(output_index).shape().rank() < 4) || - frontend_layout == backend_layout); - UNUSED_RELEASE(frontend_layout); - UNUSED_RELEASE(backend_layout); - - auto fn = acl_common::generateLayer<arm_compute::NEReshapeLayer>(input_tensor->handle(), - output_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ResizeBilinear &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::ResizeBilinear::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - auto fn = acl_common::generateLayer<arm_compute::NEScale>( - ifm_tensor->handle(), ofm_tensor->handle(), ::arm_compute::InterpolationPolicy::BILINEAR, - ::arm_compute::BorderMode::REPLICATE, ::arm_compute::PixelValue(0.f), - ::arm_compute::SamplingPolicy::TOP_LEFT); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::RNN &node) -{ - const auto output_index{node.getOutputs().at(ir::operation::RNN::Output::OUTPUT)}; - const auto hidden_state_out_index{ - node.getOutputs().at(ir::operation::RNN::Output::HIDDEN_STATE_OUT)}; - - const auto input_index{node.getInputs().at(ir::operation::RNN::Input::INPUT)}; - const auto weights_index{node.getInputs().at(ir::operation::RNN::Input::WEIGHTS)}; - const auto recurrent_weights_index{ - node.getInputs().at(ir::operation::RNN::Input::RECURRENT_WEIGHTS)}; - const auto bias_index{node.getInputs().at(ir::operation::RNN::Input::BIAS)}; - const auto hidden_state_in_index{node.getInputs().at(ir::operation::RNN::Input::HIDDEN_STATE_IN)}; - - const auto activation = node.param().activation; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto hidden_state_out_tensor = _tensor_reg->getAclTensor(hidden_state_out_index); - - auto input_tensor = _tensor_reg->getAclTensor(input_index); - auto weights_tensor = _tensor_reg->getAclTensor(weights_index); - auto recurrent_weights_tensor = _tensor_reg->getAclTensor(recurrent_weights_index); - auto bias_tensor = _tensor_reg->getAclTensor(bias_index); - auto hidden_state_in_tensor = _tensor_reg->getAclTensor(hidden_state_in_index); - auto act_info = ::onert::backend::acl_common::asActivationLayerInfo(activation); - - auto copy_layer = acl_common::generateLayer<arm_compute::NECopy>( - hidden_state_in_tensor->handle(), hidden_state_out_tensor->handle()); - _return_fn = asAclFunction(std::move(copy_layer)); - - auto fn = acl_common::generateLayer<arm_compute::NERNNLayer>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(), - weights_tensor->handle(), recurrent_weights_tensor->handle(), bias_tensor->handle(), - hidden_state_out_tensor->handle(), output_tensor->handle(), act_info); - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Squeeze &node) -{ - // Squeeze is identical to reshape except that it has an optional dimensions input. - // In addition, optional dims_index is ignored since output tensor already has squeezed shape - // by freezer and toco - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)}; - const auto dims{node.param().dims}; - const auto ndim{node.param().ndim}; - (void)dims; - (void)ndim; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - auto fn = acl_common::generateLayer<arm_compute::NEReshapeLayer>(input_tensor->handle(), - output_tensor->handle()); - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Softmax &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)}; - const auto beta = node.param().beta; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - // Disable applied dim_correction - if (input_tensor->num_dimensions() != input_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(input_tensor); - } - - auto fn = acl_common::generateLayer<arm_compute::NESoftmaxLayer>( - _tensor_builder->acl_tensor_manager()->internal_buffer_manager(), input_tensor->handle(), - output_tensor->handle(), beta); - - // Revert disabling applied dim_correction - if (input_tensor->dimension(0) == 1) - { - acl_common::disableDimCorrection(input_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::SpaceToBatchND &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)}; - const auto block_size_index{ - node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)}; - const auto paddings_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto block_size_tensor = _tensor_reg->getAclTensor(block_size_index); - auto paddings_tensor = _tensor_reg->getAclTensor(paddings_index); - - assert(_ctx.at(block_size_index).data()); - assert(_ctx.at(paddings_index).data()); - - auto fn = acl_common::generateLayer<arm_compute::NESpaceToBatchLayer>( - ifm_tensor->handle(), block_size_tensor->handle(), paddings_tensor->handle(), - ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::SpaceToDepth &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)}; - - auto block_size = node.param().block_size; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - - auto fn = acl_common::generateLayer<arm_compute::NESpaceToDepthLayer>( - ifm_tensor->handle(), ofm_tensor->handle(), block_size); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Split &node) -{ - // TODO Support this op by SubTensor - const auto ifm_index{node.getInputs().at(ir::operation::Split::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::Split::Input::AXIS)}; - - assert(node.param().num_splits == static_cast<int>(node.getOutputs().size())); - if (!_ctx.at(axis_index).isConstant()) - { - throw std::runtime_error("Non-constant axis_index NYI for acl_neon backend"); - } - - const auto ifm_rank = _ctx.at(ifm_index).shape().rank(); - std::vector<ir::OperandIndex> output_indexes; - for (const auto &output : node.getOutputs()) - output_indexes.emplace_back(output); - - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - std::vector<arm_compute::ITensor *> output_tensors; - for (const auto &ofm_ind : output_indexes) - output_tensors.emplace_back(_tensor_reg->getAclTensor(ofm_ind)->handle()); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = ifm_tensor->layout(); - auto axis = _ctx.at(axis_index).asScalar<int32_t>(); - if (axis < 0) - axis += ifm_rank; - axis = acl_common::ToARMComputeAxis(ifm_rank, axis, frontend_layout, backend_layout).value(); - - auto fn = - acl_common::generateLayer<arm_compute::NESplit>(ifm_tensor->handle(), output_tensors, axis); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::SquaredDifference &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto lhs_tensor = _tensor_reg->getAclTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getAclTensor(rhs_index); - - auto fn = acl_common::generateLayer<arm_compute::NEElementwiseSquaredDiff>( - lhs_tensor->handle(), rhs_tensor->handle(), ofm_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Slice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Slice::Input::INPUT)}; - const auto begins_index{node.getInputs().at(ir::operation::Slice::Input::BEGINS)}; - const auto sizes_index{node.getInputs().at(ir::operation::Slice::Input::SIZES)}; - - auto outputData_tensor = _tensor_reg->getAclTensor(output_index); - auto inputData_tensor = _tensor_reg->getAclTensor(input_index); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = inputData_tensor->layout(); - - // Set initializers for indices data such as order of inputData - int input_rank = _ctx.at(input_index).shape().rank(); - std::vector<int32_t> starts; - std::vector<int32_t> ends; - starts.resize(input_rank, 0); - ends.resize(input_rank, 0); - { - auto beginData_base = _ctx.at(begins_index).data()->base(); - auto sizeData_base = _ctx.at(sizes_index).data()->base(); - const int beginData_size = _ctx.at(begins_index).shape().num_elements(); - const int sizeData_size = _ctx.at(sizes_index).shape().num_elements(); - - using ir::DataType; - - UNUSED_RELEASE(beginData_size); - UNUSED_RELEASE(sizeData_size); - - assert(_ctx.at(begins_index).typeInfo().type() == DataType::INT32); - assert(_ctx.at(sizes_index).typeInfo().type() == DataType::INT32); - assert(beginData_size == input_rank); - assert(sizeData_size == input_rank); - - assert(beginData_base != nullptr); - for (int n = 0; n < input_rank; ++n) - { - auto axis = ::onert::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout, - backend_layout) - .value(); - - int32_t begin_value = *(reinterpret_cast<const int32_t *>(beginData_base) + n); - starts[axis] = begin_value; - - int32_t size_value = *(reinterpret_cast<const int32_t *>(sizeData_base) + n); - ends[axis] = begin_value + size_value; - } - } - - ::arm_compute::Coordinates starts_set; - ::arm_compute::Coordinates ends_set; - - for (size_t i = 0; i < starts.size(); ++i) - { - starts_set.set(i, starts[i]); - ends_set.set(i, ends[i]); - } - - auto fn = acl_common::generateLayer<arm_compute::NESlice>( - inputData_tensor->handle(), outputData_tensor->handle(), starts_set, ends_set); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::StridedSlice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)}; - const auto starts_index{node.getInputs().at(ir::operation::StridedSlice::Input::STARTS)}; - const auto ends_index{node.getInputs().at(ir::operation::StridedSlice::Input::ENDS)}; - const auto strides_index{node.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)}; - - auto outputData_tensor = _tensor_reg->getAclTensor(output_index); - auto inputData_tensor = _tensor_reg->getAclTensor(input_index); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = inputData_tensor->layout(); - - // Set initializers for indices data such as order of inputData - int input_rank = _ctx.at(input_index).shape().rank(); - std::vector<int32_t> starts; - std::vector<int32_t> ends; - std::vector<int32_t> strides; - starts.resize(input_rank, 0); - ends.resize(input_rank, 0); - strides.resize(input_rank, 0); - { - auto startData_base = _ctx.at(starts_index).data()->base(); - auto endData_base = _ctx.at(ends_index).data()->base(); - auto stridesData_base = _ctx.at(strides_index).data()->base(); - const int startData_size = _ctx.at(starts_index).shape().num_elements(); - const int endData_size = _ctx.at(ends_index).shape().num_elements(); - const int stridesData_size = _ctx.at(strides_index).shape().num_elements(); - - using ir::DataType; - - UNUSED_RELEASE(startData_size); - UNUSED_RELEASE(endData_size); - UNUSED_RELEASE(stridesData_size); - - assert(_ctx.at(starts_index).typeInfo().type() == DataType::INT32); - assert(_ctx.at(ends_index).typeInfo().type() == DataType::INT32); - assert(_ctx.at(strides_index).typeInfo().type() == DataType::INT32); - assert(startData_size == input_rank); - assert(endData_size == input_rank); - assert(stridesData_size == input_rank); - - assert(startData_base != nullptr); - for (int n = 0; n < input_rank; ++n) - { - auto axis = ::onert::backend::acl_common::ToARMComputeAxis(input_rank, n, frontend_layout, - backend_layout) - .value(); - - int32_t start_value = *(reinterpret_cast<const int32_t *>(startData_base) + n); - starts[axis] = start_value; - - int32_t end_value = *(reinterpret_cast<const int32_t *>(endData_base) + n); - ends[axis] = end_value; - - int32_t strides_value = *(reinterpret_cast<const int32_t *>(stridesData_base) + n); - strides[axis] = strides_value; - } - } - - // Set mask bits such as order of inputData - // FIXME Take the layouts into account. - const auto begin_mask = acl_common::ReorderBits<int32_t>(node.param().begin_mask, input_rank); - const auto end_mask = acl_common::ReorderBits<int32_t>(node.param().end_mask, input_rank); - const auto shrink_axis_mask = - acl_common::ReorderBits<int32_t>(node.param().shrink_axis_mask, input_rank); - - ::arm_compute::Coordinates starts_set; - ::arm_compute::Coordinates ends_set; - ::arm_compute::BiStrides strides_set; - - for (size_t i = 0; i < starts.size(); ++i) - { - starts_set.set(i, starts[i]); - ends_set.set(i, ends[i]); - strides_set.set(i, strides[i]); - } - - // Disable applied dim_correction - if (inputData_tensor->num_dimensions() != inputData_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(inputData_tensor); - } - - auto fn = acl_common::generateLayer<arm_compute::NEStridedSlice>( - inputData_tensor->handle(), outputData_tensor->handle(), starts_set, ends_set, strides_set, - begin_mask, end_mask, shrink_axis_mask); - - // Revert disabling applied dim_correction - if (inputData_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(inputData_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::TransposeConv &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ker_index{node.getInputs().at(ir::operation::TransposeConv::Input::KERNEL)}; - const auto ifm_index{node.getInputs().at(ir::operation::TransposeConv::Input::INPUT)}; - - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ker_shape = _ctx.at(ker_index).shape().asFeature(_current_op_seq_layout); - - const auto stride = node.param().stride; - - assert((node.param().padding.type == ir::PaddingType::SAME) || - (node.param().padding.type == ir::PaddingType::VALID)); - auto padding = ir::calculatePadding(node.param().padding, ofm_shape, ifm_shape, stride, - ker_shape.W, ker_shape.H); - - uint32_t invalid_horizontal = 0; - uint32_t invalid_vertical = 0; - if (node.param().padding.type == ir::PaddingType::VALID) - { - invalid_horizontal = - ofm_shape.W - (1 + (ifm_shape.W - 1) * stride.horizontal) - (ker_shape.W - 1); - invalid_vertical = ofm_shape.H - (1 + (ifm_shape.H - 1) * stride.vertical) - (ker_shape.H - 1); - } - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index); - auto ker_tensor = _tensor_reg->getAclTensor(ker_index); - - const auto tconv_info = acl_common::asPadStrideInfo(padding, stride); - - auto fn = acl_common::generateLayer<arm_compute::NETransposeConvLayer>( - ifm_tensor->handle(), ker_tensor->handle(), nullptr, ofm_tensor->handle(), tconv_info, - invalid_horizontal, invalid_vertical); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Transpose &node) -{ - const auto ofm_idx{node.getOutputs().at(0)}; - const auto ifm_idx{node.getInputs().at(ir::operation::Transpose::Input::INPUT)}; - const auto perm_idx{node.getInputs().at(ir::operation::Transpose::Input::PERMUTATION)}; - - auto ofm_tensor = _tensor_reg->getAclTensor(ofm_idx); - const auto ifm_tensor = _tensor_reg->getAclTensor(ifm_idx); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = ifm_tensor->layout(); - const auto rank = _ctx.at(ifm_idx).shape().rank(); - - const auto &perms = _ctx.at(perm_idx); - std::vector<int32_t> pv; - if (perms.shape() == ir::Shape{0}) - { - pv.resize(rank); - std::iota(pv.begin(), pv.end(), 0); - std::reverse(pv.begin(), pv.end()); - } - else - { - pv = _ctx.at(perm_idx).asVector<int32_t>(); - } - - std::unique_ptr<arm_compute::IFunction> fn; - if (rank == 1) - { - fn = acl_common::generateLayer<arm_compute::NECopy>(ifm_tensor->handle(), ofm_tensor->handle()); - } - else if (rank == 2) - { - assert(pv.size() == 2 && pv.at(0) == 1 && pv.at(1) == 0); - fn = acl_common::generateLayer<arm_compute::NETranspose>(ifm_tensor->handle(), - ofm_tensor->handle()); - } - else - { - auto backend_pv = - acl_common::getARMComputePermutationVector(rank, pv, frontend_layout, backend_layout); - - fn = acl_common::generateLayer<arm_compute::NEPermute>(ifm_tensor->handle(), - ofm_tensor->handle(), backend_pv); - } - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Unpack &node) -{ - const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)}; - auto axis{node.param().axis}; - - const auto input_rank = _ctx.at(input_index).shape().rank(); - - std::vector<ir::OperandIndex> output_indexes; - for (const auto &output_index : node.getOutputs()) - output_indexes.emplace_back(output_index); - - auto input_tensor = _tensor_reg->getAclTensor(input_index); - std::vector<arm_compute::ITensor *> outputs; - for (const auto &output_index : output_indexes) - outputs.emplace_back(_tensor_reg->getAclTensor(output_index)->handle()); - - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = _tensor_reg->getAclTensor(input_index)->layout(); - if (axis < 0) - axis += input_rank; - axis = acl_common::ToARMComputeAxis(input_rank, axis, frontend_layout, backend_layout).value(); - - // Disable applied dim_correction - if (input_tensor->num_dimensions() != input_tensor->info()->num_dimensions()) - { - // This means that high dimension's value is 1 and input tensor is applied dim_correction - acl_common::disableDimCorrection(input_tensor); - } - - auto fn = - acl_common::generateLayer<arm_compute::NEUnstack>(input_tensor->handle(), outputs, axis); - - // Revert disabling applied dim_correction - if (input_tensor->dimension(0) == 1) - { - acl_common::enableDimCorrection(input_tensor); - } - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::ExpandDims &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ExpandDims::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input_tensor = _tensor_reg->getAclTensor(input_index); - - auto fn = acl_common::generateLayer<arm_compute::NEReshapeLayer>(input_tensor->handle(), - output_tensor->handle()); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::Comparison &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input0_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)}; - const auto input1_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)}; - - const auto comparison_type = node.param().comparison_type; - - auto output_tensor = _tensor_reg->getAclTensor(output_index); - auto input0_tensor = _tensor_reg->getAclTensor(input0_index); - auto input1_tensor = _tensor_reg->getAclTensor(input1_index); - - auto fn = acl_common::generateLayer<arm_compute::NEElementwiseComparison>( - input0_tensor->handle(), input1_tensor->handle(), output_tensor->handle(), - (arm_compute::ComparisonOperation)comparison_type); - - _return_fn = asAclFunction(std::move(fn)); -} - -void KernelGenerator::visit(const ir::operation::OneHot &node) -{ - const auto out_idx{node.getOutputs().at(0)}; - const auto indices_idx{node.getInputs().at(ir::operation::OneHot::Input::INDICES)}; - const auto depth_idx{node.getInputs().at(ir::operation::OneHot::Input::DEPTH)}; - const auto onvalue_idx{node.getInputs().at(ir::operation::OneHot::Input::ON_VALUE)}; - const auto offvalue_idx{node.getInputs().at(ir::operation::OneHot::Input::OFF_VALUE)}; - - auto output_tensor = _tensor_reg->getAclTensor(out_idx); - auto indices_tensor = _tensor_reg->getAclTensor(indices_idx); - auto depth_tensor = _tensor_reg->getAclTensor(depth_idx); - auto onvalue_tensor = _tensor_reg->getAclTensor(onvalue_idx); - auto offvalue_tensor = _tensor_reg->getAclTensor(offvalue_idx); - - const size_t output_rank = _ctx.at(out_idx).shape().rank(); - const auto frontend_layout = _current_op_seq_layout; - const auto backend_layout = output_tensor->layout(); - int32_t axis = node.param().axis == -1 ? output_rank - 1 : node.param().axis; - axis = acl_common::ToARMComputeAxis(output_rank, axis, frontend_layout, backend_layout).value(); - - auto fn = acl_common::generateLayer<arm_compute::NEOneHot>( - indices_tensor->handle(), depth_tensor->handle(), onvalue_tensor->handle(), - offvalue_tensor->handle(), output_tensor->handle(), axis); - _return_fn = asAclFunction(std::move(fn)); -} - -} // namespace acl_neon -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_neon/KernelGenerator.h b/runtime/onert/backend/acl_neon/KernelGenerator.h deleted file mode 100644 index 4d269cde5..000000000 --- a/runtime/onert/backend/acl_neon/KernelGenerator.h +++ /dev/null @@ -1,96 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_KERNEL_GENERATOR_H__ -#define __ONERT_BACKEND_ACL_NEON_KERNEL_GENERATOR_H__ - -#include <backend/IKernelGenerator.h> - -#include "ir/Operands.h" -#include "TensorBuilder.h" -#include "AclTensorRegistry.h" -#include "TensorManager.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -class KernelGenerator : public IKernelGenerator -{ -public: - KernelGenerator(const ir::Operands &operands_ctx, const ir::Operations &operations_ctx, - const std::shared_ptr<TensorBuilder> &tensor_builder, - const std::shared_ptr<acl_common::AclTensorRegistry<TensorManager>> &_tensor_reg); - - void visit(const ir::OpSequence &) override; - void visit(const ir::operation::ArgMax &) override; - void visit(const ir::operation::BatchToSpaceND &) override; - void visit(const ir::operation::BinaryArithmetic &) override; - void visit(const ir::operation::Conv2D &) override; - void visit(const ir::operation::DepthToSpace &) override; - void visit(const ir::operation::DepthwiseConv2D &) override; - void visit(const ir::operation::Concat &) override; - void visit(const ir::operation::ElementwiseActivation &) override; - void visit(const ir::operation::ElementwiseBinary &) override; - void visit(const ir::operation::ElementwiseUnary &) override; - void visit(const ir::operation::EmbeddingLookup &) override; - void visit(const ir::operation::FullyConnected &) override; - void visit(const ir::operation::Gather &) override; - void visit(const ir::operation::HashtableLookup &) override; - void visit(const ir::operation::InstanceNorm &) override; - void visit(const ir::operation::L2Normalization &) override; - void visit(const ir::operation::LocalResponseNormalization &) override; - void visit(const ir::operation::LSTM &) override; - void visit(const ir::operation::Pack &) override; - void visit(const ir::operation::Pad &) override; - void visit(const ir::operation::Pool2D &) override; - void visit(const ir::operation::Permute &) override; - void visit(const ir::operation::PReLU &) override; - void visit(const ir::operation::Reduce &) override; - void visit(const ir::operation::Reshape &) override; - void visit(const ir::operation::ResizeBilinear &) override; - void visit(const ir::operation::RNN &) override; - void visit(const ir::operation::Squeeze &) override; - void visit(const ir::operation::Softmax &) override; - void visit(const ir::operation::SpaceToBatchND &) override; - void visit(const ir::operation::SpaceToDepth &) override; - void visit(const ir::operation::Split &) override; - void visit(const ir::operation::SquaredDifference &) override; - void visit(const ir::operation::Slice &) override; - void visit(const ir::operation::StridedSlice &) override; - void visit(const ir::operation::TransposeConv &) override; - void visit(const ir::operation::Transpose &) override; - void visit(const ir::operation::Unpack &) override; - void visit(const ir::operation::ExpandDims &) override; - void visit(const ir::operation::Comparison &) override; - void visit(const ir::operation::OneHot &) override; - -private: - const ir::Operands &_ctx; - const ir::Operations &_operations_ctx; - std::shared_ptr<TensorBuilder> _tensor_builder; - std::shared_ptr<acl_common::AclTensorRegistry<TensorManager>> _tensor_reg; - ir::Layout _current_op_seq_layout; -}; - -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_KERNEL_GENERATOR_H__ diff --git a/runtime/onert/backend/acl_neon/Optimizer.cc b/runtime/onert/backend/acl_neon/Optimizer.cc deleted file mode 100644 index ac80901cc..000000000 --- a/runtime/onert/backend/acl_neon/Optimizer.cc +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Optimizer.h" - -#include "ParentInfo.h" - -#include <cassert> -#include <compiler/LoweredGraph.h> -#include <util/logging.h> -#include "AclSubTensorAnalyzer.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -Optimizer::Optimizer(BackendContext *context) - : _context{context}, - _tensor_builder{std::dynamic_pointer_cast<TensorBuilder>(context->tensor_builder)} -{ - assert(context); -} - -void Optimizer::optimize() -{ - // Concat elimination (build subtensor info) - { - acl_common::AclSubTensorAnalyzer sa{*_context->graph()}; - for (auto op_info : _context->operation_list()) - { - auto &op = _context->graph()->operations().at(op_info.index); - sa.setLayout(op_info.layout); - op.accept(sa); - } - - _tensor_builder->parent_map(sa.releaseParentMap()); - } -} - -} // namespace acl_neon -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_neon/Optimizer.h b/runtime/onert/backend/acl_neon/Optimizer.h deleted file mode 100644 index 5fe0d519c..000000000 --- a/runtime/onert/backend/acl_neon/Optimizer.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_OPTIMIZER_H__ -#define __ONERT_BACKEND_ACL_NEON_OPTIMIZER_H__ - -#include <backend/IOptimizer.h> -#include <backend/BackendContext.h> -#include "TensorBuilder.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -class Optimizer : public IOptimizer -{ -public: - Optimizer(BackendContext *context); - - void optimize() override; - -private: - BackendContext *_context; - std::shared_ptr<TensorBuilder> _tensor_builder; -}; - -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_OPTIMIZER_H__ diff --git a/runtime/onert/backend/acl_neon/TensorBuilder.h b/runtime/onert/backend/acl_neon/TensorBuilder.h deleted file mode 100644 index 070dc20ac..000000000 --- a/runtime/onert/backend/acl_neon/TensorBuilder.h +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_TENSOR_BUILDER_H__ -#define __ONERT_BACKEND_ACL_NEON_TENSOR_BUILDER_H__ - -#include <AclTensorBuilder.h> - -#include "operand/NETensor.h" -#include "operand/NESubTensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -using TensorBuilder = - acl_common::AclTensorBuilder<operand::INETensor, operand::NETensor, operand::NESubTensor>; - -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_TENSOR_BUILDER_H__ diff --git a/runtime/onert/backend/acl_neon/TensorManager.h b/runtime/onert/backend/acl_neon/TensorManager.h deleted file mode 100644 index 3b7cfbcfd..000000000 --- a/runtime/onert/backend/acl_neon/TensorManager.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_TENSOR_MANAGER_H__ -#define __ONERT_BACKEND_ACL_NEON_TENSOR_MANAGER_H__ - -#include <arm_compute/runtime/Allocator.h> -#include <arm_compute/runtime/PoolManager.h> -#include <arm_compute/runtime/OffsetLifetimeManager.h> -#include <arm_compute/runtime/MemoryManagerOnDemand.h> -#include <arm_compute/runtime/MemoryGroup.h> - -#include <AclMemoryManager.h> -#include <AclLinearMemoryManager.h> -#include <AclInternalBufferManager.h> -#include <AclTensorManager.h> - -#include "operand/NETensor.h" -#include "operand/NESubTensor.h" - -#include "util/logging.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ - -using MemoryManager = - acl_common::AclMemoryManager<operand::INETensor, operand::NETensor, operand::NESubTensor>; - -using LinearMemoryManager = acl_common::AclLinearMemoryManager< - operand::INETensor, operand::NETensor, operand::NESubTensor, - ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager, - ::arm_compute::OffsetLifetimeManager, ::arm_compute::Allocator, ::arm_compute::MemoryGroup>; - -using InternalBufferManager = acl_common::AclInternalBufferManager< - ::arm_compute::MemoryManagerOnDemand, ::arm_compute::PoolManager, - ::arm_compute::OffsetLifetimeManager, ::arm_compute::Allocator>; - -using TensorManager = acl_common::AclTensorManager<acl_neon::operand::INETensor, operand::NETensor, - operand::NESubTensor>; - -inline TensorManager *createTensorManager(bool is_linear_executor) -{ - if (is_linear_executor) - { - VERBOSE(acl_neon_createTensorManager) << "AclTensorManager as Linear" << std::endl; - return new TensorManager(new MemoryManager(), new LinearMemoryManager(), - new InternalBufferManager()); - } - else - { - VERBOSE(acl_neon_createTensorManager) << "AclTensorManager" << std::endl; - return new TensorManager(new MemoryManager(), new MemoryManager(), new InternalBufferManager()); - } -} - -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_TENSOR_MANAGER_H__ diff --git a/runtime/onert/backend/acl_neon/acl_neon.cc b/runtime/onert/backend/acl_neon/acl_neon.cc deleted file mode 100644 index f490d132d..000000000 --- a/runtime/onert/backend/acl_neon/acl_neon.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <util/logging.h> - -#include "Backend.h" - -extern "C" { -onert::backend::Backend *onert_backend_create() -{ - VERBOSE(onert_backend_create) << "'acl_neon' loaded\n"; - return new onert::backend::acl_neon::Backend; -} - -void onert_backend_destroy(onert::backend::Backend *backend) -{ - VERBOSE(onert_backend_create) << "'acl_neon' unloaded\n"; - delete backend; -} -} diff --git a/runtime/onert/backend/acl_neon/operand/INETensor.cc b/runtime/onert/backend/acl_neon/operand/INETensor.cc deleted file mode 100644 index fddcff0f4..000000000 --- a/runtime/onert/backend/acl_neon/operand/INETensor.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "INETensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ -namespace operand -{ - -void INETensor::access(const std::function<void(ITensor &tensor)> &fn) -{ - // This is an optional input - if (total_size() == 0) - return; - - fn(*this); -} - -} // namespace operand -} // namespace acl_neon -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_neon/operand/INETensor.h b/runtime/onert/backend/acl_neon/operand/INETensor.h deleted file mode 100644 index db0ce6fdc..000000000 --- a/runtime/onert/backend/acl_neon/operand/INETensor.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_OPERAND_I_NE_TENSOR_H__ -#define __ONERT_BACKEND_ACL_NEON_OPERAND_I_NE_TENSOR_H__ - -#include <arm_compute/core/ITensor.h> - -#include <IACLTensor.h> - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ -namespace operand -{ - -class INETensor : public acl_common::IACLTensor -{ -public: - const arm_compute::ITensor *handle() const override = 0; - arm_compute::ITensor *handle() override = 0; - void access(const std::function<void(ITensor &tensor)> &fn) final; -}; - -} // namespace operand -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_OPERAND_I_NE_TENSOR_H__ diff --git a/runtime/onert/backend/acl_neon/operand/NESubTensor.cc b/runtime/onert/backend/acl_neon/operand/NESubTensor.cc deleted file mode 100644 index 457addd55..000000000 --- a/runtime/onert/backend/acl_neon/operand/NESubTensor.cc +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "NESubTensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ -namespace operand -{ - -NESubTensor::NESubTensor(INETensor *parent, const arm_compute::TensorShape &tensor_shape, - const arm_compute::Coordinates &coords, size_t rank, bool extend_parent) - : _ne_sub_tensor(std::make_shared<arm_compute::SubTensor>(parent->handle(), tensor_shape, - coords, extend_parent)), - _rank{rank} -{ - // DO NOTHING -} - -const arm_compute::SubTensor *NESubTensor::handle() const { return _ne_sub_tensor.get(); } - -arm_compute::SubTensor *NESubTensor::handle() { return _ne_sub_tensor.get(); } - -} // namespace operand -} // namespace acl_neon -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_neon/operand/NESubTensor.h b/runtime/onert/backend/acl_neon/operand/NESubTensor.h deleted file mode 100644 index e7f77d7fc..000000000 --- a/runtime/onert/backend/acl_neon/operand/NESubTensor.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_OPERAND_NE_SUB_TENSOR_H__ -#define __ONERT_BACKEND_ACL_NEON_OPERAND_NE_SUB_TENSOR_H__ - -#include <arm_compute/runtime/SubTensor.h> -#include "INETensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ -namespace operand -{ - -class NESubTensor : public INETensor -{ -public: - NESubTensor() = delete; - -public: - NESubTensor(INETensor *parent, const arm_compute::TensorShape &tensor_shape, - const arm_compute::Coordinates &coords, size_t rank, bool extend_parent = false); - -public: - size_t num_dimensions() const final { return _rank; } - -public: - const arm_compute::SubTensor *handle() const override; - arm_compute::SubTensor *handle() override; - -public: - // This method is used to prevent the use of memcpy for SubTensor - bool has_padding() const override { return true; } - -private: - std::shared_ptr<arm_compute::SubTensor> _ne_sub_tensor; - size_t _rank; -}; - -} // namespace operand -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_OPERAND_NE_SUB_TENSOR_H__ diff --git a/runtime/onert/backend/acl_neon/operand/NETensor.cc b/runtime/onert/backend/acl_neon/operand/NETensor.cc deleted file mode 100644 index 53dbb3021..000000000 --- a/runtime/onert/backend/acl_neon/operand/NETensor.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <arm_compute/runtime/Memory.h> -#include <arm_compute/runtime/MemoryRegion.h> -#include "NETensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ -namespace operand -{ - -NETensor::NETensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses) - : _ne_tensor(std::make_shared<arm_compute::Tensor>()), _rank{rank}, _num_uses{num_uses} -{ - allocator()->init(info); -} - -const arm_compute::Tensor *NETensor::handle() const { return _ne_tensor.get(); } - -arm_compute::Tensor *NETensor::handle() { return _ne_tensor.get(); } - -arm_compute::TensorAllocator *NETensor::allocator() { return _ne_tensor->allocator(); } - -} // namespace operand -} // namespace acl_neon -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/acl_neon/operand/NETensor.h b/runtime/onert/backend/acl_neon/operand/NETensor.h deleted file mode 100644 index 0dd81afec..000000000 --- a/runtime/onert/backend/acl_neon/operand/NETensor.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ACL_NEON_OPERAND_NE_TENSOR_H__ -#define __ONERT_BACKEND_ACL_NEON_OPERAND_NE_TENSOR_H__ - -#include <arm_compute/core/TensorInfo.h> -#include <arm_compute/runtime/Tensor.h> -#include "arm_compute/runtime/TensorAllocator.h" -#include "INETensor.h" - -namespace onert -{ -namespace backend -{ -namespace acl_neon -{ -namespace operand -{ - -class NETensor : public INETensor -{ -public: - NETensor() = delete; - -public: - NETensor(const arm_compute::TensorInfo &info, size_t rank, size_t num_uses); - -public: - size_t num_dimensions() const final { return _rank; } - -public: - const arm_compute::Tensor *handle() const override; - arm_compute::Tensor *handle() override; - size_t num_uses() const { return _num_uses; } - -public: - arm_compute::TensorAllocator *allocator(); - -private: - std::shared_ptr<arm_compute::Tensor> _ne_tensor; - size_t _rank; - size_t _num_uses; -}; - -} // namespace operand -} // namespace acl_neon -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ACL_NEON_OPERAND_NE_TENSOR_H__ diff --git a/runtime/onert/backend/cpu/Backend.h b/runtime/onert/backend/cpu/Backend.h deleted file mode 100644 index fc8574b26..000000000 --- a/runtime/onert/backend/cpu/Backend.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_BACKEND_H__ -#define __ONERT_BACKEND_CPU_BACKEND_H__ - -#include "BackendContext.h" -#include "Config.h" -#include "ConstantInitializer.h" -#include "KernelGenerator.h" - -#include <backend/Backend.h> - -#include <memory> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class Backend : public ::onert::backend::Backend -{ -public: - Backend() : _config{std::make_shared<Config>()} {} - - std::shared_ptr<IConfig> config() const override { return _config; } - - std::unique_ptr<onert::backend::BackendContext> - newContext(const ir::Graph &graph, const std::shared_ptr<custom::IKernelBuilder> &kb, - bool) const override - { - const auto &operands = graph.operands(); - const auto &operations = graph.operations(); - auto context = std::make_unique<BackendContext>(this, &graph); - auto tr = std::make_shared<cpu_common::TensorRegistry>(); - auto tb = std::make_shared<TensorBuilder>(tr); - context->tensor_registry = tr; - context->tensor_builder = tb; - context->constant_initializer = std::make_shared<ConstantInitializer>(operands, tr); - context->kernel_gen = std::make_shared<KernelGenerator>(operands, operations, tb, tr, kb, - context->external_context()); - context->tensor_register = nullptr; - context->optimizer = nullptr; - return context; - } - -private: - std::shared_ptr<IConfig> _config; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_BACKEND_H__ diff --git a/runtime/onert/backend/cpu/BackendContext.h b/runtime/onert/backend/cpu/BackendContext.h deleted file mode 100644 index e90b21054..000000000 --- a/runtime/onert/backend/cpu/BackendContext.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_BACKEND_CONTEXT_H__ -#define __ONERT_BACKEND_CPU_BACKEND_CONTEXT_H__ - -#include <backend/BackendContext.h> -#include "ExternalContext.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class BackendContext : public onert::backend::BackendContext -{ -public: - BackendContext(const Backend *backend, const ir::Graph *graph, - std::shared_ptr<ITensorRegistry> tensor_registry = nullptr, - std::shared_ptr<ITensorBuilder> tensor_builder = nullptr, - std::shared_ptr<IConstantInitializer> constant_initializer = nullptr, - std::shared_ptr<IKernelGenerator> kernel_gen = nullptr, - std::shared_ptr<ITensorRegister> tensor_register = nullptr, - std::shared_ptr<IOptimizer> optimizer = nullptr) - : onert::backend::BackendContext(backend, graph, tensor_registry, tensor_builder, - constant_initializer, kernel_gen, tensor_register, - optimizer), - _external_context(new ExternalContext) - { - } - - std::shared_ptr<ExternalContext> external_context() { return _external_context; } - -private: - // NOTE ruy context has a thread pool, and when multiple ruy contexts are created, - // the thread pool is also created in duplicate - // TODO Create one ruy context for session - std::shared_ptr<ExternalContext> _external_context; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_BACKEND_CONTEXT_H__ diff --git a/runtime/onert/backend/cpu/CMakeLists.txt b/runtime/onert/backend/cpu/CMakeLists.txt deleted file mode 100644 index 01a3cd178..000000000 --- a/runtime/onert/backend/cpu/CMakeLists.txt +++ /dev/null @@ -1,18 +0,0 @@ -set(LIB_ONERT_BACKEND_CPU onert_backend_cpu) - -nnfw_find_package(Ruy REQUIRED) - -file(GLOB_RECURSE SOURCES "*.cc") - -add_library(${LIB_ONERT_BACKEND_CPU} SHARED ${SOURCES}) - -target_link_libraries(${LIB_ONERT_BACKEND_CPU} PRIVATE nnfw_lib_cker) -target_link_libraries(${LIB_ONERT_BACKEND_CPU} PRIVATE onert_core) -target_link_libraries(${LIB_ONERT_BACKEND_CPU} PRIVATE nnfw_common) -target_link_libraries(${LIB_ONERT_BACKEND_CPU} PRIVATE nnfw_coverage) -target_link_libraries(${LIB_ONERT_BACKEND_CPU} PRIVATE ruy) -target_link_libraries(${LIB_ONERT_BACKEND_CPU} INTERFACE ruy_instrumentation) - -set_target_properties(${LIB_ONERT_BACKEND_CPU} PROPERTIES OUTPUT_NAME backend_cpu) - -install(TARGETS ${LIB_ONERT_BACKEND_CPU} DESTINATION lib) diff --git a/runtime/onert/backend/cpu/Config.cc b/runtime/onert/backend/cpu/Config.cc deleted file mode 100644 index 3ace47f5d..000000000 --- a/runtime/onert/backend/cpu/Config.cc +++ /dev/null @@ -1,32 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Config.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -bool Config::initialize() { return true; } - -ir::Layout Config::supportLayout(const ir::Operation &, ir::Layout) { return ir::Layout::NHWC; } - -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/Config.h b/runtime/onert/backend/cpu/Config.h deleted file mode 100644 index 37e49581a..000000000 --- a/runtime/onert/backend/cpu/Config.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_CONFIG_H__ -#define __ONERT_BACKEND_CPU_CONFIG_H__ - -#include <backend/IConfig.h> -#include <memory> -#include <util/ITimer.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class Config : public IConfig -{ -public: - std::string id() override { return "cpu"; } - bool initialize() override; - ir::Layout supportLayout(const ir::Operation &node, ir::Layout frontend_layout) override; - bool supportPermutation() override { return true; } - bool supportDynamicTensor() override { return true; } - bool supportFP16() override { return false; } - - std::unique_ptr<util::ITimer> timer() override { return std::make_unique<util::CPUTimer>(); } -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_CONFIG_H__ diff --git a/runtime/onert/backend/cpu/ConstantInitializer.cc b/runtime/onert/backend/cpu/ConstantInitializer.cc deleted file mode 100644 index 6f6eb77bc..000000000 --- a/runtime/onert/backend/cpu/ConstantInitializer.cc +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConstantInitializer.h" -#include "Tensor.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -ConstantInitializer::ConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg) - : IConstantInitializer{operands}, _tensor_reg{tensor_reg} -{ - // DO NOTHING -} - -void ConstantInitializer::registerDefaultInitializer(const ir::OperandIndex &index, - const ir::Operand &obj) -{ - registerExternalInitializer(index, obj); -} - -void ConstantInitializer::registerExternalInitializer(const ir::OperandIndex &index, - const ir::Operand &obj) -{ - // For only CONSTANTS - // TODO Add to check if tensor has been allocated - if (!obj.isConstant()) - return; - - _init_map[index] = [](const onert::ir::Operand &model_obj, onert::backend::ITensor &itensor) { - auto data = model_obj.shareData(); - assert(data && data->base()); - ExternalTensor &tensor = dynamic_cast<ExternalTensor &>(itensor); - tensor.setData(data); - }; -} - -void ConstantInitializer::visit(const ir::operation::Conv2D &node) -{ - const auto &kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL); - const auto &kernel_obj = _operands.at(kernel_index); - registerExternalInitializer(kernel_index, kernel_obj); - - const auto &bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS); - const auto &bias_obj = _operands.at(bias_index); - registerExternalInitializer(bias_index, bias_obj); -} - -void ConstantInitializer::visit(const ir::operation::DepthwiseConv2D &node) -{ - const auto &kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL); - const auto &kernel_obj = _operands.at(kernel_index); - registerExternalInitializer(kernel_index, kernel_obj); - - const auto &bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS); - const auto &bias_obj = _operands.at(bias_index); - registerExternalInitializer(bias_index, bias_obj); -} - -void ConstantInitializer::visit(const ir::operation::FullyConnected &node) -{ - const auto &weight_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT); - const auto &weight_obj = _operands.at(weight_index); - registerExternalInitializer(weight_index, weight_obj); - - const auto &bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS); - if (!bias_index.undefined()) - { - const auto &bias_obj = _operands.at(bias_index); - registerExternalInitializer(bias_index, bias_obj); - } -} - -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ConstantInitializer.h b/runtime/onert/backend/cpu/ConstantInitializer.h deleted file mode 100644 index c016c83bc..000000000 --- a/runtime/onert/backend/cpu/ConstantInitializer.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_CPU_CONSTANT_INITIALIZER_H__ -#define __ONERT_COMPILER_CPU_CONSTANT_INITIALIZER_H__ - -#include "backend/cpu_common/TensorRegistry.h" - -#include <backend/IConstantInitializer.h> -#include <ir/Operands.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class ConstantInitializer : public IConstantInitializer -{ -public: - ConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg); - -public: - void registerDefaultInitializer(const ir::OperandIndex &index, const ir::Operand &obj) override; - - // TODO: For now the only cpu backend supports constant tensor to use data from external - // If the other backend supports (to do this, - // ExternalTensor should be abstract such as IExternal, maybe), - // this can be an interface of IConstantInitializer - void registerExternalInitializer(const ir::OperandIndex &, const ir::Operand &); - -public: - void visit(const ir::operation::Conv2D &) override; - void visit(const ir::operation::DepthwiseConv2D &) override; - void visit(const ir::operation::FullyConnected &) override; - -private: - std::shared_ptr<ITensorRegistry> tensor_registry() const override { return _tensor_reg; } - -private: - std::shared_ptr<ITensorRegistry> _tensor_reg; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_COMPILER_CPU_CONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/backend/cpu/ExternalContext.h b/runtime/onert/backend/cpu/ExternalContext.h deleted file mode 100644 index 32e249f5a..000000000 --- a/runtime/onert/backend/cpu/ExternalContext.h +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_EXTERNAL_CONTEXT_H__ -#define __ONERT_BACKEND_CPU_EXTERNAL_CONTEXT_H__ - -#include <backend/IExternalContext.h> -#include <util/ConfigSource.h> -#include <ruy/context.h> - -namespace -{ -const int kDefaultNumThreadpoolThreads = 1; -} - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class ExternalContext : public IExternalContext -{ -public: - ExternalContext() : _ruy_context(new ruy::Context) - { - setMaxNumThreads(onert::util::getConfigInt(onert::util::config::RUY_THREADS)); - } - - void setMaxNumThreads(int max_num_threads) - { - const int target_num_threads = - max_num_threads > -1 ? max_num_threads : kDefaultNumThreadpoolThreads; - _ruy_context->set_max_num_threads(target_num_threads); - } - - ruy::Context *ruy_context() const { return _ruy_context.get(); } - -private: - const std::unique_ptr<ruy::Context> _ruy_context; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_EXTERNAL_CONTEXT_H__ diff --git a/runtime/onert/backend/cpu/KernelGenerator.cc b/runtime/onert/backend/cpu/KernelGenerator.cc deleted file mode 100644 index 5f330ff50..000000000 --- a/runtime/onert/backend/cpu/KernelGenerator.cc +++ /dev/null @@ -1,1289 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "KernelGenerator.h" - -#include "ops/ArgMinMaxLayer.h" -#include "ops/BatchToSpaceNDLayer.h" -#include "ops/BinaryArithmeticLayer.h" -#include "ops/CompareLayer.h" -#include "ops/ConcatLayer.h" -#include "ops/ConvolutionLayer.h" -#include "ops/DepthwiseConvolutionLayer.h" -#include "ops/EinsumLayer.h" -#include "ops/ElementwiseActivationLayer.h" -#include "ops/ElementwiseBinaryLayer.h" -#include "ops/ElementwiseUnaryLayer.h" -#include "ops/ExpandDimsLayer.h" -#include "ops/FillLayer.h" -#include "ops/FullyConnectedLayer.h" -#include "ops/GatherLayer.h" -#include "ops/MeanLayer.h" -#include "ops/OneHotLayer.h" -#include "ops/OperationUtils.h" -#include "ops/PackLayer.h" -#include "ops/PadLayer.h" -#include "ops/PoolLayer.h" -#include "ops/PowLayer.h" -#include "ops/RangeLayer.h" -#include "ops/RankLayer.h" -#include "ops/ReduceLayer.h" -#include "ops/ReshapeLayer.h" -#include "ops/ResizeBilinearLayer.h" -#include "ops/ReverseLayer.h" -#include "ops/SelectLayer.h" -#include "ops/ShapeLayer.h" -#include "ops/SliceLayer.h" -#include "ops/SoftMaxLayer.h" -#include "ops/StridedSliceLayer.h" -#include "ops/SpaceToBatchNDLayer.h" -#include "ops/SpaceToDepthLayer.h" -#include "ops/SplitLayer.h" -#include "ops/SplitVLayer.h" -#include "ops/TileLayer.h" -#include "ops/TransposeLayer.h" -#include "ops/UnpackLayer.h" -#include "ops/SquaredDiffLayer.h" -#include "ops/L2NormLayer.h" -#include "ops/MatrixBandPartLayer.h" -#include "ops/BatchMatMulLayer.h" -#include "ops/BroadcastToLayer.h" -#include "ops/FusedBatchNormLayer.h" -#include "ops/LogSoftMaxLayer.h" -#include "ops/StatelessRandomUniformLayer.h" - -#include <backend/Backend.h> -#include <backend/IConfig.h> -#include <memory> -#include <util/Utils.h> -#include <util/logging.h> -#include <exec/DynamicShapeInference.h> - -#include <stdexcept> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -namespace -{ -ops::ArithmeticType -convertArithmeticType(ir::operation::BinaryArithmetic::ArithmeticType arithmetic_type_ir) -{ - switch (arithmetic_type_ir) - { - case ir::operation::BinaryArithmetic::ArithmeticType::ADD: - return ops::ArithmeticType::kAdd; - case ir::operation::BinaryArithmetic::ArithmeticType::SUB: - return ops::ArithmeticType::kSub; - case ir::operation::BinaryArithmetic::ArithmeticType::MUL: - return ops::ArithmeticType::kMul; - case ir::operation::BinaryArithmetic::ArithmeticType::DIV: - return ops::ArithmeticType::kDiv; - default: - throw std::runtime_error("cpu KernelGenerator : Not supported operation yet"); - } -} - -ops::ElementwiseActivationType -convertElementwiseActivationType(ir::operation::ElementwiseActivation::Type type_ir) -{ - switch (type_ir) - { - case ir::operation::ElementwiseActivation::Type::LOGISTIC: - return ops::ElementwiseActivationType::kLogistic; - case ir::operation::ElementwiseActivation::Type::RELU: - return ops::ElementwiseActivationType::kReLU; - case ir::operation::ElementwiseActivation::Type::TANH: - return ops::ElementwiseActivationType::kTanh; - default: - throw std::runtime_error("cpu KernelGenerator : Not supported operation yet"); - } -} - -ops::ElementwiseBinaryType -convertElementwiseBinaryType(ir::operation::ElementwiseBinary::ElementwiseBinaryType type_ir) -{ - switch (type_ir) - { - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR: - return ops::ElementwiseBinaryType::kLogicalOr; - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MAX: - return ops::ElementwiseBinaryType::kMax; - case ir::operation::ElementwiseBinary::ElementwiseBinaryType::MIN: - return ops::ElementwiseBinaryType::kMin; - default: - throw std::runtime_error("cpu KernelGenerator : Not supported operation yet"); - } -} - -ops::ElementwiseUnaryType convertElementwiseUnaryType(ir::operation::ElementwiseUnary::Type type_ir) -{ - switch (type_ir) - { - case ir::operation::ElementwiseUnary::Type::ABS: - return ops::ElementwiseUnaryType::kAbs; - case ir::operation::ElementwiseUnary::Type::CAST: - return ops::ElementwiseUnaryType::kCast; - case ir::operation::ElementwiseUnary::Type::COS: - return ops::ElementwiseUnaryType::kCos; - case ir::operation::ElementwiseUnary::Type::ERF: - return ops::ElementwiseUnaryType::kErf; - case ir::operation::ElementwiseUnary::Type::EXP: - return ops::ElementwiseUnaryType::kExp; - case ir::operation::ElementwiseUnary::Type::LOG: - return ops::ElementwiseUnaryType::kLog; - case ir::operation::ElementwiseUnary::Type::LOGICAL_NOT: - return ops::ElementwiseUnaryType::kLogicalNot; - case ir::operation::ElementwiseUnary::Type::NEG: - return ops::ElementwiseUnaryType::kNeg; - case ir::operation::ElementwiseUnary::Type::QUANTIZE: - return ops::ElementwiseUnaryType::kQuantize; - case ir::operation::ElementwiseUnary::Type::ROUND: - return ops::ElementwiseUnaryType::kRound; - case ir::operation::ElementwiseUnary::Type::RSQRT: - return ops::ElementwiseUnaryType::kRSqrt; - case ir::operation::ElementwiseUnary::Type::SIN: - return ops::ElementwiseUnaryType::kSin; - case ir::operation::ElementwiseUnary::Type::ZEROS_LIKE: - return ops::ElementwiseUnaryType::kZerosLike; - default: - throw std::runtime_error("cpu KernelGenerator : Not supported operation yet"); - } -} - -ops::PoolType convertPoolType(ir::operation::Pool2D::PoolType type_ir) -{ - switch (type_ir) - { - case ir::operation::Pool2D::PoolType::AVG: - return ops::PoolType::kAvg; - case ir::operation::Pool2D::PoolType::MAX: - return ops::PoolType::kMax; - default: - throw std::runtime_error("cpu KernelGenerator : Not supported operation yet"); - } -} - -ops::ReduceType convertReduceType(ir::operation::Reduce::ReduceType reduce_type_ir) -{ - switch (reduce_type_ir) - { - case ir::operation::Reduce::ReduceType::ALL: - return ops::ReduceType::kAll; - case ir::operation::Reduce::ReduceType::ANY: - return ops::ReduceType::kAny; - case ir::operation::Reduce::ReduceType::MAX: - return ops::ReduceType::kMax; - case ir::operation::Reduce::ReduceType::MIN: - return ops::ReduceType::kMin; - case ir::operation::Reduce::ReduceType::PROD: - return ops::ReduceType::kProd; - case ir::operation::Reduce::ReduceType::SUM: - return ops::ReduceType::kSum; - default: - throw std::runtime_error("cpu KernelGenerator : Not supported operation yet"); - } -} -} // namespace - -KernelGenerator::KernelGenerator( - const ir::Operands &operands_ctx, const ir::Operations &operations_ctx, - const std::shared_ptr<TensorBuilder> &tensor_builder, - const std::shared_ptr<cpu_common::TensorRegistry> &tensor_reg, - const std::shared_ptr<backend::custom::IKernelBuilder> &kernel_builder, - const std::shared_ptr<ExternalContext> &external_context) - : _ctx(operands_ctx), _operations_ctx{operations_ctx}, _tensor_builder(tensor_builder), - _tensor_reg{tensor_reg}, _kernel_builder(kernel_builder), - _current_op_seq_layout(ir::Layout::UNKNOWN), _external_context(external_context) -{ - // DO NOTHING -} - -void KernelGenerator::visit(const ir::OpSequence &op_seq) -{ - assert(!_return_fn_seq); - assert(_tensor_builder->dynamicTensorManager()); - assert(_tensor_reg); - - auto dyn_shape_inferer = std::make_shared<exec::DynamicShapeInferer>(_ctx, _tensor_reg); - - _return_fn_seq = std::make_unique<exec::FunctionSequence>(); - - // Prepare to handle dynamic tensors later - auto dyn_ctx = std::make_shared<exec::FunctionSequence::DynamicTensorCtx>(); - { - dyn_ctx->op_seq = &op_seq; - dyn_ctx->operations = &_operations_ctx; - dyn_ctx->dynamic_shape_inferer = std::move(dyn_shape_inferer); - dyn_ctx->dynamic_tensor_manager = _tensor_builder->dynamicTensorManager(); - - _return_fn_seq->dynamic_tensor_ctx(dyn_ctx); - } - - _current_op_seq_layout = op_seq.getLayout(); - for (const auto &operation_idx : op_seq.operations()) - { - const auto &node = _operations_ctx.at(operation_idx); - node.accept(*this); - _return_fn_seq->append(releaseFunction()); - - for (const auto &ind : (node.getInputs() | ir::Remove::UNDEFINED) + node.getOutputs()) - { - auto portable_tensor = _tensor_reg->getPortableTensor(ind); - if (portable_tensor) - { - assert(portable_tensor->layout() == ir::Layout::NHWC); - } - - auto tensor = _tensor_reg->getNativeTensor(ind); - if (tensor) - { - tensor->increase_ref(); - } - } - } -} - -void KernelGenerator::visit(const ir::operation::Conv2D &node) -{ - using ir::operation::Conv2D; - - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(Conv2D::Input::INPUT)}; - const auto ker_index{node.getInputs().at(Conv2D::Input::KERNEL)}; - const auto bias_index{node.getInputs().at(Conv2D::Input::BIAS)}; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getPortableTensor(ifm_index); - auto ker_tensor = _tensor_reg->getPortableTensor(ker_index); - auto bias_tensor = _tensor_reg->getPortableTensor(bias_index); - - const auto stride = node.param().stride; - const auto activation = node.param().activation; - const auto param_padding = node.param().padding; - const auto dilation = node.param().dilation; - auto fn = std::make_unique<ops::ConvolutionLayer>(); - - if (_ctx.at(ifm_index).info().isDynamic() || _ctx.at(ker_index).info().isDynamic()) - { - fn->configure(ifm_tensor, ker_tensor, bias_tensor, param_padding.type, param_padding.param.left, - param_padding.param.right, param_padding.param.top, param_padding.param.bottom, - stride.horizontal, stride.vertical, dilation.width_factor, dilation.height_factor, - activation, ofm_tensor); - - _return_fn = std::move(fn); - return; - } - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]. - const auto &ker_shape = _ctx.at(ker_index).shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - - const auto padding = - ir::calculatePadding(param_padding, ifm_shape, ofm_shape, stride, ker_width, ker_height, - dilation.width_factor, dilation.height_factor); - - fn->configure(ifm_tensor, ker_tensor, bias_tensor, param_padding.type, padding.left, - padding.right, padding.top, padding.bottom, stride.horizontal, stride.vertical, - dilation.width_factor, dilation.height_factor, activation, ofm_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node) -{ - using ir::operation::DepthwiseConv2D; - - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)}; - const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)}; - const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)}; - - const auto stride = node.param().stride; - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - // Kernel format is [1, kernel_height, kernel_width, depth_out]. - const auto &ker_shape = _ctx.at(ker_index).shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - const auto padding = ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, - ker_width, ker_height); - const auto multiplier = node.param().multiplier; - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getPortableTensor(ifm_index); - auto ker_tensor = _tensor_reg->getPortableTensor(ker_index); - auto bias_tensor = _tensor_reg->getPortableTensor(bias_index); - - auto fn = std::make_unique<ops::DepthwiseConvolutionLayer>(); - - fn->configure(ifm_tensor, ker_tensor, bias_tensor, padding.left, padding.right, padding.top, - padding.bottom, stride.horizontal, stride.vertical, multiplier, activation, - ofm_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Concat &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - const auto rank = _ctx.at(ofm_index).shape().rank(); - const auto axis = ops::getAxis(rank, node.param().axis, _current_op_seq_layout); - - auto output_tensor = _tensor_reg->getPortableTensor(ofm_index); - - std::vector<const IPortableTensor *> input_tensors; - for (auto &ifm_idx : node.getInputs()) - input_tensors.emplace_back(_tensor_reg->getPortableTensor(ifm_idx)); - - auto fn = std::make_unique<ops::ConcatLayer>(); - - fn->configure(input_tensors, axis, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::BatchToSpaceND &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::BatchToSpaceND::INPUT)}; - const auto block_size_index{node.getInputs().at(ir::operation::BatchToSpaceND::BLOCK_SIZE)}; - - auto output_alloc = _tensor_reg->getPortableTensor(output_index); - auto input_alloc = _tensor_reg->getPortableTensor(input_index); - auto block_size_alloc = _tensor_reg->getPortableTensor(block_size_index); - - auto fn = std::make_unique<ops::BatchToSpaceNDLayer>(); - - IPortableTensor *crops_alloc = nullptr; - const auto NNApiInputs = 2; - - if (node.getInputs().size() != NNApiInputs) - { - const auto crops_data_index{node.getInputs().at(ir::operation::BatchToSpaceND::CROPS_DATA)}; - crops_alloc = _tensor_reg->getPortableTensor(crops_data_index); - } - - fn->configure(input_alloc, output_alloc, block_size_alloc, crops_alloc); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Fill &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Fill::Input::INPUT)}; - const auto value_index{node.getInputs().at(ir::operation::Fill::Input::VALUE)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto value_tensor = _tensor_reg->getPortableTensor(value_index); - - auto fn = std::make_unique<ops::FillLayer>(); - - fn->configure(input_tensor, value_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::FullyConnected &node) -{ - using ir::operation::FullyConnected; - - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(FullyConnected::Input::INPUT)}; - const auto weight_index{node.getInputs().at(FullyConnected::Input::WEIGHT)}; - const auto bias_index{node.getInputs().at(FullyConnected::Input::BIAS)}; - const auto activation = node.param().activation; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto weight_tensor = _tensor_reg->getPortableTensor(weight_index); - auto bias_tensor = bias_index.undefined() ? nullptr : _tensor_reg->getPortableTensor(bias_index); - - auto fn = std::make_unique<ops::FullyConnectedLayer>(); - - fn->configure(input_tensor, weight_tensor, bias_tensor, activation, output_tensor, - _external_context); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Reshape &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - // optional 2nd input - IPortableTensor *shape_tensor = nullptr; - - if (node.getInputs().size() == 2) - { - const auto shape_index{node.getInputs().at(ir::operation::Reshape::Input::SHAPE)}; - shape_tensor = _tensor_reg->getPortableTensor(shape_index); - } - - auto fn = std::make_unique<ops::ReshapeLayer>(); - - fn->configure(input_tensor, shape_tensor, output_tensor); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Squeeze &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Squeeze::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - // Squeeze can share same kernel with reshape - auto fn = std::make_unique<ops::ReshapeLayer>(); - - fn->configure(input_tensor, nullptr, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Softmax &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)}; - - const auto beta = node.param().beta; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - auto fn = std::make_unique<ops::SoftMaxLayer>(); - - fn->configure(input_tensor, beta, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::BinaryArithmetic &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)}; - - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto lhs_tensor = _tensor_reg->getPortableTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getPortableTensor(rhs_index); - - auto fn = std::make_unique<ops::BinaryArithmeticLayer>(); - - fn->configure(lhs_tensor, rhs_tensor, ofm_tensor, activation, - convertArithmeticType(node.param().arithmetic_type)); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Comparison &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)}; - const auto rhs_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)}; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto lhs_tensor = _tensor_reg->getPortableTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getPortableTensor(rhs_index); - - auto comparison_type = node.param().comparison_type; - - auto fn = std::make_unique<ops::CompareLayer>(); - - fn->configure(lhs_tensor, rhs_tensor, comparison_type, ofm_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Gather &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)}; - const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto indices_tensor = _tensor_reg->getPortableTensor(indices_index); - - const auto backend_layout = output_tensor->layout(); - UNUSED_RELEASE(backend_layout); - - // NOTE The frontend layout and backend layout must be the same for this operation. - // If not the same, we have to add a stage(?) to perform permutation of output tensor. It - // is not not efficient even if it works well. If so, it would be better to set the - // layout of these backend tensors to the same layout. - // There is also one thing we have to think about. This operation depends on the layout of - // a model. For example, if a model in NHWC has this operation as output rank == 4, indices - // rank == 2 and axis == 2, this operation should work as the axis W and C, but the axis W - // and C are not sequential in NCHW. So the backend in NCHW cannot handle this case. - assert(backend_layout == input_tensor->layout()); - assert(backend_layout == indices_tensor->layout()); - const auto &input_shape = _ctx.at(input_index).shape(); - UNUSED_RELEASE(input_shape); - assert(input_shape.rank() < 4 || _current_op_seq_layout == backend_layout); - - const auto axis_raw = node.param().axis; - const auto axis_value = (axis_raw < 0 ? (input_shape.rank() + axis_raw) : axis_raw); - - auto fn = std::make_unique<ops::GatherLayer>(); - - fn->configure(input_tensor, indices_tensor, output_tensor, axis_value); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::OneHot &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto indices_index{node.getInputs().at(ir::operation::OneHot::INDICES)}; - const auto depth_index{node.getInputs().at(ir::operation::OneHot::Input::DEPTH)}; - const auto onvalue_index{node.getInputs().at(ir::operation::OneHot::Input::ON_VALUE)}; - const auto offvalue_index{node.getInputs().at(ir::operation::OneHot::Input::OFF_VALUE)}; - - const auto axis = node.param().axis; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto indices_tensor = _tensor_reg->getPortableTensor(indices_index); - auto depth_tensor = _tensor_reg->getPortableTensor(depth_index); - auto onvalue_tensor = _tensor_reg->getPortableTensor(onvalue_index); - auto offvalue_tensor = _tensor_reg->getPortableTensor(offvalue_index); - - assert(indices_tensor->data_type() == OperandType::INT32); - assert(axis <= static_cast<int>(indices_tensor->num_dimensions())); - - auto fn = std::make_unique<ops::OneHotLayer>(); - - fn->configure(indices_tensor, depth_tensor, onvalue_tensor, offvalue_tensor, output_tensor, axis); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Einsum &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - auto output_tensor = _tensor_reg->getPortableTensor(ofm_index); - std::vector<const IPortableTensor *> input_tensors; - for (auto &ifm_idx : node.getInputs()) - input_tensors.emplace_back(_tensor_reg->getPortableTensor(ifm_idx)); - - const auto equation = node.param().equation; - - auto fn = std::make_unique<ops::EinsumLayer>(); - - fn->configure(input_tensors, equation, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Custom &node) -{ - auto fill_op_info = [&](const ir::OperandIndexSequence &opSeq, - std::vector<custom::TypeInfo> &types, - std::vector<IPortableTensor *> &tensors) { - for (auto &idx : opSeq) - { - const auto &operand = _ctx.at(idx); - // TODO make sure using `_current_op_seq_layout` is correct for custom operations - types.emplace_back(custom::TypeInfo{operand.shape(), operand.typeInfo().type()}); - auto in_tensor = _tensor_reg->getPortableTensor(idx); - tensors.emplace_back(in_tensor); - } - }; - - backend::custom::CustomKernelConfigParams params{}; - - fill_op_info(node.getInputs(), params.input_types, params.input_tensors); - fill_op_info(node.getOutputs(), params.output_types, params.output_tensors); - - params.userdata = node.userdata().data; - params.userdata_size = node.userdata().size; - - auto fn = _kernel_builder->buildKernel(node.id(), std::move(params)); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseActivation &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ElementwiseActivation::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - auto fn = std::make_unique<ops::ElementwiseActivationLayer>(); - - fn->configure(input_tensor, output_tensor, node.param().alpha, node.param().beta, - convertElementwiseActivationType(node.param().op_type)); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseBinary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::RHS)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto lhs_tensor = _tensor_reg->getPortableTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getPortableTensor(rhs_index); - - auto fn = std::make_unique<ops::ElementwiseBinaryLayer>(); - - fn->configure(lhs_tensor, rhs_tensor, output_tensor, - convertElementwiseBinaryType(node.param().op_type)); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::ElementwiseUnary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - auto fn = std::make_unique<ops::ElementwiseUnaryLayer>(); - - fn->configure(input_tensor, output_tensor, convertElementwiseUnaryType(node.param().op_type)); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::ExpandDims &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ExpandDims::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::ExpandDims::Input::AXIS)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto axis_tensor = _tensor_reg->getPortableTensor(axis_index); - - auto fn = std::make_unique<ops::ExpandDimsLayer>(); - - fn->configure(input_tensor, axis_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Pack &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - const auto rank = _ctx.at(ofm_index).shape().rank(); - const auto axis = ops::getAxis(rank, node.param().axis, _current_op_seq_layout); - - assert(-rank <= axis && axis < rank); - - auto output_tensor = _tensor_reg->getPortableTensor(ofm_index); - - std::vector<const IPortableTensor *> input_tensors; - for (auto &ifm_idx : node.getInputs()) - input_tensors.emplace_back(_tensor_reg->getPortableTensor(ifm_idx)); - - auto fn = std::make_unique<ops::PackLayer>(); - - fn->configure(input_tensors, axis, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Unpack &node) -{ - const auto input_index{node.getInputs().at(0)}; - - const auto rank = _ctx.at(input_index).shape().rank(); - const auto axis = ops::getAxis(rank, node.param().axis, _current_op_seq_layout); - - assert(rank == 0 || (-rank <= axis && axis < rank)); - - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - std::vector<IPortableTensor *> output_tensors; - for (auto &output_idx : node.getOutputs()) - output_tensors.emplace_back(_tensor_reg->getPortableTensor(output_idx)); - - auto fn = std::make_unique<ops::UnpackLayer>(); - - uint32_t axis_resolved = (axis < 0 ? axis + rank : axis); - - fn->configure(input_tensor, axis_resolved, node.param().num, output_tensors); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Pad &node) -{ - const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)}; - const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)}; - const auto output_index{node.getOutputs().at(0)}; - assert(_ctx.at(pad_index).data()); - - auto input = _tensor_reg->getPortableTensor(input_index); - auto output = _tensor_reg->getPortableTensor(output_index); - auto pad_rank = _ctx.at(pad_index).shape().dim(0); - auto pad_base = reinterpret_cast<const int32_t *>(_ctx.at(pad_index).data()->base()); - - auto fn = std::make_unique<ops::PadLayer>(); - - bool isPadV2 = node.getInputs().size() == 3 ? true : false; - const void *value = nullptr; - - if (isPadV2) - { - const auto value_index{node.getInputs().at(ir::operation::Pad::Input::VALUE)}; - value = reinterpret_cast<const void *>(_ctx.at(value_index).data()->base()); - } - - fn->configure(input, output, pad_base, pad_rank, value); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Transpose &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Transpose::Input::INPUT)}; - const auto perm_index{node.getInputs().at(ir::operation::Transpose::Input::PERMUTATION)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto perm_tensor = _tensor_reg->getPortableTensor(perm_index); - - auto fn = std::make_unique<ops::TransposeLayer>(); - - fn->configure(input_tensor, perm_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Reduce &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reduce::Input::INPUT)}; - const auto axes_index{node.getInputs().at(ir::operation::Reduce::Input::AXES)}; - - const auto keep_dims = node.param().keep_dims; - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto axes_tensor = _tensor_reg->getPortableTensor(axes_index); - - if (node.param().reduce_type == ir::operation::Reduce::ReduceType::MEAN) - { - auto fn = std::make_unique<ops::MeanLayer>(); - - fn->configure(input_tensor, axes_tensor, output_tensor, keep_dims); - - _return_fn = std::move(fn); - } - else - { - auto fn = std::make_unique<ops::ReduceLayer>(); - - const auto reduce_type = convertReduceType(node.param().reduce_type); - fn->configure(input_tensor, axes_tensor, output_tensor, reduce_type, keep_dims); - - _return_fn = std::move(fn); - } -} - -void KernelGenerator::visit(const ir::operation::Select &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto condition_index{node.getInputs().at(ir::operation::Select::Input::CONDITION)}; - const auto true_index{node.getInputs().at(ir::operation::Select::Input::INPUT_TRUE)}; - const auto false_index{node.getInputs().at(ir::operation::Select::Input::INPUT_FALSE)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto condition_tensor = _tensor_reg->getPortableTensor(condition_index); - auto true_tensor = _tensor_reg->getPortableTensor(true_index); - auto false_tensor = _tensor_reg->getPortableTensor(false_index); - - auto fn = std::make_unique<ops::SelectLayer>(); - - fn->configure(condition_tensor, true_tensor, false_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Slice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Slice::Input::INPUT)}; - const auto begins_index{node.getInputs().at(ir::operation::Slice::Input::BEGINS)}; - const auto sizes_index{node.getInputs().at(ir::operation::Slice::Input::SIZES)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto begins_tensor = _tensor_reg->getPortableTensor(begins_index); - auto sizes_tensor = _tensor_reg->getPortableTensor(sizes_index); - - auto fn = std::make_unique<ops::SliceLayer>(); - - fn->configure(input_tensor, begins_tensor, sizes_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::StridedSlice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)}; - const auto starts_index{node.getInputs().at(ir::operation::StridedSlice::Input::STARTS)}; - const auto ends_index{node.getInputs().at(ir::operation::StridedSlice::Input::ENDS)}; - const auto strides_index{node.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto starts_tensor = _tensor_reg->getPortableTensor(starts_index); - auto ends_tensor = _tensor_reg->getPortableTensor(ends_index); - auto strides_tensor = _tensor_reg->getPortableTensor(strides_index); - - auto begin_mask = node.param().begin_mask; - auto end_mask = node.param().end_mask; - auto shrink_axis_mask = node.param().shrink_axis_mask; - - auto fn = std::make_unique<ops::StridedSliceLayer>(); - - fn->configure(input_tensor, starts_tensor, ends_tensor, strides_tensor, output_tensor, begin_mask, - end_mask, shrink_axis_mask); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Split &node) -{ - const auto num_splits = node.param().num_splits; - assert(num_splits == static_cast<int>(node.getOutputs().size())); - - const auto input_idx{node.getInputs().at(ir::operation::Split::Input::INPUT)}; - const auto axis_idx{node.getInputs().at(ir::operation::Split::Input::AXIS)}; - - auto in_tensor = _tensor_reg->getPortableTensor(input_idx); - auto axis_tensor = _tensor_reg->getPortableTensor(axis_idx); - - std::vector<IPortableTensor *> out_tensors; - for (auto &output_idx : node.getOutputs()) - out_tensors.emplace_back(_tensor_reg->getPortableTensor(output_idx)); - - auto fn = std::make_unique<ops::SplitLayer>(); - - fn->configure(in_tensor, axis_tensor, num_splits, out_tensors); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Shape &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::Shape::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getPortableTensor(ifm_index); - - auto fn = std::make_unique<ops::ShapeLayer>(); - - fn->configure(ifm_tensor, ofm_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::ResizeBilinear &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ResizeBilinear::INPUT)}; - - auto align_corners = node.param().align_corners; - auto half_pixel_centers = node.param().half_pixel_centers; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - auto fn = std::make_unique<ops::ResizeBilinearLayer>(); - - if (node.getInputs().size() == 1) - { - fn->configure(input_tensor, output_tensor, node.param().height_out, node.param().width_out, - align_corners, half_pixel_centers); - } - else - { - assert(node.getInputs().size() == 2); - const auto size_index{node.getInputs().at(ir::operation::ResizeBilinear::SIZE)}; - auto size_tensor = _tensor_reg->getPortableTensor(size_index); - if (size_tensor->is_constant()) - { - auto size_vec = _ctx.at(size_index).asVector<int32_t>(); - const auto height_out = size_vec[0]; - const auto width_out = size_vec[1]; - fn->configure(input_tensor, output_tensor, height_out, width_out, align_corners, - half_pixel_centers); - } - else - { - fn->configure(input_tensor, output_tensor, size_tensor, align_corners, half_pixel_centers); - } - } - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Reverse &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reverse::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::Reverse::AXIS)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto axis_tensor = _tensor_reg->getPortableTensor(axis_index); - - auto fn = std::make_unique<ops::ReverseLayer>(); - - fn->configure(input_tensor, axis_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::ArgMax &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ArgMax::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::ArgMax::AXIS)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto axis_tensor = _tensor_reg->getPortableTensor(axis_index); - - auto fn = std::make_unique<ops::ArgMinMaxLayer>(); - - fn->configure(input_tensor, output_tensor, axis_tensor, /* is_arg_max */ true); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Pool2D &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::Pool2D::Input::INPUT)}; - - const auto kh = node.param().kh; - const auto kw = node.param().kw; - const auto stride = node.param().stride; - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(_current_op_seq_layout); - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(_current_op_seq_layout); - const auto padding = - ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, kw, kh); - const auto activation = node.param().activation; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getPortableTensor(ifm_index); - - auto fn = std::make_unique<ops::PoolLayer>(); - - fn->configure(ifm_tensor, padding.left, padding.right, padding.top, padding.bottom, - stride.horizontal, stride.vertical, kw, kh, activation, ofm_tensor, - convertPoolType(node.param().op_type)); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Pow &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::Pow::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::Pow::RHS)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto lhs_tensor = _tensor_reg->getPortableTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getPortableTensor(rhs_index); - - auto fn = std::make_unique<ops::PowLayer>(); - - fn->configure(lhs_tensor, rhs_tensor, ir::Activation::NONE, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::L2Normalization &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(0)}; - - auto output_alloc = _tensor_reg->getPortableTensor(output_index); - auto input_alloc = _tensor_reg->getPortableTensor(input_index); - - auto fn = std::make_unique<ops::L2NormLayer>(); - - fn->configure(input_alloc, output_alloc); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Range &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto start_index{node.getInputs().at(ir::operation::Range::START)}; - const auto limit_index{node.getInputs().at(ir::operation::Range::LIMIT)}; - const auto delta_index{node.getInputs().at(ir::operation::Range::DELTA)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto start_tensor = _tensor_reg->getPortableTensor(start_index); - auto limit_tensor = _tensor_reg->getPortableTensor(limit_index); - auto delta_tensor = _tensor_reg->getPortableTensor(delta_index); - - auto fn = std::make_unique<ops::RangeLayer>(); - - fn->configure(start_tensor, limit_tensor, delta_tensor, output_tensor); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Rank &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto ifm_index{node.getInputs().at(ir::operation::Shape::Input::INPUT)}; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto ifm_tensor = _tensor_reg->getPortableTensor(ifm_index); - - auto fn = std::make_unique<ops::RankLayer>(); - - fn->configure(ifm_tensor, ofm_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::SquaredDifference &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)}; - - auto ofm_tensor = _tensor_reg->getPortableTensor(ofm_index); - auto lhs_tensor = _tensor_reg->getPortableTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getPortableTensor(rhs_index); - - auto fn = std::make_unique<ops::SqDiffLayer>(); - - fn->configure(lhs_tensor, rhs_tensor, ofm_tensor); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Tile &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Tile::INPUT)}; - const auto multiples_index{node.getInputs().at(ir::operation::Tile::MULTIPLES)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto multiples_tensor = _tensor_reg->getPortableTensor(multiples_index); - - auto fn = std::make_unique<ops::TileLayer>(); - - fn->configure(input_tensor, multiples_tensor, output_tensor); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::MatrixBandPart &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::MatrixBandPart::INPUT)}; - const auto num_lower_index{node.getInputs().at(ir::operation::MatrixBandPart::NUM_LOWER_DIAG)}; - const auto num_upper_index{node.getInputs().at(ir::operation::MatrixBandPart::NUM_UPPER_DIAG)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto num_lower_tensor = _tensor_reg->getPortableTensor(num_lower_index); - auto num_upper_tensor = _tensor_reg->getPortableTensor(num_upper_index); - - auto fn = std::make_unique<ops::MatrixBandPartLayer>(); - - fn->configure(input_tensor, num_lower_tensor, num_upper_tensor, output_tensor); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::BatchMatMul &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::BatchMatMul::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::BatchMatMul::RHS)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto lhs_tensor = _tensor_reg->getPortableTensor(lhs_index); - auto rhs_tensor = _tensor_reg->getPortableTensor(rhs_index); - - const auto adj_x = node.param().adj_x; - const auto adj_y = node.param().adj_y; - - auto fn = std::make_unique<ops::BatchMatMulLayer>(); - - fn->configure(lhs_tensor, rhs_tensor, adj_x, adj_y, output_tensor); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::BroadcastTo &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::BroadcastTo::INPUT)}; - const auto shape_index{node.getInputs().at(ir::operation::BroadcastTo::SHAPE)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto shape_tensor = _tensor_reg->getPortableTensor(shape_index); - - auto fn = std::make_unique<ops::BroadcastToLayer>(); - - fn->configure(input_tensor, shape_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::FusedBatchNorm &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - - auto output_tensor = _tensor_reg->getPortableTensor(ofm_index); - std::vector<const IPortableTensor *> input_tensors; - for (auto &ifm_idx : node.getInputs()) - input_tensors.emplace_back(_tensor_reg->getPortableTensor(ifm_idx)); - - const auto epsilon = node.param().epsilon; - const auto is_training = node.param().is_training; - const auto data_format = node.param().data_format; - - auto fn = std::make_unique<ops::FusedBatchNormLayer>(); - - fn->configure(input_tensors, epsilon, is_training, data_format, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::LogSoftmax &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::LogSoftmax::Input::INPUT)}; - - const auto beta = node.param().beta; - const auto axis = node.param().axis; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - - auto fn = std::make_unique<ops::LogSoftMaxLayer>(); - - fn->configure(input_tensor, beta, axis, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::SpaceToBatchND &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::SpaceToBatchND::INPUT)}; - const auto block_shape_index{node.getInputs().at(ir::operation::SpaceToBatchND::BLOCK_SIZE)}; - const auto padding_index{node.getInputs().at(ir::operation::SpaceToBatchND::PADDINGS)}; - - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto block_shape_tensor = _tensor_reg->getPortableTensor(block_shape_index); - auto padding_tensor = _tensor_reg->getPortableTensor(padding_index); - - auto fn = std::make_unique<ops::SpaceToBatchNDLayer>(); - - fn->configure(input_tensor, block_shape_tensor, padding_tensor, output_tensor); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::SpaceToDepth &node) -{ - const auto input_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)}; - const auto output_index{node.getOutputs().at(0)}; - auto block_size = node.param().block_size; - - auto input_tensor = _tensor_reg->getPortableTensor(input_index); - auto output_tensor = _tensor_reg->getPortableTensor(output_index); - - auto fn = std::make_unique<ops::SpaceToDepthLayer>(); - - fn->configure(input_tensor, block_size, output_tensor); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::StatelessRandomUniform &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto shape_index{node.getInputs().at(ir::operation::StatelessRandomUniform::SHAPE)}; - const auto seed_index{node.getInputs().at(ir::operation::StatelessRandomUniform::SEED)}; - - auto output_alloc = _tensor_reg->getPortableTensor(output_index); - auto shape_alloc = _tensor_reg->getPortableTensor(shape_index); - auto seed_alloc = _tensor_reg->getPortableTensor(seed_index); - - auto fn = std::make_unique<ops::StatelessRandomUniformLayer>(); - - fn->configure(shape_alloc, seed_alloc, output_alloc); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::SplitV &node) -{ - const auto num_splits = node.param().num_splits; - assert(num_splits == static_cast<int>(node.getOutputs().size())); - - const auto input_idx{node.getInputs().at(ir::operation::SplitV::Input::INPUT)}; - const auto size_splits{node.getInputs().at(ir::operation::SplitV::Input::SIZE_SPLITS)}; - const auto split_dim{node.getInputs().at(ir::operation::SplitV::Input::SPLIT_DIM)}; - - auto in_tensor = _tensor_reg->getPortableTensor(input_idx); - auto in_size_splits = _tensor_reg->getPortableTensor(size_splits); - auto in_split_dim = _tensor_reg->getPortableTensor(split_dim); - - std::vector<IPortableTensor *> out_tensors; - for (auto &output_idx : node.getOutputs()) - out_tensors.emplace_back(_tensor_reg->getPortableTensor(output_idx)); - - auto fn = std::make_unique<ops::SplitVLayer>(); - - fn->configure(in_tensor, in_size_splits, in_split_dim, num_splits, out_tensors); - - _return_fn = std::move(fn); -} - -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/KernelGenerator.h b/runtime/onert/backend/cpu/KernelGenerator.h deleted file mode 100644 index 786e68ee0..000000000 --- a/runtime/onert/backend/cpu/KernelGenerator.h +++ /dev/null @@ -1,112 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_KERNEL_GENERATOR_H__ -#define __ONERT_BACKEND_CPU_KERNEL_GENERATOR_H__ - -#include "ExternalContext.h" -#include "TensorBuilder.h" -#include "backend/cpu_common/TensorRegistry.h" -#include "Tensor.h" - -#include <backend/CustomKernelBuilder.h> -#include <backend/IKernelGenerator.h> -#include <ir/Operands.h> -#include <ir/Operations.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class KernelGenerator : public IKernelGenerator -{ -public: - KernelGenerator(const ir::Operands &operands_ctx, const ir::Operations &operations_ctx, - const std::shared_ptr<TensorBuilder> &tensor_builder, - const std::shared_ptr<cpu_common::TensorRegistry> &tensor_reg, - const std::shared_ptr<custom::IKernelBuilder> &kernel_builder, - const std::shared_ptr<ExternalContext> &external_context); - - using IKernelGenerator::visit; - - void visit(const ir::OpSequence &) override; - void visit(const ir::operation::Conv2D &) override; - void visit(const ir::operation::DepthwiseConv2D &) override; - void visit(const ir::operation::Concat &) override; - void visit(const ir::operation::Fill &) override; - void visit(const ir::operation::FullyConnected &) override; - void visit(const ir::operation::Reshape &) override; - void visit(const ir::operation::Squeeze &) override; - void visit(const ir::operation::Softmax &) override; - void visit(const ir::operation::Comparison &) override; - void visit(const ir::operation::BinaryArithmetic &) override; - void visit(const ir::operation::Einsum &) override; - void visit(const ir::operation::Gather &) override; - void visit(const ir::operation::Custom &node) override; - void visit(const ir::operation::ElementwiseActivation &) override; - void visit(const ir::operation::ElementwiseBinary &) override; - void visit(const ir::operation::ElementwiseUnary &) override; - void visit(const ir::operation::ExpandDims &) override; - void visit(const ir::operation::Pad &) override; - void visit(const ir::operation::Pack &) override; - void visit(const ir::operation::Unpack &) override; - void visit(const ir::operation::OneHot &) override; - void visit(const ir::operation::Transpose &) override; - void visit(const ir::operation::Reduce &) override; - void visit(const ir::operation::Select &) override; - void visit(const ir::operation::Slice &) override; - void visit(const ir::operation::StridedSlice &) override; - void visit(const ir::operation::Split &) override; - void visit(const ir::operation::Shape &) override; - void visit(const ir::operation::ResizeBilinear &node) override; - void visit(const ir::operation::Reverse &) override; - void visit(const ir::operation::ArgMax &) override; - void visit(const ir::operation::Pool2D &) override; - void visit(const ir::operation::Pow &) override; - void visit(const ir::operation::SquaredDifference &) override; - void visit(const ir::operation::Tile &) override; - void visit(const ir::operation::L2Normalization &) override; - void visit(const ir::operation::Range &) override; - void visit(const ir::operation::Rank &) override; - void visit(const ir::operation::MatrixBandPart &) override; - void visit(const ir::operation::BatchMatMul &) override; - void visit(const ir::operation::BatchToSpaceND &) override; - void visit(const ir::operation::BroadcastTo &) override; - void visit(const ir::operation::FusedBatchNorm &) override; - void visit(const ir::operation::LogSoftmax &) override; - void visit(const ir::operation::SpaceToBatchND &) override; - void visit(const ir::operation::SpaceToDepth &) override; - void visit(const ir::operation::StatelessRandomUniform &) override; - void visit(const ir::operation::SplitV &) override; - -private: - const ir::Operands &_ctx; - const ir::Operations &_operations_ctx; - std::shared_ptr<TensorBuilder> _tensor_builder; - std::shared_ptr<cpu_common::TensorRegistry> _tensor_reg; - std::shared_ptr<backend::custom::IKernelBuilder> _kernel_builder; - ir::Layout _current_op_seq_layout; - const std::shared_ptr<ExternalContext> _external_context; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_KERNEL_GENERATOR_H__ diff --git a/runtime/onert/backend/cpu/StaticTensorManager.cc b/runtime/onert/backend/cpu/StaticTensorManager.cc deleted file mode 100644 index 3edac897c..000000000 --- a/runtime/onert/backend/cpu/StaticTensorManager.cc +++ /dev/null @@ -1,107 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "StaticTensorManager.h" -#include "Tensor.h" - -#include <util/logging.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -StaticTensorManager::StaticTensorManager(const std::shared_ptr<cpu_common::TensorRegistry> ®, - cpu_common::DynamicTensorManager *dynamic_tensor_manager) - : _nonconst_mgr{new cpu_common::MemoryManager()}, _tensors{reg}, - _dynamic_tensor_manager{dynamic_tensor_manager} -{ - // DO NOTHING -} - -void StaticTensorManager::allocateNonconsts(void) -{ - _nonconst_mgr->allocate(); - - for (auto &pair : _tensors->native_tensors()) - { - const auto &ind = pair.first; - auto tensor = pair.second.get(); - if (!_as_constants[ind] && !tensor->is_dynamic()) - { - auto *buffer = _nonconst_mgr->getBuffer(ind); - tensor->setBuffer(buffer); - - VERBOSE(CPU_StaticTensorManager) << "TENSOR(#" << ind.value() - << "): " << static_cast<void *>(buffer) << std::endl; - } - } -} - -void StaticTensorManager::deallocateNonconsts(void) { _nonconst_mgr->deallocate(); } - -void StaticTensorManager::buildTensor(const ir::OperandIndex &ind, - const ir::OperandInfo &tensor_info, ir::Layout backend_layout, - bool as_const) -{ - assert(!_tensors->getITensor(ind)); - if (as_const) - { - auto tensor = std::make_unique<ExternalTensor>(tensor_info, backend_layout); - _tensors->setNativeTensor(ind, std::move(tensor)); - } - else - { - auto tensor = std::make_unique<Tensor>(tensor_info, backend_layout, - _dynamic_tensor_manager->dynamic_mem_mgr().get()); - _tensors->setNativeTensor(ind, std::move(tensor)); - } - _as_constants[ind] = as_const; -} - -void StaticTensorManager::claimPlan(const ir::OperandIndex &ind, uint32_t size) -{ - assert(_tensors->getITensor(ind)); - - // This method is called only when a tensor has proper shape - assert(!_tensors->getITensor(ind)->is_dynamic()); - - if (!_as_constants[ind]) - _nonconst_mgr->claimPlan(ind, size); -} - -void StaticTensorManager::releasePlan(const ir::OperandIndex &ind) -{ - assert(_tensors->getITensor(ind)); - - // This method is called only when a tensor has proper shape - assert(!_tensors->getITensor(ind)->is_dynamic()); - - if (!_as_constants[ind]) - _nonconst_mgr->releasePlan(ind); -} - -void StaticTensorManager::iterate(const std::function<void(const ir::OperandIndex &)> &fn) -{ - for (const auto &it : _tensors->native_tensors()) - fn(it.first); -} - -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/StaticTensorManager.h b/runtime/onert/backend/cpu/StaticTensorManager.h deleted file mode 100644 index 2af61e4e7..000000000 --- a/runtime/onert/backend/cpu/StaticTensorManager.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_STATICTENSOR_MANAGER_H__ -#define __ONERT_BACKEND_CPU_STATICTENSOR_MANAGER_H__ - -#include "backend/IStaticTensorManager.h" -#include "backend/cpu_common/DynamicTensorManager.h" -#include "backend/cpu_common/MemoryManager.h" -#include "backend/cpu_common/TensorRegistry.h" -#include "backend/ITensorManager.h" -#include "ir/OperandIndexMap.h" -#include "ir/OperandInfo.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class StaticTensorManager : public backend::IStaticTensorManager -{ -public: - StaticTensorManager(const std::shared_ptr<cpu_common::TensorRegistry> ®, - cpu_common::DynamicTensorManager *dynamic_tensor_manager); - virtual ~StaticTensorManager() = default; - - void allocateNonconsts(void); - void deallocateNonconsts(void); - - void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info, - ir::Layout backend_layout, bool as_const); - - void claimPlan(const ir::OperandIndex &ind, uint32_t size); - void releasePlan(const ir::OperandIndex &ind); - - void iterate(const std::function<void(const ir::OperandIndex &)> &fn); - -private: - std::unique_ptr<cpu_common::MemoryManager> _nonconst_mgr; - const std::shared_ptr<cpu_common::TensorRegistry> _tensors; - ir::OperandIndexMap<bool> _as_constants; - cpu_common::DynamicTensorManager *_dynamic_tensor_manager; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_STATICTENSOR_MANAGER_H__ diff --git a/runtime/onert/backend/cpu/Tensor.cc b/runtime/onert/backend/cpu/Tensor.cc deleted file mode 100644 index dac8f898b..000000000 --- a/runtime/onert/backend/cpu/Tensor.cc +++ /dev/null @@ -1,32 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Tensor.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -// `dynamic_cast` not working across library boundaries on NDK -// With this as a key function, `dynamic_cast` works across dl -ExternalTensor::~ExternalTensor() {} - -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/Tensor.h b/runtime/onert/backend/cpu/Tensor.h deleted file mode 100644 index 2ad2ad0fb..000000000 --- a/runtime/onert/backend/cpu/Tensor.h +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_TENSOR_H__ -#define __ONERT_BACKEND_CPU_TENSOR_H__ - -#include <backend/cpu_common/Tensor.h> -#include <ir/Data.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -using Tensor = cpu_common::Tensor; - -/** - * @brief Class that uses data from external memory that is not managed by a backend - * instead of allocating and copying the data. ExternalTensor's data pointer points to - * an address of memory such as where memory is already allocated, or mmapped area. - * This is meaning that ExternalTensor can take all of types' ir::Data. - * To support this, assume below things no padding, always NHWC layout, - * constant tensor and not dynamic. - */ -class ExternalTensor : public Tensor -{ -public: - ExternalTensor() = delete; - virtual ~ExternalTensor(); - -public: - ExternalTensor(const ir::OperandInfo &info, const ir::Layout layout) - : Tensor(info, layout, nullptr) - { - assert(_layout == ir::Layout::NHWC); - assert(_info.isConstant()); - assert(_info.isDynamic() == false); - } - -public: - /** - * @brief set Data to be shared from external so that this ExternalTensor will not be - * allocated on CPU backend - * @param[in] data data of Operand to be set - */ - void setData(const std::shared_ptr<ir::Data> data) - { - assert(data != nullptr); - _data = data; - // Note. Some op such as cker::Conv could take buffer as nullptr. - // That's why _buffer also would be used - _buffer = const_cast<uint8_t *>(_data->base()); - } - -public: - uint8_t *buffer() const override { return _buffer; } - - bool is_constant() const override { return true; } - bool is_dynamic() const override { return false; } - void set_dynamic() override - { - throw std::runtime_error("This tensor does not support changing dynamic"); - } - - void setShape(const ir::Shape &) override - { - throw std::runtime_error("This tensor does not support changing shape"); - } - - void increase_ref() override { ++_num_references; } - - void decrease_ref() override - { - assert(_data != nullptr); - assert(_num_references > 0); - --_num_references; - if (_num_references == 0) - { - _data.reset(); - _buffer = nullptr; - } - } - - /** - * @brief Reset reference count to zero and release data - */ - void reset_ref() override - { - assert(_data != nullptr); - assert(_num_references > 0); - _num_references = 0; - - _data.reset(); - _buffer = nullptr; - } - - int32_t num_references() override { return _num_references; } - -private: - std::shared_ptr<const ir::Data> _data; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_TENSOR_H__ diff --git a/runtime/onert/backend/cpu/TensorBuilder.cc b/runtime/onert/backend/cpu/TensorBuilder.cc deleted file mode 100644 index e6bc55b0b..000000000 --- a/runtime/onert/backend/cpu/TensorBuilder.cc +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "TensorBuilder.h" - -#include <util/logging.h> - -#include <cassert> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -TensorBuilder::TensorBuilder(const std::shared_ptr<cpu_common::TensorRegistry> &tensor_reg) - : _tensor_reg{tensor_reg}, - _dynamic_tensor_mgr{new cpu_common::DynamicTensorManager(_tensor_reg)}, - _static_tensor_mgr{new StaticTensorManager(_tensor_reg, _dynamic_tensor_mgr.get())} -{ - /* empty */ -} - -void TensorBuilder::registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info, - ir::Layout layout) -{ - _tensor_info_map.emplace(ind, info); - - // CPU backend supports only one layout as NHWC - assert(layout == ir::Layout::NHWC); - if (info.isDynamic()) - { - _dynamic_tensor_mgr->buildTensor(ind, info, layout); - } - else - { - _static_tensor_mgr->buildTensor(ind, info, layout, info.isConstant()); - } -} - -void TensorBuilder::notifyFirstUse(const ir::OperandIndex &ind) -{ - assert(_tensor_info_map.find(ind) != _tensor_info_map.end()); - const auto tensor_info = _tensor_info_map.at(ind); - - if (!_tensor_reg->getNativeTensor(ind)->is_dynamic()) - { - const auto size = tensor_info.total_size(); - _static_tensor_mgr->claimPlan(ind, size); - } -} - -void TensorBuilder::notifyLastUse(const ir::OperandIndex &ind) -{ - if (!_tensor_reg->getNativeTensor(ind)->is_dynamic()) - { - _static_tensor_mgr->releasePlan(ind); - } -} - -bool TensorBuilder::isRegistered(const ir::OperandIndex &ind) const -{ - return _tensor_info_map.find(ind) != _tensor_info_map.end(); -} - -void TensorBuilder::prepare(void) { _static_tensor_mgr->allocateNonconsts(); } - -void TensorBuilder::allocate() -{ - // NOTE For now nothing to do. Allocation is done in prepare stage, which is not appropriate - // This is because CPU kernels require `ITensor`s to be allocated before Kernel Generation. -} - -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/TensorBuilder.h b/runtime/onert/backend/cpu/TensorBuilder.h deleted file mode 100644 index 448abc229..000000000 --- a/runtime/onert/backend/cpu/TensorBuilder.h +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_TENSOR_BUILDER_H__ -#define __ONERT_BACKEND_CPU_TENSOR_BUILDER_H__ - -#include <backend/cpu_common/DynamicTensorManager.h> -#include <backend/cpu_common/TensorRegistry.h> - -#include <backend/ITensorBuilder.h> -#include <ir/OperandIndexMap.h> - -#include "StaticTensorManager.h" -#include "Tensor.h" - -#include <unordered_map> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ - -class TensorBuilder : public ITensorBuilder -{ -public: - TensorBuilder(const std::shared_ptr<cpu_common::TensorRegistry> &tensor_reg); - - /** - * @brief Register tensor information to allocate on CPU backend - * @param[in] ind Operand index - * @param[in] info Operand information - * @param[in] layout Operand data layout - */ - void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info, - ir::Layout backend_layout) override; - - void notifyFirstUse(const ir::OperandIndex &) override; - void notifyLastUse(const ir::OperandIndex &) override; - - bool isRegistered(const ir::OperandIndex &) const override; - - void prepare(void) override; - void allocate() override; - void postFunctionPrepare() override { /* DO NOTHING */} - - IDynamicTensorManager *dynamicTensorManager(void) override { return _dynamic_tensor_mgr.get(); } - -private: - const std::shared_ptr<cpu_common::TensorRegistry> _tensor_reg; - std::unique_ptr<cpu_common::DynamicTensorManager> _dynamic_tensor_mgr; - std::unique_ptr<StaticTensorManager> _static_tensor_mgr; - ir::OperandIndexMap<ir::OperandInfo> _tensor_info_map; -}; - -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_TENSOR_BUILDER_H__ diff --git a/runtime/onert/backend/cpu/cpu.cc b/runtime/onert/backend/cpu/cpu.cc deleted file mode 100644 index 5385bb2a3..000000000 --- a/runtime/onert/backend/cpu/cpu.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Backend.h" - -#include <util/logging.h> - -extern "C" { -onert::backend::Backend *onert_backend_create() -{ - VERBOSE(onert_backend_create) << "'cpu' loaded\n"; - return new onert::backend::cpu::Backend; -} - -void onert_backend_destroy(onert::backend::Backend *backend) -{ - VERBOSE(onert_backend_create) << "'cpu' unloaded\n"; - delete backend; -} -} diff --git a/runtime/onert/backend/cpu/ops/ArgMinMaxLayer.cc b/runtime/onert/backend/cpu/ops/ArgMinMaxLayer.cc deleted file mode 100644 index 2fd284c91..000000000 --- a/runtime/onert/backend/cpu/ops/ArgMinMaxLayer.cc +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ArgMinMaxLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/ArgMinMax.h> -#include <assert.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -namespace -{ -template <typename T> std::function<bool(T, T)> GetComparefunction(bool is_arg_max) -{ - if (is_arg_max) - { - return std::greater<T>(); - } - else - { - return std::less<T>(); - } -} -} - -void ArgMinMaxLayer::configure(const IPortableTensor *input, IPortableTensor *output, - const IPortableTensor *axis, bool is_arg_max) -{ - _input = input; - _output = output; - _axis = axis; - _is_arg_max = is_arg_max; -} - -void ArgMinMaxLayer::run() -{ - if (_axis->total_size() != sizeof(int32_t)) - { - throw std::runtime_error("ArgMinMax: wrong shape of axis"); - } - auto axis = *reinterpret_cast<const int32_t *>(_axis->buffer()); - if (axis < 0) - { - axis += _input->num_dimensions(); - } -#define TF_LITE_ARG_MIN_MAX(input_type, axis_type, output_type) \ - ArgMinMax(getTensorShape(_input), reinterpret_cast<const input_type *>(_input->buffer()), \ - getTensorShape(_output), reinterpret_cast<output_type *>(_output->buffer()), axis, \ - GetComparefunction<input_type>(_is_arg_max)); - if (_output->data_type() == ir::DataType::INT32) - { - switch (_input->data_type()) - { - case ir::DataType::FLOAT32: - TF_LITE_ARG_MIN_MAX(float, int32_t, int32_t); - break; - case ir::DataType::QUANT_UINT8_ASYMM: - case ir::DataType::UINT8: - TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int32_t); - break; - case ir::DataType::INT32: - TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int32_t); - break; - default: - throw std::runtime_error("ArgMinMax: unsupported data type"); - } - } - else if (_output->data_type() == ir::DataType::INT64) - { - switch (_input->data_type()) - { - case ir::DataType::FLOAT32: - TF_LITE_ARG_MIN_MAX(float, int32_t, int64_t); - break; - case ir::DataType::QUANT_UINT8_ASYMM: - case ir::DataType::UINT8: - TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int64_t); - break; - case ir::DataType::INT32: - TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int64_t); - break; - default: - throw std::runtime_error("ArgMinMax: unsupported data type"); - } - } - else - { - throw std::runtime_error("ArgMinMax: unsupported data type"); - } - -#undef TF_LITE_ARG_MIN_MAX -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ArgMinMaxLayer.h b/runtime/onert/backend/cpu/ops/ArgMinMaxLayer.h deleted file mode 100644 index 4c864cb98..000000000 --- a/runtime/onert/backend/cpu/ops/ArgMinMaxLayer.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_ARGMINMAXLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_ARGMINMAXLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ArgMinMaxLayer : public ::onert::exec::IFunction -{ -public: - ArgMinMaxLayer() : _input(nullptr), _output(nullptr), _axis(nullptr), _is_arg_max(true) {} - -public: - void configure(const IPortableTensor *indices, IPortableTensor *output, - const IPortableTensor *axis, bool is_arg_max); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - const IPortableTensor *_axis; - bool _is_arg_max; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_ARGMINMAXLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/BatchMatMulLayer.cc b/runtime/onert/backend/cpu/ops/BatchMatMulLayer.cc deleted file mode 100644 index 7ef023788..000000000 --- a/runtime/onert/backend/cpu/ops/BatchMatMulLayer.cc +++ /dev/null @@ -1,85 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "BatchMatMulLayer.h" - -#include <cker/operation/BatchMatMul.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -BatchMatMulLayer::BatchMatMulLayer() - : _lhs(nullptr), _rhs(nullptr), _output(nullptr), _adj_x(false), _adj_y(false), - _kernel(new nnfw::cker::BatchMatMul()) -{ - // DO NOTHING -} - -BatchMatMulLayer::~BatchMatMulLayer() = default; - -void BatchMatMulLayer::batchMatMulFloat32() -{ - nnfw::cker::BatchMatMul &batchmatmul_kernel = *_kernel; - nnfw::cker::Shape lhs_shape = getTensorShape(_lhs); - nnfw::cker::Shape rhs_shape = getTensorShape(_rhs); - nnfw::cker::Shape output_shape = getTensorShape(_output); - - // TODO implement for constant input - - batchmatmul_kernel.prepare(lhs_shape, rhs_shape, _adj_x, _adj_y); - batchmatmul_kernel(lhs_shape, reinterpret_cast<const float *>(_lhs->buffer()), rhs_shape, - reinterpret_cast<const float *>(_rhs->buffer()), _adj_x, _adj_y, output_shape, - reinterpret_cast<float *>(_output->buffer())); -} - -void BatchMatMulLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs, bool adj_x, - bool adj_y, IPortableTensor *output) -{ - assert(lhs != nullptr); - assert(rhs != nullptr); - assert(output != nullptr); - - _lhs = lhs; - _rhs = rhs; - _adj_x = adj_x; - _adj_y = adj_y; - _output = output; -} - -void BatchMatMulLayer::run() -{ - if (_lhs->data_type() == OperandType::FLOAT32) - { - batchMatMulFloat32(); - } - else - { - throw std::runtime_error{"BatchMatMul: unsupported data type"}; - } -} - -#undef AVGPOOLING_PARAMETERS - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/BatchMatMulLayer.h b/runtime/onert/backend/cpu/ops/BatchMatMulLayer.h deleted file mode 100644 index 6770e218b..000000000 --- a/runtime/onert/backend/cpu/ops/BatchMatMulLayer.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_BATCH_MATMUL_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_BATCH_MATMUL_LAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace nnfw -{ -namespace cker -{ -class BatchMatMul; -} -} // namespace nnfw - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class BatchMatMulLayer : public ::onert::exec::IFunction -{ -public: - BatchMatMulLayer(); - ~BatchMatMulLayer(); - -public: - void batchMatMulFloat32(); - - void configure(const IPortableTensor *lhs, const IPortableTensor *rhs, bool adj_x, bool adj_y, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_lhs; - const IPortableTensor *_rhs; - IPortableTensor *_output; - - bool _adj_x; - bool _adj_y; - - std::unique_ptr<nnfw::cker::BatchMatMul> _kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_BATCH_MATMUL_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/BatchToSpaceNDLayer.cc b/runtime/onert/backend/cpu/ops/BatchToSpaceNDLayer.cc deleted file mode 100644 index f2f10eb9d..000000000 --- a/runtime/onert/backend/cpu/ops/BatchToSpaceNDLayer.cc +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "BatchToSpaceNDLayer.h" - -#include <cker/operation/BatchToSpaceND.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -BatchToSpaceNDLayer::BatchToSpaceNDLayer() - : _input(nullptr), _output(nullptr), _block_shape(nullptr), _crops(nullptr) -{ - // DO NOTHING -} - -template <typename T> void BatchToSpaceNDLayer::batchToSpaceNDGeneric() -{ - const int32_t NNapiCrops[]{0, 0, 0, 0}; - const int32_t *_crops_buffer; - - if (_crops == nullptr) - { - _crops_buffer = NNapiCrops; - } - else - { - _crops_buffer = reinterpret_cast<const int32_t *>(_crops->buffer()); - } - nnfw::cker::BatchToSpaceND<T>( - getTensorShape(_input), reinterpret_cast<const T *>(_input->buffer()), - reinterpret_cast<const int32_t *>(_block_shape->buffer()), _crops_buffer, - getTensorShape(_output), reinterpret_cast<T *>(_output->buffer())); -} - -void BatchToSpaceNDLayer::configure(const IPortableTensor *input, IPortableTensor *output, - IPortableTensor *block_shape, IPortableTensor *crops) -{ - _output = output; - _input = input; - _block_shape = block_shape; - _crops = crops; -} - -void BatchToSpaceNDLayer::run() -{ - if (_output->data_type() == OperandType::FLOAT32) - { - batchToSpaceNDGeneric<float>(); - } - else if (_output->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - batchToSpaceNDGeneric<uint8_t>(); - } - else - { - throw std::runtime_error{"NYI"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/BatchToSpaceNDLayer.h b/runtime/onert/backend/cpu/ops/BatchToSpaceNDLayer.h deleted file mode 100644 index 6e25b241b..000000000 --- a/runtime/onert/backend/cpu/ops/BatchToSpaceNDLayer.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_BATCHTOSPACEND_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_BATCHTOSPACEND_LAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class BatchToSpaceNDLayer : public ::onert::exec::IFunction -{ -public: - BatchToSpaceNDLayer(); - -public: - template <typename T> void batchToSpaceNDGeneric(); - - void configure(const IPortableTensor *input, IPortableTensor *output, - IPortableTensor *block_shape, IPortableTensor *crops); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - IPortableTensor *_block_shape; - IPortableTensor *_crops; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_BATCHTOSPACEND_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/BinaryArithmeticLayer.cc b/runtime/onert/backend/cpu/ops/BinaryArithmeticLayer.cc deleted file mode 100644 index 8e51daad5..000000000 --- a/runtime/onert/backend/cpu/ops/BinaryArithmeticLayer.cc +++ /dev/null @@ -1,226 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "BinaryArithmeticLayer.h" - -#include <cker/operation/BinaryArithmeticOps.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -namespace -{ - -template <nnfw::cker::BinaryArithmeticOpType arithmetic_type, typename T> -void eval(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output, - nnfw::cker::BinaryArithmeticOpParam op_params) -{ - const auto lhs_shape = getTensorShape(lhs); - const auto rhs_shape = getTensorShape(rhs); - const bool need_broadcast = nnfw::cker::ProcessBroadcastShapes(lhs_shape, rhs_shape, &op_params); - if (need_broadcast) - { - nnfw::cker::BroadcastBinaryArithmeticOp<arithmetic_type>( - op_params, lhs_shape, reinterpret_cast<const T *>(lhs->buffer()), rhs_shape, - reinterpret_cast<const T *>(rhs->buffer()), getTensorShape(output), - reinterpret_cast<T *>(output->buffer())); - return; - } - - nnfw::cker::BinaryArithmeticOp<arithmetic_type>( - op_params, lhs_shape, reinterpret_cast<const T *>(lhs->buffer()), rhs_shape, - reinterpret_cast<const T *>(rhs->buffer()), getTensorShape(output), - reinterpret_cast<T *>(output->buffer())); -} - -template <nnfw::cker::BinaryArithmeticOpType arithmetic_type> -std::function<void(const IPortableTensor *, const IPortableTensor *, IPortableTensor *)> -generateKernelGeneric(const IPortableTensor *lhs, const ir::Activation activation, - nnfw::cker::BinaryArithmeticOpParam op_params) -{ - switch (lhs->data_type()) - { - case OperandType::FLOAT32: - { - float output_activation_min = 0, output_activation_max = 0; - CalculateActivationRange(activation, &output_activation_min, &output_activation_max); - op_params.float_activation_max = output_activation_max; - op_params.float_activation_min = output_activation_min; - return std::bind(&eval<arithmetic_type, float>, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, op_params); - break; - } - case OperandType::INT32: - { - int32_t output_activation_min = 0, output_activation_max = 0; - CalculateActivationRange(activation, &output_activation_min, &output_activation_max); - op_params.quantized_activation_max = output_activation_max; - op_params.quantized_activation_min = output_activation_min; - return std::bind(eval<arithmetic_type, int32_t>, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, op_params); - break; - } - default: - throw std::runtime_error{"BinaryArithmetic(generic): Unsupported data type"}; - } -} - -void setAddOrSubQuant8Params(const IPortableTensor *lhs, const IPortableTensor *rhs, - IPortableTensor *output, ir::Activation activation, - nnfw::cker::BinaryArithmeticOpParam *params) -{ - int32_t output_activation_min, output_activation_max; - CalculateActivationRangeUint8(activation, output, &output_activation_min, &output_activation_max); - nnfw::cker::BinaryArithmeticOpParam &op_params = *params; - op_params.quantized_activation_max = output_activation_max; - op_params.quantized_activation_min = output_activation_min; - // Parameters for scaled quantized computation - op_params.left_shift = 20; - // Zero-points of input and output tensors - op_params.input1_offset = -lhs->data_offset(); - op_params.input2_offset = -rhs->data_offset(); - op_params.output_offset = output->data_offset(); - assert((op_params.input1_offset >= 0) && (op_params.input1_offset <= 255)); - assert((op_params.input2_offset >= 0) && (op_params.input2_offset <= 255)); - assert((op_params.output_offset >= 0) && (op_params.output_offset <= 255)); - - // Compute normalized scale for _lhs and _rhs values, - // and represent in 32-bit fixed point - const double norm_max_scale = 2 * std::max(lhs->data_scale(), rhs->data_scale()); - const double real_lhs_scale = lhs->data_scale() / norm_max_scale; - const double real_rhs_scale = rhs->data_scale() / norm_max_scale; - // output scale is used to normalize final result, so we invert the scale here - const double real_output_scale = - norm_max_scale / (output->data_scale() * (1 << op_params.left_shift)); - - // Represent the scales as fixed int32_t multipliers, and int32_t shifts - QuantizeMultiplier(real_lhs_scale, &op_params.input1_multiplier, &op_params.input1_shift); - QuantizeMultiplier(real_rhs_scale, &op_params.input2_multiplier, &op_params.input2_shift); - QuantizeMultiplier(real_output_scale, &op_params.output_multiplier, &op_params.output_shift); -} - -void setMulQuant8Params(const IPortableTensor *lhs, const IPortableTensor *rhs, - IPortableTensor *output, ir::Activation activation, - nnfw::cker::BinaryArithmeticOpParam *params) -{ - int32_t output_activation_min, output_activation_max; - CalculateActivationRangeUint8(activation, output, &output_activation_min, &output_activation_max); - nnfw::cker::BinaryArithmeticOpParam &op_params = *params; - - op_params.quantized_activation_max = output_activation_max; - op_params.quantized_activation_min = output_activation_min; - op_params.input1_offset = -lhs->data_offset(); - op_params.input2_offset = -rhs->data_offset(); - op_params.output_offset = output->data_offset(); - - double real_multiplier = lhs->data_scale() * rhs->data_scale() / output->data_scale(); - QuantizeMultiplier(real_multiplier, &op_params.output_multiplier, &op_params.output_shift); -} - -} // namespace - -void BinaryArithmeticLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs, - IPortableTensor *output, const ir::Activation activation, - const ArithmeticType arithmetic_type) -{ - assert(lhs != nullptr); - assert(rhs != nullptr); - assert(output != nullptr); - - _lhs = lhs; - _rhs = rhs; - _output = output; - - nnfw::cker::BinaryArithmeticOpParam op_params; - switch (arithmetic_type) - { - case ArithmeticType::kAdd: - if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - setAddOrSubQuant8Params(_lhs, _rhs, _output, activation, &op_params); - _kernel = std::bind(&eval<nnfw::cker::BinaryArithmeticOpType::ADD, uint8_t>, - std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, - op_params); - } - else - { - _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::ADD>(_lhs, activation, - op_params); - } - break; - case ArithmeticType::kSub: - if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - setAddOrSubQuant8Params(_lhs, _rhs, _output, activation, &op_params); - op_params.input2_multiplier *= -1; - _kernel = std::bind(&eval<nnfw::cker::BinaryArithmeticOpType::SUB, uint8_t>, - std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, - op_params); - } - else - { - _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::SUB>(_lhs, activation, - op_params); - } - break; - case ArithmeticType::kMul: - if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - nnfw::cker::BinaryArithmeticOpParam op_params; - setMulQuant8Params(_lhs, _rhs, _output, activation, &op_params); - _kernel = std::bind(&eval<nnfw::cker::BinaryArithmeticOpType::MUL, uint8_t>, - std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, - op_params); - } - else - { - _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::MUL>(_lhs, activation, - op_params); - } - break; - case ArithmeticType::kDiv: - if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - throw std::runtime_error{ - "BinaryArithmetic(Div): Div operation does not support quantization"}; - } - else if (_lhs->data_type() == OperandType::INT32) - { - throw std::runtime_error{"BinaryArithmetic(Div): Unsupported data type"}; - } - else - { - _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::DIV>(_lhs, activation, - op_params); - } - break; - default: - throw std::runtime_error{"BinaryArithmetic: Unsupported BinaryArithmetic type"}; - } -} - -void BinaryArithmeticLayer::run() { _kernel(_lhs, _rhs, _output); } - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/BinaryArithmeticLayer.h b/runtime/onert/backend/cpu/ops/BinaryArithmeticLayer.h deleted file mode 100644 index d6b33ad07..000000000 --- a/runtime/onert/backend/cpu/ops/BinaryArithmeticLayer.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_BINARYARITHMETICLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_BINARYARITHMETICLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -enum class ArithmeticType -{ - kAdd, - kSub, - kMul, - kDiv, -}; - -class BinaryArithmeticLayer : public ::onert::exec::IFunction -{ -public: - BinaryArithmeticLayer() : _lhs(nullptr), _rhs(nullptr), _output(nullptr) - { - // DO NOTHING - } - -public: - void configure(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output, - const ir::Activation activation, const ArithmeticType arithmetic_type); - - void run() override; - -private: - const IPortableTensor *_lhs; - const IPortableTensor *_rhs; - IPortableTensor *_output; - - std::function<void(const IPortableTensor *, const IPortableTensor *, IPortableTensor *)> _kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_BINARYARITHMETICLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/BroadcastToLayer.cc b/runtime/onert/backend/cpu/ops/BroadcastToLayer.cc deleted file mode 100644 index d9c1bbfc5..000000000 --- a/runtime/onert/backend/cpu/ops/BroadcastToLayer.cc +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "BroadcastToLayer.h" - -#include <cker/operation/BroadcastTo.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -BroadcastToLayer::BroadcastToLayer() : _input(nullptr), _shape(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void BroadcastToLayer::configure(const IPortableTensor *input, const IPortableTensor *shape, - IPortableTensor *output) -{ - _input = input; - _shape = shape; - _output = output; -} - -void BroadcastToLayer::run() -{ - // NOTE : It was implemented follows tf.broadcast_to operation works and - // Api Document(https://www.tensorflow.org/api_docs/python/tf/broadcast_to) - - switch (_output->data_type()) - { - // ToDo : It need to support INT8 and UINT8 also when will be applied quantization. - case OperandType::FLOAT32: - nnfw::cker::BroadcastTo<float>( - getTensorShape(_input), reinterpret_cast<float *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); - break; - case OperandType::INT32: - nnfw::cker::BroadcastTo<int32_t>( - getTensorShape(_input), reinterpret_cast<int32_t *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<int32_t *>(_output->buffer())); - break; - case OperandType::UINT32: - nnfw::cker::BroadcastTo<uint32_t>( - getTensorShape(_input), reinterpret_cast<uint32_t *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<uint32_t *>(_output->buffer())); - break; - default: - throw std::runtime_error{"BroadcastToLayer: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/BroadcastToLayer.h b/runtime/onert/backend/cpu/ops/BroadcastToLayer.h deleted file mode 100644 index 8e8433fc9..000000000 --- a/runtime/onert/backend/cpu/ops/BroadcastToLayer.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_BROADCASTLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_BROADCASTLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class BroadcastToLayer : public ::onert::exec::IFunction -{ -public: - BroadcastToLayer(); - -public: - void configure(const IPortableTensor *input, const IPortableTensor *shape, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_shape; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_BROADCASTLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/CompareLayer.cc b/runtime/onert/backend/cpu/ops/CompareLayer.cc deleted file mode 100644 index adf902aaf..000000000 --- a/runtime/onert/backend/cpu/ops/CompareLayer.cc +++ /dev/null @@ -1,175 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "CompareLayer.h" - -#include "OperationUtils.h" - -#include <assert.h> -#include <cker/operation/Comparison.h> -using namespace nnfw::cker; -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -namespace -{ - -using OpType = onert::ir::operation::Comparison::ComparisonType; -using namespace onert::backend::cpu; - -// Assumes these enum values to be in the order like this -static_assert(static_cast<int>(OpType::Equal) == 0, "An OpType value has changed!"); -static_assert(static_cast<int>(OpType::NotEqual) == 1, "An OpType value has changed!"); -static_assert(static_cast<int>(OpType::Greater) == 2, "An OpType value has changed!"); -static_assert(static_cast<int>(OpType::GreaterEqual) == 3, "An OpType value has changed!"); -static_assert(static_cast<int>(OpType::Less) == 4, "An OpType value has changed!"); -static_assert(static_cast<int>(OpType::LessEqual) == 5, "An OpType value has changed!"); - -template <typename T> -void compareQuant8(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output, - OpType op_type) -{ - nnfw::cker::ComparisonParams params; - params.left_shift = 8; - params.input1_offset = -lhs->data_offset(); - params.input2_offset = -rhs->data_offset(); - const double norm_max_scale = - 2 * std::max(std::abs(lhs->data_scale()), std::abs(rhs->data_scale())); - const double adjusted_lhs_scale = lhs->data_scale() / norm_max_scale; - const double adjusted_rhs_scale = rhs->data_scale() / norm_max_scale; - QuantizeMultiplierSmallerThanOneExp(adjusted_lhs_scale, ¶ms.input1_multiplier, - ¶ms.input1_shift); - QuantizeMultiplierSmallerThanOneExp(adjusted_rhs_scale, ¶ms.input2_multiplier, - ¶ms.input2_shift); - params.is_broadcast = !HaveSameShapes(lhs, rhs); - - using CompareFunction = - void (*)(ComparisonParams & params, const Shape &input1_shape, const T *input1_data, - const Shape &input2_shape, const T *input2_data, const Shape &output_shape, - bool *output_data); - - static const CompareFunction broadcast_fns[] = { - Broadcast4DSlowEqualWithScaling, Broadcast4DSlowNotEqualWithScaling, - Broadcast4DSlowGreaterWithScaling, Broadcast4DSlowGreaterEqualWithScaling, - Broadcast4DSlowLessWithScaling, Broadcast4DSlowLessEqualWithScaling, - }; - static const CompareFunction non_broadcast_fns[] = { - EqualWithScaling, NotEqualWithScaling, GreaterWithScaling, - GreaterEqualWithScaling, LessWithScaling, LessEqualWithScaling, - }; - - static_assert(sizeof(broadcast_fns) == sizeof(non_broadcast_fns), - "Sizes of broadcast_fns and non_broadcast_fns must match!"); - - auto index = static_cast<int>(op_type); - if (index < 0 || index >= static_cast<int>(sizeof(broadcast_fns) / sizeof(broadcast_fns[0]))) - throw std::runtime_error{"Invalid OpType for CompareLayer"}; - - CompareFunction fn = (params.is_broadcast ? broadcast_fns[index] : non_broadcast_fns[index]); - - fn(params, getExtendedTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()), - getExtendedTensorShape(rhs), reinterpret_cast<const T *>(rhs->buffer()), - getExtendedTensorShape(output), reinterpret_cast<bool *>(output->buffer())); -} - -template <typename T> -void compareScalar(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output, - OpType op_type) -{ - bool requires_broadcast = !HaveSameShapes(lhs, rhs); - - using CompareFunction = - void (*)(const Shape &input1_shape, const T *input1_data, const Shape &input2_shape, - const T *input2_data, const Shape &output_shape, bool *output_data); - - static const CompareFunction broadcast_fns[] = { - Broadcast4DSlowEqual, Broadcast4DSlowNotEqual, Broadcast4DSlowGreater, - Broadcast4DSlowGreaterEqual, Broadcast4DSlowLess, Broadcast4DSlowLessEqual, - }; - static const CompareFunction non_broadcast_fns[] = { - EqualNoScaling, NotEqualNoScaling, GreaterNoScaling, - GreaterEqualNoScaling, LessNoScaling, LessEqualNoScaling, - }; - - static_assert(sizeof(broadcast_fns) == sizeof(non_broadcast_fns), - "Sizes of broadcast_fns and non_broadcast_fns must match!"); - - auto index = static_cast<int>(op_type); - if (index < 0 || index >= static_cast<int>(sizeof(broadcast_fns) / sizeof(broadcast_fns[0]))) - throw std::runtime_error{"Invalid OpType for CompareLayer"}; - - CompareFunction fn = (requires_broadcast ? broadcast_fns[index] : non_broadcast_fns[index]); - - fn(getExtendedTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()), - getExtendedTensorShape(rhs), reinterpret_cast<const T *>(rhs->buffer()), - getExtendedTensorShape(output), reinterpret_cast<bool *>(output->buffer())); -} - -} // namespace - -CompareLayer::CompareLayer() - : _lhs(nullptr), _rhs(nullptr), _output(nullptr), - _op_type(ir::operation::Comparison::ComparisonType::Equal) -{ - // DO NOTHING -} - -void CompareLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs, - const OpType op_type, IPortableTensor *output) -{ - _lhs = lhs; - _rhs = rhs; - _op_type = op_type; - _output = output; -} - -void CompareLayer::run() -{ - if (_lhs->data_type() == OperandType::FLOAT32) - { - compareScalar<float>(_lhs, _rhs, _output, _op_type); - } - else if (_lhs->data_type() == OperandType::INT32) - { - compareScalar<int32_t>(_lhs, _rhs, _output, _op_type); - } - else if (_lhs->data_type() == OperandType::INT64) - { - compareScalar<int64_t>(_lhs, _rhs, _output, _op_type); - } - else if (_lhs->data_type() == OperandType::BOOL8) - { - compareScalar<uint8_t>(_lhs, _rhs, _output, _op_type); - } - else if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - compareQuant8<uint8_t>(_lhs, _rhs, _output, _op_type); - } - else - { - throw std::runtime_error{"Compare: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/CompareLayer.h b/runtime/onert/backend/cpu/ops/CompareLayer.h deleted file mode 100644 index add360ef8..000000000 --- a/runtime/onert/backend/cpu/ops/CompareLayer.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_COMPARELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_COMPARELAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> -#include <ir/operation/Comparison.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class CompareLayer : public ::onert::exec::IFunction -{ -public: - CompareLayer(); - -public: - void configure(const IPortableTensor *lhs, const IPortableTensor *rhs, - const ir::operation::Comparison::ComparisonType op_type, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_lhs; - const IPortableTensor *_rhs; - IPortableTensor *_output; - ir::operation::Comparison::ComparisonType _op_type; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_COMPARELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ConcatLayer.cc b/runtime/onert/backend/cpu/ops/ConcatLayer.cc deleted file mode 100644 index d26ed7378..000000000 --- a/runtime/onert/backend/cpu/ops/ConcatLayer.cc +++ /dev/null @@ -1,143 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConcatLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Concatenation.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -ConcatLayer::ConcatLayer() : _inputs(), _output(nullptr), _axis(0) -{ - // DO NOTHING -} - -template <typename T> void ConcatLayer::concatenationGeneral() -{ - uint32_t num_inputs = _inputs.size(); - - nnfw::cker::ConcatenationParams op_params; - op_params.axis = _axis; - op_params.inputs_count = num_inputs; - - std::vector<nnfw::cker::Shape *> inputDimsPtr; - std::vector<nnfw::cker::Shape> inputDims; - inputDimsPtr.reserve(num_inputs); - inputDims.reserve(num_inputs); - - for (uint32_t i = 0; i < num_inputs; i++) - { - inputDims.push_back(getTensorShape(_inputs[i])); - inputDimsPtr.push_back(&inputDims[i]); - } - - std::vector<const T *> inputDataPtrs; - - for (const auto input : _inputs) - { - inputDataPtrs.emplace_back(reinterpret_cast<const T *>(input->buffer())); - } - - nnfw::cker::Concatenation<T>(op_params, inputDimsPtr.data(), inputDataPtrs.data(), - getTensorShape(_output), reinterpret_cast<T *>(_output->buffer())); -} -void ConcatLayer::concatenationQuant8() -{ - uint32_t num_inputs = _inputs.size(); - - std::vector<int32_t> input_zeropoints(num_inputs); - std::vector<float> input_scales(num_inputs); - for (uint32_t i = 0; i < num_inputs; i++) - { - input_zeropoints[i] = _inputs[i]->data_offset(); - input_scales[i] = _inputs[i]->data_scale(); - } - - nnfw::cker::ConcatenationParams op_params; - op_params.axis = _axis; - op_params.inputs_count = num_inputs; - op_params.input_zeropoint = input_zeropoints.data(); - op_params.input_scale = input_scales.data(); - op_params.output_zeropoint = _output->data_offset(); - op_params.output_scale = _output->data_scale(); - - std::vector<nnfw::cker::Shape *> inputDimsPtr; - std::vector<nnfw::cker::Shape> inputDims; - inputDimsPtr.reserve(num_inputs); - inputDims.reserve(num_inputs); - for (uint32_t i = 0; i < num_inputs; i++) - { - inputDims.push_back(getTensorShape(_inputs[i])); - inputDimsPtr.push_back(&inputDims[i]); - } - - std::vector<const uint8_t *> inputDataPtrs; - for (const auto input : _inputs) - { - inputDataPtrs.emplace_back(reinterpret_cast<const uint8_t *>(input->buffer())); - } - - nnfw::cker::ConcatenationWithScaling(op_params, inputDimsPtr.data(), inputDataPtrs.data(), - getTensorShape(_output), - reinterpret_cast<uint8_t *>(_output->buffer())); -} - -void ConcatLayer::configure(const std::vector<const IPortableTensor *> &inputs, int32_t axis, - IPortableTensor *output) -{ - assert(inputs.size() > 0); - assert(output != nullptr); - - _inputs = inputs; - _axis = axis; - _output = output; -} - -void ConcatLayer::run() -{ - if (_output->data_type() == OperandType::FLOAT32) - { - concatenationGeneral<float>(); - } - else if (_output->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - concatenationQuant8(); - } - else if (_output->data_type() == OperandType::INT32) - { - concatenationGeneral<int32_t>(); - } - else if (_output->data_type() == OperandType::INT64) - { - concatenationGeneral<int64_t>(); - } - else - throw std::runtime_error("Concat: unsupported data type"); -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ConcatLayer.h b/runtime/onert/backend/cpu/ops/ConcatLayer.h deleted file mode 100644 index 0787199d6..000000000 --- a/runtime/onert/backend/cpu/ops/ConcatLayer.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_CONCATLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_CONCATLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ConcatLayer : public ::onert::exec::IFunction -{ -public: - ConcatLayer(); - -public: - template <typename T> void concatenationGeneral(); - - void concatenationQuant8(); - - void configure(const std::vector<const IPortableTensor *> &inputs, int32_t axis, - IPortableTensor *output); - - void run() override; - -private: - std::vector<const IPortableTensor *> _inputs; - IPortableTensor *_output; - int32_t _axis; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_CONCATLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ConvolutionLayer.cc b/runtime/onert/backend/cpu/ops/ConvolutionLayer.cc deleted file mode 100644 index c057267d3..000000000 --- a/runtime/onert/backend/cpu/ops/ConvolutionLayer.cc +++ /dev/null @@ -1,211 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConvolutionLayer.h" - -#include "../Tensor.h" -#include "ir/Padding.h" -#include <cker/operation/Conv.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -ConvolutionLayer::ConvolutionLayer() - : _input(nullptr), _kernel(nullptr), _bias(nullptr), _output(nullptr), - _paddingType(ir::PaddingType::EXPLICIT), _paddingLeft(0), _paddingTop(0), _paddingRight(0), - _paddingBottom(0), _strideWidth(0), _strideHeight(0), _dilationWidthFactor(1), - _dilationHeightFactor(1), _activation(ir::Activation::NONE), - _conv_kernel(new nnfw::cker::Conv()), _prepare(false) -{ - // DO NOTHING -} - -ConvolutionLayer::~ConvolutionLayer() = default; - -void ConvolutionLayer::convFloat32() -{ - float output_activation_min = 0, output_activation_max = 0; - CalculateActivationRange(_activation, &output_activation_min, &output_activation_max); - - nnfw::cker::ConvParams op_params; - op_params.padding_type = getPaddingType(_paddingType); - op_params.padding_values.width = _paddingLeft; - op_params.padding_values.height = _paddingTop; - op_params.stride_width = _strideWidth; - op_params.stride_height = _strideHeight; - op_params.dilation_width_factor = _dilationWidthFactor; - op_params.dilation_height_factor = _dilationHeightFactor; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - - nnfw::cker::Conv &kernel = *_conv_kernel; - kernel(op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_kernel), reinterpret_cast<const float *>(_kernel->buffer()), - getTensorShape(_bias), reinterpret_cast<const float *>(_bias->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); -} - -void ConvolutionLayer::convQuant8() -{ - int32_t output_activation_min = 0; - int32_t output_activation_max = 0; - CalculateActivationRangeUint8(_activation, _output, &output_activation_min, - &output_activation_max); - - double real_multiplier = 0.0; - int32_t output_multiplier = 0; - int32_t output_shift = 0; - GetQuantizedConvolutionMultiplier(_input, _kernel, _bias, _output, &real_multiplier); - QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift); - - nnfw::cker::ConvParams op_params; - op_params.stride_width = _strideWidth; - op_params.stride_height = _strideHeight; - op_params.dilation_width_factor = _dilationWidthFactor; - op_params.dilation_height_factor = _dilationHeightFactor; - op_params.padding_type = getPaddingType(_paddingType); - op_params.padding_values.width = _paddingLeft; - op_params.padding_values.height = _paddingTop; - op_params.input_offset = -_input->data_offset(); - op_params.weights_offset = -_kernel->data_offset(); - op_params.output_offset = _output->data_offset(); - op_params.output_multiplier = output_multiplier; - op_params.output_shift = output_shift; - op_params.quantized_activation_min = output_activation_min; - op_params.quantized_activation_max = output_activation_max; - op_params.is_replaced_weights = true; - - nnfw::cker::Conv &kernel = *_conv_kernel; - kernel(op_params, getTensorShape(_input), reinterpret_cast<const uint8_t *>(_input->buffer()), - getTensorShape(_kernel), reinterpret_cast<const uint8_t *>(_kernel->buffer()), - getTensorShape(_bias), reinterpret_cast<const int32_t *>(_bias->buffer()), - getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer())); -} - -void ConvolutionLayer::configure(const IPortableTensor *input, const IPortableTensor *kernel, - const IPortableTensor *bias, const ir::PaddingType paddingType, - const uint32_t paddingLeft, const uint32_t paddingRight, - const uint32_t paddingTop, const uint32_t paddingBottom, - const uint32_t strideWidth, const uint32_t strideHeight, - const uint32_t dilationWidthFactor, - const uint32_t dilationHeightFactor, - const ir::Activation activation, IPortableTensor *output) -{ - _input = input; - _kernel = kernel; - _bias = bias; - _paddingType = paddingType; - _paddingLeft = paddingLeft; - _paddingRight = paddingRight; - _paddingTop = paddingTop; - _paddingBottom = paddingBottom; - _strideWidth = strideWidth; - _strideHeight = strideHeight; - _dilationWidthFactor = dilationWidthFactor; - _dilationHeightFactor = dilationHeightFactor; - _activation = activation; - _output = output; -} - -void ConvolutionLayer::run() -{ - prepare(); - - if (_input->is_dynamic() || _kernel->is_dynamic()) - { - const auto ifm_shape = _input->getShape().asFeature(_input->layout()); - const auto ofm_shape = _output->getShape().asFeature(_input->layout()); - // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]. - const auto ker_shape = _kernel->getShape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - - ir::Stride stride; - stride.vertical = _strideWidth; - stride.horizontal = _strideWidth; - - ir::Padding param_padding; - param_padding.type = _paddingType; - param_padding.param.left = _paddingLeft; - param_padding.param.right = _paddingRight; - param_padding.param.top = _paddingTop; - param_padding.param.bottom = _paddingBottom; - - const auto padding = - ir::calculatePadding(param_padding, ifm_shape, ofm_shape, stride, ker_width, ker_height, - _dilationWidthFactor, _dilationHeightFactor); - - _paddingLeft = padding.left; - _paddingRight = padding.right; - _paddingTop = padding.top; - _paddingBottom = padding.bottom; - } - if (_input->data_type() == OperandType::FLOAT32) - { - convFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - convQuant8(); - } - else - { - throw std::runtime_error{"Conv: unsupported data type"}; - } -} - -void ConvolutionLayer::prepare() -{ - if (_prepare) - return; - - nnfw::cker::Conv &kernel = *_conv_kernel; - if (_input->data_type() == OperandType::FLOAT32 && _kernel->is_constant()) - { - bool is_transposed = false; - kernel.prepare(getTensorShape(_kernel), reinterpret_cast<const float *>(_kernel->buffer()), - getPaddingType(_paddingType), is_transposed, _dilationWidthFactor, - _dilationHeightFactor); - - // Decrease reference of _kernel(weights) only when _kernel is constant - if (is_transposed) - { - auto kernel_tensor = dynamic_cast<const Tensor *>(_kernel); - if (kernel_tensor) - // TODO Remove const_cast - const_cast<Tensor *>(kernel_tensor)->decrease_ref(); - } - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM && _kernel->is_constant() && - !_input->is_dynamic() && !_output->is_dynamic()) - { - kernel.prepareQuant(getTensorShape(_input), getTensorShape(_kernel), getTensorShape(_output), - _strideWidth, _strideHeight); - } - _prepare = true; -} - -#undef ANDROID_NN_CONV_PARAMETERS - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ConvolutionLayer.h b/runtime/onert/backend/cpu/ops/ConvolutionLayer.h deleted file mode 100644 index 398892e65..000000000 --- a/runtime/onert/backend/cpu/ops/ConvolutionLayer.h +++ /dev/null @@ -1,96 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_CONVOLUTIONLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_CONVOLUTIONLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> -#include <functional> -#include <memory> - -namespace nnfw -{ -namespace cker -{ -class Conv; -} -} // namespace nnfw - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ConvolutionLayer : public ::onert::exec::IFunction -{ -public: - ConvolutionLayer(); - ~ConvolutionLayer(); - -public: - void convFloat32(); - - void convQuant8(); - - void configure(const IPortableTensor *input, const IPortableTensor *kernel, - const IPortableTensor *bias, ir::PaddingType _paddingType, - const uint32_t paddingLeft, const uint32_t paddingRight, const uint32_t paddingTop, - const uint32_t paddingBottom, const uint32_t strideWidth, - const uint32_t strideHeight, const uint32_t dilationWidthFactor, - const uint32_t dilationHeightFactor, const ir::Activation activation, - IPortableTensor *output); - - void run() override; - - void prepare() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_kernel; - const IPortableTensor *_bias; - IPortableTensor *_output; - - ir::PaddingType _paddingType; - uint32_t _paddingLeft; - uint32_t _paddingTop; - uint32_t _paddingRight; - uint32_t _paddingBottom; - - uint32_t _strideWidth; - uint32_t _strideHeight; - uint32_t _dilationWidthFactor; - uint32_t _dilationHeightFactor; - - ir::Activation _activation; - - std::unique_ptr<nnfw::cker::Conv> _conv_kernel; - - bool _prepare; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_CONVOLUTIONLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/DepthwiseConvolutionLayer.cc b/runtime/onert/backend/cpu/ops/DepthwiseConvolutionLayer.cc deleted file mode 100644 index e67c3f390..000000000 --- a/runtime/onert/backend/cpu/ops/DepthwiseConvolutionLayer.cc +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "DepthwiseConvolutionLayer.h" - -#include <cker/operation/DepthwiseConv.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -DepthwiseConvolutionLayer::DepthwiseConvolutionLayer() - : _input(nullptr), _kernel(nullptr), _bias(nullptr), _output(nullptr), _paddingLeft(0), - _paddingTop(0), _paddingRight(0), _paddingBottom(0), _strideWidth(0), _strideHeight(0), - _multiplier(0), _activation(ir::Activation::NONE) -{ - // DO NOTHING -} - -void DepthwiseConvolutionLayer::convFloat32() -{ - float output_activation_min = 0, output_activation_max = 0; - CalculateActivationRange(_activation, &output_activation_min, &output_activation_max); - - nnfw::cker::DepthwiseConvParams op_params; - op_params.stride_width = _strideWidth; - op_params.stride_height = _strideHeight; - op_params.dilation_width_factor = 1; - op_params.dilation_height_factor = 1; - op_params.padding_values.width = _paddingLeft; - op_params.padding_values.height = _paddingTop; - op_params.depth_multiplier = _multiplier; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - - nnfw::cker::DepthwiseConv( - op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_kernel), reinterpret_cast<const float *>(_kernel->buffer()), - getTensorShape(_bias), reinterpret_cast<const float *>(_bias->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); -} - -void DepthwiseConvolutionLayer::convQuant8() -{ - int32_t output_activation_min = 0; - int32_t output_activation_max = 0; - CalculateActivationRangeUint8(_activation, _output, &output_activation_min, - &output_activation_max); - - double real_multiplier = 0.0; - int32_t output_multiplier = 0; - int32_t output_shift = 0; - GetQuantizedConvolutionMultiplier(_input, _kernel, _bias, _output, &real_multiplier); - QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift); - - nnfw::cker::DepthwiseConvParams op_params; - op_params.stride_width = _strideWidth; - op_params.stride_height = _strideHeight; - op_params.dilation_width_factor = 1; - op_params.dilation_height_factor = 1; - op_params.padding_values.width = _paddingLeft; - op_params.padding_values.height = _paddingTop; - op_params.depth_multiplier = _multiplier; - op_params.input_offset = -_input->data_offset(); - op_params.weights_offset = -_kernel->data_offset(); - op_params.output_offset = _output->data_offset(); - op_params.output_multiplier = output_multiplier; - op_params.output_shift = output_shift; - op_params.quantized_activation_min = output_activation_min; - op_params.quantized_activation_max = output_activation_max; - - nnfw::cker::DepthwiseConv( - op_params, getTensorShape(_input), reinterpret_cast<const uint8_t *>(_input->buffer()), - getTensorShape(_kernel), reinterpret_cast<const uint8_t *>(_kernel->buffer()), - getTensorShape(_bias), reinterpret_cast<const int32_t *>(_bias->buffer()), - getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer())); -} - -void DepthwiseConvolutionLayer::configure(const IPortableTensor *input, - const IPortableTensor *kernel, - const IPortableTensor *bias, const uint32_t paddingLeft, - const uint32_t paddingRight, const uint32_t paddingTop, - const uint32_t paddingBottom, const uint32_t strideWidth, - const uint32_t strideHeight, const uint32_t multiplier, - const ir::Activation activation, IPortableTensor *output) -{ - _input = input; - _kernel = kernel; - _bias = bias; - _paddingLeft = paddingLeft; - _paddingRight = paddingRight; - _paddingTop = paddingTop; - _paddingBottom = paddingBottom; - _strideWidth = strideWidth; - _strideHeight = strideHeight; - _multiplier = multiplier; - _activation = activation; - _output = output; -} - -void DepthwiseConvolutionLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - convFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - convQuant8(); - } - else - { - throw std::runtime_error{"DepthwiseConv: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/DepthwiseConvolutionLayer.h b/runtime/onert/backend/cpu/ops/DepthwiseConvolutionLayer.h deleted file mode 100644 index c898255a3..000000000 --- a/runtime/onert/backend/cpu/ops/DepthwiseConvolutionLayer.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_KERNEL_CPU_DEPTHWISECONVOLUTIONLAYER_H__ -#define __ONERT_KERNEL_CPU_DEPTHWISECONVOLUTIONLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class DepthwiseConvolutionLayer : public ::onert::exec::IFunction -{ -public: - DepthwiseConvolutionLayer(); - -public: - void convFloat32(); - - void convQuant8(); - - void configure(const IPortableTensor *input, const IPortableTensor *kernel, - const IPortableTensor *bias, const uint32_t paddingLeft, - const uint32_t paddingRight, const uint32_t paddingTop, - const uint32_t paddingBottom, const uint32_t strideW, const uint32_t strideH, - const uint32_t multiplier, const ir::Activation activation, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_kernel; - const IPortableTensor *_bias; - IPortableTensor *_output; - - uint32_t _paddingLeft; - uint32_t _paddingTop; - uint32_t _paddingRight; - uint32_t _paddingBottom; - - uint32_t _strideWidth; - uint32_t _strideHeight; - - uint32_t _multiplier; - - ir::Activation _activation; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_KERNEL_CPU_DEPTHWISECONVOLUTIONLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/EinsumLayer.cc b/runtime/onert/backend/cpu/ops/EinsumLayer.cc deleted file mode 100644 index 8c16740a3..000000000 --- a/runtime/onert/backend/cpu/ops/EinsumLayer.cc +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "EinsumLayer.h" - -#include <cker/operation/Einsum.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -EinsumLayer::EinsumLayer() - : _inputs(), _output(nullptr), _equation(), _einsum_kernel(new nnfw::cker::Einsum()) -{ - // DO NOTHING -} - -EinsumLayer::~EinsumLayer() = default; - -void EinsumLayer::einsumFloat32() -{ - uint32_t num_inputs = _inputs.size(); - nnfw::cker::Einsum &kernel = *_einsum_kernel; - - kernel.prepare(_equation); - - std::vector<nnfw::cker::Shape> inputShapes; - std::vector<const float *> inputFloatPtrs; - - for (uint32_t i = 0; i < num_inputs; i++) - { - inputShapes.emplace_back(getTensorShape(_inputs[i])); - inputFloatPtrs.emplace_back(reinterpret_cast<const float *>(_inputs[i]->buffer())); - } - - kernel(_equation, inputShapes, inputFloatPtrs, getTensorShape(_output), - reinterpret_cast<float *>(_output->buffer())); -} - -void EinsumLayer::run() -{ - if (_output->data_type() == OperandType::FLOAT32) - { - einsumFloat32(); - } - else - { - throw std::runtime_error{"Einsum: unsupported data type"}; - } -} - -void EinsumLayer::configure(const std::vector<const IPortableTensor *> &inputs, - std::string equation, IPortableTensor *output) -{ - assert(inputs.size() > 0); - assert(output != nullptr); - - _inputs = inputs; - _equation = equation; - _output = output; -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/EinsumLayer.h b/runtime/onert/backend/cpu/ops/EinsumLayer.h deleted file mode 100644 index a93f87e77..000000000 --- a/runtime/onert/backend/cpu/ops/EinsumLayer.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_EINSUM_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_EINSUM_LAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> -#include <functional> -#include <memory> - -namespace nnfw -{ -namespace cker -{ -class Einsum; -} -} // namespace nnfw - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class EinsumLayer : public ::onert::exec::IFunction -{ -public: - EinsumLayer(); - ~EinsumLayer(); - -public: - void einsumFloat32(); - - void configure(const std::vector<const IPortableTensor *> &inputs, std::string equation, - IPortableTensor *output); - - void run() override; - -private: - std::vector<const IPortableTensor *> _inputs; - IPortableTensor *_output; - - std::string _equation; - - std::unique_ptr<nnfw::cker::Einsum> _einsum_kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_EINSUM_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ElementwiseActivationLayer.cc b/runtime/onert/backend/cpu/ops/ElementwiseActivationLayer.cc deleted file mode 100644 index c1d63172b..000000000 --- a/runtime/onert/backend/cpu/ops/ElementwiseActivationLayer.cc +++ /dev/null @@ -1,173 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ElementwiseActivationLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Logistic.h> -#include <cker/operation/ReLU.h> -#include <cker/operation/ReLU6.h> -#include <cker/operation/Tanh.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -ElementwiseActivationLayer::ElementwiseActivationLayer() - : _input(nullptr), _output(nullptr), _kernel() -{ - // DO NOTHING -} - -void ElementwiseActivationLayer::PopulateLookupTable(const ElementwiseActivationType op_type) -{ - const auto input_scale = static_cast<double>(_input->data_scale()); - const auto input_zero_point = static_cast<int32_t>(_input->data_offset()); - const auto output_scale = static_cast<double>(_output->data_scale()); - const auto output_zero_point = static_cast<int32_t>(_output->data_offset()); - const float inverse_scale = 1 / output_scale; - int32_t maxval = std::numeric_limits<uint8_t>::max(); - int32_t minval = std::numeric_limits<uint8_t>::min(); - for (int32_t val = minval; val <= maxval; ++val) - { - const float dequantized = input_scale * (val - input_zero_point); - float transformed = 0.f; - if (op_type == ElementwiseActivationType::kTanh) - { - transformed = std::tanh(dequantized); - } - else if (op_type == ElementwiseActivationType::kLogistic) - { - transformed = 1.0f / (1.0f + std::exp(-dequantized)); - } - else - { - throw std::runtime_error("ElementwiseActivationLayer : unsupported activation type"); - } - const float rescaled = std::round(transformed * inverse_scale); - const int32_t quantized = static_cast<int32_t>(rescaled + output_zero_point); - _table[val] = static_cast<uint8_t>(std::max(std::min(maxval, quantized), minval)); - } -} - -void ElementwiseActivationLayer::EvalUsingLookupTable(const IPortableTensor *input, - IPortableTensor *output) -{ - const int size = MatchingFlatSize(getTensorShape(input), getTensorShape(output)); - const uint8_t *input_data = reinterpret_cast<const uint8_t *>(input->buffer()); - uint8_t *output_data = reinterpret_cast<uint8_t *>(output->buffer()); - - for (int i = 0; i < size; ++i) - { - output_data[i] = _table[input_data[i]]; - } -} - -void ElementwiseActivationLayer::configure(const IPortableTensor *input, IPortableTensor *output, - float alpha, float beta, - ElementwiseActivationType op_type) -{ - _input = input; - _output = output; - - switch (op_type) - { - case ElementwiseActivationType::kLogistic: - if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - PopulateLookupTable(op_type); - _kernel = std::bind(&ElementwiseActivationLayer::EvalUsingLookupTable, this, - std::placeholders::_1, std::placeholders::_2); - } - else if (_input->data_type() == OperandType::FLOAT32) - { - _kernel = [](const IPortableTensor *input, IPortableTensor *output) { - nnfw::cker::Logistic(getTensorShape(input), - reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); - }; - } - else - { - throw std::runtime_error{"ElementwiseActivationLayer(Logistic): unsupported data type"}; - } - break; - case ElementwiseActivationType::kReLU: - if (_input->data_type() == OperandType::FLOAT32) - { - if (alpha == std::numeric_limits<float>::infinity() && beta == 0.f) - { - _kernel = [](const IPortableTensor *input, IPortableTensor *output) { - nnfw::cker::ReLU(getTensorShape(input), - reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); - }; - } - else if (alpha == 6.f && beta == 0.f) - { - _kernel = [](const IPortableTensor *input, IPortableTensor *output) { - nnfw::cker::ReLU6(getTensorShape(input), - reinterpret_cast<const float *>(input->buffer()), - reinterpret_cast<float *>(output->buffer())); - }; - } - else - { - throw std::runtime_error( - "ElementwiseActivationLayer : This layer suppports only ReLU(0-inf) and ReLU6(0-6)"); - } - } - else - { - throw std::runtime_error{"ElementwiseActivationLayer(ReLU): unsupported data type"}; - } - break; - case ElementwiseActivationType::kTanh: - if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - PopulateLookupTable(op_type); - _kernel = std::bind(&ElementwiseActivationLayer::EvalUsingLookupTable, this, - std::placeholders::_1, std::placeholders::_2); - } - else if (_input->data_type() == OperandType::FLOAT32) - { - _kernel = [](const IPortableTensor *input, IPortableTensor *output) { - nnfw::cker::Tanh(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); - }; - } - else - { - throw std::runtime_error{"ElementwiseActivationLayer(Logistic): unsupported data type"}; - } - break; - default: - throw std::runtime_error("ElementwiseActivationLayer: unsupported op type"); - } -} - -void ElementwiseActivationLayer::run() { _kernel(_input, _output); } - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ElementwiseActivationLayer.h b/runtime/onert/backend/cpu/ops/ElementwiseActivationLayer.h deleted file mode 100644 index 3ef580041..000000000 --- a/runtime/onert/backend/cpu/ops/ElementwiseActivationLayer.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_ElementwiseActivationLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_ElementwiseActivationLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -enum class ElementwiseActivationType -{ - kLogistic, - kReLU, - kTanh -}; - -class ElementwiseActivationLayer : public ::onert::exec::IFunction -{ -public: - ElementwiseActivationLayer(); - -public: - void configure(const IPortableTensor *input, IPortableTensor *output, float alpha, float beta, - const ElementwiseActivationType op_type); - - void run() override; - - void PopulateLookupTable(const ElementwiseActivationType op_type); - - void EvalUsingLookupTable(const IPortableTensor *input, IPortableTensor *output); - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - uint8_t _table[256]; - std::function<void(const IPortableTensor *input, IPortableTensor *output)> _kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_ElementwiseActivationLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.cc b/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.cc deleted file mode 100644 index ea3c1e7cd..000000000 --- a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.cc +++ /dev/null @@ -1,151 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ElementwiseBinaryLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/LogicalOr.h> -#include <cker/operation/MaxMin.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -namespace -{ -template <typename T> -void logicalOrGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs, - IPortableTensor *output) -{ - if (!HaveSameShapes(lhs, rhs)) - { - nnfw::cker::LogicalOrBroadcast<T>( - getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()), getTensorShape(rhs), - reinterpret_cast<const T *>(rhs->buffer()), getTensorShape(output), - reinterpret_cast<T *>(output->buffer())); - } - else - { - nnfw::cker::LogicalOrElementwise<T>( - getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()), - reinterpret_cast<const T *>(rhs->buffer()), reinterpret_cast<T *>(output->buffer())); - } -} - -template <typename T> -void maximumGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output) -{ - nnfw::cker::Max<T>(getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()), - getTensorShape(rhs), reinterpret_cast<const T *>(rhs->buffer()), - getTensorShape(output), reinterpret_cast<T *>(output->buffer())); -} - -template <typename T> -void minimumGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output) -{ - nnfw::cker::Min<T>(getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()), - getTensorShape(rhs), reinterpret_cast<const T *>(rhs->buffer()), - getTensorShape(output), reinterpret_cast<T *>(output->buffer())); -} - -bool haveSameQauntInfo(const IPortableTensor *lhs, const IPortableTensor *rhs, - const IPortableTensor *output) -{ - return (lhs->data_scale() == rhs->data_scale() && lhs->data_scale() == output->data_scale()) && - (lhs->data_offset() == rhs->data_offset() && lhs->data_offset() == output->data_offset()); -} -} // namespace - -void ElementwiseBinaryLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs, - IPortableTensor *output, const ElementwiseBinaryType op_type) -{ - assert(lhs != nullptr); - assert(rhs != nullptr); - assert(output != nullptr); - - _lhs = lhs; - _rhs = rhs; - _output = output; - - switch (op_type) - { - case ElementwiseBinaryType::kLogicalOr: - if ((_lhs->data_type() == OperandType::BOOL8) && (_rhs->data_type() == OperandType::BOOL8)) - { - _kernel = logicalOrGeneric<bool>; - } - else - { - throw std::runtime_error{"LogicalOr: Unsupported data type"}; - } - break; - case ElementwiseBinaryType::kMax: - if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - if (!haveSameQauntInfo(_lhs, _rhs, _output)) - { - throw std::runtime_error("Max NYI for quantized"); - } - _kernel = maximumGeneric<uint8_t>; - } - else if (_lhs->data_type() == OperandType::FLOAT32) - { - _kernel = maximumGeneric<float>; - } - else - { - throw std::runtime_error{"Max: unsupported data type"}; - } - break; - case ElementwiseBinaryType::kMin: - if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - if (!haveSameQauntInfo(_lhs, _rhs, _output)) - { - throw std::runtime_error("Min NYI for quantized"); - } - _kernel = minimumGeneric<uint8_t>; - } - else if (_lhs->data_type() == OperandType::INT32) - { - _kernel = minimumGeneric<int32_t>; - } - else if (_lhs->data_type() == OperandType::FLOAT32) - { - _kernel = minimumGeneric<float>; - } - else - { - throw std::runtime_error{"Min: unsupported data type"}; - } - break; - default: - throw std::runtime_error{"ElementwiseBinary: Unsupported ElementwiseBinary type"}; - } -} - -void ElementwiseBinaryLayer::run() { _kernel(_lhs, _rhs, _output); } - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.h b/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.h deleted file mode 100644 index 052747a4c..000000000 --- a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_ELEMENTWISEBINARYLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_ELEMENTWISEBINARYLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -enum class ElementwiseBinaryType -{ - kLogicalAnd, - kLogicalOr, - kMax, - kMin, -}; - -class ElementwiseBinaryLayer : public ::onert::exec::IFunction -{ -public: - ElementwiseBinaryLayer() : _lhs(nullptr), _rhs(nullptr), _output(nullptr) - { - // DO NOTHING - } - -public: - void configure(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output, - const ElementwiseBinaryType op_type); - - void run() override; - -private: - const IPortableTensor *_lhs; - const IPortableTensor *_rhs; - IPortableTensor *_output; - std::function<void(const IPortableTensor *, const IPortableTensor *, IPortableTensor *)> _kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_ELEMENTWISEBINARYLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ElementwiseUnaryLayer.cc b/runtime/onert/backend/cpu/ops/ElementwiseUnaryLayer.cc deleted file mode 100644 index f8f89ab15..000000000 --- a/runtime/onert/backend/cpu/ops/ElementwiseUnaryLayer.cc +++ /dev/null @@ -1,336 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ElementwiseUnaryLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Elementwise.h> -#include <cker/operation/Erf.h> -#include <cker/operation/Exp.h> -#include <cker/operation/LogicalNot.h> -#include <cker/operation/Quantize.h> -#include <cker/operation/Round.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -namespace -{ -void absFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Abs(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -template <typename FromT> -void castPtr(const FromT *in, DataPtr out, int num_elements, ir::DataType data_type_out) -{ - switch (data_type_out) - { - case ir::DataType::FLOAT32: - std::transform(in, in + num_elements, out.f, [](FromT a) { return static_cast<float>(a); }); - return; - case ir::DataType::INT32: - std::transform(in, in + num_elements, out.i32, - [](FromT a) { return static_cast<int32_t>(a); }); - return; - case ir::DataType::UINT32: - std::transform(in, in + num_elements, out.u32, - [](FromT a) { return static_cast<uint32_t>(a); }); - return; - case ir::DataType::UINT8: - std::transform(in, in + num_elements, out.u8, - [](FromT a) { return static_cast<uint8_t>(a); }); - return; - case ir::DataType::BOOL8: - std::transform(in, in + num_elements, out.b, [](FromT a) { return static_cast<bool>(a); }); - return; - case ir::DataType::INT64: - std::transform(in, in + num_elements, out.i64, - [](FromT a) { return static_cast<int64_t>(a); }); - return; - default: - throw std::runtime_error("Cast: Not supported output type" + - std::to_string((int)data_type_out)); - } -} - -void cast(const IPortableTensor *input, IPortableTensor *output) -{ - auto input_buf = input->buffer(); - auto output_buf = output->buffer(); - const auto in = *reinterpret_cast<const DataPtr *>(&input_buf); - auto out = *reinterpret_cast<DataPtr *>(&output_buf); - - auto input_shape = getTensorShape(input); - auto output_shape = getTensorShape(output); - const auto num_elements = MatchingFlatSize(input_shape, output_shape); - - switch (input->data_type()) - { - case ir::DataType::FLOAT32: - castPtr(in.f, out, num_elements, output->data_type()); - return; - case ir::DataType::INT32: - castPtr(in.i32, out, num_elements, output->data_type()); - return; - case ir::DataType::UINT32: - castPtr(in.u32, out, num_elements, output->data_type()); - return; - case ir::DataType::UINT8: - castPtr(in.u8, out, num_elements, output->data_type()); - return; - case ir::DataType::BOOL8: - castPtr(in.b, out, num_elements, output->data_type()); - return; - case ir::DataType::INT64: - castPtr(in.i64, out, num_elements, output->data_type()); - return; - default: - throw std::runtime_error("Cast: unsupported data type" + - std::to_string((int)input->data_type())); - } -} - -void cosFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Cos(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -void expFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Exp(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -void erfFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Erf(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -void logFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Log(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -void logicalNot(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::LogicalNot(getTensorShape(input), reinterpret_cast<const bool *>(input->buffer()), - getTensorShape(output), reinterpret_cast<bool *>(output->buffer())); -} - -void negFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Neg(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -template <typename InputT, typename OutputT> -void affineQuantize(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Quantize(getTensorShape(input), reinterpret_cast<const InputT *>(input->buffer()), - getTensorShape(output), reinterpret_cast<OutputT *>(output->buffer()), - output->data_scale(), output->data_offset()); -} - -void roundFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Round(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -void rsqrtFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Rsqrt(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -void sinFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - nnfw::cker::Sin(getTensorShape(input), reinterpret_cast<const float *>(input->buffer()), - getTensorShape(output), reinterpret_cast<float *>(output->buffer())); -} - -template <typename T> void zerosLikeFloat32(const IPortableTensor *input, IPortableTensor *output) -{ - if (!HaveSameShapes(input, output)) - throw std::runtime_error{"ZerosLike: input and output shape don't match."}; - - auto element_size = getTensorShape(input).FlatSize(); - - memset(reinterpret_cast<T *>(output->buffer()), 0, element_size * sizeof(T)); -} -} // namespace - -void ElementwiseUnaryLayer::configure(const IPortableTensor *input, IPortableTensor *output, - const ElementwiseUnaryType op_type) -{ - assert(input != nullptr); - assert(output != nullptr); - - _input = input; - _output = output; - - switch (op_type) - { - case ElementwiseUnaryType::kAbs: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = absFloat32; - } - else - { - throw std::runtime_error{"Abs: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kCast: - _kernel = cast; - break; - case ElementwiseUnaryType::kCos: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = cosFloat32; - } - else - { - throw std::runtime_error{"Cos: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kExp: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = expFloat32; - } - else - { - throw std::runtime_error{"Exp: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kErf: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = erfFloat32; - } - else - { - throw std::runtime_error{"Exp: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kLog: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = logFloat32; - } - else - { - throw std::runtime_error{"Log: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kLogicalNot: - if ((input->data_type() == OperandType::BOOL8)) - { - _kernel = logicalNot; - } - else - { - throw std::runtime_error{"LogicalNot: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kNeg: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = negFloat32; - } - else - { - throw std::runtime_error{"Neg: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kQuantize: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = affineQuantize<float, uint8_t>; - } - else - { - throw std::runtime_error{"Quantize: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kRound: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = roundFloat32; - } - else - { - throw std::runtime_error{"Round: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kRSqrt: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = rsqrtFloat32; - } - else - { - throw std::runtime_error{"RSqrt: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kSin: - if ((input->data_type() == OperandType::FLOAT32)) - { - _kernel = sinFloat32; - } - else - { - throw std::runtime_error{"Sin: Unsupported data type"}; - } - break; - case ElementwiseUnaryType::kZerosLike: - if (input->data_type() == OperandType::FLOAT32) - { - _kernel = zerosLikeFloat32<float>; - } - else if (input->data_type() == OperandType::INT32) - { - _kernel = zerosLikeFloat32<int32_t>; - } - else - { - throw std::runtime_error{"ZerosLike: Unsupported data type"}; - } - break; - default: - throw std::runtime_error{"ElementwiseBinary: Unsupported ElementwiseBinary type"}; - } -} - -void ElementwiseUnaryLayer::run() { _kernel(_input, _output); } - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ElementwiseUnaryLayer.h b/runtime/onert/backend/cpu/ops/ElementwiseUnaryLayer.h deleted file mode 100644 index 74968386d..000000000 --- a/runtime/onert/backend/cpu/ops/ElementwiseUnaryLayer.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_ELEMENTWISEUNARYLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_ELEMENTWISEUNARYLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -enum class ElementwiseUnaryType -{ - kAbs, - kCast, - kCos, - kErf, - kExp, - kLog, - kLogicalNot, - kNeg, - kQuantize, - kRound, - kRSqrt, - kSin, - kZerosLike -}; - -class ElementwiseUnaryLayer : public ::onert::exec::IFunction -{ -public: - ElementwiseUnaryLayer() : _input(nullptr), _output(nullptr), _kernel() - { - // DO NOTHING - } - -public: - void configure(const IPortableTensor *input, IPortableTensor *output, - const ElementwiseUnaryType op_type); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - std::function<void(const IPortableTensor *, IPortableTensor *)> _kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_ELEMENTWISEUNARYLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ExpandDimsLayer.cc b/runtime/onert/backend/cpu/ops/ExpandDimsLayer.cc deleted file mode 100644 index b545e6743..000000000 --- a/runtime/onert/backend/cpu/ops/ExpandDimsLayer.cc +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ExpandDimsLayer.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -ExpandDimsLayer::ExpandDimsLayer() : _input(nullptr), _axis(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void ExpandDimsLayer::configure(const IPortableTensor *input, const IPortableTensor *axis, - IPortableTensor *output) -{ - _input = input; - _axis = axis; - _output = output; -} - -void ExpandDimsLayer::run() -{ - // TODO use _axis to calculate shape of output when _axis is not constant - size_t count = _input->total_size(); - memcpy(_output->buffer(), _input->buffer(), count); -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ExpandDimsLayer.h b/runtime/onert/backend/cpu/ops/ExpandDimsLayer.h deleted file mode 100644 index b5d4938b5..000000000 --- a/runtime/onert/backend/cpu/ops/ExpandDimsLayer.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef ExpandDi__ONERT_BACKEND_CPU_OPS_EXPANDDIMS_LAYER_H__ms -#define ExpandDi__ONERT_BACKEND_CPU_OPS_EXPANDDIMS_LAYER_H__ms - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ExpandDimsLayer : public ::onert::exec::IFunction -{ -public: - ExpandDimsLayer(); - -public: - void configure(const IPortableTensor *input, const IPortableTensor *axis, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_axis; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // ExpandDi__ONERT_BACKEND_CPU_OPS_EXPANDDIMS_LAYER_H__ms diff --git a/runtime/onert/backend/cpu/ops/FillLayer.cc b/runtime/onert/backend/cpu/ops/FillLayer.cc deleted file mode 100644 index 0a95ab005..000000000 --- a/runtime/onert/backend/cpu/ops/FillLayer.cc +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "FillLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Fill.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -FillLayer::FillLayer() : _input(nullptr), _value(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void FillLayer::configure(const IPortableTensor *input, const IPortableTensor *value, - IPortableTensor *output) -{ - _input = input; - _value = value; - _output = output; -} - -void FillLayer::run() -{ - switch (_output->data_type()) - { - case OperandType::FLOAT32: - nnfw::cker::Fill<float *>(getTensorShape(_input), reinterpret_cast<int *>(_input->buffer()), - reinterpret_cast<float *>(_value->buffer()), - getTensorShape(_output), - reinterpret_cast<float *>(_output->buffer())); - break; - case OperandType::INT32: - nnfw::cker::Fill<int32_t *>(getTensorShape(_input), reinterpret_cast<int *>(_input->buffer()), - reinterpret_cast<int32_t *>(_value->buffer()), - getTensorShape(_output), - reinterpret_cast<int32_t *>(_output->buffer())); - break; - case OperandType::UINT32: - nnfw::cker::Fill<uint32_t *>( - getTensorShape(_input), reinterpret_cast<int *>(_input->buffer()), - reinterpret_cast<uint32_t *>(_value->buffer()), getTensorShape(_output), - reinterpret_cast<uint32_t *>(_output->buffer())); - break; - default: - throw std::runtime_error{"Fill: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/FillLayer.h b/runtime/onert/backend/cpu/ops/FillLayer.h deleted file mode 100644 index 1f17d6b68..000000000 --- a/runtime/onert/backend/cpu/ops/FillLayer.h +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_FILLLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_FILLLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class FillLayer : public ::onert::exec::IFunction -{ -public: - FillLayer(); - - void configure(const IPortableTensor *input, const IPortableTensor *value, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_value; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_FILLLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/FullyConnectedLayer.cc b/runtime/onert/backend/cpu/ops/FullyConnectedLayer.cc deleted file mode 100644 index f873a3430..000000000 --- a/runtime/onert/backend/cpu/ops/FullyConnectedLayer.cc +++ /dev/null @@ -1,268 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "FullyConnectedLayer.h" - -#include "../Tensor.h" -#include <cker/operation/FullyConnected.h> -#include <cker/TensorUtils.h> -#include <misc/polymorphic_downcast.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -FullyConnectedLayer::FullyConnectedLayer() - : _input(nullptr), _weights(nullptr), _bias(nullptr), _output(nullptr), - _activation(ir::Activation::NONE), _temp_arena(new nnfw::cker::FCTempArena()), - _external_context(nullptr), _is_hybrid(false) -{ - // DO NOTHING -} - -FullyConnectedLayer::~FullyConnectedLayer() = default; - -void FullyConnectedLayer::fullyConnectedFloat32() -{ - float output_activation_min = 0, output_activation_max = 0; - CalculateActivationRange(_activation, &output_activation_min, &output_activation_max); - - nnfw::cker::FullyConnectedParams op_params; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - op_params.activation = convertActivationType(_activation); - - nnfw::cker::FullyConnected( - op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_weights), reinterpret_cast<const float *>(_weights->buffer()), - getTensorShape(_bias), reinterpret_cast<const float *>(_bias ? _bias->buffer() : nullptr), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); -} - -// executionMutex is used to protect concurrent access of non-threadsafe resources -// like gemmlowp::GemmContext. -void FullyConnectedLayer::fullyConnectedQuant8() -{ - double real_multiplier = 0.0; - int32_t output_multiplier = 0; - int32_t output_shift = 0; - int32_t output_activation_min = 0; - int32_t output_activation_max = 0; - GetQuantizedConvolutionMultiplier(_input, _weights, _bias, _output, &real_multiplier); - QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift); - CalculateActivationRangeUint8(_activation, _output, &output_activation_min, - &output_activation_max); - - nnfw::cker::FullyConnectedParams op_params; - op_params.input_offset = -_input->data_offset(); - op_params.weights_offset = -_weights->data_offset(); - op_params.output_offset = _output->data_offset(); - op_params.output_multiplier = output_multiplier; - op_params.output_shift = output_shift; - op_params.quantized_activation_min = output_activation_min; - op_params.quantized_activation_max = output_activation_max; - - nnfw::cker::FullyConnected( - op_params, getTensorShape(_input), reinterpret_cast<const uint8_t *>(_input->buffer()), - getTensorShape(_weights), reinterpret_cast<const uint8_t *>(_weights->buffer()), - getTensorShape(_bias), reinterpret_cast<const int32_t *>(_bias ? _bias->buffer() : nullptr), - getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer())); -} - -void FullyConnectedLayer::fullyConnectedHybrid() -{ - nnfw::cker::FCTempArena &temp_arena = *_temp_arena; - if (!temp_arena.prepared) - { - temp_arena.prepare(getTensorShape(_input), getTensorShape(_weights)); - } - - nnfw::cker::FullyConnectedParams op_params; - op_params.activation = convertActivationType(_activation); - op_params.weights_scale = _weights->data_scale(); - -#ifndef USE_RUY_GEMV - nnfw::cker::FullyConnectedHybrid( - op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_weights), reinterpret_cast<const int8_t *>(_weights->buffer()), - getTensorShape(_bias), reinterpret_cast<const float *>(_bias ? _bias->buffer() : nullptr), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()), temp_arena, - _external_context->ruy_context()); -#else - nnfw::cker::FullyConnectedHybrid( - op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_weights), - (_cached_weights) ? reinterpret_cast<const int8_t *>(_cached_weights) - : reinterpret_cast<const int8_t *>(_weights->buffer()), - getTensorShape(_bias), reinterpret_cast<const float *>(_bias ? _bias->buffer() : nullptr), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()), temp_arena, - _external_context->ruy_context()); - - if (_cached_weights == nullptr || _is_weights_freed) - return; - - // '_cached_weights is not nullptr and _is_weights_freed is false' means - // this weight shape is satisfied with the ruy kernel's prepack cache's condition. - // After entering here, it will not enter again except below the case - input is zero-vector - - // if input's elements are filled with zero, it by-passes(does not enter ruy-kernel path) - // so that handle this case - const int input_size = getTensorShape(_input).FlatSize(); - if (nnfw::cker::IsZeroVector(reinterpret_cast<float *>(_input->buffer()), input_size)) - return; - - auto weight_tensor = nnfw::misc::polymorphic_downcast<const Tensor *>(_weights); - - // This weight tensor could be other ops' const tensor. - // Therefore, below reference should be checked like following - auto tensor = const_cast<Tensor *>(weight_tensor); - if (tensor->buffer() == nullptr) // ref is already 0? - { - _is_weights_freed = true; - return; - } - - tensor->decrease_ref(); - if (tensor->buffer() == nullptr) // ref == 0? - { - _is_weights_freed = true; - } -#endif -} - -void FullyConnectedLayer::fullyConnectedSparseWeight() -{ - float output_activation_min = 0, output_activation_max = 0; - CalculateActivationRange(_activation, &output_activation_min, &output_activation_max); - - nnfw::cker::FullyConnectedParams op_params; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - op_params.activation = convertActivationType(_activation); - - const uint16_t *w1_segments = _weights->sparsity()->w1_segments(); - const uint16_t *w1_indices = _weights->sparsity()->w1_indices(); - - auto block_size = _weights->sparsity()->block_size(); - if (block_size.size() == 0) - { - nnfw::cker::FullyConnectedSparseWeightRandom( - op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_weights), reinterpret_cast<const float *>(_weights->buffer()), - getTensorShape(_bias), reinterpret_cast<const float *>(_bias ? _bias->buffer() : nullptr), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()), w1_segments, - w1_indices); - } - else if (block_size.size() == 2 && block_size[0] == 16 && block_size[1] == 1) - { - nnfw::cker::FullyConnectedSparseWeight16x1( - op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_weights), reinterpret_cast<const float *>(_weights->buffer()), - getTensorShape(_bias), reinterpret_cast<const float *>(_bias ? _bias->buffer() : nullptr), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()), w1_segments, - w1_indices); - } - else - throw std::runtime_error{"FullyConnected: unsupported sparsity"}; -} - -void FullyConnectedLayer::configure(const IPortableTensor *input, const IPortableTensor *weights, - const IPortableTensor *bias, ir::Activation activation, - IPortableTensor *output, - const std::shared_ptr<ExternalContext> &external_context) -{ - _input = input; - _weights = weights; - _bias = bias; - _activation = activation; - _output = output; - _is_hybrid = input->data_type() == OperandType::FLOAT32 && - weights->data_type() == OperandType::QUANT_INT8_SYMM; - _external_context = external_context; -} - -void FullyConnectedLayer::run() -{ - if (_is_hybrid) - { - fullyConnectedHybrid(); - } - else if (_weights->sparsity()) - { - fullyConnectedSparseWeight(); - } - else if (_input->data_type() == OperandType::FLOAT32) - { - fullyConnectedFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - fullyConnectedQuant8(); - } - else - { - throw std::runtime_error{"FullyConnected: unsupported data type"}; - } -} - -void FullyConnectedLayer::prepare() -{ - if (_bias && _bias->is_constant()) - { - const int bias_size = getTensorShape(_bias).FlatSize(); - if (nnfw::cker::IsZeroVector(reinterpret_cast<float *>(_bias->buffer()), bias_size)) - { - _bias = nullptr; - } - } - -#if (defined(__ARM_NEON__) || defined(__ARM_NEON)) && defined(USE_RUY_GEMV) - // TODO This is workaround - // The only fc hybrid will use ruy kernel - if (_input->data_type() != OperandType::FLOAT32 || - _weights->data_type() != OperandType::QUANT_INT8_SYMM) - { - return; - } - - // NOTE. The condition to enable caching on ruy kernel can be changed according to ruy's version - - // If input is dynamic, it changes total size of input - // If weights is not constant, weights cannot be cached - if (_input->is_dynamic() || !_weights->is_constant()) - return; - - const int rows = getTensorShape(_weights).Dims(0); - if (rows % 4 == 0) - { - // TODO If it's possible to extract precaching from ruy kernel, - // place this instead of below code - - // buffer will be used by ruy kernel as a cache key - _cached_weights = _weights->buffer(); - } -#endif -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/FullyConnectedLayer.h b/runtime/onert/backend/cpu/ops/FullyConnectedLayer.h deleted file mode 100644 index f1242677c..000000000 --- a/runtime/onert/backend/cpu/ops/FullyConnectedLayer.h +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_FULLYCONNECTEDLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_FULLYCONNECTEDLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "../ExternalContext.h" -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace nnfw -{ -namespace cker -{ -class FCTempArena; -} -} // namespace nnfw - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class FullyConnectedLayer : public ::onert::exec::IFunction -{ -public: - FullyConnectedLayer(); - ~FullyConnectedLayer(); - -public: - void fullyConnectedFloat32(); - - void fullyConnectedQuant8(); - - void fullyConnectedHybrid(); - - void fullyConnectedSparseWeight(); - - void configure(const IPortableTensor *input, const IPortableTensor *weights, - const IPortableTensor *bias, ir::Activation activation, IPortableTensor *output, - const std::shared_ptr<ExternalContext> &external_context); - - void run() override; - - void prepare() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_weights; - const IPortableTensor *_bias; - IPortableTensor *_output; - - ir::Activation _activation; - std::unique_ptr<nnfw::cker::FCTempArena> _temp_arena; - - std::shared_ptr<ExternalContext> _external_context; - - bool _is_hybrid; - -#ifdef USE_RUY_GEMV - uint8_t *_cached_weights = nullptr; // weights to be cached and a key - bool _is_weights_freed = false; // is weights freed? -#endif -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_FULLYCONNECTEDLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/FusedBatchNormLayer.cc b/runtime/onert/backend/cpu/ops/FusedBatchNormLayer.cc deleted file mode 100644 index c2c592db7..000000000 --- a/runtime/onert/backend/cpu/ops/FusedBatchNormLayer.cc +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "FusedBatchNormLayer.h" - -#include <cker/operation/FusedBatchNorm.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -FusedBatchNormLayer::FusedBatchNormLayer() - : _inputs(), _output(nullptr), _epsilon(0), _is_training(true), - _fusedbatchnorm_kernel(new nnfw::cker::FusedBatchNorm()) -{ - // DO NOTHING -} - -FusedBatchNormLayer::~FusedBatchNormLayer() = default; - -void FusedBatchNormLayer::fusedbatchnormFloat32() -{ - uint32_t num_inputs = _inputs.size(); - nnfw::cker::FusedBatchNorm &kernel = *_fusedbatchnorm_kernel; - - kernel.prepare(); - - std::vector<nnfw::cker::Shape> inputShapes; - std::vector<const float *> inputFloatPtrs; - - for (uint32_t i = 0; i < num_inputs; i++) - { - inputShapes.emplace_back(getTensorShape(_inputs[i])); - inputFloatPtrs.emplace_back(reinterpret_cast<const float *>(_inputs[i]->buffer())); - } - - nnfw::cker::FusedBatchNormParams param; - - param.epsilon = _epsilon; - param.is_training = _is_training; - param.data_format = _data_format; - - kernel(inputShapes, inputFloatPtrs, getTensorShape(_output), - reinterpret_cast<float *>(_output->buffer()), param); -} - -void FusedBatchNormLayer::run() -{ - if (_output->data_type() == OperandType::FLOAT32) - { - fusedbatchnormFloat32(); - } - else - { - throw std::runtime_error{"FusedBatchNorm: unsupported data type"}; - } -} - -void FusedBatchNormLayer::configure(const std::vector<const IPortableTensor *> &inputs, - float epsilon, bool is_training, std::string data_format, - IPortableTensor *output) -{ - assert(inputs.size() > 0); - assert(output != nullptr); - - _inputs = inputs; - _output = output; - _epsilon = epsilon; - _is_training = is_training; - _data_format = data_format; -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/FusedBatchNormLayer.h b/runtime/onert/backend/cpu/ops/FusedBatchNormLayer.h deleted file mode 100644 index d42b0c900..000000000 --- a/runtime/onert/backend/cpu/ops/FusedBatchNormLayer.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_FUSEDBATCHNORM_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_FUSEDBATCHNORM_LAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> -#include <functional> -#include <memory> - -namespace nnfw -{ -namespace cker -{ -class FusedBatchNorm; -} -} // namespace nnfw - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class FusedBatchNormLayer : public ::onert::exec::IFunction -{ -public: - FusedBatchNormLayer(); - ~FusedBatchNormLayer(); - -public: - void fusedbatchnormFloat32(); - - void configure(const std::vector<const IPortableTensor *> &inputs, float epsilon, - bool is_training, std::string data_format, IPortableTensor *output); - - void run() override; - -private: - std::vector<const IPortableTensor *> _inputs; - IPortableTensor *_output; - float _epsilon; - bool _is_training; - std::string _data_format; - - std::unique_ptr<nnfw::cker::FusedBatchNorm> _fusedbatchnorm_kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_FUSEDBATCHNORM_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/GatherLayer.cc b/runtime/onert/backend/cpu/ops/GatherLayer.cc deleted file mode 100644 index 641daa972..000000000 --- a/runtime/onert/backend/cpu/ops/GatherLayer.cc +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "GatherLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Gather.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -void GatherLayer::configure(const IPortableTensor *input, const IPortableTensor *indices, - IPortableTensor *output, int32_t axis) -{ - _input = input; - _indices = indices; - _axis = axis; - _output = output; -} - -template <typename InputType> void GatherLayer::runByInputType() -{ - using OutputType = InputType; - nnfw::cker::GatherParams op_params; - op_params.axis = _axis; - - switch (_indices->data_type()) - { - case OperandType::INT32: - { - using IndicesType = int32_t; - - nnfw::cker::Gather<InputType, IndicesType>( - op_params, getTensorShape(_input), reinterpret_cast<const InputType *>(_input->buffer()), - getTensorShape(_indices), reinterpret_cast<const IndicesType *>(_indices->buffer()), - getTensorShape(_output), reinterpret_cast<OutputType *>(_output->buffer())); - break; - } - case OperandType::INT64: - { - using IndicesType = int64_t; - - nnfw::cker::Gather<InputType, IndicesType>( - op_params, getTensorShape(_input), reinterpret_cast<const InputType *>(_input->buffer()), - getTensorShape(_indices), reinterpret_cast<const IndicesType *>(_indices->buffer()), - getTensorShape(_output), reinterpret_cast<OutputType *>(_output->buffer())); - break; - } - default: - throw std::runtime_error("Gather: unsupported indices data type"); - } -} - -void GatherLayer::run() -{ - switch (_input->data_type()) - { - case OperandType::FLOAT32: - runByInputType<float>(); - break; - case OperandType::QUANT_UINT8_ASYMM: - runByInputType<uint8_t>(); - break; - case OperandType::INT32: - runByInputType<int32_t>(); - break; - default: - throw std::runtime_error("Gather: unsupported input data type"); - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/GatherLayer.h b/runtime/onert/backend/cpu/ops/GatherLayer.h deleted file mode 100644 index 8fe80cc2b..000000000 --- a/runtime/onert/backend/cpu/ops/GatherLayer.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_GATHERLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_GATHERLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class GatherLayer : public ::onert::exec::IFunction -{ -public: - GatherLayer() : _input{nullptr}, _indices{nullptr}, _output{nullptr}, _axis{-1} - { - // DO NOTHING - } - -public: - void configure(const IPortableTensor *input, const IPortableTensor *indices, - IPortableTensor *output, int32_t axis); - - void run() override; - -private: - template <typename OpType> void runByInputType(); - -private: - const IPortableTensor *_input; - const IPortableTensor *_indices; - IPortableTensor *_output; - - int32_t _axis; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_GATHERLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/L2NormLayer.cc b/runtime/onert/backend/cpu/ops/L2NormLayer.cc deleted file mode 100644 index 0d99b0586..000000000 --- a/runtime/onert/backend/cpu/ops/L2NormLayer.cc +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "L2NormLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/L2Normalize.h> -#include <cker/Types.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -void L2NormLayer::configure(const IPortableTensor *input, IPortableTensor *output) -{ - assert(input != nullptr); - assert(output != nullptr); - - _input = input; - _output = output; -} - -void L2NormLayer::run() -{ - switch (_input->data_type()) - { - case OperandType::FLOAT32: - nnfw::cker::L2NormalizeFloat32( - getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); - break; - - case OperandType::QUANT_UINT8_ASYMM: - { - nnfw::cker::L2NormParams params; - assert(_input->data_offset() == 128); - params.input_zero_point = _input->data_offset(); - nnfw::cker::L2NormalizeQuant8( - params, getTensorShape(_input), reinterpret_cast<const uint8_t *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer())); - } - break; - - default: - throw std::runtime_error{"L2Norm: Unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/L2NormLayer.h b/runtime/onert/backend/cpu/ops/L2NormLayer.h deleted file mode 100644 index 63f2d1133..000000000 --- a/runtime/onert/backend/cpu/ops/L2NormLayer.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_L2NORM_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_L2NORM_LAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -class L2NormLayer : public ::onert::exec::IFunction -{ -public: - L2NormLayer() : _input(nullptr), _output(nullptr) - { - // Nothing - } - -public: - void configure(const IPortableTensor *_input, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_L2NORM_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/LogSoftMaxLayer.cc b/runtime/onert/backend/cpu/ops/LogSoftMaxLayer.cc deleted file mode 100644 index 1d7ee6caa..000000000 --- a/runtime/onert/backend/cpu/ops/LogSoftMaxLayer.cc +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "LogSoftMaxLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/LogSoftMax.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -LogSoftMaxLayer::LogSoftMaxLayer() : _input(nullptr), _output(nullptr), _beta(0.0), _axis(0) -{ - // DO NOTHING -} - -void LogSoftMaxLayer::PopulateLookupTable(const float kBeta) -{ - const float scale = -_input->data_scale() * kBeta; - const int32_t max_uint8 = std::numeric_limits<uint8_t>::max(); - for (int32_t val = 0; val <= max_uint8; ++val) - { - _table[max_uint8 - val] = expf(scale * val); - } -} - -void LogSoftMaxLayer::logsoftmaxFloat32() -{ - nnfw::cker::SoftmaxParams op_params; - op_params.beta = _beta; - op_params.axis = _axis; - nnfw::cker::LogSoftmax(op_params, getTensorShape(_input), - reinterpret_cast<const float *>(_input->buffer()), getTensorShape(_output), - reinterpret_cast<float *>(_output->buffer())); -} - -void LogSoftMaxLayer::logsoftmaxQuant8() -{ - nnfw::cker::SoftmaxParams op_params; - op_params.beta = _beta; - op_params.axis = _axis; - op_params.table = _table; - op_params.zero_point = _output->data_offset(); - op_params.scale = _output->data_scale(); - nnfw::cker::LogSoftmax(op_params, _input->data_scale(), getTensorShape(_input), - reinterpret_cast<const uint8_t *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer())); -} - -void LogSoftMaxLayer::configure(const IPortableTensor *input, const float beta, const int axis, - IPortableTensor *output) -{ - _input = input; - _output = output; - _beta = beta; - _axis = axis; - if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - PopulateLookupTable(_beta); - } -} - -void LogSoftMaxLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - logsoftmaxFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - logsoftmaxQuant8(); - } - else - { - throw std::runtime_error{"LogSoftmax : unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/LogSoftMaxLayer.h b/runtime/onert/backend/cpu/ops/LogSoftMaxLayer.h deleted file mode 100644 index 1533f3361..000000000 --- a/runtime/onert/backend/cpu/ops/LogSoftMaxLayer.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_LOGSOFTMAXLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_LOGSOFTMAXLAYER_H__ - -#include "../Tensor.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class LogSoftMaxLayer : public ::onert::exec::IFunction -{ -public: - LogSoftMaxLayer(); - -public: - void logsoftmaxFloat32(); - - void logsoftmaxQuant8(); - - void configure(const IPortableTensor *input, const float beta, const int axis, - IPortableTensor *output); - - void run(); - - void PopulateLookupTable(const float kBeta); - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - - float _beta; - int _axis; - float _table[256]; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_LOGSOFTMAXLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/MatrixBandPartLayer.cc b/runtime/onert/backend/cpu/ops/MatrixBandPartLayer.cc deleted file mode 100644 index b770cce5d..000000000 --- a/runtime/onert/backend/cpu/ops/MatrixBandPartLayer.cc +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "MatrixBandPartLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/MatrixBandPart.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -MatrixBandPartLayer::MatrixBandPartLayer() - : _input(nullptr), _num_lower_diag(nullptr), _num_upper_diag(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void MatrixBandPartLayer::matrixBandPartFloat32() -{ - if (_num_lower_diag->data_type() == OperandType::INT64) - { - nnfw::cker::MatrixBandPart<int64_t>( - *reinterpret_cast<const int64_t *>(_num_lower_diag->buffer()), - *reinterpret_cast<const int64_t *>(_num_upper_diag->buffer()), getTensorShape(_input), - reinterpret_cast<const float *>(_input->buffer()), getTensorShape(_output), - reinterpret_cast<float *>(_output->buffer())); - } - else - { - nnfw::cker::MatrixBandPart<int32_t>( - *reinterpret_cast<const int32_t *>(_num_lower_diag->buffer()), - *reinterpret_cast<const int32_t *>(_num_upper_diag->buffer()), getTensorShape(_input), - reinterpret_cast<const float *>(_input->buffer()), getTensorShape(_output), - reinterpret_cast<float *>(_output->buffer())); - } -} - -void MatrixBandPartLayer::matrixBandPartQuant8() { throw std::runtime_error{"NYI"}; } - -void MatrixBandPartLayer::configure(const IPortableTensor *input, - const IPortableTensor *num_lower_diag, - const IPortableTensor *num_upper_diag, IPortableTensor *output) -{ - _input = input; - _num_lower_diag = num_lower_diag; - _num_upper_diag = num_upper_diag; - _output = output; -} - -void MatrixBandPartLayer::run() -{ - if (_num_lower_diag->data_type() != _num_upper_diag->data_type()) - { - throw std::runtime_error{"MatrixBandpart: num_lower and num_upper must have the same type"}; - } - - if (_input->data_type() == OperandType::FLOAT32) - { - matrixBandPartFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - matrixBandPartQuant8(); - } - else - { - throw std::runtime_error{"MatrixBandpart: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/MatrixBandPartLayer.h b/runtime/onert/backend/cpu/ops/MatrixBandPartLayer.h deleted file mode 100644 index 9dcc6b277..000000000 --- a/runtime/onert/backend/cpu/ops/MatrixBandPartLayer.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_MATRIXBANDPARTLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_MATRIXBANDPARTLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class MatrixBandPartLayer : public ::onert::exec::IFunction -{ -public: - MatrixBandPartLayer(); - -public: - void matrixBandPartFloat32(); - - void matrixBandPartQuant8(); - - void configure(const IPortableTensor *input, const IPortableTensor *num_lower_diag, - const IPortableTensor *num_upper_diag, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_num_lower_diag; - const IPortableTensor *_num_upper_diag; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_MATRIXBANDPARTLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/MeanLayer.cc b/runtime/onert/backend/cpu/ops/MeanLayer.cc deleted file mode 100644 index 4921ac748..000000000 --- a/runtime/onert/backend/cpu/ops/MeanLayer.cc +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "MeanLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/ReduceMean.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -MeanLayer::MeanLayer() : _input(nullptr), _axes(nullptr), _output(nullptr), _keep_dims(false) -{ - // DO NOTHING -} - -void MeanLayer::MeanFloat32() -{ - nnfw::cker::Mean(getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()), - getReducerAxes(_axes)); -} - -void MeanLayer::MeanQuant8() -{ - nnfw::cker::MeanQ8Asymm(getTensorShape(_input), - reinterpret_cast<const uint8_t *>(_input->buffer()), _input->data_scale(), - _input->data_offset(), getTensorShape(_output), - reinterpret_cast<uint8_t *>(_output->buffer()), _output->data_scale(), - _output->data_offset(), getReducerAxes(_axes)); -} - -void MeanLayer::configure(const IPortableTensor *input, const IPortableTensor *axes, - IPortableTensor *output, bool keep_dims) -{ - _input = input; - _axes = axes; - _output = output; - _keep_dims = keep_dims; -} - -void MeanLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - MeanFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - MeanQuant8(); - } - else - { - throw std::runtime_error{"Mean: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/MeanLayer.h b/runtime/onert/backend/cpu/ops/MeanLayer.h deleted file mode 100644 index 3e95c1203..000000000 --- a/runtime/onert/backend/cpu/ops/MeanLayer.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_MEANLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_MEANLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class MeanLayer : public ::onert::exec::IFunction -{ -public: - MeanLayer(); - -public: - void MeanFloat32(); - - void MeanQuant8(); - - void configure(const IPortableTensor *input, const IPortableTensor *axes, IPortableTensor *output, - bool keep_dims); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_axes; - IPortableTensor *_output; - bool _keep_dims; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_MEANLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/OneHotLayer.cc b/runtime/onert/backend/cpu/ops/OneHotLayer.cc deleted file mode 100644 index 2a82b00ee..000000000 --- a/runtime/onert/backend/cpu/ops/OneHotLayer.cc +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OneHotLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/OneHot.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -template <typename T> void OneHotLayer::oneHotImpl() -{ - // It assumes index is int32_t type. - nnfw::cker::OneHot<T, int32_t>( - *reinterpret_cast<const int32_t *>(_depth->buffer()), - *reinterpret_cast<T *>(_on_value->buffer()), *reinterpret_cast<T *>(_off_value->buffer()), - _axis, getTensorShape(_indices), reinterpret_cast<const int32_t *>(_indices->buffer()), - getTensorShape(_output), reinterpret_cast<T *>(_output->buffer())); -} - -void OneHotLayer::configure(const IPortableTensor *indices, const IPortableTensor *depth, - const IPortableTensor *on_value, const IPortableTensor *off_value, - IPortableTensor *output, const int32_t axis) -{ - _indices = indices; - _output = output; - _depth = depth; - _on_value = on_value; - _off_value = off_value; - _axis = axis; -} - -void OneHotLayer::run() -{ - if (_output->data_type() == OperandType::FLOAT32) - { - oneHotImpl<float>(); - } - else - { - throw std::runtime_error{"OneHot: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/OneHotLayer.h b/runtime/onert/backend/cpu/ops/OneHotLayer.h deleted file mode 100644 index c05498440..000000000 --- a/runtime/onert/backend/cpu/ops/OneHotLayer.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_ONEHOTLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_ONEHOTLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class OneHotLayer : public ::onert::exec::IFunction -{ -public: - OneHotLayer() - : _indices(nullptr), _depth(nullptr), _on_value(nullptr), _off_value(nullptr), - _output(nullptr), _axis(-1) - { - // DO NOTHING - } - -public: - template <typename T> void oneHotImpl(); - - void configure(const IPortableTensor *indices, const IPortableTensor *depth, - const IPortableTensor *on_value, const IPortableTensor *off_value, - IPortableTensor *output, int32_t axis); - - void run() override; - -private: - const IPortableTensor *_indices; - const IPortableTensor *_depth; - const IPortableTensor *_on_value; - const IPortableTensor *_off_value; - IPortableTensor *_output; - - int32_t _axis; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_ONEHOTLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/OperationUtils.cc b/runtime/onert/backend/cpu/ops/OperationUtils.cc deleted file mode 100644 index 2eee6dc85..000000000 --- a/runtime/onert/backend/cpu/ops/OperationUtils.cc +++ /dev/null @@ -1,266 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OperationUtils.h" - -#include <algorithm> -#include <cassert> -#include <cmath> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -uint32_t getNumberOfDimensions(const IPortableTensor *tensor) -{ - assert(tensor); - return tensor->num_dimensions(); -} - -uint32_t getNumberOfElements(const IPortableTensor *tensor) -{ - assert(tensor); - uint32_t count = 1; - for (size_t i = 0; i < tensor->num_dimensions(); i++) - { - count *= tensor->dimension(i); - } - return count; -} - -uint32_t getSizeOfDimension(const IPortableTensor *tensor, uint32_t dimensionIdx) -{ - assert(tensor); - if (dimensionIdx >= tensor->num_dimensions()) - { - // TODO, log the error - return 0; - } - return tensor->dimension(dimensionIdx); -} - -void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift) -{ - if (double_multiplier == 0.) - { - *quantized_multiplier = 0; - *shift = 0; - return; - } - const double q = std::frexp(double_multiplier, shift); - auto q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31))); - - assert(q_fixed <= (1ll << 31)); - if (q_fixed == (1ll << 31)) - { - q_fixed /= 2; - ++*shift; - } - assert(q_fixed <= std::numeric_limits<int32_t>::max()); - *quantized_multiplier = static_cast<int32_t>(q_fixed); -} - -void GetQuantizedConvolutionMultiplier(const IPortableTensor *input, const IPortableTensor *filter, - const IPortableTensor *bias, const IPortableTensor *output, - double *multiplier) -{ - const double input_product_scale = input->data_scale() * filter->data_scale(); - const double bias_scale = (bias != nullptr) ? bias->data_scale() : input_product_scale; - const double output_scale = output->data_scale(); - // The following conditions must be guaranteed by the training pipeline. - UNUSED_RELEASE(bias_scale); - assert(std::abs(input_product_scale - bias_scale) <= - 1e-6 * std::min(input_product_scale, bias_scale)); - assert(input_product_scale >= 0); - assert(input_product_scale < output_scale); - *multiplier = input_product_scale / output_scale; -} - -void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier, - int *left_shift) -{ - assert(double_multiplier > 1.); - const double q = std::frexp(double_multiplier, left_shift); - int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31))); - assert(q_fixed <= (1ll << 31)); - if (q_fixed == (1ll << 31)) - { - q_fixed /= 2; - ++*left_shift; - } - assert(*left_shift >= 0); - assert(q_fixed <= std::numeric_limits<int32_t>::max()); - *quantized_multiplier = static_cast<int32_t>(q_fixed); -} - -void CalculateActivationRangeUint8(ir::Activation activation, const IPortableTensor *output, - int32_t *act_min, int32_t *act_max) -{ - const int32_t qmin = std::numeric_limits<uint8_t>::min(); - const int32_t qmax = std::numeric_limits<uint8_t>::max(); - const auto scale = output->data_scale(); - const auto zero_point = output->data_offset(); - auto quantize = [scale, zero_point](float f) { - return zero_point + static_cast<int32_t>(std::round(f / scale)); - }; - if (activation == ir::Activation::RELU) - { - *act_min = std::max(qmin, quantize(0.0)); - *act_max = qmax; - } - else if (activation == ir::Activation::RELU6) - { - *act_min = std::max(qmin, quantize(0.0)); - *act_max = std::min(qmax, quantize(6.0)); - } - else if (activation == ir::Activation::RELU1) - { - *act_min = std::max(qmin, quantize(-1.0)); - *act_max = std::min(qmax, quantize(1.0)); - } - else if (activation == ir::Activation::SIGMOID) - { - *act_min = std::max(qmin, quantize(0.0)); - *act_max = std::min(qmax, quantize(1.0)); - } - else if (activation == ir::Activation::NONE) - { - *act_min = qmin; - *act_max = qmax; - } - else - { - std::cout << "Unsupported fused activation function." << std::endl; - } -} - -bool HaveSameShapes(const IPortableTensor *input1, const IPortableTensor *input2) -{ - if (input1 == input2) - return true; - if (input2 == NULL || input2 == NULL) - return false; - - if (input1 == NULL) - { - return (getNumberOfDimensions(input2) == 0); - } - - if (getNumberOfDimensions(input1) != getNumberOfDimensions(input2)) - return false; - - for (uint32_t i = 0; i < getNumberOfDimensions(input1); i++) - if (input1->dimension(i) != input2->dimension(i)) - return false; - - return true; -} - -int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift) -{ - const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) * - (1ll << (31 - input_integer_bits)) / (1ll << input_left_shift); - // Tighten bound using floor. Suppose that we could use the exact value. - // After scaling the difference, the result would be at the maximum. Thus we - // must ensure that our value has lower magnitude. - return static_cast<int32_t>(std::floor(max_input_rescaled)); -} - -uint32_t sizeOfData(OperandType type, const std::vector<int32_t> &dimensions) -{ - uint32_t size = 4; - - switch (type) - { - case OperandType::FLOAT32: - case OperandType::INT32: - case OperandType::UINT32: - size = 4; - break; - case OperandType::BOOL8: - case OperandType::QUANT_UINT8_ASYMM: - case OperandType::QUANT_INT8_SYMM: - size = 1; - break; - case OperandType::INT64: - size = 8; - break; - default: - throw std::runtime_error("Not supported operand type."); - break; - } - - for (auto d : dimensions) - { - assert(d >= 0); - size *= static_cast<uint32_t>(d); - } - - return size; -} - -nnfw::cker::PaddingType getPaddingType(ir::PaddingType ir_padding_type) -{ - switch (ir_padding_type) - { - case ir::PaddingType::EXPLICIT: - return nnfw::cker::PaddingType::kNone; - case ir::PaddingType::SAME: - return nnfw::cker::PaddingType::kSame; - case ir::PaddingType::VALID: - return nnfw::cker::PaddingType::kValid; - default: - throw std::runtime_error("Wrong padding type."); - break; - } -} - -std::vector<int32_t> getReducerAxes(const IPortableTensor *axes) -{ - std::vector<int32_t> ret; - - assert(axes->layout() == ir::Layout::NHWC); - assert(axes->dimension(0) == axes->getShape().num_elements()); - switch (axes->data_type()) - { - case ir::DataType::INT32: - { - for (size_t i = 0; i < axes->dimension(0); ++i) - ret.emplace_back(*(reinterpret_cast<const int32_t *>(axes->buffer()) + i)); - break; - } - case ir::DataType::INT64: - { - for (size_t i = 0; i < axes->dimension(0); ++i) - ret.emplace_back(*(reinterpret_cast<const int64_t *>(axes->buffer()) + i)); - break; - } - default: - throw std::runtime_error("getReducerAxes: Not supported data type"); - break; - } - return ret; -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/OperationUtils.h b/runtime/onert/backend/cpu/ops/OperationUtils.h deleted file mode 100644 index eb24dd43c..000000000 --- a/runtime/onert/backend/cpu/ops/OperationUtils.h +++ /dev/null @@ -1,211 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__ -#define __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__ - -#include <backend/IPortableTensor.h> - -#include <cker/Shape.h> -#include <cker/Types.h> -#include <iostream> -#include <ir/DataType.h> -#include <ir/InternalType.h> -#include <ir/Operand.h> -#include <ir/Padding.h> - -#include <limits> -#include <vector> - -using OperandType = onert::ir::DataType; - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -union DataPtr { - uint8_t *u8; - int8_t *i8; - uint32_t *u32; - int32_t *i32; - bool *b; - float *f; - int64_t *i64; - void *v; -}; - -union ConstDataPtr { - const uint8_t *u8; - const int8_t *i8; - const uint32_t *u32; - const int32_t *i32; - const bool *b; - const float *f; - const int64_t *i64; - const void *v; -}; - -uint32_t getNumberOfDimensions(const IPortableTensor *tensor); - -uint32_t getNumberOfElements(const IPortableTensor *tensor); - -uint32_t getSizeOfDimension(const IPortableTensor *tensor, uint32_t dimensionIdx); - -inline nnfw::cker::Shape getExtendedTensorShape(const IPortableTensor *tensor) -{ - assert(tensor); - const int32_t extended_rank = 4; - int32_t raw_shape[extended_rank]; - uint32_t src = extended_rank - tensor->num_dimensions(); - for (uint32_t i = 0; i < extended_rank; ++i) - { - if (i < src) - { - raw_shape[i] = 1; - } - else - { - raw_shape[i] = tensor->dimension(i - src); - } - } - - return nnfw::cker::Shape(extended_rank, raw_shape); -} - -inline nnfw::cker::Shape getTensorShape(const IPortableTensor *tensor) -{ - if (tensor == nullptr) - return nnfw::cker::Shape(); - - const ir::Shape &shape = tensor->get_info().shape(); - - assert(tensor->layout() == ir::Layout::NHWC); - - auto rank = shape.rank(); - nnfw::cker::Shape ret(rank); - auto data = ret.DimsData(); - for (int i = 0; i < rank; ++i) - { - data[i] = shape.dim(i); - } - return ret; -} - -inline nnfw::cker::FusedActivationFunctionType -convertActivationType(const ir::Activation activation) -{ - switch (activation) - { - case ir::Activation::NONE: - return nnfw::cker::FusedActivationFunctionType::kNone; - case ir::Activation::RELU: - return nnfw::cker::FusedActivationFunctionType::kRelu; - case ir::Activation::RELU1: - return nnfw::cker::FusedActivationFunctionType::kRelu1; - case ir::Activation::RELU6: - return nnfw::cker::FusedActivationFunctionType::kRelu6; - default: - throw std::runtime_error{"CPU backend: Cannot convert activation type"}; - } -} - -inline int32_t getAxis(uint32_t rank, int32_t axis, ir::Layout frontend_layout) -{ - auto ret = axis; - - if (axis < 0) - { - ret += rank; - } - - // NCHW -> NHWC - if (frontend_layout == ir::Layout::NCHW) - { - int32_t permutation[4] = {0, 3, 1, 2}; - ret = permutation[ret]; - } - - return ret; -} - -void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift); - -void GetQuantizedConvolutionMultiplier(const IPortableTensor *inputDescr, - const IPortableTensor *filterDescr, - const IPortableTensor *biasDescr, - const IPortableTensor *outputDescr, double *multiplier); - -void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier, - int *left_shift); - -template <typename T> -void CalculateActivationRange(ir::Activation activation, T *activation_min, T *activation_max) -{ - if (activation == ir::Activation::RELU) - { - *activation_min = 0; - *activation_max = std::numeric_limits<T>::max(); - } - else if (activation == ir::Activation::RELU6) - { - *activation_min = 0; - *activation_max = 6; - } - else if (activation == ir::Activation::RELU1) - { - *activation_min = -1; - *activation_max = 1; - } - else if (activation == ir::Activation::SIGMOID) - { - *activation_min = 0; - *activation_max = 1; - } - else if (activation == ir::Activation::NONE) - { - *activation_min = std::numeric_limits<T>::lowest(); - *activation_max = std::numeric_limits<T>::max(); - } - else - { - std::cout << "Unsupported fused activation function." << std::endl; - } -} - -void CalculateActivationRangeUint8(ir::Activation activation, const IPortableTensor *output, - int32_t *act_min, int32_t *act_max); - -bool HaveSameShapes(const IPortableTensor *input1, const IPortableTensor *input2); - -int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift); - -uint32_t sizeOfData(OperandType type, const std::vector<int32_t> &dimensions); - -nnfw::cker::PaddingType getPaddingType(ir::PaddingType ir_padding_type); - -std::vector<int32_t> getReducerAxes(const IPortableTensor *axes); - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__ diff --git a/runtime/onert/backend/cpu/ops/PackLayer.cc b/runtime/onert/backend/cpu/ops/PackLayer.cc deleted file mode 100644 index 314b192a2..000000000 --- a/runtime/onert/backend/cpu/ops/PackLayer.cc +++ /dev/null @@ -1,96 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PackLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Pack.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -PackLayer::PackLayer() : _inputs(), _output(nullptr), _axis(0) -{ - // DO NOTHING -} - -template <typename T> void PackLayer::packImpl() -{ - uint32_t num_inputs = _inputs.size(); - nnfw::cker::PackParams op_params; - op_params.axis = _axis; - op_params.inputs_count = num_inputs; - - std::vector<nnfw::cker::Shape *> inputDimsPtr; - std::vector<nnfw::cker::Shape> inputDims; - inputDimsPtr.reserve(num_inputs); - inputDims.reserve(num_inputs); - - for (uint32_t i = 0; i < num_inputs; i++) - { - inputDims.push_back(getTensorShape(_inputs[i])); - inputDimsPtr.push_back(&inputDims[i]); - } - - std::vector<const T *> inputPtrs; - - for (const auto input : _inputs) - { - inputPtrs.emplace_back(reinterpret_cast<const T *>(input->buffer())); - } - - nnfw::cker::Pack<T>(op_params, inputPtrs.data(), getTensorShape(_output), - reinterpret_cast<T *>(_output->buffer())); -} - -void PackLayer::configure(const std::vector<const IPortableTensor *> &inputs, int32_t axis, - IPortableTensor *output) -{ - assert(inputs.size() > 0); - assert(output != nullptr); - - _inputs = inputs; - _axis = axis; - _output = output; -} - -void PackLayer::run() -{ - if (_output->data_type() == OperandType::FLOAT32) - { - packImpl<float>(); - } - else if (_output->data_type() == OperandType::INT32) - { - packImpl<int32_t>(); - } - else - { - throw std::runtime_error{"Pack: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/PackLayer.h b/runtime/onert/backend/cpu/ops/PackLayer.h deleted file mode 100644 index b92c8d48c..000000000 --- a/runtime/onert/backend/cpu/ops/PackLayer.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_PACKLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_PACKLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class PackLayer : public ::onert::exec::IFunction -{ -public: - PackLayer(); - -public: - template <typename T> void packImpl(); - - void configure(const std::vector<const IPortableTensor *> &inputs, int32_t axis, - IPortableTensor *output); - void run() override; - -private: - std::vector<const IPortableTensor *> _inputs; - IPortableTensor *_output; - int32_t _axis; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_PACKLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/PadLayer.cc b/runtime/onert/backend/cpu/ops/PadLayer.cc deleted file mode 100644 index 6a2bf9da0..000000000 --- a/runtime/onert/backend/cpu/ops/PadLayer.cc +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PadLayer.h" - -#include <cker/operation/Pad.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -PadLayer::PadLayer() - : _input(nullptr), _output(nullptr), _padData(), _padRank(), _constantValueData() -{ - // DO NOTHING -} - -template <typename T> void PadLayer::padImpl(const T *constant_value_data) -{ - nnfw::cker::Pad<T>(_padData, _padRank, getTensorShape(_input), - reinterpret_cast<const T *>(_input->buffer()), getTensorShape(_output), - reinterpret_cast<T *>(_output->buffer()), constant_value_data); -} - -void PadLayer::configure(const IPortableTensor *input, IPortableTensor *output, - const int32_t *padData, int32_t padRank, const void *constantValueData) -{ - _input = input; - _output = output; - memcpy(_padData, padData, sizeof(_padData)); - _padRank = padRank; - _constantValueData.v = constantValueData; -} - -void PadLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - padImpl<float>(_constantValueData.f); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - if (_constantValueData.u8 == nullptr) - { - uint8_t pad_value = static_cast<uint8_t>(_output->data_offset()); - padImpl<uint8_t>(&pad_value); - } - else - { - padImpl<uint8_t>(_constantValueData.u8); - } - } - else - { - throw std::runtime_error{"Pad: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/PadLayer.h b/runtime/onert/backend/cpu/ops/PadLayer.h deleted file mode 100644 index efd73d5e5..000000000 --- a/runtime/onert/backend/cpu/ops/PadLayer.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_PADLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_PADLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -// Note, this is pad with mode=`CONSTANT`: it doesn't support `REFLECT` and -// `SYMMETRIC` -class PadLayer : public ::onert::exec::IFunction -{ -public: - PadLayer(); - -public: - template <typename T> void padImpl(const T *constant_value_data); - - void configure(const IPortableTensor *input, IPortableTensor *output, const int32_t *padData, - int32_t padRank, const void *constantValueData = nullptr); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - - int32_t _padData[8]; - int32_t _padRank; - ConstDataPtr _constantValueData; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_PADLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/PoolLayer.cc b/runtime/onert/backend/cpu/ops/PoolLayer.cc deleted file mode 100644 index 85d02a751..000000000 --- a/runtime/onert/backend/cpu/ops/PoolLayer.cc +++ /dev/null @@ -1,132 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PoolLayer.h" - -#include <cker/operation/AveragePool.h> -#include <cker/operation/MaxPool.h> - -#include <unordered_map> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -namespace -{ -template <typename T> -void avgPool2D(const nnfw::cker::PoolParams ¶ms, const IPortableTensor *input, - IPortableTensor *output) -{ - nnfw::cker::AveragePool<T>(params, getTensorShape(input), - reinterpret_cast<const T *>(input->buffer()), getTensorShape(output), - reinterpret_cast<T *>(output->buffer())); -} - -template <typename T> -void maxPool2D(const nnfw::cker::PoolParams ¶ms, const IPortableTensor *input, - IPortableTensor *output) -{ - nnfw::cker::MaxPool<T>(params, getTensorShape(input), - reinterpret_cast<const T *>(input->buffer()), getTensorShape(output), - reinterpret_cast<T *>(output->buffer())); -} - -template <typename T> -std::function<void(const IPortableTensor *, IPortableTensor *)> -generateKernelGeneric(const nnfw::cker::PoolParams ¶ms, PoolType op_type) -{ - if (op_type == PoolType::kAvg) - { - return std::bind(&avgPool2D<T>, params, std::placeholders::_1, std::placeholders::_2); - } - else if (op_type == PoolType::kMax) - { - return std::bind(&maxPool2D<T>, params, std::placeholders::_1, std::placeholders::_2); - } - else - { - throw std::runtime_error{"Pool: unsupported pool type"}; - } -} -} // namespace - -PoolLayer::PoolLayer() : _input(nullptr), _output(nullptr), _kernel() -{ - // DO NOTHING -} - -#define POOLING_PARAMETERS \ - nnfw::cker::PoolParams op_params; \ - op_params.stride_height = strideHeight; \ - op_params.stride_width = strideWidth; \ - op_params.filter_height = kernelHeight; \ - op_params.filter_width = kernelWidth; \ - op_params.padding_values.height = (int8_t)paddingTop; \ - op_params.padding_values.width = (int8_t)paddingLeft; - -void PoolLayer::configure(const IPortableTensor *input, const uint32_t paddingLeft, const uint32_t, - const uint32_t paddingTop, const uint32_t, const uint32_t strideWidth, - const uint32_t strideHeight, const uint32_t kernelWidth, - const uint32_t kernelHeight, const ir::Activation activation, - IPortableTensor *output, const PoolType op_type) -{ - assert(input != nullptr); - assert(output != nullptr); - - _input = input; - _output = output; - - POOLING_PARAMETERS - if (_input->data_type() == OperandType::FLOAT32) - { - float output_activation_min = 0; - float output_activation_max = 0; - CalculateActivationRange<float>(activation, &output_activation_min, &output_activation_max); - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - - _kernel = generateKernelGeneric<float>(op_params, op_type); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - int32_t output_activation_min = 0; - int32_t output_activation_max = 0; - CalculateActivationRangeUint8(activation, _output, &output_activation_min, - &output_activation_max); - op_params.quantized_activation_min = output_activation_min; - op_params.quantized_activation_max = output_activation_max; - _kernel = generateKernelGeneric<uint8_t>(op_params, op_type); - } - else - { - throw std::runtime_error{"Pool: unsupported data type"}; - } -} - -void PoolLayer::run() { _kernel(_input, _output); } - -#undef AVGPOOLING_PARAMETERS - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/PoolLayer.h b/runtime/onert/backend/cpu/ops/PoolLayer.h deleted file mode 100644 index b37835946..000000000 --- a/runtime/onert/backend/cpu/ops/PoolLayer.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_POOLLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_POOLLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -enum class PoolType -{ - kAvg, - kL2, - kMax, -}; - -class PoolLayer : public ::onert::exec::IFunction -{ -public: - PoolLayer(); - -public: - void configure(const IPortableTensor *input, const uint32_t paddingLeft, - const uint32_t paddingRight, const uint32_t paddingTop, - const uint32_t paddingBottom, const uint32_t strideWidth, - const uint32_t strideHeight, const uint32_t kernelWidth, - const uint32_t kernelHeight, const ir::Activation activation, - IPortableTensor *output, const PoolType op_type); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - - std::function<void(const IPortableTensor *, IPortableTensor *)> _kernel; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_POOLLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/PowLayer.cc b/runtime/onert/backend/cpu/ops/PowLayer.cc deleted file mode 100644 index 04a1af1e1..000000000 --- a/runtime/onert/backend/cpu/ops/PowLayer.cc +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PowLayer.h" - -#include <cker/operation/Pow.h> -#include <cker/operation/BinaryArithmeticOps.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -void PowLayer::powFloat32() -{ - float output_activation_min = 0, output_activation_max = 0; - CalculateActivationRange(_activation, &output_activation_min, &output_activation_max); - nnfw::cker::BinaryArithmeticOpParam op_params; - op_params.float_activation_max = output_activation_max; - op_params.float_activation_min = output_activation_min; - - if (!HaveSameShapes(_lhs, _rhs)) - { - nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::POW>( - op_params, getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()), - getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); - return; - } - - nnfw::cker::powImpl(getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()), - getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); -} - -void PowLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs, - ir::Activation activation, IPortableTensor *output) -{ - _lhs = lhs; - _rhs = rhs; - _activation = activation; - _output = output; -} - -void PowLayer::run() -{ - if (_output->data_type() == OperandType::FLOAT32) - powFloat32(); - else - throw std::runtime_error{"Pow: unsupportted data type"}; -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/PowLayer.h b/runtime/onert/backend/cpu/ops/PowLayer.h deleted file mode 100644 index 2689aad17..000000000 --- a/runtime/onert/backend/cpu/ops/PowLayer.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_POWLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_POWLAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class PowLayer : public ::onert::exec::IFunction -{ -public: - PowLayer() : _lhs(nullptr), _rhs(nullptr), _output(nullptr) - { - // DO NOTHING - } - -public: - void powFloat32(); - - void configure(const IPortableTensor *lhs, const IPortableTensor *rhs, - const ir::Activation activation, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_lhs; - const IPortableTensor *_rhs; - IPortableTensor *_output; - - ir::Activation _activation{ir::Activation::NONE}; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_POWLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/RangeLayer.cc b/runtime/onert/backend/cpu/ops/RangeLayer.cc deleted file mode 100644 index f00101fa8..000000000 --- a/runtime/onert/backend/cpu/ops/RangeLayer.cc +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "RangeLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Range.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -RangeLayer::RangeLayer() : _start(nullptr), _limit(nullptr), _delta(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void RangeLayer::configure(const IPortableTensor *start, const IPortableTensor *limit, - const IPortableTensor *delta, IPortableTensor *output) -{ - _start = start; - _limit = limit; - _delta = delta; - _output = output; -} - -void RangeLayer::run() -{ - switch (_output->data_type()) - { - case OperandType::FLOAT32: - nnfw::cker::Range<float>(reinterpret_cast<float *>(_start->buffer()), - reinterpret_cast<float *>(_limit->buffer()), - reinterpret_cast<float *>(_delta->buffer()), - reinterpret_cast<float *>(_output->buffer())); - break; - case OperandType::INT32: - nnfw::cker::Range<int32_t>(reinterpret_cast<int32_t *>(_start->buffer()), - reinterpret_cast<int32_t *>(_limit->buffer()), - reinterpret_cast<int32_t *>(_delta->buffer()), - reinterpret_cast<int32_t *>(_output->buffer())); - break; - default: - throw std::runtime_error{"Range: unsupported data type"}; - break; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/RangeLayer.h b/runtime/onert/backend/cpu/ops/RangeLayer.h deleted file mode 100644 index 2d83b39b1..000000000 --- a/runtime/onert/backend/cpu/ops/RangeLayer.h +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_RANGELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_RANGELAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -class RangeLayer : public ::onert::exec::IFunction -{ -public: - RangeLayer(); - - void configure(const IPortableTensor *start, const IPortableTensor *limit, - const IPortableTensor *delta, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_start; - const IPortableTensor *_limit; - const IPortableTensor *_delta; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_RANGELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/RankLayer.cc b/runtime/onert/backend/cpu/ops/RankLayer.cc deleted file mode 100644 index 4690bdf72..000000000 --- a/runtime/onert/backend/cpu/ops/RankLayer.cc +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "RankLayer.h" - -#include "OperationUtils.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -RankLayer::RankLayer() : _input(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void RankLayer::configure(const IPortableTensor *input, IPortableTensor *output) -{ - _input = input; - _output = output; -} - -void RankLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32 || _input->data_type() == OperandType::INT32) - { - int32_t *output_data = reinterpret_cast<int32_t *>(_output->buffer()); - output_data[0] = _input->num_dimensions(); - } - else - { - throw std::runtime_error{"Rank : unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/RankLayer.h b/runtime/onert/backend/cpu/ops/RankLayer.h deleted file mode 100644 index 6282ceb07..000000000 --- a/runtime/onert/backend/cpu/ops/RankLayer.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_RANKLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_RANKLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class RankLayer : public ::onert::exec::IFunction -{ -public: - RankLayer(); - -public: - void configure(const IPortableTensor *input, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_RANKLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ReduceLayer.cc b/runtime/onert/backend/cpu/ops/ReduceLayer.cc deleted file mode 100644 index 4a55b2a33..000000000 --- a/runtime/onert/backend/cpu/ops/ReduceLayer.cc +++ /dev/null @@ -1,227 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ReduceLayer.h" - -#include "OperationUtils.h" - -#include "cker/neon/neon_check.h" -#include <cker/operation/Reduce.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -namespace -{ - -template <typename T> -void evalLogic(const IPortableTensor *input, IPortableTensor *output, const std::vector<int> &axes, - bool keep_dims, T init_value, nnfw::cker::Reduce &reduce_kernel, - T reducer(const T current, const T in)) -{ - reduce_kernel.prepare(input->num_dimensions(), axes.size()); - bool result = reduce_kernel.ReduceGeneric<T>( - getTensorShape(input), reinterpret_cast<const T *>(input->buffer()), getTensorShape(output), - reinterpret_cast<T *>(output->buffer()), axes, keep_dims, init_value, reducer); - - if (!result) - { - throw std::runtime_error{"Reduce: Fail to run"}; - } -} - -template <typename T> -std::function<void(const IPortableTensor *, IPortableTensor *, const std::vector<int> &)> -evalType(bool keep_dims, nnfw::cker::Reduce &reduce_kernel, ReduceType reduce_type) -{ - switch (reduce_type) - { - case ReduceType::kSum: - return std::bind(&evalLogic<T>, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, keep_dims, static_cast<T>(0), reduce_kernel, - [](const T current, const T in) -> T { return in + current; }); - break; - case ReduceType::kProd: - return std::bind(&evalLogic<T>, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, keep_dims, static_cast<T>(1), reduce_kernel, - [](const T current, const T in) -> T { return in * current; }); - break; - case ReduceType::kMax: - return std::bind( - &evalLogic<T>, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, - keep_dims, std::numeric_limits<T>::lowest(), reduce_kernel, - [](const T current, const T in) -> T { return (in > current) ? in : current; }); - break; - case ReduceType::kMin: - return std::bind( - &evalLogic<T>, std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, - keep_dims, std::numeric_limits<T>::max(), reduce_kernel, - [](const T current, const T in) -> T { return (in < current) ? in : current; }); - break; - default: - throw std::runtime_error{"Reduce: Unsupported reduce type"}; - } -} - -// Template specialization for bool type -template <> -std::function<void(const IPortableTensor *, IPortableTensor *, const std::vector<int> &)> -evalType<bool>(bool keep_dims, nnfw::cker::Reduce &reduce_kernel, ReduceType reduce_type) -{ - switch (reduce_type) - { - case ReduceType::kAny: - return std::bind(&evalLogic<bool>, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, keep_dims, false, reduce_kernel, - [](const bool current, const bool in) -> bool { return in || current; }); - break; - case ReduceType::kAll: - return std::bind(&evalLogic<bool>, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, keep_dims, true, reduce_kernel, - [](const bool current, const bool in) -> bool { return in && current; }); - break; - default: - throw std::runtime_error{"Reduce: Unsupported reduce type"}; - } -} - -std::function<void(const IPortableTensor *, IPortableTensor *, const std::vector<int> &)> -generateKernelGeneric(const IPortableTensor *input, bool keep_dims, - nnfw::cker::Reduce &reduce_kernel, ReduceType reduce_type) -{ - switch (input->data_type()) - { - case OperandType::FLOAT32: - return evalType<float>(keep_dims, reduce_kernel, reduce_type); - case OperandType::INT32: - return evalType<int32_t>(keep_dims, reduce_kernel, reduce_type); - case OperandType::BOOL8: - return evalType<bool>(keep_dims, reduce_kernel, reduce_type); - default: - throw std::runtime_error{"Reduce(generic): unsupported data type"}; - } -} - -// TODO Refine this function -void evalSumQuantized(const IPortableTensor *input, IPortableTensor *output, - const std::vector<int> &axes, bool keep_dims, - nnfw::cker::Reduce &reduce_kernel) -{ - const bool same_scale = (input->data_scale() == output->data_scale() && - input->data_offset() == output->data_offset()); - - reduce_kernel.prepare(input->num_dimensions(), axes.size()); - - if (!same_scale) - { - std::vector<int32_t> temp_sum(output->getShape().num_elements()); - bool result = reduce_kernel.QuantizedMeanOrSum<uint8_t, int32_t>( - reinterpret_cast<const uint8_t *>(input->buffer()), input->data_offset(), - input->data_scale(), getTensorShape(input), reinterpret_cast<uint8_t *>(output->buffer()), - output->data_offset(), output->data_scale(), getTensorShape(output), axes, keep_dims, - temp_sum.data(), true, [](const int32_t current, const uint8_t in) -> int32_t { - const int32_t actual_in = static_cast<int32_t>(in); - return current + actual_in; - }); - - if (!result) - { - throw std::runtime_error{"Reduce: Fail to run"}; - } - - return; - } - - const auto kernel = generateKernelGeneric(input, keep_dims, reduce_kernel, ReduceType::kSum); - kernel(input, output, axes); -} - -} // namespace - -ReduceLayer::ReduceLayer() - : _input(nullptr), _axes(nullptr), _output(nullptr), _reduce_kernel(new nnfw::cker::Reduce()), - _kernel(), _reduceType(ReduceType::kInvalid) -{ - // DO NOTHING -} - -ReduceLayer::~ReduceLayer() = default; - -void ReduceLayer::configure(const IPortableTensor *input, const IPortableTensor *axes, - IPortableTensor *output, ReduceType reduceType, bool keep_dims) -{ - _input = input; - _axes = axes; - _output = output; - _reduceType = reduceType; - - switch (_reduceType) - { - case ReduceType::kSum: - if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - _kernel = std::bind(&evalSumQuantized, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, keep_dims, *_reduce_kernel); - return; - } - _kernel = generateKernelGeneric(_input, keep_dims, *_reduce_kernel, ReduceType::kSum); - break; - case ReduceType::kProd: - _kernel = generateKernelGeneric(_input, keep_dims, *_reduce_kernel, ReduceType::kProd); - break; - case ReduceType::kMax: - _kernel = generateKernelGeneric(_input, keep_dims, *_reduce_kernel, ReduceType::kMax); - break; - case ReduceType::kMin: - _kernel = generateKernelGeneric(_input, keep_dims, *_reduce_kernel, ReduceType::kMin); - break; - case ReduceType::kAny: - _kernel = generateKernelGeneric(_input, keep_dims, *_reduce_kernel, ReduceType::kAny); - break; - case ReduceType::kAll: - _kernel = generateKernelGeneric(_input, keep_dims, *_reduce_kernel, ReduceType::kAll); - break; - default: - throw std::runtime_error{"Reduce: Unsupported reduce type"}; - } -} - -void ReduceLayer::run() -{ - const auto axes = getReducerAxes(_axes); -#ifdef USE_NEON - int32_t rank = _input->num_dimensions(); - if (_input->data_type() == ir::DataType::FLOAT32 && _reduceType == ReduceType::kSum && - axes.size() == 1 && (axes[0] == -1 || axes[0] == rank - 1)) - { - OptimizedReduceSum(reinterpret_cast<const float *>(_input->buffer()), getTensorShape(_input), - reinterpret_cast<float *>(_output->buffer())); - return; - } -#endif // NEON - _kernel(_input, _output, axes); -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ReduceLayer.h b/runtime/onert/backend/cpu/ops/ReduceLayer.h deleted file mode 100644 index 8265dd41f..000000000 --- a/runtime/onert/backend/cpu/ops/ReduceLayer.h +++ /dev/null @@ -1,85 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_REDUCESUMLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_REDUCESUMLAYER_H__ - -#include "cker/neon/neon_check.h" - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> -#include <memory> - -namespace nnfw -{ -namespace cker -{ -class Reduce; -} -} // namespace nnfw - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -enum class ReduceType -{ - kSum, - kProd, - kMax, - kMin, - kAny, - kAll, - kInvalid // For debug and initialize -}; - -class ReduceLayer : public ::onert::exec::IFunction -{ -public: - ReduceLayer(); - ~ReduceLayer(); - -public: - void configure(const IPortableTensor *input, const IPortableTensor *axes, IPortableTensor *output, - ReduceType reduceType, bool keep_dims); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_axes; - IPortableTensor *_output; - - std::unique_ptr<nnfw::cker::Reduce> _reduce_kernel; - std::function<void(const IPortableTensor *input, IPortableTensor *output, - const std::vector<int> &axes)> - _kernel; - - ReduceType _reduceType; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_REDUCESUMLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ReshapeLayer.cc b/runtime/onert/backend/cpu/ops/ReshapeLayer.cc deleted file mode 100644 index 3c2b115f4..000000000 --- a/runtime/onert/backend/cpu/ops/ReshapeLayer.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ReshapeLayer.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -ReshapeLayer::ReshapeLayer() : _input(nullptr), _shape(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void ReshapeLayer::reshapeGeneric() -{ - size_t count = _input->total_size(); - memcpy(_output->buffer(), _input->buffer(), count); -} - -void ReshapeLayer::configure(const IPortableTensor *input, const IPortableTensor *shape, - IPortableTensor *output) -{ - _input = input; - /* note : shape is optional. If not provided from model, _shape is nullptr. */ - _shape = shape; - _output = output; -} - -void ReshapeLayer::run() { reshapeGeneric(); } - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ReshapeLayer.h b/runtime/onert/backend/cpu/ops/ReshapeLayer.h deleted file mode 100644 index b49c0bf7d..000000000 --- a/runtime/onert/backend/cpu/ops/ReshapeLayer.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_RESHAPELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_RESHAPELAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ReshapeLayer : public ::onert::exec::IFunction -{ -public: - ReshapeLayer(); - -public: - void reshapeGeneric(); - - void configure(const IPortableTensor *input, const IPortableTensor *shape, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_shape; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_RESHAPELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ResizeBilinearLayer.cc b/runtime/onert/backend/cpu/ops/ResizeBilinearLayer.cc deleted file mode 100644 index 1fe56cb99..000000000 --- a/runtime/onert/backend/cpu/ops/ResizeBilinearLayer.cc +++ /dev/null @@ -1,119 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "OperationUtils.h" -#include "ResizeBilinearLayer.h" -#include "cker/operation/ResizeBilinear.h" -#include <cker/Types.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -ResizeBilinearLayer::ResizeBilinearLayer() - : _input(nullptr), _output(nullptr), _size(nullptr), _output_height(0), _output_width(0), - _align_corners(false), _half_pixel_centers(false) -{ - // DO NOTHING -} - -void ResizeBilinearLayer::configure(const IPortableTensor *input, IPortableTensor *output, - const IPortableTensor *size, bool align_corners, - bool half_pixel_centers) -{ - assert(!size->is_constant()); - _input = input; - _output = output; - _size = size; - _align_corners = align_corners; - _half_pixel_centers = half_pixel_centers; -} - -void ResizeBilinearLayer::configure(const IPortableTensor *input, IPortableTensor *output, - int32_t output_height, int32_t output_width, bool align_corners, - bool half_pixel_centers) -{ - assert(_size == nullptr); - if (output_height < 0) - { - throw std::runtime_error{"ResizeBilinear: size value must be positive value, output_height = " + - std::to_string(output_height)}; - } - if (output_width < 0) - { - throw std::runtime_error{"ResizeBilinear: size value must be positive value, output_width = " + - std::to_string(output_width)}; - } - _input = input; - _output = output; - _output_height = output_height; - _output_width = output_width; - _align_corners = align_corners; - _half_pixel_centers = half_pixel_centers; -} - -void ResizeBilinearLayer::run() -{ - nnfw::cker::ResizeBilinearParams params; - if (_size == nullptr) - { - params.output_height = _output_height; - params.output_width = _output_width; - } - else - { - const auto size_buf = reinterpret_cast<const int32_t *>(_size->buffer()); - params.output_height = size_buf[0]; - params.output_width = size_buf[1]; - } - params.align_corners = _align_corners; - params.half_pixel_centers = _half_pixel_centers; - - switch (_input->data_type()) - { - case OperandType::FLOAT32: - nnfw::cker::ResizeBilinear( - params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); - break; - - case OperandType::QUANT_UINT8_ASYMM: - nnfw::cker::ResizeBilinear( - params, getTensorShape(_input), reinterpret_cast<const uint8_t *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer())); - break; - - case OperandType::UINT8: - case OperandType::BOOL8: - case OperandType::FLOAT16: - case OperandType::INT32: - case OperandType::INT64: - case OperandType::QUANT_INT8_SYMM: - std::runtime_error("ResizeBilinear NYI"); - break; - default: - std::runtime_error("ResizeBilinear unsupported data type"); - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ResizeBilinearLayer.h b/runtime/onert/backend/cpu/ops/ResizeBilinearLayer.h deleted file mode 100644 index d7ae1c620..000000000 --- a/runtime/onert/backend/cpu/ops/ResizeBilinearLayer.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_RESIZEBILINEAR_H__ -#define __ONERT_BACKEND_CPU_OPS_RESIZEBILINEAR_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ResizeBilinearLayer : public ::onert::exec::IFunction -{ -public: - ResizeBilinearLayer(); - -public: - void configure(const IPortableTensor *input1, IPortableTensor *output, - const IPortableTensor *size, bool align_corners, bool half_pixel_centers); - - void configure(const IPortableTensor *input, IPortableTensor *output, int32_t output_height, - int32_t output_width, bool align_corners, bool half_pixel_centers); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - const IPortableTensor *_size; - int32_t _output_height; - int32_t _output_width; - bool _align_corners; - bool _half_pixel_centers; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_RESIZEBILINEAR_H__ diff --git a/runtime/onert/backend/cpu/ops/ReverseLayer.cc b/runtime/onert/backend/cpu/ops/ReverseLayer.cc deleted file mode 100644 index 7979e77a0..000000000 --- a/runtime/onert/backend/cpu/ops/ReverseLayer.cc +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ReverseLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Reverse.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -void ReverseLayer::run() -{ - - if (_axis->total_size() != 4) - { - throw std::runtime_error{"Reverse: only support 1 axis"}; - } - int32_t axis = *(reinterpret_cast<int32_t *>(_axis->buffer())); - if (axis < 0) - { - axis += _input->num_dimensions(); - } - - switch (_input->data_type()) - { - case OperandType::FLOAT32: - nnfw::cker::Reverse<float>( - axis, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); - break; - default: - throw std::runtime_error{"Reverse: unsupported data type"}; - } -} - -void ReverseLayer::configure(const IPortableTensor *input, const IPortableTensor *axis, - IPortableTensor *output) -{ - _input = input; - _axis = axis; - _output = output; -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ReverseLayer.h b/runtime/onert/backend/cpu/ops/ReverseLayer.h deleted file mode 100644 index 9591dae32..000000000 --- a/runtime/onert/backend/cpu/ops/ReverseLayer.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_REVERSE_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_REVERSE_LAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ReverseLayer : public ::onert::exec::IFunction -{ -public: - ReverseLayer() : _input{nullptr}, _axis{nullptr}, _output{nullptr} - { - // DO NOTHING - } - -public: - void configure(const IPortableTensor *input, const IPortableTensor *axis, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_axis; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_REVERSE_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SelectLayer.cc b/runtime/onert/backend/cpu/ops/SelectLayer.cc deleted file mode 100644 index 95cfe1df0..000000000 --- a/runtime/onert/backend/cpu/ops/SelectLayer.cc +++ /dev/null @@ -1,89 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SelectLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Select.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -SelectLayer::SelectLayer() - : _cond(nullptr), _input_true(nullptr), _input_false(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void SelectLayer::configure(const IPortableTensor *cond, const IPortableTensor *input_true, - const IPortableTensor *input_false, IPortableTensor *output) -{ - _cond = cond; - _input_true = input_true; - _input_false = input_false; - _output = output; -} - -void SelectLayer::run() -{ - -#define KERNEL_SELECT(type, op) \ - nnfw::cker::op(getTensorShape(_cond), reinterpret_cast<uint8_t *>(_cond->buffer()), \ - getTensorShape(_input_true), reinterpret_cast<type *>(_input_true->buffer()), \ - getTensorShape(_input_false), reinterpret_cast<type *>(_input_false->buffer()), \ - getTensorShape(_output), reinterpret_cast<type *>(_output->buffer())); - -#define KERNEL_SWITCH(type, op) \ - switch (type) \ - { \ - break; \ - case OperandType::FLOAT32: \ - KERNEL_SELECT(float, op); \ - break; \ - default: \ - throw std::runtime_error{"Select: unsupported data type"}; \ - } - - auto input_type = _input_true->data_type(); - bool require_broadcast = - !HaveSameShapes(_input_true, _cond) || !HaveSameShapes(_input_false, _cond); - bool rank_one_select = ((_input_true->num_dimensions() == 1) && !require_broadcast); - - if (rank_one_select) - { - KERNEL_SWITCH(input_type, RankOneSelect); - } - else if (require_broadcast) - { - KERNEL_SWITCH(input_type, BroadcastSelect4DSlow); - } - else - { - KERNEL_SWITCH(input_type, Select); - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SelectLayer.h b/runtime/onert/backend/cpu/ops/SelectLayer.h deleted file mode 100644 index 2ef50f369..000000000 --- a/runtime/onert/backend/cpu/ops/SelectLayer.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SELECT_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SELECT_LAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class SelectLayer : public ::onert::exec::IFunction -{ -public: - SelectLayer(); - -public: - void configure(const IPortableTensor *cond, const IPortableTensor *input_true, - const IPortableTensor *input_false, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_cond; - const IPortableTensor *_input_true; - const IPortableTensor *_input_false; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SELECT_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/ShapeLayer.cc b/runtime/onert/backend/cpu/ops/ShapeLayer.cc deleted file mode 100644 index bffb04bc6..000000000 --- a/runtime/onert/backend/cpu/ops/ShapeLayer.cc +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ShapeLayer.h" - -#include "OperationUtils.h" - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -ShapeLayer::ShapeLayer() : _input(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -template <typename T> void GetRawShape(const IPortableTensor *input, T *output_data) -{ - for (uint32_t i = 0; i < input->num_dimensions(); ++i) - { - output_data[i] = static_cast<T>(input->dimension(i)); - } -} - -void ShapeLayer::shape() -{ - if (_output->data_type() == OperandType::UINT32) - { - GetRawShape(_input, reinterpret_cast<uint32_t *>(_output->buffer())); - } - else if (_output->data_type() == OperandType::INT32) - { - GetRawShape(_input, reinterpret_cast<int32_t *>(_output->buffer())); - } - else - { - throw std::runtime_error{"NYI : not supported output type for ShapeLayer"}; - } -} - -void ShapeLayer::configure(const IPortableTensor *input, IPortableTensor *output) -{ - _input = input; - _output = output; -} - -void ShapeLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32 || _input->data_type() == OperandType::INT32 || - _input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - shape(); - } - else - { - throw std::runtime_error{"Shape : unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/ShapeLayer.h b/runtime/onert/backend/cpu/ops/ShapeLayer.h deleted file mode 100644 index fb358c7a4..000000000 --- a/runtime/onert/backend/cpu/ops/ShapeLayer.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SHAPELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SHAPELAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class ShapeLayer : public ::onert::exec::IFunction -{ -public: - ShapeLayer(); - -public: - void shape(); - - void configure(const IPortableTensor *input, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SHAPELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SliceLayer.cc b/runtime/onert/backend/cpu/ops/SliceLayer.cc deleted file mode 100644 index 449c073e6..000000000 --- a/runtime/onert/backend/cpu/ops/SliceLayer.cc +++ /dev/null @@ -1,109 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SliceLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Slice.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -SliceLayer::SliceLayer() : _input(nullptr), _begin(nullptr), _size(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -template <typename T> -void SliceLayer::GetBeginAndSizeVectors(int dimensions, const IPortableTensor *begin, - const IPortableTensor *size, std::vector<int> *begins, - std::vector<int> *sizes) -{ - for (int idx = dimensions - 1; idx >= 0; --idx) - { - begins->push_back(reinterpret_cast<T *>(begin->buffer())[idx]); - sizes->push_back(reinterpret_cast<T *>(size->buffer())[idx]); - } -} - -template <typename T> void SliceLayer::sliceImpl() -{ - const int kMaxDim = nnfw::cker::Shape::kMaxSmallSize; - - std::vector<int> begins; - std::vector<int> sizes; - begins.reserve(kMaxDim); - sizes.reserve(kMaxDim); - - GetBeginAndSizeVectors<int32_t>(_input->num_dimensions(), _begin, _size, &begins, &sizes); - - // begins : 0-based, sizes : 1-based - for (int i = _input->num_dimensions(); i < kMaxDim; ++i) - { - begins.push_back(0); - sizes.push_back(1); - } - - nnfw::cker::SliceParams op_params; - op_params.begin_count = 4; - op_params.size_count = 4; - for (int i = 0; i < 4; ++i) - { - op_params.begin[i] = begins[3 - i]; - op_params.size[i] = sizes[3 - i]; - } - - nnfw::cker::Slice(op_params, getExtendedTensorShape(_input), - reinterpret_cast<const T *>(_input->buffer()), - reinterpret_cast<T *>(_output->buffer())); -} - -void SliceLayer::configure(const IPortableTensor *input, const IPortableTensor *begin, - const IPortableTensor *size, IPortableTensor *output) -{ - _input = input; - _output = output; - _begin = begin; - _size = size; -} - -void SliceLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - sliceImpl<float>(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - sliceImpl<uint8_t>(); - } - else - { - throw std::runtime_error{"Slice: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SliceLayer.h b/runtime/onert/backend/cpu/ops/SliceLayer.h deleted file mode 100644 index 650e2c97a..000000000 --- a/runtime/onert/backend/cpu/ops/SliceLayer.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SLICELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SLICELAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class SliceLayer : public ::onert::exec::IFunction -{ -public: - SliceLayer(); - -public: - void configure(const IPortableTensor *input, const IPortableTensor *begin, - const IPortableTensor *size, IPortableTensor *output); - - void run() override; - -private: - template <typename T> void sliceImpl(); - - template <typename T> - void GetBeginAndSizeVectors(int dimensions, const IPortableTensor *begin, - const IPortableTensor *size, std::vector<int> *begins, - std::vector<int> *sizes); - -private: - const IPortableTensor *_input; - const IPortableTensor *_begin; - const IPortableTensor *_size; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SLICELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SoftMaxLayer.cc b/runtime/onert/backend/cpu/ops/SoftMaxLayer.cc deleted file mode 100644 index b42be3042..000000000 --- a/runtime/onert/backend/cpu/ops/SoftMaxLayer.cc +++ /dev/null @@ -1,148 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SoftMaxLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/SoftMax.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -SoftMaxLayer::SoftMaxLayer() : _input(nullptr), _output(nullptr), _beta(0.0) -{ - // DO NOTHING -} - -void SoftMaxLayer::softmaxFloat32() -{ - if (getNumberOfDimensions(_input) == 1) - { - uint32_t input_size = getNumberOfElements(_input); - nnfw::cker::Softmax(reinterpret_cast<const float *>(_input->buffer()), input_size, 1, _beta, - reinterpret_cast<float *>(_output->buffer())); - } - else if (getNumberOfDimensions(_input) == 2) - { - uint32_t batch_size = getSizeOfDimension(_input, 0); - if (batch_size == 0) - throw std::runtime_error("batch_size should not be 0"); - - uint32_t input_size = getNumberOfElements(_input) / batch_size; - nnfw::cker::Softmax(reinterpret_cast<const float *>(_input->buffer()), input_size, batch_size, - _beta, reinterpret_cast<float *>(_output->buffer())); - } - else if (getNumberOfDimensions(_input) == 4) - { - nnfw::cker::SoftmaxParams op_params; - op_params.beta = _beta; - nnfw::cker::Softmax(op_params, getTensorShape(_input), - reinterpret_cast<const float *>(_input->buffer()), getTensorShape(_output), - reinterpret_cast<float *>(_output->buffer())); - } - else - { - nnfw::cker::SoftmaxParams op_params; - op_params.beta = _beta; - nnfw::cker::reference::Softmax( - op_params, getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); - } -} - -void SoftMaxLayer::softmaxQuant8() -{ - nnfw::cker::Shape descrIn4D(4); - - if (getNumberOfDimensions(_input) == 2) - { - auto batch_size = getSizeOfDimension(_input, 0); - if (batch_size == 0) - throw std::runtime_error("batch_size should not be 0"); - - auto input_size = getNumberOfElements(_input) / batch_size; - descrIn4D.SetDim(0, batch_size); - descrIn4D.SetDim(1, 1); - descrIn4D.SetDim(2, 1); - descrIn4D.SetDim(3, input_size); - } - else if (getNumberOfDimensions(_input) == 4) - { - descrIn4D.SetDim(0, _input->dimension(0)); - descrIn4D.SetDim(1, _input->dimension(1)); - descrIn4D.SetDim(2, _input->dimension(2)); - descrIn4D.SetDim(3, _input->dimension(3)); - } - else - { - throw std::runtime_error{"only 2D and 4D tensors supported"}; - } - if (_output->data_offset() != 0 || _output->data_scale() != 1.f / 256) - { - throw std::runtime_error{"incorrect scale / offset for output"}; - } - static const int32_t kScaledDiffIntegerBits = 5; - const double input_beta_real_multiplier = std::min( - 1.0 * _beta * _input->data_scale() * (1 << (31 - kScaledDiffIntegerBits)), (1ll << 31) - 1.0); - int32_t input_multiplier = 0; - int32_t input_left_shift = 0; - QuantizeMultiplierGreaterThanOne(input_beta_real_multiplier, &input_multiplier, - &input_left_shift); - float diff_min = -1.0f * CalculateInputRadius(kScaledDiffIntegerBits, input_left_shift); - - nnfw::cker::SoftmaxParams op_params; - op_params.input_multiplier = input_multiplier; - op_params.input_left_shift = input_left_shift; - op_params.diff_min = diff_min; - nnfw::cker::Softmax(op_params, descrIn4D, reinterpret_cast<const uint8_t *>(_input->buffer()), - descrIn4D, reinterpret_cast<uint8_t *>(_output->buffer())); -} - -void SoftMaxLayer::configure(const IPortableTensor *input, const float beta, - IPortableTensor *output) -{ - _input = input; - _output = output; - _beta = beta; -} - -void SoftMaxLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - softmaxFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - softmaxQuant8(); - } - else - { - throw std::runtime_error{"SoftMax: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SoftMaxLayer.h b/runtime/onert/backend/cpu/ops/SoftMaxLayer.h deleted file mode 100644 index d0c704c2c..000000000 --- a/runtime/onert/backend/cpu/ops/SoftMaxLayer.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SOFTMAXLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SOFTMAXLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class SoftMaxLayer : public ::onert::exec::IFunction -{ -public: - SoftMaxLayer(); - -public: - void softmaxFloat32(); - - void softmaxQuant8(); - - void configure(const IPortableTensor *input, const float beta, IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - IPortableTensor *_output; - - float _beta; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SOFTMAXLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SpaceToBatchNDLayer.cc b/runtime/onert/backend/cpu/ops/SpaceToBatchNDLayer.cc deleted file mode 100644 index 896e262ba..000000000 --- a/runtime/onert/backend/cpu/ops/SpaceToBatchNDLayer.cc +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SpaceToBatchNDLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/SpaceToBatchND.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -SpaceToBatchNDLayer::SpaceToBatchNDLayer() - : _input(nullptr), _block_shape(nullptr), _padding(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -// TO DO : move into shape inferer -void SpaceToBatchNDLayer::checkDimension() -{ - const int kSpatialDimensionNum = 2; - if (_block_shape->dimension(0) != kSpatialDimensionNum) - { - throw std::runtime_error("SpaceToBatchND : block_shape(block_size) tensor's rank is wrong\n"); - } - - // Ensures the input height and width (with padding) is a multiple of block - // shape height and width. - for (int dim = 0; dim < kSpatialDimensionNum; ++dim) - { - int final_dim_size = - (_input->dimension(dim + 1) + reinterpret_cast<int32_t *>(_padding->buffer())[dim * 2] + - reinterpret_cast<int32_t *>(_padding->buffer())[dim * 2 + 1]); - - if (final_dim_size % reinterpret_cast<int32_t *>(_block_shape->buffer())[dim] != 0) - { - throw std::runtime_error( - "SpaceToBatchND : padded input's dimension is not a multiple of block size\n"); - } - - if ((int32_t)_output->dimension(dim + 1) != - final_dim_size / reinterpret_cast<int32_t *>(_block_shape->buffer())[dim]) - { - throw std::runtime_error("SpaceToBatchND : wrong output dimension\n"); - } - } -} - -template <> uint32_t SpaceToBatchNDLayer::getPad<float>() { return 0; } -template <> uint32_t SpaceToBatchNDLayer::getPad<uint8_t>() { return _output->data_offset(); } - -template <typename T> void SpaceToBatchNDLayer::spaceToBatchND() -{ - checkDimension(); - - nnfw::cker::SpaceToBatchParams params; - params.output_offset = getPad<T>(); - - nnfw::cker::SpaceToBatchND( - params, getTensorShape(_input), reinterpret_cast<const T *>(_input->buffer()), - getTensorShape(_block_shape), reinterpret_cast<const int32_t *>(_block_shape->buffer()), - getTensorShape(_padding), reinterpret_cast<const int32_t *>(_padding->buffer()), - getTensorShape(_output), reinterpret_cast<T *>(_output->buffer())); -} - -void SpaceToBatchNDLayer::configure(const IPortableTensor *input, - const IPortableTensor *block_shape, - const IPortableTensor *padding, IPortableTensor *output) -{ - _input = input; - _block_shape = block_shape; - _padding = padding; - _output = output; -} - -void SpaceToBatchNDLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - spaceToBatchND<float>(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - spaceToBatchND<uint8_t>(); - } - else - { - throw std::runtime_error{"SpaceToBatchND: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SpaceToBatchNDLayer.h b/runtime/onert/backend/cpu/ops/SpaceToBatchNDLayer.h deleted file mode 100644 index 6f4638719..000000000 --- a/runtime/onert/backend/cpu/ops/SpaceToBatchNDLayer.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SPACE_TO_BATCH_ND_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SPACE_TO_BATCH_ND_LAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -class SpaceToBatchNDLayer : public ::onert::exec::IFunction -{ -public: - SpaceToBatchNDLayer(); - - void configure(const IPortableTensor *input, const IPortableTensor *block_shape, - const IPortableTensor *padding, IPortableTensor *output); - - void run() override; - -private: - void checkDimension(); - - template <typename T> uint32_t getPad(); - - template <typename T> void spaceToBatchND(); - - const IPortableTensor *_input; - const IPortableTensor *_block_shape; - const IPortableTensor *_padding; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SPACE_TO_BATCH_ND_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SpaceToDepthLayer.cc b/runtime/onert/backend/cpu/ops/SpaceToDepthLayer.cc deleted file mode 100644 index a0869aed8..000000000 --- a/runtime/onert/backend/cpu/ops/SpaceToDepthLayer.cc +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SpaceToDepthLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/SpaceToDepth.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -SpaceToDepthLayer::SpaceToDepthLayer() : _input(nullptr), _block_size(0), _output(nullptr) -{ - // DO NOTHING -} - -template <typename T> void SpaceToDepthLayer::spaceToDepth() -{ - - nnfw::cker::SpaceToDepthParams params; - params.block_size = _block_size; - - nnfw::cker::SpaceToDepth(params, getTensorShape(_input), - reinterpret_cast<const T *>(_input->buffer()), getTensorShape(_output), - reinterpret_cast<T *>(_output->buffer())); -} - -void SpaceToDepthLayer::configure(const IPortableTensor *input, const int32_t block_size, - IPortableTensor *output) -{ - _input = input; - _block_size = block_size; - _output = output; -} - -void SpaceToDepthLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - spaceToDepth<float>(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - spaceToDepth<uint8_t>(); - } - else - { - throw std::runtime_error{"SpaceToDepth: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SpaceToDepthLayer.h b/runtime/onert/backend/cpu/ops/SpaceToDepthLayer.h deleted file mode 100644 index c11ef2b0a..000000000 --- a/runtime/onert/backend/cpu/ops/SpaceToDepthLayer.h +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SPACE_TO_DEPTH_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SPACE_TO_DEPTH_LAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ -class SpaceToDepthLayer : public ::onert::exec::IFunction -{ -public: - SpaceToDepthLayer(); - - void configure(const IPortableTensor *input, const int32_t block_size, IPortableTensor *output); - - void run() override; - -private: - template <typename T> void spaceToDepth(); - - const IPortableTensor *_input; - int32_t _block_size; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SPACE_TO_BATCH_ND_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SplitLayer.cc b/runtime/onert/backend/cpu/ops/SplitLayer.cc deleted file mode 100644 index 922cde2e3..000000000 --- a/runtime/onert/backend/cpu/ops/SplitLayer.cc +++ /dev/null @@ -1,103 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SplitLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Split.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -SplitLayer::SplitLayer() : _input(nullptr), _axis(nullptr), _num_splits(0), _outputs() -{ - // DO NOTHING -} - -template <typename T> void SplitLayer::split(void) -{ - nnfw::cker::SplitParams op_params; - if (_axis->total_size() != sizeof(int32_t)) - { - throw std::runtime_error("ArgMinMax: wrong shape of axis"); - } - auto axis = *reinterpret_cast<const int32_t *>(_axis->buffer()); - if (axis < 0) - { - axis += _input->num_dimensions(); - } - op_params.axis = axis; - op_params.num_split = _num_splits; - - std::vector<T *> outputPtrs; - - for (const auto output : _outputs) - { - assert(output->total_size() == sizeOfData(output->data_type(), output->getShape().dims())); - outputPtrs.emplace_back(reinterpret_cast<T *>(output->buffer())); - } - - assert(_input->total_size() == sizeOfData(_input->data_type(), _input->getShape().dims())); - nnfw::cker::Split<T>(op_params, getTensorShape(_input), reinterpret_cast<T *>(_input->buffer()), - getTensorShape(_outputs[0]), outputPtrs.data()); -} - -void SplitLayer::configure(const IPortableTensor *input, const IPortableTensor *axis, - uint16_t num_splits, std::vector<IPortableTensor *> &outputs) -{ - assert(input != nullptr); - - _num_splits = num_splits; - _input = input; - _axis = axis; - _outputs = outputs; -} - -void SplitLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - split<float>(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - split<uint8_t>(); - } - else if (_input->data_type() == OperandType::INT32) - { - split<int32_t>(); - } - else if (_input->data_type() == OperandType::INT64) - { - split<int64_t>(); - } - else - { - throw std::runtime_error{"Split: unsupported input type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SplitLayer.h b/runtime/onert/backend/cpu/ops/SplitLayer.h deleted file mode 100644 index 090f87166..000000000 --- a/runtime/onert/backend/cpu/ops/SplitLayer.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SPLITLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SPLITLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class SplitLayer : public ::onert::exec::IFunction -{ -public: - SplitLayer(); - -public: - template <typename T> void split(void); - - void configure(const IPortableTensor *input, const IPortableTensor *axis, uint16_t num_splits, - std::vector<IPortableTensor *> &outputs); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_axis; - uint16_t _num_splits; - std::vector<IPortableTensor *> _outputs; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SPLITLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SplitVLayer.cc b/runtime/onert/backend/cpu/ops/SplitVLayer.cc deleted file mode 100644 index d6ca12442..000000000 --- a/runtime/onert/backend/cpu/ops/SplitVLayer.cc +++ /dev/null @@ -1,99 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SplitVLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/SplitV.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -SplitVLayer::SplitVLayer() - : _input(nullptr), _size_splits(nullptr), _split_dim(nullptr), _num_splits(0), _outputs() -{ - // DO NOTHING -} - -template <typename T> void SplitVLayer::splitV(void) -{ - nnfw::cker::SplitVParams op_params; - op_params.axis = *(reinterpret_cast<const int32_t *>(_split_dim->buffer())); - op_params.num_split = _num_splits; - - std::vector<T *> outputPtrs; - std::vector<nnfw::cker::Shape> outshape; - - for (const auto output : _outputs) - { - assert(output->total_size() == sizeOfData(output->data_type(), output->getShape().dims())); - outputPtrs.emplace_back(reinterpret_cast<T *>(output->buffer())); - outshape.emplace_back(getTensorShape(output)); - } - - assert(_input->total_size() == sizeOfData(_input->data_type(), _input->getShape().dims())); - nnfw::cker::SplitV<T>(op_params, getTensorShape(_input), reinterpret_cast<T *>(_input->buffer()), - outshape, outputPtrs.data()); -} - -void SplitVLayer::configure(const IPortableTensor *input, const IPortableTensor *size_splits, - const IPortableTensor *split_dim, uint16_t num_splits, - std::vector<IPortableTensor *> &outputs) -{ - assert(input != nullptr); - - _num_splits = num_splits; - _size_splits = size_splits; - _input = input; - _split_dim = split_dim; - _outputs = outputs; -} - -void SplitVLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - splitV<float>(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - splitV<uint8_t>(); - } - else if (_input->data_type() == OperandType::INT32) - { - splitV<int32_t>(); - } - else if (_input->data_type() == OperandType::INT64) - { - splitV<int64_t>(); - } - else - { - throw std::runtime_error{"SplitV: unsupported input type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SplitVLayer.h b/runtime/onert/backend/cpu/ops/SplitVLayer.h deleted file mode 100644 index 98f2f4406..000000000 --- a/runtime/onert/backend/cpu/ops/SplitVLayer.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SPLIT_V_LAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SPLIT_V_LAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class SplitVLayer : public ::onert::exec::IFunction -{ -public: - SplitVLayer(); - -public: - template <typename T> void splitV(void); - - void configure(const IPortableTensor *input, const IPortableTensor *size_splits, - const IPortableTensor *size_dim, uint16_t num_splits, - std::vector<IPortableTensor *> &outputs); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_size_splits; - const IPortableTensor *_split_dim; - uint16_t _num_splits; - std::vector<IPortableTensor *> _outputs; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SPLIT_V_LAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/SquaredDiffLayer.cc b/runtime/onert/backend/cpu/ops/SquaredDiffLayer.cc deleted file mode 100644 index cf67a5c00..000000000 --- a/runtime/onert/backend/cpu/ops/SquaredDiffLayer.cc +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "SquaredDiffLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/SqDiff.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -SqDiffLayer::SqDiffLayer() : _input1(nullptr), _input2(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void SqDiffLayer::SqDiffFloat32() -{ - nnfw::cker::SqDiff(getTensorShape(_input1), reinterpret_cast<const float *>(_input1->buffer()), - getTensorShape(_input2), reinterpret_cast<const float *>(_input2->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); -} - -void SqDiffLayer::configure(const IPortableTensor *input1, const IPortableTensor *input2, - IPortableTensor *output) -{ - _input1 = input1; - _input2 = input2; - _output = output; -} - -void SqDiffLayer::run() -{ - if (_input1->data_type() == OperandType::FLOAT32) - { - SqDiffFloat32(); - } - else - { - throw std::runtime_error{"SquaredDiff: unsupported data type"}; - } -} -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/SquaredDiffLayer.h b/runtime/onert/backend/cpu/ops/SquaredDiffLayer.h deleted file mode 100644 index 386eea9ae..000000000 --- a/runtime/onert/backend/cpu/ops/SquaredDiffLayer.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in riting, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_SQDIFFLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_SQDIFFLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class SqDiffLayer : public ::onert::exec::IFunction -{ -public: - SqDiffLayer(); - -public: - void SqDiffFloat32(); - - void configure(const IPortableTensor *input1, const IPortableTensor *input2, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input1; - const IPortableTensor *_input2; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_SQDIFFLAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/StatelessRandomUniformLayer.cc b/runtime/onert/backend/cpu/ops/StatelessRandomUniformLayer.cc deleted file mode 100644 index b8dfcb4b5..000000000 --- a/runtime/onert/backend/cpu/ops/StatelessRandomUniformLayer.cc +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "StatelessRandomUniformLayer.h" - -#include <cker/operation/StatelessRandomUniform.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -StatelessRandomUniformLayer::StatelessRandomUniformLayer() - : _shape(nullptr), _seed(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void StatelessRandomUniformLayer::configure(const IPortableTensor *shape, - const IPortableTensor *seed, IPortableTensor *output) -{ - _shape = shape; - _seed = seed; - _output = output; -} - -void StatelessRandomUniformLayer::StatelessRandomUniformFloat32() -{ - nnfw::cker::StatelessRandomUniform( - getTensorShape(_shape), reinterpret_cast<const int *>(_shape->buffer()), - getTensorShape(_seed), reinterpret_cast<const int *>(_seed->buffer()), - getTensorShape(_output), reinterpret_cast<float *>(_output->buffer())); -} - -void StatelessRandomUniformLayer::run() -{ - switch (_output->data_type()) - { - // ToDo : It need to support INT8 and UINT8 also when will be applied quantization. - case OperandType::FLOAT32: - StatelessRandomUniformFloat32(); - break; - default: - throw std::runtime_error{"StatelessRandomUniformLayer: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/StatelessRandomUniformLayer.h b/runtime/onert/backend/cpu/ops/StatelessRandomUniformLayer.h deleted file mode 100644 index ef11d623d..000000000 --- a/runtime/onert/backend/cpu/ops/StatelessRandomUniformLayer.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_STATELESS_RANDOM_UNIFORM_H__ -#define __ONERT_BACKEND_CPU_OPS_STATELESS_RANDOM_UNIFORM_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class StatelessRandomUniformLayer : public ::onert::exec::IFunction -{ -public: - StatelessRandomUniformLayer(); - -public: - void configure(const IPortableTensor *shape, const IPortableTensor *seed, - IPortableTensor *output); - - void StatelessRandomUniformFloat32(); - - void run() override; - -private: - const IPortableTensor *_shape; - const IPortableTensor *_seed; - - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_STATELESS_RANDOM_UNIFORM_H__ diff --git a/runtime/onert/backend/cpu/ops/StridedSliceLayer.cc b/runtime/onert/backend/cpu/ops/StridedSliceLayer.cc deleted file mode 100644 index f77f4d691..000000000 --- a/runtime/onert/backend/cpu/ops/StridedSliceLayer.cc +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "StridedSliceLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/StridedSlice.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -StridedSliceLayer::StridedSliceLayer() - : _input(nullptr), _begin(nullptr), _end(nullptr), _strides(nullptr), _output(nullptr), - _begin_mask(0), _ellipsis_mask(0), _end_mask(0), _new_axis_mask(0), _shrink_axis_mask(0) -{ -} - -template <typename T> void StridedSliceLayer::stridedSliceImpl() -{ - const auto input_shape = getTensorShape(_input); - const auto output_shape = getTensorShape(_output); - auto op_params = nnfw::cker::buildStridedSliceParams( - reinterpret_cast<uint32_t *>(_begin->buffer()), reinterpret_cast<uint32_t *>(_end->buffer()), - reinterpret_cast<uint32_t *>(_strides->buffer()), _begin_mask, _end_mask, _shrink_axis_mask, - input_shape.DimensionsCount()); - - nnfw::cker::checkOutputSize(op_params, input_shape, output_shape, input_shape.DimensionsCount()); - - nnfw::cker::StridedSlice(op_params, input_shape, reinterpret_cast<const T *>(_input->buffer()), - output_shape, reinterpret_cast<T *>(_output->buffer())); -} - -void StridedSliceLayer::configure(const IPortableTensor *input, const IPortableTensor *begin, - const IPortableTensor *end, const IPortableTensor *strides, - IPortableTensor *output, const int32_t begin_mask, - const int32_t end_mask, const int32_t shrink_axis_mask) -{ - _input = input; - _begin = begin; - _end = end; - _strides = strides; - _output = output; - - _begin_mask = begin_mask; - _ellipsis_mask = 0; - _end_mask = end_mask; - _new_axis_mask = 0; - _shrink_axis_mask = shrink_axis_mask; -} - -void StridedSliceLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - stridedSliceImpl<float>(); - } - else if (_input->data_type() == OperandType::INT32) - { - stridedSliceImpl<int32_t>(); - } - else if (_input->data_type() == OperandType::INT64) - { - stridedSliceImpl<int64_t>(); - } - else - { - throw std::runtime_error{"StridedSlice: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/StridedSliceLayer.h b/runtime/onert/backend/cpu/ops/StridedSliceLayer.h deleted file mode 100644 index 468408152..000000000 --- a/runtime/onert/backend/cpu/ops/StridedSliceLayer.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_STRIDEDSLICELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_STRIDEDSLICELAYER_H__ - -#include <backend/IPortableTensor.h> -#include "OperationUtils.h" - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class StridedSliceLayer : public ::onert::exec::IFunction -{ -public: - StridedSliceLayer(); - -public: - void configure(const IPortableTensor *input, const IPortableTensor *begin, - const IPortableTensor *end, const IPortableTensor *strides, - IPortableTensor *output, const int32_t begin_mask, const int32_t end_mask, - const int32_t shrink_axis_mask); - void run() override; - -private: - template <typename T> void stridedSliceImpl(); - -private: - const IPortableTensor *_input; - const IPortableTensor *_begin; - const IPortableTensor *_end; - const IPortableTensor *_strides; - IPortableTensor *_output; - - int32_t _begin_mask; - int32_t _ellipsis_mask; - int32_t _end_mask; - int32_t _new_axis_mask; - int32_t _shrink_axis_mask; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_STRIDEDSLICELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/TileLayer.cc b/runtime/onert/backend/cpu/ops/TileLayer.cc deleted file mode 100644 index bfc371972..000000000 --- a/runtime/onert/backend/cpu/ops/TileLayer.cc +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "TileLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Tile.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -TileLayer::TileLayer() : _input(nullptr), _multipliers(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -void TileLayer::tileFloat32() -{ - TileOneDimension(getTensorShape(_input), reinterpret_cast<const float *>(_input->buffer()), - reinterpret_cast<const int *>(_multipliers->buffer()), - reinterpret_cast<float *>(_output->buffer()), 0); -} - -void TileLayer::tileQuant8() -{ - // cker quant8 tile is not implemented yet - throw std::runtime_error{"NYI"}; -} - -void TileLayer::configure(const IPortableTensor *input, const IPortableTensor *multipliers, - IPortableTensor *output) -{ - _input = input; - _multipliers = multipliers; - _output = output; -} - -void TileLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - tileFloat32(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - tileQuant8(); - } - else - { - throw std::runtime_error{"Tile: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/TileLayer.h b/runtime/onert/backend/cpu/ops/TileLayer.h deleted file mode 100644 index d7b793ecc..000000000 --- a/runtime/onert/backend/cpu/ops/TileLayer.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_TILELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_TILELAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class TileLayer : public ::onert::exec::IFunction -{ -public: - TileLayer(); - -public: - void tileFloat32(); - - void tileQuant8(); - - void configure(const IPortableTensor *input, const IPortableTensor *_multipliers, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_multipliers; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_TILELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/TransposeLayer.cc b/runtime/onert/backend/cpu/ops/TransposeLayer.cc deleted file mode 100644 index 3362c3396..000000000 --- a/runtime/onert/backend/cpu/ops/TransposeLayer.cc +++ /dev/null @@ -1,111 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "TransposeLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Transpose.h> -#include <numeric> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -TransposeLayer::TransposeLayer() : _input(nullptr), _perm(nullptr), _output(nullptr) -{ - // DO NOTHING -} - -template <typename T> void TransposeLayer::transpose() -{ - nnfw::cker::TransposeParams param; - assert(_perm->num_dimensions() == 1); - - param.perm_count = _input->num_dimensions(); - if (_perm->dimension(0) == 0) // This means _perm is (n-1...0) - { - const auto begin = param.perm; - const auto end = param.perm + _input->num_dimensions(); - std::iota(begin, end, 0); - std::reverse(begin, end); - } - else - { - assert(param.perm_count == static_cast<int>(_perm->dimension(0))); - for (auto i = 0; i < param.perm_count; i++) - { - param.perm[i] = *(reinterpret_cast<const int32_t *>(_perm->buffer()) + i); - } - } - - nnfw::cker::Transpose(param, getTensorShape(_input), - reinterpret_cast<const T *>(_input->buffer()), getTensorShape(_output), - reinterpret_cast<T *>(_output->buffer())); -} - -void TransposeLayer::transposeQuant8() -{ - if (_input->data_offset() != _output->data_offset()) - { - throw std::runtime_error("TransposeLayer : qassym8 input and output offsets unmatched"); - } - - if (_input->data_scale() != _output->data_scale()) - { - throw std::runtime_error("TransposeLayer : qassym8 input and output scales unmatched"); - } - - transpose<uint8_t>(); -} - -void TransposeLayer::configure(const IPortableTensor *input, const IPortableTensor *perm, - IPortableTensor *output) -{ - _input = input; - _perm = perm; - _output = output; -} - -void TransposeLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - { - transpose<float>(); - } - else if (_input->data_type() == OperandType::INT32) - { - transpose<int32_t>(); - } - else if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM) - { - transposeQuant8(); - } - else - { - throw std::runtime_error{"Transpose: unsupported data type"}; - } -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/TransposeLayer.h b/runtime/onert/backend/cpu/ops/TransposeLayer.h deleted file mode 100644 index c8e9f8ae7..000000000 --- a/runtime/onert/backend/cpu/ops/TransposeLayer.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_TRANSPOSELAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_TRANSPOSELAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class TransposeLayer : public ::onert::exec::IFunction -{ -public: - TransposeLayer(); - -public: - template <typename T> void transpose(); - - void transposeQuant8(); - - void configure(const IPortableTensor *input, const IPortableTensor *perm, - IPortableTensor *output); - - void run() override; - -private: - const IPortableTensor *_input; - const IPortableTensor *_perm; - IPortableTensor *_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_TRANSPOSELAYER_H__ diff --git a/runtime/onert/backend/cpu/ops/UnpackLayer.cc b/runtime/onert/backend/cpu/ops/UnpackLayer.cc deleted file mode 100644 index 428b38588..000000000 --- a/runtime/onert/backend/cpu/ops/UnpackLayer.cc +++ /dev/null @@ -1,91 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "UnpackLayer.h" - -#include "OperationUtils.h" - -#include <cker/operation/Unpack.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -UnpackLayer::UnpackLayer() : _input(nullptr), _outputs(), _axis(0), _num_output(0) -{ - // DO NOTHING -} - -template <typename T> void UnpackLayer::unpackImpl() -{ - nnfw::cker::UnpackParams op_params; - op_params.axis = _axis; - op_params.num_split = _num_output; - - std::vector<nnfw::cker::Shape *> outputDimsPtr; - std::vector<nnfw::cker::Shape> outputDims; - outputDimsPtr.reserve(_num_output); - outputDims.reserve(_num_output); - - for (int32_t i = 0; i < _num_output; i++) - { - outputDims.push_back(getTensorShape(_outputs[i])); - outputDimsPtr.push_back(&outputDims[i]); - } - - std::vector<T *> outputPtrs; - - for (const auto output : _outputs) - { - outputPtrs.emplace_back(reinterpret_cast<T *>(output->buffer())); - } - - nnfw::cker::Unpack<T>(op_params, getTensorShape(_input), reinterpret_cast<T *>(_input->buffer()), - getTensorShape(_outputs[0]), outputPtrs.data()); -} - -void UnpackLayer::configure(const IPortableTensor *input, uint32_t axis, int32_t num, - std::vector<IPortableTensor *> &outputs) -{ - assert(input != nullptr); - assert(outputs.size() > 0); - assert(outputs.size() == (size_t)num); - - _input = input; - _axis = axis; - _num_output = num; - _outputs = outputs; -} - -void UnpackLayer::run() -{ - if (_input->data_type() == OperandType::FLOAT32) - unpackImpl<float>(); - else if (_input->data_type() == OperandType::INT32) - unpackImpl<int32_t>(); - else - throw std::runtime_error{"Unpack: Unsupported data type"}; -} - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert diff --git a/runtime/onert/backend/cpu/ops/UnpackLayer.h b/runtime/onert/backend/cpu/ops/UnpackLayer.h deleted file mode 100644 index a185b31a0..000000000 --- a/runtime/onert/backend/cpu/ops/UnpackLayer.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_OPS_UNPACKLAYER_H__ -#define __ONERT_BACKEND_CPU_OPS_UNPACKLAYER_H__ - -#include <backend/IPortableTensor.h> - -#include <exec/IFunction.h> - -namespace onert -{ -namespace backend -{ -namespace cpu -{ -namespace ops -{ - -class UnpackLayer : public ::onert::exec::IFunction -{ -public: - UnpackLayer(); - -public: - void configure(const IPortableTensor *input, uint32_t axis, int32_t num_output, - std::vector<IPortableTensor *> &output); - void run() override; - -private: - template <typename T> void unpackImpl(); - -private: - const IPortableTensor *_input; - std::vector<IPortableTensor *> _outputs; - uint32_t _axis; - int32_t _num_output; -}; - -} // namespace ops -} // namespace cpu -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_OPS_UNPACKLAYER_H__ diff --git a/runtime/onert/core/CMakeLists.txt b/runtime/onert/core/CMakeLists.txt deleted file mode 100644 index 344b2a972..000000000 --- a/runtime/onert/core/CMakeLists.txt +++ /dev/null @@ -1,45 +0,0 @@ -file(GLOB_RECURSE SOURCES "src/*.cc") -file(GLOB_RECURSE TESTS "*.test.cc") -list(REMOVE_ITEM SOURCES ${TESTS}) - -add_library(onert_core SHARED ${SOURCES}) -set_target_properties(onert_core PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(onert_core PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) -target_include_directories(onert_core PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src) -target_link_libraries(onert_core PUBLIC nnfw_lib_misc half) -target_link_libraries(onert_core PRIVATE nnfw_lib_cker) -target_link_libraries(onert_core PRIVATE nnfw_common) -target_link_libraries(onert_core PRIVATE nnfw_coverage) -target_link_libraries(onert_core PRIVATE dl ${LIB_PTHREAD}) -target_link_libraries(onert_core PRIVATE jsoncpp) -target_link_libraries(onert_core INTERFACE ruy_instrumentation) -# NOTE Below line is added to remove warning for android build -# It will be removed after android build uses gold linker -if (ANDROID) - target_link_libraries(onert_core INTERFACE log) -endif (ANDROID) - -if(ENVVAR_ONERT_CONFIG) - target_compile_definitions(onert_core PRIVATE ENVVAR_FOR_DEFAULT_CONFIG) -endif(ENVVAR_ONERT_CONFIG) - -install(TARGETS onert_core LIBRARY DESTINATION lib) -install(DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include/" - DESTINATION "include/onert" - FILES_MATCHING PATTERN "*.h" PATTERN "*.lst" - ) - -if(NOT ENABLE_TEST) - return() -endif(NOT ENABLE_TEST) - -# Unit Tests -set(TEST_ONERT_BACKEND_CPU_COMMON test_onert_backend_cpu_common) - -add_executable(${TEST_ONERT_BACKEND_CPU_COMMON} ${TESTS}) - -target_link_libraries(${TEST_ONERT_BACKEND_CPU_COMMON} onert_core) -target_link_libraries(${TEST_ONERT_BACKEND_CPU_COMMON} gtest gtest_main dl ${LIB_PTHREAD}) - -add_test(${TEST_ONERT_BACKEND_CPU_COMMON} ${TEST_ONERT_BACKEND_CPU_COMMON}) -install(TARGETS ${TEST_ONERT_BACKEND_CPU_COMMON} DESTINATION unittest_standalone) diff --git a/runtime/onert/core/include/backend/Backend.h b/runtime/onert/core/include/backend/Backend.h deleted file mode 100644 index 4f6ebbba7..000000000 --- a/runtime/onert/core/include/backend/Backend.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_BACKEND_H__ -#define __ONERT_BACKEND_BACKEND_H__ - -#include <memory> - -#include "ir/Graph.h" -#include "backend/IConfig.h" -#include "backend/BackendContext.h" - -namespace onert -{ -namespace backend -{ - -namespace custom -{ -class IKernelBuilder; -} - -class Backend -{ -public: - virtual ~Backend() = default; - virtual std::shared_ptr<onert::backend::IConfig> config() const = 0; - - virtual std::unique_ptr<BackendContext> - newContext(const ir::Graph &graph, const std::shared_ptr<backend::custom::IKernelBuilder> &kb, - bool is_linear_executor) const = 0; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_BACKEND_H__ diff --git a/runtime/onert/core/include/backend/BackendContext.h b/runtime/onert/core/include/backend/BackendContext.h deleted file mode 100644 index 1eba29550..000000000 --- a/runtime/onert/core/include/backend/BackendContext.h +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_BACKEND_CONTEXT_H__ -#define __ONERT_BACKEND_BACKEND_CONTEXT_H__ - -#include <memory> -#include "ir/Graph.h" - -namespace onert -{ -namespace backend -{ - -class Backend; -class IConstantInitializer; -class IKernelGenerator; -class ITensorRegister; -struct ITensorRegistry; -struct ITensorBuilder; -struct IOptimizer; - -class BackendContext -{ -public: - struct OperationInfo - { - ir::OperationIndex index; - ir::Layout layout; - - OperationInfo(ir::OperationIndex index, ir::Layout layout) : index{index}, layout{layout} {} - }; - -public: - BackendContext(const Backend *backend, const ir::Graph *graph, - std::shared_ptr<ITensorRegistry> tensor_registry = nullptr, - std::shared_ptr<ITensorBuilder> tensor_builder = nullptr, - std::shared_ptr<IConstantInitializer> constant_initializer = nullptr, - std::shared_ptr<IKernelGenerator> kernel_gen = nullptr, - std::shared_ptr<ITensorRegister> tensor_register = nullptr, - std::shared_ptr<IOptimizer> optimizer = nullptr) - : _backend{backend}, _graph{graph}, tensor_registry{tensor_registry}, - tensor_builder{tensor_builder}, constant_initializer{constant_initializer}, - kernel_gen{kernel_gen}, tensor_register{tensor_register}, optimizer{optimizer} - { - } - - virtual ~BackendContext() = default; - - void initialize(const std::vector<OperationInfo> &operation_list, - const std::vector<ir::OperandIndex> &operand_list); - void initConsts(); - - const Backend *backend() const { return _backend; } - const ir::Graph *graph() const { return _graph; } - const std::vector<OperationInfo> &operation_list() { return _operation_list; } - const std::vector<ir::OperandIndex> &operand_list() { return _operand_list; } - -private: - const Backend *_backend{nullptr}; - const ir::Graph *_graph{nullptr}; - std::vector<OperationInfo> _operation_list; - std::vector<ir::OperandIndex> _operand_list; - -public: - std::shared_ptr<ITensorRegistry> tensor_registry; - std::shared_ptr<ITensorBuilder> tensor_builder; - std::shared_ptr<IConstantInitializer> constant_initializer; - std::shared_ptr<IKernelGenerator> kernel_gen; - std::shared_ptr<ITensorRegister> tensor_register; - std::shared_ptr<IOptimizer> optimizer; -}; - -using BackendContexts = std::unordered_map<const Backend *, std::unique_ptr<BackendContext>>; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_BACKEND_CONTEXT_H__ diff --git a/runtime/onert/core/include/backend/CustomKernelBuilder.h b/runtime/onert/core/include/backend/CustomKernelBuilder.h deleted file mode 100644 index cae2fc1a3..000000000 --- a/runtime/onert/core/include/backend/CustomKernelBuilder.h +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CUSTOM_KERNEL_BUILDER_H__ -#define __ONERT_BACKEND_CUSTOM_KERNEL_BUILDER_H__ - -#include "backend/IPortableTensor.h" -#include "ir/Shape.h" -#include "ir/DataType.h" - -#include <vector> -#include <memory> - -namespace onert -{ -namespace exec -{ - -class IFunction; - -} // namespace exec -} // namespace onert - -namespace onert -{ -namespace backend -{ -namespace custom -{ - -struct TypeInfo -{ - ir::Shape shape; - ir::DataType dtype; -}; - -struct CustomKernelConfigParams -{ - std::vector<backend::IPortableTensor *> input_tensors; - std::vector<TypeInfo> input_types; - - std::vector<backend::IPortableTensor *> output_tensors; - std::vector<TypeInfo> output_types; - - char *userdata; - size_t userdata_size; -}; - -class IKernelBuilder -{ -public: - virtual ~IKernelBuilder() = default; - virtual std::unique_ptr<exec::IFunction> buildKernel(const std::string &id, - CustomKernelConfigParams &¶ms) const = 0; -}; - -} // namespace custom - -} // namespace backend - -} // namespace onert - -#endif // __ONERT_BACKEND_CUSTOM_KERNEL_BUILDER_H__ diff --git a/runtime/onert/core/include/backend/IConfig.h b/runtime/onert/core/include/backend/IConfig.h deleted file mode 100644 index ef9c5cdb2..000000000 --- a/runtime/onert/core/include/backend/IConfig.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ICONFIG_H__ -#define __ONERT_BACKEND_ICONFIG_H__ - -#include "ir/Layout.h" -#include "ir/Operation.h" -#include "util/ITimer.h" - -#include <memory> -#include <string> - -namespace onert -{ -namespace backend -{ - -struct IConfig -{ - virtual ~IConfig() = default; - /** - * @brief Returns ID of the backend - * - * @return std::string ID of this backend - */ - virtual std::string id() = 0; - /** - * @brief Initialize the backend. This is called as soon as the backend is loaded. - * - * @return true Initialization succeeded - * @return false Initialization failed, so it cannot use this backend - */ - virtual bool initialize() = 0; - /** - * @brief Returns supported layout for the given \p node and \p frontend_layout - * - * @param node Operation - * @param frontend_layout The layout defined in the model - * @return ir::Layout The layout that the backend kernel actually uses - */ - virtual ir::Layout supportLayout(const ir::Operation &node, ir::Layout frontend_layout) = 0; - /** - * @brief The function that is called after each OpSequence run on profiling mode. - * This may be useful for profiling GPU-based or special computing units. - */ - virtual void sync() const {} - /** - * @brief Returns Timer object for this backend. For some computing units, it may need its own - * Timer implementation. - * - * @return std::unique_ptr<util::ITimer> Timer object for this backend - */ - virtual std::unique_ptr<util::ITimer> timer() { return nullptr; } - - virtual bool supportPermutation() = 0; - virtual bool supportDynamicTensor() = 0; - virtual bool supportFP16() = 0; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ICONFIG_H__ diff --git a/runtime/onert/core/include/backend/IConstantInitializer.h b/runtime/onert/core/include/backend/IConstantInitializer.h deleted file mode 100644 index 149acecb4..000000000 --- a/runtime/onert/core/include/backend/IConstantInitializer.h +++ /dev/null @@ -1,230 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ICONSTANT_INITIALIZER_H__ -#define __ONERT_BACKEND_ICONSTANT_INITIALIZER_H__ - -#include <unordered_map> -#include <functional> - -#include "ITensorBuilder.h" -#include "ir/Coordinates.h" -#include "ir/Layout.h" -#include "ir/Operand.h" -#include "ir/Operands.h" -#include "ir/OperationVisitor.h" -#include "ir/OpSequence.h" -#include "util/logging.h" - -namespace -{ -template <typename T> -static void Init(const onert::ir::Operand &model_obj, onert::backend::ITensor &obj, const bool copy, - const onert::ir::Layout frontend_layout = onert::ir::Layout::UNKNOWN) -{ - const auto shape = model_obj.shape(); - assert(model_obj.data()); - auto base = reinterpret_cast<const T *>(model_obj.data()->base()); - - obj.access([&](::onert::backend::ITensor &tensor) { - switch (shape.rank()) - { - case 0: - { - assert(model_obj.data()->size() == sizeof(T)); - const auto value = *reinterpret_cast<const T *>(base); - T *into = reinterpret_cast<T *>(tensor.buffer()); - *into = value; - break; - } - case 1: - { - auto vec_size = shape.dim(0); - for (int32_t n = 0; n < vec_size; ++n) - { - const T *from = reinterpret_cast<const T *>(base) + n; - const auto value = *from; - - T *into = reinterpret_cast<T *>(tensor.buffer()) + n; - - *into = value; - } - break; - } - case 2: - { - const int32_t copy_len = shape.dim(1); - - for (auto i = 0; i < shape.dim(0); ++i) - { - ::onert::ir::Coordinates coords{i, 0}; - memcpy(tensor.buffer() + tensor.calcOffset(coords), base + i * copy_len, - copy_len * sizeof(T)); - } - break; - } - case 3: - { - const int32_t width = shape.dim(1); - const int32_t copy_len = shape.dim(2); - - for (auto i = 0; i < shape.dim(0); ++i) - { - for (auto j = 0; j < shape.dim(1); ++j) - { - ::onert::ir::Coordinates coords{i, j, 0}; - memcpy(tensor.buffer() + tensor.calcOffset(coords), - base + i * width * copy_len + j * copy_len, copy_len * sizeof(T)); - } - } - break; - } - case 4: - { - const int32_t height = shape.dim(1); - const int32_t width = shape.dim(2); - const int32_t copy_len = shape.dim(3); - for (auto i = 0; i < shape.dim(0); ++i) - { - for (auto j = 0; j < shape.dim(1); ++j) - { - for (auto k = 0; k < shape.dim(2); ++k) - { - if (copy) - { - ::onert::ir::Coordinates coords{i, j, k, 0}; - memcpy(tensor.buffer() + tensor.calcOffset(coords), - base + i * height * width * copy_len + j * width * copy_len + k * copy_len, - copy_len * sizeof(T)); - } - else - { - for (auto l = 0; l < shape.dim(3); ++l) - { - const auto coords = ::onert::ir::convertCoordinates({i, j, k, l}, frontend_layout, - tensor.layout()); - T *into = reinterpret_cast<T *>(tensor.buffer() + tensor.calcOffset(coords)); - T value = *(base + i * height * width * copy_len + j * width * copy_len + - k * copy_len + l); - *into = value; - } - } - } - } - } - break; - } - default: - throw std::runtime_error{"Not yet supported"}; - } - }); -} - -template <typename T> -void copyInit(const onert::ir::Operand &model_obj, onert::backend::ITensor &obj) -{ - Init<T>(model_obj, obj, true); -} - -template <typename T> -void permuteInit(const onert::ir::Operand &model_obj, onert::backend::ITensor &obj, - const onert::ir::Layout frontend_layout) -{ - const bool copy = frontend_layout == obj.layout(); - Init<T>(model_obj, obj, copy, frontend_layout); -} - -} // namespace - -namespace onert -{ -namespace backend -{ - -class IConstantInitializer : public ir::OperationVisitor -{ -public: - virtual ~IConstantInitializer() = default; - -public: - void run() - { - assert(tensor_registry()); - for (const auto &it : _init_map) - { - const auto &ind = it.first; - const auto &fn = it.second; - - const auto &model_obj = _operands.at(ind); - auto tensor_obj = tensor_registry()->getNativeITensor(ind); - assert(tensor_obj != nullptr); - fn(model_obj, *tensor_obj); - VERBOSE(FillOperandData) << "Fill data for operand " << ind.value() << std::endl; - } - _init_map.clear(); - } - -public: - IConstantInitializer(const ir::Operands &operands) - : _operands{operands}, _current_op_seq_layout{ir::Layout::UNKNOWN} - { - } - -public: - using Initializer = std::function<void(const ir::Operand &, backend::ITensor &)>; - - void setLayout(ir::Layout layout) { _current_op_seq_layout = layout; } - -protected: - virtual std::shared_ptr<ITensorRegistry> tensor_registry() const = 0; - -public: - virtual void registerDefaultInitializer(const ir::OperandIndex &index, const ir::Operand &obj) - { - registerPermuteInitializer(index, obj); // as default - } - -public: - void registerCopyInitializer(const ir::OperandIndex &index, const ir::Operand &obj); - void registerPermuteInitializer(const ir::OperandIndex &index, const ir::Operand &obj); - -public: - void registerCustomInitializer(const ir::OperandIndex &index, const ir::Operand &obj, - void (*customInit)(const onert::ir::Operand &model_obj, - onert::backend::ITensor &obj)) - { - // For only CONSTANTS - // TODO Add to check if tensor has been allocated - if (!obj.isConstant()) - return; - - using namespace std::placeholders; - _init_map[index] = std::bind(customInit, _1, _2); - } - -public: - bool exist(const ir::OperandIndex &ind) { return _init_map.find(ind) != _init_map.end(); } - -protected: - const ir::Operands &_operands; - std::unordered_map<ir::OperandIndex, Initializer> _init_map; - ir::Layout _current_op_seq_layout; // TODO Rename this to _current_layout -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ICONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/core/include/backend/IDynamicTensorManager.h b/runtime/onert/core/include/backend/IDynamicTensorManager.h deleted file mode 100644 index 67cfda24e..000000000 --- a/runtime/onert/core/include/backend/IDynamicTensorManager.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_IDYNAMICTENSOR_MANAGER_H__ -#define __ONERT_BACKEND_IDYNAMICTENSOR_MANAGER_H__ - -#include "ITensorManager.h" - -#include <ir/Index.h> -#include <ir/Operation.h> -#include <ir/Shape.h> -#include <backend/ITensor.h> - -namespace onert -{ -namespace backend -{ - -/** - * @brief Interface as an abstract tensor manager, providing ways to handle memory - * for dynamic tensors. - */ -struct IDynamicTensorManager : public ITensorManager -{ - virtual ~IDynamicTensorManager() = default; - -public: - /** - * @brief Plan when to delete a tensor. Note this planning is done at compilation time. - * @param op_ind operation index - * @param tensor candidate ITensor to dealloc. Tensor can be static - * or dynamic since tensor type may not be clearly known at compilation time. - */ - virtual void planDealloc(ir::OperationIndex op_ind, backend::ITensor *tensor) = 0; - - /** - * @brief Deallocate input tensors of op if an input tensor is a dynamic tensor and it won't - * be used anymore - * @note This will work after calling planDealloc - */ - virtual void deallocInput(ir::OperationIndex op_ind) = 0; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_IDYNAMICTENSOR_MANAGER_H__ diff --git a/runtime/onert/core/include/backend/IExternalContext.h b/runtime/onert/core/include/backend/IExternalContext.h deleted file mode 100644 index 88ffb502c..000000000 --- a/runtime/onert/core/include/backend/IExternalContext.h +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_IEXTERNAL_CONTEXT_H__ -#define __ONERT_BACKEND_IEXTERNAL_CONTEXT_H__ - -namespace onert -{ -namespace backend -{ - -struct IExternalContext -{ - virtual ~IExternalContext() = default; - virtual void setMaxNumThreads(int) = 0; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_IEXTERNAL_CONTEXT__ diff --git a/runtime/onert/core/include/backend/IKernelGenerator.h b/runtime/onert/core/include/backend/IKernelGenerator.h deleted file mode 100644 index afc34ec21..000000000 --- a/runtime/onert/core/include/backend/IKernelGenerator.h +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_IKERNEL_GENERATOR_H__ -#define __ONERT_BACKEND_IKERNEL_GENERATOR_H__ - -#include <assert.h> -#include <memory> -#include <functional> - -#include "ITensorBuilder.h" -#include "ir/OperationVisitor.h" -#include "ir/OpSequence.h" -#include <memory> -#include "exec/FunctionSequence.h" - -namespace onert -{ -namespace backend -{ - -class IKernelGenerator : public ir::OperationVisitor -{ -public: - virtual ~IKernelGenerator() = default; - - std::unique_ptr<exec::IFunction> releaseFunction() - { - assert(_return_fn); - return std::move(_return_fn); - } - - std::unique_ptr<exec::FunctionSequence> generate(const ir::OpSequence &op_seq) - { - op_seq.accept(*this); - return std::move(_return_fn_seq); - } - -protected: - using OperationVisitor::visit; - - void visit(const ir::OpSequence &) override - { - throw std::runtime_error("KernelGenerator: NYI for operation 'OpSequence'"); - } - -#define OP(InternalName) \ - void visit(const ir::operation::InternalName &) override \ - { \ - throw std::runtime_error("KernelGenerator: NYI for operation '" #InternalName "'"); \ - } -#include "ir/Operations.lst" -#undef OP - -protected: - std::unique_ptr<exec::IFunction> _return_fn; - std::unique_ptr<exec::FunctionSequence> _return_fn_seq; // TODO Extract this out -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_IKERNEL_GENERATOR_H__ diff --git a/runtime/onert/core/include/backend/IMemoryManager.h b/runtime/onert/core/include/backend/IMemoryManager.h deleted file mode 100644 index bad2fd51a..000000000 --- a/runtime/onert/core/include/backend/IMemoryManager.h +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_IMEMORY_MANAGER_H__ -#define __ONERT_BACKEND_IMEMORY_MANAGER_H__ - -namespace onert -{ -namespace backend -{ - -struct IMemoryManager -{ - virtual ~IMemoryManager() = default; - - virtual void allocate(void) = 0; - virtual void deallocate(void) = 0; -}; - -} // namespace backend -} // namespace onert - -#include <unordered_set> -#include <memory> - -namespace onert -{ -namespace backend -{ - -using MemoryManagerSet = std::unordered_set<std::unique_ptr<backend::IMemoryManager>>; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_IMEMORY_MANAGER_H__ diff --git a/runtime/onert/core/include/backend/IOptimizer.h b/runtime/onert/core/include/backend/IOptimizer.h deleted file mode 100644 index 4844d21b9..000000000 --- a/runtime/onert/core/include/backend/IOptimizer.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_I_OPTIMIZER_H__ -#define __ONERT_BACKEND_I_OPTIMIZER_H__ - -namespace onert -{ -namespace ir -{ -class LoweredGraph; -} -} // namespace onert - -namespace onert -{ -namespace backend -{ - -/** - * @brief Class for backend optimizations. This is an optional class so not all backends must have - * it. - * - */ -struct IOptimizer -{ - virtual ~IOptimizer() = default; - /** - * @brief Run optimization - * - */ - virtual void optimize() = 0; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_I_OPTIMIZER_H__ diff --git a/runtime/onert/core/include/backend/IPortableTensor.h b/runtime/onert/core/include/backend/IPortableTensor.h deleted file mode 100644 index 1b1f05fe1..000000000 --- a/runtime/onert/core/include/backend/IPortableTensor.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_I_PORTABLE_TENSOR_H__ -#define __ONERT_BACKEND_I_PORTABLE_TENSOR_H__ - -#include "backend/ITensor.h" -#include "ir/OperandInfo.h" -#include "ir/Sparsity.h" - -namespace onert -{ -namespace backend -{ - -/** - * @brief A tensor class that is portable for other backends - * - * Backends that use derivatives of this interface can reuse each other's tensors without copying. - * Here's criterion to be a portable tensor: - * - it must not have any paddings - * - No special operations on @c access method - * - e.g. CL memory must map/unmap to use it from CPU, the memory so it cannot be portable - */ -class IPortableTensor : public ITensor -{ -public: - IPortableTensor(const ir::OperandInfo &info) : _info(info) {} - - virtual ~IPortableTensor(); - virtual const ir::Sparsity *sparsity() const { return nullptr; } - const ir::OperandInfo &get_info() const { return _info; } - -public: - bool has_padding() const final { return false; } - void access(const std::function<void(ITensor &tensor)> &fn) final { fn(*this); } - -protected: - ir::OperandInfo _info; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_I_PORTABLE_TENSOR_H__ diff --git a/runtime/onert/core/include/backend/IStaticTensorManager.h b/runtime/onert/core/include/backend/IStaticTensorManager.h deleted file mode 100644 index cef1f8a0a..000000000 --- a/runtime/onert/core/include/backend/IStaticTensorManager.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ISTATICTENSOR_MANAGER_H__ -#define __ONERT_BACKEND_ISTATICTENSOR_MANAGER_H__ - -#include "ITensorManager.h" - -namespace onert -{ -namespace backend -{ - -struct IStaticTensorManager : public ITensorManager -{ - virtual ~IStaticTensorManager() = default; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ISTATICTENSOR_MANAGER_H__ diff --git a/runtime/onert/core/include/backend/ITensor.h b/runtime/onert/core/include/backend/ITensor.h deleted file mode 100644 index b18dd30a2..000000000 --- a/runtime/onert/core/include/backend/ITensor.h +++ /dev/null @@ -1,111 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_OPERAND_I_TENSOR_H__ -#define __ONERT_BACKEND_OPERAND_I_TENSOR_H__ - -#include <cstring> -#include <cstdint> -#include <functional> - -#include "ir/DataType.h" -#include "ir/Layout.h" -#include "ir/Shape.h" -#include "ir/Coordinates.h" -#include "util/Utils.h" - -namespace onert -{ -namespace backend -{ - -struct IDynamicTensorManager; - -class ITensor -{ -public: - virtual ~ITensor() = default; - -public: - virtual uint8_t *buffer() const = 0; - virtual size_t total_size() const = 0; - virtual size_t dimension(size_t index) const = 0; - virtual size_t num_dimensions() const = 0; - virtual size_t calcOffset(const ir::Coordinates &coords) const = 0; - virtual ir::Layout layout() const = 0; - virtual ir::DataType data_type() const = 0; - virtual float data_scale() const = 0; - virtual int32_t data_offset() const = 0; - virtual bool has_padding() const = 0; - virtual void access(const std::function<void(ITensor &tensor)> &fn) = 0; - - /** - * @brief Set the shape to @c shape and possibly re-allocate the buffer - * - * If a tensor is dynamic tensor and previously allocated memory exists, - * it will be deallocated. - * If a tensor is static tensor (with previously allocated memory by StaticTensorManager), - * @c buffer() will be overwriten - * - * @param shape tensor's new shape. While allocating memory for this new_shape, - * tensor's shape is set to new_shape - * @return true If applying shape is successful - * @return false If not applying shape is not supported (it throws for other errors) - */ - virtual bool applyShape(const ir::Shape &) { return false; } - - /** - * @brief Return true if the tensor is constant - */ - virtual bool is_constant() const - { - throw std::runtime_error("This backend does not support checking constant"); - } - - /** - * @brief Return true if the tensor needs dynamic allocation, meaning that during compile-time - * the outpus shape cannot be known and the output shape is calculated during - * kernel execution-time. - */ - virtual bool is_dynamic() const = 0; - - /// @brief set this tensor dynamic - virtual void set_dynamic() - { - throw std::runtime_error("This backend does not support dynamic tensor"); - } - - /** - * @brief Set the shape of tenser to new_shape - * @note Higer dimension will be placed on front. - */ - virtual void setShape(const ir::Shape &new_shape) - { - UNUSED_RELEASE(new_shape); - throw std::runtime_error("This backend does not support dynamic setShape"); - } - - /** - * @brief Get ir::Shape of tensor - * @note Higer dimension will be placed on front. - */ - virtual ir::Shape getShape() const; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_OPERAND_I_TENSOR_H__ diff --git a/runtime/onert/core/include/backend/ITensorBuilder.h b/runtime/onert/core/include/backend/ITensorBuilder.h deleted file mode 100644 index 97721cf19..000000000 --- a/runtime/onert/core/include/backend/ITensorBuilder.h +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ITENSOR_BUILDER_H__ -#define __ONERT_BACKEND_ITENSOR_BUILDER_H__ - -#include <map> - -#include "ir/Index.h" -#include "ir/OperandInfo.h" -#include "ir/Operation.h" -#include "ir/Layout.h" -#include "ITensor.h" -#include "ITensorManager.h" -#include "ITensorRegistry.h" -#include "IDynamicTensorManager.h" - -namespace onert -{ -namespace backend -{ - -struct ITensorBuilder -{ - using IterateFunction = std::function<void(const ir::OperandIndex &)>; - - virtual ~ITensorBuilder(void) = default; - - /** - * @brief Register tensor information to allocate on backend - * - * @param ind Index - * @param info Info - * @param backend_layout Backend layout - * @param as_const Whether this tensor is constant - */ - virtual void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info, - ir::Layout backend_layout) = 0; - - /** - * @brief Check if the tensor has been registered with @c registerTensorInfo - * - * @return true If the tensor has been registered - * @return false Otherwise - */ - virtual bool isRegistered(const ir::OperandIndex &) const = 0; - -public: // methods for static tensor allocation - /** - * @brief Let the tensor builder know first use(start of lifetime) of a tensor - * Must be called before calling @c prepare - * Must be run up to once for each tensor before calling @c notifyLastUse - * NOTE: Useful only for static models - */ - virtual void notifyFirstUse(const ir::OperandIndex &) = 0; - /** - * @brief Let the tensor builder know last use(end of lifetime) of a tensor - * Must be run up to once for each tensor after calling @c notifyFirstUse - * NOTE: Useful only for static models - */ - virtual void notifyLastUse(const ir::OperandIndex &) = 0; - /** - * @brief Prepare the tensors - * Before calling this, all the tensors must be registered - */ - virtual void prepare(void) = 0; - /** - * @brief Allocate the tensors - * Before calling this, @c prepare must be called - */ - virtual void allocate() = 0; - /** - * @brief Some actions after functions' @c IFunction::prepare method. - * This is called right after each function's @c IFunction::prepare function has been - * called. - */ - virtual void postFunctionPrepare() = 0; - -public: // methods for dynamic tensor allocation - /** - * @brief Get dynamicTensorManager. If a backend does not support dynamic tensor, exception - * will be thrown. - * - * @return pointer of IDynamicTensorManager object - * - * @note Since it is a pointer, its life time is from the cration of TensorBuilder - * to the end of execution - */ - virtual IDynamicTensorManager *dynamicTensorManager(void) { return nullptr; } -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ITENSOR_BUILDER_H__ diff --git a/runtime/onert/core/include/backend/ITensorManager.h b/runtime/onert/core/include/backend/ITensorManager.h deleted file mode 100644 index 4974b6645..000000000 --- a/runtime/onert/core/include/backend/ITensorManager.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ITENSOR_MANAGER_H__ -#define __ONERT_BACKEND_ITENSOR_MANAGER_H__ - -namespace onert -{ -namespace backend -{ - -// NOTE This name ITensorManager has been discussed whether or not the name is proper. -// Anyone can argue with any better name. -/** - * @brief Interface as an abstract tensor manager which has MemoryManager - * This is used as a base class for IStaticTensorManager and IDynamicTensorManager - */ -struct ITensorManager -{ - virtual ~ITensorManager() = default; -}; - -} // namespace backend -} // namespace onert - -#include <unordered_set> -#include <memory> - -namespace onert -{ -namespace backend -{ - -using TensorManagerSet = std::unordered_set<std::unique_ptr<backend::ITensorManager>>; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ITENSOR_MANAGER_H__ diff --git a/runtime/onert/core/include/backend/ITensorRegister.h b/runtime/onert/core/include/backend/ITensorRegister.h deleted file mode 100644 index b8e521ce3..000000000 --- a/runtime/onert/core/include/backend/ITensorRegister.h +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ITENSOR_REGISTER_H__ -#define __ONERT_BACKEND_ITENSOR_REGISTER_H__ - -#include "ir/LowerInfoMap.h" -#include "ITensorBuilder.h" -#include "ir/Layout.h" -#include "ir/OperandIndexSequence.h" -#include "ir/OperandInfo.h" -#include "ir/Operands.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace backend -{ - -class ITensorRegister : public ir::OperationVisitor -{ -public: - virtual ~ITensorRegister() = default; - -public: - void registerTensors(const ir::OpSequence &op_seq, const ir::LowerInfoMap *lower_info_map) - { - _current_op_seq_layout = op_seq.getLayout(); - _lower_info_map = lower_info_map; - assert(_lower_info_map != nullptr); - assert(tensor_builder().get() != nullptr); - op_seq.accept(*this); - } - -protected: - virtual const ir::Operands &operands() const = 0; - virtual std::shared_ptr<ITensorBuilder> tensor_builder() const = 0; - -protected: -#define OP(InternalName) \ - void visit(const ir::operation::InternalName &node) override \ - { \ - for (const auto &ind : (node.getInputs() | ir::Remove::UNDEFINED) + node.getOutputs()) \ - { \ - defaultRegisterTensorInfo(ind); \ - } \ - } -#include "ir/Operations.lst" -#undef OP - -protected: - void defaultRegisterTensorInfo(const ir::OperandIndex &index) const - { - if (tensor_builder()->isRegistered(index)) - { - return; - } - - const auto &obj = operands().at(index); - const auto frontend_layout = frontendLayout(); - const auto backend_layout = backendLayout(index); - ir::OperandInfo backend_info{permuteShape(obj.shape(), frontend_layout, backend_layout), - obj.typeInfo(), obj.info().memAllocType(), obj.isConstant()}; - tensor_builder()->registerTensorInfo(index, backend_info, backend_layout); - } - -protected: - ir::Layout frontendLayout() const { return _current_op_seq_layout; } - ir::Layout backendLayout(const ir::OperandIndex &index) const - { - assert(_lower_info_map != nullptr); - const auto lower_info = _lower_info_map->operand.at(index).get(); - return lower_info->def_factors().getOnlyElement().layout(); - } - -private: - ir::Layout _current_op_seq_layout; - const ir::LowerInfoMap *_lower_info_map{nullptr}; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ITENSOR_REGISTER_H__ diff --git a/runtime/onert/core/include/backend/ITensorRegistry.h b/runtime/onert/core/include/backend/ITensorRegistry.h deleted file mode 100644 index b256a1fb8..000000000 --- a/runtime/onert/core/include/backend/ITensorRegistry.h +++ /dev/null @@ -1,146 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_ITENSOR_REGISTRY__ -#define __ONERT_BACKEND_ITENSOR_REGISTRY__ - -#include <memory> - -#include "ir/Index.h" -#include "backend/ITensor.h" -#include "backend/IPortableTensor.h" - -namespace onert -{ -namespace backend -{ - -struct ITensorRegistry -{ - /** - * @brief Deconstruct itself - */ - virtual ~ITensorRegistry() = default; - - /** - * @brief Returns pointer of ITensor among native and migrant tensors - * - * Native Tensor is a tensor that is managed by this backend - * Migrant Tensor is a tensor that is imported from another backend - * - * @note Return tensor cannot be used longer than dynamic tensor manager - */ - virtual ITensor *getITensor(const ir::OperandIndex &) = 0; - /** - * @brief Returns pointer of ITensor among native tensors - * - * Unlike @c getITensor , this function only searches from native tensors - * - * @note Returned tensor cannot be used longer than dynamic tensor manager - */ - virtual ITensor *getNativeITensor(const ir::OperandIndex &) = 0; - /** - * @brief Set the Migrant Tensor which are from other backends - * - * @return true if supported - * @return false if not supported - */ - virtual bool setMigrantTensor(const ir::OperandIndex &, IPortableTensor *) { return false; } -}; - -} // namespace backend -} // namespace onert - -#include "ir/OperandIndexMap.h" - -namespace onert -{ -namespace backend -{ - -/** - * @brief TensorRegistry template class for the convenience of backend implementations - * - * If a backend uses @c IPortableTensor , and there is no special reason to implement @c - * ITensorRegistry on your own, you may just use this default implementation. - * - * @tparam T_Tensor Tensor type. Must be a subclass of @c onert::backend::IPortableTensor . - */ -template <typename T_Tensor> class PortableTensorRegistryTemplate : public ITensorRegistry -{ -public: - ITensor *getITensor(const ir::OperandIndex &ind) override - { - static_assert(std::is_base_of<ITensor, T_Tensor>::value, "T_Tensor must derive from ITensor."); - auto _migrant_tensor = _migrant.find(ind); - if (_migrant_tensor != _migrant.end()) - return _migrant_tensor->second; - return getNativeTensor(ind); - } - - ITensor *getNativeITensor(const ir::OperandIndex &ind) override { return getNativeTensor(ind); } - - IPortableTensor *getPortableTensor(const ir::OperandIndex &ind) - { - auto _migrant_tensor = _migrant.find(ind); - if (_migrant_tensor != _migrant.end()) - { - if (_migrant_tensor->second) - return _migrant_tensor->second; - } - return getNativeTensor(ind); - } - - T_Tensor *getNativeTensor(const ir::OperandIndex &ind) - { - auto tensor = _native.find(ind); - if (tensor != _native.end()) - return tensor->second.get(); - return nullptr; - } - - bool setMigrantTensor(const ir::OperandIndex &ind, IPortableTensor *tensor) override - { - assert(tensor != nullptr); - auto itr = _native.find(ind); - if (itr != _native.end()) - throw std::runtime_error{"Tried to set a migrant tensor but a native tensor already exists."}; - _migrant[ind] = tensor; - return true; - } - - void setNativeTensor(const ir::OperandIndex &ind, std::unique_ptr<T_Tensor> &&tensor) - { - assert(tensor != nullptr); - auto itr = _migrant.find(ind); - if (itr != _migrant.end()) - throw std::runtime_error{"Tried to set a native tensor but a migrant tensor already exists."}; - _native[ind] = std::move(tensor); - } - - const ir::OperandIndexMap<std::unique_ptr<T_Tensor>> &native_tensors() { return _native; } - - const ir::OperandIndexMap<IPortableTensor *> &migrant_tensors() { return _migrant; } - -private: - ir::OperandIndexMap<IPortableTensor *> _migrant; - ir::OperandIndexMap<std::unique_ptr<T_Tensor>> _native; -}; - -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_ITENSOR_REGISTRY__ diff --git a/runtime/onert/core/include/backend/cpu_common/Allocator.h b/runtime/onert/core/include/backend/cpu_common/Allocator.h deleted file mode 100644 index fa67fc7c4..000000000 --- a/runtime/onert/core/include/backend/cpu_common/Allocator.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file       Allocator.h - * @brief      This file contains Allocator related classes - */ - -#ifndef __ONERT_BACKEND_CPU_COMMON_ALLOCATOR_H__ -#define __ONERT_BACKEND_CPU_COMMON_ALLOCATOR_H__ - -#include <memory> - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -/** - * @brief Class to allocate memory - */ -class Allocator -{ -public: - Allocator(uint32_t capacity); - /** - * @brief Get memory base pointer - * @return base pointer - */ - uint8_t *base() const { return _base.get(); } - void release() { _base.reset(); } - -private: - std::unique_ptr<uint8_t[]> _base; -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_COMMON_ALLOCATOR_H__ diff --git a/runtime/onert/core/include/backend/cpu_common/DynamicTensorManager.h b/runtime/onert/core/include/backend/cpu_common/DynamicTensorManager.h deleted file mode 100644 index c4e06aa82..000000000 --- a/runtime/onert/core/include/backend/cpu_common/DynamicTensorManager.h +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_COMMON_DYNAMICTENSOR_MANAGER_H__ -#define __ONERT_BACKEND_CPU_COMMON_DYNAMICTENSOR_MANAGER_H__ - -#include "MemoryManager.h" -#include "TensorRegistry.h" - -#include <backend/IDynamicTensorManager.h> -#include <ir/OperandInfo.h> -#include <ir/Operation.h> -#include <ir/Index.h> - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -// TODO Find optimized algorithm to manage memory. - -/** - * @brief Class to manage dynamic tensor and its memory - */ -class DynamicTensorManager : public backend::IDynamicTensorManager -{ -public: - DynamicTensorManager(const std::shared_ptr<TensorRegistry> ®); - - virtual ~DynamicTensorManager() = default; - - void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info, - ir::Layout backend_layout); - - void planDealloc(ir::OperationIndex op_ind, backend::ITensor *tensor) override; - void deallocInput(ir::OperationIndex op_ind) override; - - std::shared_ptr<DynamicMemoryManager> dynamic_mem_mgr() { return _dynamic_mem_mgr; } - -private: - const ITensor *getRawITensor(ir::OperandIndex ind); - -private: - /** - * @brief Memory manager for dynamic tensor. - * @todo DynamicMemoryManager is not optimized. Optimized one is needed - */ - std::shared_ptr<DynamicMemoryManager> _dynamic_mem_mgr; - const std::shared_ptr<TensorRegistry> _tensors; - - // contains list of dynamic tensor index, which can be deallocated after running operation - // note: this map could contain static tensor index too. Careful use is required. - std::unordered_map<ir::OperationIndex, std::unordered_set<backend::ITensor *>> - _dealloc_tensor_map; -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_COMMON_DYNAMICTENSOR_MANAGER_H__ diff --git a/runtime/onert/core/include/backend/cpu_common/IMemoryPlanner.h b/runtime/onert/core/include/backend/cpu_common/IMemoryPlanner.h deleted file mode 100644 index 335f8f5c0..000000000 --- a/runtime/onert/core/include/backend/cpu_common/IMemoryPlanner.h +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_IMEMORY_PLANNER_H__ -#define __ONERT_BACKEND_IMEMORY_PLANNER_H__ - -#include "ir/OperandIndexMap.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -/** - * @brief Structure to have memory offset and size - */ -struct Block -{ - uint32_t offset; - size_t size; -}; - -/** - * @brief Interface to plan memory - */ -struct IMemoryPlanner -{ - using MemoryPlans = ir::OperandIndexMap<Block>; - - /** - * @brief Claim memory for operand - * @param[in] index The operand index - * @param[in] size The size of the memory - */ - virtual void claim(const ir::OperandIndex &, size_t) = 0; - /** - * @brief Release memory for operand - * @param[in] index The operand index - */ - virtual void release(const ir::OperandIndex &) = 0; - /** - * @brief Get capacity for memory planning - * @return The value of capacity - */ - virtual uint32_t capacity() = 0; - /** - * @brief Get MemoryPlans - * @return MemoryPlans - */ - virtual MemoryPlans &memory_plans() = 0; - - virtual ~IMemoryPlanner() = default; -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_IMEMORY_PLANNER_H__ diff --git a/runtime/onert/core/include/backend/cpu_common/MemoryManager.h b/runtime/onert/core/include/backend/cpu_common/MemoryManager.h deleted file mode 100644 index 28ec6b803..000000000 --- a/runtime/onert/core/include/backend/cpu_common/MemoryManager.h +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_MEMORY_MANAGER_H__ -#define __ONERT_BACKEND_CPU_MEMORY_MANAGER_H__ - -#include "Allocator.h" -#include "backend/IMemoryManager.h" -#include "IMemoryPlanner.h" - -namespace onert -{ -namespace backend -{ - -class ITensor; - -namespace cpu_common -{ - -class MemoryManager : public backend::IMemoryManager -{ -public: - MemoryManager(); - MemoryManager(const std::string); - virtual ~MemoryManager() = default; - - void allocate(void) override; - uint8_t *getBuffer(const ir::OperandIndex &ind) const; - void deallocate(void) override { _mem_alloc->release(); } - - void claimPlan(const ir::OperandIndex &ind, uint32_t size); - void releasePlan(const ir::OperandIndex &ind); - -private: - IMemoryPlanner *createMemoryPlanner(); - IMemoryPlanner *createMemoryPlanner(const std::string); - -private: - ir::OperandIndexMap<Block> _tensor_mem_map; - std::shared_ptr<IMemoryPlanner> _mem_planner; - std::shared_ptr<Allocator> _mem_alloc; -}; - -class DynamicMemoryManager -{ -public: - DynamicMemoryManager() = default; - virtual ~DynamicMemoryManager() = default; - - std::shared_ptr<Allocator> allocate(const ITensor *tensor, uint32_t capacity); - void deallocate(const ITensor *tensor); - void deallocate(void); - -private: - std::unordered_map<const ITensor *, std::shared_ptr<Allocator>> _mem_alloc_map; -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_MEMORY_MANAGER_H__ diff --git a/runtime/onert/core/include/backend/cpu_common/StaticTensorManager.h b/runtime/onert/core/include/backend/cpu_common/StaticTensorManager.h deleted file mode 100644 index fa50b551e..000000000 --- a/runtime/onert/core/include/backend/cpu_common/StaticTensorManager.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_COMMON_STATICTENSOR_MANAGER_H__ -#define __ONERT_BACKEND_CPU_COMMON_STATICTENSOR_MANAGER_H__ - -#include "MemoryManager.h" - -#include "backend/IStaticTensorManager.h" -#include "ir/OperandIndexMap.h" -#include "ir/OperandInfo.h" -#include "TensorRegistry.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -class DynamicTensorManager; - -class StaticTensorManager : public backend::IStaticTensorManager -{ -public: - StaticTensorManager(const std::shared_ptr<TensorRegistry> ®, - DynamicMemoryManager *dynamic_mem_mgr); - virtual ~StaticTensorManager() = default; - - void allocateConsts(void); - void allocateNonconsts(void); - void deallocateConsts(void); - void deallocateNonconsts(void); - - void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info, - ir::Layout backend_layout, bool as_const); - - void claimPlan(const ir::OperandIndex &ind, uint32_t size); - void releasePlan(const ir::OperandIndex &ind); - - void iterate(const std::function<void(const ir::OperandIndex &)> &fn); - -private: - std::unique_ptr<DynamicMemoryManager> _const_mgr; - std::unique_ptr<MemoryManager> _nonconst_mgr; - const std::shared_ptr<TensorRegistry> _tensors; - ir::OperandIndexMap<bool> _as_constants; - DynamicMemoryManager *_dynamic_mem_mgr; -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_COMMON_STATICTENSOR_MANAGER_H__ diff --git a/runtime/onert/core/include/backend/cpu_common/Tensor.h b/runtime/onert/core/include/backend/cpu_common/Tensor.h deleted file mode 100644 index 5fa20e15d..000000000 --- a/runtime/onert/core/include/backend/cpu_common/Tensor.h +++ /dev/null @@ -1,184 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_COMMON_TENSOR_H__ -#define __ONERT_BACKEND_CPU_COMMON_TENSOR_H__ - -#include "Allocator.h" - -#include <backend/IPortableTensor.h> -#include <ir/OperandInfo.h> - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -class DynamicMemoryManager; - -class Tensor : public IPortableTensor -{ -public: - Tensor() = delete; - virtual ~Tensor(); - -public: - Tensor(const ir::OperandInfo &info, const ir::Layout layout, - DynamicMemoryManager *dynamic_mem_mgr) - : IPortableTensor(info), _layout(layout), _buffer(nullptr), _num_references(0), - _dynamic_mem_mgr(dynamic_mem_mgr), _allocator(nullptr) - { - // DO NOTHING - } - -public: - // Only one of two method 'setBuffer' must be called once - - /** - * @brief Set the Buffer object. This method is called for static and non-const tensor - */ - void setBuffer(uint8_t *buffer) - { - assert(_buffer == nullptr); - _buffer = buffer; - } - - /** - * @brief Set the Buffer object. This method is called for dynamic or const tensor - */ - void setBuffer(const std::shared_ptr<Allocator> &alloc) - { - assert(_buffer == nullptr); - _allocator = alloc; - _buffer = alloc->base(); - } - - // This works just as setBuffer but it simply overwrite existing Allocator without nullptr check - void overwriteBuffer(const std::shared_ptr<Allocator> &alloc) - { - _allocator = alloc; - _buffer = alloc->base(); - } - - /** - * @brief Mark this tensor does not have memory. - * Real memory deallocation should be done by caller. - */ - void resetBuffer() - { - _allocator.reset(); - _buffer = nullptr; - } - -public: - uint8_t *buffer() const override { return _buffer; } - /** - * @brief Get dimension by index - * - * @param index Index to get diemension - * @return size_t Dimension at index - * @note N : dimension(0) - * H : dimension(1) - * W : dimension(2) - * C : dimension(3) - */ - size_t dimension(size_t index) const final override { return _info.shape().dim(index); } - size_t num_dimensions() const override { return _info.shape().rank(); } - size_t total_size() const override { return _info.total_size(); } - size_t calcOffset(const ir::Coordinates &coords) const override; - ir::Layout layout() const override { return _layout; } - ir::DataType data_type() const override { return _info.typeInfo().type(); } - float data_scale() const override { return _info.typeInfo().scale(); } - int32_t data_offset() const override { return _info.typeInfo().offset(); } - bool is_constant() const override { return _info.isConstant(); } - bool is_dynamic() const override { return _info.isDynamic(); } - void set_dynamic() override { _info.setDynamic(); } - bool applyShape(const ir::Shape &new_shape) override; - const ir::Sparsity *sparsity() const override { return _info.typeInfo().sparsity(); } - - virtual void increase_ref() - { - assert(is_dynamic() || - // when not dynamic - (_buffer != nullptr)); - - ++_num_references; - } - - virtual void decrease_ref() - { - assert(_buffer != nullptr || _allocator != nullptr); - assert(_num_references > 0); - --_num_references; - // constant tensor and dynamic tensor has _allocator - if (_num_references == 0) - { - if (_buffer != nullptr) - _buffer = nullptr; - if (_allocator != nullptr) - { - _allocator->release(); - _allocator = nullptr; - } - } - } - - /** - * @brief Reset reference count to zero and release data - */ - virtual void reset_ref() - { - assert(_buffer != nullptr || _allocator != nullptr); - assert(_num_references > 0); - _num_references = 0; - - // Only constant tensor has allocator pointer - if (_buffer != nullptr) - _buffer = nullptr; - else - { - _allocator->release(); - _allocator = nullptr; - } - } - - virtual int32_t num_references() { return _num_references; } - - void setShape(const ir::Shape &new_shape) override; - -protected: - ir::Layout _layout; - uint8_t *_buffer; - int32_t _num_references; - DynamicMemoryManager *_dynamic_mem_mgr; - -private: - /** - * @brief Memory allocator for dynamic tensor and const tensor - * Since maintaing _allocator and also _buffer makes confusion, - * we will mainly use _buffer (not _allocator.base()) for memory pointer in this code. - * _allocator(shared_ptr) is used to guarantee that we have valid _buffer. - */ - std::shared_ptr<Allocator> _allocator; -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_COMMON_TENSOR_H__ diff --git a/runtime/onert/core/include/backend/cpu_common/TensorRegistry.h b/runtime/onert/core/include/backend/cpu_common/TensorRegistry.h deleted file mode 100644 index 5896fb7ad..000000000 --- a/runtime/onert/core/include/backend/cpu_common/TensorRegistry.h +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_COMMON_TENSOR_REGISTRY__ -#define __ONERT_BACKEND_CPU_COMMON_TENSOR_REGISTRY__ - -#include "backend/ITensorRegistry.h" -#include "Tensor.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -using TensorRegistry = PortableTensorRegistryTemplate<cpu_common::Tensor>; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_COMMON_TENSOR_REGISTRY__ diff --git a/runtime/onert/core/include/compiler/BackendManager.h b/runtime/onert/core/include/compiler/BackendManager.h deleted file mode 100644 index af13d13f7..000000000 --- a/runtime/onert/core/include/compiler/BackendManager.h +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_BACKEND_MANAGER_H__ -#define __ONERT_COMPILER_BACKEND_MANAGER_H__ - -#include <memory> -#include <map> - -#include "ir/Operands.h" -#include "backend/Backend.h" -#include "backend/controlflow/Backend.h" - -namespace onert -{ -namespace compiler -{ - -class BackendManager -{ -public: - using backend_create_t = backend::Backend *(*)(); - using backend_destroy_t = void (*)(backend::Backend *); - using dlhandle_destroy_t = void (*)(void *); - - static BackendManager &get(); - -public: - backend::Backend *get(const std::string &key); - const backend::Backend *get(const std::string &key) const; - const backend::controlflow::Backend *getControlflow() const; - const std::vector<const backend::Backend *> getAll() const - { - std::vector<const backend::Backend *> v; - for (const auto &p : _gen_map) - v.emplace_back(p.second.get()); - return v; - } - size_t num_backends() const { return _gen_map.size(); } - /** - * @brief load backend plugin - * - * @param backend backend to be loaded - * - * @return - */ - void loadBackend(const std::string &backend); - -private: - BackendManager(); - -private: - std::map<std::string, std::unique_ptr<void, dlhandle_destroy_t>> _handle_map; - std::map<std::string, std::unique_ptr<backend::Backend, backend_destroy_t>> _gen_map; - backend::controlflow::Backend *_controlflow{nullptr}; - /** - * @brief load controlflow backend - * - * @param backend backend to be loaded - * - * @return - */ - void loadControlflowBackend(); -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_BACKEND_MANAGER_H__ diff --git a/runtime/onert/core/include/compiler/BackendResolver.h b/runtime/onert/core/include/compiler/BackendResolver.h deleted file mode 100644 index a316b4335..000000000 --- a/runtime/onert/core/include/compiler/BackendResolver.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_BACKEND_RESOLVER_H__ -#define __ONERT_COMPILER_BACKEND_RESOLVER_H__ - -#include <unordered_map> -#include <typeindex> - -#include "backend/Backend.h" -#include "ir/OperationIndexMap.h" - -namespace onert -{ -namespace compiler -{ - -class BackendResolver -{ -public: - const backend::Backend *getBackend(const ir::OperationIndex &index) const - { - return _gen_map.at(index); - } - - void setBackend(const ir::OperationIndex &index, const backend::Backend *backend) - { - _gen_map[index] = backend; - } - - void - iterate(const std::function<void(const ir::OperationIndex &, const backend::Backend &)> &fn) const - { - for (const auto &e : _gen_map) - { - fn(e.first, *e.second); - } - } - -private: - ir::OperationIndexMap<const backend::Backend *> _gen_map; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_BACKEND_RESOLVER_H__ diff --git a/runtime/onert/core/include/compiler/CodeMap.h b/runtime/onert/core/include/compiler/CodeMap.h deleted file mode 100644 index e13d3334c..000000000 --- a/runtime/onert/core/include/compiler/CodeMap.h +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_CODE_MAP_H__ -#define __ONERT_COMPILER_CODE_MAP_H__ - -#include <unordered_map> - -namespace onert -{ -namespace compiler -{ - -struct CodeAndInfo -{ - const ir::OpSequence *op_seq; - const ir::operation::LowerInfo *lower_info; - std::unique_ptr<exec::FunctionSequence> fn_seq; - - CodeAndInfo(const ir::OpSequence *op_seq, const ir::operation::LowerInfo *lower_info, - std::unique_ptr<exec::FunctionSequence> &&fn_seq) - : op_seq{op_seq}, lower_info{lower_info}, fn_seq{std::move(fn_seq)} - { - } -}; - -using CodeMap = std::unordered_map<ir::OpSequenceIndex, CodeAndInfo>; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_CODE_MAP_H__ diff --git a/runtime/onert/core/include/compiler/Compiler.h b/runtime/onert/core/include/compiler/Compiler.h deleted file mode 100644 index 3098be7ba..000000000 --- a/runtime/onert/core/include/compiler/Compiler.h +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Compiler.h - * @brief This file contains Compiler class to define and run compilation phase - */ - -#ifndef __ONERT_COMPILER_COMPILE_H_ -#define __ONERT_COMPILER_COMPILE_H_ - -#include "ir/Graph.h" -#include "exec/IExecutor.h" - -namespace onert -{ - -namespace compiler -{ - -enum class State -{ - CREATED, // Before compilation - COMPILED // Success compilation -}; - -struct ManualSchedulerOptions -{ - std::string backend_for_all; - std::unordered_map<ir::OpCode, std::string> opcode_to_backend; - std::unordered_map<ir::OperationIndex, std::string> index_to_backend; -}; - -struct CompilerOptions -{ - // GENERAL OPTIONS - std::vector<std::string> backend_list; - bool is_primary_subgraph; // TODO Remove this out of this struct as it is not user-given option - - // OPTIONS ONLY FOR DEBUGGING/PROFILING - std::string trace_filepath; //< File path to save trace records - int graph_dump_level; //< Graph dump level, values between 0 and 2 are valid - int op_seq_max_node; //< Number of nodes that can be - std::string executor; //< Executor name to use - ManualSchedulerOptions manual_scheduler_options; //< Options for ManualScheduler - bool he_scheduler; //< HEScheduler if true, ManualScheduler otherwise - bool he_profiling_mode; //< Whether HEScheduler profiling mode ON/OFF - bool disable_compile; //< Run with Interpreter if true, try compilation otherwise - bool fp16_enable; //< Whether fp16 mode ON/OFF -}; - -CompilerOptions fetchCompilerOptionsFromGlobalConfig(const ir::Subgraphs &subgs); - -/** - * @brief Class to compile graph model - */ -class Compiler -{ -public: - /** - * @brief Construct a new Compiler object - * @param[in] subgs All subgraphs of a model - */ - Compiler(const std::shared_ptr<ir::Subgraphs> &subgs); - -public: - /** - * @brief Do compilation with the options - * - * @return std::shared_ptr<exec::ExecutorMap> Executors as a result of compilation - */ - std::shared_ptr<exec::ExecutorMap> compile(void); - - State state(void) const { return _state; } - - /** - * @brief Check if model can compile - * @return @c true if model can compile, otherwise @c false - * @note This method don't check model correctness,\n - * so model verification should be done before calling this method - */ - bool checkCompilable(); - CompilerOptions &options() { return _options; } - - /** - * @brief Allow to compute float32 using float16 data type - */ - void enableToFp16(); - -private: - void checkProfilerConditions(); - std::shared_ptr<ir::Graph> &primary_subgraph() { return _subgraphs->at(ir::SubgraphIndex{0}); } - -private: - std::shared_ptr<ir::Subgraphs> _subgraphs; - // NOTE These executors does not have duplicated subgraph. This mean they do not allow support - // subgraphs being called recursively because data of non-constant tensor of parent executor will - // be updated by child executor. If you want to support subgraphs being called recursively, you - // have to add allocate non-constant tensor memory of executors in execution time when each - // subgraph is called. - State _state; - CompilerOptions _options; -}; - -} // namespace compiler - -} // namespace onert - -#endif // __ONERT_COMPILER_COMPILE_H_ diff --git a/runtime/onert/core/include/compiler/ExecutionBuilder.h b/runtime/onert/core/include/compiler/ExecutionBuilder.h deleted file mode 100644 index d54d9d046..000000000 --- a/runtime/onert/core/include/compiler/ExecutionBuilder.h +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_EXECUTION_BUILDER_H__ -#define __ONERT_COMPILER_EXECUTION_BUILDER_H__ - -#include <memory> - -#include "ir/operation/LowerInfo.h" -#include "ir/OpSequence.h" -#include "exec/FunctionSequence.h" -#include "CodeMap.h" - -namespace onert -{ -namespace compiler -{ - -class ExecutionBuilder -{ -public: - void append(const ir::OpSequenceIndex index, CodeAndInfo &&code_and_info) - { - _code_map.emplace(index, std::move(code_and_info)); - } - - CodeMap releaseCodeMap() { return std::move(_code_map); } - -private: - CodeMap _code_map; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_EXECUTION_BUILDER_H__ diff --git a/runtime/onert/core/include/compiler/LoweredGraph.h b/runtime/onert/core/include/compiler/LoweredGraph.h deleted file mode 100644 index aadba6857..000000000 --- a/runtime/onert/core/include/compiler/LoweredGraph.h +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_LOWERED_GRAPH_H__ -#define __ONERT_IR_LOWERED_GRAPH_H__ - -#include "ir/Graph.h" -#include "ir/LowerInfoMap.h" -#include "ir/OpSequences.h" -#include "compiler/BackendResolver.h" -#include "compiler/Compiler.h" - -namespace onert -{ -namespace compiler -{ - -/** - * @brief Class that contains lowering information on graph. - * In addition, after lowering, operands in graph will be set to "dynamic" - * if the shape of output of an operation cannot be decided at compilation time. - */ -class LoweredGraph -{ -public: - LoweredGraph(const ir::Graph &graph, const compiler::CompilerOptions &options); - - ir::Graph &graph() { return _graph; } - const ir::Graph &graph() const { return _graph; } - const ir::LowerInfoMap *getLowerInfo() const { return &_lower_info_map; } - const ir::operation::LowerInfo *getLowerInfo(const ir::OpSequenceIndex &op_seq_index) const; - void setLowerInfo(const ir::OpSequenceIndex &op_seq_index, - std::unique_ptr<ir::operation::LowerInfo> &&lower_info); - void removeLowerInfo(const ir::OpSequenceIndex &op_seq_index); - const ir::operand::LowerInfo *getLowerInfo(const ir::OperandIndex &index) const; - ir::operand::LowerInfo *getLowerInfo(const ir::OperandIndex &index); - void setLowerInfo(const ir::OperandIndex &index, - std::unique_ptr<ir::operand::LowerInfo> &&lower_info); - void removeLowerInfo(const ir::OperandIndex &index); - ir::OpSequences &op_seqs() { return _op_seqs; } - const ir::OpSequences &op_seqs() const { return _op_seqs; } - void iterateTopolOpSeqs( - const std::function<void(const ir::OpSequenceIndex &, const ir::OpSequence &)> &fn) const; - void - iterateTopolOpSeqs(const std::function<void(const ir::OpSequenceIndex &, ir::OpSequence &)> &fn); - const backend::BackendContexts &backend_contexts() { return _backend_contexts; } - const backend::BackendContexts &backend_contexts() const { return _backend_contexts; } - std::shared_ptr<ir::OperationIndexMap<int64_t>> indexed_ranks() { return _indexed_ranks; } - -private: - void - makeOpSequences(ir::OperandIndexMap<std::unique_ptr<ir::operand::LowerInfo>> &operands_lower_info, - const compiler::CompilerOptions &options, - const compiler::BackendResolver &backend_resolver); - - void manipulateLowerInfo( - ir::OperandIndexMap<std::unique_ptr<ir::operand::LowerInfo>> &operands_lower_info, - bool is_primary); - void dumpLowerInfo(); - bool mergeable(const ir::OpSequenceIndex &op_seq_index, const ir::OperationIndex &node_index, - ir::Layout layout, const compiler::BackendResolver &backend_resolver); - ir::OpSequenceIndex appendFreshSingleOpSequence(const ir::OperationIndex &node_index, - const ir::Operation &node); - -private: - ir::Graph _graph; - backend::BackendContexts _backend_contexts; - std::shared_ptr<ir::OperationIndexMap<int64_t>> _indexed_ranks; - ir::LowerInfoMap _lower_info_map; - // Pass(for Perm) can accept only graph so that Graph has OpSequences as a member - ir::OpSequences _op_seqs; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_IR_LOWERED_GRAPH_H__ diff --git a/runtime/onert/core/include/compiler/StaticShapeInference.h b/runtime/onert/core/include/compiler/StaticShapeInference.h deleted file mode 100644 index 5af11074e..000000000 --- a/runtime/onert/core/include/compiler/StaticShapeInference.h +++ /dev/null @@ -1,141 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_STATIC_SHAPE_INFERENCE_H__ -#define __ONERT_COMPILER_STATIC_SHAPE_INFERENCE_H__ - -#include "ir/OperationVisitor.h" -#include "ir/OpSequence.h" -#include "compiler/LoweredGraph.h" -#include "ir/Index.h" - -#include <memory> -#include <unordered_map> - -namespace onert -{ -namespace compiler -{ - -/** - * @brief Class to infer shape before running kernels. It does the following: - * - re-calculate and set output shape at compile time (before running kernels) - * - if calculation cannot be done at compile time, mark the outputs to be dynamic, meaning - * shapes of outputs will be calculated during running kernels - */ -class StaticShapeInferer : public ir::OperationVisitor -{ -public: - StaticShapeInferer( - const ir::SubgraphIndex &subg_idx, - const std::unordered_map<ir::SubgraphIndex, std::unique_ptr<compiler::LoweredGraph>> - &lowered_subgs) - : _lowered_subgs(lowered_subgs), _operands(lowered_subgs.at(subg_idx)->graph().operands()), - _operations(lowered_subgs.at(subg_idx)->graph().operations()), - _return_has_dynamic_tensor(false) - { /* empty */ - } - virtual ~StaticShapeInferer() = default; - -public: - /** - * @brief Infer shape of operands beloning to ops and set the output shape. - * If output shape cannot be known without running op, mark it so that it can be allocated - * when running kernel. - * @param op_seq sequence of operations - * @return @c true if op_seq's input or output has any dynamic tensor; @c false otherwise. - */ - bool infer(const ir::OpSequence &op_seq); - - void dump(); - -private: - bool checkDynamicInput(const ir::Operation &op); - void setDynamicOutput(const ir::Operation &op); - -private: - // TODO Define visitors for operations. List them in alphabetic order. - void visit(const ir::operation::ArgMax &op) override; - void visit(const ir::operation::BatchMatMul &op) override; - void visit(const ir::operation::BCQFullyConnected &op) override; - void visit(const ir::operation::BCQGather &op) override; - void visit(const ir::operation::BinaryArithmetic &op) override; - void visit(const ir::operation::BroadcastTo &op) override; - void visit(const ir::operation::Comparison &op) override; - void visit(const ir::operation::Concat &op) override; - void visit(const ir::operation::Conv2D &op) override; - void visit(const ir::operation::ElementwiseActivation &op) override; - void visit(const ir::operation::ElementwiseBinary &op) override; - void visit(const ir::operation::ElementwiseUnary &op) override; - void visit(const ir::operation::ExpandDims &op) override; - void visit(const ir::operation::Fill &op) override; - void visit(const ir::operation::FullyConnected &op) override; - void visit(const ir::operation::FusedBatchNorm &op) override; - void visit(const ir::operation::Gather &op) override; - void visit(const ir::operation::If &op) override; - void visit(const ir::operation::L2Normalization &op) override; - void visit(const ir::operation::LSTM &op) override; - void visit(const ir::operation::MatrixBandPart &op) override; - void visit(const ir::operation::OneHot &op) override; - void visit(const ir::operation::Pack &op) override; - void visit(const ir::operation::Pad &op) override; - void visit(const ir::operation::Permute &op) override; - void visit(const ir::operation::Pow &op) override; - void visit(const ir::operation::Range &op) override; - void visit(const ir::operation::Reduce &op) override; - void visit(const ir::operation::Reshape &op) override; - void visit(const ir::operation::ResizeBilinear &op) override; - void visit(const ir::operation::Reverse &op) override; - void visit(const ir::operation::Select &op) override; - void visit(const ir::operation::Shape &op) override; - void visit(const ir::operation::Slice &op) override; - void visit(const ir::operation::Softmax &op) override; - void visit(const ir::operation::SpaceToBatchND &op) override; - void visit(const ir::operation::Split &op) override; - void visit(const ir::operation::Squeeze &op) override; - void visit(const ir::operation::StridedSlice &op) override; - void visit(const ir::operation::SquaredDifference &op) override; - void visit(const ir::operation::Tile &op) override; - void visit(const ir::operation::Transpose &op) override; - void visit(const ir::operation::Unpack &op) override; - void visit(const ir::operation::While &op) override; - -private: - /** - * @brief Performs shape inference for arithmetic operation - */ - void handleBinaryArithmeticOp(const ir::Operation &op, const ir::OperandIndex lhs_idx, - const ir::OperandIndex rhs_idx); - - /** - * @brief Performs shape inference for unary op whose output shape is - * always same with input shape - */ - void handleSimpleUnaryOp(const ir::Operation &op, const ir::OperandIndex input_idx); - -private: - const std::unordered_map<ir::SubgraphIndex, std::unique_ptr<compiler::LoweredGraph>> - &_lowered_subgs; - // _operands and _operations can be changed by controlflow operation - ir::Operands &_operands; // operands of current subgraph - ir::Operations &_operations; // operations of current subgraph - bool _return_has_dynamic_tensor; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_STATIC_SHAPE_INFERENCE_H__ diff --git a/runtime/onert/core/include/exec/DynamicShapeInference.h b/runtime/onert/core/include/exec/DynamicShapeInference.h deleted file mode 100644 index 4a86708d0..000000000 --- a/runtime/onert/core/include/exec/DynamicShapeInference.h +++ /dev/null @@ -1,123 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_DYNAMIC_SHAPE_INFERENCE_H__ -#define __ONERT_EXEC_DYNAMIC_SHAPE_INFERENCE_H__ - -#include "ir/Operands.h" -#include "ir/OperationVisitor.h" -#include "ir/Index.h" -#include "backend/IDynamicTensorManager.h" -#include "backend/ITensorManager.h" -#include "backend/ITensorRegistry.h" - -#include <map> - -namespace onert -{ -namespace exec -{ - -/** - * @brief Class to infer shape of output tensor at execution time and - * allocate memory fo output tensor if needed - */ -class DynamicShapeInferer : public ir::OperationVisitor -{ -public: - DynamicShapeInferer(const ir::Operands &operands, - const std::shared_ptr<backend::ITensorRegistry> &tensor_registry) - : _operands(operands), _tensor_registry(tensor_registry) - { - UNUSED_RELEASE(_operands); - UNUSED_RELEASE(_tensor_registry); - } - -public: - // TODO Define visitors for operations. List them in alphabetic order. - // Remove TODO when any op starting from the alphabet is added - void visit(const ir::operation::ArgMax &op) override; - void visit(const ir::operation::BatchMatMul &op) override; - void visit(const ir::operation::BCQFullyConnected &op) override; - void visit(const ir::operation::BCQGather &op) override; - void visit(const ir::operation::BinaryArithmetic &op) override; - void visit(const ir::operation::BroadcastTo &op) override; - void visit(const ir::operation::Comparison &op) override; - void visit(const ir::operation::Concat &op) override; - void visit(const ir::operation::Conv2D &op) override; - void visit(const ir::operation::ElementwiseActivation &op) override; - void visit(const ir::operation::ElementwiseBinary &op) override; - void visit(const ir::operation::ElementwiseUnary &op) override; - void visit(const ir::operation::ExpandDims &op) override; - void visit(const ir::operation::Fill &op) override; - void visit(const ir::operation::FullyConnected &op) override; - void visit(const ir::operation::FusedBatchNorm &op) override; - void visit(const ir::operation::Gather &op) override; - void visit(const ir::operation::L2Normalization &op) override; - void visit(const ir::operation::LSTM &op) override; - void visit(const ir::operation::MatrixBandPart &op) override; - void visit(const ir::operation::OneHot &op) override; - void visit(const ir::operation::Pack &op) override; - void visit(const ir::operation::Pad &op) override; - void visit(const ir::operation::Permute &op) override; - void visit(const ir::operation::Pow &op) override; - // TODO write op starting from Q - void visit(const ir::operation::Range &op) override; - void visit(const ir::operation::Reduce &op) override; - void visit(const ir::operation::Reshape &op) override; - void visit(const ir::operation::ResizeBilinear &op) override; - void visit(const ir::operation::Reverse &op) override; - void visit(const ir::operation::Select &op) override; - void visit(const ir::operation::Shape &op) override; - void visit(const ir::operation::Slice &op) override; - void visit(const ir::operation::Softmax &op) override; - void visit(const ir::operation::SpaceToBatchND &op) override; - void visit(const ir::operation::Split &op) override; - void visit(const ir::operation::Squeeze &op) override; - void visit(const ir::operation::StridedSlice &op) override; - void visit(const ir::operation::SquaredDifference &op) override; - void visit(const ir::operation::Tile &op) override; - void visit(const ir::operation::Transpose &op) override; - void visit(const ir::operation::Unpack &op) override; - // TODO write op starting from V - -private: - /** - * @brief Performs shape inference and memory allocation for arithmetic operation - */ - void handleBinaryArithmeticOp(const ir::Operation &op, const ir::OperandIndex lhs_idx, - const ir::OperandIndex rhs_idx); - /** - * @brief Performs shape inference and memory allocation for unary op whose output shape is - * always same with input shape - */ - void handleSimpleUnaryOp(const ir::Operation &op, const ir::OperandIndex input_idx); - -private: - /** - * @brief To get operand-level info, e.g., ir::Operand::isConstant() - */ - const ir::Operands &_operands; - /** - * @brief To get tensor object and access tensor-level info, e.g., ITensor::buffer() - */ - std::shared_ptr<backend::ITensorRegistry> _tensor_registry; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_DYNAMIC_SHAPE_INFERENCE_H__ diff --git a/runtime/onert/core/include/exec/Execution.h b/runtime/onert/core/include/exec/Execution.h deleted file mode 100644 index d3c5b6dda..000000000 --- a/runtime/onert/core/include/exec/Execution.h +++ /dev/null @@ -1,162 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Execution.h - * @brief This file defines execution - */ -#ifndef __ONERT_EXEC_EXECUTION_H__ -#define __ONERT_EXEC_EXECUTION_H__ - -#include "ir/Layout.h" -#include "exec/IExecutor.h" -#include "IODescription.h" - -#include <thread> - -namespace onert -{ -namespace exec -{ - -/** - * @brief Class to define execution instance to collect input/output information for inference - * and prepare executor run (TODO) - */ -class Execution -{ - -public: - /** - * @brief Construct a new Execution object - * @param[in] executor Model executor - */ - Execution(const std::shared_ptr<ExecutorMap> &executors); - -public: - /** - * @brief Returns primary graph object - * @return Graph object - */ - const ir::Graph &primary_subgraph() const { return primary_executor()->graph(); } - - /** - * @brief Change input shape - * @param[in] index Input index - * @param[in] new_shape shape to change - */ - void changeInputShape(const ir::IOIndex &index, const ir::Shape &new_shape); - - /** - * @brief Set input data's information - * @param[in] index Input index - * @param[in] buffer Input data's buffer pointer - * @param[in] length Input data's length - * @param[in] layout Input data's data format - */ - void setInput(const ir::IOIndex &index, const void *buffer, size_t length, - ir::Layout layout = ir::Layout::NHWC); - /** - * @brief Set input data's information, especially to specify unknown dimensions on model - * build time. - * @param[in] index Input index - * @param[in] type Input data's type info - * @param[in] shape Input data's shape - * @param[in] buffer Input data's buffer pointer - * @param[in] length Input data's length - * @param[in] layout Input data's data format - */ - void setInput(const ir::IOIndex &index, const ir::TypeInfo &type, const ir::Shape &shape, - const void *buffer, size_t length, ir::Layout layout = ir::Layout::NHWC); - /** - * @brief Set output data's information - * @param[in] index Output index - * @param[in] buffer Output data's buffer pointer - * @param[in] length Output data's length - * @param[in] layout Output data's data format - */ - void setOutput(const ir::IOIndex &index, void *buffer, size_t length, - ir::Layout layout = ir::Layout::NHWC); - /** - * @brief Set output data's information, especially to specify unknown dimensions on model - * build time. - * @param[in] index Output index - * @param[in] type Output data's type info - * @param[in] shape Output data's shape - * @param[in] buffer Output data's buffer pointer - * @param[in] length Output data's length - * @param[in] layout Output data's data format - */ - void setOutput(const ir::IOIndex &index, const ir::TypeInfo &type, const ir::Shape &shape, - void *buffer, size_t length, ir::Layout layout = ir::Layout::NHWC); - /** - * @brief Set input data's data format - * @param[in] index Input index - * @param[in] layout Input data's data format - */ - void setInputLayout(const ir::IOIndex &index, ir::Layout layout); - /** - * @brief Set output data's data format - * @param[in] index Output index - * @param[in] layout Output data's data format - */ - void setOutputLayout(const ir::IOIndex &index, ir::Layout layout); - /** - * @brief Execution - * @note It should be called after setting input and output buffer - */ - void execute(); - - /** - * @brief Start asynchronous execution - * @note It returns after execution thread is started - * It should be called after setting input and output buffer - */ - void startExecute(void); - - /** - * @brief Return when execution is finished - * @note It waits until execution is finished - */ - void waitFinish(void); - - /** - * @brief Check execution is finished - * @return @c true if execution is finished, otherwise @c false - */ - bool isFinished(void) const; - - ir::Shape getInputShape(ir::IOIndex ind) const; - ir::Shape getOutputShape(ir::IOIndex ind) const; - -private: - const std::unique_ptr<IExecutor> &primary_executor() const - { - return _executors->at(ir::SubgraphIndex{0}); - }; - std::unique_ptr<IExecutor> &primary_executor() { return _executors->at(ir::SubgraphIndex{0}); }; - -private: - const std::shared_ptr<ExecutorMap> _executors; - IODescription _io_desc; - std::unique_ptr<std::thread> _exec_thread; - bool finished{false}; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_EXECUTION_H__ diff --git a/runtime/onert/core/include/exec/FunctionSequence.h b/runtime/onert/core/include/exec/FunctionSequence.h deleted file mode 100644 index 49f00dba1..000000000 --- a/runtime/onert/core/include/exec/FunctionSequence.h +++ /dev/null @@ -1,132 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_FUNCTION_SEQUENCE_H__ -#define __ONERT_EXEC_FUNCTION_SEQUENCE_H__ - -#include <memory> -#include <cassert> -#include <vector> -#include <functional> - -#include "exec/IFunction.h" -#include "exec/DynamicShapeInference.h" -#include "ir/Operations.h" -#include "backend/ITensorRegistry.h" -#include "backend/IDynamicTensorManager.h" - -namespace onert -{ -namespace exec -{ - -class FunctionSequence : public IFunction -{ -public: - template <typename... Args> FunctionSequence(Args &&... args) { initialize(std::move(args)...); } - -private: - void initialize() - { - // Template base case : do nothing - } - - template <typename T, typename... Args> void initialize(std::unique_ptr<T> &&fn, Args &&... args) - { - _functions.emplace_back(std::move(fn)); - initialize(std::move(args)...); - } - -public: - virtual ~FunctionSequence() = default; - - void run() override; - void prepare() override; - - /** - * @brief Appends an IFunction object to the function sequence - * - * @param function IFunction object to be appended - */ - void append(std::unique_ptr<IFunction> &&function); - - void iterate(const std::function<void(IFunction &)> &fn); - - template <typename T, typename... Args> void wrap(Args &&... args) - { - for (auto &function : _functions) - { - function = std::make_unique<T>(std::move(function), args...); - } - } - -public: // methods related to dynamic tensor - struct DynamicTensorCtx - { - const ir::OpSequence *op_seq = nullptr; - const ir::Operations *operations = nullptr; - std::shared_ptr<exec::DynamicShapeInferer> dynamic_shape_inferer = nullptr; - backend::IDynamicTensorManager *dynamic_tensor_manager = nullptr; - }; - - /** - * @brief Prepare to run FunctionSequence which "might" handle dynamic tensor - * @note Calling this does not mean that run() will handle dynamic tensor. - * enableDynamicShapeInferer(true) will make run() will handle dynamic tensor. - */ - void dynamic_tensor_ctx(std::shared_ptr<DynamicTensorCtx> &dynamic_tensor_ctx) - { - _dynamic_tensor_ctx = dynamic_tensor_ctx; - } - - std::shared_ptr<DynamicTensorCtx> &dynamic_tensor_ctx() { return _dynamic_tensor_ctx; } - - /** - * @brief Call this function by passing @c true if this FunctionSequence handles dynamic tensors - * and should run DynamicShapeInferer. This function can be called multiple times and - * if @c false is passed during multiple calls, DynamicShapeInfere will not be run. - * @note This must be called before run(). If not called, run() assumes that all tensors are - * dynamic and DynamicShapeInferer will be run. - */ - void enableDynamicShapeInferer(bool enable) - { - _enable_dynamic_shape_inferer = _enable_dynamic_shape_inferer || enable; - } - - /** - * @brief Call this function to initialize vars before running - * @note When we run a model with static tensor input and then run with dynamic tensor input, - * _enable_dynamic_shape_inferer is set to @c false at first run. - * Once _enable_dynamic_shape_inferer is set to @c true it cannot be changed to @c false - * only with calling enableDynamicShapeInferer(). So initializing it to @c false is - * necessary. - * @todo This is a quick fix. Adding this will increase time for run(). Find way to optimize. - */ - void initRunning() { _enable_dynamic_shape_inferer = false; } - -protected: - std::vector<std::unique_ptr<IFunction>> _functions; - -protected: - bool _enable_dynamic_shape_inferer = false; - - std::shared_ptr<DynamicTensorCtx> _dynamic_tensor_ctx = nullptr; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_FUNCTION_SEQUENCE_H__ diff --git a/runtime/onert/core/include/exec/IExecutor.h b/runtime/onert/core/include/exec/IExecutor.h deleted file mode 100644 index 1d2831dd0..000000000 --- a/runtime/onert/core/include/exec/IExecutor.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file IExecutor.h - * @brief This file defines interface of Executor - */ -#ifndef __ONERT_EXEC_I_EXECUTOR_H_ -#define __ONERT_EXEC_I_EXECUTOR_H_ - -#include "ir/Graph.h" -#include "IFunction.h" -#include "IODescription.h" -#include "ir/OperationIndexMap.h" -#include "backend/IDynamicTensorManager.h" - -namespace onert -{ -namespace exec -{ -class IExecutionObserver; -/** - * @brief Struct to define interface of Executor - */ -struct IExecutor -{ - /** - * @brief Construct a new IExecutor object - */ - IExecutor() = default; - /** - * @brief Destroy the IExecutor object - */ - virtual ~IExecutor() = default; - - /** - * @brief Returns graph object - * - * @return Graph object - */ - virtual const ir::Graph &graph() = 0; - - /** - * @brief Set an ordering on operations - * @param[in] ranks The table encoding the ordering - */ - virtual void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>>) = 0; - - /** - * @brief Start execution - * @param[in] desc Input and output description - * @note This method should be thread-safe - */ - virtual void execute(const IODescription &desc) = 0; -}; - -using ExecutorMap = std::unordered_map<ir::SubgraphIndex, std::unique_ptr<IExecutor>>; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_I_EXECUTOR_H_ diff --git a/runtime/onert/core/include/exec/IFunction.h b/runtime/onert/core/include/exec/IFunction.h deleted file mode 100644 index 18ba2457a..000000000 --- a/runtime/onert/core/include/exec/IFunction.h +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_I_FUNCTION_H__ -#define __ONERT_EXEC_I_FUNCTION_H__ - -namespace onert -{ -namespace exec -{ - -class IFunction -{ -public: - virtual ~IFunction() = default; - virtual void run() = 0; - virtual void prepare() {} -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_I_FUNCTION_H__ diff --git a/runtime/onert/core/include/exec/IODescription.h b/runtime/onert/core/include/exec/IODescription.h deleted file mode 100644 index d1810ec3b..000000000 --- a/runtime/onert/core/include/exec/IODescription.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_IO_DESCRIPTION_H__ -#define __ONERT_EXEC_IO_DESCRIPTION_H__ - -#include <vector> -#include <unordered_map> - -#include "ir/OperandInfo.h" -#include "ir/Index.h" - -namespace onert -{ -namespace exec -{ - -struct InputDesc -{ - const ir::OperandInfo info; - const void *buffer; - const size_t size; - const ir::Layout layout; - - InputDesc(void) = delete; - InputDesc(const ir::OperandInfo &info, const void *buffer, const size_t size, ir::Layout layout) - : info(info), buffer(buffer), size(size), layout(layout) - { - } -}; - -struct OutputDesc -{ - // not `const` because shape should be modified after execution in case when output is - // a dynamic tensor - ir::OperandInfo info; - void *buffer; - const size_t size; - const ir::Layout layout; - - OutputDesc(void) = delete; - OutputDesc(const ir::OperandInfo &info, void *buffer, const size_t size, ir::Layout layout) - : info(info), buffer(buffer), size(size), layout(layout) - { - } -}; - -struct IODescription -{ - std::vector<std::unique_ptr<InputDesc>> inputs; - std::vector<std::unique_ptr<OutputDesc>> outputs; - // Contains shape of input set by nnfw_set_input_tensorinfo(..) - std::unordered_map<ir::IOIndex, ir::Shape> dynamic_input_shapes; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_IO_DESCRIPTION_H__ diff --git a/runtime/onert/core/include/exec/NopFunction.h b/runtime/onert/core/include/exec/NopFunction.h deleted file mode 100644 index d0ed55921..000000000 --- a/runtime/onert/core/include/exec/NopFunction.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file NopFunction.h - * @brief This file defines NopFunction - */ -#ifndef __ONERT_EXEC_NOP_FUNCTION_H_ -#define __ONERT_EXEC_NOP_FUNCTION_H_ - -#include "IFunction.h" - -namespace onert -{ -namespace exec -{ - -/** - * @brief A derivative of IFunction tha does nothing - * - */ -class NopFunction : public IFunction -{ -public: - NopFunction() = default; - void run() override - { - // DO NOTHING - } -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_NOP_FUNCTION_H_ diff --git a/runtime/onert/core/include/ir/Coordinates.h b/runtime/onert/core/include/ir/Coordinates.h deleted file mode 100644 index 3849a5509..000000000 --- a/runtime/onert/core/include/ir/Coordinates.h +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_COORDINATES_H__ -#define __ONERT_IR_COORDINATES_H__ - -#include <cassert> -#include <stdint.h> -#include <vector> - -#include "Layout.h" - -namespace onert -{ -namespace ir -{ - -/** - * @brief Class to represent position(offset) of tensor.\n - * Assume that the front is higher dimensional. - * i.g. N: 0, C: 1, H: 2, W: 3 for NCHW layout - */ -class Coordinates final -{ -public: - static constexpr size_t num_max_dimensions = 4; - -public: - /** - * @brief Construct a new Coordinates object with zero dimension - * @return N/A - */ - Coordinates() = default; - /** - * @brief Construct a new Coordinates object - * @param[in] init The initialzer_list with coordinates - * @return - */ - Coordinates(std::initializer_list<int32_t> init) : _coordinates{init} - { - assert(init.size() <= num_max_dimensions); - } - /** - * @brief Construct a new Coordinates object - * @param[in] init The initialzer_list with coordinates - * @return - */ - Coordinates(std::initializer_list<uint32_t> init) : _coordinates{init.begin(), init.end()} - { - assert(init.size() <= num_max_dimensions); - } - -public: - /** - * @brief Set the coordinate of one of the coordinates. - * - * @param[in] dimension Dimension for which the coordinate is set. - * @param[in] Coordinate Coordinate to be set for the dimension. - */ - void set(size_t dimension, int32_t coordinate) - { - assert(dimension < num_max_dimensions); - if (dimension >= _coordinates.size()) - { - _coordinates.resize(dimension + 1, 0); - } - _coordinates[dimension] = coordinate; - } - -public: - /** - * @brief Return size of coordinates - * - * @return size of coordinates - */ - size_t size() const { return _coordinates.size(); } - -public: - int32_t operator[](size_t dimension) const - { - assert(dimension < _coordinates.size()); - return _coordinates[dimension]; - } - -public: - /** - * @brief begin() of const_iterator for this class - * - * @return The first iterator of the coordinates - */ - std::vector<int32_t>::const_iterator begin() const { return _coordinates.begin(); } - /** - * @brief end() of const_iterator for this class - * - * @return The last iterator of the coordinates - */ - std::vector<int32_t>::const_iterator end() const { return _coordinates.end(); } - -private: - std::vector<int32_t> _coordinates; -}; - -Coordinates convertCoordinates(const Coordinates &from_coordinates, Layout from_layout, - Layout to_layout); - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_COORDINATES_H__ diff --git a/runtime/onert/core/include/ir/Data.h b/runtime/onert/core/include/ir/Data.h deleted file mode 100644 index d31191b4f..000000000 --- a/runtime/onert/core/include/ir/Data.h +++ /dev/null @@ -1,107 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_DATA_H__ -#define __ONERT_IR_DATA_H__ - -#include <algorithm> -#include <sys/mman.h> - -namespace onert -{ -namespace ir -{ - -struct Data -{ - virtual ~Data() = default; - - virtual size_t size(void) const = 0; - virtual const uint8_t *base(void) const = 0; -}; - -class CachedData final : public Data -{ -public: - CachedData(const uint8_t *base, size_t size) : _base{new uint8_t[size]}, _size{size} - { - std::copy(base, base + size, _base); - } - -public: - ~CachedData() { delete[] _base; } - -public: - size_t size(void) const override { return _size; } - const uint8_t *base(void) const override { return _base; } - -private: - uint8_t *_base; - size_t _size; -}; - -class ExternalData : public Data -{ -public: - ExternalData(const uint8_t *base, size_t size) : _base{base}, _size{size} - { - // DO NOTHING - } - -public: - size_t size(void) const override { return _size; } - const uint8_t *base(void) const override { return _base; } - -private: - const uint8_t *_base; - const size_t _size; -}; - -class MMapedData final : public ExternalData -{ -public: - MMapedData(int fd, const std::ptrdiff_t mmap_offset, const size_t mmap_size, - const std::ptrdiff_t data_offset, const size_t data_size) - : ExternalData(nullptr, data_size), - _mmap_base( - static_cast<uint8_t *>(mmap(NULL, mmap_size, PROT_READ, MAP_PRIVATE, fd, mmap_offset))), - _mmap_size(mmap_size), _offset(data_offset - mmap_offset) - { - // DO NOTHING - } - -public: - ~MMapedData() - { - if (_mmap_size > 0) - { - munmap(const_cast<uint8_t *>(_mmap_base), _mmap_size); - } - } - -public: - const uint8_t *base(void) const override { return _mmap_base + _offset; } - -private: - const uint8_t *_mmap_base; - size_t _mmap_size; - std::ptrdiff_t _offset; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_DATA_H__ diff --git a/runtime/onert/core/include/ir/DataType.h b/runtime/onert/core/include/ir/DataType.h deleted file mode 100644 index fe10b9283..000000000 --- a/runtime/onert/core/include/ir/DataType.h +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_DATATYPE_H__ -#define __ONERT_IR_DATATYPE_H__ - -#include <cstdlib> - -namespace onert -{ -namespace ir -{ - -enum class DataType -{ - FLOAT32 = 0, - INT32 = 1, - UINT32 = 2, - QUANT_UINT8_ASYMM = 3, - BOOL8 = 4, - UINT8 = 5, - QUANT_INT8_SYMM = 6, - FLOAT16 = 7, - INT64 = 8, -}; - -size_t sizeOfDataType(DataType data_type); - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_DATATYPE_H__ diff --git a/runtime/onert/core/include/ir/Graph.h b/runtime/onert/core/include/ir/Graph.h deleted file mode 100644 index 2103e6e64..000000000 --- a/runtime/onert/core/include/ir/Graph.h +++ /dev/null @@ -1,124 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_GRAPH_H__ -#define __ONERT_IR_GRAPH_H__ - -#include <functional> -#include <unordered_map> - -#include "ir/Operands.h" -#include "ir/Operations.h" -#include "ir/OpSequence.h" -#include "ir/OpSequences.h" -#include "ir/Subgraphs.h" - -namespace onert -{ -namespace backend -{ -namespace custom -{ -class IKernelBuilder; -} // namespace custom -} // namespace backend -} // namespace onert - -namespace onert -{ -namespace ir -{ - -class Graph -{ -private: - enum class Phase - { - BUILDING, - MODEL - }; - -public: - Graph(void); - ~Graph(void); - - // Graph Building -public: - OperandIndex addOperand(const Shape &shape, const TypeInfo &type); - OperationIndex addOperation(std::unique_ptr<Operation> &&node); - void setOperandValue(const OperandIndex &ind, std::shared_ptr<Data> data); - void addInput(const OperandIndex &ind, const std::string &name = ""); - void addOutput(const OperandIndex &ind, const std::string &name = ""); - void finishBuilding(void); - void removeOperand(const OperandIndex &ind) { _operands.remove(ind); } - bool isBuildingPhase(void) const { return _phase == Phase::BUILDING; } - void setLayout(Layout layout) { _layout = layout; } - void setSubgraphs(const std::shared_ptr<Subgraphs> &subgs) { _subgraphs = subgs; } - -private: - void initializeUseDef(); - void sweepGarbageOperands(); - - // Custom operations support -public: - void - bindKernelBuilder(const std::shared_ptr<onert::backend::custom::IKernelBuilder> &kernel_builder) - { - _kernel_builder = kernel_builder; - } - - const std::shared_ptr<backend::custom::IKernelBuilder> &getKernelBuilder() const - { - return _kernel_builder; - } - -private: - std::shared_ptr<backend::custom::IKernelBuilder> _kernel_builder; - - // Accessors -public: - const OperandIndexSequence &getInputs() const { return _inputs; } - OperandIndexSequence &getInputs() { return _inputs; } - const OperandIndexSequence &getOutputs() const { return _outputs; } - OperandIndexSequence &getOutputs() { return _outputs; } - IOIndex getInputIndex(const std::string &name) const; - IOIndex getOutputIndex(const std::string &name) const; - const Operands &operands() const { return _operands; } - Operands &operands() { return _operands; } // TODO Remove this non-const accessor - const Operations &operations() const { return _operations; } - Operations &operations() { return _operations; } - const std::shared_ptr<Subgraphs> &subgraphs() const { return _subgraphs; } - std::shared_ptr<Subgraphs> &subgraphs() { return _subgraphs; } - Layout layout() const { return _layout; } - -private: - Phase _phase{Phase::BUILDING}; - Operations _operations; - Operands _operands; - OperandIndexSequence _inputs; - OperandIndexSequence _outputs; - std::unordered_map<std::string, IOIndex> _name_to_input; - std::unordered_map<std::string, IOIndex> _name_to_output; - // Child subgraphs - std::shared_ptr<Subgraphs> _subgraphs; - // TFLite and circle's default layout is NHWC; - Layout _layout{Layout::NHWC}; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_GRAPH_H__ diff --git a/runtime/onert/core/include/ir/Index.h b/runtime/onert/core/include/ir/Index.h deleted file mode 100644 index 2538301a4..000000000 --- a/runtime/onert/core/include/ir/Index.h +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_INDEX_H__ -#define __ONERT_IR_INDEX_H__ - -#include "util/Index.h" - -namespace onert -{ -namespace ir -{ - -struct OperationIndexTag; -using OperationIndex = ::onert::util::Index<uint32_t, OperationIndexTag>; - -struct OperandIndexTag; -using OperandIndex = ::onert::util::Index<uint32_t, OperandIndexTag>; - -struct IOIndexTag; -using IOIndex = ::onert::util::Index<uint32_t, IOIndexTag>; - -struct OpSequenceIndexTag; -using OpSequenceIndex = ::onert::util::Index<uint32_t, OpSequenceIndexTag>; - -struct SubgraphIndexTag; -using SubgraphIndex = ::onert::util::Index<uint32_t, SubgraphIndexTag>; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_INDEX_H__ diff --git a/runtime/onert/core/include/ir/InternalType.h b/runtime/onert/core/include/ir/InternalType.h deleted file mode 100644 index 1d962c185..000000000 --- a/runtime/onert/core/include/ir/InternalType.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_INTERNAL_TYPE_H__ -#define __ONERT_IR_INTERNAL_TYPE_H__ - -#include <cstdint> - -namespace onert -{ -namespace ir -{ - -enum class Activation -{ - NONE = 0, - RELU = 1, - RELU1 = 2, - RELU6 = 3, - TANH = 4, - SIGMOID = 5 -}; - -struct Stride -{ - uint32_t vertical; - uint32_t horizontal; -}; - -struct Dilation -{ - uint32_t width_factor; - uint32_t height_factor; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_INTERNAL_TYPE_H__ diff --git a/runtime/onert/core/include/ir/Layout.h b/runtime/onert/core/include/ir/Layout.h deleted file mode 100644 index 082810172..000000000 --- a/runtime/onert/core/include/ir/Layout.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_LAYOUT_H__ -#define __ONERT_IR_LAYOUT_H__ - -#include <functional> -#include <string> - -namespace onert -{ -namespace ir -{ - -enum class Layout -{ - UNKNOWN = 0, - NHWC, - NCHW -}; - -inline std::string to_string(Layout layout) -{ - switch (layout) - { - case Layout::NHWC: - return std::string{"NHWC"}; - case Layout::NCHW: - return std::string{"NCHW"}; - case Layout::UNKNOWN: - return std::string{"UNKNOWN"}; - default: - throw std::runtime_error("WRONG LAYOUT"); - } -} - -} // namespace ir -} // namespace onert - -namespace std -{ - -template <> struct hash<onert::ir::Layout> -{ - size_t operator()(onert::ir::Layout value) const noexcept - { - using type = typename std::underlying_type<onert::ir::Layout>::type; - return hash<type>()(static_cast<type>(value)); - } -}; - -} // namespace std - -#endif // __ONERT_IR_LAYOUT_H__ diff --git a/runtime/onert/core/include/ir/LowerInfoMap.h b/runtime/onert/core/include/ir/LowerInfoMap.h deleted file mode 100644 index fbabaf39d..000000000 --- a/runtime/onert/core/include/ir/LowerInfoMap.h +++ /dev/null @@ -1,42 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_LOWER_INFO_MAP_H__ -#define __ONERT_IR_LOWER_INFO_MAP_H__ - -#include <memory> -#include <unordered_map> - -#include "ir/operand/LowerInfo.h" -#include "ir/operation/LowerInfo.h" -#include "ir/OperandIndexMap.h" -#include "ir/Index.h" - -namespace onert -{ -namespace ir -{ - -struct LowerInfoMap -{ - std::unordered_map<OpSequenceIndex, std::unique_ptr<operation::LowerInfo>> op_seq; - OperandIndexMap<std::unique_ptr<operand::LowerInfo>> operand; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_LOWER_INFO_MAP_H__ diff --git a/runtime/onert/core/include/ir/OpCode.h b/runtime/onert/core/include/ir/OpCode.h deleted file mode 100644 index 32e47796e..000000000 --- a/runtime/onert/core/include/ir/OpCode.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OP_CODE_H__ -#define __ONERT_IR_OP_CODE_H__ - -#include <functional> -#include <stdint.h> -#include <string> - -namespace onert -{ -namespace ir -{ - -enum class OpCode -{ - Invalid, //< Unused -#define OP(Name) Name, //< All operations -#include "ir/Operations.lst" -#undef OP - COUNT -}; - -const char *toString(OpCode opcode); -OpCode toOpCode(const std::string str); - -} // namespace ir -} // namespace onert - -namespace std -{ - -template <> struct hash<onert::ir::OpCode> -{ - size_t operator()(onert::ir::OpCode value) const noexcept - { - using type = typename std::underlying_type<onert::ir::OpCode>::type; - return hash<type>()(static_cast<type>(value)); - } -}; - -} // namespace std - -#endif // __ONERT_IR_OP_CODE_H__ diff --git a/runtime/onert/core/include/ir/OpSequence.h b/runtime/onert/core/include/ir/OpSequence.h deleted file mode 100644 index 754cf3b34..000000000 --- a/runtime/onert/core/include/ir/OpSequence.h +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OP_SEQUENCE_H__ -#define __ONERT_IR_OP_SEQUENCE_H__ - -#include <vector> -#include <string> -#include <memory> - -#include "ir/Layout.h" -#include "ir/Index.h" -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ - -class Operations; - -class OpSequence -{ -public: - explicit OpSequence(Layout layout); - OpSequence(const OpSequence &) = delete; - -public: - void accept(OperationVisitor &v) const; - -public: - const OperandIndexSequence &getInputs() const { return _inputs; } - const OperandIndexSequence &getOutputs() const { return _outputs; } - void setInputs(const OperandIndexSequence &indexes) { _inputs = indexes; } - void setOutputs(const OperandIndexSequence &indexes) { _outputs = indexes; } - void replaceInputs(const OperandIndex &from, const OperandIndex &to) - { - _inputs.replace(from, to); - } - void replaceOutputs(const OperandIndex &from, const OperandIndex &to) - { - _outputs.replace(from, to); - } - - void appendOperation(const OperationIndex &index) { _operations.emplace_back(index); } - - std::vector<OperationIndex> &operations(void) { return _operations; } - - const std::vector<OperationIndex> &operations(void) const { return _operations; } - - uint32_t size(void) const { return _operations.size(); } - -public: - void remove(const OperationIndex &index); - - bool exist(const OperationIndex &index) const; - -public: - Layout getLayout() const { return _layout; } - -public: - std::vector<OperationIndex>::const_iterator begin() const { return _operations.begin(); } - std::vector<OperationIndex>::const_iterator end() const { return _operations.end(); } - -public: - /** - * @brief Set @c true if any operation in this opSequence has dynamic input - * or dynamic output; - * @c false if all operations' inputs and outputs are static tensors - */ - void has_dynamic_tensor(bool has_dynamic_tensor) { _has_dynamic_tensor = has_dynamic_tensor; } - bool has_dynamic_tensor() const { return _has_dynamic_tensor; } - -private: - OperandIndexSequence _inputs; - OperandIndexSequence _outputs; - std::vector<OperationIndex> _operations; - -private: - Layout _layout; - bool _has_dynamic_tensor; -}; - -std::string getStrFromOpSeq(const OpSequence &op_seq, const Operations &operations); - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OP_SEQUENCE_H__ diff --git a/runtime/onert/core/include/ir/OpSequences.h b/runtime/onert/core/include/ir/OpSequences.h deleted file mode 100644 index ab258f395..000000000 --- a/runtime/onert/core/include/ir/OpSequences.h +++ /dev/null @@ -1,91 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OP_SEQUENCES_H__ -#define __ONERT_IR_OP_SEQUENCES_H__ - -#include "ir/Index.h" -#include "ir/OpSequence.h" -#include "util/ObjectManager.h" - -namespace onert -{ -namespace ir -{ - -/** - * @brief Class that manages OpSequence objects - */ -class OpSequences : public util::ObjectManager<OpSequenceIndex, OpSequence> -{ -public: - /** - * @brief Create an instance of OpSequence with given op and push it to objects - * - * @param[in] op_idx Operation index that is emplaced - * @param[in] layout OpSequence's layout - * @return OpSequenceIndex - */ - OpSequenceIndex emplace(const OperationIndex &op_index, Layout layout); - - /** - * @brief Push an instance of OpSequence to objects - * - * @param[in] op_seq An instance of OpSequence - * @return OpSequenceIndex - */ - OpSequenceIndex emplace(std::unique_ptr<OpSequence> &&op_seq); - /** - * @brief Check if an operation does exist in any OpSequences - * - * @param operation_index Operation index to find - * @return true If such operation exists in any OpSequences otherwise false - */ - bool containsOperation(const OperationIndex &operation_index) const; - /** - * @brief Find an operation from all OpSequences - * - * @param operation_index Operation index to find - * @return OpSequenceIndex Index of OpSequence that contains given operation index - */ - OpSequenceIndex getOperation(const OperationIndex &operation_index) const; - /** - * @brief Remove an operation from OpSequence - * - * @param operation_index Operation index to be removed - */ - void removeFromOpSequence(const OperationIndex &operation_index); - -private: - void cacheSequenceIndex(const OpSequenceIndex &seq_index, const OperationIndex &op_index) const; - OpSequenceIndex *findSequenceIndex(const OperationIndex &operation_index) const; - - OpSequenceIndex findOperation(const OperationIndex &operation_index) const; - mutable std::unordered_map<OperationIndex, OpSequenceIndex> _seq_indexes; -}; - -/** - * @brief Dump OpSequences - * - * @param op_seqs Operation Sequences - * @param operations Operation context - */ -void dumpOpSequences(const OpSequences &op_seqs, const Operations &operations); - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OP_SEQUENCES_H__ diff --git a/runtime/onert/core/include/ir/Operand.h b/runtime/onert/core/include/ir/Operand.h deleted file mode 100644 index f149a744b..000000000 --- a/runtime/onert/core/include/ir/Operand.h +++ /dev/null @@ -1,117 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERAND_H__ -#define __ONERT_IR_OPERAND_H__ - -#include <cassert> -#include <cstdint> -#include <memory> -#include <algorithm> - -#include "ir/Data.h" -#include "ir/DataType.h" -#include "ir/OperandInfo.h" -#include "ir/OperationIndexSet.h" - -namespace onert -{ -namespace ir -{ - -class Operand -{ -public: - explicit Operand(const Shape &shape, const TypeInfo &type) - : _info{shape, type, MemAllocType::STATIC} - { - // DO NOTHING - } - explicit Operand(const Operand &) = default; - -public: - const Shape &shape(void) const { return _info.shape(); } - const TypeInfo &typeInfo(void) const { return _info.typeInfo(); } - const OperandInfo &info(void) const { return _info; } - OperandInfo &info(void) { return _info; } - size_t operandSize(void) const; - - const OperationIndexSet &getUses() const { return _uses; } - OperationIndex getDef() const { return _def; } - void insertUse(const OperationIndex &idx); - void removeUse(const OperationIndex &idx); - void setDef(const OperationIndex &idx); - void unsetDef(); - -public: - void type(const DataType type) { _info.type(type); }; - -public: - void data(std::shared_ptr<Data> &&data) - { - _data = std::move(data); - _info.setAsConstant(); - } - const Data *data(void) const { return _data.get(); } - - void releaseData(void) { _data.reset(); } - - std::shared_ptr<Data> shareData(void) const { return _data; } - - /** - * @brief Get true if Operand is const, otherwise @c false - a @return @c true if Operand is const, otherwise @c false - */ - bool isConstant(void) const { return _info.isConstant(); } - -public: - template <typename T, typename... Args> void data(Args &&... args) - { - data(std::make_unique<T>(std::forward<Args>(args)...)); - } - -public: - template <typename T> T asScalar(void) const - { - assert((shape().rank() == 0) || ((shape().rank() == 1) && (shape().dim(0) == 1))); - assert(_data != nullptr); - assert((_data->base() != nullptr) && (_data->size() == sizeof(T))); - - return *(reinterpret_cast<const T *>(_data->base())); - } - - template <typename T> std::vector<T> asVector() const - { - assert(_data != nullptr); - assert(_data->size() % sizeof(T) == 0); - - const auto *base = reinterpret_cast<const T *>(_data->base()); - const std::size_t size = _data->size() / sizeof(T); - return std::vector<T>(base, base + size); - } - -private: - OperandInfo _info; - std::shared_ptr<Data> _data; - - OperationIndexSet _uses; - OperationIndex _def; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERAND_H__ diff --git a/runtime/onert/core/include/ir/OperandConstraint.h b/runtime/onert/core/include/ir/OperandConstraint.h deleted file mode 100644 index 8da922bea..000000000 --- a/runtime/onert/core/include/ir/OperandConstraint.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_MODEL_OPERAND_CONSTRAINT_H__ -#define __ONERT_MODEL_OPERAND_CONSTRAINT_H__ - -#include <stdint.h> -#include <limits> -#include <set> - -namespace onert -{ -namespace ir -{ - -class OperandConstraint -{ -private: - static const uint32_t INF = std::numeric_limits<uint32_t>::max(); - -public: - static OperandConstraint createAny() { return OperandConstraint{0u, INF}; } - static OperandConstraint createExact(uint32_t exact) { return OperandConstraint{exact, exact}; } - static OperandConstraint createAtMost(uint32_t end) { return OperandConstraint{0u, end}; } - static OperandConstraint createAtLeast(uint32_t begin) { return OperandConstraint{begin, INF}; } - static OperandConstraint createInRange(uint32_t begin, uint32_t end) - { - return OperandConstraint{begin, end}; - } - -private: - OperandConstraint(uint32_t begin, uint32_t end) : _begin{begin}, _end{end} {} - -public: - bool check(uint32_t ind) const { return _begin <= ind && ind <= _end; } - -private: - uint32_t _begin; - uint32_t _end; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_MODEL_OPERAND_CONSTRAINT_H__ diff --git a/runtime/onert/core/include/ir/OperandIndexMap.h b/runtime/onert/core/include/ir/OperandIndexMap.h deleted file mode 100644 index 468162ffb..000000000 --- a/runtime/onert/core/include/ir/OperandIndexMap.h +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERAND_INDEX_MAP_H__ -#define __ONERT_IR_OPERAND_INDEX_MAP_H__ - -#include <unordered_map> - -#include "ir/Index.h" - -namespace onert -{ -namespace ir -{ - -template <typename T> using OperandIndexMap = std::unordered_map<OperandIndex, T>; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERAND_INDEX_MAP_H__ diff --git a/runtime/onert/core/include/ir/OperandIndexSequence.h b/runtime/onert/core/include/ir/OperandIndexSequence.h deleted file mode 100644 index 2f78cc832..000000000 --- a/runtime/onert/core/include/ir/OperandIndexSequence.h +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_MODEL_OPERAND_INDEX_SEQUENCE_H__ -#define __ONERT_MODEL_OPERAND_INDEX_SEQUENCE_H__ - -#include <initializer_list> -#include <vector> - -#include "ir/Index.h" - -namespace onert -{ -namespace ir -{ - -enum class Remove -{ - DUPLICATED = 1, - UNDEFINED = 2 -}; - -class OperandIndexSequence -{ -public: - OperandIndexSequence(void) = default; - OperandIndexSequence(std::initializer_list<OperandIndex> list); - OperandIndexSequence(std::initializer_list<int32_t> list); - OperandIndexSequence(std::initializer_list<uint32_t> list); - -public: - void append(const OperandIndex &index) { _vec.emplace_back(index); } - void append(const OperandIndexSequence &l) { _vec.insert(_vec.end(), l.begin(), l.end()); } - -public: - uint32_t size() const { return static_cast<uint32_t>(_vec.size()); } - const OperandIndex &at(IOIndex set_index) const { return _vec.at(set_index.value()); } - const OperandIndex &at(uint32_t index) const { return _vec.at(index); } - bool contains(const OperandIndex &index) const; - void replace(const OperandIndex &from, const OperandIndex &to); - OperandIndexSequence operator|(ir::Remove filter) const - { - switch (filter) - { - case ir::Remove::DUPLICATED: - { - ir::OperandIndexSequence seq; - for (const auto &ind : _vec) - if (!seq.contains(ind)) - seq.append(ind); - return seq; - } - case ir::Remove::UNDEFINED: - { - ir::OperandIndexSequence seq; - for (const auto &ind : _vec) - if (!ind.undefined()) - seq.append(ind); - return seq; - } - } - return *this; - } - -public: - OperandIndexSequence operator+(const OperandIndexSequence &other) const; - friend std::ostream &operator<<(std::ostream &o, const OperandIndexSequence &op_seq); - -public: - std::vector<OperandIndex>::const_iterator begin(void) const { return _vec.begin(); } - std::vector<OperandIndex>::const_iterator end(void) const { return _vec.end(); } - std::vector<OperandIndex>::iterator begin(void) { return _vec.begin(); } - std::vector<OperandIndex>::iterator end(void) { return _vec.end(); } - -private: - std::vector<OperandIndex> _vec; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_MODEL_OPERAND_INDEX_SET_H__ diff --git a/runtime/onert/core/include/ir/OperandInfo.h b/runtime/onert/core/include/ir/OperandInfo.h deleted file mode 100644 index 67aeb0e65..000000000 --- a/runtime/onert/core/include/ir/OperandInfo.h +++ /dev/null @@ -1,141 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file OperandInfo.h - * @brief This file contains OperandInfo class - */ -#ifndef __ONERT_IR_OPERAND_INFO_H__ -#define __ONERT_IR_OPERAND_INFO_H__ - -#include "ir/Shape.h" -#include "ir/TypeInfo.h" -#include "ir/Layout.h" - -namespace onert -{ -namespace ir -{ - -/** - * @brief enum class indicating when the memory for a tensor is allocated - */ -enum class MemAllocType -{ - /** - * @brief At compile time, shape for a tensor is known, thus requried memory capacity can be - * calculated - */ - STATIC, - - /** - * @brief At kernel execution time, shape for a tensor is known, thus requried memory capacity - * can be calculated - */ - DYNAMIC -}; - -/** - * @brief Class to save tensor's shape and type - */ -class OperandInfo -{ -public: - /** - * @brief Construct a new OperandInfo object (deleted) - */ - OperandInfo() = delete; - - /** - * @brief Construct a new OperandInfo object - * @param[in] shape Tensor shape - * @param[in] typeInfo Tensor data type - * @param[in] alloc_type When the thesor needs memory allocation - */ - OperandInfo(const Shape &shape, const TypeInfo &typeInfo, MemAllocType alloc_type, - bool is_const = false) - : _shape(shape), _typeInfo(typeInfo), _alloc_type(alloc_type), _const(is_const) - { - // DO NOTHING - } - /** - * @brief Construct a new OperandInfo object - * @param[in] origin info for copy - */ - OperandInfo(const OperandInfo &origin) = default; - - /** - * @brief Create a static OperandInfo object - */ - static OperandInfo createStaticInfo(const Shape &shape, const TypeInfo &typeInfo) - { - return OperandInfo(shape, typeInfo, MemAllocType::STATIC); - } - -public: - /** - * @brief Return tensor shape - * @return Tensor shape - */ - const Shape &shape() const { return _shape; } - /** - * @brief Return mutable tensor shape - * @return Tensor shape - */ - Shape &shape() { return _shape; } - /** - * @brief set shape - */ - void shape(const ir::Shape &new_shape) { _shape = new_shape; } - /** - * @brief Return tensor data type info - * @return Tensor data type - */ - const TypeInfo &typeInfo() const { return _typeInfo; } - /** - * @brief Set tensor data type - */ - void type(const DataType type) { _typeInfo.type(type); } - /** - * @brief Return size of tensor (bytes) - * @return Tensor size - */ - size_t total_size() const { return _shape.num_elements() * sizeOfDataType(_typeInfo.type()); } - - MemAllocType memAllocType() const { return _alloc_type; } - void setAsConstant() { _const = true; } - void setAsNonConst() { _const = false; } - bool isConstant() const - { - // Impossible case: constant and dynamic operand - assert(!(isDynamic() && _const)); - return _const; - } - bool isDynamic() const { return _alloc_type == MemAllocType::DYNAMIC; } - void setDynamic() { _alloc_type = MemAllocType::DYNAMIC; } - -private: - Shape _shape; - TypeInfo _typeInfo; - - MemAllocType _alloc_type; - bool _const; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERAND_INFO_H__ diff --git a/runtime/onert/core/include/ir/Operands.h b/runtime/onert/core/include/ir/Operands.h deleted file mode 100644 index be7b7061f..000000000 --- a/runtime/onert/core/include/ir/Operands.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERANDS_H__ -#define __ONERT_IR_OPERANDS_H__ - -#include <memory> -#include <unordered_map> - -#include "ir/Operand.h" -#include "ir/Index.h" -#include "util/ObjectManager.h" - -namespace onert -{ -namespace ir -{ - -class Operands : public util::ObjectManager<OperandIndex, Operand> -{ -public: - Operands() = default; - Operands(const Operands &obj); - Operands(Operands &&) = default; - Operands &operator=(const Operands &) = delete; - Operands &operator=(Operands &&) = default; - ~Operands() = default; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_MODEL_OPERAND_SET_H__ diff --git a/runtime/onert/core/include/ir/Operation.h b/runtime/onert/core/include/ir/Operation.h deleted file mode 100644 index 89f7e340d..000000000 --- a/runtime/onert/core/include/ir/Operation.h +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_H__ -#define __ONERT_IR_OPERATION_H__ - -#include <memory> - -#include "ir/OpCode.h" -#include "ir/Operand.h" -#include "ir/OperandIndexSequence.h" -#include "ir/OperandConstraint.h" - -namespace onert -{ -namespace ir -{ - -struct OperationVisitor; - -class Operation -{ -public: - // TODO Remove default parameter - Operation(OperandConstraint input_constr, const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, - OperandConstraint output_constr = OperandConstraint::createAny()); - explicit Operation(OperandConstraint input_constr, - OperandConstraint output_constr = OperandConstraint::createAny()); - - Operation(const Operation &) = default; - Operation(Operation &&) = default; - Operation &operator=(const Operation &) = default; - Operation &operator=(Operation &&) = default; - - virtual ~Operation(); - -public: - virtual void accept(OperationVisitor &v) const = 0; - virtual std::string name() const { return std::string{toString(opcode())}; } - virtual OpCode opcode() const = 0; - -public: - void replaceInputs(const OperandIndex &from, const OperandIndex &to); - void replaceOutputs(const OperandIndex &from, const OperandIndex &to); - OperandIndexSequence &getInputs() { return _inputs; } - const OperandIndexSequence &getInputs() const { return _inputs; } - const OperandIndexSequence &getOutputs() const { return _outputs; } - // It's for only input/output tensors but const data. - void setInputs(const OperandIndexSequence &indexes); - void setOutputs(const OperandIndexSequence &indexes); - -private: - OperandConstraint _input_constr; - OperandConstraint _output_constr; - OperandIndexSequence _inputs; - OperandIndexSequence _outputs; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_H__ diff --git a/runtime/onert/core/include/ir/OperationIndexMap.h b/runtime/onert/core/include/ir/OperationIndexMap.h deleted file mode 100644 index 50c21c0ab..000000000 --- a/runtime/onert/core/include/ir/OperationIndexMap.h +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_INDEX_MAP_H__ -#define __ONERT_IR_OPERATION_INDEX_MAP_H__ - -#include <unordered_map> - -#include "ir/Index.h" - -namespace onert -{ -namespace ir -{ - -template <typename T> using OperationIndexMap = std::unordered_map<OperationIndex, T>; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_INDEX_MAP_H__ diff --git a/runtime/onert/core/include/ir/OperationIndexSet.h b/runtime/onert/core/include/ir/OperationIndexSet.h deleted file mode 100644 index 067aa19e1..000000000 --- a/runtime/onert/core/include/ir/OperationIndexSet.h +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_MODEL_OPERATION_INDEX_SET_H__ -#define __ONERT_MODEL_OPERATION_INDEX_SET_H__ - -#include <algorithm> -#include <cassert> -#include <initializer_list> -#include <unordered_set> - -#include "ir/Index.h" - -namespace onert -{ -namespace ir -{ - -class OperationIndexSet -{ -public: - OperationIndexSet(void) = default; - OperationIndexSet(std::initializer_list<OperationIndex> list); - -public: - void insert(const OperationIndex &index) { _set.insert(index); } - void clear(void) { _set.clear(); } - void remove(const OperationIndex &index) - { - auto itr = std::find(_set.begin(), _set.end(), index); - assert(itr != _set.end()); - _set.erase(itr); - } - -public: - uint32_t size() const { return static_cast<uint32_t>(_set.size()); } - bool contains(const OperationIndex &index) const; - -public: - std::unordered_set<OperationIndex>::iterator begin(void) { return _set.begin(); } - std::unordered_set<OperationIndex>::iterator end(void) { return _set.end(); } - std::unordered_set<OperationIndex>::const_iterator begin(void) const { return _set.begin(); } - std::unordered_set<OperationIndex>::const_iterator end(void) const { return _set.end(); } - -private: - std::unordered_set<OperationIndex> _set; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_MODEL_OPERATION_INDEX_SET_H__ diff --git a/runtime/onert/core/include/ir/OperationVisitor.h b/runtime/onert/core/include/ir/OperationVisitor.h deleted file mode 100644 index a27770744..000000000 --- a/runtime/onert/core/include/ir/OperationVisitor.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_VISITOR_H__ -#define __ONERT_IR_OPERATION_VISITOR_H__ - -#include "ir/Operations.Include.h" -#include "ir/OpSequence.h" - -namespace onert -{ -namespace ir -{ - -struct OperationVisitor -{ - virtual ~OperationVisitor() = default; - -#define OP(InternalName) \ - virtual void visit(const operation::InternalName &) {} -#include "ir/Operations.lst" -#undef OP - - // This OpSequence node should be handled specially so that - // Op.lst doesn't have OpSequence - // TODO Remove by pushing it down to derived classes. - virtual void visit(const OpSequence &) - { - throw std::runtime_error{ - "OperationVisitor: This does not privide visit function in OpSequence"}; - } -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_VISITOR_H__ diff --git a/runtime/onert/core/include/ir/Operations.Include.h b/runtime/onert/core/include/ir/Operations.Include.h deleted file mode 100644 index 17bbbc29c..000000000 --- a/runtime/onert/core/include/ir/Operations.Include.h +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// This file has no ifdef guard intentionally - -#include "ir/operation/BatchToSpaceND.h" -#include "ir/operation/BinaryArithmetic.h" -#include "ir/operation/BroadcastTo.h" -#include "ir/operation/Conv2D.h" -#include "ir/operation/Pool2D.h" -#include "ir/operation/Concat.h" -#include "ir/operation/Reshape.h" -#include "ir/operation/Fill.h" -#include "ir/operation/FullyConnected.h" -#include "ir/operation/Softmax.h" -#include "ir/operation/Transpose.h" -#include "ir/operation/Permute.h" -#include "ir/operation/Reduce.h" -#include "ir/operation/DepthwiseConv2D.h" -#include "ir/operation/Slice.h" -#include "ir/operation/StridedSlice.h" -#include "ir/operation/Squeeze.h" -#include "ir/operation/ElementwiseActivation.h" -#include "ir/operation/ElementwiseBinary.h" -#include "ir/operation/ElementwiseUnary.h" -#include "ir/operation/ExpandDims.h" -#include "ir/operation/Comparison.h" -#include "ir/operation/LSTM.h" -#include "ir/operation/ResizeBilinear.h" -#include "ir/operation/ResizeNearestNeighbor.h" -#include "ir/operation/Reverse.h" -#include "ir/operation/RNN.h" -#include "ir/operation/SpaceToBatchND.h" -#include "ir/operation/SpaceToDepth.h" -#include "ir/operation/EmbeddingLookup.h" -#include "ir/operation/L2Normalization.h" -#include "ir/operation/HashtableLookup.h" -#include "ir/operation/InstanceNorm.h" -#include "ir/operation/PReLU.h" -#include "ir/operation/TransposeConv.h" -#include "ir/operation/SquaredDifference.h" -#include "ir/operation/TopKV2.h" -#include "ir/operation/Gather.h" -#include "ir/operation/ArgMax.h" -#include "ir/operation/LocalResponseNormalization.h" -#include "ir/operation/DepthToSpace.h" -#include "ir/operation/Pack.h" -#include "ir/operation/Select.h" -#include "ir/operation/Split.h" -#include "ir/operation/SplitV.h" -#include "ir/operation/Unpack.h" -#include "ir/operation/Pad.h" -#include "ir/operation/Custom.h" -#include "ir/operation/Einsum.h" -#include "ir/operation/OneHot.h" -#include "ir/operation/Shape.h" -#include "ir/operation/ConvertFp32ToFp16.h" -#include "ir/operation/ConvertFp16ToFp32.h" -#include "ir/operation/If.h" -#include "ir/operation/While.h" -#include "ir/operation/Pow.h" -#include "ir/operation/Tile.h" -#include "ir/operation/Range.h" -#include "ir/operation/Rank.h" -#include "ir/operation/BCQFullyConnected.h" -#include "ir/operation/BCQGather.h" -#include "ir/operation/MatrixBandPart.h" -#include "ir/operation/BatchMatMul.h" -#include "ir/operation/FusedBatchNorm.h" -#include "ir/operation/LogSoftmax.h" -#include "ir/operation/StatelessRandomUniform.h" diff --git a/runtime/onert/core/include/ir/Operations.h b/runtime/onert/core/include/ir/Operations.h deleted file mode 100644 index 0b5fbf529..000000000 --- a/runtime/onert/core/include/ir/Operations.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATIONS_H__ -#define __ONERT_IR_OPERATIONS_H__ - -#include "ir/Index.h" -#include "ir/Operation.h" -#include "util/ObjectManager.h" - -namespace onert -{ -namespace ir -{ - -class Operations : public util::ObjectManager<OperationIndex, Operation> -{ -public: - Operations() = default; - Operations(const Operations &obj); - Operations(Operations &&) = default; - Operations &operator=(const Operations &) = delete; - Operations &operator=(Operations &&) = default; - ~Operations() = default; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_MODEL_OPERATION_MANAGER_H__ diff --git a/runtime/onert/core/include/ir/Operations.lst b/runtime/onert/core/include/ir/Operations.lst deleted file mode 100644 index ab2146821..000000000 --- a/runtime/onert/core/include/ir/Operations.lst +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef OP -#error Define OP before including this file -#endif - -// Internal Name -OP(BatchToSpaceND) -OP(BinaryArithmetic) -OP(BroadcastTo) -OP(Conv2D) -OP(DepthwiseConv2D) -OP(Pool2D) -OP(Concat) -OP(Fill) -OP(FullyConnected) -OP(Reduce) -OP(Reshape) -OP(Softmax) -OP(Squeeze) -OP(Slice) -OP(StridedSlice) -OP(Transpose) -OP(ElementwiseActivation) -OP(ElementwiseBinary) -OP(ElementwiseUnary) -OP(ExpandDims) -OP(Comparison) -OP(LSTM) -OP(ResizeBilinear) -OP(ResizeNearestNeighbor) -OP(Reverse) -OP(RNN) -OP(SpaceToBatchND) -OP(SpaceToDepth) -OP(EmbeddingLookup) -OP(L2Normalization) -OP(HashtableLookup) -OP(InstanceNorm) -OP(PReLU) -OP(TransposeConv) -OP(SquaredDifference) -OP(TopKV2) -OP(Gather) -OP(ArgMax) -OP(Einsum) -OP(LocalResponseNormalization) -OP(DepthToSpace) -OP(Pack) -OP(Select) -OP(Split) -OP(SplitV) -OP(Unpack) -OP(Pad) -OP(Custom) -OP(Permute) -OP(OneHot) -OP(Shape) -OP(ConvertFp32ToFp16) -OP(ConvertFp16ToFp32) -OP(If) -OP(While) -OP(Pow) -OP(Tile) -OP(Range) -OP(Rank) -OP(BCQFullyConnected) -OP(BCQGather) -OP(MatrixBandPart) -OP(BatchMatMul) -OP(FusedBatchNorm) -OP(LogSoftmax) -OP(StatelessRandomUniform) diff --git a/runtime/onert/core/include/ir/Padding.h b/runtime/onert/core/include/ir/Padding.h deleted file mode 100644 index 8a7bcdbeb..000000000 --- a/runtime/onert/core/include/ir/Padding.h +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_PADDIGN_H__ -#define __ONERT_IR_PADDIGN_H__ - -#include "Shape.h" -#include "InternalType.h" - -#include <cstdint> -#include <string> - -namespace onert -{ -namespace ir -{ - -enum class PaddingType -{ - EXPLICIT = 0, - SAME = 1, - VALID = 2 -}; - -/** - * @brief Converts a internal padding type to const char* - * @param[in] type Padding type to be converted - * @return A string holding the converted value - */ -inline std::string to_string(const PaddingType type); - -struct ExplicitPadding -{ - uint32_t left; - uint32_t right; - uint32_t top; - uint32_t bottom; -}; - -// TODO Resolve explicit padding param at frontend and save in value field -struct Padding -{ - Padding(void); - Padding(PaddingType paddingType); - Padding(uint32_t left, uint32_t right, uint32_t top, uint32_t bottom); - - // TODO Change to private field - PaddingType type; - ExplicitPadding param; -}; - -// TODO Change to Padding struct's method -const ExplicitPadding calculatePadding(const Padding &padding, const FeatureShape &ifm_shape, - const FeatureShape &ofm_shape, const Stride &stride, - uint32_t kw, uint32_t kh, uint32_t dwf = 1, - uint32_t dhf = 1); - -} // namespace ir -} // namespace onert - -#endif diff --git a/runtime/onert/core/include/ir/Shape.h b/runtime/onert/core/include/ir/Shape.h deleted file mode 100644 index a0b4bb196..000000000 --- a/runtime/onert/core/include/ir/Shape.h +++ /dev/null @@ -1,151 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. -*/ - -#ifndef __ONERT_IR_SHAPE_H__ -#define __ONERT_IR_SHAPE_H__ - -#include "ir/Layout.h" - -#include <cassert> -#include <cstdint> -#include <vector> -#include <algorithm> - -namespace onert -{ -namespace ir -{ - -/** - * @brief Structure to have values of dimensions for feature - */ -struct FeatureShape -{ - int32_t N; /**< The batch value */ - int32_t C; /**< The depth value */ - int32_t H; /**< The height value */ - int32_t W; /**< The width value */ - - /** - * @brief Construct FeatureShape object using default constrcutor - */ - FeatureShape() = default; - /** - * @brief Construct FeatureShape object with three values of dimensions - * @param[in] depth The depth value - * @param[in] height The height value - * @param[in] width The width value - */ - FeatureShape(int32_t depth, int32_t height, int32_t width) : N{1}, C{depth}, H{height}, W{width} - { - // DO NOTHING - } - /** - * @brief Construct FeatureShape object with four values of dimensions - * @param[in] batch The batch value - * @param[in] depth The depth value - * @param[in] height The height value - * @param[in] width The width value - */ - FeatureShape(int32_t batch, int32_t depth, int32_t height, int32_t width) - : N{batch}, C{depth}, H{height}, W{width} - { - // DO NOTHING - } -}; - -struct Shape -{ -public: - static int32_t const UNSPECIFIED_DIM; - static int32_t const MAX_RANK; - - Shape() = default; - - explicit Shape(int rank) : _dimensions(rank) {} - - Shape(std::initializer_list<int32_t> dimensions) : _dimensions(dimensions) {} - - int rank() const { return _dimensions.size(); } - - const std::vector<int32_t> &dims() const { return _dimensions; } - - int32_t dim(int i) const - { - assert(rank() != 0 || i == 0); - return rank() == 0 ? 1 : _dimensions.at(i); - } - - int32_t &dim(int i) { return _dimensions.at(i); } - - /** - * @brief Returns number of elements when rank or dim is specified - */ - uint64_t num_elements() const; - -public: - FeatureShape asFeature(Layout layout) const; - - /** - * @brief Add dimension to the beginning - * @param[in] d dimension to add to the beginning - */ - void prepend(int32_t d) { _dimensions.insert(_dimensions.cbegin(), d); } - - /** - * @brief Add dimension to the end - * @param[in] d dimension to add to the end - */ - void append(int32_t d) { _dimensions.emplace_back(d); } - - /** - * @brief Extend rank of Shape object for operand with param. - * @param[in] to_rank The rank value to be extended to - */ - void extendRank(int to_rank); - - /** - * @brief Find out if any dimension is unspecified. If the rank is not specified, it returns - * false. - * \see https://developer.android.com/ndk/reference/struct/a-neural-networks-operand-type - * @note base_loader set dim to -1 when there is unknown dim in input tensor - */ - bool hasUnspecifiedDims() const - { - return (std::find(_dimensions.begin(), _dimensions.end(), UNSPECIFIED_DIM) != - _dimensions.end()); - } - -private: - std::vector<int32_t> _dimensions; -}; - -inline bool operator==(const Shape &lhs, const Shape &rhs) { return lhs.dims() == rhs.dims(); } -inline bool operator!=(const Shape &lhs, const Shape &rhs) { return lhs.dims() != rhs.dims(); } - -Shape permuteShape(const Shape &shape, Layout frontend_layout, Layout backend_layout); - -/** -* @brief Find out if tha rank in this shape is "maybe" unspecified. -* Note that when rank == 0, shape could represent scalar or unspecified rank -* \see https://developer.android.com/ndk/reference/struct/a-neural-networks-operand-type -*/ -inline bool rankMaybeUnspecified(const ir::Shape &shape) { return (shape.rank() == 0); } - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_SHAPE_H__ diff --git a/runtime/onert/core/include/ir/Sparsity.h b/runtime/onert/core/include/ir/Sparsity.h deleted file mode 100644 index ad4d8259b..000000000 --- a/runtime/onert/core/include/ir/Sparsity.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. -*/ - -#ifndef __ONERT_IR_SPARSITY_H__ -#define __ONERT_IR_SPARSITY_H__ - -#include <cassert> -#include <cstdint> -#include <vector> - -namespace onert -{ -namespace ir -{ - -/** - * @brief Structure for Sparse Tensor - */ -struct Sparsity -{ -public: - Sparsity() = default; - Sparsity(std::vector<uint16_t> &&w1_segments, std::vector<uint16_t> &&w1_indices, - std::vector<int32_t> &&block_size) - : _w1_segments(w1_segments), _w1_indices(w1_indices), _block_size(block_size) - { - } - - /** - * @brief Returns segments array. See compressed sparse row format. - */ - const uint16_t *w1_segments() const { return _w1_segments.data(); } - /** - * @brief Returns indices array. See compressed sparse row format. - */ - const uint16_t *w1_indices() const { return _w1_indices.data(); } - /** - * @brief Returns block size which is used for block sparsity - */ - const std::vector<int32_t> &block_size() const { return _block_size; } - -private: - std::vector<uint16_t> _w1_segments; - std::vector<uint16_t> _w1_indices; - std::vector<int32_t> _block_size; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_SPARSITY_H__ diff --git a/runtime/onert/core/include/ir/Subgraphs.h b/runtime/onert/core/include/ir/Subgraphs.h deleted file mode 100644 index 7b4c33b76..000000000 --- a/runtime/onert/core/include/ir/Subgraphs.h +++ /dev/null @@ -1,139 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_SUBGRAPHS_H__ -#define __ONERT_IR_SUBGRAPHS_H__ - -#include <memory> -#include <unordered_map> - -#include "ir/Index.h" -#include "util/ObjectManager.h" - -namespace onert -{ -namespace ir -{ - -class Graph; - -class Subgraphs -{ -public: - Subgraphs() = default; - Subgraphs(const Subgraphs &obj) = default; - Subgraphs(Subgraphs &&) = default; - Subgraphs &operator=(const Subgraphs &) = default; - Subgraphs &operator=(Subgraphs &&) = default; - ~Subgraphs() = default; - - /** - * @brief Put subgraph in the container with a new Index for that - * - * @param[in] subg Subgraph to be pushed - * @param[in] index Index of subgraph to be pushed - * @return Created - */ - void push(SubgraphIndex index, const std::shared_ptr<Graph> &subg) { _subgraphs[index] = subg; } - - /** - * @brief Remove the subgraph that is associated with the given index - * - * @param[in] index Index of the subgraph to be removed - * @return N/A - */ - void remove(const SubgraphIndex &index) { _subgraphs.erase(index); } - - /** - * @brief Get the subgraph that is associated with the given index - * - * @param[in] index Index of the subgraph to be returned - * @return Graph - */ - const std::shared_ptr<Graph> &at(const SubgraphIndex &index) const - { - return _subgraphs.at(index); - } - /** - * @brief Get the subgraph that is associated with the given index - * - * @param[in] index Index of the subgraph to be returned - * @return Graph - */ - std::shared_ptr<Graph> &at(const SubgraphIndex &index) { return _subgraphs.at(index); } - - /** - * @brief Get the subgraph that is associated with the given index - * - * @param[in] index Index of the subgraph to be returned - * @return true if such entry exists otherwise false - */ - bool exist(const SubgraphIndex &index) const - { - auto it = _subgraphs.find(index); - return it != _subgraphs.end(); - } - - /** - * @brief Iterate over the container with given function - * - * @param[in] fn Function to be run for every container entry - * @return N/A - */ - void iterate(const std::function<void(const SubgraphIndex &, const Graph &)> &fn) const - { - for (const auto &e : _subgraphs) - { - fn(e.first, *e.second); - } - } - - /** - * @brief Iterate over the container with given function - * - * @param[in] fn Function to be run for every container entry - * @return N/A - */ - void iterate(const std::function<void(const SubgraphIndex &, Graph &)> &fn) - { - for (const auto &e : _subgraphs) - { - fn(e.first, *e.second); - } - } - - /** - * @brief Get count of Subgraphs - * - * @return count of Subgraphs - */ - size_t count() { return _subgraphs.size(); } - - /** - * @brief Return the primary subgraph - * - * @return std::shared_ptr<Graph> Primary sugraph - */ - std::shared_ptr<Graph> primary() const { return _subgraphs.at(SubgraphIndex{0}); } - -private: - std::unordered_map<SubgraphIndex, std::shared_ptr<Graph>> _subgraphs; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_SUBGRAPHS_H__ diff --git a/runtime/onert/core/include/ir/TypeInfo.h b/runtime/onert/core/include/ir/TypeInfo.h deleted file mode 100644 index a1ae4d2e4..000000000 --- a/runtime/onert/core/include/ir/TypeInfo.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_TYPEINFO_H__ -#define __ONERT_IR_TYPEINFO_H__ - -#include <cstdint> -#include <memory> -#include <vector> - -#include "ir/DataType.h" -#include "ir/Sparsity.h" - -namespace onert -{ -namespace ir -{ - -class TypeInfo -{ -public: - TypeInfo() = delete; - - explicit TypeInfo(DataType type, float scale = 0, int32_t offset = 0) - : _type(type), _scale(scale), _offset(offset), _sparsity(nullptr) - { - } - -public: - DataType type() const { return _type; } - float scale() const { return _scale; } - int32_t offset() const { return _offset; } - const ir::Sparsity *sparsity() const { return _sparsity.get(); } - void sparsity(std::shared_ptr<ir::Sparsity> sparsity) { _sparsity = sparsity; } - -public: - void type(const DataType type) { _type = type; } - -private: - DataType _type; - // for quantization - float _scale; - int32_t _offset; - // for sparsity - std::shared_ptr<ir::Sparsity> _sparsity; -}; - -bool operator==(const TypeInfo &lhs, const TypeInfo &rhs); -bool operator!=(const TypeInfo &lhs, const TypeInfo &rhs); - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_TYPEINFO_H__ diff --git a/runtime/onert/core/include/ir/operand/LowerInfo.h b/runtime/onert/core/include/ir/operand/LowerInfo.h deleted file mode 100644 index b7f032b02..000000000 --- a/runtime/onert/core/include/ir/operand/LowerInfo.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERAND_LOWER_INFO_H__ -#define __ONERT_IR_OPERAND_LOWER_INFO_H__ - -#include <functional> -#include <stdint.h> - -#include "ir/operand/PermuteFactor.h" -#include "util/Set.h" - -namespace onert -{ -namespace backend -{ -class Backend; -} // namespace backend -} // namespace onert - -namespace onert -{ -namespace ir -{ -namespace operand -{ -using PermuteFactorSet = util::Set<PermuteFactor>; - -class LowerInfo -{ -public: - LowerInfo() - { - // DO NOTHING - } - -public: - const PermuteFactorSet &def_factors(void) const { return _def_factors; } - const PermuteFactorSet &use_factors(void) const { return _use_factors; } - -public: - void addDefPermuteFactor(const PermuteFactor &factor) { _def_factors.add(factor); } - void addUsePermuteFactor(const PermuteFactor &factor) { _use_factors.add(factor); } - void removeDefPermuteFactor(const PermuteFactor &factor) { _def_factors.remove(factor); } - void removeUsePermuteFactor(const PermuteFactor &factor) { _use_factors.remove(factor); } - -private: - PermuteFactorSet _def_factors; - PermuteFactorSet _use_factors; -}; - -} // namespace operand -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERAND_LOWER_INFO_H__ diff --git a/runtime/onert/core/include/ir/operand/PermuteFactor.h b/runtime/onert/core/include/ir/operand/PermuteFactor.h deleted file mode 100644 index d0bfed337..000000000 --- a/runtime/onert/core/include/ir/operand/PermuteFactor.h +++ /dev/null @@ -1,130 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file PermuteFactor.h - * @brief This file contains onert::ir::operand::PermuteFactor class - * @ingroup COM_AI_RUNTIME - */ - -#ifndef __ONERT_IR_OPERAND_PERMUTE_FACTOR_H__ -#define __ONERT_IR_OPERAND_PERMUTE_FACTOR_H__ - -#include <functional> - -#include "ir/Layout.h" - -namespace onert -{ -namespace backend -{ -class Backend; -} // namespace backend -} // namespace onert - -namespace onert -{ -namespace ir -{ -namespace operand -{ - -/** - * @brief Class that has factors of permutation - */ -class PermuteFactor -{ -public: - /** - * @brief Construct PermuteFactor object. - * @param backend The backend factor - * @param layout The layout factor - */ - PermuteFactor(const backend::Backend *backend, Layout layout) : _backend{backend}, _layout{layout} - { - // DO NOTHING - } - /** - * @brief Construct PermuteFactor object by copy semantics. - */ - PermuteFactor(const PermuteFactor &f) : _backend{f._backend}, _layout{f._layout} - { - // DO NOTHING - } - /** - * @brief Construct PermuteFactor object by move semantics. - */ - PermuteFactor(PermuteFactor &&) = default; - -public: - /** - * @brief Get backend - * - * @return Backend factor - */ - const backend::Backend *backend() const { return _backend; } - /** - * @brief Get layout - * - * @return Layout factor - */ - Layout layout() const { return _layout; } - -public: - /** - * @brief operator overloading function for `==` - * - * @return Whether two PermuteFactor are the same - */ - bool operator==(const PermuteFactor &other) const - { - return _backend == other.backend() && _layout == other.layout(); - } - /** - * @brief operator overloading function for `!=` - * - * @return Whether two PermuteFactor are differenct - */ - bool operator!=(const PermuteFactor &other) const { return !(*this == other); } - -private: - const backend::Backend *_backend{nullptr}; - Layout _layout{Layout::UNKNOWN}; -}; - -} // namespace operand -} // namespace ir -} // namespace onert - -namespace std -{ - -/** - * @brief Structure that provides hash value of PermuteFactor - */ -template <> struct hash<onert::ir::operand::PermuteFactor> -{ - size_t operator()(const onert::ir::operand::PermuteFactor &factor) const noexcept - { - hash<const onert::backend::Backend *> b_hash{}; - hash<onert::ir::Layout> l_hash{}; - return b_hash(factor.backend()) ^ (l_hash(factor.layout()) << 1); - } -}; - -} // namespace std - -#endif // __ONERT_IR_OPERAND_PERMUTE_FACTOR_H__ diff --git a/runtime/onert/core/include/ir/operation/ArgMax.h b/runtime/onert/core/include/ir/operation/ArgMax.h deleted file mode 100644 index ea7eabb83..000000000 --- a/runtime/onert/core/include/ir/operation/ArgMax.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_ARG_MAX_H__ -#define __ONERT_IR_OPERATION_ARG_MAX_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ArgMax : public Operation -{ -public: - enum Input - { - INPUT = 0, - AXIS = 1 - }; - - struct Param - { - DataType output_type; - }; - -public: - ArgMax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::ArgMax; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_ARG_MAX_H__ diff --git a/runtime/onert/core/include/ir/operation/BCQFullyConnected.h b/runtime/onert/core/include/ir/operation/BCQFullyConnected.h deleted file mode 100644 index 4bf3a0bdb..000000000 --- a/runtime/onert/core/include/ir/operation/BCQFullyConnected.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_BCQFULLYCONNECTED_H__ -#define __ONERT_IR_OPERATION_BCQFULLYCONNECTED_H__ - -#include <memory> - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class BCQFullyConnected : public Operation -{ -public: - enum Input - { - INPUT = 0, - WEIGHTS_SCALES, - WEIGHTS_BINARY, - BIAS, - WEIGHTS_CLUSTERS, - }; - - struct Param - { - uint32_t weights_hidden_size; - Activation activation; - }; - -public: - BCQFullyConnected(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::BCQFullyConnected; } - - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_BCQFULLYCONNECTED_H__ diff --git a/runtime/onert/core/include/ir/operation/BCQGather.h b/runtime/onert/core/include/ir/operation/BCQGather.h deleted file mode 100644 index 1349b8c6d..000000000 --- a/runtime/onert/core/include/ir/operation/BCQGather.h +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_BCQGATHER_H__ -#define __ONERT_IR_OPERATION_BCQGATHER_H__ - -#include <memory> - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class BCQGather : public Operation -{ -public: - enum Input - { - INPUT_SCALES = 0, - INPUT_BINARY, - INDICES, - INPUT_CLUSTERS, - }; - - struct Param - { - uint32_t input_hidden_size; - uint32_t axis; - }; - -public: - BCQGather(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::BCQGather; } - - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_BCQGATHER_H__ diff --git a/runtime/onert/core/include/ir/operation/BatchMatMul.h b/runtime/onert/core/include/ir/operation/BatchMatMul.h deleted file mode 100644 index 183f60abe..000000000 --- a/runtime/onert/core/include/ir/operation/BatchMatMul.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_BATCH_MATMUL_H__ -#define __ONERT_IR_OPERATION_BATCH_MATMUL_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class BatchMatMul : public Operation -{ -public: - enum Input - { - LHS = 0, - RHS - }; - - struct Param - { - bool adj_x; - bool adj_y; - }; - -public: - BatchMatMul(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::BatchMatMul; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_BATCH_MATMUL_H__ diff --git a/runtime/onert/core/include/ir/operation/BatchToSpaceND.h b/runtime/onert/core/include/ir/operation/BatchToSpaceND.h deleted file mode 100644 index 3e69b42c7..000000000 --- a/runtime/onert/core/include/ir/operation/BatchToSpaceND.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_BATCH_TO_SPACE_ND_H__ -#define __ONERT_IR_OPERATION_BATCH_TO_SPACE_ND_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class BatchToSpaceND : public Operation -{ -public: - enum Input - { - INPUT = 0, - BLOCK_SIZE = 1, - CROPS_DATA = 2 - }; - -public: - BatchToSpaceND(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::BatchToSpaceND; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_BATCH_TO_SPACE_ND_H__ diff --git a/runtime/onert/core/include/ir/operation/BinaryArithmetic.h b/runtime/onert/core/include/ir/operation/BinaryArithmetic.h deleted file mode 100644 index 110fff565..000000000 --- a/runtime/onert/core/include/ir/operation/BinaryArithmetic.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_BINARY_ARITHMETIC_H__ -#define __ONERT_IR_OPERATION_BINARY_ARITHMETIC_H__ - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class BinaryArithmetic final : public Operation -{ -public: - enum Input - { - LHS = 0, - RHS - }; - - enum class ArithmeticType - { - ADD, - SUB, - MUL, - DIV - }; - - struct Param - { - ArithmeticType arithmetic_type; - Activation activation; - }; - -public: - BinaryArithmetic(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - std::string name() const override; - OpCode opcode() const final { return OpCode::BinaryArithmetic; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_BINARY_ARITHMETIC_H__ diff --git a/runtime/onert/core/include/ir/operation/BroadcastTo.h b/runtime/onert/core/include/ir/operation/BroadcastTo.h deleted file mode 100644 index 06c033497..000000000 --- a/runtime/onert/core/include/ir/operation/BroadcastTo.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_BROADCAST_TO_H__ -#define __ONERT_IR_OPERATION_BROADCAST_TO_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class BroadcastTo : public Operation -{ -public: - enum Input - { - INPUT = 0, - SHAPE = 1 - }; - -public: - BroadcastTo(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::BroadcastTo; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_BROADCAST_TO_H__ diff --git a/runtime/onert/core/include/ir/operation/Comparison.h b/runtime/onert/core/include/ir/operation/Comparison.h deleted file mode 100644 index 8b53f163b..000000000 --- a/runtime/onert/core/include/ir/operation/Comparison.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_COMPARISON_H__ -#define __ONERT_IR_OPERATION_COMPARISON_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Comparison : public Operation -{ -public: - enum Input - { - INPUT0 = 0, - INPUT1 - }; - - enum class ComparisonType - { - Equal, - NotEqual, - Greater, - GreaterEqual, - Less, - LessEqual - }; - - struct Param - { - ComparisonType comparison_type; - }; - -public: - Comparison(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Comparison; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_COMPARISON_H__ diff --git a/runtime/onert/core/include/ir/operation/Concat.h b/runtime/onert/core/include/ir/operation/Concat.h deleted file mode 100644 index 2dff04e93..000000000 --- a/runtime/onert/core/include/ir/operation/Concat.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_CONCAT_H__ -#define __ONERT_IR_OPERATION_CONCAT_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Concat : public Operation -{ -public: - struct Param - { - int32_t axis; - }; - -public: - Concat(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Concat; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_CONCAT_H__ diff --git a/runtime/onert/core/include/ir/operation/Conv2D.h b/runtime/onert/core/include/ir/operation/Conv2D.h deleted file mode 100644 index d8c7b671b..000000000 --- a/runtime/onert/core/include/ir/operation/Conv2D.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_CONV2D_H__ -#define __ONERT_IR_OPERATION_CONV2D_H__ - -#include <memory> - -#include "ir/Operation.h" -#include "ir/InternalType.h" -#include "ir/Padding.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Conv2D : public Operation -{ -public: - enum Input - { - INPUT = 0, - KERNEL, - BIAS - }; - - struct Param - { - Stride stride; - Padding padding; - Activation activation; - Dilation dilation; - }; - -public: - Conv2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Conv2D; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_CONV2D_H__ diff --git a/runtime/onert/core/include/ir/operation/ConvertFp16ToFp32.h b/runtime/onert/core/include/ir/operation/ConvertFp16ToFp32.h deleted file mode 100644 index 15c48357f..000000000 --- a/runtime/onert/core/include/ir/operation/ConvertFp16ToFp32.h +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_CONVERT_FP16_TO_FP32_H__ -#define __ONERT_IR_OPERATION_CONVERT_FP16_TO_FP32_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ConvertFp16ToFp32 : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - -public: - ConvertFp16ToFp32(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::ConvertFp16ToFp32; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_CONVERT_FP16_TO_FP32_H__ diff --git a/runtime/onert/core/include/ir/operation/ConvertFp32ToFp16.h b/runtime/onert/core/include/ir/operation/ConvertFp32ToFp16.h deleted file mode 100644 index 983ce4891..000000000 --- a/runtime/onert/core/include/ir/operation/ConvertFp32ToFp16.h +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_CONVERT_FP32_TO_FP16_H__ -#define __ONERT_IR_OPERATION_CONVERT_FP32_TO_FP16_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ConvertFp32ToFp16 : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - -public: - ConvertFp32ToFp16(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::ConvertFp32ToFp16; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_CONVERT_FP32_TO_FP16_H__ diff --git a/runtime/onert/core/include/ir/operation/Custom.h b/runtime/onert/core/include/ir/operation/Custom.h deleted file mode 100644 index c2a4b354a..000000000 --- a/runtime/onert/core/include/ir/operation/Custom.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ONERT_IR_OPERATION_CUSTOM_H__ -#define __ONERT_IR_OPERATION_CUSTOM_H__ - -#include "ir/Operation.h" - -#include <cstring> - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Custom : public Operation -{ -public: - struct Userdata - { - char *data; - size_t size; - - Userdata() : data{nullptr}, size{0} {} - Userdata(const Userdata &o) - { - size = o.size; - data = new char[size]; - std::memcpy(data, o.data, size); - } - ~Userdata() { delete[] data; } - }; - - Custom(OperandConstraint input_constr, const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, std::string id, const Userdata &userdata); - - void accept(OperationVisitor &v) const override; - -public: - /** - * @return unique operation identifier - */ - const std::string &id() const; - - std::string name() const override; - OpCode opcode() const final { return OpCode::Custom; } - - /** - * @return user-provided data - */ - const Userdata &userdata() const; - -private: - std::string _id; - Userdata _userdata; -}; - -} // namespace operation -} // namespace ir -} // namespace onert -#endif // __ONERT_IR_OPERATION_CUSTOM_H__ diff --git a/runtime/onert/core/include/ir/operation/DepthToSpace.h b/runtime/onert/core/include/ir/operation/DepthToSpace.h deleted file mode 100644 index a5315051d..000000000 --- a/runtime/onert/core/include/ir/operation/DepthToSpace.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_DEPTH_TO_SPACE_H__ -#define __ONERT_IR_OPERATION_DEPTH_TO_SPACE_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class DepthToSpace : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - struct Param - { - std::int32_t block_size; - }; - -public: - DepthToSpace(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::DepthToSpace; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_DEPTH_TO_SPACE_H__ diff --git a/runtime/onert/core/include/ir/operation/DepthwiseConv2D.h b/runtime/onert/core/include/ir/operation/DepthwiseConv2D.h deleted file mode 100644 index b10bf708c..000000000 --- a/runtime/onert/core/include/ir/operation/DepthwiseConv2D.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_DEPTHWISECONV2D_H__ -#define __ONERT_IR_OPERATION_DEPTHWISECONV2D_H__ - -#include <memory> - -#include "ir/Operation.h" -#include "ir/InternalType.h" -#include "ir/Padding.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class DepthwiseConv2D : public Operation -{ -public: - enum Input - { - INPUT = 0, - KERNEL, - BIAS - }; - - struct Param - { - Stride stride; - Padding padding; - uint32_t multiplier; - Activation activation; - }; - -public: - DepthwiseConv2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::DepthwiseConv2D; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_DEPTHWISECONV2D_H__ diff --git a/runtime/onert/core/include/ir/operation/Einsum.h b/runtime/onert/core/include/ir/operation/Einsum.h deleted file mode 100644 index 9892c24b8..000000000 --- a/runtime/onert/core/include/ir/operation/Einsum.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_EINSUM_H__ -#define __ONERT_IR_OPERATION_EINSUM_H__ - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Einsum : public Operation -{ -public: - struct Param - { - std::string equation; - }; - -public: - Einsum(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Einsum; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_EINSUM_H__ diff --git a/runtime/onert/core/include/ir/operation/ElementwiseActivation.h b/runtime/onert/core/include/ir/operation/ElementwiseActivation.h deleted file mode 100644 index b2a1d3d2d..000000000 --- a/runtime/onert/core/include/ir/operation/ElementwiseActivation.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_ELEMENTWISE_ACTIVATION_H__ -#define __ONERT_IR_OPERATION_ELEMENTWISE_ACTIVATION_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ElementwiseActivation : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - enum class Type - { - ELU, - LOGISTIC, - RELU, - TANH, - LEAKY_RELU - }; - - struct Param - { - Type op_type; - float alpha; - float beta; - Param() : op_type(Type::ELU), alpha(0.0f), beta(0.0f) {} - }; - -public: - ElementwiseActivation(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - std::string name() const override; - OpCode opcode() const final { return OpCode::ElementwiseActivation; } - -public: - const Param ¶m() const { return _param; } - -public: - static float infinity; - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_ELEMENTWISE_ACTIVATION_H__ diff --git a/runtime/onert/core/include/ir/operation/ElementwiseBinary.h b/runtime/onert/core/include/ir/operation/ElementwiseBinary.h deleted file mode 100644 index dd07f6058..000000000 --- a/runtime/onert/core/include/ir/operation/ElementwiseBinary.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_ELEMENTWISEBINARY_H__ -#define __ONERT_IR_OPERATION_ELEMENTWISEBINARY_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ElementwiseBinary : public Operation -{ -public: - enum Input - { - LHS = 0, - RHS - }; - - enum class ElementwiseBinaryType - { - LOGICAL_AND, - LOGICAL_OR, - MAX, - MIN - }; - - struct Param - { - ElementwiseBinaryType op_type; - }; - -public: - ElementwiseBinary(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - std::string name() const override; - OpCode opcode() const final { return OpCode::ElementwiseBinary; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_ELEMENTWISEBINARY_H__ diff --git a/runtime/onert/core/include/ir/operation/ElementwiseUnary.h b/runtime/onert/core/include/ir/operation/ElementwiseUnary.h deleted file mode 100644 index c40778a56..000000000 --- a/runtime/onert/core/include/ir/operation/ElementwiseUnary.h +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_ELEMENTWISEUNARY_H__ -#define __ONERT_IR_OPERATION_ELEMENTWISEUNARY_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ElementwiseUnary : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - enum class Type - { - ABS, - CAST, - COS, - DEQUANTIZE, - ERF, - EXP, - FLOOR, - LOG, - LOGICAL_NOT, - NEG, - QUANTIZE, - ROUND, - RSQRT, - SIN, - SQRT, - SQURE, - ZEROS_LIKE - }; - - struct Param - { - Type op_type; - }; - -public: - ElementwiseUnary(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - std::string name() const override; - OpCode opcode() const final { return OpCode::ElementwiseUnary; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_ELEMENTWISEUNARY_H__ diff --git a/runtime/onert/core/include/ir/operation/EmbeddingLookup.h b/runtime/onert/core/include/ir/operation/EmbeddingLookup.h deleted file mode 100644 index 54064faf0..000000000 --- a/runtime/onert/core/include/ir/operation/EmbeddingLookup.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_EMBEDDING_LOOKUP_H__ -#define __ONERT_IR_OPERATION_EMBEDDING_LOOKUP_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class EmbeddingLookup : public Operation -{ -public: - enum Input - { - LOOKUPS = 0, - VALUES = 1 - }; - -public: - EmbeddingLookup(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::EmbeddingLookup; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_EMBEDDING_LOOKUP_H__ diff --git a/runtime/onert/core/include/ir/operation/ExpandDims.h b/runtime/onert/core/include/ir/operation/ExpandDims.h deleted file mode 100644 index 09669a40b..000000000 --- a/runtime/onert/core/include/ir/operation/ExpandDims.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_EXPANDDIMS_H__ -#define __ONERT_IR_OPERATION_EXPANDDIMS_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ExpandDims : public Operation -{ -public: - enum Input - { - INPUT = 0, - AXIS = 1 - }; - -public: - ExpandDims(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::ExpandDims; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_EXPANDDIMS_H__ diff --git a/runtime/onert/core/include/ir/operation/Fill.h b/runtime/onert/core/include/ir/operation/Fill.h deleted file mode 100644 index 524e41385..000000000 --- a/runtime/onert/core/include/ir/operation/Fill.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_FILL_H__ -#define __ONERT_IR_OPERATION_FILL_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Fill : public Operation -{ -public: - enum Input - { - INPUT = 0, - VALUE, - }; - -public: - Fill(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Fill; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_FILL_H__ diff --git a/runtime/onert/core/include/ir/operation/FullyConnected.h b/runtime/onert/core/include/ir/operation/FullyConnected.h deleted file mode 100644 index b6484ae4d..000000000 --- a/runtime/onert/core/include/ir/operation/FullyConnected.h +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_FULLYCONNECTED_H__ -#define __ONERT_IR_OPERATION_FULLYCONNECTED_H__ - -#include <memory> - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class FullyConnected : public Operation -{ -public: - enum Input - { - INPUT = 0, - WEIGHT, - BIAS - }; - - struct Param - { - Activation activation; - }; - -public: - FullyConnected(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::FullyConnected; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_FULLYCONNECTED_H__ diff --git a/runtime/onert/core/include/ir/operation/FusedBatchNorm.h b/runtime/onert/core/include/ir/operation/FusedBatchNorm.h deleted file mode 100644 index 989ee2b98..000000000 --- a/runtime/onert/core/include/ir/operation/FusedBatchNorm.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_FUSEDBATCHNORM_H__ -#define __ONERT_IR_OPERATION_FUSEDBATCHNORM_H__ - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class FusedBatchNorm : public Operation -{ -public: - enum Input - { - INPUT = 0, - SCALE, - OFFSET, - MEAN, - VARIANCE - }; - - struct Param - { - bool is_training; - std::string data_format; - float epsilon; - }; - -public: - FusedBatchNorm(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::FusedBatchNorm; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_FUSEDBATCHNORM_H__ diff --git a/runtime/onert/core/include/ir/operation/Gather.h b/runtime/onert/core/include/ir/operation/Gather.h deleted file mode 100644 index 544eb3b19..000000000 --- a/runtime/onert/core/include/ir/operation/Gather.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_GATHER_H__ -#define __ONERT_IR_OPERATION_GATHER_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Gather : public Operation -{ -public: - enum Input - { - INPUT = 0, - INDICES, - }; - - struct Param - { - int32_t axis; - }; - -public: - Gather(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Gather; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_GATHER_H__ diff --git a/runtime/onert/core/include/ir/operation/HashtableLookup.h b/runtime/onert/core/include/ir/operation/HashtableLookup.h deleted file mode 100644 index 4b6cf9362..000000000 --- a/runtime/onert/core/include/ir/operation/HashtableLookup.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_HASHTABLE_LOOKUP_H__ -#define __ONERT_IR_OPERATION_HASHTABLE_LOOKUP_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class HashtableLookup : public Operation -{ -public: - enum Input - { - LOOKUPS = 0, - KEYS = 1, - VALUES = 2 - }; - - enum Output - { - OUTPUT = 0, - HITS = 1 - }; - -public: - HashtableLookup(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::HashtableLookup; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_HASHTABLE_LOOKUP_H__ diff --git a/runtime/onert/core/include/ir/operation/If.h b/runtime/onert/core/include/ir/operation/If.h deleted file mode 100644 index 41cd4e239..000000000 --- a/runtime/onert/core/include/ir/operation/If.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_IF_H__ -#define __ONERT_IR_OPERATION_IF_H__ - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class If : public Operation -{ -public: - struct Param - { - SubgraphIndex then_subg_index; - SubgraphIndex else_subg_index; - }; - -public: - If(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::If; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_IF_H__ diff --git a/runtime/onert/core/include/ir/operation/InstanceNorm.h b/runtime/onert/core/include/ir/operation/InstanceNorm.h deleted file mode 100644 index 6a3bb5189..000000000 --- a/runtime/onert/core/include/ir/operation/InstanceNorm.h +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_INSTANCE_NORM_H__ -#define __ONERT_IR_OPERATION_INSTANCE_NORM_H__ - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class InstanceNorm : public Operation -{ -public: - enum Input - { - INPUT = 0, - GAMMA, - BETA - }; - - struct Param - { - Activation activation; - float epsilon; - }; - -public: - InstanceNorm(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::InstanceNorm; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_INSTANCE_NORM_H__ diff --git a/runtime/onert/core/include/ir/operation/L2Normalization.h b/runtime/onert/core/include/ir/operation/L2Normalization.h deleted file mode 100644 index abbd68c97..000000000 --- a/runtime/onert/core/include/ir/operation/L2Normalization.h +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_L2_NORMALIZATION_H__ -#define __ONERT_IR_OPERATION_L2_NORMALIZATION_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class L2Normalization : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - -public: - L2Normalization(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::L2Normalization; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_L2_NORMALIZATION_H__ diff --git a/runtime/onert/core/include/ir/operation/LSTM.h b/runtime/onert/core/include/ir/operation/LSTM.h deleted file mode 100644 index 027bc6b42..000000000 --- a/runtime/onert/core/include/ir/operation/LSTM.h +++ /dev/null @@ -1,96 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ONERT_IR_OPERATION_LSTM_H__ -#define __ONERT_IR_OPERATION_LSTM_H__ - -#include "ir/InternalType.h" -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -// This operation supports only unidirectional sequence lstm -class LSTM : public Operation -{ -public: - enum Input - { - INPUT = 0, - INPUT_TO_INPUT_WEIGHTS = 1, - INPUT_TO_FORGET_WEIGHTS = 2, - INPUT_TO_CELL_WEIGHTS = 3, - INPUT_TO_OUTPUT_WEIGHTS = 4, - RECURRENT_TO_INPUT_WEIGHTS = 5, - RECURRENT_TO_FORGET_WEIGHTS = 6, - RECURRENT_TO_CELL_WEIGHTS = 7, - RECURRENT_TO_OUTPUT_WEIGHTS = 8, - CELL_TO_INPUT_WEIGHTS = 9, - CELL_TO_FORGET_WEIGHTS = 10, - CELL_TO_OUTPUT_WEIGHTS = 11, - INPUT_GATE_BIAS = 12, - FORGET_GATE_BIAS = 13, - CELL_BIAS = 14, - OUTPUT_GATE_BIAS = 15, - PROJECTION_WEIGHTS = 16, - PROJECTION_BIAS = 17, - OUTPUT_STATE_IN = 18, - CELL_STATE_IN = 19, - INPUT_LAYER_NORMALIZATION_WEIGHTS = 20, - FORGET_LAYER_NORMALIZATION_WEIGHTS = 21, - CELL_LAYER_NORMALIZATION_WEIGHTS = 22, - OUTPUT_LAYER_NORMALIZATION_WEIGHTS = 23, - }; - - enum Output - { - SCRATCH_BUFFER = 0, - OUTPUT_STATE_OUT = 1, - CELL_STATE_OUT = 2, - OUTPUT = 3 - }; - - struct Param - { - Activation activation; - float cell_threshold; - float projection_threshold; - bool time_major; - }; - -public: - LSTM(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - std::string name() const override; - OpCode opcode() const final { return OpCode::LSTM; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_LSTM_H__ diff --git a/runtime/onert/core/include/ir/operation/LocalResponseNormalization.h b/runtime/onert/core/include/ir/operation/LocalResponseNormalization.h deleted file mode 100644 index 2946cfbad..000000000 --- a/runtime/onert/core/include/ir/operation/LocalResponseNormalization.h +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_LOCAL_RESPONSE_NORMALIZATION_H__ -#define __ONERT_IR_OPERATION_LOCAL_RESPONSE_NORMALIZATION_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class LocalResponseNormalization : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - struct Param - { - int radius; - float bias; - float alpha; - float beta; - }; - -public: - LocalResponseNormalization(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::LocalResponseNormalization; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_LOCAL_RESPONSE_NORMALIZATION_H__ diff --git a/runtime/onert/core/include/ir/operation/LogSoftmax.h b/runtime/onert/core/include/ir/operation/LogSoftmax.h deleted file mode 100644 index 391b4ba4a..000000000 --- a/runtime/onert/core/include/ir/operation/LogSoftmax.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_LOGSOFTMAX_H__ -#define __ONERT_IR_OPERATION_LOGSOFTMAX_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class LogSoftmax : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - struct Param - { - float beta; - int axis; - }; - -public: - LogSoftmax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::LogSoftmax; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_LOGSOFTMAX_H__ diff --git a/runtime/onert/core/include/ir/operation/LowerInfo.h b/runtime/onert/core/include/ir/operation/LowerInfo.h deleted file mode 100644 index 7ef53b8c7..000000000 --- a/runtime/onert/core/include/ir/operation/LowerInfo.h +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_LOWER_INFO_H__ -#define __ONERT_IR_OPERATION_LOWER_INFO_H__ - -#include <string> - -#include <ir/operand/PermuteFactor.h> - -namespace onert -{ -namespace backend -{ -class Backend; -} // namespace backend -} // namespace onert - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class LowerInfo -{ -public: - LowerInfo(const backend::Backend *backend, Layout layout); - const backend::Backend *backend() const { return _permute_factor.backend(); } - Layout layout() const { return _permute_factor.layout(); } - -private: - operand::PermuteFactor _permute_factor; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_LOWER_INFO_H__ diff --git a/runtime/onert/core/include/ir/operation/MatrixBandPart.h b/runtime/onert/core/include/ir/operation/MatrixBandPart.h deleted file mode 100644 index 291826635..000000000 --- a/runtime/onert/core/include/ir/operation/MatrixBandPart.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_MATRIX_BAND_PART_H__ -#define __ONERT_IR_OPERATION_MATRIX_BAND_PART_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class MatrixBandPart : public Operation -{ -public: - enum Input - { - INPUT = 0, - NUM_LOWER_DIAG, - NUM_UPPER_DIAG, - }; - -public: - MatrixBandPart(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::MatrixBandPart; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_MATRIX_BAND_PART_H__ diff --git a/runtime/onert/core/include/ir/operation/OneHot.h b/runtime/onert/core/include/ir/operation/OneHot.h deleted file mode 100644 index 6264cd15e..000000000 --- a/runtime/onert/core/include/ir/operation/OneHot.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_ONEHOT_H__ -#define __ONERT_IR_OPERATION_ONEHOT_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class OneHot : public Operation -{ -public: - enum Input - { - INDICES = 0, - DEPTH = 1, - ON_VALUE = 2, - OFF_VALUE = 3, - }; - - struct Param - { - int axis; - }; - -public: - OneHot(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::OneHot; } - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_ONEHOT_H__ diff --git a/runtime/onert/core/include/ir/operation/PReLU.h b/runtime/onert/core/include/ir/operation/PReLU.h deleted file mode 100644 index 2981ffc6a..000000000 --- a/runtime/onert/core/include/ir/operation/PReLU.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_PRELU_H__ -#define __ONERT_IR_OPERATION_PRELU_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class PReLU : public Operation -{ -public: - enum Input - { - INPUT = 0, - ALPHA = 1 - }; - -public: - PReLU(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::PReLU; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_PRELU_H__ diff --git a/runtime/onert/core/include/ir/operation/Pack.h b/runtime/onert/core/include/ir/operation/Pack.h deleted file mode 100644 index cf07541e0..000000000 --- a/runtime/onert/core/include/ir/operation/Pack.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ONERT_IR_OPERATION_PACK_H__ -#define __ONERT_IR_OPERATION_PACK_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -class Pack : public Operation -{ -public: - struct Param - { - int32_t num; - int32_t axis; - }; - -public: - Pack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Pack; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; -} // namespace operation -} // namespace ir -} // namespace onert -#endif // __ONERT_IR_OPERATION_PACK_H__ diff --git a/runtime/onert/core/include/ir/operation/Pad.h b/runtime/onert/core/include/ir/operation/Pad.h deleted file mode 100644 index 00481cd50..000000000 --- a/runtime/onert/core/include/ir/operation/Pad.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_PAD_H__ -#define __ONERT_IR_OPERATION_PAD_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Pad : public Operation -{ -public: - enum Input - { - INPUT = 0, - PAD = 1, - VALUE = 2 - }; - -public: - Pad(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Pad; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_PAD_H__ diff --git a/runtime/onert/core/include/ir/operation/Permute.h b/runtime/onert/core/include/ir/operation/Permute.h deleted file mode 100644 index 10f09b9a0..000000000 --- a/runtime/onert/core/include/ir/operation/Permute.h +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_PERMUTE_H__ -#define __ONERT_IR_OPERATION_PERMUTE_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace backend -{ -class BackendContext; -} // namespace backend -} // namespace onert - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Permute : public Operation -{ -public: - enum class Type - { - NHWC_TO_NCHW, - NCHW_TO_NHWC, - COPY - }; - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Permute; } - -public: - Permute(const OperandIndex &input, const OperandIndex &output, Type type); - -public: - Type getPermuteType() const { return _type; } - -private: - Type _type; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_PERMUTE_H__ diff --git a/runtime/onert/core/include/ir/operation/Pool2D.h b/runtime/onert/core/include/ir/operation/Pool2D.h deleted file mode 100644 index 22425b4c2..000000000 --- a/runtime/onert/core/include/ir/operation/Pool2D.h +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_POOL2D_H__ -#define __ONERT_IR_OPERATION_POOL2D_H__ - -#include <memory> - -#include "ir/Operation.h" -#include "ir/InternalType.h" -#include "ir/Padding.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Pool2D : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - enum class PoolType - { - AVG, - L2, - MAX, - }; - - struct Param - { - PoolType op_type; - uint32_t kh; - uint32_t kw; - Stride stride; - Padding padding; - Activation activation; - }; - -public: - Pool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - std::string name() const override; - OpCode opcode() const final { return OpCode::Pool2D; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_POOL2D_H__ diff --git a/runtime/onert/core/include/ir/operation/Pow.h b/runtime/onert/core/include/ir/operation/Pow.h deleted file mode 100644 index ca28ddfe7..000000000 --- a/runtime/onert/core/include/ir/operation/Pow.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_POW_H__ -#define __ONERT_IR_OPERATION_POW_H__ - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Pow : public Operation -{ -public: - enum Input - { - LHS = 0, - RHS - }; - -public: - Pow(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Pow; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_POW_H__ diff --git a/runtime/onert/core/include/ir/operation/RNN.h b/runtime/onert/core/include/ir/operation/RNN.h deleted file mode 100644 index 087075da2..000000000 --- a/runtime/onert/core/include/ir/operation/RNN.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ONERT_IR_OPERATION_RNN_H__ -#define __ONERT_IR_OPERATION_RNN_H__ - -#include "ir/InternalType.h" -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class RNN : public Operation -{ -public: - enum Input - { - INPUT = 0, - WEIGHTS = 1, - RECURRENT_WEIGHTS = 2, - BIAS = 3, - HIDDEN_STATE_IN = 4 - }; - - enum Output - { - OUTPUT = 0, - HIDDEN_STATE_OUT = 1 - }; - - struct Param - { - Activation activation; - }; - -public: - RNN(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::RNN; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_RNN_H__ diff --git a/runtime/onert/core/include/ir/operation/Range.h b/runtime/onert/core/include/ir/operation/Range.h deleted file mode 100644 index 81e170be9..000000000 --- a/runtime/onert/core/include/ir/operation/Range.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_RANGE_H__ -#define __ONERT_IR_OPERATION_RANGE_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Range : public Operation -{ -public: - enum Input - { - START = 0, - LIMIT = 1, - DELTA = 2 - }; - -public: - Range(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Range; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_RANGE_H__ diff --git a/runtime/onert/core/include/ir/operation/Rank.h b/runtime/onert/core/include/ir/operation/Rank.h deleted file mode 100644 index 2fd24ce23..000000000 --- a/runtime/onert/core/include/ir/operation/Rank.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_RANK_H__ -#define __ONERT_IR_OPERATION_RANK_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Rank : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - -public: - Rank(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Rank; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_RANK_H__ diff --git a/runtime/onert/core/include/ir/operation/Reduce.h b/runtime/onert/core/include/ir/operation/Reduce.h deleted file mode 100644 index 26bcf5ec9..000000000 --- a/runtime/onert/core/include/ir/operation/Reduce.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_REDUCE_H__ -#define __ONERT_IR_OPERATION_REDUCE_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Reduce : public Operation -{ -public: - enum Input - { - INPUT = 0, - AXES = 1 - }; - - enum class ReduceType - { - ALL, - ANY, - MAX, - MEAN, - MIN, - PROD, - SUM - }; - - struct Param - { - ReduceType reduce_type; - bool keep_dims; - }; - -public: - Reduce(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - std::string name() const override; - OpCode opcode() const final { return OpCode::Reduce; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_REDUCE_ALL_H__ diff --git a/runtime/onert/core/include/ir/operation/Reshape.h b/runtime/onert/core/include/ir/operation/Reshape.h deleted file mode 100644 index c2c0e8c99..000000000 --- a/runtime/onert/core/include/ir/operation/Reshape.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_RESHAPE_H__ -#define __ONERT_IR_OPERATION_RESHAPE_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Reshape : public Operation -{ -public: - enum Input - { - INPUT = 0, - SHAPE = 1 - }; - - struct Param - { - std::vector<int32_t> new_shape; - }; - -public: - Reshape(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Reshape; } - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_RESHAPE_H__ diff --git a/runtime/onert/core/include/ir/operation/ResizeBilinear.h b/runtime/onert/core/include/ir/operation/ResizeBilinear.h deleted file mode 100644 index ab330c826..000000000 --- a/runtime/onert/core/include/ir/operation/ResizeBilinear.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_RESIZE_BILINEAR_H__ -#define __ONERT_IR_OPERATION_RESIZE_BILINEAR_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ResizeBilinear : public Operation -{ -public: - enum Input - { - INPUT = 0, - SIZE = 1, - }; - - struct Param - { - // If the input SIZE exists in inputs, height_out and width_out are not set. Ignore these params - int32_t height_out; - int32_t width_out; - bool align_corners; - bool half_pixel_centers; - }; - -public: - ResizeBilinear(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::ResizeBilinear; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_RESIZE_BILINEAR_H__ diff --git a/runtime/onert/core/include/ir/operation/ResizeNearestNeighbor.h b/runtime/onert/core/include/ir/operation/ResizeNearestNeighbor.h deleted file mode 100644 index 10827803e..000000000 --- a/runtime/onert/core/include/ir/operation/ResizeNearestNeighbor.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_RESIZE_NEAREST_NEIGHBOR_H__ -#define __ONERT_IR_OPERATION_RESIZE_NEAREST_NEIGHBOR_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class ResizeNearestNeighbor : public Operation -{ -public: - enum Input - { - INPUT = 0, - SIZE = 1, - }; - - struct Param - { - // If the input SIZE exists in inputs, Be height_out and width_out not set. Ignore these params - int32_t height_out; - int32_t width_out; - bool align_corners; - }; - -public: - ResizeNearestNeighbor(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::ResizeNearestNeighbor; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_RESIZE_NEAREST_NEIGHBOR_H__ diff --git a/runtime/onert/core/include/ir/operation/Reverse.h b/runtime/onert/core/include/ir/operation/Reverse.h deleted file mode 100644 index 3d7f3fc89..000000000 --- a/runtime/onert/core/include/ir/operation/Reverse.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_REVERSE_H__ -#define __ONERT_IR_OPERATION_REVERSE_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Reverse : public Operation -{ -public: - enum Input - { - INPUT = 0, - AXIS = 1 - }; - -public: - Reverse(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Reverse; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_REVERSE_H__ diff --git a/runtime/onert/core/include/ir/operation/Select.h b/runtime/onert/core/include/ir/operation/Select.h deleted file mode 100644 index 33bf67886..000000000 --- a/runtime/onert/core/include/ir/operation/Select.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SELECT_H__ -#define __ONERT_IR_OPERATION_SELECT_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Select : public Operation -{ -public: - enum Input - { - CONDITION = 0, - INPUT_TRUE = 1, - INPUT_FALSE = 2 - }; - -public: - Select(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Select; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SELECT_H__ diff --git a/runtime/onert/core/include/ir/operation/Shape.h b/runtime/onert/core/include/ir/operation/Shape.h deleted file mode 100644 index 4dea7e424..000000000 --- a/runtime/onert/core/include/ir/operation/Shape.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SHAPE_H__ -#define __ONERT_IR_OPERATION_SHAPE_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Shape : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - -public: - Shape(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Shape; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SHAPE_H__ diff --git a/runtime/onert/core/include/ir/operation/Slice.h b/runtime/onert/core/include/ir/operation/Slice.h deleted file mode 100644 index c86a9893a..000000000 --- a/runtime/onert/core/include/ir/operation/Slice.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SLICE_H__ -#define __ONERT_IR_OPERATION_SLICE_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Slice : public Operation -{ -public: - enum Input - { - INPUT = 0, - BEGINS = 1, - SIZES = 2, - }; - -public: - Slice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Slice; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SLICE_H__ diff --git a/runtime/onert/core/include/ir/operation/Softmax.h b/runtime/onert/core/include/ir/operation/Softmax.h deleted file mode 100644 index db7ae910e..000000000 --- a/runtime/onert/core/include/ir/operation/Softmax.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SOFTMAX_H__ -#define __ONERT_IR_OPERATION_SOFTMAX_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Softmax : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - struct Param - { - float beta; - }; - -public: - Softmax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Softmax; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SOFTMAX_H__ diff --git a/runtime/onert/core/include/ir/operation/SpaceToBatchND.h b/runtime/onert/core/include/ir/operation/SpaceToBatchND.h deleted file mode 100644 index 99928ff24..000000000 --- a/runtime/onert/core/include/ir/operation/SpaceToBatchND.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SPACE_TO_BATCH_ND_H__ -#define __ONERT_IR_OPERATION_SPACE_TO_BATCH_ND_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class SpaceToBatchND : public Operation -{ -public: - enum Input - { - INPUT = 0, - BLOCK_SIZE = 1, - PADDINGS = 2 - }; - -public: - SpaceToBatchND(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::SpaceToBatchND; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SPACE_TO_BATCH_ND_H__ diff --git a/runtime/onert/core/include/ir/operation/SpaceToDepth.h b/runtime/onert/core/include/ir/operation/SpaceToDepth.h deleted file mode 100644 index 6c8b09130..000000000 --- a/runtime/onert/core/include/ir/operation/SpaceToDepth.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SPACE_TO_DEPTH_H__ -#define __ONERT_IR_OPERATION_SPACE_TO_DEPTH_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class SpaceToDepth : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - struct Param - { - std::int32_t block_size; - }; - -public: - SpaceToDepth(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::SpaceToDepth; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SPACE_TO_DEPTH_H__ diff --git a/runtime/onert/core/include/ir/operation/Split.h b/runtime/onert/core/include/ir/operation/Split.h deleted file mode 100644 index c415941a4..000000000 --- a/runtime/onert/core/include/ir/operation/Split.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ONERT_IR_OPERATION_SPLIT_H__ -#define __ONERT_IR_OPERATION_SPLIT_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -class Split : public Operation -{ -public: - enum Input - { - AXIS = 0, - INPUT = 1, - }; - - struct Param - { - int num_splits; - }; - -public: - Split(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Split; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; -} // namespace operation -} // namespace ir -} // namespace onert -#endif // __ONERT_IR_OPERATION_SPLIT_H__ diff --git a/runtime/onert/core/include/ir/operation/SplitV.h b/runtime/onert/core/include/ir/operation/SplitV.h deleted file mode 100644 index 99a06ee7f..000000000 --- a/runtime/onert/core/include/ir/operation/SplitV.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ONERT_IR_OPERATION_SPLIT_V_H__ -#define __ONERT_IR_OPERATION_SPLIT_V_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -class SplitV : public Operation -{ -public: - enum Input - { - INPUT = 0, - SIZE_SPLITS = 1, - SPLIT_DIM = 2 - }; - - struct Param - { - int num_splits; - }; - -public: - SplitV(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::SplitV; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; -} // namespace operation -} // namespace ir -} // namespace onert -#endif // __ONERT_IR_OPERATION_SPLIT_V_H__ diff --git a/runtime/onert/core/include/ir/operation/SquaredDifference.h b/runtime/onert/core/include/ir/operation/SquaredDifference.h deleted file mode 100644 index 392b11448..000000000 --- a/runtime/onert/core/include/ir/operation/SquaredDifference.h +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SQUARED_DIFFERENCE_H__ -#define __ONERT_IR_OPERATION_SQUARED_DIFFERENCE_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class SquaredDifference : public Operation -{ -public: - enum Input - { - LHS = 0, - RHS - }; - -public: - SquaredDifference(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::SquaredDifference; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SQUARED_DIFFERENCE_H__ diff --git a/runtime/onert/core/include/ir/operation/Squeeze.h b/runtime/onert/core/include/ir/operation/Squeeze.h deleted file mode 100644 index c370472b7..000000000 --- a/runtime/onert/core/include/ir/operation/Squeeze.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_SQUEEZE_H__ -#define __ONERT_IR_OPERATION_SQUEEZE_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Squeeze : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - struct Param - { - // Please see tensorflow/lite/c/builtin_op_data.h and squeeze.cc. - // tensorflow lite supports only for ndim <= 8. - int dims[8]; - int ndim; - }; - -public: - Squeeze(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Squeeze; } - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_SQUEEZE_H__ diff --git a/runtime/onert/core/include/ir/operation/StatelessRandomUniform.h b/runtime/onert/core/include/ir/operation/StatelessRandomUniform.h deleted file mode 100644 index 112a748fd..000000000 --- a/runtime/onert/core/include/ir/operation/StatelessRandomUniform.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_STATELESS_RANDOM_UNIFORM_H__ -#define __ONERT_IR_OPERATION_STATELESS_RANDOM_UNIFORM_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class StatelessRandomUniform : public Operation -{ -public: - enum Input - { - SHAPE = 0, - SEED = 1 - }; - -public: - StatelessRandomUniform(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::StatelessRandomUniform; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_STATELESS_RANDOM_UNIFORM_H__ diff --git a/runtime/onert/core/include/ir/operation/StridedSlice.h b/runtime/onert/core/include/ir/operation/StridedSlice.h deleted file mode 100644 index 4a5e06410..000000000 --- a/runtime/onert/core/include/ir/operation/StridedSlice.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_STRIDED_SLICE_H__ -#define __ONERT_IR_OPERATION_STRIDED_SLICE_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class StridedSlice : public Operation -{ -public: - enum Input - { - INPUT = 0, - STARTS = 1, - ENDS = 2, - STRIDES = 3 - }; - - struct Param - { - int32_t begin_mask; - int32_t end_mask; - int32_t shrink_axis_mask; - }; - -public: - StridedSlice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::StridedSlice; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_STRIDED_SLICE_H__ diff --git a/runtime/onert/core/include/ir/operation/Tile.h b/runtime/onert/core/include/ir/operation/Tile.h deleted file mode 100644 index 388c452c8..000000000 --- a/runtime/onert/core/include/ir/operation/Tile.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_TILE_H__ -#define __ONERT_IR_OPERATION_TILE_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Tile : public Operation -{ -public: - enum Input - { - INPUT = 0, - MULTIPLES, - }; - -public: - Tile(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Tile; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_TILE_H__ diff --git a/runtime/onert/core/include/ir/operation/TopKV2.h b/runtime/onert/core/include/ir/operation/TopKV2.h deleted file mode 100644 index 179a599ca..000000000 --- a/runtime/onert/core/include/ir/operation/TopKV2.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_TOPK_V2_H__ -#define __ONERT_IR_OPERATION_TOPK_V2_H__ - -#include <memory> - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class TopKV2 : public Operation -{ -public: - enum Input - { - INPUT - }; - - enum Output - { - OUTPUT_VALUES = 0, - OUTPUT_INDICES, - }; - - struct Param - { - std::int32_t k; - }; - -public: - TopKV2(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::TopKV2; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_TOPK_V2_H__ diff --git a/runtime/onert/core/include/ir/operation/Transpose.h b/runtime/onert/core/include/ir/operation/Transpose.h deleted file mode 100644 index 665c9bbce..000000000 --- a/runtime/onert/core/include/ir/operation/Transpose.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_TRANSPOSE_H__ -#define __ONERT_IR_OPERATION_TRANSPOSE_H__ - -#include "ir/Operation.h" - -#include <utility> - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class Transpose : public Operation -{ -public: - enum Input - { - INPUT = 0, // for an n-D tensor, specifying the tensor to be transposed. - PERMUTATION = 1, - }; - -public: - Transpose(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Transpose; } -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_TRANSPOSE_H__ diff --git a/runtime/onert/core/include/ir/operation/TransposeConv.h b/runtime/onert/core/include/ir/operation/TransposeConv.h deleted file mode 100644 index 05137ccf8..000000000 --- a/runtime/onert/core/include/ir/operation/TransposeConv.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_TRANSPOSE_CONV_H__ -#define __ONERT_IR_OPERATION_TRANSPOSE_CONV_H__ - -#include <memory> - -#include "ir/Operation.h" -#include "ir/InternalType.h" -#include "ir/Padding.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class TransposeConv : public Operation -{ -public: - enum Input - { - OUTPUT_SHAPE = 0, - KERNEL, - INPUT - }; - - struct Param - { - Padding padding; - Stride stride; - }; - -public: - TransposeConv(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::TransposeConv; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_TRANSPOSE_CONV_H__ diff --git a/runtime/onert/core/include/ir/operation/Unpack.h b/runtime/onert/core/include/ir/operation/Unpack.h deleted file mode 100644 index 092583a97..000000000 --- a/runtime/onert/core/include/ir/operation/Unpack.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef __ONERT_IR_OPERATION_UNPACK_H__ -#define __ONERT_IR_OPERATION_UNPACK_H__ - -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -class Unpack : public Operation -{ -public: - enum Input - { - INPUT = 0 - }; - - struct Param - { - int32_t num; - int32_t axis; - }; - -public: - Unpack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::Unpack; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; -} // namespace operation -} // namespace ir -} // namespace onert -#endif // __ONERT_IR_OPERATION_UNPACK_H__ diff --git a/runtime/onert/core/include/ir/operation/While.h b/runtime/onert/core/include/ir/operation/While.h deleted file mode 100644 index cf310d596..000000000 --- a/runtime/onert/core/include/ir/operation/While.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_WHILE_H__ -#define __ONERT_IR_OPERATION_WHILE_H__ - -#include "ir/Operation.h" -#include "ir/InternalType.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -class While : public Operation -{ -public: - struct Param - { - SubgraphIndex cond_subg_index; - SubgraphIndex body_subg_index; - }; - -public: - While(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m); - -public: - void accept(OperationVisitor &v) const override; - OpCode opcode() const final { return OpCode::While; } - -public: - const Param ¶m() const { return _param; } - -private: - Param _param; -}; - -} // namespace operation -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_WHILE_H__ diff --git a/runtime/onert/core/include/util/Config.lst b/runtime/onert/core/include/util/Config.lst deleted file mode 100644 index 30f211011..000000000 --- a/runtime/onert/core/include/util/Config.lst +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef CONFIG -#error Define CONFIG before including this file -#endif - -// Name | Type | Default -CONFIG(GRAPH_DOT_DUMP , int , "0") -CONFIG(BACKENDS , std::string , "cpu;acl_cl;acl_neon;bcq") // FIXME Remove bcq -CONFIG(OP_BACKEND_ALLOPS , std::string , "") -CONFIG(OP_BACKEND_MAP , std::string , "") -CONFIG(DISABLE_COMPILE , bool , "0") -CONFIG(ONERT_LOG_ENABLE , bool , "0") -CONFIG(CPU_MEMORY_PLANNER , std::string , "WIC") -CONFIG(EXECUTOR , std::string , "Linear") -CONFIG(ACL_LAYOUT , std::string , "none") -CONFIG(NCNN_LAYOUT , std::string , "NCHW") -CONFIG(PROFILING_MODE , bool , "0") -CONFIG(USE_SCHEDULER , bool , "0") -CONFIG(OP_SEQ_MAX_NODE , int , "0") -CONFIG(TRACE_FILEPATH , std::string , "") -CONFIG(FP16_ENABLE , bool , "0") -CONFIG(RUY_THREADS , int , "-1") -CONFIG(USE_MMAPED_DATA , bool , "0") - -// Auto-generate all operations - -#define OP(InternalName) \ - CONFIG(OP_BACKEND_ ## InternalName, std::string, "") -#include "ir/Operations.lst" -#undef OP - diff --git a/runtime/onert/core/include/util/ConfigSource.h b/runtime/onert/core/include/util/ConfigSource.h deleted file mode 100644 index b6a8144fd..000000000 --- a/runtime/onert/core/include/util/ConfigSource.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_CONFIG_SOURCE_H__ -#define __ONERT_UTIL_CONFIG_SOURCE_H__ - -#include <memory> - -#include "IConfigSource.h" - -namespace onert -{ -namespace util -{ - -void config_source(std::unique_ptr<IConfigSource> &&source); - -bool toBool(const std::string &val); -int toInt(const std::string &val); - -bool getConfigBool(const std::string &key); -int getConfigInt(const std::string &key); -std::string getConfigString(const std::string &key); - -} // namespace util -} // namespace onert - -namespace onert -{ -namespace util -{ -namespace config -{ - -#define CONFIG(Name, Type, Default) extern const char *Name; - -#include "Config.lst" - -#undef CONFIG - -} // namespace config -} // namespace util -} // namespace onert - -#endif // __ONERT_UTIL_CONFIG_SOURCE_H__ diff --git a/runtime/onert/core/include/util/EnvConfigSource.h b/runtime/onert/core/include/util/EnvConfigSource.h deleted file mode 100644 index 8c5d0e8e9..000000000 --- a/runtime/onert/core/include/util/EnvConfigSource.h +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_ENV_CONFIG_SOURCE_H__ -#define __ONERT_UTIL_ENV_CONFIG_SOURCE_H__ - -#include <unordered_map> - -#include "util/GeneralConfigSource.h" - -namespace onert -{ -namespace util -{ - -class EnvConfigSource final : public GeneralConfigSource -{ -public: - std::string get(const std::string &key) const override; - -private: - std::unordered_map<std::string, std::string> _default_attributes; -}; - -} // namespace util -} // namespace onert - -#endif // __ONERT_UTIL_ENV_CONFIG_SOURCE_H__ diff --git a/runtime/onert/core/include/util/Exceptions.h b/runtime/onert/core/include/util/Exceptions.h deleted file mode 100644 index fc3fa0f64..000000000 --- a/runtime/onert/core/include/util/Exceptions.h +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_ONERTEXCEPTION_H__ -#define __ONERT_UTIL_ONERTEXCEPTION_H__ - -#include <string> - -namespace onert -{ - -class OnertException : public std::exception -{ -public: - OnertException(const std::string &msg) : _msg{msg} {} - OnertException(const std::string &tag, const std::string &msg) : _msg{tag + " : " + msg} {} - - const char *what() const noexcept override { return _msg.c_str(); } - -private: - std::string _msg; -}; - -class InsufficientBufferSizeException : public OnertException -{ -public: - InsufficientBufferSizeException(const std::string &msg) - : OnertException{"InsufficientBufferSize", msg} - { - } -}; - -} // namespace onert - -#endif // __ONERT_UTIL_ONERTEXCEPTION_H__ diff --git a/runtime/onert/core/include/util/GeneralConfigSource.h b/runtime/onert/core/include/util/GeneralConfigSource.h deleted file mode 100644 index dedc820ec..000000000 --- a/runtime/onert/core/include/util/GeneralConfigSource.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_GLOBAL_CONFIG_SOURCE_H__ -#define __ONERT_UTIL_GLOBAL_CONFIG_SOURCE_H__ - -#include <unordered_map> - -#include "util/IConfigSource.h" - -namespace onert -{ -namespace util -{ - -class GeneralConfigSource : public IConfigSource -{ -public: - GeneralConfigSource() = default; - - std::string get(const std::string &key) const override; - void set(const std::string &key, const std::string &val); - -private: - std::unordered_map<std::string, std::string> _map; -}; - -} // namespace util -} // namespace onert - -#endif // __ONERT_UTIL_GLOBAL_CONFIG_SOURCE_H__ diff --git a/runtime/onert/core/include/util/IConfigSource.h b/runtime/onert/core/include/util/IConfigSource.h deleted file mode 100644 index 07b09848a..000000000 --- a/runtime/onert/core/include/util/IConfigSource.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_I_CONFIG_SOURCE_H__ -#define __ONERT_UTIL_I_CONFIG_SOURCE_H__ - -#include <string> - -namespace onert -{ -namespace util -{ - -struct IConfigSource -{ - /** - * @brief Destroy the IConfigSource object - */ - virtual ~IConfigSource() = default; - - /** - * @brief get the value for the matching key - * - * @param key string key to search - * @return string value associated with the key - */ - virtual std::string get(const std::string &key) const = 0; -}; - -} // namespace util -} // namespace onert - -#endif // __ONERT_UTIL_I_CONFIG_SOURCE_H__ diff --git a/runtime/onert/core/include/util/ITimer.h b/runtime/onert/core/include/util/ITimer.h deleted file mode 100644 index d5a4e1eb0..000000000 --- a/runtime/onert/core/include/util/ITimer.h +++ /dev/null @@ -1,59 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_ITIMER_H__ -#define __ONERT_UTIL_ITIMER_H__ - -#include <chrono> - -namespace onert -{ -namespace util -{ - -class ITimer -{ -public: - virtual void handleBegin() = 0; - virtual void handleEnd() = 0; - int getTime() { return _timer_res; }; - - virtual ~ITimer() = default; - -protected: - int _timer_res{0}; -}; - -class CPUTimer : public ITimer -{ -public: - void handleBegin() override { _start_time = std::chrono::steady_clock::now(); }; - - void handleEnd() override - { - const auto end_time = std::chrono::steady_clock::now(); - _timer_res = - std::chrono::duration_cast<std::chrono::microseconds>(end_time - _start_time).count(); - }; - -private: - std::chrono::steady_clock::time_point _start_time; // in microseconds -}; - -} // namespace util -} // namespace onert - -#endif // __ONERT_UTIL_ITIMER_H__ diff --git a/runtime/onert/core/include/util/Index.h b/runtime/onert/core/include/util/Index.h deleted file mode 100644 index e8f59282d..000000000 --- a/runtime/onert/core/include/util/Index.h +++ /dev/null @@ -1,169 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_INDEX_H__ -#define __ONERT_UTIL_INDEX_H__ - -#include <functional> -#include <limits> -#include <stdint.h> -#include <string> - -namespace onert -{ -namespace util -{ - -/** - * @brief A wrapper class for unsigned integral Index - * NOTE : Max value of the underlying type is used as the invalid value - * - * @tparam T Underlying type. Must be unsigned integral type otherwise its behavior is undefined. - * @tparam DummyTag Dummy type to distinguish types with a same underlying type. Using an opaque - * type is recommended. - */ -template <typename T, typename DummyTag> class Index -{ -private: - static const T UNDEFINED = std::numeric_limits<T>::max(); - -public: - /** - * @brief Construct a new Index object - */ - explicit Index(void) : _index{UNDEFINED} {} - /** - * @brief Construct a new Index object with a value in the underlying type - * - * @param o Value in the underlying type - */ - explicit Index(const T o) : _index{o} {} - /** - * @brief Copy Constructor - * - * @param o Object to be copied - */ - Index(const Index &o) = default; - - /** - * @brief Assign a value in the underlying time - * - * @param o Value in the underlying type - * @return Index& Reference of this pointer - */ - Index &operator=(const T o) - { - _index = o; - return *this; - } - - /** - * @brief Copy assignment operator - * - * @param o Object to be copied - * @return Index& Reference of this pointer - */ - Index &operator=(const Index &o) = default; - - /** - * @brief Equality operator - * - * @param o The other value in the underlying type to compare - * @return true if underlying value is the same, false otherwise - */ - bool operator==(T o) const { return _index == o; } - /** - * @brief Equality operator - * - * @param o The other object to compare - * @return true if underlying value is the same, false otherwise - */ - bool operator==(const Index &o) const { return _index == o._index; } - /** - * @brief Inquality operator - * - * @param o The other value in the underlying type to compare - * @return true if underlying value is different, false otherwise - */ - bool operator!=(T o) const { return !(*this == o); } - /** - * @brief Inquality operator - * - * @param o The other object to compare - * @return true if underlying value is different, false otherwise - */ - bool operator!=(const Index &o) const { return !(*this == o); } - - /** - * @brief Post increment operator - * - * @return Index Index before increment - */ - Index operator++(int) - { - Index temp = *this; - _index++; - return temp; - } - - /** - * @brief Check whether the value is valid or not - * - * @return true if valid, false otherwise - */ - bool valid() const { return _index != UNDEFINED; } - /** - * @brief Check whether the value is undefined - * - * @return true if undefined, false otherwise - */ - bool undefined() const { return _index == UNDEFINED; } - /** - * @brief Return underlying value - * - * @return T Underlying value - */ - T value() const { return _index; } - - friend std::ostream &operator<<(std::ostream &o, const Index &t) - { - if (t.undefined()) - return o << std::string("undefined"); - else - return o << t.value(); - } - -private: - T _index; -}; - -} // namespace util -} // namespace onert - -namespace std -{ - -template <typename T, typename Tag> struct hash<::onert::util::Index<T, Tag>> -{ - size_t operator()(const ::onert::util::Index<T, Tag> &index) const noexcept - { - return hash<T>()(index.value()); - } -}; - -} // namespace std - -#endif // __ONERT_UTIL_INDEX_H__ diff --git a/runtime/onert/core/include/util/ObjectManager.h b/runtime/onert/core/include/util/ObjectManager.h deleted file mode 100644 index d2dd881a8..000000000 --- a/runtime/onert/core/include/util/ObjectManager.h +++ /dev/null @@ -1,148 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_OBJECT_MANAGER_H__ -#define __ONERT_UTIL_OBJECT_MANAGER_H__ - -#include <unordered_map> -#include <memory> -#include <list> -#include <functional> - -#include <memory> - -namespace onert -{ -namespace util -{ - -/** - * @brief Class that owns objects and maps them with indices as a handle for them - * - */ -template <typename Index, typename Object> class ObjectManager -{ -public: - ObjectManager() : _index_count{0u} {} - -public: - /** - * @brief Create an object with args and put it in the container with a new Index for that - * - * @param[in] args Arguments for creating Operand object - * @return Created index that is associated to the object - */ - template <class... Args> Index emplace(Args &&... args) - { - auto index = generateIndex(); - _objects.emplace(index, std::make_unique<Object>(std::forward<Args>(args)...)); - return index; - } - - /** - * @brief Put object in the container with a new Index for that - * - * @param[in] object Object to be pushed - * @return Created index that is associated to the object - */ - Index push(std::unique_ptr<Object> &&object) - { - auto index = generateIndex(); - _objects.emplace(index, std::move(object)); - return index; - } - - /** - * @brief Remove the object that is associated with the given index - * - * @param[in] index Index of the object to be removed - * @return N/A - */ - void remove(const Index &index) { _objects.erase(index); } - - /** - * @brief Get the object that is associated with the given index - * - * @param[in] index Index of the object to be returned - * @return Object - */ - const Object &at(const Index &index) const { return *(_objects.at(index)); } - /** - * @brief Get the object that is associated with the given index - * - * @param[in] index Index of the object to be returned - * @return Object - */ - Object &at(const Index &index) { return *(_objects.at(index)); } - /** - * @brief Get the object that is associated with the given index - * - * @param[in] index Index of the object to be returned - * @return true if such entry exists otherwise false - */ - bool exist(const Index &index) const - { - auto it = _objects.find(index); - return it != _objects.end(); - } - /** - * @brief Iterate over the container with given function - * - * @param[in] fn Function to be run for every container entry - * @return N/A - */ - void iterate(const std::function<void(const Index &, const Object &)> &fn) const - { - for (const auto &e : _objects) - { - fn(e.first, *e.second); - } - } - /** - * @brief Iterate over the container with given function - * - * @param[in] fn Function to be run for every container entry - * @return N/A - */ - void iterate(const std::function<void(const Index &, Object &)> &fn) - { - // TODO Remove this workaround - // This implementation is a workaround in case of adding operands while iteration - std::list<Index> l; - - for (auto &e : _objects) - { - l.push_back(e.first); - } - - for (auto index : l) - { - fn(index, *_objects[index]); - } - } - -private: - Index generateIndex() { return Index{_index_count++}; } - -protected: - std::unordered_map<Index, std::unique_ptr<Object>> _objects; - uint32_t _index_count; -}; - -} // namespace util -} // namespace onert - -#endif // __ONERT_UTIL_OBJECT_MANAGER_H__ diff --git a/runtime/onert/core/include/util/Set.h b/runtime/onert/core/include/util/Set.h deleted file mode 100644 index ee4062d25..000000000 --- a/runtime/onert/core/include/util/Set.h +++ /dev/null @@ -1,166 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Set.h - * @brief This file contains onert::util::Set class - * @ingroup COM_AI_RUNTIME - */ - -#ifndef __ONERT_UTIL_SET_H__ -#define __ONERT_UTIL_SET_H__ - -#include <cassert> -#include <unordered_set> - -namespace onert -{ -namespace util -{ - -/** - * @brief Class for set of custom element - & @tparam Element Key type of Set - */ -template <typename Element> class Set -{ -public: - /** - * @brief Construct default Set object. - */ - Set() = default; - /** - * @brief Construct Set object by copy semantics. - */ - Set(const Set<Element> &) = default; - /** - * @brief Construct move Set object by move semantics. - */ - Set(Set<Element> &&) = default; - -public: - /** - * @brief Add a given element to the set - * - * @param e Element added - */ - void add(const Element &e) { _set.insert(e); } - /** - * @brief remove a given element from the set - * - * @param e Element removed - */ - void remove(const Element &e) { _set.erase(e); } - /** - * @brief Get size of the set - * - * @return The size of the set - */ - uint32_t size() const { return static_cast<uint32_t>(_set.size()); } - /** - * @brief Get whether the set is empty - * - * @return Whether the set is empty - */ - bool empty() const { return _set.empty(); } - /** - * @brief Get whether a given element exists in the set - * - * @param e A given element - * - * @return Whether a given element exists in the set - */ - bool contains(const Element &e) const { return _set.find(e) != _set.end(); } - /** - * @brief Get first element of the set - * - * @return first element of the set - */ - const Element &getOnlyElement() const - { - assert(_set.size() == 1u); - return *_set.begin(); - } - -public: - /** - * @brief operator overloading function for `|` - * - * @return A set with two sets combined - */ - Set<Element> operator|(const Set<Element> &other) const // Union - { - auto ret = *this; - for (auto e : other) - { - ret.add(e); - } - return ret; - } - /** - * @brief operator overloading function for `&` - * - * @return A set of elements that overlap in two sets - */ - Set<Element> operator&(const Set<Element> &other) const // Intersect - { - Set<Element> ret; - for (auto e : other) - { - if (contains(e)) - { - ret.add(e); - } - } - return ret; - } - /** - * @brief operator overloading function for `-` - * - * @return A set of subtracted from another set - */ - Set<Element> operator-(const Set<Element> &other) const // Minus - { - auto ret = *this; - for (auto e : other) - { - ret.remove(e); - } - return ret; - } - -public: - /** - * @brief begin() of const_iterator for this class - * - * @return The first iterator of the set - */ - typename std::unordered_set<Element>::const_iterator begin() const { return _set.begin(); } - /** - * @brief end() of const_iterator for this class - * - * @return The last iterator of the set - */ - typename std::unordered_set<Element>::const_iterator end() const { return _set.end(); } - -private: - std::unordered_set<Element> _set; -}; - -} // namespace util -} // namespace onert - -#endif // __ONERT_UTIL_SET_H__ diff --git a/runtime/onert/core/include/util/ShapeInference.h b/runtime/onert/core/include/util/ShapeInference.h deleted file mode 100644 index 701b835d2..000000000 --- a/runtime/onert/core/include/util/ShapeInference.h +++ /dev/null @@ -1,153 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_GRAPH_SHAPE_INFERENCE_H__ -#define __ONERT_GRAPH_SHAPE_INFERENCE_H__ - -#include "Utils.h" - -#include "ir/operation/Concat.h" -#include "ir/operation/Conv2D.h" -#include "ir/operation/DepthwiseConv2D.h" -#include "ir/operation/Pool2D.h" -#include "ir/operation/Reshape.h" -#include "ir/operation/StridedSlice.h" -#include "compiler/LoweredGraph.h" -#include "ir/Index.h" -#include "ir/Layout.h" -#include "ir/OperationVisitor.h" -#include "backend/IDynamicTensorManager.h" -#include "backend/ITensor.h" -#include "backend/ITensorRegistry.h" - -namespace onert -{ -namespace shape_inference -{ - -using Shapes = std::vector<ir::Shape>; - -// Define shape calculation for operations. List them in alphabetic order. - -ir::Shape inferArgMaxShape(const ir::Shape &input_shape, int axis, int rank); - -ir::Shape inferBatchMatMulShape(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape, - const ir::operation::BatchMatMul::Param ¶m); - -ir::Shape inferBCQFullyConnectedShape(const ir::Shape &in_shape, const ir::Shape &cluster_shape, - const int32_t *cluster_buf); - -ir::Shape inferBCQGatherShape(const ir::Shape &indices_shape, const ir::Shape &cluster_shape, - const int32_t *cluster_buf, int rank, - const ir::operation::BCQGather::Param ¶m); - -ir::Shape inferBroadcastToShape(const ir::Shape shp_shape, const int32_t *shp_buf); - -ir::Shape inferConcatShape(const Shapes &in_shapes, const ir::operation::Concat::Param ¶m); - -ir::Shape inferConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape, - const ir::operation::Conv2D::Param ¶m, - ir::Layout layout = ir::Layout::NHWC); - -ir::Shape inferDepthwiseConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape, - const ir::operation::DepthwiseConv2D::Param ¶m, - ir::Layout layout = ir::Layout::NHWC); - -ir::Shape inferEltwiseShape(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape); - -ir::Shape inferExpandDimsShape(const ir::Shape &in_shape, int32_t axis); - -ir::Shape inferFillShape(const ir::Shape &in_shape, const int32_t *in_buf); - -ir::Shape inferFullyConnectedShape(const ir::Shape &in_shape, const ir::Shape &ker_shape); - -ir::Shape inferGatherShape(const ir::Shape &input_shape, const ir::Shape &indices_shape, int axis, - int rank); - -ir::Shape inferOnehotShape(const ir::Shape &input_shape, const int depth, int axis); - -ir::Shape inferPackShape(const ir::Shape &input_shape, int axis, int rank, int num); - -ir::Shape inferPadShape(const ir::Shape &in_shape, const int32_t *pad_buf, const size_t num_pads); - -ir::Shape inferPoolShape(const ir::Shape &in_shape, const ir::operation::Pool2D::Param ¶m, - ir::Layout layout = ir::Layout::NHWC); - -template <typename T> ir::Shape inferRangeShape(T start_val, T limit_val, T delta_val); - -ir::Shape inferReshapeShape(const int32_t *shape_buf, const int32_t shape_num_elements, - const size_t total_num_elements); - -ir::Shape inferReduceShape(const ir::Shape &input_shape, const std::vector<int> &axes, - bool keep_dims); - -template <float *> ir::Shape inferRangeShape(float *start_val, float *limit_val, float *delta_val); - -template <typename T> ir::Shape inferRangeShape(T start_val, T limit_val, T delta_val); - -ir::Shape inferResizeBilinearShape(const ir::Shape &in_shape, const int32_t output_height, - const int32_t output_width); - -ir::Shape inferSelectShape(const ir::Shape &input_cond_shape, const ir::Shape &input_true_shape, - const ir::Shape &input_false_shape); - -ir::Shape inferSliceShape(const ir::Shape &input_shape, const int32_t *begins_buf, - const int32_t *sizes_buf); - -ir::Shape inferSpaceToBatchNDShape(const ir::Shape &input_shape, const ir::Shape &block_shape_shape, - const ir::Shape &padding_shape, const int32_t *block_shape_buf, - const int32_t *padding_buf); - -ir::Shape inferSplitShape(const ir::Shape input_shape, int axis_value, int num_splits); - -ir::Shape inferSqueezeShape(const ir::Shape &in_shape, const ir::operation::Squeeze::Param ¶m); - -struct StridedSliceParams -{ - int8_t start_indices_count; - int16_t start_indices[4]; - int8_t stop_indices_count; - int16_t stop_indices[4]; - int8_t strides_count; - int16_t strides[4]; - - int16_t begin_mask; - int16_t ellipsis_mask; - int16_t end_mask; - int16_t new_axis_mask; - int16_t shrink_axis_mask; -}; - -template <typename T> -StridedSliceParams buildStridedSliceParams(const T *begin, const T *end, const T *strides, - const uint32_t begin_mask, const uint32_t end_mask, - const uint32_t shrink_axis_mask, const uint8_t rank); - -ir::Shape inferStridedSliceShape(const ir::Shape &input_shape, const StridedSliceParams &op_params, - uint32_t rank); - -ir::Shape inferTileShape(const ir::Shape &in_shape, const int32_t *multiplier_buf, - const int32_t multiplier_size); - -ir::Shape inferTransposeShape(const ir::Shape &in_shape, const int32_t *perm_buf, - const int32_t rank); - -ir::Shape inferUnpackShape(const ir::Shape &input_shape, int axis, int rank); - -} // namespace shape_inference -} // namespace onert - -#endif // __ONERT_GRAPH_SHAPE_INFERENCE_H__ diff --git a/runtime/onert/core/include/util/Utils.h b/runtime/onert/core/include/util/Utils.h deleted file mode 100644 index 8a4eea32b..000000000 --- a/runtime/onert/core/include/util/Utils.h +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Utils.h - * @brief This file contains utility macro - */ - -#ifndef __ONERT_UTIL_UTILS_H__ -#define __ONERT_UTIL_UTILS_H__ - -#include "ir/Coordinates.h" -#include "ir/Shape.h" - -#define UNUSED_RELEASE(a) (void)(a) - -template <size_t from, size_t to, typename Enable = void> struct ForEachDimension -{ - template <typename L, typename... Args> - static void unroll(const onert::ir::Shape &shape, onert::ir::Coordinates &coords, - L &&lambda_function, Args &&... args) - { - static_assert(from < to, "from must not be less than to"); - assert(static_cast<int>(to) <= shape.rank()); - const auto &d = shape.dim(from); - - for (auto v = 0; v < d; v++) - { - coords.set(from, v); - ForEachDimension<from + 1, to>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - } - } -}; - -template <size_t from, size_t to> -struct ForEachDimension<from, to, typename std::enable_if<from == to>::type> -{ - template <typename L, typename... Args> - static void unroll(const onert::ir::Shape &shape, onert::ir::Coordinates &coords, - L &&lambda_function, Args &&... args) - { - UNUSED_RELEASE(shape); - assert(static_cast<int>(to) <= shape.rank()); - lambda_function(coords, std::forward<Args>(args)...); - } -}; - -template <typename L, typename... Args> -inline void ShapeLoop(const onert::ir::Shape &shape, L &&lambda_function, Args &&... args) -{ - assert(shape.rank() > 0); - for (auto i = 0; i < shape.rank(); ++i) - { - assert(shape.dim(i) > 0); - } - - onert::ir::Coordinates coords; - switch (shape.rank()) - { - case 0: - coords.set(0, 0); - ForEachDimension<0, 0>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - break; - case 1: - ForEachDimension<0, 1>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - break; - case 2: - ForEachDimension<0, 2>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - break; - case 3: - ForEachDimension<0, 3>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - break; - case 4: - ForEachDimension<0, 4>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - break; - case 5: - ForEachDimension<0, 5>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - break; - case 6: - ForEachDimension<0, 6>::unroll(shape, coords, std::forward<L>(lambda_function), - std::forward<Args>(args)...); - break; - default: - assert(false && "ShapeLoop, 1 <= Shape'rank <= 6"); - break; - } -} -#endif // __ONERT_UTIL_UTILS_H__ diff --git a/runtime/onert/core/include/util/logging.h b/runtime/onert/core/include/util/logging.h deleted file mode 100644 index 76cfb8d60..000000000 --- a/runtime/onert/core/include/util/logging.h +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_LOGGING_H__ -#define __ONERT_UTIL_LOGGING_H__ - -#include <iostream> - -#include "util/ConfigSource.h" - -namespace onert -{ -namespace util -{ -namespace logging -{ - -class Context -{ -public: - Context() noexcept : _enabled{false} - { - const auto env = util::getConfigBool(util::config::ONERT_LOG_ENABLE); - - if (env) - { - _enabled = true; - } - } - - static Context &get() noexcept; - -public: - bool enabled(void) const { return _enabled; } - -private: - bool _enabled; -}; - -static Context &ctx = Context::get(); - -} // namespace logging -} // namespace util -} // namespace onert - -#define VERBOSE(name) \ - if (::onert::util::logging::ctx.enabled()) \ - std::cout << "[" << #name << "] " - -#define VERBOSE_F() \ - if (::onert::util::logging::ctx.enabled()) \ - std::cout << "[" << __func__ << "] " - -#endif // __ONERT_UTIL_LOGGING_H__ diff --git a/runtime/onert/core/src/backend/BackendContext.cc b/runtime/onert/core/src/backend/BackendContext.cc deleted file mode 100644 index bafa36d28..000000000 --- a/runtime/onert/core/src/backend/BackendContext.cc +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/BackendContext.h" - -#include "ir/Operation.h" -#include "backend/IConstantInitializer.h" - -namespace onert -{ -namespace backend -{ - -void BackendContext::initialize(const std::vector<OperationInfo> &operation_list, - const std::vector<ir::OperandIndex> &operand_list) -{ - _operation_list = operation_list; - _operand_list = operand_list; -} - -void BackendContext::initConsts() -{ - for (auto &op : _operation_list) - { - constant_initializer->setLayout(op.layout); - _graph->operations().at(op.index).accept(*constant_initializer); - } - - for (auto ind : _operand_list) - { - const auto &obj = _graph->operands().at(ind); - if (obj.isConstant() && !constant_initializer->exist(ind)) - { - constant_initializer->registerDefaultInitializer(ind, obj); - } - } - - constant_initializer->run(); -} - -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/IConstantInitializer.cc b/runtime/onert/core/src/backend/IConstantInitializer.cc deleted file mode 100644 index 934a42753..000000000 --- a/runtime/onert/core/src/backend/IConstantInitializer.cc +++ /dev/null @@ -1,112 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/IConstantInitializer.h" - -#include <Half.h> - -using float16 = Half; - -namespace onert -{ -namespace backend -{ - -void IConstantInitializer::registerCopyInitializer(const ir::OperandIndex &index, - const ir::Operand &obj) -{ - // For only CONSTANTS - // TODO Add to check if tensor has been allocated - if (!obj.isConstant()) - return; - - const auto type = obj.typeInfo().type(); - using ir::DataType; - - switch (type) - { - case DataType::FLOAT32: - _init_map[index] = copyInit<float>; - break; - case DataType::INT32: - _init_map[index] = copyInit<int32_t>; - break; - case DataType::UINT32: - _init_map[index] = copyInit<uint32_t>; - break; - case DataType::BOOL8: - case DataType::QUANT_UINT8_ASYMM: - _init_map[index] = copyInit<uint8_t>; - break; - case DataType::QUANT_INT8_SYMM: - _init_map[index] = copyInit<int8_t>; - break; - case DataType::FLOAT16: - _init_map[index] = copyInit<float16>; - break; - case DataType::INT64: - _init_map[index] = copyInit<int64_t>; - break; - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} - -void IConstantInitializer::registerPermuteInitializer(const ir::OperandIndex &index, - const ir::Operand &obj) -{ - // For only CONSTANTS - // TODO Add to check if tensor has been allocated - if (!obj.isConstant()) - return; - - const auto type = obj.typeInfo().type(); - using ir::DataType; - using namespace std::placeholders; - - switch (type) - { - case DataType::FLOAT32: - _init_map[index] = std::bind(permuteInit<float>, _1, _2, _current_op_seq_layout); - break; - case DataType::INT32: - _init_map[index] = std::bind(permuteInit<int32_t>, _1, _2, _current_op_seq_layout); - break; - case DataType::UINT32: - _init_map[index] = std::bind(permuteInit<uint32_t>, _1, _2, _current_op_seq_layout); - break; - case DataType::BOOL8: - case DataType::QUANT_UINT8_ASYMM: - _init_map[index] = std::bind(permuteInit<uint8_t>, _1, _2, _current_op_seq_layout); - break; - case DataType::QUANT_INT8_SYMM: - _init_map[index] = std::bind(permuteInit<int8_t>, _1, _2, _current_op_seq_layout); - break; - case DataType::FLOAT16: - _init_map[index] = std::bind(permuteInit<float16>, _1, _2, _current_op_seq_layout); - break; - case DataType::INT64: - _init_map[index] = std::bind(permuteInit<int64_t>, _1, _2, _current_op_seq_layout); - break; - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} - -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/IPortableTensor.cc b/runtime/onert/core/src/backend/IPortableTensor.cc deleted file mode 100644 index cec34e780..000000000 --- a/runtime/onert/core/src/backend/IPortableTensor.cc +++ /dev/null @@ -1,29 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/IPortableTensor.h" - -namespace onert -{ -namespace backend -{ - -// `dynamic_cast` not working across library boundaries on NDK -// With this as a key function, `dynamic_cast` works across dl -IPortableTensor::~IPortableTensor() {} - -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/ITensor.cc b/runtime/onert/core/src/backend/ITensor.cc deleted file mode 100644 index 7127ed93d..000000000 --- a/runtime/onert/core/src/backend/ITensor.cc +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/ITensor.h" - -namespace onert -{ -namespace backend -{ - -ir::Shape ITensor::getShape() const -{ - onert::ir::Shape shape(num_dimensions()); - for (uint32_t d = 0; d < num_dimensions(); d++) - shape.dim(d) = dimension(d); - - return shape; -} - -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/Backend.h b/runtime/onert/core/src/backend/controlflow/Backend.h deleted file mode 100644 index 670f7750f..000000000 --- a/runtime/onert/core/src/backend/controlflow/Backend.h +++ /dev/null @@ -1,86 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_BACKEND_H__ -#define __ONERT_BACKEND_CONTROLFLOW_BACKEND_H__ - -#include "Config.h" -#include "ConstantInitializer.h" -#include "KernelGenerator.h" -#include "TensorBuilder.h" -#include "Tensor.h" - -#include <backend/Backend.h> - -#include <memory> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -class Backend : public ::onert::backend::Backend -{ -public: - Backend() : _config{std::make_shared<Config>()} {} - - std::shared_ptr<IConfig> config() const override { return _config; } - - std::unique_ptr<BackendContext> newContext(const ir::Graph &graph, - const std::shared_ptr<custom::IKernelBuilder> &, - bool) const override - { - const auto &operands = graph.operands(); - auto context = std::make_unique<BackendContext>(this, &graph); - // ControlFlow backend may not build tensors for itself because the backend's operation uses - // tensors of other baceknd instead - // But the backend builds tensors in case of that the controlflow operation may have constant - // input or that consecutive controflow operations exist. We have to make them not to be built - // later - // 1. Constant input - // These tensors cannot be dynamic tensor, so let's do it as follows: - // - always skip copying - // - if it is operation's input in child subgraph: register "use" as constant input of the - // operations in child subgraph - // - if it is child subgraph's output: register "use" as constant input of the operations - // using it - // 2. Consecutive controflow operation's intermediate tensor - // These tensors can be dynamic tensor and this is complicated to support without copying. But - // there is no such case until now, let's support it later - // TODO Remove TensorBuilder and ConstantInitializer - // TODO Support Consecutive controflow operation's intermediate tensor - auto tr = std::make_shared<TensorRegistry>(); - auto tb = std::make_shared<TensorBuilder>(tr); - context->tensor_registry = tr; - context->tensor_builder = tb; - context->constant_initializer = std::make_shared<ConstantInitializer>(operands, tr); - context->kernel_gen = std::make_shared<KernelGenerator>(graph, tb->dynamicTensorManager(), tr); - context->tensor_register = nullptr; - context->optimizer = nullptr; - return context; - } - -private: - std::shared_ptr<IConfig> _config; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_BACKEND_H__ diff --git a/runtime/onert/core/src/backend/controlflow/BackendContext.h b/runtime/onert/core/src/backend/controlflow/BackendContext.h deleted file mode 100644 index d179bfde4..000000000 --- a/runtime/onert/core/src/backend/controlflow/BackendContext.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_BACKEND_CONTEXT_H__ -#define __ONERT_BACKEND_CPU_BACKEND_CONTEXT_H__ - -#include <backend/BackendContext.h> -#include "ExternalContext.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -class BackendContext : public onert::backend::BackendContext -{ -public: - BackendContext(const Backend *backend, const ir::Graph *graph, - std::shared_ptr<ITensorRegistry> tensor_registry = nullptr, - std::shared_ptr<ITensorBuilder> tensor_builder = nullptr, - std::shared_ptr<IConstantInitializer> constant_initializer = nullptr, - std::shared_ptr<IKernelGenerator> kernel_gen = nullptr, - std::shared_ptr<ITensorRegister> tensor_register = nullptr, - std::shared_ptr<IOptimizer> optimizer = nullptr) - : onert::backend::BackendContext(backend, graph, tensor_registry, tensor_builder, - constant_initializer, kernel_gen, tensor_register, - optimizer), - _external_context(std::make_shared<ExternalContext>()) - { - } - - std::shared_ptr<ExternalContext> external_context() { return _external_context; } - -private: - // NOTE ruy context has a thread pool, and when multiple ruy contexts are created, - // the thread pool is also created in duplicate - // TODO Create one ruy context for session - std::shared_ptr<ExternalContext> _external_context; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_BACKEND_CONTEXT_H__ diff --git a/runtime/onert/core/src/backend/controlflow/Config.cc b/runtime/onert/core/src/backend/controlflow/Config.cc deleted file mode 100644 index 5ec01fe11..000000000 --- a/runtime/onert/core/src/backend/controlflow/Config.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Config.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -std::string Config::ID = "controlflow"; - -bool Config::initialize() { return true; } - -ir::Layout Config::supportLayout(const ir::Operation &, ir::Layout frontend_layout) -{ - return frontend_layout; -} - -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/Config.h b/runtime/onert/core/src/backend/controlflow/Config.h deleted file mode 100644 index 6645ed59d..000000000 --- a/runtime/onert/core/src/backend/controlflow/Config.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_CONFIG_H__ -#define __ONERT_BACKEND_CONTROLFLOW_CONFIG_H__ - -#include <backend/IConfig.h> -#include <memory> -#include <util/ITimer.h> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -class Config : public IConfig -{ -public: - static std::string ID; - std::string id() override { return ID; } - bool initialize() override; - ir::Layout supportLayout(const ir::Operation &node, ir::Layout frontend_layout) override; - bool supportPermutation() override { return false; } - bool supportDynamicTensor() override - { - // TODO Make this backend to support dynamic tensor or not to build non-constant tensor - return true; - } - bool supportFP16() override { return false; } - - std::unique_ptr<util::ITimer> timer() override { return std::make_unique<util::CPUTimer>(); } -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_CONFIG_H__ diff --git a/runtime/onert/core/src/backend/controlflow/ConstantInitializer.h b/runtime/onert/core/src/backend/controlflow/ConstantInitializer.h deleted file mode 100644 index e21a8f357..000000000 --- a/runtime/onert/core/src/backend/controlflow/ConstantInitializer.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_CONTROLFLOW_CONSTANT_INITIALIZER_H__ -#define __ONERT_COMPILER_CONTROLFLOW_CONSTANT_INITIALIZER_H__ - -#include "TensorRegistry.h" - -#include <backend/IConstantInitializer.h> -#include <ir/Operands.h> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -class ConstantInitializer : public IConstantInitializer -{ -public: - ConstantInitializer(const ir::Operands &operands, - const std::shared_ptr<ITensorRegistry> &tensor_reg) - : IConstantInitializer{operands}, _tensor_reg{tensor_reg} - { - } - -private: - std::shared_ptr<ITensorRegistry> tensor_registry() const override { return _tensor_reg; } - -private: - std::shared_ptr<ITensorRegistry> _tensor_reg; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_COMPILER_CONTROLFLOW_CONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/core/src/backend/controlflow/DynamicTensorManager.cc b/runtime/onert/core/src/backend/controlflow/DynamicTensorManager.cc deleted file mode 100644 index 77f02969d..000000000 --- a/runtime/onert/core/src/backend/controlflow/DynamicTensorManager.cc +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "DynamicTensorManager.h" - -#include "util/logging.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -DynamicTensorManager::DynamicTensorManager(const std::shared_ptr<TensorRegistry> &tensors) - : _dynamic_mem_mgr{new cpu_common::DynamicMemoryManager()}, _tensors{tensors} -{ - // DO NOTHING -} - -void DynamicTensorManager::buildTensor(const ir::OperandIndex &ind, - const ir::OperandInfo &tensor_info, - ir::Layout backend_layout) -{ - auto tensor = - std::make_unique<cpu_common::Tensor>(tensor_info, backend_layout, _dynamic_mem_mgr.get()); - _tensors->setNativeOwnTensor(ind, std::move(tensor)); -} - -void DynamicTensorManager::planDealloc(ir::OperationIndex op_ind, backend::ITensor *tensor) -{ - _dealloc_tensor_map[op_ind].emplace(tensor); -} - -void DynamicTensorManager::deallocInput(ir::OperationIndex op_ind) -{ - auto find = _dealloc_tensor_map.find(op_ind); - if (find == _dealloc_tensor_map.end()) - return; - - auto &input_set = find->second; - for (auto *tensor : input_set) - { - if (!tensor->is_dynamic()) - continue; - - _dynamic_mem_mgr->deallocate(tensor); - - auto *cpu_tensor = nnfw::misc::polymorphic_downcast<cpu_common::Tensor *>(tensor); - cpu_tensor->resetBuffer(); - - VERBOSE(DynamicTensorManager) << "Deallocating a tensor " << (void *)tensor - << " (input of op_ind: " << op_ind.value() << ")" << std::endl; - } -} - -const ITensor *DynamicTensorManager::getRawITensor(ir::OperandIndex ind) -{ - auto ptr = _tensors->getITensor(ind); - assert(ptr); - return ptr; -} - -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/DynamicTensorManager.h b/runtime/onert/core/src/backend/controlflow/DynamicTensorManager.h deleted file mode 100644 index fb822a917..000000000 --- a/runtime/onert/core/src/backend/controlflow/DynamicTensorManager.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_DYNAMICTENSOR_MANAGER_H__ -#define __ONERT_BACKEND_CONTROLFLOW_DYNAMICTENSOR_MANAGER_H__ - -#include "TensorRegistry.h" -#include "Tensor.h" - -#include <backend/IDynamicTensorManager.h> -#include <backend/cpu_common/MemoryManager.h> -#include <ir/OperandInfo.h> -#include <ir/Operation.h> -#include <ir/Index.h> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -/** - * @brief Class to manage dynamic tensor and its memory - */ -class DynamicTensorManager : public backend::IDynamicTensorManager -{ -public: - DynamicTensorManager(const std::shared_ptr<TensorRegistry> &tensors); - - virtual ~DynamicTensorManager() = default; - - void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &tensor_info, - ir::Layout backend_layout); - - void planDealloc(ir::OperationIndex op_ind, backend::ITensor *tensor) override; - void deallocInput(ir::OperationIndex op_ind) override; - - std::shared_ptr<cpu_common::DynamicMemoryManager> dynamic_mem_mgr() { return _dynamic_mem_mgr; } - -private: - const ITensor *getRawITensor(ir::OperandIndex ind); - -private: - /** - * @brief Memory manager for dynamic tensor. - * @todo DynamicMemoryManager is not optimized. Optimized one is needed - */ - std::shared_ptr<cpu_common::DynamicMemoryManager> _dynamic_mem_mgr; - const std::shared_ptr<TensorRegistry> _tensors; - - // contains list of dynamic tensor, which can be deallocated after running operation - // note: this map could contain static tensor too. Careful use is required. - std::unordered_map<ir::OperationIndex, std::unordered_set<backend::ITensor *>> - _dealloc_tensor_map; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_DYNAMICTENSOR_MANAGER_H__ diff --git a/runtime/onert/core/src/backend/controlflow/ExternalContext.h b/runtime/onert/core/src/backend/controlflow/ExternalContext.h deleted file mode 100644 index 58bccb6c6..000000000 --- a/runtime/onert/core/src/backend/controlflow/ExternalContext.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_EXTERNAL_CONTEXT_H__ -#define __ONERT_BACKEND_CONTROLFLOW_EXTERNAL_CONTEXT_H__ - -#include <backend/IExternalContext.h> -#include <util/ConfigSource.h> -#include <ruy/context.h> - -namespace -{ -const int kDefaultNumThreadpoolThreads = 1; -} - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -// TODO Unify this with cpu::ExternalContext -class ExternalContext : public IExternalContext -{ -public: - ExternalContext() : _ruy_context(nullptr) - { - // setMaxNumThreads(onert::util::getConfigInt(onert::util::config::RUY_THREADS)); - } - - void setMaxNumThreads(int max_num_threads) - { - const int target_num_threads = - max_num_threads > -1 ? max_num_threads : kDefaultNumThreadpoolThreads; - _ruy_context->set_max_num_threads(target_num_threads); - } - - ruy::Context *ruy_context() const { return _ruy_context.get(); } - -private: - const std::unique_ptr<ruy::Context> _ruy_context; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_EXTERNAL_CONTEXT_H__ diff --git a/runtime/onert/core/src/backend/controlflow/KernelGenerator.cc b/runtime/onert/core/src/backend/controlflow/KernelGenerator.cc deleted file mode 100644 index d76ca53e3..000000000 --- a/runtime/onert/core/src/backend/controlflow/KernelGenerator.cc +++ /dev/null @@ -1,158 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "KernelGenerator.h" - -#include <backend/BackendContext.h> -#include <util/Utils.h> -#include "kernel/IfLayer.h" -#include "kernel/WhileLayer.h" -#include "kernel/PermuteLayer.h" -#include "exec/ExecutorBase.h" -#include "exec/FunctionSequence.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -KernelGenerator::KernelGenerator(const ir::Graph &graph, IDynamicTensorManager *dyn_tensor_manager, - const std::shared_ptr<TensorRegistry> &tensor_reg) - : _graph{graph}, _dyn_tensor_manager{dyn_tensor_manager}, _tensor_reg{tensor_reg}, - _tensor_registries{}, _executor_map{nullptr} -{ - UNUSED_RELEASE(_graph); - UNUSED_RELEASE(_tensor_registries); - UNUSED_RELEASE(_executor_map); -} - -void KernelGenerator::visit(const ir::OpSequence &op_seq) -{ - assert(!_return_fn_seq); - assert(_dyn_tensor_manager); - assert(_tensor_reg); - - auto dyn_shape_inferer = - std::make_unique<exec::DynamicShapeInferer>(_graph.operands(), _tensor_reg); - - _return_fn_seq = std::make_unique<exec::FunctionSequence>(); - - // Prepare to handle dynamic tensors later - auto dyn_ctx = std::make_shared<exec::FunctionSequence::DynamicTensorCtx>(); - { - dyn_ctx->op_seq = &op_seq; - dyn_ctx->operations = &_graph.operations(); - dyn_ctx->dynamic_shape_inferer = std::move(dyn_shape_inferer); - dyn_ctx->dynamic_tensor_manager = _dyn_tensor_manager; - - _return_fn_seq->dynamic_tensor_ctx(dyn_ctx); - } - - for (const auto &op_idx : op_seq.operations()) - { - const auto &node = _graph.operations().at(op_idx); - node.accept(*this); - _return_fn_seq->append(releaseFunction()); - } -} - -void KernelGenerator::visit(const ir::operation::If &node) -{ - const auto then_subg_index = node.param().then_subg_index; - const auto else_subg_index = node.param().else_subg_index; - - std::vector<backend::ITensor *> input_tensors; - for (const auto input_index : node.getInputs()) - { - auto input_tensor = getTensor(input_index); - - input_tensors.emplace_back(input_tensor); - } - - std::vector<backend::ITensor *> output_tensors; - for (const auto output_index : node.getOutputs()) - { - auto output_tensor = getTensor(output_index); - output_tensors.emplace_back(output_tensor); - } - - // IfLayer just set ExecutorMap instead of then and else executor to avoid complexity of - // creating executor recusively - const auto cond_tensor = input_tensors.front(); - input_tensors.erase(input_tensors.begin()); - auto fn = std::make_unique<::onert::backend::controlflow::kernel::IfLayer>( - cond_tensor, input_tensors, output_tensors, node.getOutputs(), _graph, then_subg_index, - else_subg_index, _executor_map); - - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::Permute &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(0)}; - - // Add PermuteLayer - std::vector<ITensor *> output_tensors{getTensor(output_index)}; - std::vector<ITensor *> input_tensors{getTensor(input_index)}; - - auto fn = std::make_unique<kernel::PermuteLayer>(input_tensors, output_tensors); - _return_fn = std::move(fn); -} - -void KernelGenerator::visit(const ir::operation::While &node) -{ - const auto cond_subg_index = node.param().cond_subg_index; - const auto body_subg_index = node.param().body_subg_index; - - // This op does not support input as a constant, because controlflow backend does not have - // TensorBuilder - std::vector<backend::ITensor *> input_tensors; - for (const auto input_index : node.getInputs()) - { - auto input_tensor = getTensor(input_index); - - input_tensors.emplace_back(input_tensor); - } - - std::vector<backend::ITensor *> output_tensors; - for (const auto output_index : node.getOutputs()) - { - auto output_tensor = getTensor(output_index); - output_tensors.emplace_back(output_tensor); - } - - // WhileLayer just set ExecutorMap instead of cond and body executor to avoid complexity of - // creating executor recusively - auto fn = std::make_unique<::onert::backend::controlflow::kernel::WhileLayer>( - input_tensors, output_tensors, node.getOutputs(), _graph, cond_subg_index, body_subg_index, - _executor_map); - - _return_fn = std::move(fn); -} - -backend::ITensor *KernelGenerator::getTensor(const ir::OperandIndex &index) -{ - backend::ITensor *ret = _tensor_registries.getITensor(index); - assert(ret != nullptr); - return ret; -} - -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/KernelGenerator.h b/runtime/onert/core/src/backend/controlflow/KernelGenerator.h deleted file mode 100644 index ce248913f..000000000 --- a/runtime/onert/core/src/backend/controlflow/KernelGenerator.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_KERNEL_GENERATOR_H__ -#define __ONERT_BACKEND_CONTROLFLOW_KERNEL_GENERATOR_H__ - -#include <backend/IKernelGenerator.h> -#include <backend/ITensorBuilder.h> -#include <exec/IExecutor.h> -#include <ir/Graph.h> -#include "TensorBuilder.h" -#include "compiler/TensorRegistries.h" -#include "TensorRegistry.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -class KernelGenerator : public IKernelGenerator -{ -public: - KernelGenerator(const ir::Graph &graph, IDynamicTensorManager *dyn_tensor_manager, - const std::shared_ptr<TensorRegistry> &tensor_reg); - - void setTensorRegistries(const compiler::TensorRegistries &tensor_registries) - { - _tensor_registries = tensor_registries; - } - void setExecutorMap(const std::shared_ptr<exec::ExecutorMap> &executor_map) - { - // FIXME Using shared_ptr's raw pointer! - _executor_map = executor_map.get(); - } - - using IKernelGenerator::visit; - - void visit(const ir::OpSequence &) override; - void visit(const ir::operation::If &) override; - void visit(const ir::operation::Permute &) override; - void visit(const ir::operation::While &) override; - -private: - backend::ITensor *getTensor(const ir::OperandIndex &index); - -private: - const ir::Graph &_graph; - IDynamicTensorManager *_dyn_tensor_manager; - std::shared_ptr<TensorRegistry> _tensor_reg; - compiler::TensorRegistries _tensor_registries; - exec::ExecutorMap *_executor_map; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_KERNEL_GENERATOR_H__ diff --git a/runtime/onert/core/src/backend/controlflow/Tensor.h b/runtime/onert/core/src/backend/controlflow/Tensor.h deleted file mode 100644 index ba5bafd75..000000000 --- a/runtime/onert/core/src/backend/controlflow/Tensor.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_TENSOR_H__ -#define __ONERT_BACKEND_CONTROLFLOW_TENSOR_H__ - -#include <backend/cpu_common/Tensor.h> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -using Tensor = cpu_common::Tensor; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_TENSOR_H__ diff --git a/runtime/onert/core/src/backend/controlflow/TensorBuilder.cc b/runtime/onert/core/src/backend/controlflow/TensorBuilder.cc deleted file mode 100644 index 7d0ff201f..000000000 --- a/runtime/onert/core/src/backend/controlflow/TensorBuilder.cc +++ /dev/null @@ -1,116 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "TensorBuilder.h" - -#include <util/logging.h> - -#include <cassert> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -TensorBuilder::TensorBuilder(const std::shared_ptr<TensorRegistry> &tensor_reg) - : _tensor_reg{tensor_reg}, _dynamic_tensor_mgr{new DynamicTensorManager(_tensor_reg)}, - _static_tensor_mgr{new cpu_common::StaticTensorManager( - _tensor_reg->base_reg(), _dynamic_tensor_mgr->dynamic_mem_mgr().get())} -{ - /* empty */ -} - -void TensorBuilder::registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info, - ir::Layout backend_layout) -{ - _tensor_info_map.emplace(ind, info); - - _tensor_layout_map.insert({ind, backend_layout}); - - if (info.isDynamic()) - { - _dynamic_tensor_mgr->buildTensor(ind, info, _tensor_layout_map[ind]); - } - else - { - _static_tensor_mgr->buildTensor(ind, info, _tensor_layout_map[ind], info.isConstant()); - } -} - -void TensorBuilder::notifyFirstUse(const ir::OperandIndex &ind) -{ - // TODO Enhance the way of checking user tensors - if (_tensor_info_map.find(ind) == _tensor_info_map.end()) // Do not proceed for user tensors - return; - - const auto tensor_info = _tensor_info_map.at(ind); - - if (!nativeOwnTensorAt(ind)->is_dynamic()) - { - const auto size = tensor_info.total_size(); - _static_tensor_mgr->claimPlan(ind, size); - } -} - -void TensorBuilder::notifyLastUse(const ir::OperandIndex &ind) -{ - // TODO Enhance the way of checking user tensors - if (_tensor_info_map.find(ind) == _tensor_info_map.end()) // Do not proceed for user tensors - return; - - if (!nativeOwnTensorAt(ind)->is_dynamic()) - { - _static_tensor_mgr->releasePlan(ind); - } -} - -bool TensorBuilder::isRegistered(const ir::OperandIndex &ind) const -{ - // User tensors are not registered in _tensor_info_map but objects for them are exist - // in the tensor registry. - // TODO Enhance the way of checking user tensors - if (_tensor_reg->getITensor(ind)) - return true; - return _tensor_info_map.find(ind) != _tensor_info_map.end(); -} - -void TensorBuilder::prepare(void) -{ - _static_tensor_mgr->allocateConsts(); - _static_tensor_mgr->allocateNonconsts(); -} - -void TensorBuilder::allocate() -{ - // NOTE For now nothing to do. Allocation is done in prepare stage, which is not appropriate - // This is because CPU kernels require `ITensor`s to be allocated before Kernel Generation. -} - -IDynamicTensorManager *TensorBuilder::dynamicTensorManager(void) -{ - return _dynamic_tensor_mgr.get(); -} - -cpu_common::Tensor *TensorBuilder::nativeOwnTensorAt(const ir::OperandIndex &ind) -{ - return _tensor_reg->getNativeOwnTensor(ind); -} - -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/TensorBuilder.h b/runtime/onert/core/src/backend/controlflow/TensorBuilder.h deleted file mode 100644 index 695994761..000000000 --- a/runtime/onert/core/src/backend/controlflow/TensorBuilder.h +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_TENSOR_BUILDER_H__ -#define __ONERT_BACKEND_CONTROLFLOW_TENSOR_BUILDER_H__ - -#include <backend/cpu_common/StaticTensorManager.h> -#include <backend/cpu_common/TensorRegistry.h> -#include <backend/cpu_common/Tensor.h> - -#include <backend/ITensorBuilder.h> -#include <ir/OperandIndexMap.h> - -#include <unordered_map> - -#include "DynamicTensorManager.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -class TensorBuilder : public ITensorBuilder -{ -public: - TensorBuilder(const std::shared_ptr<TensorRegistry> &tensor_reg); - - /** - * @brief Register tensor information to allocate on CPU backend - * @param[in] ind Operand index - * @param[in] info Operand information - * @param[in] layout Operand data layout - */ - void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info, - ir::Layout backend_layout) override; - - void notifyFirstUse(const ir::OperandIndex &) override; - void notifyLastUse(const ir::OperandIndex &) override; - - bool isRegistered(const ir::OperandIndex &) const override; - - void prepare(void) override; - void allocate() override; - void postFunctionPrepare() override { /* DO NOTHING */} - - IDynamicTensorManager *dynamicTensorManager(void) override; - - /** - * @brief Get tensor with a specific OperandIndex. - * @param ind OperandIndex for the tensor. There must exist a tensor with this ind. - * If not, program will crash with assert or exception. - * @return operand::Tensor * - */ - cpu_common::Tensor *nativeOwnTensorAt(const ir::OperandIndex &ind); - -private: - const std::shared_ptr<TensorRegistry> _tensor_reg; - std::unique_ptr<DynamicTensorManager> _dynamic_tensor_mgr; - std::unique_ptr<cpu_common::StaticTensorManager> _static_tensor_mgr; - ir::OperandIndexMap<ir::OperandInfo> _tensor_info_map; - ir::OperandIndexMap<ir::Layout> _tensor_layout_map; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_TENSOR_BUILDER_H__ diff --git a/runtime/onert/core/src/backend/controlflow/TensorRegistry.h b/runtime/onert/core/src/backend/controlflow/TensorRegistry.h deleted file mode 100644 index 94f71bb9c..000000000 --- a/runtime/onert/core/src/backend/controlflow/TensorRegistry.h +++ /dev/null @@ -1,133 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_TENSOR_REGISTRY_H__ -#define __ONERT_BACKEND_CONTROLFLOW_TENSOR_REGISTRY_H__ - -#include "backend/cpu_common/TensorRegistry.h" -#include "backend/ITensorRegistry.h" -#include "Tensor.h" -#include "UserTensor.h" -#include <assert.h> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -/** - * @brief Tensor registry class for controlflow backend - * - * This class contains three types of tensors. Two native tensors(tensors that are managed by this - * backend) and the other is migrant tensor. - * - * - NativeUserTensor - @c UserTensor managed by this backend, buffer is user-given - * - NativeOwnTensor - @c cpu_common::Tensor managed by this backend ( in @c _base_reg ) - * - MigrantTensor - @c IPortableTensor managed by other backends ( in @c _base_reg ) - * - * @note @c _base_reg is used in implementation to reuse @c cpu_common::StaticTensorManager - * - */ -class TensorRegistry : public ITensorRegistry -{ -public: - TensorRegistry() : _base_reg{new cpu_common::TensorRegistry} {} - - ITensor *getITensor(const ir::OperandIndex &ind) override - { - auto base_tensor = _base_reg->getITensor(ind); - if (base_tensor) - return base_tensor; - return getNativeUserTensor(ind); - } - - ITensor *getNativeITensor(const ir::OperandIndex &ind) override - { - auto base_tensor = _base_reg->getNativeITensor(ind); - if (base_tensor) - return base_tensor; - return getNativeUserTensor(ind); - } - - IPortableTensor *getPortableTensor(const ir::OperandIndex &ind) - { - auto base_tensor = _base_reg->getPortableTensor(ind); - if (base_tensor) - return base_tensor; - return getNativeUserTensor(ind); - } - - IPortableTensor *getNativeTensor(const ir::OperandIndex &ind) - { - auto base_tensor = _base_reg->getNativeTensor(ind); - if (base_tensor) - return base_tensor; - return getNativeUserTensor(ind); - } - - Tensor *getNativeOwnTensor(const ir::OperandIndex &ind) - { - return _base_reg->getNativeTensor(ind); - } - - UserTensor *getNativeUserTensor(const ir::OperandIndex &ind) - { - auto tensor = _native_user_tensors.find(ind); - if (tensor != _native_user_tensors.end()) - return tensor->second.get(); - return nullptr; - } - - bool setMigrantTensor(const ir::OperandIndex &ind, IPortableTensor *tensor) override - { - assert(tensor); - assert(!getITensor(ind)); // For the ind, tensor is not registered yet - _base_reg->setMigrantTensor(ind, tensor); - return true; - } - - void setNativeOwnTensor(ir::OperandIndex ind, std::unique_ptr<Tensor> &&tensor) - { - assert(tensor); - assert(!getITensor(ind)); // For the ind, tensor is not registered yet - _base_reg->setNativeTensor(ind, std::move(tensor)); - } - - void setNativeUserTensor(ir::OperandIndex ind, std::unique_ptr<UserTensor> &&tensor) - { - assert(tensor); - assert(!getITensor(ind)); // For the ind, tensor is not registered yet - _native_user_tensors[ind] = std::move(tensor); - } - - const ir::OperandIndexMap<std::unique_ptr<UserTensor>> &native_user_tensors() - { - return _native_user_tensors; - } - std::shared_ptr<cpu_common::TensorRegistry> base_reg() { return _base_reg; } - -private: - std::shared_ptr<cpu_common::TensorRegistry> _base_reg; - ir::OperandIndexMap<std::unique_ptr<UserTensor>> _native_user_tensors; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // ifndef __ONERT_BACKEND_CONTROLFLOW_TENSOR_REGISTRY_H__ diff --git a/runtime/onert/core/src/backend/controlflow/UserTensor.cc b/runtime/onert/core/src/backend/controlflow/UserTensor.cc deleted file mode 100644 index 5081a90ea..000000000 --- a/runtime/onert/core/src/backend/controlflow/UserTensor.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "UserTensor.h" - -#include "util/Exceptions.h" -#include "ir/DataType.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -size_t UserTensor::calcOffset(const ir::Coordinates &coords) const -{ - size_t rank = num_dimensions(); - size_t offset = 0; - for (size_t i = 0; i < rank; ++i) - { - offset = offset * dimension(i) + coords[i]; - } - offset *= sizeOfDataType(data_type()); - return offset; -} - -bool UserTensor::applyShape(const ir::Shape &new_shape) -{ - // User tensors cannot be reallocated. - auto new_size = new_shape.num_elements() * ir::sizeOfDataType(data_type()); - if (total_size() < new_size) - throw InsufficientBufferSizeException{"User given buffer size is too small."}; - setShape(new_shape); - return true; -} - -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/UserTensor.h b/runtime/onert/core/src/backend/controlflow/UserTensor.h deleted file mode 100644 index 7aa62a8a9..000000000 --- a/runtime/onert/core/src/backend/controlflow/UserTensor.h +++ /dev/null @@ -1,85 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_USER_TENSOR_H__ -#define __ONERT_BACKEND_CONTROLFLOW_USER_TENSOR_H__ - -#include "ir/OperandInfo.h" -#include "backend/IPortableTensor.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ - -/** - * @brief Tensor object that is for Input and Output tensors from the user. - * - * This class is a wrapped buffer that is allocated by the user. So it does not have resposibility - * on allocation nor deallocation. All the model input/output tensors are wrapped with this class - * for execution. - * - */ -class UserTensor : public IPortableTensor -{ -public: - UserTensor(const ir::OperandInfo &info, ir::Layout layout, uint8_t *buffer, size_t size) - : IPortableTensor{info}, _layout{layout}, _buffer{buffer}, _size{size}, _dynamic{false} - { - } - - UserTensor(const ir::OperandInfo &info, ir::Layout layout) : UserTensor{info, layout, nullptr, 0} - { - } - -public: - void setBuffer(uint8_t *buffer, size_t size) - { - _buffer = buffer; - _size = size; - } - -public: - uint8_t *buffer() const override { return _buffer; } - size_t total_size() const override { return _size; } - size_t dimension(size_t index) const override { return _info.shape().dim(index); } - size_t num_dimensions() const override { return _info.shape().rank(); } - size_t calcOffset(const ir::Coordinates &coords) const override; - ir::Layout layout() const override { return _layout; } - ir::DataType data_type() const override { return _info.typeInfo().type(); } - float data_scale() const override { return _info.typeInfo().scale(); } - int32_t data_offset() const override { return _info.typeInfo().offset(); } - bool is_dynamic() const override { return _dynamic; } - void set_dynamic() override { _dynamic = true; } - ir::Shape getShape() const override { return _info.shape(); } - void setShape(const ir::Shape &new_shape) override { _info.shape(new_shape); } - bool is_constant() const override { return false; } - bool applyShape(const ir::Shape &) override; - -private: - ir::Layout _layout; - uint8_t *_buffer; - size_t _size; - bool _dynamic; -}; - -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_USER_TENSOR_H__ diff --git a/runtime/onert/core/src/backend/controlflow/kernel/IfLayer.cc b/runtime/onert/core/src/backend/controlflow/kernel/IfLayer.cc deleted file mode 100644 index c0329acd8..000000000 --- a/runtime/onert/core/src/backend/controlflow/kernel/IfLayer.cc +++ /dev/null @@ -1,129 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "IfLayer.h" - -#include <backend/ITensor.h> -#include "exec/ExecutorBase.h" -#include <misc/polymorphic_downcast.h> -#include "PermuteLayer.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ -namespace kernel -{ - -IfLayer::IfLayer(backend::ITensor *cond_tensor, const std::vector<backend::ITensor *> input_tensors, - const std::vector<backend::ITensor *> output_tensors, - const ir::OperandIndexSequence &output_indices, const ir::Graph &graph, - const ir::SubgraphIndex &then_subg_index, const ir::SubgraphIndex &else_subg_index, - exec::ExecutorMap *executor_map) - : _cond_tensor{cond_tensor}, _input_tensors{input_tensors}, _output_tensors{output_tensors}, - _output_indices{output_indices}, _graph{graph}, _then_subg_index{then_subg_index}, - _else_subg_index{else_subg_index}, _executor_map{executor_map} -{ - // At this point, executor_map may not have executors of then subg and else subg -} - -void IfLayer::run() -{ - // Check condition - // // If true - // // // Copy _input_tensors -> then subg's inputs - // // // Run then subg - // // // Copy outputs of then subg -> _output_tensors - // // Else - // // // Copy _input_tensors -> else subg's inputs if false - // // // Run else subg - // // // Copy outputs of else subg -> _output_tensors - auto getResultCond = [](backend::ITensor *tensor) -> bool { - bool ret = false; - tensor->access([&](ITensor &tensor) { ret = *reinterpret_cast<bool *>(tensor.buffer()); }); - return ret; - }; - - exec::ExecutorBase *subg_exec = nullptr; - bool cond_result = getResultCond(_cond_tensor); - if (cond_result) - { - VERBOSE(If) << "Call to $" << _then_subg_index << " (then)" << std::endl; - subg_exec = nnfw::misc::polymorphic_downcast<exec::ExecutorBase *>( - _executor_map->at(_then_subg_index).get()); - } - else - { - VERBOSE(If) << "Call to $" << _else_subg_index << " (else)" << std::endl; - subg_exec = nnfw::misc::polymorphic_downcast<exec::ExecutorBase *>( - _executor_map->at(_else_subg_index).get()); - } - - const auto &subg_graph = subg_exec->graph(); - - std::vector<backend::ITensor *> src_tensors; - std::vector<backend::ITensor *> dst_tensors; - // Add tensors used in subgraph or contained in outputs of subgraph - assert(subg_graph.getInputs().size() == _input_tensors.size()); - assert(subg_graph.getInputs().size() == subg_exec->getInputTensors().size()); - for (uint32_t i = 0; i < subg_graph.getInputs().size(); ++i) - { - const auto &subg_input_index = subg_graph.getInputs().at(i); - const auto &subg_input = subg_graph.operands().at(subg_input_index); - if (subg_input.getUses().size() > 0 || subg_graph.getOutputs().contains(subg_input_index)) - { - src_tensors.emplace_back(_input_tensors.at(i)); - dst_tensors.emplace_back(subg_exec->getInputTensors().at(i)); - } - } - const auto permute_op_input_to_subg_input = - std::make_shared<PermuteLayer>(src_tensors, dst_tensors); - - // Add tensors used as output of operation or contained in outputs of operation - src_tensors.clear(); - dst_tensors.clear(); - assert(_output_indices.size() == subg_exec->getOutputTensors().size()); - assert(_output_indices.size() == _output_tensors.size()); - for (uint32_t i = 0; i < _output_indices.size(); ++i) - { - const auto &output_index = _output_indices.at(i); - const auto &output = _graph.operands().at(output_index); - if (output.getUses().size() > 0 || _graph.getOutputs().contains(output_index)) - { - src_tensors.emplace_back(subg_exec->getOutputTensors().at(i)); - dst_tensors.emplace_back(_output_tensors.at(i)); - } - } - const auto permute_subg_output_to_op_output = - std::make_shared<PermuteLayer>(src_tensors, dst_tensors); - - // Remove copying of unused tensor - permute_op_input_to_subg_input->prepare(); - permute_subg_output_to_op_output->prepare(); - - // Copy & run - subg_exec->execute(_input_tensors, permute_op_input_to_subg_input); - permute_subg_output_to_op_output->run(); - VERBOSE(If) << "Return from $" << (cond_result ? _then_subg_index : _else_subg_index) - << std::endl; -} - -} // namespace kernel -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/kernel/IfLayer.h b/runtime/onert/core/src/backend/controlflow/kernel/IfLayer.h deleted file mode 100644 index 1461388dc..000000000 --- a/runtime/onert/core/src/backend/controlflow/kernel/IfLayer.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_KERNEL_IF_LAYER_H__ -#define __ONERT_BACKEND_CONTROLFLOW_KERNEL_IF_LAYER_H__ - -#include <backend/ITensor.h> -#include <exec/IExecutor.h> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ -namespace kernel -{ - -class IfLayer : public ::onert::exec::IFunction -{ -public: - IfLayer(backend::ITensor *cond_tensor, const std::vector<backend::ITensor *> input_tensors, - const std::vector<backend::ITensor *> output_tensors, - const ir::OperandIndexSequence &output_indices, const ir::Graph &graph, - const ir::SubgraphIndex &then_subg_index, const ir::SubgraphIndex &else_subg_index, - exec::ExecutorMap *executor_map); - -public: - void run() override; - -private: - backend::ITensor *_cond_tensor; - const std::vector<backend::ITensor *> _input_tensors; - const std::vector<backend::ITensor *> _output_tensors; - const ir::OperandIndexSequence &_output_indices; - const ir::Graph &_graph; - const ir::SubgraphIndex _then_subg_index; - const ir::SubgraphIndex _else_subg_index; - exec::ExecutorMap *_executor_map; -}; - -} // namespace kernel -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_KERNEL_IF_LAYER_H__ diff --git a/runtime/onert/core/src/backend/controlflow/kernel/PermuteLayer.cc b/runtime/onert/core/src/backend/controlflow/kernel/PermuteLayer.cc deleted file mode 100644 index 49fbb33c4..000000000 --- a/runtime/onert/core/src/backend/controlflow/kernel/PermuteLayer.cc +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PermuteLayer.h" - -#include "exec/ShapeConverter.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ -namespace kernel -{ - -void PermuteLayer::run() -{ - assert(_src_tensors.size() == _dst_tensors.size()); - // PermuteLayer infers dynamic shape inside itself whenever run is called for the following - // reasons: - // 1. PermuteLayer has to access dynamic tensor manager for input/output tensors of other backends - // 2. Other controlflow operation(If/While) uses this layout for copying tensors of other - // subgraphs(with other backends) - // 3. This infering code is placed here to avoid duplicated code that can be caused by above 2 - // reasons - - // check if output is not dynamic - for (size_t i = 0; i < _src_tensors.size(); ++i) - { - auto dst_tensor = _dst_tensors.at(i); - auto src_tensor = _src_tensors.at(i); - if (src_tensor->is_dynamic() || dst_tensor->is_dynamic()) - { - // getting output shape - auto src_shape = src_tensor->getShape(); - - // set output shape and output buffer - ir::Shape new_shape = - exec::convertShape(src_shape, src_tensor->layout(), dst_tensor->layout()); - - try - { - if (!dst_tensor->applyShape(new_shape)) - throw std::runtime_error{ - "Error: PermuteLayer: output's TensorManager does not support dynamic tensor"}; - assert(dst_tensor->buffer() != nullptr); - } - catch (const std::out_of_range &e) - { - std::cerr << "Error: out_of_range in PermuteLayer: output's TensorManager does not support " - "dynamic tensor" - << '\n'; - throw; - } - } - assert(exec::convertShape(src_tensor->getShape(), src_tensor->layout(), dst_tensor->layout()) == - dst_tensor->getShape()); - } - IPermuteFunction::run(); -} - -} // namespace kernel -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/kernel/PermuteLayer.h b/runtime/onert/core/src/backend/controlflow/kernel/PermuteLayer.h deleted file mode 100644 index 8129403a5..000000000 --- a/runtime/onert/core/src/backend/controlflow/kernel/PermuteLayer.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_KERNEL_PERMUTELAYER_H__ -#define __ONERT_BACKEND_CONTROLFLOW_KERNEL_PERMUTELAYER_H__ - -#include "backend/ITensorBuilder.h" -#include "exec/IPermuteFunction.h" -#include "exec/IExecutor.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ -namespace kernel -{ - -class PermuteLayer : public onert::exec::IPermuteFunction -{ -public: - PermuteLayer(const std::vector<ITensor *> &src_tensors, const std::vector<ITensor *> &dst_tensors) - { - assert(src_tensors.size() == dst_tensors.size()); - _src_tensors = src_tensors; - _dst_tensors = dst_tensors; - } - - void optimize() override - { - // Remove copying of tensor as nullptr - auto src_it = _src_tensors.begin(); - auto dst_it = _dst_tensors.begin(); - while (src_it != _src_tensors.end()) - { - if ((*src_it == *dst_it) || (*src_it == nullptr || *dst_it == nullptr)) - { - src_it = _src_tensors.erase(src_it); - dst_it = _dst_tensors.erase(dst_it); - } - else - { - ++src_it; - ++dst_it; - } - } - } - - void run() override; -}; - -} // namespace kernel -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_KERNEL_PERMUTELAYER_H__ diff --git a/runtime/onert/core/src/backend/controlflow/kernel/WhileLayer.cc b/runtime/onert/core/src/backend/controlflow/kernel/WhileLayer.cc deleted file mode 100644 index 225f0dd7c..000000000 --- a/runtime/onert/core/src/backend/controlflow/kernel/WhileLayer.cc +++ /dev/null @@ -1,220 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "WhileLayer.h" - -#include <backend/ITensor.h> -#include "exec/ExecutorBase.h" -#include <misc/polymorphic_downcast.h> -#include "PermuteLayer.h" - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ -namespace kernel -{ - -WhileLayer::WhileLayer(const std::vector<backend::ITensor *> input_tensors, - const std::vector<backend::ITensor *> output_tensors, - const ir::OperandIndexSequence &output_indices, const ir::Graph &graph, - const ir::SubgraphIndex &cond_subg_index, - const ir::SubgraphIndex &body_subg_index, exec::ExecutorMap *executor_map) - : _cond_subg_index{cond_subg_index}, _body_subg_index{body_subg_index}, - _output_indices{output_indices}, _graph{graph}, _input_tensors{input_tensors}, - _output_tensors{output_tensors}, _executor_map{executor_map} -{ - // At this point, executor_map may not have executors of cond subg and body subg -} - -void WhileLayer::run() -{ - // Copy "_input_tensors" -> "cond subg inputs" - // Run cond subg - // Start loop while output of cond subg is ture - // // Copy "_input_tensors" -> "body subg inputs" in the first iteration, then copy "body subg - // outputs" -> "body subg inputs" in the second or more iterations - // // Run body subg - // // Copy "body subg outputs" -> "cond subg inputs" - // // Run cond subg - // If there is no loop copy "_input_tensors" -> "_dst_tensors", else copy "cond subg inputs" -> - // "_dst_tensors" - auto cond_exec = nnfw::misc::polymorphic_downcast<exec::ExecutorBase *>( - _executor_map->at(_cond_subg_index).get()); - auto body_exec = nnfw::misc::polymorphic_downcast<exec::ExecutorBase *>( - _executor_map->at(_body_subg_index).get()); - - const auto &cond_graph = cond_exec->graph(); - const auto &body_graph = body_exec->graph(); - - std::vector<backend::ITensor *> input_tensors; - std::vector<backend::ITensor *> cond_input_tensors; - std::vector<backend::ITensor *> body_input_tensors; - std::vector<backend::ITensor *> body_output_tensors; - std::vector<backend::ITensor *> output_tensors; - - // Add only used tensors in cond subgraph - assert(cond_graph.getInputs().size() == _input_tensors.size()); - assert(cond_graph.getInputs().size() == cond_exec->getInputTensors().size()); - for (uint32_t i = 0; i < cond_graph.getInputs().size(); ++i) - { - const auto &cond_input = cond_graph.operands().at(cond_graph.getInputs().at(i)); - if (cond_input.getUses().size() > 0) - { - input_tensors.emplace_back(_input_tensors.at(i)); - cond_input_tensors.emplace_back(cond_exec->getInputTensors().at(i)); - } - } - const auto permute_op_input_to_cond_input = - std::make_shared<PermuteLayer>(input_tensors, cond_input_tensors); - - // Add only used tensors among outputs of while operation - assert(_output_indices.size() == _input_tensors.size()); - assert(_output_indices.size() == _output_tensors.size()); - input_tensors.clear(); - output_tensors.clear(); - for (size_t i = 0; i < _output_indices.size(); ++i) - { - const auto &output_index = _output_indices.at(i); - const auto &output = _graph.operands().at(output_index); - if (output.getUses().size() > 0 || _graph.getOutputs().contains(output_index)) - { - input_tensors.emplace_back(_input_tensors.at(i)); - output_tensors.emplace_back(_output_tensors.at(i)); - } - } - const auto permute_op_input_to_op_output = - std::make_shared<PermuteLayer>(input_tensors, output_tensors); - - // Add all tensors with unused tensors in body subgraph because unused input tensors will be - // copied output tensors in body subgraph - assert(_input_tensors.size() == body_exec->getInputTensors().size()); - input_tensors = _input_tensors; - body_input_tensors = body_exec->getInputTensors(); - const auto permute_op_input_to_body_input = - std::make_shared<PermuteLayer>(input_tensors, body_input_tensors); - - // Add only used tensors in cond subgraph - assert(cond_graph.getInputs().size() == body_exec->getOutputTensors().size()); - assert(cond_graph.getInputs().size() == cond_exec->getInputTensors().size()); - body_output_tensors.clear(); - cond_input_tensors.clear(); - for (uint32_t i = 0; i < cond_graph.getInputs().size(); ++i) - { - const auto &cond_input = cond_graph.operands().at(cond_graph.getInputs().at(i)); - if (cond_input.getUses().size() > 0) - { - body_output_tensors.emplace_back(body_exec->getOutputTensors().at(i)); - cond_input_tensors.emplace_back(cond_exec->getInputTensors().at(i)); - } - } - const auto permute_body_output_to_cond_input = - std::make_shared<PermuteLayer>(body_output_tensors, cond_input_tensors); - - // Add only used tensors in body subgraph - assert(body_graph.getInputs().size() == body_exec->getOutputTensors().size()); - assert(body_graph.getInputs().size() == body_exec->getInputTensors().size()); - body_output_tensors.clear(); - body_input_tensors.clear(); - for (uint32_t i = 0; i < body_graph.getInputs().size(); ++i) - { - const auto &body_input_index = body_graph.getInputs().at(i); - const auto &body_input = body_graph.operands().at(body_input_index); - if (body_input.getUses().size() > 0 && - !body_exec->graph().getOutputs().contains(body_input_index)) - { - body_output_tensors.emplace_back(body_exec->getOutputTensors().at(i)); - body_input_tensors.emplace_back(body_exec->getInputTensors().at(i)); - } - } - const auto permute_body_output_to_body_input = - std::make_shared<PermuteLayer>(body_output_tensors, body_input_tensors); - - // Add only used tensors among outputs of while operation - assert(_output_indices.size() == body_exec->getOutputTensors().size()); - assert(_output_indices.size() == _output_tensors.size()); - body_output_tensors.clear(); - output_tensors.clear(); - for (size_t i = 0; i < _output_indices.size(); ++i) - { - const auto &output_index = _output_indices.at(i); - const auto &output = _graph.operands().at(output_index); - if (output.getUses().size() > 0 || _graph.getOutputs().contains(output_index)) - { - body_output_tensors.emplace_back(body_exec->getOutputTensors().at(i)); - output_tensors.emplace_back(_output_tensors.at(i)); - } - } - const auto permute_body_output_to_op_output = - std::make_shared<PermuteLayer>(body_output_tensors, output_tensors); - - // Remove copying of unused tensor - permute_op_input_to_cond_input->prepare(); - permute_op_input_to_op_output->prepare(); - permute_op_input_to_body_input->prepare(); - permute_body_output_to_cond_input->prepare(); - permute_body_output_to_body_input->prepare(); - permute_body_output_to_op_output->prepare(); - - VERBOSE(While) << "Call to $" << _cond_subg_index << " (cond)" << std::endl; - cond_exec->execute(_input_tensors, permute_op_input_to_cond_input); - VERBOSE(While) << "Return from $" << _cond_subg_index << std::endl; - - assert(cond_exec->getOutputTensors().size() == 1); - auto &cond_output_tensor = cond_exec->getOutputTensors().at(0); - auto getResultCond = [](backend::ITensor *tensor) -> bool { - bool ret = false; - tensor->access([&](ITensor &tensor) { ret = *reinterpret_cast<bool *>(tensor.buffer()); }); - return ret; - }; - - const auto body_execute_with_op_inputs = [&]() { - VERBOSE(While) << "Call to $" << _body_subg_index << " (body)" << std::endl; - body_exec->execute(_input_tensors, permute_op_input_to_body_input); - VERBOSE(While) << "Return from $" << _body_subg_index << std::endl; - }; - - const auto body_execute_with_body_outputs = [&]() { - VERBOSE(While) << "Call to $" << _body_subg_index << " (body)" << std::endl; - body_exec->execute(body_exec->getOutputTensors(), permute_body_output_to_body_input); - VERBOSE(While) << "Return from $" << _body_subg_index << std::endl; - }; - - std::function<void()> body_execute = body_execute_with_op_inputs; - const auto cond_execute = [&]() { - VERBOSE(While) << "Call to $" << _cond_subg_index << " (cond)" << std::endl; - cond_exec->execute(body_exec->getOutputTensors(), permute_body_output_to_cond_input); - VERBOSE(While) << "Return from $" << _cond_subg_index << std::endl; - }; - auto permute_to_outputs_fn = permute_op_input_to_op_output; - - // Loop while Cond subgraph's output is true - while (getResultCond(cond_output_tensor)) - { - body_execute(); - cond_execute(); - body_execute = body_execute_with_body_outputs; - permute_to_outputs_fn = permute_body_output_to_op_output; - } - permute_to_outputs_fn->run(); -} - -} // namespace kernel -} // namespace controlflow -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/controlflow/kernel/WhileLayer.h b/runtime/onert/core/src/backend/controlflow/kernel/WhileLayer.h deleted file mode 100644 index 9dae49281..000000000 --- a/runtime/onert/core/src/backend/controlflow/kernel/WhileLayer.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CONTROLFLOW_KERNEL_WHILE_LAYER_H__ -#define __ONERT_BACKEND_CONTROLFLOW_KERNEL_WHILE_LAYER_H__ - -#include <backend/ITensor.h> -#include <exec/IExecutor.h> -#include <exec/IFunction.h> -#include <ir/OperandIndexSequence.h> -#include <ir/Graph.h> - -namespace onert -{ -namespace backend -{ -namespace controlflow -{ -namespace kernel -{ - -class WhileLayer : public ::onert::exec::IFunction -{ -public: - WhileLayer(const std::vector<backend::ITensor *> input_tensors, - const std::vector<backend::ITensor *> output_tensors, - const ir::OperandIndexSequence &output_indices, const ir::Graph &graph, - const ir::SubgraphIndex &cond_subg_index, const ir::SubgraphIndex &body_subg_index, - exec::ExecutorMap *executor_map); - -public: - void run() override; - -private: - const ir::SubgraphIndex _cond_subg_index; - const ir::SubgraphIndex _body_subg_index; - const ir::OperandIndexSequence &_output_indices; - const ir::Graph &_graph; - const std::vector<backend::ITensor *> _input_tensors; - const std::vector<backend::ITensor *> _output_tensors; - exec::ExecutorMap *_executor_map; -}; - -} // namespace kernel -} // namespace controlflow -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CONTROLFLOW_KERNEL_WHILE_LAYER_H__ diff --git a/runtime/onert/core/src/backend/cpu_common/Allocator.cc b/runtime/onert/core/src/backend/cpu_common/Allocator.cc deleted file mode 100644 index 0ba444ee6..000000000 --- a/runtime/onert/core/src/backend/cpu_common/Allocator.cc +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/cpu_common/Allocator.h" - -#include "util/logging.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -Allocator::Allocator(uint32_t capacity) -{ - _base = std::make_unique<uint8_t[]>(capacity); - - VERBOSE(ALLOC) << "allocation capacity: " << capacity << std::endl; - VERBOSE(ALLOC) << "base pointer: " << static_cast<void *>(_base.get()) << std::endl; -} - -} // namespace cpu_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/cpu_common/DynamicTensorManager.cc b/runtime/onert/core/src/backend/cpu_common/DynamicTensorManager.cc deleted file mode 100644 index 740248ccd..000000000 --- a/runtime/onert/core/src/backend/cpu_common/DynamicTensorManager.cc +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/cpu_common/DynamicTensorManager.h" - -#include "util/logging.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -DynamicTensorManager::DynamicTensorManager(const std::shared_ptr<TensorRegistry> ®) - : _dynamic_mem_mgr{new DynamicMemoryManager()}, _tensors{reg} -{ - // DO NOTHING -} - -void DynamicTensorManager::buildTensor(const ir::OperandIndex &ind, - const ir::OperandInfo &tensor_info, - ir::Layout backend_layout) -{ - assert(_tensors->getNativeTensor(ind) == nullptr); - auto tensor = std::make_unique<Tensor>(tensor_info, backend_layout, _dynamic_mem_mgr.get()); - _tensors->setNativeTensor(ind, std::move(tensor)); -} - -void DynamicTensorManager::planDealloc(ir::OperationIndex op_ind, backend::ITensor *tensor) -{ - _dealloc_tensor_map[op_ind].emplace(tensor); -} - -void DynamicTensorManager::deallocInput(ir::OperationIndex op_ind) -{ - auto find = _dealloc_tensor_map.find(op_ind); - if (find == _dealloc_tensor_map.end()) - return; - - auto &input_set = find->second; - for (auto *tensor : input_set) - { - if (!tensor->is_dynamic()) - continue; - - _dynamic_mem_mgr->deallocate(tensor); - - auto *cpu_tensor = nnfw::misc::polymorphic_downcast<cpu_common::Tensor *>(tensor); - cpu_tensor->resetBuffer(); - - VERBOSE(DynamicTensorManager) << "Deallocating tensor " << (void *)cpu_tensor - << " (input of op_ind: " << op_ind.value() << ")" << std::endl; - } -} - -const ITensor *DynamicTensorManager::getRawITensor(ir::OperandIndex ind) -{ - auto ptr = _tensors->getITensor(ind); - assert(ptr); - return ptr; -} - -} // namespace cpu_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/cpu_common/MemoryManager.cc b/runtime/onert/core/src/backend/cpu_common/MemoryManager.cc deleted file mode 100644 index 9f179d9ee..000000000 --- a/runtime/onert/core/src/backend/cpu_common/MemoryManager.cc +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <backend/cpu_common/MemoryManager.h> - -#include <cassert> - -#include "MemoryPlannerFactory.h" -#include "util/ConfigSource.h" -#include "util/logging.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -MemoryManager::MemoryManager() : _mem_planner{createMemoryPlanner()} -{ - // DO NOTHING -} - -MemoryManager::MemoryManager(const std::string planner_id) - : _mem_planner{createMemoryPlanner(planner_id)} -{ - // DO NOTHING -} - -cpu_common::IMemoryPlanner *MemoryManager::createMemoryPlanner() -{ - auto planner_id = util::getConfigString(util::config::CPU_MEMORY_PLANNER); - return cpu_common::MemoryPlannerFactory::get().create(planner_id); -} - -cpu_common::IMemoryPlanner *MemoryManager::createMemoryPlanner(const std::string planner_id) -{ - return cpu_common::MemoryPlannerFactory::get().create(planner_id); -} - -void MemoryManager::claimPlan(const ir::OperandIndex &ind, uint32_t size) -{ - _mem_planner->claim(ind, size); -} - -void MemoryManager::releasePlan(const ir::OperandIndex &ind) { _mem_planner->release(ind); } - -void MemoryManager::allocate(void) -{ - _mem_alloc = std::make_shared<cpu_common::Allocator>(_mem_planner->capacity()); - assert(_mem_alloc->base()); -} - -uint8_t *MemoryManager::getBuffer(const ir::OperandIndex &ind) const -{ - assert(_mem_planner->memory_plans().find(ind) != _mem_planner->memory_plans().end()); - const auto &mem_blk = _mem_planner->memory_plans().at(ind); - return _mem_alloc->base() + mem_blk.offset; -} - -std::shared_ptr<cpu_common::Allocator> DynamicMemoryManager::allocate(const ITensor *tensor, - uint32_t capacity) -{ - auto find = _mem_alloc_map.find(tensor); - if (find != _mem_alloc_map.end()) - throw std::runtime_error("Cannot allocate memory for a tensor. It was already allocated."); - - _mem_alloc_map[tensor] = std::make_shared<cpu_common::Allocator>(capacity); - return _mem_alloc_map[tensor]; -} - -void DynamicMemoryManager::deallocate(const ITensor *tensor) -{ - auto find = _mem_alloc_map.find(tensor); - if (find == _mem_alloc_map.end()) - throw std::runtime_error("Cannot find Allocator for the requested index"); - - find->second->release(); // explicitly erase memory - _mem_alloc_map.erase(find); // remove tensor and alloc -} - -void DynamicMemoryManager::deallocate(void) -{ - for (auto &mem_alloc : _mem_alloc_map) - { - // Release memory buffer of mem_alloc - mem_alloc.second->release(); - } - - _mem_alloc_map.clear(); -} - -} // namespace cpu_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.cc b/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.cc deleted file mode 100644 index 75c2da7d2..000000000 --- a/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.cc +++ /dev/null @@ -1,215 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "MemoryPlanner.h" -#include "util/logging.h" -#include <cassert> - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -void BumpPlanner::claim(const ir::OperandIndex &ind, size_t size) -{ - assert(size != 0); - - Block blk{_capacity, size}; - _mem_plans[ind] = blk; - _capacity += size; - - VERBOSE(BP_PLANNER) << "CLAIM(#" << ind.value() << "): " << blk.offset << ", " << blk.size - << std::endl; -} - -void BumpPlanner::release(const ir::OperandIndex &ind) -{ - VERBOSE(BP_PLANNER) << "RELEASE(#" << ind.value() << "): " - << "NOTHING does" << std::endl; -} - -// There are some assumptions for claiming memory(== making a reservation for memory). -// 1. About _claim_table(std::map). -// - The table's data structure is std::map so that it always sorts -// value(OperandIndex) by key(base_offset). -// - This claim() inserts key/value into _claim_table and the release() removes the key/value from -// _claim_table. -// - _claim_table shows the memory status at a certain point in time. Therefore, -// - If _claim_table has an offset and a certain size at a certain point in time, -// it means the place at the offset has been already claimed(== can't claim now. need to find -// someplace new). -// - If _claim_table doesn't have any element for an offset and a certain size at a certain -// point in time, it means the place at the offset can be claimed. -// 2. In the loop for _claim_table, we can assume the current claim_base_offset value is bigger than -// the previous claim_base_offset. -void FirstFitPlanner::claim(const ir::OperandIndex &ind, size_t size) -{ - assert(size != 0); - - // Find the right position for claiming - uint32_t next_offset = 0; - for (auto &mem_claim : _claim_table) - { - auto claimed_base_offset = mem_claim.first; - auto claimed_size = _mem_plans[mem_claim.second].size; - if (next_offset + size <= claimed_base_offset) - { - break; - } - else - { - next_offset = claimed_base_offset + claimed_size; - } - } - - // Now next_offset is set to the proper offset - _claim_table[next_offset] = ind; - _mem_plans[ind] = {next_offset, size}; - - VERBOSE(FF_PLANNER) << "claim(#" << ind.value() << "): [+" << next_offset << ", " << size << "sz]" - << std::endl; - - if (_capacity < next_offset + size) - { - _capacity = next_offset + size; - } -} - -void FirstFitPlanner::release(const ir::OperandIndex &ind) -{ - for (auto it = _claim_table.cbegin(); it != _claim_table.cend(); ++it) - { - if (it->second == ind) - { - uint32_t offset = it->first; - uint32_t index = ind.value(); - uint32_t size = _mem_plans[ind].size; - - _claim_table.erase(it); - - VERBOSE(FF_PLANNER) << "release(#" << index << "): [+" << offset << ", " << size << "sz]" - << std::endl; - return; - } - } - assert(!"Cannot release for given index. It has been not claimed or released already."); -} - -WICPlanner::WICPlanner() - : _initialized(false), _capacity(0), _mem_plans(), _live_operands(), _interference_graph(), - _operands() -{ - // DO NOTHING -} - -void WICPlanner::claim(const ir::OperandIndex &ind, size_t size) -{ - assert(size != 0); - - _operands.emplace(size, ind); - _interference_graph[ind].insert(_interference_graph[ind].end(), _live_operands.cbegin(), - _live_operands.cend()); - for (const auto &live_operand : _live_operands) - { - _interference_graph[live_operand].emplace_back(ind); - } - _live_operands.emplace(ind); - - VERBOSE(WIC_PLANNER) << "claim(#" << ind.value() << "): [" << size << "sz]" << std::endl; -} - -void WICPlanner::release(const ir::OperandIndex &ind) -{ - _live_operands.erase(ind); - VERBOSE(WIC_PLANNER) << "release(#" << ind.value() << ")" << std::endl; -} - -/* - * Build memory plans using liveness and size of operands - * 1. Build inference graph at claim - * - Two operands interfere if they have overlapped live range - * 2. Sort operands in descending order of size - * - Use std::multimap to sort operands - * 3. Allocate memory block for sorted operands - * - Find free memory block which does not overlap with interfered operands - */ -void WICPlanner::buildMemoryPlans() -{ - for (const auto &operand : _operands) - { - uint32_t size = operand.first; - const ir::OperandIndex &ind = operand.second; - VERBOSE(WIC_PLANNER) << "build_plan(#" << ind.value() << "): [" << size << "sz]" << std::endl; - - uint32_t next_offset = 0; - if (_interference_graph.count(ind)) - { - // Find interfered memory plans and sort them by offset - std::multimap<uint32_t, uint32_t> interfered_plans; - for (const auto &interference : _interference_graph[ind]) - { - if (_mem_plans.count(interference)) - interfered_plans.emplace(_mem_plans[interference].offset, _mem_plans[interference].size); - } - - // Find free memory block in first-fit manner - for (const auto &interfered_plan : interfered_plans) - { - auto claimed_base_offset = interfered_plan.first; - auto claimed_size = interfered_plan.second; - VERBOSE(WIC_PLANNER) << "interfere : [+" << claimed_base_offset << ", " << claimed_size - << "sz]" << std::endl; - if (next_offset + size <= claimed_base_offset) - { - break; - } - else if (next_offset < claimed_base_offset + claimed_size) - { - next_offset = claimed_base_offset + claimed_size; - } - } - } - else - { - VERBOSE(WIC_PLANNER) << "No interference" << std::endl; - } - - _mem_plans[ind] = {next_offset, size}; - VERBOSE(WIC_PLANNER) << "alloc(#" << ind.value() << "): [+" << next_offset << ", " << size - << "sz]" << std::endl; - - if (_capacity < next_offset + size) - { - _capacity = next_offset + size; - } - } - _initialized = true; - _interference_graph.clear(); - _operands.clear(); -} - -WICPlanner::MemoryPlans &WICPlanner::memory_plans() -{ - if (!_initialized) - buildMemoryPlans(); - return _mem_plans; -} - -} // namespace cpu_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.h b/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.h deleted file mode 100644 index 7c387e542..000000000 --- a/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.h +++ /dev/null @@ -1,160 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file       MemoryPlanner.h - * @brief      This file contains Memory Planning related classes - */ - -#ifndef __ONERT_BACKEND_CPU_COMMON_MEMORY_PLANNER_H__ -#define __ONERT_BACKEND_CPU_COMMON_MEMORY_PLANNER_H__ - -#include <map> -#include <vector> -#include <unordered_set> -#include <memory> - -#include "backend/cpu_common/Allocator.h" -#include "backend/cpu_common/IMemoryPlanner.h" -#include "ir/OperandIndexMap.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -/** - * @brief Class to plan memory by bump way - */ -class BumpPlanner : public IMemoryPlanner -{ -public: - /** - * @brief Claim memory for operand by bump way - * @param[in] index The operand index - * @param[in] size The size of the memory - */ - void claim(const ir::OperandIndex &, size_t) override; - /** - * @brief Release memory for operand by bump way - * @param[in] index The operand index - */ - void release(const ir::OperandIndex &) override; - /** - * @brief Get capacity for memory planning - * @return The value of capacity - */ - uint32_t capacity() override { return _capacity; } - /** - * @brief Get MemoryPlans - * @return MemoryPlans - */ - MemoryPlans &memory_plans() override { return _mem_plans; } - -private: - uint32_t _capacity = 0; - MemoryPlans _mem_plans; -}; - -/** - * @brief Class to plan memory by firstfit way - */ -class FirstFitPlanner : public IMemoryPlanner -{ -public: - /** - * @brief Claim memory for operand by firstfit way - * @param[in] index The operand index - * @param[in] size The size of the memory - */ - void claim(const ir::OperandIndex &, size_t) override; - /** - * @brief Release memory for operand by firstfit way - * @param[in] index The operand index - */ - void release(const ir::OperandIndex &) override; - /** - * @brief Get capacity for memory planning - * @return The value of capacity - */ - uint32_t capacity() override { return _capacity; } - /** - * @brief Get MemoryPlans - * @return MemoryPlans - */ - MemoryPlans &memory_plans() override { return _mem_plans; } - -private: - uint32_t _capacity = 0; - MemoryPlans _mem_plans; - // Use std::map because claim() assumes that _claim_table is sorted by uint32_t(base_offset) - std::map<uint32_t, ir::OperandIndex> _claim_table; -}; - -/** - * @brief Class to plan memory by Weighted Interval Color algorithm - */ -class WICPlanner : public IMemoryPlanner -{ -public: - WICPlanner(); - - /** - * @brief Claim memory for operand by WIC algorithm - * @param[in] index The operand index - * @param[in] size The size of the memory - */ - void claim(const ir::OperandIndex &, size_t) override; - /** - * @brief Release memory for operand by WIC algorithm - * @param[in] index The operand index - */ - void release(const ir::OperandIndex &) override; - /** - * @brief Get capacity for memory planning - * @return The value of capacity - */ - uint32_t capacity() override - { - if (!_initialized) - buildMemoryPlans(); - return _capacity; - } - /** - * @brief Get MemoryPlans - * @return MemoryPlans - */ - MemoryPlans &memory_plans() override; - -private: - void buildMemoryPlans(); - - bool _initialized; - uint32_t _capacity; - MemoryPlans _mem_plans; - std::unordered_set<ir::OperandIndex> _live_operands; - ir::OperandIndexMap<std::vector<ir::OperandIndex>> _interference_graph; - // Sort operands by descending order of size - std::multimap<uint32_t, ir::OperandIndex, std::greater<uint32_t>> _operands; -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_COMMON_MEMORY_PLANNER_H__ diff --git a/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.test.cc b/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.test.cc deleted file mode 100644 index 5208a94d4..000000000 --- a/runtime/onert/core/src/backend/cpu_common/MemoryPlanner.test.cc +++ /dev/null @@ -1,193 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "MemoryPlanner.h" -#include "ir/Index.h" - -TEST(Allocator, allocate_test) -{ - ::onert::backend::cpu_common::Allocator allocator(1024); - ASSERT_NE(allocator.base(), nullptr); -} - -TEST(BumpPlanner, claim_test) -{ - ::onert::backend::cpu_common::BumpPlanner planner; - - auto claim = [&planner](uint32_t index, size_t size, uint32_t expected_offset) { - onert::ir::OperandIndex mem_idx(index); - planner.claim(mem_idx, size); - auto mem_blk = planner.memory_plans()[mem_idx]; - ASSERT_EQ(mem_blk.offset, expected_offset); - ASSERT_EQ(mem_blk.size, size); - }; - - claim(0, 10, 0); - claim(1, 20, 10); - claim(2, 30, 30); -} - -TEST(FirstFitPlanner, claim_release_test) -{ - ::onert::backend::cpu_common::FirstFitPlanner planner; - - auto claim = [&planner](uint32_t index, size_t size, uint32_t expected_offset) { - onert::ir::OperandIndex mem_idx(index); - planner.claim(mem_idx, size); - auto mem_blk = planner.memory_plans()[mem_idx]; - ASSERT_EQ(mem_blk.offset, expected_offset); - ASSERT_EQ(mem_blk.size, size); - }; - - auto release = [&planner](uint32_t index) { - onert::ir::OperandIndex mem_idx(index); - planner.release(mem_idx); - }; - - // 0 CLAIM - 10 - claim(0, 10, 0); - - // 1 CLAIM - 20 - claim(1, 20, 10); - - // 2 CLAIM - 30 - claim(2, 30, 30); - - // 0 RELEASE - 10 - release(0); - - // 3 CLAIM - 20 - claim(3, 20, 60); - - // 4 CLAIM - 5 - claim(4, 5, 0); - - // 5 CLAIM - 10 - claim(5, 10, 80); - - // 6 CLAIM - 5 - claim(6, 5, 5); - - // 2 RELEASE - 30 - release(2); - - // 7 CLAIM - 35 - claim(7, 35, 90); - - // 8 CLAIM - 10 - claim(8, 10, 30); - - // 4 RELEASE - 5 - release(4); - - // 9 CLAIM - 10 - claim(9, 10, 40); - - // 10 CLAIM - 10 - claim(10, 10, 50); - - // 6 RELEASE - release(6); - - // 1 RELEASE - release(1); - - // 8 RELEASE - release(8); - - // 9 RELEASE - release(9); - - // 10 RELEASE - release(10); - - // 3 RELEASE - release(3); - - // 5 RELEASE - release(5); - - // 7 RELEASE - release(7); -} - -TEST(WICPlanner, claim_release_test) -{ - ::onert::backend::cpu_common::WICPlanner planner; - - auto claim = [&planner](uint32_t index, size_t size) { - onert::ir::OperandIndex mem_idx(index); - planner.claim(mem_idx, size); - }; - - auto release = [&planner](uint32_t index) { - onert::ir::OperandIndex mem_idx(index); - planner.release(mem_idx); - }; - - auto verify = [&planner](uint32_t index, uint32_t size, uint32_t expected_offset) { - onert::ir::OperandIndex mem_idx(index); - auto mem_blk = planner.memory_plans()[mem_idx]; - ASSERT_EQ(mem_blk.offset, expected_offset); - ASSERT_EQ(mem_blk.size, size); - }; - - auto capacity = [&planner](uint32_t expected_capacity) { - auto actual_capacity = planner.capacity(); - ASSERT_EQ(actual_capacity, expected_capacity); - }; - - claim(0, 20); - claim(1, 5); - release(0); - claim(2, 10); - release(1); - claim(3, 10); - release(2); - claim(4, 10); - release(3); - claim(5, 20); - release(4); - claim(6, 20); - release(5); - release(7); - - // VERIFY 0 - 0 - verify(0, 20, 0); - - // VERIFY 1 - 20 - verify(1, 5, 20); - - // VERIFY 2 - 0 - verify(2, 10, 0); - - // VERIFY 3 - 10 - verify(3, 10, 10); - - // VERIFY 4 - 20 - verify(4, 10, 20); - - // VERIFY 5 - 0 - verify(5, 20, 0); - - // VERIFY 6 - 20 - verify(6, 20, 20); - - // CAPACITY - 40 - capacity(40); -} diff --git a/runtime/onert/core/src/backend/cpu_common/MemoryPlannerFactory.cc b/runtime/onert/core/src/backend/cpu_common/MemoryPlannerFactory.cc deleted file mode 100644 index ead4f3294..000000000 --- a/runtime/onert/core/src/backend/cpu_common/MemoryPlannerFactory.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "MemoryPlannerFactory.h" - -#include "MemoryPlanner.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -MemoryPlannerFactory &MemoryPlannerFactory::get() -{ - static MemoryPlannerFactory instance; - return instance; -} - -IMemoryPlanner *MemoryPlannerFactory::create(const std::string &key) -{ - if (key == "FirstFit") - { - return new FirstFitPlanner; - } - else if (key == "Bump") - { - return new BumpPlanner; - } - else if (key == "WIC") - { - return new WICPlanner; - } - return new FirstFitPlanner; // Default Planner -} - -} // namespace cpu_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/cpu_common/MemoryPlannerFactory.h b/runtime/onert/core/src/backend/cpu_common/MemoryPlannerFactory.h deleted file mode 100644 index d14ec13ca..000000000 --- a/runtime/onert/core/src/backend/cpu_common/MemoryPlannerFactory.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_BACKEND_CPU_COMMON_MEMORY_PLANNER_FACTORY_H__ -#define __ONERT_BACKEND_CPU_COMMON_MEMORY_PLANNER_FACTORY_H__ - -#include "backend/cpu_common/IMemoryPlanner.h" - -#include <string> - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -class MemoryPlannerFactory -{ -public: - static MemoryPlannerFactory &get(); - -private: - MemoryPlannerFactory() = default; - -public: - IMemoryPlanner *create(const std::string &key); -}; - -} // namespace cpu_common -} // namespace backend -} // namespace onert - -#endif // __ONERT_BACKEND_CPU_COMMON_MEMORY_PLANNER_FACTORY_H__ diff --git a/runtime/onert/core/src/backend/cpu_common/StaticTensorManager.cc b/runtime/onert/core/src/backend/cpu_common/StaticTensorManager.cc deleted file mode 100644 index cac43babe..000000000 --- a/runtime/onert/core/src/backend/cpu_common/StaticTensorManager.cc +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/cpu_common/StaticTensorManager.h" - -#include "backend/cpu_common/DynamicTensorManager.h" -#include <util/logging.h> - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -StaticTensorManager::StaticTensorManager(const std::shared_ptr<TensorRegistry> ®, - DynamicMemoryManager *dynamic_mem_mgr) - : _const_mgr{new DynamicMemoryManager()}, _nonconst_mgr{new MemoryManager()}, _tensors{reg}, - _dynamic_mem_mgr{dynamic_mem_mgr} -{ - // DO NOTHING -} - -void StaticTensorManager::allocateConsts(void) -{ - for (auto &pair : _tensors->native_tensors()) - { - const auto &ind = pair.first; - auto tensor = pair.second.get(); - if (_as_constants[ind]) - { - auto mem_alloc = _const_mgr->allocate(_tensors->getITensor(ind), tensor->total_size()); - tensor->setBuffer(mem_alloc); - auto buffer = mem_alloc->base(); - VERBOSE(CPU_COMMON_StaticTensorManager) << "CONSTANT TENSOR(#" << ind.value() - << "): " << static_cast<void *>(buffer) - << "size : " << tensor->total_size() << std::endl; - } - } -} - -void StaticTensorManager::allocateNonconsts(void) -{ - _nonconst_mgr->allocate(); - - for (auto &pair : _tensors->native_tensors()) - { - const auto &ind = pair.first; - auto tensor = pair.second.get(); - if (!_as_constants[ind] && !tensor->is_dynamic()) - { - auto *buffer = _nonconst_mgr->getBuffer(ind); - tensor->setBuffer(buffer); - - VERBOSE(CPU_COMMON_StaticTensorManager) << "TENSOR(#" << ind.value() - << "): " << static_cast<void *>(buffer) << std::endl; - } - } -} - -void StaticTensorManager::deallocateConsts(void) { _const_mgr->deallocate(); } - -void StaticTensorManager::deallocateNonconsts(void) { _nonconst_mgr->deallocate(); } - -void StaticTensorManager::buildTensor(const ir::OperandIndex &ind, - const ir::OperandInfo &tensor_info, ir::Layout backend_layout, - bool as_const) -{ - assert(!_tensors->getNativeTensor(ind)); - auto tensor = std::make_unique<Tensor>(tensor_info, backend_layout, _dynamic_mem_mgr); - _tensors->setNativeTensor(ind, std::move(tensor)); - _as_constants[ind] = as_const; -} - -void StaticTensorManager::claimPlan(const ir::OperandIndex &ind, uint32_t size) -{ - assert(_tensors->getNativeTensor(ind)); - - // This method is called only when a tensor has proper shape - assert(!_tensors->getNativeTensor(ind)->is_dynamic()); - - if (!_as_constants[ind]) - _nonconst_mgr->claimPlan(ind, size); -} - -void StaticTensorManager::releasePlan(const ir::OperandIndex &ind) -{ - assert(_tensors->getNativeTensor(ind)); - - // This method is called only when a tensor has proper shape - assert(!_tensors->getNativeTensor(ind)->is_dynamic()); - - if (!_as_constants[ind]) - _nonconst_mgr->releasePlan(ind); -} - -void StaticTensorManager::iterate(const std::function<void(const ir::OperandIndex &)> &fn) -{ - for (const auto &it : _tensors->native_tensors()) - fn(it.first); -} - -} // namespace cpu_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/backend/cpu_common/Tensor.cc b/runtime/onert/core/src/backend/cpu_common/Tensor.cc deleted file mode 100644 index d3dcf9a6d..000000000 --- a/runtime/onert/core/src/backend/cpu_common/Tensor.cc +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "backend/cpu_common/Tensor.h" - -#include "ir/DataType.h" -#include "backend/cpu_common/MemoryManager.h" - -namespace onert -{ -namespace backend -{ -namespace cpu_common -{ - -Tensor::~Tensor() {} - -size_t Tensor::calcOffset(const ir::Coordinates &coords) const -{ - size_t rank = num_dimensions(); - rank = rank == 0 ? 1 : rank; - size_t offset = 0; - for (size_t i = 0; i < rank; ++i) - { - offset = offset * dimension(i) + coords[i]; - } - offset *= sizeOfDataType(data_type()); - return offset; -} - -void Tensor::setShape(const ir::Shape &new_shape) { _info.shape(new_shape); } - -bool Tensor::applyShape(const ir::Shape &new_shape) -{ - bool previously_dynamic = is_dynamic(); - - auto allocTensorMem = [&](bool overwrite = false) { - auto capacity = total_size(); - auto alloc = _dynamic_mem_mgr->allocate(this, capacity); - - if (overwrite) - overwriteBuffer(alloc); - else - setBuffer(alloc); - }; - - if (!previously_dynamic) - { - // TODO deallocate tensor->buffer() - // issue is that staticTensorManager might have allocate this memory - setShape(new_shape); - set_dynamic(); - allocTensorMem(true); - } - else if (buffer() == nullptr) - { - setShape(new_shape); - set_dynamic(); - allocTensorMem(); - } - // when buffer was already allocated and new_shape requires different size - else - { - auto previous_size = total_size(); - auto new_size = new_shape.num_elements() * ir::sizeOfDataType(data_type()); - if (previous_size != new_size) - { - _dynamic_mem_mgr->deallocate(this); - - setShape(new_shape); - set_dynamic(); - allocTensorMem(true); - } - else - { // when buffer with same size was already allocated, shape could differ - setShape(new_shape); - } - } - return true; -} - -} // namespace cpu_common -} // namespace backend -} // namespace onert diff --git a/runtime/onert/core/src/compiler/BackendManager.cc b/runtime/onert/core/src/compiler/BackendManager.cc deleted file mode 100644 index 0093f50fd..000000000 --- a/runtime/onert/core/src/compiler/BackendManager.cc +++ /dev/null @@ -1,146 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "compiler/BackendManager.h" - -#include <memory> -#include <dlfcn.h> - -#include "backend/Backend.h" -#include "backend/controlflow/Backend.h" -#include "backend/controlflow/Config.h" -#include "backend/IConfig.h" -#include "util/logging.h" -#include "util/ConfigSource.h" -#include "misc/string_helpers.h" - -static const char *SHARED_LIB_EXT = -#if defined(__APPLE__) && defined(__MACH__) - ".dylib"; -#else - ".so"; -#endif - -namespace onert -{ -namespace compiler -{ - -BackendManager &BackendManager::get() -{ - static BackendManager object; - return object; -} - -BackendManager::BackendManager() { loadControlflowBackend(); } - -void BackendManager::loadControlflowBackend() -{ - auto backend_object = std::unique_ptr<backend::controlflow::Backend, backend_destroy_t>( - new backend::controlflow::Backend, [](backend::Backend *backend) { delete backend; }); - - bool initialized = backend_object->config()->initialize(); // Call initialize here? - if (!initialized) - { - throw std::runtime_error(backend::controlflow::Config::ID + " backend initialization failed"); - } - _controlflow = backend_object.get(); // Save the controlflow backend implementation pointer - assert(_controlflow); - _gen_map.emplace(backend_object->config()->id(), std::move(backend_object)); -} - -void BackendManager::loadBackend(const std::string &backend) -{ - if (get(backend) != nullptr) - { - return; - } - - // TODO Remove indentation - { - const std::string backend_so = "libbackend_" + backend + SHARED_LIB_EXT; - void *handle = dlopen(backend_so.c_str(), RTLD_LAZY | RTLD_LOCAL); - - if (handle == nullptr) - { - VERBOSE_F() << "Failed to load backend '" << backend << "' - " << dlerror() << std::endl; - return; - } - - VERBOSE_F() << "Successfully loaded '" << backend << "' - " << backend_so << "\n"; - - { - // load object creator function - auto backend_create = (backend_create_t)dlsym(handle, "onert_backend_create"); - if (backend_create == nullptr) - { - fprintf(stderr, "BackendManager: unable to open function onert_backend_create : %s\n", - dlerror()); - abort(); - } - - // load object creator function - auto backend_destroy = (backend_destroy_t)dlsym(handle, "onert_backend_destroy"); - if (backend_destroy == nullptr) - { - fprintf(stderr, "BackendManager: unable to open function onert_backend_destroy : %s\n", - dlerror()); - abort(); - } - - auto backend_object = - std::unique_ptr<backend::Backend, backend_destroy_t>(backend_create(), backend_destroy); - bool initialized = backend_object->config()->initialize(); // Call initialize here? - if (!initialized) - { - VERBOSE_F() << backend.c_str() << " backend initialization failed. Don't use this backend" - << std::endl; - dlclose(handle); - return; - } - _gen_map.emplace(backend_object->config()->id(), std::move(backend_object)); - } - - // Save backend handle (avoid warning by handle lost without dlclose()) - auto u_handle = std::unique_ptr<void, dlhandle_destroy_t>{handle, [](void *h) { dlclose(h); }}; - _handle_map.emplace(backend, std::move(u_handle)); - } -} - -backend::Backend *BackendManager::get(const std::string &key) -{ - if (_gen_map.find(key) != _gen_map.end()) - { - return _gen_map.at(key).get(); - } - - return nullptr; -} - -const backend::Backend *BackendManager::get(const std::string &key) const -{ - if (_gen_map.find(key) != _gen_map.end()) - { - return _gen_map.at(key).get(); - } - - return nullptr; -} - -const backend::controlflow::Backend *BackendManager::getControlflow() const { return _controlflow; } - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/BackendResolver.cc b/runtime/onert/core/src/compiler/BackendResolver.cc deleted file mode 100644 index a47d8d2d5..000000000 --- a/runtime/onert/core/src/compiler/BackendResolver.cc +++ /dev/null @@ -1,25 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "compiler/BackendResolver.h" - -namespace onert -{ -namespace compiler -{ - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/Compiler.cc b/runtime/onert/core/src/compiler/Compiler.cc deleted file mode 100644 index 12b582b35..000000000 --- a/runtime/onert/core/src/compiler/Compiler.cc +++ /dev/null @@ -1,326 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "compiler/Compiler.h" - -#include "ParamChecker.h" -#include "ExecutorFactory.h" -#include "OperationValidator.h" -#include "ShapeValidator.h" -#include "Fp32ToFp16Converter.h" - -#include <backend/controlflow/Config.h> -#include "compiler/BackendManager.h" -#include "compiler/IScheduler.h" -#include "compiler/ManualScheduler.h" -#include "compiler/HEScheduler.h" -#include "compiler/StaticShapeInference.h" -#include "compiler/pass/ConstantOutputPass.h" -#include "compiler/pass/OddOutputPass.h" -#include "compiler/pass/PassRunner.h" -#include "exec/ExecTime.h" -#include "ir/operation/LowerInfo.h" -#include "ir/verifier/Verifier.h" -#include "dumper/dot/DotDumper.h" -#include "compiler/Linear.h" -#include "interp/InterpExecutor.h" -#include "util/ConfigSource.h" -#include "util/logging.h" -#include "ir/OperationDumper.h" -#include "misc/string_helpers.h" - -namespace onert -{ - -namespace compiler -{ - -CompilerOptions fetchCompilerOptionsFromGlobalConfig(const ir::Subgraphs &subgs) -{ - CompilerOptions options; - options.backend_list = nnfw::misc::split(util::getConfigString(util::config::BACKENDS), ';'); - options.is_primary_subgraph = false; - options.trace_filepath = util::getConfigString(util::config::TRACE_FILEPATH); - options.graph_dump_level = util::getConfigInt(util::config::GRAPH_DOT_DUMP); - options.op_seq_max_node = util::getConfigInt(util::config::OP_SEQ_MAX_NODE); - options.executor = util::getConfigString(util::config::EXECUTOR); - options.he_scheduler = util::getConfigBool(util::config::USE_SCHEDULER); - options.he_profiling_mode = util::getConfigBool(util::config::PROFILING_MODE); - options.disable_compile = util::getConfigBool(util::config::DISABLE_COMPILE); - options.fp16_enable = util::getConfigBool(util::config::FP16_ENABLE); -#ifdef RUY_PROFILER - options.op_seq_max_node = 1; -#endif - - { - // Backend for all - auto &ms_options = options.manual_scheduler_options; - - // Default value for op_backend_all is first element in the backend list - ms_options.backend_for_all = util::getConfigString(util::config::OP_BACKEND_ALLOPS); - -// Opcode to Backend -#define OP(OpName) \ - { \ - const auto &backend_str = util::getConfigString(util::config::OP_BACKEND_##OpName); \ - if (!backend_str.empty()) \ - { \ - ms_options.opcode_to_backend[ir::OpCode::OpName] = backend_str; \ - } \ - } -#include "ir/Operations.lst" -#undef OP - - // Index to Backend - // TODO Support multiple subgraphs for manual scheduling - auto map_str = util::getConfigString(util::config::OP_BACKEND_MAP); - auto key_val_list = nnfw::misc::split(map_str, ';'); - for (const auto &key_val_str : key_val_list) - { - if (key_val_str.empty()) - { - continue; - } - - auto key_val = nnfw::misc::split(key_val_str, '='); - const auto &key_str = key_val.at(0); - const auto &val = key_val.at(1); - auto key = static_cast<uint32_t>(std::stoi(key_str)); - - subgs.at(ir::SubgraphIndex{0}) - ->operations() - .at(ir::OperationIndex{key}); // Check if exist, or this wil throw - ms_options.index_to_backend.emplace(ir::OperationIndex{key}, val); - } - } - return options; -} - -Compiler::Compiler(const std::shared_ptr<ir::Subgraphs> &subgs) - : _subgraphs{subgs}, _state{State::CREATED} -{ - // Set default values for CompilerOptions - // All these default values should not be fetched from Env, when we stop supporting Android NN - // API. - _options = fetchCompilerOptionsFromGlobalConfig(*subgs); -} - -void Compiler::enableToFp16() { _options.fp16_enable = true; } - -void Compiler::checkProfilerConditions() -{ - if (!_options.he_scheduler) - throw std::runtime_error("Heterogeneous scheduler must be enabled during profiling."); - - if (_options.executor != "Dataflow") - throw std::runtime_error("Profiling mode works only with 'Dataflow' executor"); -} - -std::shared_ptr<exec::ExecutorMap> Compiler::compile(void) -{ - // Set control flow backend for control flow operators - { - _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::If] = - backend::controlflow::Config::ID; - _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::While] = - backend::controlflow::Config::ID; - _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::Permute] = - backend::controlflow::Config::ID; - } - - // FIXME This is a workaround for bcq operations, should remove it - { - _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::BCQFullyConnected] = "bcq"; - _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::BCQGather] = "bcq"; - } - - { - VERBOSE(Compiler) << std::boolalpha; - VERBOSE(Compiler) << "==== Compiler Options ====" << std::endl; - VERBOSE(Compiler) << "backend_list : " - << nnfw::misc::join(_options.backend_list.begin(), - _options.backend_list.end(), "/") - << std::endl; - VERBOSE(Compiler) << "trace_filepath : " << _options.trace_filepath << std::endl; - VERBOSE(Compiler) << "graph_dump_level : " << _options.graph_dump_level << std::endl; - VERBOSE(Compiler) << "op_seq_max_node : " << _options.op_seq_max_node << std::endl; - VERBOSE(Compiler) << "executor : " << _options.executor << std::endl; - VERBOSE(Compiler) << "manual_scheduler_options : (Too many things to print)" << std::endl; - VERBOSE(Compiler) << "he_scheduler : " << _options.he_scheduler << std::endl; - VERBOSE(Compiler) << "he_profiling_mode : " << _options.he_profiling_mode << std::endl; - VERBOSE(Compiler) << "disable_compile : " << _options.disable_compile << std::endl; - VERBOSE(Compiler) << "fp16_enable : " << _options.fp16_enable << std::endl; - VERBOSE(Compiler) << std::noboolalpha; - } - - _subgraphs->iterate([&](const ir::SubgraphIndex &, ir::Graph &subg) { - // Mandatory passes - pass::PassRunner{} - .append(std::make_unique<pass::ConstantOutputPass>(subg)) - .append(std::make_unique<pass::OddOutputPass>(subg)) - .run(); - }); - - /*************************************************** - * Prepare compilation phase - ***************************************************/ - - // Check shape independent operation feature - // - Operand type - // - Shape independent parameter - _subgraphs->iterate( - [](const onert::ir::SubgraphIndex &, const ir::Graph &subg) { OperationValidator{subg}(); }); - - auto executors = std::make_shared<exec::ExecutorMap>(); - - // Compilable check - // TODO: Support hybrid execution - - // execution between interpreter and compiled executor (including control flow) - if (!checkCompilable()) - { - _subgraphs->iterate([&](const ir::SubgraphIndex &index, ir::Graph &subg) { - executors->emplace(index, std::make_unique<interp::InterpExecutor>(subg)); - }); - _state = State::COMPILED; - return executors; - } - - // Mode check - if (_options.he_profiling_mode) - checkProfilerConditions(); - - /*************************************************** - * Backend independent analysis & optimization phase - ***************************************************/ - auto dump_level = static_cast<dumper::dot::DotDumper::Level>(_options.graph_dump_level); - - // Lower: Assign backend - std::unordered_map<ir::SubgraphIndex, std::unique_ptr<compiler::LoweredGraph>> lowered_subgs; - _subgraphs->iterate([&](const ir::SubgraphIndex &index, ir::Graph &subg) { - _options.is_primary_subgraph = (index == ir::SubgraphIndex{0}); - onert::dumper::dot::DotDumper dot_dumper(subg, dump_level); - dot_dumper.dump(nnfw::misc::str("before_lower_subg-", index.value())); - - // Lower: Assign backend - lowered_subgs[index] = std::make_unique<compiler::LoweredGraph>(subg, _options); - - // Check backend(s) for subgraph support FP16 - bool backends_support_fp16 = true; - auto &contexts = (*lowered_subgs[index]).backend_contexts(); - for (auto it = contexts.begin(); it != contexts.end(); it++) - { - // Controlflow backend is not for actual computaion of operations so it is an exception - if (it->first->config()->id() != backend::controlflow::Config::ID) - backends_support_fp16 &= it->first->config()->supportFP16(); - } - - if (_options.fp16_enable && backends_support_fp16) - { - // NOTE: the only acl_cl backend enables fp16 mode - Fp32ToFp16Converter(*lowered_subgs[index]).run(); - } - - subg.setSubgraphs(nullptr); - }); - - _subgraphs.reset(); - - // Shape inference. - { - const auto primary_subg_idx = ir::SubgraphIndex{0}; - StaticShapeInferer inferer(primary_subg_idx, lowered_subgs); - lowered_subgs.at(primary_subg_idx) - ->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - auto has_dynamic_tensor = inferer.infer(op_seq); - op_seq.has_dynamic_tensor(has_dynamic_tensor); - }); - inferer.dump(); - } - - // Shape validation - // TODO Move shape independent feature check from ShapeValidator to OperationValidator - // TODO Move ShapeValidator into shape inference - // - Check input tensor shape validation - // - Check parameter value validation which valid value is depend on input tensor shape - // - Output tensor shape validation check is needless because - // static/dynamic shape inferer will make valid output shape - for (auto &pair : lowered_subgs) - { - auto &lowered_subg = pair.second; - compiler::ShapeValidator{lowered_subg->graph()}(); - } - - /************************************************************* - * Backend independent analysis & optimization phase finished - *************************************************************/ - - executors = std::make_shared<exec::ExecutorMap>(); - for (auto &pair : lowered_subgs) - { - const auto &subg_index = pair.first; - auto &lowered_subg = pair.second; - auto indexed_ranks = lowered_subg->indexed_ranks(); - - _options.is_primary_subgraph = (subg_index == ir::SubgraphIndex{0}); - - onert::dumper::dot::DotDumper dot_dumper_lowered(lowered_subg.get(), dump_level); - dot_dumper_lowered.dump("after_lower_subg-" + std::to_string(subg_index.value())); - - ir::OperationDumper dumper("START SUBGRAPH " + std::to_string(subg_index.value())); - lowered_subg->graph().operations().iterate( - [&](const ir::OperationIndex &, const ir::Operation &op) { op.accept(dumper); }); - auto executor = std::unique_ptr<exec::IExecutor>{ - ExecutorFactory::get().create(std::move(lowered_subg), _options, executors)}; - executor->setIndexedRanks(indexed_ranks); - executors->insert(std::make_pair(subg_index, std::move(executor))); - } - - /******************************** - * Code generation phase finished - ********************************/ - _state = State::COMPILED; - return executors; -} - -bool Compiler::checkCompilable() -{ - // Disable compile phase - // When ready to use interpreter backend, remove this config and use backend setting - if (_options.disable_compile) - { - return false; - } - - // TODO check unspecified operand shape - - // Check compilable parameter - for (uint32_t i = 0; i < _subgraphs->count(); ++i) - { - auto graph = _subgraphs->at(ir::SubgraphIndex{i}); - ParamChecker paramChecker{graph}; - paramChecker(); - if (paramChecker.haveNoneConstParam()) - { - return false; - } - } - - return true; -} - -} // namespace compiler - -} // namespace onert diff --git a/runtime/onert/core/src/compiler/ExecutorFactory.cc b/runtime/onert/core/src/compiler/ExecutorFactory.cc deleted file mode 100644 index bb325ffbc..000000000 --- a/runtime/onert/core/src/compiler/ExecutorFactory.cc +++ /dev/null @@ -1,501 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ExecutorFactory.h" - -#include <functional> -#include "exec/ExecutionObservers.h" -#include "exec/LinearExecutor.h" -#include "exec/DataflowExecutor.h" -#include "exec/ParallelExecutor.h" -#include "compiler/BackendManager.h" -#include "compiler/ExecutionBuilder.h" -#include "exec/ExecTime.h" -#include "compiler/Linear.h" -#include "compiler/TensorBuilders.h" -#include "backend/IConstantInitializer.h" -#include "backend/IKernelGenerator.h" -#include "backend/IOptimizer.h" -#include "backend/IPortableTensor.h" -#include "backend/ITensorRegister.h" -#include "backend/controlflow/Config.h" -#include "backend/controlflow/KernelGenerator.h" -#include "backend/controlflow/UserTensor.h" -#include "backend/controlflow/TensorBuilder.h" -#include <memory> - -namespace onert -{ -namespace -{ - -class SyncFunction final : public exec::IFunction -{ -public: - virtual ~SyncFunction() = default; - SyncFunction(std::unique_ptr<exec::IFunction> fn, const std::shared_ptr<backend::IConfig> config) - : _fn{std::move(fn)}, _config{config} - { - assert(_fn); - assert(_config); - } - - void run() override - { - _fn->run(); - _config->sync(); - } - - void prepare() override { _fn->prepare(); } - -private: - std::unique_ptr<exec::IFunction> _fn; - std::shared_ptr<backend::IConfig> _config; -}; - -} // namespace -} // namespace onert - -namespace onert -{ -namespace compiler -{ - -ExecutorFactory &ExecutorFactory::get() -{ - static ExecutorFactory singleton; - return singleton; -} - -ExecutorFactory::ExecutorFactory() -{ - _map["Linear"] = createLinearExecutor; - _map["Dataflow"] = std::bind(createDataflowExecutor, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, false); - _map["Parallel"] = std::bind(createDataflowExecutor, std::placeholders::_1, std::placeholders::_2, - std::placeholders::_3, true); -} - -exec::IExecutor *ExecutorFactory::create(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const compiler::CompilerOptions &options, - const std::shared_ptr<exec::ExecutorMap> &executor_map) -{ - return _map.at(options.executor)(std::move(lowered_graph), options, executor_map); -} - -void ExecutorFactory::initializeBackendContext(compiler::LoweredGraph *lowered_graph) -{ - struct Entry - { - std::vector<backend::BackendContext::OperationInfo> operation_list; - std::vector<ir::OperandIndex> operand_list; - }; - std::unordered_map<const backend::Backend *, Entry> backend_assets; - - // Build lists for operations - lowered_graph->op_seqs().iterate( - [&](const ir::OpSequenceIndex &op_seq_index, const ir::OpSequence &op_seq) { - auto &op_seq_li = lowered_graph->getLowerInfo()->op_seq; - auto backend = op_seq_li.at(op_seq_index)->backend(); - for (auto &operation_idx : op_seq.operations()) - { - backend_assets[backend].operation_list.emplace_back(operation_idx, op_seq.getLayout()); - } - }); - - // Build lists for operands - lowered_graph->graph().operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &) { - const auto lower_info = lowered_graph->getLowerInfo(ind); - for (auto factor : lower_info->def_factors()) - { - auto backend = factor.backend(); - backend_assets[backend].operand_list.emplace_back(ind); - } - }); - - for (auto &pair : backend_assets) - { - auto backend = pair.first; - auto &arg = pair.second; - lowered_graph->backend_contexts().at(backend)->initialize(arg.operation_list, arg.operand_list); - } -} - -void ExecutorFactory::runTensorRegistration(compiler::LoweredGraph *lowered_graph, - const std::vector<ir::OpSequenceIndex> &order) -{ - for (const auto index : order) - { - const auto &op_seq = lowered_graph->op_seqs().at(index); - const auto backend = lowered_graph->getLowerInfo(index)->backend(); - const auto tensor_register = lowered_graph->backend_contexts().at(backend)->tensor_register; - auto tensor_builder = lowered_graph->backend_contexts().at(backend)->tensor_builder; - auto model_io = lowered_graph->graph().getInputs() + lowered_graph->graph().getOutputs(); - - if (tensor_register) - { - // Custom registration - tensor_register->registerTensors(op_seq, lowered_graph->getLowerInfo()); - } - else - { - // Default registration - for (const auto op_idx : op_seq) - { - const auto &op = lowered_graph->graph().operations().at(op_idx); - for (const auto &index : - (op.getInputs() | ir::Remove::UNDEFINED) + (op.getOutputs() | ir::Remove::UNDEFINED)) - { - if (!tensor_builder->isRegistered(index) && !model_io.contains(index)) - { - const auto &operand_lower_info = - lowered_graph->getLowerInfo(index)->def_factors().getOnlyElement(); - - // E.g., permute (CPU) -> tensor A -> MaxPool2D(acl_cl) - // op.getOutputs() of permute (CPU) returns tensor A - // but tensor A belongs to the backend of acl_cl. - // So, we have to make this tensor NOT registered for CPU. - if (operand_lower_info.backend() != backend) - continue; - - const auto &obj = lowered_graph->graph().operands().at(index); - const auto frontend_layout = op_seq.getLayout(); - const auto backend_layout = operand_lower_info.layout(); - ir::OperandInfo backend_info{permuteShape(obj.shape(), frontend_layout, backend_layout), - obj.typeInfo(), obj.info().memAllocType(), - obj.isConstant()}; - tensor_builder->registerTensorInfo(index, backend_info, backend_layout); - } - } - } - } - } -} - -std::vector<backend::ITensor *> -ExecutorFactory::initializeModelIOTensors(compiler::LoweredGraph &lowered_graph, - const ir::OperandIndexSequence &indices) -{ - std::vector<backend::ITensor *> ret; - - // TODO Store controlflow backend in BackendContext - std::shared_ptr<backend::controlflow::TensorBuilder> cf_tensor_builder; - std::shared_ptr<backend::controlflow::TensorRegistry> cf_tensor_reg; - for (const auto &e : lowered_graph.backend_contexts()) - { - auto backend = e.first; - auto &context = e.second; - if (backend->config()->id() == backend::controlflow::Config::ID) - { - cf_tensor_builder = - std::dynamic_pointer_cast<backend::controlflow::TensorBuilder>(context->tensor_builder); - cf_tensor_reg = - std::dynamic_pointer_cast<backend::controlflow::TensorRegistry>(context->tensor_registry); - } - } - assert(cf_tensor_builder); - assert(cf_tensor_reg); - - for (auto ind : indices) - { - const auto &operand = lowered_graph.graph().operands().at(ind); - auto tensor = std::make_unique<backend::controlflow::UserTensor>( - operand.info(), - ir::Layout::NHWC /* FIXME find op_seq for this operand and use frontend_layout */ - ); - - // Add tensor to controlflow TensorRegistry. - cf_tensor_reg->setNativeUserTensor(ind, std::move(tensor)); - auto *itensor = cf_tensor_reg->getITensor(ind); - ret.push_back(itensor); - } - return ret; -} - -void ExecutorFactory::prepareMigrantTensors(compiler::LoweredGraph &lowered_graph) -{ - TensorRegistries tensor_regs{lowered_graph.backend_contexts(), true}; - - lowered_graph.op_seqs().iterate( - [&](const ir::OpSequenceIndex &op_seq_index, const ir::OpSequence &op_seq) { - auto lower_info = lowered_graph.getLowerInfo(op_seq_index); - auto &backend_ctx = lowered_graph.backend_contexts().at(lower_info->backend()); - for (auto ind : (op_seq.getInputs() + op_seq.getOutputs()) | ir::Remove::DUPLICATED | - ir::Remove::UNDEFINED) - { - // If an OpSequence input/output tensor does not have a own tensor object, - // it must be using migrant tensors, so find the tensor from other tensor builders and - // set the tensor to this tensor builder if portable - if (!backend_ctx->tensor_registry->getITensor(ind)) - { - auto tensor = tensor_regs.getITensor(ind); - assert(tensor); // The tensor must have been registered - auto ptensor = dynamic_cast<backend::IPortableTensor *>(tensor); - if (ptensor) - backend_ctx->tensor_registry->setMigrantTensor(ind, ptensor); - } - } - }); -} - -exec::IExecutor * -ExecutorFactory::createLinearExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const compiler::CompilerOptions &options, - const std::shared_ptr<exec::ExecutorMap> &executor_map) -{ - const auto &backend_contexts = lowered_graph->backend_contexts(); - - initializeBackendContext(lowered_graph.get()); - - // linearize - assert(!lowered_graph->graph().isBuildingPhase()); - - /************************************************* - * Backend dependent analysis & optimization phase - *************************************************/ - - for (auto &pair : backend_contexts) - { - auto &optimizer = pair.second->optimizer; - if (optimizer) - optimizer->optimize(); - } - - /********************************************************** - * Backend dependent analysis & optimization phase finished - **********************************************************/ - - /*********************** - * Code generation phase - ***********************/ - - auto order = Linear::linearize(*lowered_graph); - runTensorRegistration(lowered_graph.get(), order); - - std::vector<backend::ITensor *> input_tensors; - std::vector<backend::ITensor *> output_tensors; - if (options.is_primary_subgraph) - { - input_tensors = initializeModelIOTensors(*lowered_graph, lowered_graph->graph().getInputs()); - output_tensors = initializeModelIOTensors(*lowered_graph, lowered_graph->graph().getOutputs()); - } - - Linear::dump(*lowered_graph, order); - Linear::planTensors(*lowered_graph, order); - - TensorBuilders tensor_builders{lowered_graph->backend_contexts(), true}; - TensorRegistries tensor_regs{lowered_graph->backend_contexts(), true}; - - for (auto &tensor_builder : tensor_builders) - { - tensor_builder->prepare(); - } - - prepareMigrantTensors(*lowered_graph); - - ExecutionBuilder builder; - - // Generate kernels - lowered_graph->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &op_seq_index, - const ir::OpSequence &op_seq) { - auto lower_info = lowered_graph->getLowerInfo(op_seq_index); - auto kernel_gen = lowered_graph->backend_contexts().at(lower_info->backend())->kernel_gen; - // Set TensorBuilderSet and ExecutorMap to kernel_gen of control flow - auto cf_kernel_gen = dynamic_cast<backend::controlflow::KernelGenerator *>(kernel_gen.get()); - if (cf_kernel_gen != nullptr) - { - cf_kernel_gen->setTensorRegistries(tensor_regs); - cf_kernel_gen->setExecutorMap(executor_map); - } - auto fn_seq = kernel_gen->generate(op_seq); - if (options.he_profiling_mode) - { - fn_seq->wrap<SyncFunction>(lower_info->backend()->config()); - } - builder.append(op_seq_index, {&op_seq, lower_info, std::move(fn_seq)}); - }); - - for (auto &tensor_builder : tensor_builders) - { - tensor_builder->allocate(); - } - - for (auto &pair : backend_contexts) - { - pair.second->initConsts(); - } - - lowered_graph->graph().operands().iterate( - [](const ir::OperandIndex &, ir::Operand &obj) { obj.releaseData(); }); - - auto code_map = builder.releaseCodeMap(); - - for (auto &it : code_map) - { - auto op_seq_index = it.first; - auto &fn_seq = it.second.fn_seq; - - fn_seq->iterate([&](exec::IFunction &ifunc) { - ifunc.prepare(); - auto backend = lowered_graph->getLowerInfo(op_seq_index)->backend(); - auto tensor_builder = lowered_graph->backend_contexts().at(backend)->tensor_builder; - tensor_builder->postFunctionPrepare(); - }); - } - - auto exec = - new exec::LinearExecutor{std::move(lowered_graph), input_tensors, output_tensors, tensor_regs, - std::move(code_map), order}; - - if (!options.trace_filepath.empty()) - { - std::unique_ptr<exec::IExecutionObserver> ctp = - std::make_unique<exec::ChromeTracingObserver>(options.trace_filepath, exec->graph()); - exec->addObserver(std::move(ctp)); - } - - return exec; -} - -exec::IExecutor *ExecutorFactory::createDataflowExecutor( - std::unique_ptr<compiler::LoweredGraph> lowered_graph, const compiler::CompilerOptions &options, - const std::shared_ptr<exec::ExecutorMap> &executor_map, bool parallel) -{ - const auto &backend_contexts = lowered_graph->backend_contexts(); - - initializeBackendContext(lowered_graph.get()); - - auto order = Linear::linearize(*lowered_graph); - runTensorRegistration(lowered_graph.get(), order); - - std::vector<backend::ITensor *> input_tensors; - std::vector<backend::ITensor *> output_tensors; - if (options.is_primary_subgraph) - { - input_tensors = initializeModelIOTensors(*lowered_graph, lowered_graph->graph().getInputs()); - output_tensors = initializeModelIOTensors(*lowered_graph, lowered_graph->graph().getOutputs()); - } - - TensorBuilders tensor_builders{lowered_graph->backend_contexts(), true}; - TensorRegistries tensor_regs{lowered_graph->backend_contexts(), true}; - - // To make tensors never be deallocated, this is a workaround to use static memory planner - for (auto &tensor_builder : tensor_builders) - { - lowered_graph->graph().operands().iterate( - [&](const ir::OperandIndex &ind, const ir::Operand &) { - if (tensor_builder->isRegistered(ind)) - { - tensor_builder->notifyFirstUse(ind); - } - }); - } - - for (auto &tensor_builder : tensor_builders) - { - tensor_builder->prepare(); - } - - prepareMigrantTensors(*lowered_graph); - - ExecutionBuilder builder; - - // Generate kernels - lowered_graph->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &op_seq_index, - const ir::OpSequence &op_seq) { - auto lower_info = lowered_graph->getLowerInfo(op_seq_index); - auto kernel_gen = lowered_graph->backend_contexts().at(lower_info->backend())->kernel_gen; - // Set TensorBuilderSet and ExecutorMap to kernel_gen of control flow - auto cf_kernel_gen = dynamic_cast<backend::controlflow::KernelGenerator *>(kernel_gen.get()); - if (cf_kernel_gen != nullptr) - { - assert(cf_kernel_gen != nullptr); - cf_kernel_gen->setTensorRegistries(tensor_regs); - cf_kernel_gen->setExecutorMap(executor_map); - } - auto fn_seq = kernel_gen->generate(op_seq); - if (options.he_profiling_mode) - { - fn_seq->wrap<SyncFunction>(lower_info->backend()->config()); - } - builder.append(op_seq_index, {&op_seq, lower_info, std::move(fn_seq)}); - }); - - for (const auto &tensor_builder : tensor_builders) - { - tensor_builder->allocate(); - } - - for (auto &pair : backend_contexts) - { - pair.second->initConsts(); - } - - lowered_graph->graph().operands().iterate( - [](const ir::OperandIndex &, ir::Operand &obj) { obj.releaseData(); }); - - auto code_map = builder.releaseCodeMap(); - - for (auto &it : code_map) - { - auto op_seq_index = it.first; - auto &fn_seq = it.second.fn_seq; - - fn_seq->iterate([&](exec::IFunction &ifunc) { - ifunc.prepare(); - auto backend = lowered_graph->getLowerInfo(op_seq_index)->backend(); - auto tensor_builder = lowered_graph->backend_contexts().at(backend)->tensor_builder; - tensor_builder->postFunctionPrepare(); - }); - } - - exec::ExecutorBase *exec = nullptr; - if (parallel) - { - exec = new exec::ParallelExecutor{std::move(lowered_graph), input_tensors, output_tensors, - tensor_regs, std::move(code_map)}; - } - else - { - auto dataflow_exec = new exec::DataflowExecutor{ - std::move(lowered_graph), input_tensors, output_tensors, tensor_regs, std::move(code_map)}; - if (options.he_profiling_mode) - { - std::vector<const backend::Backend *> backends; - for (const auto &pair : backend_contexts) - { - backends.push_back(pair.first); - } - auto et = std::make_shared<exec::ExecTime>(backends); - std::unique_ptr<exec::IExecutionObserver> obs = - std::make_unique<exec::ProfileObserver>(et, dataflow_exec->graph()); - dataflow_exec->addObserver(std::move(obs)); - } - exec = dataflow_exec; - } - - if (!options.trace_filepath.empty()) - { - std::unique_ptr<exec::IExecutionObserver> ctp = - std::make_unique<exec::ChromeTracingObserver>(options.trace_filepath, exec->graph()); - exec->addObserver(std::move(ctp)); - } - - return exec; -} - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/ExecutorFactory.h b/runtime/onert/core/src/compiler/ExecutorFactory.h deleted file mode 100644 index e76b721ea..000000000 --- a/runtime/onert/core/src/compiler/ExecutorFactory.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_EXECUTOR_FACTORY_H__ -#define __ONERT_COMPILER_EXECUTOR_FACTORY_H__ - -#include <unordered_map> - -#include "backend/ITensor.h" -#include "exec/IExecutor.h" -#include "compiler/LoweredGraph.h" -#include "TensorRegistries.h" - -namespace onert -{ -namespace compiler -{ - -class ExecutorFactory -{ -public: - static ExecutorFactory &get(); - -public: - exec::IExecutor *create(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const compiler::CompilerOptions &options, - const std::shared_ptr<exec::ExecutorMap> &executor_map); - -private: - ExecutorFactory(); - -private: - static void initializeBackendContext(compiler::LoweredGraph *lowered_graph); - static void runTensorRegistration(compiler::LoweredGraph *lowered_graph, - const std::vector<ir::OpSequenceIndex> &order); - static std::vector<backend::ITensor *> - initializeModelIOTensors(compiler::LoweredGraph &lowered_graph, - const ir::OperandIndexSequence &indices); - static void prepareMigrantTensors(compiler::LoweredGraph &lowered_graph); - static exec::IExecutor * - createLinearExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const compiler::CompilerOptions &options, - const std::shared_ptr<exec::ExecutorMap> &executor_map); - static exec::IExecutor * - createDataflowExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const compiler::CompilerOptions &options, - const std::shared_ptr<exec::ExecutorMap> &executor_map, bool parallel); - -private: - std::unordered_map<std::string, std::function<exec::IExecutor *( - std::unique_ptr<compiler::LoweredGraph>, - const compiler::CompilerOptions &options, - const std::shared_ptr<exec::ExecutorMap> &executor_map)>> - _map; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_EXECUTOR_FACTORY_H__ diff --git a/runtime/onert/core/src/compiler/Fp32ToFp16Converter.cc b/runtime/onert/core/src/compiler/Fp32ToFp16Converter.cc deleted file mode 100644 index 23a6a253d..000000000 --- a/runtime/onert/core/src/compiler/Fp32ToFp16Converter.cc +++ /dev/null @@ -1,954 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Fp32ToFp16Converter.h" -#include "ir/operation/ConvertFp32ToFp16.h" -#include "ir/operation/ConvertFp16ToFp32.h" -#include "util/logging.h" - -#include <Half.h> - -using float16 = Half; - -namespace -{ - -const std::string kAclClBackendConfigId = "acl_cl"; - -void copyDataFromFp32ToFp16(const float *from, float16 *into, size_t num_elements) -{ - for (size_t i = 0; i < num_elements; ++i) - { - into[i] = static_cast<float16>(from[i]); - } -} - -} // namespace - -namespace onert -{ - -namespace compiler -{ - -Fp32ToFp16Converter::Fp32ToFp16Converter(compiler::LoweredGraph &lowered_graph) - : _lowered_graph{lowered_graph} -{ - VERBOSE(Fp32ToFp16Converter) << "Fp16 Enable on" << std::endl; -} - -// For example, two OpSequences are there and each OpSequence has an Operation -// -// OP#0 // model input -// | -// [OPERATION] // OpSeq#0 -// | -// OP#1 -// | -// [OPERATION] // OpSeq#1 -// | -// OP#2 // model output -// -// -// AFTER `appendOpSequences()`, -// note that model_input and model_output are not changed. -// -// OP#0 -// | -// [FP32TO16] // OpSeq#2 -// | -// OP#3 -// | -// [OPERATION] // OpSeq#0 -// | -// OP#4 -// | -// [FP16TO32] // OpSeq#3 -// | -// OP#1 -// | -// [FP32TO16] // OpSeq#4 -// | -// OP#5 -// | -// [OPERATION] // OpSeq#1 -// | -// OP#6 -// | -// [FP16TO32] // OpSeq#5 -// | -// OP#2 -// -// -// AFTER `optimize()`, -// -// OP#0 -// | -// [FP32TO16] // OpSeq#2 -// | -// OP#3 -// | -// [OPERATION] // OpSeq#0 -// | -// OP#4 -// | -// [OPERATION] // OpSeq#1 -// | -// OP#6 -// | -// [FP16TO32] // OpSeq#5 -// | -// OP#2 -// -// -// AFTER `convertOperands()`, -// -// OP#0 // model_input, not fp16 -// | -// [FP32TO16] // OpSeq#2 -// | -// OP#3 // fp16 -// | -// [OPERATION] // OpSeq#0 -// | -// OP#4 // fp16 -// | -// [OPERATION] // OpSeq#1 -// | -// OP#6 // fp16 -// | -// [FP16TO32] // OpSeq#5 -// | -// OP#2 // model_output, notfp16 -// -// -// AFTER `convertDatas()`, -// -// OP#0 // model_input, not fp16 -// | -// [FP32TO16] // OpSeq#2 -// | -// OP#3 // fp16 -// | -// [OPERATION] // OpSeq#0, constants are fp16 -// | -// OP#4 // fp16 -// | -// [OPERATION] // OpSeq#1, constants are fp16 -// | -// OP#6 // fp16 -// | -// [FP16TO32] // OpSeq#5 -// | -// OP#2 // model_output, notfp16 -// -void Fp32ToFp16Converter::run() -{ - // Append new OpSequence which includes ConvertFp32ToFp16 - // and append new OpSequence which includes ConvertFp16ToFp32 - appendOpSequences(); - - // Remove unnecessary converting operations - optimize(); - - // Convert operands' data types from fp32 to fp16 - convertOperands(); - - // Convert Datas - convertDatas(); - - // Print the result - printOpSequences("FINAL OpSequences"); -} - -void Fp32ToFp16Converter::appendOpSequences() -{ - _lowered_graph.op_seqs().iterate( - [&](const ir::OpSequenceIndex &op_seq_ind, ir::OpSequence &op_seq) { - const auto lower_info = _lowered_graph.getLowerInfo(op_seq_ind); - assert(lower_info != nullptr); - - // For now, the only acl_cl supports fully fp16 type - // TODO Support fp16 on acl_neon. Current acl_neon supports the only reshape and concat - // operations. - // To do this, we could check the support by `operation by operation`. After that, we - // would partition an op_seq if it contains unsupported operations. - if (lower_info->backend()->config()->id() != kAclClBackendConfigId) - return; - - // OpSeq's input set should be included in the first operation's input set or - // OpSeq's output set should be included in the last operation's output set - assert(checkOperandsOfOpSequence(op_seq)); - - // Append converting OpSequence for fp16 but all operands' types are not fp16 still. - appendNewOpSeqForConvertFp32ToFp16(op_seq_ind, op_seq); - appendNewOpSeqForConvertFp16ToFp32(op_seq_ind, op_seq); - }); -} - -// -// BEFORE -// -// OP#0 // model input -// | -// [OPERATION] // OpSeq#0 -// | -// OP#1 // model output -// -// -// AFTER -// -// OP#0 // model input -// | -// [FP32TO16] // OpSeq#1 -// | -// OP#2 -// | -// [OPERATION] // OpSeq#0 -// | -// OP#1 // model output -// -void Fp32ToFp16Converter::appendNewOpSeqForConvertFp32ToFp16(const ir::OpSequenceIndex &op_seq_ind, - ir::OpSequence &op_seq) -{ - // OpSeq's input set is included in the first operation's input set - const ir::OperandIndexSequence op_seq_inputs = op_seq.getInputs(); // copied - - // NOTE Please do not change sequence of op_seq_inputs. It can change the sequence of inputs of - // Subgraph - for (const auto &op_seq_input_ind : - op_seq_inputs | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - if (checkOperandType(op_seq_input_ind) == false) - continue; - - // new operand w/ datatype fp32 - const auto new_op_ind = newCopiedOperand(op_seq_input_ind); - - // set new lower_info for operand - setNewOperandLowerInfo(op_seq_ind, new_op_ind); - - // manipulate input of operation and op_seq - // - replace the first operation's input to new operand - // with old operand's removeUse and new operand's appendUse() - manipulateInput(op_seq_ind, op_seq_input_ind, new_op_ind); - - // new op - const auto new_node_ind = newOperationConvertFp32ToFp16(op_seq_input_ind, new_op_ind); - - // new op_seq - const auto new_op_seq_ind = newOpSequence(op_seq_ind, new_node_ind); - - // set new lower_info for op_seq - setNewOpSequenceLowerInfo(op_seq_ind, new_op_seq_ind); - - _list_fp32_to_fp16.insert(new_op_seq_ind); - - VERBOSE(Fp32ToFp16Converter) << "NEW |Fp32To16]" - << ir::getStrFromOpSeq(_lowered_graph.op_seqs().at(new_op_seq_ind), - _lowered_graph.graph().operations()) - << std::endl; - } -} - -// -// BEFORE -// -// OP#0 // model input -// | -// [FP32TO16] // OpSeq#1 -// | -// OP#2 -// | -// [OPERATION] // OpSeq#0 -// | -// OP#1 // model output -// -// -// AFTER -// -// OP#0 // model input -// | -// [FP32TO16] // OpSeq#1 -// | -// OP#2 -// | -// [OPERATION] // OpSeq#0 -// | -// OP#3 -// | -// [FP16TO32] // OpSeq#2 -// | -// OP#1 // model output -// -void Fp32ToFp16Converter::appendNewOpSeqForConvertFp16ToFp32(const ir::OpSequenceIndex &op_seq_ind, - ir::OpSequence &op_seq) -{ - // OpSeq's output set is included in the last operation's output set - const ir::OperandIndexSequence op_seq_outputs = op_seq.getOutputs(); // copied - - // NOTE Please do not change sequence of op_seq_outputs. It can change the sequence of outputs of - // Subgraph - for (const auto &op_seq_output_ind : - op_seq_outputs | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - if (checkOperandType(op_seq_output_ind) == false) - continue; - - // new operand w/ datatype fp32 - const auto new_op_ind = newCopiedOperand(op_seq_output_ind); - - // set new lower_info for operand - setNewOperandLowerInfo(op_seq_ind, new_op_ind); - - // manipulate output of operation and op_seq - // - replace output of the last operation's output to new operand - // with old operand's unsetDef and new operand's appendDef() - manipulateOutput(op_seq_ind, op_seq_output_ind, new_op_ind); - - // new op - auto new_node_ind = newOperationConvertFp16ToFp32(op_seq_output_ind, new_op_ind); - - // new op_seq - auto new_op_seq_ind = newOpSequence(op_seq_ind, new_node_ind); - - // set new lower_info for op_seq - setNewOpSequenceLowerInfo(op_seq_ind, new_op_seq_ind); - - _list_fp16_to_fp32.insert(new_op_seq_ind); - - VERBOSE(Fp32ToFp16Converter) << "NEW |Fp16To32]" - << ir::getStrFromOpSeq(_lowered_graph.op_seqs().at(new_op_seq_ind), - _lowered_graph.graph().operations()) - << std::endl; - } -} - -void Fp32ToFp16Converter::optimize() -{ - printOpSequences("BEFORE opt"); - - removeContiguousConvertOpSequences(); - - printOpSequences("AFTER removeContiguousConverts"); - - // TODO Handle Split from the beginning of the model. ex) MODELS/inception_module - // - // BEFORE) - // - // OP#0---------------------. // model_input - // | | - // [FP32TO16] // OpSeq#0 [FP32TO16] // OpSeq#1 - // | | - // OP#1 OP#2 - // | | - // [OPERATION] // OpSeq#2 [OPERATION] // OpSeq#3 - // - // - // AFTER) - // - // OP#0 // model_input - // | - // [FP32TO16] // OpSeq#4 - // | - // OP#3---------------------------. - // | | - // [OPERATION] // OpSeq#2 [OPERATION] // OpSeq#3 -} - -void Fp32ToFp16Converter::convertOperands() -{ - _lowered_graph.op_seqs().iterate( - [&](const ir::OpSequenceIndex &op_seq_ind, ir::OpSequence &op_seq) { - const auto lower_info = _lowered_graph.getLowerInfo(op_seq_ind); - assert(lower_info != nullptr); - // For now, the only acl_cl supports fully fp16 - if (lower_info->backend()->config()->id() != kAclClBackendConfigId) - return; - - // Convert input,output operands' type to fp16 - convertOperandsOfOpSequence(op_seq); - }); -} - -void Fp32ToFp16Converter::convertOperandsOfOpSequence(ir::OpSequence &op_seq) -{ - auto &operands = _lowered_graph.graph().operands(); - const auto &operations = _lowered_graph.graph().operations(); - const auto &op_seq_inputs = _lowered_graph.graph().getInputs(); - const auto &op_seq_outputs = _lowered_graph.graph().getOutputs(); - - for (auto &op_idx : op_seq) - { - const auto &node = operations.at(op_idx); - for (auto &ind : node.getInputs() | ir::Remove::UNDEFINED) - { - if (node.opcode() == ir::OpCode::ConvertFp32ToFp16 || op_seq_inputs.contains(ind)) - continue; - - auto &obj = operands.at(ind); - if (obj.isConstant() || obj.typeInfo().type() != ir::DataType::FLOAT32) - continue; - - obj.type(ir::DataType::FLOAT16); - - VERBOSE(Fp32ToFp16Converter) << "Input Operand #" << ind.value() << ": fp16" << std::endl; - } - - for (auto &ind : node.getOutputs()) - { - if (node.opcode() == ir::OpCode::ConvertFp16ToFp32 || op_seq_outputs.contains(ind)) - continue; - - auto &obj = operands.at(ind); - if (obj.isConstant() || obj.typeInfo().type() != ir::DataType::FLOAT32) - continue; - - obj.type(ir::DataType::FLOAT16); - - VERBOSE(Fp32ToFp16Converter) << "Output Operand #" << ind.value() << ": fp16" << std::endl; - } - } -} - -void Fp32ToFp16Converter::convertDatas() -{ - _lowered_graph.graph().operands().iterate([&](const ir::OperandIndex &ind, ir::Operand &obj) { - const auto type = obj.typeInfo().type(); - if (type == ir::DataType::FLOAT32 && obj.isConstant()) - { - auto data = obj.data(); - assert(data != nullptr); - - size_t num_elements = obj.operandSize() / ir::sizeOfDataType(type); - size_t new_ptr_size = num_elements * sizeof(float16); - auto new_ptr = std::make_unique<uint8_t[]>(new_ptr_size); - copyDataFromFp32ToFp16(reinterpret_cast<const float *>(data->base()), - reinterpret_cast<float16 *>(new_ptr.get()), num_elements); - obj.releaseData(); - - auto new_data = std::make_unique<ir::CachedData>(new_ptr.get(), new_ptr_size); - - obj.data(std::move(new_data)); - obj.type(ir::DataType::FLOAT16); - VERBOSE(Fp32ToFp16Converter) << "Constant Operand #" << ind.value() << ": fp16" << std::endl; - } - }); -} - -void Fp32ToFp16Converter::printOpSequences(const std::string &pre_msg, const std::string &post_msg) -{ - if (pre_msg.empty() == false) - { - VERBOSE(Fp32ToFp16Converter) << pre_msg << std::endl; - } - - _lowered_graph.op_seqs().iterate([&](const ir::OpSequenceIndex &, const ir::OpSequence &op_seq) { - VERBOSE(Fp32ToFp16Converter) << ir::getStrFromOpSeq(op_seq, _lowered_graph.graph().operations()) - << std::endl; - }); - - if (post_msg.empty() == false) - { - VERBOSE(Fp32ToFp16Converter) << post_msg << std::endl; - } -} - -bool Fp32ToFp16Converter::checkOperandType(const ir::OperandIndex &op_ind) const -{ - const auto &operands = _lowered_graph.graph().operands(); - const auto &obj = operands.at(op_ind); - return (obj.isConstant() == false && obj.typeInfo().type() == ir::DataType::FLOAT32); -} - -bool Fp32ToFp16Converter::checkOperandsOfOpSequence(const ir::OpSequence &op_seq) const -{ - const auto &operations = _lowered_graph.graph().operations(); - - // the first node's input - const auto &first_node_ind = op_seq.operations().at(0); - const auto &first_node = operations.at(first_node_ind); - const auto &first_node_inputs = first_node.getInputs(); - for (const auto &op_seq_input_ind : op_seq.getInputs() | ir::Remove::UNDEFINED) - { - if (first_node_inputs.contains(op_seq_input_ind) == false) - return false; - } - - // the last node's output - size_t last_ind = op_seq.size() - 1; - const auto &last_node_ind = op_seq.operations().at(last_ind); - const auto &last_node = operations.at(last_node_ind); - const auto &last_node_outputs = last_node.getOutputs(); - for (const auto &op_seq_output_ind : op_seq.getOutputs()) - { - if (last_node_outputs.contains(op_seq_output_ind) == false) - return false; - } - - return true; -} - -ir::OperandIndex Fp32ToFp16Converter::newCopiedOperand(const ir::OperandIndex &op_ind) -{ - auto &operands = _lowered_graph.graph().operands(); - const auto &obj = operands.at(op_ind); - auto new_op_ind = operands.emplace(obj.shape(), obj.typeInfo()); - return new_op_ind; -} - -void Fp32ToFp16Converter::setNewOperandLowerInfo(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperandIndex &new_op_ind) -{ - const auto lower_info = _lowered_graph.getLowerInfo(op_seq_ind); - assert(lower_info != nullptr); - auto new_lower_info = std::make_unique<ir::operand::LowerInfo>(); - auto permute_factor = ir::operand::PermuteFactor(lower_info->backend(), lower_info->layout()); - new_lower_info->addDefPermuteFactor(permute_factor); - new_lower_info->addUsePermuteFactor(permute_factor); - _lowered_graph.setLowerInfo(new_op_ind, std::move(new_lower_info)); -} - -void Fp32ToFp16Converter::setNewOpSequenceLowerInfo(const ir::OpSequenceIndex &op_seq_ind, - const ir::OpSequenceIndex &new_op_seq_ind) -{ - const auto lower_info = _lowered_graph.getLowerInfo(op_seq_ind); - assert(lower_info != nullptr); - - auto new_lower_info = - std::make_unique<ir::operation::LowerInfo>(lower_info->backend(), lower_info->layout()); - _lowered_graph.setLowerInfo(new_op_seq_ind, std::move(new_lower_info)); -} - -void Fp32ToFp16Converter::manipulateInput(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperandIndex &op_seq_input_ind, - const ir::OperandIndex &new_op_ind) -{ - auto &operands = _lowered_graph.graph().operands(); - auto &operations = _lowered_graph.graph().operations(); - - auto &op_seq = _lowered_graph.op_seqs().at(op_seq_ind); - - auto &first_node_ind = op_seq.operations().at(0); - auto &first_node = operations.at(first_node_ind); - assert(first_node.getInputs().contains(op_seq_input_ind)); - - auto &input_obj = operands.at(op_seq_input_ind); - assert(input_obj.isConstant() == false); - - auto &new_op_obj = operands.at(new_op_ind); - - // The same inputs having the index as op_seq_input_ind are replaced all at once - op_seq.replaceInputs(op_seq_input_ind, new_op_ind); - first_node.replaceInputs(op_seq_input_ind, new_op_ind); - - // op_seq_obj doesn't have uses/def - input_obj.removeUse(first_node_ind); - new_op_obj.insertUse(first_node_ind); -} - -void Fp32ToFp16Converter::manipulateOutput(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperandIndex &op_seq_output_ind, - const ir::OperandIndex &new_op_ind) -{ - auto &operands = _lowered_graph.graph().operands(); - auto &operations = _lowered_graph.graph().operations(); - - auto &op_seq = _lowered_graph.op_seqs().at(op_seq_ind); - - size_t last_ind = op_seq.size() - 1; - auto &last_node_ind = op_seq.operations().at(last_ind); - auto &last_node = operations.at(last_node_ind); - assert(last_node.getOutputs().contains(op_seq_output_ind)); - - auto &output_obj = operands.at(op_seq_output_ind); - assert(output_obj.isConstant() == false); - - auto &new_op_obj = operands.at(new_op_ind); - - // The same outputs having the index as op_seq_output_ind are replaced all at once - op_seq.replaceOutputs(op_seq_output_ind, new_op_ind); - last_node.replaceOutputs(op_seq_output_ind, new_op_ind); - - // op_seq_obj doesn't have uses/def - assert(output_obj.getDef() == last_node_ind); - output_obj.unsetDef(); - new_op_obj.setDef(last_node_ind); -} - -ir::OperationIndex -Fp32ToFp16Converter::newOperationConvertFp32ToFp16(const ir::OperandIndex &op_seq_input_ind, - const ir::OperandIndex &new_op_ind) -{ - auto &operands = _lowered_graph.graph().operands(); - auto &operations = _lowered_graph.graph().operations(); - - auto &input_obj = operands.at(op_seq_input_ind); - auto &new_op_obj = operands.at(new_op_ind); - - std::unique_ptr<ir::Operation> new_node( - new ir::operation::ConvertFp32ToFp16({op_seq_input_ind}, {new_op_ind})); - const auto new_node_ind = operations.push(std::move(new_node)); - - input_obj.insertUse(new_node_ind); - new_op_obj.setDef(new_node_ind); - - return new_node_ind; -} - -ir::OperationIndex -Fp32ToFp16Converter::newOperationConvertFp16ToFp32(const ir::OperandIndex &op_seq_output_ind, - const ir::OperandIndex &new_op_ind) -{ - auto &operands = _lowered_graph.graph().operands(); - auto &operations = _lowered_graph.graph().operations(); - - auto &output_obj = operands.at(op_seq_output_ind); - auto &new_op_obj = operands.at(new_op_ind); - - std::unique_ptr<ir::Operation> new_node( - new ir::operation::ConvertFp16ToFp32({new_op_ind}, {op_seq_output_ind})); - const auto new_node_ind = operations.push(std::move(new_node)); - - new_op_obj.insertUse(new_node_ind); - output_obj.setDef(new_node_ind); - - return new_node_ind; -} - -ir::OpSequenceIndex Fp32ToFp16Converter::newOpSequence(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperationIndex &node_index) -{ - auto &node = _lowered_graph.graph().operations().at(node_index); - const auto lower_info = _lowered_graph.getLowerInfo(op_seq_ind); - assert(lower_info != nullptr); - auto layout = lower_info->layout(); - - auto op_seq = std::make_unique<ir::OpSequence>(layout); - op_seq->appendOperation(node_index); - op_seq->setOutputs(node.getOutputs()); - op_seq->setInputs(node.getInputs()); - - return _lowered_graph.op_seqs().emplace(std::move(op_seq)); -} - -// The op_seq(Fp16To32)'s output operand is the next to op_seq (Fp32To16)? -// If so, connect Fp16To32's previous OpSeq to Fp32To16's next OpSeq -// -// Assume that an OpSequence has an operation for easy explaination -// -// BEFORE) -// -// [OPERATION] // OpSeq#0 -// | -// OP#0 -// | -// [FP16TO32] // OpSeq#1 -// | -// OP#1 -// | -// [FP32TO16] // OpSeq#2 -// | -// OP#2 -// | -// [OPERATION] // OpSeq#3 -// -// -// AFTER) -// -// [OPERATION] // OpSeq#0 -// | -// OP#0 -// | -// [OPERATION] // OpSeq#3 -// -void Fp32ToFp16Converter::removeContiguousConvertOpSequences() -{ - // Prepare InputToOpSeqs map - const auto input_to_op_seqs = prepareInputToOpSeqs(); - - // Find OpSequences to delete while manipulating input of OpSeq. - auto opseq_map_to_delete = findOpSequencesContiguous(input_to_op_seqs); - - // Find Operations to delete - auto list_to_delete_op_seqs = getListOpSequences(opseq_map_to_delete); - auto list_to_delete_ops = findOperationsToDelete(list_to_delete_op_seqs); - - // Before deleting, manipulateInputs of OpSeq & Operation - manipulateContiguousOpSequences(input_to_op_seqs, opseq_map_to_delete); - - // Delete OpSequences & Operations & obj's use/def & operands - deleteContiguousOpSequences(list_to_delete_op_seqs, list_to_delete_ops); -} - -Fp32ToFp16Converter::OpSeqIndexToOpSeqIndexList -Fp32ToFp16Converter::findOpSequencesContiguous(const InputToOpSeqs &input_to_op_seqs) const -{ - const auto &op_seqs = _lowered_graph.op_seqs(); - OpSeqIndexToOpSeqIndexList opseq_map_to_delete; - - // - // Assume that an Operation an OpSequence for easy explaination - // - // [OPERATION] - // | - // OP#0 - // | - // [FP16TO32] // op_seq_ind_fp16_to_fp32 & op_seq_fp16_to_fp32 - // | - // OP#1 // output_ind_fp16_fp32 - // | - // [FP32TO16] // op_seq_ind - // | - // OP#2 - // | - // [OPERATION] - // - for (auto it = _list_fp16_to_fp32.cbegin(); it != _list_fp16_to_fp32.cend(); ++it) - { - // fp16_to_fp32's input/output num is always 1 - auto &op_seq_ind_fp16_to_fp32 = *it; - auto &op_seq_fp16_to_fp32 = op_seqs.at(op_seq_ind_fp16_to_fp32); - assert(op_seq_fp16_to_fp32.size() == 1); - assert(op_seq_fp16_to_fp32.getInputs().size() == 1); - - auto &output_ind_fp16_to_fp32 = op_seq_fp16_to_fp32.getOutputs().at(0); - auto found_input_in_op_seqs = input_to_op_seqs.find(output_ind_fp16_to_fp32); - if (found_input_in_op_seqs == input_to_op_seqs.end()) - { - continue; - } - - // DO NOT FORGET THE CASE - // - // | - // [FP16TO32] - // | - // OP#0---------------------. - // | | - // [FP32TO16] [FP32TO16] - // | | - // OP#1 OP#2 - // | | - // [OPERATION] [OPERATION] - // - for (auto &op_seq_ind : found_input_in_op_seqs->second) - { - auto found_in_fp32_to_fp16 = _list_fp32_to_fp16.find(op_seq_ind); - if (found_in_fp32_to_fp16 != _list_fp32_to_fp16.end()) - { - if (opseq_map_to_delete.find(op_seq_ind_fp16_to_fp32) == opseq_map_to_delete.end()) - { - opseq_map_to_delete[op_seq_ind_fp16_to_fp32].emplace(op_seq_ind); - } - else - { - opseq_map_to_delete[op_seq_ind_fp16_to_fp32].insert(op_seq_ind); - } - - VERBOSE(Fp32ToFp16Converter) - << "Contiguous from OpSeq#" << op_seq_ind_fp16_to_fp32.value() << "(ToFp32)" - << " to OpSeq#" << op_seq_ind.value() << "(ToFp16)" << std::endl; - } - } - } - - return opseq_map_to_delete; -} - -Fp32ToFp16Converter::InputToOpSeqs Fp32ToFp16Converter::prepareInputToOpSeqs() const -{ - const auto &op_seqs = _lowered_graph.op_seqs(); - - InputToOpSeqs input_to_op_seqs; - op_seqs.iterate([&](const ir::OpSequenceIndex &op_seq_idx, const ir::OpSequence &op_seq) { - for (auto input : op_seq.getInputs() | ir::Remove::UNDEFINED) - { - auto it = input_to_op_seqs.find(input); - if (it == input_to_op_seqs.end()) - { - input_to_op_seqs[input].emplace(op_seq_idx); - } - else - { - input_to_op_seqs[input].insert(op_seq_idx); - } - } - }); - - return input_to_op_seqs; -} - -Fp32ToFp16Converter::OpSeqIndexList -Fp32ToFp16Converter::getListOpSequences(const OpSeqIndexToOpSeqIndexList &opseq_map_to_delete) const -{ - OpSeqIndexList list; - for (const auto &it : opseq_map_to_delete) - { - auto &opseq_ind_fp16_to_fp32 = it.first; - if (list.find(opseq_ind_fp16_to_fp32) == list.end()) - { - list.emplace(opseq_ind_fp16_to_fp32); - } - - for (auto &opseq_ind_fp32_to_fp16 : it.second) - { - if (list.find(opseq_ind_fp32_to_fp16) == list.end()) - { - list.emplace(opseq_ind_fp32_to_fp16); - } - } - } - return list; -} - -ir::OperandIndexSequence -Fp32ToFp16Converter::findOperationsToDelete(const OpSeqIndexList &list_to_delete_op_seqs) const -{ - const auto &operations = _lowered_graph.graph().operations(); - const auto &op_seqs = _lowered_graph.op_seqs(); - - ir::OperandIndexSequence list_to_delete_ops; - for (const auto &op_seq_ind : list_to_delete_op_seqs) - { - const auto &op_seq = op_seqs.at(op_seq_ind); - assert(op_seq.size() == 1); - - const auto &first_node_ind = op_seq.operations().at(0); - const auto &first_node = operations.at(first_node_ind); - assert(first_node.opcode() == ir::OpCode::ConvertFp32ToFp16 || - first_node.opcode() == ir::OpCode::ConvertFp16ToFp32); - - for (const auto &ind : first_node.getOutputs()) - { - list_to_delete_ops.append(ind); - } - } - - return list_to_delete_ops; -} - -void Fp32ToFp16Converter::manipulateContiguousOpSequences( - const InputToOpSeqs &input_to_op_seqs, const OpSeqIndexToOpSeqIndexList &opseq_map_to_delete) -{ - auto &op_seqs = _lowered_graph.op_seqs(); - - // - // [OPERATION] - // | - // OP#0 // input_ind_fp16_to_fp32 - // | - // [FP16TO32] // op_seq_ind_fp16_to_fp32 & op_seq_fp16_to_fp32 - // | - // OP#1 - // | - // [FP32TO16] // op_seq_ind_fp32_to_fp16, op_seq_fp32_to_fp16 - // | - // OP#2 // output_ind_fp32_to_fp16 - // | - // [OPERATION] // op_seq_ind_next_to_fp16 - // - for (auto it : opseq_map_to_delete) - { - // fp16_to_fp32's input/output num is always 1 - auto &op_seq_ind_fp16_to_fp32 = it.first; - auto &op_seq_fp16_to_fp32 = op_seqs.at(op_seq_ind_fp16_to_fp32); - auto &input_ind_fp16_to_fp32 = op_seq_fp16_to_fp32.getInputs().at(0); - - for (auto &op_seq_ind_fp32_to_fp16 : it.second) - { - auto &op_seq_fp32_to_fp16 = op_seqs.at(op_seq_ind_fp32_to_fp16); - assert(op_seq_fp32_to_fp16.size() == 1); - assert(op_seq_fp32_to_fp16.getInputs().size() == 1); - - auto &output_ind_fp32_to_fp16 = op_seq_fp32_to_fp16.getOutputs().at(0); - auto found_next_to_fp16 = input_to_op_seqs.find(output_ind_fp32_to_fp16); - assert(found_next_to_fp16 != input_to_op_seqs.end()); - - for (auto &op_seq_ind_next_to_fp16 : found_next_to_fp16->second) - { - manipulateInput(op_seq_ind_next_to_fp16, output_ind_fp32_to_fp16, input_ind_fp16_to_fp32); - } - // - // [OPERATION] - // | - // OP#0 // input_ind_fp16_to_fp32 - // | - // [OPERATION] // op_seq_ind_next_to_fp16 - // - } - } -} - -void Fp32ToFp16Converter::deleteContiguousOpSequences( - const OpSeqIndexList &list_to_delete_op_seqs, - const ir::OperandIndexSequence &list_to_delete_ops) -{ - auto &operands = _lowered_graph.graph().operands(); - auto &operations = _lowered_graph.graph().operations(); - auto &op_seqs = _lowered_graph.op_seqs(); - - for (auto &op_seq_ind : list_to_delete_op_seqs) - { - auto &op_seq = op_seqs.at(op_seq_ind); - assert(op_seq.size() == 1); - VERBOSE(Fp32ToFp16Converter) << "Delete OpSeq #" << op_seq_ind.value() << std::endl; - - auto &first_node_ind = op_seq.operations().at(0); - auto &first_node = operations.at(first_node_ind); - assert(first_node.opcode() == ir::OpCode::ConvertFp32ToFp16 || - first_node.opcode() == ir::OpCode::ConvertFp16ToFp32); - VERBOSE(Fp32ToFp16Converter) << "Delete Node #" << first_node_ind.value() << std::endl; - - // Uses - for (auto &ind : first_node.getInputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - auto &obj = operands.at(ind); - obj.removeUse(first_node_ind); - VERBOSE(Fp32ToFp16Converter) << "Operand #" << ind.value() << "'s Use(Node#" - << first_node_ind.value() << ") is removed" << std::endl; - } - - // Def - for (auto &ind : first_node.getOutputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - auto &obj = operands.at(ind); - assert(obj.getDef() == first_node_ind); - obj.unsetDef(); - VERBOSE(Fp32ToFp16Converter) << "Operand #" << ind.value() << "'s Def(Node#" - << first_node_ind.value() << ") is removed" << std::endl; - } - - // Operation - operations.remove(first_node_ind); - VERBOSE(Fp32ToFp16Converter) << "Node#" << first_node_ind.value() << " is removed" << std::endl; - - // OpSequence - op_seqs.remove(op_seq_ind); - VERBOSE(Fp32ToFp16Converter) << "OpSeq#" << op_seq_ind.value() << " is removed" << std::endl; - } - - // Operand - for (auto &ind : list_to_delete_ops) - { - operands.remove(ind); - VERBOSE(Fp32ToFp16Converter) << "Operand #" << ind.value() << " is removed" << std::endl; - } -} - -} // namespace compiler - -} // namespace onert diff --git a/runtime/onert/core/src/compiler/Fp32ToFp16Converter.h b/runtime/onert/core/src/compiler/Fp32ToFp16Converter.h deleted file mode 100644 index eeecb9846..000000000 --- a/runtime/onert/core/src/compiler/Fp32ToFp16Converter.h +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_FP32_TO_FP16_CONVERTER_H__ -#define __ONERT_COMPILER_FP32_TO_FP16_CONVERTER_H__ - -#include "compiler/LoweredGraph.h" - -namespace onert -{ - -namespace compiler -{ - -class Fp32ToFp16Converter -{ -public: - Fp32ToFp16Converter(compiler::LoweredGraph &lowered_graph); - -public: - void run(); - -private: - using OpSeqIndexList = std::unordered_set<ir::OpSequenceIndex>; - using InputToOpSeqs = std::unordered_map<ir::OperandIndex, OpSeqIndexList>; - using OpSeqIndexToOpSeqIndexList = std::unordered_map<ir::OpSequenceIndex, OpSeqIndexList>; - -private: - void appendOpSequences(); - void optimize(); - void convertOperands(); - void convertDatas(); - void printOpSequences(const std::string &pre_msg = std::string(), - const std::string &post_msg = std::string()); - - bool checkOperandType(const ir::OperandIndex &op_ind) const; - bool checkOperandsOfOpSequence(const ir::OpSequence &op_seq) const; - - void appendNewOpSeqForConvertFp32ToFp16(const ir::OpSequenceIndex &op_seq_ind, - ir::OpSequence &op_seq); - void appendNewOpSeqForConvertFp16ToFp32(const ir::OpSequenceIndex &op_seq_ind, - ir::OpSequence &op_seq); - - ir::OperandIndex newCopiedOperand(const ir::OperandIndex &op_ind); - ir::OperationIndex newOperationConvertFp32ToFp16(const ir::OperandIndex &op_seq_input_ind, - const ir::OperandIndex &new_op_ind); - ir::OperationIndex newOperationConvertFp16ToFp32(const ir::OperandIndex &op_seq_output_ind, - const ir::OperandIndex &new_op_ind); - ir::OpSequenceIndex newOpSequence(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperationIndex &node_index); - - void setNewOperandLowerInfo(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperandIndex &new_op_ind); - void setNewOpSequenceLowerInfo(const ir::OpSequenceIndex &op_seq_ind, - const ir::OpSequenceIndex &new_op_seq_ind); - - void manipulateInput(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperandIndex &op_seq_input_ind, - const ir::OperandIndex &new_op_ind); - void manipulateOutput(const ir::OpSequenceIndex &op_seq_ind, - const ir::OperandIndex &op_seq_output_ind, - const ir::OperandIndex &new_op_ind); - - void removeContiguousConvertOpSequences(); - InputToOpSeqs prepareInputToOpSeqs() const; - OpSeqIndexToOpSeqIndexList - findOpSequencesContiguous(const InputToOpSeqs &intput_to_op_seqs) const; - OpSeqIndexList getListOpSequences(const OpSeqIndexToOpSeqIndexList &opseq_map_to_delete) const; - ir::OperandIndexSequence - findOperationsToDelete(const OpSeqIndexList &list_to_delete_op_seqs) const; - void manipulateContiguousOpSequences(const InputToOpSeqs &input_to_op_seqs, - const OpSeqIndexToOpSeqIndexList &opseq_map_to_delete); - void deleteContiguousOpSequences(const OpSeqIndexList &list_to_delete_op_seqs, - const ir::OperandIndexSequence &list_to_delete_ops); - - void convertOperandsOfOpSequence(ir::OpSequence &op_seq); - -private: - compiler::LoweredGraph &_lowered_graph; - OpSeqIndexList _list_fp32_to_fp16; - OpSeqIndexList _list_fp16_to_fp32; -}; - -} // namespace compiler - -} // namespace onert - -#endif // __ONERT_COMPILER_FP32_TO_FP16_CONVERTER_H__ diff --git a/runtime/onert/core/src/compiler/HEScheduler.cc b/runtime/onert/core/src/compiler/HEScheduler.cc deleted file mode 100644 index fe54b0fdd..000000000 --- a/runtime/onert/core/src/compiler/HEScheduler.cc +++ /dev/null @@ -1,593 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Operand.h" -#include "compiler/HEScheduler.h" -#include "ir/Graph.h" -#include "util/ConfigSource.h" -#include "compiler/BackendResolver.h" -#include "util/logging.h" -#include "util/Utils.h" -#include "exec/FunctionSequence.h" -#include <cassert> -#include <cmath> -#include <chrono> - -namespace onert -{ - -namespace compiler -{ -static uint32_t getOperationsFlattenedIOSize(const ir::Graph &graph, const ir::Operation &node) -{ - uint32_t size = 0; - for (const auto &ind : - (node.getInputs() | ir::Remove::UNDEFINED) + (node.getOutputs() | ir::Remove::UNDEFINED)) - { - size += graph.operands().at(ind).info().total_size(); - } - return size; -} - -static bool isQuant(const ir::Graph &graph, const ir::Operation &node) -{ - for (const auto &input : node.getInputs() | ir::Remove::UNDEFINED) - { - const auto &obj = graph.operands().at(input); - if (obj.typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM) - { - return true; - } - } - return false; -} - -static bool isWorkaroundSkip(const ir::Graph &, const backend::Backend *, const ir::Operation &, - bool) -{ - // Now, there is no workaround - return false; -} - -// if a node can be merged into op_seq -static bool isMergeable(const ir::Graph &graph, const ir::Operation &node) -{ - size_t prev_op_cnt = 0; - for (const auto &input : node.getInputs()) - { - // only valid_inputs - const auto &operand = graph.operands().at(input); - if (operand.isConstant()) - continue; - - // This operand is output of operation, not weight or bias - if (operand.getDef().valid()) - ++prev_op_cnt; - - // Current node has multiple inputs as concat or at the beginning of the separated branch - if (prev_op_cnt > 1 || operand.getUses().size() > 1) - { - return false; - } - } - return true; -} - -void HEScheduler::scheduleShufflingBackends() -{ - VERBOSE(HEScheduler::schedule) - << "Started task scheduling: uses all backends to get more metrics for data transfer" - << std::endl; - size_t backend_ind = 0; - for (const auto &rank : _rank_to_op) - { - VERBOSE(HEScheduler::schedule) << "scheduling (" << rank.second.value() << ")" << std::endl; - const auto &node = _graph->operations().at(rank.second); - const bool quant = isQuant(*_graph, node); - const auto size = getOperationsFlattenedIOSize(*_graph, node); - for (size_t i = 0;; ++i) - { - if (i == _all_backends.size()) - { - // wasn't able to find backend - assert(false); - break; - } - if (backend_ind == _all_backends.size()) - { - backend_ind = 0; - } - if (isWorkaroundSkip(*_graph, _all_backends[backend_ind], node, quant)) - { - ++backend_ind; - continue; - } - const auto exec_time = - _exec_time->getOperationExecTime(_all_backends[backend_ind], node.name(), quant, size); - // Scheduling to measure data transfer must be done after measuring all backends separately - assert(exec_time != _exec_time->NOT_FOUND); - if (exec_time == _exec_time->getMax()) - { - ++backend_ind; - continue; - } - _backend_resolver->setBackend(rank.second, _all_backends[backend_ind]); - VERBOSE(HEScheduler::schedule) << "backend for " << node.name() << " is " - << _all_backends[backend_ind]->config()->id() << std::endl; - ++backend_ind; - break; - } - } -} - -bool HEScheduler::isNodeProfiled(const ir::Operation &node) -{ - const bool quant = isQuant(*_graph, node); - const auto size = getOperationsFlattenedIOSize(*_graph, node); - for (const auto *backend : _all_backends) - { - const auto exec_time = _exec_time->getOperationExecTime(backend, node.name(), quant, size); - if (exec_time == _exec_time->NOT_FOUND) - return false; - } - return true; -} - -void HEScheduler::scheduleBranch(const ir::OperationIndex &index, - ir::OperationIndexMap<bool> &scheduled) -{ - auto loc_index = index; - const backend::Backend *parent_backend = nullptr; - while (true) - { - if (scheduled[loc_index]) - { - return; - } - if (!schedule(loc_index, parent_backend)) - { - return; - } - scheduled[loc_index] = true; - parent_backend = _backend_resolver->getBackend(loc_index); - - const auto &node = _graph->operations().at(loc_index); - /* get the only output operand, that is input of the next single operation - * and just this nodes output.*/ - if (node.getOutputs().size() != 1) - { - return; - } - const auto &only_out_operand = _graph->operands().at(*node.getOutputs().begin()); - // One of the last nodes - if (only_out_operand.getUses().size() == 0) - { - return; - } - loc_index = *only_out_operand.getUses().begin(); - /* verify, that next node is neither beginning nor ending node of a branch*/ - const auto &next_node = _graph->operations().at(loc_index); - if (!isMergeable(*_graph, next_node)) - { - return; - } - } -} - -std::unique_ptr<compiler::BackendResolver> HEScheduler::schedule(const ir::Graph &graph) -{ - _graph = &graph; - VERBOSE(HEScheduler::schedule) << "task scheduling started" << std::endl; - // Make ranks and save in descending order - makeRank(); - - for (const auto *backend : _all_backends) - { - _backends_avail_time.emplace(backend, std::map<int64_t, int64_t>{{0, 0}}); - } - - if (_is_profiling_mode) - { - // Check if profiling info about all backend/node pairs already exists - bool all_nodes_are_profiled = true; - _graph->operations().iterate([&](const ir::OperationIndex &, const ir::Operation &op) { - if (all_nodes_are_profiled) - all_nodes_are_profiled = isNodeProfiled(op); - }); - - // If all nodes are already profiled - schedule backends in such order, so more profiling - // information about between-backends data transfer could be collected - if (all_nodes_are_profiled) - { - scheduleShufflingBackends(); - VERBOSE(HEScheduler::schedule) << "task scheduling finished" << std::endl; - return std::move(_backend_resolver); - } - } - - ir::OperationIndexMap<bool> visited; - graph.operations().iterate( - [&](const ir::OperationIndex &index, const ir::Operation &) { visited[index] = false; }); - // for each task select the backend with the smallest earliest finishing time(eft) - for (const auto &rank : _rank_to_op) - { - scheduleBranch(rank.second, visited); - } - VERBOSE(HEScheduler::schedule) << "task scheduling finished" << std::endl; - return std::move(_backend_resolver); -} - -int64_t HEScheduler::getOpTime(const backend::Backend *backend, const std::string &operation, - bool quant, uint32_t size) -{ - const auto time = _exec_time->getOperationExecTime(backend, operation, quant, size); - if (time != _exec_time->NOT_FOUND) - return time; - - return _is_supported.at(backend).at(operation) ? 1 : _exec_time->getMax(); -} - -int64_t HEScheduler::getPermuteTime(const backend::Backend *src_backend, - const backend::Backend *dst_backend, bool quant, uint32_t size) -{ - // TODO Change it to getOperationExecTime() - const auto time = _exec_time->getPermuteTime(src_backend, dst_backend, quant, size); - - if (time != _exec_time->NOT_FOUND) - return time; - - // FIXME permute time is not recorded so the control reaches here always - // Makes the scheduler prefer keeping computations on one backend - return size / 400; -} - -int64_t HEScheduler::tryBackend(const ir::Operation &node, const backend::Backend *backend) -{ - // if there is no profiling info don't use this backend during scheduling - if (!_is_profiling_mode) - { - VERBOSE(HEScheduler::tryBackend) - << "Trying to HE schedule while there is no profiling info for " << node.name() - << " on backend " << backend->config()->id() << ". So this backend won't be used. " - << std::endl; - _is_supported[backend][node.name()] = false; - return _exec_time->getMax(); - } - auto iter = _is_supported.find(backend); - if (iter != _is_supported.end()) - { - auto it2 = iter->second.find(node.name()); - if (it2 != iter->second.end()) - { - return _is_supported[backend][node.name()] ? 1 : _exec_time->getMax(); - } - } - try - { - // DO NOTHING - - _is_supported[backend][node.name()] = true; - } - catch (std::runtime_error &e) - { - _is_supported[backend][node.name()] = false; - } - return _is_supported[backend][node.name()] ? 1 : _exec_time->getMax(); -} - -void HEScheduler::makeRank() -{ - VERBOSE(HEScheduler::makeRank) << "task prioritizing" << std::endl; - - _graph->operations().iterate( - [&](const ir::OperationIndex &index, const ir::Operation &) { DFSMaxRank(index); }); - - // Check that ranks are calculated for all operations(nodes) - _graph->operations().iterate([&](const ir::OperationIndex &index, const ir::Operation &) { - UNUSED_RELEASE(index); - assert(_op_to_rank->find(index) != _op_to_rank->end()); - }); - VERBOSE(HEScheduler::makeRank) << "task prioritizing finished" << std::endl; -} - -int64_t HEScheduler::DFSMaxRank(const ir::OperationIndex &index) -{ - auto op_to_rank_it = _op_to_rank->find(index); - if (op_to_rank_it != _op_to_rank->end()) - return op_to_rank_it->second; - - const auto &node = _graph->operations().at(index); - int64_t rank = 0; - const bool quant = isQuant(*_graph, node); - const auto size = getOperationsFlattenedIOSize(*_graph, node); - auto supported_backends_quantity = static_cast<int64_t>(_all_backends.size()); - - const auto max_child_rank = DFSChildrenMaxRank(index); - - // get average exec time of this op - for (const auto &backend : _all_backends) - { - auto exec_time = _exec_time->getOperationExecTime(backend, node.name(), quant, size); - if (exec_time == _exec_time->NOT_FOUND) - { - exec_time = tryBackend(node, backend); - } - if (exec_time < _exec_time->getMax()) - { - rank += exec_time; - } - else - { - // this operation isn't supported in this backend - --supported_backends_quantity; - } - } - if (supported_backends_quantity == 0) - { - throw std::runtime_error{"Encountered unsupported op: " + node.name()}; - } - rank /= supported_backends_quantity; - - // get standard deviation - int64_t std = 0; - for (const auto backend : _all_backends) - { - const auto exec_time = getOpTime(backend, node.name(), quant, size); - if (exec_time < _exec_time->getMax()) - { - std += (exec_time - rank) * (exec_time - rank); - } - } - std /= supported_backends_quantity; - if (std > 0) - { - std = static_cast<int>(std::sqrt(std)); - rank *= std; - } - rank += max_child_rank; - - assert(rank >= 0); - _rank_to_op.emplace(rank, index); - _op_to_rank->emplace(index, rank); - VERBOSE(HEScheduler::DFSMaxRank) << "rank of operation (" << index.value() << ")" << node.name() - << " is " << rank << std::endl; - - return rank; -} - -int64_t HEScheduler::DFSChildrenMaxRank(const ir::OperationIndex &index) -{ - const auto &node = _graph->operations().at(index); - int64_t max_child_rank = 0; - for (const auto &output : node.getOutputs() | ir::Remove::UNDEFINED) - { - const auto &operand = _graph->operands().at(output); - const bool quant = operand.typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM; - // average data transfer cost of this operand's data - int64_t avg_transfer_cost = 1; - for (const auto *backend : _all_backends) - { - for (const auto *other_backend : _all_backends) - { - if (backend == other_backend) - { - continue; - } - // TODO Change it to controlflow backend - auto transfer_cost = - getPermuteTime(backend, other_backend, quant, operand.info().total_size()); - avg_transfer_cost += transfer_cost; - } - } - avg_transfer_cost /= _all_backends.size(); - for (const auto &use : operand.getUses()) - { - const auto cur_child_rank = DFSMaxRank(use); - max_child_rank = std::max(max_child_rank, cur_child_rank + avg_transfer_cost); - } - } - return max_child_rank; -} - -int64_t HEScheduler::backendAvailableTime(const backend::Backend *backend, - const int64_t &starting_time, const int64_t &time_amount) -{ - const auto backend_times = _backends_avail_time.at(backend); - // finishing and starting times of an op, that will come after current op - auto next_op_fst = backend_times.upper_bound(starting_time); - // finishing time of an op, that will come before current op - auto prev_op_ft = starting_time; - // until reach the "hole/gap", that is enough to run this op - while (next_op_fst != backend_times.end() && next_op_fst->second - prev_op_ft <= time_amount) - { - prev_op_ft = next_op_fst->first + 1; - ++next_op_fst; - } - return prev_op_ft; -} - -bool HEScheduler::schedule(const ir::OperationIndex &index, const backend::Backend *parent_backend) -{ - VERBOSE(HEScheduler::schedule) << "scheduling (" << index.value() << ")" << std::endl; - int64_t eft = std::numeric_limits<int64_t>::max(), selected_exec_time = 0; - const auto &node = _graph->operations().at(index); - - std::multimap<int64_t, int64_t> selected_transfer_st_exec_time; - // select the backend with the smallest eft of this task - const backend::Backend *chosen_backend = nullptr; - for (const auto *backend : _all_backends) - { - std::multimap<int64_t, int64_t> transfer_st_exec_time; - const auto est_and_et = ESTAndExecTime(backend, index, transfer_st_exec_time); - - if (eft > est_and_et.first + est_and_et.second) - { - eft = est_and_et.first + est_and_et.second; - selected_exec_time = est_and_et.second; - chosen_backend = backend; - selected_transfer_st_exec_time = transfer_st_exec_time; - } - } - - if (chosen_backend == nullptr) - { - throw std::runtime_error{"Fail to choose backend on scheduler"}; - } - - // this is part of a branch and it is assigned another backend - if (parent_backend && parent_backend != chosen_backend) - { - return false; - } - for (const auto &it : selected_transfer_st_exec_time) - { - auto prev_op_ft = backendAvailableTime(_cpu_backend, it.first, it.second); - _backends_avail_time[_cpu_backend].insert({prev_op_ft + it.second, prev_op_ft}); - } - - _ops_eft[index] = eft; - _backends_avail_time[chosen_backend].emplace(eft, eft - selected_exec_time); - _backend_resolver->setBackend(index, chosen_backend); - - VERBOSE(HEScheduler::schedule) << "backend for " << node.name() << " is " - << chosen_backend->config()->id() << ". Its eft: " << eft - << std::endl; - return true; -} - -std::pair<int64_t, int64_t> -HEScheduler::ESTAndExecTime(const backend::Backend *backend, const ir::OperationIndex &index, - std::multimap<int64_t, int64_t> &transfer_st_exec_time) -{ - // Permutation will cause creating a separate op_seq that contains just this permutation node. - // This isn't needed for Linear executor since it doesn't use op_seqs - // Number 1 ms is picked experimentally - int64_t permute_fine = 1000; - // Multiply cpu operations' exec time by 2 because in parallel executor it might be busy with - // permutation on other branches or non-nnfw specific tasks and have to wait for it. - // Number 2 is picked experimentally - const int64_t CPU_DELAY = 2; - const auto &node = _graph->operations().at(index); - const bool quant = isQuant(*_graph, node); - const auto size = getOperationsFlattenedIOSize(*_graph, node); - // if this node can be part of a op_seq, then assigning different backend will cause creating - // another op_seq - if (isMergeable(*_graph, node)) - { - permute_fine *= 2; - } - if (isWorkaroundSkip(*_graph, backend, node, quant)) - { - return {_exec_time->getMax(), _exec_time->getMax()}; - } - // get average exec time of the op on this backend - auto exec_time = getOpTime(backend, node.name(), quant, size); - if (backend->config()->id() == "cpu" && _is_parallel_exec) - { - exec_time *= CPU_DELAY; - } - - // get max eft of direct (one level above) predecessors - auto max_pred_eft = predMaxEFT(backend, node, transfer_st_exec_time); - - int64_t total_transfer_cost = 0; - std::vector<std::multimap<int64_t, int64_t>::iterator> inserted_permutations; - // Find free time for data transferring and insert it into backend taskset. This is needed: - // 1. Time for multiple permutations for this node's input is found correctly - // 2. If backend==cpu, then free time for this node must come after permutations - for (auto &it : transfer_st_exec_time) - { - if (_is_parallel_exec) - { - it.second *= CPU_DELAY; - } - if (!_is_linear_exec) - { - it.second += permute_fine; - } - total_transfer_cost += it.second; - - const auto prev_op_ft = backendAvailableTime(_cpu_backend, it.first, it.second); - - max_pred_eft = std::max(max_pred_eft, prev_op_ft + it.second); - - const auto tmp = _backends_avail_time[_cpu_backend].emplace(prev_op_ft + it.second, prev_op_ft); - inserted_permutations.push_back(tmp.first); - } - // find the hole/gap, where this op can be put or the finishing time of the last assigned op - auto prev_op_ft = backendAvailableTime(backend, max_pred_eft, exec_time); - - // Remove inserted permutation from cpu's task set - for (const auto &it : inserted_permutations) - { - _backends_avail_time[_cpu_backend].erase(it); - } - - /* In case non-parallel executor measure just exec time and data transfer time - * because EFT(prev_op_ft) is the same for all backends. Since two operations - * can't be run simultaneously, finish of running operation must be waited for. - * When an operation starts, all backends are free. So, they need time just for - * data transfer.*/ - if (!_is_parallel_exec) - { - VERBOSE(HEScheduler::ESTAndExecTime) - << "exec_time of (" << index.value() << ") " << node.name() << " quant==" << quant << " on " - << backend->config()->id() << " is " << exec_time - << " microseconds. Data transfer cost: " << total_transfer_cost << std::endl; - - return {total_transfer_cost, exec_time}; - } - VERBOSE(HEScheduler::ESTAndExecTime) - << "exec_time of (" << index.value() << ") " << node.name() << " quant==" << quant << " on " - << backend->config()->id() << ": " << exec_time - << " microseconds. Backend available time: " << prev_op_ft - << " Parent's max eft: " << max_pred_eft - total_transfer_cost - << " data transfer cost: " << total_transfer_cost << std::endl; - - return {prev_op_ft, exec_time}; -} - -int64_t HEScheduler::predMaxEFT(const backend::Backend *backend, const ir::Operation &node, - std::multimap<int64_t, int64_t> &transfer_st_exec_time) -{ - int64_t max_pred_eft = 0; - for (const auto &input_operand_idx : node.getInputs()) - { - const auto &input_operand = _graph->operands().at(input_operand_idx); - const bool quant = input_operand.typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM; - - auto input_node_idx = input_operand.getDef(); - if (input_node_idx.valid()) - { - // Data transfer cost from parent's node backend to current node's backend: - auto parent_backend = _backend_resolver->getBackend(input_node_idx); - - max_pred_eft = std::max(max_pred_eft, _ops_eft.at(input_node_idx)); - if (parent_backend != backend) - { - // Multiply operand size by 2 because size must describe input+output size - int64_t transfer_cost = - getPermuteTime(parent_backend, backend, quant, input_operand.info().total_size() * 2); - transfer_st_exec_time.emplace(_ops_eft.at(input_node_idx), transfer_cost); - } - } - } - return max_pred_eft; -} - -} // namespace compiler - -} // namespace onert diff --git a/runtime/onert/core/src/compiler/HEScheduler.h b/runtime/onert/core/src/compiler/HEScheduler.h deleted file mode 100644 index b9cee5881..000000000 --- a/runtime/onert/core/src/compiler/HEScheduler.h +++ /dev/null @@ -1,186 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file HEScheduler.h - * @brief This file contains HEScheduler class to define and run task Heterogeneous Execution - * Scheduler - */ - -#ifndef __ONERT_COMPILER_H_E_SCHEDULER_H_ -#define __ONERT_COMPILER_H_E_SCHEDULER_H_ - -#include "compiler/IScheduler.h" -#include "compiler/BackendManager.h" -#include "compiler/Compiler.h" -#include "ir/Graph.h" -#include "exec/ExecTime.h" -#include "backend/Backend.h" -#include <memory> -#include "ir/OperationIndexMap.h" -#include <map> -#include <memory> - -namespace onert -{ - -namespace compiler -{ -/** - * @brief Class to schedule tasks - */ -class HEScheduler : IScheduler -{ -public: - /** - * @brief Construct a new Heterogeneous Execution Scheduler object - * @param[in] model Graph model - * @param[in] backend_resolver backend resolver - */ - HEScheduler(const backend::BackendContexts &backend_contexts, const CompilerOptions &options) - : _is_supported{}, _backends_avail_time{}, _ops_eft{}, - _op_to_rank{std::make_shared<ir::OperationIndexMap<int64_t>>()}, - _is_profiling_mode{options.he_profiling_mode}, - _is_linear_exec{options.executor == "Linear"}, - _is_parallel_exec{options.executor == "Parallel"} - { - for (auto &entry : backend_contexts) - { - if (entry.first->config()->id() == backend::controlflow::Config::ID) - continue; - _all_backends.push_back(entry.first); - } - _backend_resolver = std::make_unique<compiler::BackendResolver>(); - _exec_time = std::make_unique<exec::ExecTime>(_all_backends); - - // Find cpu backend - auto cpu_backend_it = std::find_if( - _all_backends.begin(), _all_backends.end(), - [](const backend::Backend *backend) { return backend->config()->id() == "cpu"; }); - if (cpu_backend_it == _all_backends.end()) - throw std::runtime_error("HEScheduler could be used only if 'cpu' backend is available"); - _cpu_backend = *cpu_backend_it; - } - -public: - /** - * @brief Task scheduling - * - * @note The main idea is taken from HSIP algo: - * https://www.hindawi.com/journals/sp/2016/3676149/ - */ - std::unique_ptr<compiler::BackendResolver> schedule(const ir::Graph &graph) final; - std::shared_ptr<ir::OperationIndexMap<int64_t>> getIndexedRanks() { return _op_to_rank; } - -private: - bool isNodeProfiled(const ir::Operation &); - - bool schedule(const ir::OperationIndex &, const backend::Backend *parent_backend); - /** - * @brief Get earliest starting time and execution time of an operation on a backend. - * - * @note Returns a time when operation's inputs are ready and backend is available - * It also returns exec time. If this is "cpu" backend, then exec_time*CPU_DELAY - * - * @param[in] backend: backend, for which to return the time - * @param[in] index: index of an operation - * @param[out] transfer_st_exec_time: est and exec time of data transfer operation - * - * @return earliest starting time and execution time - */ - std::pair<int64_t, int64_t> - ESTAndExecTime(const backend::Backend *backend, const ir::OperationIndex &index, - std::multimap<int64_t, int64_t> &transfer_st_exec_time); - /** - * @brief Returns the latest finishing time of parents of a node. - * - * @param[in] backend: backend, for which to return the time - * @param[in] node: node to get eft of parents - * @param[out] transfer_st_exec_time: est and exec time of data transfer operation - * - * @return earliest finishing time of parent nodes - */ - int64_t predMaxEFT(const backend::Backend *backend, const ir::Operation &node, - std::multimap<int64_t, int64_t> &transfer_st_exec_time); - - void makeRank(); - - int64_t DFSMaxRank(const ir::OperationIndex &index); - - int64_t DFSChildrenMaxRank(const ir::OperationIndex &index); - /** - * @brief Returns the time, when backend is available for at least given amount of time. - * - * @note Returns either hole/gap between two performing two already scheduled operations, - * or the finishing time of the last scheduled operation - * - * @param[in] backend backend, for which to return the time - * @param[in] starting_time time, starting which to look for gap - * @param[in] time_amount amount of the time, for which to look gap - * - * @return time, when backend has at least time_amount free time - */ - int64_t backendAvailableTime(const backend::Backend *backend, const int64_t &starting_time, - const int64_t &time_amount); - - int64_t getOpTime(const backend::Backend *backend, const std::string &operation, bool quant, - uint32_t size); - - int64_t getPermuteTime(const backend::Backend *src_backend, const backend::Backend *dst_backend, - bool quant, uint32_t size); - - void scheduleShufflingBackends(); - - int64_t tryBackend(const ir::Operation &node, const backend::Backend *backend); - - /** - * @brief Schedule a node and its successor until: - * 1. there is no branching or connection of multiple branches - * 2. for subsequent nodes: other than predecessor's backend is prefered - * - * @param[in] index: index of an operation - * @param[in] scheduled: a map to check if this node has already been scheduled - * - * @return N/A - */ - void scheduleBranch(const ir::OperationIndex &index, ir::OperationIndexMap<bool> &scheduled); - -private: - // This variable stores backend/node pairs with unknown execution time, and hints scheduler - // whether it should assign these backends to these nodes: - // * It stores false for unsupported nodes - // * During rank calculation with enabled profiling mode it stores true for supported nodes - std::unordered_map<const backend::Backend *, std::unordered_map<std::string, bool>> _is_supported; - // Finishing and starting time of each backend - std::unordered_map<const backend::Backend *, std::map<int64_t, int64_t>> _backends_avail_time; - ir::OperationIndexMap<int64_t> _ops_eft; - std::multimap<int64_t, ir::OperationIndex, std::greater<int64_t>> _rank_to_op; - std::shared_ptr<ir::OperationIndexMap<int64_t>> _op_to_rank; - std::unique_ptr<compiler::BackendResolver> _backend_resolver; - std::unique_ptr<exec::ExecTime> _exec_time; - const ir::Graph *_graph{nullptr}; - std::vector<const backend::Backend *> _all_backends; - const backend::Backend *_cpu_backend{nullptr}; // TODO Change this to controlflow_backend - bool _is_profiling_mode; - bool _is_linear_exec; - bool _is_parallel_exec; -}; - -} // namespace compiler - -} // namespace onert - -#endif // __ONERT_COMPILER_H_E_SCHEDULER_H_ diff --git a/runtime/onert/core/src/compiler/IScheduler.h b/runtime/onert/core/src/compiler/IScheduler.h deleted file mode 100644 index 5e9b9bd3c..000000000 --- a/runtime/onert/core/src/compiler/IScheduler.h +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_CORE_COMPILER_I_SCHEDULER_H__ -#define __ONERT_CORE_COMPILER_I_SCHEDULER_H__ - -#include "compiler/BackendResolver.h" -#include "ir/Graph.h" - -namespace onert -{ -namespace compiler -{ - -struct IScheduler -{ - virtual ~IScheduler() = default; - - virtual std::unique_ptr<BackendResolver> schedule(const ir::Graph &graph) = 0; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_CORE_COMPILER_I_SCHEDULER_H__ diff --git a/runtime/onert/core/src/compiler/Linear.cc b/runtime/onert/core/src/compiler/Linear.cc deleted file mode 100644 index 39e58fe11..000000000 --- a/runtime/onert/core/src/compiler/Linear.cc +++ /dev/null @@ -1,231 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <algorithm> - -#include "Linear.h" - -#include "backend/IConfig.h" -#include "backend/IConstantInitializer.h" -#include "backend/ITensorRegister.h" -#include "backend/Backend.h" -#include "util/logging.h" - -namespace onert -{ -namespace compiler -{ - -std::vector<ir::OpSequenceIndex> Linear::linearize(const compiler::LoweredGraph &lowered_graph) -{ - std::vector<ir::OpSequenceIndex> order; - lowered_graph.iterateTopolOpSeqs( - [&](const ir::OpSequenceIndex &index, const ir::OpSequence &) -> void { - order.emplace_back(index); - }); - return order; -} - -void Linear::dump(const compiler::LoweredGraph &lowered_graph, - const std::vector<ir::OpSequenceIndex> &order) -{ - { - const auto &toString = [](const onert::backend::Backend *backend) { - assert(backend); - std::string str; - str += backend->config()->id(); - return "{" + str + "}"; - }; - - VERBOSE(Linear) << "Final OpSequence" << std::endl; - for (const auto index : order) - { - const auto &op_seq = lowered_graph.op_seqs().at(index); - const auto lower_info = lowered_graph.getLowerInfo(index); - const auto &operations = lowered_graph.graph().operations(); - VERBOSE(Linear) << "* OP_SEQ " << toString(lower_info->backend()) << " " - << ir::getStrFromOpSeq(op_seq, operations) << std::endl; - } - } -} - -void Linear::planTensors(const compiler::LoweredGraph &lowered_graph, - const std::vector<ir::OpSequenceIndex> &order) -{ - const auto &graph = lowered_graph.graph(); - ir::OperandIndexMap<std::shared_ptr<backend::ITensorBuilder>> tensor_builder_map; - - ir::OperandIndexMap<uint32_t> uses_map; - ir::OperandIndexMap<uint32_t> def_map; - ir::OperandIndexSequence constants; - - // Prepare scanning - graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) { - const auto lower_info = lowered_graph.getLowerInfo(ind); - // TODO Remove if onert doesn't support anymore such as - // GeneratedTests.reshape_quant8_weights_as_inputs - if (lower_info->def_factors().size() == 0 && lower_info->use_factors().size() == 0 && - !graph.getInputs().contains(ind)) - { - VERBOSE(LINEAR) << "Operand #" << ind.value() << " will not be used. no more process." - << std::endl; - return; - } - - // Unused input of subgraph - // TODO Register unused input as nullptr in tensor_builder - if (lower_info->def_factors().size() == 0 && lower_info->use_factors().size() == 0 && - graph.getInputs().contains(ind)) - { - VERBOSE(LINEAR) << "Operand #" << ind.value() << " will not be used. no more process." - << std::endl; - return; - } - - uses_map[ind] = obj.getUses().size(); - def_map[ind] = obj.getDef().valid() ? 1 : 0; - - bool is_const = obj.isConstant(); - if (is_const) - { - constants.append(ind); - } - - auto factor = lower_info->def_factors().getOnlyElement(); - auto backend = factor.backend(); - auto tensor_builder = lowered_graph.backend_contexts().at(backend)->tensor_builder; - if (!tensor_builder->isRegistered(ind)) - { - // These tensors do not exist in any op_seq (No use and def) - const auto info = obj.info(); - const auto backend_layout = factor.layout(); - // TODO Change tensor info to have permuted shape - tensor_builder->registerTensorInfo(ind, info, backend_layout); - } - - tensor_builder_map[ind] = tensor_builder; - }); - - // If a tensor is model output, increase the use of the tensor. - // This aim is same to above one. - for (const auto &ind : graph.getOutputs() | ir::Remove::DUPLICATED) - { - uses_map[ind]++; - } - - // Start scanning to do notify{First|Last}Use for each tensor - - // If a tensor is a constant, increase the use of the tensor. - // It makes the tensor not be dealloced. It means these will be deallocated last. - // And allocate constant operands first - VERBOSE(LINEAR) << "TENSORS as CONSTANT" << std::endl; - for (const auto &ind : constants) - { - uses_map[ind]++; - tensor_builder_map[ind]->notifyFirstUse(ind); - } - - // Allocate Model's inputs - VERBOSE(LINEAR) << "TENSORS as MODEL INPUT" << std::endl; - for (const auto &ind : graph.getInputs() | ir::Remove::DUPLICATED) - { - auto tensor_builder = tensor_builder_map[ind]; - if (!tensor_builder) // for GeneratedTests.xxx_weights_as_inputs - continue; - tensor_builder->notifyFirstUse(ind); - } - - const auto io_tensors = - (graph.getInputs() + graph.getOutputs()) | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED; - - // At each operation, - // 1. Scan DEF of outputs. If the DEF, allocate it - // 2. Scan USE of inputs. Decrease the USE and deallocate if the USE is 0 - VERBOSE(LINEAR) << "TENSORS" << std::endl; - for (const auto op_seq_ind : order) - { - const auto &op_seq = lowered_graph.op_seqs().at(op_seq_ind); - for (const auto &op_idx : op_seq.operations()) - { - for (const auto &ind : graph.operations().at(op_idx).getOutputs() | ir::Remove::DUPLICATED | - ir::Remove::UNDEFINED) - { - assert(def_map.find(ind) != def_map.end()); - if (def_map[ind]) - { - def_map[ind] = 0; - tensor_builder_map[ind]->notifyFirstUse(ind); - } - } - - for (const auto &ind : graph.operations().at(op_idx).getInputs() | ir::Remove::DUPLICATED | - ir::Remove::UNDEFINED) - { - assert(uses_map.find(ind) != uses_map.end()); - assert(uses_map[ind] > 0); - uses_map[ind]--; - if (uses_map[ind] == 0) - { - // plan for deallocation of static tensornode - tensor_builder_map[ind]->notifyLastUse(ind); - - // plan for deallocation of dynamic tensor - auto dyn_tensor_manager = tensor_builder_map[ind]->dynamicTensorManager(); - if (dyn_tensor_manager) - { - const auto *backend = - lowered_graph.getLowerInfo(ind)->def_factors().getOnlyElement().backend(); - auto &tensor_registry = lowered_graph.backend_contexts().at(backend)->tensor_registry; - auto *tensor = tensor_registry->getITensor(ind); - assert(tensor); - if (!io_tensors.contains(ind)) // I/O tensors cannot be deallocated - dyn_tensor_manager->planDealloc(op_idx, tensor); - } - } - } - } - } - - // Dispose and validate - for (const auto &ind : graph.getOutputs() | ir::Remove::DUPLICATED) - { - --uses_map[ind]; - if (uses_map[ind] == 0) // To prevent notifyLastUse from being called twice - { - tensor_builder_map[ind]->notifyLastUse(ind); - } - } - - for (const auto &ind : constants) - { - --uses_map[ind]; - if (uses_map[ind] == 0) // To prevent notifyLastUse from being called twice - { - tensor_builder_map[ind]->notifyLastUse(ind); - } - } - - assert( - std::all_of(uses_map.begin(), uses_map.end(), - [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; })); - - assert( - std::all_of(def_map.begin(), def_map.end(), - [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; })); -} - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/Linear.h b/runtime/onert/core/src/compiler/Linear.h deleted file mode 100644 index 1e24cf92b..000000000 --- a/runtime/onert/core/src/compiler/Linear.h +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_LINEAR_H__ -#define __ONERT_COMPILER_LINEAR_H__ - -#include <vector> -#include <memory> - -#include "ir/OpSequences.h" -#include "ir/Index.h" -#include "backend/ITensorBuilder.h" -#include "compiler/LoweredGraph.h" - -namespace onert -{ -namespace ir -{ -struct OperationVisitor; -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace compiler -{ - -class Linear -{ -public: - static std::vector<ir::OpSequenceIndex> linearize(const compiler::LoweredGraph &lowered_graph); - static void dump(const compiler::LoweredGraph &lowered_graph, - const std::vector<ir::OpSequenceIndex> &order); - static void planTensors(const compiler::LoweredGraph &lowered_graph, - const std::vector<ir::OpSequenceIndex> &order); -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_LINEAR_H__ diff --git a/runtime/onert/core/src/compiler/LoweredGraph.cc b/runtime/onert/core/src/compiler/LoweredGraph.cc deleted file mode 100644 index cdf1a8158..000000000 --- a/runtime/onert/core/src/compiler/LoweredGraph.cc +++ /dev/null @@ -1,565 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "compiler/LoweredGraph.h" - -#include <assert.h> -#include <sstream> -#include "util/logging.h" -#include "compiler/pass/ConstantInsertionPass.h" -#include "compiler/pass/ConstantLoweringPass.h" -#include "compiler/pass/PassRunner.h" -#include "compiler/pass/PermutationOperationPass.h" -#include "compiler/pass/PermutationInsertionPass.h" -#include "compiler/pass/PermutationEliminationPass.h" -#include "ir/GraphIterator.h" -#include "ir/verifier/Verifier.h" -#include "backend/Backend.h" -#include "backend/IConfig.h" -#include "compiler/BackendResolver.h" -#include "compiler/ManualScheduler.h" -#include "compiler/HEScheduler.h" - -namespace onert -{ -namespace compiler -{ - -LoweredGraph::LoweredGraph(const ir::Graph &graph, const CompilerOptions &options) : _graph{graph} -{ - bool linear_executor = (options.executor == "Linear"); - - // Build backend contexts - auto &backend_manager = BackendManager::get(); - - // Always create Controlflow backend context - auto cf_backend = backend_manager.getControlflow(); - _backend_contexts.emplace( - cf_backend, cf_backend->newContext(_graph, _graph.getKernelBuilder(), linear_executor)); - - // Create contexts for other backends - for (auto backend_str : options.backend_list) - { - backend_manager.loadBackend(backend_str); - auto backend = backend_manager.get(backend_str); - - // TODO As the default value of backend list contains "cpu", "acl_cl" and "acl_neon", and some - // are not available on x64 or some other platforms. So this may be a workaround for x64 and - // we should change it back(throw if backend is not loaded) later. - if (!backend) - { - VERBOSE(LoweredGraph) << "Cannot load backend - " << backend_str; - continue; - } - - _backend_contexts.emplace( - backend, backend->newContext(_graph, _graph.getKernelBuilder(), linear_executor)); - } - if (backend_manager.num_backends() == 0) - throw std::runtime_error{"No available backends loaded."}; - - // TODO Move "schedule" phase out of here - // Schedule - std::unique_ptr<BackendResolver> backend_resolver; - if (options.he_scheduler) - { - auto scheduler = HEScheduler(_backend_contexts, options); - backend_resolver = scheduler.schedule(_graph); - _indexed_ranks = scheduler.getIndexedRanks(); - } - else - { - auto scheduler = ManualScheduler(_backend_contexts, options); - backend_resolver = scheduler.schedule(_graph); - } - - { - // operand::LowerInfo holder - ir::OperandIndexMap<std::unique_ptr<ir::operand::LowerInfo>> operands_lower_info; - - _graph.operands().iterate([&](const ir::OperandIndex &index, const ir::Operand &) { - operands_lower_info[index] = std::make_unique<ir::operand::LowerInfo>(); - }); - - // Make op_seqs while checking whether a node can be merged into a op_seq. - makeOpSequences(operands_lower_info, options, *backend_resolver); - - _op_seqs.iterate([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - assert(op_seq.operations().size() > 0); - std::reverse(std::begin(op_seq.operations()), std::end(op_seq.operations())); - }); - - VERBOSE(OpSequences) << "dump before permutation insertion" << std::endl; - dumpOpSequences(_op_seqs, _graph.operations()); - - // Mandatory passes - pass::PassRunner{} - .append(std::make_unique<pass::ConstantInsertionPass>(*this)) - .append(std::make_unique<pass::ConstantLoweringPass>(*this)) - .run(); - - // Set LowerInfo for each operand from the operand::LowerInfo holder - manipulateLowerInfo(operands_lower_info, options.is_primary_subgraph); - - dumpLowerInfo(); - } - - // Mandatory passes - pass::PassRunner{} - .append(std::make_unique<pass::PermutationOperationPass>(*this)) - .append(std::make_unique<pass::PermutationInsertionPass>(*this)) - .run(); - - // Optimization passes - pass::PassRunner{}.append(std::make_unique<pass::PermutationEliminationPass>(*this)).run(); - - VERBOSE(OpSequences) << "Dump after permutation insertion" << std::endl; - dumpOpSequences(_op_seqs, _graph.operations()); - - // Graph verifications - { - assert(ir::verifier::DAGChecker().verify(_graph)); - assert(ir::verifier::EdgeConsistencyChecker().verify(_graph)); - } -} - -const ir::operation::LowerInfo * -LoweredGraph::getLowerInfo(const ir::OpSequenceIndex &op_seq_index) const -{ - auto itr = _lower_info_map.op_seq.find(op_seq_index); - if (itr == _lower_info_map.op_seq.end()) - return nullptr; - return itr->second.get(); -} - -void LoweredGraph::setLowerInfo(const ir::OpSequenceIndex &op_seq_index, - std::unique_ptr<ir::operation::LowerInfo> &&lower_info) -{ - _lower_info_map.op_seq.insert(std::make_pair(op_seq_index, std::move(lower_info))); -} - -void LoweredGraph::removeLowerInfo(const ir::OpSequenceIndex &op_seq_index) -{ - auto &op_seq_lower_info = _lower_info_map.op_seq; - assert(op_seq_lower_info.find(op_seq_index) != op_seq_lower_info.end()); - for (auto it = op_seq_lower_info.begin(); it != op_seq_lower_info.end(); ++it) - { - if (it->first == op_seq_index) - { - op_seq_lower_info.erase(it); - break; - } - } -} - -const ir::operand::LowerInfo *LoweredGraph::getLowerInfo(const ir::OperandIndex &index) const -{ - auto itr = _lower_info_map.operand.find(index); - if (itr == _lower_info_map.operand.end()) - return nullptr; - return itr->second.get(); -} - -ir::operand::LowerInfo *LoweredGraph::getLowerInfo(const ir::OperandIndex &index) -{ - auto itr = _lower_info_map.operand.find(index); - if (itr == _lower_info_map.operand.end()) - return nullptr; - return itr->second.get(); -} - -void LoweredGraph::setLowerInfo(const ir::OperandIndex &index, - std::unique_ptr<ir::operand::LowerInfo> &&lower_info) -{ - _lower_info_map.operand.insert(std::make_pair(index, std::move(lower_info))); -} - -void LoweredGraph::removeLowerInfo(const ir::OperandIndex &index) -{ - _lower_info_map.operand.erase(index); -} - -void LoweredGraph::iterateTopolOpSeqs( - const std::function<void(const ir::OpSequenceIndex &, const ir::OpSequence &)> &fn) const -{ - // Topological Sorting for ir::OpSequences - std::vector<ir::OpSequenceIndex> topol_sorted; - ir::PostDfsIterator<true>{}.iterateOpSeqs( - *this, [&](const ir::OpSequenceIndex &index, const ir::OpSequence &) { - topol_sorted.emplace_back(index); - }); - std::reverse(topol_sorted.begin(), topol_sorted.end()); - for (const auto op_seq_idx : topol_sorted) - { - const auto &op_seq = _op_seqs.at(op_seq_idx); - fn(op_seq_idx, op_seq); - } -} - -void LoweredGraph::iterateTopolOpSeqs( - const std::function<void(const ir::OpSequenceIndex &, ir::OpSequence &)> &fn) -{ - // Topological Sorting for ir::OpSequences - std::vector<ir::OpSequenceIndex> topol_sorted; - ir::PostDfsIterator<false>{}.iterateOpSeqs( - *this, [&](const ir::OpSequenceIndex &index, ir::OpSequence &) { - topol_sorted.emplace_back(index); - }); - std::reverse(topol_sorted.begin(), topol_sorted.end()); - for (const auto op_seq_idx : topol_sorted) - { - auto &op_seq = _op_seqs.at(op_seq_idx); - fn(op_seq_idx, op_seq); - } -} - -ir::OpSequenceIndex LoweredGraph::appendFreshSingleOpSequence(const ir::OperationIndex &node_index, - const ir::Operation &node) -{ - // Create a fresh op_seq with one operation, and append it to op_seqs - // Create a fresh op_seq - auto op_seq = std::make_unique<ir::OpSequence>(_graph.layout()); - - // Add an operation - op_seq->appendOperation(node_index); - - // Update input/output - op_seq->setOutputs(node.getOutputs()); - op_seq->setInputs(node.getInputs()); - - return _op_seqs.emplace(std::move(op_seq)); -} - -void LoweredGraph::makeOpSequences( - ir::OperandIndexMap<std::unique_ptr<ir::operand::LowerInfo>> &operands_lower_info, - const CompilerOptions &options, const BackendResolver &backend_resolver) -{ - // if SUBG_MAX_NODE == 0, no limit on nodes of a op_seq - const int op_seq_max_node = options.op_seq_max_node; - assert(op_seq_max_node >= 0); - - bool is_profiling = options.he_profiling_mode; - ir::OpSequence *op_seq = nullptr; - ir::OpSequenceIndex op_seq_index; - - // NOTE: The below method appends nodes while making one op_seq if needed. If something better - // ways, happy to update this code. - ir::PostDfsConstIterator{}.iterate( - _graph, [&](const ir::OperationIndex &node_index, const ir::Operation &node) { - // LowerInfo for in/output operands - auto backend = backend_resolver.getBackend(node_index); - - // Get frontend's layout - auto frontend_layout = _graph.layout(); - - // The layout of each backend should be set at another place - // TODO Change setting layout of each backend at another place - auto backend_layout = backend->config()->supportLayout(node, frontend_layout); - - for (auto operand : node.getInputs() | ir::Remove::UNDEFINED) - { - auto &&lower_info = operands_lower_info.at(operand); - lower_info->addUsePermuteFactor(ir::operand::PermuteFactor{backend, backend_layout}); - } - for (auto operand : node.getOutputs() | ir::Remove::UNDEFINED) - { - auto &&lower_info = operands_lower_info.at(operand); - lower_info->addDefPermuteFactor(ir::operand::PermuteFactor{backend, backend_layout}); - } - - bool new_op_seq = (op_seq == nullptr || - (op_seq_max_node != 0 && - op_seq->operations().size() >= static_cast<size_t>(op_seq_max_node))); - - // for profiling each op_seq must contain just one node, - // so that we can measure a node separately - if (new_op_seq || is_profiling || - !mergeable(op_seq_index, node_index, backend_layout, backend_resolver)) - { - auto new_op_seq_index = appendFreshSingleOpSequence(node_index, node); - - // ir::OpSequence LowerInfo - setLowerInfo(new_op_seq_index, - std::make_unique<ir::operation::LowerInfo>(backend, backend_layout)); - - op_seq_index = new_op_seq_index; - op_seq = &(_op_seqs.at(new_op_seq_index)); - - VERBOSE(Lower) << "OpSequence#" << op_seq_index.value() << " is created for " - << "NODE#" << node_index.value() << "(" << node.name() << ")" << std::endl; - } - else - { - op_seq->appendOperation(node_index); - // Set inputs - auto new_inputs = node.getInputs(); - // Add inputs except outputs of the previous node - for (auto ind : op_seq->getInputs()) - { - if (!node.getOutputs().contains(ind)) - new_inputs.append(ind); - } - op_seq->setInputs(new_inputs); - - VERBOSE(Lower) << "OpSequence#" << op_seq_index.value() << " merges " - << "NODE#" << node_index.value() << "(" << node.name() << ")" << std::endl; - } - }); -} - -void LoweredGraph::manipulateLowerInfo( - ir::OperandIndexMap<std::unique_ptr<ir::operand::LowerInfo>> &operands_lower_info, - bool is_primary) -{ - const auto controlflow_backend = BackendManager::get().getControlflow(); - - // TODO Rather than handling primary graph specially, - // let the permute inserted and remove it later - if (is_primary) - { - // TODO Rather than using NHWC Get frontend layout of this node from IR - auto factor = ir::operand::PermuteFactor{controlflow_backend, ir::Layout::NHWC}; - for (auto index : _graph.getInputs() | ir::Remove::UNDEFINED) - { - auto &&lower_info = operands_lower_info.at(index); - assert(lower_info->def_factors().empty()); - lower_info->addDefPermuteFactor(factor); - } - for (auto index : _graph.getOutputs() | ir::Remove::UNDEFINED) - { - auto &&lower_info = operands_lower_info.at(index); - lower_info->addUsePermuteFactor(factor); - } - } - else - { - for (auto index : _graph.getInputs() | ir::Remove::UNDEFINED) - { - auto &&lower_info = operands_lower_info.at(index); - if (!(lower_info->def_factors().size() == 0 && lower_info->use_factors().size() == 0)) - { - // In case of not that Graph's input is not used in any operation and not the graph's - // output. - // In other words, it is not unused input in Graph. - lower_info->addDefPermuteFactor(*lower_info->use_factors().begin()); - } - else - { - // In case of that an operand is Graph's input and not input or output of any operation - lower_info->addDefPermuteFactor(ir::operand::PermuteFactor{ - controlflow_backend, - ir::Layout::NHWC // TODO Get frontend layout of this node from IR - }); - } - } - } - for (auto index : _graph.getOutputs() | ir::Remove::UNDEFINED) - { - auto &&lower_info = operands_lower_info.at(index); - if (lower_info->def_factors().size() == 0) - { - // In case of that an operand is Graph's output and not input or output of any operation - lower_info->addDefPermuteFactor(ir::operand::PermuteFactor{ - controlflow_backend, - ir::Layout::NHWC // TODO Get frontend layout of this node from IR - }); - } - } - - // Set LowerInfo for each operand from the operand::LowerInfo holder - _graph.operands().iterate([&](const ir::OperandIndex &index, ir::Operand &) { - setLowerInfo(index, std::move(operands_lower_info[index])); - }); -} - -void LoweredGraph::dumpLowerInfo() -{ - if (::onert::util::logging::ctx.enabled() == false) - return; - - std::map<uint32_t, std::string> dumps; - - _graph.operands().iterate([&](const ir::OperandIndex &index, ir::Operand &object) { - std::stringstream sstream; - if (!getLowerInfo(index)->def_factors().empty() || !getLowerInfo(index)->use_factors().empty()) - { - auto factors_to_string = [](const ir::operand::PermuteFactorSet &factors) { - std::string str; - for (auto factor : factors) - { - str += factor.backend()->config()->id(); - str += "(" + to_string(factor.layout()) + ")"; - str += " "; - } - return "{ " + str + "}"; - }; - - auto operation_index_to_string = [](const ir::OperationIndexSet &operations) { - std::string str; - for (auto op : operations) - { - str += std::to_string(op.value()); - str += " "; - } - return "{ " + str + "}"; - }; - - const auto lower_info = getLowerInfo(index); - const auto &shape = object.shape(); - std::string def_ops = - object.getDef().valid() ? std::to_string(object.getDef().value()) : "N/A"; - std::string use_ops = operation_index_to_string(object.getUses()); - std::string def_layouts = factors_to_string(lower_info->def_factors()); - std::string use_layouts = factors_to_string(lower_info->use_factors()); - sstream << "Operand #" << index.value() << " LowerInfo" << std::endl; - sstream << " - Shape : { "; - for (auto i = 0; i < shape.rank(); ++i) - { - sstream << (shape.dim(i)) << " "; - } - sstream << "}" << std::endl; - sstream << " - Def ir::Operations : " << def_ops << std::endl; - sstream << " - Use ir::Operations : " << use_ops << std::endl; - sstream << " - Lower Info" << std::endl; - sstream << " - Def Backends : " << def_layouts << std::endl; - sstream << " - Use Backends : " << use_layouts << std::endl; - } - dumps.emplace(index.value(), sstream.str()); - }); - - for (const auto &e : dumps) - { - if (!e.second.empty()) - { - VERBOSE(Lower) << e.second; - } - } -} - -bool LoweredGraph::mergeable(const ir::OpSequenceIndex &op_seq_index, - const ir::OperationIndex &node_index, ir::Layout layout, - const BackendResolver &backend_resolver) -{ - // Are they mergeable? - // 1. the same backend id and layout? - // 2. Is op_seq or node branched? - // 3. if 1 is true, the op_seq and a node are connected? - const auto &op_seq = _op_seqs.at(op_seq_index); - const auto &node = _graph.operations().at(node_index); - - // The same backend id and layout? - { - const auto op_seq_backend_layout = getLowerInfo(op_seq_index)->layout(); - const auto &op_seq_backend_id = getLowerInfo(op_seq_index)->backend()->config()->id(); - const auto &node_backend_id = backend_resolver.getBackend(node_index)->config()->id(); - VERBOSE(Lower) << "OpSequence#" << op_seq_index.value() << " { " << op_seq_backend_id << "(" - << to_string(op_seq_backend_layout) << ") } " - << " NODE#" << node_index.value() << " (" << node.name() << ") { " - << node_backend_id << "(" << to_string(layout) << ") } " << std::endl; - if (op_seq_backend_id != node_backend_id || op_seq_backend_layout != layout) - return false; - } - - // Branched? - { - std::unordered_set<ir::OperationIndex> branched_set; - - // Check for branching up - for (const auto &input : op_seq.getInputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - const auto &input_obj = _graph.operands().at(input); - auto def = input_obj.getDef(); - if (def.valid()) - { - branched_set.insert(def); - if (branched_set.size() > 1) - { - return false; - } - } - } - branched_set.clear(); - - // Check for branching down - for (const auto &output : node.getOutputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - // TODO Fix this workaround for the case of model outputs that are used by another operation - // This is needed since the branching is decided by operation, but for model outputs, - // there is controlflow backen(use backend) but no actual use operation exists - if (_graph.getOutputs().contains(output)) - return false; - - const auto &output_obj = _graph.operands().at(output); - for (const auto &use : output_obj.getUses()) - { - branched_set.insert(use); - if (branched_set.size() > 1) - { - return false; - } - } - } - } - - // Connected? - // an input of one node is an output of the other node? or vice-versa? - { - const auto &node_inputs = node.getInputs(); - const auto &node_outputs = node.getOutputs(); - - // op_seq's operations are in order so that we just check the first and the last - std::vector<ir::OperationIndex> op_seq_ops{op_seq.operations()[0]}; - if (op_seq.operations().size() > 1) - op_seq_ops.emplace_back(op_seq.operations()[op_seq.operations().size() - 1]); - - for (const auto &n_index : op_seq_ops) - { - const auto &n = _graph.operations().at(n_index); - - // node's output == op_seq's input? - for (const auto input : n.getInputs() | ir::Remove::UNDEFINED) - { - if (node_outputs.contains(input)) - { - VERBOSE(Lower) << "OpSequence#" << op_seq_index.value() << " 's NODE#" << n_index.value() - << "(" << n.name() << ") is connected to NODE#" << node_index.value() - << "(" << node.name() << ")" << std::endl; - return true; - } - } - - // node's input == op_seq's output? - for (const auto output : n.getOutputs() | ir::Remove::UNDEFINED) - { - if (node_inputs.contains(output)) - { - VERBOSE(Lower) << "OpSequence#" << op_seq_index.value() << " 's NODE#" << n_index.value() - << " (" << n.name() << ") is connected to NODE#" << node_index.value() - << std::endl; - return true; - } - } - } - - VERBOSE(Lower) << "OpSequence#" << op_seq_index.value() << " is not connected to NODE#" - << node_index.value() << "(" << node.name() << ")" << std::endl; - } - - return false; -} - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/ManualScheduler.cc b/runtime/onert/core/src/compiler/ManualScheduler.cc deleted file mode 100644 index ed49ee56f..000000000 --- a/runtime/onert/core/src/compiler/ManualScheduler.cc +++ /dev/null @@ -1,124 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ManualScheduler.h" -#include "ir/OpCode.h" -#include "ir/Operations.Include.h" -#include "backend/Backend.h" -#include "backend/IConfig.h" -#include "compiler/BackendManager.h" -#include "util/ConfigSource.h" -#include "util/logging.h" -#include "misc/string_helpers.h" - -namespace onert -{ -namespace compiler -{ - -ManualScheduler::ManualScheduler(const backend::BackendContexts &backend_contexts, - const compiler::CompilerOptions &options) - : _backend_contexts{backend_contexts}, _options{options} -{ -} - -std::unique_ptr<BackendResolver> ManualScheduler::schedule(const ir::Graph &graph) -{ - const auto &manual_options = _options.manual_scheduler_options; - auto backend_resolver = std::make_unique<compiler::BackendResolver>(); - - // This fallback will be used in case that `backend_for_all` is unavailable - auto fallback = [&]() -> const backend::Backend * { - for (auto backend_id : _options.backend_list) - { - auto backend = resolveBackend(backend_id); - if (backend) - return backend; - } - return nullptr; - }(); - if (fallback == nullptr) - throw std::runtime_error{"No loaded backends available."}; - - // 1. Backend for All operations - const backend::Backend *backend_all = resolveBackend(manual_options.backend_for_all, fallback); - VERBOSE(ManualScheduler) << "Default backend for all ops: " << backend_all->config()->id() - << std::endl; - - graph.operations().iterate([&](const ir::OperationIndex &index, const ir::Operation &) { - backend_resolver->setBackend(index, backend_all); - }); - - // 2. Backend per operation type - std::unordered_map<ir::OpCode, backend::Backend *> op_type_map; - for (auto &pair : manual_options.opcode_to_backend) - { - op_type_map.emplace(pair.first, BackendManager::get().get(pair.second)); - } - // By default, Custom uses cpu backend - op_type_map[ir::OpCode::Custom] = BackendManager::get().get("cpu"); - - graph.operations().iterate([&](const ir::OperationIndex &index, const ir::Operation &operation) { - auto itr = op_type_map.find(operation.opcode()); - if (itr != op_type_map.end()) - { - backend_resolver->setBackend(index, itr->second); - } - }); - - // 3. Backend per operation - for (auto &pair : manual_options.index_to_backend) - { - const auto &key = pair.first; - const auto &val = pair.second; - - try - { - graph.operations().at(key); // Check if exist, or this will throw - backend_resolver->setBackend( - key, BackendManager::get().get( - val)); // TODO Ensure this backend is available in backend contexts - } - catch (...) - { - VERBOSE(ManualScheduler) << "Invalid value while OperationIndex to Backend mapping : @" - << key.value() << " -> \"" << val << "\"" << std::endl; - } - } - - // Dump final assignment - backend_resolver->iterate([&](const ir::OperationIndex &index, const backend::Backend &backend) { - VERBOSE(ManualScheduler) << "backend for operation #" << index.value() << ": " - << backend.config()->id() << std::endl; - }); - - return backend_resolver; -} - -const backend::Backend *ManualScheduler::resolveBackend(const std::string &id, - const backend::Backend *fallback) -{ - // Ensure if the backend is available in the current backend context - const backend::Backend *backend = BackendManager::get().get(id); - if (!backend || _backend_contexts.find(backend) == _backend_contexts.end()) - { - backend = fallback; - } - return backend; -} - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/ManualScheduler.h b/runtime/onert/core/src/compiler/ManualScheduler.h deleted file mode 100644 index 41503f7ff..000000000 --- a/runtime/onert/core/src/compiler/ManualScheduler.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_CORE_COMPILER_MANUAL_SCHEDULER_H__ -#define __ONERT_CORE_COMPILER_MANUAL_SCHEDULER_H__ - -#include "IScheduler.h" -#include "compiler/Compiler.h" - -namespace onert -{ -namespace compiler -{ - -class ManualScheduler : public IScheduler -{ -public: - ManualScheduler(const backend::BackendContexts &backend_contexts, - const compiler::CompilerOptions &options); - std::unique_ptr<BackendResolver> schedule(const ir::Graph &graph) override; - -private: - const backend::Backend *resolveBackend(const std::string &id, - const backend::Backend *fallback = nullptr); - -private: - const backend::BackendContexts &_backend_contexts; - compiler::CompilerOptions _options; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_CORE_COMPILER_MANUAL_SCHEDULER_H__ diff --git a/runtime/onert/core/src/compiler/OperationValidator.cc b/runtime/onert/core/src/compiler/OperationValidator.cc deleted file mode 100644 index 0582cf154..000000000 --- a/runtime/onert/core/src/compiler/OperationValidator.cc +++ /dev/null @@ -1,244 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OperationValidator.h" - -#include "ir/Graph.h" - -#define OP_REQUIRES(EXP) \ - do \ - { \ - if (!(EXP)) \ - throw std::runtime_error("OperationValidator failed at line " + std::to_string(__LINE__)); \ - } while (0) - -namespace onert -{ -namespace compiler -{ - -OperationValidator::OperationValidator(const ir::Graph &graph) - : _graph{graph}, _ctx{graph.operands()} -{ -} - -void OperationValidator::operator()() -{ - assert(_graph.subgraphs() == nullptr); - - _graph.operations().iterate( - [&](const ir::OperationIndex &, const ir::Operation &node) { node.accept(*this); }); -} - -void OperationValidator::visit(const ir::operation::BatchMatMul &node) -{ - const auto lhs_index(node.getInputs().at(ir::operation::BatchMatMul::Input::LHS)); - const auto rhs_index(node.getInputs().at(ir::operation::BatchMatMul::Input::RHS)); - - // Constant lhs and rhs is not implemented yet - OP_REQUIRES(!_ctx.at(lhs_index).isConstant() && !_ctx.at(rhs_index).isConstant()); -} - -void OperationValidator::visit(const ir::operation::BatchToSpaceND &node) -{ - const auto block_size_index{ - node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)}; - - // Non-constant block_size is not implemented yet - OP_REQUIRES(_ctx.at(block_size_index).isConstant()); -} - -void OperationValidator::visit(const ir::operation::Comparison &node) -{ - const auto output_index{node.getOutputs().at(0)}; - - const auto lhs_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT0)}; - const auto rhs_index{node.getInputs().at(ir::operation::Comparison::Input::INPUT1)}; - - OP_REQUIRES(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(rhs_index).typeInfo().type()); - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == ir::DataType::BOOL8); -} - -void OperationValidator::visit(const ir::operation::DepthToSpace &node) -{ - int32_t block_size = node.param().block_size; - - OP_REQUIRES(block_size > 0); -} - -void OperationValidator::visit(const ir::operation::ElementwiseActivation &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(0)}; - - // Check if I/O types match - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == _ctx.at(input_index).typeInfo().type()); -} - -void OperationValidator::visit(const ir::operation::ElementwiseBinary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::ElementwiseBinary::Input::RHS)}; - - OP_REQUIRES(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(rhs_index).typeInfo().type()); - OP_REQUIRES(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(output_index).typeInfo().type()); -} - -void OperationValidator::visit(const ir::operation::ElementwiseUnary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)}; - - // Check if I/O types match - if (node.param().op_type == ir::operation::ElementwiseUnary::Type::DEQUANTIZE) - { - OP_REQUIRES(_ctx.at(input_index).typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM); - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == ir::DataType::FLOAT32); - } - else if (node.param().op_type == ir::operation::ElementwiseUnary::Type::QUANTIZE) - { - OP_REQUIRES(_ctx.at(input_index).typeInfo().type() == ir::DataType::FLOAT32); - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM); - } - else if (node.param().op_type != ir::operation::ElementwiseUnary::Type::CAST) - { - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == _ctx.at(input_index).typeInfo().type()); - } -} - -void OperationValidator::visit(const ir::operation::EmbeddingLookup &node) -{ - const auto lookups_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::LOOKUPS)}; - - OP_REQUIRES(_ctx.at(lookups_index).typeInfo().type() == ir::DataType::INT32); -} - -void OperationValidator::visit(const ir::operation::ExpandDims &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ExpandDims::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::ExpandDims::Input::AXIS)}; - - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == _ctx.at(input_index).typeInfo().type()); - OP_REQUIRES(_ctx.at(axis_index).typeInfo().type() == ir::DataType::INT32); -} - -void OperationValidator::visit(const ir::operation::HashtableLookup &node) -{ - const auto hits_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::HITS)}; - const auto lookups_index{node.getInputs().at(ir::operation::HashtableLookup::Input::LOOKUPS)}; - const auto keys_index{node.getInputs().at(ir::operation::HashtableLookup::Input::KEYS)}; - - OP_REQUIRES(_ctx.at(lookups_index).typeInfo().type() == ir::DataType::INT32); - OP_REQUIRES(_ctx.at(keys_index).typeInfo().type() == ir::DataType::INT32); - OP_REQUIRES(_ctx.at(hits_index).typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM); -} - -void OperationValidator::visit(const ir::operation::Pack &node) -{ - const auto num{node.param().num}; - - OP_REQUIRES(num == static_cast<int32_t>(node.getInputs().size())); -} - -void OperationValidator::visit(const ir::operation::Pad &node) -{ - const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)}; - - OP_REQUIRES(_ctx.at(pad_index).typeInfo().type() == ir::DataType::INT32); -} - -void OperationValidator::visit(const ir::operation::ResizeBilinear &node) -{ - auto align_corners = node.param().align_corners; - auto half_pixel_centers = node.param().half_pixel_centers; - - OP_REQUIRES(!align_corners || !half_pixel_centers); -} - -void OperationValidator::visit(const ir::operation::Reverse &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reverse::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::Reverse::Input::AXIS)}; - - OP_REQUIRES(_ctx.at(axis_index).typeInfo().type() == ir::DataType::INT32); - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == _ctx.at(input_index).typeInfo().type()); -} - -void OperationValidator::visit(const ir::operation::SpaceToBatchND &node) -{ - const auto block_size_index{ - node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)}; - const auto paddings_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)}; - - // Non-constant block_size and padding is not implemented yet - OP_REQUIRES(_ctx.at(block_size_index).isConstant()); - OP_REQUIRES(_ctx.at(paddings_index).isConstant()); -} - -void OperationValidator::visit(const ir::operation::SpaceToDepth &node) -{ - const auto block_size = node.param().block_size; - OP_REQUIRES(block_size >= 1); -} - -void OperationValidator::visit(const ir::operation::Split &node) -{ - const auto num_splits = node.param().num_splits; - - OP_REQUIRES(num_splits > 0 && num_splits <= 0xFFFF); - OP_REQUIRES(node.getOutputs().size() == static_cast<uint32_t>(num_splits)); -} - -void OperationValidator::visit(const ir::operation::SquaredDifference &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)}; - - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == _ctx.at(lhs_index).typeInfo().type()); - OP_REQUIRES(_ctx.at(lhs_index).typeInfo().type() == _ctx.at(rhs_index).typeInfo().type()); -} - -void OperationValidator::visit(const ir::operation::StridedSlice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)}; - - OP_REQUIRES(_ctx.at(output_index).typeInfo().type() == _ctx.at(input_index).typeInfo().type()); -} - -void OperationValidator::visit(const ir::operation::TransposeConv &node) -{ - OP_REQUIRES((node.param().padding.type == ir::PaddingType::SAME) || - (node.param().padding.type == ir::PaddingType::VALID)); -} - -void OperationValidator::visit(const ir::operation::Unpack &node) -{ - const auto num{node.param().num}; - OP_REQUIRES(num == static_cast<int32_t>(node.getOutputs().size())); -} - -void OperationValidator::visit(const ir::operation::While &node) -{ - OP_REQUIRES(node.getInputs().size() == node.getOutputs().size()); -} - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/OperationValidator.h b/runtime/onert/core/src/compiler/OperationValidator.h deleted file mode 100644 index f884a3765..000000000 --- a/runtime/onert/core/src/compiler/OperationValidator.h +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_OPERATION_VALIDATOR_H__ -#define __ONERT_COMPILER_OPERATION_VALIDATOR_H__ - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -class Graph; -class Operands; -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace compiler -{ - -class OperationValidator : public ir::OperationVisitor -{ -public: - OperationValidator(void) = delete; - OperationValidator(const ir::Graph &graph); - -public: - void operator()(); - -public: - void visit(const ir::operation::BatchMatMul &node) override; - void visit(const ir::operation::BatchToSpaceND &node) override; - void visit(const ir::operation::Comparison &node) override; - void visit(const ir::operation::DepthToSpace &node) override; - void visit(const ir::operation::ElementwiseActivation &node) override; - void visit(const ir::operation::ElementwiseBinary &node) override; - void visit(const ir::operation::ElementwiseUnary &node) override; - void visit(const ir::operation::EmbeddingLookup &node) override; - void visit(const ir::operation::ExpandDims &node) override; - void visit(const ir::operation::HashtableLookup &node) override; - void visit(const ir::operation::Pack &node) override; - void visit(const ir::operation::Pad &node) override; - void visit(const ir::operation::ResizeBilinear &node) override; - void visit(const ir::operation::Reverse &node) override; - void visit(const ir::operation::SpaceToBatchND &node) override; - void visit(const ir::operation::SpaceToDepth &node) override; - void visit(const ir::operation::Split &node) override; - void visit(const ir::operation::SquaredDifference &node) override; - void visit(const ir::operation::StridedSlice &node) override; - void visit(const ir::operation::TransposeConv &node) override; - void visit(const ir::operation::Unpack &node) override; - void visit(const ir::operation::While &node) override; - -private: - // TODO Remove _ctx field - const ir::Graph &_graph; - const ir::Operands &_ctx; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_OPERATION_VALIDATOR_H__ diff --git a/runtime/onert/core/src/compiler/ParamChecker.cc b/runtime/onert/core/src/compiler/ParamChecker.cc deleted file mode 100644 index c4f80f087..000000000 --- a/runtime/onert/core/src/compiler/ParamChecker.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ParamChecker.h" - -#include "ir/Graph.h" - -namespace onert -{ -namespace compiler -{ - -void ParamChecker::operator()() -{ - _model->operations().iterate( - [&](const ir::OperationIndex &, const ir::Operation &node) { node.accept(*this); }); -} - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/ParamChecker.h b/runtime/onert/core/src/compiler/ParamChecker.h deleted file mode 100644 index 61429d521..000000000 --- a/runtime/onert/core/src/compiler/ParamChecker.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file ParamChecker.h - * @brief This file contains ParamChecker to check\n - * operations' parameters are compilable at machine independent phase\n - * ex) Check param is constant - */ -#ifndef __ONERT_COMPILER_PARAM_CHECKER_H__ -#define __ONERT_COMPILER_PARAM_CHECKER_H__ - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -class Graph; -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace compiler -{ - -class ParamChecker : public ir::OperationVisitor -{ -public: - /** - * @brief Construct a new Param Checker object (deleted) - */ - ParamChecker(void) = delete; - /** - * @brief Construct a new Param Checker object - * @param[in] model Graph model to check - */ - ParamChecker(std::shared_ptr<ir::Graph> model) : _model{model} {} - -public: - /** - * @brief Run parameter analysis - */ - void operator()(); - /** - * @brief Return analysis result if model have non-const parameter - * @return @c true if there is non-const parameter, otherwise @c false - */ - bool haveNoneConstParam(void) { return _nonConstParam; } - -private: - const std::shared_ptr<ir::Graph> _model; - bool _nonConstParam{false}; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_OPERATION_VALIDATOR_H__ diff --git a/runtime/onert/core/src/compiler/ShapeValidator.cc b/runtime/onert/core/src/compiler/ShapeValidator.cc deleted file mode 100644 index 8be4fe6ec..000000000 --- a/runtime/onert/core/src/compiler/ShapeValidator.cc +++ /dev/null @@ -1,1021 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ShapeValidator.h" - -#include <typeinfo> - -#include "ir/Graph.h" -#include "ir/operation/LowerInfo.h" - -#include "util/logging.h" -#include "util/Utils.h" - -#define OP_REQUIRES(EXP) \ - do \ - { \ - if (!(EXP)) \ - throw std::runtime_error("ShapeValidator failed at line " + std::to_string(__LINE__)); \ - } while (0) - -namespace onert -{ -namespace compiler -{ - -ShapeValidator::ShapeValidator(const ir::Graph &graph) - : _graph{graph}, _ctx{graph.operands()}, _current_op_seq_layout{ir::Layout::UNKNOWN} -{ -} - -void ShapeValidator::checkUnaryOp(const ir::Operation &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(0)}; - - if (_ctx.at(output_index).info().isDynamic()) - return; - - // Check if I/O shapes match - OP_REQUIRES(_ctx.at(output_index).shape() == _ctx.at(input_index).shape()); -} - -void ShapeValidator::operator()() -{ - // There is no reason for each subgraph to have subgraphs since compiler has subgraphs when - // creating Compiler - assert(_graph.subgraphs() == nullptr); - - _current_op_seq_layout = _graph.layout(); - - _graph.operations().iterate( - [&](const ir::OperationIndex &, const ir::Operation &node) { node.accept(*this); }); -} - -void ShapeValidator::visit(const ir::operation::BatchMatMul &node) -{ - const auto lhs_index(node.getInputs().at(ir::operation::BatchMatMul::Input::LHS)); - const auto rhs_index(node.getInputs().at(ir::operation::BatchMatMul::Input::RHS)); - const auto out_index{node.getOutputs().at(0)}; - - if (_ctx.at(out_index).info().isDynamic()) - return; - - OP_REQUIRES(_ctx.at(lhs_index).shape().rank() <= 4); - OP_REQUIRES(_ctx.at(rhs_index).shape().rank() <= 4); - OP_REQUIRES(_ctx.at(lhs_index).shape().rank() >= 2); - OP_REQUIRES(_ctx.at(rhs_index).shape().rank() >= 2); -} - -void ShapeValidator::visit(const ir::operation::BatchToSpaceND &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::BatchToSpaceND::Input::INPUT)}; - const auto block_size_index{ - node.getInputs().at(ir::operation::BatchToSpaceND::Input::BLOCK_SIZE)}; - - const auto frontend_layout = _current_op_seq_layout; - const auto input_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout); - const auto output_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout); - - // All requirement as per NNAPI specification. - OP_REQUIRES(_ctx.at(ifm_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(ofm_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(block_size_index).shape().rank() == 1); - - OP_REQUIRES(_ctx.at(block_size_index).shape().dim(0) == 2); - - OP_REQUIRES(input_shape.C == output_shape.C); -} - -void ShapeValidator::visit(const ir::operation::BCQFullyConnected &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::BCQFullyConnected::Input::INPUT)}; - const auto weight_scales_index{ - node.getInputs().at(ir::operation::BCQFullyConnected::Input::WEIGHTS_SCALES)}; - const auto weight_binary_index{ - node.getInputs().at(ir::operation::BCQFullyConnected::Input::WEIGHTS_BINARY)}; - const auto weight_cluster_index{ - node.getInputs().at(ir::operation::BCQFullyConnected::Input::WEIGHTS_CLUSTERS)}; - // const auto bias_index{node.getInputs().at(ir::operation::BCQFullyConnected::Input::BIAS)}; - - OP_REQUIRES(_ctx.at(ifm_index).shape().rank() == 2); - OP_REQUIRES(_ctx.at(ofm_index).shape().rank() == 2); - OP_REQUIRES(_ctx.at(weight_scales_index).shape().rank() == 1); - OP_REQUIRES(_ctx.at(weight_binary_index).shape().rank() == 2); - OP_REQUIRES(_ctx.at(weight_cluster_index).shape().rank() == 2); - - OP_REQUIRES(_ctx.at(ifm_index).shape().dim(1) == _ctx.at(ofm_index).shape().dim(1)); - - OP_REQUIRES(_ctx.at(weight_cluster_index).shape().dim(0) > 0); - OP_REQUIRES(_ctx.at(weight_cluster_index).shape().dim(1) == 2); - - // more shape validation will be done inside kernel. - - // TODO Check bias dimension (can be null tensor) -} - -void ShapeValidator::visit(const ir::operation::BCQGather &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto indices_index{node.getInputs().at(ir::operation::BCQGather::Input::INDICES)}; - const auto input_binary_index{node.getInputs().at(ir::operation::BCQGather::Input::INPUT_BINARY)}; - const auto input_scales_index{node.getInputs().at(ir::operation::BCQGather::Input::INPUT_SCALES)}; - const auto input_clusters_index{ - node.getInputs().at(ir::operation::BCQGather::Input::INPUT_CLUSTERS)}; - - OP_REQUIRES(_ctx.at(indices_index).shape().rank() <= 2); // TODO : support rank up to 4 or more - OP_REQUIRES(_ctx.at(input_binary_index).shape().rank() == 2); - OP_REQUIRES(_ctx.at(input_scales_index).shape().rank() == 1); - OP_REQUIRES(_ctx.at(input_clusters_index).shape().rank() == 2); - - OP_REQUIRES(_ctx.at(input_clusters_index).shape().dim(0) > 0); - OP_REQUIRES(_ctx.at(input_clusters_index).shape().dim(1) == 2); - - // more shape validation will be done inside kernel. -} - -void ShapeValidator::visit(const ir::operation::Comparison &) -{ - // TODO Shape validation of comparison -} - -void ShapeValidator::visit(const ir::operation::Softmax &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(0)}; - - OP_REQUIRES(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank()); -} - -void ShapeValidator::visit(const ir::operation::InstanceNorm &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::InstanceNorm::Input::INPUT)}; - const auto gamma_index{node.getInputs().at(ir::operation::InstanceNorm::Input::GAMMA)}; - const auto beta_index{node.getInputs().at(ir::operation::InstanceNorm::Input::BETA)}; - - OP_REQUIRES(_ctx.at(ifm_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(ifm_index).shape() == _ctx.at(ofm_index).shape()); - OP_REQUIRES(_ctx.at(gamma_index).shape().rank() == 1); - OP_REQUIRES(_ctx.at(beta_index).shape().rank() == 1); -} - -void ShapeValidator::visit(const ir::operation::Pool2D &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::Pool2D::Input::INPUT)}; - - OP_REQUIRES(_ctx.at(ifm_index).shape().rank() == 4); -} - -void ShapeValidator::visit(const ir::operation::Permute &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(0)}; - - OP_REQUIRES(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank()); -} - -void ShapeValidator::visit(const ir::operation::Reduce &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(ir::operation::Reduce::Input::INPUT)}; - const auto input_shape = _ctx.at(input_index).shape(); - const auto output_shape = _ctx.at(output_index).shape(); - - OP_REQUIRES(input_shape.rank() <= 4); - OP_REQUIRES(output_shape.rank() <= input_shape.rank()); - - // NOTE For the 4-dimensions, if the rank of input and output are different, this runtime only - // supports cases reducing height and width or reducing depth. - // TODO We have to support all cases of dimensions up to 4. - // For correct permuting, we have to set output's shape to be equal in dimension position of the - // input. But the positions of the same dimensions in the input and output may be set differently. - // For example {2,3,4,5}(input's shape) can be reduced to {3,5}(output's shape). The original - // output shape should be {1,3,1,5}, but real output shape may be {3,5}. If you simply try to - // extend it in 4 dimensions, it should be {1,1,3,5}. - // Even if output shape is changed to {1,3,1,5}, there is another problem. It is that shape of - // output tensor used at next operation is changed to {1,3,1,5} after this operation even if the - // next operation is not desired. - if (input_shape.rank() == 4 && input_shape.rank() != output_shape.rank()) - { - if (output_shape.rank() == 2) - { - // Reducing HW - OP_REQUIRES(input_shape.dim(0) == output_shape.dim(0) && - input_shape.dim(3) == output_shape.dim(1)); - } - else if (output_shape.rank() == 3) - { - // Reducing C or - // (Reducing H and C(input and output) == 1) or (Reducing W and C(input and output) == 1) - OP_REQUIRES((input_shape.dim(0) == output_shape.dim(0) && - input_shape.dim(1) == output_shape.dim(1) && - input_shape.dim(2) == output_shape.dim(2)) || - (input_shape.dim(0) == output_shape.dim(0) && - (input_shape.dim(1) == output_shape.dim(1) || - input_shape.dim(2) == output_shape.dim(1)) && - input_shape.dim(3) == 1 && output_shape.dim(2) == 1)); - } - } -} - -void ShapeValidator::visit(const ir::operation::Transpose &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(ir::operation::Transpose::Input::INPUT)}; - const auto perm_index{node.getInputs().at(ir::operation::Transpose::Input::PERMUTATION)}; - - const auto &output_shape = _ctx.at(output_index).shape(); - const auto &input_shape = _ctx.at(input_index).shape(); - - OP_REQUIRES(_ctx.at(perm_index).shape().num_elements() == 0 || - input_shape.rank() == static_cast<int>(_ctx.at(perm_index).shape().num_elements())); - OP_REQUIRES(input_shape.rank() == output_shape.rank()); -} - -void ShapeValidator::visit(const ir::operation::RNN &node) -{ - // NOTE This validation is for static rnn(non-dynamic shape), but not for dynamic rnn - // TODO Support dynamic rnn - const auto output_index{node.getOutputs().at(ir::operation::RNN::Output::OUTPUT)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto hidden_state_out_index{ - node.getOutputs().at(ir::operation::RNN::Output::HIDDEN_STATE_OUT)}; - - const auto input_index{node.getInputs().at(ir::operation::RNN::Input::INPUT)}; - const auto weights_index{node.getInputs().at(ir::operation::RNN::Input::WEIGHTS)}; - const auto recurrent_weights_index{ - node.getInputs().at(ir::operation::RNN::Input::RECURRENT_WEIGHTS)}; - const auto bias_index{node.getInputs().at(ir::operation::RNN::Input::BIAS)}; - const auto hidden_state_in_index{node.getInputs().at(ir::operation::RNN::Input::HIDDEN_STATE_IN)}; - - const auto batch_size = _ctx.at(output_index).shape().dim(0); - const auto num_units = _ctx.at(output_index).shape().dim(1); - - OP_REQUIRES(_ctx.at(output_index).shape().rank() == 2 && - _ctx.at(hidden_state_out_index).shape().rank() == 2 && - _ctx.at(input_index).shape().rank() == 2 && - _ctx.at(weights_index).shape().rank() == 2 && - _ctx.at(recurrent_weights_index).shape().rank() == 2 && - _ctx.at(hidden_state_in_index).shape().rank() == 2); - OP_REQUIRES(_ctx.at(bias_index).shape().rank() == 1); - - OP_REQUIRES(batch_size == _ctx.at(input_index).shape().dim(0) && - batch_size == _ctx.at(hidden_state_in_index).shape().dim(0) && - batch_size == _ctx.at(hidden_state_out_index).shape().dim(0)); - OP_REQUIRES(_ctx.at(input_index).shape().dim(1) == _ctx.at(weights_index).shape().dim(1)); - - OP_REQUIRES(num_units == _ctx.at(weights_index).shape().dim(0) && - num_units == _ctx.at(recurrent_weights_index).shape().dim(0) && - num_units == _ctx.at(bias_index).shape().dim(0)); - OP_REQUIRES(num_units == _ctx.at(output_index).shape().dim(1) && - num_units == _ctx.at(recurrent_weights_index).shape().dim(1) && - num_units == _ctx.at(hidden_state_in_index).shape().dim(1) && - num_units == _ctx.at(hidden_state_out_index).shape().dim(1)); -} - -void ShapeValidator::visit(const ir::operation::SpaceToBatchND &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)}; - const auto block_size_index{ - node.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)}; - const auto paddings_index{node.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)}; - - const auto frontend_layout = _current_op_seq_layout; - const auto input_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout); - const auto output_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout); - - // All requirement as per NNAPI specification. - OP_REQUIRES(_ctx.at(ifm_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(ofm_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(block_size_index).shape().rank() == 1); - OP_REQUIRES(_ctx.at(paddings_index).shape().rank() == 2); - - OP_REQUIRES(_ctx.at(block_size_index).shape().dim(0) == 2); - OP_REQUIRES(_ctx.at(paddings_index).shape().dim(0) == 2); - OP_REQUIRES(_ctx.at(paddings_index).shape().dim(1) == 2); - - OP_REQUIRES(input_shape.C == output_shape.C); -} - -void ShapeValidator::visit(const ir::operation::SpaceToDepth &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::SpaceToDepth::Input::INPUT)}; - - const auto frontend_layout = _current_op_seq_layout; - const auto input_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout); - const auto output_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout); - const auto block_size = node.param().block_size; - - // All assertions as per NNAPI specification. - OP_REQUIRES(_ctx.at(ifm_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(ofm_index).shape().rank() == 4); - OP_REQUIRES((input_shape.H % block_size == 0) && (input_shape.W % block_size == 0)); - OP_REQUIRES(input_shape.N == output_shape.N); - OP_REQUIRES(input_shape.C * block_size * block_size == output_shape.C); -} - -void ShapeValidator::visit(const ir::operation::ElementwiseActivation &node) { checkUnaryOp(node); } - -void ShapeValidator::visit(const ir::operation::ElementwiseBinary &) -{ - // TODO Shape validation of ElementwiseBinary -} - -void ShapeValidator::visit(const ir::operation::ElementwiseUnary &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)}; - - if (_ctx.at(output_index).info().isDynamic()) - return; - - OP_REQUIRES(_ctx.at(output_index).shape() == _ctx.at(input_index).shape()); -} - -void ShapeValidator::visit(const ir::operation::EmbeddingLookup &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lookups_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::LOOKUPS)}; - const auto values_index{node.getInputs().at(ir::operation::EmbeddingLookup::Input::VALUES)}; - - const auto &output_obj = _ctx.at(output_index); - const auto &lookups_obj = _ctx.at(lookups_index); - const auto &values_obj = _ctx.at(values_index); - - // Verify operand here, not at SimpleEmbeddingLookup::configure() to avoid acl's modifying - // TensorShape sometimes(Issue: https://github.sec.samsung.net/STAR/nnfw/issues/729) - { - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto &output_shape = output_obj.shape(); - const auto &lookups_shape = lookups_obj.shape(); - const auto &values_shape = values_obj.shape(); - - OP_REQUIRES(lookups_shape.rank() == 1); - OP_REQUIRES(values_shape.rank() >= 2); - - // output should be a n-D tensor with the same rank and shape as the values tensor, except for - // the first dimension which has the same size as lookups' only dimension. - OP_REQUIRES(output_shape.rank() == values_shape.rank()); - OP_REQUIRES(output_shape.dim(0) == lookups_shape.dim(0)); - for (int n = 1; n < output_shape.rank(); ++n) - { - OP_REQUIRES(output_shape.dim(n) == values_shape.dim(n)); - } - } -} - -void ShapeValidator::visit(const ir::operation::ExpandDims &node) -{ - const auto axis_index{node.getInputs().at(ir::operation::ExpandDims::Input::AXIS)}; - - if (_ctx.at(axis_index).info().isDynamic()) - return; - OP_REQUIRES(_ctx.at(axis_index).shape().rank() <= 1); -} - -void ShapeValidator::visit(const ir::operation::HashtableLookup &node) -{ - const auto output_index{node.getOutputs().at(ir::operation::HashtableLookup::Output::OUTPUT)}; - const auto lookups_index{node.getInputs().at(ir::operation::HashtableLookup::Input::LOOKUPS)}; - const auto keys_index{node.getInputs().at(ir::operation::HashtableLookup::Input::KEYS)}; - const auto values_index{node.getInputs().at(ir::operation::HashtableLookup::Input::VALUES)}; - - const auto &output_obj = _ctx.at(output_index); - const auto &lookups_obj = _ctx.at(lookups_index); - const auto &keys_obj = _ctx.at(keys_index); - const auto &values_obj = _ctx.at(values_index); - - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto &output_shape = output_obj.shape(); - const auto &lookups_shape = lookups_obj.shape(); - const auto &keys_shape = keys_obj.shape(); - const auto &values_shape = values_obj.shape(); - - OP_REQUIRES(values_shape.rank() == output_shape.rank()); - OP_REQUIRES(lookups_shape.rank() == 1); - OP_REQUIRES(keys_shape.rank() == 1); - OP_REQUIRES(values_shape.dim(0) == keys_shape.dim(0)); - OP_REQUIRES(lookups_shape.dim(0) == output_shape.dim(0)); -} - -void ShapeValidator::visit(const ir::operation::TransposeConv &node) -{ - // shape check - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::TransposeConv::Input::INPUT)}; - const auto ker_index{node.getInputs().at(ir::operation::TransposeConv::Input::KERNEL)}; - - // Only 4D tensors are supported - OP_REQUIRES(_ctx.at(ofm_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(ofm_index).shape().rank() == _ctx.at(ifm_index).shape().rank()); - OP_REQUIRES(_ctx.at(ofm_index).shape().rank() == _ctx.at(ker_index).shape().rank()); - - const auto frontend_layout = _current_op_seq_layout; - const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature(frontend_layout); - const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature(frontend_layout); - // The kernel has only IHWO layout on frontend - // So ker_shape is treated here below - // I -> N - // H -> H - // W -> W - // O -> C - const auto ker_shape = _ctx.at(ker_index).shape().asFeature(ir::Layout::NHWC); - - OP_REQUIRES(ifm_shape.N == ofm_shape.N); - OP_REQUIRES(ifm_shape.C == ker_shape.C); - OP_REQUIRES(ker_shape.N == ofm_shape.C); -} - -void ShapeValidator::visit(const ir::operation::Gather &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::Gather::Input::INPUT)}; - const auto indices_index{node.getInputs().at(ir::operation::Gather::Input::INDICES)}; - - const auto ifm_shape = _ctx.at(ifm_index).shape(); - const auto indices_shape = _ctx.at(indices_index).shape(); - const auto ofm_shape = _ctx.at(ofm_index).shape(); - - OP_REQUIRES(ifm_shape.rank() <= 4); - OP_REQUIRES(indices_shape.rank() <= 3); - OP_REQUIRES(ofm_shape.rank() <= 4); -} - -void ShapeValidator::visit(const ir::operation::DepthToSpace &node) -{ - int32_t block_size = node.param().block_size; - - // shape check - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(ir::operation::DepthToSpace::Input::INPUT)}; - - const auto frontend_layout = _current_op_seq_layout; - const auto output_shape = _ctx.at(output_index).shape().asFeature(frontend_layout); - const auto input_shape = _ctx.at(input_index).shape().asFeature(frontend_layout); - - OP_REQUIRES(_ctx.at(input_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(output_index).shape().rank() == 4); - - { - OP_REQUIRES(output_shape.N == input_shape.N); - OP_REQUIRES(output_shape.H == input_shape.H * block_size); - OP_REQUIRES(output_shape.W == input_shape.W * block_size); - OP_REQUIRES(input_shape.C % (block_size * block_size) == 0); - OP_REQUIRES(output_shape.C == input_shape.C / (block_size * block_size)); - } -} - -void ShapeValidator::visit(const ir::operation::Pack &node) -{ - const auto axis{node.param().axis}; - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - // shape check - const auto &output_shape = _ctx.at(output_index).shape(); - const auto output_rank = static_cast<int32_t>(output_shape.rank()); - - const auto input1_index{node.getInputs().at(0)}; - const auto input_shape = _ctx.at(input1_index).shape(); - - OP_REQUIRES(axis >= -output_rank && axis < output_rank); - for (const auto &index : node.getInputs()) - { - OP_REQUIRES(input_shape == _ctx.at(index).shape()); - } -} - -void ShapeValidator::visit(const ir::operation::LSTM &node) -{ - // NOTE This validation is for static rnn(non-dynamic shape), but not for dynamic rnn - // TODO Support dynamic rnn - const auto output_index{node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto scratch_buffer_index{ - node.getOutputs().at(ir::operation::LSTM::Output::SCRATCH_BUFFER)}; - const auto output_state_out_index{ - node.getOutputs().at(ir::operation::LSTM::Output::OUTPUT_STATE_OUT)}; - const auto cell_state_out_index{ - node.getOutputs().at(ir::operation::LSTM::Output::CELL_STATE_OUT)}; - - const auto input_index{node.getInputs().at(ir::operation::LSTM::Input::INPUT)}; - const auto input_to_input_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_INPUT_WEIGHTS)}; - const auto input_to_forget_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_FORGET_WEIGHTS)}; - const auto input_to_cell_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_CELL_WEIGHTS)}; - const auto input_to_output_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS)}; - const auto recurrent_to_input_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS)}; - const auto recurrent_to_forget_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_FORGET_WEIGHTS)}; - const auto recurrent_to_cell_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_CELL_WEIGHTS)}; - const auto recurrent_to_output_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS)}; - const auto cell_to_input_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_INPUT_WEIGHTS)}; - const auto cell_to_forget_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_FORGET_WEIGHTS)}; - const auto cell_to_output_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::CELL_TO_OUTPUT_WEIGHTS)}; - const auto input_gate_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::INPUT_GATE_BIAS)}; - const auto forget_gate_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::FORGET_GATE_BIAS)}; - const auto cell_bias_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_BIAS)}; - const auto output_gate_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_GATE_BIAS)}; - const auto projection_weights_index{ - node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_WEIGHTS)}; - const auto projection_bias_index{ - node.getInputs().at(ir::operation::LSTM::Input::PROJECTION_BIAS)}; - const auto output_state_in_index{ - node.getInputs().at(ir::operation::LSTM::Input::OUTPUT_STATE_IN)}; - const auto cell_state_in_index{node.getInputs().at(ir::operation::LSTM::Input::CELL_STATE_IN)}; - - OP_REQUIRES(_ctx.at(input_index).shape().rank() == _ctx.at(output_index).shape().rank()); - for (int i = 0; i < _ctx.at(input_index).shape().rank() - 1; ++i) - { - OP_REQUIRES(_ctx.at(input_index).shape().dim(i) == _ctx.at(output_index).shape().dim(i)); - } - OP_REQUIRES( - (_ctx.at(output_index).shape().rank() == 2 || _ctx.at(output_index).shape().rank() == 3) && - (_ctx.at(input_index).shape().rank() == 2 || _ctx.at(input_index).shape().rank() == 3) && - _ctx.at(input_to_input_weights_index).shape().rank() == 2 && - _ctx.at(input_to_forget_weights_index).shape().rank() == 2 && - _ctx.at(input_to_cell_weights_index).shape().rank() == 2 && - _ctx.at(input_to_output_weights_index).shape().rank() == 2 && - _ctx.at(recurrent_to_input_weights_index).shape().rank() == 2 && - _ctx.at(recurrent_to_forget_weights_index).shape().rank() == 2 && - _ctx.at(recurrent_to_cell_weights_index).shape().rank() == 2 && - _ctx.at(recurrent_to_output_weights_index).shape().rank() == 2 && - _ctx.at(projection_weights_index).shape().rank() == 2 && - _ctx.at(output_state_in_index).shape().rank() == 2 && - _ctx.at(cell_state_in_index).shape().rank() == 2); - - OP_REQUIRES(_ctx.at(cell_to_input_weights_index).shape().rank() == 1 && - _ctx.at(cell_to_forget_weights_index).shape().rank() == 1 && - _ctx.at(cell_to_output_weights_index).shape().rank() == 1 && - _ctx.at(input_gate_bias_index).shape().rank() == 1 && - _ctx.at(forget_gate_bias_index).shape().rank() == 1 && - _ctx.at(cell_bias_index).shape().rank() == 1 && - _ctx.at(output_gate_bias_index).shape().rank() == 1 && - _ctx.at(projection_bias_index).shape().rank() == 1); - - // CIFG assertion - OP_REQUIRES((_ctx.at(input_to_input_weights_index).shape().dim(0) == 0 && - _ctx.at(input_to_input_weights_index).shape().dim(1) == 0 && - _ctx.at(recurrent_to_input_weights_index).shape().dim(0) == 0 && - _ctx.at(recurrent_to_input_weights_index).shape().dim(1) == 0 && - _ctx.at(input_gate_bias_index).shape().dim(0) == 0 && - _ctx.at(cell_to_input_weights_index).shape().dim(0) == 0) || - (_ctx.at(input_to_input_weights_index).shape().dim(0) != 0 && - _ctx.at(input_to_input_weights_index).shape().dim(1) != 0 && - _ctx.at(recurrent_to_input_weights_index).shape().dim(0) != 0 && - _ctx.at(recurrent_to_input_weights_index).shape().dim(1) != 0 && - _ctx.at(input_gate_bias_index).shape().dim(0) != 0)); - - // Peephole assertion - OP_REQUIRES((_ctx.at(cell_to_forget_weights_index).shape().dim(0) == 0 && - _ctx.at(cell_to_output_weights_index).shape().dim(0) == 0) || - (_ctx.at(cell_to_forget_weights_index).shape().dim(0) != 0 && - _ctx.at(cell_to_output_weights_index).shape().dim(0) != 0)); - - bool has_input_to_input_weights = _ctx.at(input_to_input_weights_index).shape().dim(0) != 0 && - _ctx.at(input_to_input_weights_index).shape().dim(1) != 0; - bool has_recurrent_to_input_weights = - _ctx.at(recurrent_to_input_weights_index).shape().dim(0) != 0 && - _ctx.at(recurrent_to_input_weights_index).shape().dim(1) != 0; - bool has_input_gate_bias = _ctx.at(input_gate_bias_index).shape().dim(0) != 0; - bool has_cell_to_input_weights = _ctx.at(cell_to_input_weights_index).shape().dim(0) != 0; - bool has_cell_to_forget_weights = _ctx.at(cell_to_forget_weights_index).shape().dim(0) != 0; - bool has_cell_to_output_weights = _ctx.at(cell_to_output_weights_index).shape().dim(0) != 0; - bool has_projection_weights = _ctx.at(projection_weights_index).shape().dim(0) != 0 && - _ctx.at(projection_weights_index).shape().dim(1) != 0; - bool has_projection_bias = _ctx.at(projection_bias_index).shape().dim(0); - - // NOTE The cell_to_input_weights do not exist in non-peephole although regular LSTM(non-CIFG). - // true: no CIFG - // false: CIFG - bool has_cifg_param = has_input_to_input_weights && has_recurrent_to_input_weights; - - // NOTE The cell_to_input_weights do not exist in regular CIFG although peephole. - // true: peephole - // false: no peephole - bool has_peephole_param = has_cell_to_forget_weights && has_cell_to_output_weights; - - // NOTE The projection weights may have data but the projection bias may not. - bool has_projection_param = has_projection_weights; - - const auto batch_size = (_ctx.at(input_index).shape().rank() == 3 && node.param().time_major) - ? _ctx.at(input_index).shape().dim(1) - : _ctx.at(input_index).shape().dim(0); - OP_REQUIRES(batch_size == _ctx.at(output_state_in_index).shape().dim(0) && - batch_size == _ctx.at(cell_state_in_index).shape().dim(0)); - - const auto input_size = _ctx.at(input_index).shape().dim(_ctx.at(input_index).shape().rank() - 1); - OP_REQUIRES(input_size == _ctx.at(input_to_forget_weights_index).shape().dim(1) && - input_size == _ctx.at(input_to_cell_weights_index).shape().dim(1) && - input_size == _ctx.at(input_to_output_weights_index).shape().dim(1)); - - const auto num_units = _ctx.at(input_to_output_weights_index).shape().dim(0); - OP_REQUIRES(num_units == _ctx.at(input_to_cell_weights_index).shape().dim(0) && - num_units == _ctx.at(input_to_output_weights_index).shape().dim(0) && - num_units == _ctx.at(recurrent_to_forget_weights_index).shape().dim(0) && - num_units == _ctx.at(recurrent_to_cell_weights_index).shape().dim(0) && - num_units == _ctx.at(recurrent_to_output_weights_index).shape().dim(0) && - num_units == _ctx.at(forget_gate_bias_index).shape().dim(0) && - num_units == _ctx.at(cell_bias_index).shape().dim(0) && - num_units == _ctx.at(output_gate_bias_index).shape().dim(0) && - num_units == _ctx.at(cell_state_in_index).shape().dim(1)); - - const auto output_size = - _ctx.at(output_index).shape().dim(_ctx.at(output_index).shape().rank() - 1); - OP_REQUIRES(output_size == _ctx.at(recurrent_to_forget_weights_index).shape().dim(1) && - output_size == _ctx.at(recurrent_to_cell_weights_index).shape().dim(1) && - output_size == _ctx.at(recurrent_to_output_weights_index).shape().dim(1) && - output_size == _ctx.at(output_state_in_index).shape().dim(1)); - - if (has_cifg_param) - { - OP_REQUIRES(input_size == _ctx.at(input_to_input_weights_index).shape().dim(1)); - OP_REQUIRES(num_units == _ctx.at(input_to_input_weights_index).shape().dim(0) && - num_units == _ctx.at(recurrent_to_input_weights_index).shape().dim(0) && - (num_units == _ctx.at(cell_to_input_weights_index).shape().dim(0) || - _ctx.at(cell_to_input_weights_index).shape().dim(0) == 0 /* non-peephole */) && - num_units == _ctx.at(input_gate_bias_index).shape().dim(0)); - OP_REQUIRES(output_size == _ctx.at(recurrent_to_input_weights_index).shape().dim(1)); - OP_REQUIRES(has_input_to_input_weights && has_recurrent_to_input_weights && - has_input_gate_bias); - if (has_cell_to_input_weights) - { - // NOTE The cell_to_input_weights exist only in case of non-CIFG and peephole. - OP_REQUIRES(has_peephole_param); - } - if (_ctx.exist(scratch_buffer_index)) - OP_REQUIRES(_ctx.at(scratch_buffer_index).shape().dim(1) == num_units * 4); - } - else - { - if (_ctx.exist(scratch_buffer_index)) - OP_REQUIRES(_ctx.at(scratch_buffer_index).shape().dim(1) == num_units * 3); - } - - if (has_peephole_param) - { - OP_REQUIRES(num_units == _ctx.at(cell_to_forget_weights_index).shape().dim(0) && - num_units == _ctx.at(cell_to_output_weights_index).shape().dim(0) && - (num_units == _ctx.at(cell_to_input_weights_index).shape().dim(0) || - _ctx.at(cell_to_input_weights_index).shape().dim(0) == 0 /* CIFG */)); - } - - if (has_projection_param) - { - OP_REQUIRES(num_units == _ctx.at(projection_weights_index).shape().dim(1)); - OP_REQUIRES(output_size == _ctx.at(projection_weights_index).shape().dim(0)); - if (has_projection_bias) - { - OP_REQUIRES(output_size == _ctx.at(projection_bias_index).shape().dim(0)); - } - } - - if (_ctx.exist(scratch_buffer_index)) - { - OP_REQUIRES(_ctx.at(scratch_buffer_index).shape().rank() == 2); - OP_REQUIRES(batch_size == _ctx.at(scratch_buffer_index).shape().dim(0)); - } - - if (_ctx.exist(output_state_out_index)) - { - OP_REQUIRES(_ctx.at(output_state_out_index).shape().rank() == 2); - OP_REQUIRES(batch_size == _ctx.at(output_state_out_index).shape().dim(0)); - OP_REQUIRES(output_size == _ctx.at(output_state_out_index).shape().dim(1)); - } - - if (_ctx.exist(cell_state_out_index)) - { - OP_REQUIRES(_ctx.at(cell_state_out_index).shape().rank() == 2); - OP_REQUIRES(batch_size == _ctx.at(cell_state_out_index).shape().dim(0)); - OP_REQUIRES(num_units == _ctx.at(cell_state_out_index).shape().dim(1)); - } -} - -void ShapeValidator::visit(const ir::operation::L2Normalization &node) -{ - const auto ofm_index{node.getOutputs().at(0)}; - if (_ctx.at(ofm_index).info().isDynamic()) - return; - - const auto ifm_index{node.getInputs().at(ir::operation::L2Normalization::Input::INPUT)}; - - auto ifm_shape = _ctx.at(ifm_index).shape(); - auto ofm_shape = _ctx.at(ofm_index).shape(); - - OP_REQUIRES(ifm_shape.rank() == ofm_shape.rank()); - - for (auto i = 0; i < ifm_shape.rank(); i++) - { - OP_REQUIRES(ifm_shape.dim(i) == ofm_shape.dim(i)); - } -} - -void ShapeValidator::visit(const ir::operation::Unpack &node) -{ - const auto axis{node.param().axis}; - const auto output_index{node.getInputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(ir::operation::Unpack::Input::INPUT)}; - - const auto &input_shape = _ctx.at(input_index).shape(); - const auto input_rank = static_cast<int32_t>(input_shape.rank()); - - OP_REQUIRES(axis >= -input_rank && axis < input_rank); -} - -void ShapeValidator::visit(const ir::operation::Pad &node) -{ - const auto pad_index{node.getInputs().at(ir::operation::Pad::Input::PAD)}; - OP_REQUIRES(_ctx.at(pad_index).typeInfo().type() == ir::DataType::INT32); - - const auto output_index{node.getInputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(ir::operation::Pad::Input::INPUT)}; - - const auto &pad_shape = _ctx.at(pad_index).shape(); - const auto input_rank = static_cast<int32_t>(_ctx.at(input_index).shape().rank()); - - OP_REQUIRES(pad_shape.rank() == 2); - OP_REQUIRES(pad_shape.dim(0) == input_rank); - OP_REQUIRES(pad_shape.dim(1) == 2); - OP_REQUIRES(_ctx.at(input_index).shape().rank() == _ctx.at(output_index).shape().rank()); -} - -void ShapeValidator::visit(const ir::operation::Select &node) -{ - const auto output_index{node.getOutputs().at(0)}; - // This validator does not check shape. So checking isDynamic() is skipped. - - const auto condition_index{node.getInputs().at(ir::operation::Select::Input::CONDITION)}; - const auto input_true_index{node.getInputs().at(ir::operation::Select::Input::INPUT_TRUE)}; - const auto input_false_index{node.getInputs().at(ir::operation::Select::Input::INPUT_FALSE)}; - UNUSED_RELEASE(output_index); - UNUSED_RELEASE(input_true_index); - UNUSED_RELEASE(input_false_index); - - OP_REQUIRES(_ctx.at(condition_index).typeInfo().type() == ir::DataType::BOOL8); -} - -void ShapeValidator::visit(const ir::operation::StridedSlice &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::StridedSlice::Input::INPUT)}; - - if (_ctx.at(output_index).info().isDynamic()) - return; - - OP_REQUIRES(_ctx.at(input_index).shape().rank() <= 4); -} - -void ShapeValidator::visit(const ir::operation::Split &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(ir::operation::Split::Input::INPUT)}; - const auto axis_index{node.getInputs().at(ir::operation::Split::Input::AXIS)}; - - const auto num_splits = node.param().num_splits; - const auto input_rank = _ctx.at(input_index).shape().rank(); - auto axis = *reinterpret_cast<const int32_t *>(_ctx.at(axis_index).data()->base()); - axis = axis < 0 ? axis + input_rank : axis; - - OP_REQUIRES(axis >= 0 && axis < input_rank); - OP_REQUIRES(_ctx.at(input_index).shape().dim(axis) % num_splits == 0); -} - -void ShapeValidator::visit(const ir::operation::Shape &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(0)}; - UNUSED_RELEASE(input_index); - OP_REQUIRES(_ctx.at(output_index).shape().rank() == 1); -} - -void ShapeValidator::visit(const ir::operation::ResizeBilinear &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::ResizeBilinear::Input::INPUT)}; - - if (_ctx.at(output_index).info().isDynamic()) - { - return; - } - OP_REQUIRES(_ctx.at(input_index).shape().rank() == 4); - OP_REQUIRES(_ctx.at(output_index).shape().rank() == 4); -} - -void ShapeValidator::visit(const ir::operation::Reverse &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::Reverse::Input::INPUT)}; - - if (_ctx.at(output_index).info().isDynamic()) - return; - OP_REQUIRES(_ctx.at(output_index).shape() == _ctx.at(input_index).shape()); -} - -void ShapeValidator::visit(const ir::operation::If &) -{ - // TODO Add to validate with subgraphs -} - -void ShapeValidator::visit(const ir::operation::While &) -{ - // This validator does not check shape. So checking isDynamic() is skipped. - // TODO Add to validate with subgraphs -} - -void ShapeValidator::visit(const ir::operation::SquaredDifference &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto lhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::LHS)}; - const auto rhs_index{node.getInputs().at(ir::operation::SquaredDifference::Input::RHS)}; - - // Check for dimension constraints - if (_ctx.at(output_index).info().isDynamic()) - return; - - auto output_shape = _ctx.at(output_index).shape(); - auto lhs_shape = _ctx.at(lhs_index).shape(); - auto rhs_shape = _ctx.at(rhs_index).shape(); - // Check for output rank - OP_REQUIRES(output_shape.rank() == std::max(lhs_shape.rank(), rhs_shape.rank())); - auto min_rank = std::min(lhs_shape.rank(), rhs_shape.rank()); - - for (int idx = 1; idx <= min_rank; idx++) - { - int l_idx = lhs_shape.rank() - idx; - int r_idx = rhs_shape.rank() - idx; - int out_idx = output_shape.rank() - idx; - - OP_REQUIRES((l_idx >= 0) && (r_idx >= 0) && (out_idx >= 0)); - - auto l_dims = lhs_shape.dim(l_idx); - auto r_dims = rhs_shape.dim(r_idx); - auto out_dims = output_shape.dim(out_idx); - - OP_REQUIRES(((l_dims == r_dims) && (out_dims == l_dims)) || - ((l_dims == 1) && (out_dims == r_dims)) || ((r_dims == 1) && (out_dims == l_dims))); - } - auto &tmp_shape = (lhs_shape.rank() > rhs_shape.rank()) ? lhs_shape : rhs_shape; - for (int idx = min_rank + 1; idx <= output_shape.rank(); idx++) - { - int out_idx = output_shape.rank() - idx; - int tmp_idx = tmp_shape.rank() - idx; - - OP_REQUIRES((out_idx >= 0) && (tmp_idx >= 0) && - (output_shape.dim(out_idx) == tmp_shape.dim(tmp_idx))); - } -} -void ShapeValidator::visit(const ir::operation::Tile &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(0)}; - const auto multiple_index{node.getInputs().at(1)}; - - OP_REQUIRES(_ctx.at(multiple_index).shape().rank() == 1); - OP_REQUIRES(_ctx.at(multiple_index).shape().dim(0) == _ctx.at(input_index).shape().rank()); - OP_REQUIRES(_ctx.at(input_index).shape().rank() == _ctx.at(output_index).shape().rank()); -} - -void ShapeValidator::visit(const ir::operation::Range &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto start_index{node.getInputs().at(ir::operation::Range::Input::START)}; - const auto limit_index{node.getInputs().at(ir::operation::Range::Input::LIMIT)}; - const auto delta_index{node.getInputs().at(ir::operation::Range::Input::DELTA)}; - - // Check for dimension constraints - if (_ctx.at(output_index).info().isDynamic()) - return; - - OP_REQUIRES(_ctx.at(start_index).shape().rank() == 0); - OP_REQUIRES(_ctx.at(limit_index).shape().rank() == 0); - OP_REQUIRES(_ctx.at(delta_index).shape().rank() == 0); -} - -void ShapeValidator::visit(const ir::operation::MatrixBandPart &node) -{ - const auto output_index{node.getOutputs().at(0)}; - const auto input_index{node.getInputs().at(ir::operation::MatrixBandPart::Input::INPUT)}; - const auto num_lower_index{ - node.getInputs().at(ir::operation::MatrixBandPart::Input::NUM_LOWER_DIAG)}; - const auto num_upper_index{ - node.getInputs().at(ir::operation::MatrixBandPart::Input::NUM_UPPER_DIAG)}; - - // Check for dimension constraints - if (_ctx.at(output_index).info().isDynamic()) - return; - - OP_REQUIRES(_ctx.at(input_index).shape().rank() >= 2); // input must be more than 2 dim matrix - OP_REQUIRES(_ctx.at(num_upper_index).shape().rank() == 0); // num_lower must be scalar - OP_REQUIRES(_ctx.at(num_lower_index).shape().rank() == 0); // num_upper must be scalar -} - -void ShapeValidator::visit(const ir::operation::LogSoftmax &node) -{ - const auto output_index{node.getOutputs().at(0)}; - if (_ctx.at(output_index).info().isDynamic()) - return; - - const auto input_index{node.getInputs().at(0)}; - - OP_REQUIRES(_ctx.at(output_index).shape().rank() == _ctx.at(input_index).shape().rank()); -} - -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/ShapeValidator.h b/runtime/onert/core/src/compiler/ShapeValidator.h deleted file mode 100644 index f40c098d5..000000000 --- a/runtime/onert/core/src/compiler/ShapeValidator.h +++ /dev/null @@ -1,102 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_SHAPE_VALIDATOR_H__ -#define __ONERT_COMPILER_SHAPE_VALIDATOR_H__ - -#include "ir/Layout.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -class Graph; -class Operands; -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace compiler -{ - -class ShapeValidator : public ir::OperationVisitor -{ -public: - ShapeValidator(void) = delete; - ShapeValidator(const ir::Graph &graph); - -public: - void operator()(); - -public: - void visit(const ir::operation::BatchMatMul &node) override; - void visit(const ir::operation::BatchToSpaceND &node) override; - void visit(const ir::operation::BCQFullyConnected &node) override; - void visit(const ir::operation::BCQGather &node) override; - void visit(const ir::operation::Comparison &node) override; - void visit(const ir::operation::Softmax &node) override; - void visit(const ir::operation::InstanceNorm &node) override; - void visit(const ir::operation::Permute &node) override; - void visit(const ir::operation::Pool2D &node) override; - void visit(const ir::operation::Reduce &node) override; - void visit(const ir::operation::Transpose &node) override; - void visit(const ir::operation::RNN &node) override; - void visit(const ir::operation::SpaceToBatchND &node) override; - void visit(const ir::operation::SpaceToDepth &node) override; - void visit(const ir::operation::ElementwiseActivation &node) override; - void visit(const ir::operation::ElementwiseBinary &node) override; - void visit(const ir::operation::ElementwiseUnary &node) override; - void visit(const ir::operation::EmbeddingLookup &node) override; - void visit(const ir::operation::ExpandDims &node) override; - void visit(const ir::operation::HashtableLookup &node) override; - void visit(const ir::operation::TransposeConv &node) override; - void visit(const ir::operation::Gather &node) override; - void visit(const ir::operation::DepthToSpace &node) override; - void visit(const ir::operation::Pack &node) override; - void visit(const ir::operation::LSTM &node) override; - void visit(const ir::operation::L2Normalization &node) override; - void visit(const ir::operation::Unpack &node) override; - void visit(const ir::operation::Pad &node) override; - void visit(const ir::operation::Select &node) override; - void visit(const ir::operation::StridedSlice &node) override; - void visit(const ir::operation::Split &node) override; - void visit(const ir::operation::Shape &node) override; - void visit(const ir::operation::ResizeBilinear &node) override; - void visit(const ir::operation::Reverse &node) override; - void visit(const ir::operation::If &node) override; - void visit(const ir::operation::While &node) override; - void visit(const ir::operation::SquaredDifference &node) override; - void visit(const ir::operation::Tile &node) override; - void visit(const ir::operation::Range &node) override; - void visit(const ir::operation::MatrixBandPart &node) override; - void visit(const ir::operation::LogSoftmax &node) override; - -private: - void checkUnaryOp(const ir::Operation &node); - -private: - // TODO Remove _ctx field - const ir::Graph &_graph; - const ir::Operands &_ctx; - ir::Layout _current_op_seq_layout; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_SHAPE_VALIDATOR_H__ diff --git a/runtime/onert/core/src/compiler/StaticShapeInference.cc b/runtime/onert/core/src/compiler/StaticShapeInference.cc deleted file mode 100644 index df129d98b..000000000 --- a/runtime/onert/core/src/compiler/StaticShapeInference.cc +++ /dev/null @@ -1,1302 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "compiler/StaticShapeInference.h" -#include "util/ShapeInference.h" -#include "util/logging.h" - -#include <sstream> - -namespace onert -{ -namespace compiler -{ - -bool StaticShapeInferer::infer(const ir::OpSequence &op_seq) -{ - bool has_dynamic_tensor = false; - - for (const auto &operation_idx : op_seq.operations()) - { - auto &op = _operations.at(operation_idx); - auto opcode = op.opcode(); - - _return_has_dynamic_tensor = false; // this is used as a return value inside operation's visit() - - // IF: need shape inference for then, else - // While: need shape inference for condition, body - if (opcode == ir::OpCode::If || opcode == ir::OpCode::While) - { - op.accept(*this); - } - else - { - _return_has_dynamic_tensor = checkDynamicInput(op); - - if (_return_has_dynamic_tensor) - { - setDynamicOutput(op); - } - else - { - op.accept(*this); - } - } - - has_dynamic_tensor = has_dynamic_tensor || _return_has_dynamic_tensor; - } - - return has_dynamic_tensor; -} - -bool StaticShapeInferer::checkDynamicInput(const ir::Operation &op) -{ - for (auto input_idx : op.getInputs() | ir::Remove::UNDEFINED | ir::Remove::DUPLICATED) - { - if (_operands.at(input_idx).info().isDynamic()) - { - return true; - } - } - - return false; -} - -void StaticShapeInferer::setDynamicOutput(const ir::Operation &op) -{ - for (auto output_idx : op.getOutputs()) - { - _operands.at(output_idx).info().setDynamic(); - } -} - -void StaticShapeInferer::handleBinaryArithmeticOp(const ir::Operation &op, - const ir::OperandIndex lhs_idx, - const ir::OperandIndex rhs_idx) -{ - const auto &lhs = _operands.at(lhs_idx); - const auto &rhs = _operands.at(rhs_idx); - - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferEltwiseShape(lhs.info().shape(), rhs.info().shape()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::handleSimpleUnaryOp(const ir::Operation &op, - const ir::OperandIndex input_idx) -{ - const auto &input = _operands.at(input_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // re-sizing output shape - ir::Shape new_shape = input.info().shape(); - output.info().shape(new_shape); -} - -void StaticShapeInferer::dump() -{ - auto get_shape_str = [](const ir::Shape &shape) { - std::stringstream sstream; - sstream << "shape : {"; - for (int i = 0; i < shape.rank(); i++) - { - if (i == 0) - sstream << shape.dim(i); - else - sstream << " " << shape.dim(i); - } - sstream << "}"; - return sstream.str(); - }; - - for (const auto &pair : _lowered_subgs) - { - const auto index = pair.first; - const auto &lowered_subg = pair.second; - VERBOSE(StaticShapeInferer) << "SubGraph #" << index.value() << std::endl; - lowered_subg->graph().operands().iterate( - [&](const ir::OperandIndex &ind, const ir::Operand &operand) { - VERBOSE(StaticShapeInferer) << "Operand #" << ind.value() << ", " - << (operand.info().isDynamic() ? "Dynamic" : "Static") << ", " - << get_shape_str(operand.info().shape()) << std::endl; - }); - } -} - -void StaticShapeInferer::visit(const ir::operation::ArgMax &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::ArgMax::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto axis_idx{op.getInputs().at(ir::operation::ArgMax::Input::AXIS)}; - const auto &axis = _operands.at(axis_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - if (!axis.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - const auto rank = input.info().shape().rank(); - auto axis_value = axis.asScalar<int32_t>(); - axis_value = axis_value < 0 ? axis_value + rank : axis_value; - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferArgMaxShape(input.info().shape(), axis_value, rank); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::BatchMatMul &op) -{ - const auto lhs_index = op.getInputs().at(ir::operation::BatchMatMul::Input::LHS); - const auto rhs_index = op.getInputs().at(ir::operation::BatchMatMul::Input::RHS); - const auto output_index = op.getOutputs().at(0); - const auto &lhs = _operands.at(lhs_index); - const auto &rhs = _operands.at(rhs_index); - auto &output = _operands.at(output_index); - auto new_shape = shape_inference::inferBatchMatMulShape(lhs.shape(), rhs.shape(), op.param()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::BCQFullyConnected &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::BCQFullyConnected::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto cluster_idx{ - op.getInputs().at(ir::operation::BCQFullyConnected::Input::WEIGHTS_CLUSTERS)}; - const auto &cluster = _operands.at(cluster_idx); - - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - auto cluster_buf = reinterpret_cast<const int32_t *>(cluster.data()->base()); - assert(cluster_buf); - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferBCQFullyConnectedShape( - input.info().shape(), cluster.info().shape(), cluster_buf); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::BCQGather &op) -{ - const auto indices_idx{op.getInputs().at(ir::operation::BCQGather::Input::INDICES)}; - const auto &indices = _operands.at(indices_idx); - - const auto input_binary_idx{op.getInputs().at(ir::operation::BCQGather::Input::INPUT_BINARY)}; - const auto &input_binary = _operands.at(input_binary_idx); - - const auto cluster_idx{op.getInputs().at(ir::operation::BCQGather::Input::INPUT_CLUSTERS)}; - const auto &cluster = _operands.at(cluster_idx); - - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - auto cluster_buf = reinterpret_cast<const int32_t *>(cluster.data()->base()); - assert(cluster_buf); - - auto rank = input_binary.shape().rank(); - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferBCQGatherShape( - indices.info().shape(), cluster.info().shape(), cluster_buf, rank, op.param()); - - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::BinaryArithmetic &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS), - op.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)); -} - -void StaticShapeInferer::visit(const ir::operation::BroadcastTo &op) -{ - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - const auto shape_idx{op.getInputs().at(ir::operation::BroadcastTo::Input::SHAPE)}; - const auto &shape = _operands.at(shape_idx); - - if (!shape.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - // assert(shape.typeInfo().type() == ir::DataType::INT32); - auto shape_buffer = reinterpret_cast<const int32_t *>(shape.data()->base()); - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferBroadcastToShape(shape.info().shape(), shape_buffer); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Comparison &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::Comparison::Input::INPUT0), - op.getInputs().at(ir::operation::Comparison::Input::INPUT1)); -} - -void StaticShapeInferer::visit(const ir::operation::Concat &op) -{ - const auto input_count = op.getInputs().size(); - - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - shape_inference::Shapes input_shapes; - for (uint32_t i = 0; i < input_count; i++) - { - const auto input_idx{op.getInputs().at(i)}; - const auto &input = _operands.at(input_idx); - input_shapes.emplace_back(input.shape()); - } - - ir::Shape out_shape = shape_inference::inferConcatShape(input_shapes, op.param()); - - // re-sizing output shape - output.info().shape(out_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Conv2D &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Conv2D::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - const auto ker_idx{op.getInputs().at(ir::operation::Conv2D::Input::KERNEL)}; - const auto &ker = _operands.at(ker_idx); - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // re-sizing output shape - ir::Shape new_shape = - shape_inference::inferConv2DShape(input.info().shape(), ker.info().shape(), op.param()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::ElementwiseActivation &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::ElementwiseActivation::Input::INPUT)); -} - -void StaticShapeInferer::visit(const ir::operation::ElementwiseBinary &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::ElementwiseBinary::Input::LHS), - op.getInputs().at(ir::operation::ElementwiseBinary::Input::RHS)); -} - -void StaticShapeInferer::visit(const ir::operation::ElementwiseUnary &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)); -} - -void StaticShapeInferer::visit(const ir::operation::ExpandDims &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::ExpandDims::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - const auto axis_idx{op.getInputs().at(ir::operation::ExpandDims::Input::AXIS)}; - const auto &axis = _operands.at(axis_idx); - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - if (!axis.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - // even when axis is constant, output shape should be recalculated since user might call - // nnfw_set_input_tensorinfo(input, some_new_shape) - auto axis_buf = reinterpret_cast<const int32_t *>(axis.data()->base()); - assert(axis_buf); - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferExpandDimsShape(input.info().shape(), axis_buf[0]); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Fill &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Fill::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - if (!input.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - assert(input.typeInfo().type() == ir::DataType::INT32); - - auto input_buf = reinterpret_cast<const int32_t *>(input.data()->base()); - assert(input_buf); - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferFillShape(input.info().shape(), input_buf); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::FullyConnected &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::FullyConnected::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto ker_idx{op.getInputs().at(ir::operation::FullyConnected::Input::WEIGHT)}; - const auto &ker = _operands.at(ker_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - // re-sizing output shape - ir::Shape new_shape = - shape_inference::inferFullyConnectedShape(input.info().shape(), ker.info().shape()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::FusedBatchNorm &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::FusedBatchNorm::Input::INPUT)); -} - -void StaticShapeInferer::visit(const ir::operation::Gather &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Gather::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - const auto indices_idx{op.getInputs().at(ir::operation::Gather::Input::INDICES)}; - const auto &indices = _operands.at(indices_idx); - const auto rank = input.info().shape().rank(); - const auto axis = ((op.param().axis < 0) ? rank + op.param().axis : op.param().axis); - - assert(0 <= axis && axis < rank); - - // re-sizing output shape - ir::Shape new_shape = - shape_inference::inferGatherShape(input.info().shape(), indices.info().shape(), axis, rank); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::If &op) -{ - auto &then_graph = _lowered_subgs.at(op.param().then_subg_index)->graph(); - auto &else_graph = _lowered_subgs.at(op.param().else_subg_index)->graph(); - const std::vector<ir::OperandIndex> inputs{op.getInputs().begin() + 1, op.getInputs().end()}; - const auto &outputs = op.getOutputs(); - - // re-sizing input shapes of then subgraph - const auto &then_inputs = then_graph.getInputs(); - assert(inputs.size() == then_inputs.size()); - for (size_t i = 0; i < inputs.size(); ++i) - { - auto &then_input = then_graph.operands().at(then_inputs.at(i)); - if (_operands.at(inputs.at(i)).info().isDynamic()) - { - then_input.info().setDynamic(); - } - else - { - auto new_shape = _operands.at(inputs.at(i)).info().shape(); - then_input.info().shape(new_shape); - } - } - - // re-sizing input shapes of else subgraph - const auto &else_inputs = else_graph.getInputs(); - assert(inputs.size() == else_inputs.size()); - for (size_t i = 0; i < inputs.size(); ++i) - { - auto &else_input = else_graph.operands().at(else_inputs.at(i)); - if (_operands.at(inputs.at(i)).info().isDynamic()) - { - else_input.info().setDynamic(); - } - else - { - const auto &new_shape = _operands.at(inputs.at(i)).info().shape(); - else_input.info().shape(new_shape); - } - } - - // re-sizing operands of then subgraph - StaticShapeInferer then_inferer(op.param().then_subg_index, _lowered_subgs); - _lowered_subgs.at(op.param().then_subg_index) - ->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - bool has_dynamic_tensor = then_inferer.infer(op_seq); - op_seq.has_dynamic_tensor(has_dynamic_tensor); - }); - - // re-sizing operands of else subgraph - StaticShapeInferer else_inferer(op.param().else_subg_index, _lowered_subgs); - _lowered_subgs.at(op.param().else_subg_index) - ->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - bool has_dynamic_tensor = else_inferer.infer(op_seq); - op_seq.has_dynamic_tensor(has_dynamic_tensor); - }); - - // re-sizing output shapes - const auto &then_outputs = _lowered_subgs.at(op.param().then_subg_index)->graph().getOutputs(); - const auto &else_outputs = _lowered_subgs.at(op.param().else_subg_index)->graph().getOutputs(); - assert(outputs.size() == then_outputs.size()); - assert(outputs.size() == else_outputs.size()); - for (size_t i = 0; i < outputs.size(); ++i) - { - const auto &then_output = then_graph.operands().at(then_outputs.at(i)); - const auto &else_output = else_graph.operands().at(else_outputs.at(i)); - auto &output = _operands.at(outputs.at(i)); - if (!then_output.info().isDynamic() && !else_output.info().isDynamic() && - then_output.shape() == else_output.shape()) - { - output.info().shape(then_output.shape()); - } - else - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - } - } -} - -void StaticShapeInferer::visit(const ir::operation::L2Normalization &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::L2Normalization::Input::INPUT)); -} - -void StaticShapeInferer::visit(const ir::operation::LSTM &op) -{ - const auto output_index{op.getOutputs().at(ir::operation::LSTM::Output::OUTPUT)}; - auto &output = _operands.at(output_index); - - const auto output_state_out_index{ - op.getOutputs().at(ir::operation::LSTM::Output::OUTPUT_STATE_OUT)}; - - const auto cell_state_out_index{op.getOutputs().at(ir::operation::LSTM::Output::CELL_STATE_OUT)}; - - const auto scratch_buffer_index{op.getOutputs().at(ir::operation::LSTM::Output::SCRATCH_BUFFER)}; - - if (output.info().isDynamic() || (_operands.exist(output_state_out_index) && - _operands.at(output_state_out_index).info().isDynamic()) || - (_operands.exist(cell_state_out_index) && - _operands.at(cell_state_out_index).info().isDynamic()) || - (_operands.exist(scratch_buffer_index) && - _operands.at(scratch_buffer_index).info().isDynamic())) - return; - - const auto input_index{op.getInputs().at(ir::operation::LSTM::Input::INPUT)}; - const auto &input = _operands.at(input_index); - - const auto input_to_output_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS)}; - const auto &input_to_output_weights = _operands.at(input_to_output_weights_index); - - const auto recurrent_to_output_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS)}; - const auto &recurrent_to_output_weights = _operands.at(recurrent_to_output_weights_index); - - // re-sizing outputs - const int n_batch = (input.shape().rank() == 3 && op.param().time_major) ? input.shape().dim(1) - : input.shape().dim(0); - const int n_cell = input_to_output_weights.shape().dim(0); - const int n_output = recurrent_to_output_weights.shape().dim(1); - if (input.shape().rank() == 3) - { - if (op.param().time_major) - output.info().shape(ir::Shape{input.shape().dim(0), n_batch, n_output}); - else - output.info().shape(ir::Shape{n_batch, input.shape().dim(1), n_output}); - } - else - { - assert(input.shape().rank() == 2); - output.info().shape(ir::Shape{n_batch, n_output}); - } - - if (_operands.exist(output_state_out_index)) - { - auto &output_state_out = _operands.at(output_state_out_index); - output_state_out.info().shape(ir::Shape{n_batch, n_output}); - } - - if (_operands.exist(cell_state_out_index)) - { - auto &cell_state_out = _operands.at(cell_state_out_index); - cell_state_out.info().shape(ir::Shape{n_batch, n_cell}); - } - - if (_operands.exist(scratch_buffer_index)) - { - auto &scratch_buffer = _operands.at(scratch_buffer_index); - - const auto input_to_input_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_INPUT_WEIGHTS)}; - const auto recurrent_to_input_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS)}; - - bool has_input_to_input_weights = - _operands.at(input_to_input_weights_index).shape().dim(0) != 0 && - _operands.at(input_to_input_weights_index).shape().dim(1) != 0; - bool has_recurrent_to_input_weights = - _operands.at(recurrent_to_input_weights_index).shape().dim(0) != 0 && - _operands.at(recurrent_to_input_weights_index).shape().dim(1) != 0; - - // NOTE The cell_to_input_weights do not exist in non-peephole although regular LSTM(non-CIFG). - // true: no CIFG - // false: CIFG - bool has_cifg_param = has_input_to_input_weights && has_recurrent_to_input_weights; - if (has_cifg_param) - { - scratch_buffer.info().shape(ir::Shape{n_batch, n_cell * 4}); - } - else - { - scratch_buffer.info().shape(ir::Shape{n_batch, n_cell * 3}); - } - } -} - -void StaticShapeInferer::visit(const ir::operation::MatrixBandPart &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::MatrixBandPart::Input::INPUT)); -} - -void StaticShapeInferer::visit(const ir::operation::OneHot &op) -{ - const auto indice_idx{op.getInputs().at(ir::operation::OneHot::Input::INDICES)}; - const auto &indice = _operands.at(indice_idx); - const auto depth_idx{op.getInputs().at(ir::operation::OneHot::Input::DEPTH)}; - const auto &depth = _operands.at(depth_idx); - - const auto axis = op.param().axis; - - auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - if (!depth.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - const auto *depth_buf = reinterpret_cast<const int32_t *>(depth.data()->base()); - assert(depth_buf); - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferOnehotShape(indice.info().shape(), *depth_buf, axis); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Pack &op) -{ - const auto input_idx{op.getInputs().at(0)}; - const auto &input = _operands.at(input_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - const auto rank = input.shape().rank() + 1; - const auto axis = ((op.param().axis < 0) ? rank + op.param().axis : op.param().axis); - const auto num = op.param().num; - - assert(0 <= axis && axis < rank); - - // re-sizing output shape - ir::Shape new_shape = shape_inference::inferPackShape(input.info().shape(), axis, rank, num); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Pad &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Pad::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto pad_idx{op.getInputs().at(ir::operation::Pad::Input::PAD)}; - const auto &pad = _operands.at(pad_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // if pad is not constant, output also becomes dynamic - if (!pad.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - // re-sizing output shape - const auto new_shape = shape_inference::inferPadShape( - input.shape(), reinterpret_cast<const int32_t *>(pad.data()->base()), - pad.shape().num_elements()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Permute &op) -{ - const auto input_idx{op.getInputs().at(0)}; - const auto &input = _operands.at(input_idx); - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // re-sizing output shape - // Permute is a special operation that layouts of input/output may be different on backend - // However, it is not applied here, so input/output have the same layout of frontend. Because - // "ExecutorFactory" would convert shape of input/output accoding to the layouts when registering - // operand info to "TensorBuilder" after calling "StaticShapeInferer" - const auto new_shape = input.info().shape(); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Pow &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::Pow::Input::LHS), - op.getInputs().at(ir::operation::Pow::Input::RHS)); -} - -void StaticShapeInferer::visit(const ir::operation::Range &op) -{ - const auto start_idx{op.getInputs().at(ir::operation::Range::Input::START)}; - const auto limit_idx{op.getInputs().at(ir::operation::Range::Input::LIMIT)}; - const auto delta_idx{op.getInputs().at(ir::operation::Range::Input::DELTA)}; - const auto &start_op = _operands.at(start_idx); - const auto &limit_op = _operands.at(limit_idx); - const auto &delta_op = _operands.at(delta_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - ir::Shape new_shape; - if (start_op.isConstant() && limit_op.isConstant() && delta_op.isConstant()) - { - assert(start_op.typeInfo().type() == limit_op.typeInfo().type() && - start_op.typeInfo().type() == delta_op.typeInfo().type()); - if (output.typeInfo().type() == ir::DataType::FLOAT32) - { - new_shape = shape_inference::inferRangeShape<float>( - start_op.asScalar<float>(), limit_op.asScalar<float>(), delta_op.asScalar<float>()); - } - else if (output.typeInfo().type() == ir::DataType::INT32) - { - new_shape = shape_inference::inferRangeShape<int32_t>( - start_op.asScalar<int32_t>(), limit_op.asScalar<int32_t>(), delta_op.asScalar<int32_t>()); - } - assert(output.shape() == new_shape); - } - else - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - } -} - -void StaticShapeInferer::visit(const ir::operation::Reduce &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Reduce::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto axes_idx{op.getInputs().at(ir::operation::Reduce::Input::AXES)}; - const auto &axes = _operands.at(axes_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - std::vector<int32_t> axes_vec; - for (size_t i = 0; i < axes.shape().num_elements(); ++i) - { - switch (axes.typeInfo().type()) - { - case ir::DataType::INT32: - { - axes_vec.emplace_back(reinterpret_cast<const int32_t *>(axes.data()->base())[i]); - break; - } - case ir::DataType::INT64: - { - axes_vec.emplace_back(reinterpret_cast<const int64_t *>(axes.data()->base())[i]); - break; - } - default: - throw std::runtime_error("StaticShapeInferer " + op.name() + ": Not supported data type"); - break; - } - } - const auto keep_dims = op.param().keep_dims; - - // re-sizing output shape - ir::Shape new_shape = - shape_inference::inferReduceShape(input.info().shape(), axes_vec, keep_dims); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Reshape &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Reshape::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // New shape is given by second input tensor - if (op.getInputs().size() == 2) - { - // Let's check the second input - const auto shape_idx{op.getInputs().at(ir::operation::Reshape::Input::SHAPE)}; - const auto &shape = _operands.at(shape_idx); - - if (shape.isConstant()) - { - const auto *shape_buf = reinterpret_cast<const int32_t *>(shape.data()->base()); - assert(shape_buf); - - ir::Shape new_shape = shape_inference::inferReshapeShape( - shape_buf, shape.shape().num_elements(), input.shape().num_elements()); - - // if shape is from Const, TFLC put the shape of output into tensor - if (new_shape != output.shape()) - { - // change on output shape - output.info().shape(new_shape); - } - } - else - { - // if shape is NOT Const, set output shape to be dynamic_ - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - } - } - // New shape is given by option - else if (op.param().new_shape.size() != 0) - { - // Let's check the new_shape option - auto shape = op.param().new_shape; - ir::Shape new_shape = shape_inference::inferReshapeShape(shape.data(), shape.size(), - input.shape().num_elements()); - - if (new_shape != output.shape()) - { - // change on output shape - output.info().shape(new_shape); - } - } - else - { - throw std::runtime_error("Reshape: new shape is missing"); - } -} - -void StaticShapeInferer::visit(const ir::operation::ResizeBilinear &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::ResizeBilinear::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - int32_t height_out, width_out; - if (op.getInputs().size() == 2) - { - auto &size = _operands.at(op.getInputs().at(ir::operation::ResizeBilinear::Input::SIZE)); - if (!size.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - const auto size_v = size.asVector<std::int32_t>(); - height_out = size_v[0]; - width_out = size_v[1]; - } - else - { - height_out = op.param().height_out; - width_out = op.param().width_out; - } - - // Shape inferencing logic based on Params - ir::Shape new_shape = - shape_inference::inferResizeBilinearShape(input.shape(), height_out, width_out); - - // if size_op is from Const, TFLC put the shape of output into tensor - if (new_shape != output.shape()) - { - // change on output shape - output.info().shape(new_shape); - } -} - -void StaticShapeInferer::visit(const ir::operation::Reverse &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::Reverse::Input::INPUT)); -} - -void StaticShapeInferer::visit(const ir::operation::Select &op) -{ - const auto input_cond_idx{op.getInputs().at(ir::operation::Select::Input::CONDITION)}; - const auto &input_cond = _operands.at(input_cond_idx); - - const auto input_true_idx{op.getInputs().at(ir::operation::Select::Input::INPUT_TRUE)}; - const auto &input_true = _operands.at(input_true_idx); - - const auto input_false_idx{op.getInputs().at(ir::operation::Select::Input::INPUT_FALSE)}; - const auto &input_false = _operands.at(input_false_idx); - - auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // Select output shpae - ir::Shape new_shape = shape_inference::inferSelectShape( - input_cond.info().shape(), input_true.info().shape(), input_false.info().shape()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Shape &op) -{ - const auto input_idx{op.getInputs().at(0)}; - const auto &input = _operands.at(input_idx); - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // re-sizing output shape - ir::Shape output_shape; - output_shape.append(input.info().shape().rank()); - - output.info().shape(output_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Slice &op) -{ - const auto input_index{op.getInputs().at(ir::operation::Slice::Input::INPUT)}; - const auto &input = _operands.at(input_index); - const auto begins_index{op.getInputs().at(ir::operation::Slice::Input::BEGINS)}; - const auto &begins = _operands.at(begins_index); - const auto sizes_index{op.getInputs().at(ir::operation::Slice::Input::SIZES)}; - const auto &sizes = _operands.at(sizes_index); - const auto output_index = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_index); - - // Whether input is constant or not does not affect whether output is dynamic or not - if (!(begins.isConstant() && sizes.isConstant())) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - auto begins_buf = reinterpret_cast<const int32_t *>(begins.data()->base()); - auto sizes_buf = reinterpret_cast<const int32_t *>(sizes.data()->base()); - - ir::Shape new_shape = - shape_inference::inferSliceShape(input.info().shape(), begins_buf, sizes_buf); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Softmax &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::Softmax::Input::INPUT)); -} - -void StaticShapeInferer::visit(const ir::operation::SpaceToBatchND &op) -{ - const auto output_index = op.getOutputs().at(0); - const auto input_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)}; - const auto block_shape_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)}; - const auto padding_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)}; - - ir::Operand &output = _operands.at(output_index); - const auto &input = _operands.at(input_idx); - const auto &block_shape = _operands.at(block_shape_idx); - const auto &padding = _operands.at(padding_idx); - - // Whether input is constant or not does not affect whether output is dynamic or not - if (!(block_shape.isConstant() && padding.isConstant())) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - auto input_shape = input.info().shape(); - auto block_shape_shape = block_shape.info().shape(); - auto padding_shape = padding.info().shape(); - - auto block_shape_data = reinterpret_cast<const int32_t *>(block_shape.data()->base()); - auto padding_data = reinterpret_cast<const int32_t *>(padding.data()->base()); - - ir::Shape new_shape = shape_inference::inferSpaceToBatchNDShape( - input_shape, block_shape_shape, padding_shape, block_shape_data, padding_data); - - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Split &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Split::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto axis_idx{op.getInputs().at(ir::operation::Split::Input::AXIS)}; - const auto &axis = _operands.at(axis_idx); - - auto outputs = op.getOutputs(); - if (!axis.isConstant()) - { - for (auto output_idx : outputs) - { - ir::Operand &output = _operands.at(output_idx); - output.info().setDynamic(); - } - _return_has_dynamic_tensor = true; - return; - } - - const auto num_splits = op.param().num_splits; - - const auto rank = input.info().shape().rank(); - auto axis_value = axis.asScalar<int32_t>(); - axis_value = axis_value < 0 ? axis_value + rank : axis_value; - - assert(0 <= axis_value && axis_value < rank); - - ir::Shape new_shape = - shape_inference::inferSplitShape(input.info().shape(), axis_value, num_splits); - for (auto output_idx : outputs) - { - ir::Operand &output = _operands.at(output_idx); - output.info().shape(new_shape); - } -} - -void StaticShapeInferer::visit(const ir::operation::SquaredDifference &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::SquaredDifference::Input::LHS), - op.getInputs().at(ir::operation::SquaredDifference::Input::RHS)); -} - -void StaticShapeInferer::visit(const ir::operation::Squeeze &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Squeeze::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - // Squeeze output shpae - ir::Shape new_shape = shape_inference::inferSqueezeShape(input.info().shape(), op.param()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::StridedSlice &op) -{ - const auto input_index{op.getInputs().at(ir::operation::StridedSlice::Input::INPUT)}; - const auto &input = _operands.at(input_index); - const auto starts_index{op.getInputs().at(ir::operation::StridedSlice::Input::STARTS)}; - const auto &starts = _operands.at(starts_index); - const auto ends_index{op.getInputs().at(ir::operation::StridedSlice::Input::ENDS)}; - const auto &ends = _operands.at(ends_index); - const auto strides_index{op.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)}; - const auto &strides = _operands.at(strides_index); - const auto output_index = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_index); - - if (!(starts.isConstant() && ends.isConstant() && strides.isConstant())) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - const auto begin_mask = op.param().begin_mask; - const auto end_mask = op.param().end_mask; - const auto shrink_axis_mask = op.param().shrink_axis_mask; - const auto rank = input.info().shape().rank(); - - auto starts_buf = reinterpret_cast<const uint32_t *>(starts.data()->base()); - auto ends_buf = reinterpret_cast<const uint32_t *>(ends.data()->base()); - auto strides_buf = reinterpret_cast<const uint32_t *>(strides.data()->base()); - - auto op_params = shape_inference::buildStridedSliceParams( - starts_buf, ends_buf, strides_buf, begin_mask, end_mask, shrink_axis_mask, rank); - - ir::Shape new_shape = - shape_inference::inferStridedSliceShape(input.info().shape(), op_params, rank); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Tile &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Tile::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto multiplier_idx{op.getInputs().at(ir::operation::Tile::Input::MULTIPLES)}; - const auto &multiplier = _operands.at(multiplier_idx); - - const auto output_idx = op.getOutputs().at(0); - ir::Operand &output = _operands.at(output_idx); - - if (!multiplier.isConstant()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - auto multiplier_buffer = reinterpret_cast<const int32_t *>(multiplier.data()->base()); - assert(multiplier_buffer); - - // re-sizing output shape - auto new_shape = shape_inference::inferTileShape(input.info().shape(), multiplier_buffer, - multiplier.shape().num_elements()); - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Transpose &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Transpose::Input::INPUT)}; - const auto &input = _operands.at(input_idx); - - const auto perm_idx{op.getInputs().at(ir::operation::Transpose::Input::PERMUTATION)}; - const auto &perm = _operands.at(perm_idx); - - // perm.shape() != ir::Shape{0} means that perm is (n-1...0) - // TODO This condition changes to perm.num_elements() == 0 - const auto is_regular_transpose = perm.shape() == ir::Shape{0}; - - // get mutable output operand - const auto output_idx = op.getOutputs().at(0); - auto &output = _operands.at(output_idx); - if (!perm.isConstant() && !is_regular_transpose) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - return; - } - - ir::Shape new_shape; - if (is_regular_transpose) - { - // Call by (n-1...0) - new_shape = shape_inference::inferTransposeShape(input.info().shape(), nullptr, 0); - } - else - { - // Check rank - if (input.info().shape().rank() != static_cast<int>(perm.info().shape().num_elements())) - { - throw std::runtime_error("StaticShapeInferer failed, bad rank size: " + - std::to_string(perm.info().shape().num_elements())); - } - - // set output shape, based on input and params - const auto perm_buf = reinterpret_cast<const int32_t *>(perm.data()->base()); - new_shape = shape_inference::inferTransposeShape(input.info().shape(), perm_buf, - perm.shape().num_elements()); - } - output.info().shape(new_shape); -} - -void StaticShapeInferer::visit(const ir::operation::Unpack &op) -{ - const auto input_idx{op.getInputs().at(0)}; - const auto &input = _operands.at(input_idx); - const auto num = op.param().num; - const auto rank = input.shape().rank(); - const auto axis = ((op.param().axis < 0) ? rank + op.param().axis : op.param().axis); - - assert(axis < rank); - if (axis < 0) - { - for (int out_tensor_idx = 0; out_tensor_idx < num; out_tensor_idx++) - { - const auto output_idx = op.getOutputs().at(out_tensor_idx); - ir::Operand &output = _operands.at(output_idx); - output.info().setDynamic(); - } - _return_has_dynamic_tensor = true; - return; - } - - ir::Shape new_shape = shape_inference::inferUnpackShape(input.info().shape(), axis, rank); - - // re-sizing output shape - for (int out_tensor_idx = 0; out_tensor_idx < num; out_tensor_idx++) - { - const auto output_idx = op.getOutputs().at(out_tensor_idx); - ir::Operand &output = _operands.at(output_idx); - output.info().shape(new_shape); - } -} - -void StaticShapeInferer::visit(const ir::operation::While &op) -{ - auto &cond_graph = _lowered_subgs.at(op.param().cond_subg_index)->graph(); - auto &body_graph = _lowered_subgs.at(op.param().body_subg_index)->graph(); - const auto inputs = op.getInputs(); - const auto &outputs = op.getOutputs(); - - // re-sizing input shapes of then subgraph - const auto &cond_inputs = cond_graph.getInputs(); - assert(inputs.size() == cond_inputs.size()); - for (size_t i = 0; i < inputs.size(); ++i) - { - const auto &input = _operands.at(inputs.at(i)); - auto &cond_input = cond_graph.operands().at(cond_inputs.at(i)); - if (input.info().isDynamic()) - { - cond_input.info().setDynamic(); - } - else - { - auto new_shape = input.info().shape(); - cond_input.info().shape(new_shape); - } - } - - // re-sizing input shapes of body subgraph - const auto &body_inputs = body_graph.getInputs(); - assert(cond_inputs.size() == body_inputs.size()); - for (size_t i = 0; i < cond_inputs.size(); ++i) - { - const auto &cond_input = cond_graph.operands().at(cond_inputs.at(i)); - auto &body_input = body_graph.operands().at(body_inputs.at(i)); - if (cond_input.info().isDynamic()) - { - body_input.info().setDynamic(); - } - else - { - const auto &new_shape = cond_input.info().shape(); - body_input.info().shape(new_shape); - } - } - - // re-sizing operands of body subgraph - StaticShapeInferer body_inferer(op.param().body_subg_index, _lowered_subgs); - _lowered_subgs.at(op.param().body_subg_index) - ->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - bool has_dynamic_tensor = body_inferer.infer(op_seq); - op_seq.has_dynamic_tensor(has_dynamic_tensor); - }); - - // Check whether while operation's shapes are predictable - // If any of shape of body outputs and cond inputs are different, non-constant operands would be - // set to dynamic - bool check_unpredictable_dynamic = false; - const auto &body_outputs = body_graph.getOutputs(); - assert(body_outputs.size() == cond_inputs.size()); - for (size_t i = 0; i < body_outputs.size(); ++i) - { - const auto &body_output = body_graph.operands().at(body_outputs.at(i)); - auto &cond_input = cond_graph.operands().at(cond_inputs.at(i)); - if ((cond_input.info().isDynamic() != body_output.info().isDynamic()) || - (cond_input.shape() != body_output.shape())) - { - check_unpredictable_dynamic = true; - break; - } - } - - if (check_unpredictable_dynamic) - { - // Set inputs of body subgraph - for (const auto &input_index : body_inputs) - { - auto &input = body_graph.operands().at(input_index); - if (!input.isConstant()) - { - input.info().setDynamic(); - } - } - - // Set inputs of cond subgraph - for (const auto &input_index : cond_inputs) - { - auto &input = cond_graph.operands().at(input_index); - if (!input.isConstant()) - { - input.info().setDynamic(); - } - } - - // Set non-constant operands of body subgraph to dynamic - StaticShapeInferer body_inferer(op.param().body_subg_index, _lowered_subgs); - _lowered_subgs.at(op.param().body_subg_index) - ->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - bool has_dynamic_tensor = body_inferer.infer(op_seq); - op_seq.has_dynamic_tensor(has_dynamic_tensor); - }); - } - - // re-sizing operands of cond subgraph - // If check_unpredictable_dynamic is true, non-constant operands of cond subgraph would be set to - // dynamic - StaticShapeInferer cond_inferer(op.param().cond_subg_index, _lowered_subgs); - _lowered_subgs.at(op.param().cond_subg_index) - ->iterateTopolOpSeqs([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - bool has_dynamic_tensor = cond_inferer.infer(op_seq); - op_seq.has_dynamic_tensor(has_dynamic_tensor); - }); - - // re-sizing outputs of while operation - // If check_unpredictable_dynamic is true, outputs of while operation would be set to dynamic - assert(cond_inputs.size() == outputs.size()); - for (size_t i = 0; i < cond_inputs.size(); ++i) - { - const auto &cond_input = cond_graph.operands().at(cond_inputs.at(i)); - auto &output = _operands.at(outputs.at(i)); - if (cond_input.info().isDynamic()) - { - output.info().setDynamic(); - _return_has_dynamic_tensor = true; - } - else - { - const auto new_shape = cond_input.info().shape(); - output.info().shape(new_shape); - } - } -} - -} // namespace compiler - -} // namespace onert diff --git a/runtime/onert/core/src/compiler/TensorBuilders.h b/runtime/onert/core/src/compiler/TensorBuilders.h deleted file mode 100644 index 3b0360b4b..000000000 --- a/runtime/onert/core/src/compiler/TensorBuilders.h +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_TENSOR_BUILDERS_H__ -#define __ONERT_COMPILER_TENSOR_BUILDERS_H__ - -#include <unordered_set> -#include <memory> -#include "backend/BackendContext.h" -#include "backend/Backend.h" -#include "backend/controlflow/Config.h" -#include "backend/controlflow/TensorBuilder.h" -#include "util/logging.h" - -namespace onert -{ -namespace compiler -{ - -class TensorBuilders -{ -public: - TensorBuilders() = default; - - TensorBuilders(const onert::backend::BackendContexts &backend_contexts, bool include_controlflow) - { - for (const auto &e : backend_contexts) - { - if (e.first->config()->id() == backend::controlflow::Config::ID) - { - _cf_tensor_builder = std::dynamic_pointer_cast<backend::controlflow::TensorBuilder>( - e.second->tensor_builder); - if (include_controlflow) - _tensor_builders.insert(e.second->tensor_builder); - } - else - { - _tensor_builders.insert(e.second->tensor_builder); - } - } - } - - std::unordered_set<std::shared_ptr<onert::backend::ITensorBuilder>>::const_iterator begin() const - { - return _tensor_builders.cbegin(); - } - std::unordered_set<std::shared_ptr<onert::backend::ITensorBuilder>>::const_iterator end() const - { - return _tensor_builders.cend(); - } - - std::shared_ptr<backend::controlflow::TensorBuilder> getControlflowTensorBuilder() const - { - return _cf_tensor_builder; - } - -private: - std::unordered_set<std::shared_ptr<backend::ITensorBuilder>> _tensor_builders; - std::shared_ptr<backend::controlflow::TensorBuilder> _cf_tensor_builder; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_TENSOR_BUILDERS_H__ diff --git a/runtime/onert/core/src/compiler/TensorRegistries.h b/runtime/onert/core/src/compiler/TensorRegistries.h deleted file mode 100644 index e42225cbf..000000000 --- a/runtime/onert/core/src/compiler/TensorRegistries.h +++ /dev/null @@ -1,91 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_TENSOR_REGISTRIES_H__ -#define __ONERT_COMPILER_TENSOR_REGISTRIES_H__ - -#include <unordered_set> -#include <memory> -#include "backend/BackendContext.h" -#include "backend/Backend.h" -#include "backend/controlflow/Config.h" -#include "backend/controlflow/TensorBuilder.h" -#include "backend/controlflow/TensorRegistry.h" - -namespace onert -{ -namespace compiler -{ - -class TensorRegistries -{ -public: - TensorRegistries() = default; - - TensorRegistries(const onert::backend::BackendContexts &backend_contexts, - bool include_controlflow) - { - for (const auto &e : backend_contexts) - { - auto tensor_reg = e.second->tensor_registry; - if (e.first->config()->id() == backend::controlflow::Config::ID) - { - _cf_tensor_reg = - std::dynamic_pointer_cast<backend::controlflow::TensorRegistry>(tensor_reg); - if (include_controlflow) - _tensor_regs.insert(tensor_reg); - } - else - { - _tensor_regs.insert(tensor_reg); - } - } - } - - std::unordered_set<std::shared_ptr<onert::backend::ITensorRegistry>>::const_iterator begin() const - { - return _tensor_regs.cbegin(); - } - std::unordered_set<std::shared_ptr<onert::backend::ITensorRegistry>>::const_iterator end() const - { - return _tensor_regs.cend(); - } - - std::shared_ptr<backend::controlflow::TensorRegistry> getControlflowTensorRegistry() const - { - return _cf_tensor_reg; - } - - backend::ITensor *getITensor(ir::OperandIndex ind) const - { - for (auto &tensor_reg : _tensor_regs) - { - auto tensor = tensor_reg->getITensor(ind); - if (tensor) - return tensor; - } - return nullptr; - } - -private: - std::unordered_set<std::shared_ptr<backend::ITensorRegistry>> _tensor_regs; - std::shared_ptr<backend::controlflow::TensorRegistry> _cf_tensor_reg; -}; - -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_TENSOR_REGISTRIES_H__ diff --git a/runtime/onert/core/src/compiler/pass/ConstantInsertionPass.cc b/runtime/onert/core/src/compiler/pass/ConstantInsertionPass.cc deleted file mode 100644 index ef6240894..000000000 --- a/runtime/onert/core/src/compiler/pass/ConstantInsertionPass.cc +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConstantInsertionPass.h" - -#include "backend/Backend.h" -#include <ir/Graph.h> -#include <util/Utils.h> - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void ConstantInsertionPass::callback(const ir::OperationIndex &node_index, ir::Operation &node) -{ - const auto &op_sequence_index = _lowered_graph.op_seqs().getOperation(node_index); - const auto op_seq_lower_info = _lowered_graph.getLowerInfo(op_sequence_index); - const auto backend = op_seq_lower_info->backend(); - const auto layout = op_seq_lower_info->layout(); - const auto factor = ir::operand::PermuteFactor{backend, layout}; - - for (const auto input : node.getInputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - auto &object = _graph.operands().at(input); - - if (object.isConstant()) - { - const auto key = ReplaceKey{input, factor}; - if (_replace_operands_map.count(key) == 0) - { - ir::Operand new_object(object); - new_object.unsetDef(); - // TODO Remove const_case - const_cast<ir::OperationIndexSet &>(new_object.getUses()).clear(); - const auto new_index = _graph.operands().emplace(new_object); - _replace_operands_map[key] = new_index; - } - - const auto replaced_input = _replace_operands_map[key]; - // Update op_seq - if (_lowered_graph.op_seqs().at(op_sequence_index).getInputs().contains(input)) - { - // All inputs of op_seq have the same PermuteFactor because those inputs are inputs of first - // operation - _lowered_graph.op_seqs().at(op_sequence_index).replaceInputs(input, replaced_input); - } - - // Update the same inputs of a node at once because inputs of an operation have the same - // PermuteFactor - node.replaceInputs(input, replaced_input); - - // Update operand - auto &replaced_object = _graph.operands().at(replaced_input); - replaced_object.insertUse(node_index); - - // Remove this node from uses of origin operand - // Constant operand has no def. - assert(!object.getDef().valid()); - object.removeUse(node_index); - - // Remove origin operand - if (object.getUses().size() == 0) - _graph.removeOperand(input); - } - } - - // Now this runtime does not support the node making output as constant - for (const auto &output : node.getOutputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - UNUSED_RELEASE(output); - assert(!_graph.operands().at(output).isConstant()); - } -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/ConstantInsertionPass.h b/runtime/onert/core/src/compiler/pass/ConstantInsertionPass.h deleted file mode 100644 index 052883c92..000000000 --- a/runtime/onert/core/src/compiler/pass/ConstantInsertionPass.h +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_CONSTANT_INSERTION_PASS_H__ -#define __ONERT_COMPILER_PASS_CONSTANT_INSERTION_PASS_H__ - -#include <ir/operand/PermuteFactor.h> -#include <ir/Index.h> -#include "LoweredOperationPass.h" -#include <unordered_map> -#include <utility> - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class ConstantInsertionPass : public LoweredOperationPass -{ -public: - using LoweredOperationPass::LoweredOperationPass; - -public: - std::string id() final { return "ConstantInsertionPass"; } - -public: - void callback(const ir::OperationIndex &index, ir::Operation &node) final; - -private: - struct ReplaceKey - { - ir::OperandIndex index; - ir::operand::PermuteFactor factor; - - bool operator==(const ReplaceKey &other) const - { - return index == other.index && factor == other.factor; - } - }; - - /** - * @brief Structure that provides hash function of ReplaceKey - */ - struct KeyHasher - { - std::size_t operator()(const ReplaceKey &key) const noexcept - { - using std::hash; - return hash<ir::OperandIndex>()(key.index) ^ - (hash<ir::operand::PermuteFactor>()(key.factor) << 1); - } - }; - - std::unordered_map<ReplaceKey, ir::OperandIndex, KeyHasher> _replace_operands_map; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_CONSTANT_INSERTION_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/ConstantLoweringPass.cc b/runtime/onert/core/src/compiler/pass/ConstantLoweringPass.cc deleted file mode 100644 index 1c1dbe0ee..000000000 --- a/runtime/onert/core/src/compiler/pass/ConstantLoweringPass.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConstantLoweringPass.h" - -#include "backend/Backend.h" -#include <ir/Graph.h> -#include <ir/operand/PermuteFactor.h> -#include <util/Utils.h> - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void ConstantLoweringPass::callback(const ir::OperationIndex &node_index, ir::Operation &node) -{ - const auto &op_sequence_index = _lowered_graph.op_seqs().getOperation(node_index); - const auto op_seq_lower_info = _lowered_graph.getLowerInfo(op_sequence_index); - const auto backend = op_seq_lower_info->backend(); - const auto layout = op_seq_lower_info->layout(); - const auto factor = ir::operand::PermuteFactor{backend, layout}; - - // Now this runtime does not support the node making output of operation as constant - for (const auto input : node.getInputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED) - { - auto &object = _graph.operands().at(input); - if (object.isConstant()) - { - // All constant operand are already assinged at each backend by ContantInsertionPass. So a - // constant has `def` and `use` as the same PermuteFactor - _lowered_graph.setLowerInfo(input, std::make_unique<ir::operand::LowerInfo>()); - _lowered_graph.getLowerInfo(input)->addDefPermuteFactor(factor); - _lowered_graph.getLowerInfo(input)->addUsePermuteFactor(factor); - } - } -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/ConstantLoweringPass.h b/runtime/onert/core/src/compiler/pass/ConstantLoweringPass.h deleted file mode 100644 index e17d776d1..000000000 --- a/runtime/onert/core/src/compiler/pass/ConstantLoweringPass.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_CONSTANT_LOWERING_PASS_H__ -#define __ONERT_COMPILER_PASS_CONSTANT_LOWERING_PASS_H__ - -#include <ir/Index.h> -#include "LoweredOperationPass.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class ConstantLoweringPass : public LoweredOperationPass -{ -public: - using LoweredOperationPass::LoweredOperationPass; - -public: - std::string id() final { return "ConstantLoweringPass"; } - -public: - void callback(const ir::OperationIndex &index, ir::Operation &node) final; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_CONSTANT_LOWERING_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/ConstantOutputPass.cc b/runtime/onert/core/src/compiler/pass/ConstantOutputPass.cc deleted file mode 100644 index c176f6ffb..000000000 --- a/runtime/onert/core/src/compiler/pass/ConstantOutputPass.cc +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ConstantOutputPass.h" - -#include "ir/Graph.h" -#include "ir/operation/Permute.h" -#include "util/logging.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void ConstantOutputPass::callback(const ir::OperandIndex &ind, ir::Operand &obj) -{ - if (!_graph.getOutputs().contains(ind) || !obj.isConstant()) - return; - - auto permute_input_ind = _graph.addOperand(obj.shape(), obj.typeInfo()); - auto &permute_input_obj = _graph.operands().at(permute_input_ind); - - // Move the const data - permute_input_obj.data(obj.shareData()); - obj.releaseData(); - obj.info().setAsNonConst(); - - using ir::operation::Permute; - auto permute_obj = std::make_unique<Permute>(permute_input_ind, ind, Permute::Type::COPY); - auto permute_ind = _graph.operations().push(std::move(permute_obj)); - - permute_input_obj.insertUse(permute_ind); - obj.setDef(permute_ind); - - // Make the operations that uses this operand to use the generated operand - auto orig_uses = obj.getUses(); - for (auto use : orig_uses) - { - permute_input_obj.insertUse(use); - obj.removeUse(use); - _graph.operations().at(use).replaceInputs(ind, permute_input_ind); - } - - VERBOSE(ConstantOutputPass) << "Permute Op inserted for a constant ouput, node index : " - << permute_ind << std::endl; - VERBOSE(ConstantOutputPass) << " - Input (inserted) Operand : " << permute_input_ind - << std::endl; - VERBOSE(ConstantOutputPass) << " - Output(original) Operand : " << ind << std::endl; -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/ConstantOutputPass.h b/runtime/onert/core/src/compiler/pass/ConstantOutputPass.h deleted file mode 100644 index 193dd3a68..000000000 --- a/runtime/onert/core/src/compiler/pass/ConstantOutputPass.h +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_CONSTANT_OUTPUT_PASS_H__ -#define __ONERT_COMPILER_PASS_CONSTANT_OUTPUT_PASS_H__ - -#include "OperandPass.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -/** - * @brief Pass to specially handle constant model outputs - * - * As an output buffer is given right before an execution but constant initialization is done at - * prepare phase, the current runtime structure cannot handle when an output is constant. - * To resolve this problem, this pass inserts a Permute layer with a const input and make the model - * output tensor to be its output. - * - * e.g.) - * - * ((Const Output)) - * - * becomes - * - * (Const) -> [Permute] -> ((Output)) - * - * Note that this is a mandatory pass for Graph. - */ -class ConstantOutputPass : public OperandPass -{ -public: - using OperandPass::OperandPass; - -public: - std::string id() final { return "ConstantOutputPass"; } - -public: - void callback(const ir::OperandIndex &i, ir::Operand &o) final; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_CONSTANT_INSERTION_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/LoweredOperandPass.h b/runtime/onert/core/src/compiler/pass/LoweredOperandPass.h deleted file mode 100644 index 0c5f7d745..000000000 --- a/runtime/onert/core/src/compiler/pass/LoweredOperandPass.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_PASS_LOWERED_OPERAND_PASS_H__ -#define __ONERT_IR_PASS_LOWERED_OPERAND_PASS_H__ - -#include "OperandPass.h" -#include "compiler/LoweredGraph.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class LoweredOperandPass : public OperandPass -{ -public: - LoweredOperandPass(compiler::LoweredGraph &lowered_graph) - : OperandPass{lowered_graph.graph()}, _lowered_graph{lowered_graph} - { - // DO NOTHING - } - - virtual ~LoweredOperandPass() = default; - - std::string id() override = 0; - void callback(const ir::OperandIndex &i, ir::Operand &o) override = 0; - -protected: - compiler::LoweredGraph &_lowered_graph; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_IR_PASS_LOWERED_OPERAND_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/LoweredOperationPass.h b/runtime/onert/core/src/compiler/pass/LoweredOperationPass.h deleted file mode 100644 index 5c8569be2..000000000 --- a/runtime/onert/core/src/compiler/pass/LoweredOperationPass.h +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_PASS_LOWERED_OPERATION_PASS_H__ -#define __ONERT_IR_PASS_LOWERED_OPERATION_PASS_H__ - -#include "OperationPass.h" -#include "compiler/LoweredGraph.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class LoweredOperationPass : public OperationPass -{ -public: - LoweredOperationPass(LoweredGraph &lowered_graph) - : OperationPass{lowered_graph.graph()}, _lowered_graph{lowered_graph} - { - // DO NOTHING - } - - virtual ~LoweredOperationPass() = default; - - std::string id() override = 0; - void callback(const ir::OperationIndex &i, ir::Operation &o) override = 0; - -protected: - LoweredGraph &_lowered_graph; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_IR_PASS_LOWERED_OPERATION_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/OddOutputPass.cc b/runtime/onert/core/src/compiler/pass/OddOutputPass.cc deleted file mode 100644 index f50fae0d3..000000000 --- a/runtime/onert/core/src/compiler/pass/OddOutputPass.cc +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OddOutputPass.h" - -#include "ir/Graph.h" -#include "ir/operation/Permute.h" -#include "util/logging.h" -#include "util/Utils.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void OddOutputPass::run() -{ - auto &outputs = _graph.getOutputs(); - - VERBOSE(OddOutputPass) << "Case 1 : An operand which is a model output and a model input" - << std::endl; - for (auto &ind : outputs) - { - if (_graph.getInputs().contains(ind)) - { - auto permute_output_ind = insertPermute(ind); - // Update the output to be newly added operand - _graph.getOutputs().replace(ind, permute_output_ind); - } - } - - VERBOSE(OddOutputPass) << "Case 2 : Two or more duplicated outputs" << std::endl; - std::unordered_set<ir::OperandIndex> occurence; - for (auto &ind : outputs) - { - auto &obj = _graph.operands().at(ind); - if (occurence.count(ind) == 0) - { - occurence.insert(ind); - continue; - } - - // Panic when it is const, it must have been handled earlier in another pass - UNUSED_RELEASE(obj); - assert(!obj.isConstant()); - - auto permute_output_ind = insertPermute(ind); - ind = permute_output_ind; // Replace output index to fix output duplication - } -} - -ir::OperandIndex OddOutputPass::insertPermute(ir::OperandIndex ind) -{ - auto &obj = _graph.operands().at(ind); - auto output_ind = _graph.addOperand(obj.shape(), obj.typeInfo()); - auto &output_obj = _graph.operands().at(output_ind); - - using ir::operation::Permute; - auto permute_obj = std::make_unique<Permute>(ind, output_ind, Permute::Type::COPY); - auto permute_ind = _graph.operations().push(std::move(permute_obj)); - - output_obj.setDef(permute_ind); - obj.insertUse(permute_ind); - - VERBOSE(OddOutputPass) << "Permute Op inserted for a constant output, node index : " - << permute_ind << std::endl; - VERBOSE(OddOutputPass) << " - Input (original) Operand : " << ind << std::endl; - VERBOSE(OddOutputPass) << " - Output(inserted) Operand : " << output_ind << std::endl; - - return output_ind; -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/OddOutputPass.h b/runtime/onert/core/src/compiler/pass/OddOutputPass.h deleted file mode 100644 index 2accbac60..000000000 --- a/runtime/onert/core/src/compiler/pass/OddOutputPass.h +++ /dev/null @@ -1,89 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_ODD_OUTPUT_PASS_H__ -#define __ONERT_COMPILER_PASS_ODD_OUTPUT_PASS_H__ - -#include <unordered_set> - -#include "Pass.h" -#include "ir/Index.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -/** - * @brief Pass to specially handle odd outputs in a subgraph - * - * Runtime Graph IR requires every input or output must have distinct tensor index, this is onert's - * restriction. However we allow duplication of indices in the models(or API). So we should - * transform the graph after model-loading. - * - * This is necessary since our API lets users to set different buffers for each input and output so - * it is unavoidable that we must copy the value at runtime. - * - * Note that this is a mandatory pass for Graph. - * - * Case 1 : An operand which is a model output and a model input - * - * Create an operand and insert a Permute(copy) op between them. And change the output to be the - * newly generated operand. - * - * e.g.) - * - * ``` - * ((#0 Input0 and also Output0)) - * becomes - * ((#0 Input0)) -> [#0 Permute] -> ((#1 Output0)) - * ``` - * - * Case 2 : Two or more duplicated outputs - * - * Do the same with Case 1, but between two outputs of the same tensor index. - * - * e.g.) - * - * ``` - * ((#0 Input0)) -> [#0 Some Operation] -> ((#1 Output0 and also Output1)) - * becomes - * ((#0 Input0)) -> [#0 Some Operation] -> ((#1 Output0)) [#1 Permute] -> ((#2 Output1)) - * ``` - * - */ -class OddOutputPass : public Pass -{ -public: - using Pass::Pass; - -public: - std::string id() final { return "OddOutputPass"; } - -public: - void run() override; - -private: - ir::OperandIndex insertPermute(ir::OperandIndex input); -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_ODD_OUTPUT_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/OperandPass.cc b/runtime/onert/core/src/compiler/pass/OperandPass.cc deleted file mode 100644 index 50c001c30..000000000 --- a/runtime/onert/core/src/compiler/pass/OperandPass.cc +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OperandPass.h" - -#include "ir/Graph.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void OperandPass::run() -{ - _graph.operands().iterate( - [&](const ir::OperandIndex &index, ir::Operand &object) { callback(index, object); }); -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/OperandPass.h b/runtime/onert/core/src/compiler/pass/OperandPass.h deleted file mode 100644 index b094879c5..000000000 --- a/runtime/onert/core/src/compiler/pass/OperandPass.h +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_OPERAND_PASS_H__ -#define __ONERT_COMPILER_PASS_OPERAND_PASS_H__ - -#include "Pass.h" -#include "ir/Index.h" - -namespace onert -{ -namespace ir -{ -class Operand; -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class OperandPass : public Pass -{ -public: - using Pass::Pass; - virtual ~OperandPass() = default; - -public: - std::string id() override = 0; - void run() override final; - virtual void callback(const ir::OperandIndex &i, ir::Operand &o) = 0; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_OPERAND_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/OperationPass.cc b/runtime/onert/core/src/compiler/pass/OperationPass.cc deleted file mode 100644 index d7a55cb22..000000000 --- a/runtime/onert/core/src/compiler/pass/OperationPass.cc +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OperationPass.h" - -#include "ir/Index.h" -#include "ir/Operation.h" -#include "ir/Graph.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void OperationPass::run() -{ - _graph.operations().iterate( - [&](const ir::OperationIndex &index, ir::Operation &node) { callback(index, node); }); -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/OperationPass.h b/runtime/onert/core/src/compiler/pass/OperationPass.h deleted file mode 100644 index ac4d818a2..000000000 --- a/runtime/onert/core/src/compiler/pass/OperationPass.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file OperationPass.h - * @brief This file contains OperationPass class - */ - -#ifndef __ONERT_COMPILER_PASS_OPERATION_PASS_H__ -#define __ONERT_COMPILER_PASS_OPERATION_PASS_H__ - -#include "Pass.h" -#include "ir/Index.h" - -namespace onert -{ -namespace ir -{ -class Operation; -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -/** - * @brief Class to iterate over operations and calls callback() method - */ -class OperationPass : public Pass -{ -public: - using Pass::Pass; - virtual ~OperationPass() = default; - -public: - /** - * @brief Returns string id for this pass. Same with class name. - * - * @return string id - */ - std::string id() override = 0; - - /** - * @brief Be called for all nodes of graph. - * @param index is the index of a node in graph - * @param node is the node in graph - */ - virtual void callback(const ir::OperationIndex &index, ir::Operation &node) = 0; - - /** - * @brief Run the pass - */ - void run() final; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_OPERATION_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/Pass.h b/runtime/onert/core/src/compiler/pass/Pass.h deleted file mode 100644 index 3f356c337..000000000 --- a/runtime/onert/core/src/compiler/pass/Pass.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_PASS_H__ -#define __ONERT_COMPILER_PASS_PASS_H__ - -#include <string> - -namespace onert -{ -namespace ir -{ -class Graph; -} // namespace compiler -} // namespace onert - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class Pass -{ -public: - Pass(ir::Graph &graph) : _graph{graph} {} - virtual ~Pass() = default; - -public: - virtual std::string id() = 0; - virtual void run() = 0; - -protected: - ir::Graph &_graph; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/PassRunner.cc b/runtime/onert/core/src/compiler/pass/PassRunner.cc deleted file mode 100644 index 2a058c8ac..000000000 --- a/runtime/onert/core/src/compiler/pass/PassRunner.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PassRunner.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -PassRunner &PassRunner::append(std::unique_ptr<Pass> pass) -{ - _passes.emplace_back(std::move(pass)); - return *this; -} - -void PassRunner::run() -{ - for (auto &pass : _passes) - { - VERBOSE(PassRunner) << "Start running '" << pass->id() << "'" << std::endl; - pass->run(); - VERBOSE(PassRunner) << "Finished running '" << pass->id() << "'" << std::endl; - // TODO Dump graph(LowerInfo, OpSequence, ...)? - } -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/PassRunner.h b/runtime/onert/core/src/compiler/pass/PassRunner.h deleted file mode 100644 index a43c83f89..000000000 --- a/runtime/onert/core/src/compiler/pass/PassRunner.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_PASS_RUNNER_H__ -#define __ONERT_COMPILER_PASS_PASS_RUNNER_H__ - -#include <initializer_list> -#include <memory> -#include <vector> - -#include "Pass.h" -#include "util/logging.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -/** - * @brief Composite passes with logging - */ -class PassRunner -{ -public: - PassRunner() = default; - PassRunner &append(std::unique_ptr<Pass> pass); - - void run(); - -private: - std::vector<std::unique_ptr<Pass>> _passes; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_PASS_RUNNER_H__ diff --git a/runtime/onert/core/src/compiler/pass/PermutationEliminationPass.cc b/runtime/onert/core/src/compiler/pass/PermutationEliminationPass.cc deleted file mode 100644 index 504f1b995..000000000 --- a/runtime/onert/core/src/compiler/pass/PermutationEliminationPass.cc +++ /dev/null @@ -1,167 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PermutationEliminationPass.h" -#include "backend/controlflow/Config.h" - -#include "util/logging.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void PermutationEliminationPass::callback(const ir::OperationIndex &ind, ir::Operation &node) -{ - _op_ind = ind; - node.accept(*this); -}; - -void PermutationEliminationPass::visit(const ir::operation::Permute &node) -{ - auto in_operand = node.getInputs().at(0); - auto out_operand = node.getOutputs().at(0); - - // Check if two tensors are both portable if not, we can't eliminate the node - { - auto in_def_factor = _lowered_graph.getLowerInfo(in_operand)->def_factors().getOnlyElement(); - auto out_def_factor = _lowered_graph.getLowerInfo(out_operand)->def_factors().getOnlyElement(); - - auto in_config = in_def_factor.backend()->config(); - auto out_config = out_def_factor.backend()->config(); - - // FIXME Supporting dynamic tensor does not exactly mean those are portable. - // It may need to have another config option for checking if each uses `IPortableTensor`. - if (!(in_config->supportDynamicTensor() && out_config->supportDynamicTensor())) - return; - } - - if (_graph.getOutputs().contains(out_operand)) - { - // If the input is a const, we cannot remove it since we cannot put the constant data in the - // output buffer during prepare phase. - auto permute_input = node.getInputs().at(0); - if (_graph.operands().at(permute_input).isConstant()) - return; - // If the input is a model input, we cannot remove it since our API lets users to set different - // buffers for inputs and outputs even though one tensor is both at the same time. - auto permute_output = node.getOutputs().at(0); - if (_graph.getInputs().contains(permute_input) && _graph.getOutputs().contains(permute_output)) - return; - // Likewise, if copying between outputs to outputs, keep it. - if (_graph.getOutputs().contains(permute_input) && _graph.getOutputs().contains(permute_output)) - return; - - // Exceptional case : When the output operand is a model output - // In this case we keep the output and remove the input - - auto &out_operand_obj = _graph.operands().at(out_operand); - assert(out_operand_obj.getDef() == _op_ind); - out_operand_obj.unsetDef(); - _lowered_graph.op_seqs().iterate([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - if (!op_seq.getOutputs().contains(in_operand)) - return; - - // Update OpSequence/ir::Operation edges and ir::Operand edges - op_seq.replaceOutputs(in_operand, out_operand); - for (auto op : op_seq.operations()) - { - auto &operation_obj = _graph.operations().at(op); - if (operation_obj.getOutputs().contains(in_operand)) - { - operation_obj.replaceOutputs(in_operand, out_operand); - out_operand_obj.setDef(op); - } - } - }); - - // Remove Permute operation, enclosing OpSequence and the operand - { - _graph.removeOperand(in_operand); - - auto op_seq_ind = _lowered_graph.op_seqs().getOperation(_op_ind); - // Assumes enclosing OpSequence contatins just this Permute operation - assert(_lowered_graph.op_seqs().at(op_seq_ind).size() == 1); - _lowered_graph.op_seqs().remove(op_seq_ind); - _graph.operations().remove(_op_ind); - } - - _lowered_graph.op_seqs().iterate([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - if (!op_seq.getInputs().contains(in_operand)) - return; - - op_seq.replaceInputs(in_operand, out_operand); - for (auto op : op_seq.operations()) - { - auto &operation_obj = _graph.operations().at(op); - if (operation_obj.getInputs().contains(in_operand)) - { - operation_obj.replaceInputs(in_operand, out_operand); - out_operand_obj.insertUse(op); - } - } - }); - - VERBOSE(removePermute) << "Permute Op removed, node index : " << _op_ind << std::endl; - VERBOSE(removePermute) << " - Input (removed) ir::Operand : " << in_operand << std::endl; - VERBOSE(removePermute) << " - Output(kept) ir::Operand : " << out_operand << std::endl; - } - else - { - // Otherwise keep the input and remove the output - - auto &in_operand_obj = _graph.operands().at(in_operand); - in_operand_obj.removeUse(_op_ind); - - // Make OpSequences(that use the output) use the input - _lowered_graph.op_seqs().iterate([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) { - if (!op_seq.getInputs().contains(out_operand)) - return; - - op_seq.replaceInputs(out_operand, in_operand); - for (auto op : op_seq.operations()) - { - auto &operation_obj = _graph.operations().at(op); - if (operation_obj.getInputs().contains(out_operand)) - { - operation_obj.replaceInputs(out_operand, in_operand); - in_operand_obj.insertUse(op); - } - } - }); - - // Remove Permute operation, enclosing OpSequence and the operand - { - _graph.removeOperand(out_operand); - - auto op_seq_ind = _lowered_graph.op_seqs().getOperation(_op_ind); - // Assumes enclosing OpSequence contatins just this Permute operation - assert(_lowered_graph.op_seqs().at(op_seq_ind).size() == 1); - _lowered_graph.op_seqs().remove(op_seq_ind); - _graph.operations().remove(_op_ind); - } - - VERBOSE(removePermute) << "Permute Op removed, node index : " << _op_ind << std::endl; - VERBOSE(removePermute) << " - Input (kept) ir::Operand : " << in_operand << std::endl; - VERBOSE(removePermute) << " - Output(removed) ir::Operand : " << out_operand << std::endl; - } -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/PermutationEliminationPass.h b/runtime/onert/core/src/compiler/pass/PermutationEliminationPass.h deleted file mode 100644 index 29daf1a82..000000000 --- a/runtime/onert/core/src/compiler/pass/PermutationEliminationPass.h +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_PERMUTATION_ELIMINATION_PASS_H__ -#define __ONERT_COMPILER_PASS_PERMUTATION_ELIMINATION_PASS_H__ - -#include "ir/OperationVisitor.h" -#include "LoweredOperationPass.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -/** - * @brief An optimization pass that removes Permute operations if possible - * - * There may be some Permute operations that are inserted by PermutationInsertionPass or other - * passes. This pass checks all Permute operations and eliminates them if Permute in/out tensors - * are compatible and layouts match. - * - * Permute input tensor is kept and the output is removed for all the cases, except model outputs. - * As all output tensors have to be controlflow backend, so the output is kept. - * - * @note This is an optimization pass which means that everything should work fine even if this pass - * was skipped. - */ -class PermutationEliminationPass : public LoweredOperationPass, public ir::OperationVisitor -{ -public: - using LoweredOperationPass::LoweredOperationPass; - -public: - std::string id() final { return "PermutationEliminationPass"; } - -public: - void callback(const ir::OperationIndex &i, ir::Operation &n) final; - -private: - void visit(const ir::operation::Permute &) final; - -private: - ir::OperationIndex _op_ind; -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_PERMUTATION_ELIMINATION_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.cc b/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.cc deleted file mode 100644 index c83a72ada..000000000 --- a/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.cc +++ /dev/null @@ -1,216 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PermutationInsertionPass.h" - -#include <cassert> -#include <utility> -#include <unordered_map> - -#include "backend/controlflow/Config.h" -#include "ir/Operand.h" -#include "ir/operation/LowerInfo.h" -#include "ir/Graph.h" -#include "backend/IConfig.h" -#include "util/logging.h" -#include <memory> -#include "ir/operation/Permute.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -void PermutationInsertionPass::callback(const ir::OperandIndex &index, ir::Operand &object) -{ - auto &&operand_li = _lowered_graph.getLowerInfo(index); - assert(operand_li); - - // NOTE Later, constants also will have Def - // Ignore constants - if (operand_li->def_factors().size() == 0) - { - return; - } - - std::list<ir::OperationIndex> permute_indexes; - - // Build a map for all necessary type of operands - std::unordered_map<ir::operand::PermuteFactor, ir::OperandIndex> factor_to_index; - { - assert(operand_li->def_factors().size() == 1); - for (auto factor : operand_li->def_factors()) - { - factor_to_index.emplace(factor, index); - } - - auto insert_set = operand_li->use_factors() - operand_li->def_factors(); - for (auto factor : insert_set) - { - const auto permute_operation_index = insertPermute(index, factor); - permute_indexes.push_back(permute_operation_index); - const auto &permute_operation = _graph.operations().at(permute_operation_index); - const auto permuted_operand_index = permute_operation.getOutputs().at(0); - factor_to_index.emplace(factor, permuted_operand_index); - } - } - - // Update operations' input that uses this operand - { - std::list<ir::OperationIndex> remove_list; - - auto uses = object.getUses(); - for (auto use : uses) - { - // If permute operation, ignore it - if (std::find(permute_indexes.begin(), permute_indexes.end(), use) != permute_indexes.end()) - continue; - - auto &operation = _graph.operations().at(use); - assert(_lowered_graph.op_seqs().containsOperation(use)); - auto op_seq_index = _lowered_graph.op_seqs().getOperation(use); - auto op_seq_li = _lowered_graph.getLowerInfo(op_seq_index); - assert(op_seq_li); - const auto op_seq_layout = op_seq_li->layout(); - const backend::Backend *backend = op_seq_li->backend(); - assert(backend); - auto use_node_inputs = operation.getInputs(); - assert(use_node_inputs.contains(index)); - - auto new_index = factor_to_index.at({backend, op_seq_layout}); - if (index != new_index) - { - // Update from op_seq - // Replace the same inputs of an OpSequence at once for the following reasons: - // 1. An OpSequence's inputs are the same inputs of first operation - // 2. An OpSequence may have inputs as the same operand (2 or more). - // 3. The same inputs of OpSequence have the same PermuteFactor. - _lowered_graph.op_seqs().at(op_seq_index).replaceInputs(index, new_index); - - // Update from operation - // Replace the same inputs of an operation at once for the following reasons: - // No. 2 and 3 above - operation.replaceInputs(index, new_index); - - // Update from operand - remove_list.push_back( - use); // Removal should be done in another loop since we are in the loop - _graph.operands().at(new_index).insertUse(use); - } - } - - for (auto &operation : remove_list) - { - object.removeUse(operation); - } - } -} - -ir::OperationIndex PermutationInsertionPass::insertPermute(const ir::OperandIndex &operand_index, - const ir::operand::PermuteFactor &factor) -{ - assert(!_graph.isBuildingPhase()); - - auto &operand = _graph.operands().at(operand_index); - - // Generate output operand and permute operation - auto out_operand_index = _graph.addOperand(operand.shape(), operand.typeInfo()); - // change model output if operand_index is model output index - auto &model_outputs = _graph.getOutputs(); - if (model_outputs.contains(operand_index)) - { - model_outputs.replace(operand_index, out_operand_index); - } - - // Find Permute information - auto input_factor = _lowered_graph.getLowerInfo(operand_index)->def_factors().getOnlyElement(); - auto input_backend = input_factor.backend(); - auto output_backend = factor.backend(); - // NOTE Permute may not have specific layout because the layout of input and output may be - // different. - const auto permute_node_layout = ir::Layout::UNKNOWN; - // NOTE If one backend supports several layout, the backend must support Permute operation - const backend::Backend *permute_node_backend = compiler::BackendManager::get().getControlflow(); - if (input_backend == output_backend) - { - permute_node_backend = input_backend; - } - const ir::operand::PermuteFactor permute_node_factor{permute_node_backend, permute_node_layout}; - - // Update LowerInfo of input operand - auto operand_lower_info = _lowered_graph.getLowerInfo(operand_index); - operand_lower_info->removeUsePermuteFactor(factor); - operand_lower_info->addUsePermuteFactor(permute_node_factor); - - // Update LowerInfo of output operand - auto out_operand_li = std::make_unique<ir::operand::LowerInfo>(); - - // The input and output factors of all nodes will be the same except Permute. So Tensor's - // allocators allocates memory using only the information of def permutation factor now. - // TODO Change param to permute_node_factor - out_operand_li->addDefPermuteFactor(factor); - out_operand_li->addUsePermuteFactor(factor); - _lowered_graph.setLowerInfo(out_operand_index, std::move(out_operand_li)); - - // Insert permute operation to the graph - const auto input_layout = input_factor.layout(); - const auto output_layout = factor.layout(); - using Permute = ir::operation::Permute; - const auto permute_type = [&]() { - if (input_layout == ir::Layout::NHWC && output_layout == ir::Layout::NCHW) - { - return Permute::Type::NHWC_TO_NCHW; - } - else if (input_layout == ir::Layout::NCHW && output_layout == ir::Layout::NHWC) - { - return Permute::Type::NCHW_TO_NHWC; - } - else - { - return Permute::Type::COPY; - } - }(); - auto insert_node = std::make_unique<Permute>(operand_index, out_operand_index, permute_type); - - auto node_index = _graph.operations().push(std::move(insert_node)); - const auto &node = _graph.operations().at(node_index); - - VERBOSE_F() << "Permute Op inserted, node index : " << node_index << std::endl; - VERBOSE_F() << " - Input (original) Operand : " << operand_index << std::endl; - VERBOSE_F() << " - Output(inserted) Operand : " << out_operand_index << std::endl; - - // OpSequence - { - auto op_seq_index = _lowered_graph.op_seqs().emplace(node_index, permute_node_layout); - auto &op_seq = _lowered_graph.op_seqs().at(op_seq_index); - op_seq.setInputs(node.getInputs()); - op_seq.setOutputs(node.getOutputs()); - _lowered_graph.setLowerInfo(op_seq_index, std::make_unique<ir::operation::LowerInfo>( - permute_node_backend, permute_node_layout)); - } - - // Update Use/Def info - { - _graph.operands().at(operand_index).insertUse(node_index); - _graph.operands().at(out_operand_index).setDef(node_index); - } - return node_index; -} -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.h b/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.h deleted file mode 100644 index 758515385..000000000 --- a/runtime/onert/core/src/compiler/pass/PermutationInsertionPass.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_PERMUTATION_INSERTION_PASS_H__ -#define __ONERT_COMPILER_PASS_PERMUTATION_INSERTION_PASS_H__ - -#include "LoweredOperandPass.h" -#include "compiler/BackendManager.h" -#include "ir/Operand.h" -#include "ir/operand/PermuteFactor.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class PermutationInsertionPass : public LoweredOperandPass -{ -public: - using LoweredOperandPass::LoweredOperandPass; - -public: - std::string id() override { return "PermutationInsertionPass"; } - void callback(const ir::OperandIndex &index, ir::Operand &object) override; - -private: - /** - * @brief Insert Permute operation that has given operand as input - * - * @param operand_index is the target operand index for the insertion - * @param factor is the output operand's backend type and layout - * - * @return ir::OperationIndex - */ - ir::OperationIndex insertPermute(const ir::OperandIndex &operand_index, - const ir::operand::PermuteFactor &factor); -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_PERMUTATION_INSERTION_PASS_H__ diff --git a/runtime/onert/core/src/compiler/pass/PermutationOperationPass.cc b/runtime/onert/core/src/compiler/pass/PermutationOperationPass.cc deleted file mode 100644 index 93d125307..000000000 --- a/runtime/onert/core/src/compiler/pass/PermutationOperationPass.cc +++ /dev/null @@ -1,351 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "PermutationOperationPass.h" - -#include "backend/Backend.h" -#include "backend/IConfig.h" -#include "ir/Graph.h" -#include "util/logging.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -using namespace ir; - -void PermutationOperationPass::callback(const OperationIndex &, Operation &node) -{ - node.accept(*this); -}; - -// TODO Remove this. Expanding ranks of Operand is dangerous -void PermutationOperationPass::applyExpandRanks(const Operation &node) -{ - const auto &output_ind = node.getOutputs().at(0); - const auto &output = _graph.operands().at(output_ind); - - assert(output.getDef().valid()); - const auto node_index = output.getDef(); - const auto &op_seq_index = _lowered_graph.op_seqs().getOperation(node_index); - const auto frontend_layout = _lowered_graph.op_seqs().at(op_seq_index).getLayout(); - const auto backend_layout = _lowered_graph.getLowerInfo(op_seq_index)->layout(); - - if (frontend_layout == backend_layout) - { - return; - } - - int32_t expanded_rank = 0; - for (const auto &index : - (node.getInputs() + node.getOutputs()) | Remove::DUPLICATED | Remove::UNDEFINED) - { - expanded_rank = std::max(expanded_rank, _graph.operands().at(index).shape().rank()); - } - if (expanded_rank < 4) - return; - - for (const auto &index : - (node.getInputs() + node.getOutputs()) | Remove::DUPLICATED | Remove::UNDEFINED) - { - const auto &operand = _graph.operands().at(index); - if (operand.shape().rank() < expanded_rank) - { - if (operand.getUses().size() > 1) - throw std::runtime_error("PermutationOperationPass: not supported expanding rank of " - "operand used in more than one node"); - // TODO remove const_cast later. For example, _ctx may need to be a non const variable or - // a node to extend shape may be inserted in front of this operation - const_cast<Shape &>(operand.shape()).extendRank(expanded_rank); - } - } -} - -void PermutationOperationPass::changeToKeepLayout(const Operation &node) -{ - const auto &output_ind = node.getOutputs().at(0); - const auto &output_obj = _graph.operands().at(output_ind); - - assert(output_obj.getDef().valid()); - const auto node_index = output_obj.getDef(); - const auto &op_seq_index = _lowered_graph.op_seqs().getOperation(node_index); - - const auto frontend_layout = _lowered_graph.op_seqs().at(op_seq_index).getLayout(); - const auto backend_layout = _lowered_graph.getLowerInfo(op_seq_index)->layout(); - - if (frontend_layout == backend_layout) - { - return; - } - - // Permutation changing layout beyond 4-D is not supported yet - assert(output_obj.shape().rank() <= 4); - - // Divide op_seq based on target operation - { - auto &prev_op_seq = _lowered_graph.op_seqs().at(op_seq_index); - auto &operations = _lowered_graph.graph().operations(); - - // Create new op_seq and move information from existing op_seq to new op_seq if target - // node is the end of op_seq - auto it = prev_op_seq.begin(); - // Find iterator of target node in op_seq - while (*(it++) != node_index) - ; - if (it != prev_op_seq.end()) - { - const auto &target_op_idx = *it; - const auto &target_node = operations.at(target_op_idx); - const auto &next_op_seq_index = - _lowered_graph.op_seqs().emplace(target_op_idx, prev_op_seq.getLayout()); - auto &next_op_seq = _lowered_graph.op_seqs().at(next_op_seq_index); - next_op_seq.setInputs(target_node.getInputs()); - next_op_seq.setOutputs(target_node.getOutputs()); - - std::vector<OperationIndex> remove_list; - remove_list.emplace_back(target_op_idx); - while (++it != prev_op_seq.end()) - { - next_op_seq.appendOperation(target_op_idx); - next_op_seq.setOutputs(target_node.getOutputs()); - remove_list.emplace_back(target_op_idx); - } - - prev_op_seq.setOutputs(node.getOutputs()); - for (const auto &index : remove_list) - { - prev_op_seq.remove(index); - } - - const auto op_seq_li = _lowered_graph.getLowerInfo(op_seq_index); - _lowered_graph.setLowerInfo( - next_op_seq_index, - std::make_unique<ir::operation::LowerInfo>(op_seq_li->backend(), op_seq_li->layout())); - } - } - - // Remove target operation from op_seq and insert the target operation to new op_seq - { - const auto backend = _lowered_graph.getLowerInfo(op_seq_index)->backend(); - - // Remove target operation from op_sequence - _lowered_graph.op_seqs().removeFromOpSequence(node_index); - - if (!_lowered_graph.op_seqs().exist(op_seq_index)) - { - // Remove lowerinfo for op_seq of target operation if the op_seq does not exist - _lowered_graph.removeLowerInfo(op_seq_index); - } - else - { - // Update op_seq of target operation if the op_seq exists - auto &prev_op_seq = _lowered_graph.op_seqs().at(op_seq_index); - const auto &last_node_idx = *(--prev_op_seq.end()); - const auto &last_node = _lowered_graph.graph().operations().at(last_node_idx); - prev_op_seq.setOutputs(last_node.getOutputs()); - } - - // Create new op_seq and set information to the op_seq - auto new_op_seq_index = _lowered_graph.op_seqs().emplace(node_index, frontend_layout); - auto &new_op_seq = _lowered_graph.op_seqs().at(new_op_seq_index); - new_op_seq.setInputs(node.getInputs()); - new_op_seq.setOutputs(node.getOutputs()); - _lowered_graph.setLowerInfo( - new_op_seq_index, std::make_unique<ir::operation::LowerInfo>(backend, frontend_layout)); - } - - // Change PermuteFactors of operands of target node - { - const auto &op_seq_index = _lowered_graph.op_seqs().getOperation(node_index); - const auto op_seq_li = _lowered_graph.getLowerInfo(op_seq_index); - const auto backend = op_seq_li->backend(); - const operand::PermuteFactor removed_factor{backend, backend_layout}; - const operand::PermuteFactor new_factor{backend, frontend_layout}; - for (const auto &input : node.getInputs() | Remove::DUPLICATED | Remove::UNDEFINED) - { - bool canRemove = true; - for (const auto &use : _graph.operands().at(input).getUses()) - { - if (use != node_index) - { - const auto &use_op_seq_index = _lowered_graph.op_seqs().getOperation(use); - auto use_op_seq_li = _lowered_graph.getLowerInfo(use_op_seq_index); - if (use_op_seq_li->backend() == backend && use_op_seq_li->layout() == backend_layout) - { - canRemove = false; - break; - } - } - } - - auto lower_info = _lowered_graph.getLowerInfo(input); - if (canRemove) - { - lower_info->removeUsePermuteFactor(removed_factor); - } - lower_info->addUsePermuteFactor(new_factor); - - // Whether if node's input is an input of model or a constant - if (!_graph.operands().at(input).getDef().valid() && - (lower_info->def_factors().size() == 1 && - lower_info->def_factors().getOnlyElement() == removed_factor)) - { - assert(_graph.getInputs().contains(input) || _graph.operands().at(input).isConstant()); - lower_info->removeDefPermuteFactor(removed_factor); - lower_info->addDefPermuteFactor(new_factor); - } - } - - for (const auto &output : node.getOutputs() | Remove::DUPLICATED | Remove::UNDEFINED) - { - auto lower_info = _lowered_graph.getLowerInfo(output); - lower_info->removeDefPermuteFactor(removed_factor); - lower_info->addDefPermuteFactor(new_factor); - - // Whether if node's output is an output of model - if (_graph.operands().at(output).getUses().size() == 0) - { - assert(_graph.getOutputs().contains(output)); - lower_info->removeUsePermuteFactor(removed_factor); - lower_info->addUsePermuteFactor(new_factor); - } - } - } -} - -void PermutationOperationPass::visit(const ir::operation::BinaryArithmetic &node) -{ - applyExpandRanks(node); -} - -void PermutationOperationPass::visit(const ir::operation::Concat &node) { applyExpandRanks(node); } - -void PermutationOperationPass::visit(const ir::operation::Comparison &node) -{ - applyExpandRanks(node); -} - -void PermutationOperationPass::visit(const ir::operation::ElementwiseBinary &node) -{ - applyExpandRanks(node); -} - -void PermutationOperationPass::visit(const ir::operation::ElementwiseUnary &node) -{ - applyExpandRanks(node); -} - -void PermutationOperationPass::visit(const ir::operation::FullyConnected &node) -{ - const auto &input_ind = node.getInputs().at(ir::operation::FullyConnected::Input::INPUT); - const auto &input_obj = _graph.operands().at(input_ind); - const auto &input_shape = input_obj.shape(); - - if (input_shape.rank() >= 4) - { - changeToKeepLayout(node); - } -} - -void PermutationOperationPass::visit(const ir::operation::Gather &node) -{ - const auto &input_ind = node.getInputs().at(ir::operation::Gather::Input::INPUT); - const auto &input_obj = _graph.operands().at(input_ind); - const auto &input_shape = input_obj.shape(); - - const auto &output_ind = node.getOutputs().at(0); - const auto &output_obj = _graph.operands().at(output_ind); - const auto &output_shape = output_obj.shape(); - - if (input_shape.rank() >= 4 || output_shape.rank() >= 4) - { - changeToKeepLayout(node); - } -} - -void PermutationOperationPass::visit(const ir::operation::OneHot &node) -{ - const auto &output_ind = node.getOutputs().at(0); - const auto &output_obj = _graph.operands().at(output_ind); - const auto &output_shape = output_obj.shape(); - - if (output_shape.rank() >= 4) - { - changeToKeepLayout(node); - } -} - -void PermutationOperationPass::visit(const ir::operation::Pack &node) -{ - const auto &input_ind = node.getInputs().at(ir::operation::Reshape::Input::INPUT); - const auto &input_obj = _graph.operands().at(input_ind); - const auto &input_shape = input_obj.shape(); - - const auto &output_ind = node.getOutputs().at(0); - const auto &output_obj = _graph.operands().at(output_ind); - const auto &output_shape = output_obj.shape(); - - if (input_shape.rank() < 4 || output_shape.rank() >= 4) - { - changeToKeepLayout(node); - } -} - -void PermutationOperationPass::visit(const ir::operation::PReLU &node) { applyExpandRanks(node); } - -void PermutationOperationPass::visit(const ir::operation::Reshape &node) -{ - const auto &input_ind = node.getInputs().at(ir::operation::Reshape::Input::INPUT); - const auto &input_obj = _graph.operands().at(input_ind); - const auto &input_shape = input_obj.shape(); - - const auto &output_ind = node.getOutputs().at(0); - const auto &output_obj = _graph.operands().at(output_ind); - const auto &output_shape = output_obj.shape(); - - if (input_shape.rank() >= 4 || output_shape.rank() >= 4) - { - changeToKeepLayout(node); - } -} - -void PermutationOperationPass::visit(const ir::operation::SquaredDifference &node) -{ - applyExpandRanks(node); -} - -void PermutationOperationPass::visit(const ir::operation::Unpack &node) -{ - const auto &input_ind = node.getInputs().at(ir::operation::Reshape::Input::INPUT); - const auto &input_obj = _graph.operands().at(input_ind); - const auto &input_shape = input_obj.shape(); - - const auto &output_ind = node.getOutputs().at(0); - const auto &output_obj = _graph.operands().at(output_ind); - const auto &output_shape = output_obj.shape(); - - if (input_shape.rank() < 4 || output_shape.rank() >= 4) - { - changeToKeepLayout(node); - } -} - -} // namespace pass -} // namespace compiler -} // namespace onert diff --git a/runtime/onert/core/src/compiler/pass/PermutationOperationPass.h b/runtime/onert/core/src/compiler/pass/PermutationOperationPass.h deleted file mode 100644 index cea5de288..000000000 --- a/runtime/onert/core/src/compiler/pass/PermutationOperationPass.h +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_COMPILER_PASS_PERMUTATION_OPERATION_PASS_H__ -#define __ONERT_COMPILER_PASS_PERMUTATION_OPERATION_PASS_H__ - -#include "ir/OperationVisitor.h" -#include "LoweredOperationPass.h" - -namespace onert -{ -namespace compiler -{ -namespace pass -{ - -class PermutationOperationPass : public LoweredOperationPass, public ir::OperationVisitor -{ -public: - using LoweredOperationPass::LoweredOperationPass; - -public: - std::string id() final { return "PermutationOperationPass"; } - -public: - void callback(const ir::OperationIndex &i, ir::Operation &n) final; - -public: - void visit(const ir::operation::BinaryArithmetic &) final; - void visit(const ir::operation::Comparison &) final; - void visit(const ir::operation::Concat &) final; - void visit(const ir::operation::ElementwiseBinary &) final; - void visit(const ir::operation::ElementwiseUnary &) final; - void visit(const ir::operation::OneHot &) final; - void visit(const ir::operation::Pack &) final; - void visit(const ir::operation::PReLU &) final; - void visit(const ir::operation::SquaredDifference &) final; - void visit(const ir::operation::Unpack &) final; - void visit(const ir::operation::FullyConnected &) final; - void visit(const ir::operation::Gather &) final; - void visit(const ir::operation::Reshape &) final; - -private: - void applyExpandRanks(const ir::Operation &); - void changeToKeepLayout(const ir::Operation &); -}; - -} // namespace pass -} // namespace compiler -} // namespace onert - -#endif // __ONERT_COMPILER_PASS_PERMUTATION_OPERATION_PASS_H__ diff --git a/runtime/onert/core/src/dumper/dot/DotBuilder.cc b/runtime/onert/core/src/dumper/dot/DotBuilder.cc deleted file mode 100644 index 38a69696e..000000000 --- a/runtime/onert/core/src/dumper/dot/DotBuilder.cc +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "DotBuilder.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -// DotDumper -DotBuilder::DotBuilder() {} - -void DotBuilder::update(const Node &node_info) -{ - add(node_info); - for (auto edge : node_info.out_edges()) - { - addEdge(node_info, *edge); - } -} - -void DotBuilder::addOpSequence(const DotSubgraphInfo &subgraph_info) -{ - _dot << "subgraph cluster_" << subgraph_info.index().value() << " {\n"; - _dot << " label=\"" << subgraph_info.label() << "\";\n"; - _dot << " style=filled;\n"; - _dot << " color=lightgrey;\n"; - _dot << " "; - for (auto op : subgraph_info.operations()) - { - _dot << "operation" << op.value() << "; "; - } - for (auto op : subgraph_info.operands()) - { - _dot << "operand" << op.value() << "; "; - } - _dot << "\n"; - _dot << "}\n"; -} - -void DotBuilder::writeDot(std::ostream &os) -{ - os << "digraph D {\n" - << _dot.str() << "\n" - << "}\n"; -} - -void DotBuilder::add(const Node &node) -{ - _dot << node.id(); - std::stringstream ss; - _dot << "["; - for (auto attr : node.attributes()) - { - _dot << attr.first << "=\"" << attr.second << "\" "; - } - _dot << "];\n"; -} - -void DotBuilder::addEdge(const Node &node1, const Node &node2) -{ - _dot << node1.id() << " -> " << node2.id() << ";\n"; -} - -} // namespace dot -} // namespace dumper -} // namespace onert diff --git a/runtime/onert/core/src/dumper/dot/DotBuilder.h b/runtime/onert/core/src/dumper/dot/DotBuilder.h deleted file mode 100644 index 681cbbf5d..000000000 --- a/runtime/onert/core/src/dumper/dot/DotBuilder.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_DUMPER_DOT_DOT_BUILDER_H__ -#define __ONERT_DUMPER_DOT_DOT_BUILDER_H__ - -#include <sstream> - -#include "ir/Index.h" -#include "ir/Operation.h" -#include "ir/Operand.h" - -#include "OperationNode.h" -#include "OperandNode.h" -#include "DotSubgraphInfo.h" - -using Operation = onert::ir::Operation; -using Object = onert::ir::Operand; - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -class DotBuilder -{ -public: - DotBuilder(); - -public: - void update(const Node &dotinfo); - void addOpSequence(const DotSubgraphInfo &subgraph_info); - - void writeDot(std::ostream &os); - -private: - void add(const Node &dotinfo); - void addEdge(const Node &dotinfo1, const Node &dotinfo2); - - std::stringstream _dot; -}; - -} // namespace dot -} // namespace dumper -} // namespace onert - -#endif // __ONERT_DUMPER_DOT_DOT_BUILDER_H__ diff --git a/runtime/onert/core/src/dumper/dot/DotDumper.cc b/runtime/onert/core/src/dumper/dot/DotDumper.cc deleted file mode 100644 index 8f3cf328c..000000000 --- a/runtime/onert/core/src/dumper/dot/DotDumper.cc +++ /dev/null @@ -1,201 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <fstream> -#include <unordered_map> - -#include "DotDumper.h" -#include "DotBuilder.h" -#include "DotSubgraphInfo.h" -#include "ir/OpSequence.h" -#include "ir/OperationIndexMap.h" -#include "backend/Backend.h" -#include "backend/IConfig.h" -#include "compiler/BackendManager.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -void DotDumper::dump(const std::string &tag) -{ - if (_level == Level::OFF) - { - return; - } - - onert::dumper::dot::DotBuilder dot_builder; - - auto &operations = _graph.operations(); - auto &operands = _graph.operands(); - - ir::OperationIndexMap<std::unique_ptr<Operation>> operation_nodes; - std::unordered_map<ir::OperandIndex, std::unique_ptr<Operand>> operand_nodes; - - auto backend_to_fillcolor = [](const backend::Backend *backend) { - static const auto map = []() { - std::unordered_map<const backend::Backend *, std::string> ret; - uint32_t index = 1; // Start from 1 to avoid 0(red) which is too dark :( - for (const auto backend : compiler::BackendManager::get().getAll()) - { - ret.emplace(backend, Node::BG_COLORS[index]); - index = (index + 1) % (sizeof(Node::BG_COLORS) / sizeof(Node::BG_COLORS[0])); - } - return ret; - }(); - - auto itr = map.find(backend); - if (itr == map.end()) - { - return Node::DEFAULT_FILLCOLOR; - } - else - { - return itr->second; - } - }; - - util::Set<ir::OperandIndex> shown_operand_set; - - operands.iterate([&](const ir::OperandIndex &index, const ir::Operand &object) { - bool showing_cond = false; - if (_level == Level::ALL) - { - showing_cond = true; - } - else - { - showing_cond = - !object.isConstant() || (_graph.getInputs() + _graph.getOutputs()).contains(index); - } - if (showing_cond) - { - shown_operand_set.add(index); - - auto type = [&]() { - using onert::dumper::dot::Operand; - if (_graph.getInputs().contains(index)) - return Operand::Type::MODEL_INPUT; - if (_graph.getOutputs().contains(index)) - return Operand::Type::MODEL_OUTPUT; - return Operand::Type::INTERNAL; - }(); - - auto node = std::make_unique<Operand>(index, type); - - { - // Display LowerInfo attributes - std::string label = std::to_string(index.value()); - std::string fillcolor = ""; - if (_lowered_graph) - { - auto lower_info = _lowered_graph->getLowerInfo(index); - const auto &def_factors = lower_info->def_factors(); - if (def_factors.size() > 0) - { - label += "\\n["; - label += def_factors.getOnlyElement().backend()->config()->id(); - label += "]"; - - fillcolor = backend_to_fillcolor(lower_info->def_factors().getOnlyElement().backend()); - } - } - node->setAttribute("label", label); - node->setAttribute("fillcolor", fillcolor); - } - - operand_nodes.emplace(index, std::move(node)); - } - }); - - operations.iterate([&](const ir::OperationIndex &index, const ir::Operation &op) { - auto node = std::make_unique<Operation>(index, op); - - for (auto input : op.getInputs()) - { - using onert::dumper::dot::Operand; - - // Constant input and dump level is ALL_BUT_CONSTANTS - if (operand_nodes.find(input) == operand_nodes.end()) - continue; - - auto &input_node = operand_nodes.at(input); - input_node->addOutEdge(node.get()); - } - - for (auto output : op.getOutputs()) - { - using onert::dumper::dot::Operand; - auto &output_node = operand_nodes.at(output); - node->addOutEdge(output_node.get()); - } - - operation_nodes.emplace(index, std::move(node)); - }); - - if (_lowered_graph) - { - const auto &op_seqs = _lowered_graph->op_seqs(); - op_seqs.iterate([&](const ir::OpSequenceIndex &index, const ir::OpSequence &op_seq) { - const auto lower_info = _lowered_graph->getLowerInfo(index); - auto fillcolor = backend_to_fillcolor(lower_info->backend()); - std::string label = - std::to_string(index.value()) + " [" + lower_info->backend()->config()->id() + "]"; - DotSubgraphInfo subgraph_info{index, op_seq, shown_operand_set, _graph.operations()}; - subgraph_info.label(label); - subgraph_info.fillcolor(fillcolor); - dot_builder.addOpSequence(subgraph_info); - - // Set fillcolor of all operations in the op_seq - for (const auto &op_idx : op_seq.operations()) - { - auto found = operation_nodes.find(op_idx); - if (found != operation_nodes.end()) - { - auto &&op = found->second; - op->setAttribute("fillcolor", fillcolor); - } - } - }); - } - - for (const auto &e : operation_nodes) - dot_builder.update(*e.second); - for (const auto &e : operand_nodes) - dot_builder.update(*e.second); - - // Dump to file - { - std::string file_name; - file_name += tag; - file_name += ".dot"; - std::filebuf fb; - - fb.open(file_name, std::ios::out); - std::ostream os(&fb); - - dot_builder.writeDot(os); - - fb.close(); - } -} - -} // namespace dot -} // namespace dumper -} // namespace onert diff --git a/runtime/onert/core/src/dumper/dot/DotDumper.h b/runtime/onert/core/src/dumper/dot/DotDumper.h deleted file mode 100644 index fdbca1642..000000000 --- a/runtime/onert/core/src/dumper/dot/DotDumper.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Graph.h" -#include "compiler/LoweredGraph.h" - -#ifndef __ONERT_DUMPER_DOT_DOT_DUMPER_H__ -#define __ONERT_DUMPER_DOT_DOT_DUMPER_H__ - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -class DotDumper -{ -public: - enum Level - { - OFF = 0, //< Do not dump - ALL_BUT_CONSTANTS = 1, //< Emit all operations and operands but constants - ALL = 2 //< Emit all operations and operands - }; - -public: - DotDumper(const ir::Graph &graph, Level level) - : _lowered_graph{nullptr}, _graph(graph), _level{level} - { - } - DotDumper(const compiler::LoweredGraph *lowered_graph, Level level) - : _lowered_graph{lowered_graph}, _graph(_lowered_graph->graph()), _level{level} - { - } - -public: - /** - * @brief Dump to dot file as tag name if "GRAPH_DOT_DUMP" is set - * - * @param[in] tag The name of dot file that would be created - * @return N/A - */ - void dump(const std::string &tag); - -private: - const compiler::LoweredGraph *_lowered_graph; - const ir::Graph &_graph; - Level _level; -}; - -} // namespace dot -} // namespace dumper -} // namespace onert - -#endif // __ONERT_DUMPER_DOT_DOT_DUMPER_H__ diff --git a/runtime/onert/core/src/dumper/dot/DotSubgraphInfo.cc b/runtime/onert/core/src/dumper/dot/DotSubgraphInfo.cc deleted file mode 100644 index 52e9c758d..000000000 --- a/runtime/onert/core/src/dumper/dot/DotSubgraphInfo.cc +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "DotSubgraphInfo.h" - -#include <sstream> - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -DotSubgraphInfo::DotSubgraphInfo(const ir::OpSequenceIndex &index, const ir::OpSequence &op_seq, - const util::Set<ir::OperandIndex> &shown_operands, - const ir::Operations &operations_ctx) - : _index{index} -{ - for (const auto &op_idx : op_seq.operations()) - { - _operations.insert(op_idx); - const auto &node = operations_ctx.at(op_idx); - for (auto o : node.getInputs()) - { - // Must be a shown operand, not op_seq's inputs - if (shown_operands.contains(o) && !op_seq.getInputs().contains(o)) - { - _operands.insert(o); - } - } - for (auto o : node.getOutputs()) - { - // Must be a shown operand, not op_seq's inputs - if (shown_operands.contains(o) && !op_seq.getOutputs().contains(o)) - { - _operands.insert(o); - } - } - } -} - -} // namespace dot -} // namespace dumper -} // namespace onert diff --git a/runtime/onert/core/src/dumper/dot/DotSubgraphInfo.h b/runtime/onert/core/src/dumper/dot/DotSubgraphInfo.h deleted file mode 100644 index 95ba8953e..000000000 --- a/runtime/onert/core/src/dumper/dot/DotSubgraphInfo.h +++ /dev/null @@ -1,61 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_CORE_DUMPER_DOT_DOT_SUBGRAPH_INFO_H__ -#define __ONERT_CORE_DUMPER_DOT_DOT_SUBGRAPH_INFO_H__ - -#include <unordered_set> - -#include "ir/Index.h" -#include <ir/Operations.h> -#include "ir/OpSequence.h" -#include "util/Set.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -class DotSubgraphInfo -{ -public: - DotSubgraphInfo(const ir::OpSequenceIndex &index, const ir::OpSequence &op_seq, - const util::Set<ir::OperandIndex> &shown_operands, - const ir::Operations &operations_ctx); - - ir::OpSequenceIndex index() const { return _index; } - std::string label() const { return _label; } - void label(const std::string &val) { _label = val; } - std::string fillcolor() const { return _fillcolor; } - void fillcolor(const std::string &val) { _fillcolor = val; } - const std::unordered_set<ir::OperationIndex> &operations() const { return _operations; } - const std::unordered_set<ir::OperandIndex> &operands() const { return _operands; } - -private: - ir::OpSequenceIndex _index; - std::string _label; - std::string _fillcolor; - std::unordered_set<ir::OperationIndex> _operations; - std::unordered_set<ir::OperandIndex> _operands; -}; - -} // namespace dot -} // namespace dumper -} // namespace onert - -#endif // __ONERT_CORE_DUMPER_DOT_DOT_SUBGRAPH_INFO_H__ diff --git a/runtime/onert/core/src/dumper/dot/Node.cc b/runtime/onert/core/src/dumper/dot/Node.cc deleted file mode 100644 index 85d6e67a4..000000000 --- a/runtime/onert/core/src/dumper/dot/Node.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Node.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -const std::string Node::DEFAULT_COLORSCHEME = "x11"; -const std::string Node::DEFAULT_FILLCOLOR = "white"; -// RED, BLUE, GREEN, PURPLE, ORANGE, YELLOW, BROWN, PINK -const std::string Node::BG_COLORS[8] = {"1", "2", "3", "4", "5", "6", "7", "8"}; - -Node::Node(const std::string &id) : _id{id} -{ - // Set default values - _attributes["style"] = "filled"; - _attributes["colorscheme"] = DEFAULT_COLORSCHEME; - _attributes["fillcolor"] = DEFAULT_FILLCOLOR; -} - -void Node::setAttribute(const std::string &key, const std::string &val) { _attributes[key] = val; } - -std::string Node::getAttribute(const std::string &key) -{ - auto itr = _attributes.find(key); - if (itr == _attributes.end()) - { - return ""; - } - else - { - return itr->second; - } -} - -} // namespace dot -} // namespace dumper -} // namespace onert diff --git a/runtime/onert/core/src/dumper/dot/Node.h b/runtime/onert/core/src/dumper/dot/Node.h deleted file mode 100644 index 9b09b92e7..000000000 --- a/runtime/onert/core/src/dumper/dot/Node.h +++ /dev/null @@ -1,127 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Node.h - * @brief This file contains Node class - * @ingroup COM_AI_RUNTIME - * - */ - -#ifndef __ONERT_DUMPER_DOT_NODE_H__ -#define __ONERT_DUMPER_DOT_NODE_H__ - -#include <string> -#include <memory> -#include <vector> -#include <unordered_map> - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -enum BGCOLORS : int -{ - RED, - BLUE, - GREEN, - PUPLE, - ORANGE, - YELLOW, - BROWN, - PINK -}; - -/** - * @brief Class that represents a Node in "dot" format - * - */ -class Node -{ -public: - const static std::string DEFAULT_FILLCOLOR; - const static std::string DEFAULT_COLORSCHEME; - const static std::string BG_COLORS[8]; - -public: - /** - * @brief Destroy the Node object - * - */ - virtual ~Node() = default; - - /** - * @brief Construct a new Node object - * - * @param id - */ - Node(const std::string &id); - - /** - * @brief return id - * - * @return id - */ - std::string id() const { return _id; } - - /** - * @brief return attributes - * - * @return const reference of attributes object - */ - const std::unordered_map<std::string, std::string> &attributes() const { return _attributes; } - /** - * @brief Store an attribute with key-value pair - * - * @param[in] key attribute's key - * @param[in] val attribute's value that is associated with the key - */ - void setAttribute(const std::string &key, const std::string &val); - /** - * @brief Get the attributte value that is associated with key - * - * @param[in] key key of the attribute - * @return value that is associated with the key - */ - std::string getAttribute(const std::string &key); - - /** - * @brief Add an edge in the graph, which is an outgoing edge - * - * @param[in] dotinfo A node that the new edge will be connected to - */ - void addOutEdge(Node *dotinfo) { _out_edges.emplace_back(dotinfo); } - /** - * @brief Return list of out edges - * - * @return Edges - */ - const std::vector<Node *> &out_edges() const { return _out_edges; } - -private: - std::string _id; - std::unordered_map<std::string, std::string> _attributes; - std::vector<Node *> _out_edges; -}; - -} // namespace dot -} // namespace dumper -} // namespace onert - -#endif // __ONERT_DUMPER_DOT_NODE_H__ diff --git a/runtime/onert/core/src/dumper/dot/OperandNode.cc b/runtime/onert/core/src/dumper/dot/OperandNode.cc deleted file mode 100644 index 5a6015ca9..000000000 --- a/runtime/onert/core/src/dumper/dot/OperandNode.cc +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <sstream> - -#include "OperandNode.h" -#include "ir/Graph.h" -#include "ir/operand/LowerInfo.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -const std::string Operand::INPUT_SHAPE = "doublecircle"; -const std::string Operand::OUTPUT_SHAPE = "doublecircle"; -const std::string Operand::OPERAND_SHAPE = "ellipse"; -const std::string Operand::BG_COLOR_SCHEME = "set18"; - -Operand::Operand(const ir::OperandIndex &index, Type type) - : Node{"operand" + std::to_string(index.value())} -{ - { - auto type_to_shape = [](Type type) { - switch (type) - { - case Type::MODEL_INPUT: - return INPUT_SHAPE; - case Type::MODEL_OUTPUT: - return OUTPUT_SHAPE; - case Type::UNDEFINED: - case Type::INTERNAL: - default: - return OPERAND_SHAPE; - } - }; - setAttribute("shape", type_to_shape(type)); - } - - setAttribute("colorscheme", BG_COLOR_SCHEME); -} - -} // namespace dot -} // namespace dumper -} // namespace onert diff --git a/runtime/onert/core/src/dumper/dot/OperandNode.h b/runtime/onert/core/src/dumper/dot/OperandNode.h deleted file mode 100644 index 2e7cc5861..000000000 --- a/runtime/onert/core/src/dumper/dot/OperandNode.h +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Operand.h - * @brief This file contains Operand - * @ingroup COM_AI_RUNTIME - * - */ - -#ifndef __ONERT_DUMPER_DOT_DOT_OPERAND_INFO_H__ -#define __ONERT_DUMPER_DOT_DOT_OPERAND_INFO_H__ - -#include <vector> - -#include "Node.h" -#include "ir/Operand.h" -#include "ir/Index.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -/** - * @brief Class that represents an Operand - * - */ -class Operand : public Node -{ -public: - enum class Type - { - UNDEFINED, - MODEL_INPUT, - MODEL_OUTPUT, - INTERNAL - }; - -public: - static const std::string INPUT_SHAPE; - static const std::string OUTPUT_SHAPE; - static const std::string OPERAND_SHAPE; - static const std::string BG_COLOR_SCHEME; - -public: - /** - * @brief Construct a new Operand Node object - * - * @param[in] index Operand index - * @param[in] type Operand type - * @param[in] lower_info Operand LowerInfo - */ - Operand(const ir::OperandIndex &index, Type type); - -private: - void addBackendLabel(); -}; - -} // namespace dot -} // namespace dumper -} // namespace onert - -#endif // __ONERT_DUMPER_DOT_DOT_OPERAND_INFO_H__ diff --git a/runtime/onert/core/src/dumper/dot/OperationNode.cc b/runtime/onert/core/src/dumper/dot/OperationNode.cc deleted file mode 100644 index bee137e7c..000000000 --- a/runtime/onert/core/src/dumper/dot/OperationNode.cc +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <sstream> - -#include "OperationNode.h" -#include "ir/Graph.h" -#include "ir/operation/LowerInfo.h" -#include "backend/IConfig.h" -#include "backend/Backend.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -const std::string Operation::OPERATION_SHAPE = "rect"; -const std::string Operation::BG_COLOR_SCHEME = "pastel18"; - -Operation::Operation(const ir::OperationIndex &index, const ir::Operation &node) - : Node{"operation" + std::to_string(index.value())} -{ - setAttribute("label", std::to_string(index.value()) + " : " + node.name()); - setAttribute("shape", OPERATION_SHAPE); - setAttribute("colorscheme", BG_COLOR_SCHEME); - setAttribute("fillcolor", DEFAULT_FILLCOLOR); -} - -} // namespace dot -} // namespace dumper -} // namespace onert diff --git a/runtime/onert/core/src/dumper/dot/OperationNode.h b/runtime/onert/core/src/dumper/dot/OperationNode.h deleted file mode 100644 index 74a37d3fb..000000000 --- a/runtime/onert/core/src/dumper/dot/OperationNode.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Operation.h - * @brief This file contains Operation - * @ingroup COM_AI_RUNTIME - * - */ - -#ifndef __ONERT_DUMPER_DOT_DOT_NODE_INFO_H__ -#define __ONERT_DUMPER_DOT_DOT_NODE_INFO_H__ - -#include "Node.h" -#include "ir/Operation.h" -#include "ir/Index.h" - -namespace onert -{ -namespace dumper -{ -namespace dot -{ - -/** - * @brief Class that represents an Operation - * - */ -class Operation : public Node -{ -public: - static const std::string OPERATION_SHAPE; - static const std::string BG_COLOR_SCHEME; - -public: - /** - * @brief Construct a new Operation Node object - * - * @param[in] index operation index - * @param[in] node operation object - */ - Operation(const ir::OperationIndex &index, const ir::Operation &node); -}; - -} // namespace dot -} // namespace dumper -} // namespace onert - -#endif // __ONERT_DUMPER_DOT_DOT_NODE_INFO_H__ diff --git a/runtime/onert/core/src/exec/BackendSet.h b/runtime/onert/core/src/exec/BackendSet.h deleted file mode 100644 index 33ec75e4b..000000000 --- a/runtime/onert/core/src/exec/BackendSet.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_BACKEND_SET_H__ -#define __ONERT_EXEC_BACKEND_SET_H__ - -#include "util/Set.h" - -namespace onert -{ -namespace backend -{ -class Backend; -} // namespace backend -} // namespace onert - -namespace onert -{ -namespace exec -{ - -using BackendSet = util::Set<const backend::Backend *>; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_BACKEND_SET_H__ diff --git a/runtime/onert/core/src/exec/DataflowExecutor.cc b/runtime/onert/core/src/exec/DataflowExecutor.cc deleted file mode 100644 index 53bc3c204..000000000 --- a/runtime/onert/core/src/exec/DataflowExecutor.cc +++ /dev/null @@ -1,183 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "DataflowExecutor.h" - -#include <cassert> - -#include "util/logging.h" - -namespace onert -{ -namespace exec -{ - -int64_t DataflowExecutor::calculateRank(const std::vector<ir::OperationIndex> &operations) -{ - int64_t rank = 0; - if (!_indexed_ranks) - { - return rank; - } - for (const auto &operation_idx : operations) - { - auto it = _indexed_ranks->find(operation_idx); - if (it == _indexed_ranks->end()) - { - assert(_graph.operations().at(operation_idx).opcode() == ir::OpCode::Permute && - operations.size() == 1); - // run Permute ASAP for next operations to be ready for other backends - return std::numeric_limits<int64_t>::max(); - } - else - { - rank += it->second; - } - } - return rank; -} - -void DataflowExecutor::emplaceToReadyJobs(const uint32_t &id) -{ - auto &job = _waiting_jobs[id]; - assert(job != nullptr); - auto &op_seq = _lowered_graph->op_seqs().at(_job_to_op_seq[job->index()]); - auto rank = calculateRank(op_seq.operations()); - _ready_jobs.emplace(rank, std::move(job)); -} - -void DataflowExecutor::notify(uint32_t finished_job_id) -{ - for (auto id : _output_info[finished_job_id]) - { - assert(_input_info[id] > 0); - auto count = --_input_info[id]; - if (count == 0) // No dependent jobs left, ready for execution - { - emplaceToReadyJobs(id); - } - } -} -bool DataflowExecutor::noWaitingJobs() -{ - return std::all_of(_waiting_jobs.begin(), _waiting_jobs.end(), - [](const std::unique_ptr<Job> &job) { return job == nullptr; }); -} - -DataflowExecutor::DataflowExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const std::vector<backend::ITensor *> &input_tensors, - const std::vector<backend::ITensor *> &output_tensors, - const compiler::TensorRegistries &tensor_regs, - compiler::CodeMap &&code_map) - : ExecutorBase{std::move(lowered_graph), input_tensors, output_tensors, tensor_regs}, - _code_map{std::move(code_map)} -{ - VERBOSE(DataflowExecutor) << "Constructing Dataflow Executor" << std::endl; - - const auto &op_seqs = _lowered_graph->op_seqs(); - // Assign jobs convert OpSequenceIndex to job index(uint32_t) - uint32_t next_job_index = 0; - std::unordered_map<ir::OpSequenceIndex, uint32_t> op_seq_to_job; - op_seqs.iterate([&](const ir::OpSequenceIndex &op_seq_index, const ir::OpSequence &) { - VERBOSE(DataflowExecutor) << "Create a job #" << next_job_index << " with OpSequenceIndex " - << op_seq_index.value() << std::endl; - _finished_jobs.emplace_back( - std::make_unique<Job>(next_job_index, _code_map.at(op_seq_index).fn_seq.get())); - op_seq_to_job[op_seq_index] = next_job_index++; - }); - - _waiting_jobs.resize(next_job_index); - _output_info.resize(next_job_index); - _initial_input_info.resize(next_job_index, 0); - - op_seqs.iterate([&](const ir::OpSequenceIndex &op_seq_index, const ir::OpSequence &op_seq) { - auto job_index = op_seq_to_job[op_seq_index]; - for (auto output : op_seq.getOutputs()) - { - // Update output and input info - op_seqs.iterate( - [&](const ir::OpSequenceIndex &op_seq_cur_index, const ir::OpSequence &op_seq_cur) { - if (op_seq_cur.getInputs().contains(output)) - { - auto dep_index = op_seq_to_job[op_seq_cur_index]; - ++_initial_input_info[dep_index]; - _output_info[job_index].push_back(dep_index); - } - }); - } - }); - for (const auto &s : op_seq_to_job) - _job_to_op_seq.emplace(s.second, s.first); - - _input_info = _initial_input_info; -} - -void DataflowExecutor::executeImpl() -{ - assert(noWaitingJobs()); - - bool dynamic_input_exists = hasDynamicInput(); - - // Execution setup - _waiting_jobs.swap(_finished_jobs); // Move finished jobs to waiting jobs - - for (uint32_t i = 0; i < _waiting_jobs.size(); ++i) - { - if (_input_info[i] == 0) - { - emplaceToReadyJobs(i); - } - } - assert(!_ready_jobs.empty()); // Cannot begin if there is no initial jobs - - _subject.notifyModelBegin(this); - - while (!_ready_jobs.empty()) - { - auto job = std::move((_ready_jobs.begin())->second); - _ready_jobs.erase(_ready_jobs.begin()); - auto job_index = job->index(); - VERBOSE(DataflowExecutor) << "Run job #" << job_index << std::endl; - - auto op_seq_index = _job_to_op_seq[job_index]; - auto op_seq = &_lowered_graph->op_seqs().at(op_seq_index); - const backend::Backend *backend = - _lowered_graph->getLowerInfo()->op_seq.at(op_seq_index)->backend(); - - _subject.notifyJobBegin(this, op_seq, backend); - - job->fn_seq()->initRunning(); - - // check if FunctionSequence needs to handle dynamic tensor - bool handle_dynamic_tensor = op_seq->has_dynamic_tensor() || dynamic_input_exists; - job->fn_seq()->enableDynamicShapeInferer(handle_dynamic_tensor); - - job->run(); - - _subject.notifyJobEnd(this, op_seq, backend); - notify(job_index); - _finished_jobs[job_index] = std::move(job); - } - assert(noWaitingJobs()); - - _subject.notifyModelEnd(this); - - // Reset input info for the next execution - _input_info = _initial_input_info; -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/DataflowExecutor.h b/runtime/onert/core/src/exec/DataflowExecutor.h deleted file mode 100644 index 69dfda15c..000000000 --- a/runtime/onert/core/src/exec/DataflowExecutor.h +++ /dev/null @@ -1,96 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_DATAFLOW_EXECUTOR_H__ -#define __ONERT_EXEC_DATAFLOW_EXECUTOR_H__ - -#include <list> -#include <map> -#include <unordered_map> - -#include "exec/FunctionSequence.h" -#include "Job.h" -#include "ir/OperandIndexSequence.h" -#include "ir/Index.h" -#include <memory> -#include "exec/ExecutorBase.h" -#include "compiler/CodeMap.h" - -namespace onert -{ -namespace exec -{ - -class DataflowExecutor : public ExecutorBase -{ - -protected: - virtual void notify(uint32_t finished_job_id); - bool noWaitingJobs(); - -public: - /** - * @brief Constructs a DataflowExecutor object - * - * @param lowered_graph LoweredGraph object - * @param tensor_builders Tensor builders that are currently used - * @param code_map OpSequence and its code map - */ - DataflowExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const std::vector<backend::ITensor *> &input_tensors, - const std::vector<backend::ITensor *> &output_tensors, - const compiler::TensorRegistries &tensor_regs, compiler::CodeMap &&code_map); - - void executeImpl() override; - -protected: - int64_t calculateRank(const std::vector<ir::OperationIndex> &operations); - void emplaceToReadyJobs(const uint32_t &id); - -protected: - compiler::CodeMap _code_map; - /** - * @brief A vector of finished jobs for current execution - * After a run it has all the jobs of this execution for the next run - */ - std::vector<std::unique_ptr<Job>> _finished_jobs; - /** - * @brief A vector of waiting jobs for current execution - * All the jobs are moved from #_finished_jobs to it when start a run - */ - std::vector<std::unique_ptr<Job>> _waiting_jobs; - /** - * @brief Jobs' output info - * Used for notifying after finishing a job - */ - std::vector<std::list<uint32_t>> _output_info; - std::vector<uint32_t> _initial_input_info; - std::vector<uint32_t> _input_info; - /** - * @brief A collection of jobs that are ready for execution - * Jobs in it are ready to be scheduled. - * Ordered by priority from `_indexed_ranks` - */ - std::multimap<int64_t, std::unique_ptr<Job>, std::greater<int64_t>> _ready_jobs; - - /// @brief Which job runs which op and function. - std::unordered_map<uint32_t, ir::OpSequenceIndex> _job_to_op_seq; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_DATAFLOW_EXECUTOR_H__ diff --git a/runtime/onert/core/src/exec/DynamicShapeInference.cc b/runtime/onert/core/src/exec/DynamicShapeInference.cc deleted file mode 100644 index 0f604c43f..000000000 --- a/runtime/onert/core/src/exec/DynamicShapeInference.cc +++ /dev/null @@ -1,1236 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "exec/DynamicShapeInference.h" -#include "util/ShapeInference.h" -#include <assert.h> - -namespace onert -{ -namespace exec -{ - -void DynamicShapeInferer::handleBinaryArithmeticOp(const ir::Operation &op, - const ir::OperandIndex lhs_idx, - const ir::OperandIndex rhs_idx) -{ - auto lhs = _tensor_registry->getITensor(lhs_idx); - auto lhs_shape = lhs->getShape(); - - auto rhs = _tensor_registry->getITensor(rhs_idx); - auto rhs_shape = rhs->getShape(); - - /* - Here, the state after compilation (satic shape inference) could be one of the following: - - lhs rhs output execution-time shape inf required - ------------------------------------------ --------------------------------- - case 1) static static static X - case 2) one or both are dynamic dynamic O - - Then nnfw_apply_tensorinf() could change one or both inputs dynamic. - So, in this method, we have one more state and we have to re-calculate shape for this shape. - - case 3) one or both are dynamic static O - - So, only when all inputs are static, we can skip dynamic shape inference. - */ - if ((!lhs->is_dynamic()) && (!rhs->is_dynamic())) - return; - - auto output_idx = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_idx); - - ir::Shape new_shape = shape_inference::inferEltwiseShape(lhs_shape, rhs_shape); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::handleSimpleUnaryOp(const ir::Operation &op, - const ir::OperandIndex input_ind) -{ - // check if input is not dynamic - auto input = _tensor_registry->getITensor(input_ind); - auto output_shape = input->getShape(); - - /* - Here, the state after compilation (satic shape inference) could be one of the following: - - input output execution-time shape inf required - ------------------------- --------------------------------- - case 1) static static X - case 2) dynamic dynamic O - - Then nnfw_apply_tensorinf() could change input dynamic. - So, in this method, we have one more state and we have to re-calculate shape for this shape. - - case 3) dynamic static O - - So, only when input is static, we can skip dynamic shape inference. - */ - if (!input->is_dynamic()) - return; - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::ArgMax &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::ArgMax::Input::INPUT)}; - const auto input = _tensor_registry->getITensor(input_idx); - - const auto axis_idx{op.getInputs().at(ir::operation::ArgMax::Input::AXIS)}; - const auto axis = _tensor_registry->getITensor(axis_idx); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - if (!input->is_dynamic()) - return; - - auto input_shape = input->getShape(); - auto axis_value = *reinterpret_cast<const int32_t *>(axis->buffer()); - const auto rank = input_shape.rank(); - axis_value = axis_value < 0 ? axis_value + rank : axis_value; - - ir::Shape new_shape = shape_inference::inferArgMaxShape(input_shape, axis_value, rank); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::BatchMatMul &op) -{ - const auto lhs_index = op.getInputs().at(ir::operation::BatchMatMul::Input::LHS); - const auto rhs_index = op.getInputs().at(ir::operation::BatchMatMul::Input::RHS); - auto lhs = _tensor_registry->getITensor(lhs_index); - auto rhs = _tensor_registry->getITensor(rhs_index); - - if (!lhs->is_dynamic() && !rhs->is_dynamic()) - return; - - const auto output_index = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_index); - - auto lhs_shape = lhs->getShape(); - auto rhs_shape = rhs->getShape(); - // TODO - - auto new_shape = shape_inference::inferBatchMatMulShape(lhs_shape, rhs_shape, op.param()); - output->applyShape(new_shape); -} - -void DynamicShapeInferer::visit(const ir::operation::BCQFullyConnected &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::BCQFullyConnected::Input::INPUT)}; - const auto &input = _tensor_registry->getITensor(input_idx); - - const auto cluster_idx{ - op.getInputs().at(ir::operation::BCQFullyConnected::Input::WEIGHTS_CLUSTERS)}; - const auto &cluster = _tensor_registry->getITensor(cluster_idx); - assert(cluster->is_constant()); - - if (!input->is_dynamic()) - return; - - auto input_shape = input->getShape(); - auto cluster_shape = cluster->getShape(); - - auto cluster_buf = reinterpret_cast<const int32_t *>(cluster->buffer()); - assert(cluster_buf); - - ir::Shape new_shape = - shape_inference::inferBCQFullyConnectedShape(input_shape, cluster_shape, cluster_buf); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::BCQGather &op) -{ - const auto indices_idx{op.getInputs().at(ir::operation::BCQGather::Input::INDICES)}; - const auto &indices = _tensor_registry->getITensor(indices_idx); - - const auto input_binary_idx{op.getInputs().at(ir::operation::BCQGather::Input::INDICES)}; - const auto &input_binary = _tensor_registry->getITensor(input_binary_idx); - - const auto cluster_idx{op.getInputs().at(ir::operation::BCQGather::Input::INPUT_CLUSTERS)}; - const auto &cluster = _tensor_registry->getITensor(cluster_idx); - assert(cluster->is_constant()); - - if (!indices->is_dynamic()) - return; - - auto indices_shape = indices->getShape(); - auto cluster_shape = cluster->getShape(); - auto rank = input_binary->getShape().rank(); - - auto cluster_buf = reinterpret_cast<const int32_t *>(cluster->buffer()); - assert(cluster_buf); - - ir::Shape new_shape = shape_inference::inferBCQGatherShape(indices_shape, cluster_shape, - cluster_buf, rank, op.param()); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::BinaryArithmetic &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS), - op.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)); -} - -void DynamicShapeInferer::visit(const ir::operation::BroadcastTo &op) -{ - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - auto input_idx = op.getInputs().at(ir::operation::BroadcastTo::INPUT); - auto input = _tensor_registry->getITensor(input_idx); - - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - auto shape_idx = op.getInputs().at(ir::operation::Tile::Input::MULTIPLES); - const auto &shape = _tensor_registry->getITensor(shape_idx); - - assert(shape); // It shouldn't be 0. - - auto output_shape = shape_inference::inferBroadcastToShape( - shape->getShape(), reinterpret_cast<const int32_t *>(shape->buffer())); - - // set output shape and output buffer - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Comparison &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::Comparison::Input::INPUT0), - op.getInputs().at(ir::operation::Comparison::Input::INPUT1)); -} - -void DynamicShapeInferer::visit(const ir::operation::Concat &op) -{ - /* - The state after compilation (satic shape inference) could be one of the following: - - inputs output execution-time shape inf required - ------------------------------------------ --------------------------------- - case 1) all static static X - case 2) at least on is dynamic dynamic O - - Then nnfw_apply_tensorinf() could change one or both inputs dynamic. - So, in this method, we have one more state and we have to re-calculate shape for this shape. - - case 3) at least on is dynamic static O - - So, only when all inputs are static, we can skip dynamic shape inference. - */ - bool all_static = true; - for (auto input_ind : op.getInputs()) - { - auto input = _tensor_registry->getITensor(input_ind); - if (input->is_dynamic()) - { - all_static = false; - break; - } - } - - if (all_static) - return; - - // sanity check - { - auto isConcatible = [](const backend::ITensor *input1, const backend::ITensor *input2, - int32_t axis) { - if (input1->num_dimensions() != input2->num_dimensions()) - return false; - - for (size_t i = 0; i < input1->num_dimensions(); i++) - { - auto positive_axis = (axis >= 0) ? axis : axis + input1->num_dimensions(); - - if (i != positive_axis) - if (input1->dimension(i) != input2->dimension(i)) - return false; - } - - return true; - }; - - auto first_input_ind = op.getInputs().at(0); - auto first_input = _tensor_registry->getITensor(first_input_ind); - - for (auto input_ind : op.getInputs()) - { - auto input = _tensor_registry->getITensor(input_ind); - if (input != first_input && !isConcatible(first_input, input, op.param().axis)) - throw std::runtime_error("input shapes does not matched for concat"); - } - } - - // getting output shape - onert::shape_inference::Shapes in_shapes; - for (auto input_ind : op.getInputs()) - { - auto input = _tensor_registry->getITensor(input_ind); - ir::Shape shape = input->getShape(); - - in_shapes.emplace_back(shape); - } - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - auto output_shape = shape_inference::inferConcatShape(in_shapes, op.param()); - - output->applyShape(output_shape); -} - -void DynamicShapeInferer::visit(const ir::operation::Conv2D &op) -{ - // check if input is not dynamic - auto input_ind = op.getInputs().at(ir::operation::Conv2D::INPUT); - auto input = _tensor_registry->getITensor(input_ind); - - auto ker_ind = op.getInputs().at(ir::operation::Conv2D::KERNEL); - auto ker = _tensor_registry->getITensor(ker_ind); - - if ((!input->is_dynamic()) && (!ker->is_dynamic())) - return; - - ir::Shape input_shape = input->getShape(); - ir::Shape ker_shape = ker->getShape(); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - ir::Shape output_shape = shape_inference::inferConv2DShape(input_shape, ker_shape, op.param()); - - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::ElementwiseActivation &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::ElementwiseActivation::INPUT)); -} - -void DynamicShapeInferer::visit(const ir::operation::ElementwiseBinary &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::ElementwiseBinary::Input::LHS), - op.getInputs().at(ir::operation::ElementwiseBinary::Input::RHS)); -} - -void DynamicShapeInferer::visit(const ir::operation::ElementwiseUnary &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT)); -} - -void DynamicShapeInferer::visit(const ir::operation::ExpandDims &op) -{ - // check if input is not dynamic - auto input_ind = op.getInputs().at(ir::operation::ExpandDims::INPUT); - auto input = _tensor_registry->getITensor(input_ind); - - // check if output is not dynamic, meaning when 1st input is static and 2nd input is const - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - /* - Here, the state after compilation (satic shape inference) could be one of the following: - - input1 input2 output execution-time shape inf required - ----------------------------- -------------------------------- - case 1) static const static X - case 2) static placeholder dynamic O - case 3) dynamic const dynamic O - case 4) dynamic placeholder dynamic O - - Then nnfw_apply_tensorinf() could change input dynamic. - So, in this method, we could have one more state and we have to re-calculate shape - for this shape. - - case 5) dynamic const static O - - So, only when input1 and ouput are static, we can skip dynamic shape inference. - */ - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - ir::Shape input_shape = input->getShape(); - - auto axis_ind = op.getInputs().at(ir::operation::ExpandDims::AXIS); - auto axis = _tensor_registry->getITensor(axis_ind); - auto axis_buf = reinterpret_cast<const int32_t *>(axis->buffer()); - assert(axis_buf); - - auto output_shape = shape_inference::inferExpandDimsShape(input_shape, axis_buf[0]); - - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Fill &op) -{ - // check if output is not dynamic - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - auto input_ind = op.getInputs().at(ir::operation::Fill::Input::INPUT); - auto input = _tensor_registry->getITensor(input_ind); - ir::Shape input_shape = input->getShape(); - - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - assert(input->data_type() == ir::DataType::INT32); - - auto input_buf = reinterpret_cast<const int32_t *>(input->buffer()); - assert(input_buf); - - auto output_shape = shape_inference::inferFillShape(input_shape, input_buf); - - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::FullyConnected &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::FullyConnected::Input::INPUT)}; - const auto &input = _tensor_registry->getITensor(input_idx); - - const auto ker_idx{op.getInputs().at(ir::operation::FullyConnected::Input::WEIGHT)}; - const auto &ker = _tensor_registry->getITensor(ker_idx); - - if (!input->is_dynamic() && !ker->is_dynamic()) - return; - - auto input_shape = input->getShape(); - auto ker_shape = ker->getShape(); - - ir::Shape new_shape = shape_inference::inferFullyConnectedShape(input_shape, ker_shape); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::FusedBatchNorm &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::FusedBatchNorm::Input::INPUT)); -} - -void DynamicShapeInferer::visit(const ir::operation::Gather &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Gather::Input::INPUT)}; - const auto &input = _tensor_registry->getITensor(input_idx); - auto input_shape = input->getShape(); - - const auto indices_idx{op.getInputs().at(ir::operation::Gather::Input::INDICES)}; - const auto &indices = _tensor_registry->getITensor(indices_idx); - auto indices_shape = indices->getShape(); - - if (!(input->is_dynamic()) && !(indices->is_dynamic())) - return; - - const auto rank = input_shape.rank(); - const auto axis = ((op.param().axis < 0) ? rank + op.param().axis : op.param().axis); - - assert(0 <= axis && axis < rank); - - ir::Shape new_shape = shape_inference::inferGatherShape(input_shape, indices_shape, axis, rank); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::L2Normalization &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::L2Normalization::INPUT)); -} - -void DynamicShapeInferer::visit(const ir::operation::LSTM &op) -{ - const auto output_index{op.getOutputs().at(ir::operation::LSTM::Output::OUTPUT)}; - auto output = _tensor_registry->getITensor(output_index); - - const auto output_state_out_index{ - op.getOutputs().at(ir::operation::LSTM::Output::OUTPUT_STATE_OUT)}; - - const auto cell_state_out_index{op.getOutputs().at(ir::operation::LSTM::Output::CELL_STATE_OUT)}; - - const auto scratch_buffer_index{op.getOutputs().at(ir::operation::LSTM::Output::SCRATCH_BUFFER)}; - - if (!output->is_dynamic() && - !(_tensor_registry->getITensor(output_state_out_index) != nullptr && - _tensor_registry->getITensor(output_state_out_index)->is_dynamic()) && - !(_tensor_registry->getITensor(cell_state_out_index) != nullptr && - _tensor_registry->getITensor(cell_state_out_index)->is_dynamic()) && - !(_tensor_registry->getITensor(scratch_buffer_index) != nullptr && - _tensor_registry->getITensor(cell_state_out_index)->is_dynamic())) - return; - - const auto input_index{op.getInputs().at(ir::operation::LSTM::Input::INPUT)}; - const auto input = _tensor_registry->getITensor(input_index); - const auto input_shape = input->getShape(); - - const auto input_to_output_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS)}; - const auto input_to_output_weights = _tensor_registry->getITensor(input_to_output_weights_index); - const auto input_to_output_weights_shape = input_to_output_weights->getShape(); - - const auto recurrent_to_output_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS)}; - const auto recurrent_to_output_weights = - _tensor_registry->getITensor(recurrent_to_output_weights_index); - const auto recurrent_to_output_weights_shape = recurrent_to_output_weights->getShape(); - - // re-sizing outputs - const int n_batch = - (input_shape.rank() == 3 && op.param().time_major) ? input_shape.dim(1) : input_shape.dim(0); - const int n_cell = input_to_output_weights_shape.dim(0); - const int n_output = recurrent_to_output_weights_shape.dim(1); - if (input_shape.rank() == 3) - { - if (op.param().time_major) - output->applyShape(ir::Shape{input_shape.dim(0), n_batch, n_output}); - else - output->applyShape(ir::Shape{n_batch, input_shape.dim(1), n_output}); - } - else - { - assert(input_shape.rank() == 2); - output->applyShape(ir::Shape{n_batch, n_output}); - } - assert(output->buffer() != nullptr); - - auto output_state_out = _tensor_registry->getITensor(output_state_out_index); - if (output_state_out != nullptr) - { - output_state_out->applyShape(ir::Shape{n_batch, n_output}); - assert(output_state_out->buffer() != nullptr); - } - - auto cell_state_out = _tensor_registry->getITensor(cell_state_out_index); - if (cell_state_out != nullptr) - { - cell_state_out->applyShape(ir::Shape{n_batch, n_cell}); - assert(cell_state_out->buffer() != nullptr); - } - - auto scratch_buffer = _tensor_registry->getITensor(scratch_buffer_index); - if (scratch_buffer != nullptr) - { - const auto input_to_input_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::INPUT_TO_INPUT_WEIGHTS)}; - const auto recurrent_to_input_weights_index{ - op.getInputs().at(ir::operation::LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS)}; - - const auto input_to_input_weights_shape = - _tensor_registry->getITensor(input_to_input_weights_index)->getShape(); - bool has_input_to_input_weights = - input_to_input_weights_shape.dim(0) != 0 && input_to_input_weights_shape.dim(1) != 0; - - const auto recurrent_to_input_weights_shape = - _tensor_registry->getITensor(recurrent_to_input_weights_index)->getShape(); - bool has_recurrent_to_input_weights = recurrent_to_input_weights_shape.dim(0) != 0 && - recurrent_to_input_weights_shape.dim(1) != 0; - - // NOTE The cell_to_input_weights do not exist in non-peephole although regular LSTM(non-CIFG). - // true: no CIFG - // false: CIFG - bool has_cifg_param = has_input_to_input_weights && has_recurrent_to_input_weights; - if (has_cifg_param) - { - scratch_buffer->applyShape(ir::Shape{n_batch, n_cell * 4}); - } - else - { - scratch_buffer->applyShape(ir::Shape{n_batch, n_cell * 3}); - } - assert(scratch_buffer->buffer() != nullptr); - } -} - -void DynamicShapeInferer::visit(const ir::operation::MatrixBandPart &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::MatrixBandPart::INPUT)); -} - -void DynamicShapeInferer::visit(const ir::operation::OneHot &op) -{ - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - auto indices_ind = op.getInputs().at(ir::operation::OneHot::INDICES); - const auto &indices = _tensor_registry->getITensor(indices_ind); - auto indices_shape = indices->getShape(); - - auto depth_ind = op.getInputs().at(ir::operation::OneHot::DEPTH); - const auto &depth = _tensor_registry->getITensor(depth_ind); - - if (!indices->is_dynamic() && !depth->is_dynamic()) - { - return; - } - - int32_t *depth_buf = reinterpret_cast<int32_t *>(depth->buffer()); - assert(depth_buf); - const auto axis_val = op.param().axis; - - ir::Shape new_shape = shape_inference::inferOnehotShape(indices_shape, *depth_buf, axis_val); - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Pack &op) -{ - bool is_any_of_inputs_dynamic = [&]() -> bool { - for (uint32_t i = 0; i < op.getInputs().size(); ++i) - { - const auto &input = _tensor_registry->getITensor(op.getInputs().at(i)); - if (input->is_dynamic()) - { - return true; - } - } - return false; - }(); - - const auto input_idx{op.getInputs().at(0)}; - const auto &input = _tensor_registry->getITensor(input_idx); - auto input_shape = input->getShape(); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - if (!is_any_of_inputs_dynamic && !output->is_dynamic()) - return; - - const auto rank = input_shape.rank() + 1; - const auto axis = ((op.param().axis < 0) ? rank + op.param().axis : op.param().axis); - const auto num = op.param().num; - - assert(0 <= axis && axis < rank); - - ir::Shape new_shape = shape_inference::inferPackShape(input_shape, axis, rank, num); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Pad &op) -{ - // check if output is not dynamic - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - auto input_ind = op.getInputs().at(ir::operation::Pad::Input::INPUT); - auto input = _tensor_registry->getITensor(input_ind); - - auto pad_ind = op.getInputs().at(ir::operation::Pad::Input::PAD); - auto pad = _tensor_registry->getITensor(pad_ind); - - // check if input and output are not dynamic - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - int32_t *pad_buf = reinterpret_cast<int32_t *>(pad->buffer()); - assert(pad_buf); - - auto output_shape = - shape_inference::inferPadShape(input->getShape(), pad_buf, pad->getShape().num_elements()); - - // change output shape and reallocate output tensor memory - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Permute & /* op */) -{ - // NOTE Permute is a special operation which does not do shape inference before the actual - // function(kernel) execution. Shape inference and output allocation will be done in the kernel - // on-the-fly, as it must support inter-backend inference/allocation. -} - -void DynamicShapeInferer::visit(const ir::operation::Pow &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::Pow::Input::LHS), - op.getInputs().at(ir::operation::Pow::Input::RHS)); -} - -void DynamicShapeInferer::visit(const ir::operation::Range &op) -{ - // check if output is not dynamic - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - // from op, access the buffer of second input to read new shape - auto start_idx = op.getInputs().at(ir::operation::Range::Input::START); - auto start_tensor = _tensor_registry->getITensor(start_idx); - - auto limit_idx = op.getInputs().at(ir::operation::Range::Input::LIMIT); - auto limit_tensor = _tensor_registry->getITensor(limit_idx); - - auto delta_idx = op.getInputs().at(ir::operation::Range::Input::DELTA); - auto delta_tensor = _tensor_registry->getITensor(delta_idx); - - if (!start_tensor->is_dynamic() && !limit_tensor->is_dynamic() && !delta_tensor->is_dynamic() && - !output->is_dynamic()) - return; - - ir::Shape new_shape; - if (output->data_type() == ir::DataType::FLOAT32) - { - new_shape = - shape_inference::inferRangeShape<float>(*reinterpret_cast<float *>(start_tensor->buffer()), - *reinterpret_cast<float *>(limit_tensor->buffer()), - *reinterpret_cast<float *>(delta_tensor->buffer())); - } - else if (output->data_type() == ir::DataType::INT32) - { - new_shape = shape_inference::inferRangeShape<int32_t>( - *reinterpret_cast<int32_t *>(start_tensor->buffer()), - *reinterpret_cast<int32_t *>(limit_tensor->buffer()), - *reinterpret_cast<int32_t *>(delta_tensor->buffer())); - } - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Reduce &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Reduce::Input::INPUT)}; - const auto &input = _tensor_registry->getITensor(input_idx); - auto input_shape = input->getShape(); - - const auto axes_idx{op.getInputs().at(ir::operation::Reduce::Input::AXES)}; - const auto &axes = _tensor_registry->getITensor(axes_idx); - - if (!input->is_dynamic()) - return; - - std::vector<int32_t> axes_vec; - for (uint32_t i = 0; i < axes->getShape().num_elements(); ++i) - { - const auto buffer = axes->buffer() + axes->calcOffset({i}); - switch (axes->data_type()) - { - case ir::DataType::INT32: - { - axes_vec.emplace_back(*reinterpret_cast<const int32_t *>(buffer)); - break; - } - case ir::DataType::INT64: - { - axes_vec.emplace_back(*reinterpret_cast<const int64_t *>(buffer)); - break; - } - default: - throw std::runtime_error("DynamicShapeInferer " + op.name() + ": Not supported data type"); - break; - } - } - const auto keep_dims = op.param().keep_dims; - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - ir::Shape new_shape = shape_inference::inferReduceShape(input_shape, axes_vec, keep_dims); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Reshape &op) -{ - // check if output is not dynamic - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - auto input_ind = op.getInputs().at(ir::operation::Reshape::Input::INPUT); - auto input = _tensor_registry->getITensor(input_ind); - - /* - Here, the state after compilation (satic shape inference) could be one of the following: - - input1 input2 (or option) output execution-time shape inf required - ------------------------------------ -------------------------------- - case 1) static const static X - case 2) static placeholder dynamic O - case 3) dynamic const dynamic O - case 4) dynamic placeholder dynamic O - - Then nnfw_apply_tensorinf() could change input dynamic. - So, in this method, we could have one more state and we have to re-calculate shape - for this shape. - - case 5) dynamic const static O - - So, only when both input1 and ouput are static, we can skip dynamic shape inference. - */ - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - // New shape is given by second input tensor - if (op.getInputs().size() == 2) - { - // from op, access the buffer of second input to read new shape - auto new_shape_ind = op.getInputs().at(ir::operation::Reshape::Input::SHAPE); - - // getting output shape by reading new_shape tensor buffer - auto new_shape = _tensor_registry->getITensor(new_shape_ind); - assert(new_shape); - - int32_t *new_shape_buf = reinterpret_cast<int32_t *>(new_shape->buffer()); - assert(new_shape_buf); - - auto output_shape = shape_inference::inferReshapeShape( - new_shape_buf, new_shape->getShape().num_elements(), input->getShape().num_elements()); - - // if shape is changed, change output shape and reallocate output tensor memory - if (output_shape != output->getShape() || output->buffer() == nullptr) - { - // change on output shape - output->applyShape(output_shape); - } - assert(output->buffer() != nullptr); - } - // New shape is given by option - else if (op.param().new_shape.size() != 0) - { - // Let's check the new_shape option - auto shape = op.param().new_shape; - auto output_shape = shape_inference::inferReshapeShape(shape.data(), shape.size(), - input->getShape().num_elements()); - - // if shape is changed, change output shape and reallocate output tensor memory - if (output_shape != output->getShape() || output->buffer() == nullptr) - { - // change on output shape - output->applyShape(output_shape); - } - assert(output->buffer() != nullptr); - } - else - { - throw std::runtime_error("Reshape: new shape is missing"); - return; - } -} - -void DynamicShapeInferer::visit(const ir::operation::ResizeBilinear &op) -{ - // check if output is not dynamic - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - auto input_ind = op.getInputs().at(ir::operation::Reshape::Input::INPUT); - auto input = _tensor_registry->getITensor(input_ind); - - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - // getting output shape from input shape and Params - int32_t height_out, width_out; - if (op.getInputs().size() == 2) - { - auto size_ind = op.getInputs().at(ir::operation::ResizeBilinear::Input::SIZE); - auto size = _tensor_registry->getITensor(size_ind); - if (size->data_type() == ir::DataType::INT32) - { - auto size_buf = reinterpret_cast<const int32_t *>(size->buffer()); - height_out = size_buf[0]; - width_out = size_buf[1]; - } - else - { - throw std::runtime_error("DynamicShapeInferer ResizeBilinear : Unsupported data type"); - } - } - else - { - height_out = op.param().height_out; - width_out = op.param().width_out; - } - auto output_shape = - shape_inference::inferResizeBilinearShape(input->getShape(), height_out, width_out); - - // if shape is changed, change output shape and reallocate output tensor memory - if (output_shape != output->getShape() || output->buffer() == nullptr) - { - // change on output shape - output->applyShape(output_shape); - } - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Reverse &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::Reverse::INPUT)); -} - -void DynamicShapeInferer::visit(const ir::operation::Select &op) -{ - const auto input_cond_idx = op.getInputs().at(ir::operation::Select::Input::CONDITION); - const auto &input_cond = _tensor_registry->getITensor(input_cond_idx); - - const auto input_true_idx = op.getInputs().at(ir::operation::Select::Input::INPUT_TRUE); - const auto &input_true = _tensor_registry->getITensor(input_true_idx); - - const auto input_false_idx = op.getInputs().at(ir::operation::Select::Input::INPUT_FALSE); - const auto &input_false = _tensor_registry->getITensor(input_false_idx); - - if ((!input_cond->is_dynamic()) && (!input_true->is_dynamic()) && (!input_false->is_dynamic())) - { - return; - } - - auto input_cond_shape = input_cond->getShape(); - auto input_true_shape = input_true->getShape(); - auto input_false_shape = input_false->getShape(); - - // Select output shpae - ir::Shape new_shape = - shape_inference::inferSelectShape(input_cond_shape, input_true_shape, input_false_shape); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Shape &op) -{ - const auto input_idx{op.getInputs().at(0)}; - const auto &input = _tensor_registry->getITensor(input_idx); - auto input_shape = input->getShape(); - - if (!input->is_dynamic()) - return; - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - ir::Shape output_shape; - output_shape.append(input_shape.rank()); - - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Slice &op) -{ - const auto input_index{op.getInputs().at(ir::operation::Slice::Input::INPUT)}; - const auto input = _tensor_registry->getITensor(input_index); - const auto begins_index{op.getInputs().at(ir::operation::Slice::Input::BEGINS)}; - const auto begins = _tensor_registry->getITensor(begins_index); - const auto sizes_index{op.getInputs().at(ir::operation::Slice::Input::SIZES)}; - const auto sizes = _tensor_registry->getITensor(sizes_index); - auto output_index = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_index); - - if (!(input->is_dynamic() || begins->is_dynamic() || sizes->is_dynamic() || output->is_dynamic())) - { - return; - } - - ir::Shape input_shape = input->getShape(); - auto begins_buf = reinterpret_cast<const int32_t *>(begins->buffer()); - auto sizes_buf = reinterpret_cast<const int32_t *>(sizes->buffer()); - - ir::Shape new_shape = shape_inference::inferSliceShape(input_shape, begins_buf, sizes_buf); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Softmax &op) -{ - handleSimpleUnaryOp(op, op.getInputs().at(ir::operation::Softmax::INPUT)); -} - -void DynamicShapeInferer::visit(const ir::operation::SpaceToBatchND &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::INPUT)}; - const auto block_shape_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::BLOCK_SIZE)}; - const auto padding_idx{op.getInputs().at(ir::operation::SpaceToBatchND::Input::PADDINGS)}; - auto output_idx{op.getOutputs().at(0)}; - - const auto &input = _tensor_registry->getITensor(input_idx); - const auto &block_shape = _tensor_registry->getITensor(block_shape_idx); - const auto &padding = _tensor_registry->getITensor(padding_idx); - auto output = _tensor_registry->getITensor(output_idx); - - if (!(input->is_dynamic() || block_shape->is_dynamic() || padding->is_dynamic() || - output->is_dynamic())) - { - return; - } - - auto input_shape = input->getShape(); - auto block_shape_shape = block_shape->getShape(); - auto padding_shape = padding->getShape(); - - auto block_shape_data = reinterpret_cast<int32_t *>(block_shape->buffer()); - auto padding_data = reinterpret_cast<int32_t *>(padding->buffer()); - - ir::Shape new_shape = shape_inference::inferSpaceToBatchNDShape( - input_shape, block_shape_shape, padding_shape, block_shape_data, padding_data); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Split &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Split::Input::INPUT)}; - const auto &input = _tensor_registry->getITensor(input_idx); - - // Return if all tensors are not dynamic - bool has_dynamic = false; - for (const auto output_idx : op.getOutputs()) - { - auto output = _tensor_registry->getITensor(output_idx); - has_dynamic |= output->is_dynamic(); - } - if (!input->is_dynamic() && !has_dynamic) - { - return; - } - - auto input_shape = input->getShape(); - - const auto axis_idx{op.getInputs().at(ir::operation::Split::Input::AXIS)}; - const auto &axis = _tensor_registry->getITensor(axis_idx); - - auto axis_value = *reinterpret_cast<const int32_t *>(axis->buffer()); - const auto num_splits = op.param().num_splits; - const auto rank = input_shape.rank(); - axis_value = axis_value < 0 ? axis_value + rank : axis_value; - - assert(0 <= axis_value && axis_value < rank); - - ir::Shape new_shape = shape_inference::inferSplitShape(input_shape, axis_value, num_splits); - for (int out_tensor_idx = 0; out_tensor_idx < num_splits; out_tensor_idx++) - { - auto output_ind = op.getOutputs().at(out_tensor_idx); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); - } -} - -void DynamicShapeInferer::visit(const ir::operation::SquaredDifference &op) -{ - handleBinaryArithmeticOp(op, op.getInputs().at(ir::operation::SquaredDifference::Input::LHS), - op.getInputs().at(ir::operation::SquaredDifference::Input::RHS)); -} - -void DynamicShapeInferer::visit(const ir::operation::Squeeze &op) -{ - const auto input_idx{op.getInputs().at(ir::operation::Squeeze::Input::INPUT)}; - const auto &input = _tensor_registry->getITensor(input_idx); - - if (!input->is_dynamic()) - { - return; - } - - auto input_shape = input->getShape(); - - // Squeeze output shpae - ir::Shape new_shape = shape_inference::inferSqueezeShape(input_shape, op.param()); - - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::StridedSlice &op) -{ - - const auto input_index{op.getInputs().at(ir::operation::StridedSlice::Input::INPUT)}; - auto input = _tensor_registry->getITensor(input_index); - ir::Shape input_shape = input->getShape(); - - const auto starts_index{op.getInputs().at(ir::operation::StridedSlice::Input::STARTS)}; - auto starts = _tensor_registry->getITensor(starts_index); - - const auto ends_index{op.getInputs().at(ir::operation::StridedSlice::Input::ENDS)}; - auto ends = _tensor_registry->getITensor(ends_index); - - const auto strides_index{op.getInputs().at(ir::operation::StridedSlice::Input::STRIDES)}; - auto strides = _tensor_registry->getITensor(strides_index); - - if (!(input->is_dynamic() || starts->is_dynamic() || ends->is_dynamic() || strides->is_dynamic())) - { - return; - } - - const auto begin_mask = op.param().begin_mask; - const auto end_mask = op.param().end_mask; - const auto shrink_axis_mask = op.param().shrink_axis_mask; - const auto rank = input_shape.rank(); - - auto op_params = shape_inference::buildStridedSliceParams( - reinterpret_cast<uint32_t *>(starts->buffer()), reinterpret_cast<uint32_t *>(ends->buffer()), - reinterpret_cast<uint32_t *>(strides->buffer()), begin_mask, end_mask, shrink_axis_mask, - rank); - - auto output_index = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_index); - - ir::Shape output_shape = - onert::shape_inference::inferStridedSliceShape(input_shape, op_params, rank); - - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Tile &op) -{ - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - auto input_idx = op.getInputs().at(ir::operation::Tile::Input::INPUT); - auto input = _tensor_registry->getITensor(input_idx); - - auto multiplier_idx = op.getInputs().at(ir::operation::Tile::Input::MULTIPLES); - auto multiplier = _tensor_registry->getITensor(multiplier_idx); - - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - auto input_shape = input->getShape(); - auto multiplier_buffer = reinterpret_cast<const int32_t *>(multiplier->buffer()); - assert(multiplier_buffer); - - auto output_shape = - shape_inference::inferTileShape(input_shape, multiplier_buffer, multiplier->dimension(0)); - - // set output shape and output buffer - output->applyShape(output_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Transpose &op) -{ - // check if output is not dynamic - auto output_ind = op.getOutputs().at(0); - auto output = _tensor_registry->getITensor(output_ind); - - // from op, access the buffer of second input to read new shape - auto input_ind = op.getInputs().at(ir::operation::Transpose::Input::INPUT); - auto input = _tensor_registry->getITensor(input_ind); - auto input_shape = input->getShape(); - - /* - Here, the state after compilation (static shape inference) could be one of the following: - - input perms output execution-time shape inf required - ------------------------------------ -------------------------------- - case 1) static const static X - case 2) static non-const dynamic O - case 3) dynamic const dynamic O - case 4) dynamic non-const dynamic O - - So, only when both input1 and ouput are static, we can skip dynamic shape inference. - */ - if ((!input->is_dynamic()) && (!output->is_dynamic())) - return; - - auto perm_ind = op.getInputs().at(ir::operation::Transpose::Input::PERMUTATION); - auto perm = _tensor_registry->getITensor(perm_ind); - - ir::Shape new_shape; - // TODO Change perm->dimension(0) == 0 to perm->num_elements() == 0 - if (perm->dimension(0) == 0) // This condition means that perm is (n-1...0) - { - // Call by (n-1...0) - new_shape = shape_inference::inferTransposeShape(input_shape, nullptr, 0); - } - else - { - // Check rank - if (input->num_dimensions() != perm->getShape().num_elements()) - { - throw std::runtime_error("DynamicShapeInferer failed, bad rank size: " + - std::to_string(perm->getShape().num_elements())); - } - - // set output shape, based on input and params - const auto perm_buffer = reinterpret_cast<const int32_t *>(perm->buffer()); - new_shape = shape_inference::inferTransposeShape(input_shape, perm_buffer, perm->dimension(0)); - } - output->applyShape(new_shape); - assert(output->buffer() != nullptr); -} - -void DynamicShapeInferer::visit(const ir::operation::Unpack &op) -{ - // check if output is not dynamic - const auto input_idx{op.getInputs().at(0)}; - const auto &input = _tensor_registry->getITensor(input_idx); - - if (!input->is_dynamic()) - return; - - auto input_shape = input->getShape(); - - const auto rank = input_shape.rank(); - const auto axis = ((op.param().axis < 0) ? rank + op.param().axis : op.param().axis); - const auto num = op.param().num; - - assert(0 <= axis && axis < rank); - - ir::Shape new_shape = shape_inference::inferUnpackShape(input_shape, axis, rank); - - for (int out_tensor_idx = 0; out_tensor_idx < num; out_tensor_idx++) - { - auto output_ind = op.getOutputs().at(out_tensor_idx); - auto output = _tensor_registry->getITensor(output_ind); - - output->applyShape(new_shape); - - assert(output->buffer() != nullptr); - } -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ExecTime.cc b/runtime/onert/core/src/exec/ExecTime.cc deleted file mode 100644 index 6bf2744a9..000000000 --- a/runtime/onert/core/src/exec/ExecTime.cc +++ /dev/null @@ -1,137 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "exec/ExecTime.h" - -#include <fstream> -#include <cassert> -#include <limits> -#include <algorithm> - -namespace onert -{ -namespace exec -{ - -int64_t ExecTime::getOperationExecTime(const backend::Backend *backend, - const std::string &operation, bool quant, - uint32_t op_size) const -{ - auto found_backend = _measurements.find(backend); - if (found_backend == _measurements.end()) - return NOT_FOUND; // no execution time for this backend - - auto found_operation_with_type = found_backend->second.find(operation); - if (found_operation_with_type == found_backend->second.end()) - // no execution time for this operation - return NOT_FOUND; - - auto found_operation = found_operation_with_type->second.find(quant); - if (found_operation == found_operation_with_type->second.end()) - // no execution time for this operation - return NOT_FOUND; - - auto found_size = found_operation->second.find(op_size); - if (found_size != found_operation->second.end()) - return found_size->second; // found execution time - - // Try to interpolate - if (found_operation->second.size() < 2) - // not possible to do linear interpolation - return found_operation->second.begin()->second; - - // if we reach here, then this means, that there is no record, that is equal to op_size - auto upper_bound = found_operation->second.upper_bound(op_size); // > op_size - auto lower_bound = upper_bound; - - if (upper_bound == found_operation->second.end()) // all values <= op_size - { - upper_bound--; - lower_bound = upper_bound; - lower_bound--; - } - else if (upper_bound == found_operation->second.begin()) // all values > op_size - { - upper_bound++; - } - else // op_size between - { - lower_bound--; - } - - // Linear interpolation - const auto x0 = static_cast<int64_t>(lower_bound->first); // size - const auto x1 = static_cast<int64_t>(upper_bound->first); // size - const int64_t y0 = lower_bound->second; // time - const int64_t y1 = upper_bound->second; // time - const auto x = static_cast<int64_t>(op_size); - - int64_t interpolated_value = y0 + (x - x0) * (y1 - y0) / (x1 - x0); - - // In some cases ops with smaller inputs is executed slower than the one - // with larger inputs, more likely because of a backend's load difference - if (interpolated_value < 0 && x > x1) - { - return y0; - } - // It must be non-positive ONLY if it's lesser than both of them - assert(interpolated_value > 0 || x < x0); - - // execution time must be non-negative - return std::max<int64_t>(interpolated_value, 1); -} - -void ExecTime::updateOperationExecTime(const backend::Backend *backend, - const std::string &operation, bool quant, uint32_t op_size, - int64_t time) -{ - // If the op is not implemented for some input, it should not be scheduled - const auto &recs = _measurements[backend][operation][quant]; - if (time == getMax() || - std::any_of(recs.begin(), recs.end(), - [](std::pair<const uint32_t, const int64_t> p) { return p.second == getMax(); })) - { - _measurements[backend][operation][quant].clear(); - _measurements[backend][operation][quant].emplace(op_size, getMax()); - } - else - { - auto it = _measurements[backend][operation][quant].emplace(op_size, time); - if (!it.second) - { - // affect of the last measurement is bigger than the previous ones: - // this prefers new metrics than older once, so will adapt backend changes - it.first->second = (it.first->second + time) / 2; - } - } -} - -void ExecTime::updatePermuteTime(const backend::Backend *from_backend, - const backend::Backend *to_backend, bool quant, uint32_t op_size, - int64_t time) -{ - updateOperationExecTime(from_backend, to_backend->config()->id(), quant, op_size, time); -} - -int64_t ExecTime::getPermuteTime(const backend::Backend *from_backend, - const backend::Backend *to_backend, bool quant, - uint32_t op_size) const -{ - return getOperationExecTime(from_backend, to_backend->config()->id(), quant, op_size); -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ExecTime.h b/runtime/onert/core/src/exec/ExecTime.h deleted file mode 100644 index 846d0930b..000000000 --- a/runtime/onert/core/src/exec/ExecTime.h +++ /dev/null @@ -1,112 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_EXEC_TIME_H__ -#define __ONERT_EXEC_EXEC_TIME_H__ - -#include "backend/Backend.h" -#include "backend/IConfig.h" -#include "JSONExecTime.h" -#include <memory> -#include <limits> -#include <map> -#include <unordered_map> -#include <vector> - -namespace onert -{ -namespace exec -{ -class ExecTime -{ -public: - explicit ExecTime(const std::vector<const backend::Backend *> &backends) - : _json(backends, _measurements) - { - } - -public: - /** - * @brief Get exec time of an operation with input size - * or linearly interpolated value based on size if there is no record for given size - * - * @param[in] backend id of a backend - * @param[in] operation name of an operation - * @param[in] quant if input type quantized - * @param[in] op_size sum of operation's flattened sizes of inputs and outputs - * @return execution time for given input sizes - * -1 if there are no records for given parameters (backend, op, quantization). - */ - int64_t getOperationExecTime(const backend::Backend *backend, const std::string &operation, - bool quant, uint32_t op_size) const; - /** - * @brief Update exec time of the operation on a backend with given input size or - * add new entity if there is no one. - * - * @param[in] backend id of a backend - * @param[in] operation name of an operation - * @param[in] quant if input type quantized - * @param[in] op_size sum of operation's flattened sizes of inputs and outputs - * @param[in] time real measured value - */ - void updateOperationExecTime(const backend::Backend *backend, const std::string &operation, - bool quant, uint32_t op_size, int64_t time); - /** - * @brief Get the permute time from one backend to another - * - * @param[in] from_backend - * @param[in] to_backend - * @param[in] quant if input type quantized - * @param[in] op_size sum of operation's flattened sizes of inputs and outputs - * @return permutation time for operation size - */ - int64_t getPermuteTime(const backend::Backend *from_backend, const backend::Backend *to_backend, - bool quant, uint32_t op_size) const; - /** - * @brief Update permute time from one backend to another - * - * @param[in] from_backend - * @param[in] to_backend - * @param[in] quant if input type quantized - * @param[in] time measured permutation time - * @param[in] op_size sum of operation's flattened sizes of inputs and outputs - */ - void updatePermuteTime(const backend::Backend *from_backend, const backend::Backend *to_backend, - bool quant, uint32_t op_size, int64_t time); - /** - * @brief Get the max value of int32_t in int64_t - * @return max value - */ - static int64_t getMax() { return _MAX; } - /** - * @brief Update metrics file with new data. - */ - void uploadOperationsExecTime() const { _json.uploadOperationsExecTime(); } - static const int64_t NOT_FOUND = -1; - -private: - /// @brief Measurement data, which is shared with serializer - MeasurementData _measurements; - // int64_t::max may cause integer overflow - static const int64_t _MAX = std::numeric_limits<int32_t>::max(); - /// @brief Serializer - JSON _json; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_EXEC_TIME_H__ diff --git a/runtime/onert/core/src/exec/Execution.cc b/runtime/onert/core/src/exec/Execution.cc deleted file mode 100644 index 21fdd9c05..000000000 --- a/runtime/onert/core/src/exec/Execution.cc +++ /dev/null @@ -1,182 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "exec/Execution.h" - -#include "util/logging.h" - -namespace onert -{ -namespace exec -{ - -Execution::Execution(const std::shared_ptr<ExecutorMap> &executors) : _executors{executors} -{ - assert(executors != nullptr); - assert(executors->at(ir::SubgraphIndex{0}) != nullptr); - const auto &primary_subg = primary_subgraph(); - _io_desc.inputs.resize(primary_subg.getInputs().size()); - _io_desc.outputs.resize(primary_subg.getOutputs().size()); -} - -void Execution::changeInputShape(const ir::IOIndex &index, const ir::Shape &new_shape) -{ - // This will be used later to set input tensor dynamic - // Note that 'compiled' model will not be updated with new_shape - // but new_shape will change model input shape while 'running' the model - _io_desc.dynamic_input_shapes[index] = new_shape; - - VERBOSE(Execution) << "Model input shape will be changed at the start of execute()" - << "(index: " << index.value() << ")" << std::endl; -} - -// TODO Remove default parameter -void Execution::setInput(const ir::IOIndex &index, const void *buffer, size_t length, - ir::Layout layout) -{ - const auto input_index = primary_subgraph().getInputs().at(index); - const auto info = primary_subgraph().operands().at(input_index).info(); - - // TODO handle when (!buffer && length != 0) : setting the input as an optional tensor - - // check if size enough for input is passed - // if input_shape_sig is set, input_shape_sig overrides shape in info - // note: input_shape_sig contains shape passed by nnfw_set_input_tensorinfo() - { - auto input_shape_sig = _io_desc.dynamic_input_shapes.find(index); - auto size_required = (input_shape_sig != _io_desc.dynamic_input_shapes.end()) - ? input_shape_sig->second.num_elements() * - onert::ir::sizeOfDataType(info.typeInfo().type()) - : info.total_size(); - - if (length < size_required) - { - throw std::runtime_error{"Too small length"}; - } - } - - _io_desc.inputs.at(index.value()) = std::make_unique<InputDesc>(info, buffer, length, layout); -} - -// TODO Remove default parameter -void Execution::setInput(const ir::IOIndex &index, const ir::TypeInfo &type, const ir::Shape &shape, - const void *buffer, size_t length, ir::Layout layout) -{ - auto info = ir::OperandInfo::createStaticInfo(shape, type); - - if (length < info.total_size()) - { - throw std::runtime_error{"Too small length"}; - } - - _io_desc.inputs.at(index.value()) = std::make_unique<InputDesc>(info, buffer, length, layout); -} - -// TODO Remove default parameter -void Execution::setOutput(const ir::IOIndex &index, void *buffer, size_t length, ir::Layout layout) -{ - const auto output_index = primary_subgraph().getOutputs().at(index); - const auto info = primary_subgraph().operands().at(output_index).info(); - - if (length < info.total_size()) - { - throw std::runtime_error{"Too small length"}; - } - - _io_desc.outputs.at(index.value()) = std::make_unique<OutputDesc>(info, buffer, length, layout); -} - -// TODO Remove default parameter -void Execution::setOutput(const ir::IOIndex &index, const ir::TypeInfo &type, - const ir::Shape &shape, void *buffer, size_t length, ir::Layout layout) -{ - auto info = ir::OperandInfo::createStaticInfo(shape, type); - - if (length < info.total_size()) - { - throw std::runtime_error{"Too small length"}; - } - - _io_desc.outputs.at(index.value()) = std::make_unique<OutputDesc>(info, buffer, length, layout); -} - -void Execution::setInputLayout(const ir::IOIndex &index, ir::Layout layout) -{ - const auto &input_desc = _io_desc.inputs.at(index.value()); - _io_desc.inputs.at(index.value()) = - std::make_unique<InputDesc>(input_desc->info, input_desc->buffer, input_desc->size, layout); -} - -void Execution::setOutputLayout(const ir::IOIndex &index, ir::Layout layout) -{ - const auto &output_desc = _io_desc.outputs.at(index.value()); - _io_desc.outputs.at(index.value()) = std::make_unique<OutputDesc>( - output_desc->info, output_desc->buffer, output_desc->size, layout); -} - -void Execution::execute() -{ - VERBOSE(Execution) << "Start execution" << std::endl; - - primary_executor()->execute(_io_desc); - finished = true; - - VERBOSE(Execution) << "Execution finished" << std::endl; -} - -void Execution::startExecute() -{ - VERBOSE(Execution) << "Create asynchronous execution thread" << std::endl; - - _exec_thread = std::make_unique<std::thread>(&Execution::execute, this); -} - -void Execution::waitFinish() -{ - VERBOSE(Execution) << "Wait to finish execution" << std::endl; - - _exec_thread->join(); - finished = true; -} - -bool Execution::isFinished(void) const { return finished; } - -ir::Shape Execution::getInputShape(ir::IOIndex ind) const -{ - auto itr = _io_desc.dynamic_input_shapes.find(ind); - if (itr == _io_desc.dynamic_input_shapes.end()) - { - auto operand_idx = primary_subgraph().getInputs().at(ind.value()); - return primary_subgraph().operands().at(operand_idx).shape(); - } - else - { - return itr->second; - } -} - -ir::Shape Execution::getOutputShape(ir::IOIndex ind) const -{ - if (!isFinished()) - throw std::runtime_error("Cannot get output shape before execution is finished"); - - const auto &output_desc = _io_desc.outputs.at(ind.value()); - - return output_desc->info.shape(); -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ExecutionObservee.cc b/runtime/onert/core/src/exec/ExecutionObservee.cc deleted file mode 100644 index ddb1fb6a0..000000000 --- a/runtime/onert/core/src/exec/ExecutionObservee.cc +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ExecutionObservee.h" - -namespace onert -{ -namespace exec -{ - -void ExecutionObservee::add(std::unique_ptr<IExecutionObserver> observer) -{ - _observers.emplace_back(std::move(observer)); -} - -void ExecutionObservee::notifyModelBegin(IExecutor *executor) -{ - for (auto &o : _observers) - { - o->handleBegin(executor); - } -} - -void ExecutionObservee::notifyModelEnd(IExecutor *executor) -{ - for (auto &o : _observers) - { - o->handleEnd(executor); - } -} - -void ExecutionObservee::notifyJobBegin(IExecutor *executor, const ir::OpSequence *op_seq, - const backend::Backend *backend) -{ - for (auto &o : _observers) - { - o->handleBegin(executor, op_seq, backend); - } -} - -void ExecutionObservee::notifyJobEnd(IExecutor *executor, const ir::OpSequence *op_seq, - const backend::Backend *backend) -{ - for (auto &o : _observers) - { - o->handleEnd(executor, op_seq, backend); - } -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ExecutionObservee.h b/runtime/onert/core/src/exec/ExecutionObservee.h deleted file mode 100644 index 49d409a3a..000000000 --- a/runtime/onert/core/src/exec/ExecutionObservee.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_EXECUTION_OBSERVEE_H__ -#define __ONERT_EXEC_EXECUTION_OBSERVEE_H__ - -#include <list> - -#include "exec/ExecutionObservers.h" - -namespace onert -{ -namespace exec -{ - -/** - * @brief Class that - * - */ -class ExecutionObservee -{ -public: - /** - * @brief Register an observer - * - * @param observer Observer to be added - */ - void add(std::unique_ptr<IExecutionObserver> observer); - void notifyModelBegin(IExecutor *executor); - void notifyModelEnd(IExecutor *executor); - void notifyJobBegin(IExecutor *executor, const ir::OpSequence *op_seq, - const backend::Backend *backend); - void notifyJobEnd(IExecutor *executor, const ir::OpSequence *op_seq, - const backend::Backend *backend); - -private: - std::list<std::unique_ptr<IExecutionObserver>> _observers; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_EXECUTION_OBSERVEE__ diff --git a/runtime/onert/core/src/exec/ExecutionObservers.cc b/runtime/onert/core/src/exec/ExecutionObservers.cc deleted file mode 100644 index 5883d9a1c..000000000 --- a/runtime/onert/core/src/exec/ExecutionObservers.cc +++ /dev/null @@ -1,135 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "exec/ExecutionObservers.h" - -#include <string> - -#include "util/logging.h" -#include "exec/IExecutor.h" -#include "misc/polymorphic_downcast.h" -#include "ir/OpSequence.h" -#include "util/EventWriter.h" - -namespace onert -{ - -namespace exec -{ - -void ProfileObserver::handleBegin(onert::exec::IExecutor *, const ir::OpSequence *, - const onert::backend::Backend *backend) -{ - _timer = backend->config()->timer(); - if (_timer == nullptr) - throw std::runtime_error("To profile backend timer() method must be implemented"); - _timer->handleBegin(); -} - -void ProfileObserver::handleEnd(IExecutor *exec, const ir::OpSequence *op_seq, - const backend::Backend *backend) -{ - _timer->handleEnd(); - const auto timer_res = _timer->getTime(); - - // NOTE This assumes there is just one operation in a op_seq - const auto &node = _graph.operations().at(op_seq->operations().at(0)); - auto node_name = node.name(); - VERBOSE(ProfileInfo) << "Time for " << node_name << " : " << timer_res << std::endl; - - // fill ExecTime: - bool is_quantized = exec->graph().operands().at(node.getInputs().at(0)).typeInfo().type() == - ir::DataType::QUANT_UINT8_ASYMM; - - uint32_t size = 0; - for (const auto &ind : node.getInputs() + node.getOutputs()) - { - size += exec->graph().operands().at(ind).info().total_size(); - } - if (node_name == "Permute") - { - // TODO Change it to updateOperationExecTime() - _et->updatePermuteTime(backend, backend, is_quantized, size, timer_res); - } - else - { - _et->updateOperationExecTime(backend, node_name, is_quantized, size, timer_res); - } -}; - -ChromeTracingObserver::ChromeTracingObserver(const std::string &filepath, const ir::Graph &graph) - : _base_filepath(filepath), _recorder{}, _collector{&_recorder}, _graph{graph} -{ -} - -ChromeTracingObserver::~ChromeTracingObserver() -{ - try - { - EventWriter{_recorder}.writeToFiles(_base_filepath); - } - catch (const std::exception &e) - { - std::cerr << "E: Fail to record event in ChromeTracingObserver: " << e.what() << std::endl; - } -} - -void ChromeTracingObserver::handleBegin(IExecutor *) -{ - _collector.onEvent(EventCollector::Event{EventCollector::Edge::BEGIN, "runtime", "Graph"}); -} - -void ChromeTracingObserver::handleBegin(IExecutor *, const ir::OpSequence *op_seq, - const backend::Backend *backend) -{ - std::string backend_id = backend->config()->id(); - _collector.onEvent(EventCollector::Event{EventCollector::Edge::BEGIN, backend_id, - opSequenceTag(op_seq, _graph.operations())}); -} - -void ChromeTracingObserver::handleEnd(IExecutor *, const ir::OpSequence *op_seq, - const backend::Backend *backend) -{ - std::string backend_id = backend->config()->id(); - _collector.onEvent(EventCollector::Event{EventCollector::Edge::END, backend_id, - opSequenceTag(op_seq, _graph.operations())}); -} - -void ChromeTracingObserver::handleEnd(IExecutor *) -{ - _collector.onEvent(EventCollector::Event{EventCollector::Edge::END, "runtime", "Graph"}); -} - -std::string ChromeTracingObserver::opSequenceTag(const ir::OpSequence *op_seq, - const ir::Operations &operations) -{ - if (op_seq->size() == 0) - return "Empty OpSequence"; - - const auto &first_op_idx = op_seq->operations().at(0); - const auto &first_op_node = operations.at(first_op_idx); - std::string tag = "$" + std::to_string(first_op_idx.value()); - tag += " " + first_op_node.name(); - if (op_seq->size() > 1) - { - tag += " (+" + std::to_string(op_seq->size() - 1) + ")"; - } - return tag; -} - -} // namespace exec - -} // namespace onert diff --git a/runtime/onert/core/src/exec/ExecutionObservers.h b/runtime/onert/core/src/exec/ExecutionObservers.h deleted file mode 100644 index f8c2acca5..000000000 --- a/runtime/onert/core/src/exec/ExecutionObservers.h +++ /dev/null @@ -1,88 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_OBSREVERS_H__ -#define __ONERT_EXEC_OBSREVERS_H__ - -#include "exec/IFunction.h" -#include "ir/OpSequence.h" -#include "ExecTime.h" -#include "util/ITimer.h" -#include "exec/IExecutor.h" -#include "util/EventCollector.h" -#include "util/EventRecorder.h" - -namespace onert -{ -namespace exec -{ -class IExecutionObserver -{ -public: - /// @brief Invoked just before model (not individual operation) execution begins - virtual void handleBegin(IExecutor *) { return; } - - virtual void handleBegin(IExecutor *, const ir::OpSequence *, const backend::Backend *) = 0; - virtual void handleEnd(IExecutor *, const ir::OpSequence *, const backend::Backend *) = 0; - - /// @brief Invoked just after model (not individual operation) execution ends - virtual void handleEnd(IExecutor *) { return; } - - virtual ~IExecutionObserver() = default; -}; - -class ProfileObserver : public IExecutionObserver -{ -public: - explicit ProfileObserver(std::shared_ptr<ExecTime> et, const ir::Graph &graph) - : _et(std::move(et)), _graph(graph) - { - } - void handleBegin(IExecutor *, const ir::OpSequence *, const backend::Backend *) override; - void handleEnd(IExecutor *, const ir::OpSequence *, const backend::Backend *) override; - - void handleEnd(IExecutor *) override { _et->uploadOperationsExecTime(); } - -private: - std::unique_ptr<util::ITimer> _timer; - std::shared_ptr<ExecTime> _et; - const ir::Graph &_graph; -}; - -class ChromeTracingObserver : public IExecutionObserver -{ -public: - ChromeTracingObserver(const std::string &filepath, const ir::Graph &graph); - ~ChromeTracingObserver(); - void handleBegin(IExecutor *) override; - void handleBegin(IExecutor *, const ir::OpSequence *, const backend::Backend *) override; - void handleEnd(IExecutor *, const ir::OpSequence *, const backend::Backend *) override; - void handleEnd(IExecutor *) override; - -private: - static std::string opSequenceTag(const ir::OpSequence *op_seq, const ir::Operations &operations); - -private: - const std::string &_base_filepath; - EventRecorder _recorder; - EventCollector _collector; - const ir::Graph &_graph; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_OBSREVERS_H__ diff --git a/runtime/onert/core/src/exec/ExecutorBase.cc b/runtime/onert/core/src/exec/ExecutorBase.cc deleted file mode 100644 index 018a0bba0..000000000 --- a/runtime/onert/core/src/exec/ExecutorBase.cc +++ /dev/null @@ -1,203 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ExecutorBase.h" - -#include "backend/ITensor.h" -#include "backend/controlflow/UserTensor.h" -#include "backend/cpu_common/Tensor.h" -#include "util/logging.h" - -namespace onert -{ -namespace exec -{ - -ExecutorBase::ExecutorBase(std::unique_ptr<compiler::LoweredGraph> &&lowered_graph, - const std::vector<backend::ITensor *> &input_tensors, - const std::vector<backend::ITensor *> &output_tensors, - const compiler::TensorRegistries &tensor_regs) - : _lowered_graph{std::move(lowered_graph)}, _graph{_lowered_graph->graph()}, - _input_tensors{input_tensors}, _output_tensors{output_tensors}, _mutex() -{ - // TODO Fix the way of knowing whether it is primary or not - bool primary_executor = !(_input_tensors.empty() && _output_tensors.empty()); - if (!primary_executor) - { - auto build_input_tensor_list = [&](const onert::ir::OperandIndexSequence &ind_seq) { - std::vector<backend::ITensor *> list; - for (auto ind : ind_seq) - { - backend::ITensor *tensor = tensor_regs.getITensor(ind); - assert(tensor != nullptr); - list.push_back(tensor); - } - return list; - }; - auto build_output_tensor_list = [&](const onert::ir::OperandIndexSequence &ind_seq) { - std::vector<backend::ITensor *> list; - for (auto ind : ind_seq) - { - backend::ITensor *tensor = tensor_regs.getITensor(ind); - assert(tensor != nullptr); - list.push_back(tensor); - } - return list; - }; - _input_tensors = build_input_tensor_list(_graph.getInputs()); - _output_tensors = build_output_tensor_list(_graph.getOutputs()); - } -} - -void ExecutorBase::execute(const std::vector<backend::ITensor *> &src_tensors, - const std::shared_ptr<IPermuteFunction> &pre_fn) -{ - // For thread-safe, use mutex - // TODO: if all used backends on this executor are thread-safe, - // do not need to use mutex (otherwise, use mutex) - // Deadlock occurs when an Executor is called recursively. - std::lock_guard<std::mutex> lock(_mutex); - - assert(src_tensors.size() == _graph.getInputs().size()); - assert(src_tensors.size() == _input_tensors.size()); - for (uint32_t n = 0; n < _graph.getInputs().size(); ++n) - { - // when user changes input shape, the input tensor is dynamic and its memory is not allocated. - // This code find the info to allocate dynamic tensor, and allocate memory based on the source - // tensor's shape set by caller. - const auto src_tensor = src_tensors[n]; - auto input_tensor = _input_tensors[n]; - // If src_tensor or input_tensor is nullptr, pre_fn does not copy the tensors - if (src_tensor != nullptr && input_tensor != nullptr) - { - const auto orig_input_shape = input_tensor->getShape(); - const auto changed_input_shape = - convertShape(src_tensor->getShape(), src_tensor->layout(), input_tensor->layout()); - if (orig_input_shape != changed_input_shape) - { - input_tensor->set_dynamic(); - } - } - } - - // TODO Move calling permute_fn.run() into executeImpl() - assert(pre_fn); - pre_fn->run(); - - executeImpl(); -} - -void ExecutorBase::execute(const IODescription &desc) -{ - // For thread-safe, use mutex - // TODO: if all used backends on this executor are thread-safe, - // do not need to use mutex (otherwise, use mutex) - std::lock_guard<std::mutex> lock(_mutex); - - // Set input(s) - assert(_input_tensors.size() == desc.inputs.size()); - for (uint32_t i = 0; i < _input_tensors.size(); ++i) - { - // TODO Remove dynamic_cast - auto *tensor = dynamic_cast<backend::controlflow::UserTensor *>(_input_tensors[i]); - assert(tensor); - auto input_shape = desc.dynamic_input_shapes.find(ir::IOIndex{i}); - if (input_shape != desc.dynamic_input_shapes.end()) - { - tensor->set_dynamic(); - tensor->setShape(input_shape->second); - } - // TODO Check if (desc.inputs[i] == nullptr) - // TODO Better design for ITensor? (we need const_cast as ITensor is writable) - tensor->setBuffer(static_cast<uint8_t *>(const_cast<void *>(desc.inputs[i]->buffer)), - desc.inputs[i]->size); - - handleDynamicInputTensor(ir::IOIndex{i}, desc); - } - - assert(_output_tensors.size() == desc.outputs.size()); - for (uint32_t i = 0; i < _output_tensors.size(); ++i) - { - // TODO Remove dynamic_cast - auto *tensor = dynamic_cast<backend::controlflow::UserTensor *>(_output_tensors[i]); - assert(tensor); - tensor->set_dynamic(); // It can't be resized but shape could change - if (desc.outputs[i] == nullptr) - throw std::runtime_error{"Output " + std::to_string(i) + "'s buffer is not set."}; - tensor->setBuffer(static_cast<uint8_t *>(desc.outputs[i]->buffer), desc.outputs[i]->size); - } - - executeImpl(); - - // Update output(s) desc - for (uint32_t n = 0; n < _graph.getOutputs().size(); ++n) - { - ir::IOIndex output_index{n}; - // Optional output - if (desc.outputs.at(n) == nullptr) - { - continue; - } - auto &output = *desc.outputs.at(n); - - // set shape of outputDesc to tensor shape since tensor can be dynamic - const auto output_tensor_shape = _output_tensors[n]->getShape(); - output.info.shape( - convertShape(output_tensor_shape, _output_tensors[n]->layout(), output.layout)); - } -} - -/** - * @brief Changes tensor shape and allocate memory - * if input shape was changed by nnfw_set_input_tensorinfo() - * - * @note Cases are: - * 1) static operand -> nnfw_set_input_tensorinfo() -> execute() -> execute() - * (a) (b) - * - * at (a), operand is static, tensor is static - memory dealloc is not needed - * (DynamicTensorManager cannot dealloc memory allocated by StaticTensorManager) - * at (b), operand is static, tensor is dynamic - memory dealloc is needed - * - * 2) dynamic operand -> nnfw_set_input_tensorinfo() -> execute() -> execute() - * (a) (b) - * - * at (a), operand is dynamic, tensor is dynamic - memory dealloc is not needed - * since it has not been allocated yet - * at (b), operand is dynamic, tensor is dynamic - memory dealloc is needed - */ -void ExecutorBase::handleDynamicInputTensor(ir::IOIndex io_ind, const IODescription &desc) -{ - auto shape_sig_found = desc.dynamic_input_shapes.find(io_ind); - if (shape_sig_found != desc.dynamic_input_shapes.end()) - { - auto changed_input_shape = shape_sig_found->second; - _input_tensors[io_ind.value()]->applyShape(changed_input_shape); - } -} - -bool ExecutorBase::hasDynamicInput() -{ - for (auto &tensor : _input_tensors) - { - if (tensor->is_dynamic()) - return true; - } - return false; -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ExecutorBase.h b/runtime/onert/core/src/exec/ExecutorBase.h deleted file mode 100644 index 8a6ec9174..000000000 --- a/runtime/onert/core/src/exec/ExecutorBase.h +++ /dev/null @@ -1,107 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_EXECUTOR_BASE_H__ -#define __ONERT_EXEC_EXECUTOR_BASE_H__ - -#include <mutex> - -#include "IPermuteFunction.h" -#include "exec/ExecutionObservers.h" -#include "ShapeConverter.h" -#include "exec/IExecutor.h" -#include "compiler/LoweredGraph.h" -#include "ir/LowerInfoMap.h" -#include "backend/IConfig.h" -#include "backend/Backend.h" -#include "exec/ExecTime.h" -#include "exec/IFunction.h" -#include "backend/IDynamicTensorManager.h" -#include "backend/ITensorManager.h" -#include "exec/ExecutionObservee.h" -#include "compiler/TensorRegistries.h" -#include <list> - -namespace onert -{ -namespace exec -{ - -class ExecutorBase : public IExecutor -{ -public: - /** - * @brief Construct a new ExecutorBase object - * @param graph Graph object - * @param tensor_builders Tensor builders that are currently used - */ - ExecutorBase(std::unique_ptr<compiler::LoweredGraph> &&lowered_graph, - const std::vector<backend::ITensor *> &input_tensors, - const std::vector<backend::ITensor *> &output_tensors, - const compiler::TensorRegistries &tensor_regs); - - virtual ~ExecutorBase() = default; - - const ir::Graph &graph() final { return _graph; } - - /** - * @brief Execute without IODescription - * - * @param src_tensor Tensor list that will be copied to input tensors of this - * @param pre_fn The permutation function that copy from src_tensor to input tensors of this - */ - void execute(const std::vector<backend::ITensor *> &src_tensors, - const std::shared_ptr<IPermuteFunction> &pre_fn); - - void execute(const IODescription &desc) final; - - // Used only in Dataflow and Parallel Executors - void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>> ranks) final - { - _indexed_ranks = std::move(ranks); - }; - - virtual void executeImpl(void) = 0; - - void addObserver(std::unique_ptr<IExecutionObserver> ref) { _subject.add(std::move(ref)); }; - - const std::vector<backend::ITensor *> &getInputTensors() const { return _input_tensors; } - - const std::vector<backend::ITensor *> &getOutputTensors() const { return _output_tensors; } - -protected: - /** - * @brief Returns @c true if any input tensor is dynamic; @c false if all are static tensors - */ - bool hasDynamicInput(); - -protected: - ExecutionObservee _subject; - std::shared_ptr<ir::OperationIndexMap<int64_t>> _indexed_ranks; - std::unique_ptr<compiler::LoweredGraph> _lowered_graph; - const ir::Graph &_graph; - std::vector<backend::ITensor *> _input_tensors; - std::vector<backend::ITensor *> _output_tensors; - std::mutex _mutex; - -private: - void handleDynamicInputTensor(ir::IOIndex input_index, const IODescription &desc); -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_EXECUTOR_BASE_H__ diff --git a/runtime/onert/core/src/exec/FunctionSequence.cc b/runtime/onert/core/src/exec/FunctionSequence.cc deleted file mode 100644 index 8aefa5eeb..000000000 --- a/runtime/onert/core/src/exec/FunctionSequence.cc +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "exec/FunctionSequence.h" - -#include "ir/Operation.h" -#include "backend/IDynamicTensorManager.h" -#include "backend/ITensorRegistry.h" -#include "util/logging.h" - -namespace onert -{ -namespace exec -{ - -void FunctionSequence::run() -{ - if (_enable_dynamic_shape_inferer && _dynamic_tensor_ctx) - { - // acl_cl and acl_neon backend don't support dynamic shape. - // _dynamic_tensor_ctx is always nullptr for acl_cl and acl_neon - // Thus, those two bakends cannot reach here. - if (_dynamic_tensor_ctx->op_seq->size() != _functions.size()) - throw std::runtime_error("operation and functions should be mapped one by one"); - - auto op_seq_iter = _dynamic_tensor_ctx->op_seq->begin(); - for (const auto &function : _functions) - { - // set shape of output and allocate memory when needed - auto &op = _dynamic_tensor_ctx->operations->at(*op_seq_iter); - op.accept(*_dynamic_tensor_ctx->dynamic_shape_inferer); - - auto *sub_func_seq = dynamic_cast<FunctionSequence *>(function.get()); - if (sub_func_seq != nullptr) - { - sub_func_seq->enableDynamicShapeInferer(true); - sub_func_seq->dynamic_tensor_ctx(dynamic_tensor_ctx()); - } - - // run kernel - function->run(); - - // deallocate input tensors which is no longer used - _dynamic_tensor_ctx->dynamic_tensor_manager->deallocInput(*op_seq_iter); - - op_seq_iter++; - } - } - else - { - for (const auto &function : _functions) - { - function->run(); - } - } -} - -void FunctionSequence::prepare() -{ - for (const auto &function : _functions) - { - function->prepare(); - } -} - -void FunctionSequence::append(std::unique_ptr<IFunction> &&function) -{ - _functions.push_back(std::move(function)); -} - -void FunctionSequence::iterate(const std::function<void(IFunction &)> &fn) -{ - for (const auto &func : _functions) - { - fn(*func); - } -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/IPermuteFunction.h b/runtime/onert/core/src/exec/IPermuteFunction.h deleted file mode 100644 index 94bc2e436..000000000 --- a/runtime/onert/core/src/exec/IPermuteFunction.h +++ /dev/null @@ -1,224 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_I_PERMUTE_FUNCTION_H__ -#define __ONERT_EXEC_I_PERMUTE_FUNCTION_H__ - -#include "feature/IndexIterator.h" -#include "feature/nchw/Reader.h" -#include "feature/nchw/View.h" -#include "feature/nhwc/Reader.h" -#include "feature/nhwc/View.h" - -#include "backend/ITensor.h" -#include "exec/IFunction.h" -#include "ir/Index.h" -#include "ir/Shape.h" -#include <memory> -#include <typeinfo> -#include "util/Utils.h" -#include <vector> - -namespace onert -{ -namespace exec -{ - -class IPermuteFunction : public IFunction -{ -private: - enum class PermuteType - { - NHWC_TO_NCHW, - NCHW_TO_NHWC, - COPY - }; - -public: - virtual void run() override - { - // TODO Optimization : Make control does not reach here? when (_src_tensors.size() == 0) - assert(_src_tensors.size() == _dst_tensors.size()); - auto src_it = _src_tensors.begin(); - auto dst_it = _dst_tensors.begin(); - while (src_it != _src_tensors.end()) - { - auto src_tensor = *src_it; - auto dst_tensor = *dst_it; - if (src_tensor != dst_tensor) - { - // TODO Change to permute in parallel - assert(underlying_type(src_tensor->data_type()) == - underlying_type(dst_tensor->data_type())); - const auto rank = src_tensor->num_dimensions(); - switch (src_tensor->data_type()) - { - case ir::DataType::FLOAT32: - permute<float>(src_tensor, dst_tensor, rank); - break; - case ir::DataType::INT32: - permute<int32_t>(src_tensor, dst_tensor, rank); - break; - case ir::DataType::UINT32: - permute<uint32_t>(src_tensor, dst_tensor, rank); - break; - case ir::DataType::BOOL8: - case ir::DataType::QUANT_UINT8_ASYMM: - case ir::DataType::UINT8: - permute<uint8_t>(src_tensor, dst_tensor, rank); - break; - case ir::DataType::QUANT_INT8_SYMM: - permute<int8_t>(src_tensor, dst_tensor, rank); - break; - case ir::DataType::INT64: - permute<int64_t>(src_tensor, dst_tensor, rank); - break; - default: - throw std::runtime_error("IPermuteFunction: Not supported data type"); - break; - } - } - src_it++; - dst_it++; - } - } - - virtual void prepare() override { optimize(); } - - virtual void optimize() = 0; - -private: - // TODO make src const by proving const access() - template <class T> void permute(backend::ITensor *src, backend::ITensor *dst, size_t rank) - { - const auto permute_type = [&]() -> PermuteType { - if (src->layout() == ir::Layout::NHWC && dst->layout() == ir::Layout::NCHW) - { - return PermuteType::NHWC_TO_NCHW; - } - else if (src->layout() == ir::Layout::NCHW && dst->layout() == ir::Layout::NHWC) - { - return PermuteType::NCHW_TO_NHWC; - } - else - { - return PermuteType::COPY; - } - }(); - auto fn = [&](backend::ITensor &src_tensor) { - dst->access([&](backend::ITensor &dst_tensor) { - if (rank == 4 && permute_type != PermuteType::COPY) - { - switch (permute_type) - { - case PermuteType::NHWC_TO_NCHW: - { - ir::FeatureShape shape; - shape.N = dst_tensor.dimension(0); - shape.C = dst_tensor.dimension(1); - shape.H = dst_tensor.dimension(2); - shape.W = dst_tensor.dimension(3); - const feature::nhwc::Reader<T> from(&src_tensor); - feature::nchw::View<T> into(&dst_tensor); - feature::iterate(shape) - << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(batch, row, col, ch); - into.at(batch, ch, row, col) = value; - }; - break; - } - case PermuteType::NCHW_TO_NHWC: - { - ir::FeatureShape shape; - shape.N = src_tensor.dimension(0); - shape.C = src_tensor.dimension(1); - shape.H = src_tensor.dimension(2); - shape.W = src_tensor.dimension(3); - const feature::nchw::Reader<T> from(&src_tensor); - feature::nhwc::View<T> into(&dst_tensor); - feature::iterate(shape) - << [&](uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) { - const auto value = from.at(batch, ch, row, col); - into.at(batch, row, col, ch) = value; - }; - break; - } - default: - { - throw std::runtime_error("Unsupported Permutation"); - break; - } - } - } - else if (!src_tensor.has_padding() && !dst_tensor.has_padding()) - { - auto src_size = src_tensor.total_size(); - assert(src_size <= dst_tensor.total_size()); - memcpy(dst_tensor.buffer(), src_tensor.buffer(), src_size); - } - else - { - auto loop_shape = src_tensor.getShape(); - const auto copy_axis = loop_shape.rank() - 1; - const auto copy_len = loop_shape.dim(copy_axis) * sizeof(T); - loop_shape.dim(copy_axis) = 1; - ShapeLoop(loop_shape, [&](const onert::ir::Coordinates &coords) { - memcpy(dst_tensor.buffer() + dst_tensor.calcOffset(coords), - src_tensor.buffer() + src_tensor.calcOffset(coords), copy_len); - }); - } - }); - }; - src->access(fn); - } - - // NOTE The typeid expression is lvalue expression which refers to an object with static storage - // duration, of the polymorphic type const std::type_info or of some type derived from it. - // So std::type_info is non-copyable - const std::type_info &underlying_type(ir::DataType type) const - { - switch (type) - { - case ir::DataType::FLOAT32: - return typeid(float); - case ir::DataType::INT32: - return typeid(int32_t); - case ir::DataType::UINT32: - return typeid(uint32_t); - case ir::DataType::INT64: - return typeid(int64_t); - case ir::DataType::BOOL8: - case ir::DataType::QUANT_UINT8_ASYMM: - case ir::DataType::UINT8: - return typeid(uint8_t); - case ir::DataType::QUANT_INT8_SYMM: - return typeid(int8_t); - default: - throw std::runtime_error("IPermuteFunction: Not supported data type"); - } - } - -protected: - std::vector<backend::ITensor *> _src_tensors; - std::vector<backend::ITensor *> _dst_tensors; - // TODO Remove this member if it is possible - std::vector<size_t> _ranks; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_I_PERMUTE_FUNCTION_H__ diff --git a/runtime/onert/core/src/exec/JSONExecTime.cc b/runtime/onert/core/src/exec/JSONExecTime.cc deleted file mode 100644 index 72a18def1..000000000 --- a/runtime/onert/core/src/exec/JSONExecTime.cc +++ /dev/null @@ -1,231 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "exec/JSONExecTime.h" -#include "backend/IConfig.h" -#include <fstream> - -namespace onert -{ -namespace exec -{ -/** - * @brief Helper function for reading string from stream - * - * @param str Output string - * @param stream File stream - */ -void readString(std::string &str, std::ifstream &stream) -{ - str.clear(); - char buf; - while (stream.good()) - { - stream.get(buf); - if (buf == '"') - break; - str.push_back(buf); - } -} - -/** - * @brief Helper function for reading bool from stream - * - * @param quant Output bool - * @param stream File stream - */ -void readBool(bool &quant, std::ifstream &stream) -{ - char buf; - stream.get(buf); - quant = (buf == '1'); - stream.get(buf); -} - -void printString(const std::string &str, std::ofstream &stream) { stream << "\"" << str << "\""; } - -void printBool(bool quant, std::ofstream &stream) { stream << "\"" << quant << "\""; } - -void JSON::readOperation(const std::string &backend, const std::string &operation, bool quant, - std::ifstream &stream) -{ - uint32_t size = 0; - int64_t time = 0; - - std::string int_buf; - char buf; - int number_of_closed_braces = 0; - int number_of_commas = 0; - - while (stream.good()) - { - stream.get(buf); - - switch (buf) - { - case ']': - { - number_of_closed_braces++; - break; - } - case '[': - { - number_of_closed_braces--; - break; - } - default: - { - if (std::isdigit(buf)) - { - int_buf.push_back(buf); - } - break; - } - } - - if (number_of_closed_braces == 1) - break; - - if ((buf == ']' && number_of_closed_braces == 0) || - (buf == ',' && number_of_closed_braces == -1)) - { - switch (number_of_commas % 2) - { - case 0: - { - size = static_cast<uint32_t>(std::atoi(int_buf.c_str())); - break; - } - case 1: - { - time = static_cast<int64_t>(std::atol(int_buf.c_str())); - auto bf = _backends.find(backend); - if (bf != _backends.end()) - { - _measurements[bf->second][operation][quant][size] = time; - } // we ignore the records for unsupported backends - break; - } - } - number_of_commas++; - int_buf.clear(); - } - } -} -void JSON::printOperation(const std::map<uint32_t, int64_t> &operation_info, - std::ofstream &stream) const -{ - for (const auto &items : operation_info) - { - stream << "[" << items.first << ", " << items.second << "], "; - } - stream.seekp(-2, std::ofstream::end); -} - -void JSON::uploadOperationsExecTime() const -{ - std::ofstream stream(_measurement_file); - if (!stream.is_open()) - { - throw std::runtime_error("Failed to save backend config file"); - } - else - { - stream << "{"; - for (const auto &backend : _measurements) - { - printString(backend.first->config()->id(), stream); - stream << ": {"; - for (const auto &operation : backend.second) - { - printString(operation.first, stream); - stream << ": {"; - for (const auto &type : operation.second) - { - printBool(type.first, stream); - stream << ": ["; - printOperation(type.second, stream); - stream << "], "; - } - stream.seekp(-2, std::ofstream::end); - stream << "}, "; - } - stream.seekp(-2, std::ofstream::end); - stream << "}, "; - } - stream.seekp(-2, std::ofstream::end); - stream << "}"; - stream.close(); - } -} - -void JSON::loadOperationsExecTime() -{ - std::ifstream stream(_measurement_file); - if (stream.is_open()) - { - std::string backend; - std::string operation; - bool quant = false; - char buf; - int number_of_open_braces = 0; - - while (stream.good()) - { - stream.get(buf); - switch (buf) - { - case '{': - number_of_open_braces++; - break; - case '}': - number_of_open_braces--; - break; - case '"': - { - if (number_of_open_braces == 1) - { - // read backend string - readString(backend, stream); - } - if (number_of_open_braces == 2) - { - // read operation string - readString(operation, stream); - } - if (number_of_open_braces == 3) - { - // read operation string - readBool(quant, stream); - } - break; - } - case '[': - { - // reading and creating all info for operation - readOperation(backend, operation, quant, stream); - break; - } - default: - break; - } - } - stream.close(); - } -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/JSONExecTime.h b/runtime/onert/core/src/exec/JSONExecTime.h deleted file mode 100644 index a64cb3133..000000000 --- a/runtime/onert/core/src/exec/JSONExecTime.h +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_JSON_EXEC_TIME_H__ -#define __ONERT_EXEC_JSON_EXEC_TIME_H__ - -#include <fstream> -#include <unordered_map> -#include <map> -#include <vector> -#include "backend/Backend.h" -#include "backend/IConfig.h" - -namespace onert -{ -namespace exec -{ - -/** - * @brief table, that contains execution time of an operation on some backend for different input - * sizes and transfer time from one backend to another for various input sizes (permutation time) - * - * backend -> op -> quant-> size --> time - * _measurements[Backend*]["string"][bool][uint32_t] = int64_t - */ -using MeasurementData = std::unordered_map< - const backend::Backend *, - std::unordered_map<std::string, std::unordered_map<bool, std::map<uint32_t, int64_t>>>>; - -class JSON -{ -public: - explicit JSON(const std::vector<const backend::Backend *> &backends, - MeasurementData &measurements) - : _measurement_file("exec_time.json"), _backends(), _measurements(measurements) - { - for (const auto b : backends) - { - _backends.emplace(b->config()->id(), b); - } - loadOperationsExecTime(); - }; - /** - * @brief Update _operations_exec_time_file with new data. - */ - void uploadOperationsExecTime() const; - -private: - ///@brief file containing measurements - std::string _measurement_file; - std::unordered_map<std::string, const backend::Backend *> _backends; - std::unordered_map< - const backend::Backend *, - std::unordered_map<std::string, std::unordered_map<bool, std::map<uint32_t, int64_t>>>> - &_measurements; - /** - * @brief Helper function for inserting data to OperationExecTimes - * - * @param backend String name of backend - * @param operation String name of operation - * @param quant if input type quantized - * @param stream File stream - */ - void readOperation(const std::string &backend, const std::string &operation, bool quant, - std::ifstream &stream); - - /** - * @brief Helper function for writing OperationExecTimes to stream - * - * @param operation_info Map of operations execution information - * @param stream File stream - */ - void printOperation(const std::map<uint32_t, int64_t> &operation_info, - std::ofstream &stream) const; - /** - * @brief Parse and load operations_exec_time from _operations_exec_time_file. - */ - void loadOperationsExecTime(); -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_JSON_EXEC_TIME_H__ diff --git a/runtime/onert/core/src/exec/Job.cc b/runtime/onert/core/src/exec/Job.cc deleted file mode 100644 index 27925a93c..000000000 --- a/runtime/onert/core/src/exec/Job.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Job.h" - -#include <cassert> - -#include "util/logging.h" - -namespace onert -{ -namespace exec -{ - -Job::Job(uint32_t index, FunctionSequence *fn_seq) : _index{index}, _fn_seq{fn_seq} {} - -void Job::run() { _fn_seq->run(); } - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/Job.h b/runtime/onert/core/src/exec/Job.h deleted file mode 100644 index 6de9c31a0..000000000 --- a/runtime/onert/core/src/exec/Job.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_JOB_H__ -#define __ONERT_EXEC_JOB_H__ - -#include <unordered_set> - -#include "exec/FunctionSequence.h" -#include "ir/Index.h" -#include "ir/OperandIndexSequence.h" -#include "backend/Backend.h" - -namespace onert -{ -namespace exec -{ - -class Job -{ -public: - /** - * @brief Constructs a Job object - * - * @param index Operation index for this job - * @param fn_seq compiled code to run this job - * @param inputs Input operand list - * @param outputs Output operand list - */ - Job(uint32_t index, FunctionSequence *fn_seq); - /** - * @brief Execute the compiled code - */ - void run(); - /** - * @brief Return job index - * - * @return Job index - */ - uint32_t index() const { return _index; } - /** - * @brief Return the function to be executed - * - * @return Pointer of the function - */ - FunctionSequence *fn_seq() { return _fn_seq; } - -private: - uint32_t _index; - FunctionSequence *_fn_seq; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_JOB_H__ diff --git a/runtime/onert/core/src/exec/LinearExecutor.cc b/runtime/onert/core/src/exec/LinearExecutor.cc deleted file mode 100644 index 6e6ca110f..000000000 --- a/runtime/onert/core/src/exec/LinearExecutor.cc +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "LinearExecutor.h" -#ifdef RUY_PROFILER -#include "ruy/profiler/instrumentation.h" -#endif - -namespace onert -{ -namespace exec -{ - -#ifdef RUY_PROFILER -namespace -{ -char *seq_to_label(const onert::ir::OpSequence *op_seq, const onert::ir::Operations &operations) -{ - auto node_name = operations.at(*op_seq->begin()).name(); - char *cstr = new char[node_name.length() + 1]; - std::strcpy(cstr, node_name.c_str()); - return cstr; -} -} // namespace -#endif - -void LinearExecutor::executeImpl() -{ - _subject.notifyModelBegin(this); - for (auto &&code : _code) - { - const auto op_seq = code.op_seq; - const auto backend = code.lower_info->backend(); -// TODO : Move ruy profiler into ExecutionObserver -#ifdef RUY_PROFILER - ruy::profiler::ScopeLabel label(seq_to_label(op_seq, _graph.operations())); -#endif - _subject.notifyJobBegin(this, op_seq, backend); - - auto &fn_seq = code.fn_seq; - - fn_seq->initRunning(); - - bool handle_dynamic_tensor = op_seq->has_dynamic_tensor() || hasDynamicInput(); - fn_seq->enableDynamicShapeInferer(handle_dynamic_tensor); - fn_seq->run(); - - _subject.notifyJobEnd(this, op_seq, backend); - } - _subject.notifyModelEnd(this); -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/LinearExecutor.h b/runtime/onert/core/src/exec/LinearExecutor.h deleted file mode 100644 index 22d00ec30..000000000 --- a/runtime/onert/core/src/exec/LinearExecutor.h +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file LinearExecutor.h - * @brief This file contains LinearExecutor class to define and run execution phase - */ - -#ifndef __ONERT_EXEC_EXECUTOR_H_ -#define __ONERT_EXEC_EXECUTOR_H_ - -#include "ir/Index.h" -#include "ExecutorBase.h" -#include "compiler/Linear.h" -#include "exec/FunctionSequence.h" -#include "compiler/CodeMap.h" - -namespace onert -{ -namespace exec -{ - -/** - * @brief Class to handle execution phase. Simple run the sequence of operations that is sorted in - * topological order - */ -class LinearExecutor final : public ExecutorBase -{ -public: - /** - * @brief Construct a new LinearExecutor object - * @param lowered_graph LoweredGraph object - * @param tensor_builders Tensor builders that are currently used - * @param code_map OpSequence and its code map - */ - LinearExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const std::vector<backend::ITensor *> &input_tensors, - const std::vector<backend::ITensor *> &output_tensors, - const compiler::TensorRegistries &tensor_regs, compiler::CodeMap &&code_map, - const std::vector<ir::OpSequenceIndex> &order) - : ExecutorBase{std::move(lowered_graph), input_tensors, output_tensors, tensor_regs} - { - for (auto index : order) - { - _code.emplace_back(std::move(code_map.at(index))); - } - } - -public: - void executeImpl(void) override; - -private: - std::vector<compiler::CodeAndInfo> _code; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_EXECUTOR_H_ diff --git a/runtime/onert/core/src/exec/ParallelExecutor.cc b/runtime/onert/core/src/exec/ParallelExecutor.cc deleted file mode 100644 index 676bdb5fa..000000000 --- a/runtime/onert/core/src/exec/ParallelExecutor.cc +++ /dev/null @@ -1,156 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ParallelExecutor.h" - -#include <cassert> - -#include "util/logging.h" -#include "exec/IFunction.h" - -namespace onert -{ -namespace exec -{ - -class HookFunction : public IFunction -{ -public: - HookFunction(IFunction *fn, const std::function<void()> &setup, - const std::function<void()> &teardown) - : _fn{fn}, _setup{setup}, _teardown{teardown} - { - } - -public: - void run() override - { - _setup(); - _fn->run(); - _teardown(); - } - -private: - IFunction *_fn; - std::function<void()> _setup; - std::function<void()> _teardown; -}; - -void ParallelExecutor::notify(uint32_t finished_job_id) -{ - std::unique_lock<std::mutex> lock{_mu_jobs}; - - DataflowExecutor::notify(finished_job_id); - - lock.unlock(); - _cv_jobs.notify_all(); -} - -ParallelExecutor::ParallelExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const std::vector<backend::ITensor *> &input_tensors, - const std::vector<backend::ITensor *> &output_tensors, - const compiler::TensorRegistries &tensor_regs, - compiler::CodeMap &&code_map) - : DataflowExecutor{std::move(lowered_graph), input_tensors, output_tensors, tensor_regs, - std::move(code_map)} -{ - VERBOSE(ParallelExecutor) << "Constructing Parallel Executor" << std::endl; -} - -void ParallelExecutor::executeImpl() -{ - bool dynamic_input_exists = hasDynamicInput(); - - // Init scheduler - // TODO Consider to have distinct backend set in LowerInfoMap - BackendSet backends; - for (auto &itr : _lowered_graph->getLowerInfo()->op_seq) - { - backends.add(itr.second->backend()); - } - _scheduler = std::make_unique<ParallelScheduler>(backends); - - assert(noWaitingJobs()); - - // Execution setup - _waiting_jobs.swap(_finished_jobs); // Move finished jobs to waiting jobs - - for (uint32_t i = 0; i < _waiting_jobs.size(); ++i) - { - VERBOSE(ParallelExecutor) << i << ": " << _input_info[i] << std::endl; - if (_input_info[i] == 0) - { - emplaceToReadyJobs(i); - } - } - assert(!_ready_jobs.empty()); // Cannot begin if there is no initial jobs - - VERBOSE(ParallelExecutor) << "INITIAL JOBS : " << _ready_jobs.size() << std::endl; - - _subject.notifyModelBegin(this); - while (true) - { - std::unique_lock<std::mutex> lock{_mu_jobs}; - - if (_ready_jobs.empty()) - { - _cv_jobs.wait(lock, [this] { return !_ready_jobs.empty() || noWaitingJobs(); }); - // Check finish condition - if (_ready_jobs.empty() && noWaitingJobs()) - { - break; - } - } - - auto job = std::move(_ready_jobs.begin()->second); - _ready_jobs.erase(_ready_jobs.begin()); - - lock.unlock(); - - VERBOSE(ParallelExecutor) << "Assigning fn #" << job->index() << std::endl; - - auto job_index = job->index(); - auto op_sequence_index = _job_to_op_seq[job_index]; - auto op_seq = &_lowered_graph->op_seqs().at(op_sequence_index); - auto backend = _lowered_graph->getLowerInfo()->op_seq.at(op_sequence_index)->backend(); - auto setup = [&, op_seq, backend]() { _subject.notifyJobBegin(this, op_seq, backend); }; - auto teardown = [&, job_index, op_seq, backend]() { - _subject.notifyJobEnd(this, op_seq, backend); - notify(job_index); - }; - - job->fn_seq()->initRunning(); - - // dynamic tensor setting - bool handle_dynamic_tensor = op_seq->has_dynamic_tensor() || dynamic_input_exists; - job->fn_seq()->enableDynamicShapeInferer(handle_dynamic_tensor); - - _scheduler->assign(std::make_unique<HookFunction>(job->fn_seq(), setup, teardown), backend); - _finished_jobs[job_index] = std::move(job); - } - - assert(noWaitingJobs()); - - // Wait for all the jobs done - _scheduler->finish(); - _subject.notifyModelEnd(this); - - // Reset input info for the next execution - _input_info = _initial_input_info; -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ParallelExecutor.h b/runtime/onert/core/src/exec/ParallelExecutor.h deleted file mode 100644 index 111c20c0c..000000000 --- a/runtime/onert/core/src/exec/ParallelExecutor.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_PARALLEL_EXECUTOR_H__ -#define __ONERT_EXEC_PARALLEL_EXECUTOR_H__ - -#include <list> -#include <queue> -#include <unordered_map> - -#include "exec/FunctionSequence.h" -#include "Job.h" -#include "ir/OperandIndexSequence.h" -#include "ir/Index.h" -#include <memory> -#include "exec/DataflowExecutor.h" -#include "ParallelScheduler.h" - -namespace onert -{ -namespace exec -{ - -/** - * @brief Class to execute Graph in parallel - */ -class ParallelExecutor : public DataflowExecutor -{ -protected: - void notify(uint32_t finished_job_id) override; - -public: - /** - * @brief Constructs a ParallelExecutor object - * - * @param lowered_graph LoweredGraph object - * @param tensor_builders Tensor builders that are currently used - * @param code_map OpSequence and its code map - */ - ParallelExecutor(std::unique_ptr<compiler::LoweredGraph> lowered_graph, - const std::vector<backend::ITensor *> &input_tensors, - const std::vector<backend::ITensor *> &output_tensors, - const compiler::TensorRegistries &tensor_regs, compiler::CodeMap &&code_map); - - void executeImpl() override; - -private: - std::condition_variable _cv_jobs; - std::mutex _mu_jobs; - std::unique_ptr<ParallelScheduler> _scheduler; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_PARALLEL_EXECUTOR_H__ diff --git a/runtime/onert/core/src/exec/ParallelScheduler.cc b/runtime/onert/core/src/exec/ParallelScheduler.cc deleted file mode 100644 index 70c9c3dd6..000000000 --- a/runtime/onert/core/src/exec/ParallelScheduler.cc +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ParallelScheduler.h" - -#include <cassert> - -#include <memory> -#include "util/logging.h" - -namespace onert -{ -namespace exec -{ - -ParallelScheduler::ParallelScheduler(const BackendSet &backends) -{ - assert(!backends.empty()); - - for (auto backend : backends) - { - _thread_pools[backend] = std::make_unique<ThreadPool>(); - } -} - -void ParallelScheduler::assign(std::unique_ptr<IFunction> &&fn, const backend::Backend *backend) -{ - assert(!_thread_pools.empty()); - - _thread_pools.at(backend)->enqueue(std::move(fn)); -} - -void ParallelScheduler::finish() -{ - for (auto &itr : _thread_pools) - { - itr.second->finish(); - } -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ParallelScheduler.h b/runtime/onert/core/src/exec/ParallelScheduler.h deleted file mode 100644 index 6802c9e43..000000000 --- a/runtime/onert/core/src/exec/ParallelScheduler.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_PARALLEL_SCHEDULER_H__ -#define __ONERT_EXEC_PARALLEL_SCHEDULER_H__ - -#include <unordered_map> -#include <memory> - -#include "exec/IFunction.h" -#include "BackendSet.h" -#include "ThreadPool.h" - -namespace onert -{ -namespace exec -{ - -class ParallelScheduler -{ -public: - /** - * @brief Constructs ParallelScheduler object - * - * @param backends Backend set - */ - ParallelScheduler(const BackendSet &backends); - /** - * @brief Assign a task to the given backend - * - * @param[in] fn Function to be assigned - * @param[in] fn Target backend - */ - void assign(std::unique_ptr<IFunction> &&fn, const backend::Backend *backend); - /** - * @brief Block until all jobs are finished - */ - void finish(); - -private: - std::unordered_map<const backend::Backend *, std::unique_ptr<ThreadPool>> _thread_pools; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_PARALLEL_SCHEDULER_H__ diff --git a/runtime/onert/core/src/exec/ShapeConverter.cc b/runtime/onert/core/src/exec/ShapeConverter.cc deleted file mode 100644 index 707aef29b..000000000 --- a/runtime/onert/core/src/exec/ShapeConverter.cc +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ShapeConverter.h" - -namespace onert -{ -namespace exec -{ - -ir::Shape convertShape(const ir::Shape &shape, ir::Layout src_layout, ir::Layout dst_layout) -{ - if (shape.rank() != 4) - return shape; - - if (src_layout == dst_layout) - return shape; - - if (src_layout == ir::Layout::NCHW && dst_layout == ir::Layout::NHWC) - { - const ir::Shape &src_NCHW = shape; - ir::Shape dst_NHWC(4); - dst_NHWC.dim(0) = src_NCHW.dim(0); // N - dst_NHWC.dim(1) = src_NCHW.dim(2); // H - dst_NHWC.dim(2) = src_NCHW.dim(3); // W - dst_NHWC.dim(3) = src_NCHW.dim(1); // C - - return dst_NHWC; - } - - if (src_layout == ir::Layout::NHWC && dst_layout == ir::Layout::NCHW) - { - const ir::Shape &src_NHWC = shape; - ir::Shape dst_NCHW(4); - dst_NCHW.dim(0) = src_NHWC.dim(0); // N - dst_NCHW.dim(1) = src_NHWC.dim(3); // C - dst_NCHW.dim(2) = src_NHWC.dim(1); // H - dst_NCHW.dim(3) = src_NHWC.dim(2); // W - - return dst_NCHW; - } - - throw std::runtime_error("Should not reach here"); -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ShapeConverter.h b/runtime/onert/core/src/exec/ShapeConverter.h deleted file mode 100644 index 7dc7e7536..000000000 --- a/runtime/onert/core/src/exec/ShapeConverter.h +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_SHAPE_CONVERTER_H__ -#define __ONERT_EXEC_SHAPE_CONVERTER_H__ - -#include <ir/Layout.h> -#include <ir/Shape.h> - -namespace onert -{ -namespace exec -{ - -/** - * @brief Converts shape when its rank is 4 - * - * @return ir::Shape Return a shape based on dst_layout. If rank is not 4, input shape is - * returned without conversion. - */ -ir::Shape convertShape(const ir::Shape &shape, ir::Layout src_layout, ir::Layout dst_layout); - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_SHAPE_CONVERTER_H__ diff --git a/runtime/onert/core/src/exec/ThreadPool.cc b/runtime/onert/core/src/exec/ThreadPool.cc deleted file mode 100644 index c8e0e3265..000000000 --- a/runtime/onert/core/src/exec/ThreadPool.cc +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ThreadPool.h" - -#include <cassert> - -namespace onert -{ -namespace exec -{ - -ThreadPool::ThreadPool(uint32_t num_threads) -{ - assert(num_threads >= 1); - - for (uint32_t i = 0; i < num_threads; i++) - { - _threads.emplace_back(std::ref(_worker)); - } -} - -ThreadPool::~ThreadPool() -{ - if (!_threads.empty()) - { - _worker.terminate(); - join(); - } -} - -void ThreadPool::enqueue(std::unique_ptr<IFunction> &&fn) { _worker.enqueue(std::move(fn)); } - -uint32_t ThreadPool::numJobsInQueue() { return _worker.numJobsInQueue(); } - -void ThreadPool::join() -{ - for (auto &thread : _threads) - { - thread.join(); - } - _threads.clear(); -} - -void ThreadPool::finish() -{ - _worker.finish(); - join(); -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/ThreadPool.h b/runtime/onert/core/src/exec/ThreadPool.h deleted file mode 100644 index b638bd94c..000000000 --- a/runtime/onert/core/src/exec/ThreadPool.h +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_THREAD_POOL_H__ -#define __ONERT_EXEC_THREAD_POOL_H__ - -#include <thread> -#include <memory> -#include <vector> - -#include "WorkQueue.h" - -namespace onert -{ -namespace exec -{ - -class ThreadPool -{ -public: - /** - * @brief Coustruct ThreadPool object - * - * @param num_threads Number of threads - */ - ThreadPool(uint32_t num_threads = 1); - /** - * @brief Destroy ThreadPool object - */ - ~ThreadPool(); - /** - * @brief Enqueue a function - * - * @param fn A function to be queued - */ - void enqueue(std::unique_ptr<IFunction> &&fn); - /** - * @brief Get number of jobs in worker's queue - * - * @return Number of jobs - */ - uint32_t numJobsInQueue(); - - /** - * @brief Block until all jobs are finished - */ - void finish(); - -private: - void join(); - -private: - WorkQueue _worker; - std::vector<std::thread> _threads; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_THREAD_POOL_H__ diff --git a/runtime/onert/core/src/exec/WorkQueue.cc b/runtime/onert/core/src/exec/WorkQueue.cc deleted file mode 100644 index b37f6a387..000000000 --- a/runtime/onert/core/src/exec/WorkQueue.cc +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "WorkQueue.h" - -#include <cassert> - -namespace onert -{ -namespace exec -{ - -WorkQueue::~WorkQueue() -{ - { - std::unique_lock<std::mutex> lock(_mu); - _state = State::FORCE_FINISHING; - } - _cv.notify_all(); -} - -void WorkQueue::operator()() -{ - while (true) - { - std::unique_ptr<IFunction> fn = nullptr; - - { - std::unique_lock<std::mutex> lock{_mu}; - _cv.wait(lock, [this] { - return (_state == State::FORCE_FINISHING) || (_state == State::FINISHING) || - (_state == State::ONLINE && !_functions.empty()); - }); - - if (_state == State::FORCE_FINISHING) - { - assert(_functions.empty() && "Terminating with unfinished jobs"); - return; - } - else if (_state == State::FINISHING && _functions.empty()) - { - return; - } - else - { - assert(((_state == State::FINISHING) || (_state == State::ONLINE)) && !_functions.empty()); - fn = std::move(_functions.front()); - _functions.pop(); - } - } - - assert(fn); - fn->run(); - } -} - -void WorkQueue::enqueue(std::unique_ptr<IFunction> &&fn) -{ - { - std::unique_lock<std::mutex> lock{_mu}; - _functions.emplace(std::move(fn)); - } - _cv.notify_one(); -} - -void WorkQueue::terminate() -{ - { - std::unique_lock<std::mutex> lock{_mu}; - _state = State::FORCE_FINISHING; - } - _cv.notify_all(); -} - -void WorkQueue::finish() -{ - { - std::unique_lock<std::mutex> lock{_mu}; - _state = State::FINISHING; - } - _cv.notify_all(); -} - -uint32_t WorkQueue::numJobsInQueue() -{ - std::unique_lock<std::mutex> lock{_mu}; - return _functions.size(); -} - -} // namespace exec -} // namespace onert diff --git a/runtime/onert/core/src/exec/WorkQueue.h b/runtime/onert/core/src/exec/WorkQueue.h deleted file mode 100644 index 2e56d85e8..000000000 --- a/runtime/onert/core/src/exec/WorkQueue.h +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_WORK_QUEUE_H__ -#define __ONERT_EXEC_WORK_QUEUE_H__ - -#include <condition_variable> -#include <memory> -#include <mutex> -#include <queue> - -#include "exec/IFunction.h" - -namespace onert -{ -namespace exec -{ - -class WorkQueue -{ -public: - enum class State - { - ONLINE, - FINISHING, - FORCE_FINISHING - }; - -public: - /** - * @brief Create WorkQueue object - */ - WorkQueue() = default; - /** - * @brief Destroy WorkQueue object - */ - ~WorkQueue(); - /** - * @brief Thread entry function - */ - void operator()(); - /** - * @brief Push the given Task to the job queue - * - * @param fn Function to be executed(a job) - */ - void enqueue(std::unique_ptr<IFunction> &&fn); - /** - * @brief Flag as terminating so all the worker threads can terminate - */ - void terminate(); - /** - * @brief Flag as terminating so all the worker threads can terminate - */ - void finish(); - /** - * @brief Check if it has pending jobs. Even if this returns fals, WorkQueue threads may be still - * running - * - * @return true if the job queue not empty otherwise false - */ - uint32_t numJobsInQueue(); - -private: - State _state{State::ONLINE}; - std::queue<std::unique_ptr<IFunction>> _functions; - std::mutex _mu; - std::condition_variable _cv; -}; - -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_WORK_QUEUE_H__ diff --git a/runtime/onert/core/src/exec/feature/IndexIterator.h b/runtime/onert/core/src/exec/feature/IndexIterator.h deleted file mode 100644 index 9613f5a30..000000000 --- a/runtime/onert/core/src/exec/feature/IndexIterator.h +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file IndexIterator.h - * @brief This file contains IndexIterator class - */ - -#ifndef __ONERT_EXEC_FEATURE_INDEX_ITERATOR_H__ -#define __ONERT_EXEC_FEATURE_INDEX_ITERATOR_H__ - -#include "ir/Shape.h" - -namespace onert -{ -namespace exec -{ -namespace feature -{ - -/** - * @brief Class to iterate Callable with Index of feature - */ -class IndexIterator -{ -public: - /** - * @brief Construct IndexIterator object with Shape of feature - * @param[in] shape Shape reference of feature - */ - IndexIterator(const ir::FeatureShape &shape) : _shape{shape} - { - // DO NOTHING - } - -public: - /** - * @brief Call a function iterated - * @param[in] cb A callback function - * @return Current IndexIterator object - */ - template <typename Callable> IndexIterator &iter(Callable cb) - { - for (int32_t batch = 0; batch < _shape.N; ++batch) - { - for (int32_t ch = 0; ch < _shape.C; ++ch) - { - for (int32_t row = 0; row < _shape.H; ++row) - { - for (int32_t col = 0; col < _shape.W; ++col) - { - cb(batch, ch, row, col); - } - } - } - } - - return (*this); - } - -private: - /** - * @brief Shape for feature - */ - const ir::FeatureShape _shape; -}; - -/** - * @brief Create an object of IndexIterator for feature - * @param[in] Shape reference of feature - * @return Created IndexIterator object - */ -static inline IndexIterator iterate(const ir::FeatureShape &shape) { return IndexIterator{shape}; } - -/** - * @brief Call a function iterated using IndexIterator of feature - * Overloaded operator<< - * @param[in] it An IndexIterator reference - * @param[in] cb A callback function - * @return created IndexIterator object - */ -template <typename Callable> IndexIterator &operator<<(IndexIterator &&it, Callable cb) -{ - return it.iter(cb); -} - -} // namespace feature -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_FEATURE_INDEX_ITERATOR_H__ diff --git a/runtime/onert/core/src/exec/feature/Reader.h b/runtime/onert/core/src/exec/feature/Reader.h deleted file mode 100644 index ed87bb990..000000000 --- a/runtime/onert/core/src/exec/feature/Reader.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Reader.h - * @brief This file contains Reader class - */ - -#ifndef __ONERT_EXEC_FEATURE_READER_H__ -#define __ONERT_EXEC_FEATURE_READER_H__ - -#include <cstdint> - -namespace onert -{ -namespace exec -{ -namespace feature -{ - -/** - * @brief Class reads values of feature - * The interface class - */ -template <typename T> struct Reader -{ - /** - * @brief Destruct Reader object using default destructor - */ - virtual ~Reader() = default; - - /** - * @brief Get the value used by three indexes - * @param[in] ch The depth index - * @param[in] row The height index - * @param[in] col The width index - * @return The value at the offset - */ - virtual T at(uint32_t ch, uint32_t row, uint32_t col) const = 0; - /** - * @brief Get the value used by four indexes - * @param[in] batch The batch index - * @param[in] ch The depth index - * @param[in] row The height index - * @param[in] col The width index - * @return The value at the offset - */ - virtual T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const = 0; -}; - -} // namespace feature -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_FEATURE_READER_H__ diff --git a/runtime/onert/core/src/exec/feature/nchw/Reader.h b/runtime/onert/core/src/exec/feature/nchw/Reader.h deleted file mode 100644 index 7be9df4d5..000000000 --- a/runtime/onert/core/src/exec/feature/nchw/Reader.h +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_FEATURE_NCHW_READER_H__ -#define __ONERT_EXEC_FEATURE_NCHW_READER_H__ - -#include "../Reader.h" - -#include <cassert> - -#include "backend/ITensor.h" -#include "ir/Shape.h" - -namespace onert -{ -namespace exec -{ -namespace feature -{ -namespace nchw -{ - -template <typename T> class Reader : public feature::Reader<T> -{ -public: - // Construct for buffer of model inputs - Reader(const ir::FeatureShape &shape, const T *ptr, size_t len) - : _shape{shape}, _ptr{reinterpret_cast<const uint8_t *>(ptr)}, _len{len} - { - assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len); - - // No padding - _strides.W = sizeof(T); - _strides.H = shape.W * sizeof(T); - _strides.C = shape.W * shape.H * sizeof(T); - _strides.N = shape.W * shape.H * shape.C * sizeof(T); - } - - // Construct for backend tensor - Reader(backend::ITensor *tensor) - : _ptr{tensor->buffer() + tensor->calcOffset({0, 0, 0, 0})}, _len{tensor->total_size()} - { - assert(tensor->layout() == ir::Layout::NCHW); - - const auto start_offset = tensor->calcOffset({0, 0, 0, 0}); - _strides.W = tensor->dimension(3) == 1 ? 0 : tensor->calcOffset({0, 0, 0, 1}) - start_offset; - _strides.H = tensor->dimension(2) == 1 ? 0 : tensor->calcOffset({0, 0, 1, 0}) - start_offset; - _strides.C = tensor->dimension(1) == 1 ? 0 : tensor->calcOffset({0, 1, 0, 0}) - start_offset; - _strides.N = tensor->dimension(0) == 1 ? 0 : tensor->calcOffset({1, 0, 0, 0}) - start_offset; - - _shape.W = tensor->dimension(3); - _shape.H = tensor->dimension(2); - _shape.C = tensor->dimension(1); - _shape.N = tensor->dimension(0); - } - -public: - T at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const final - { - return getRef(batch, ch, row, col); - } - T at(uint32_t ch, uint32_t row, uint32_t col) const final { return getRef(0, ch, row, col); } - -protected: - const T &getRef(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const - { - const auto offset = feature_index_to_byte_offset(batch, ch, row, col); - - const T *ptr = reinterpret_cast<const T *>(_ptr + offset); - - return *ptr; - } - -private: - size_t feature_index_to_byte_offset(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) const - { - assert(1u * _shape.N > batch); // shape.N > batch - assert(1u * _shape.C > ch); // shape.C > ch - assert(1u * _shape.H > row); // shape.H > row - assert(1u * _shape.W > col); // shape.W > col - - uint32_t res = 0; - res += batch * _strides.N; - res += ch * _strides.C; - res += row * _strides.H; - res += col * _strides.W; - - return res; - } - -private: - // TODO Remove _shape - ir::FeatureShape _shape; - using Strides = ir::FeatureShape; - Strides _strides; - const uint8_t *_ptr; - size_t _len; -}; - -} // namespace nchw -} // namespace feature -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_FEATURE_NCHW_READER_H__ diff --git a/runtime/onert/core/src/exec/feature/nchw/View.h b/runtime/onert/core/src/exec/feature/nchw/View.h deleted file mode 100644 index dbaf1a91e..000000000 --- a/runtime/onert/core/src/exec/feature/nchw/View.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_FEATURE_NCHW_VIEW_H__ -#define __ONERT_EXEC_FEATURE_NCHW_VIEW_H__ - -#include "Reader.h" - -#include "backend/ITensor.h" -#include "ir/Shape.h" -#include "util/logging.h" - -#include <cassert> - -namespace onert -{ -namespace exec -{ -namespace feature -{ -namespace nchw -{ - -template <typename T> class View final : public Reader<T> -{ -public: - // Construct for buffer of model inputs - View(const ir::FeatureShape &shape, T *ptr, size_t len) : Reader<T>{shape, ptr, len} - { - // DO NOTHING - } - - // Construct for backend tensor - View(::onert::backend::ITensor *tensor) : Reader<T>{tensor} - { - // DO NOTHING - } - -public: - using Reader<T>::at; - T &at(uint32_t batch, uint32_t ch, uint32_t row, uint32_t col) - { - return const_cast<T &>(Reader<T>::getRef(batch, ch, row, col)); - } - T &at(uint32_t ch, uint32_t row, uint32_t col) - { - return const_cast<T &>(Reader<T>::getRef(0, ch, row, col)); - } -}; - -} // namespace nchw -} // namespace feature -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_FEATURE_NCHW_VIEW_H__ diff --git a/runtime/onert/core/src/exec/feature/nhwc/Reader.h b/runtime/onert/core/src/exec/feature/nhwc/Reader.h deleted file mode 100644 index 7730cee72..000000000 --- a/runtime/onert/core/src/exec/feature/nhwc/Reader.h +++ /dev/null @@ -1,120 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_FEATURE_NHWC_READER_H__ -#define __ONERT_EXEC_FEATURE_NHWC_READER_H__ - -#include "../Reader.h" - -#include <cassert> - -#include "backend/ITensor.h" -#include "ir/Shape.h" -#include "util/Utils.h" - -namespace onert -{ -namespace exec -{ -namespace feature -{ -namespace nhwc -{ - -template <typename T> class Reader : public feature::Reader<T> -{ -public: - // Construct for buffer of model inputs - Reader(const ir::FeatureShape &shape, const T *ptr, size_t len) - : _shape{shape}, _ptr{reinterpret_cast<const uint8_t *>(ptr)}, _len{len} - { - UNUSED_RELEASE(len); // Workaround for unused variable in release mode - assert(shape.N * shape.C * shape.H * shape.W * sizeof(T) == len); - - // No padding - _strides.C = sizeof(T); - _strides.W = shape.C * sizeof(T); - _strides.H = shape.C * shape.W * sizeof(T); - _strides.N = shape.C * shape.W * shape.H * sizeof(T); - } - - // Construct for backend tensor - Reader(const backend::ITensor *tensor) - : _ptr{tensor->buffer() + tensor->calcOffset({0, 0, 0, 0})}, _len{tensor->total_size()} - { - assert(tensor->layout() == ir::Layout::NHWC); - - const auto start_offset = tensor->calcOffset({0, 0, 0, 0}); - _strides.C = tensor->dimension(3) == 1 ? 0 : tensor->calcOffset({0, 0, 0, 1}) - start_offset; - _strides.W = tensor->dimension(2) == 1 ? 0 : tensor->calcOffset({0, 0, 1, 0}) - start_offset; - _strides.H = tensor->dimension(1) == 1 ? 0 : tensor->calcOffset({0, 1, 0, 0}) - start_offset; - _strides.N = tensor->dimension(0) == 1 ? 0 : tensor->calcOffset({1, 0, 0, 0}) - start_offset; - - _shape.C = tensor->dimension(3); - _shape.W = tensor->dimension(2); - _shape.H = tensor->dimension(1); - _shape.N = tensor->dimension(0); - } - -public: - T at(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) const final - { - return getRef(batch, row, col, ch); - } - T at(uint32_t row, uint32_t col, uint32_t ch) const final { return getRef(0, row, col, ch); } - -protected: - const T &getRef(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) const - { - const auto offset = feature_index_to_byte_offset(batch, row, col, ch); - - const T *ptr = reinterpret_cast<const T *>(_ptr + offset); - - return *ptr; - } - -private: - size_t feature_index_to_byte_offset(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) const - { - assert(1u * _shape.N > batch); // shape.N > batch - assert(1u * _shape.H > row); // shape.H > row - assert(1u * _shape.W > col); // shape.W > col - assert(1u * _shape.C > ch); // shape.C > ch - - uint32_t res = 0; - res += batch * _strides.N; - res += row * _strides.H; - res += col * _strides.W; - res += ch * _strides.C; - - return res; - } - -private: - // TODO Remove _shape - ir::FeatureShape _shape; - using Strides = ir::FeatureShape; - Strides _strides; - const uint8_t *_ptr; - size_t _len; -}; - -} // namespace nhwc -} // namespace feature -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_FEATURE_NHWC_READER_H__ diff --git a/runtime/onert/core/src/exec/feature/nhwc/View.h b/runtime/onert/core/src/exec/feature/nhwc/View.h deleted file mode 100644 index 72c8c3415..000000000 --- a/runtime/onert/core/src/exec/feature/nhwc/View.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_EXEC_FEATURE_NHWC_VIEW_H__ -#define __ONERT_EXEC_FEATURE_NHWC_VIEW_H__ - -#include "../Reader.h" - -#include <cassert> -#include <cstddef> - -#include "backend/ITensor.h" -#include "ir/Shape.h" -#include "util/Utils.h" - -namespace onert -{ -namespace exec -{ -namespace feature -{ -namespace nhwc -{ - -template <typename T> class View final : public Reader<T> -{ -public: - // Construct for buffer of model inputs - View(const ir::FeatureShape &shape, T *ptr, size_t len) : Reader<T>{shape, ptr, len} - { - // DO NOTHING - } - - // Construct for backend tensor - View(backend::ITensor *tensor) : Reader<T>{tensor} - { - // DO NOTHING - } - -public: - using Reader<T>::at; - T &at(uint32_t batch, uint32_t row, uint32_t col, uint32_t ch) - { - return const_cast<T &>(Reader<T>::getRef(batch, row, col, ch)); - } - T &at(uint32_t row, uint32_t col, uint32_t ch) - { - return const_cast<T &>(Reader<T>::getRef(0, row, col, ch)); - } -}; - -} // namespace nhwc -} // namespace feature -} // namespace exec -} // namespace onert - -#endif // __ONERT_EXEC_FEATURE_NHWC_VIEW_H__ diff --git a/runtime/onert/core/src/interp/Buffer.h b/runtime/onert/core/src/interp/Buffer.h deleted file mode 100644 index 24938f74f..000000000 --- a/runtime/onert/core/src/interp/Buffer.h +++ /dev/null @@ -1,91 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Buffer.h - * @brief This file contains Buffer interface and InternalBuffer, ExternalBuffer class - */ -#ifndef __ONERT_INTERP_BUFFER_H__ -#define __ONERT_INTERP_BUFFER_H__ - -#include <memory> - -#include "ir/Data.h" - -namespace onert -{ -namespace interp -{ - -/** - * @brief Interface for writable data area - */ -class Buffer : public ir::Data -{ -public: - /** - * @brief Return writable pointer for data area - * @return Writable pointer - */ - virtual uint8_t *baseWritable(void) const = 0; -}; - -/** - * @brief Class for internally allocated data area - */ -class InternalBuffer final : public Buffer -{ -public: - InternalBuffer(size_t size) : _base{std::make_unique<uint8_t[]>(size)}, _size{size} - { - // DO NOTHING - } - -public: - size_t size(void) const override { return _size; } - const uint8_t *base(void) const override { return _base.get(); } - uint8_t *baseWritable(void) const override { return _base.get(); } - -private: - std::unique_ptr<uint8_t[]> _base; - size_t _size; -}; - -/** - * @brief Class for data area from outside - */ -class ExternalBuffer final : public Buffer -{ -public: - ExternalBuffer(uint8_t *base, size_t size) : _base{base}, _size{size} - { - // DO NOTHING - } - -public: - size_t size(void) const override { return _size; } - const uint8_t *base(void) const override { return _base; } - uint8_t *baseWritable(void) const override { return _base; } - -private: - uint8_t *_base; - size_t _size; -}; - -} // namespace interp -} // namespace onert - -#endif // __ONERT_INTERP_BUFFER_H__ diff --git a/runtime/onert/core/src/interp/ExecEnv.h b/runtime/onert/core/src/interp/ExecEnv.h deleted file mode 100644 index 7f577ea6e..000000000 --- a/runtime/onert/core/src/interp/ExecEnv.h +++ /dev/null @@ -1,212 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file ExecEnv.h - * @brief This file contains ExecEnv to access interpreter tensor and execution status - */ -#ifndef __ONERT_INTERP_EXEC_ENV_H_ -#define __ONERT_INTERP_EXEC_ENV_H_ - -#include <unordered_set> - -#include "ir/Graph.h" -#include "Tensor.h" - -namespace onert -{ -namespace interp -{ - -/** - * @brief Class to gather interpreter execution environment - * Each interpreter instance own execution environment - */ -class ExecEnv -{ -public: - /** - * @brief Construct a new Exec Env object (deleted) - */ - ExecEnv(void) = delete; - /** - * @brief Construct a new ExecEnv object - * @param[in] graph Graph to execute by interpreter - */ - explicit ExecEnv(const ir::Graph &graph) : _graph(graph) - { - // DO NOTHING - } - -public: - /** - * @brief Return graph to execute - * @return Graph - */ - const ir::Graph &graph(void) const { return _graph; } - /** - * @brief Assign tensor to environment which have allocated or assigned buffer - * @param[in] index Tensor index - * @param[in] tensor Tensor - */ - void assignTensor(const ir::OperandIndex index, std::shared_ptr<ITensor> tensor) - { - assert(tensor->bufferRO() != nullptr); - _tensors.emplace(index, tensor); - } - - /** - * @brief Return tensor pointer in environment - * @param[in] index Tensor index - * can_optional @c True if tensor can be optional input, otherwise @c false - * @return Tensor pointer - */ - const ITensor *tensorAt(const ir::OperandIndex index, bool can_optional = false) const - { - if (_tensors.find(index) == _tensors.end()) - { - // It may optional input, - // otherwise input is not set by runtime user - if (can_optional) - { - return nullptr; - } - - throw std::runtime_error{"ExecEnv: Input is not set"}; - } - - return _tensors.at(index).get(); - } - - /** - * @brief Check environment contains tensor - * @param[in] index Tensor index - * @return @c true if environment contain tensor, otherwise @c false - */ - bool contains(const ir::OperandIndex index) const - { - return (_tensors.find(index) != _tensors.end()); - } - - /** - * @brief Allocate tensor using operand info - * @param[in] index Tensor index - * @param[in] info Operand info - * @note If already allocated, just return - * @TODO More smart allocation policy - */ - void allocateIfNeeded(const ir::OperandIndex index, const ir::OperandInfo &info) - { - // already allocated, or constant - if (contains(index)) - { - return; - } - - // Buffer from external (ex. model output) - auto tensor = std::make_shared<Tensor>(info); - if (isExtBuffer(index)) - { - tensor->setBuffer(_external_buffers.at(index)); - assignTensor(index, tensor); - - return; - } - - tensor->setBuffer(std::make_shared<InternalBuffer>(tensor->total_size())); - assignTensor(index, tensor); - _buffers.insert(index); - } - - /** - * @brief Allocate read-only tensor and share data with other tensor - * @param[in] index Tensor index - * @param[in] info Operand info - * @param[in] index_to_share Tensor index that have data to share - */ - void allocateAndShareIfNeeded(const ir::OperandIndex index, const ir::OperandInfo &info, - const ir::OperandIndex index_to_share) - { - if (!contains(index_to_share)) - { - throw std::runtime_error{"Cannot find tensor to share data"}; - } - - // already allocated - if (contains(index)) - { - return; - } - - if (isExtBuffer(index)) - { - auto tensor = std::make_shared<Tensor>(info); - tensor->setBuffer(_external_buffers.at(index)); - assignTensor(index, tensor); - } - else - { - auto tensor = std::make_shared<ROTensor>(info); - tensor->setData(tensorAt(index_to_share)->shareData()); - assignTensor(index, tensor); - _buffers.insert(index); - } - } - - /** - * @brief Free buffer if allocated by allocateIfNeed - * @param[in] index Tensor index - * @note If allocated by outside, just return - */ - void freeIfAllocated(const ir::OperandIndex index) - { - if (_buffers.find(index) != _buffers.end()) - { - _tensors.at(index)->releaseData(); - } - } - - /** - * @brief Assign ExternalBuffer into external buffer map - * @param[in] index Tensor index - * @param[in] buffer External buffer - */ - void assignExternalBuffer(const ir::OperandIndex index, std::shared_ptr<ExternalBuffer> buffer) - { - _external_buffers.emplace(index, buffer); - } - -private: - bool isExtBuffer(const ir::OperandIndex index) - { - return (_external_buffers.find(index) != _external_buffers.end()); - } - -private: - const ir::Graph &_graph; - // Tensor map to use in interpreter - // It should map tensors that have allocated or assigned buffer pointer - std::unordered_map<ir::OperandIndex, std::shared_ptr<ITensor>> _tensors; - // Tensors allocated by allocateIfNeed (buffer) - std::unordered_set<ir::OperandIndex> _buffers; - // Tensor buffer from external - std::unordered_map<ir::OperandIndex, std::shared_ptr<ExternalBuffer>> _external_buffers; -}; - -} // namespace interp -} // namespace onert - -#endif // __ONERT_INTERP_EXEC_ENV_H_ diff --git a/runtime/onert/core/src/interp/InterpExecutor.cc b/runtime/onert/core/src/interp/InterpExecutor.cc deleted file mode 100644 index cd31a4dca..000000000 --- a/runtime/onert/core/src/interp/InterpExecutor.cc +++ /dev/null @@ -1,126 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "interp/InterpExecutor.h" -#include "interp/ExecEnv.h" -#include "interp/Interpreter.h" - -#include "util/logging.h" - -#include <memory> - -namespace onert -{ -namespace interp -{ - -void InterpExecutor::execute(const exec::IODescription &desc) -{ - /************************************************************************ - * Prepare execution model (submodel) - It may execute divided model - but now consider model inference is done at interpreter - ***********************************************************************/ - ir::OperandIndexMap<std::shared_ptr<ITensor>> tensor_map; - - for (uint32_t n = 0; n < _graph.getInputs().size(); n++) - { - ir::IOIndex index{n}; - const auto input_index = _graph.getInputs().at(index); - - const auto input = desc.inputs.at(n).get(); - if (input == nullptr) - { - // Optional input - continue; - } - - auto input_tensor = std::make_shared<ROTensor>(input->info); - input_tensor->setData(std::make_shared<const ir::ExternalData>( - reinterpret_cast<const uint8_t *>(input->buffer), input->size)); - tensor_map[input_index] = input_tensor; - } - - /************************************************************************ - * Prepare execution environment - Execution environment will be assigned to invoked interpreter instance - ***********************************************************************/ - - std::unique_ptr<ExecEnv> interp_env = std::make_unique<ExecEnv>(_graph); - - // Assign input/output tensor into interpreter execution environment - for (auto index : _graph.getInputs()) - { - if (tensor_map.find(index) != tensor_map.end()) - { - VERBOSE(INTERPRETER) << "Assign input tensor. operand index:" << index.value() << std::endl; - interp_env->assignTensor(index, tensor_map.at(index)); - } - } - - for (uint32_t n = 0; n < _graph.getOutputs().size(); n++) - { - ir::IOIndex index{n}; - const auto output_index = _graph.getOutputs().at(index); - const auto output = desc.outputs.at(n).get(); - if (output == nullptr) - { - // Optional output - continue; - } - - VERBOSE(INTERPRETER) << "Set out buffer to ExecEnv. operand index:" << output_index.value() - << std::endl; - - interp_env->assignExternalBuffer( - output_index, std::make_shared<ExternalBuffer>(reinterpret_cast<uint8_t *>(output->buffer), - output->size)); - } - - // Allocate constant tensor - _graph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) { - if (obj.isConstant()) - { - VERBOSE(INTERPRETER) << "Allocate and assign constant tensor. operand index:" << ind.value() - << std::endl; - - assert(obj.data()); - auto const_tensor = std::make_shared<ROTensor>(obj.info()); - // Assume that interpreter's tensor layout is same with model (NHWC) - const_tensor->setData( - std::make_shared<ir::ExternalData>(obj.data()->base(), obj.info().total_size())); - interp_env->assignTensor(ind, const_tensor); - } - }); - - /***************************************************************************** - * Invoke interpreter - ****************************************************************************/ - - interp::Interpreter interp(std::move(interp_env)); - interp.run(); - - /***************************************************************************** - * Invoked interpreter run is finished - ****************************************************************************/ - - // If interpreter execute submodel - // 1. Get tensor output of submodel into tensor_map to save result - // 2. Generate new ExecEnv for next interpretation -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/InterpExecutor.h b/runtime/onert/core/src/interp/InterpExecutor.h deleted file mode 100644 index 2e3f3ca54..000000000 --- a/runtime/onert/core/src/interp/InterpExecutor.h +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file InterpExecutor.h - * @brief This file contains InterpExecutor class\n - * to manage interpreter execution and environment - */ -#ifndef __ONERT_INTERP_INTERP_EXECUTOR_H__ -#define __ONERT_INTERP_INTERP_EXECUTOR_H__ - -#include "ir/OperandIndexMap.h" -#include "ir/Graph.h" -#include "exec/IExecutor.h" - -namespace onert -{ -namespace interp -{ - -class ITensor; - -/** - * @brief Class to execute model using interpreter - */ -class InterpExecutor final : public exec::IExecutor -{ -public: - explicit InterpExecutor(const ir::Graph &graph) : _graph(graph) - { - // DO NOTHING - } - -public: - /** - * @brief Return graph object - * @return Graph object - */ - const ir::Graph &graph() final { return _graph; } - void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>>) override{ - // Not implemented - }; - /** - * @brief Start execution - * @note It should be called after setting input and output buffer - */ - void execute(const exec::IODescription &desc) final; - -private: - const ir::Graph &_graph; - ir::OperandIndexMap<std::shared_ptr<ITensor>> _tensor_map; -}; - -} // namespace interp -} // namespace onert - -#endif // __ONERT_INTERP_INTERP_EXECUTOR_H__ diff --git a/runtime/onert/core/src/interp/InterpOps.lst b/runtime/onert/core/src/interp/InterpOps.lst deleted file mode 100644 index 0714df38a..000000000 --- a/runtime/onert/core/src/interp/InterpOps.lst +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef INTERP_OP -#error Define INTERP_OP before including this file -#endif - -// Supported operation name in interpreter -// -// Same list with Operations.lst -// Make comment out if operation is not supported in interpreter -INTERP_OP(BinaryArithmetic) -//INTERP_OP(BatchToSpaceND) -//INTERP_OP(Cast) -INTERP_OP(Conv2D) -INTERP_OP(DepthwiseConv2D) -INTERP_OP(Pool2D) -INTERP_OP(Concat) -INTERP_OP(FullyConnected) -//INTERP_OP(Reduce) -INTERP_OP(Reshape) -INTERP_OP(Softmax) -//INTERP_OP(Squeeze) -//INTERP_OP(Slice) -//INTERP_OP(StridedSlice) -INTERP_OP(ElementwiseActivation) -//INTERP_OP(Transpose) -//INTERP_OP(Exp) -//INTERP_OP(Comparison) -//INTERP_OP(LogicalNot) -//INTERP_OP(LSTM) -//INTERP_OP(RSQRT) -//INTERP_OP(ResizeBilinear) -//INTERP_OP(RNN) -//INTERP_OP(Floor) -//INTERP_OP(SpaceToBatchND) -//INTERP_OP(SpaceToDepth) -//INTERP_OP(EmbeddingLookup) -//INTERP_OP(L2Normalization) -//INTERP_OP(HashtableLookup) -INTERP_OP(InstanceNorm) -//INTERP_OP(PReLU) -INTERP_OP(TransposeConv) -//INTERP_OP(SQRT) -//INTERP_OP(SquaredDifference) -//INTERP_OP(TopKV2) -INTERP_OP(Gather) -//INTERP_OP(Neg) -//INTERP_OP(Abs) -//INTERP_OP(ArgMax) -//INTERP_OP(Dequantize) -//INTERP_OP(LocalResponseNormalization) -//INTERP_OP(DepthToSpace) -//INTERP_OP(Pack) -//INTERP_OP(Split) -//INTERP_OP(Unpack) -INTERP_OP(Pad) -//INTERP_OP(Custom) -//INTERP_OP(Permute) -//INTERP_OP(OneHot) diff --git a/runtime/onert/core/src/interp/Interpreter.cc b/runtime/onert/core/src/interp/Interpreter.cc deleted file mode 100644 index b92afbe73..000000000 --- a/runtime/onert/core/src/interp/Interpreter.cc +++ /dev/null @@ -1,184 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Interpreter.h" - -#include <stack> -#include <unordered_set> - -#include "Registration.h" - -#include "ir/OperandIndexMap.h" -#include "util/logging.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace interp -{ - -// TODO more structured execution kernel implementation -// TODO use cker for execution -// TODO divide tensor prepare and execution -// TODO introduce memory manager (buffer allocate and free) -class OperationExecutor -{ -public: - OperationExecutor(ExecEnv *env) : _env{env} - { -#define INTERP_OP(InternalName) _kernels[ir::OpCode::InternalName] = get##InternalName(); -#include "InterpOps.lst" -#undef INTERP_OP - } - - void execute(const ir::OperationIndex &idx) - { - const ir::Operation &node = _env->graph().operations().at(idx); - const auto nodeName = node.name(); - VERBOSE(INTERPRETER) << "Prepare output operands and execute " << nodeName - << " operation (id: " << idx.value() << ")" << std::endl; - - const auto nodeOpCode = node.opcode(); - if (_kernels.find(nodeOpCode) == _kernels.end()) - { - throw std::runtime_error{"Interpreter: Operation " + nodeName + " is not yet implemented"}; - } - - if (_kernels[nodeOpCode]->prepare != nullptr) - { - _kernels[nodeOpCode]->prepare(_env, node); - } - _kernels[nodeOpCode]->invoke(_env, node); - } - -private: - ExecEnv *_env; - std::unordered_map<ir::OpCode, OpKernel *> _kernels; -}; - -void Interpreter::run() -{ - VERBOSE(INTERPRETER) << "Interpreter is invoked " << std::endl; - - // operand_stack: save operands prepared to use - std::stack<ir::OperandIndex> operand_stack; - - // Note: We should push input first, then constant. - // We use use-def for find operators ready to execution, - // but Use-Def cannot handle parameters (maybe constant, but not always) - // Note: If all model inputs are constant, it may not work (depend on tensors' order). - // But that scenario may not exist - for (auto ind : _env->graph().getInputs()) - { - VERBOSE(INTERPRETER) << "Input: Push to operand stack " << ind.value() << std::endl; - - operand_stack.push(ind); - } - - _env->graph().operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) { - if (obj.isConstant()) - { - VERBOSE(INTERPRETER) << "Constant: Push to operand stack " << ind.value() << std::endl; - - operand_stack.push(ind); - } - }); - - // Execution - std::unordered_set<ir::OperandIndex> ready_check; - std::unordered_set<ir::OperationIndex> executed; - OperationExecutor executor{_env.get()}; - while (!operand_stack.empty()) - { - const auto current_operand_index = operand_stack.top(); - operand_stack.pop(); - VERBOSE(INTERPRETER) << "Poped operand " << current_operand_index.value() - << " is checked ready to use" << std::endl; - - assert(ready_check.find(current_operand_index) == ready_check.end()); - ready_check.insert(current_operand_index); - - // Find prepared operations by scan use of current operand - std::stack<ir::OperationIndex> operation_stack; - const auto use_operators = _env->graph().operands().at(current_operand_index).getUses(); - for (const auto &use_operator : use_operators) - { - // Assumption: all parameters are ready to use - bool operator_ready = true; - for (auto input_index : _env->graph().operations().at(use_operator).getInputs()) - { - if (ready_check.find(input_index) == ready_check.end()) - { - operator_ready = false; - break; - } - } - - if (operator_ready) - { - VERBOSE(INTERPRETER) << "Ready to execute operation " << use_operator.value() << std::endl; - operation_stack.push(use_operator); - } - } - - while (!operation_stack.empty()) - { - const auto current_operation_index = operation_stack.top(); - operation_stack.pop(); - VERBOSE(INTERPRETER) << "Poped operation: " << current_operation_index.value() << "(" - << _env->graph().operations().at(current_operation_index).name() << ")" - << std::endl; - - // execution - // 1. Prepare output tensor - // 2. Call operation kernel - executor.execute(current_operation_index); - executed.insert(current_operation_index); - - // 3. Push each output into operand stack - const auto def_operands = _env->graph().operations().at(current_operation_index).getOutputs(); - for (auto def_operand : def_operands) - { - VERBOSE(INTERPRETER) << "Buffer: Push to operand stack " << def_operand.value() - << std::endl; - operand_stack.push(def_operand); - } - - // 4. Free if lifetime of buffer operands used by input is finished - for (auto input_index : _env->graph().operations().at(current_operation_index).getInputs()) - { - const auto use_operators = _env->graph().operands().at(input_index).getUses(); - bool dead_buffer = true; - for (const auto &use_operator : use_operators) - { - if (executed.find(use_operator) == executed.end()) - { - dead_buffer = false; - break; - } - } - - if (dead_buffer) - { - _env->freeIfAllocated(input_index); - } - } - } - } -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/Interpreter.h b/runtime/onert/core/src/interp/Interpreter.h deleted file mode 100644 index d2165f538..000000000 --- a/runtime/onert/core/src/interp/Interpreter.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Interpreter.h - * @brief This file contains Interpreter class for interpretation - */ -#ifndef __ONERT_INTERP_INTERPRETER_H__ -#define __ONERT_INTERP_INTERPRETER_H__ - -#include "ExecEnv.h" - -namespace onert -{ -namespace interp -{ - -/** - * @brief Class for interpretation - */ -class Interpreter -{ - -public: - /** - * @brief Construct a new Interpreter object (deleted) - */ - Interpreter() = delete; - /** - * @brief Construct a new Interpreter object - * @param[in] env Execution environment variable for interpreter object - */ - Interpreter(std::unique_ptr<ExecEnv> env) : _env{std::move(env)} - { - // DO NOTHING - } - -public: - /** - * @brief Run interpreter until there is no operation to execute - */ - void run(); - -private: - std::unique_ptr<ExecEnv> _env; -}; - -} // namespace interp -} // namespace onert - -#endif // __ONERT_INTERP_INTERPRETER_H__ diff --git a/runtime/onert/core/src/interp/Registration.h b/runtime/onert/core/src/interp/Registration.h deleted file mode 100644 index 956b92a53..000000000 --- a/runtime/onert/core/src/interp/Registration.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_INTERP_REGISTRATION_H__ -#define __ONERT_INTERP_REGISTRATION_H__ - -#include "ExecEnv.h" - -#include "ir/Operation.h" - -namespace onert -{ -namespace interp -{ - -struct OpKernel -{ - std::function<void(ExecEnv *, const ir::Operation &)> prepare; - std::function<void(const ExecEnv *, const ir::Operation &)> invoke; -}; - -// Defined in operations/ directory -#define INTERP_OP(InternalName) OpKernel *get##InternalName(); -#include "InterpOps.lst" -#undef INTERP_OP - -} // namespace interp -} // namespace onert - -#endif // __ONERT_INTERP_REGISTRATION_H__ diff --git a/runtime/onert/core/src/interp/Tensor.cc b/runtime/onert/core/src/interp/Tensor.cc deleted file mode 100644 index 07f8b75dc..000000000 --- a/runtime/onert/core/src/interp/Tensor.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Tensor.h" - -#define NO_USE(a) (void)(a) - -namespace onert -{ -namespace interp -{ - -void ITensor::access(const std::function<void(backend::ITensor &tensor)> &fn) { fn(*this); } - -size_t ROTensor::calcOffset(const ir::Coordinates &coords) const -{ - NO_USE(coords); - throw std::runtime_error("offset_element_in_bytes is not supported for cpu::Tensor now."); -} - -size_t Tensor::calcOffset(const ir::Coordinates &coords) const -{ - NO_USE(coords); - throw std::runtime_error("offset_element_in_bytes is not supported for cpu::Tensor now."); -} - -ir::Layout ROTensor::layout() const -{ - // TODO Changes to return frontend layout - return ir::Layout::NHWC; -} - -ir::Layout Tensor::layout() const -{ - // TODO Changes to return frontend layout - return ir::Layout::NHWC; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/Tensor.h b/runtime/onert/core/src/interp/Tensor.h deleted file mode 100644 index 8b72d537d..000000000 --- a/runtime/onert/core/src/interp/Tensor.h +++ /dev/null @@ -1,183 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file Tensor.h - * @brief This file contains ITensor interface, ROTensor class, and Tensor class - */ -#ifndef __ONERT_INTERP_TENSOR_H__ -#define __ONERT_INTERP_TENSOR_H__ - -#include "Buffer.h" - -#include "ir/OperandInfo.h" -#include "backend/ITensor.h" -#include "ir/Layout.h" - -namespace onert -{ -namespace interp -{ - -/** - * @brief Interface to handle Tensor in interpreter - */ -class ITensor : public backend::ITensor -{ -public: - virtual ~ITensor() = default; - -public: - virtual uint8_t *buffer() const = 0; - /** - * @brief Return shared pointer for buffer - * @return Buffer shared pointer - */ - virtual std::shared_ptr<const Buffer> shareBuffer() const = 0; - /** - * @brief Return read-only buffer pointer - * @return Read-only buffer pointer - */ - virtual const uint8_t *bufferRO() const = 0; - /** - * @brief Return shared pointer for data - * @return Data shared pointer - */ - virtual std::shared_ptr<const ir::Data> shareData() const = 0; - /** - * @brief Set internal/external buffer - * @param[in] buffer Buffer pointer - */ - virtual void setBuffer(std::shared_ptr<const Buffer> buffer) = 0; - /** - * @brief Set data reference (including constant, input) - * @param[in] data Data pointer - */ - virtual void setData(std::shared_ptr<const ir::Data> data) = 0; - virtual void releaseData() = 0; - - virtual size_t total_size() const = 0; - virtual size_t dimension(size_t index) const = 0; - virtual size_t num_dimensions() const = 0; - virtual size_t calcOffset(const ir::Coordinates &coords) const = 0; - - virtual bool has_padding() const = 0; - /** - * @brief Return data type of tensor - * @return Data type of tensor - */ - virtual ir::DataType data_type() const = 0; - /** - * @brief Return TensorInfo - * @return TensorInfo - */ - virtual const ir::OperandInfo &tensorInfo() const = 0; - /** - * @brief Return number of elements - * @return Number of elements - */ - virtual uint64_t num_elements() const = 0; - void access(const std::function<void(backend::ITensor &tensor)> &fn) final; -}; - -/** - * @brief Class to handle tensor in interpreter as read-only - */ -class ROTensor final : public ITensor -{ -public: - ROTensor() = delete; - ROTensor(const ir::OperandInfo &info) : _info(info) - { - // DO NOTHING - } - -public: - uint8_t *buffer() const override { throw std::runtime_error{"Read only tensor"}; } - std::shared_ptr<const Buffer> shareBuffer() const override - { - throw std::runtime_error{"Read only tensor"}; - } - const uint8_t *bufferRO() const override { return _data->base(); } - std::shared_ptr<const ir::Data> shareData() const override { return _data; } - void setBuffer(std::shared_ptr<const Buffer> buffer) override { _data = buffer; } - void setData(std::shared_ptr<const ir::Data> data) override { _data = data; } - void releaseData() override { _data = nullptr; } - - size_t total_size() const override { return _info.total_size(); } - size_t dimension(size_t index) const override { return _info.shape().dim(index); } - size_t num_dimensions() const override { return _info.shape().rank(); } - size_t calcOffset(const ir::Coordinates &coords) const override; - ir::Layout layout() const override; - bool is_dynamic() const override { return false; } - bool has_padding() const override { return false; } - ir::DataType data_type() const override { return _info.typeInfo().type(); } - float data_scale() const override { return _info.typeInfo().scale(); } - int32_t data_offset() const override { return _info.typeInfo().offset(); } - const ir::OperandInfo &tensorInfo() const override { return _info; } - uint64_t num_elements() const override { return _info.shape().num_elements(); }; - -private: - const ir::OperandInfo _info; - std::shared_ptr<const ir::Data> _data{nullptr}; -}; - -/** - * @brief Class to handle tensor in interpreter as writable - */ -class Tensor final : public ITensor -{ -public: - Tensor() = delete; - Tensor(const ir::OperandInfo &info) : _info(info) - { - // DO NOTHING - } - -public: - uint8_t *buffer() const override { return _buffer->baseWritable(); } - std::shared_ptr<const Buffer> shareBuffer() const override { return _buffer; }; - const uint8_t *bufferRO() const override { return _buffer->base(); } - std::shared_ptr<const ir::Data> shareData() const override { return _buffer; } - void setBuffer(std::shared_ptr<const Buffer> buffer) override { _buffer = buffer; } - void setData(std::shared_ptr<const ir::Data>) override - { - throw std::runtime_error{"Passed data may read-only"}; - } - void releaseData() override { _buffer = nullptr; } - - size_t total_size() const override { return _info.total_size(); } - size_t dimension(size_t index) const override { return _info.shape().dim(index); } - size_t num_dimensions() const override { return _info.shape().rank(); } - size_t calcOffset(const ir::Coordinates &coords) const override; - ir::Layout layout() const override; - bool is_dynamic() const override { return false; } - bool has_padding() const override { return false; } - ir::DataType data_type() const override { return _info.typeInfo().type(); } - float data_scale() const override { return _info.typeInfo().scale(); } - int32_t data_offset() const override { return _info.typeInfo().offset(); } - const ir::OperandInfo &tensorInfo() const override { return _info; } - uint64_t num_elements() const override { return _info.shape().num_elements(); }; - -private: - const ir::OperandInfo _info; - std::shared_ptr<const Buffer> _buffer{nullptr}; -}; - -} // namespace interp -} // namespace onert - -#endif // __ONERT_INTERP_TENSOR_H__ diff --git a/runtime/onert/core/src/interp/operations/BinaryArithmeticOps.cc b/runtime/onert/core/src/interp/operations/BinaryArithmeticOps.cc deleted file mode 100644 index 86e883524..000000000 --- a/runtime/onert/core/src/interp/operations/BinaryArithmeticOps.cc +++ /dev/null @@ -1,205 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/BinaryArithmeticOps.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/BinaryArithmetic.h" -#include "misc/polymorphic_downcast.h" -#include "cker/Types.h" - -namespace onert -{ -namespace interp -{ -namespace -{ - -enum class OpType -{ - ADD, - SUB, - MUL -}; - -void prepare(ExecEnv *env, const ir::Operation &node) -{ - const auto &arithmetic_node = - nnfw::misc::polymorphic_downcast<const ir::operation::BinaryArithmetic &>(node); - - const auto lhs_index = node.getInputs().at(arithmetic_node.LHS); - const auto rhs_index = node.getInputs().at(arithmetic_node.RHS); - const auto out_index = node.getOutputs().at(0); - - const auto lhs_tensor = env->tensorAt(lhs_index); - const auto rhs_tensor = env->tensorAt(rhs_index); - - // Check shape and type lhs is same with rhs - // TODO Util function to compare TensorInfo - if (lhs_tensor->data_type() != rhs_tensor->data_type()) - { - throw std::runtime_error{"Interp(" + arithmetic_node.name() + "): Different input types"}; - } - - bool try_broadcast = (lhs_tensor->tensorInfo().shape() != rhs_tensor->tensorInfo().shape()); - if (try_broadcast) - { - bool success = true; - auto out_shape = calcBroadcastShape(lhs_tensor->tensorInfo().shape(), - rhs_tensor->tensorInfo().shape(), success); - if (!success) - { - throw std::runtime_error{"Interp(" + arithmetic_node.name() + "): Fail to brodcasting"}; - } - - auto output_info = - ir::OperandInfo::createStaticInfo(out_shape, lhs_tensor->tensorInfo().typeInfo()); - // We can handle already allocated (ex. model output) - env->allocateIfNeeded(out_index, output_info); - } - else - { - // Output's shape and type is same with input - auto output_info = lhs_tensor->tensorInfo(); - // We can handle already allocated (ex. model output) - env->allocateIfNeeded(out_index, output_info); - } - - auto out_tensor = env->tensorAt(out_index); - // Check shape and type lhs is same with output - // TODO Util function to compare TensorInfo - if (lhs_tensor->data_type() != out_tensor->data_type()) - { - throw std::runtime_error{"Interp(" + arithmetic_node.name() + "): Invalid output type"}; - } -} - -inline void setActivationParams(float min, float max, nnfw::cker::BinaryArithmeticOpParam *params) -{ - params->float_activation_min = min; - params->float_activation_max = max; -} - -inline void setActivationParams(int32_t min, int32_t max, - nnfw::cker::BinaryArithmeticOpParam *params) -{ - params->quantized_activation_min = min; - params->quantized_activation_max = max; -} - -template <typename raw_type, OpType op_type> -void invoke(const ITensor *lhs_tensor, const ITensor *rhs_tensor, const ITensor *out_tensor, - const ir::operation::BinaryArithmetic::Param ¶m) -{ - const auto lhs_buffer = lhs_tensor->bufferRO(); - const auto rhs_buffer = rhs_tensor->bufferRO(); - auto out_buffer = out_tensor->buffer(); - - nnfw::cker::BinaryArithmeticOpParam cker_param; - raw_type activation_min, activation_max; - calculateActivationRange(param.activation, &activation_min, &activation_max); - setActivationParams(activation_min, activation_max, &cker_param); - const raw_type *lhs_ptr = reinterpret_cast<const raw_type *>(lhs_buffer); - const raw_type *rhs_ptr = reinterpret_cast<const raw_type *>(rhs_buffer); - raw_type *out_ptr = reinterpret_cast<raw_type *>(out_buffer); - - const auto cker_op_type = - (op_type == OpType::ADD) - ? nnfw::cker::BinaryArithmeticOpType::ADD - : ((op_type == OpType::SUB) ? nnfw::cker::BinaryArithmeticOpType::SUB - : nnfw::cker::BinaryArithmeticOpType::MUL); - - const bool need_broadcast = nnfw::cker::ProcessBroadcastShapes( - convertShape(lhs_tensor->tensorInfo().shape()), - convertShape(rhs_tensor->tensorInfo().shape()), &cker_param); - - if (need_broadcast) - { - const auto lhs_shape = convertShape(lhs_tensor->tensorInfo().shape()); - const auto rhs_shape = convertShape(rhs_tensor->tensorInfo().shape()); - const auto out_shape = convertShape(out_tensor->tensorInfo().shape()); - nnfw::cker::BroadcastBinaryArithmeticOp<cker_op_type>(cker_param, lhs_shape, lhs_ptr, rhs_shape, - rhs_ptr, out_shape, out_ptr); - return; - } - - const auto lhs_shape = convertShape(lhs_tensor->tensorInfo().shape()); - const auto rhs_shape = convertShape(rhs_tensor->tensorInfo().shape()); - const auto out_shape = convertShape(out_tensor->tensorInfo().shape()); - nnfw::cker::BinaryArithmeticOp<cker_op_type>(cker_param, lhs_shape, lhs_ptr, rhs_shape, rhs_ptr, - out_shape, out_ptr); -} - -template <OpType op_type> -void invokeBinaryArithmetic(const ExecEnv *env, const ir::operation::BinaryArithmetic &node) -{ - const auto lhs_index = node.getInputs().at(node.LHS); - const auto rhs_index = node.getInputs().at(node.RHS); - const auto out_index = node.getOutputs().at(0); - const auto lhs_tensor = env->tensorAt(lhs_index); - const auto rhs_tensor = env->tensorAt(rhs_index); - const auto out_tensor = env->tensorAt(out_index); - const auto data_type = lhs_tensor->data_type(); - - if (data_type == ir::DataType::INT32) - { - invoke<int32_t, op_type>(lhs_tensor, rhs_tensor, out_tensor, node.param()); - } - else if (data_type == ir::DataType::FLOAT32) - { - invoke<float, op_type>(lhs_tensor, rhs_tensor, out_tensor, node.param()); - } - else - { - throw std::runtime_error{"NYI: Unsupported data type"}; - } -} - -void invokeBinaryArithmeticOps(const ExecEnv *env, const ir::Operation &node) -{ - const auto &arithmetic_node = - nnfw::misc::polymorphic_downcast<const ir::operation::BinaryArithmetic &>(node); - - switch (arithmetic_node.param().arithmetic_type) - { - case ir::operation::BinaryArithmetic::ArithmeticType::ADD: - invokeBinaryArithmetic<OpType::ADD>(env, arithmetic_node); - break; - case ir::operation::BinaryArithmetic::ArithmeticType::SUB: - invokeBinaryArithmetic<OpType::SUB>(env, arithmetic_node); - break; - case ir::operation::BinaryArithmetic::ArithmeticType::MUL: - invokeBinaryArithmetic<OpType::MUL>(env, arithmetic_node); - break; - default: - throw std::runtime_error{"Interp(BinaryArithmetic): NYI unsupported operation " + - arithmetic_node.name()}; - break; - } -} - -} // namespace - -OpKernel *getBinaryArithmetic() -{ - static OpKernel kernel = {prepare, invokeBinaryArithmeticOps}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/Concat.cc b/runtime/onert/core/src/interp/operations/Concat.cc deleted file mode 100644 index efc46c66b..000000000 --- a/runtime/onert/core/src/interp/operations/Concat.cc +++ /dev/null @@ -1,147 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/Concatenation.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/Concat.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace interp -{ -namespace concat -{ - -void prepareConcat(ExecEnv *env, const ir::Operation &node) -{ - const auto &concat_node = nnfw::misc::polymorphic_downcast<const ir::operation::Concat &>(node); - - const auto first_index = node.getInputs().at(0); - const auto out_index = node.getOutputs().at(0); - - const auto first_tensor = env->tensorAt(first_index); - uint32_t out_axis_dimension = 0; - const int32_t axis_raw = concat_node.param().axis; - const uint32_t axis = (axis_raw < 0) ? (axis_raw + first_tensor->num_dimensions()) : axis_raw; - - // All inputs shape should be same except axis dimension - // All inputs type should be same - for (auto input : node.getInputs()) - { - assert(first_tensor->num_dimensions() == env->tensorAt(input)->num_dimensions()); - assert(first_tensor->data_type() == env->tensorAt(input)->data_type()); - for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++) - { - if (i == axis) - { - out_axis_dimension += env->tensorAt(input)->dimension(i); - continue; - } - assert(first_tensor->dimension(i) == env->tensorAt(input)->dimension(i)); - } - } - - // Make output tensor info using first input tensor info, and accumulated axis dimension value - auto out_shape = first_tensor->tensorInfo().shape(); - out_shape.dim(axis) = out_axis_dimension; - env->allocateIfNeeded(out_index, ir::OperandInfo::createStaticInfo( - out_shape, first_tensor->tensorInfo().typeInfo())); - - auto out_tensor = env->tensorAt(out_index); - UNUSED_RELEASE(out_tensor); - - // Output shape should be same with input except axis dimension - // Output type should be same with input - assert(first_tensor->data_type() == out_tensor->data_type()); - for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++) - { - if (i == axis) - { - continue; - } - assert(first_tensor->dimension(i) == out_tensor->dimension(i)); - } -} - -void invoke(const std::vector<const ITensor *> in_tensors, const ITensor *out_tensor, uint32_t axis) -{ - const uint32_t count = in_tensors.size(); - - // Calculate - nnfw::cker::ConcatenationParams cker_param; - cker_param.axis = (int8_t)axis; - cker_param.inputs_count = count; - - const auto out_shape = convertShape(out_tensor->tensorInfo().shape()); - - std::vector<nnfw::cker::Shape> in_shapes; - std::vector<const nnfw::cker::Shape *> in_shape_ptrs; - in_shapes.reserve(count); - in_shape_ptrs.reserve(count); - std::vector<const float *> in_ptrs; - for (uint32_t i = 0; i < count; i++) - { - in_shapes.push_back(convertShape(in_tensors[i]->tensorInfo().shape())); - in_shape_ptrs.push_back(&in_shapes[i]); - in_ptrs.push_back(reinterpret_cast<const float *>(in_tensors[i]->bufferRO())); - } - - auto out_buffer = out_tensor->buffer(); - float *out_ptr = reinterpret_cast<float *>(out_buffer); - - nnfw::cker::Concatenation<float>(cker_param, in_shape_ptrs.data(), in_ptrs.data(), out_shape, - out_ptr); -} - -void invokeConcat(const ExecEnv *env, const ir::Operation &node) -{ - const auto &concat_node = nnfw::misc::polymorphic_downcast<const ir::operation::Concat &>(node); - const int32_t axis_raw = concat_node.param().axis; - - std::vector<const ITensor *> in_tensors; - for (const auto &e : concat_node.getInputs()) - { - in_tensors.emplace_back(env->tensorAt(e)); - } - - const auto out_index = node.getOutputs().at(0); - const auto out_tensor = env->tensorAt(out_index); - const uint32_t axis = (axis_raw < 0) ? (axis_raw + out_tensor->num_dimensions()) : axis_raw; - - const auto data_type = in_tensors[0]->data_type(); - if (data_type == ir::DataType::FLOAT32) - { - invoke(in_tensors, out_tensor, axis); - } - else - { - throw std::runtime_error{"NYI: Support float32 only"}; - } -} -} // namespace concat - -OpKernel *getConcat() -{ - static OpKernel kernel = {concat::prepareConcat, concat::invokeConcat}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/Conv2D.cc b/runtime/onert/core/src/interp/operations/Conv2D.cc deleted file mode 100644 index bb00b828c..000000000 --- a/runtime/onert/core/src/interp/operations/Conv2D.cc +++ /dev/null @@ -1,151 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/Conv.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/Conv2D.h" -#include "util/Utils.h" -#include "util/ShapeInference.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace interp -{ -namespace conv2d -{ - -void prepareConv2D(ExecEnv *env, const ir::Operation &node) -{ - const auto in_index = node.getInputs().at(ir::operation::Conv2D::INPUT); - const auto kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL); - const auto bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS); - const auto out_index = node.getOutputs().at(0); - - const auto in_tensor = env->tensorAt(in_index); - const auto kernel_tensor = env->tensorAt(kernel_index); - const auto bias_tensor = env->tensorAt(bias_index); - - assert(in_tensor->num_dimensions() == 4); - assert(kernel_tensor->num_dimensions() == 4); - assert(bias_tensor->num_dimensions() == 1); - - UNUSED_RELEASE(in_tensor); - UNUSED_RELEASE(kernel_tensor); - UNUSED_RELEASE(bias_tensor); - - const auto output_info = env->graph().operands().at(out_index).info(); - if (output_info.total_size() == 0) - { - // Handle unspecified output shape - const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node); - const auto infered_output_shape = shape_inference::inferConv2DShape( - in_tensor->tensorInfo().shape(), kernel_tensor->tensorInfo().shape(), conv_node.param()); - env->allocateIfNeeded( - out_index, ir::OperandInfo::createStaticInfo(infered_output_shape, output_info.typeInfo())); - } - else - { - env->allocateIfNeeded(out_index, output_info); - } - - auto out_tensor = env->tensorAt(out_index); - UNUSED_RELEASE(out_tensor); - - // Handle same ifm & ofm data type only - assert(in_tensor->data_type() == out_tensor->data_type()); - assert(out_tensor->num_dimensions() == 4); -} - -void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor, - const ITensor *ofm_tensor, const ir::operation::Conv2D::Param ¶m) -{ - // TODO Support NCHW frontned - const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]. - const auto &ker_shape = ker_tensor->tensorInfo().shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - const auto padding = ir::calculatePadding(param.padding, ifm_shape, ofm_shape, param.stride, - ker_width, ker_height); - - // Calculate - float activation_min, activation_max; - calculateActivationRange(param.activation, &activation_min, &activation_max); - - nnfw::cker::ConvParams cker_param; - cker_param.padding_type = convertPaddingType(param.padding.type); - cker_param.padding_values.width = padding.left; - cker_param.padding_values.height = padding.top; - cker_param.stride_width = param.stride.horizontal; - cker_param.stride_height = param.stride.vertical; - cker_param.dilation_width_factor = 1; - cker_param.dilation_height_factor = 1; - cker_param.float_activation_min = activation_min; - cker_param.float_activation_max = activation_max; - - const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape()); - const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape()); - const auto cker_bias_shape = convertShape(bias_tensor->tensorInfo().shape()); - const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape()); - const float *ifm_ptr = reinterpret_cast<const float *>(ifm_tensor->bufferRO()); - const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO()); - const float *bias_ptr = reinterpret_cast<const float *>(bias_tensor->bufferRO()); - float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer()); - - nnfw::cker::Conv conv_kernel; - conv_kernel(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr, cker_bias_shape, - bias_ptr, cker_ofm_shape, ofm_ptr); -} - -void invokeConv2D(const ExecEnv *env, const ir::Operation &node) -{ - const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node); - - const auto ifm_index = node.getInputs().at(ir::operation::Conv2D::INPUT); - const auto ker_index = node.getInputs().at(ir::operation::Conv2D::KERNEL); - const auto bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS); - const auto ofm_index = node.getOutputs().at(0); - - const auto ifm_tensor = env->tensorAt(ifm_index); - const auto ker_tensor = env->tensorAt(ker_index); - const auto bias_tensor = env->tensorAt(bias_index); - const auto ofm_tensor = env->tensorAt(ofm_index); - - const auto data_type = ifm_tensor->data_type(); - if (data_type == ir::DataType::FLOAT32) - { - invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param()); - } - else - { - throw std::runtime_error{"NYI: Support float32 only"}; - } -} -} // namespace conv2d - -OpKernel *getConv2D() -{ - static OpKernel kernel = {conv2d::prepareConv2D, conv2d::invokeConv2D}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/DepthwiseConv2D.cc b/runtime/onert/core/src/interp/operations/DepthwiseConv2D.cc deleted file mode 100644 index 0473855d9..000000000 --- a/runtime/onert/core/src/interp/operations/DepthwiseConv2D.cc +++ /dev/null @@ -1,156 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/DepthwiseConv.h> -#include <misc/polymorphic_downcast.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/DepthwiseConv2D.h" -#include "util/Utils.h" -#include "util/ShapeInference.h" - -namespace onert -{ -namespace interp -{ - -namespace -{ - -void prepareDepthwiseConv(ExecEnv *env, const ir::Operation &node) -{ - const auto in_index = node.getInputs().at(ir::operation::DepthwiseConv2D::INPUT); - const auto kernel_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL); - const auto bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS); - const auto out_index = node.getOutputs().at(0); - - const auto in_tensor = env->tensorAt(in_index); - const auto kernel_tensor = env->tensorAt(kernel_index); - const auto bias_tensor = env->tensorAt(bias_index); - - assert(in_tensor->num_dimensions() == 4); - assert(kernel_tensor->num_dimensions() == 4); - assert(bias_tensor->num_dimensions() == 1); - - UNUSED_RELEASE(in_tensor); - UNUSED_RELEASE(kernel_tensor); - UNUSED_RELEASE(bias_tensor); - - // TODO handle unspecified output shape: - // calculate output shape using ifm shape, kernel shape, padding, stride - const auto output_info = env->graph().operands().at(out_index).info(); - if (output_info.total_size() == 0) - { - // Handle unspecified output shape - const auto &depth_conv_node = - nnfw::misc::polymorphic_downcast<const ir::operation::DepthwiseConv2D &>(node); - const auto infered_output_shape = shape_inference::inferDepthwiseConv2DShape( - in_tensor->tensorInfo().shape(), kernel_tensor->tensorInfo().shape(), - depth_conv_node.param()); - env->allocateIfNeeded( - out_index, ir::OperandInfo::createStaticInfo(infered_output_shape, output_info.typeInfo())); - } - else - { - env->allocateIfNeeded(out_index, output_info); - } - - auto out_tensor = env->tensorAt(out_index); - UNUSED_RELEASE(out_tensor); - - // Handle same ifm & ofm data type only - assert(in_tensor->data_type() == out_tensor->data_type()); - assert(out_tensor->num_dimensions() == 4); -} - -void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor, - const ITensor *ofm_tensor, const ir::operation::DepthwiseConv2D::Param ¶m) -{ - // TODO Support NCHW frontend - const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - // Kernel format is [1, kernel_height, kernel_width, depth_out]. - const auto &ker_shape = ker_tensor->tensorInfo().shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - const auto padding = ir::calculatePadding(param.padding, ifm_shape, ofm_shape, param.stride, - ker_width, ker_height); - - // Calculate - float activation_min, activation_max; - calculateActivationRange(param.activation, &activation_min, &activation_max); - - nnfw::cker::DepthwiseConvParams cker_param; - cker_param.padding_values.width = padding.left; - cker_param.padding_values.height = padding.top; - cker_param.depth_multiplier = param.multiplier; - cker_param.stride_width = param.stride.horizontal; - cker_param.stride_height = param.stride.vertical; - cker_param.dilation_width_factor = 1; - cker_param.dilation_height_factor = 1; - cker_param.float_activation_min = activation_min; - cker_param.float_activation_max = activation_max; - - const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape()); - const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape()); - const auto cker_bias_shape = convertShape(bias_tensor->tensorInfo().shape()); - const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape()); - const float *ifm_ptr = reinterpret_cast<const float *>(ifm_tensor->bufferRO()); - const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO()); - const float *bias_ptr = reinterpret_cast<const float *>(bias_tensor->bufferRO()); - float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer()); - - nnfw::cker::DepthwiseConv(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr, - cker_bias_shape, bias_ptr, cker_ofm_shape, ofm_ptr); -} - -void invokeDepthwiseConv(const ExecEnv *env, const ir::Operation &node) -{ - const auto &conv_node = static_cast<const ir::operation::DepthwiseConv2D &>(node); - - const auto ifm_index = node.getInputs().at(ir::operation::DepthwiseConv2D::INPUT); - const auto ker_index = node.getInputs().at(ir::operation::DepthwiseConv2D::KERNEL); - const auto bias_index = node.getInputs().at(ir::operation::DepthwiseConv2D::BIAS); - const auto ofm_index = node.getOutputs().at(0); - - const auto ifm_tensor = env->tensorAt(ifm_index); - const auto ker_tensor = env->tensorAt(ker_index); - const auto bias_tensor = env->tensorAt(bias_index); - const auto ofm_tensor = env->tensorAt(ofm_index); - - const auto data_type = ifm_tensor->data_type(); - if (data_type == ir::DataType::FLOAT32) - { - invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param()); - } - else - { - throw std::runtime_error{"NYI: Support float32 only"}; - } -} - -} // namespace - -OpKernel *getDepthwiseConv2D() -{ - static OpKernel kernel = {prepareDepthwiseConv, invokeDepthwiseConv}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/ElementwiseActivations.cc b/runtime/onert/core/src/interp/operations/ElementwiseActivations.cc deleted file mode 100644 index c8773bef4..000000000 --- a/runtime/onert/core/src/interp/operations/ElementwiseActivations.cc +++ /dev/null @@ -1,161 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cmath> - -#include "OperationUtil.h" - -#include "interp/Registration.h" - -#include "ir/operation/ElementwiseActivation.h" - -#include <misc/polymorphic_downcast.h> -#include <cker/operation/Logistic.h> -#include <cker/operation/Tanh.h> - -namespace onert -{ -namespace interp -{ -namespace -{ - -enum class ActivationType -{ - Logistic, - ReLU, - Tanh -}; - -void prepare(ExecEnv *env, const ir::Operation &node) -{ - const auto input_index = node.getInputs().at(0); - const auto output_index = node.getOutputs().at(0); - - const auto input_tensor = env->tensorAt(input_index); - - const auto output_info = env->graph().operands().at(output_index).info(); - if (output_info.total_size() == 0) - { - // Output's shape and type is same with input - auto input_info = input_tensor->tensorInfo(); - // We can handle already allocated (ex. model output) - env->allocateIfNeeded(output_index, input_info); - } - else - { - env->allocateIfNeeded(output_index, output_info); - } - - const auto output_tensor = env->tensorAt(output_index); - // Check shape and type lhs is same with output - // TODO Util function to compare TensorInfo - if (input_tensor->data_type() != output_tensor->data_type()) - { - throw std::runtime_error{"Interp(ElementwiseActivation): Invalid output type"}; - } -} - -template <ActivationType act_type> -void evalFloat(const float *input_ptr, float *output_ptr, uint64_t num_elements, float alpha, - float beta) -{ - std::function<float(const float &)> fn = [](const float &) { return std::nanf(""); }; - switch (act_type) - { - case ActivationType::ReLU: - fn = [alpha, beta](const float &in) { return std::min(std::max(beta, in), alpha); }; - break; - case ActivationType::Tanh: - fn = [](const float &in) { return std::tanh(in); }; - break; - default: - throw std::runtime_error{"Interp(ElementwiseActivation): NYI - Unsupported activation"}; - break; - } - - const float *input_end = input_ptr + num_elements; - for (; input_ptr < input_end; input_ptr++, output_ptr++) - { - *output_ptr = fn(*input_ptr); - } -} - -template <ActivationType act_type> void invoke(const ExecEnv *env, const ir::Operation &node) -{ - const auto input_index = node.getInputs().at(0); - const auto output_index = node.getOutputs().at(0); - - // Check lhs shape is same with rhs (with broadcast) - const auto input_tensor = env->tensorAt(input_index); - const auto output_tensor = env->tensorAt(output_index); - - const auto data_type = input_tensor->data_type(); - if (data_type == ir::DataType::FLOAT32) - { - uint64_t elements = input_tensor->num_elements(); - const float *input_start = reinterpret_cast<const float *>(input_tensor->bufferRO()); - float *out = reinterpret_cast<float *>(output_tensor->buffer()); - if (act_type == ActivationType::Logistic) - { - const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape()); - const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape()); - nnfw::cker::Logistic(cker_input_shape, input_start, cker_output_shape, out); - } - else - { - const auto &act_node = - nnfw::misc::polymorphic_downcast<const ir::operation::ElementwiseActivation &>(node); - evalFloat<act_type>(input_start, out, elements, act_node.param().alpha, - act_node.param().beta); - } - } - else - { - throw std::runtime_error{"Interp(" + node.name() + "): NYI - Support float only"}; - } -} - -void invokeElementwiseActivation(const ExecEnv *env, const ir::Operation &node) -{ - const auto &act_node = - nnfw::misc::polymorphic_downcast<const ir::operation::ElementwiseActivation &>(node); - switch (act_node.param().op_type) - { - case ir::operation::ElementwiseActivation::Type::LOGISTIC: - invoke<ActivationType::Logistic>(env, node); - break; - case ir::operation::ElementwiseActivation::Type::RELU: - invoke<ActivationType::ReLU>(env, node); - break; - case ir::operation::ElementwiseActivation::Type::TANH: - invoke<ActivationType::Tanh>(env, node); - break; - default: - throw std::runtime_error("Interp(" + node.name() + "): NYI - Unsupported activation"); - } -} - -} // namespace - -OpKernel *getElementwiseActivation() -{ - static OpKernel kernel = {prepare, invokeElementwiseActivation}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/FullyConnected.cc b/runtime/onert/core/src/interp/operations/FullyConnected.cc deleted file mode 100644 index 12f529dab..000000000 --- a/runtime/onert/core/src/interp/operations/FullyConnected.cc +++ /dev/null @@ -1,136 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/FullyConnected.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/FullyConnected.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace interp -{ -namespace fc -{ - -void prepareFC(ExecEnv *env, const ir::Operation &node) -{ - const auto in_index = node.getInputs().at(ir::operation::FullyConnected::INPUT); - const auto kernel_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT); - const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS); - const auto out_index = node.getOutputs().at(0); - - const auto in_tensor = env->tensorAt(in_index); - const auto kernel_tensor = env->tensorAt(kernel_index); - const auto bias_tensor = env->tensorAt(bias_index); - - UNUSED_RELEASE(in_tensor); - UNUSED_RELEASE(kernel_tensor); - UNUSED_RELEASE(bias_tensor); - - assert(in_tensor->num_dimensions() >= 2); - assert(kernel_tensor->num_dimensions() == 2); - assert(bias_tensor->num_dimensions() == 1); - - const auto input_size_with_batch = in_tensor->num_elements(); - const auto num_units = kernel_tensor->dimension(0); - const auto input_size = kernel_tensor->dimension(1); - const auto batch_size = input_size_with_batch / input_size; - assert(input_size_with_batch % input_size == 0); - assert(num_units == bias_tensor->dimension(0)); - - // Make output tensor info - ir::Shape output_shape(2); - output_shape.dim(0) = batch_size; - output_shape.dim(1) = num_units; - const auto out_info = - ir::OperandInfo::createStaticInfo(output_shape, in_tensor->tensorInfo().typeInfo()); - env->allocateIfNeeded(out_index, out_info); - - auto out_tensor = env->tensorAt(out_index); - UNUSED_RELEASE(out_tensor); - - // Handle same ifm & ofm data type only - assert(in_tensor->data_type() == out_tensor->data_type()); - assert(out_tensor->num_dimensions() == 2); - assert(out_tensor->dimension(0) == batch_size); - assert(out_tensor->dimension(1) == num_units); -} - -void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor, - const ITensor *ofm_tensor, const ir::operation::FullyConnected::Param ¶m) -{ - const auto ifm_buffer = ifm_tensor->bufferRO(); - const auto ker_buffer = ker_tensor->bufferRO(); - const auto bias_buffer = bias_tensor->bufferRO(); - auto ofm_buffer = ofm_tensor->buffer(); - - // Calculate - nnfw::cker::FullyConnectedParams cker_param; - cker_param.activation = convertActivationType(param.activation); - calculateActivationRange(param.activation, &cker_param.float_activation_min, - &cker_param.float_activation_max); - const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape()); - const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape()); - const auto cker_bias_shape = convertShape(bias_tensor->tensorInfo().shape()); - const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape()); - const float *ifm_ptr = reinterpret_cast<const float *>(ifm_buffer); - const float *ker_ptr = reinterpret_cast<const float *>(ker_buffer); - const float *bias_ptr = reinterpret_cast<const float *>(bias_buffer); - float *ofm_ptr = reinterpret_cast<float *>(ofm_buffer); - - nnfw::cker::FullyConnected(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr, - cker_bias_shape, bias_ptr, cker_ofm_shape, ofm_ptr); -} - -void invokeFC(const ExecEnv *env, const ir::Operation &node) -{ - const auto &conv_node = - nnfw::misc::polymorphic_downcast<const ir::operation::FullyConnected &>(node); - - const auto ifm_index = node.getInputs().at(ir::operation::FullyConnected::INPUT); - const auto ker_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT); - const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS); - const auto ofm_index = node.getOutputs().at(0); - - const auto ifm_tensor = env->tensorAt(ifm_index); - const auto ker_tensor = env->tensorAt(ker_index); - const auto bias_tensor = env->tensorAt(bias_index); - const auto ofm_tensor = env->tensorAt(ofm_index); - - const auto data_type = ifm_tensor->data_type(); - if (data_type == ir::DataType::FLOAT32) - { - invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param()); - } - else - { - throw std::runtime_error{"NYI: Support float only"}; - } -} -} // namespace fc - -OpKernel *getFullyConnected() -{ - static OpKernel kernel = {fc::prepareFC, fc::invokeFC}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/Gather.cc b/runtime/onert/core/src/interp/operations/Gather.cc deleted file mode 100644 index 9e82def5f..000000000 --- a/runtime/onert/core/src/interp/operations/Gather.cc +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/Gather.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/Gather.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace interp -{ -namespace -{ - -void prepareGather(ExecEnv *env, const ir::Operation &node) -{ - const auto input_index = node.getInputs().at(ir::operation::Gather::INPUT); - const auto indices_index = node.getInputs().at(ir::operation::Gather::INDICES); - const auto output_index = node.getOutputs().at(0); - - const auto input_tensor = env->tensorAt(input_index); - const auto indices_tensor = env->tensorAt(indices_index); - - // TODO handle unspecified output shape: - // calculate output shape using ifm shape, kernel shape, padding, stride - const auto output_info = env->graph().operands().at(output_index).info(); - if (output_info.total_size() == 0) - { - throw std::runtime_error{"Interp(Gather): NYI for unspecified output shape"}; - } - else - { - env->allocateIfNeeded(output_index, output_info); - } - - if (indices_tensor->data_type() != ir::DataType::INT32) - { - throw std::runtime_error{"Interp(Gather): Invalid indices data type"}; - } - - auto output_tensor = env->tensorAt(output_index); - auto output_rank = input_tensor->num_dimensions() + indices_tensor->num_dimensions() - 1; - - if (output_rank != output_tensor->num_dimensions()) - { - throw std::runtime_error{"Interp(Gather): Invalid output rank"}; - } - if (output_tensor->data_type() != input_tensor->data_type()) - { - throw std::runtime_error{"Interp(Gather): Invalid output data type"}; - } - - if (input_tensor->data_type() == ir::DataType::QUANT_UINT8_ASYMM && - input_tensor->tensorInfo().typeInfo() != output_tensor->tensorInfo().typeInfo()) - { - throw std::runtime_error{ - "Interp(Gather): Cannot handle different I/O QUANT_UINT8_ASYMM scale/offset"}; - } -} - -template <typename raw_type> -void invoke(const ITensor *input_tensors, const ITensor *indices_tensors, - const ITensor *output_tensor, uint32_t axis) -{ - // Calculate - nnfw::cker::GatherParams cker_param; - cker_param.axis = (int8_t)axis; - - const auto cker_input_shapes = convertShape(input_tensors->tensorInfo().shape()); - const auto cker_indices_shape = convertShape(indices_tensors->tensorInfo().shape()); - const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape()); - const raw_type *input_ptr = reinterpret_cast<const raw_type *>(input_tensors->bufferRO()); - const int32_t *indices_ptr = reinterpret_cast<const int32_t *>(indices_tensors->bufferRO()); - raw_type *output_ptr = reinterpret_cast<raw_type *>(output_tensor->buffer()); - - nnfw::cker::Gather<raw_type>(cker_param, cker_input_shapes, input_ptr, cker_indices_shape, - indices_ptr, cker_output_shape, output_ptr); -} - -void invokeGather(const ExecEnv *env, const ir::Operation &node) -{ - const auto &gather_node = nnfw::misc::polymorphic_downcast<const ir::operation::Gather &>(node); - const int32_t axis_raw = gather_node.param().axis; - - const auto input_index = node.getInputs().at(ir::operation::Gather::INPUT); - const auto indices_index = node.getInputs().at(ir::operation::Gather::INDICES); - const auto output_index = node.getOutputs().at(0); - - const auto input_tensor = env->tensorAt(input_index); - const auto indices_tensor = env->tensorAt(indices_index); - const auto output_tensor = env->tensorAt(output_index); - const uint32_t axis = (axis_raw < 0) ? (axis_raw + input_tensor->num_dimensions()) : axis_raw; - - const auto data_type = input_tensor->data_type(); - - switch (data_type) - { - case ir::DataType::FLOAT32: - invoke<float>(input_tensor, indices_tensor, output_tensor, axis); - break; - case ir::DataType::INT32: - invoke<int32_t>(input_tensor, indices_tensor, output_tensor, axis); - break; - case ir::DataType::QUANT_UINT8_ASYMM: - invoke<uint8_t>(input_tensor, indices_tensor, output_tensor, axis); - break; - default: - throw std::runtime_error{"Interp(Gather): NYI - Not supported type"}; - } -} - -} // namespace - -OpKernel *getGather() -{ - static OpKernel kernel = {prepareGather, invokeGather}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/InstanceNorm.cc b/runtime/onert/core/src/interp/operations/InstanceNorm.cc deleted file mode 100644 index 2538bcc39..000000000 --- a/runtime/onert/core/src/interp/operations/InstanceNorm.cc +++ /dev/null @@ -1,121 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/InstanceNorm.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/InstanceNorm.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace interp -{ -namespace instancenorm -{ - -void prepareInstanceNorm(ExecEnv *env, const ir::Operation &node) -{ - const auto &instancenorm_node = - nnfw::misc::polymorphic_downcast<const ir::operation::InstanceNorm &>(node); - - const auto input_index = node.getInputs().at(instancenorm_node.INPUT); - const auto output_index = node.getOutputs().at(0); - const auto input_tensor = env->tensorAt(input_index); - - if (input_tensor->num_dimensions() != 4) - { - throw std::runtime_error{"Interp(InstanceNorm): Input should be 4D-tensor"}; - } - - // Output shape should be same with input - env->allocateIfNeeded(output_index, input_tensor->tensorInfo()); - - auto output_tensor = env->tensorAt(output_index); - UNUSED_RELEASE(output_tensor); - - // Handle same ifm & ofm data type only - assert(input_tensor->data_type() == output_tensor->data_type()); - assert(input_tensor->tensorInfo().shape() == output_tensor->tensorInfo().shape()); -} - -inline void setActivationParams(float min, float max, nnfw::cker::InstanceNormParams *params) -{ - params->float_activation_min = min; - params->float_activation_max = max; -} - -void invoke(const ITensor *input_tensor, const ITensor *gamma_tensor, const ITensor *beta_tensor, - const ITensor *output_tensor, const ir::operation::InstanceNorm::Param ¶m) -{ - // Calculate - float activation_min, activation_max; - calculateActivationRange(param.activation, &activation_min, &activation_max); - - nnfw::cker::InstanceNormParams cker_param; - cker_param.epsilon = param.epsilon; - cker_param.float_activation_min = activation_min; - cker_param.float_activation_max = activation_max; - - const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape()); - const auto cker_gamma_shape = convertShape(gamma_tensor->tensorInfo().shape()); - const auto cker_beta_shape = convertShape(beta_tensor->tensorInfo().shape()); - const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape()); - const float *input_ptr = reinterpret_cast<const float *>(input_tensor->bufferRO()); - const float *gamma_ptr = reinterpret_cast<const float *>(gamma_tensor->bufferRO()); - const float *beta_ptr = reinterpret_cast<const float *>(beta_tensor->bufferRO()); - float *output_ptr = reinterpret_cast<float *>(output_tensor->buffer()); - - nnfw::cker::InstanceNorm(cker_param, cker_input_shape, input_ptr, cker_gamma_shape, gamma_ptr, - cker_beta_shape, beta_ptr, cker_output_shape, output_ptr); -} - -void invokeInstanceNorm(const ExecEnv *env, const ir::Operation &node) -{ - const auto &instancenorm_node = - nnfw::misc::polymorphic_downcast<const ir::operation::InstanceNorm &>(node); - - const auto input_index = node.getInputs().at(instancenorm_node.INPUT); - const auto gamma_index = node.getInputs().at(instancenorm_node.GAMMA); - const auto beta_index = node.getInputs().at(instancenorm_node.BETA); - const auto out_index = node.getOutputs().at(0); - const auto input_tensor = env->tensorAt(input_index); - const auto gamma_tensor = env->tensorAt(gamma_index); - const auto beta_tensor = env->tensorAt(beta_index); - const auto out_tensor = env->tensorAt(out_index); - const auto data_type = input_tensor->data_type(); - - if (data_type == ir::DataType::FLOAT32) - { - invoke(input_tensor, gamma_tensor, beta_tensor, out_tensor, instancenorm_node.param()); - } - else - { - throw std::runtime_error{"NYI: Unsupported data type"}; - } -} -} // namespace instancenorm - -OpKernel *getInstanceNorm() -{ - static OpKernel kernel = {instancenorm::prepareInstanceNorm, instancenorm::invokeInstanceNorm}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/OperationUtil.h b/runtime/onert/core/src/interp/operations/OperationUtil.h deleted file mode 100644 index 2fdf098f0..000000000 --- a/runtime/onert/core/src/interp/operations/OperationUtil.h +++ /dev/null @@ -1,203 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_INTERP_OPERATIONS_OPERATION_UTILS_H_ -#define __ONERT_INTERP_OPERATIONS_OPERATION_UTILS_H_ - -#include "ir/Shape.h" -#include "ir/InternalType.h" -#include "ir/Padding.h" - -#include <cker/Shape.h> -#include <cker/Types.h> - -namespace onert -{ -namespace interp -{ - -inline nnfw::cker::Shape convertShape(const ir::Shape &shape) -{ - auto dimensions = std::vector<uint32_t>(shape.dims().begin(), shape.dims().end()); - - std::vector<int32_t> raw_shape; - raw_shape.resize(dimensions.size()); - - for (uint32_t i = 0; i < dimensions.size(); ++i) - { - raw_shape[i] = dimensions[i]; - } - - return nnfw::cker::GetShape(raw_shape); -} - -inline nnfw::cker::Shape convertExtendShape(const ir::Shape &shape) -{ - auto dimensions = std::vector<uint32_t>(shape.dims().begin(), shape.dims().end()); - - const int32_t extended_rank = 4; - int32_t raw_shape[extended_rank]; - uint32_t start = extended_rank - dimensions.size(); - - for (uint32_t i = 0; i < extended_rank; ++i) - { - if (i < start) - { - raw_shape[i] = 1; - } - else - { - raw_shape[i] = dimensions[i - start]; - } - } - - return nnfw::cker::Shape(extended_rank, raw_shape); -} - -inline nnfw::cker::FusedActivationFunctionType -convertActivationType(const ir::Activation activation) -{ - switch (activation) - { - case ir::Activation::NONE: - return nnfw::cker::FusedActivationFunctionType::kNone; - case ir::Activation::RELU: - return nnfw::cker::FusedActivationFunctionType::kRelu; - case ir::Activation::RELU1: - return nnfw::cker::FusedActivationFunctionType::kRelu1; - case ir::Activation::RELU6: - return nnfw::cker::FusedActivationFunctionType::kRelu6; - default: - throw std::runtime_error{"CPU backend: Cannot convert activation type"}; - } -} - -template <typename T> -void calculateActivationRange(ir::Activation activation, T *activation_min, T *activation_max) -{ - if (activation == ir::Activation::RELU) - { - *activation_min = 0; - *activation_max = std::numeric_limits<T>::max(); - } - else if (activation == ir::Activation::RELU6) - { - *activation_min = 0; - *activation_max = 6; - } - else if (activation == ir::Activation::RELU1) - { - *activation_min = -1; - *activation_max = 1; - } - else if (activation == ir::Activation::NONE) - { - *activation_min = std::numeric_limits<T>::lowest(); - *activation_max = std::numeric_limits<T>::max(); - } - else - { - throw std::runtime_error{"Unsupported activation type"}; - } -} - -inline ir::Shape calcBroadcastShape(const ir::Shape &lhs, const ir::Shape &rhs, bool &success) -{ - int lhs_rank = lhs.rank(); - int rhs_rank = rhs.rank(); - - int out_rank = (lhs_rank > rhs_rank ? lhs_rank : rhs_rank); - ir::Shape out_shape(out_rank); - - int lhs_idim = lhs_rank - 1; - int rhs_idim = rhs_rank - 1; - success = true; - for (int out_idim = out_rank - 1; out_idim >= 0; out_idim--) - { - if (lhs_idim == -1 && rhs_idim == -1) - { - // invalid result - success = false; - break; - } - - if (lhs_idim == -1) - { - out_shape.dim(out_idim) = rhs.dim(rhs_idim); - rhs_idim--; - } - else if (rhs_idim == -1) - { - out_shape.dim(out_idim) = lhs.dim(lhs_idim); - lhs_idim--; - } - else - { - if (lhs.dim(lhs_idim) == rhs.dim(rhs_idim)) - { - out_shape.dim(out_idim) = lhs.dim(lhs_idim); - lhs_idim--; - rhs_idim--; - } - else if (lhs.dim(lhs_idim) == 1) - { - out_shape.dim(out_idim) = rhs.dim(rhs_idim); - lhs_idim--; - rhs_idim--; - } - else if (rhs.dim(rhs_idim) == 1) - { - out_shape.dim(out_idim) = lhs.dim(lhs_idim); - lhs_idim--; - rhs_idim--; - } - else - { - // invalid result - success = false; - break; - } - } - } - - if (lhs_idim != -1 || rhs_idim != -1) - { - // invalid result - success = false; - } - return out_shape; -} - -inline nnfw::cker::PaddingType convertPaddingType(ir::PaddingType ir_padding_type) -{ - switch (ir_padding_type) - { - case ir::PaddingType::EXPLICIT: - return nnfw::cker::PaddingType::kNone; - case ir::PaddingType::SAME: - return nnfw::cker::PaddingType::kSame; - case ir::PaddingType::VALID: - return nnfw::cker::PaddingType::kValid; - default: - throw std::runtime_error("Wrong padding type."); - break; - } -} - -} // namespace interp -} // namespace onert - -#endif // __ONERT_INTERP_OPERATIONS_OPERATION_UTILS_H_ diff --git a/runtime/onert/core/src/interp/operations/Pad.cc b/runtime/onert/core/src/interp/operations/Pad.cc deleted file mode 100644 index c8dce698d..000000000 --- a/runtime/onert/core/src/interp/operations/Pad.cc +++ /dev/null @@ -1,106 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/Pad.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/Pad.h" - -namespace onert -{ -namespace interp -{ -namespace -{ - -void preparePad(ExecEnv *env, const ir::Operation &node) -{ - const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT); - const auto output_index = node.getOutputs().at(0); - - const auto input_tensor = env->tensorAt(input_index); - - const auto output_info = env->graph().operands().at(output_index).info(); - - // Check shape and type lhs is same with rhs - // TODO Util function to compare TensorInfo - if (output_info.total_size() == 0) - { - throw std::runtime_error{"Interp(Pad): NYI unspecified output shape"}; - } - else - { - env->allocateIfNeeded(output_index, output_info); - } - - const auto output_tensor = env->tensorAt(output_index); - if (input_tensor->data_type() != output_tensor->data_type()) - { - throw std::runtime_error{"Interp(Pad): Invalid output type"}; - } -} - -void invoke(const ITensor *input_tensor, const ITensor *pad_tensor, const ITensor *output_tensor) -{ - const auto input_buffer = input_tensor->bufferRO(); - const auto pad_buffer = pad_tensor->bufferRO(); - auto output_buffer = output_tensor->buffer(); - - int32_t pad_rank = pad_tensor->dimension(0); - - const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape()); - const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape()); - const float *input_ptr = reinterpret_cast<const float *>(input_buffer); - const int32_t *pad_ptr = reinterpret_cast<const int32_t *>(pad_buffer); - float *output_ptr = reinterpret_cast<float *>(output_buffer); - - nnfw::cker::Pad<float>(pad_ptr, pad_rank, cker_input_shape, input_ptr, cker_output_shape, - output_ptr, nullptr); -} - -void invokePad(const ExecEnv *env, const ir::Operation &node) -{ - const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT); - const auto pad_index = node.getInputs().at(ir::operation::Pad::PAD); - const auto output_index = node.getOutputs().at(0); - - const auto input_tensor = env->tensorAt(input_index); - const auto pad_tensor = env->tensorAt(pad_index); - const auto output_tensor = env->tensorAt(output_index); - - const auto data_type = input_tensor->data_type(); - - if (data_type == ir::DataType::FLOAT32) - { - invoke(input_tensor, pad_tensor, output_tensor); - } - else - { - throw std::runtime_error{"Interp(Pad): NYI - Unsupported data type"}; - } -} -} // namespace - -OpKernel *getPad() -{ - static OpKernel kernel = {preparePad, invokePad}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/Pool2D.cc b/runtime/onert/core/src/interp/operations/Pool2D.cc deleted file mode 100644 index 92f9d70b2..000000000 --- a/runtime/onert/core/src/interp/operations/Pool2D.cc +++ /dev/null @@ -1,140 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/AveragePool.h> -#include <cker/operation/MaxPool.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/Pool2D.h" -#include "util/Utils.h" -#include "util/ShapeInference.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace interp -{ -namespace pool2d -{ - -void preparePool2D(ExecEnv *env, const ir::Operation &node) -{ - const auto &pool_node = nnfw::misc::polymorphic_downcast<const ir::operation::Pool2D &>(node); - const auto in_index = node.getInputs().at(pool_node.INPUT); - const auto out_index = node.getOutputs().at(0); - - const auto in_tensor = env->tensorAt(in_index); - UNUSED_RELEASE(in_tensor); - - assert(in_tensor->num_dimensions() == 4); - - const auto output_info = env->graph().operands().at(out_index).info(); - if (output_info.total_size() == 0) - { - // Handle unspecified output shape - const auto infered_output_shape = - shape_inference::inferPoolShape(in_tensor->tensorInfo().shape(), pool_node.param()); - env->allocateIfNeeded( - out_index, ir::OperandInfo::createStaticInfo(infered_output_shape, output_info.typeInfo())); - } - else - { - env->allocateIfNeeded(out_index, output_info); - } - - auto out_tensor = env->tensorAt(out_index); - UNUSED_RELEASE(out_tensor); - - // Handle same ifm & ofm data type only - assert(in_tensor->data_type() == out_tensor->data_type()); - assert(out_tensor->num_dimensions() == 4); -} - -template <typename T> -void invoke(const nnfw::cker::PoolParams ¶ms, const nnfw::cker::Shape &in_shape, - const T *in_ptr, const nnfw::cker::Shape &out_shape, T *out_ptr, - ir::operation::Pool2D::PoolType op_type) -{ - switch (op_type) - { - case ir::operation::Pool2D::PoolType::AVG: - nnfw::cker::AveragePool<T>(params, in_shape, in_ptr, out_shape, out_ptr); - break; - case ir::operation::Pool2D::PoolType::MAX: - nnfw::cker::MaxPool<T>(params, in_shape, in_ptr, out_shape, out_ptr); - break; - default: - throw std::runtime_error{"Interp(Pool2D): NYI unsupported operation"}; - break; - } -} - -void invokePool2DOps(const ExecEnv *env, const ir::Operation &node) -{ - const auto &pool_node = nnfw::misc::polymorphic_downcast<const ir::operation::Pool2D &>(node); - - const auto in_index = node.getInputs().at(0); - const auto out_index = node.getOutputs().at(0); - - // Check lhs shape is same with rhs (with broadcast) - const auto in_tensor = env->tensorAt(in_index); - const auto out_tensor = env->tensorAt(out_index); - - // TODO support NCHW frontend - const auto ifm_shape = in_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - const auto ofm_shape = out_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - const auto param = pool_node.param(); - const auto padding = - ir::calculatePadding(param.padding, ifm_shape, ofm_shape, param.stride, param.kw, param.kh); - // Calculate - nnfw::cker::PoolParams cker_param; - cker_param.filter_width = param.kw; - cker_param.filter_height = param.kh; - cker_param.padding_values.width = padding.left; - cker_param.padding_values.height = padding.top; - cker_param.stride_width = param.stride.horizontal; - cker_param.stride_height = param.stride.vertical; - - const auto data_type = in_tensor->data_type(); - if (data_type == ir::DataType::FLOAT32) - { - calculateActivationRange(param.activation, &cker_param.float_activation_min, - &cker_param.float_activation_max); - - const auto in_shape = convertShape(in_tensor->tensorInfo().shape()); - const auto out_shape = convertShape(out_tensor->tensorInfo().shape()); - const float *in_ptr = reinterpret_cast<const float *>(in_tensor->bufferRO()); - float *out_ptr = reinterpret_cast<float *>(out_tensor->buffer()); - // Now, invoke() supports only Pool2D in float - invoke<float>(cker_param, in_shape, in_ptr, out_shape, out_ptr, param.op_type); - } - else - { - throw std::runtime_error{"NYI: Support float only"}; - } -} -} // namespace pool2d - -OpKernel *getPool2D() -{ - static OpKernel kernel = {pool2d::preparePool2D, pool2d::invokePool2DOps}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/Reshape.cc b/runtime/onert/core/src/interp/operations/Reshape.cc deleted file mode 100644 index 3a118456b..000000000 --- a/runtime/onert/core/src/interp/operations/Reshape.cc +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "interp/Registration.h" - -namespace onert -{ -namespace interp -{ -namespace -{ - -void prepare(ExecEnv *env, const ir::Operation &node) -{ - const auto in_index = node.getInputs().at(0); - const auto out_index = node.getOutputs().at(0); - - // Unspecified shape is not supported in operation node spec now - const auto output_info = env->graph().operands().at(out_index).info(); - env->allocateAndShareIfNeeded(out_index, output_info, in_index); - - assert(output_info.total_size() == env->graph().operands().at(in_index).info().total_size()); -} - -void invoke(const ExecEnv *env, const ir::Operation &node) -{ - const auto in_index = node.getInputs().at(0); - const auto out_index = node.getOutputs().at(0); - - if (env->tensorAt(in_index)->bufferRO() == env->tensorAt(out_index)->bufferRO()) - { - // Same data - return; - } - - const auto output_info = env->graph().operands().at(out_index).info(); - memcpy(env->tensorAt(out_index)->buffer(), env->tensorAt(in_index)->bufferRO(), - output_info.total_size()); -} - -} // namespace - -OpKernel *getReshape() -{ - static OpKernel kernel = {prepare, invoke}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/Softmax.cc b/runtime/onert/core/src/interp/operations/Softmax.cc deleted file mode 100644 index d30f78deb..000000000 --- a/runtime/onert/core/src/interp/operations/Softmax.cc +++ /dev/null @@ -1,123 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/SoftMax.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/Softmax.h" -#include "misc/polymorphic_downcast.h" - -namespace onert -{ -namespace interp -{ -namespace -{ - -void prepareSoftMax(ExecEnv *env, const ir::Operation &node) -{ - const auto in_index = node.getInputs().at(0); - const auto out_index = node.getOutputs().at(0); - - const auto in_tensor = env->tensorAt(in_index); - UNUSED_RELEASE(in_tensor); - - assert((in_tensor->num_dimensions() == 4) || (in_tensor->num_dimensions() == 2)); - - // Output shape should be same with input - // Output type is pre-defined in model - const auto output_shape = env->graph().operands().at(in_index).info().shape(); - const auto output_type = env->graph().operands().at(out_index).info().typeInfo(); - - const auto output_info = ir::OperandInfo::createStaticInfo(output_shape, output_type); - env->allocateIfNeeded(out_index, output_info); - - auto out_tensor = env->tensorAt(out_index); - UNUSED_RELEASE(out_tensor); - - // Check output shape is same with input - assert(out_tensor->num_dimensions() == out_tensor->num_dimensions()); - for (uint32_t i = 0; i < in_tensor->num_dimensions(); i++) - { - assert(in_tensor->dimension(i) == out_tensor->dimension(i)); - } -} - -void invoke(const ITensor *in_tensor, const ITensor *out_tensor, - const ir::operation::Softmax::Param ¶m) -{ - const float *in_ptr = reinterpret_cast<const float *>(in_tensor->bufferRO()); - float *out_ptr = reinterpret_cast<float *>(out_tensor->buffer()); - - float beta = param.beta; - - if (in_tensor->num_dimensions() == 2) - { - uint32_t batch_size = in_tensor->dimension(0); - uint32_t input_size = in_tensor->dimension(1); - - nnfw::cker::Softmax(in_ptr, input_size, batch_size, beta, out_ptr); - } - else if (in_tensor->num_dimensions() == 4) - { - const auto in_shape = convertShape(in_tensor->tensorInfo().shape()); - const auto out_shape = convertShape(out_tensor->tensorInfo().shape()); - - nnfw::cker::SoftmaxParams cker_param; - cker_param.beta = beta; - - nnfw::cker::Softmax(cker_param, in_shape, in_ptr, out_shape, out_ptr); - } - else - { - throw std::runtime_error{"Unsuported input dimension: support 2D or 4D"}; - } -} - -void invokeSoftMax(const ExecEnv *env, const ir::Operation &node) -{ - const auto &softmax_node = nnfw::misc::polymorphic_downcast<const ir::operation::Softmax &>(node); - - const auto in_index = node.getInputs().at(0); - const auto out_index = node.getOutputs().at(0); - - const auto in_tensor = env->tensorAt(in_index); - const auto out_tensor = env->tensorAt(out_index); - - const auto in_data_type = in_tensor->data_type(); - const auto out_data_type = out_tensor->data_type(); - if ((in_data_type == ir::DataType::FLOAT32) && (out_data_type == ir::DataType::FLOAT32)) - { - invoke(in_tensor, out_tensor, softmax_node.param()); - } - else - { - throw std::runtime_error{"NYI: Support float32 only"}; - } -} - -} // namespace - -OpKernel *getSoftmax() -{ - static OpKernel kernel = {prepareSoftMax, invokeSoftMax}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/interp/operations/TransposeConv.cc b/runtime/onert/core/src/interp/operations/TransposeConv.cc deleted file mode 100644 index cc2ced26b..000000000 --- a/runtime/onert/core/src/interp/operations/TransposeConv.cc +++ /dev/null @@ -1,141 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <cker/operation/TransposeConv.h> -#include <misc/polymorphic_downcast.h> - -#include "OperationUtil.h" - -#include "interp/Registration.h" -#include "ir/operation/TransposeConv.h" - -namespace onert -{ -namespace interp -{ -namespace -{ - -void prepareTransposeConv(ExecEnv *env, const ir::Operation &node) -{ - const auto ifm_index = node.getInputs().at(ir::operation::TransposeConv::INPUT); - const auto ker_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL); - const auto ofm_shape_index = node.getInputs().at(ir::operation::TransposeConv::OUTPUT_SHAPE); - const auto ofm_index = node.getOutputs().at(0); - - const auto ifm_tensor = env->tensorAt(ifm_index); - const auto ker_tensor = env->tensorAt(ker_index); - const auto ofm_shape_tensor = env->tensorAt(ofm_shape_index); - - assert(ifm_tensor->num_dimensions() == 4); - assert(ker_tensor->num_dimensions() == 4); - assert(ofm_shape_tensor->num_dimensions() == 1); - - UNUSED_RELEASE(ifm_tensor); - UNUSED_RELEASE(ker_tensor); - UNUSED_RELEASE(ofm_shape_tensor); - - const auto output_info = env->graph().operands().at(ofm_index).info(); - if (output_info.total_size() == 0) - { - // TODO: Handle unspecified output shape - throw std::runtime_error{"Interp(TConv): NYI unspecified output shape"}; - } - else - { - env->allocateIfNeeded(ofm_index, output_info); - } - - auto ofm_tensor = env->tensorAt(ofm_index); - UNUSED_RELEASE(ofm_tensor); - - // Handle same ifm & ofm data type only - if (ifm_tensor->data_type() != ofm_tensor->data_type()) - { - throw std::runtime_error{"Interp(TConv): Different I/O data dype"}; - } - - if (ofm_tensor->num_dimensions() != 4) - { - throw std::runtime_error{"Interp(TConv): Invalid output rank"}; - } -} - -void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *ofm_tensor, - const ir::operation::TransposeConv::Param ¶m) -{ - const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC); - // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]. - const auto ker_shape = ker_tensor->tensorInfo().shape(); - const auto ker_height = ker_shape.dim(1); - const auto ker_width = ker_shape.dim(2); - const auto padding = ir::calculatePadding(param.padding, ofm_shape, ifm_shape, param.stride, - ker_width, ker_height); - - nnfw::cker::TransposeConvParams cker_param; - cker_param.padding_values.width = padding.left; - cker_param.padding_values.height = padding.top; - cker_param.stride_width = param.stride.horizontal; - cker_param.stride_height = param.stride.vertical; - cker_param.dilation_width_factor = 1; - cker_param.dilation_height_factor = 1; - - const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape()); - const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape()); - const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape()); - const float *ifm_ptr = reinterpret_cast<const float *>(ifm_tensor->bufferRO()); - const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO()); - float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer()); - - nnfw::cker::TransposeConv(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr, - cker_ofm_shape, ofm_ptr); -} - -void invokeTransposeConv(const ExecEnv *env, const ir::Operation &node) -{ - const auto &tconv_node = - nnfw::misc::polymorphic_downcast<const ir::operation::TransposeConv &>(node); - - const auto ifm_index = node.getInputs().at(ir::operation::TransposeConv::INPUT); - const auto ker_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL); - const auto ofm_index = node.getOutputs().at(0); - - const auto ifm_tensor = env->tensorAt(ifm_index); - const auto ker_tensor = env->tensorAt(ker_index); - const auto ofm_tensor = env->tensorAt(ofm_index); - - const auto data_type = ifm_tensor->data_type(); - if (data_type == ir::DataType::FLOAT32) - { - invoke(ifm_tensor, ker_tensor, ofm_tensor, tconv_node.param()); - } - else - { - throw std::runtime_error{"Interp(TConv): Support float32 only"}; - } -} - -} // namespace - -OpKernel *getTransposeConv() -{ - static OpKernel kernel = {prepareTransposeConv, invokeTransposeConv}; - return &kernel; -} - -} // namespace interp -} // namespace onert diff --git a/runtime/onert/core/src/ir/Coordinates.cc b/runtime/onert/core/src/ir/Coordinates.cc deleted file mode 100644 index a02a56567..000000000 --- a/runtime/onert/core/src/ir/Coordinates.cc +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Coordinates.h" - -#include <cassert> - -namespace onert -{ -namespace ir -{ - -Coordinates convertCoordinates(const Coordinates &from_coordinates, Layout from_layout, - Layout to_layout) -{ - assert(from_coordinates.size() == 4); - Coordinates to{from_coordinates}; - if (from_layout == Layout::NHWC && to_layout == Layout::NCHW) - { - to.set(0, from_coordinates[0]); - to.set(1, from_coordinates[3]); - to.set(2, from_coordinates[1]); - to.set(3, from_coordinates[2]); - } - else if (from_layout == Layout::NCHW && to_layout == Layout::NHWC) - { - to.set(0, from_coordinates[0]); - to.set(1, from_coordinates[2]); - to.set(2, from_coordinates[3]); - to.set(3, from_coordinates[1]); - } - - return to; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/DataType.cc b/runtime/onert/core/src/ir/DataType.cc deleted file mode 100644 index 80c659b3a..000000000 --- a/runtime/onert/core/src/ir/DataType.cc +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/DataType.h" - -#include <stdexcept> -#include <Half.h> - -using float16 = Half; - -namespace onert -{ -namespace ir -{ - -size_t sizeOfDataType(DataType data_type) -{ - switch (data_type) - { - case DataType::FLOAT32: - return sizeof(float); - case DataType::INT32: - return sizeof(int32_t); - case DataType::UINT32: - return sizeof(uint32_t); - case DataType::BOOL8: - case DataType::QUANT_UINT8_ASYMM: - case DataType::UINT8: - return sizeof(uint8_t); - case DataType::QUANT_INT8_SYMM: - return sizeof(int8_t); - case DataType::FLOAT16: - return sizeof(float16); - case DataType::INT64: - return sizeof(int64_t); - default: - throw std::runtime_error{"Unsupported type size"}; - } -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/Graph.cc b/runtime/onert/core/src/ir/Graph.cc deleted file mode 100644 index 605562ebc..000000000 --- a/runtime/onert/core/src/ir/Graph.cc +++ /dev/null @@ -1,146 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Graph.h" - -#include <algorithm> -#include <bitset> -#include <sstream> - -#include "util/logging.h" -#include "verifier/Verifier.h" -#include "ir/operation/LowerInfo.h" -#include "ir/operand/LowerInfo.h" -#include "ir/operand/PermuteFactor.h" -#include "ir/OperandIndexMap.h" -#include "ir/GraphIterator.h" -#include "backend/IConfig.h" - -namespace onert -{ -namespace ir -{ - -Graph::Graph() = default; - -Graph::~Graph(void) = default; - -OperandIndex Graph::addOperand(const Shape &shape, const TypeInfo &type) -{ - return _operands.emplace(shape, type); -} - -OperationIndex Graph::addOperation(std::unique_ptr<Operation> &&node) -{ - assert(isBuildingPhase()); - return _operations.push(std::move(node)); -} - -void Graph::setOperandValue(const OperandIndex &ind, std::shared_ptr<Data> data) -{ - assert(isBuildingPhase()); - assert(_operands.exist(ind)); - _operands.at(ind).data(std::move(data)); -} - -void Graph::addInput(const OperandIndex &ind, const std::string &name) -{ - assert(isBuildingPhase()); - if (!name.empty()) - _name_to_input.emplace(name, IOIndex{_inputs.size()}); - _inputs.append(ind); -} - -void Graph::addOutput(const OperandIndex &ind, const std::string &name) -{ - assert(isBuildingPhase()); - if (!name.empty()) - _name_to_output.emplace(name, IOIndex{_outputs.size()}); - _outputs.append(ind); -} - -IOIndex Graph::getInputIndex(const std::string &name) const -{ - auto itr = _name_to_input.find(name); - return (itr == _name_to_input.end()) ? IOIndex{} : itr->second; -} - -IOIndex Graph::getOutputIndex(const std::string &name) const -{ - auto itr = _name_to_output.find(name); - return (itr == _name_to_output.end()) ? IOIndex{} : itr->second; -} - -void Graph::finishBuilding(void) -{ - assert(isBuildingPhase()); - _phase = Phase::MODEL; - - initializeUseDef(); - sweepGarbageOperands(); - - // Call graph verifications for the MODEL phase - { - assert(verifier::DAGChecker().verify(*this)); - assert(verifier::EdgeConsistencyChecker().verify(*this)); - } -} - -void Graph::initializeUseDef() -{ - operations().iterate([&](const OperationIndex &index, const Operation &node) -> void { - auto outputs = node.getOutputs(); - for (auto output : outputs | ir::Remove::UNDEFINED) - { - operands().at(output).setDef(index); - } - - for (auto input : node.getInputs() | ir::Remove::UNDEFINED) - { - operands().at(input).insertUse(index); - } - }); -} - -void Graph::sweepGarbageOperands() -{ - // Remove operands that are not used by any operations, except Graph inputs/outputs - ir::OperandIndexMap<bool> visited; - - operations().iterate([&](const OperationIndex &, const Operation &node) { - for (auto ind : node.getInputs() + node.getOutputs()) - { - visited[ind] = true; - } - }); - - // Graph's inputs/outputs are always reachable - for (auto ind : getInputs() + getOutputs()) - { - visited[ind] = true; - } - - operands().iterate([&](const OperandIndex &ind, const Operand &) { - if (!visited[ind]) - { - VERBOSE(Graph::sweepGarbageOperands) << "Sweep garbage operand " << ind.value() << std::endl; - operands().remove(ind); - } - }); -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/GraphIterator.cc b/runtime/onert/core/src/ir/GraphIterator.cc deleted file mode 100644 index ac67771c4..000000000 --- a/runtime/onert/core/src/ir/GraphIterator.cc +++ /dev/null @@ -1,121 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "GraphIterator.h" - -#include "ir/OperationIndexMap.h" -#include "compiler/LoweredGraph.h" - -namespace onert -{ -namespace ir -{ - -// -// Graph::DefaultIterator -// - -template <bool is_const> -void DefaultIterator<is_const>::iterate(GraphRef graph, const IterFn &fn) const -{ - graph.operations().iterate( - [&](const OperationIndex &index, NodeRef node) -> void { fn(index, node); }); -} - -// -// Graph::PostDfsIterator -// - -template <bool is_const> -void PostDfsIterator<is_const>::iterate(GraphRef graph, const IterFn &fn) const -{ - assert(!graph.isBuildingPhase()); // Restrict iteration condition - - OperationIndexMap<bool> visited; - graph.operations().iterate([&](const OperationIndex &index, NodeRef) { visited[index] = false; }); - - std::function<void(const OperationIndex &, NodeRef)> dfs_recursive = - [&](const OperationIndex &index, NodeRef node) -> void { - if (visited[index]) - return; - visited[index] = true; - - for (const auto output : node.getOutputs() | Remove::DUPLICATED | Remove::UNDEFINED) - { - const auto &operand = graph.operands().at(output); - for (const auto &use : operand.getUses()) - { - dfs_recursive(use, graph.operations().at(use)); - } - } - - fn(index, node); - }; - - graph.operations().iterate(dfs_recursive); - - // All of the operations(nodes) must have been visited. - assert(std::all_of(visited.begin(), visited.end(), - [](const std::pair<const OperationIndex, bool> &v) { return v.second; })); -} - -template <bool is_const> -void PostDfsIterator<is_const>::iterateOpSeqs(LoweredGraphRef lowered_graph, - const OpSeqIterFn &fn) const -{ - std::unordered_map<OpSequenceIndex, bool> visited; - lowered_graph.op_seqs().iterate( - [&](const OpSequenceIndex &index, OpSequenceRef) { visited[index] = false; }); - - std::function<void(const OpSequenceIndex &, OpSequenceRef)> dfs_recursive = - [&](const OpSequenceIndex &index, OpSequenceRef op_seq) -> void { - if (visited[index]) - return; - visited[index] = true; - - for (const auto output : op_seq.getOutputs() | Remove::DUPLICATED | Remove::UNDEFINED) - { - const auto &operand = lowered_graph.graph().operands().at(output); - for (const auto &use : operand.getUses()) - { - const auto use_op_seq_index = lowered_graph.op_seqs().getOperation(use); - dfs_recursive(use_op_seq_index, lowered_graph.op_seqs().at(use_op_seq_index)); - } - } - - fn(index, op_seq); - }; - - lowered_graph.op_seqs().iterate(dfs_recursive); - - // All of the operations(nodes) must have been visited. - assert(std::all_of(visited.begin(), visited.end(), - [](const std::pair<const OpSequenceIndex, bool> &v) { return v.second; })); -} - -// Explicit instantiations to have implementation in the source file. -// NOTE If these instatiations were in the top of this file, `iterate` is compiled and saved in -// `GraphIterator.cc.o` but `iterateOpSeqs`. This happens only when cross-building for Android. -// (Maybe a bug of NDK toolchain(clang)?) - -template class DefaultIterator<true>; -template class DefaultIterator<false>; - -template class PostDfsIterator<true>; -template class PostDfsIterator<false>; - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/GraphIterator.h b/runtime/onert/core/src/ir/GraphIterator.h deleted file mode 100644 index b54314e0e..000000000 --- a/runtime/onert/core/src/ir/GraphIterator.h +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_GRAPH_ITERATOR_H__ -#define __ONERT_IR_GRAPH_ITERATOR_H__ - -#include <type_traits> - -#include "ir/Index.h" - -namespace onert -{ -namespace compiler -{ -class LoweredGraph; -} // namespace compiler -} // namespace onert - -namespace onert -{ -namespace ir -{ - -class Graph; -class Operation; -class OpSequence; - -template <bool is_const> class Iterator -{ -public: - using GraphRef = typename std::conditional<is_const, const Graph &, Graph &>::type; - using IndexRef = const OperationIndex &; - using NodeRef = typename std::conditional<is_const, const Operation &, Operation &>::type; - using IterFn = std::function<void(IndexRef, NodeRef)>; - -public: - virtual ~Iterator() = default; - virtual void iterate(GraphRef graph, const IterFn &fn) const = 0; -}; - -template <bool is_const = false> class DefaultIterator final : public Iterator<is_const> -{ -public: - using GraphRef = typename Iterator<is_const>::GraphRef; - using IndexRef = typename Iterator<is_const>::IndexRef; - using NodeRef = typename Iterator<is_const>::NodeRef; - using IterFn = typename Iterator<is_const>::IterFn; - -public: - void iterate(GraphRef graph, const IterFn &fn) const; -}; -using DefaultConstIterator = DefaultIterator<true>; - -template <bool is_const = false> class PostDfsIterator final : public Iterator<is_const> -{ -public: - using GraphRef = typename Iterator<is_const>::GraphRef; - using IndexRef = typename Iterator<is_const>::IndexRef; - using NodeRef = typename Iterator<is_const>::NodeRef; - using IterFn = typename Iterator<is_const>::IterFn; - using LoweredGraphRef = - typename std::conditional<is_const, const typename compiler::LoweredGraph &, - typename compiler::LoweredGraph &>::type; - using OpSequenceRef = typename std::conditional<is_const, const OpSequence &, OpSequence &>::type; - using OpSeqIndexRef = const OpSequenceIndex &; - using OpSeqIterFn = std::function<void(OpSeqIndexRef, OpSequenceRef)>; - -public: - void iterate(GraphRef graph, const IterFn &fn) const; - void iterateOpSeqs(LoweredGraphRef lowered_graph, const OpSeqIterFn &f) const; -}; -using PostDfsConstIterator = PostDfsIterator<true>; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_GRAPH_ITERATOR_H__ diff --git a/runtime/onert/core/src/ir/LayoutSet.cc b/runtime/onert/core/src/ir/LayoutSet.cc deleted file mode 100644 index bd3f438ad..000000000 --- a/runtime/onert/core/src/ir/LayoutSet.cc +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "LayoutSet.h" - -namespace onert -{ -namespace ir -{ - -LayoutSet::LayoutSet(std::initializer_list<Layout> layouts) -{ - for (auto layout : layouts) - { - _set.insert(layout); - } -} - -LayoutSet LayoutSet::operator|(const LayoutSet &other) const -{ - auto ret = *this; - for (auto layout : other) - { - ret.add(layout); - } - return ret; -} - -LayoutSet LayoutSet::operator&(const LayoutSet &other) const -{ - LayoutSet ret; - for (auto layout : other) - { - if (contains(layout)) - { - ret.add(layout); - } - } - return ret; -} - -LayoutSet LayoutSet::operator-(const LayoutSet &other) const -{ - auto ret = *this; - for (auto layout : other) - { - ret.remove(layout); - } - return ret; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/LayoutSet.h b/runtime/onert/core/src/ir/LayoutSet.h deleted file mode 100644 index 6ce4e38c6..000000000 --- a/runtime/onert/core/src/ir/LayoutSet.h +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_LAYOUT_SET_H__ -#define __ONERT_IR_LAYOUT_SET_H__ - -#include <initializer_list> -#include <unordered_set> - -#include "ir/Layout.h" - -namespace onert -{ -namespace ir -{ - -class LayoutSet -{ -public: - LayoutSet() = default; - LayoutSet(std::initializer_list<Layout> layouts); - -public: - void add(const Layout &layout) { _set.insert(layout); } - void remove(const Layout &layout) { _set.erase(layout); } - uint32_t size() const { return static_cast<uint32_t>(_set.size()); } - bool contains(const Layout &layout) const { return _set.find(layout) != _set.end(); } - -public: - LayoutSet operator|(const LayoutSet &other) const; // Union - LayoutSet operator&(const LayoutSet &other) const; // Intersect - LayoutSet operator-(const LayoutSet &other) const; // Minus - -public: - std::unordered_set<Layout>::const_iterator begin() const { return _set.begin(); } - std::unordered_set<Layout>::const_iterator end() const { return _set.end(); } - -private: - std::unordered_set<Layout> _set; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_LAYOUT_SET_H__ diff --git a/runtime/onert/core/src/ir/OpCode.cc b/runtime/onert/core/src/ir/OpCode.cc deleted file mode 100644 index ef3411f6d..000000000 --- a/runtime/onert/core/src/ir/OpCode.cc +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/OpCode.h" - -#include <unordered_map> - -namespace onert -{ -namespace ir -{ - -const char *toString(OpCode opcode) -{ - static const std::unordered_map<OpCode, const char *> map{{OpCode::Invalid, "Invalid"}, -#define OP(Name) {OpCode::Name, #Name}, -#include "ir/Operations.lst" -#undef OP - {OpCode::COUNT, "COUNT"}}; - return map.at(opcode); -} - -OpCode toOpCode(const std::string str) -{ - static const std::unordered_map<std::string, OpCode> map{ -#define OP(Name) {#Name, OpCode::Name}, -#include "ir/Operations.lst" -#undef OP - }; - return map.at(str); -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/OpSequence.cc b/runtime/onert/core/src/ir/OpSequence.cc deleted file mode 100644 index e2b989d8c..000000000 --- a/runtime/onert/core/src/ir/OpSequence.cc +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/OpSequence.h" - -#include "ir/Operations.h" -#include "ir/OperationVisitor.h" -#include <sstream> - -namespace -{ - -std::string getStrFromIndice(const onert::ir::OperandIndexSequence &indice) -{ - std::string str; - for (const auto &ind : indice) - { - str += std::to_string(ind.value()); - str.push_back(','); - } - if (str.back() == ',') - str.pop_back(); - - return str; -} -} - -namespace onert -{ -namespace ir -{ - -OpSequence::OpSequence(Layout layout) : _layout{layout}, _has_dynamic_tensor{false} -{ - // DO NOTHING -} - -void OpSequence::accept(OperationVisitor &v) const { v.visit(*this); } - -// TODO: Impl Dumper instead of this method -std::string getStrFromOpSeq(const OpSequence &op_seq, const Operations &operations) -{ - // " OpSequence IN(0,1,2) -> { op0(0,1,2:3), op1(3:4), op2(4:5) } -> OUT(5)" - std::stringstream ss; - ss << " OpSequence IN(" << getStrFromIndice(op_seq.getInputs()) << ") -> {"; - for (const auto &op_idx : op_seq) - { - ss << " " << op_idx.value() << "(" << operations.at(op_idx).name() << ":" - << getStrFromIndice(operations.at(op_idx).getInputs()) << ":" - << getStrFromIndice(operations.at(op_idx).getOutputs()) << ")"; - } - ss << " } -> OUT(" << getStrFromIndice(op_seq.getOutputs()) << ")"; - return ss.str(); -} - -void OpSequence::remove(const OperationIndex &index) -{ - assert(exist(index)); - for (auto it = _operations.cbegin(); it != _operations.cend(); ++it) - { - if (*it == index) - { - _operations.erase(it); - break; - } - } -} - -bool OpSequence::exist(const OperationIndex &index) const -{ - for (const auto &inner_op_idx : _operations) - { - if (inner_op_idx == index) - { - return true; - } - } - return false; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/OpSequences.cc b/runtime/onert/core/src/ir/OpSequences.cc deleted file mode 100644 index 68884783e..000000000 --- a/runtime/onert/core/src/ir/OpSequences.cc +++ /dev/null @@ -1,124 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/OpSequences.h" -#include "util/logging.h" -#include <memory> - -#include <cassert> -#include <string> - -namespace onert -{ -namespace ir -{ - -OpSequenceIndex OpSequences::emplace(const OperationIndex &index, Layout layout) -{ - std::unique_ptr<OpSequence> op_seq = std::make_unique<OpSequence>(layout); - op_seq->appendOperation(index); - const OpSequenceIndex &seq_index = push(std::move(op_seq)); - cacheSequenceIndex(seq_index, index); - return seq_index; -} - -OpSequenceIndex OpSequences::emplace(std::unique_ptr<OpSequence> &&op_seq) -{ - auto &operations = op_seq->operations(); - const OpSequenceIndex &seq_index = push(std::move(op_seq)); - for (const auto &op_idx : operations) - { - cacheSequenceIndex(seq_index, op_idx); - } - return seq_index; -} - -void OpSequences::cacheSequenceIndex(const OpSequenceIndex &seq_index, - const OperationIndex &op_index) const -{ - _seq_indexes.emplace(op_index, seq_index); -} - -OpSequenceIndex *OpSequences::findSequenceIndex(const OperationIndex &operation_index) const -{ - // If opration_index is cached, return sequence_index from cache - if (_seq_indexes.count(operation_index)) - { - auto &op_seq_index = _seq_indexes.at(operation_index); - if (_objects.count(op_seq_index) && _objects.at(op_seq_index)->exist(operation_index)) - { - return &op_seq_index; - } - else - { - _seq_indexes.erase(operation_index); - return nullptr; - } - } - return nullptr; -} - -bool OpSequences::containsOperation(const OperationIndex &operation_index) const -{ - return findOperation(operation_index).valid(); -} - -OpSequenceIndex OpSequences::getOperation(const OperationIndex &operation_index) const -{ - OpSequenceIndex ret = findOperation(operation_index); - assert(ret.valid()); - return ret; -} - -void OpSequences::removeFromOpSequence(const OperationIndex &operation_index) -{ - const auto op_seq_index = findOperation(operation_index); - auto &op_seq = at(op_seq_index); - _seq_indexes.erase(operation_index); - op_seq.remove(operation_index); - if (op_seq.size() == 0) - { - remove(op_seq_index); - } -} - -OpSequenceIndex OpSequences::findOperation(const OperationIndex &operation_index) const -{ - if (OpSequenceIndex *op_seq_index = findSequenceIndex(operation_index)) - return *op_seq_index; - - for (auto &e : _objects) - { - OpSequence &object = *e.second; - auto it = find(object.operations().begin(), object.operations().end(), operation_index); - if (it != object.operations().end()) - { - cacheSequenceIndex(e.first, operation_index); - return e.first; - } - } - throw std::runtime_error("Operation not found"); -} - -void dumpOpSequences(const OpSequences &op_seqs, const Operations &operations) -{ - op_seqs.iterate([&](const OpSequenceIndex &idx, const OpSequence &op_seq) { - VERBOSE(OpSequences) << idx.value() << "] " << getStrFromOpSeq(op_seq, operations) << std::endl; - }); -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/Operand.cc b/runtime/onert/core/src/ir/Operand.cc deleted file mode 100644 index e29c7a6ec..000000000 --- a/runtime/onert/core/src/ir/Operand.cc +++ /dev/null @@ -1,50 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Operand.h" - -namespace onert -{ -namespace ir -{ - -size_t Operand::operandSize(void) const -{ - const uint32_t ranks = shape().rank(); - int32_t elements = 1; - - for (uint32_t rank = 0; rank < ranks; rank++) - { - elements *= shape().dim(rank); - } - - DataType type = typeInfo().type(); - size_t element_size = sizeOfDataType(type); - - // Value of type is matched with OperandCode enum in NeuralNetworks.h - return element_size * elements; -} - -void Operand::insertUse(const OperationIndex &idx) { _uses.insert(idx); } - -void Operand::removeUse(const OperationIndex &idx) { _uses.remove(idx); } - -void Operand::setDef(const OperationIndex &idx) { _def = idx; } - -void Operand::unsetDef() { _def = OperationIndex{}; } - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/OperandIndexSequence.cc b/runtime/onert/core/src/ir/OperandIndexSequence.cc deleted file mode 100644 index 73f928280..000000000 --- a/runtime/onert/core/src/ir/OperandIndexSequence.cc +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/OperandIndexSequence.h" - -#include <algorithm> -#include <sstream> - -namespace onert -{ -namespace ir -{ - -OperandIndexSequence::OperandIndexSequence(std::initializer_list<OperandIndex> list) : _vec(list) -{ - // DO NOTHING -} - -OperandIndexSequence::OperandIndexSequence(std::initializer_list<int32_t> list) -{ - for (auto val : list) - { - _vec.emplace_back(static_cast<uint32_t>(val)); - } -} - -OperandIndexSequence::OperandIndexSequence(std::initializer_list<uint32_t> list) -{ - for (auto val : list) - { - _vec.emplace_back(val); - } -} - -bool OperandIndexSequence::contains(const OperandIndex &index) const -{ - return std::find(_vec.begin(), _vec.end(), index) != _vec.end(); -} - -void OperandIndexSequence::replace(const OperandIndex &from, const OperandIndex &to) -{ - std::replace(_vec.begin(), _vec.end(), from, to); -} - -OperandIndexSequence OperandIndexSequence::operator+(const OperandIndexSequence &other) const -{ - OperandIndexSequence ret = *this; - ret.append(other); - return ret; -} - -std::ostream &operator<<(std::ostream &o, const OperandIndexSequence &op_seq) -{ - std::string delimeter; - for (const auto &ind : op_seq._vec) - { - o << delimeter << ind; - delimeter = ','; - } - return o; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/Operands.cc b/runtime/onert/core/src/ir/Operands.cc deleted file mode 100644 index ab32e478a..000000000 --- a/runtime/onert/core/src/ir/Operands.cc +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Operands.h" - -#include <memory> -#include "util/logging.h" - -namespace onert -{ -namespace ir -{ - -Operands::Operands(const Operands &obj) -{ - obj.iterate([&](const OperandIndex &index, const Operand &operand) { - _objects.emplace(index, std::make_unique<Operand>(operand)); - }); - _index_count = obj._index_count; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/Operation.cc b/runtime/onert/core/src/ir/Operation.cc deleted file mode 100644 index 4af878541..000000000 --- a/runtime/onert/core/src/ir/Operation.cc +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Operation.h" - -#include <cassert> - -namespace onert -{ -namespace ir -{ - -Operation::Operation(OperandConstraint input_constr, const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, OperandConstraint output_constr) - : _input_constr{input_constr}, _output_constr{output_constr} -{ - setInputs(inputs); - setOutputs(outputs); -} - -Operation::Operation(OperandConstraint input_constr, OperandConstraint output_constr) - : _input_constr{input_constr}, _output_constr{output_constr} -{ -} - -Operation::~Operation() = default; - -void Operation::setInputs(const OperandIndexSequence &indexes) -{ - if (!_input_constr.check(indexes.size())) - throw std::runtime_error{"Invalid number of input tensors for this operation."}; - _inputs = indexes; -} - -void Operation::setOutputs(const OperandIndexSequence &indexes) -{ - if (!_output_constr.check(indexes.size())) - throw std::runtime_error{"Invalid number of output tensors for this operation."}; - _outputs = indexes; -} - -void Operation::replaceInputs(const OperandIndex &from, const OperandIndex &to) -{ - _inputs.replace(from, to); -} - -void Operation::replaceOutputs(const OperandIndex &from, const OperandIndex &to) -{ - _outputs.replace(from, to); -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/OperationCloner.cc b/runtime/onert/core/src/ir/OperationCloner.cc deleted file mode 100644 index b4e60f0bc..000000000 --- a/runtime/onert/core/src/ir/OperationCloner.cc +++ /dev/null @@ -1,42 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OperationCloner.h" - -#include <assert.h> - -namespace onert -{ -namespace ir -{ - -#define OP(Name) \ - void OperationCloner::visit(const operation::Name &o) \ - { \ - assert(!_return_op); \ - _return_op = std::make_unique<operation::Name>(o); \ - } -#include "ir/Operations.lst" -#undef OP - -std::unique_ptr<Operation> OperationCloner::releaseClone() -{ - assert(_return_op); - return std::move(_return_op); -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/OperationCloner.h b/runtime/onert/core/src/ir/OperationCloner.h deleted file mode 100644 index 0e8cda2a0..000000000 --- a/runtime/onert/core/src/ir/OperationCloner.h +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_IR_OPERATION_CLONER_H__ -#define __ONERT_IR_OPERATION_CLONER_H__ - -#include <memory> -#include "ir/OperationVisitor.h" -#include "ir/Operation.h" - -namespace onert -{ -namespace ir -{ - -class OperationCloner : public OperationVisitor -{ -public: -#define OP(Name) void visit(const operation::Name &o) override; -#include "ir/Operations.lst" -#undef OP - -public: - std::unique_ptr<Operation> releaseClone(); - -private: - std::unique_ptr<Operation> _return_op; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_IR_OPERATION_CLONER_H__ diff --git a/runtime/onert/core/src/ir/OperationDumper.cc b/runtime/onert/core/src/ir/OperationDumper.cc deleted file mode 100644 index eecfe81cc..000000000 --- a/runtime/onert/core/src/ir/OperationDumper.cc +++ /dev/null @@ -1,521 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OperationDumper.h" - -#include <string> - -#include "util/logging.h" - -namespace onert -{ -namespace ir -{ - -using namespace operation; - -namespace -{ -void dumpUnaryInputOp(const Operation &node, const std::string &adding_input = "") -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - VERBOSE(LIR) << " - Inputs : Input(" << node.getInputs().at(0) << ") " << adding_input - << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void dumpBinaryInputOp(const Operation &node, const std::string &adding_input = "") -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - VERBOSE(LIR) << " - Inputs : Input(" << node.getInputs().at(0) << ", " << node.getInputs().at(1) - << ") " << adding_input << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void dumpConvOp(const Operation &node, const std::string &padding_type) -{ - VERBOSE(LIR) << "* " << node.name() << "(" << padding_type << ")" << std::endl; - VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(Conv2D::Input::INPUT) << ") Kernel(" - << node.getInputs().at(Conv2D::Input::KERNEL) << ") Bias(" - << node.getInputs().at(Conv2D::Input::BIAS) << ")" << std::endl; - VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void dumpPackingOp(const Operation &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - std::string inputs; - for (auto i : node.getInputs()) - { - inputs += std::to_string(i.value()) + ","; - } - VERBOSE(LIR) << " - Inputs : Inputs(" << inputs << ")" << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} -} // namespace - -OperationDumper::OperationDumper(const std::string &start_msg) -{ - VERBOSE(LIR) << start_msg << std::endl; -} - -void OperationDumper::visit(const ArgMax &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const BatchToSpaceND &node) -{ - std::string block_size = - "BlockSize(" + - std::to_string(node.getInputs().at(BatchToSpaceND::Input::BLOCK_SIZE).value()) + ")"; - dumpUnaryInputOp(node, block_size); -} - -void OperationDumper::visit(const BCQFullyConnected &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(BCQFullyConnected::Input::INPUT) - << ") WeightsBinary(" - << node.getInputs().at(BCQFullyConnected::Input::WEIGHTS_BINARY) - << ") WeightsScales(" - << node.getInputs().at(BCQFullyConnected::Input::WEIGHTS_SCALES) - << ") WeightsClusters(" - << node.getInputs().at(BCQFullyConnected::Input::WEIGHTS_CLUSTERS) << ") Bias(" - << node.getInputs().at(BCQFullyConnected::Input::BIAS) << ")" << std::endl; - VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const BinaryArithmetic &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const operation::BroadcastTo &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const Comparison &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const Concat &node) { dumpPackingOp(node); } - -void OperationDumper::visit(const Conv2D &node) -{ - std::string padding_type = - node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit"; - dumpConvOp(node, padding_type); -} - -void OperationDumper::visit(const ConvertFp16ToFp32 &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const ConvertFp32ToFp16 &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const DepthToSpace &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const DepthwiseConv2D &node) -{ - std::string padding_type = - node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit"; - dumpConvOp(node, padding_type); -} - -void OperationDumper::visit(const ElementwiseActivation &node) -{ - std::string params; - if (node.param().op_type == ElementwiseActivation::Type::RELU) - { - params = " lower value(" + std::to_string(node.param().alpha) + ") upper value(" + - std::to_string(node.param().beta) + ")"; - } - else if (node.param().op_type == ElementwiseActivation::Type::LEAKY_RELU) - { - params = " alpha value(" + std::to_string(node.param().alpha) + ")"; - } - dumpUnaryInputOp(node, params); -} - -void OperationDumper::visit(const ElementwiseBinary &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const ElementwiseUnary &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const EmbeddingLookup &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - VERBOSE(LIR) << " - Inputs : Lookups(" << node.getInputs().at(EmbeddingLookup::Input::LOOKUPS) - << ") VALUES(" << node.getInputs().at(EmbeddingLookup::Input::VALUES) << ")" - << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const ExpandDims &node) -{ - std::string axis = - "AXIS(" + std::to_string(node.getInputs().at(ExpandDims::Input::AXIS).value()) + ")"; - dumpUnaryInputOp(node, axis); -} - -void OperationDumper::visit(const FullyConnected &node) -{ - std::string inputs = - "Weight(" + std::to_string(node.getInputs().at(FullyConnected::Input::WEIGHT).value()) + - ") Bias(" + std::to_string(node.getInputs().at(FullyConnected::Input::BIAS).value()) + ")"; - dumpUnaryInputOp(node, inputs); -} - -void OperationDumper::visit(const Gather &node) -{ - std::string indices = - "Indices(" + std::to_string(node.getInputs().at(Gather::Input::INDICES).value()) + ")"; - dumpUnaryInputOp(node, indices); -} - -void OperationDumper::visit(const HashtableLookup &node) -{ - VERBOSE(LIR) << "* HashTableLookup" << std::endl; - VERBOSE(LIR) << " - Inputs : Lookups(" << node.getInputs().at(HashtableLookup::Input::LOOKUPS) - << ") Keys(" << node.getInputs().at(HashtableLookup::Input::KEYS) << ") Values(" - << node.getInputs().at(HashtableLookup::Input::VALUES) << ")" << std::endl; - VERBOSE(LIR) << " - Outputs : Output(" << node.getInputs().at(HashtableLookup::Output::OUTPUT) - << ") Hits(" << node.getInputs().at(HashtableLookup::Output::HITS) << ")" - << std::endl; -} - -void OperationDumper::visit(const InstanceNorm &node) -{ - std::string inputs = - "Gamma(" + std::to_string(node.getInputs().at(InstanceNorm::Input::GAMMA).value()) + - ") Beta(" + std::to_string(node.getInputs().at(InstanceNorm::Input::BETA).value()) + ")"; - dumpUnaryInputOp(node, inputs); -} - -void OperationDumper::visit(const L2Normalization &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const LocalResponseNormalization &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const LSTM &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - VERBOSE(LIR) - << " - Inputs : Input(" << node.getInputs().at(LSTM::Input::INPUT) - << ") Input To Input Weights(" << node.getInputs().at(LSTM::Input::INPUT_TO_INPUT_WEIGHTS) - << ") Input To Forget Weights(" << node.getInputs().at(LSTM::Input::INPUT_TO_FORGET_WEIGHTS) - << ") Input To Cell Weights(" << node.getInputs().at(LSTM::Input::INPUT_TO_CELL_WEIGHTS) - << ") Input To Output Weights(" << node.getInputs().at(LSTM::Input::INPUT_TO_OUTPUT_WEIGHTS) - << ") Recurrent To Input Weights(" - << node.getInputs().at(LSTM::Input::RECURRENT_TO_INPUT_WEIGHTS) - << ") Recurrent To Forget Weights(" - << node.getInputs().at(LSTM::Input::RECURRENT_TO_FORGET_WEIGHTS) - << ") Recurrent To Cell Weights(" - << node.getInputs().at(LSTM::Input::RECURRENT_TO_CELL_WEIGHTS) - << ") Recurrent To Output Weights(" - << node.getInputs().at(LSTM::Input::RECURRENT_TO_OUTPUT_WEIGHTS) << ") Cell To Input Weights(" - << node.getInputs().at(LSTM::Input::CELL_TO_INPUT_WEIGHTS) << ") Cell To Forget Weights(" - << node.getInputs().at(LSTM::Input::CELL_TO_FORGET_WEIGHTS) << ") Cell To OUTPUT Weights(" - << node.getInputs().at(LSTM::Input::CELL_TO_OUTPUT_WEIGHTS) << ") Input Gate Bias(" - << node.getInputs().at(LSTM::Input::INPUT_GATE_BIAS) << ") Forget Gate Bias(" - << node.getInputs().at(LSTM::Input::FORGET_GATE_BIAS) << ") Cell Bias(" - << node.getInputs().at(LSTM::Input::CELL_BIAS) << ") Output Gate Bias(" - << node.getInputs().at(LSTM::Input::OUTPUT_GATE_BIAS) << ") Projection Weights(" - << node.getInputs().at(LSTM::Input::PROJECTION_WEIGHTS) << ") Projection Bias(" - << node.getInputs().at(LSTM::Input::PROJECTION_BIAS) << ") Output State In(" - << node.getInputs().at(LSTM::Input::OUTPUT_STATE_IN) << ") Cell State In(" - << node.getInputs().at(LSTM::Input::CELL_STATE_IN); - if (node.getInputs().size() == 24) - { - VERBOSE(LIR) << ") Input Layer Normalization Weights(" - << node.getInputs().at(LSTM::Input::INPUT_LAYER_NORMALIZATION_WEIGHTS) - << ") Forget Layer Normalization Weights(" - << node.getInputs().at(LSTM::Input::FORGET_LAYER_NORMALIZATION_WEIGHTS) - << ") Cell Layer Normalization Weights(" - << node.getInputs().at(LSTM::Input::CELL_LAYER_NORMALIZATION_WEIGHTS) - << ") Ouput Layer Normalization Weights(" - << node.getInputs().at(LSTM::Input::OUTPUT_LAYER_NORMALIZATION_WEIGHTS); - } - VERBOSE(LIR) << ")" << std::endl; - VERBOSE(LIR) << " - Output : Scratch Buffer(" - << node.getOutputs().at(LSTM::Output::SCRATCH_BUFFER) << ") Output State Out(" - << node.getOutputs().at(LSTM::Output::OUTPUT_STATE_OUT) << ") Cell State Out(" - << node.getOutputs().at(LSTM::Output::CELL_STATE_OUT) << ") Output(" - << node.getOutputs().at(LSTM::Output::OUTPUT) << ")" << std::endl; -} - -void OperationDumper::visit(const Pack &node) { dumpPackingOp(node); } - -void OperationDumper::visit(const Pad &node) -{ - std::string pad = "Pad(" + std::to_string(node.getInputs().at(Pad::Input::PAD).value()) + ")"; - dumpUnaryInputOp(node, pad); -} - -void OperationDumper::visit(const Permute &node) -{ - std::string permute_type = "Unknown"; - switch (node.getPermuteType()) - { - case Permute::Type::COPY: - permute_type = "Copy"; - break; - case Permute::Type::NHWC_TO_NCHW: - permute_type = "NHWC to NCHW"; - break; - case Permute::Type::NCHW_TO_NHWC: - permute_type = "NCHW to NHWC"; - break; - } - - VERBOSE(LIR) << "* Permute(" + permute_type + ")" << std::endl; - VERBOSE(LIR) << " - Inputs : Input(" << node.getInputs().at(0) << ")" << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const Pool2D &node) -{ - std::string padding_type = - node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit"; - VERBOSE(LIR) << "* " << node.name() << "(" << padding_type << ")" << std::endl; - VERBOSE(LIR) << " - Inputs : IFM(" << node.getInputs().at(Pool2D::Input::INPUT) << ")" - << std::endl; - VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const Pow &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const PReLU &node) -{ - std::string alpha = - "Alpha(" + std::to_string(node.getInputs().at(PReLU::Input::ALPHA).value()) + ")"; - dumpUnaryInputOp(node, alpha); -} - -void OperationDumper::visit(const Rank &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const Reduce &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const Reshape &node) -{ - // optional param - std::string shape = - node.getInputs().size() == 2 - ? "Shape(" + std::to_string(node.getInputs().at(Reshape::Input::SHAPE).value()) + ")" - : "Shape(not provided)"; - dumpUnaryInputOp(node, shape); -} - -void OperationDumper::visit(const ResizeBilinear &node) -{ - if (node.getInputs().size() == 1) - { - dumpUnaryInputOp(node); - } - else if (node.getInputs().size() == 2) - { - dumpBinaryInputOp(node); - } - else - { - VERBOSE(LIR) << "* " << node.name() << " is set wrong" << std::endl; - } -} - -void OperationDumper::visit(const ResizeNearestNeighbor &node) -{ - if (node.getInputs().size() == 1) - { - dumpUnaryInputOp(node); - } - else if (node.getInputs().size() == 2) - { - dumpBinaryInputOp(node); - } - else - { - VERBOSE(LIR) << "* " << node.name() << " is set wrong" << std::endl; - } -} - -void OperationDumper::visit(const Reverse &node) -{ - std::string axis = - "Axis(" + std::to_string(node.getInputs().at(Reverse::Input::AXIS).value()) + ")"; - dumpUnaryInputOp(node, axis); -} - -void OperationDumper::visit(const RNN &node) -{ - VERBOSE(LIR) << "* RNN" << std::endl; - VERBOSE(LIR) << " - Inputs : Input(" << node.getInputs().at(RNN::Input::INPUT) << ") Weights(" - << node.getInputs().at(RNN::Input::WEIGHTS) << ") Recurrent Weights(" - << node.getInputs().at(RNN::Input::RECURRENT_WEIGHTS) << ") Bias(" - << node.getInputs().at(RNN::Input::BIAS) << ") Hidden State(" - << node.getInputs().at(RNN::Input::HIDDEN_STATE_IN) << ")" << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(RNN::Output::OUTPUT) - << ") Hidden State(" << node.getInputs().at(RNN::Output::HIDDEN_STATE_OUT) << ")" - << std::endl; -} - -void OperationDumper::visit(const Range &node) -{ - VERBOSE(LIR) << "* Range" << std::endl; - VERBOSE(LIR) << " - Inputs : Start(" << node.getInputs().at(Range::Input::START) << ")" - << " Limit(" << node.getInputs().at(Range::Input::LIMIT) << ")" - << " Delta(" << node.getInputs().at(Range::Input::DELTA) << ")" << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const Select &node) -{ - VERBOSE(LIR) << "* Select" << std::endl; - VERBOSE(LIR) << " - Inputs : Condition(" << node.getInputs().at(Select::Input::CONDITION) << ")" - << " Input_X(" << node.getInputs().at(Select::Input::INPUT_TRUE) << ")" - << " Input_Y(" << node.getInputs().at(Select::Input::INPUT_FALSE) << ")" - << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const ir::operation::Shape &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const Softmax &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const SpaceToBatchND &node) -{ - std::string inputs = - "BlockSize(" + - std::to_string(node.getInputs().at(SpaceToBatchND::Input::BLOCK_SIZE).value()) + - ") Paddings(" + std::to_string(node.getInputs().at(SpaceToBatchND::Input::PADDINGS).value()) + - ")"; - dumpUnaryInputOp(node, inputs); -} - -void OperationDumper::visit(const SpaceToDepth &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const Split &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const SquaredDifference &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const StatelessRandomUniform &node) -{ - VERBOSE(LIR) << "* StatelessRandomUniform" << std::endl; - VERBOSE(LIR) << " - Inputs : Shape(" << node.getInputs().at(StatelessRandomUniform::Input::SHAPE) - << " Seed(" << node.getInputs().at(StatelessRandomUniform::Input::SEED) << ")" - << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const Squeeze &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const Slice &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const StridedSlice &node) { dumpUnaryInputOp(node); } - -void OperationDumper::visit(const Tile &node) -{ - std::string multiples = - "Multiples(" + std::to_string(node.getInputs().at(Tile::Input::MULTIPLES).value()) + ")"; - dumpUnaryInputOp(node, multiples); -} - -void OperationDumper::visit(const TopKV2 &node) -{ - VERBOSE(LIR) << "* TopKV2" << std::endl; - VERBOSE(LIR) << " - Inputs : Input(" << node.getInputs().at(TopKV2::Input::INPUT) << ")" - << std::endl; - VERBOSE(LIR) << " - Outputs : Values(" << node.getOutputs().at(TopKV2::Output::OUTPUT_VALUES) - << ") Indices(" << node.getOutputs().at(TopKV2::Output::OUTPUT_INDICES) << ")" - << std::endl; -} - -void OperationDumper::visit(const TransposeConv &node) -{ - std::string padding_type = - node.param().padding.type == PaddingType::EXPLICIT ? "Explicit" : "Implicit"; - VERBOSE(LIR) << "* TransposeConv(" << padding_type << ")" << std::endl; - VERBOSE(LIR) << " - Inputs : Output Shape(" - << node.getInputs().at(TransposeConv::Input::OUTPUT_SHAPE) << ") KERNEL(" - << node.getInputs().at(TransposeConv::Input::KERNEL) << ") IFM(" - << node.getInputs().at(TransposeConv::Input::INPUT) << ")" << std::endl; - VERBOSE(LIR) << " - Output : OFM(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const Transpose &node) { dumpBinaryInputOp(node); } - -void OperationDumper::visit(const Unpack &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - VERBOSE(LIR) << " - Inputs : Input(" << node.getInputs().at(Unpack::Input::INPUT) << ")" - << std::endl; - std::string outputs; - const auto &output_indices = node.getOutputs(); - for (auto it = std::begin(output_indices); it != std::end(output_indices); ++it) - { - outputs += std::to_string(it->value()); - if (std::next(it) != std::end(output_indices)) - outputs += ", "; - } - VERBOSE(LIR) << " - Outputs : Outputs(" << outputs << ")" << std::endl; -} - -void OperationDumper::visit(const OneHot &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - VERBOSE(LIR) << " - Inputs : " - << "Indices(" << node.getInputs().at(OneHot::Input::INDICES) << ") " << std::endl; - VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(0) << ")" << std::endl; -} - -void OperationDumper::visit(const If &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - std::string inputs; - const auto &input_indices = node.getInputs(); - for (auto it = std::begin(input_indices); it != std::end(input_indices); ++it) - { - inputs += std::to_string(it->value()); - if (std::next(it) != std::end(input_indices)) - inputs += ", "; - } - VERBOSE(LIR) << " - Inputs : " - << "Then subgraph (" << node.param().then_subg_index << ") Else subgraph (" - << node.param().else_subg_index << ") Inputs(" << inputs << ")" << std::endl; - std::string outputs; - const auto &output_indices = node.getOutputs(); - for (auto it = std::begin(output_indices); it != std::end(output_indices); ++it) - { - outputs += std::to_string(it->value()); - if (std::next(it) != std::end(output_indices)) - outputs += ", "; - } - VERBOSE(LIR) << " - Output : Outputs(" << outputs << ")" << std::endl; -} - -void OperationDumper::visit(const While &node) -{ - VERBOSE(LIR) << "* " << node.name() << std::endl; - std::string inputs; - const auto &input_indices = node.getInputs(); - for (auto it = std::begin(input_indices); it != std::end(input_indices); ++it) - { - inputs += std::to_string(it->value()); - if (std::next(it) != std::end(input_indices)) - inputs += ", "; - } - VERBOSE(LIR) << " - Inputs : " - << "Cond subgraph (" << node.param().cond_subg_index << ") Body subgraph (" - << node.param().cond_subg_index << ") Inputs(" << inputs << ")" << std::endl; - std::string outputs; - const auto &output_indices = node.getOutputs(); - for (auto it = std::begin(output_indices); it != std::end(output_indices); ++it) - { - outputs += std::to_string(it->value()); - if (std::next(it) != std::end(output_indices)) - outputs += ", "; - } - VERBOSE(LIR) << " - Output : Outputs(" << outputs << ")" << std::endl; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/OperationDumper.h b/runtime/onert/core/src/ir/OperationDumper.h deleted file mode 100644 index 91642ab13..000000000 --- a/runtime/onert/core/src/ir/OperationDumper.h +++ /dev/null @@ -1,96 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_OPERATION_DUMPER_H__ -#define __ONERT_OPERATION_DUMPER_H__ - -#include "ir/OperationVisitor.h" -#include <string> - -namespace onert -{ -namespace ir -{ - -class OperationDumper : public OperationVisitor -{ -public: - OperationDumper(const std::string &start_msg); - -public: - void visit(const operation::ArgMax &) override; - void visit(const operation::BatchToSpaceND &node) override; - void visit(const operation::BCQFullyConnected &node) override; - void visit(const operation::BinaryArithmetic &node) override; - void visit(const operation::BroadcastTo &) override; - void visit(const operation::Comparison &) override; - void visit(const operation::Concat &node) override; - void visit(const operation::Conv2D &node) override; - void visit(const operation::ConvertFp16ToFp32 &node) override; - void visit(const operation::ConvertFp32ToFp16 &node) override; - void visit(const operation::DepthToSpace &) override; - void visit(const operation::DepthwiseConv2D &node) override; - void visit(const operation::ElementwiseActivation &) override; - void visit(const operation::ElementwiseBinary &) override; - void visit(const operation::ElementwiseUnary &) override; - void visit(const operation::EmbeddingLookup &) override; - void visit(const operation::ExpandDims &) override; - void visit(const operation::FullyConnected &node) override; - void visit(const operation::Gather &) override; - void visit(const operation::HashtableLookup &) override; - void visit(const operation::InstanceNorm &) override; - void visit(const operation::L2Normalization &) override; - void visit(const operation::LocalResponseNormalization &) override; - void visit(const operation::LSTM &) override; - void visit(const operation::Pack &) override; - void visit(const operation::Pad &) override; - void visit(const operation::Permute &node) override; - void visit(const operation::Pool2D &node) override; - void visit(const operation::Pow &node) override; - void visit(const operation::PReLU &) override; - void visit(const operation::Range &) override; - void visit(const operation::Rank &) override; - void visit(const operation::Reduce &) override; - void visit(const operation::Reshape &node) override; - void visit(const operation::ResizeBilinear &) override; - void visit(const operation::ResizeNearestNeighbor &) override; - void visit(const operation::Reverse &) override; - void visit(const operation::RNN &) override; - void visit(const operation::Select &node) override; - void visit(const operation::Shape &node) override; - void visit(const operation::Softmax &node) override; - void visit(const operation::SpaceToBatchND &) override; - void visit(const operation::SpaceToDepth &) override; - void visit(const operation::Split &) override; - void visit(const operation::SquaredDifference &) override; - void visit(const operation::Squeeze &) override; - void visit(const operation::Slice &) override; - void visit(const operation::StridedSlice &) override; - void visit(const operation::StatelessRandomUniform &) override; - void visit(const operation::Tile &) override; - void visit(const operation::TopKV2 &) override; - void visit(const operation::TransposeConv &) override; - void visit(const operation::Transpose &) override; - void visit(const operation::Unpack &) override; - void visit(const operation::OneHot &) override; - void visit(const operation::If &) override; - void visit(const operation::While &) override; -}; - -} // namespace ir -} // namespace onert - -#endif // __ONERT_OPERATION_DUMPER_H__ diff --git a/runtime/onert/core/src/ir/OperationIndexSet.cc b/runtime/onert/core/src/ir/OperationIndexSet.cc deleted file mode 100644 index 750ffffa6..000000000 --- a/runtime/onert/core/src/ir/OperationIndexSet.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/OperationIndexSet.h" - -#include <algorithm> - -namespace onert -{ -namespace ir -{ - -OperationIndexSet::OperationIndexSet(std::initializer_list<OperationIndex> list) : _set(list) -{ - // DO NOTHING -} - -bool OperationIndexSet::contains(const OperationIndex &index) const -{ - return std::find(_set.begin(), _set.end(), index) != _set.end(); -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/Operations.cc b/runtime/onert/core/src/ir/Operations.cc deleted file mode 100644 index 64d0bd6f0..000000000 --- a/runtime/onert/core/src/ir/Operations.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Operations.h" - -#include "OperationCloner.h" - -namespace onert -{ -namespace ir -{ - -Operations::Operations(const Operations &obj) -{ - obj.iterate([&](const OperationIndex &index, const Operation &op) { - OperationCloner cloner; - op.accept(cloner); - _objects.emplace(index, cloner.releaseClone()); - }); - _index_count = obj._index_count; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/Padding.cc b/runtime/onert/core/src/ir/Padding.cc deleted file mode 100644 index d74f80217..000000000 --- a/runtime/onert/core/src/ir/Padding.cc +++ /dev/null @@ -1,160 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Padding.h" - -#include "util/Utils.h" - -#include <stdexcept> -#include <cassert> - -namespace onert -{ -namespace ir -{ -namespace -{ - -inline ExplicitPadding validPadding(void) -{ - // - // ANEURALNETWORKS_PADDING_VALID - // - // VALID padding. No padding. - // - // When the input size is not evenly divisible by the filter size, - // the input at the end that could not fill the whole filter tile - // will simply be ignored. - // - ExplicitPadding padding; - - padding.top = 0; - padding.bottom = 0; - padding.left = 0; - padding.right = 0; - - return padding; -} - -inline ExplicitPadding samePaddingUsingIFM(const FeatureShape &ifm_shape, const Stride &stride, - uint32_t kw, uint32_t kh, uint32_t dwf, uint32_t dhf) -{ - ExplicitPadding padding; - - // ANEURALNETWORKS_PADDING_SAME (from NNAPI spec) - // - // SAME padding. Padding on both ends are the "same": - // - // padding_to_beginning = total_padding / 2 - // padding_to_end = (total_padding + 1)/2. - // - const int32_t effective_filter_h_size = (kh - 1) * dhf + 1; - const int32_t effective_filter_w_size = (kw - 1) * dwf + 1; - - const int32_t vertical_expected_output = (ifm_shape.H + stride.vertical - 1) / stride.vertical; - const int32_t horizontal_expected_output = - (ifm_shape.W + stride.horizontal - 1) / stride.horizontal; - - const int32_t vertical_needed_input = - (vertical_expected_output - 1) * stride.vertical + effective_filter_h_size; - const int32_t vertical_total_padding = std::max(0, vertical_needed_input - ifm_shape.H); - - const int32_t horizontal_needed_input = - (horizontal_expected_output - 1) * stride.horizontal + effective_filter_w_size; - const int32_t horizontal_total_padding = std::max(0, horizontal_needed_input - ifm_shape.W); - - padding.top = vertical_total_padding / 2; - padding.bottom = (vertical_total_padding + 1) / 2; - padding.left = horizontal_total_padding / 2; - padding.right = (horizontal_total_padding + 1) / 2; - - return padding; -} - -inline ExplicitPadding samePadding(const FeatureShape &ifm_shape, const FeatureShape &ofm_shape, - const Stride &stride, uint32_t kw, uint32_t kh, uint32_t dwf, - uint32_t dhf) -{ - const int32_t vertical_expected_output = (ifm_shape.H + stride.vertical - 1) / stride.vertical; - const int32_t horizontal_expected_output = - (ifm_shape.W + stride.horizontal - 1) / stride.horizontal; - assert(vertical_expected_output == ofm_shape.H); - assert(horizontal_expected_output == ofm_shape.W); - - UNUSED_RELEASE(ofm_shape); - UNUSED_RELEASE(vertical_expected_output); - UNUSED_RELEASE(horizontal_expected_output); - - return samePaddingUsingIFM(ifm_shape, stride, kw, kh, dwf, dhf); -} - -} // namespace - -inline std::string to_string(const PaddingType type) -{ - switch (type) - { - case PaddingType::EXPLICIT: - return "Padding::EXPLICIT"; - case PaddingType::SAME: - return "Padding::SAME"; - case PaddingType::VALID: - return "Padding::VALID"; - default: - throw std::runtime_error{"Fail to convert string: wrong padding type"}; - } -} - -Padding::Padding(void) : type{PaddingType::EXPLICIT}, param{0, 0, 0, 0} -{ - // DO NOTHING -} - -Padding::Padding(PaddingType paddingType) : type{paddingType}, param{0, 0, 0, 0} -{ - assert(paddingType != PaddingType::EXPLICIT); -} - -Padding::Padding(uint32_t left, uint32_t right, uint32_t top, uint32_t bottom) - : type{PaddingType::EXPLICIT}, param{left, right, top, bottom} -{ - // DO NOTHING -} - -const ExplicitPadding calculatePadding(const Padding &padding, const FeatureShape &ifm_shape, - const FeatureShape &ofm_shape, const Stride &stride, - uint32_t kw, uint32_t kh, uint32_t dwf, uint32_t dhf) -{ - if (padding.type == PaddingType::EXPLICIT) - { - return padding.param; - } - else if (padding.type == PaddingType::SAME) - { - return samePadding(ifm_shape, ofm_shape, stride, kw, kh, dwf, dhf); - } - else if (padding.type == PaddingType::VALID) - { - return validPadding(); - } - else - { - throw std::runtime_error{"Cannot handle padding type"}; - } -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/Shape.cc b/runtime/onert/core/src/ir/Shape.cc deleted file mode 100644 index 322df7b4c..000000000 --- a/runtime/onert/core/src/ir/Shape.cc +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/Shape.h" - -#include <cassert> -#include <functional> -#include <numeric> -#include <algorithm> - -namespace onert -{ -namespace ir -{ - -int32_t const Shape::UNSPECIFIED_DIM = -1; - -// NNFW_MAX_RANK is 6 -int32_t const Shape::MAX_RANK = 6; - -FeatureShape Shape::asFeature(Layout layout) const -{ - assert(rank() == 4); - - if (layout == Layout::NHWC) - { - // Feature Map in NHWC layout - // - Dimension(0) -> Batch - // - Dimension(1) -> Height - // - Dimension(2) -> Width - // - Dimension(3) -> Depth - const auto batch = dim(0); - const auto depth = dim(3); - const auto height = dim(1); - const auto width = dim(2); - - return {batch, depth, height, width}; - } - else if (layout == Layout::NCHW) - { - // Feature Map in NHWC layout - // - Dimension(0) -> Batch - // - Dimension(1) -> Depth - // - Dimension(2) -> Height - // - Dimension(3) -> Width - const auto batch = dim(0); - const auto depth = dim(1); - const auto height = dim(2); - const auto width = dim(3); - - return {batch, depth, height, width}; - } - else - { - throw std::runtime_error("Wrong Layout"); - } -} - -// Extended dimension is filled with 1. -void Shape::extendRank(int to_rank) -{ - assert(to_rank - rank() >= 0); - _dimensions.insert(_dimensions.cbegin(), to_rank - rank(), 1); -} - -uint64_t Shape::num_elements() const -{ - // if dimension is 0, it means unspecified and cannot calculate the total number of elements - if (std::any_of(_dimensions.begin(), _dimensions.end(), - [](const int32_t &v) { return v == UNSPECIFIED_DIM; })) - throw std::runtime_error("num_elements() cannot calculate when any dimension is unspecified"); - - return std::accumulate(_dimensions.cbegin(), _dimensions.cend(), UINT64_C(1), - std::multiplies<uint64_t>()); -} - -Shape permuteShape(const Shape &shape, Layout frontend_layout, Layout backend_layout) -{ - assert(shape.rank() <= Shape::MAX_RANK); - Shape backend_shape{shape}; - if (shape.rank() >= 4 && frontend_layout == Layout::NHWC && backend_layout == Layout::NCHW) - { - // Permutation changing layout beyond 4-D is not supported yet - assert(shape.rank() <= 4); - backend_shape.dim(1) = shape.dim(3); - backend_shape.dim(2) = shape.dim(1); - backend_shape.dim(3) = shape.dim(2); - } - else if (shape.rank() >= 4 && frontend_layout == Layout::NCHW && backend_layout == Layout::NHWC) - { - // Permutation changing layout beyond 4-D is not supported yet - assert(shape.rank() <= 4); - backend_shape.dim(1) = shape.dim(2); - backend_shape.dim(2) = shape.dim(3); - backend_shape.dim(3) = shape.dim(1); - } - return backend_shape; -} - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/TypeInfo.cc b/runtime/onert/core/src/ir/TypeInfo.cc deleted file mode 100644 index ab8af287e..000000000 --- a/runtime/onert/core/src/ir/TypeInfo.cc +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/TypeInfo.h" - -namespace onert -{ -namespace ir -{ - -bool operator==(const TypeInfo &lhs, const TypeInfo &rhs) -{ - if (lhs.type() != rhs.type()) - { - return false; - } - - if (lhs.offset() != rhs.offset()) - { - return false; - } - - if (lhs.scale() != rhs.scale()) - { - return false; - } - - return true; -} - -bool operator!=(const TypeInfo &lhs, const TypeInfo &rhs) { return !(lhs == rhs); } - -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ArgMax.cc b/runtime/onert/core/src/ir/operation/ArgMax.cc deleted file mode 100644 index f3bd8fd73..000000000 --- a/runtime/onert/core/src/ir/operation/ArgMax.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ArgMax.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ArgMax::accept(OperationVisitor &v) const { v.visit(*this); } - -ArgMax::ArgMax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/BCQFullyConnected.cc b/runtime/onert/core/src/ir/operation/BCQFullyConnected.cc deleted file mode 100644 index 9dc54e6e9..000000000 --- a/runtime/onert/core/src/ir/operation/BCQFullyConnected.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/BCQFullyConnected.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void BCQFullyConnected::accept(OperationVisitor &v) const { v.visit(*this); } - -BCQFullyConnected::BCQFullyConnected(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createExact(5u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/BCQGather.cc b/runtime/onert/core/src/ir/operation/BCQGather.cc deleted file mode 100644 index 80efa6460..000000000 --- a/runtime/onert/core/src/ir/operation/BCQGather.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/BCQGather.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void BCQGather::accept(OperationVisitor &v) const { v.visit(*this); } - -BCQGather::BCQGather(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(4u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/BatchMatMul.cc b/runtime/onert/core/src/ir/operation/BatchMatMul.cc deleted file mode 100644 index b9616158d..000000000 --- a/runtime/onert/core/src/ir/operation/BatchMatMul.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/BatchMatMul.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void BatchMatMul::accept(OperationVisitor &v) const { v.visit(*this); } - -BatchMatMul::BatchMatMul(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/BatchToSpaceND.cc b/runtime/onert/core/src/ir/operation/BatchToSpaceND.cc deleted file mode 100644 index 34be79dd2..000000000 --- a/runtime/onert/core/src/ir/operation/BatchToSpaceND.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/BatchToSpaceND.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void BatchToSpaceND::accept(OperationVisitor &v) const { v.visit(*this); } - -BatchToSpaceND::BatchToSpaceND(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createInRange(2u, 3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/BinaryArithmetic.cc b/runtime/onert/core/src/ir/operation/BinaryArithmetic.cc deleted file mode 100644 index 2b1422c73..000000000 --- a/runtime/onert/core/src/ir/operation/BinaryArithmetic.cc +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/BinaryArithmetic.h" - -#include <cassert> -#include <unordered_map> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void BinaryArithmetic::accept(OperationVisitor &v) const { v.visit(*this); } - -BinaryArithmetic::BinaryArithmetic(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} - -std::string BinaryArithmetic::name() const -{ - using ArithmeticType = onert::ir::operation::BinaryArithmetic::ArithmeticType; - static const std::unordered_map<ArithmeticType, std::string> name_map{ - {ArithmeticType::ADD, std::string{"Add"}}, - {ArithmeticType::SUB, std::string{"Sub"}}, - {ArithmeticType::MUL, std::string{"Mul"}}, - {ArithmeticType::DIV, std::string{"Div"}}}; - return name_map.at(_param.arithmetic_type); -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/BroadcastTo.cc b/runtime/onert/core/src/ir/operation/BroadcastTo.cc deleted file mode 100644 index a8f5e59cf..000000000 --- a/runtime/onert/core/src/ir/operation/BroadcastTo.cc +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/BroadcastTo.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -void BroadcastTo::accept(OperationVisitor &v) const { v.visit(*this); } - -BroadcastTo::BroadcastTo(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Comparison.cc b/runtime/onert/core/src/ir/operation/Comparison.cc deleted file mode 100644 index 2f6775411..000000000 --- a/runtime/onert/core/src/ir/operation/Comparison.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Comparison.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Comparison::accept(OperationVisitor &v) const { v.visit(*this); } - -Comparison::Comparison(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Concat.cc b/runtime/onert/core/src/ir/operation/Concat.cc deleted file mode 100644 index 608bc29a6..000000000 --- a/runtime/onert/core/src/ir/operation/Concat.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Concat.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Concat::accept(OperationVisitor &v) const { v.visit(*this); } - -Concat::Concat(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createAtLeast(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Conv2D.cc b/runtime/onert/core/src/ir/operation/Conv2D.cc deleted file mode 100644 index 3a2e1d1fe..000000000 --- a/runtime/onert/core/src/ir/operation/Conv2D.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Conv2D.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Conv2D::accept(OperationVisitor &v) const { v.visit(*this); } - -Conv2D::Conv2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ConvertFp16ToFp32.cc b/runtime/onert/core/src/ir/operation/ConvertFp16ToFp32.cc deleted file mode 100644 index 676e039fa..000000000 --- a/runtime/onert/core/src/ir/operation/ConvertFp16ToFp32.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ConvertFp16ToFp32.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ConvertFp16ToFp32::accept(OperationVisitor &v) const { v.visit(*this); } - -ConvertFp16ToFp32::ConvertFp16ToFp32(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(1u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ConvertFp32ToFp16.cc b/runtime/onert/core/src/ir/operation/ConvertFp32ToFp16.cc deleted file mode 100644 index bcfcbfc04..000000000 --- a/runtime/onert/core/src/ir/operation/ConvertFp32ToFp16.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ConvertFp32ToFp16.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ConvertFp32ToFp16::accept(OperationVisitor &v) const { v.visit(*this); } - -ConvertFp32ToFp16::ConvertFp32ToFp16(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(1u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Custom.cc b/runtime/onert/core/src/ir/operation/Custom.cc deleted file mode 100644 index 25c53e1ba..000000000 --- a/runtime/onert/core/src/ir/operation/Custom.cc +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Custom.h" - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Custom::accept(OperationVisitor &v) const { v.visit(*this); } - -Custom::Custom(OperandConstraint input_constr, const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, std::string id, const Userdata &userdata) - : Operation{input_constr, inputs, outputs}, _id(std::move(id)), _userdata(userdata) -{ -} - -const std::string &Custom::id() const { return _id; } - -const Custom::Userdata &Custom::userdata() const { return _userdata; } - -std::string Custom::name() const { return id(); } - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/DepthToSpace.cc b/runtime/onert/core/src/ir/operation/DepthToSpace.cc deleted file mode 100644 index f2d6c7c1b..000000000 --- a/runtime/onert/core/src/ir/operation/DepthToSpace.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/DepthToSpace.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void DepthToSpace::accept(OperationVisitor &v) const { v.visit(*this); } - -DepthToSpace::DepthToSpace(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/DepthwiseConv2D.cc b/runtime/onert/core/src/ir/operation/DepthwiseConv2D.cc deleted file mode 100644 index d587a5591..000000000 --- a/runtime/onert/core/src/ir/operation/DepthwiseConv2D.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/DepthwiseConv2D.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void DepthwiseConv2D::accept(OperationVisitor &v) const { v.visit(*this); } - -DepthwiseConv2D::DepthwiseConv2D(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Einsum.cc b/runtime/onert/core/src/ir/operation/Einsum.cc deleted file mode 100644 index 3c1473aaa..000000000 --- a/runtime/onert/core/src/ir/operation/Einsum.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Einsum.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Einsum::accept(OperationVisitor &v) const { v.visit(*this); } - -Einsum::Einsum(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createAtLeast(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ElementwiseActivation.cc b/runtime/onert/core/src/ir/operation/ElementwiseActivation.cc deleted file mode 100644 index f6718b656..000000000 --- a/runtime/onert/core/src/ir/operation/ElementwiseActivation.cc +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ElementwiseActivation.h" - -#include <cassert> -#include <unordered_map> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ElementwiseActivation::accept(OperationVisitor &v) const { v.visit(*this); } - -ElementwiseActivation::ElementwiseActivation(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ - if (param.op_type == Type::LOGISTIC) - { - assert(param.alpha == 0.0f && param.beta == 0.0f && "Logistic will be supported only as " - "sigmoid function(L=1, k=1, x0=0). So, do " - "not use alpha and beta"); - } - else if (param.op_type == Type::RELU) - { - assert(param.alpha >= param.beta && "ReLU's alpha must be equal or greater than beta"); - } - else if (param.op_type == Type::TANH) - { - assert(param.alpha == 1.0f && param.beta == 1.0f && "f(x) = alpha * tanh(beta * x), Tanh is " - "supported only the values of alpha and " - "beta are 1.f"); - } -} - -std::string ElementwiseActivation::name() const -{ - using ElementwiseActivationType = onert::ir::operation::ElementwiseActivation::Type; - static const std::unordered_map<Type, std::string> name_map{ - {ElementwiseActivationType::ELU, "ELU"}, - {ElementwiseActivationType::LOGISTIC, "Logistic"}, - {ElementwiseActivationType::RELU, "ReLU"}, - {ElementwiseActivationType::TANH, "Tanh"}, - {ElementwiseActivationType::LEAKY_RELU, "LeakyRelu"}}; - return name_map.at(_param.op_type); -} - -float ElementwiseActivation::infinity = std::numeric_limits<float>::infinity(); - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ElementwiseBinary.cc b/runtime/onert/core/src/ir/operation/ElementwiseBinary.cc deleted file mode 100644 index 3287fc0a3..000000000 --- a/runtime/onert/core/src/ir/operation/ElementwiseBinary.cc +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ElementwiseBinary.h" - -#include <cassert> -#include <unordered_map> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ElementwiseBinary::accept(OperationVisitor &v) const { v.visit(*this); } - -ElementwiseBinary::ElementwiseBinary(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} - -std::string ElementwiseBinary::name() const -{ - using ElementwiseBinaryType = onert::ir::operation::ElementwiseBinary::ElementwiseBinaryType; - static const std::unordered_map<ElementwiseBinaryType, std::string> name_map{ - {ElementwiseBinaryType::LOGICAL_AND, std::string{"LogicalAnd"}}, - {ElementwiseBinaryType::LOGICAL_OR, std::string{"LogicalOr"}}, - {ElementwiseBinaryType::MAX, std::string{"Max"}}, - {ElementwiseBinaryType::MIN, std::string{"Min"}}}; - return name_map.at(_param.op_type); -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ElementwiseUnary.cc b/runtime/onert/core/src/ir/operation/ElementwiseUnary.cc deleted file mode 100644 index 6a0be7eb8..000000000 --- a/runtime/onert/core/src/ir/operation/ElementwiseUnary.cc +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ElementwiseUnary.h" - -#include <cassert> -#include <unordered_map> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ElementwiseUnary::accept(OperationVisitor &v) const { v.visit(*this); } - -ElementwiseUnary::ElementwiseUnary(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs, - OperandConstraint::createExact(1u)}, - _param{param} -{ -} - -std::string ElementwiseUnary::name() const -{ - using ElementwiseUnaryType = onert::ir::operation::ElementwiseUnary::Type; - static const std::unordered_map<ElementwiseUnaryType, std::string> name_map{ - {ElementwiseUnaryType::ABS, std::string{"Abs"}}, - {ElementwiseUnaryType::CAST, std::string{"Cast"}}, - {ElementwiseUnaryType::COS, std::string{"Cos"}}, - {ElementwiseUnaryType::DEQUANTIZE, std::string{"Dequantize"}}, - {ElementwiseUnaryType::ERF, std::string{"Erf"}}, - {ElementwiseUnaryType::EXP, std::string{"Exp"}}, - {ElementwiseUnaryType::FLOOR, std::string{"Floor"}}, - {ElementwiseUnaryType::LOG, std::string{"Log"}}, - {ElementwiseUnaryType::LOGICAL_NOT, std::string{"LogicalNot"}}, - {ElementwiseUnaryType::NEG, std::string{"Neg"}}, - {ElementwiseUnaryType::QUANTIZE, std::string{"Quantize"}}, - {ElementwiseUnaryType::ROUND, std::string{"Round"}}, - {ElementwiseUnaryType::RSQRT, std::string{"RSqrt"}}, - {ElementwiseUnaryType::SIN, std::string{"Sin"}}, - {ElementwiseUnaryType::SQRT, std::string{"Sqrt"}}, - {ElementwiseUnaryType::SQURE, std::string{"Squre"}}, - {ElementwiseUnaryType::ZEROS_LIKE, std::string{"ZerosLike"}}}; - return name_map.at(_param.op_type); -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/EmbeddingLookup.cc b/runtime/onert/core/src/ir/operation/EmbeddingLookup.cc deleted file mode 100644 index b300b004e..000000000 --- a/runtime/onert/core/src/ir/operation/EmbeddingLookup.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/EmbeddingLookup.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void EmbeddingLookup::accept(OperationVisitor &v) const { v.visit(*this); } - -EmbeddingLookup::EmbeddingLookup(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ExpandDims.cc b/runtime/onert/core/src/ir/operation/ExpandDims.cc deleted file mode 100644 index 3f555bd23..000000000 --- a/runtime/onert/core/src/ir/operation/ExpandDims.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ExpandDims.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ExpandDims::accept(OperationVisitor &v) const { v.visit(*this); } - -ExpandDims::ExpandDims(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Fill.cc b/runtime/onert/core/src/ir/operation/Fill.cc deleted file mode 100644 index b8b97d1c0..000000000 --- a/runtime/onert/core/src/ir/operation/Fill.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Fill.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Fill::accept(OperationVisitor &v) const { v.visit(*this); } - -Fill::Fill(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/FullyConnected.cc b/runtime/onert/core/src/ir/operation/FullyConnected.cc deleted file mode 100644 index 9837a3137..000000000 --- a/runtime/onert/core/src/ir/operation/FullyConnected.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/FullyConnected.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void FullyConnected::accept(OperationVisitor &v) const { v.visit(*this); } - -FullyConnected::FullyConnected(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createInRange(2u, 3u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/FusedBatchNorm.cc b/runtime/onert/core/src/ir/operation/FusedBatchNorm.cc deleted file mode 100644 index 7b9301ea6..000000000 --- a/runtime/onert/core/src/ir/operation/FusedBatchNorm.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/FusedBatchNorm.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void FusedBatchNorm::accept(OperationVisitor &v) const { v.visit(*this); } - -FusedBatchNorm::FusedBatchNorm(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createAtLeast(5u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Gather.cc b/runtime/onert/core/src/ir/operation/Gather.cc deleted file mode 100644 index 11d46e75b..000000000 --- a/runtime/onert/core/src/ir/operation/Gather.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Gather.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Gather::accept(OperationVisitor &v) const { v.visit(*this); } - -Gather::Gather(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/HashtableLookup.cc b/runtime/onert/core/src/ir/operation/HashtableLookup.cc deleted file mode 100644 index e9a7a82ff..000000000 --- a/runtime/onert/core/src/ir/operation/HashtableLookup.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/HashtableLookup.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void HashtableLookup::accept(OperationVisitor &v) const { v.visit(*this); } - -HashtableLookup::HashtableLookup(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/If.cc b/runtime/onert/core/src/ir/operation/If.cc deleted file mode 100644 index 599751dfd..000000000 --- a/runtime/onert/core/src/ir/operation/If.cc +++ /dev/null @@ -1,32 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "ir/operation/If.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -void If::accept(OperationVisitor &v) const { v.visit(*this); } -If::If(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createAny(), inputs, outputs}, _param{param} -{ -} -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/InstanceNorm.cc b/runtime/onert/core/src/ir/operation/InstanceNorm.cc deleted file mode 100644 index 2334560ef..000000000 --- a/runtime/onert/core/src/ir/operation/InstanceNorm.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/InstanceNorm.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void InstanceNorm::accept(OperationVisitor &v) const { v.visit(*this); } - -InstanceNorm::InstanceNorm(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/L2Normalization.cc b/runtime/onert/core/src/ir/operation/L2Normalization.cc deleted file mode 100644 index 9a7d3eb61..000000000 --- a/runtime/onert/core/src/ir/operation/L2Normalization.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/L2Normalization.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void L2Normalization::accept(OperationVisitor &v) const { v.visit(*this); } - -L2Normalization::L2Normalization(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(1u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/LSTM.cc b/runtime/onert/core/src/ir/operation/LSTM.cc deleted file mode 100644 index 5cd7c793a..000000000 --- a/runtime/onert/core/src/ir/operation/LSTM.cc +++ /dev/null @@ -1,48 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/LSTM.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void LSTM::accept(OperationVisitor &v) const { v.visit(*this); } - -LSTM::LSTM(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createInRange(20u, 24u), inputs, outputs}, _param{param} -{ -} - -std::string LSTM::name() const -{ - if (getOutputs().at(Output::SCRATCH_BUFFER).undefined()) - return std::string{"UnidirectionalSequenceLSTM"}; - else - return Operation::name(); -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/LocalResponseNormalization.cc b/runtime/onert/core/src/ir/operation/LocalResponseNormalization.cc deleted file mode 100644 index 1ae97c142..000000000 --- a/runtime/onert/core/src/ir/operation/LocalResponseNormalization.cc +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/LocalResponseNormalization.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void LocalResponseNormalization::accept(OperationVisitor &v) const { v.visit(*this); } - -LocalResponseNormalization::LocalResponseNormalization(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/LogSoftmax.cc b/runtime/onert/core/src/ir/operation/LogSoftmax.cc deleted file mode 100644 index 73c6580ec..000000000 --- a/runtime/onert/core/src/ir/operation/LogSoftmax.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/LogSoftmax.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void LogSoftmax::accept(OperationVisitor &v) const { v.visit(*this); } - -LogSoftmax::LogSoftmax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/LowerInfo.cc b/runtime/onert/core/src/ir/operation/LowerInfo.cc deleted file mode 100644 index 249918bd6..000000000 --- a/runtime/onert/core/src/ir/operation/LowerInfo.cc +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/LowerInfo.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -LowerInfo::LowerInfo(const backend::Backend *backend, Layout layout) - : _permute_factor{backend, layout} -{ - // DO NOTHING -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/MatrixBandPart.cc b/runtime/onert/core/src/ir/operation/MatrixBandPart.cc deleted file mode 100644 index bac31f13e..000000000 --- a/runtime/onert/core/src/ir/operation/MatrixBandPart.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/MatrixBandPart.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void MatrixBandPart::accept(OperationVisitor &v) const { v.visit(*this); } - -MatrixBandPart::MatrixBandPart(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/OneHot.cc b/runtime/onert/core/src/ir/operation/OneHot.cc deleted file mode 100644 index 22935e7d6..000000000 --- a/runtime/onert/core/src/ir/operation/OneHot.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/OneHot.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void OneHot::accept(OperationVisitor &v) const { v.visit(*this); } - -OneHot::OneHot(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(4u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/PReLU.cc b/runtime/onert/core/src/ir/operation/PReLU.cc deleted file mode 100644 index a2e37e0ad..000000000 --- a/runtime/onert/core/src/ir/operation/PReLU.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/PReLU.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void PReLU::accept(OperationVisitor &v) const { v.visit(*this); } - -PReLU::PReLU(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Pack.cc b/runtime/onert/core/src/ir/operation/Pack.cc deleted file mode 100644 index 784d4162a..000000000 --- a/runtime/onert/core/src/ir/operation/Pack.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "ir/operation/Pack.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -void Pack::accept(OperationVisitor &v) const { v.visit(*this); } -Pack::Pack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createAtLeast(1u), inputs, outputs}, _param{param} -{ -} -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Pad.cc b/runtime/onert/core/src/ir/operation/Pad.cc deleted file mode 100644 index 0c56e92e3..000000000 --- a/runtime/onert/core/src/ir/operation/Pad.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Pad.h" - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Pad::accept(OperationVisitor &v) const { v.visit(*this); } - -// PAD: 2 inputs -// PADV2: 3 inputs -Pad::Pad(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createInRange(2u, 3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Permute.cc b/runtime/onert/core/src/ir/operation/Permute.cc deleted file mode 100644 index eefb6c542..000000000 --- a/runtime/onert/core/src/ir/operation/Permute.cc +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Permute.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Permute::accept(OperationVisitor &v) const { v.visit(*this); } - -Permute::Permute(const OperandIndex &input, const OperandIndex &output, Type type) - : Operation{OperandConstraint::createExact(1u)}, _type{type} -{ - setInputs({input}); - setOutputs({output}); -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Pool2D.cc b/runtime/onert/core/src/ir/operation/Pool2D.cc deleted file mode 100644 index 761d14c3d..000000000 --- a/runtime/onert/core/src/ir/operation/Pool2D.cc +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Pool2D.h" - -#include <cassert> -#include <unordered_map> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Pool2D::accept(OperationVisitor &v) const { v.visit(*this); } - -Pool2D::Pool2D(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} - -std::string Pool2D::name() const -{ - using PoolType = onert::ir::operation::Pool2D::PoolType; - static const std::unordered_map<PoolType, std::string> name_map{ - {PoolType::AVG, "Avg" + std::string{toString(opcode())}}, - {PoolType::L2, "L2" + std::string{toString(opcode())}}, - {PoolType::MAX, "Max" + std::string{toString(opcode())}}}; - return name_map.at(_param.op_type); -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Pow.cc b/runtime/onert/core/src/ir/operation/Pow.cc deleted file mode 100644 index 940b1391a..000000000 --- a/runtime/onert/core/src/ir/operation/Pow.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Pow.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Pow::accept(OperationVisitor &v) const { v.visit(*this); } - -Pow::Pow(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/RNN.cc b/runtime/onert/core/src/ir/operation/RNN.cc deleted file mode 100644 index 298c5e745..000000000 --- a/runtime/onert/core/src/ir/operation/RNN.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/RNN.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void RNN::accept(OperationVisitor &v) const { v.visit(*this); } - -RNN::RNN(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(5u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Range.cc b/runtime/onert/core/src/ir/operation/Range.cc deleted file mode 100644 index 96ab04c1b..000000000 --- a/runtime/onert/core/src/ir/operation/Range.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Range.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Range::accept(OperationVisitor &v) const { v.visit(*this); } - -Range::Range(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Rank.cc b/runtime/onert/core/src/ir/operation/Rank.cc deleted file mode 100644 index c357e9018..000000000 --- a/runtime/onert/core/src/ir/operation/Rank.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Rank.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Rank::accept(OperationVisitor &v) const { v.visit(*this); } - -Rank::Rank(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(1u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Reduce.cc b/runtime/onert/core/src/ir/operation/Reduce.cc deleted file mode 100644 index d6a1d953c..000000000 --- a/runtime/onert/core/src/ir/operation/Reduce.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Reduce.h" - -#include <cassert> -#include <unordered_map> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Reduce::accept(OperationVisitor &v) const { v.visit(*this); } - -Reduce::Reduce(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} - -std::string Reduce::name() const -{ - using ReduceType = onert::ir::operation::Reduce::ReduceType; - static const std::unordered_map<ReduceType, std::string> name_map{ - {ReduceType::ALL, std::string{toString(opcode())} + "All"}, - {ReduceType::ANY, std::string{toString(opcode())} + "Any"}, - {ReduceType::MAX, std::string{toString(opcode())} + "Max"}, - {ReduceType::MEAN, std::string{toString(opcode())} + "Mean"}, - {ReduceType::MIN, std::string{toString(opcode())} + "Min"}, - {ReduceType::PROD, std::string{toString(opcode())} + "Prod"}, - {ReduceType::SUM, std::string{toString(opcode())} + "SUM"}}; - return name_map.at(_param.reduce_type); - // return std::string(toString(opcode())) + reduce_type_str_map.at(_param.reduce_type); -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Reshape.cc b/runtime/onert/core/src/ir/operation/Reshape.cc deleted file mode 100644 index 92aa89ac6..000000000 --- a/runtime/onert/core/src/ir/operation/Reshape.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Reshape.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Reshape::accept(OperationVisitor &v) const { v.visit(*this); } - -Reshape::Reshape(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param(param) -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ResizeBilinear.cc b/runtime/onert/core/src/ir/operation/ResizeBilinear.cc deleted file mode 100644 index 71925bb44..000000000 --- a/runtime/onert/core/src/ir/operation/ResizeBilinear.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ResizeBilinear.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ResizeBilinear::accept(OperationVisitor &v) const { v.visit(*this); } - -ResizeBilinear::ResizeBilinear(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createInRange(1u, 2u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/ResizeNearestNeighbor.cc b/runtime/onert/core/src/ir/operation/ResizeNearestNeighbor.cc deleted file mode 100644 index 98d0b5f26..000000000 --- a/runtime/onert/core/src/ir/operation/ResizeNearestNeighbor.cc +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/ResizeNearestNeighbor.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void ResizeNearestNeighbor::accept(OperationVisitor &v) const { v.visit(*this); } - -ResizeNearestNeighbor::ResizeNearestNeighbor(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createInRange(1u, 2u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Reverse.cc b/runtime/onert/core/src/ir/operation/Reverse.cc deleted file mode 100644 index 4b3c1e1af..000000000 --- a/runtime/onert/core/src/ir/operation/Reverse.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Reverse.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Reverse::accept(OperationVisitor &v) const { v.visit(*this); } - -Reverse::Reverse(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Select.cc b/runtime/onert/core/src/ir/operation/Select.cc deleted file mode 100644 index 1f22b5234..000000000 --- a/runtime/onert/core/src/ir/operation/Select.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Select.h" - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Select::accept(OperationVisitor &v) const { v.visit(*this); } - -Select::Select(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Shape.cc b/runtime/onert/core/src/ir/operation/Shape.cc deleted file mode 100644 index 2a63d6dcf..000000000 --- a/runtime/onert/core/src/ir/operation/Shape.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Shape.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Shape::accept(OperationVisitor &v) const { v.visit(*this); } - -Shape::Shape(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(1u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Slice.cc b/runtime/onert/core/src/ir/operation/Slice.cc deleted file mode 100644 index 888b563fb..000000000 --- a/runtime/onert/core/src/ir/operation/Slice.cc +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Slice.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Slice::accept(OperationVisitor &v) const { v.visit(*this); } - -Slice::Slice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Softmax.cc b/runtime/onert/core/src/ir/operation/Softmax.cc deleted file mode 100644 index 3f1aa0af1..000000000 --- a/runtime/onert/core/src/ir/operation/Softmax.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Softmax.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Softmax::accept(OperationVisitor &v) const { v.visit(*this); } - -Softmax::Softmax(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/SpaceToBatchND.cc b/runtime/onert/core/src/ir/operation/SpaceToBatchND.cc deleted file mode 100644 index 53fab4fa9..000000000 --- a/runtime/onert/core/src/ir/operation/SpaceToBatchND.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/SpaceToBatchND.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void SpaceToBatchND::accept(OperationVisitor &v) const { v.visit(*this); } - -SpaceToBatchND::SpaceToBatchND(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(3u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/SpaceToDepth.cc b/runtime/onert/core/src/ir/operation/SpaceToDepth.cc deleted file mode 100644 index d8a45aee5..000000000 --- a/runtime/onert/core/src/ir/operation/SpaceToDepth.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/SpaceToDepth.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void SpaceToDepth::accept(OperationVisitor &v) const { v.visit(*this); } - -SpaceToDepth::SpaceToDepth(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Split.cc b/runtime/onert/core/src/ir/operation/Split.cc deleted file mode 100644 index b538e9206..000000000 --- a/runtime/onert/core/src/ir/operation/Split.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "ir/operation/Split.h" -#include <cassert> -#include "ir/OperationVisitor.h" -namespace onert -{ -namespace ir -{ -namespace operation -{ -void Split::accept(OperationVisitor &v) const { v.visit(*this); } -Split::Split(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(2u), inputs, outputs}, _param{param} -{ -} -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/SplitV.cc b/runtime/onert/core/src/ir/operation/SplitV.cc deleted file mode 100644 index e638c9ac9..000000000 --- a/runtime/onert/core/src/ir/operation/SplitV.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "ir/operation/SplitV.h" -#include <cassert> -#include "ir/OperationVisitor.h" -namespace onert -{ -namespace ir -{ -namespace operation -{ -void SplitV::accept(OperationVisitor &v) const { v.visit(*this); } -SplitV::SplitV(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param} -{ -} -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/SquaredDifference.cc b/runtime/onert/core/src/ir/operation/SquaredDifference.cc deleted file mode 100644 index 49e58aaf2..000000000 --- a/runtime/onert/core/src/ir/operation/SquaredDifference.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/SquaredDifference.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void SquaredDifference::accept(OperationVisitor &v) const { v.visit(*this); } - -SquaredDifference::SquaredDifference(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Squeeze.cc b/runtime/onert/core/src/ir/operation/Squeeze.cc deleted file mode 100644 index 8cf928fb4..000000000 --- a/runtime/onert/core/src/ir/operation/Squeeze.cc +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Squeeze.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Squeeze::accept(OperationVisitor &v) const { v.visit(*this); } - -Squeeze::Squeeze(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param(param) -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/StatelessRandomUniform.cc b/runtime/onert/core/src/ir/operation/StatelessRandomUniform.cc deleted file mode 100644 index cbb0ff251..000000000 --- a/runtime/onert/core/src/ir/operation/StatelessRandomUniform.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/StatelessRandomUniform.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -void StatelessRandomUniform::accept(OperationVisitor &v) const { v.visit(*this); } - -StatelessRandomUniform::StatelessRandomUniform(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/StridedSlice.cc b/runtime/onert/core/src/ir/operation/StridedSlice.cc deleted file mode 100644 index 2a7905995..000000000 --- a/runtime/onert/core/src/ir/operation/StridedSlice.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/StridedSlice.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void StridedSlice::accept(OperationVisitor &v) const { v.visit(*this); } - -StridedSlice::StridedSlice(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(4u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Tile.cc b/runtime/onert/core/src/ir/operation/Tile.cc deleted file mode 100644 index 5ba3df2ad..000000000 --- a/runtime/onert/core/src/ir/operation/Tile.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Tile.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Tile::accept(OperationVisitor &v) const { v.visit(*this); } - -Tile::Tile(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/TopKV2.cc b/runtime/onert/core/src/ir/operation/TopKV2.cc deleted file mode 100644 index a5e6c6a85..000000000 --- a/runtime/onert/core/src/ir/operation/TopKV2.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/TopKV2.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void TopKV2::accept(OperationVisitor &v) const { v.visit(*this); } - -TopKV2::TopKV2(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Transpose.cc b/runtime/onert/core/src/ir/operation/Transpose.cc deleted file mode 100644 index 997f98ab0..000000000 --- a/runtime/onert/core/src/ir/operation/Transpose.cc +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/Transpose.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void Transpose::accept(OperationVisitor &v) const { v.visit(*this); } - -Transpose::Transpose(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) - : Operation{OperandConstraint::createExact(2u), inputs, outputs} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/TransposeConv.cc b/runtime/onert/core/src/ir/operation/TransposeConv.cc deleted file mode 100644 index 7f29ca44e..000000000 --- a/runtime/onert/core/src/ir/operation/TransposeConv.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ir/operation/TransposeConv.h" - -#include <cassert> - -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ - -void TransposeConv::accept(OperationVisitor &v) const { v.visit(*this); } - -TransposeConv::TransposeConv(const OperandIndexSequence &inputs, - const OperandIndexSequence &outputs, const Param ¶m) - : Operation{OperandConstraint::createExact(3u), inputs, outputs}, _param{param} -{ -} - -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/Unpack.cc b/runtime/onert/core/src/ir/operation/Unpack.cc deleted file mode 100644 index 67aa54ab5..000000000 --- a/runtime/onert/core/src/ir/operation/Unpack.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "ir/operation/Unpack.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -void Unpack::accept(OperationVisitor &v) const { v.visit(*this); } -Unpack::Unpack(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createExact(1u), inputs, outputs}, _param{param} -{ -} -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/operation/While.cc b/runtime/onert/core/src/ir/operation/While.cc deleted file mode 100644 index 2505c60e3..000000000 --- a/runtime/onert/core/src/ir/operation/While.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "ir/operation/While.h" -#include "ir/OperationVisitor.h" - -namespace onert -{ -namespace ir -{ -namespace operation -{ -void While::accept(OperationVisitor &v) const { v.visit(*this); } -While::While(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs, - const Param ¶m) - : Operation{OperandConstraint::createAny(), inputs, outputs}, _param{param} -{ -} -} // namespace operation -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/verifier/Verifier.cc b/runtime/onert/core/src/ir/verifier/Verifier.cc deleted file mode 100644 index 489845971..000000000 --- a/runtime/onert/core/src/ir/verifier/Verifier.cc +++ /dev/null @@ -1,132 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Verifier.h" - -#include "ir/Graph.h" -#include "ir/OperationIndexMap.h" - -#include "util/logging.h" - -namespace onert -{ -namespace ir -{ -namespace verifier -{ - -// -// DAGChecker -// - -bool DAGChecker::verify(const Graph &graph) const noexcept -{ - auto &operations = graph.operations(); - bool cyclic = false; - - OperationIndexMap<bool> visited; - operations.iterate( - [&](const OperationIndex &index, const Operation &) { visited[index] = false; }); - OperationIndexMap<bool> on_stack = visited; // Copy from visited - - std::function<void(const OperationIndex &index, const Operation &)> dfs_recursive = - [&](const OperationIndex &index, const Operation &node) -> void { - if (on_stack[index]) - cyclic = true; - if (visited[index]) - return; - visited[index] = true; - on_stack[index] = true; - - for (auto output : node.getOutputs() | Remove::DUPLICATED | Remove::UNDEFINED) - { - const auto &operand = graph.operands().at(output); - for (const auto &use : operand.getUses()) - { - dfs_recursive(use, graph.operations().at(use)); - } - } - - on_stack[index] = false; - }; - - operations.iterate(dfs_recursive); - - return !cyclic; -} - -// -// EdgeConsistencyVerifier -// - -bool EdgeConsistencyChecker::verify(const Graph &graph) const noexcept -{ - auto &operations = graph.operations(); - uint32_t errors = 0; - operations.iterate([&](const OperationIndex &index, const Operation &node) { - for (auto operand_index : node.getInputs() | ir::Remove::UNDEFINED) - { - try - { - auto &operand = graph.operands().at(operand_index); - bool operand_has_use = operand.getUses().contains(index); - if (!operand_has_use) - { - VERBOSE(EdgeConsistencyChecker) << "[ERROR] EDGE MISMATCH : Missing USE edge - Operand " - << operand_index << " to Operation " << index - << std::endl; - errors += 1; - } - } - catch (const std::out_of_range &e) - { - VERBOSE(EdgeConsistencyChecker) - << "[ERROR] OPEARAND NOT FOUND : Operation " << index << " has Operand " - << operand_index << ", but the operand object is not present in the graph" << std::endl; - errors += 1; - } - } - for (auto operand_index : node.getOutputs() | ir::Remove::UNDEFINED) - { - try - { - auto &operand = graph.operands().at(operand_index); - if (operand.getDef() != index) - { - VERBOSE(EdgeConsistencyChecker) << "[ERROR] EDGE MISMATCH : Missing DEF edge - Operand" - << operand_index << " to Operation " << index - << std::endl; - errors += 1; - } - } - catch (const std::out_of_range &e) - { - VERBOSE(EdgeConsistencyChecker) - << "[ERROR] OPEARAND NOT FOUND : Operation " << index << " has Operand " - << operand_index << ", but the operand object is not present in the graph" << std::endl; - errors += 1; - } - } - }); - - VERBOSE(EdgeConsistencyChecker) << "Total Number of errors : " << errors << std::endl; - - return errors == 0; -} - -} // namespace verifier -} // namespace ir -} // namespace onert diff --git a/runtime/onert/core/src/ir/verifier/Verifier.h b/runtime/onert/core/src/ir/verifier/Verifier.h deleted file mode 100644 index 0c7b57b04..000000000 --- a/runtime/onert/core/src/ir/verifier/Verifier.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_GRAPH_VERIFIER_VERIFIER_H__ -#define __ONERT_GRAPH_VERIFIER_VERIFIER_H__ - -namespace onert -{ -namespace ir -{ -class Graph; -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace ir -{ -namespace verifier -{ - -struct IVerifier -{ - virtual ~IVerifier() = default; - virtual bool verify(const Graph &graph) const noexcept = 0; -}; - -} // namespace verifier -} // namespace ir -} // namespace onert - -namespace onert -{ -namespace ir -{ -namespace verifier -{ - -class DAGChecker : public IVerifier -{ -public: - bool verify(const Graph &graph) const noexcept override; -}; - -class EdgeConsistencyChecker : public IVerifier -{ -public: - bool verify(const Graph &graph) const noexcept override; -}; - -} // namespace verifier -} // namespace ir -} // namespace onert - -#endif // __ONERT_GRAPH_VERIFIER_VERIFIER_H__ diff --git a/runtime/onert/core/src/library_info.cc b/runtime/onert/core/src/library_info.cc deleted file mode 100644 index 6d7579cca..000000000 --- a/runtime/onert/core/src/library_info.cc +++ /dev/null @@ -1,17 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -volatile const char info[] = "library information : runtime=onert"; diff --git a/runtime/onert/core/src/util/ConfigSource.cc b/runtime/onert/core/src/util/ConfigSource.cc deleted file mode 100644 index 45cce662e..000000000 --- a/runtime/onert/core/src/util/ConfigSource.cc +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/ConfigSource.h" -#include "util/GeneralConfigSource.h" -#include "util/EnvConfigSource.h" - -#include <array> -#include <algorithm> -#include <cassert> - -#include <memory> - -namespace onert -{ -namespace util -{ - -static std::unique_ptr<IConfigSource> _source; - -void config_source(std::unique_ptr<IConfigSource> &&source) { _source = std::move(source); } - -static IConfigSource *config_source() -{ - if (!_source) - { -#ifdef ENVVAR_FOR_DEFAULT_CONFIG - // Default ConfigSource is EnvConfigSource - _source = std::make_unique<EnvConfigSource>(); -#else - _source = std::make_unique<GeneralConfigSource>(); -#endif // ENVVAR_FOR_DEFAULT_CONFIG - } - return _source.get(); -} - -static std::string getConfigOrDefault(const std::string &key) -{ - static std::unordered_map<std::string, std::string> defaults; - if (defaults.empty()) - { -#define CONFIG(Name, Type, Default) \ - { \ - auto name = std::string{#Name}; \ - defaults.emplace(name, std::string{Default}); \ - } - -#include "util/Config.lst" - -#undef CONFIG - } - - // Treat empty string and absence of the value to be the same - auto ret = config_source()->get(key); - if (ret.empty()) - { - auto itr = defaults.find(key); - if (itr != defaults.end()) - { - // Return the default value if exists - ret = itr->second; - } - } - - return ret; -} - -bool toBool(const std::string &val) -{ - static const std::array<std::string, 5> false_list{"0", "OFF", "FALSE", "N", "NO"}; - auto false_found = std::find(false_list.begin(), false_list.end(), val); - return false_found == false_list.end(); -} - -int toInt(const std::string &val) { return std::stoi(val); } - -bool getConfigBool(const std::string &key) -{ - auto raw = getConfigOrDefault(key); - return toBool(raw); -} - -int getConfigInt(const std::string &key) -{ - auto raw = getConfigOrDefault(key); - return toInt(raw); -} - -std::string getConfigString(const std::string &key) { return getConfigOrDefault(key); } - -} // namespace util -} // namespace onert - -namespace onert -{ -namespace util -{ -namespace config -{ - -#define CONFIG(Name, Type, Default) const char *Name = #Name; - -#include "util/Config.lst" - -#undef CONFIG - -} // namespace config -} // namespace util -} // namespace onert diff --git a/runtime/onert/core/src/util/EnvConfigSource.cc b/runtime/onert/core/src/util/EnvConfigSource.cc deleted file mode 100644 index 0d25b7353..000000000 --- a/runtime/onert/core/src/util/EnvConfigSource.cc +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/EnvConfigSource.h" - -#include <cstdlib> - -namespace onert -{ -namespace util -{ - -std::string EnvConfigSource::get(const std::string &key) const -{ - const char *value = std::getenv(key.c_str()); - if (value != nullptr) - { - return value; - } - else - { - return GeneralConfigSource::get(key); - } -} - -} // namespace util -} // namespace onert diff --git a/runtime/onert/core/src/util/EventCollector.cc b/runtime/onert/core/src/util/EventCollector.cc deleted file mode 100644 index de37276bf..000000000 --- a/runtime/onert/core/src/util/EventCollector.cc +++ /dev/null @@ -1,109 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/EventCollector.h" - -// C++ standard libraries -#include <chrono> - -// POSIX standard libraries -#include <sys/time.h> -#include <sys/resource.h> - -namespace -{ - -std::string timestamp(void) -{ - auto now = std::chrono::steady_clock::now(); - return std::to_string( - std::chrono::duration_cast<std::chrono::microseconds>(now.time_since_epoch()).count()); -} - -class DurationEventBuilder -{ -public: - DurationEventBuilder(const std::string &ts) : _ts{ts} {} - - DurationEvent build(const std::string &tid, const std::string &name, const std::string &ph) const - { - DurationEvent evt; - - evt.name = name; - evt.tid = tid; - evt.ph = ph; - evt.ts = _ts; - - return evt; - } - -private: - std::string _ts; -}; - -#ifdef DEBUG -inline void emit_rusage(EventRecorder *rec, const std::string &ts) -{ - struct rusage ru; - - getrusage(RUSAGE_SELF, &ru); - { - CounterEvent evt; - - evt.name = "maxrss"; - evt.ph = "C"; - evt.ts = ts; - evt.values["value"] = std::to_string(ru.ru_maxrss); - - rec->emit(evt); - } - - { - CounterEvent evt; - - evt.name = "minflt"; - evt.ph = "C"; - evt.ts = ts; - evt.values["value"] = std::to_string(ru.ru_minflt); - - rec->emit(evt); - } -} -#endif - -} // namespace - -void EventCollector::onEvent(const Event &event) -{ - auto ts = timestamp(); - - switch (event.edge) - { - case Edge::BEGIN: - _rec->emit(DurationEventBuilder(ts).build(event.backend, event.label, "B")); - break; - - case Edge::END: - _rec->emit(DurationEventBuilder(ts).build(event.backend, event.label, "E")); - break; - } - -// TODO: Add resurece measurement(e.g. RSS) -// when ready with low overhead in release build -#ifdef DEBUG - emit_rusage(_rec, ts); -#endif -} diff --git a/runtime/onert/core/src/util/EventCollector.h b/runtime/onert/core/src/util/EventCollector.h deleted file mode 100644 index 8154be592..000000000 --- a/runtime/onert/core/src/util/EventCollector.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_EVENT_COLLECTOR_H__ -#define __ONERT_UTIL_EVENT_COLLECTOR_H__ - -#include "util/EventRecorder.h" - -class EventCollector -{ -public: - enum class Edge - { - BEGIN, - END - }; - - struct Event - { - Edge edge; - std::string backend; - std::string label; - }; - -public: - EventCollector(EventRecorder *rec) : _rec{rec} - { - // DO NOTHING - } - -public: - void onEvent(const Event &event); - -protected: - EventRecorder *_rec; -}; - -#endif // __ONERT_UTIL_EVENT_COLLECTOR_H__ diff --git a/runtime/onert/core/src/util/EventCollectorGlobal.cc b/runtime/onert/core/src/util/EventCollectorGlobal.cc deleted file mode 100644 index 6c03a5b9a..000000000 --- a/runtime/onert/core/src/util/EventCollectorGlobal.cc +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/EventCollectorGlobal.h" - -#include <cassert> -#include <fstream> -#include <iostream> - -#include "util/ConfigSource.h" -#include "util/EventWriter.h" - -namespace onert -{ -namespace util -{ - -EventCollectorGlobal::EventCollectorGlobal() : _recorder{}, _collector{&_recorder} -{ - // DO NOTHING -} - -EventCollectorGlobal::~EventCollectorGlobal() -{ - if (!_recorder.empty()) - { - try - { - // TODO Need better way for saved file path than the hardcoded path - EventWriter{_recorder}.writeToFile("trace.global.json", - EventWriter::WriteFormat::CHROME_TRACING); - } - catch (const std::exception &e) - { - std::cerr << "E: Fail to record event in EventCollectorGlobal: " << e.what() << std::endl; - } - } -} - -EventCollectorGlobal &EventCollectorGlobal::get() -{ - static EventCollectorGlobal instance; - return instance; -} - -EventDurationBlock::EventDurationBlock(const std::string &tag) : _tag{tag} -{ - auto &glob = EventCollectorGlobal::get(); - glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::BEGIN, "0", _tag}); -} -EventDurationBlock::~EventDurationBlock() -{ - auto &glob = EventCollectorGlobal::get(); - glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::END, "0", _tag}); -} - -EventDurationManual::EventDurationManual(const std::string &tag) : _tag{tag}, _pair{true} {} - -EventDurationManual::~EventDurationManual() -{ - // Check if it has called begin-end pair - assert(_pair); -} - -void EventDurationManual::begin() -{ - _pair = false; - auto &glob = EventCollectorGlobal::get(); - glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::BEGIN, "0", _tag}); -} - -void EventDurationManual::end() -{ - assert(!_pair); - _pair = true; - auto &glob = EventCollectorGlobal::get(); - glob.collector().onEvent(EventCollector::Event{EventCollector::Edge::END, "0", _tag}); -} - -} // namespace util -} // namespace onert diff --git a/runtime/onert/core/src/util/EventCollectorGlobal.h b/runtime/onert/core/src/util/EventCollectorGlobal.h deleted file mode 100644 index 1027ec84d..000000000 --- a/runtime/onert/core/src/util/EventCollectorGlobal.h +++ /dev/null @@ -1,155 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_EVENT_COLLECTOR_GLOBAL_H__ -#define __ONERT_UTIL_EVENT_COLLECTOR_GLOBAL_H__ - -#include "util/EventRecorder.h" -#include "util/EventCollector.h" - -namespace onert -{ -namespace util -{ - -/** - * @brief Singleton class for event collection from anywhere in code - * - */ -class EventCollectorGlobal -{ -public: - /** - * @brief Get the singleton object of this class - * - * @return EventCollectorGlobal& Singleton object - */ - static EventCollectorGlobal &get(); - -public: - /** - * @brief Getter for event collector object - * - * @return EventCollector& Collector object - */ - EventCollector &collector() { return _collector; } - -private: - EventCollectorGlobal(); - ~EventCollectorGlobal(); - -private: - EventRecorder _recorder; - EventCollector _collector; -}; - -/** - * @brief Helper class for emitting duration event which is handled automatically with ctor/dtor - * - */ -class EventDurationBlock -{ -public: - /** - * @brief Raise a duration event with type of BEGIN - * - * @param tag A label for the duration event - */ - EventDurationBlock(const std::string &tag); - /** - * @brief Raise a duration event with type of END - * - */ - ~EventDurationBlock(); - -private: - std::string _tag; -}; - -/** - * @brief Helper class for emitting duration event which is handled manually - * - * Usage: - * { - * ... - * EventDurationManual duration("some tag"); - * duration.begin(); - * ... - * ... // Code for duration - * ... - * duration.end(); - * } - * - */ -class EventDurationManual -{ -public: - /** - * @brief Construct a new Event Duration Manual object - * - * @param tag A label for the duration object - */ - EventDurationManual(const std::string &tag); - /** - * @brief Destroy the Event Duration Manual object - * - */ - ~EventDurationManual(); - - /** - * @brief Raise a duration event with type of BEGIN - * - */ - void begin(); - /** - * @brief Raise a duration event with type of END - * - */ - void end(); - -private: - std::string _tag; - bool _pair; -}; - -} // namespace util -} // namespace onert - -/** - * Helper Macro Definitions - * - * HOW TO USE - * - * void f(args) - * { - * EVENT_DURATION_FUNCTION(); - * ... - * if(cond) - * { - * EVENT_DURATION_REGION("if branch"); - * ... - * } - * ... - * } - */ - -#define EVENT_DURATION_FUNCTION() \ - ::onert::util::EventDurationBlock __event_duration__##__LINE__ { __FUNCTION__ } - -#define EVENT_DURATION_REGION(tag) \ - ::onert::util::EventDurationBlock __event_duration__##__LINE__ { tag } - -#endif // __ONERT_UTIL_EVENT_COLLECTOR_GLOBAL_H__ diff --git a/runtime/onert/core/src/util/EventRecorder.cc b/runtime/onert/core/src/util/EventRecorder.cc deleted file mode 100644 index 3714e4f02..000000000 --- a/runtime/onert/core/src/util/EventRecorder.cc +++ /dev/null @@ -1,31 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/EventRecorder.h" - -void EventRecorder::emit(const DurationEvent &evt) -{ - std::lock_guard<std::mutex> lock{_mu}; - - _duration_events.push_back(evt); -} - -void EventRecorder::emit(const CounterEvent &evt) -{ - std::lock_guard<std::mutex> lock{_mu}; - - _counter_events.push_back(evt); -} diff --git a/runtime/onert/core/src/util/EventRecorder.h b/runtime/onert/core/src/util/EventRecorder.h deleted file mode 100644 index 7af4c7ddb..000000000 --- a/runtime/onert/core/src/util/EventRecorder.h +++ /dev/null @@ -1,69 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_EVENT_RECORDER_H__ -#define __ONERT_UTIL_EVENT_RECORDER_H__ - -#include <map> -#include <memory> -#include <mutex> - -#include <vector> - -struct Event -{ - std::string name; - std::string tid; - std::string ph; /* REQUIRED */ - std::string ts; /* REQUIRED */ -}; - -struct DurationEvent : public Event -{ - // TO BE FILLED -}; - -struct CounterEvent : public Event -{ - std::map<std::string, std::string> values; -}; - -// -// Record Event as Chrome Trace Event File Format -// -// Refrence: https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/edit -// -class EventRecorder -{ -public: - EventRecorder() = default; - -public: - void emit(const DurationEvent &evt); - void emit(const CounterEvent &evt); - -public: - bool empty() { return _duration_events.empty() && _counter_events.empty(); } - const std::vector<DurationEvent> &duration_events() const { return _duration_events; } - const std::vector<CounterEvent> &counter_events() const { return _counter_events; } - -private: - std::mutex _mu; - std::vector<DurationEvent> _duration_events; - std::vector<CounterEvent> _counter_events; -}; - -#endif // __ONERT_UTIL_EVENT_RECORDER_H__ diff --git a/runtime/onert/core/src/util/EventWriter.cc b/runtime/onert/core/src/util/EventWriter.cc deleted file mode 100644 index dacb40e64..000000000 --- a/runtime/onert/core/src/util/EventWriter.cc +++ /dev/null @@ -1,574 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/EventWriter.h" - -#include <sstream> -#include <vector> -#include <unordered_map> -#include <json/json.h> -#include <assert.h> -#include <utility> -#include <map> -#include <set> -#include <stdint.h> -#include <fstream> - -// json type for Chrome Event Trace -namespace -{ - -std::string quote(const std::string &value) -{ - std::stringstream ss; - ss << '"' << value << '"'; - return ss.str(); -} - -std::string field(const std::string &k, const std::string &v) -{ - std::stringstream ss; - ss << quote(k) << " : " << quote(v); - return ss.str(); -} - -struct Content // One Entry in Chrome Event Trace -{ - std::vector<std::pair<std::string, std::string>> flds; - std::vector<std::pair<std::string, std::string>> args; -}; - -std::string object(const Content &content) -{ - std::stringstream ss; - - ss << "{ "; - - ss << field(content.flds[0].first, content.flds[0].second); - - for (uint32_t n = 1; n < content.flds.size(); ++n) - { - ss << ", " << field(content.flds.at(n).first, content.flds.at(n).second); - } - - if (content.args.size() > 0) - { - ss << ", " << quote("args") << " : { "; - ss << field(content.args.at(0).first, content.args.at(0).second); - - for (uint32_t n = 1; n < content.args.size(); ++n) - { - ss << ", " << field(content.args.at(n).first, content.args.at(n).second); - } - - ss << "}"; - } - - ss << " }"; - - return ss.str(); -} - -void fill(Content &content, const Event &evt) -{ - content.flds.emplace_back("name", evt.name); - content.flds.emplace_back("pid", "0"); - content.flds.emplace_back("tid", evt.tid); - content.flds.emplace_back("ph", evt.ph); - content.flds.emplace_back("ts", evt.ts); -} - -std::string object(const DurationEvent &evt) -{ - Content content; - - fill(content, evt); - - return ::object(content); -} - -std::string object(const CounterEvent &evt) -{ - Content content; - - fill(content, evt); - - for (auto it = evt.values.begin(); it != evt.values.end(); ++it) - { - content.args.emplace_back(it->first, it->second); - } - - return ::object(content); -} - -} // namespace - -// md table type -namespace -{ - -void writeMDTableRow(std::ostream &os, const std::vector<std::string> &list) -{ - os << "| "; - for (auto &key : list) - { - os << key << " | "; - } - os << "\n"; -} - -struct MDContent -{ - std::string name; - uint64_t begin_ts; - uint64_t end_ts; - uint32_t min_rss; - uint32_t max_rss; - uint32_t min_page_reclaims; - uint32_t max_page_reclaims; - - MDContent() - : begin_ts(0), end_ts(0), min_rss(UINT32_MAX), max_rss(0), min_page_reclaims(UINT32_MAX), - max_page_reclaims(0) - { - // DO NOTHING - } - - virtual ~MDContent() = default; - - void updateRss(uint32_t rss) - { - if (min_rss == UINT32_MAX) - min_rss = rss; - if (max_rss == 0) - max_rss = rss; - - if (min_rss > rss) - min_rss = rss; - else if (max_rss < rss) - max_rss = rss; - } - - void updateMinflt(uint32_t minflt) - { - if (min_page_reclaims == UINT32_MAX) - min_page_reclaims = minflt; - if (max_page_reclaims == 0) - max_page_reclaims = minflt; - - if (min_page_reclaims > minflt) - min_page_reclaims = minflt; - else if (max_page_reclaims < minflt) - max_page_reclaims = minflt; - } - - virtual void write(std::ostream &os) const = 0; -}; - -struct OpSeq : public MDContent -{ - std::string backend; - uint64_t graph_latency; - - struct OpSeqCmp - { - bool operator()(const OpSeq &lhs, const OpSeq &rhs) const - { - return lhs.begin_ts < rhs.begin_ts; - } - bool operator()(const OpSeq &lhs, const OpSeq &rhs) { return lhs.begin_ts < rhs.begin_ts; } - bool operator()(OpSeq &lhs, OpSeq &rhs) { return lhs.begin_ts < rhs.begin_ts; } - }; - - void write(std::ostream &os) const override - { - uint64_t opseq_latency = end_ts - begin_ts; - double opseq_per = static_cast<double>(opseq_latency) / graph_latency * 100.0; - writeMDTableRow(os, {name, backend, std::to_string(opseq_latency), std::to_string(opseq_per), - std::to_string(min_rss), std::to_string(max_rss), - std::to_string(min_page_reclaims), std::to_string(max_page_reclaims)}); - } -}; - -struct Graph : public MDContent -{ - std::set<OpSeq, OpSeq::OpSeqCmp> opseqs; - - void setOpSeqs(const std::map<std::string, OpSeq> &name_to_opseq) - { - uint64_t graph_latency = end_ts - begin_ts; - for (auto it : name_to_opseq) - { - auto opseq = it.second; - opseq.graph_latency = graph_latency; - - opseqs.insert(opseq); - - updateRss(opseq.min_rss); - updateRss(opseq.max_rss); - updateMinflt(opseq.min_page_reclaims); - updateMinflt(opseq.max_page_reclaims); - } - } - - void write(std::ostream &os) const override - { - static std::vector<std::string> graph_headers{"latency(us)", "rss_min(kb)", "rss_max(kb)", - "page_reclaims_min", "page_reclaims_max"}; - - static std::vector<std::string> graph_headers_line{"-----------", "-------", "-------", - "-----------------", "-----------------"}; - - // Graph's Header - writeMDTableRow(os, graph_headers); - writeMDTableRow(os, graph_headers_line); - - // Graph's contents - writeMDTableRow(os, {std::to_string(end_ts - begin_ts), std::to_string(min_rss), - std::to_string(max_rss), std::to_string(min_page_reclaims), - std::to_string(max_page_reclaims)}); - - os << "\n"; - - static std::vector<std::string> opseq_headers{ - "OpSeq name", "backend", "latency(us)", "latency(%)", - "rss_min(kb)", "rss_max(kb)", "page_reclaims_min", "page_reclaims_max"}; - - static std::vector<std::string> opseq_headers_line{ - "----------", "-------", "-----------", "-----------", - "-------", "-------", "-----------------", "-----------------"}; - - os << "## OpSequences \n"; - - // OpSeq's Header - writeMDTableRow(os, opseq_headers); - writeMDTableRow(os, opseq_headers_line); - - // OpSeq's contents - for (auto opseq : opseqs) - { - opseq.write(os); - } - - os << "\n"; - } -}; - -struct MDTableBuilder -{ - MDTableBuilder(const std::vector<DurationEvent> &duration_events, - const std::vector<CounterEvent> &counter_events) - : _duration_events(duration_events), _counter_events(counter_events) - { -// when ready with low overhead in release build -#ifdef DEBUG - for (const auto &evt : _counter_events) - { - uint64_t ts = std::stoull(evt.ts); - auto &name = evt.name; - assert(name.compare("maxrss") == 0 || name.compare("minflt") == 0); - assert(evt.values.size() == 1); - auto &val = evt.values.begin()->second; - if (_ts_to_values.find(ts) == _ts_to_values.end()) - { - std::pair<uint32_t, uint32_t> values; - if (name.compare("maxrss") == 0) - values.first = std::stoul(val); - else - values.second = std::stoul(val); - _ts_to_values.insert({ts, values}); - } - else - { - auto &values = _ts_to_values.at(ts); - if (name.compare("maxrss") == 0) - values.first = std::stoul(val); - else - values.second = std::stoul(val); - } - } -#endif - } - - MDTableBuilder &build() - { - for (auto &it : divideGraph()) - { - size_t begin_idx = it.first; - size_t end_idx = it.second; - std::map<std::string, OpSeq> name_to_opseq; - for (size_t i = begin_idx + 1; i < end_idx; ++i) - { - const auto &evt = _duration_events[i]; - assert(evt.name.compare("Graph") != 0); - assert(evt.ph.compare("B") == 0 || evt.ph.compare("E") == 0); - if (evt.ph.compare("B") == 0) - { - assert(name_to_opseq.find(evt.name) == name_to_opseq.end()); - name_to_opseq.insert({evt.name, makeOpSeq(evt)}); - } - else - { - assert(name_to_opseq.find(evt.name) != name_to_opseq.end()); - auto &opseq = name_to_opseq.at(evt.name); - updateOpSeq(opseq, evt); - } - } - - _graphs.emplace_back(makeGraph(begin_idx, end_idx, name_to_opseq)); - } - - return *this; - } - - std::vector<std::pair<size_t, size_t>> divideGraph() - { - std::vector<std::pair<size_t, size_t>> graph_idx_list; // pair<begin_idx, end_idx> - for (size_t i = 0, begin_idx = 0; i < _duration_events.size(); ++i) - { - const auto &evt = _duration_events.at(i); - if (evt.name.compare("Graph") == 0) - { - if (evt.ph.compare("B") == 0) - begin_idx = i; - else - graph_idx_list.emplace_back(begin_idx, i); - } - } - return graph_idx_list; - } - - OpSeq makeOpSeq(const DurationEvent &evt) - { - OpSeq opseq; - opseq.name = evt.name; - opseq.begin_ts = std::stoull(evt.ts); - opseq.backend = evt.tid; -#ifdef DEBUG - opseq.updateRss(_ts_to_values.at(opseq.begin_ts).first); - opseq.updateMinflt(_ts_to_values.at(opseq.begin_ts).second); -#else - opseq.updateRss(0); - opseq.updateMinflt(0); -#endif - return opseq; - } - - void updateOpSeq(OpSeq &opseq, const DurationEvent &evt) - { - opseq.end_ts = std::stoull(evt.ts); -#ifdef DEBUG - opseq.updateRss(_ts_to_values.at(opseq.end_ts).first); - opseq.updateMinflt(_ts_to_values.at(opseq.end_ts).second); -#else - opseq.updateRss(0); - opseq.updateMinflt(0); -#endif - } - - Graph makeGraph(size_t begin_idx, size_t end_idx, - const std::map<std::string, OpSeq> &name_to_opseq) - { - Graph graph; - graph.name = "Graph"; - graph.begin_ts = std::stoull(_duration_events[begin_idx].ts); - graph.end_ts = std::stoull(_duration_events[end_idx].ts); - graph.setOpSeqs(name_to_opseq); -#ifdef DEBUG - graph.updateRss(_ts_to_values.at(graph.begin_ts).first); - graph.updateMinflt(_ts_to_values.at(graph.begin_ts).second); - graph.updateRss(_ts_to_values.at(graph.end_ts).first); - graph.updateMinflt(_ts_to_values.at(graph.end_ts).second); -#else - graph.updateRss(0); - graph.updateMinflt(0); -#endif - return graph; - } - - void write(std::ostream &os) - { - // Write contents - for (size_t i = 0; i < _graphs.size(); ++i) - { - os << "# Graph " << i << "\n"; - _graphs.at(i).write(os); - } - } - - const std::vector<DurationEvent> &_duration_events; - const std::vector<CounterEvent> &_counter_events; - // timestamp to std::pair<maxrss, minflt> - std::unordered_map<uint64_t, std::pair<uint32_t, uint32_t>> _ts_to_values; - std::vector<Graph> _graphs; -}; - -} // namespace - -EventWriter::EventWriter(const EventRecorder &recorder) : _recorder(recorder) -{ - // DO NOTHING -} - -void EventWriter::writeToFiles(const std::string &base_filepath) -{ - // Note. According to an internal issue, let snpe json as just file name not '.snpe.json' - writeToFile(base_filepath, WriteFormat::SNPE_BENCHMARK); - writeToFile(base_filepath + ".chrome.json", WriteFormat::CHROME_TRACING); - writeToFile(base_filepath + ".table.md", WriteFormat::MD_TABLE); -} - -void EventWriter::writeToFile(const std::string &filepath, WriteFormat write_format) -{ - std::ofstream os{filepath, std::ofstream::out}; - switch (write_format) - { - case WriteFormat::CHROME_TRACING: - writeChromeTrace(os); - break; - case WriteFormat::SNPE_BENCHMARK: - writeSNPEBenchmark(os); - break; - case WriteFormat::MD_TABLE: - writeMDTable(os); - break; - default: - assert(!"Invalid value"); - break; - } -} - -void EventWriter::writeSNPEBenchmark(std::ostream &os) -{ - Json::Value root; - auto &exec_data = root["Execution_Data"] = Json::Value{Json::objectValue}; - - struct Stat - { - uint64_t sum = 0; - uint64_t count = 0; - uint64_t max = 0; - uint64_t min = std::numeric_limits<uint64_t>::max(); - - void accumulate(uint64_t val) - { - sum += val; - count++; - max = std::max(max, val); - min = std::min(min, val); - } - }; - - // Memory - { - std::unordered_map<std::string, Stat> mem_stats; - for (auto &evt : _recorder.counter_events()) - { - auto &mem_stat = mem_stats[evt.name]; - uint64_t val = std::stoull(evt.values.at("value")); - mem_stat.accumulate(val); - } - - auto &mem = exec_data["memory"] = Json::Value{Json::objectValue}; - for (auto &kv : mem_stats) - { - auto &key = kv.first; - auto &val = kv.second; - mem[key]["Avg_Size"] = val.sum / val.count; - mem[key]["Max_Size"] = val.max; - mem[key]["Min_Size"] = val.min; - mem[key]["Runtime"] = "NA"; - } - } - - // Operation Execution Time - { - // NOTE This assumes _duration_events is sorted by "ts" ascending - - // 2D keys : stats[tid][name] - std::unordered_map<std::string, std::unordered_map<std::string, Stat>> stats; - std::unordered_map<std::string, std::unordered_map<std::string, uint64_t>> begin_timestamps; - for (auto &evt : _recorder.duration_events()) - { - auto &stat = stats[evt.tid][evt.name]; - auto &begin_ts = begin_timestamps[evt.tid][evt.name]; - uint64_t timestamp = std::stoull(evt.ts); - if (evt.ph == "B") - { - if (begin_ts != 0) - throw std::runtime_error{"Invalid Data"}; - begin_ts = timestamp; - } - else if (evt.ph == "E") - { - if (begin_ts == 0 || timestamp < begin_ts) - throw std::runtime_error{"Invalid Data"}; - stat.accumulate(timestamp - begin_ts); - begin_ts = 0; - } - else - throw std::runtime_error{"Invalid Data - invalid value for \"ph\" : \"" + evt.ph + "\""}; - } - - for (auto &kv : begin_timestamps) - for (auto &kv2 : kv.second) - if (kv2.second != 0) - throw std::runtime_error{"Invalid Data - B and E pair does not match."}; - - for (auto &kv : stats) - { - auto &tid = kv.first; - auto &map = kv.second; - auto &json_tid = exec_data[tid] = Json::Value{Json::objectValue}; - for (auto &kv : map) - { - auto &name = kv.first; - auto &val = kv.second; - json_tid[name]["Avg_Time"] = val.sum / val.count; - json_tid[name]["Max_Time"] = val.max; - json_tid[name]["Min_Time"] = val.min; - json_tid[name]["Runtime"] = tid; - } - } - } - - os << root; -} - -void EventWriter::writeChromeTrace(std::ostream &os) -{ - os << "{\n"; - os << " " << quote("traceEvents") << ": [\n"; - - for (auto &evt : _recorder.duration_events()) - { - os << " " << object(evt) << ",\n"; - } - - for (auto &evt : _recorder.counter_events()) - { - os << " " << object(evt) << ",\n"; - } - - os << " { }\n"; - os << " ]\n"; - os << "}\n"; -} - -void EventWriter::writeMDTable(std::ostream &os) -{ - MDTableBuilder(_recorder.duration_events(), _recorder.counter_events()).build().write(os); -} diff --git a/runtime/onert/core/src/util/EventWriter.h b/runtime/onert/core/src/util/EventWriter.h deleted file mode 100644 index 7e838ca82..000000000 --- a/runtime/onert/core/src/util/EventWriter.h +++ /dev/null @@ -1,51 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_UTIL_EVENT_WRITER_H__ -#define __ONERT_UTIL_EVENT_WRITER_H__ - -#include "EventRecorder.h" - -#include <string> -#include <ostream> - -class EventWriter -{ -public: - enum class WriteFormat - { - CHROME_TRACING, - SNPE_BENCHMARK, - MD_TABLE, - }; - -public: - EventWriter(const EventRecorder &recorder); - -public: - void writeToFiles(const std::string &base_filepath); - void writeToFile(const std::string &filepath, WriteFormat write_format); - -private: - void writeSNPEBenchmark(std::ostream &os); - void writeChromeTrace(std::ostream &os); - void writeMDTable(std::ostream &os); - -private: - const EventRecorder &_recorder; -}; - -#endif // __ONERT_UTIL_EVENT_WRITER_H__ diff --git a/runtime/onert/core/src/util/GeneralConfigSource.cc b/runtime/onert/core/src/util/GeneralConfigSource.cc deleted file mode 100644 index 7d2757e58..000000000 --- a/runtime/onert/core/src/util/GeneralConfigSource.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/GeneralConfigSource.h" -#include "util/logging.h" - -namespace onert -{ -namespace util -{ - -std::string GeneralConfigSource::get(const std::string &key) const -{ - auto itr = _map.find(key); - if (itr == _map.end()) - { - return ""; - } - else - { - return itr->second; - } -} - -void GeneralConfigSource::set(const std::string &key, const std::string &val) -{ - VERBOSE(GeneralConfigSource) << key << " : " << val << std::endl; - _map[key] = val; -} - -} // namespace util -} // namespace onert diff --git a/runtime/onert/core/src/util/ShapeInference.cc b/runtime/onert/core/src/util/ShapeInference.cc deleted file mode 100644 index 0278df4d2..000000000 --- a/runtime/onert/core/src/util/ShapeInference.cc +++ /dev/null @@ -1,1130 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright 2017 The TensorFlow Authors. All Rights Reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/Utils.h" -#include "ir/InternalType.h" -#include "ir/Shape.h" -#include "util/ShapeInference.h" -#include "util/logging.h" - -#include <cassert> -#include <numeric> -#include <sstream> -#include <cmath> - -namespace onert -{ -namespace shape_inference -{ - -// -// Helper functions -// - -namespace -{ - -template <typename T, typename U> -typename std::enable_if<std::is_integral<T>::value && std::is_integral<U>::value, - typename std::common_type<T, U>::type>::type -ceil_div(T dividend, U divisor) -{ - assert(dividend > 0 && divisor > 0 && "this implementations is for positive numbers only"); - return (dividend + divisor - 1) / divisor; -} - -// Calculate the result of broadcast of two shapes -ir::Shape broadcastShapes(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape) -{ - ir::Shape out_shape; - auto max_rank = std::max(lhs_shape.rank(), rhs_shape.rank()); - - for (int idx = 0; idx < max_rank; ++idx) - { - // Go over operands dimensions from right to left - int lhs_idx = lhs_shape.rank() - idx - 1; - int rhs_idx = rhs_shape.rank() - idx - 1; - - int32_t lhs_dim = lhs_idx >= 0 ? lhs_shape.dim(lhs_idx) : 1; - int32_t rhs_dim = rhs_idx >= 0 ? rhs_shape.dim(rhs_idx) : 1; - - if (lhs_dim != 1 && rhs_dim != 1 && lhs_dim != rhs_dim) - throw std::runtime_error("Incompatible shapes for broadcast"); - - out_shape.prepend(std::max(lhs_dim, rhs_dim)); - } - - return out_shape; -} - -} // namespace - -namespace bcq -{ -inline int getOutputSize(const ir::Shape &cluster_shape, const int32_t *cluster_buf) -{ - int size = 0; - for (int idx = 0; idx < cluster_shape.dim(0); idx++) - { - size += cluster_buf[idx * 2 + 1]; - } - return size; -} -} // namespace bcq - -// -// Shape inference -// - -// Calculate output height and width of convolution-like operation -std::pair<int, int> calcConvLikeHeightAndWidth(const int in_h, const int in_w, const int ker_h, - const int ker_w, const ir::Padding pad, - const ir::Stride stride, - const ir::Dilation dilation = {1, 1}) -{ - int32_t out_h = 0, out_w = 0; - int32_t effective_filter_w_size = (ker_w - 1) * dilation.width_factor + 1; - int32_t effective_filter_h_size = (ker_h - 1) * dilation.height_factor + 1; - switch (pad.type) - { - case ir::PaddingType::SAME: - out_h = ceil_div(in_h, stride.vertical); - out_w = ceil_div(in_w, stride.horizontal); - break; - case ir::PaddingType::VALID: - out_h = ceil_div(in_h - effective_filter_h_size + 1, stride.vertical); - out_w = ceil_div(in_w - effective_filter_w_size + 1, stride.horizontal); - break; - case ir::PaddingType::EXPLICIT: - out_h = - (in_h + pad.param.top + pad.param.bottom - effective_filter_h_size) / stride.vertical + 1; - out_w = - (in_w + pad.param.left + pad.param.right - effective_filter_w_size) / stride.horizontal + - 1; - break; - default: - assert(false); - } - - return {out_h, out_w}; -} - -ir::Shape inferEltwiseShape(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape) -{ - return broadcastShapes(lhs_shape, rhs_shape); -} - -ir::Shape inferArgMaxShape(const ir::Shape &input_shape, int axis, int rank) -{ - if (axis < 0 || axis >= rank) - { - throw std::runtime_error("ArgMax shape inference: Wrong axis value " + std::to_string(axis)); - } - - ir::Shape out_shape; - for (int idx = 0; idx < rank; ++idx) - { - if (idx != axis) - { - int32_t input_dim = input_shape.dim(idx); - out_shape.append(input_dim); - } - } - - return out_shape; -} - -ir::Shape inferReduceShape(const ir::Shape &input_shape, const std::vector<int> &axes, - bool keep_dims) -{ - int num_axis = axes.size(); - int input_num_dims = input_shape.rank(); - if (input_num_dims == 0) - { - ir::Shape out_shape(0); - return out_shape; - } - if (keep_dims) - { - ir::Shape out_shape; - for (int idx = 0; idx < input_num_dims; ++idx) - { - bool is_axis = false; - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (axes[axis_idx] == idx || axes[axis_idx] + input_num_dims == idx) - { - is_axis = true; - break; - } - } - if (is_axis) - { - out_shape.append(1); - } - else - { - out_shape.append(input_shape.dim(idx)); - } - } - return out_shape; - } - else - { - // Calculates size of reducing axis. - int num_reduce_axis = num_axis; - for (int i = 0; i < num_axis; ++i) - { - int current = axes[i]; - if (current < 0) - { - current += input_num_dims; - } - assert(0 <= current && current < input_num_dims); - for (int j = 0; j < i; ++j) - { - int previous = axes[j]; - if (previous < 0) - { - previous += input_num_dims; - } - if (current == previous) - { - --num_reduce_axis; - break; - } - } - } - // Determines output dimensions. - ir::Shape out_shape; - int num_skip_axis = 0; - for (int idx = 0; idx < input_num_dims; ++idx) - { - bool is_axis = false; - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (axes[axis_idx] == idx || axes[axis_idx] + input_num_dims == idx) - { - ++num_skip_axis; - is_axis = true; - break; - } - } - if (!is_axis) - { - out_shape.append(input_shape.dim(idx)); - } - } - return out_shape; - } -} - -ir::Shape inferBatchMatMulShape(const ir::Shape &lhs_shape, const ir::Shape &rhs_shape, - const ir::operation::BatchMatMul::Param ¶m) -{ - bool adj_x = param.adj_x; - bool adj_y = param.adj_y; - ir::Shape output_shape; - - int output_rank = std::max(lhs_shape.rank(), rhs_shape.rank()); - - // Extend lhs and rhs shape - ir::Shape extended_lhs_shape(lhs_shape); - ir::Shape extended_rhs_shape(rhs_shape); - extended_lhs_shape.extendRank(output_rank); - extended_rhs_shape.extendRank(output_rank); - - for (int i = 0; i < output_rank - 2; i++) - { - const int lhs_dim = extended_lhs_shape.dim(i); - const int rhs_dim = extended_rhs_shape.dim(i); - int broadcast_dim = lhs_dim; - if (lhs_dim != rhs_dim) - { - if (lhs_dim == 1) - { - broadcast_dim = rhs_dim; - } - else if (rhs_dim != 1) - { - throw std::runtime_error{"BatchMatMul shape inference: invalid brodcasting input shape"}; - } - } - - output_shape.append(broadcast_dim); - } - - // Fill in the matmul dimensions. - int lhs_rows_index = adj_x ? output_rank - 1 : output_rank - 2; - int rhs_cols_index = adj_y ? output_rank - 2 : output_rank - 1; - - output_shape.append(extended_lhs_shape.dim(lhs_rows_index)); - output_shape.append(extended_rhs_shape.dim(rhs_cols_index)); - - return output_shape; -} - -/* - * shp_shape : SHAPE input tensor's shape - * shp_buf : SHAPE input tensor's buffer - */ -ir::Shape inferBroadcastToShape(const ir::Shape shp_shape, const int32_t *shp_buf) -{ - - const int num_elements = shp_shape.num_elements(); - - assert(num_elements != 0); - assert(shp_buf); - - ir::Shape new_shape(num_elements); - - for (int i = 0; i < num_elements; ++i) - { - assert(shp_buf[i] != 0); // It shouldn't be 0. - new_shape.dim(i) = shp_buf[i]; - } - - return new_shape; -} - -ir::Shape inferConcatShape(const Shapes &in_shapes, const ir::operation::Concat::Param ¶m) -{ - const int32_t concat_axis = param.axis >= 0 ? param.axis : in_shapes[0].rank() + param.axis; - const auto &first_in_shape = in_shapes[0]; - - // Check that all shapes are equal except for concat axis dimension - for (const auto &in_shape : in_shapes) - { - if (in_shape.rank() != first_in_shape.rank()) - throw std::runtime_error("Rank in all input tensors should be same"); - - for (int64_t dim_idx = 0; dim_idx < in_shape.rank(); ++dim_idx) - if (!(dim_idx == concat_axis || in_shape.dim(dim_idx) == first_in_shape.dim(dim_idx))) - throw std::runtime_error("All tensor should have same dimension " - "except dimension on passed axis"); - } - - // Calculate output shape - ir::Shape out_shape(first_in_shape); - out_shape.dim(concat_axis) = 0; - for (const auto &in_shape : in_shapes) - out_shape.dim(concat_axis) += in_shape.dim(concat_axis); - return out_shape; -} - -ir::Shape inferConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape, - const ir::operation::Conv2D::Param ¶m, ir::Layout layout) -{ - if (param.stride.horizontal == 0 || param.stride.vertical == 0) - throw std::runtime_error{"Conv2D: stride values must be positive"}; - - auto ifm_shape = in_shape.asFeature(layout); - - // Kernel format is [depth_out, kernel_height, kernel_width, depth_in] - auto kf_shape = ker_shape.asFeature(layout); - assert(ifm_shape.C == kf_shape.C); - - const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, kf_shape.H, kf_shape.W, - param.padding, param.stride, param.dilation); - - return ir::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, kf_shape.N}; -} - -ir::Shape inferDepthwiseConv2DShape(const ir::Shape &in_shape, const ir::Shape &ker_shape, - const ir::operation::DepthwiseConv2D::Param ¶m, - ir::Layout layout) -{ - if (param.stride.horizontal == 0 || param.stride.vertical == 0) - throw std::runtime_error{"DepthwiseConv2D: stride values must be positive"}; - - assert(layout == ir::Layout::NHWC); - auto ifm_shape = in_shape.asFeature(layout); - - // Kernel format is [1, kernel_height, kernel_width, depth_out] - auto kf_shape = ker_shape.asFeature(layout); - assert(kf_shape.C == static_cast<int32_t>(ifm_shape.C * param.multiplier)); - assert(kf_shape.N == 1); - - const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, kf_shape.H, kf_shape.W, - param.padding, param.stride); - - return ir::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, kf_shape.C}; -} - -ir::Shape inferExpandDimsShape(const ir::Shape &in_shape, int32_t axis) -{ - ir::Shape out_shape(in_shape.rank() + 1); - - axis = ((axis >= 0) ? axis : /* when axis < 0 */ (out_shape.rank() + axis)); - if (!(0 <= axis && axis <= in_shape.rank())) - throw std::runtime_error("axis of dim is out of range"); - - for (int x = 0, out_x = 0; out_x < out_shape.rank(); ++out_x) - { - if (out_x == axis) - out_shape.dim(out_x) = 1; - else - out_shape.dim(out_x) = in_shape.dim(x++); - } - - return out_shape; -} - -ir::Shape inferFillShape(const ir::Shape &in_shape, const int32_t *in_buf) -{ - ir::Shape out_shape(in_shape.dim(0)); - - for (int out_x = 0; out_x < out_shape.rank(); ++out_x) - { - out_shape.dim(out_x) = in_buf[out_x]; - } - - return out_shape; -} - -ir::Shape inferFullyConnectedShape(const ir::Shape &in_shape, const ir::Shape &ker_shape) -{ - assert(in_shape.rank() >= 2); - assert(ker_shape.rank() == 2); - - const auto input_size_with_batch = in_shape.num_elements(); - const auto num_units = ker_shape.dim(0); - const auto input_size = ker_shape.dim(1); - const auto batch_size = input_size_with_batch / input_size; - assert(input_size_with_batch % input_size == 0); - - return {ir::Shape({static_cast<int32_t>(batch_size), num_units})}; -} - -ir::Shape inferBCQFullyConnectedShape(const ir::Shape &in_shape, const ir::Shape &cluster_shape, - const int32_t *cluster_buf) -{ - assert(cluster_shape.rank() == 2); - assert(cluster_shape.dim(1) == 2); - - const auto input_size = in_shape.dim(1); - const auto output_size = bcq::getOutputSize(cluster_shape, cluster_buf); - - return {ir::Shape({output_size, input_size})}; -} - -ir::Shape inferBCQGatherShape(const ir::Shape &indices_shape, const ir::Shape &cluster_shape, - const int32_t *cluster_buf, int rank, - const ir::operation::BCQGather::Param ¶m) -{ - ir::Shape out_shape; - ir::Shape in_original_shape; - - assert(cluster_shape.rank() == 2); - assert(cluster_shape.dim(1) == 2); - - auto hidden_size = param.input_hidden_size; - auto axis = param.axis; - - in_original_shape.append(bcq::getOutputSize(cluster_shape, cluster_buf)); - in_original_shape.append(hidden_size); - - const int indices_rank = indices_shape.rank(); - for (int idx = 0; idx < rank; ++idx) - { - if (idx == (int)axis) - { - for (int indices_idx = 0; indices_idx < indices_rank; indices_idx++) - { - out_shape.append(indices_shape.dim(indices_idx)); - } - } - else - { - out_shape.append(in_original_shape.dim(idx)); - } - } - - return out_shape; -} - -ir::Shape inferGatherShape(const ir::Shape &input_shape, const ir::Shape &indices_shape, int axis, - int rank) -{ - ir::Shape out_shape; - - const int indices_rank = indices_shape.rank(); - - for (int idx = 0; idx < rank; ++idx) - { - if (idx == axis) - { - for (int indices_idx = 0; indices_idx < indices_rank; indices_idx++) - { - out_shape.append(indices_shape.dim(indices_idx)); - } - } - else - { - out_shape.append(input_shape.dim(idx)); - } - } - - return out_shape; -} - -ir::Shape inferOnehotShape(const ir::Shape &input_shape, const int depth, int axis) -{ - assert(depth >= 0); - const auto rank = input_shape.rank() + 1; - ir::Shape newShape(rank); - - axis = (axis == -1) ? (rank - 1) : axis; - - for (int i = 0; i < rank; ++i) - { - if (i < axis) - { - newShape.dim(i) = input_shape.dim(i); - } - else if (i == axis) - { - newShape.dim(i) = depth; - } - else - { - newShape.dim(i) = input_shape.dim(i - 1); - } - } - - return newShape; -} - -ir::Shape inferPackShape(const ir::Shape &input_shape, int axis, int rank, int num) -{ - ir::Shape out_shape; - int in_idx = 0; - - for (int out_idx = 0; out_idx < rank; ++out_idx) - { - if (out_idx == axis) - { - out_shape.append(num); - } - else - { - out_shape.append(input_shape.dim(in_idx++)); - } - } - - return out_shape; -} - -ir::Shape inferPadShape(const ir::Shape &in_shape, const int32_t *pad_buf, const size_t num_pads) -{ - assert(num_pads % 2 == 0); - const int32_t rank = num_pads / 2; - - ir::Shape ret(rank); - for (int32_t i = 0; i < rank; ++i) - { - const auto before_padding = pad_buf[i * 2]; - const auto after_padding = pad_buf[i * 2 + 1]; - - ret.dim(i) = in_shape.dim(i) + before_padding + after_padding; - } - - return ret; -} - -ir::Shape inferPoolShape(const ir::Shape &in_shape, const ir::operation::Pool2D::Param ¶m, - const ir::Layout layout) -{ - if (param.stride.horizontal == 0 || param.stride.vertical == 0) - throw std::runtime_error{"Pool2D: stride values must be positive"}; - - assert(layout == ir::Layout::NHWC); - auto ifm_shape = in_shape.asFeature(layout); - const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, param.kh, param.kw, - param.padding, param.stride); - // Pooling don't change number of channels and batch size - return ir::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, ifm_shape.C}; -} - -ir::Shape inferResizeBilinearShape(const ir::Shape &in_shape, const int32_t output_height, - const int32_t output_width) -{ - assert(in_shape.rank() == 4); - if (output_height < 0) - { - throw std::runtime_error{"ResizeBilinear: size value must be positive value, output_height = " + - std::to_string(output_height)}; - } - if (output_width < 0) - { - throw std::runtime_error{"ResizeBilinear: size value must be positive value, output_width = " + - std::to_string(output_width)}; - } - - ir::Shape ret(in_shape.rank()); - - ret.dim(0) = in_shape.dim(0); - ret.dim(1) = output_height; - ret.dim(2) = output_width; - ret.dim(3) = in_shape.dim(3); - - return ret; -} - -template <typename T> ir::Shape inferRangeShape(T start_val, T limit_val, T delta_val) -{ - ir::Shape out_shape(static_cast<int>(1)); - - out_shape.dim(0) = - (std::is_integral<T>::value - ? ((std::abs(start_val - limit_val) + std::abs(delta_val) - 1) / std::abs(delta_val)) - : std::ceil(std::abs((start_val - limit_val) / delta_val))); - return out_shape; -} - -// template instantiation -template ir::Shape inferRangeShape(int start_val, int limit_val, int delta_val); -template ir::Shape inferRangeShape(float start_val, float limit_val, float delta_val); - -ir::Shape inferReshapeShape(const int32_t *shape_buf, const int32_t shape_num_elements, - const size_t total_num_elements) -{ - ir::Shape ret(shape_num_elements); - int32_t flatten_dim = ir::Shape::UNSPECIFIED_DIM; - for (int32_t i = 0; i < shape_num_elements; ++i) - { - if (shape_buf[i] < 0) - { - if (flatten_dim != ir::Shape::UNSPECIFIED_DIM) - throw std::runtime_error("Reshape: 2nd param has special dim(for flatten) more than twice"); - flatten_dim = i; - ret.dim(i) = 1; - } - else - { - ret.dim(i) = shape_buf[i]; - } - } - if (flatten_dim != ir::Shape::UNSPECIFIED_DIM) - ret.dim(flatten_dim) = total_num_elements / ret.num_elements(); - - // Check reshapable - if (total_num_elements != static_cast<size_t>(ret.num_elements())) - throw std::runtime_error("Reshape: 2nd param is not compatible with the shape of input"); - - return ret; -} - -ir::Shape inferSelectShape(const ir::Shape &input_cond_shape, const ir::Shape &input_true_shape, - const ir::Shape &input_false_shape) -{ - auto haveSameShapes = [](const ir::Shape &input_cond_shape, const ir::Shape &input_true_shape, - const ir::Shape &input_false_shape) { - if ((input_cond_shape.rank() != input_true_shape.rank()) || - input_cond_shape.rank() != input_false_shape.rank()) - { - return false; - } - - int rank = input_cond_shape.rank(); - for (int i = 0; i < rank; ++i) - { - if (input_cond_shape.dim(i) != input_true_shape.dim(i) || - input_cond_shape.dim(i) != input_false_shape.dim(i)) - { - return false; - } - } - - return true; - }; - - auto calculateShape = [](const ir::Shape &input_cond_shape, const ir::Shape &input_true_shape, - const ir::Shape &input_false_shape, ir::Shape &new_shape) { - ir::Shape cond_shape = input_cond_shape; - ir::Shape true_shape = input_true_shape; - ir::Shape false_shape = input_false_shape; - int most_rank = - (cond_shape.rank() >= true_shape.rank()) && (cond_shape.rank() >= false_shape.rank()) - ? cond_shape.rank() - : (false_shape.rank() >= true_shape.rank() ? false_shape.rank() : true_shape.rank()); - - ir::Shape calculate_shape(most_rank); - - cond_shape.extendRank(most_rank); - true_shape.extendRank(most_rank); - false_shape.extendRank(most_rank); - - for (int i = 0; i < most_rank; ++i) - { - calculate_shape.dim(i) = - (cond_shape.dim(i) >= true_shape.dim(i)) && (cond_shape.dim(i) >= false_shape.dim(i)) - ? cond_shape.dim(i) - : (false_shape.dim(i) >= true_shape.dim(i) ? false_shape.dim(i) : true_shape.dim(i)); - - if ((cond_shape.dim(i) != calculate_shape.dim(i) && cond_shape.dim(i) != 1) || - (true_shape.dim(i) != calculate_shape.dim(i) && true_shape.dim(i) != 1) || - (false_shape.dim(i) != calculate_shape.dim(i) && false_shape.dim(i) != 1)) - { - return false; - } - } - - new_shape = calculate_shape; - - return true; - }; - - bool havesame = haveSameShapes(input_cond_shape, input_true_shape, input_false_shape); - if (havesame) - { - return input_cond_shape; - } - - ir::Shape new_shape; - bool possible = calculateShape(input_cond_shape, input_true_shape, input_false_shape, new_shape); - - if (!possible) - { - throw std::runtime_error("Broadcasting is not possible."); - } - - return new_shape; -} - -ir::Shape inferSliceShape(const ir::Shape &input_shape, const int32_t *begins_buf, - const int32_t *sizes_buf) -{ - const uint32_t rank = input_shape.rank(); - ir::Shape out_shape(rank); - - for (uint32_t idx = 0; idx < rank; ++idx) - { - const auto input_dim = input_shape.dim(idx); - - // begin is zero-based - auto begin = begins_buf[idx]; - if (begin < 0) - throw std::runtime_error("shape inference Slice: Invalid begin."); - - // size is one-based - auto size = sizes_buf[idx]; - if (size < -1) - throw std::runtime_error("shape inference Slice: Invalid size."); - - if (size == -1) - { - size = input_dim - begin; - } - else - { - if (input_dim < begin + size) - throw std::runtime_error("shape inference Slice: Invalid begin and size."); - } - out_shape.dim(idx) = size; - } - - return out_shape; -} - -ir::Shape inferSpaceToBatchNDShape(const ir::Shape &input_shape, const ir::Shape &block_shape_shape, - const ir::Shape &padding_shape, const int32_t *block_shape_buf, - const int32_t *padding_buf) -{ - const uint32_t rank = input_shape.rank(); - ir::Shape out_shape(rank); - - // Currently, only 4D NHWC input/output op_context are supported. - // The 4D array need to have exactly 2 spatial dimensions. - // TODO(nupurgarg): Support arbitrary dimension in SpaceToBatchND. - const int32_t kInputDimensionNum = 4; - const int32_t kBlockSizeDimensionNum = 1; - const int32_t kSpatialDimensionNum = 2; - - UNUSED_RELEASE(kInputDimensionNum); - UNUSED_RELEASE(kBlockSizeDimensionNum); - UNUSED_RELEASE(block_shape_shape); - UNUSED_RELEASE(padding_shape); - - assert(block_shape_shape.rank() == kBlockSizeDimensionNum); - assert(block_shape_shape.dim(0) == kSpatialDimensionNum); - assert(padding_shape.dim(0) == kSpatialDimensionNum); - assert(padding_shape.dim(1) == 2); // fixed, meaning left/right padding for each element - assert(padding_shape.rank() == 2); // fixed, meaning dimension(dim 0) and padding length(dim 1) - - // Ensures the input height and width (with padding) is a multiple of block - // shape height and width. - for (int dim = 0; dim < kSpatialDimensionNum; ++dim) - { - int final_dim_size = - (input_shape.dim(dim + 1) + padding_buf[dim * 2] + padding_buf[dim * 2 + 1]); - - assert(final_dim_size % block_shape_buf[dim] == 0); - - out_shape.dim(dim + 1) = final_dim_size / block_shape_buf[dim]; - } - - const int output_batch_size = input_shape.dim(0) * block_shape_buf[0] * block_shape_buf[1]; - const int output_channel_size = input_shape.dim(3); - - out_shape.dim(0) = output_batch_size; - out_shape.dim(3) = output_channel_size; - - return out_shape; -} - -ir::Shape inferSplitShape(const ir::Shape input_shape, int axis_value, int num_splits) -{ - ir::Shape newShape(input_shape); - - assert(axis_value >= 0); - assert(axis_value < input_shape.rank()); - - const int input_size = input_shape.dim(axis_value); - assert(input_size % num_splits == 0); - const int slice_size = input_size / num_splits; - - newShape.dim(axis_value) = slice_size; - - return newShape; -} - -ir::Shape inferSqueezeShape(const ir::Shape &in_shape, const ir::operation::Squeeze::Param ¶m) -{ - const int ndims = param.ndim; - const int *squeeze_dims = param.dims; - bool should_squeeze[8] = {false}; - int num_squeezed_dims = 0; - int shape_rank = in_shape.rank(); - if (ndims == 0) - { - for (int idx = 0; idx < shape_rank; ++idx) - { - if (in_shape.dim(idx) == 1) - { - should_squeeze[idx] = true; - ++num_squeezed_dims; - } - } - } - else - { - for (int idx = 0; idx < ndims; ++idx) - { - int current = squeeze_dims[idx]; - if (current < 0) - { - current += shape_rank; - } - - if (!(current >= 0 && current < shape_rank && in_shape.dim(current) == 1)) - { - throw std::runtime_error( - "The following conditions must be met: 0 <= dim < Shape rank, dim == 1"); - } - - if (!should_squeeze[current]) - { - ++num_squeezed_dims; - } - should_squeeze[current] = true; - } - } - - // Set output shape. - ir::Shape out_shape(shape_rank - num_squeezed_dims); - for (int in_idx = 0, out_idx = 0; in_idx < shape_rank; ++in_idx) - { - if (!should_squeeze[in_idx]) - { - out_shape.dim(out_idx++) = in_shape.dim(in_idx); - } - } - - return out_shape; -} - -// helper for for StridedSlice -template <typename T> -StridedSliceParams buildStridedSliceParams(const T *begin, const T *end, const T *strides, - const uint32_t begin_mask, const uint32_t end_mask, - const uint32_t shrink_axis_mask, const uint8_t rank) -{ - StridedSliceParams op_params; - op_params.start_indices_count = rank; - op_params.stop_indices_count = rank; - op_params.strides_count = rank; - - for (int i = 0; i < op_params.strides_count; ++i) - { - op_params.start_indices[i] = begin[i]; - op_params.stop_indices[i] = end[i]; - op_params.strides[i] = strides[i]; - - assert(op_params.strides[i] != 0); - } - - op_params.begin_mask = begin_mask; - op_params.ellipsis_mask = 0; // NYI - op_params.end_mask = end_mask; - op_params.new_axis_mask = 0; // NYI - op_params.shrink_axis_mask = shrink_axis_mask; - - assert(sizeof(op_params.begin_mask) * 4 >= rank); - - return op_params; -} - -// template instantiation -template StridedSliceParams -buildStridedSliceParams(const uint32_t *begin, const uint32_t *end, const uint32_t *strides, - const uint32_t begin_mask, const uint32_t end_mask, - const uint32_t shrink_axis_mask, const uint8_t rank); - -int Clamp(const int v, const int lo, const int hi) -{ - assert(!(hi < lo)); - if (hi < v) - return hi; - if (v < lo) - return lo; - return v; -} - -int StartForAxis(const StridedSliceParams ¶ms, const ir::Shape &input_shape, int axis) -{ - const auto begin_mask = params.begin_mask; - const auto *start_indices = params.start_indices; - const auto *strides = params.strides; - // Begin with the specified index. - int start = start_indices[axis]; - - // begin_mask override - if (begin_mask & 1 << axis) - { - if (strides[axis] > 0) - { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axis_size-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits<int>::lowest(); - } - else - { - // Backward iteration - use the last element. - start = std::numeric_limits<int>::max(); - } - } - - // Handle negative indices - int axis_size = input_shape.dim(axis); - if (start < 0) - { - start += axis_size; - } - - // Clamping - start = Clamp(start, 0, axis_size - 1); - - return start; -} - -// Return the "real" index for the end of iteration along that axis. This is an -// "end" in the traditional C sense, in that it points to one past the last -// element. ie. So if you were iterating through all elements of a 1D array of -// size 4, this function would return 4 as the stop, because it is one past the -// "real" indices of 0, 1, 2 & 3. -int StopForAxis(const StridedSliceParams ¶ms, const ir::Shape &input_shape, int axis, - int start_for_axis) -{ - const auto end_mask = params.end_mask; - const auto shrink_axis_mask = params.shrink_axis_mask; - const auto *stop_indices = params.stop_indices; - const auto *strides = params.strides; - - // Begin with the specified index - const bool shrink_axis = shrink_axis_mask & (1 << axis); - int stop = stop_indices[axis]; - - // When shrinking an axis, the end position does not matter (and can be - // incorrect when negative indexing is used, see Issue #19260). Always use - // start_for_axis + 1 to generate a length 1 slice, since start_for_axis has - // already been adjusted for negative indices. - if (shrink_axis) - { - stop = start_for_axis + 1; - } - - // end_mask override - if (end_mask & (1 << axis)) - { - if (strides[axis] > 0) - { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits<int>::max(); - } - else - { - // Backward iteration - use the first element. - stop = std::numeric_limits<int>::lowest(); - } - } - - // Handle negative indices - - const int axis_size = input_shape.dim(axis); - if (stop < 0) - { - stop += axis_size; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (strides[axis] > 0) - { - // Forward iteration - stop = Clamp(stop, 0, axis_size); - } - else - { - // Backward iteration - stop = Clamp(stop, -1, axis_size - 1); - } - - return stop; -} - -ir::Shape inferStridedSliceShape(const ir::Shape &input_shape, const StridedSliceParams &op_params, - uint32_t rank) -{ - ir::Shape out_shape; - - for (uint32_t idx = 0; idx < rank; ++idx) - { - int32_t stride = op_params.strides[idx]; - int32_t begin = StartForAxis(op_params, input_shape, idx); - int32_t end = StopForAxis(op_params, input_shape, idx, begin); - - // When shrinking an axis, the end position does not matter (and can be - // incorrect when negative indexing is used, see Issue #19260). Always use - // begin + 1 to generate a length 1 slice, since begin has - // already been adjusted for negative indices by StartForAxis. - const bool shrink_axis = op_params.shrink_axis_mask & (1 << idx); - if (shrink_axis) - { - end = begin + 1; - } - - int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride)); - dim_shape = dim_shape < 0 ? 0 : dim_shape; - if (!shrink_axis) - { - out_shape.append(dim_shape); - } - } - - return out_shape; -} - -ir::Shape inferTileShape(const ir::Shape &in_shape, const int32_t *multiplier_buf, - const int32_t multiplier_size) -{ - if (multiplier_size != in_shape.rank()) - { - throw std::runtime_error("inferTileShape failed, input rank: " + - std::to_string(in_shape.rank()) + ", bad multipliers size: " + - std::to_string(multiplier_size) + ""); - } - ir::Shape new_Shape(in_shape.rank()); - - for (int i = 0; i < in_shape.rank(); ++i) - { - assert(multiplier_buf[i]); // multiplier_buf[i] shuld not be 0. - new_Shape.dim(i) = in_shape.dim(i) * multiplier_buf[i]; - } - return new_Shape; -} - -ir::Shape inferTransposeShape(const ir::Shape &in_shape, const int32_t *perm_buf, - const int32_t perm_size) -{ - const auto rank = in_shape.rank(); - if (perm_size > rank) - { - throw std::runtime_error("inferTransposeShape failed, bad permutation size: " + - std::to_string(perm_size)); - } - - const int32_t *perm_data = perm_buf; - std::vector<int32_t> regular_perm_vec; - if (perm_size == 0) - { - // perm_data will be set to (n-1...0) - regular_perm_vec.resize(rank); - std::iota(regular_perm_vec.begin(), regular_perm_vec.end(), 0); - std::reverse(regular_perm_vec.begin(), regular_perm_vec.end()); - perm_data = regular_perm_vec.data(); - } - else - { - assert(rank == perm_size); - } - - ir::Shape out_shape(rank); - std::vector<bool> visit_perms(rank, false); - for (int idx = 0; idx < rank; idx++) - { - const auto perm_val = perm_data[idx]; - // Check invalid permutation value - if (perm_val < 0 || perm_val >= rank) - { - throw std::runtime_error("inferTransposeShape failed, bad permutation value: " + - std::to_string(perm_val)); - } - - // Check duplicated permutation value - if (visit_perms.at(perm_val)) - { - throw std::runtime_error("inferTransposeShape failed, duplicated permutation value: " + - std::to_string(perm_val)); - } - visit_perms.at(perm_val) = true; - - out_shape.dim(idx) = in_shape.dim(perm_val); - } - return out_shape; -} - -ir::Shape inferUnpackShape(const ir::Shape &input_shape, int axis, int rank) -{ - ir::Shape out_shape; - - for (int out_idx = 0; out_idx < rank; out_idx++) - { - if (out_idx != axis) - { - out_shape.append(input_shape.dim(out_idx)); - } - } - - return out_shape; -} - -} // namespace shape_inference -} // namespace onert diff --git a/runtime/onert/core/src/util/logging.cc b/runtime/onert/core/src/util/logging.cc deleted file mode 100644 index 6309d25e5..000000000 --- a/runtime/onert/core/src/util/logging.cc +++ /dev/null @@ -1,23 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the License); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "util/logging.h" - -onert::util::logging::Context &onert::util::logging::Context::get() noexcept -{ - static Context ctx; - return ctx; -} diff --git a/runtime/onert/frontend/CMakeLists.txt b/runtime/onert/frontend/CMakeLists.txt deleted file mode 100644 index 5ea6cdadd..000000000 --- a/runtime/onert/frontend/CMakeLists.txt +++ /dev/null @@ -1 +0,0 @@ -add_subdirectories() diff --git a/runtime/onert/frontend/base_loader/CMakeLists.txt b/runtime/onert/frontend/base_loader/CMakeLists.txt deleted file mode 100644 index 6f700710c..000000000 --- a/runtime/onert/frontend/base_loader/CMakeLists.txt +++ /dev/null @@ -1,11 +0,0 @@ -if(NOT BUILD_TFLITE_LOADER AND NOT BUILD_CIRCLE_LOADER) - return() -endif(NOT BUILD_TFLITE_LOADER AND NOT BUILD_CIRCLE_LOADER) - -nnfw_find_package(FlatBuffers REQUIRED) - -add_library(base_loader INTERFACE) -target_include_directories(base_loader INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include) - -target_link_libraries(base_loader INTERFACE onert_core) -target_link_libraries(base_loader INTERFACE flatbuffers::flatbuffers) diff --git a/runtime/onert/frontend/base_loader/include/base_loader.h b/runtime/onert/frontend/base_loader/include/base_loader.h deleted file mode 100644 index d21001e59..000000000 --- a/runtime/onert/frontend/base_loader/include/base_loader.h +++ /dev/null @@ -1,1523 +0,0 @@ -/* - * Copyright 2017 The TensorFlow Authors. All Rights Reserved. - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __BASE_LOADER_BASE_LOADER_H__ -#define __BASE_LOADER_BASE_LOADER_H__ - -#include "ir/Graph.h" -#include "ir/Shape.h" -#include "ir/Operations.Include.h" - -#include "flatbuffers/flexbuffers.h" - -#include <map> -#include <memory> -#include <fstream> -#include <limits> -#include <fcntl.h> -#include <sys/stat.h> -#include <sys/mman.h> -#include <unistd.h> -#include <util/logging.h> - -namespace onert -{ -namespace base_loader -{ - -template <typename LoaderDomain> class BaseLoader -{ -protected: - using Verifier = typename LoaderDomain::Verifier; - using ActivationFunctionType = typename LoaderDomain::ActivationFunctionType; - using Buffer = typename LoaderDomain::Buffer; - using BuiltinOperator = typename LoaderDomain::BuiltinOperator; - using CustomOptionsFormat = typename LoaderDomain::CustomOptionsFormat; - using Model = typename LoaderDomain::Model; - using Operator = typename LoaderDomain::Operator; - using Padding = typename LoaderDomain::Padding; - using Pool2DOptions = typename LoaderDomain::Pool2DOptions; - using SubGraph = typename LoaderDomain::SubGraph; - using Tensor = typename LoaderDomain::Tensor; - using TensorType = typename LoaderDomain::TensorType; - using DimensionType = typename LoaderDomain::DimensionType; - using SparseIndexVector = typename LoaderDomain::SparseIndexVector; - -protected: - bool isOptionalInputTensor(std::int32_t idx) { return idx == -1; } - virtual bool allowOptionalInputTensor(BuiltinOperator) = 0; - -public: - /** - * @brief Construct a new Loader object - * - * @param graph reference on subgraphs - */ - explicit BaseLoader(std::unique_ptr<ir::Subgraphs> &subgs) - : _base{nullptr}, _pagesize(getpagesize()), _fd(-1), _subgraphs(subgs), _model{nullptr} - { - _use_mmaped_data = util::getConfigBool(util::config::USE_MMAPED_DATA); - } - - /** - * @brief Load a model from file - * - * @param file_path - */ - void loadFromFile(const char *file_path); - /** - * @brief Load a model from a buffer - * - * @param buffer buffer pointer - * @param size buffer size - */ - void loadFromBuffer(uint8_t *buffer, size_t size); - -protected: - ~BaseLoader() = default; - void loadModel(); - - // Helper functions - ir::Activation convertActivation(ActivationFunctionType type); - ir::DataType tensorTypeToDataType(TensorType type); - ir::OperandIndex tensorIdxToOperandIdx(int32_t tensorIdx); - - // Create operands form tflite::Tensor - ir::OperandIndex loadOperand(const Tensor *tensor, ir::Graph &subg); - void loadOperationIO(const Operator *op, ir::OperandIndexSequence &inputs, - ir::OperandIndexSequence &outputs); - // Create operations from Operator - void loadOperation(const Operator *op, ir::Graph &subg); - // Load Strides and Paddings from options to param - template <typename Param, typename OptionsType> - void loadStridesAndPaddings(Param ¶m, const OptionsType *options); - // Load Pool2D param - template <typename Param> void loadPool2DOptions(Param ¶m, const Pool2DOptions *options); - -private: - virtual std::unique_ptr<ir::Graph> loadSubgraph(const SubGraph *subg) = 0; - // Operations - template <typename OpIR, typename... Args> - const OpIR *loadOperationTo(const Operator *op, ir::Graph &subg, Args &&... args); - void loadConv2D(const Operator *op, ir::Graph &subg); - void loadDepthwiseConv2D(const Operator *op, ir::Graph &subg); - void loadTransposeConv(const Operator *op, ir::Graph &subg); - void loadPool2D(const Operator *op, ir::Graph &subg, ir::operation::Pool2D::PoolType op_type); - void loadReshape(const Operator *op, ir::Graph &subg); - void loadSoftmax(const Operator *op, ir::Graph &subg); - void loadConcatenation(const Operator *op, ir::Graph &subg); - void loadFC(const Operator *op, ir::Graph &subg); - void loadBinaryArithmetic(const Operator *op, ir::Graph &subg, - ir::operation::BinaryArithmetic::ArithmeticType op_type); - void loadAddV2(const Operator *op, ir::Graph &subg); - void loadPack(const Operator *op, ir::Graph &subg); - void loadResizeBilinear(const Operator *op, ir::Graph &subg); - void loadResizeNearestNeighbor(const Operator *op, ir::Graph &subg); - void loadReduce(const Operator *op, ir::Graph &subg, - ir::operation::Reduce::ReduceType reduce_type); - void loadReduceAll(const Operator *op, ir::Graph &subg); - void loadElementwiseActivation(const Operator *op, ir::Graph &subg, - ir::operation::ElementwiseActivation::Type op_type, - float alpha = 0.f, float beta = 0.f); - void loadElementwiseBinary(const Operator *op, ir::Graph &subg, - ir::operation::ElementwiseBinary::ElementwiseBinaryType op_type); - void loadElementwiseUnary(const Operator *op, ir::Graph &subg, - ir::operation::ElementwiseUnary::Type op_type); - void loadGather(const Operator *op, ir::Graph &subg); - void loadCustom(const Operator *op, ir::Graph &subg); - void loadBatchMatMul(const Operator *op, ir::Graph &subg); - void loadSqueeze(const Operator *op, ir::Graph &subg); - void loadSplit(const Operator *op, ir::Graph &subg); - void loadSplitV(const Operator *op, ir::Graph &subg); - void loadStridedSlice(const Operator *op, ir::Graph &subg); - void loadUnpack(const Operator *op, ir::Graph &subg); - void loadComparison(const Operator *op, ir::Graph &subg); - void loadEinsum(const Operator *op, ir::Graph &subg); - void loadOneHot(const Operator *op, ir::Graph &subg); - void loadIf(const Operator *op, ir::Graph &subg); - void loadWhile(const Operator *op, ir::Graph &subg); - void loadArgMax(const Operator *op, ir::Graph &subg); - void loadFusedBatchNorm(const Operator *op, ir::Graph &subg); - void loadLogSoftmax(const Operator *op, ir::Graph &subg); - void loadSpaceToDepth(const Operator *op, ir::Graph &subg); - void loadLeakyRelu(const Operator *op, ir::Graph &subg); - - void verifySubgraphIndex(int subg_index) - { - const auto num_subgraphs = _model->subgraphs()->size(); - if (subg_index < 0 || subg_index >= static_cast<int32_t>(num_subgraphs)) - throw std::runtime_error{std::string{"Invalid subgraph index - "} + - std::to_string(subg_index)}; - } - -protected: - // Base address for mapped region for loading (if needed) - uint8_t *_base; - // Memory page size - int32_t _pagesize; - // loaded file description - int _fd; - // Reference on loadable subgraphs - std::unique_ptr<ir::Subgraphs> &_subgraphs; - const Model *_model; - // Maps Tensor indices to onert Operands. - std::vector<ir::OperandIndex> _tensor_to_operand; - std::unordered_map<ir::OperandIndex, std::string> _tensor_names; - // Verifier - std::unique_ptr<Verifier> _verifier; - // Boolean flag to use MMAPED_DATA - bool _use_mmaped_data = false; -}; - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::BaseLoader::loadFromFile(const char *file_path) -{ - _fd = open(file_path, O_RDONLY); - if (_fd < 0) - { - throw std::runtime_error("Failed to open file " + std::string(file_path)); - } - - struct stat file_stat; - if (fstat(_fd, &file_stat) != 0) - { - throw std::runtime_error("Fstat failed or file " + std::string(file_path) + - " is not a regular file"); - } - int size = file_stat.st_size; - - // Map model file into memory region - _base = static_cast<uint8_t *>(mmap(NULL, size, PROT_READ, MAP_PRIVATE, _fd, 0)); - if (_base == MAP_FAILED) - { - close(_fd); - throw std::runtime_error("mmap failed - " + std::string(strerror(errno))); - } - - _verifier = std::make_unique<Verifier>(reinterpret_cast<const std::uint8_t *>(_base), size); - - loadModel(); - munmap(_base, size); - - close(_fd); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::BaseLoader::loadFromBuffer(uint8_t *buffer, size_t size) -{ - _base = buffer; - _verifier = std::make_unique<Verifier>(reinterpret_cast<const std::uint8_t *>(_base), size); - loadModel(); -} - -template <typename LoaderDomain> -ir::Activation -BaseLoader<LoaderDomain>::BaseLoader::convertActivation(const ActivationFunctionType type) -{ - switch (type) - { - case ActivationFunctionType::ActivationFunctionType_NONE: - return ir::Activation::NONE; - case ActivationFunctionType::ActivationFunctionType_RELU: - return ir::Activation::RELU; - case ActivationFunctionType::ActivationFunctionType_RELU_N1_TO_1: - return ir::Activation::RELU1; - case ActivationFunctionType::ActivationFunctionType_RELU6: - return ir::Activation::RELU6; - case ActivationFunctionType::ActivationFunctionType_TANH: - return ir::Activation::TANH; - default: - throw std::runtime_error(std::string("Unsupported or invalid activation type: ") + - std::to_string(static_cast<int>(type))); - } -} - -template <typename LoaderDomain> -ir::DataType BaseLoader<LoaderDomain>::BaseLoader::tensorTypeToDataType(const TensorType type) -{ - switch (type) - { - case TensorType::TensorType_FLOAT32: - return ir::DataType::FLOAT32; - case TensorType::TensorType_INT32: - return ir::DataType::INT32; - case TensorType::TensorType_BOOL: - return ir::DataType::BOOL8; - case TensorType::TensorType_UINT8: - return ir::DataType::QUANT_UINT8_ASYMM; - case TensorType::TensorType_INT8: - return ir::DataType::QUANT_INT8_SYMM; - case TensorType::TensorType_INT64: - return ir::DataType::INT64; - default: - throw std::runtime_error( - std::string("Unsupported tensor type: ").append(EnumNameTensorType(type))); - } -} - -template <typename LoaderDomain> -ir::OperandIndex BaseLoader<LoaderDomain>::BaseLoader::tensorIdxToOperandIdx(int32_t tensorIdx) -{ - return isOptionalInputTensor(tensorIdx) ? ir::OperandIndex() : _tensor_to_operand[tensorIdx]; -} - -/* Copy is copied from tensorflow lite */ -template <typename T> bool Copy(const T *data_ptr, std::vector<uint16_t> &arr) -{ - if (data_ptr->values() == nullptr) - { - return false; - } - - int size = data_ptr->values()->size(); - arr.reserve(size); - for (int i = 0; i < size; i++) - { - arr.emplace_back(static_cast<uint16_t>(data_ptr->values()->Get(i))); - } - return true; -} - -template <typename LoaderDomain> -ir::OperandIndex BaseLoader<LoaderDomain>::loadOperand(const Tensor *tensor, ir::Graph &subg) -{ - ir::Shape shape; - // Shape - const auto *tensor_shape = tensor->shape(); - if (tensor_shape != nullptr) - { - for (const auto &dim : *tensor_shape) - { - shape.append(dim); - } - } - - // Note for tensor->shape_signature() - // We don't handle shape signature - // How we handle: - // If shape_signature[k] == -1, we will use tensor->shape()[k] == 1 - // If app wants to change the input shape, call nnfw_apply_input_tensorinfo() can - // be used. - - // Type - ir::DataType data_type = tensorTypeToDataType(tensor->type()); - // Quantization - auto q_params = tensor->quantization(); - float scale = 0.0; - long zero_point = 0; - if (q_params != nullptr) - { - if (q_params->scale()) - { - if (q_params->scale()->size() != 1) - { - throw std::runtime_error("Only 1 scale for a tensor is supported."); - } - scale = q_params->scale()->Get(0); - } - - if (q_params->zero_point()) - { - if (q_params->zero_point()->size() != 1) - { - throw std::runtime_error("Only 1 zero_point value for a tensor is supported."); - } - zero_point = q_params->zero_point()->Get(0); - // zero_point is long while TypeInfo.zero_point is defined as int32_t. - assert(zero_point >= std::numeric_limits<int32_t>::min()); - assert(zero_point <= std::numeric_limits<int32_t>::max()); - } - auto details = q_params->details_as_CustomQuantization(); - if (details != nullptr) - throw std::runtime_error("Custom Quantization is not supported"); - } - // Create TypeInfo - ir::TypeInfo type_info(data_type, scale, zero_point); - // Sparsity - auto src_sparsity = tensor->sparsity(); - if (src_sparsity != nullptr) - { - std::vector<uint16_t> w1_segments; - std::vector<uint16_t> w1_indices; - // check traversal_order - if (src_sparsity->traversal_order()) - { - const int traversal_order_size = src_sparsity->traversal_order()->size(); - for (int i = 0; i < traversal_order_size; ++i) - { - if (i != src_sparsity->traversal_order()->Get(i)) - throw std::runtime_error("traversal_order [0, 1, ..., n-1] is only supported."); - } - } - // check block_map - int block_rank = 0; - if (src_sparsity->block_map()) - { - block_rank = src_sparsity->block_map()->size(); - for (int i = 0; i < block_rank; ++i) - { - if (i != src_sparsity->block_map()->Get(i)) - throw std::runtime_error("block_map [0, 1, ..., n-1] is only supported."); - } - } - // load metadata - const int dim_metadata_size = src_sparsity->dim_metadata()->size(); - auto dense_rank = shape.rank(); - if (dense_rank + block_rank != dim_metadata_size) - throw std::runtime_error("sparsity dim_metadata length is wrong."); - bool random_sparsity = dim_metadata_size == 2 && block_rank == 0; - bool block2D_sparsity = dim_metadata_size == 4 && block_rank == 2; - if (dim_metadata_size != !random_sparsity && !block2D_sparsity) - throw std::runtime_error( - "sparsity is supported only for 2D tensor with random or 16x1 block sparsity."); - - const auto *src_metadata = src_sparsity->dim_metadata()->Get(0); - if (src_metadata->format() != DimensionType::DimensionType_DENSE) - throw std::runtime_error("sparse tensor dim[0] is not DENSE"); - src_metadata = src_sparsity->dim_metadata()->Get(1); - if (src_metadata->format() != DimensionType::DimensionType_SPARSE_CSR) - throw std::runtime_error("sparse tensor dim[0] is not SPARSE_CSR"); - auto ParseSparseIndexVector = [src_metadata, &w1_segments, &w1_indices]() { - if (src_metadata->array_segments() == nullptr || src_metadata->array_indices() == nullptr) - return false; - bool status = true; - switch (src_metadata->array_segments_type()) - { - case SparseIndexVector::SparseIndexVector_Int32Vector: - status = Copy(src_metadata->array_segments_as_Int32Vector(), w1_segments); - break; - case SparseIndexVector::SparseIndexVector_Uint16Vector: - status = Copy(src_metadata->array_segments_as_Uint16Vector(), w1_segments); - break; - case SparseIndexVector::SparseIndexVector_Uint8Vector: - status = Copy(src_metadata->array_segments_as_Uint8Vector(), w1_segments); - break; - default: - return false; - } - if (status != true) - return false; - switch (src_metadata->array_indices_type()) - { - case SparseIndexVector::SparseIndexVector_Int32Vector: - return Copy(src_metadata->array_indices_as_Int32Vector(), w1_indices); - case SparseIndexVector::SparseIndexVector_Uint16Vector: - return Copy(src_metadata->array_indices_as_Uint16Vector(), w1_indices); - case SparseIndexVector::SparseIndexVector_Uint8Vector: - return Copy(src_metadata->array_indices_as_Uint8Vector(), w1_indices); - default: - break; - } - return false; - }; - if (ParseSparseIndexVector() == false) - throw std::runtime_error("Error during parsing sparsity index information"); - // Get block size - std::vector<int32_t> block_size; - for (int i = 0; i < block_rank; ++i) - { - auto block_metadata = src_sparsity->dim_metadata()->Get(dense_rank + i); - if (block_metadata->format() != DimensionType::DimensionType_DENSE) - throw std::runtime_error("block dimension must be DENSE."); - block_size.push_back(block_metadata->dense_size()); - } - type_info.sparsity(std::make_shared<ir::Sparsity>(std::move(w1_segments), std::move(w1_indices), - std::move(block_size))); - } - // Create operand - const auto operand_index = subg.addOperand(shape, type_info); - - // Constant tensors are indicated by non-empty data. - const auto *data = _model->buffers()->Get(tensor->buffer())->data(); - if (data != nullptr) - { - using std::ptrdiff_t; - std::unique_ptr<ir::Data> data_obj; - if (_fd == -1) // Model is from memory - { - data_obj = std::make_unique<ir::ExternalData>(data->data(), data->size()); - } - else // Model is loaded(mmap'd) from a file - { - size_t data_size = data->size(); - ptrdiff_t unaligned_offset_start = data->data() - _base; - ptrdiff_t offset_end = unaligned_offset_start + data_size; - - // Calculated aligned offset from base address of mapped region - // munmap accepts memory address which is a multiple of the pagesize - ptrdiff_t aligned_offset_start = (unaligned_offset_start / _pagesize) * _pagesize; - size_t mmap_size = offset_end - aligned_offset_start; - - if (_use_mmaped_data) - { - data_obj = std::make_unique<ir::MMapedData>(_fd, aligned_offset_start, mmap_size, - unaligned_offset_start, data_size); - } - else - { - size_t offset = unaligned_offset_start - aligned_offset_start; - uint8_t *mmap_base = static_cast<uint8_t *>( - mmap(NULL, mmap_size, PROT_READ, MAP_PRIVATE, _fd, aligned_offset_start)); - data_obj = std::make_unique<ir::CachedData>(mmap_base + offset, data_size); - munmap(mmap_base, mmap_size); - } - } - subg.setOperandValue(operand_index, std::move(data_obj)); - } - - _tensor_names.emplace(operand_index, tensor->name()->str()); - - // Variablie - if (tensor->is_variable()) - throw std::runtime_error("Variable tensor not supported!"); - - return operand_index; -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadOperationIO(const Operator *op, ir::OperandIndexSequence &inputs, - ir::OperandIndexSequence &outputs) -{ - for (const std::int32_t idx : *op->inputs()) - { - // Optional tensors are not supported yet except for FULLY_CONNECTED and BCQ_FULLY_CONNECTED - auto check_optional_input = [&]() { - auto builtin_code = _model->operator_codes()->Get(op->opcode_index())->builtin_code(); - if (isOptionalInputTensor(idx) && !allowOptionalInputTensor(builtin_code)) - throw std::runtime_error( - std::string("loader doesn't support optional input tensor yet for ") - .append(EnumNameBuiltinOperator(builtin_code))); - }; - check_optional_input(); - inputs.append(tensorIdxToOperandIdx(idx)); - } - - for (const std::int32_t idx : *op->outputs()) - { - outputs.append(tensorIdxToOperandIdx(idx)); - } -} - -template <typename LoaderDomain> -template <typename Param, typename OptionsType> -void BaseLoader<LoaderDomain>::loadStridesAndPaddings(Param ¶m, const OptionsType *options) -{ - // Strides - param.stride.vertical = options->stride_h(); - param.stride.horizontal = options->stride_w(); - // Paddings - switch (options->padding()) - { - case Padding::Padding_SAME: - param.padding.type = ir::PaddingType::SAME; - break; - case Padding::Padding_VALID: - param.padding.type = ir::PaddingType::VALID; - break; - default: - throw std::runtime_error{"Invalid padding type"}; - } - // param paddings indexes unused -} - -template <typename LoaderDomain> -template <typename Param> -void BaseLoader<LoaderDomain>::loadPool2DOptions(Param ¶m, const Pool2DOptions *options) -{ - // Strides and Paddings - if (options->stride_h() <= 0 || options->stride_w() <= 0) - throw std::runtime_error{"Invalid stride vertical or horizontal - both must be bigger than 0"}; - loadStridesAndPaddings(param, options); - // Filter width and height - // Strides - if (options->filter_width() <= 0 || options->filter_height() <= 0) - throw std::runtime_error{"Invalid filter width or height - both must be bigger than 0"}; - param.kw = options->filter_width(); - param.kh = options->filter_height(); - // Activation - param.activation = convertActivation(options->fused_activation_function()); -} - -template <typename LoaderDomain> -template <typename OpIR, typename... Args> -const OpIR *BaseLoader<LoaderDomain>::loadOperationTo(const Operator *op, ir::Graph &subg, - Args &&... args) -{ - static_assert(sizeof...(args) <= 1, "You can't have more than 1 arguments!"); - ir::OperandIndexSequence inputs; - ir::OperandIndexSequence outputs; - - loadOperationIO(op, inputs, outputs); - - std::unique_ptr<OpIR> new_op(new OpIR(inputs, outputs, std::forward<Args>(args)...)); - auto ret = new_op.get(); - subg.addOperation(std::move(new_op)); - - return ret; -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadConv2D(const Operator *op, ir::Graph &subg) -{ - ir::operation::Conv2D::Param param; - const auto *options = op->builtin_options_as_Conv2DOptions(); - param.activation = convertActivation(options->fused_activation_function()); - loadStridesAndPaddings(param, options); - param.dilation.width_factor = options->dilation_w_factor(); - param.dilation.height_factor = options->dilation_h_factor(); - - loadOperationTo<ir::operation::Conv2D>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadDepthwiseConv2D(const Operator *op, ir::Graph &subg) -{ - ir::operation::DepthwiseConv2D::Param param; - const auto *options = op->builtin_options_as_DepthwiseConv2DOptions(); - param.activation = convertActivation(options->fused_activation_function()); - loadStridesAndPaddings(param, options); - param.multiplier = options->depth_multiplier(); - // Dilation h/w factor unused - - loadOperationTo<ir::operation::DepthwiseConv2D>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadTransposeConv(const Operator *op, ir::Graph &subg) -{ - ir::operation::TransposeConv::Param param; - const auto *options = op->builtin_options_as_TransposeConvOptions(); - loadStridesAndPaddings(param, options); - - loadOperationTo<ir::operation::TransposeConv>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadPool2D(const Operator *op, ir::Graph &subg, - ir::operation::Pool2D::PoolType op_type) -{ - ir::operation::Pool2D::Param param; - param.op_type = op_type; - const auto *options = op->builtin_options_as_Pool2DOptions(); - - loadPool2DOptions(param, options); - - loadOperationTo<ir::operation::Pool2D>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadReshape(const Operator *op, ir::Graph &subg) -{ - ir::operation::Reshape::Param param{}; - const auto *options = op->builtin_options_as_ReshapeOptions(); - if (options != nullptr) - { - const auto *new_shape = options->new_shape(); - if (new_shape) - { - for (uint i = 0; i < new_shape->size(); ++i) - { - param.new_shape.push_back(new_shape->Get(i)); - } - } - } - - loadOperationTo<ir::operation::Reshape>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadSoftmax(const Operator *op, ir::Graph &subg) -{ - ir::operation::Softmax::Param param; - const auto *options = op->builtin_options_as_SoftmaxOptions(); - // Beta - param.beta = options->beta(); - - loadOperationTo<ir::operation::Softmax>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadConcatenation(const Operator *op, ir::Graph &subg) -{ - ir::operation::Concat::Param param; - const auto *options = op->builtin_options_as_ConcatenationOptions(); - // Axis - param.axis = options->axis(); - // activation unused - - loadOperationTo<ir::operation::Concat>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadFC(const Operator *op, ir::Graph &subg) -{ - ir::operation::FullyConnected::Param param; - const auto *options = op->builtin_options_as_FullyConnectedOptions(); - - param.activation = convertActivation(options->fused_activation_function()); - // weights_format unused - - const auto fc = loadOperationTo<ir::operation::FullyConnected>(op, subg, param); - - const auto &input_operand = - subg.operands().at(fc->getInputs().at(ir::operation::FullyConnected::INPUT)); - auto &weights_operand = - subg.operands().at(fc->getInputs().at(ir::operation::FullyConnected::WEIGHT)); - if (input_operand.typeInfo().type() == ir::DataType::FLOAT32 && - weights_operand.typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM) - { - weights_operand.type(ir::DataType::QUANT_INT8_SYMM); - } -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadAddV2(const Operator *op, ir::Graph &subg) -{ - ir::operation::BinaryArithmetic::Param param; - param.arithmetic_type = ir::operation::BinaryArithmetic::ArithmeticType::ADD; - - if (op->custom_options() == nullptr) - { - param.activation = ir::Activation::NONE; - } - else - { - size_t custom_op_data_size = op->custom_options()->size(); - auto custom_op_data = op->custom_options()->Data(); - auto data_root = flexbuffers::GetRoot(custom_op_data, custom_op_data_size); - auto attr_map = data_root.AsMap(); - const auto fused_activation_func = static_cast<typename LoaderDomain::ActivationFunctionType>( - attr_map["fused_activation_function"].AsInt8()); - param.activation = convertActivation(fused_activation_func); - } - - loadOperationTo<ir::operation::BinaryArithmetic>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadBinaryArithmetic( - const Operator *op, ir::Graph &subg, ir::operation::BinaryArithmetic::ArithmeticType op_type) -{ - ir::operation::BinaryArithmetic::Param param; - param.arithmetic_type = op_type; - switch (op_type) - { - case ir::operation::BinaryArithmetic::ArithmeticType::ADD: - { - const auto *add_options = op->builtin_options_as_AddOptions(); - param.activation = convertActivation(add_options->fused_activation_function()); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::SUB: - { - const auto *sub_options = op->builtin_options_as_SubOptions(); - param.activation = convertActivation(sub_options->fused_activation_function()); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::MUL: - { - const auto *mul_options = op->builtin_options_as_MulOptions(); - param.activation = convertActivation(mul_options->fused_activation_function()); - break; - } - case ir::operation::BinaryArithmetic::ArithmeticType::DIV: - { - const auto *div_options = op->builtin_options_as_DivOptions(); - param.activation = convertActivation(div_options->fused_activation_function()); - break; - } - default: - assert(false && - "The function 'loadBinaryArithmetic' supports only BinaryArithmetic operations"); - break; - } - - loadOperationTo<ir::operation::BinaryArithmetic>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadPack(const Operator *op, ir::Graph &subg) -{ - ir::operation::Pack::Param param; - const auto *options = op->builtin_options_as_PackOptions(); - param.num = options->values_count(); - param.axis = options->axis(); - - loadOperationTo<ir::operation::Pack>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadElementwiseActivation( - const Operator *op, ir::Graph &subg, ir::operation::ElementwiseActivation::Type op_type, - float alpha, float beta) -{ - ir::operation::ElementwiseActivation::Param param; - param.op_type = op_type; - param.alpha = alpha; - param.beta = beta; - - loadOperationTo<ir::operation::ElementwiseActivation>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadResizeBilinear(const Operator *op, ir::Graph &subg) -{ - ir::operation::ResizeBilinear::Param param; - param.align_corners = op->builtin_options_as_ResizeBilinearOptions()->align_corners(); - param.half_pixel_centers = op->builtin_options_as_ResizeBilinearOptions()->half_pixel_centers(); - - loadOperationTo<ir::operation::ResizeBilinear>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadResizeNearestNeighbor(const Operator *op, ir::Graph &subg) -{ - ir::operation::ResizeNearestNeighbor::Param param; - param.align_corners = op->builtin_options_as_ResizeNearestNeighborOptions()->align_corners(); - - loadOperationTo<ir::operation::ResizeNearestNeighbor>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadReduce(const Operator *op, ir::Graph &subg, - ir::operation::Reduce::ReduceType reduce_type) -{ - ir::operation::Reduce::Param param; - param.reduce_type = reduce_type; - param.keep_dims = op->builtin_options_as_ReducerOptions()->keep_dims(); - - loadOperationTo<ir::operation::Reduce>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadReduceAll(const Operator *op, ir::Graph &subg) -{ - ir::operation::Reduce::Param param; - param.reduce_type = ir::operation::Reduce::ReduceType::ALL; - if (op->custom_options() == nullptr) - { - param.keep_dims = false; - } - else - { - size_t custom_op_data_size = op->custom_options()->size(); - auto custom_op_data = op->custom_options()->Data(); - auto data_root = flexbuffers::GetRoot(custom_op_data, custom_op_data_size); - auto attr_map = data_root.AsMap(); - param.keep_dims = attr_map["keep_dims"].AsBool(); - } - - loadOperationTo<ir::operation::Reduce>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadElementwiseBinary( - const Operator *op, ir::Graph &subg, - ir::operation::ElementwiseBinary::ElementwiseBinaryType op_type) -{ - ir::operation::ElementwiseBinary::Param param; - param.op_type = op_type; - - loadOperationTo<ir::operation::ElementwiseBinary>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadElementwiseUnary(const Operator *op, ir::Graph &subg, - ir::operation::ElementwiseUnary::Type op_type) -{ - ir::operation::ElementwiseUnary::Param param; - param.op_type = op_type; - - const auto eu = loadOperationTo<ir::operation::ElementwiseUnary>(op, subg, param); - if (op_type == ir::operation::ElementwiseUnary::Type::CAST) - { - auto qasymm8ToUint8 = [](ir::Operand &operand) { - if (operand.typeInfo().type() == ir::DataType::QUANT_UINT8_ASYMM) - { - operand.type(ir::DataType::UINT8); - } - }; - qasymm8ToUint8( - subg.operands().at(eu->getInputs().at(ir::operation::ElementwiseUnary::Input::INPUT))); - qasymm8ToUint8(subg.operands().at(eu->getOutputs().at(0))); - } -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadGather(const Operator *op, ir::Graph &subg) -{ - ir::operation::Gather::Param param; - param.axis = op->builtin_options_as_GatherOptions()->axis(); - - loadOperationTo<ir::operation::Gather>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadBatchMatMul(const Operator *op, ir::Graph &subg) -{ - ir::operation::BatchMatMul::Param param; - - const auto builtin_op = _model->operator_codes()->Get(op->opcode_index())->builtin_code(); - - switch (builtin_op) - { - case BuiltinOperator::BuiltinOperator_BATCH_MATMUL: - param.adj_x = op->builtin_options_as_BatchMatMulOptions()->adjoint_lhs(); - param.adj_y = op->builtin_options_as_BatchMatMulOptions()->adjoint_rhs(); - break; - case BuiltinOperator::BuiltinOperator_CUSTOM: - if (op->custom_options() == nullptr) - { - param.adj_x = false; - param.adj_y = false; - } - else - { - size_t custom_op_data_size = op->custom_options()->size(); - auto custom_op_data = op->custom_options()->Data(); - auto data_root = flexbuffers::GetRoot(custom_op_data, custom_op_data_size); - auto attr_map = data_root.AsMap(); - param.adj_x = attr_map["adj_x"].AsBool(); - param.adj_y = attr_map["adj_y"].AsBool(); - } - break; - default: - throw std::runtime_error( - std::string("Wrong loaded operation: ").append(EnumNameBuiltinOperator(builtin_op)) + - " as " + EnumNameBuiltinOperator(BuiltinOperator::BuiltinOperator_BATCH_MATMUL)); - } - - loadOperationTo<ir::operation::BatchMatMul>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadSpaceToDepth(const Operator *op, ir::Graph &subg) -{ - ir::operation::SpaceToDepth::Param param; - const auto *options = op->builtin_options_as_SpaceToDepthOptions(); - param.block_size = options->block_size(); - - loadOperationTo<ir::operation::SpaceToDepth>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadCustom(const Operator *op, ir::Graph &subg) -{ - ir::OperandIndexSequence inputs; - ir::OperandIndexSequence outputs; - - assert(op->custom_options_format() == CustomOptionsFormat::CustomOptionsFormat_FLEXBUFFERS && - "Unsupported custom operation options format"); - - auto *op_code = _model->operator_codes()->Get(op->opcode_index()); - auto custom_op_name = op_code->custom_code()->str(); - - enum class BuiltinOP - { - AddV2, - ReduceAll, - MatrixBandPart, - BatchMatMul, - Einsum, - BroadcastTo, - FusedBatchNorm, - StatelessRandomUniform, - Erf - }; - - // Mapping from custom op name string to BuiltinOP enum - std::map<std::string, BuiltinOP> builtin_map = { - {"AddV2", BuiltinOP::AddV2}, - {"All", BuiltinOP::ReduceAll}, - {"MatrixBandPart", BuiltinOP::MatrixBandPart}, - {"BatchMatMulV2", BuiltinOP::BatchMatMul}, - {"Einsum", BuiltinOP::Einsum}, - {"FusedBatchNormV3", BuiltinOP::FusedBatchNorm}, - {"BroadcastTo", BuiltinOP::BroadcastTo}, - {"StatelessRandomUniform", BuiltinOP::StatelessRandomUniform}, - {"Erf", BuiltinOP::Erf}, - }; - - try - { - // Throw out_of_range if it is unknown custom op - auto custom_op_id = builtin_map.at(custom_op_name); - switch (custom_op_id) - { - case BuiltinOP::AddV2: - loadAddV2(op, subg); - break; - case BuiltinOP::ReduceAll: - loadReduceAll(op, subg); - break; - case BuiltinOP::MatrixBandPart: - loadOperationTo<ir::operation::MatrixBandPart>(op, subg); - break; - case BuiltinOP::BatchMatMul: - loadBatchMatMul(op, subg); - break; - case BuiltinOP::Einsum: - loadEinsum(op, subg); - break; - case BuiltinOP::BroadcastTo: - loadOperationTo<ir::operation::BroadcastTo>(op, subg); - break; - case BuiltinOP::FusedBatchNorm: - loadFusedBatchNorm(op, subg); - break; - case BuiltinOP::StatelessRandomUniform: - loadOperationTo<ir::operation::StatelessRandomUniform>(op, subg); - break; - case BuiltinOP::Erf: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::ERF); - break; - default: - throw std::runtime_error{ - "Loader: Custom OP map is defined but operation loader function is not defined"}; - } - - return; - } - catch (...) - { - loadOperationIO(op, inputs, outputs); - - auto constraint = ir::OperandConstraint::createExact(inputs.size()); - - size_t custom_op_data_size = op->custom_options()->size(); - auto custom_op_data = new char[custom_op_data_size]; - std::copy(op->custom_options()->begin(), op->custom_options()->end(), custom_op_data); - - ir::operation::Custom::Userdata userdata{}; - userdata.data = custom_op_data; - userdata.size = custom_op_data_size; - - auto new_op = std::make_unique<ir::operation::Custom>(constraint, inputs, outputs, - custom_op_name, userdata); - - subg.addOperation(std::move(new_op)); - } -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadSqueeze(const Operator *op, ir::Graph &subg) -{ - ir::operation::Squeeze::Param param; - const auto *options = op->builtin_options_as_SqueezeOptions(); - const auto *dims = options->squeeze_dims(); - if (dims) - { - if (dims->size() > sizeof(param.dims) / sizeof(param.dims[0])) - throw std::runtime_error("Squeeze: 'param.ndims' is out of range."); - param.ndim = dims->size(); - for (int i = 0; i < param.ndim; ++i) - param.dims[i] = dims->Get(i); - } - - loadOperationTo<ir::operation::Squeeze>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadSplit(const Operator *op, ir::Graph &subg) -{ - ir::operation::Split::Param param; - const auto *options = op->builtin_options_as_SplitOptions(); - param.num_splits = options->num_splits(); - - loadOperationTo<ir::operation::Split>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadSplitV(const Operator *op, ir::Graph &subg) -{ - ir::operation::SplitV::Param param; - const auto *options = op->builtin_options_as_SplitVOptions(); - param.num_splits = options->num_splits(); - - loadOperationTo<ir::operation::SplitV>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadStridedSlice(const Operator *op, ir::Graph &subg) -{ - ir::operation::StridedSlice::Param param; - const auto *options = op->builtin_options_as_StridedSliceOptions(); - param.begin_mask = options->begin_mask(); - param.end_mask = options->end_mask(); - param.shrink_axis_mask = options->shrink_axis_mask(); - - loadOperationTo<ir::operation::StridedSlice>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadUnpack(const Operator *op, ir::Graph &subg) -{ - ir::operation::Unpack::Param param; - const auto *options = op->builtin_options_as_UnpackOptions(); - param.num = options->num(); - param.axis = options->axis(); - - loadOperationTo<ir::operation::Unpack>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadComparison(const Operator *op, ir::Graph &subg) -{ - ir::operation::Comparison::Param param; - const auto builtin_op = _model->operator_codes()->Get(op->opcode_index())->builtin_code(); - - switch (builtin_op) - { - case BuiltinOperator::BuiltinOperator_EQUAL: - param.comparison_type = ir::operation::Comparison::ComparisonType::Equal; - break; - case BuiltinOperator::BuiltinOperator_NOT_EQUAL: - param.comparison_type = ir::operation::Comparison::ComparisonType::NotEqual; - break; - case BuiltinOperator::BuiltinOperator_GREATER_EQUAL: - param.comparison_type = ir::operation::Comparison::ComparisonType::GreaterEqual; - break; - case BuiltinOperator::BuiltinOperator_GREATER: - param.comparison_type = ir::operation::Comparison::ComparisonType::Greater; - break; - case BuiltinOperator::BuiltinOperator_LESS_EQUAL: - param.comparison_type = ir::operation::Comparison::ComparisonType::LessEqual; - break; - case BuiltinOperator::BuiltinOperator_LESS: - param.comparison_type = ir::operation::Comparison::ComparisonType::Less; - break; - default: - throw std::runtime_error( - std::string("Unsupported operation: ").append(EnumNameBuiltinOperator(builtin_op))); - } - - loadOperationTo<ir::operation::Comparison>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadEinsum(const Operator *op, ir::Graph &subg) -{ - ir::operation::Einsum::Param param; - if (op->custom_options() == nullptr) - { - throw std::runtime_error{"Einsum: empty equation"}; - } - else - { - size_t custom_op_data_size = op->custom_options()->size(); - auto custom_op_data = op->custom_options()->Data(); - auto data_root = flexbuffers::GetRoot(custom_op_data, custom_op_data_size); - auto attr_map = data_root.AsMap(); - param.equation = attr_map["equation"].ToString(); - } - - const auto es = loadOperationTo<ir::operation::Einsum>(op, subg, param); - if (es->getInputs().size() != 2) - { - throw std::runtime_error{"Einsum: NYI input - only support two inputs"}; - } -} -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadFusedBatchNorm(const Operator *op, ir::Graph &subg) -{ - ir::operation::FusedBatchNorm::Param param; - if (op->custom_options() == nullptr) - { - throw std::runtime_error{"FusedBatchNorm: empty option"}; - } - else - { - size_t custom_op_data_size = op->custom_options()->size(); - auto custom_op_data = op->custom_options()->Data(); - auto data_root = flexbuffers::GetRoot(custom_op_data, custom_op_data_size); - auto attr_map = data_root.AsMap(); - param.is_training = attr_map["is_training"].AsBool(); - param.epsilon = attr_map["epsilon"].AsFloat(); - param.data_format = attr_map["data_format"].ToString(); - } - - const auto fbn = loadOperationTo<ir::operation::FusedBatchNorm>(op, subg, param); - - if (fbn->getInputs().size() != 5) - { - throw std::runtime_error{"FusedBatchNorm: NYI input - only support five inputs"}; - } -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadOneHot(const Operator *op, ir::Graph &subg) -{ - if (op->inputs()->size() != 4 || op->outputs()->size() != 1) - throw std::runtime_error("OneHot Op has wrong number of input or output tensors."); - - // Set parameter - ir::operation::OneHot::Param param; - param.axis = op->builtin_options_as_OneHotOptions()->axis(); - - loadOperationTo<ir::operation::OneHot>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadIf(const Operator *op, ir::Graph &subg) -{ - const auto *options = op->builtin_options_as_IfOptions(); - const int32_t then_index = options->then_subgraph_index(); - const int32_t else_index = options->else_subgraph_index(); - - verifySubgraphIndex(then_index); - verifySubgraphIndex(else_index); - - ir::operation::If::Param param; - param.then_subg_index = ir::SubgraphIndex{static_cast<uint32_t>(then_index)}; - param.else_subg_index = ir::SubgraphIndex{static_cast<uint32_t>(else_index)}; - - loadOperationTo<ir::operation::If>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadWhile(const Operator *op, ir::Graph &subg) -{ - const auto *options = op->builtin_options_as_WhileOptions(); - const int32_t cond_index = options->cond_subgraph_index(); - const int32_t body_index = options->body_subgraph_index(); - - verifySubgraphIndex(cond_index); - verifySubgraphIndex(body_index); - - ir::operation::While::Param param; - param.cond_subg_index = ir::SubgraphIndex{static_cast<uint32_t>(cond_index)}; - param.body_subg_index = ir::SubgraphIndex{static_cast<uint32_t>(body_index)}; - - loadOperationTo<ir::operation::While>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadArgMax(const Operator *op, ir::Graph &subg) -{ - ir::operation::ArgMax::Param param; - const auto output_type = op->builtin_options_as_ArgMaxOptions()->output_type(); - switch (output_type) - { - case TensorType::TensorType_INT32: - case TensorType::TensorType_INT64: - param.output_type = tensorTypeToDataType(output_type); - break; - default: - throw std::runtime_error("ArgMax: `output_type` must be either int32 or int64."); - } - auto am = loadOperationTo<ir::operation::ArgMax>(op, subg, param); - - auto &axisOperand = subg.operands().at(am->getInputs().at(ir::operation::ArgMax::Input::AXIS)); - if (!(axisOperand.operandSize() == 4 && (axisOperand.typeInfo().type() == ir::DataType::INT32 || - axisOperand.typeInfo().type() == ir::DataType::INT64))) - throw std::runtime_error("ArgMax: `axis` with an int32 or int64 element is only supported."); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadLogSoftmax(const Operator *op, ir::Graph &subg) -{ - ir::operation::LogSoftmax::Param param; - // In tflite, beta is fixed to 1.0 and axis is fixed to -1. - param.beta = 1.0f; - param.axis = -1; - - loadOperationTo<ir::operation::LogSoftmax>(op, subg, param); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadLeakyRelu(const Operator *op, ir::Graph &subg) -{ - float alpha = op->builtin_options_as_LeakyReluOptions()->alpha(); - loadElementwiseActivation(op, subg, ir::operation::ElementwiseActivation::Type::LEAKY_RELU, alpha, - 1.f); -} - -template <typename LoaderDomain> -void BaseLoader<LoaderDomain>::loadOperation(const Operator *op, ir::Graph &subg) -{ - const auto builtin_op = _model->operator_codes()->Get(op->opcode_index())->builtin_code(); - - switch (builtin_op) - { - case BuiltinOperator::BuiltinOperator_CONV_2D: - loadConv2D(op, subg); - return; - case BuiltinOperator::BuiltinOperator_AVERAGE_POOL_2D: - loadPool2D(op, subg, ir::operation::Pool2D::PoolType::AVG); - return; - case BuiltinOperator::BuiltinOperator_DEPTHWISE_CONV_2D: - loadDepthwiseConv2D(op, subg); - return; - case BuiltinOperator::BuiltinOperator_TRANSPOSE_CONV: - loadTransposeConv(op, subg); - return; - case BuiltinOperator::BuiltinOperator_RESHAPE: - loadReshape(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SOFTMAX: - loadSoftmax(op, subg); - return; - case BuiltinOperator::BuiltinOperator_MAX_POOL_2D: - loadPool2D(op, subg, ir::operation::Pool2D::PoolType::MAX); - return; - case BuiltinOperator::BuiltinOperator_CONCATENATION: - loadConcatenation(op, subg); - return; - case BuiltinOperator::BuiltinOperator_FULLY_CONNECTED: - loadFC(op, subg); - return; - case BuiltinOperator::BuiltinOperator_ADD: - loadBinaryArithmetic(op, subg, ir::operation::BinaryArithmetic::ArithmeticType::ADD); - return; - case BuiltinOperator::BuiltinOperator_SUB: - loadBinaryArithmetic(op, subg, ir::operation::BinaryArithmetic::ArithmeticType::SUB); - return; - case BuiltinOperator::BuiltinOperator_MUL: - loadBinaryArithmetic(op, subg, ir::operation::BinaryArithmetic::ArithmeticType::MUL); - return; - case BuiltinOperator::BuiltinOperator_DIV: - loadBinaryArithmetic(op, subg, ir::operation::BinaryArithmetic::ArithmeticType::DIV); - return; - case BuiltinOperator::BuiltinOperator_PACK: - loadPack(op, subg); - return; - case BuiltinOperator::BuiltinOperator_RELU: - loadElementwiseActivation(op, subg, ir::operation::ElementwiseActivation::Type::RELU, - ir::operation::ElementwiseActivation::infinity, 0.f); - return; - case BuiltinOperator::BuiltinOperator_RELU_N1_TO_1: - loadElementwiseActivation(op, subg, ir::operation::ElementwiseActivation::Type::RELU, 1.f, - -1.f); - return; - case BuiltinOperator::BuiltinOperator_RELU6: - loadElementwiseActivation(op, subg, ir::operation::ElementwiseActivation::Type::RELU, 6.f, - 0.f); - return; - case BuiltinOperator::BuiltinOperator_RESIZE_BILINEAR: - loadResizeBilinear(op, subg); - return; - case BuiltinOperator::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: - loadResizeNearestNeighbor(op, subg); - return; - case BuiltinOperator::BuiltinOperator_RSQRT: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::RSQRT); - return; - case BuiltinOperator::BuiltinOperator_SELECT: - case BuiltinOperator::BuiltinOperator_SELECT_V2: - loadOperationTo<ir::operation::Select>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SQRT: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::SQRT); - return; - case BuiltinOperator::BuiltinOperator_SQUARED_DIFFERENCE: - loadOperationTo<ir::operation::SquaredDifference>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_TANH: - loadElementwiseActivation(op, subg, ir::operation::ElementwiseActivation::Type::TANH, 1.f, - 1.f); - return; - case BuiltinOperator::BuiltinOperator_TRANSPOSE: - loadOperationTo<ir::operation::Transpose>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_MEAN: - loadReduce(op, subg, ir::operation::Reduce::ReduceType::MEAN); - return; - case BuiltinOperator::BuiltinOperator_REDUCE_ANY: - loadReduce(op, subg, ir::operation::Reduce::ReduceType::ANY); - return; - case BuiltinOperator::BuiltinOperator_REDUCE_MAX: - loadReduce(op, subg, ir::operation::Reduce::ReduceType::MAX); - return; - case BuiltinOperator::BuiltinOperator_REVERSE_V2: - loadOperationTo<ir::operation::Reverse>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_PAD: - case BuiltinOperator::BuiltinOperator_PADV2: - loadOperationTo<ir::operation::Pad>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_LOGISTIC: - loadElementwiseActivation(op, subg, ir::operation::ElementwiseActivation::Type::LOGISTIC); - return; - case BuiltinOperator::BuiltinOperator_EXP: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::EXP); - return; - case BuiltinOperator::BuiltinOperator_EXPAND_DIMS: - loadOperationTo<ir::operation::ExpandDims>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_GATHER: - loadGather(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SPACE_TO_BATCH_ND: - loadOperationTo<ir::operation::SpaceToBatchND>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_BATCH_TO_SPACE_ND: - loadOperationTo<ir::operation::BatchToSpaceND>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SUM: - loadReduce(op, subg, ir::operation::Reduce::ReduceType::SUM); - return; - case BuiltinOperator::BuiltinOperator_CUSTOM: - loadCustom(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SQUEEZE: - loadSqueeze(op, subg); - return; - case BuiltinOperator::BuiltinOperator_PRELU: - loadOperationTo<ir::operation::PReLU>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SPLIT: - loadSplit(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SPLIT_V: - loadSplitV(op, subg); - return; - case BuiltinOperator::BuiltinOperator_SLICE: - loadOperationTo<ir::operation::Slice>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_STRIDED_SLICE: - loadStridedSlice(op, subg); - return; - case BuiltinOperator::BuiltinOperator_UNPACK: - loadUnpack(op, subg); - return; - case BuiltinOperator::BuiltinOperator_MINIMUM: - loadElementwiseBinary(op, subg, ir::operation::ElementwiseBinary::ElementwiseBinaryType::MIN); - return; - case BuiltinOperator::BuiltinOperator_MAXIMUM: - loadElementwiseBinary(op, subg, ir::operation::ElementwiseBinary::ElementwiseBinaryType::MAX); - return; - case BuiltinOperator::BuiltinOperator_CAST: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::CAST); - return; - case BuiltinOperator::BuiltinOperator_EQUAL: - case BuiltinOperator::BuiltinOperator_NOT_EQUAL: - case BuiltinOperator::BuiltinOperator_GREATER_EQUAL: - case BuiltinOperator::BuiltinOperator_GREATER: - case BuiltinOperator::BuiltinOperator_LESS_EQUAL: - case BuiltinOperator::BuiltinOperator_LESS: - loadComparison(op, subg); - return; - case BuiltinOperator::BuiltinOperator_ONE_HOT: - loadOneHot(op, subg); - return; - case BuiltinOperator::BuiltinOperator_ABS: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::ABS); - return; - case BuiltinOperator::BuiltinOperator_COS: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::COS); - return; - case BuiltinOperator::BuiltinOperator_SIN: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::SIN); - return; - case BuiltinOperator::BuiltinOperator_SHAPE: - loadOperationTo<ir::operation::Shape>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_REDUCE_PROD: - loadReduce(op, subg, ir::operation::Reduce::ReduceType::PROD); - return; - case BuiltinOperator::BuiltinOperator_IF: - loadIf(op, subg); - return; - case BuiltinOperator::BuiltinOperator_WHILE: - loadWhile(op, subg); - return; - case BuiltinOperator::BuiltinOperator_NEG: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::NEG); - return; - case BuiltinOperator::BuiltinOperator_ARG_MAX: - loadArgMax(op, subg); - return; - case BuiltinOperator::BuiltinOperator_LOG: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::LOG); - return; - case BuiltinOperator::BuiltinOperator_ROUND: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::ROUND); - return; - case BuiltinOperator::BuiltinOperator_POW: - loadOperationTo<ir::operation::Pow>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_LOGICAL_NOT: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::LOGICAL_NOT); - return; - case BuiltinOperator::BuiltinOperator_LOGICAL_OR: - loadElementwiseBinary(op, subg, - ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR); - return; - case BuiltinOperator::BuiltinOperator_FILL: - loadOperationTo<ir::operation::Fill>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_ZEROS_LIKE: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::ZEROS_LIKE); - return; - case BuiltinOperator::BuiltinOperator_TILE: - loadOperationTo<ir::operation::Tile>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_RANGE: - loadOperationTo<ir::operation::Range>(op, subg); - return; - case BuiltinOperator::BuiltinOperator_BATCH_MATMUL: - loadBatchMatMul(op, subg); - return; - case BuiltinOperator::BuiltinOperator_LOG_SOFTMAX: - loadLogSoftmax(op, subg); - return; - case BuiltinOperator::BuiltinOperator_QUANTIZE: - loadElementwiseUnary(op, subg, ir::operation::ElementwiseUnary::Type::QUANTIZE); - return; - case BuiltinOperator::BuiltinOperator_SPACE_TO_DEPTH: - loadSpaceToDepth(op, subg); - return; - case BuiltinOperator::BuiltinOperator_L2_NORMALIZATION: - loadOperationTo<ir::operation::L2Normalization>(op, subg); - break; - case BuiltinOperator::BuiltinOperator_LEAKY_RELU: - loadLeakyRelu(op, subg); - return; - case BuiltinOperator::BuiltinOperator_RANK: - loadOperationTo<ir::operation::Rank>(op, subg); - return; - default: - throw std::runtime_error( - std::string("Unsupported operation: ").append(EnumNameBuiltinOperator(builtin_op))); - } -} - -template <typename LoaderDomain> void BaseLoader<LoaderDomain>::loadModel() -{ - LoaderDomain::VerifyModelBuffer(*_verifier.get()); - _model = LoaderDomain::GetModel(_base); - // Version unused - // const auto version = _model->version(); - // Description unused - // const auto *description = _model->description(); - // Metabuffer unsued - // const auto *metadata_buffer = _model->metadata_buffer(); - // Load subgraphs and map operations on subgraph - const auto domain_subgraphs = _model->subgraphs(); - auto subgraphs = std::make_unique<ir::Subgraphs>(); - for (uint32_t subgraph_index = 0; subgraph_index < domain_subgraphs->size(); ++subgraph_index) - { - auto subg = loadSubgraph((*_model->subgraphs())[subgraph_index]); - subgraphs->push(ir::SubgraphIndex{subgraph_index}, std::move(subg)); - } - _subgraphs = std::move(subgraphs); -} - -} // namespace base_loader -} // namespace onert - -#endif //__BASE_LOADER_BASE_LOADER_H__ diff --git a/runtime/onert/frontend/circle/CMakeLists.txt b/runtime/onert/frontend/circle/CMakeLists.txt deleted file mode 100644 index 76dca9989..000000000 --- a/runtime/onert/frontend/circle/CMakeLists.txt +++ /dev/null @@ -1,15 +0,0 @@ -if (NOT BUILD_CIRCLE_LOADER) - return() -endif () - -set(CIRCLE_LOADER_SOURCES src/circle_loader.cc) - -add_library(circle_loader SHARED ${CIRCLE_LOADER_SOURCES}) - -target_include_directories(circle_loader PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) - -target_link_libraries(circle_loader PRIVATE onert_core) -target_link_libraries(circle_loader PRIVATE base_loader nnfw_common nnfw_coverage) -target_link_libraries(circle_loader PRIVATE circle_schema) - -install(TARGETS circle_loader DESTINATION lib) diff --git a/runtime/onert/frontend/circle/include/circle_loader.h b/runtime/onert/frontend/circle/include/circle_loader.h deleted file mode 100644 index 675a5b3e7..000000000 --- a/runtime/onert/frontend/circle/include/circle_loader.h +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __CIRCLE_CIRCLE_LOADER_H__ -#define __CIRCLE_CIRCLE_LOADER_H__ - -#include "ir/Graph.h" - -#include <memory> - -namespace onert -{ -namespace circle_loader -{ -std::unique_ptr<ir::Subgraphs> loadModel(const char *filename); -std::unique_ptr<ir::Subgraphs> loadModel(uint8_t *buffer, size_t size); -} // namespace circle_loader -} // namespace onert - -#endif // __CIRCLE_CIRCLE_LOADER_H__ diff --git a/runtime/onert/frontend/circle/src/circle_loader.cc b/runtime/onert/frontend/circle/src/circle_loader.cc deleted file mode 100644 index 4565ffc00..000000000 --- a/runtime/onert/frontend/circle/src/circle_loader.cc +++ /dev/null @@ -1,221 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "circle_loader.h" -#include "base_loader.h" -#include "circle_schema_generated.h" - -namespace onert -{ -namespace circle_loader -{ - -namespace -{ - -ir::Layout convertDataFormat(circle::DataFormat data_format) -{ - switch (data_format) - { - case circle::DataFormat::DataFormat_CHANNELS_FIRST: - return ir::Layout::NCHW; - case circle::DataFormat::DataFormat_CHANNELS_LAST: - return ir::Layout::NHWC; - default: - throw std::runtime_error("Unsupported DataFormat"); - } -} - -struct LoaderDomain -{ - using Verifier = flatbuffers::Verifier; - using ActivationFunctionType = circle::ActivationFunctionType; - using Buffer = circle::Buffer; - using BuiltinOperator = circle::BuiltinOperator; - using CustomOptionsFormat = circle::CustomOptionsFormat; - using Model = circle::Model; - using Operator = circle::Operator; - using Padding = circle::Padding; - using Pool2DOptions = circle::Pool2DOptions; - using Tensor = circle::Tensor; - using TensorType = circle::TensorType; - using SubGraph = circle::SubGraph; - using DimensionType = circle::DimensionType; - using SparseIndexVector = circle::SparseIndexVector; - - static const char *EnumNameBuiltinOperator(BuiltinOperator e) - { - return circle::EnumNameBuiltinOperator(e); - } - static const char *EnumNameActivationFunctionType(ActivationFunctionType e) - { - return circle::EnumNameActivationFunctionType(e); - } - static const char *EnumNameTensorType(TensorType e) { return circle::EnumNameTensorType(e); } - static const Model *GetModel(const void *buf) { return circle::GetModel(buf); } - static bool VerifyModelBuffer(Verifier &verifier) { return circle::VerifyModelBuffer(verifier); } -}; - -class CircleLoader final : public base_loader::BaseLoader<LoaderDomain> -{ -protected: - void loadInstanceNorm(const Operator *op, ir::Graph &subg); - void loadBCQFullyConnected(const Operator *op, ir::Graph &subg); - void loadBCQGather(const Operator *op, ir::Graph &subg); - -public: - using BaseLoader::BaseLoader; - - bool allowOptionalInputTensor(BuiltinOperator op) override - { - switch (op) - { - case BuiltinOperator::BuiltinOperator_FULLY_CONNECTED: - case BuiltinOperator::BuiltinOperator_BCQ_FULLY_CONNECTED: - return true; - default: - return false; - } - } - -private: - std::unique_ptr<ir::Graph> loadSubgraph(const circle::SubGraph *circle_subg) override - { - auto subg = std::make_unique<ir::Graph>(); - // Load tensors - _tensor_to_operand.resize(circle_subg->tensors()->size()); - for (flatbuffers::uoffset_t i = 0; i < circle_subg->tensors()->size(); ++i) - { - _tensor_to_operand[i] = loadOperand(circle_subg->tensors()->Get(i), *subg); - } - // Set inputs - for (const std::int32_t input_ind : *circle_subg->inputs()) - { - subg->addInput(tensorIdxToOperandIdx(input_ind), - _tensor_names.at(_tensor_to_operand[input_ind])); - } - // Set outputs - for (const std::int32_t output_ind : *circle_subg->outputs()) - { - subg->addOutput(tensorIdxToOperandIdx(output_ind), - _tensor_names.at(_tensor_to_operand[output_ind])); - } - // Create operations - for (const auto *op : *circle_subg->operators()) - { - CircleLoader::loadOperation(op, *subg); - } - - subg->setLayout(convertDataFormat(circle_subg->data_format())); - - subg->finishBuilding(); - - return subg; - } - - void loadOperation(const circle::Operator *op, ir::Graph &subg) - { - const auto builtin_op = _model->operator_codes()->Get(op->opcode_index())->builtin_code(); - - switch (builtin_op) - { - case circle::BuiltinOperator::BuiltinOperator_INSTANCE_NORM: - loadInstanceNorm(op, subg); - return; - case circle::BuiltinOperator::BuiltinOperator_BCQ_FULLY_CONNECTED: - loadBCQFullyConnected(op, subg); - return; - case circle::BuiltinOperator::BuiltinOperator_BCQ_GATHER: - loadBCQGather(op, subg); - return; - default: - BaseLoader::loadOperation(op, subg); - return; - } - } -}; - -void CircleLoader::loadInstanceNorm(const Operator *op, ir::Graph &subg) -{ - ir::OperandIndexSequence inputs; - ir::OperandIndexSequence outputs; - - loadOperationIO(op, inputs, outputs); - - ir::operation::InstanceNorm::Param param; - const auto *options = op->builtin_options_as_InstanceNormOptions(); - - param.activation = convertActivation(options->fused_activation_function()); - // Use default value 1e-5 if value of epsilon is zero - param.epsilon = options->epsilon() == 0.f ? 1e-5 : options->epsilon(); - - std::unique_ptr<ir::Operation> new_op(new ir::operation::InstanceNorm(inputs, outputs, param)); - subg.addOperation(std::move(new_op)); -} - -void CircleLoader::loadBCQGather(const Operator *op, ir::Graph &subg) -{ - ir::OperandIndexSequence inputs; - ir::OperandIndexSequence outputs; - - loadOperationIO(op, inputs, outputs); - - ir::operation::BCQGather::Param param; - const auto *options = op->builtin_options_as_BCQGatherOptions(); - param.input_hidden_size = options->input_hidden_size(); - param.axis = options->axis(); - - std::unique_ptr<ir::Operation> new_op(new ir::operation::BCQGather(inputs, outputs, param)); - subg.addOperation(std::move(new_op)); -} - -void CircleLoader::loadBCQFullyConnected(const Operator *op, ir::Graph &subg) -{ - ir::OperandIndexSequence inputs; - ir::OperandIndexSequence outputs; - - loadOperationIO(op, inputs, outputs); - - ir::operation::BCQFullyConnected::Param param; - const auto *options = op->builtin_options_as_BCQFullyConnectedOptions(); - param.weights_hidden_size = options->weights_hidden_size(); - param.activation = convertActivation(options->fused_activation_function()); - - std::unique_ptr<ir::Operation> new_op( - new ir::operation::BCQFullyConnected(inputs, outputs, param)); - subg.addOperation(std::move(new_op)); -} - -} // namespace - -std::unique_ptr<ir::Subgraphs> loadModel(const char *filename) -{ - auto subgraphs = std::make_unique<ir::Subgraphs>(); - CircleLoader loader(subgraphs); - loader.loadFromFile(filename); - return subgraphs; -} - -std::unique_ptr<ir::Subgraphs> loadModel(uint8_t *buffer, size_t size) -{ - auto subgraphs = std::make_unique<ir::Subgraphs>(); - CircleLoader loader(subgraphs); - loader.loadFromBuffer(buffer, size); - return subgraphs; -} - -} // namespace circle_loader -} // namespace onert diff --git a/runtime/onert/frontend/circle_schema/CMakeLists.txt b/runtime/onert/frontend/circle_schema/CMakeLists.txt deleted file mode 100644 index 208103f1c..000000000 --- a/runtime/onert/frontend/circle_schema/CMakeLists.txt +++ /dev/null @@ -1,7 +0,0 @@ -add_library(circle_schema INTERFACE) - -nnfw_find_package(FlatBuffers REQUIRED) - -target_link_libraries(circle_schema INTERFACE flatbuffers::flatbuffers) - -target_include_directories(circle_schema INTERFACE include) diff --git a/runtime/onert/frontend/circle_schema/include/circle_schema_generated.h b/runtime/onert/frontend/circle_schema/include/circle_schema_generated.h deleted file mode 100644 index 190c84d98..000000000 --- a/runtime/onert/frontend/circle_schema/include/circle_schema_generated.h +++ /dev/null @@ -1,10112 +0,0 @@ -/* - * Copyright (c) 2019-2020 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright 2018 The TensorFlow Authors. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -// automatically generated by the FlatBuffers compiler, do not modify - -#ifndef FLATBUFFERS_GENERATED_CIRCLESCHEMA_CIRCLE_H_ -#define FLATBUFFERS_GENERATED_CIRCLESCHEMA_CIRCLE_H_ - -#include "flatbuffers/flatbuffers.h" - -namespace circle -{ - -struct CustomQuantization; - -struct QuantizationParameters; - -struct Int32Vector; - -struct Uint16Vector; - -struct Uint8Vector; - -struct DimensionMetadata; - -struct SparsityParameters; - -struct Tensor; - -struct Conv2DOptions; - -struct Pool2DOptions; - -struct DepthwiseConv2DOptions; - -struct ConcatEmbeddingsOptions; - -struct LSHProjectionOptions; - -struct SVDFOptions; - -struct RNNOptions; - -struct SequenceRNNOptions; - -struct BidirectionalSequenceRNNOptions; - -struct FullyConnectedOptions; - -struct SoftmaxOptions; - -struct ConcatenationOptions; - -struct AddOptions; - -struct MulOptions; - -struct L2NormOptions; - -struct LocalResponseNormalizationOptions; - -struct LSTMOptions; - -struct UnidirectionalSequenceLSTMOptions; - -struct BidirectionalSequenceLSTMOptions; - -struct ResizeBilinearOptions; - -struct ResizeNearestNeighborOptions; - -struct CallOptions; - -struct PadOptions; - -struct PadV2Options; - -struct ReshapeOptions; - -struct SpaceToBatchNDOptions; - -struct BatchToSpaceNDOptions; - -struct SkipGramOptions; - -struct SpaceToDepthOptions; - -struct DepthToSpaceOptions; - -struct SubOptions; - -struct DivOptions; - -struct TopKV2Options; - -struct EmbeddingLookupSparseOptions; - -struct GatherOptions; - -struct TransposeOptions; - -struct ExpOptions; - -struct CosOptions; - -struct ReducerOptions; - -struct SqueezeOptions; - -struct SplitOptions; - -struct SplitVOptions; - -struct StridedSliceOptions; - -struct LogSoftmaxOptions; - -struct CastOptions; - -struct DequantizeOptions; - -struct MaximumMinimumOptions; - -struct TileOptions; - -struct ArgMaxOptions; - -struct ArgMinOptions; - -struct GreaterOptions; - -struct GreaterEqualOptions; - -struct LessOptions; - -struct LessEqualOptions; - -struct NegOptions; - -struct SelectOptions; - -struct SliceOptions; - -struct TransposeConvOptions; - -struct ExpandDimsOptions; - -struct SparseToDenseOptions; - -struct EqualOptions; - -struct NotEqualOptions; - -struct ShapeOptions; - -struct RankOptions; - -struct PowOptions; - -struct FakeQuantOptions; - -struct PackOptions; - -struct LogicalOrOptions; - -struct OneHotOptions; - -struct AbsOptions; - -struct HardSwishOptions; - -struct LogicalAndOptions; - -struct LogicalNotOptions; - -struct UnpackOptions; - -struct FloorDivOptions; - -struct SquareOptions; - -struct ZerosLikeOptions; - -struct FillOptions; - -struct FloorModOptions; - -struct RangeOptions; - -struct LeakyReluOptions; - -struct SquaredDifferenceOptions; - -struct MirrorPadOptions; - -struct UniqueOptions; - -struct ReverseV2Options; - -struct AddNOptions; - -struct GatherNdOptions; - -struct WhereOptions; - -struct ReverseSequenceOptions; - -struct MatrixDiagOptions; - -struct QuantizeOptions; - -struct MatrixSetDiagOptions; - -struct IfOptions; - -struct WhileOptions; - -struct NonMaxSuppressionV4Options; - -struct NonMaxSuppressionV5Options; - -struct ScatterNdOptions; - -struct SelectV2Options; - -struct DensifyOptions; - -struct SegmentSumOptions; - -struct BatchMatMulOptions; - -struct BCQGatherOptions; - -struct BCQFullyConnectedOptions; - -struct InstanceNormOptions; - -struct OperatorCode; - -struct Operator; - -struct SubGraph; - -struct Buffer; - -struct Metadata; - -struct Model; - -enum TensorType -{ - TensorType_FLOAT32 = 0, - TensorType_FLOAT16 = 1, - TensorType_INT32 = 2, - TensorType_UINT8 = 3, - TensorType_INT64 = 4, - TensorType_STRING = 5, - TensorType_BOOL = 6, - TensorType_INT16 = 7, - TensorType_COMPLEX64 = 8, - TensorType_INT8 = 9, - TensorType_FLOAT64 = 10, - TensorType_MIN = TensorType_FLOAT32, - TensorType_MAX = TensorType_FLOAT64 -}; - -inline const TensorType (&EnumValuesTensorType())[11] -{ - static const TensorType values[] = {TensorType_FLOAT32, TensorType_FLOAT16, TensorType_INT32, - TensorType_UINT8, TensorType_INT64, TensorType_STRING, - TensorType_BOOL, TensorType_INT16, TensorType_COMPLEX64, - TensorType_INT8, TensorType_FLOAT64}; - return values; -} - -inline const char *const *EnumNamesTensorType() -{ - static const char *const names[] = {"FLOAT32", "FLOAT16", "INT32", "UINT8", - "INT64", "STRING", "BOOL", "INT16", - "COMPLEX64", "INT8", "FLOAT64", nullptr}; - return names; -} - -inline const char *EnumNameTensorType(TensorType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesTensorType()[index]; -} - -enum QuantizationDetails -{ - QuantizationDetails_NONE = 0, - QuantizationDetails_CustomQuantization = 1, - QuantizationDetails_MIN = QuantizationDetails_NONE, - QuantizationDetails_MAX = QuantizationDetails_CustomQuantization -}; - -inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2] -{ - static const QuantizationDetails values[] = {QuantizationDetails_NONE, - QuantizationDetails_CustomQuantization}; - return values; -} - -inline const char *const *EnumNamesQuantizationDetails() -{ - static const char *const names[] = {"NONE", "CustomQuantization", nullptr}; - return names; -} - -inline const char *EnumNameQuantizationDetails(QuantizationDetails e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesQuantizationDetails()[index]; -} - -template <typename T> struct QuantizationDetailsTraits -{ - static const QuantizationDetails enum_value = QuantizationDetails_NONE; -}; - -template <> struct QuantizationDetailsTraits<CustomQuantization> -{ - static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; -}; - -bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, - QuantizationDetails type); -bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types); - -enum DimensionType -{ - DimensionType_DENSE = 0, - DimensionType_SPARSE_CSR = 1, - DimensionType_MIN = DimensionType_DENSE, - DimensionType_MAX = DimensionType_SPARSE_CSR -}; - -inline const DimensionType (&EnumValuesDimensionType())[2] -{ - static const DimensionType values[] = {DimensionType_DENSE, DimensionType_SPARSE_CSR}; - return values; -} - -inline const char *const *EnumNamesDimensionType() -{ - static const char *const names[] = {"DENSE", "SPARSE_CSR", nullptr}; - return names; -} - -inline const char *EnumNameDimensionType(DimensionType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesDimensionType()[index]; -} - -enum SparseIndexVector -{ - SparseIndexVector_NONE = 0, - SparseIndexVector_Int32Vector = 1, - SparseIndexVector_Uint16Vector = 2, - SparseIndexVector_Uint8Vector = 3, - SparseIndexVector_MIN = SparseIndexVector_NONE, - SparseIndexVector_MAX = SparseIndexVector_Uint8Vector -}; - -inline const SparseIndexVector (&EnumValuesSparseIndexVector())[4] -{ - static const SparseIndexVector values[] = {SparseIndexVector_NONE, SparseIndexVector_Int32Vector, - SparseIndexVector_Uint16Vector, - SparseIndexVector_Uint8Vector}; - return values; -} - -inline const char *const *EnumNamesSparseIndexVector() -{ - static const char *const names[] = {"NONE", "Int32Vector", "Uint16Vector", "Uint8Vector", - nullptr}; - return names; -} - -inline const char *EnumNameSparseIndexVector(SparseIndexVector e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesSparseIndexVector()[index]; -} - -template <typename T> struct SparseIndexVectorTraits -{ - static const SparseIndexVector enum_value = SparseIndexVector_NONE; -}; - -template <> struct SparseIndexVectorTraits<Int32Vector> -{ - static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; -}; - -template <> struct SparseIndexVectorTraits<Uint16Vector> -{ - static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; -}; - -template <> struct SparseIndexVectorTraits<Uint8Vector> -{ - static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; -}; - -bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, - SparseIndexVector type); -bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types); - -enum BuiltinOperator -{ - BuiltinOperator_ADD = 0, - BuiltinOperator_AVERAGE_POOL_2D = 1, - BuiltinOperator_CONCATENATION = 2, - BuiltinOperator_CONV_2D = 3, - BuiltinOperator_DEPTHWISE_CONV_2D = 4, - BuiltinOperator_DEPTH_TO_SPACE = 5, - BuiltinOperator_DEQUANTIZE = 6, - BuiltinOperator_EMBEDDING_LOOKUP = 7, - BuiltinOperator_FLOOR = 8, - BuiltinOperator_FULLY_CONNECTED = 9, - BuiltinOperator_HASHTABLE_LOOKUP = 10, - BuiltinOperator_L2_NORMALIZATION = 11, - BuiltinOperator_L2_POOL_2D = 12, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13, - BuiltinOperator_LOGISTIC = 14, - BuiltinOperator_LSH_PROJECTION = 15, - BuiltinOperator_LSTM = 16, - BuiltinOperator_MAX_POOL_2D = 17, - BuiltinOperator_MUL = 18, - BuiltinOperator_RELU = 19, - BuiltinOperator_RELU_N1_TO_1 = 20, - BuiltinOperator_RELU6 = 21, - BuiltinOperator_RESHAPE = 22, - BuiltinOperator_RESIZE_BILINEAR = 23, - BuiltinOperator_RNN = 24, - BuiltinOperator_SOFTMAX = 25, - BuiltinOperator_SPACE_TO_DEPTH = 26, - BuiltinOperator_SVDF = 27, - BuiltinOperator_TANH = 28, - BuiltinOperator_CONCAT_EMBEDDINGS = 29, - BuiltinOperator_SKIP_GRAM = 30, - BuiltinOperator_CALL = 31, - BuiltinOperator_CUSTOM = 32, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33, - BuiltinOperator_PAD = 34, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35, - BuiltinOperator_GATHER = 36, - BuiltinOperator_BATCH_TO_SPACE_ND = 37, - BuiltinOperator_SPACE_TO_BATCH_ND = 38, - BuiltinOperator_TRANSPOSE = 39, - BuiltinOperator_MEAN = 40, - BuiltinOperator_SUB = 41, - BuiltinOperator_DIV = 42, - BuiltinOperator_SQUEEZE = 43, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, - BuiltinOperator_STRIDED_SLICE = 45, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, - BuiltinOperator_EXP = 47, - BuiltinOperator_TOPK_V2 = 48, - BuiltinOperator_SPLIT = 49, - BuiltinOperator_LOG_SOFTMAX = 50, - BuiltinOperator_DELEGATE = 51, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, - BuiltinOperator_CAST = 53, - BuiltinOperator_PRELU = 54, - BuiltinOperator_MAXIMUM = 55, - BuiltinOperator_ARG_MAX = 56, - BuiltinOperator_MINIMUM = 57, - BuiltinOperator_LESS = 58, - BuiltinOperator_NEG = 59, - BuiltinOperator_PADV2 = 60, - BuiltinOperator_GREATER = 61, - BuiltinOperator_GREATER_EQUAL = 62, - BuiltinOperator_LESS_EQUAL = 63, - BuiltinOperator_SELECT = 64, - BuiltinOperator_SLICE = 65, - BuiltinOperator_SIN = 66, - BuiltinOperator_TRANSPOSE_CONV = 67, - BuiltinOperator_SPARSE_TO_DENSE = 68, - BuiltinOperator_TILE = 69, - BuiltinOperator_EXPAND_DIMS = 70, - BuiltinOperator_EQUAL = 71, - BuiltinOperator_NOT_EQUAL = 72, - BuiltinOperator_LOG = 73, - BuiltinOperator_SUM = 74, - BuiltinOperator_SQRT = 75, - BuiltinOperator_RSQRT = 76, - BuiltinOperator_SHAPE = 77, - BuiltinOperator_POW = 78, - BuiltinOperator_ARG_MIN = 79, - BuiltinOperator_FAKE_QUANT = 80, - BuiltinOperator_REDUCE_PROD = 81, - BuiltinOperator_REDUCE_MAX = 82, - BuiltinOperator_PACK = 83, - BuiltinOperator_LOGICAL_OR = 84, - BuiltinOperator_ONE_HOT = 85, - BuiltinOperator_LOGICAL_AND = 86, - BuiltinOperator_LOGICAL_NOT = 87, - BuiltinOperator_UNPACK = 88, - BuiltinOperator_REDUCE_MIN = 89, - BuiltinOperator_FLOOR_DIV = 90, - BuiltinOperator_REDUCE_ANY = 91, - BuiltinOperator_SQUARE = 92, - BuiltinOperator_ZEROS_LIKE = 93, - BuiltinOperator_FILL = 94, - BuiltinOperator_FLOOR_MOD = 95, - BuiltinOperator_RANGE = 96, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97, - BuiltinOperator_LEAKY_RELU = 98, - BuiltinOperator_SQUARED_DIFFERENCE = 99, - BuiltinOperator_MIRROR_PAD = 100, - BuiltinOperator_ABS = 101, - BuiltinOperator_SPLIT_V = 102, - BuiltinOperator_UNIQUE = 103, - BuiltinOperator_CEIL = 104, - BuiltinOperator_REVERSE_V2 = 105, - BuiltinOperator_ADD_N = 106, - BuiltinOperator_GATHER_ND = 107, - BuiltinOperator_COS = 108, - BuiltinOperator_WHERE = 109, - BuiltinOperator_RANK = 110, - BuiltinOperator_ELU = 111, - BuiltinOperator_REVERSE_SEQUENCE = 112, - BuiltinOperator_MATRIX_DIAG = 113, - BuiltinOperator_QUANTIZE = 114, - BuiltinOperator_MATRIX_SET_DIAG = 115, - BuiltinOperator_ROUND = 116, - BuiltinOperator_HARD_SWISH = 117, - BuiltinOperator_IF = 118, - BuiltinOperator_WHILE = 119, - BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120, - BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121, - BuiltinOperator_SCATTER_ND = 122, - BuiltinOperator_SELECT_V2 = 123, - BuiltinOperator_DENSIFY = 124, - BuiltinOperator_SEGMENT_SUM = 125, - BuiltinOperator_BATCH_MATMUL = 126, - BuiltinOperator_BCQ_GATHER = 252, - BuiltinOperator_BCQ_FULLY_CONNECTED = 253, - BuiltinOperator_INSTANCE_NORM = 254, - BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_INSTANCE_NORM -}; - -inline const BuiltinOperator (&EnumValuesBuiltinOperator())[130] -{ - static const BuiltinOperator values[] = {BuiltinOperator_ADD, - BuiltinOperator_AVERAGE_POOL_2D, - BuiltinOperator_CONCATENATION, - BuiltinOperator_CONV_2D, - BuiltinOperator_DEPTHWISE_CONV_2D, - BuiltinOperator_DEPTH_TO_SPACE, - BuiltinOperator_DEQUANTIZE, - BuiltinOperator_EMBEDDING_LOOKUP, - BuiltinOperator_FLOOR, - BuiltinOperator_FULLY_CONNECTED, - BuiltinOperator_HASHTABLE_LOOKUP, - BuiltinOperator_L2_NORMALIZATION, - BuiltinOperator_L2_POOL_2D, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, - BuiltinOperator_LOGISTIC, - BuiltinOperator_LSH_PROJECTION, - BuiltinOperator_LSTM, - BuiltinOperator_MAX_POOL_2D, - BuiltinOperator_MUL, - BuiltinOperator_RELU, - BuiltinOperator_RELU_N1_TO_1, - BuiltinOperator_RELU6, - BuiltinOperator_RESHAPE, - BuiltinOperator_RESIZE_BILINEAR, - BuiltinOperator_RNN, - BuiltinOperator_SOFTMAX, - BuiltinOperator_SPACE_TO_DEPTH, - BuiltinOperator_SVDF, - BuiltinOperator_TANH, - BuiltinOperator_CONCAT_EMBEDDINGS, - BuiltinOperator_SKIP_GRAM, - BuiltinOperator_CALL, - BuiltinOperator_CUSTOM, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, - BuiltinOperator_PAD, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_GATHER, - BuiltinOperator_BATCH_TO_SPACE_ND, - BuiltinOperator_SPACE_TO_BATCH_ND, - BuiltinOperator_TRANSPOSE, - BuiltinOperator_MEAN, - BuiltinOperator_SUB, - BuiltinOperator_DIV, - BuiltinOperator_SQUEEZE, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_STRIDED_SLICE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_EXP, - BuiltinOperator_TOPK_V2, - BuiltinOperator_SPLIT, - BuiltinOperator_LOG_SOFTMAX, - BuiltinOperator_DELEGATE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_CAST, - BuiltinOperator_PRELU, - BuiltinOperator_MAXIMUM, - BuiltinOperator_ARG_MAX, - BuiltinOperator_MINIMUM, - BuiltinOperator_LESS, - BuiltinOperator_NEG, - BuiltinOperator_PADV2, - BuiltinOperator_GREATER, - BuiltinOperator_GREATER_EQUAL, - BuiltinOperator_LESS_EQUAL, - BuiltinOperator_SELECT, - BuiltinOperator_SLICE, - BuiltinOperator_SIN, - BuiltinOperator_TRANSPOSE_CONV, - BuiltinOperator_SPARSE_TO_DENSE, - BuiltinOperator_TILE, - BuiltinOperator_EXPAND_DIMS, - BuiltinOperator_EQUAL, - BuiltinOperator_NOT_EQUAL, - BuiltinOperator_LOG, - BuiltinOperator_SUM, - BuiltinOperator_SQRT, - BuiltinOperator_RSQRT, - BuiltinOperator_SHAPE, - BuiltinOperator_POW, - BuiltinOperator_ARG_MIN, - BuiltinOperator_FAKE_QUANT, - BuiltinOperator_REDUCE_PROD, - BuiltinOperator_REDUCE_MAX, - BuiltinOperator_PACK, - BuiltinOperator_LOGICAL_OR, - BuiltinOperator_ONE_HOT, - BuiltinOperator_LOGICAL_AND, - BuiltinOperator_LOGICAL_NOT, - BuiltinOperator_UNPACK, - BuiltinOperator_REDUCE_MIN, - BuiltinOperator_FLOOR_DIV, - BuiltinOperator_REDUCE_ANY, - BuiltinOperator_SQUARE, - BuiltinOperator_ZEROS_LIKE, - BuiltinOperator_FILL, - BuiltinOperator_FLOOR_MOD, - BuiltinOperator_RANGE, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - BuiltinOperator_LEAKY_RELU, - BuiltinOperator_SQUARED_DIFFERENCE, - BuiltinOperator_MIRROR_PAD, - BuiltinOperator_ABS, - BuiltinOperator_SPLIT_V, - BuiltinOperator_UNIQUE, - BuiltinOperator_CEIL, - BuiltinOperator_REVERSE_V2, - BuiltinOperator_ADD_N, - BuiltinOperator_GATHER_ND, - BuiltinOperator_COS, - BuiltinOperator_WHERE, - BuiltinOperator_RANK, - BuiltinOperator_ELU, - BuiltinOperator_REVERSE_SEQUENCE, - BuiltinOperator_MATRIX_DIAG, - BuiltinOperator_QUANTIZE, - BuiltinOperator_MATRIX_SET_DIAG, - BuiltinOperator_ROUND, - BuiltinOperator_HARD_SWISH, - BuiltinOperator_IF, - BuiltinOperator_WHILE, - BuiltinOperator_NON_MAX_SUPPRESSION_V4, - BuiltinOperator_NON_MAX_SUPPRESSION_V5, - BuiltinOperator_SCATTER_ND, - BuiltinOperator_SELECT_V2, - BuiltinOperator_DENSIFY, - BuiltinOperator_SEGMENT_SUM, - BuiltinOperator_BATCH_MATMUL, - BuiltinOperator_BCQ_GATHER, - BuiltinOperator_BCQ_FULLY_CONNECTED, - BuiltinOperator_INSTANCE_NORM}; - return values; -} - -inline const char *const *EnumNamesBuiltinOperator() -{ - static const char *const names[] = {"ADD", - "AVERAGE_POOL_2D", - "CONCATENATION", - "CONV_2D", - "DEPTHWISE_CONV_2D", - "DEPTH_TO_SPACE", - "DEQUANTIZE", - "EMBEDDING_LOOKUP", - "FLOOR", - "FULLY_CONNECTED", - "HASHTABLE_LOOKUP", - "L2_NORMALIZATION", - "L2_POOL_2D", - "LOCAL_RESPONSE_NORMALIZATION", - "LOGISTIC", - "LSH_PROJECTION", - "LSTM", - "MAX_POOL_2D", - "MUL", - "RELU", - "RELU_N1_TO_1", - "RELU6", - "RESHAPE", - "RESIZE_BILINEAR", - "RNN", - "SOFTMAX", - "SPACE_TO_DEPTH", - "SVDF", - "TANH", - "CONCAT_EMBEDDINGS", - "SKIP_GRAM", - "CALL", - "CUSTOM", - "EMBEDDING_LOOKUP_SPARSE", - "PAD", - "UNIDIRECTIONAL_SEQUENCE_RNN", - "GATHER", - "BATCH_TO_SPACE_ND", - "SPACE_TO_BATCH_ND", - "TRANSPOSE", - "MEAN", - "SUB", - "DIV", - "SQUEEZE", - "UNIDIRECTIONAL_SEQUENCE_LSTM", - "STRIDED_SLICE", - "BIDIRECTIONAL_SEQUENCE_RNN", - "EXP", - "TOPK_V2", - "SPLIT", - "LOG_SOFTMAX", - "DELEGATE", - "BIDIRECTIONAL_SEQUENCE_LSTM", - "CAST", - "PRELU", - "MAXIMUM", - "ARG_MAX", - "MINIMUM", - "LESS", - "NEG", - "PADV2", - "GREATER", - "GREATER_EQUAL", - "LESS_EQUAL", - "SELECT", - "SLICE", - "SIN", - "TRANSPOSE_CONV", - "SPARSE_TO_DENSE", - "TILE", - "EXPAND_DIMS", - "EQUAL", - "NOT_EQUAL", - "LOG", - "SUM", - "SQRT", - "RSQRT", - "SHAPE", - "POW", - "ARG_MIN", - "FAKE_QUANT", - "REDUCE_PROD", - "REDUCE_MAX", - "PACK", - "LOGICAL_OR", - "ONE_HOT", - "LOGICAL_AND", - "LOGICAL_NOT", - "UNPACK", - "REDUCE_MIN", - "FLOOR_DIV", - "REDUCE_ANY", - "SQUARE", - "ZEROS_LIKE", - "FILL", - "FLOOR_MOD", - "RANGE", - "RESIZE_NEAREST_NEIGHBOR", - "LEAKY_RELU", - "SQUARED_DIFFERENCE", - "MIRROR_PAD", - "ABS", - "SPLIT_V", - "UNIQUE", - "CEIL", - "REVERSE_V2", - "ADD_N", - "GATHER_ND", - "COS", - "WHERE", - "RANK", - "ELU", - "REVERSE_SEQUENCE", - "MATRIX_DIAG", - "QUANTIZE", - "MATRIX_SET_DIAG", - "ROUND", - "HARD_SWISH", - "IF", - "WHILE", - "NON_MAX_SUPPRESSION_V4", - "NON_MAX_SUPPRESSION_V5", - "SCATTER_ND", - "SELECT_V2", - "DENSIFY", - "SEGMENT_SUM", - "BATCH_MATMUL", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "BCQ_GATHER", - "BCQ_FULLY_CONNECTED", - "INSTANCE_NORM", - nullptr}; - return names; -} - -inline const char *EnumNameBuiltinOperator(BuiltinOperator e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesBuiltinOperator()[index]; -} - -enum BuiltinOptions -{ - BuiltinOptions_NONE = 0, - BuiltinOptions_Conv2DOptions = 1, - BuiltinOptions_DepthwiseConv2DOptions = 2, - BuiltinOptions_ConcatEmbeddingsOptions = 3, - BuiltinOptions_LSHProjectionOptions = 4, - BuiltinOptions_Pool2DOptions = 5, - BuiltinOptions_SVDFOptions = 6, - BuiltinOptions_RNNOptions = 7, - BuiltinOptions_FullyConnectedOptions = 8, - BuiltinOptions_SoftmaxOptions = 9, - BuiltinOptions_ConcatenationOptions = 10, - BuiltinOptions_AddOptions = 11, - BuiltinOptions_L2NormOptions = 12, - BuiltinOptions_LocalResponseNormalizationOptions = 13, - BuiltinOptions_LSTMOptions = 14, - BuiltinOptions_ResizeBilinearOptions = 15, - BuiltinOptions_CallOptions = 16, - BuiltinOptions_ReshapeOptions = 17, - BuiltinOptions_SkipGramOptions = 18, - BuiltinOptions_SpaceToDepthOptions = 19, - BuiltinOptions_EmbeddingLookupSparseOptions = 20, - BuiltinOptions_MulOptions = 21, - BuiltinOptions_PadOptions = 22, - BuiltinOptions_GatherOptions = 23, - BuiltinOptions_BatchToSpaceNDOptions = 24, - BuiltinOptions_SpaceToBatchNDOptions = 25, - BuiltinOptions_TransposeOptions = 26, - BuiltinOptions_ReducerOptions = 27, - BuiltinOptions_SubOptions = 28, - BuiltinOptions_DivOptions = 29, - BuiltinOptions_SqueezeOptions = 30, - BuiltinOptions_SequenceRNNOptions = 31, - BuiltinOptions_StridedSliceOptions = 32, - BuiltinOptions_ExpOptions = 33, - BuiltinOptions_TopKV2Options = 34, - BuiltinOptions_SplitOptions = 35, - BuiltinOptions_LogSoftmaxOptions = 36, - BuiltinOptions_CastOptions = 37, - BuiltinOptions_DequantizeOptions = 38, - BuiltinOptions_MaximumMinimumOptions = 39, - BuiltinOptions_ArgMaxOptions = 40, - BuiltinOptions_LessOptions = 41, - BuiltinOptions_NegOptions = 42, - BuiltinOptions_PadV2Options = 43, - BuiltinOptions_GreaterOptions = 44, - BuiltinOptions_GreaterEqualOptions = 45, - BuiltinOptions_LessEqualOptions = 46, - BuiltinOptions_SelectOptions = 47, - BuiltinOptions_SliceOptions = 48, - BuiltinOptions_TransposeConvOptions = 49, - BuiltinOptions_SparseToDenseOptions = 50, - BuiltinOptions_TileOptions = 51, - BuiltinOptions_ExpandDimsOptions = 52, - BuiltinOptions_EqualOptions = 53, - BuiltinOptions_NotEqualOptions = 54, - BuiltinOptions_ShapeOptions = 55, - BuiltinOptions_PowOptions = 56, - BuiltinOptions_ArgMinOptions = 57, - BuiltinOptions_FakeQuantOptions = 58, - BuiltinOptions_PackOptions = 59, - BuiltinOptions_LogicalOrOptions = 60, - BuiltinOptions_OneHotOptions = 61, - BuiltinOptions_LogicalAndOptions = 62, - BuiltinOptions_LogicalNotOptions = 63, - BuiltinOptions_UnpackOptions = 64, - BuiltinOptions_FloorDivOptions = 65, - BuiltinOptions_SquareOptions = 66, - BuiltinOptions_ZerosLikeOptions = 67, - BuiltinOptions_FillOptions = 68, - BuiltinOptions_BidirectionalSequenceLSTMOptions = 69, - BuiltinOptions_BidirectionalSequenceRNNOptions = 70, - BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71, - BuiltinOptions_FloorModOptions = 72, - BuiltinOptions_RangeOptions = 73, - BuiltinOptions_ResizeNearestNeighborOptions = 74, - BuiltinOptions_LeakyReluOptions = 75, - BuiltinOptions_SquaredDifferenceOptions = 76, - BuiltinOptions_MirrorPadOptions = 77, - BuiltinOptions_AbsOptions = 78, - BuiltinOptions_SplitVOptions = 79, - BuiltinOptions_UniqueOptions = 80, - BuiltinOptions_ReverseV2Options = 81, - BuiltinOptions_AddNOptions = 82, - BuiltinOptions_GatherNdOptions = 83, - BuiltinOptions_CosOptions = 84, - BuiltinOptions_WhereOptions = 85, - BuiltinOptions_RankOptions = 86, - BuiltinOptions_ReverseSequenceOptions = 87, - BuiltinOptions_MatrixDiagOptions = 88, - BuiltinOptions_QuantizeOptions = 89, - BuiltinOptions_MatrixSetDiagOptions = 90, - BuiltinOptions_HardSwishOptions = 91, - BuiltinOptions_IfOptions = 92, - BuiltinOptions_WhileOptions = 93, - BuiltinOptions_DepthToSpaceOptions = 94, - BuiltinOptions_NonMaxSuppressionV4Options = 95, - BuiltinOptions_NonMaxSuppressionV5Options = 96, - BuiltinOptions_ScatterNdOptions = 97, - BuiltinOptions_SelectV2Options = 98, - BuiltinOptions_DensifyOptions = 99, - BuiltinOptions_SegmentSumOptions = 100, - BuiltinOptions_BatchMatMulOptions = 101, - BuiltinOptions_BCQGatherOptions = 252, - BuiltinOptions_BCQFullyConnectedOptions = 253, - BuiltinOptions_InstanceNormOptions = 254, - BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_InstanceNormOptions -}; - -inline const BuiltinOptions (&EnumValuesBuiltinOptions())[105] -{ - static const BuiltinOptions values[] = {BuiltinOptions_NONE, - BuiltinOptions_Conv2DOptions, - BuiltinOptions_DepthwiseConv2DOptions, - BuiltinOptions_ConcatEmbeddingsOptions, - BuiltinOptions_LSHProjectionOptions, - BuiltinOptions_Pool2DOptions, - BuiltinOptions_SVDFOptions, - BuiltinOptions_RNNOptions, - BuiltinOptions_FullyConnectedOptions, - BuiltinOptions_SoftmaxOptions, - BuiltinOptions_ConcatenationOptions, - BuiltinOptions_AddOptions, - BuiltinOptions_L2NormOptions, - BuiltinOptions_LocalResponseNormalizationOptions, - BuiltinOptions_LSTMOptions, - BuiltinOptions_ResizeBilinearOptions, - BuiltinOptions_CallOptions, - BuiltinOptions_ReshapeOptions, - BuiltinOptions_SkipGramOptions, - BuiltinOptions_SpaceToDepthOptions, - BuiltinOptions_EmbeddingLookupSparseOptions, - BuiltinOptions_MulOptions, - BuiltinOptions_PadOptions, - BuiltinOptions_GatherOptions, - BuiltinOptions_BatchToSpaceNDOptions, - BuiltinOptions_SpaceToBatchNDOptions, - BuiltinOptions_TransposeOptions, - BuiltinOptions_ReducerOptions, - BuiltinOptions_SubOptions, - BuiltinOptions_DivOptions, - BuiltinOptions_SqueezeOptions, - BuiltinOptions_SequenceRNNOptions, - BuiltinOptions_StridedSliceOptions, - BuiltinOptions_ExpOptions, - BuiltinOptions_TopKV2Options, - BuiltinOptions_SplitOptions, - BuiltinOptions_LogSoftmaxOptions, - BuiltinOptions_CastOptions, - BuiltinOptions_DequantizeOptions, - BuiltinOptions_MaximumMinimumOptions, - BuiltinOptions_ArgMaxOptions, - BuiltinOptions_LessOptions, - BuiltinOptions_NegOptions, - BuiltinOptions_PadV2Options, - BuiltinOptions_GreaterOptions, - BuiltinOptions_GreaterEqualOptions, - BuiltinOptions_LessEqualOptions, - BuiltinOptions_SelectOptions, - BuiltinOptions_SliceOptions, - BuiltinOptions_TransposeConvOptions, - BuiltinOptions_SparseToDenseOptions, - BuiltinOptions_TileOptions, - BuiltinOptions_ExpandDimsOptions, - BuiltinOptions_EqualOptions, - BuiltinOptions_NotEqualOptions, - BuiltinOptions_ShapeOptions, - BuiltinOptions_PowOptions, - BuiltinOptions_ArgMinOptions, - BuiltinOptions_FakeQuantOptions, - BuiltinOptions_PackOptions, - BuiltinOptions_LogicalOrOptions, - BuiltinOptions_OneHotOptions, - BuiltinOptions_LogicalAndOptions, - BuiltinOptions_LogicalNotOptions, - BuiltinOptions_UnpackOptions, - BuiltinOptions_FloorDivOptions, - BuiltinOptions_SquareOptions, - BuiltinOptions_ZerosLikeOptions, - BuiltinOptions_FillOptions, - BuiltinOptions_BidirectionalSequenceLSTMOptions, - BuiltinOptions_BidirectionalSequenceRNNOptions, - BuiltinOptions_UnidirectionalSequenceLSTMOptions, - BuiltinOptions_FloorModOptions, - BuiltinOptions_RangeOptions, - BuiltinOptions_ResizeNearestNeighborOptions, - BuiltinOptions_LeakyReluOptions, - BuiltinOptions_SquaredDifferenceOptions, - BuiltinOptions_MirrorPadOptions, - BuiltinOptions_AbsOptions, - BuiltinOptions_SplitVOptions, - BuiltinOptions_UniqueOptions, - BuiltinOptions_ReverseV2Options, - BuiltinOptions_AddNOptions, - BuiltinOptions_GatherNdOptions, - BuiltinOptions_CosOptions, - BuiltinOptions_WhereOptions, - BuiltinOptions_RankOptions, - BuiltinOptions_ReverseSequenceOptions, - BuiltinOptions_MatrixDiagOptions, - BuiltinOptions_QuantizeOptions, - BuiltinOptions_MatrixSetDiagOptions, - BuiltinOptions_HardSwishOptions, - BuiltinOptions_IfOptions, - BuiltinOptions_WhileOptions, - BuiltinOptions_DepthToSpaceOptions, - BuiltinOptions_NonMaxSuppressionV4Options, - BuiltinOptions_NonMaxSuppressionV5Options, - BuiltinOptions_ScatterNdOptions, - BuiltinOptions_SelectV2Options, - BuiltinOptions_DensifyOptions, - BuiltinOptions_SegmentSumOptions, - BuiltinOptions_BatchMatMulOptions, - BuiltinOptions_BCQGatherOptions, - BuiltinOptions_BCQFullyConnectedOptions, - BuiltinOptions_InstanceNormOptions}; - return values; -} - -inline const char *const *EnumNamesBuiltinOptions() -{ - static const char *const names[] = {"NONE", - "Conv2DOptions", - "DepthwiseConv2DOptions", - "ConcatEmbeddingsOptions", - "LSHProjectionOptions", - "Pool2DOptions", - "SVDFOptions", - "RNNOptions", - "FullyConnectedOptions", - "SoftmaxOptions", - "ConcatenationOptions", - "AddOptions", - "L2NormOptions", - "LocalResponseNormalizationOptions", - "LSTMOptions", - "ResizeBilinearOptions", - "CallOptions", - "ReshapeOptions", - "SkipGramOptions", - "SpaceToDepthOptions", - "EmbeddingLookupSparseOptions", - "MulOptions", - "PadOptions", - "GatherOptions", - "BatchToSpaceNDOptions", - "SpaceToBatchNDOptions", - "TransposeOptions", - "ReducerOptions", - "SubOptions", - "DivOptions", - "SqueezeOptions", - "SequenceRNNOptions", - "StridedSliceOptions", - "ExpOptions", - "TopKV2Options", - "SplitOptions", - "LogSoftmaxOptions", - "CastOptions", - "DequantizeOptions", - "MaximumMinimumOptions", - "ArgMaxOptions", - "LessOptions", - "NegOptions", - "PadV2Options", - "GreaterOptions", - "GreaterEqualOptions", - "LessEqualOptions", - "SelectOptions", - "SliceOptions", - "TransposeConvOptions", - "SparseToDenseOptions", - "TileOptions", - "ExpandDimsOptions", - "EqualOptions", - "NotEqualOptions", - "ShapeOptions", - "PowOptions", - "ArgMinOptions", - "FakeQuantOptions", - "PackOptions", - "LogicalOrOptions", - "OneHotOptions", - "LogicalAndOptions", - "LogicalNotOptions", - "UnpackOptions", - "FloorDivOptions", - "SquareOptions", - "ZerosLikeOptions", - "FillOptions", - "BidirectionalSequenceLSTMOptions", - "BidirectionalSequenceRNNOptions", - "UnidirectionalSequenceLSTMOptions", - "FloorModOptions", - "RangeOptions", - "ResizeNearestNeighborOptions", - "LeakyReluOptions", - "SquaredDifferenceOptions", - "MirrorPadOptions", - "AbsOptions", - "SplitVOptions", - "UniqueOptions", - "ReverseV2Options", - "AddNOptions", - "GatherNdOptions", - "CosOptions", - "WhereOptions", - "RankOptions", - "ReverseSequenceOptions", - "MatrixDiagOptions", - "QuantizeOptions", - "MatrixSetDiagOptions", - "HardSwishOptions", - "IfOptions", - "WhileOptions", - "DepthToSpaceOptions", - "NonMaxSuppressionV4Options", - "NonMaxSuppressionV5Options", - "ScatterNdOptions", - "SelectV2Options", - "DensifyOptions", - "SegmentSumOptions", - "BatchMatMulOptions", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "BCQGatherOptions", - "BCQFullyConnectedOptions", - "InstanceNormOptions", - nullptr}; - return names; -} - -inline const char *EnumNameBuiltinOptions(BuiltinOptions e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesBuiltinOptions()[index]; -} - -template <typename T> struct BuiltinOptionsTraits -{ - static const BuiltinOptions enum_value = BuiltinOptions_NONE; -}; - -template <> struct BuiltinOptionsTraits<Conv2DOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; -}; - -template <> struct BuiltinOptionsTraits<DepthwiseConv2DOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; -}; - -template <> struct BuiltinOptionsTraits<ConcatEmbeddingsOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; -}; - -template <> struct BuiltinOptionsTraits<LSHProjectionOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; -}; - -template <> struct BuiltinOptionsTraits<Pool2DOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; -}; - -template <> struct BuiltinOptionsTraits<SVDFOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; -}; - -template <> struct BuiltinOptionsTraits<RNNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; -}; - -template <> struct BuiltinOptionsTraits<FullyConnectedOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; -}; - -template <> struct BuiltinOptionsTraits<SoftmaxOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; -}; - -template <> struct BuiltinOptionsTraits<ConcatenationOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; -}; - -template <> struct BuiltinOptionsTraits<AddOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; -}; - -template <> struct BuiltinOptionsTraits<L2NormOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; -}; - -template <> struct BuiltinOptionsTraits<LocalResponseNormalizationOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; -}; - -template <> struct BuiltinOptionsTraits<LSTMOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; -}; - -template <> struct BuiltinOptionsTraits<ResizeBilinearOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; -}; - -template <> struct BuiltinOptionsTraits<CallOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; -}; - -template <> struct BuiltinOptionsTraits<ReshapeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; -}; - -template <> struct BuiltinOptionsTraits<SkipGramOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; -}; - -template <> struct BuiltinOptionsTraits<SpaceToDepthOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; -}; - -template <> struct BuiltinOptionsTraits<EmbeddingLookupSparseOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; -}; - -template <> struct BuiltinOptionsTraits<MulOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; -}; - -template <> struct BuiltinOptionsTraits<PadOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; -}; - -template <> struct BuiltinOptionsTraits<GatherOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; -}; - -template <> struct BuiltinOptionsTraits<BatchToSpaceNDOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; -}; - -template <> struct BuiltinOptionsTraits<SpaceToBatchNDOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; -}; - -template <> struct BuiltinOptionsTraits<TransposeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; -}; - -template <> struct BuiltinOptionsTraits<ReducerOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; -}; - -template <> struct BuiltinOptionsTraits<SubOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; -}; - -template <> struct BuiltinOptionsTraits<DivOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; -}; - -template <> struct BuiltinOptionsTraits<SqueezeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; -}; - -template <> struct BuiltinOptionsTraits<SequenceRNNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; -}; - -template <> struct BuiltinOptionsTraits<StridedSliceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; -}; - -template <> struct BuiltinOptionsTraits<ExpOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; -}; - -template <> struct BuiltinOptionsTraits<TopKV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; -}; - -template <> struct BuiltinOptionsTraits<SplitOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; -}; - -template <> struct BuiltinOptionsTraits<LogSoftmaxOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; -}; - -template <> struct BuiltinOptionsTraits<CastOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; -}; - -template <> struct BuiltinOptionsTraits<DequantizeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; -}; - -template <> struct BuiltinOptionsTraits<MaximumMinimumOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; -}; - -template <> struct BuiltinOptionsTraits<ArgMaxOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; -}; - -template <> struct BuiltinOptionsTraits<LessOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; -}; - -template <> struct BuiltinOptionsTraits<NegOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; -}; - -template <> struct BuiltinOptionsTraits<PadV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; -}; - -template <> struct BuiltinOptionsTraits<GreaterOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; -}; - -template <> struct BuiltinOptionsTraits<GreaterEqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; -}; - -template <> struct BuiltinOptionsTraits<LessEqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; -}; - -template <> struct BuiltinOptionsTraits<SelectOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; -}; - -template <> struct BuiltinOptionsTraits<SliceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; -}; - -template <> struct BuiltinOptionsTraits<TransposeConvOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; -}; - -template <> struct BuiltinOptionsTraits<SparseToDenseOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; -}; - -template <> struct BuiltinOptionsTraits<TileOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; -}; - -template <> struct BuiltinOptionsTraits<ExpandDimsOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; -}; - -template <> struct BuiltinOptionsTraits<EqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; -}; - -template <> struct BuiltinOptionsTraits<NotEqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; -}; - -template <> struct BuiltinOptionsTraits<ShapeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; -}; - -template <> struct BuiltinOptionsTraits<PowOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; -}; - -template <> struct BuiltinOptionsTraits<ArgMinOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; -}; - -template <> struct BuiltinOptionsTraits<FakeQuantOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; -}; - -template <> struct BuiltinOptionsTraits<PackOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; -}; - -template <> struct BuiltinOptionsTraits<LogicalOrOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; -}; - -template <> struct BuiltinOptionsTraits<OneHotOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; -}; - -template <> struct BuiltinOptionsTraits<LogicalAndOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; -}; - -template <> struct BuiltinOptionsTraits<LogicalNotOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; -}; - -template <> struct BuiltinOptionsTraits<UnpackOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; -}; - -template <> struct BuiltinOptionsTraits<FloorDivOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; -}; - -template <> struct BuiltinOptionsTraits<SquareOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; -}; - -template <> struct BuiltinOptionsTraits<ZerosLikeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; -}; - -template <> struct BuiltinOptionsTraits<FillOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; -}; - -template <> struct BuiltinOptionsTraits<BidirectionalSequenceLSTMOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; -}; - -template <> struct BuiltinOptionsTraits<BidirectionalSequenceRNNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; -}; - -template <> struct BuiltinOptionsTraits<UnidirectionalSequenceLSTMOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; -}; - -template <> struct BuiltinOptionsTraits<FloorModOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; -}; - -template <> struct BuiltinOptionsTraits<RangeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; -}; - -template <> struct BuiltinOptionsTraits<ResizeNearestNeighborOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; -}; - -template <> struct BuiltinOptionsTraits<LeakyReluOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; -}; - -template <> struct BuiltinOptionsTraits<SquaredDifferenceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; -}; - -template <> struct BuiltinOptionsTraits<MirrorPadOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; -}; - -template <> struct BuiltinOptionsTraits<AbsOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; -}; - -template <> struct BuiltinOptionsTraits<SplitVOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; -}; - -template <> struct BuiltinOptionsTraits<UniqueOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; -}; - -template <> struct BuiltinOptionsTraits<ReverseV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; -}; - -template <> struct BuiltinOptionsTraits<AddNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; -}; - -template <> struct BuiltinOptionsTraits<GatherNdOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; -}; - -template <> struct BuiltinOptionsTraits<CosOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; -}; - -template <> struct BuiltinOptionsTraits<WhereOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; -}; - -template <> struct BuiltinOptionsTraits<RankOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; -}; - -template <> struct BuiltinOptionsTraits<ReverseSequenceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; -}; - -template <> struct BuiltinOptionsTraits<MatrixDiagOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; -}; - -template <> struct BuiltinOptionsTraits<QuantizeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; -}; - -template <> struct BuiltinOptionsTraits<MatrixSetDiagOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; -}; - -template <> struct BuiltinOptionsTraits<HardSwishOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; -}; - -template <> struct BuiltinOptionsTraits<IfOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; -}; - -template <> struct BuiltinOptionsTraits<WhileOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; -}; - -template <> struct BuiltinOptionsTraits<DepthToSpaceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; -}; - -template <> struct BuiltinOptionsTraits<NonMaxSuppressionV4Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; -}; - -template <> struct BuiltinOptionsTraits<NonMaxSuppressionV5Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; -}; - -template <> struct BuiltinOptionsTraits<ScatterNdOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; -}; - -template <> struct BuiltinOptionsTraits<SelectV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; -}; - -template <> struct BuiltinOptionsTraits<DensifyOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; -}; - -template <> struct BuiltinOptionsTraits<SegmentSumOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; -}; - -template <> struct BuiltinOptionsTraits<BatchMatMulOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; -}; - -template <> struct BuiltinOptionsTraits<BCQGatherOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BCQGatherOptions; -}; - -template <> struct BuiltinOptionsTraits<BCQFullyConnectedOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BCQFullyConnectedOptions; -}; - -template <> struct BuiltinOptionsTraits<InstanceNormOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_InstanceNormOptions; -}; - -bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); -bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types); - -enum Padding -{ - Padding_SAME = 0, - Padding_VALID = 1, - Padding_MIN = Padding_SAME, - Padding_MAX = Padding_VALID -}; - -inline const Padding (&EnumValuesPadding())[2] -{ - static const Padding values[] = {Padding_SAME, Padding_VALID}; - return values; -} - -inline const char *const *EnumNamesPadding() -{ - static const char *const names[] = {"SAME", "VALID", nullptr}; - return names; -} - -inline const char *EnumNamePadding(Padding e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesPadding()[index]; -} - -enum ActivationFunctionType -{ - ActivationFunctionType_NONE = 0, - ActivationFunctionType_RELU = 1, - ActivationFunctionType_RELU_N1_TO_1 = 2, - ActivationFunctionType_RELU6 = 3, - ActivationFunctionType_TANH = 4, - ActivationFunctionType_SIGN_BIT = 5, - ActivationFunctionType_MIN = ActivationFunctionType_NONE, - ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT -}; - -inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6] -{ - static const ActivationFunctionType values[] = { - ActivationFunctionType_NONE, ActivationFunctionType_RELU, - ActivationFunctionType_RELU_N1_TO_1, ActivationFunctionType_RELU6, - ActivationFunctionType_TANH, ActivationFunctionType_SIGN_BIT}; - return values; -} - -inline const char *const *EnumNamesActivationFunctionType() -{ - static const char *const names[] = {"NONE", "RELU", "RELU_N1_TO_1", "RELU6", - "TANH", "SIGN_BIT", nullptr}; - return names; -} - -inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesActivationFunctionType()[index]; -} - -enum LSHProjectionType -{ - LSHProjectionType_UNKNOWN = 0, - LSHProjectionType_SPARSE = 1, - LSHProjectionType_DENSE = 2, - LSHProjectionType_MIN = LSHProjectionType_UNKNOWN, - LSHProjectionType_MAX = LSHProjectionType_DENSE -}; - -inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3] -{ - static const LSHProjectionType values[] = {LSHProjectionType_UNKNOWN, LSHProjectionType_SPARSE, - LSHProjectionType_DENSE}; - return values; -} - -inline const char *const *EnumNamesLSHProjectionType() -{ - static const char *const names[] = {"UNKNOWN", "SPARSE", "DENSE", nullptr}; - return names; -} - -inline const char *EnumNameLSHProjectionType(LSHProjectionType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesLSHProjectionType()[index]; -} - -enum FullyConnectedOptionsWeightsFormat -{ - FullyConnectedOptionsWeightsFormat_DEFAULT = 0, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1, - FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 -}; - -inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] -{ - static const FullyConnectedOptionsWeightsFormat values[] = { - FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8}; - return values; -} - -inline const char *const *EnumNamesFullyConnectedOptionsWeightsFormat() -{ - static const char *const names[] = {"DEFAULT", "SHUFFLED4x16INT8", nullptr}; - return names; -} - -inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesFullyConnectedOptionsWeightsFormat()[index]; -} - -enum LSTMKernelType -{ - LSTMKernelType_FULL = 0, - LSTMKernelType_BASIC = 1, - LSTMKernelType_MIN = LSTMKernelType_FULL, - LSTMKernelType_MAX = LSTMKernelType_BASIC -}; - -inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2] -{ - static const LSTMKernelType values[] = {LSTMKernelType_FULL, LSTMKernelType_BASIC}; - return values; -} - -inline const char *const *EnumNamesLSTMKernelType() -{ - static const char *const names[] = {"FULL", "BASIC", nullptr}; - return names; -} - -inline const char *EnumNameLSTMKernelType(LSTMKernelType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesLSTMKernelType()[index]; -} - -enum CombinerType -{ - CombinerType_SUM = 0, - CombinerType_MEAN = 1, - CombinerType_SQRTN = 2, - CombinerType_MIN = CombinerType_SUM, - CombinerType_MAX = CombinerType_SQRTN -}; - -inline const CombinerType (&EnumValuesCombinerType())[3] -{ - static const CombinerType values[] = {CombinerType_SUM, CombinerType_MEAN, CombinerType_SQRTN}; - return values; -} - -inline const char *const *EnumNamesCombinerType() -{ - static const char *const names[] = {"SUM", "MEAN", "SQRTN", nullptr}; - return names; -} - -inline const char *EnumNameCombinerType(CombinerType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesCombinerType()[index]; -} - -enum MirrorPadMode -{ - MirrorPadMode_REFLECT = 0, - MirrorPadMode_SYMMETRIC = 1, - MirrorPadMode_MIN = MirrorPadMode_REFLECT, - MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC -}; - -inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2] -{ - static const MirrorPadMode values[] = {MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC}; - return values; -} - -inline const char *const *EnumNamesMirrorPadMode() -{ - static const char *const names[] = {"REFLECT", "SYMMETRIC", nullptr}; - return names; -} - -inline const char *EnumNameMirrorPadMode(MirrorPadMode e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesMirrorPadMode()[index]; -} - -enum CustomOptionsFormat -{ - CustomOptionsFormat_FLEXBUFFERS = 0, - CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS, - CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS -}; - -inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] -{ - static const CustomOptionsFormat values[] = {CustomOptionsFormat_FLEXBUFFERS}; - return values; -} - -inline const char *const *EnumNamesCustomOptionsFormat() -{ - static const char *const names[] = {"FLEXBUFFERS", nullptr}; - return names; -} - -inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesCustomOptionsFormat()[index]; -} - -enum DataFormat -{ - DataFormat_CHANNELS_LAST = 0, - DataFormat_CHANNELS_FIRST = 1, - DataFormat_MIN = DataFormat_CHANNELS_LAST, - DataFormat_MAX = DataFormat_CHANNELS_FIRST -}; - -inline const DataFormat (&EnumValuesDataFormat())[2] -{ - static const DataFormat values[] = {DataFormat_CHANNELS_LAST, DataFormat_CHANNELS_FIRST}; - return values; -} - -inline const char *const *EnumNamesDataFormat() -{ - static const char *const names[] = {"CHANNELS_LAST", "CHANNELS_FIRST", nullptr}; - return names; -} - -inline const char *EnumNameDataFormat(DataFormat e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesDataFormat()[index]; -} - -struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_CUSTOM = 4 - }; - const flatbuffers::Vector<uint8_t> *custom() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_CUSTOM) && - verifier.VerifyVector(custom()) && verifier.EndTable(); - } -}; - -struct CustomQuantizationBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_custom(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom) - { - fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom); - } - explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CustomQuantizationBuilder &operator=(const CustomQuantizationBuilder &); - flatbuffers::Offset<CustomQuantization> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CustomQuantization>(end); - return o; - } -}; - -inline flatbuffers::Offset<CustomQuantization> -CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom = 0) -{ - CustomQuantizationBuilder builder_(_fbb); - builder_.add_custom(custom); - return builder_.Finish(); -} - -inline flatbuffers::Offset<CustomQuantization> -CreateCustomQuantizationDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint8_t> *custom = nullptr) -{ - return circle::CreateCustomQuantization(_fbb, custom ? _fbb.CreateVector<uint8_t>(*custom) : 0); -} - -struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_MIN = 4, - VT_MAX = 6, - VT_SCALE = 8, - VT_ZERO_POINT = 10, - VT_DETAILS_TYPE = 12, - VT_DETAILS = 14, - VT_QUANTIZED_DIMENSION = 16 - }; - const flatbuffers::Vector<float> *min() const - { - return GetPointer<const flatbuffers::Vector<float> *>(VT_MIN); - } - const flatbuffers::Vector<float> *max() const - { - return GetPointer<const flatbuffers::Vector<float> *>(VT_MAX); - } - const flatbuffers::Vector<float> *scale() const - { - return GetPointer<const flatbuffers::Vector<float> *>(VT_SCALE); - } - const flatbuffers::Vector<int64_t> *zero_point() const - { - return GetPointer<const flatbuffers::Vector<int64_t> *>(VT_ZERO_POINT); - } - QuantizationDetails details_type() const - { - return static_cast<QuantizationDetails>(GetField<uint8_t>(VT_DETAILS_TYPE, 0)); - } - const void *details() const { return GetPointer<const void *>(VT_DETAILS); } - template <typename T> const T *details_as() const; - const CustomQuantization *details_as_CustomQuantization() const - { - return details_type() == QuantizationDetails_CustomQuantization - ? static_cast<const CustomQuantization *>(details()) - : nullptr; - } - int32_t quantized_dimension() const { return GetField<int32_t>(VT_QUANTIZED_DIMENSION, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_MIN) && - verifier.VerifyVector(min()) && VerifyOffset(verifier, VT_MAX) && - verifier.VerifyVector(max()) && VerifyOffset(verifier, VT_SCALE) && - verifier.VerifyVector(scale()) && VerifyOffset(verifier, VT_ZERO_POINT) && - verifier.VerifyVector(zero_point()) && VerifyField<uint8_t>(verifier, VT_DETAILS_TYPE) && - VerifyOffset(verifier, VT_DETAILS) && - VerifyQuantizationDetails(verifier, details(), details_type()) && - VerifyField<int32_t>(verifier, VT_QUANTIZED_DIMENSION) && verifier.EndTable(); - } -}; - -template <> -inline const CustomQuantization *QuantizationParameters::details_as<CustomQuantization>() const -{ - return details_as_CustomQuantization(); -} - -struct QuantizationParametersBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(flatbuffers::Offset<flatbuffers::Vector<float>> min) - { - fbb_.AddOffset(QuantizationParameters::VT_MIN, min); - } - void add_max(flatbuffers::Offset<flatbuffers::Vector<float>> max) - { - fbb_.AddOffset(QuantizationParameters::VT_MAX, max); - } - void add_scale(flatbuffers::Offset<flatbuffers::Vector<float>> scale) - { - fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale); - } - void add_zero_point(flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point) - { - fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point); - } - void add_details_type(QuantizationDetails details_type) - { - fbb_.AddElement<uint8_t>(QuantizationParameters::VT_DETAILS_TYPE, - static_cast<uint8_t>(details_type), 0); - } - void add_details(flatbuffers::Offset<void> details) - { - fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details); - } - void add_quantized_dimension(int32_t quantized_dimension) - { - fbb_.AddElement<int32_t>(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, - 0); - } - explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - QuantizationParametersBuilder &operator=(const QuantizationParametersBuilder &); - flatbuffers::Offset<QuantizationParameters> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<QuantizationParameters>(end); - return o; - } -}; - -inline flatbuffers::Offset<QuantizationParameters> -CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<float>> min = 0, - flatbuffers::Offset<flatbuffers::Vector<float>> max = 0, - flatbuffers::Offset<flatbuffers::Vector<float>> scale = 0, - flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point = 0, - QuantizationDetails details_type = QuantizationDetails_NONE, - flatbuffers::Offset<void> details = 0, int32_t quantized_dimension = 0) -{ - QuantizationParametersBuilder builder_(_fbb); - builder_.add_quantized_dimension(quantized_dimension); - builder_.add_details(details); - builder_.add_zero_point(zero_point); - builder_.add_scale(scale); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_details_type(details_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, const std::vector<float> *min = nullptr, - const std::vector<float> *max = nullptr, const std::vector<float> *scale = nullptr, - const std::vector<int64_t> *zero_point = nullptr, - QuantizationDetails details_type = QuantizationDetails_NONE, - flatbuffers::Offset<void> details = 0, int32_t quantized_dimension = 0) -{ - return circle::CreateQuantizationParameters( - _fbb, min ? _fbb.CreateVector<float>(*min) : 0, max ? _fbb.CreateVector<float>(*max) : 0, - scale ? _fbb.CreateVector<float>(*scale) : 0, - zero_point ? _fbb.CreateVector<int64_t>(*zero_point) : 0, details_type, details, - quantized_dimension); -} - -struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES = 4 - }; - const flatbuffers::Vector<int32_t> *values() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && verifier.EndTable(); - } -}; - -struct Int32VectorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset<flatbuffers::Vector<int32_t>> values) - { - fbb_.AddOffset(Int32Vector::VT_VALUES, values); - } - explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Int32VectorBuilder &operator=(const Int32VectorBuilder &); - flatbuffers::Offset<Int32Vector> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Int32Vector>(end); - return o; - } -}; - -inline flatbuffers::Offset<Int32Vector> -CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> values = 0) -{ - Int32VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Int32Vector> -CreateInt32VectorDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *values = nullptr) -{ - return circle::CreateInt32Vector(_fbb, values ? _fbb.CreateVector<int32_t>(*values) : 0); -} - -struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES = 4 - }; - const flatbuffers::Vector<uint16_t> *values() const - { - return GetPointer<const flatbuffers::Vector<uint16_t> *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && verifier.EndTable(); - } -}; - -struct Uint16VectorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values) - { - fbb_.AddOffset(Uint16Vector::VT_VALUES, values); - } - explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Uint16VectorBuilder &operator=(const Uint16VectorBuilder &); - flatbuffers::Offset<Uint16Vector> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Uint16Vector>(end); - return o; - } -}; - -inline flatbuffers::Offset<Uint16Vector> -CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values = 0) -{ - Uint16VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Uint16Vector> -CreateUint16VectorDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint16_t> *values = nullptr) -{ - return circle::CreateUint16Vector(_fbb, values ? _fbb.CreateVector<uint16_t>(*values) : 0); -} - -struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES = 4 - }; - const flatbuffers::Vector<uint8_t> *values() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && verifier.EndTable(); - } -}; - -struct Uint8VectorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values) - { - fbb_.AddOffset(Uint8Vector::VT_VALUES, values); - } - explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Uint8VectorBuilder &operator=(const Uint8VectorBuilder &); - flatbuffers::Offset<Uint8Vector> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Uint8Vector>(end); - return o; - } -}; - -inline flatbuffers::Offset<Uint8Vector> -CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values = 0) -{ - Uint8VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Uint8Vector> -CreateUint8VectorDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint8_t> *values = nullptr) -{ - return circle::CreateUint8Vector(_fbb, values ? _fbb.CreateVector<uint8_t>(*values) : 0); -} - -struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FORMAT = 4, - VT_DENSE_SIZE = 6, - VT_ARRAY_SEGMENTS_TYPE = 8, - VT_ARRAY_SEGMENTS = 10, - VT_ARRAY_INDICES_TYPE = 12, - VT_ARRAY_INDICES = 14 - }; - DimensionType format() const - { - return static_cast<DimensionType>(GetField<int8_t>(VT_FORMAT, 0)); - } - int32_t dense_size() const { return GetField<int32_t>(VT_DENSE_SIZE, 0); } - SparseIndexVector array_segments_type() const - { - return static_cast<SparseIndexVector>(GetField<uint8_t>(VT_ARRAY_SEGMENTS_TYPE, 0)); - } - const void *array_segments() const { return GetPointer<const void *>(VT_ARRAY_SEGMENTS); } - template <typename T> const T *array_segments_as() const; - const Int32Vector *array_segments_as_Int32Vector() const - { - return array_segments_type() == SparseIndexVector_Int32Vector - ? static_cast<const Int32Vector *>(array_segments()) - : nullptr; - } - const Uint16Vector *array_segments_as_Uint16Vector() const - { - return array_segments_type() == SparseIndexVector_Uint16Vector - ? static_cast<const Uint16Vector *>(array_segments()) - : nullptr; - } - const Uint8Vector *array_segments_as_Uint8Vector() const - { - return array_segments_type() == SparseIndexVector_Uint8Vector - ? static_cast<const Uint8Vector *>(array_segments()) - : nullptr; - } - SparseIndexVector array_indices_type() const - { - return static_cast<SparseIndexVector>(GetField<uint8_t>(VT_ARRAY_INDICES_TYPE, 0)); - } - const void *array_indices() const { return GetPointer<const void *>(VT_ARRAY_INDICES); } - template <typename T> const T *array_indices_as() const; - const Int32Vector *array_indices_as_Int32Vector() const - { - return array_indices_type() == SparseIndexVector_Int32Vector - ? static_cast<const Int32Vector *>(array_indices()) - : nullptr; - } - const Uint16Vector *array_indices_as_Uint16Vector() const - { - return array_indices_type() == SparseIndexVector_Uint16Vector - ? static_cast<const Uint16Vector *>(array_indices()) - : nullptr; - } - const Uint8Vector *array_indices_as_Uint8Vector() const - { - return array_indices_type() == SparseIndexVector_Uint8Vector - ? static_cast<const Uint8Vector *>(array_indices()) - : nullptr; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_FORMAT) && - VerifyField<int32_t>(verifier, VT_DENSE_SIZE) && - VerifyField<uint8_t>(verifier, VT_ARRAY_SEGMENTS_TYPE) && - VerifyOffset(verifier, VT_ARRAY_SEGMENTS) && - VerifySparseIndexVector(verifier, array_segments(), array_segments_type()) && - VerifyField<uint8_t>(verifier, VT_ARRAY_INDICES_TYPE) && - VerifyOffset(verifier, VT_ARRAY_INDICES) && - VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) && - verifier.EndTable(); - } -}; - -template <> inline const Int32Vector *DimensionMetadata::array_segments_as<Int32Vector>() const -{ - return array_segments_as_Int32Vector(); -} - -template <> inline const Uint16Vector *DimensionMetadata::array_segments_as<Uint16Vector>() const -{ - return array_segments_as_Uint16Vector(); -} - -template <> inline const Uint8Vector *DimensionMetadata::array_segments_as<Uint8Vector>() const -{ - return array_segments_as_Uint8Vector(); -} - -template <> inline const Int32Vector *DimensionMetadata::array_indices_as<Int32Vector>() const -{ - return array_indices_as_Int32Vector(); -} - -template <> inline const Uint16Vector *DimensionMetadata::array_indices_as<Uint16Vector>() const -{ - return array_indices_as_Uint16Vector(); -} - -template <> inline const Uint8Vector *DimensionMetadata::array_indices_as<Uint8Vector>() const -{ - return array_indices_as_Uint8Vector(); -} - -struct DimensionMetadataBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_format(DimensionType format) - { - fbb_.AddElement<int8_t>(DimensionMetadata::VT_FORMAT, static_cast<int8_t>(format), 0); - } - void add_dense_size(int32_t dense_size) - { - fbb_.AddElement<int32_t>(DimensionMetadata::VT_DENSE_SIZE, dense_size, 0); - } - void add_array_segments_type(SparseIndexVector array_segments_type) - { - fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, - static_cast<uint8_t>(array_segments_type), 0); - } - void add_array_segments(flatbuffers::Offset<void> array_segments) - { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments); - } - void add_array_indices_type(SparseIndexVector array_indices_type) - { - fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_INDICES_TYPE, - static_cast<uint8_t>(array_indices_type), 0); - } - void add_array_indices(flatbuffers::Offset<void> array_indices) - { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices); - } - explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DimensionMetadataBuilder &operator=(const DimensionMetadataBuilder &); - flatbuffers::Offset<DimensionMetadata> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DimensionMetadata>(end); - return o; - } -}; - -inline flatbuffers::Offset<DimensionMetadata> -CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, - DimensionType format = DimensionType_DENSE, int32_t dense_size = 0, - SparseIndexVector array_segments_type = SparseIndexVector_NONE, - flatbuffers::Offset<void> array_segments = 0, - SparseIndexVector array_indices_type = SparseIndexVector_NONE, - flatbuffers::Offset<void> array_indices = 0) -{ - DimensionMetadataBuilder builder_(_fbb); - builder_.add_array_indices(array_indices); - builder_.add_array_segments(array_segments); - builder_.add_dense_size(dense_size); - builder_.add_array_indices_type(array_indices_type); - builder_.add_array_segments_type(array_segments_type); - builder_.add_format(format); - return builder_.Finish(); -} - -struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TRAVERSAL_ORDER = 4, - VT_BLOCK_MAP = 6, - VT_DIM_METADATA = 8 - }; - const flatbuffers::Vector<int32_t> *traversal_order() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_TRAVERSAL_ORDER); - } - const flatbuffers::Vector<int32_t> *block_map() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_BLOCK_MAP); - } - const flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>> *dim_metadata() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>> *>( - VT_DIM_METADATA); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_TRAVERSAL_ORDER) && - verifier.VerifyVector(traversal_order()) && VerifyOffset(verifier, VT_BLOCK_MAP) && - verifier.VerifyVector(block_map()) && VerifyOffset(verifier, VT_DIM_METADATA) && - verifier.VerifyVector(dim_metadata()) && verifier.VerifyVectorOfTables(dim_metadata()) && - verifier.EndTable(); - } -}; - -struct SparsityParametersBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_traversal_order(flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order) - { - fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order); - } - void add_block_map(flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map) - { - fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map); - } - void add_dim_metadata( - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>>> dim_metadata) - { - fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata); - } - explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SparsityParametersBuilder &operator=(const SparsityParametersBuilder &); - flatbuffers::Offset<SparsityParameters> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SparsityParameters>(end); - return o; - } -}; - -inline flatbuffers::Offset<SparsityParameters> CreateSparsityParameters( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>>> dim_metadata = - 0) -{ - SparsityParametersBuilder builder_(_fbb); - builder_.add_dim_metadata(dim_metadata); - builder_.add_block_map(block_map); - builder_.add_traversal_order(traversal_order); - return builder_.Finish(); -} - -inline flatbuffers::Offset<SparsityParameters> CreateSparsityParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, const std::vector<int32_t> *traversal_order = nullptr, - const std::vector<int32_t> *block_map = nullptr, - const std::vector<flatbuffers::Offset<DimensionMetadata>> *dim_metadata = nullptr) -{ - return circle::CreateSparsityParameters( - _fbb, traversal_order ? _fbb.CreateVector<int32_t>(*traversal_order) : 0, - block_map ? _fbb.CreateVector<int32_t>(*block_map) : 0, - dim_metadata ? _fbb.CreateVector<flatbuffers::Offset<DimensionMetadata>>(*dim_metadata) : 0); -} - -struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SHAPE = 4, - VT_TYPE = 6, - VT_BUFFER = 8, - VT_NAME = 10, - VT_QUANTIZATION = 12, - VT_IS_VARIABLE = 14, - VT_SPARSITY = 16, - VT_SHAPE_SIGNATURE = 18 - }; - const flatbuffers::Vector<int32_t> *shape() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE); - } - TensorType type() const { return static_cast<TensorType>(GetField<int8_t>(VT_TYPE, 0)); } - uint32_t buffer() const { return GetField<uint32_t>(VT_BUFFER, 0); } - const flatbuffers::String *name() const - { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - const QuantizationParameters *quantization() const - { - return GetPointer<const QuantizationParameters *>(VT_QUANTIZATION); - } - bool is_variable() const { return GetField<uint8_t>(VT_IS_VARIABLE, 0) != 0; } - const SparsityParameters *sparsity() const - { - return GetPointer<const SparsityParameters *>(VT_SPARSITY); - } - const flatbuffers::Vector<int32_t> *shape_signature() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE_SIGNATURE); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SHAPE) && - verifier.VerifyVector(shape()) && VerifyField<int8_t>(verifier, VT_TYPE) && - VerifyField<uint32_t>(verifier, VT_BUFFER) && VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && VerifyOffset(verifier, VT_QUANTIZATION) && - verifier.VerifyTable(quantization()) && VerifyField<uint8_t>(verifier, VT_IS_VARIABLE) && - VerifyOffset(verifier, VT_SPARSITY) && verifier.VerifyTable(sparsity()) && - VerifyOffset(verifier, VT_SHAPE_SIGNATURE) && verifier.VerifyVector(shape_signature()) && - verifier.EndTable(); - } -}; - -struct TensorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) - { - fbb_.AddOffset(Tensor::VT_SHAPE, shape); - } - void add_type(TensorType type) - { - fbb_.AddElement<int8_t>(Tensor::VT_TYPE, static_cast<int8_t>(type), 0); - } - void add_buffer(uint32_t buffer) { fbb_.AddElement<uint32_t>(Tensor::VT_BUFFER, buffer, 0); } - void add_name(flatbuffers::Offset<flatbuffers::String> name) - { - fbb_.AddOffset(Tensor::VT_NAME, name); - } - void add_quantization(flatbuffers::Offset<QuantizationParameters> quantization) - { - fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); - } - void add_is_variable(bool is_variable) - { - fbb_.AddElement<uint8_t>(Tensor::VT_IS_VARIABLE, static_cast<uint8_t>(is_variable), 0); - } - void add_sparsity(flatbuffers::Offset<SparsityParameters> sparsity) - { - fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity); - } - void add_shape_signature(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature) - { - fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature); - } - explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TensorBuilder &operator=(const TensorBuilder &); - flatbuffers::Offset<Tensor> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Tensor>(end); - return o; - } -}; - -inline flatbuffers::Offset<Tensor> -CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0, - TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, - flatbuffers::Offset<flatbuffers::String> name = 0, - flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false, - flatbuffers::Offset<SparsityParameters> sparsity = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature = 0) -{ - TensorBuilder builder_(_fbb); - builder_.add_shape_signature(shape_signature); - builder_.add_sparsity(sparsity); - builder_.add_quantization(quantization); - builder_.add_name(name); - builder_.add_buffer(buffer); - builder_.add_shape(shape); - builder_.add_is_variable(is_variable); - builder_.add_type(type); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Tensor> CreateTensorDirect( - flatbuffers::FlatBufferBuilder &_fbb, const std::vector<int32_t> *shape = nullptr, - TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, const char *name = nullptr, - flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false, - flatbuffers::Offset<SparsityParameters> sparsity = 0, - const std::vector<int32_t> *shape_signature = nullptr) -{ - return circle::CreateTensor(_fbb, shape ? _fbb.CreateVector<int32_t>(*shape) : 0, type, buffer, - name ? _fbb.CreateString(name) : 0, quantization, is_variable, - sparsity, - shape_signature ? _fbb.CreateVector<int32_t>(*shape_signature) : 0); -} - -struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FUSED_ACTIVATION_FUNCTION = 10, - VT_DILATION_W_FACTOR = 12, - VT_DILATION_H_FACTOR = 14 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); } - int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) && - VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable(); - } -}; - -struct Conv2DOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(Conv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Conv2DOptionsBuilder &operator=(const Conv2DOptionsBuilder &); - flatbuffers::Offset<Conv2DOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Conv2DOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<Conv2DOptions> -CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1) -{ - Conv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FILTER_WIDTH = 10, - VT_FILTER_HEIGHT = 12, - VT_FUSED_ACTIVATION_FUNCTION = 14 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - int32_t filter_width() const { return GetField<int32_t>(VT_FILTER_WIDTH, 0); } - int32_t filter_height() const { return GetField<int32_t>(VT_FILTER_HEIGHT, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && - VerifyField<int32_t>(verifier, VT_FILTER_WIDTH) && - VerifyField<int32_t>(verifier, VT_FILTER_HEIGHT) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct Pool2DOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(Pool2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_filter_width(int32_t filter_width) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0); - } - void add_filter_height(int32_t filter_height) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Pool2DOptionsBuilder &operator=(const Pool2DOptionsBuilder &); - flatbuffers::Offset<Pool2DOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Pool2DOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<Pool2DOptions> -CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0, int32_t filter_width = 0, - int32_t filter_height = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - Pool2DOptionsBuilder builder_(_fbb); - builder_.add_filter_height(filter_height); - builder_.add_filter_width(filter_width); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_DEPTH_MULTIPLIER = 10, - VT_FUSED_ACTIVATION_FUNCTION = 12, - VT_DILATION_W_FACTOR = 14, - VT_DILATION_H_FACTOR = 16 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - int32_t depth_multiplier() const { return GetField<int32_t>(VT_DEPTH_MULTIPLIER, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); } - int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && - VerifyField<int32_t>(verifier, VT_DEPTH_MULTIPLIER) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) && - VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable(); - } -}; - -struct DepthwiseConv2DOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_depth_multiplier(int32_t depth_multiplier) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DepthwiseConv2DOptionsBuilder &operator=(const DepthwiseConv2DOptionsBuilder &); - flatbuffers::Offset<DepthwiseConv2DOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DepthwiseConv2DOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, int32_t stride_w = 0, - int32_t stride_h = 0, int32_t depth_multiplier = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1) -{ - DepthwiseConv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_depth_multiplier(depth_multiplier); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM_CHANNELS = 4, - VT_NUM_COLUMNS_PER_CHANNEL = 6, - VT_EMBEDDING_DIM_PER_CHANNEL = 8 - }; - int32_t num_channels() const { return GetField<int32_t>(VT_NUM_CHANNELS, 0); } - const flatbuffers::Vector<int32_t> *num_columns_per_channel() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NUM_COLUMNS_PER_CHANNEL); - } - const flatbuffers::Vector<int32_t> *embedding_dim_per_channel() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_EMBEDDING_DIM_PER_CHANNEL); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_CHANNELS) && - VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) && - verifier.VerifyVector(num_columns_per_channel()) && - VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) && - verifier.VerifyVector(embedding_dim_per_channel()) && verifier.EndTable(); - } -}; - -struct ConcatEmbeddingsOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_channels(int32_t num_channels) - { - fbb_.AddElement<int32_t>(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0); - } - void add_num_columns_per_channel( - flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel) - { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel); - } - void add_embedding_dim_per_channel( - flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel) - { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, - embedding_dim_per_channel); - } - explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ConcatEmbeddingsOptionsBuilder &operator=(const ConcatEmbeddingsOptionsBuilder &); - flatbuffers::Offset<ConcatEmbeddingsOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ConcatEmbeddingsOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel = 0) -{ - ConcatEmbeddingsOptionsBuilder builder_(_fbb); - builder_.add_embedding_dim_per_channel(embedding_dim_per_channel); - builder_.add_num_columns_per_channel(num_columns_per_channel); - builder_.add_num_channels(num_channels); - return builder_.Finish(); -} - -inline flatbuffers::Offset<ConcatEmbeddingsOptions> -CreateConcatEmbeddingsOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0, - const std::vector<int32_t> *num_columns_per_channel = nullptr, - const std::vector<int32_t> *embedding_dim_per_channel = nullptr) -{ - return circle::CreateConcatEmbeddingsOptions( - _fbb, num_channels, - num_columns_per_channel ? _fbb.CreateVector<int32_t>(*num_columns_per_channel) : 0, - embedding_dim_per_channel ? _fbb.CreateVector<int32_t>(*embedding_dim_per_channel) : 0); -} - -struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TYPE = 4 - }; - LSHProjectionType type() const - { - return static_cast<LSHProjectionType>(GetField<int8_t>(VT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_TYPE) && - verifier.EndTable(); - } -}; - -struct LSHProjectionOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_type(LSHProjectionType type) - { - fbb_.AddElement<int8_t>(LSHProjectionOptions::VT_TYPE, static_cast<int8_t>(type), 0); - } - explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LSHProjectionOptionsBuilder &operator=(const LSHProjectionOptionsBuilder &); - flatbuffers::Offset<LSHProjectionOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LSHProjectionOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LSHProjectionOptions> -CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, - LSHProjectionType type = LSHProjectionType_UNKNOWN) -{ - LSHProjectionOptionsBuilder builder_(_fbb); - builder_.add_type(type); - return builder_.Finish(); -} - -struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_RANK = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - int32_t rank() const { return GetField<int32_t>(VT_RANK, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RANK) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct SVDFOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_rank(int32_t rank) { fbb_.AddElement<int32_t>(SVDFOptions::VT_RANK, rank, 0); } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SVDFOptionsBuilder &operator=(const SVDFOptionsBuilder &); - flatbuffers::Offset<SVDFOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SVDFOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SVDFOptions> -CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t rank = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) -{ - SVDFOptionsBuilder builder_(_fbb); - builder_.add_rank(rank); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 6 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct RNNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - RNNOptionsBuilder &operator=(const RNNOptionsBuilder &); - flatbuffers::Offset<RNNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<RNNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<RNNOptions> -CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) -{ - RNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct SequenceRNNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), - 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SequenceRNNOptionsBuilder &operator=(const SequenceRNNOptionsBuilder &); - flatbuffers::Offset<SequenceRNNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SequenceRNNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) -{ - SequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_MERGE_OUTPUTS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 - }; - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct BidirectionalSequenceRNNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, - static_cast<uint8_t>(time_major), 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_merge_outputs(bool merge_outputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS, - static_cast<uint8_t>(merge_outputs), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BidirectionalSequenceRNNOptionsBuilder &operator=(const BidirectionalSequenceRNNOptionsBuilder &); - flatbuffers::Offset<BidirectionalSequenceRNNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BidirectionalSequenceRNNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool merge_outputs = false, bool asymmetric_quantize_inputs = false) -{ - BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_WEIGHTS_FORMAT = 6, - VT_KEEP_NUM_DIMS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - FullyConnectedOptionsWeightsFormat weights_format() const - { - return static_cast<FullyConnectedOptionsWeightsFormat>(GetField<int8_t>(VT_WEIGHTS_FORMAT, 0)); - } - bool keep_num_dims() const { return GetField<uint8_t>(VT_KEEP_NUM_DIMS, 0) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<int8_t>(verifier, VT_WEIGHTS_FORMAT) && - VerifyField<uint8_t>(verifier, VT_KEEP_NUM_DIMS) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct FullyConnectedOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_weights_format(FullyConnectedOptionsWeightsFormat weights_format) - { - fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_WEIGHTS_FORMAT, - static_cast<int8_t>(weights_format), 0); - } - void add_keep_num_dims(bool keep_num_dims) - { - fbb_.AddElement<uint8_t>(FullyConnectedOptions::VT_KEEP_NUM_DIMS, - static_cast<uint8_t>(keep_num_dims), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FullyConnectedOptionsBuilder &operator=(const FullyConnectedOptionsBuilder &); - flatbuffers::Offset<FullyConnectedOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FullyConnectedOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions( - flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - FullyConnectedOptionsWeightsFormat weights_format = FullyConnectedOptionsWeightsFormat_DEFAULT, - bool keep_num_dims = false, bool asymmetric_quantize_inputs = false) -{ - FullyConnectedOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_keep_num_dims(keep_num_dims); - builder_.add_weights_format(weights_format); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BETA = 4 - }; - float beta() const { return GetField<float>(VT_BETA, 0.0f); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_BETA) && - verifier.EndTable(); - } -}; - -struct SoftmaxOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_beta(float beta) { fbb_.AddElement<float>(SoftmaxOptions::VT_BETA, beta, 0.0f); } - explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SoftmaxOptionsBuilder &operator=(const SoftmaxOptionsBuilder &); - flatbuffers::Offset<SoftmaxOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SoftmaxOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SoftmaxOptions> -CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, float beta = 0.0f) -{ - SoftmaxOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - return builder_.Finish(); -} - -struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_AXIS = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6 - }; - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct ConcatenationOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(ConcatenationOptions::VT_AXIS, axis, 0); } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ConcatenationOptionsBuilder &operator=(const ConcatenationOptionsBuilder &); - flatbuffers::Offset<ConcatenationOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ConcatenationOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t axis = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - ConcatenationOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct AddOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - AddOptionsBuilder &operator=(const AddOptionsBuilder &); - flatbuffers::Offset<AddOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<AddOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<AddOptions> -CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - AddOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct MulOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MulOptionsBuilder &operator=(const MulOptionsBuilder &); - flatbuffers::Offset<MulOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MulOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MulOptions> -CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - MulOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct L2NormOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - L2NormOptionsBuilder &operator=(const L2NormOptionsBuilder &); - flatbuffers::Offset<L2NormOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<L2NormOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<L2NormOptions> -CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - L2NormOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_RADIUS = 4, - VT_BIAS = 6, - VT_ALPHA = 8, - VT_BETA = 10 - }; - int32_t radius() const { return GetField<int32_t>(VT_RADIUS, 0); } - float bias() const { return GetField<float>(VT_BIAS, 0.0f); } - float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); } - float beta() const { return GetField<float>(VT_BETA, 0.0f); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RADIUS) && - VerifyField<float>(verifier, VT_BIAS) && VerifyField<float>(verifier, VT_ALPHA) && - VerifyField<float>(verifier, VT_BETA) && verifier.EndTable(); - } -}; - -struct LocalResponseNormalizationOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_radius(int32_t radius) - { - fbb_.AddElement<int32_t>(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0); - } - void add_bias(float bias) - { - fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f); - } - void add_alpha(float alpha) - { - fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f); - } - void add_beta(float beta) - { - fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f); - } - explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LocalResponseNormalizationOptionsBuilder & - operator=(const LocalResponseNormalizationOptionsBuilder &); - flatbuffers::Offset<LocalResponseNormalizationOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LocalResponseNormalizationOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LocalResponseNormalizationOptions> -CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t radius = 0, - float bias = 0.0f, float alpha = 0.0f, float beta = 0.0f) -{ - LocalResponseNormalizationOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - builder_.add_alpha(alpha); - builder_.add_bias(bias); - builder_.add_radius(radius); - return builder_.Finish(); -} - -struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_KERNEL_TYPE = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); } - float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); } - LSTMKernelType kernel_type() const - { - return static_cast<LSTMKernelType>(GetField<int8_t>(VT_KERNEL_TYPE, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<float>(verifier, VT_CELL_CLIP) && - VerifyField<float>(verifier, VT_PROJ_CLIP) && - VerifyField<int8_t>(verifier, VT_KERNEL_TYPE) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct LSTMOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) - { - fbb_.AddElement<float>(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) - { - fbb_.AddElement<float>(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_kernel_type(LSTMKernelType kernel_type) - { - fbb_.AddElement<int8_t>(LSTMOptions::VT_KERNEL_TYPE, static_cast<int8_t>(kernel_type), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LSTMOptionsBuilder &operator=(const LSTMOptionsBuilder &); - flatbuffers::Offset<LSTMOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LSTMOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LSTMOptions> -CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - float cell_clip = 0.0f, float proj_clip = 0.0f, - LSTMKernelType kernel_type = LSTMKernelType_FULL, - bool asymmetric_quantize_inputs = false) -{ - LSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_kernel_type(kernel_type); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_TIME_MAJOR = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); } - float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); } - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<float>(verifier, VT_CELL_CLIP) && - VerifyField<float>(verifier, VT_PROJ_CLIP) && - VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct UnidirectionalSequenceLSTMOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) - { - fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) - { - fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, - static_cast<uint8_t>(time_major), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - UnidirectionalSequenceLSTMOptionsBuilder & - operator=(const UnidirectionalSequenceLSTMOptionsBuilder &); - flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> -CreateUnidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - float cell_clip = 0.0f, float proj_clip = 0.0f, bool time_major = false, - bool asymmetric_quantize_inputs = false) -{ - UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_MERGE_OUTPUTS = 10, - VT_TIME_MAJOR = 12, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 14 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); } - float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); } - bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; } - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 1) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<float>(verifier, VT_CELL_CLIP) && - VerifyField<float>(verifier, VT_PROJ_CLIP) && - VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && - VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct BidirectionalSequenceLSTMOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) - { - fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) - { - fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_merge_outputs(bool merge_outputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS, - static_cast<uint8_t>(merge_outputs), 0); - } - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, - static_cast<uint8_t>(time_major), 1); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BidirectionalSequenceLSTMOptionsBuilder & - operator=(const BidirectionalSequenceLSTMOptionsBuilder &); - flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BidirectionalSequenceLSTMOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - float cell_clip = 0.0f, float proj_clip = 0.0f, bool merge_outputs = false, - bool time_major = true, bool asymmetric_quantize_inputs = false) -{ - BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ALIGN_CORNERS = 8, - VT_HALF_PIXEL_CENTERS = 10 - }; - bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; } - bool half_pixel_centers() const { return GetField<uint8_t>(VT_HALF_PIXEL_CENTERS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) && - VerifyField<uint8_t>(verifier, VT_HALF_PIXEL_CENTERS) && verifier.EndTable(); - } -}; - -struct ResizeBilinearOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) - { - fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_ALIGN_CORNERS, - static_cast<uint8_t>(align_corners), 0); - } - void add_half_pixel_centers(bool half_pixel_centers) - { - fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, - static_cast<uint8_t>(half_pixel_centers), 0); - } - explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ResizeBilinearOptionsBuilder &operator=(const ResizeBilinearOptionsBuilder &); - flatbuffers::Offset<ResizeBilinearOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ResizeBilinearOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ResizeBilinearOptions> -CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false, - bool half_pixel_centers = false) -{ - ResizeBilinearOptionsBuilder builder_(_fbb); - builder_.add_half_pixel_centers(half_pixel_centers); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ALIGN_CORNERS = 4 - }; - bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) && - verifier.EndTable(); - } -}; - -struct ResizeNearestNeighborOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) - { - fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, - static_cast<uint8_t>(align_corners), 0); - } - explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ResizeNearestNeighborOptionsBuilder &operator=(const ResizeNearestNeighborOptionsBuilder &); - flatbuffers::Offset<ResizeNearestNeighborOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ResizeNearestNeighborOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ResizeNearestNeighborOptions> -CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false) -{ - ResizeNearestNeighborOptionsBuilder builder_(_fbb); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SUBGRAPH = 4 - }; - uint32_t subgraph() const { return GetField<uint32_t>(VT_SUBGRAPH, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_SUBGRAPH) && - verifier.EndTable(); - } -}; - -struct CallOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_subgraph(uint32_t subgraph) - { - fbb_.AddElement<uint32_t>(CallOptions::VT_SUBGRAPH, subgraph, 0); - } - explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CallOptionsBuilder &operator=(const CallOptionsBuilder &); - flatbuffers::Offset<CallOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CallOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, - uint32_t subgraph = 0) -{ - CallOptionsBuilder builder_(_fbb); - builder_.add_subgraph(subgraph); - return builder_.Finish(); -} - -struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct PadOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PadOptionsBuilder &operator=(const PadOptionsBuilder &); - flatbuffers::Offset<PadOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PadOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - PadOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct PadV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PadV2OptionsBuilder &operator=(const PadV2OptionsBuilder &); - flatbuffers::Offset<PadV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PadV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - PadV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NEW_SHAPE = 4 - }; - const flatbuffers::Vector<int32_t> *new_shape() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NEW_SHAPE); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_NEW_SHAPE) && - verifier.VerifyVector(new_shape()) && verifier.EndTable(); - } -}; - -struct ReshapeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_new_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape) - { - fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape); - } - explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReshapeOptionsBuilder &operator=(const ReshapeOptionsBuilder &); - flatbuffers::Offset<ReshapeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReshapeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReshapeOptions> -CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape = 0) -{ - ReshapeOptionsBuilder builder_(_fbb); - builder_.add_new_shape(new_shape); - return builder_.Finish(); -} - -inline flatbuffers::Offset<ReshapeOptions> -CreateReshapeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *new_shape = nullptr) -{ - return circle::CreateReshapeOptions(_fbb, new_shape ? _fbb.CreateVector<int32_t>(*new_shape) : 0); -} - -struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SpaceToBatchNDOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SpaceToBatchNDOptionsBuilder &operator=(const SpaceToBatchNDOptionsBuilder &); - flatbuffers::Offset<SpaceToBatchNDOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SpaceToBatchNDOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SpaceToBatchNDOptions> -CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SpaceToBatchNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct BatchToSpaceNDOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BatchToSpaceNDOptionsBuilder &operator=(const BatchToSpaceNDOptionsBuilder &); - flatbuffers::Offset<BatchToSpaceNDOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BatchToSpaceNDOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BatchToSpaceNDOptions> -CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - BatchToSpaceNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NGRAM_SIZE = 4, - VT_MAX_SKIP_SIZE = 6, - VT_INCLUDE_ALL_NGRAMS = 8 - }; - int32_t ngram_size() const { return GetField<int32_t>(VT_NGRAM_SIZE, 0); } - int32_t max_skip_size() const { return GetField<int32_t>(VT_MAX_SKIP_SIZE, 0); } - bool include_all_ngrams() const { return GetField<uint8_t>(VT_INCLUDE_ALL_NGRAMS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NGRAM_SIZE) && - VerifyField<int32_t>(verifier, VT_MAX_SKIP_SIZE) && - VerifyField<uint8_t>(verifier, VT_INCLUDE_ALL_NGRAMS) && verifier.EndTable(); - } -}; - -struct SkipGramOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_ngram_size(int32_t ngram_size) - { - fbb_.AddElement<int32_t>(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0); - } - void add_max_skip_size(int32_t max_skip_size) - { - fbb_.AddElement<int32_t>(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0); - } - void add_include_all_ngrams(bool include_all_ngrams) - { - fbb_.AddElement<uint8_t>(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, - static_cast<uint8_t>(include_all_ngrams), 0); - } - explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SkipGramOptionsBuilder &operator=(const SkipGramOptionsBuilder &); - flatbuffers::Offset<SkipGramOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SkipGramOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SkipGramOptions> -CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t ngram_size = 0, - int32_t max_skip_size = 0, bool include_all_ngrams = false) -{ - SkipGramOptionsBuilder builder_(_fbb); - builder_.add_max_skip_size(max_skip_size); - builder_.add_ngram_size(ngram_size); - builder_.add_include_all_ngrams(include_all_ngrams); - return builder_.Finish(); -} - -struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { return GetField<int32_t>(VT_BLOCK_SIZE, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BLOCK_SIZE) && - verifier.EndTable(); - } -}; - -struct SpaceToDepthOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) - { - fbb_.AddElement<int32_t>(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SpaceToDepthOptionsBuilder &operator=(const SpaceToDepthOptionsBuilder &); - flatbuffers::Offset<SpaceToDepthOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SpaceToDepthOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SpaceToDepthOptions> -CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t block_size = 0) -{ - SpaceToDepthOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { return GetField<int32_t>(VT_BLOCK_SIZE, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BLOCK_SIZE) && - verifier.EndTable(); - } -}; - -struct DepthToSpaceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) - { - fbb_.AddElement<int32_t>(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DepthToSpaceOptionsBuilder &operator=(const DepthToSpaceOptionsBuilder &); - flatbuffers::Offset<DepthToSpaceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DepthToSpaceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DepthToSpaceOptions> -CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t block_size = 0) -{ - DepthToSpaceOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct SubOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SubOptionsBuilder &operator=(const SubOptionsBuilder &); - flatbuffers::Offset<SubOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SubOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SubOptions> -CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - SubOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct DivOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DivOptionsBuilder &operator=(const DivOptionsBuilder &); - flatbuffers::Offset<DivOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DivOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DivOptions> -CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - DivOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct TopKV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TopKV2OptionsBuilder &operator=(const TopKV2OptionsBuilder &); - flatbuffers::Offset<TopKV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TopKV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - TopKV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_COMBINER = 4 - }; - CombinerType combiner() const - { - return static_cast<CombinerType>(GetField<int8_t>(VT_COMBINER, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_COMBINER) && - verifier.EndTable(); - } -}; - -struct EmbeddingLookupSparseOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_combiner(CombinerType combiner) - { - fbb_.AddElement<int8_t>(EmbeddingLookupSparseOptions::VT_COMBINER, - static_cast<int8_t>(combiner), 0); - } - explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - EmbeddingLookupSparseOptionsBuilder &operator=(const EmbeddingLookupSparseOptionsBuilder &); - flatbuffers::Offset<EmbeddingLookupSparseOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<EmbeddingLookupSparseOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<EmbeddingLookupSparseOptions> -CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, - CombinerType combiner = CombinerType_SUM) -{ - EmbeddingLookupSparseOptionsBuilder builder_(_fbb); - builder_.add_combiner(combiner); - return builder_.Finish(); -} - -struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_AXIS = 4 - }; - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) && - verifier.EndTable(); - } -}; - -struct GatherOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(GatherOptions::VT_AXIS, axis, 0); } - explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GatherOptionsBuilder &operator=(const GatherOptionsBuilder &); - flatbuffers::Offset<GatherOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GatherOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) -{ - GatherOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct TransposeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TransposeOptionsBuilder &operator=(const TransposeOptionsBuilder &); - flatbuffers::Offset<TransposeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TransposeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<TransposeOptions> -CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - TransposeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ExpOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ExpOptionsBuilder &operator=(const ExpOptionsBuilder &); - flatbuffers::Offset<ExpOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ExpOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ExpOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct CosOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CosOptionsBuilder &operator=(const CosOptionsBuilder &); - flatbuffers::Offset<CosOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CosOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<CosOptions> CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - CosOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_KEEP_DIMS = 4 - }; - bool keep_dims() const { return GetField<uint8_t>(VT_KEEP_DIMS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_KEEP_DIMS) && - verifier.EndTable(); - } -}; - -struct ReducerOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_keep_dims(bool keep_dims) - { - fbb_.AddElement<uint8_t>(ReducerOptions::VT_KEEP_DIMS, static_cast<uint8_t>(keep_dims), 0); - } - explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReducerOptionsBuilder &operator=(const ReducerOptionsBuilder &); - flatbuffers::Offset<ReducerOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReducerOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReducerOptions> -CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false) -{ - ReducerOptionsBuilder builder_(_fbb); - builder_.add_keep_dims(keep_dims); - return builder_.Finish(); -} - -struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SQUEEZE_DIMS = 4 - }; - const flatbuffers::Vector<int32_t> *squeeze_dims() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SQUEEZE_DIMS); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SQUEEZE_DIMS) && - verifier.VerifyVector(squeeze_dims()) && verifier.EndTable(); - } -}; - -struct SqueezeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_squeeze_dims(flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims) - { - fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims); - } - explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SqueezeOptionsBuilder &operator=(const SqueezeOptionsBuilder &); - flatbuffers::Offset<SqueezeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SqueezeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SqueezeOptions> -CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims = 0) -{ - SqueezeOptionsBuilder builder_(_fbb); - builder_.add_squeeze_dims(squeeze_dims); - return builder_.Finish(); -} - -inline flatbuffers::Offset<SqueezeOptions> -CreateSqueezeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *squeeze_dims = nullptr) -{ - return circle::CreateSqueezeOptions(_fbb, - squeeze_dims ? _fbb.CreateVector<int32_t>(*squeeze_dims) : 0); -} - -struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) && - verifier.EndTable(); - } -}; - -struct SplitOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) - { - fbb_.AddElement<int32_t>(SplitOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SplitOptionsBuilder &operator=(const SplitOptionsBuilder &); - flatbuffers::Offset<SplitOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SplitOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) -{ - SplitOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) && - verifier.EndTable(); - } -}; - -struct SplitVOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) - { - fbb_.AddElement<int32_t>(SplitVOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SplitVOptionsBuilder &operator=(const SplitVOptionsBuilder &); - flatbuffers::Offset<SplitVOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SplitVOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) -{ - SplitVOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BEGIN_MASK = 4, - VT_END_MASK = 6, - VT_ELLIPSIS_MASK = 8, - VT_NEW_AXIS_MASK = 10, - VT_SHRINK_AXIS_MASK = 12 - }; - int32_t begin_mask() const { return GetField<int32_t>(VT_BEGIN_MASK, 0); } - int32_t end_mask() const { return GetField<int32_t>(VT_END_MASK, 0); } - int32_t ellipsis_mask() const { return GetField<int32_t>(VT_ELLIPSIS_MASK, 0); } - int32_t new_axis_mask() const { return GetField<int32_t>(VT_NEW_AXIS_MASK, 0); } - int32_t shrink_axis_mask() const { return GetField<int32_t>(VT_SHRINK_AXIS_MASK, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BEGIN_MASK) && - VerifyField<int32_t>(verifier, VT_END_MASK) && - VerifyField<int32_t>(verifier, VT_ELLIPSIS_MASK) && - VerifyField<int32_t>(verifier, VT_NEW_AXIS_MASK) && - VerifyField<int32_t>(verifier, VT_SHRINK_AXIS_MASK) && verifier.EndTable(); - } -}; - -struct StridedSliceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_begin_mask(int32_t begin_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0); - } - void add_end_mask(int32_t end_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_END_MASK, end_mask, 0); - } - void add_ellipsis_mask(int32_t ellipsis_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0); - } - void add_new_axis_mask(int32_t new_axis_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0); - } - void add_shrink_axis_mask(int32_t shrink_axis_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); - } - explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - StridedSliceOptionsBuilder &operator=(const StridedSliceOptionsBuilder &); - flatbuffers::Offset<StridedSliceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<StridedSliceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<StridedSliceOptions> -CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t begin_mask = 0, - int32_t end_mask = 0, int32_t ellipsis_mask = 0, - int32_t new_axis_mask = 0, int32_t shrink_axis_mask = 0) -{ - StridedSliceOptionsBuilder builder_(_fbb); - builder_.add_shrink_axis_mask(shrink_axis_mask); - builder_.add_new_axis_mask(new_axis_mask); - builder_.add_ellipsis_mask(ellipsis_mask); - builder_.add_end_mask(end_mask); - builder_.add_begin_mask(begin_mask); - return builder_.Finish(); -} - -struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogSoftmaxOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogSoftmaxOptionsBuilder &operator=(const LogSoftmaxOptionsBuilder &); - flatbuffers::Offset<LogSoftmaxOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogSoftmaxOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogSoftmaxOptions> -CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogSoftmaxOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_IN_DATA_TYPE = 4, - VT_OUT_DATA_TYPE = 6 - }; - TensorType in_data_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_IN_DATA_TYPE, 0)); - } - TensorType out_data_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_OUT_DATA_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_IN_DATA_TYPE) && - VerifyField<int8_t>(verifier, VT_OUT_DATA_TYPE) && verifier.EndTable(); - } -}; - -struct CastOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_in_data_type(TensorType in_data_type) - { - fbb_.AddElement<int8_t>(CastOptions::VT_IN_DATA_TYPE, static_cast<int8_t>(in_data_type), 0); - } - void add_out_data_type(TensorType out_data_type) - { - fbb_.AddElement<int8_t>(CastOptions::VT_OUT_DATA_TYPE, static_cast<int8_t>(out_data_type), 0); - } - explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CastOptionsBuilder &operator=(const CastOptionsBuilder &); - flatbuffers::Offset<CastOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CastOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<CastOptions> -CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType in_data_type = TensorType_FLOAT32, - TensorType out_data_type = TensorType_FLOAT32) -{ - CastOptionsBuilder builder_(_fbb); - builder_.add_out_data_type(out_data_type); - builder_.add_in_data_type(in_data_type); - return builder_.Finish(); -} - -struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct DequantizeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DequantizeOptionsBuilder &operator=(const DequantizeOptionsBuilder &); - flatbuffers::Offset<DequantizeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DequantizeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DequantizeOptions> -CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - DequantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct MaximumMinimumOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MaximumMinimumOptionsBuilder &operator=(const MaximumMinimumOptionsBuilder &); - flatbuffers::Offset<MaximumMinimumOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MaximumMinimumOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MaximumMinimumOptions> -CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - MaximumMinimumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct TileOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TileOptionsBuilder &operator=(const TileOptionsBuilder &); - flatbuffers::Offset<TileOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TileOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - TileOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OUTPUT_TYPE = 4 - }; - TensorType output_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) && - verifier.EndTable(); - } -}; - -struct ArgMaxOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(TensorType output_type) - { - fbb_.AddElement<int8_t>(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0); - } - explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ArgMaxOptionsBuilder &operator=(const ArgMaxOptionsBuilder &); - flatbuffers::Offset<ArgMaxOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ArgMaxOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ArgMaxOptions> -CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType output_type = TensorType_FLOAT32) -{ - ArgMaxOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OUTPUT_TYPE = 4 - }; - TensorType output_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) && - verifier.EndTable(); - } -}; - -struct ArgMinOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(TensorType output_type) - { - fbb_.AddElement<int8_t>(ArgMinOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0); - } - explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ArgMinOptionsBuilder &operator=(const ArgMinOptionsBuilder &); - flatbuffers::Offset<ArgMinOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ArgMinOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ArgMinOptions> -CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType output_type = TensorType_FLOAT32) -{ - ArgMinOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct GreaterOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GreaterOptionsBuilder &operator=(const GreaterOptionsBuilder &); - flatbuffers::Offset<GreaterOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GreaterOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GreaterOptions> -CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - GreaterOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct GreaterEqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GreaterEqualOptionsBuilder &operator=(const GreaterEqualOptionsBuilder &); - flatbuffers::Offset<GreaterEqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GreaterEqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GreaterEqualOptions> -CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - GreaterEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LessOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LessOptionsBuilder &operator=(const LessOptionsBuilder &); - flatbuffers::Offset<LessOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LessOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LessOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LessEqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LessEqualOptionsBuilder &operator=(const LessEqualOptionsBuilder &); - flatbuffers::Offset<LessEqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LessEqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LessEqualOptions> -CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LessEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NegOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NegOptionsBuilder &operator=(const NegOptionsBuilder &); - flatbuffers::Offset<NegOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NegOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - NegOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SelectOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SelectOptionsBuilder &operator=(const SelectOptionsBuilder &); - flatbuffers::Offset<SelectOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SelectOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SelectOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SliceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SliceOptionsBuilder &operator=(const SliceOptionsBuilder &); - flatbuffers::Offset<SliceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SliceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SliceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && verifier.EndTable(); - } -}; - -struct TransposeConvOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(TransposeConvOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_H, stride_h, 0); - } - explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TransposeConvOptionsBuilder &operator=(const TransposeConvOptionsBuilder &); - flatbuffers::Offset<TransposeConvOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TransposeConvOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<TransposeConvOptions> -CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0) -{ - TransposeConvOptionsBuilder builder_(_fbb); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ExpandDimsOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ExpandDimsOptionsBuilder &operator=(const ExpandDimsOptionsBuilder &); - flatbuffers::Offset<ExpandDimsOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ExpandDimsOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ExpandDimsOptions> -CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ExpandDimsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALIDATE_INDICES = 4 - }; - bool validate_indices() const { return GetField<uint8_t>(VT_VALIDATE_INDICES, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_VALIDATE_INDICES) && - verifier.EndTable(); - } -}; - -struct SparseToDenseOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_validate_indices(bool validate_indices) - { - fbb_.AddElement<uint8_t>(SparseToDenseOptions::VT_VALIDATE_INDICES, - static_cast<uint8_t>(validate_indices), 0); - } - explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SparseToDenseOptionsBuilder &operator=(const SparseToDenseOptionsBuilder &); - flatbuffers::Offset<SparseToDenseOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SparseToDenseOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SparseToDenseOptions> -CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, bool validate_indices = false) -{ - SparseToDenseOptionsBuilder builder_(_fbb); - builder_.add_validate_indices(validate_indices); - return builder_.Finish(); -} - -struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct EqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - EqualOptionsBuilder &operator=(const EqualOptionsBuilder &); - flatbuffers::Offset<EqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<EqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - EqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NotEqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NotEqualOptionsBuilder &operator=(const NotEqualOptionsBuilder &); - flatbuffers::Offset<NotEqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NotEqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<NotEqualOptions> -CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - NotEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OUT_TYPE = 4 - }; - TensorType out_type() const { return static_cast<TensorType>(GetField<int8_t>(VT_OUT_TYPE, 0)); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUT_TYPE) && - verifier.EndTable(); - } -}; - -struct ShapeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_out_type(TensorType out_type) - { - fbb_.AddElement<int8_t>(ShapeOptions::VT_OUT_TYPE, static_cast<int8_t>(out_type), 0); - } - explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ShapeOptionsBuilder &operator=(const ShapeOptionsBuilder &); - flatbuffers::Offset<ShapeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ShapeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ShapeOptions> -CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, TensorType out_type = TensorType_FLOAT32) -{ - ShapeOptionsBuilder builder_(_fbb); - builder_.add_out_type(out_type); - return builder_.Finish(); -} - -struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct RankOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - RankOptionsBuilder &operator=(const RankOptionsBuilder &); - flatbuffers::Offset<RankOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<RankOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<RankOptions> CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - RankOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct PowOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PowOptionsBuilder &operator=(const PowOptionsBuilder &); - flatbuffers::Offset<PowOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PowOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - PowOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_MIN = 4, - VT_MAX = 6, - VT_NUM_BITS = 8, - VT_NARROW_RANGE = 10 - }; - float min() const { return GetField<float>(VT_MIN, 0.0f); } - float max() const { return GetField<float>(VT_MAX, 0.0f); } - int32_t num_bits() const { return GetField<int32_t>(VT_NUM_BITS, 0); } - bool narrow_range() const { return GetField<uint8_t>(VT_NARROW_RANGE, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_MIN) && - VerifyField<float>(verifier, VT_MAX) && VerifyField<int32_t>(verifier, VT_NUM_BITS) && - VerifyField<uint8_t>(verifier, VT_NARROW_RANGE) && verifier.EndTable(); - } -}; - -struct FakeQuantOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(float min) { fbb_.AddElement<float>(FakeQuantOptions::VT_MIN, min, 0.0f); } - void add_max(float max) { fbb_.AddElement<float>(FakeQuantOptions::VT_MAX, max, 0.0f); } - void add_num_bits(int32_t num_bits) - { - fbb_.AddElement<int32_t>(FakeQuantOptions::VT_NUM_BITS, num_bits, 0); - } - void add_narrow_range(bool narrow_range) - { - fbb_.AddElement<uint8_t>(FakeQuantOptions::VT_NARROW_RANGE, static_cast<uint8_t>(narrow_range), - 0); - } - explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FakeQuantOptionsBuilder &operator=(const FakeQuantOptionsBuilder &); - flatbuffers::Offset<FakeQuantOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FakeQuantOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FakeQuantOptions> -CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, float min = 0.0f, float max = 0.0f, - int32_t num_bits = 0, bool narrow_range = false) -{ - FakeQuantOptionsBuilder builder_(_fbb); - builder_.add_num_bits(num_bits); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_narrow_range(narrow_range); - return builder_.Finish(); -} - -struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES_COUNT = 4, - VT_AXIS = 6 - }; - int32_t values_count() const { return GetField<int32_t>(VT_VALUES_COUNT, 0); } - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_VALUES_COUNT) && - VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable(); - } -}; - -struct PackOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values_count(int32_t values_count) - { - fbb_.AddElement<int32_t>(PackOptions::VT_VALUES_COUNT, values_count, 0); - } - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(PackOptions::VT_AXIS, axis, 0); } - explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PackOptionsBuilder &operator=(const PackOptionsBuilder &); - flatbuffers::Offset<PackOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PackOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<PackOptions> -CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t values_count = 0, int32_t axis = 0) -{ - PackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_values_count(values_count); - return builder_.Finish(); -} - -struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogicalOrOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogicalOrOptionsBuilder &operator=(const LogicalOrOptionsBuilder &); - flatbuffers::Offset<LogicalOrOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogicalOrOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogicalOrOptions> -CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogicalOrOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_AXIS = 4 - }; - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) && - verifier.EndTable(); - } -}; - -struct OneHotOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(OneHotOptions::VT_AXIS, axis, 0); } - explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - OneHotOptionsBuilder &operator=(const OneHotOptionsBuilder &); - flatbuffers::Offset<OneHotOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<OneHotOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) -{ - OneHotOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct AbsOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - AbsOptionsBuilder &operator=(const AbsOptionsBuilder &); - flatbuffers::Offset<AbsOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<AbsOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - AbsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct HardSwishOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - HardSwishOptionsBuilder &operator=(const HardSwishOptionsBuilder &); - flatbuffers::Offset<HardSwishOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<HardSwishOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<HardSwishOptions> -CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - HardSwishOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogicalAndOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogicalAndOptionsBuilder &operator=(const LogicalAndOptionsBuilder &); - flatbuffers::Offset<LogicalAndOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogicalAndOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogicalAndOptions> -CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogicalAndOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogicalNotOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogicalNotOptionsBuilder &operator=(const LogicalNotOptionsBuilder &); - flatbuffers::Offset<LogicalNotOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogicalNotOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogicalNotOptions> -CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogicalNotOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM = 4, - VT_AXIS = 6 - }; - int32_t num() const { return GetField<int32_t>(VT_NUM, 0); } - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM) && - VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable(); - } -}; - -struct UnpackOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num(int32_t num) { fbb_.AddElement<int32_t>(UnpackOptions::VT_NUM, num, 0); } - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(UnpackOptions::VT_AXIS, axis, 0); } - explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - UnpackOptionsBuilder &operator=(const UnpackOptionsBuilder &); - flatbuffers::Offset<UnpackOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<UnpackOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t num = 0, int32_t axis = 0) -{ - UnpackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_num(num); - return builder_.Finish(); -} - -struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct FloorDivOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FloorDivOptionsBuilder &operator=(const FloorDivOptionsBuilder &); - flatbuffers::Offset<FloorDivOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FloorDivOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FloorDivOptions> -CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - FloorDivOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SquareOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SquareOptionsBuilder &operator=(const SquareOptionsBuilder &); - flatbuffers::Offset<SquareOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SquareOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SquareOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ZerosLikeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ZerosLikeOptionsBuilder &operator=(const ZerosLikeOptionsBuilder &); - flatbuffers::Offset<ZerosLikeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ZerosLikeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ZerosLikeOptions> -CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ZerosLikeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct FillOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FillOptionsBuilder &operator=(const FillOptionsBuilder &); - flatbuffers::Offset<FillOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FillOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - FillOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct FloorModOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FloorModOptionsBuilder &operator=(const FloorModOptionsBuilder &); - flatbuffers::Offset<FloorModOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FloorModOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FloorModOptions> -CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - FloorModOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct RangeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - RangeOptionsBuilder &operator=(const RangeOptionsBuilder &); - flatbuffers::Offset<RangeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<RangeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - RangeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ALPHA = 4 - }; - float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_ALPHA) && - verifier.EndTable(); - } -}; - -struct LeakyReluOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_alpha(float alpha) { fbb_.AddElement<float>(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); } - explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LeakyReluOptionsBuilder &operator=(const LeakyReluOptionsBuilder &); - flatbuffers::Offset<LeakyReluOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LeakyReluOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LeakyReluOptions> -CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, float alpha = 0.0f) -{ - LeakyReluOptionsBuilder builder_(_fbb); - builder_.add_alpha(alpha); - return builder_.Finish(); -} - -struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SquaredDifferenceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SquaredDifferenceOptionsBuilder &operator=(const SquaredDifferenceOptionsBuilder &); - flatbuffers::Offset<SquaredDifferenceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SquaredDifferenceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SquaredDifferenceOptions> -CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SquaredDifferenceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_MODE = 4 - }; - MirrorPadMode mode() const { return static_cast<MirrorPadMode>(GetField<int8_t>(VT_MODE, 0)); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_MODE) && - verifier.EndTable(); - } -}; - -struct MirrorPadOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_mode(MirrorPadMode mode) - { - fbb_.AddElement<int8_t>(MirrorPadOptions::VT_MODE, static_cast<int8_t>(mode), 0); - } - explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MirrorPadOptionsBuilder &operator=(const MirrorPadOptionsBuilder &); - flatbuffers::Offset<MirrorPadOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MirrorPadOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MirrorPadOptions> -CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, - MirrorPadMode mode = MirrorPadMode_REFLECT) -{ - MirrorPadOptionsBuilder builder_(_fbb); - builder_.add_mode(mode); - return builder_.Finish(); -} - -struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_IDX_OUT_TYPE = 4 - }; - TensorType idx_out_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_IDX_OUT_TYPE, 2)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_IDX_OUT_TYPE) && - verifier.EndTable(); - } -}; - -struct UniqueOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_idx_out_type(TensorType idx_out_type) - { - fbb_.AddElement<int8_t>(UniqueOptions::VT_IDX_OUT_TYPE, static_cast<int8_t>(idx_out_type), 2); - } - explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - UniqueOptionsBuilder &operator=(const UniqueOptionsBuilder &); - flatbuffers::Offset<UniqueOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<UniqueOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<UniqueOptions> -CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType idx_out_type = TensorType_INT32) -{ - UniqueOptionsBuilder builder_(_fbb); - builder_.add_idx_out_type(idx_out_type); - return builder_.Finish(); -} - -struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ReverseV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReverseV2OptionsBuilder &operator=(const ReverseV2OptionsBuilder &); - flatbuffers::Offset<ReverseV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReverseV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReverseV2Options> -CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - ReverseV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct AddNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - AddNOptionsBuilder &operator=(const AddNOptionsBuilder &); - flatbuffers::Offset<AddNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<AddNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<AddNOptions> CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - AddNOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct GatherNdOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GatherNdOptionsBuilder &operator=(const GatherNdOptionsBuilder &); - flatbuffers::Offset<GatherNdOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GatherNdOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GatherNdOptions> -CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - GatherNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct WhereOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - WhereOptionsBuilder &operator=(const WhereOptionsBuilder &); - flatbuffers::Offset<WhereOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<WhereOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<WhereOptions> CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - WhereOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SEQ_DIM = 4, - VT_BATCH_DIM = 6 - }; - int32_t seq_dim() const { return GetField<int32_t>(VT_SEQ_DIM, 0); } - int32_t batch_dim() const { return GetField<int32_t>(VT_BATCH_DIM, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_SEQ_DIM) && - VerifyField<int32_t>(verifier, VT_BATCH_DIM) && verifier.EndTable(); - } -}; - -struct ReverseSequenceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_seq_dim(int32_t seq_dim) - { - fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0); - } - void add_batch_dim(int32_t batch_dim) - { - fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0); - } - explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReverseSequenceOptionsBuilder &operator=(const ReverseSequenceOptionsBuilder &); - flatbuffers::Offset<ReverseSequenceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReverseSequenceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReverseSequenceOptions> -CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t seq_dim = 0, - int32_t batch_dim = 0) -{ - ReverseSequenceOptionsBuilder builder_(_fbb); - builder_.add_batch_dim(batch_dim); - builder_.add_seq_dim(seq_dim); - return builder_.Finish(); -} - -struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct MatrixDiagOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MatrixDiagOptionsBuilder &operator=(const MatrixDiagOptionsBuilder &); - flatbuffers::Offset<MatrixDiagOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MatrixDiagOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MatrixDiagOptions> -CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - MatrixDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct QuantizeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - QuantizeOptionsBuilder &operator=(const QuantizeOptionsBuilder &); - flatbuffers::Offset<QuantizeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<QuantizeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<QuantizeOptions> -CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - QuantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct MatrixSetDiagOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MatrixSetDiagOptionsBuilder &operator=(const MatrixSetDiagOptionsBuilder &); - flatbuffers::Offset<MatrixSetDiagOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MatrixSetDiagOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MatrixSetDiagOptions> -CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - MatrixSetDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_THEN_SUBGRAPH_INDEX = 4, - VT_ELSE_SUBGRAPH_INDEX = 6 - }; - int32_t then_subgraph_index() const { return GetField<int32_t>(VT_THEN_SUBGRAPH_INDEX, 0); } - int32_t else_subgraph_index() const { return GetField<int32_t>(VT_ELSE_SUBGRAPH_INDEX, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_THEN_SUBGRAPH_INDEX) && - VerifyField<int32_t>(verifier, VT_ELSE_SUBGRAPH_INDEX) && verifier.EndTable(); - } -}; - -struct IfOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_then_subgraph_index(int32_t then_subgraph_index) - { - fbb_.AddElement<int32_t>(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0); - } - void add_else_subgraph_index(int32_t else_subgraph_index) - { - fbb_.AddElement<int32_t>(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0); - } - explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - IfOptionsBuilder &operator=(const IfOptionsBuilder &); - flatbuffers::Offset<IfOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<IfOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<IfOptions> CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t then_subgraph_index = 0, - int32_t else_subgraph_index = 0) -{ - IfOptionsBuilder builder_(_fbb); - builder_.add_else_subgraph_index(else_subgraph_index); - builder_.add_then_subgraph_index(then_subgraph_index); - return builder_.Finish(); -} - -struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_COND_SUBGRAPH_INDEX = 4, - VT_BODY_SUBGRAPH_INDEX = 6 - }; - int32_t cond_subgraph_index() const { return GetField<int32_t>(VT_COND_SUBGRAPH_INDEX, 0); } - int32_t body_subgraph_index() const { return GetField<int32_t>(VT_BODY_SUBGRAPH_INDEX, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_COND_SUBGRAPH_INDEX) && - VerifyField<int32_t>(verifier, VT_BODY_SUBGRAPH_INDEX) && verifier.EndTable(); - } -}; - -struct WhileOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_cond_subgraph_index(int32_t cond_subgraph_index) - { - fbb_.AddElement<int32_t>(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); - } - void add_body_subgraph_index(int32_t body_subgraph_index) - { - fbb_.AddElement<int32_t>(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); - } - explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - WhileOptionsBuilder &operator=(const WhileOptionsBuilder &); - flatbuffers::Offset<WhileOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<WhileOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<WhileOptions> CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t cond_subgraph_index = 0, - int32_t body_subgraph_index = 0) -{ - WhileOptionsBuilder builder_(_fbb); - builder_.add_body_subgraph_index(body_subgraph_index); - builder_.add_cond_subgraph_index(cond_subgraph_index); - return builder_.Finish(); -} - -struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NonMaxSuppressionV4OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NonMaxSuppressionV4OptionsBuilder &operator=(const NonMaxSuppressionV4OptionsBuilder &); - flatbuffers::Offset<NonMaxSuppressionV4Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NonMaxSuppressionV4Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<NonMaxSuppressionV4Options> -CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - NonMaxSuppressionV4OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NonMaxSuppressionV5OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NonMaxSuppressionV5OptionsBuilder &operator=(const NonMaxSuppressionV5OptionsBuilder &); - flatbuffers::Offset<NonMaxSuppressionV5Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NonMaxSuppressionV5Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<NonMaxSuppressionV5Options> -CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - NonMaxSuppressionV5OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ScatterNdOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ScatterNdOptionsBuilder &operator=(const ScatterNdOptionsBuilder &); - flatbuffers::Offset<ScatterNdOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ScatterNdOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ScatterNdOptions> -CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ScatterNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SelectV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SelectV2OptionsBuilder &operator=(const SelectV2OptionsBuilder &); - flatbuffers::Offset<SelectV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SelectV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<SelectV2Options> -CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - SelectV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct DensifyOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DensifyOptionsBuilder &operator=(const DensifyOptionsBuilder &); - flatbuffers::Offset<DensifyOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DensifyOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DensifyOptions> -CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - DensifyOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SegmentSumOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SegmentSumOptionsBuilder &operator=(const SegmentSumOptionsBuilder &); - flatbuffers::Offset<SegmentSumOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SegmentSumOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SegmentSumOptions> -CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SegmentSumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ADJOINT_LHS = 4, - VT_ADJOINT_RHS = 6 - }; - bool adjoint_lhs() const { return GetField<uint8_t>(VT_ADJOINT_LHS, 0) != 0; } - bool adjoint_rhs() const { return GetField<uint8_t>(VT_ADJOINT_RHS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ADJOINT_LHS) && - VerifyField<uint8_t>(verifier, VT_ADJOINT_RHS) && verifier.EndTable(); - } -}; - -struct BatchMatMulOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_adjoint_lhs(bool adjoint_lhs) - { - fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ADJOINT_LHS, static_cast<uint8_t>(adjoint_lhs), - 0); - } - void add_adjoint_rhs(bool adjoint_rhs) - { - fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ADJOINT_RHS, static_cast<uint8_t>(adjoint_rhs), - 0); - } - explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BatchMatMulOptionsBuilder &operator=(const BatchMatMulOptionsBuilder &); - flatbuffers::Offset<BatchMatMulOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BatchMatMulOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BatchMatMulOptions> -CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, bool adjoint_lhs = false, - bool adjoint_rhs = false) -{ - BatchMatMulOptionsBuilder builder_(_fbb); - builder_.add_adjoint_rhs(adjoint_rhs); - builder_.add_adjoint_lhs(adjoint_lhs); - return builder_.Finish(); -} - -struct BCQGatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_INPUT_HIDDEN_SIZE = 4, - VT_AXIS = 6 - }; - int32_t input_hidden_size() const { return GetField<int32_t>(VT_INPUT_HIDDEN_SIZE, 0); } - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_INPUT_HIDDEN_SIZE) && - VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable(); - } -}; - -struct BCQGatherOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_input_hidden_size(int32_t input_hidden_size) - { - fbb_.AddElement<int32_t>(BCQGatherOptions::VT_INPUT_HIDDEN_SIZE, input_hidden_size, 0); - } - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(BCQGatherOptions::VT_AXIS, axis, 0); } - explicit BCQGatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BCQGatherOptionsBuilder &operator=(const BCQGatherOptionsBuilder &); - flatbuffers::Offset<BCQGatherOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BCQGatherOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BCQGatherOptions> -CreateBCQGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t input_hidden_size = 0, - int32_t axis = 0) -{ - BCQGatherOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_input_hidden_size(input_hidden_size); - return builder_.Finish(); -} - -struct BCQFullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_WEIGHTS_HIDDEN_SIZE = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6 - }; - int32_t weights_hidden_size() const { return GetField<int32_t>(VT_WEIGHTS_HIDDEN_SIZE, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_WEIGHTS_HIDDEN_SIZE) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct BCQFullyConnectedOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_weights_hidden_size(int32_t weights_hidden_size) - { - fbb_.AddElement<int32_t>(BCQFullyConnectedOptions::VT_WEIGHTS_HIDDEN_SIZE, weights_hidden_size, - 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(BCQFullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit BCQFullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BCQFullyConnectedOptionsBuilder &operator=(const BCQFullyConnectedOptionsBuilder &); - flatbuffers::Offset<BCQFullyConnectedOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BCQFullyConnectedOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BCQFullyConnectedOptions> CreateBCQFullyConnectedOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t weights_hidden_size = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - BCQFullyConnectedOptionsBuilder builder_(_fbb); - builder_.add_weights_hidden_size(weights_hidden_size); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct InstanceNormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_EPSILON = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6 - }; - float epsilon() const { return GetField<float>(VT_EPSILON, 0.0f); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_EPSILON) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct InstanceNormOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_epsilon(float epsilon) - { - fbb_.AddElement<float>(InstanceNormOptions::VT_EPSILON, epsilon, 0.0f); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(InstanceNormOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit InstanceNormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - InstanceNormOptionsBuilder &operator=(const InstanceNormOptionsBuilder &); - flatbuffers::Offset<InstanceNormOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<InstanceNormOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<InstanceNormOptions> CreateInstanceNormOptions( - flatbuffers::FlatBufferBuilder &_fbb, float epsilon = 0.0f, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - InstanceNormOptionsBuilder builder_(_fbb); - builder_.add_epsilon(epsilon); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BUILTIN_CODE = 4, - VT_CUSTOM_CODE = 6, - VT_VERSION = 8 - }; - BuiltinOperator builtin_code() const - { - return static_cast<BuiltinOperator>(GetField<uint8_t>(VT_BUILTIN_CODE, 0)); - } - const flatbuffers::String *custom_code() const - { - return GetPointer<const flatbuffers::String *>(VT_CUSTOM_CODE); - } - int32_t version() const { return GetField<int32_t>(VT_VERSION, 1); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_BUILTIN_CODE) && - VerifyOffset(verifier, VT_CUSTOM_CODE) && verifier.VerifyString(custom_code()) && - VerifyField<int32_t>(verifier, VT_VERSION) && verifier.EndTable(); - } -}; - -struct OperatorCodeBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_builtin_code(BuiltinOperator builtin_code) - { - fbb_.AddElement<uint8_t>(OperatorCode::VT_BUILTIN_CODE, static_cast<uint8_t>(builtin_code), 0); - } - void add_custom_code(flatbuffers::Offset<flatbuffers::String> custom_code) - { - fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code); - } - void add_version(int32_t version) - { - fbb_.AddElement<int32_t>(OperatorCode::VT_VERSION, version, 1); - } - explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - OperatorCodeBuilder &operator=(const OperatorCodeBuilder &); - flatbuffers::Offset<OperatorCode> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<OperatorCode>(end); - return o; - } -}; - -inline flatbuffers::Offset<OperatorCode> -CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, - BuiltinOperator builtin_code = BuiltinOperator_ADD, - flatbuffers::Offset<flatbuffers::String> custom_code = 0, int32_t version = 1) -{ - OperatorCodeBuilder builder_(_fbb); - builder_.add_version(version); - builder_.add_custom_code(custom_code); - builder_.add_builtin_code(builtin_code); - return builder_.Finish(); -} - -inline flatbuffers::Offset<OperatorCode> -CreateOperatorCodeDirect(flatbuffers::FlatBufferBuilder &_fbb, - BuiltinOperator builtin_code = BuiltinOperator_ADD, - const char *custom_code = nullptr, int32_t version = 1) -{ - return circle::CreateOperatorCode(_fbb, builtin_code, - custom_code ? _fbb.CreateString(custom_code) : 0, version); -} - -struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OPCODE_INDEX = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_BUILTIN_OPTIONS_TYPE = 10, - VT_BUILTIN_OPTIONS = 12, - VT_CUSTOM_OPTIONS = 14, - VT_CUSTOM_OPTIONS_FORMAT = 16, - VT_MUTATING_VARIABLE_INPUTS = 18, - VT_INTERMEDIATES = 20 - }; - uint32_t opcode_index() const { return GetField<uint32_t>(VT_OPCODE_INDEX, 0); } - const flatbuffers::Vector<int32_t> *inputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS); - } - const flatbuffers::Vector<int32_t> *outputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS); - } - BuiltinOptions builtin_options_type() const - { - return static_cast<BuiltinOptions>(GetField<uint8_t>(VT_BUILTIN_OPTIONS_TYPE, 0)); - } - const void *builtin_options() const { return GetPointer<const void *>(VT_BUILTIN_OPTIONS); } - template <typename T> const T *builtin_options_as() const; - const Conv2DOptions *builtin_options_as_Conv2DOptions() const - { - return builtin_options_type() == BuiltinOptions_Conv2DOptions - ? static_cast<const Conv2DOptions *>(builtin_options()) - : nullptr; - } - const DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const - { - return builtin_options_type() == BuiltinOptions_DepthwiseConv2DOptions - ? static_cast<const DepthwiseConv2DOptions *>(builtin_options()) - : nullptr; - } - const ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const - { - return builtin_options_type() == BuiltinOptions_ConcatEmbeddingsOptions - ? static_cast<const ConcatEmbeddingsOptions *>(builtin_options()) - : nullptr; - } - const LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const - { - return builtin_options_type() == BuiltinOptions_LSHProjectionOptions - ? static_cast<const LSHProjectionOptions *>(builtin_options()) - : nullptr; - } - const Pool2DOptions *builtin_options_as_Pool2DOptions() const - { - return builtin_options_type() == BuiltinOptions_Pool2DOptions - ? static_cast<const Pool2DOptions *>(builtin_options()) - : nullptr; - } - const SVDFOptions *builtin_options_as_SVDFOptions() const - { - return builtin_options_type() == BuiltinOptions_SVDFOptions - ? static_cast<const SVDFOptions *>(builtin_options()) - : nullptr; - } - const RNNOptions *builtin_options_as_RNNOptions() const - { - return builtin_options_type() == BuiltinOptions_RNNOptions - ? static_cast<const RNNOptions *>(builtin_options()) - : nullptr; - } - const FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const - { - return builtin_options_type() == BuiltinOptions_FullyConnectedOptions - ? static_cast<const FullyConnectedOptions *>(builtin_options()) - : nullptr; - } - const SoftmaxOptions *builtin_options_as_SoftmaxOptions() const - { - return builtin_options_type() == BuiltinOptions_SoftmaxOptions - ? static_cast<const SoftmaxOptions *>(builtin_options()) - : nullptr; - } - const ConcatenationOptions *builtin_options_as_ConcatenationOptions() const - { - return builtin_options_type() == BuiltinOptions_ConcatenationOptions - ? static_cast<const ConcatenationOptions *>(builtin_options()) - : nullptr; - } - const AddOptions *builtin_options_as_AddOptions() const - { - return builtin_options_type() == BuiltinOptions_AddOptions - ? static_cast<const AddOptions *>(builtin_options()) - : nullptr; - } - const L2NormOptions *builtin_options_as_L2NormOptions() const - { - return builtin_options_type() == BuiltinOptions_L2NormOptions - ? static_cast<const L2NormOptions *>(builtin_options()) - : nullptr; - } - const LocalResponseNormalizationOptions * - builtin_options_as_LocalResponseNormalizationOptions() const - { - return builtin_options_type() == BuiltinOptions_LocalResponseNormalizationOptions - ? static_cast<const LocalResponseNormalizationOptions *>(builtin_options()) - : nullptr; - } - const LSTMOptions *builtin_options_as_LSTMOptions() const - { - return builtin_options_type() == BuiltinOptions_LSTMOptions - ? static_cast<const LSTMOptions *>(builtin_options()) - : nullptr; - } - const ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const - { - return builtin_options_type() == BuiltinOptions_ResizeBilinearOptions - ? static_cast<const ResizeBilinearOptions *>(builtin_options()) - : nullptr; - } - const CallOptions *builtin_options_as_CallOptions() const - { - return builtin_options_type() == BuiltinOptions_CallOptions - ? static_cast<const CallOptions *>(builtin_options()) - : nullptr; - } - const ReshapeOptions *builtin_options_as_ReshapeOptions() const - { - return builtin_options_type() == BuiltinOptions_ReshapeOptions - ? static_cast<const ReshapeOptions *>(builtin_options()) - : nullptr; - } - const SkipGramOptions *builtin_options_as_SkipGramOptions() const - { - return builtin_options_type() == BuiltinOptions_SkipGramOptions - ? static_cast<const SkipGramOptions *>(builtin_options()) - : nullptr; - } - const SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const - { - return builtin_options_type() == BuiltinOptions_SpaceToDepthOptions - ? static_cast<const SpaceToDepthOptions *>(builtin_options()) - : nullptr; - } - const EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const - { - return builtin_options_type() == BuiltinOptions_EmbeddingLookupSparseOptions - ? static_cast<const EmbeddingLookupSparseOptions *>(builtin_options()) - : nullptr; - } - const MulOptions *builtin_options_as_MulOptions() const - { - return builtin_options_type() == BuiltinOptions_MulOptions - ? static_cast<const MulOptions *>(builtin_options()) - : nullptr; - } - const PadOptions *builtin_options_as_PadOptions() const - { - return builtin_options_type() == BuiltinOptions_PadOptions - ? static_cast<const PadOptions *>(builtin_options()) - : nullptr; - } - const GatherOptions *builtin_options_as_GatherOptions() const - { - return builtin_options_type() == BuiltinOptions_GatherOptions - ? static_cast<const GatherOptions *>(builtin_options()) - : nullptr; - } - const BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const - { - return builtin_options_type() == BuiltinOptions_BatchToSpaceNDOptions - ? static_cast<const BatchToSpaceNDOptions *>(builtin_options()) - : nullptr; - } - const SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const - { - return builtin_options_type() == BuiltinOptions_SpaceToBatchNDOptions - ? static_cast<const SpaceToBatchNDOptions *>(builtin_options()) - : nullptr; - } - const TransposeOptions *builtin_options_as_TransposeOptions() const - { - return builtin_options_type() == BuiltinOptions_TransposeOptions - ? static_cast<const TransposeOptions *>(builtin_options()) - : nullptr; - } - const ReducerOptions *builtin_options_as_ReducerOptions() const - { - return builtin_options_type() == BuiltinOptions_ReducerOptions - ? static_cast<const ReducerOptions *>(builtin_options()) - : nullptr; - } - const SubOptions *builtin_options_as_SubOptions() const - { - return builtin_options_type() == BuiltinOptions_SubOptions - ? static_cast<const SubOptions *>(builtin_options()) - : nullptr; - } - const DivOptions *builtin_options_as_DivOptions() const - { - return builtin_options_type() == BuiltinOptions_DivOptions - ? static_cast<const DivOptions *>(builtin_options()) - : nullptr; - } - const SqueezeOptions *builtin_options_as_SqueezeOptions() const - { - return builtin_options_type() == BuiltinOptions_SqueezeOptions - ? static_cast<const SqueezeOptions *>(builtin_options()) - : nullptr; - } - const SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const - { - return builtin_options_type() == BuiltinOptions_SequenceRNNOptions - ? static_cast<const SequenceRNNOptions *>(builtin_options()) - : nullptr; - } - const StridedSliceOptions *builtin_options_as_StridedSliceOptions() const - { - return builtin_options_type() == BuiltinOptions_StridedSliceOptions - ? static_cast<const StridedSliceOptions *>(builtin_options()) - : nullptr; - } - const ExpOptions *builtin_options_as_ExpOptions() const - { - return builtin_options_type() == BuiltinOptions_ExpOptions - ? static_cast<const ExpOptions *>(builtin_options()) - : nullptr; - } - const TopKV2Options *builtin_options_as_TopKV2Options() const - { - return builtin_options_type() == BuiltinOptions_TopKV2Options - ? static_cast<const TopKV2Options *>(builtin_options()) - : nullptr; - } - const SplitOptions *builtin_options_as_SplitOptions() const - { - return builtin_options_type() == BuiltinOptions_SplitOptions - ? static_cast<const SplitOptions *>(builtin_options()) - : nullptr; - } - const LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const - { - return builtin_options_type() == BuiltinOptions_LogSoftmaxOptions - ? static_cast<const LogSoftmaxOptions *>(builtin_options()) - : nullptr; - } - const CastOptions *builtin_options_as_CastOptions() const - { - return builtin_options_type() == BuiltinOptions_CastOptions - ? static_cast<const CastOptions *>(builtin_options()) - : nullptr; - } - const DequantizeOptions *builtin_options_as_DequantizeOptions() const - { - return builtin_options_type() == BuiltinOptions_DequantizeOptions - ? static_cast<const DequantizeOptions *>(builtin_options()) - : nullptr; - } - const MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const - { - return builtin_options_type() == BuiltinOptions_MaximumMinimumOptions - ? static_cast<const MaximumMinimumOptions *>(builtin_options()) - : nullptr; - } - const ArgMaxOptions *builtin_options_as_ArgMaxOptions() const - { - return builtin_options_type() == BuiltinOptions_ArgMaxOptions - ? static_cast<const ArgMaxOptions *>(builtin_options()) - : nullptr; - } - const LessOptions *builtin_options_as_LessOptions() const - { - return builtin_options_type() == BuiltinOptions_LessOptions - ? static_cast<const LessOptions *>(builtin_options()) - : nullptr; - } - const NegOptions *builtin_options_as_NegOptions() const - { - return builtin_options_type() == BuiltinOptions_NegOptions - ? static_cast<const NegOptions *>(builtin_options()) - : nullptr; - } - const PadV2Options *builtin_options_as_PadV2Options() const - { - return builtin_options_type() == BuiltinOptions_PadV2Options - ? static_cast<const PadV2Options *>(builtin_options()) - : nullptr; - } - const GreaterOptions *builtin_options_as_GreaterOptions() const - { - return builtin_options_type() == BuiltinOptions_GreaterOptions - ? static_cast<const GreaterOptions *>(builtin_options()) - : nullptr; - } - const GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const - { - return builtin_options_type() == BuiltinOptions_GreaterEqualOptions - ? static_cast<const GreaterEqualOptions *>(builtin_options()) - : nullptr; - } - const LessEqualOptions *builtin_options_as_LessEqualOptions() const - { - return builtin_options_type() == BuiltinOptions_LessEqualOptions - ? static_cast<const LessEqualOptions *>(builtin_options()) - : nullptr; - } - const SelectOptions *builtin_options_as_SelectOptions() const - { - return builtin_options_type() == BuiltinOptions_SelectOptions - ? static_cast<const SelectOptions *>(builtin_options()) - : nullptr; - } - const SliceOptions *builtin_options_as_SliceOptions() const - { - return builtin_options_type() == BuiltinOptions_SliceOptions - ? static_cast<const SliceOptions *>(builtin_options()) - : nullptr; - } - const TransposeConvOptions *builtin_options_as_TransposeConvOptions() const - { - return builtin_options_type() == BuiltinOptions_TransposeConvOptions - ? static_cast<const TransposeConvOptions *>(builtin_options()) - : nullptr; - } - const SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const - { - return builtin_options_type() == BuiltinOptions_SparseToDenseOptions - ? static_cast<const SparseToDenseOptions *>(builtin_options()) - : nullptr; - } - const TileOptions *builtin_options_as_TileOptions() const - { - return builtin_options_type() == BuiltinOptions_TileOptions - ? static_cast<const TileOptions *>(builtin_options()) - : nullptr; - } - const ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const - { - return builtin_options_type() == BuiltinOptions_ExpandDimsOptions - ? static_cast<const ExpandDimsOptions *>(builtin_options()) - : nullptr; - } - const EqualOptions *builtin_options_as_EqualOptions() const - { - return builtin_options_type() == BuiltinOptions_EqualOptions - ? static_cast<const EqualOptions *>(builtin_options()) - : nullptr; - } - const NotEqualOptions *builtin_options_as_NotEqualOptions() const - { - return builtin_options_type() == BuiltinOptions_NotEqualOptions - ? static_cast<const NotEqualOptions *>(builtin_options()) - : nullptr; - } - const ShapeOptions *builtin_options_as_ShapeOptions() const - { - return builtin_options_type() == BuiltinOptions_ShapeOptions - ? static_cast<const ShapeOptions *>(builtin_options()) - : nullptr; - } - const PowOptions *builtin_options_as_PowOptions() const - { - return builtin_options_type() == BuiltinOptions_PowOptions - ? static_cast<const PowOptions *>(builtin_options()) - : nullptr; - } - const ArgMinOptions *builtin_options_as_ArgMinOptions() const - { - return builtin_options_type() == BuiltinOptions_ArgMinOptions - ? static_cast<const ArgMinOptions *>(builtin_options()) - : nullptr; - } - const FakeQuantOptions *builtin_options_as_FakeQuantOptions() const - { - return builtin_options_type() == BuiltinOptions_FakeQuantOptions - ? static_cast<const FakeQuantOptions *>(builtin_options()) - : nullptr; - } - const PackOptions *builtin_options_as_PackOptions() const - { - return builtin_options_type() == BuiltinOptions_PackOptions - ? static_cast<const PackOptions *>(builtin_options()) - : nullptr; - } - const LogicalOrOptions *builtin_options_as_LogicalOrOptions() const - { - return builtin_options_type() == BuiltinOptions_LogicalOrOptions - ? static_cast<const LogicalOrOptions *>(builtin_options()) - : nullptr; - } - const OneHotOptions *builtin_options_as_OneHotOptions() const - { - return builtin_options_type() == BuiltinOptions_OneHotOptions - ? static_cast<const OneHotOptions *>(builtin_options()) - : nullptr; - } - const LogicalAndOptions *builtin_options_as_LogicalAndOptions() const - { - return builtin_options_type() == BuiltinOptions_LogicalAndOptions - ? static_cast<const LogicalAndOptions *>(builtin_options()) - : nullptr; - } - const LogicalNotOptions *builtin_options_as_LogicalNotOptions() const - { - return builtin_options_type() == BuiltinOptions_LogicalNotOptions - ? static_cast<const LogicalNotOptions *>(builtin_options()) - : nullptr; - } - const UnpackOptions *builtin_options_as_UnpackOptions() const - { - return builtin_options_type() == BuiltinOptions_UnpackOptions - ? static_cast<const UnpackOptions *>(builtin_options()) - : nullptr; - } - const FloorDivOptions *builtin_options_as_FloorDivOptions() const - { - return builtin_options_type() == BuiltinOptions_FloorDivOptions - ? static_cast<const FloorDivOptions *>(builtin_options()) - : nullptr; - } - const SquareOptions *builtin_options_as_SquareOptions() const - { - return builtin_options_type() == BuiltinOptions_SquareOptions - ? static_cast<const SquareOptions *>(builtin_options()) - : nullptr; - } - const ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const - { - return builtin_options_type() == BuiltinOptions_ZerosLikeOptions - ? static_cast<const ZerosLikeOptions *>(builtin_options()) - : nullptr; - } - const FillOptions *builtin_options_as_FillOptions() const - { - return builtin_options_type() == BuiltinOptions_FillOptions - ? static_cast<const FillOptions *>(builtin_options()) - : nullptr; - } - const BidirectionalSequenceLSTMOptions * - builtin_options_as_BidirectionalSequenceLSTMOptions() const - { - return builtin_options_type() == BuiltinOptions_BidirectionalSequenceLSTMOptions - ? static_cast<const BidirectionalSequenceLSTMOptions *>(builtin_options()) - : nullptr; - } - const BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const - { - return builtin_options_type() == BuiltinOptions_BidirectionalSequenceRNNOptions - ? static_cast<const BidirectionalSequenceRNNOptions *>(builtin_options()) - : nullptr; - } - const UnidirectionalSequenceLSTMOptions * - builtin_options_as_UnidirectionalSequenceLSTMOptions() const - { - return builtin_options_type() == BuiltinOptions_UnidirectionalSequenceLSTMOptions - ? static_cast<const UnidirectionalSequenceLSTMOptions *>(builtin_options()) - : nullptr; - } - const FloorModOptions *builtin_options_as_FloorModOptions() const - { - return builtin_options_type() == BuiltinOptions_FloorModOptions - ? static_cast<const FloorModOptions *>(builtin_options()) - : nullptr; - } - const RangeOptions *builtin_options_as_RangeOptions() const - { - return builtin_options_type() == BuiltinOptions_RangeOptions - ? static_cast<const RangeOptions *>(builtin_options()) - : nullptr; - } - const ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const - { - return builtin_options_type() == BuiltinOptions_ResizeNearestNeighborOptions - ? static_cast<const ResizeNearestNeighborOptions *>(builtin_options()) - : nullptr; - } - const LeakyReluOptions *builtin_options_as_LeakyReluOptions() const - { - return builtin_options_type() == BuiltinOptions_LeakyReluOptions - ? static_cast<const LeakyReluOptions *>(builtin_options()) - : nullptr; - } - const SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const - { - return builtin_options_type() == BuiltinOptions_SquaredDifferenceOptions - ? static_cast<const SquaredDifferenceOptions *>(builtin_options()) - : nullptr; - } - const MirrorPadOptions *builtin_options_as_MirrorPadOptions() const - { - return builtin_options_type() == BuiltinOptions_MirrorPadOptions - ? static_cast<const MirrorPadOptions *>(builtin_options()) - : nullptr; - } - const AbsOptions *builtin_options_as_AbsOptions() const - { - return builtin_options_type() == BuiltinOptions_AbsOptions - ? static_cast<const AbsOptions *>(builtin_options()) - : nullptr; - } - const SplitVOptions *builtin_options_as_SplitVOptions() const - { - return builtin_options_type() == BuiltinOptions_SplitVOptions - ? static_cast<const SplitVOptions *>(builtin_options()) - : nullptr; - } - const UniqueOptions *builtin_options_as_UniqueOptions() const - { - return builtin_options_type() == BuiltinOptions_UniqueOptions - ? static_cast<const UniqueOptions *>(builtin_options()) - : nullptr; - } - const ReverseV2Options *builtin_options_as_ReverseV2Options() const - { - return builtin_options_type() == BuiltinOptions_ReverseV2Options - ? static_cast<const ReverseV2Options *>(builtin_options()) - : nullptr; - } - const AddNOptions *builtin_options_as_AddNOptions() const - { - return builtin_options_type() == BuiltinOptions_AddNOptions - ? static_cast<const AddNOptions *>(builtin_options()) - : nullptr; - } - const GatherNdOptions *builtin_options_as_GatherNdOptions() const - { - return builtin_options_type() == BuiltinOptions_GatherNdOptions - ? static_cast<const GatherNdOptions *>(builtin_options()) - : nullptr; - } - const CosOptions *builtin_options_as_CosOptions() const - { - return builtin_options_type() == BuiltinOptions_CosOptions - ? static_cast<const CosOptions *>(builtin_options()) - : nullptr; - } - const WhereOptions *builtin_options_as_WhereOptions() const - { - return builtin_options_type() == BuiltinOptions_WhereOptions - ? static_cast<const WhereOptions *>(builtin_options()) - : nullptr; - } - const RankOptions *builtin_options_as_RankOptions() const - { - return builtin_options_type() == BuiltinOptions_RankOptions - ? static_cast<const RankOptions *>(builtin_options()) - : nullptr; - } - const ReverseSequenceOptions *builtin_options_as_ReverseSequenceOptions() const - { - return builtin_options_type() == BuiltinOptions_ReverseSequenceOptions - ? static_cast<const ReverseSequenceOptions *>(builtin_options()) - : nullptr; - } - const MatrixDiagOptions *builtin_options_as_MatrixDiagOptions() const - { - return builtin_options_type() == BuiltinOptions_MatrixDiagOptions - ? static_cast<const MatrixDiagOptions *>(builtin_options()) - : nullptr; - } - const QuantizeOptions *builtin_options_as_QuantizeOptions() const - { - return builtin_options_type() == BuiltinOptions_QuantizeOptions - ? static_cast<const QuantizeOptions *>(builtin_options()) - : nullptr; - } - const MatrixSetDiagOptions *builtin_options_as_MatrixSetDiagOptions() const - { - return builtin_options_type() == BuiltinOptions_MatrixSetDiagOptions - ? static_cast<const MatrixSetDiagOptions *>(builtin_options()) - : nullptr; - } - const HardSwishOptions *builtin_options_as_HardSwishOptions() const - { - return builtin_options_type() == BuiltinOptions_HardSwishOptions - ? static_cast<const HardSwishOptions *>(builtin_options()) - : nullptr; - } - const IfOptions *builtin_options_as_IfOptions() const - { - return builtin_options_type() == BuiltinOptions_IfOptions - ? static_cast<const IfOptions *>(builtin_options()) - : nullptr; - } - const WhileOptions *builtin_options_as_WhileOptions() const - { - return builtin_options_type() == BuiltinOptions_WhileOptions - ? static_cast<const WhileOptions *>(builtin_options()) - : nullptr; - } - const DepthToSpaceOptions *builtin_options_as_DepthToSpaceOptions() const - { - return builtin_options_type() == BuiltinOptions_DepthToSpaceOptions - ? static_cast<const DepthToSpaceOptions *>(builtin_options()) - : nullptr; - } - const NonMaxSuppressionV4Options *builtin_options_as_NonMaxSuppressionV4Options() const - { - return builtin_options_type() == BuiltinOptions_NonMaxSuppressionV4Options - ? static_cast<const NonMaxSuppressionV4Options *>(builtin_options()) - : nullptr; - } - const NonMaxSuppressionV5Options *builtin_options_as_NonMaxSuppressionV5Options() const - { - return builtin_options_type() == BuiltinOptions_NonMaxSuppressionV5Options - ? static_cast<const NonMaxSuppressionV5Options *>(builtin_options()) - : nullptr; - } - const ScatterNdOptions *builtin_options_as_ScatterNdOptions() const - { - return builtin_options_type() == BuiltinOptions_ScatterNdOptions - ? static_cast<const ScatterNdOptions *>(builtin_options()) - : nullptr; - } - const SelectV2Options *builtin_options_as_SelectV2Options() const - { - return builtin_options_type() == BuiltinOptions_SelectV2Options - ? static_cast<const SelectV2Options *>(builtin_options()) - : nullptr; - } - const DensifyOptions *builtin_options_as_DensifyOptions() const - { - return builtin_options_type() == BuiltinOptions_DensifyOptions - ? static_cast<const DensifyOptions *>(builtin_options()) - : nullptr; - } - const SegmentSumOptions *builtin_options_as_SegmentSumOptions() const - { - return builtin_options_type() == BuiltinOptions_SegmentSumOptions - ? static_cast<const SegmentSumOptions *>(builtin_options()) - : nullptr; - } - const BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const - { - return builtin_options_type() == BuiltinOptions_BatchMatMulOptions - ? static_cast<const BatchMatMulOptions *>(builtin_options()) - : nullptr; - } - const BCQGatherOptions *builtin_options_as_BCQGatherOptions() const - { - return builtin_options_type() == BuiltinOptions_BCQGatherOptions - ? static_cast<const BCQGatherOptions *>(builtin_options()) - : nullptr; - } - const BCQFullyConnectedOptions *builtin_options_as_BCQFullyConnectedOptions() const - { - return builtin_options_type() == BuiltinOptions_BCQFullyConnectedOptions - ? static_cast<const BCQFullyConnectedOptions *>(builtin_options()) - : nullptr; - } - const InstanceNormOptions *builtin_options_as_InstanceNormOptions() const - { - return builtin_options_type() == BuiltinOptions_InstanceNormOptions - ? static_cast<const InstanceNormOptions *>(builtin_options()) - : nullptr; - } - const flatbuffers::Vector<uint8_t> *custom_options() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS); - } - CustomOptionsFormat custom_options_format() const - { - return static_cast<CustomOptionsFormat>(GetField<int8_t>(VT_CUSTOM_OPTIONS_FORMAT, 0)); - } - const flatbuffers::Vector<uint8_t> *mutating_variable_inputs() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_MUTATING_VARIABLE_INPUTS); - } - const flatbuffers::Vector<int32_t> *intermediates() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INTERMEDIATES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_OPCODE_INDEX) && - VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) && - VerifyField<uint8_t>(verifier, VT_BUILTIN_OPTIONS_TYPE) && - VerifyOffset(verifier, VT_BUILTIN_OPTIONS) && - VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) && - VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && verifier.VerifyVector(custom_options()) && - VerifyField<int8_t>(verifier, VT_CUSTOM_OPTIONS_FORMAT) && - VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && - verifier.VerifyVector(mutating_variable_inputs()) && - VerifyOffset(verifier, VT_INTERMEDIATES) && verifier.VerifyVector(intermediates()) && - verifier.EndTable(); - } -}; - -template <> inline const Conv2DOptions *Operator::builtin_options_as<Conv2DOptions>() const -{ - return builtin_options_as_Conv2DOptions(); -} - -template <> -inline const DepthwiseConv2DOptions *Operator::builtin_options_as<DepthwiseConv2DOptions>() const -{ - return builtin_options_as_DepthwiseConv2DOptions(); -} - -template <> -inline const ConcatEmbeddingsOptions *Operator::builtin_options_as<ConcatEmbeddingsOptions>() const -{ - return builtin_options_as_ConcatEmbeddingsOptions(); -} - -template <> -inline const LSHProjectionOptions *Operator::builtin_options_as<LSHProjectionOptions>() const -{ - return builtin_options_as_LSHProjectionOptions(); -} - -template <> inline const Pool2DOptions *Operator::builtin_options_as<Pool2DOptions>() const -{ - return builtin_options_as_Pool2DOptions(); -} - -template <> inline const SVDFOptions *Operator::builtin_options_as<SVDFOptions>() const -{ - return builtin_options_as_SVDFOptions(); -} - -template <> inline const RNNOptions *Operator::builtin_options_as<RNNOptions>() const -{ - return builtin_options_as_RNNOptions(); -} - -template <> -inline const FullyConnectedOptions *Operator::builtin_options_as<FullyConnectedOptions>() const -{ - return builtin_options_as_FullyConnectedOptions(); -} - -template <> inline const SoftmaxOptions *Operator::builtin_options_as<SoftmaxOptions>() const -{ - return builtin_options_as_SoftmaxOptions(); -} - -template <> -inline const ConcatenationOptions *Operator::builtin_options_as<ConcatenationOptions>() const -{ - return builtin_options_as_ConcatenationOptions(); -} - -template <> inline const AddOptions *Operator::builtin_options_as<AddOptions>() const -{ - return builtin_options_as_AddOptions(); -} - -template <> inline const L2NormOptions *Operator::builtin_options_as<L2NormOptions>() const -{ - return builtin_options_as_L2NormOptions(); -} - -template <> -inline const LocalResponseNormalizationOptions * -Operator::builtin_options_as<LocalResponseNormalizationOptions>() const -{ - return builtin_options_as_LocalResponseNormalizationOptions(); -} - -template <> inline const LSTMOptions *Operator::builtin_options_as<LSTMOptions>() const -{ - return builtin_options_as_LSTMOptions(); -} - -template <> -inline const ResizeBilinearOptions *Operator::builtin_options_as<ResizeBilinearOptions>() const -{ - return builtin_options_as_ResizeBilinearOptions(); -} - -template <> inline const CallOptions *Operator::builtin_options_as<CallOptions>() const -{ - return builtin_options_as_CallOptions(); -} - -template <> inline const ReshapeOptions *Operator::builtin_options_as<ReshapeOptions>() const -{ - return builtin_options_as_ReshapeOptions(); -} - -template <> inline const SkipGramOptions *Operator::builtin_options_as<SkipGramOptions>() const -{ - return builtin_options_as_SkipGramOptions(); -} - -template <> -inline const SpaceToDepthOptions *Operator::builtin_options_as<SpaceToDepthOptions>() const -{ - return builtin_options_as_SpaceToDepthOptions(); -} - -template <> -inline const EmbeddingLookupSparseOptions * -Operator::builtin_options_as<EmbeddingLookupSparseOptions>() const -{ - return builtin_options_as_EmbeddingLookupSparseOptions(); -} - -template <> inline const MulOptions *Operator::builtin_options_as<MulOptions>() const -{ - return builtin_options_as_MulOptions(); -} - -template <> inline const PadOptions *Operator::builtin_options_as<PadOptions>() const -{ - return builtin_options_as_PadOptions(); -} - -template <> inline const GatherOptions *Operator::builtin_options_as<GatherOptions>() const -{ - return builtin_options_as_GatherOptions(); -} - -template <> -inline const BatchToSpaceNDOptions *Operator::builtin_options_as<BatchToSpaceNDOptions>() const -{ - return builtin_options_as_BatchToSpaceNDOptions(); -} - -template <> -inline const SpaceToBatchNDOptions *Operator::builtin_options_as<SpaceToBatchNDOptions>() const -{ - return builtin_options_as_SpaceToBatchNDOptions(); -} - -template <> inline const TransposeOptions *Operator::builtin_options_as<TransposeOptions>() const -{ - return builtin_options_as_TransposeOptions(); -} - -template <> inline const ReducerOptions *Operator::builtin_options_as<ReducerOptions>() const -{ - return builtin_options_as_ReducerOptions(); -} - -template <> inline const SubOptions *Operator::builtin_options_as<SubOptions>() const -{ - return builtin_options_as_SubOptions(); -} - -template <> inline const DivOptions *Operator::builtin_options_as<DivOptions>() const -{ - return builtin_options_as_DivOptions(); -} - -template <> inline const SqueezeOptions *Operator::builtin_options_as<SqueezeOptions>() const -{ - return builtin_options_as_SqueezeOptions(); -} - -template <> -inline const SequenceRNNOptions *Operator::builtin_options_as<SequenceRNNOptions>() const -{ - return builtin_options_as_SequenceRNNOptions(); -} - -template <> -inline const StridedSliceOptions *Operator::builtin_options_as<StridedSliceOptions>() const -{ - return builtin_options_as_StridedSliceOptions(); -} - -template <> inline const ExpOptions *Operator::builtin_options_as<ExpOptions>() const -{ - return builtin_options_as_ExpOptions(); -} - -template <> inline const TopKV2Options *Operator::builtin_options_as<TopKV2Options>() const -{ - return builtin_options_as_TopKV2Options(); -} - -template <> inline const SplitOptions *Operator::builtin_options_as<SplitOptions>() const -{ - return builtin_options_as_SplitOptions(); -} - -template <> inline const LogSoftmaxOptions *Operator::builtin_options_as<LogSoftmaxOptions>() const -{ - return builtin_options_as_LogSoftmaxOptions(); -} - -template <> inline const CastOptions *Operator::builtin_options_as<CastOptions>() const -{ - return builtin_options_as_CastOptions(); -} - -template <> inline const DequantizeOptions *Operator::builtin_options_as<DequantizeOptions>() const -{ - return builtin_options_as_DequantizeOptions(); -} - -template <> -inline const MaximumMinimumOptions *Operator::builtin_options_as<MaximumMinimumOptions>() const -{ - return builtin_options_as_MaximumMinimumOptions(); -} - -template <> inline const ArgMaxOptions *Operator::builtin_options_as<ArgMaxOptions>() const -{ - return builtin_options_as_ArgMaxOptions(); -} - -template <> inline const LessOptions *Operator::builtin_options_as<LessOptions>() const -{ - return builtin_options_as_LessOptions(); -} - -template <> inline const NegOptions *Operator::builtin_options_as<NegOptions>() const -{ - return builtin_options_as_NegOptions(); -} - -template <> inline const PadV2Options *Operator::builtin_options_as<PadV2Options>() const -{ - return builtin_options_as_PadV2Options(); -} - -template <> inline const GreaterOptions *Operator::builtin_options_as<GreaterOptions>() const -{ - return builtin_options_as_GreaterOptions(); -} - -template <> -inline const GreaterEqualOptions *Operator::builtin_options_as<GreaterEqualOptions>() const -{ - return builtin_options_as_GreaterEqualOptions(); -} - -template <> inline const LessEqualOptions *Operator::builtin_options_as<LessEqualOptions>() const -{ - return builtin_options_as_LessEqualOptions(); -} - -template <> inline const SelectOptions *Operator::builtin_options_as<SelectOptions>() const -{ - return builtin_options_as_SelectOptions(); -} - -template <> inline const SliceOptions *Operator::builtin_options_as<SliceOptions>() const -{ - return builtin_options_as_SliceOptions(); -} - -template <> -inline const TransposeConvOptions *Operator::builtin_options_as<TransposeConvOptions>() const -{ - return builtin_options_as_TransposeConvOptions(); -} - -template <> -inline const SparseToDenseOptions *Operator::builtin_options_as<SparseToDenseOptions>() const -{ - return builtin_options_as_SparseToDenseOptions(); -} - -template <> inline const TileOptions *Operator::builtin_options_as<TileOptions>() const -{ - return builtin_options_as_TileOptions(); -} - -template <> inline const ExpandDimsOptions *Operator::builtin_options_as<ExpandDimsOptions>() const -{ - return builtin_options_as_ExpandDimsOptions(); -} - -template <> inline const EqualOptions *Operator::builtin_options_as<EqualOptions>() const -{ - return builtin_options_as_EqualOptions(); -} - -template <> inline const NotEqualOptions *Operator::builtin_options_as<NotEqualOptions>() const -{ - return builtin_options_as_NotEqualOptions(); -} - -template <> inline const ShapeOptions *Operator::builtin_options_as<ShapeOptions>() const -{ - return builtin_options_as_ShapeOptions(); -} - -template <> inline const PowOptions *Operator::builtin_options_as<PowOptions>() const -{ - return builtin_options_as_PowOptions(); -} - -template <> inline const ArgMinOptions *Operator::builtin_options_as<ArgMinOptions>() const -{ - return builtin_options_as_ArgMinOptions(); -} - -template <> inline const FakeQuantOptions *Operator::builtin_options_as<FakeQuantOptions>() const -{ - return builtin_options_as_FakeQuantOptions(); -} - -template <> inline const PackOptions *Operator::builtin_options_as<PackOptions>() const -{ - return builtin_options_as_PackOptions(); -} - -template <> inline const LogicalOrOptions *Operator::builtin_options_as<LogicalOrOptions>() const -{ - return builtin_options_as_LogicalOrOptions(); -} - -template <> inline const OneHotOptions *Operator::builtin_options_as<OneHotOptions>() const -{ - return builtin_options_as_OneHotOptions(); -} - -template <> inline const LogicalAndOptions *Operator::builtin_options_as<LogicalAndOptions>() const -{ - return builtin_options_as_LogicalAndOptions(); -} - -template <> inline const LogicalNotOptions *Operator::builtin_options_as<LogicalNotOptions>() const -{ - return builtin_options_as_LogicalNotOptions(); -} - -template <> inline const UnpackOptions *Operator::builtin_options_as<UnpackOptions>() const -{ - return builtin_options_as_UnpackOptions(); -} - -template <> inline const FloorDivOptions *Operator::builtin_options_as<FloorDivOptions>() const -{ - return builtin_options_as_FloorDivOptions(); -} - -template <> inline const SquareOptions *Operator::builtin_options_as<SquareOptions>() const -{ - return builtin_options_as_SquareOptions(); -} - -template <> inline const ZerosLikeOptions *Operator::builtin_options_as<ZerosLikeOptions>() const -{ - return builtin_options_as_ZerosLikeOptions(); -} - -template <> inline const FillOptions *Operator::builtin_options_as<FillOptions>() const -{ - return builtin_options_as_FillOptions(); -} - -template <> -inline const BidirectionalSequenceLSTMOptions * -Operator::builtin_options_as<BidirectionalSequenceLSTMOptions>() const -{ - return builtin_options_as_BidirectionalSequenceLSTMOptions(); -} - -template <> -inline const BidirectionalSequenceRNNOptions * -Operator::builtin_options_as<BidirectionalSequenceRNNOptions>() const -{ - return builtin_options_as_BidirectionalSequenceRNNOptions(); -} - -template <> -inline const UnidirectionalSequenceLSTMOptions * -Operator::builtin_options_as<UnidirectionalSequenceLSTMOptions>() const -{ - return builtin_options_as_UnidirectionalSequenceLSTMOptions(); -} - -template <> inline const FloorModOptions *Operator::builtin_options_as<FloorModOptions>() const -{ - return builtin_options_as_FloorModOptions(); -} - -template <> inline const RangeOptions *Operator::builtin_options_as<RangeOptions>() const -{ - return builtin_options_as_RangeOptions(); -} - -template <> -inline const ResizeNearestNeighborOptions * -Operator::builtin_options_as<ResizeNearestNeighborOptions>() const -{ - return builtin_options_as_ResizeNearestNeighborOptions(); -} - -template <> inline const LeakyReluOptions *Operator::builtin_options_as<LeakyReluOptions>() const -{ - return builtin_options_as_LeakyReluOptions(); -} - -template <> -inline const SquaredDifferenceOptions * -Operator::builtin_options_as<SquaredDifferenceOptions>() const -{ - return builtin_options_as_SquaredDifferenceOptions(); -} - -template <> inline const MirrorPadOptions *Operator::builtin_options_as<MirrorPadOptions>() const -{ - return builtin_options_as_MirrorPadOptions(); -} - -template <> inline const AbsOptions *Operator::builtin_options_as<AbsOptions>() const -{ - return builtin_options_as_AbsOptions(); -} - -template <> inline const SplitVOptions *Operator::builtin_options_as<SplitVOptions>() const -{ - return builtin_options_as_SplitVOptions(); -} - -template <> inline const UniqueOptions *Operator::builtin_options_as<UniqueOptions>() const -{ - return builtin_options_as_UniqueOptions(); -} - -template <> inline const ReverseV2Options *Operator::builtin_options_as<ReverseV2Options>() const -{ - return builtin_options_as_ReverseV2Options(); -} - -template <> inline const AddNOptions *Operator::builtin_options_as<AddNOptions>() const -{ - return builtin_options_as_AddNOptions(); -} - -template <> inline const GatherNdOptions *Operator::builtin_options_as<GatherNdOptions>() const -{ - return builtin_options_as_GatherNdOptions(); -} - -template <> inline const CosOptions *Operator::builtin_options_as<CosOptions>() const -{ - return builtin_options_as_CosOptions(); -} - -template <> inline const WhereOptions *Operator::builtin_options_as<WhereOptions>() const -{ - return builtin_options_as_WhereOptions(); -} - -template <> inline const RankOptions *Operator::builtin_options_as<RankOptions>() const -{ - return builtin_options_as_RankOptions(); -} - -template <> -inline const ReverseSequenceOptions *Operator::builtin_options_as<ReverseSequenceOptions>() const -{ - return builtin_options_as_ReverseSequenceOptions(); -} - -template <> inline const MatrixDiagOptions *Operator::builtin_options_as<MatrixDiagOptions>() const -{ - return builtin_options_as_MatrixDiagOptions(); -} - -template <> inline const QuantizeOptions *Operator::builtin_options_as<QuantizeOptions>() const -{ - return builtin_options_as_QuantizeOptions(); -} - -template <> -inline const MatrixSetDiagOptions *Operator::builtin_options_as<MatrixSetDiagOptions>() const -{ - return builtin_options_as_MatrixSetDiagOptions(); -} - -template <> inline const HardSwishOptions *Operator::builtin_options_as<HardSwishOptions>() const -{ - return builtin_options_as_HardSwishOptions(); -} - -template <> inline const IfOptions *Operator::builtin_options_as<IfOptions>() const -{ - return builtin_options_as_IfOptions(); -} - -template <> inline const WhileOptions *Operator::builtin_options_as<WhileOptions>() const -{ - return builtin_options_as_WhileOptions(); -} - -template <> -inline const DepthToSpaceOptions *Operator::builtin_options_as<DepthToSpaceOptions>() const -{ - return builtin_options_as_DepthToSpaceOptions(); -} - -template <> -inline const NonMaxSuppressionV4Options * -Operator::builtin_options_as<NonMaxSuppressionV4Options>() const -{ - return builtin_options_as_NonMaxSuppressionV4Options(); -} - -template <> -inline const NonMaxSuppressionV5Options * -Operator::builtin_options_as<NonMaxSuppressionV5Options>() const -{ - return builtin_options_as_NonMaxSuppressionV5Options(); -} - -template <> inline const ScatterNdOptions *Operator::builtin_options_as<ScatterNdOptions>() const -{ - return builtin_options_as_ScatterNdOptions(); -} - -template <> inline const SelectV2Options *Operator::builtin_options_as<SelectV2Options>() const -{ - return builtin_options_as_SelectV2Options(); -} - -template <> inline const DensifyOptions *Operator::builtin_options_as<DensifyOptions>() const -{ - return builtin_options_as_DensifyOptions(); -} - -template <> inline const SegmentSumOptions *Operator::builtin_options_as<SegmentSumOptions>() const -{ - return builtin_options_as_SegmentSumOptions(); -} - -template <> -inline const BatchMatMulOptions *Operator::builtin_options_as<BatchMatMulOptions>() const -{ - return builtin_options_as_BatchMatMulOptions(); -} - -template <> inline const BCQGatherOptions *Operator::builtin_options_as<BCQGatherOptions>() const -{ - return builtin_options_as_BCQGatherOptions(); -} - -template <> -inline const BCQFullyConnectedOptions * -Operator::builtin_options_as<BCQFullyConnectedOptions>() const -{ - return builtin_options_as_BCQFullyConnectedOptions(); -} - -template <> -inline const InstanceNormOptions *Operator::builtin_options_as<InstanceNormOptions>() const -{ - return builtin_options_as_InstanceNormOptions(); -} - -struct OperatorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_opcode_index(uint32_t opcode_index) - { - fbb_.AddElement<uint32_t>(Operator::VT_OPCODE_INDEX, opcode_index, 0); - } - void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) - { - fbb_.AddOffset(Operator::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) - { - fbb_.AddOffset(Operator::VT_OUTPUTS, outputs); - } - void add_builtin_options_type(BuiltinOptions builtin_options_type) - { - fbb_.AddElement<uint8_t>(Operator::VT_BUILTIN_OPTIONS_TYPE, - static_cast<uint8_t>(builtin_options_type), 0); - } - void add_builtin_options(flatbuffers::Offset<void> builtin_options) - { - fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options); - } - void add_custom_options(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options) - { - fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options); - } - void add_custom_options_format(CustomOptionsFormat custom_options_format) - { - fbb_.AddElement<int8_t>(Operator::VT_CUSTOM_OPTIONS_FORMAT, - static_cast<int8_t>(custom_options_format), 0); - } - void add_mutating_variable_inputs( - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs) - { - fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); - } - void add_intermediates(flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates) - { - fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates); - } - explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - OperatorBuilder &operator=(const OperatorBuilder &); - flatbuffers::Offset<Operator> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Operator>(end); - return o; - } -}; - -inline flatbuffers::Offset<Operator> -CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0, - BuiltinOptions builtin_options_type = BuiltinOptions_NONE, - flatbuffers::Offset<void> builtin_options = 0, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options = 0, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates = 0) -{ - OperatorBuilder builder_(_fbb); - builder_.add_intermediates(intermediates); - builder_.add_mutating_variable_inputs(mutating_variable_inputs); - builder_.add_custom_options(custom_options); - builder_.add_builtin_options(builtin_options); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_opcode_index(opcode_index); - builder_.add_custom_options_format(custom_options_format); - builder_.add_builtin_options_type(builtin_options_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Operator> -CreateOperatorDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0, - const std::vector<int32_t> *inputs = nullptr, - const std::vector<int32_t> *outputs = nullptr, - BuiltinOptions builtin_options_type = BuiltinOptions_NONE, - flatbuffers::Offset<void> builtin_options = 0, - const std::vector<uint8_t> *custom_options = nullptr, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, - const std::vector<uint8_t> *mutating_variable_inputs = nullptr, - const std::vector<int32_t> *intermediates = nullptr) -{ - return circle::CreateOperator( - _fbb, opcode_index, inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0, - outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0, builtin_options_type, builtin_options, - custom_options ? _fbb.CreateVector<uint8_t>(*custom_options) : 0, custom_options_format, - mutating_variable_inputs ? _fbb.CreateVector<uint8_t>(*mutating_variable_inputs) : 0, - intermediates ? _fbb.CreateVector<int32_t>(*intermediates) : 0); -} - -struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TENSORS = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_OPERATORS = 10, - VT_NAME = 12, - VT_DATA_FORMAT = 14 - }; - const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *tensors() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *>(VT_TENSORS); - } - const flatbuffers::Vector<int32_t> *inputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS); - } - const flatbuffers::Vector<int32_t> *outputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS); - } - const flatbuffers::Vector<flatbuffers::Offset<Operator>> *operators() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Operator>> *>(VT_OPERATORS); - } - const flatbuffers::String *name() const - { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - DataFormat data_format() const - { - return static_cast<DataFormat>(GetField<int8_t>(VT_DATA_FORMAT, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_TENSORS) && - verifier.VerifyVector(tensors()) && verifier.VerifyVectorOfTables(tensors()) && - VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) && - VerifyOffset(verifier, VT_OPERATORS) && verifier.VerifyVector(operators()) && - verifier.VerifyVectorOfTables(operators()) && VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && VerifyField<int8_t>(verifier, VT_DATA_FORMAT) && - verifier.EndTable(); - } -}; - -struct SubGraphBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors) - { - fbb_.AddOffset(SubGraph::VT_TENSORS, tensors); - } - void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) - { - fbb_.AddOffset(SubGraph::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) - { - fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs); - } - void - add_operators(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators) - { - fbb_.AddOffset(SubGraph::VT_OPERATORS, operators); - } - void add_name(flatbuffers::Offset<flatbuffers::String> name) - { - fbb_.AddOffset(SubGraph::VT_NAME, name); - } - void add_data_format(DataFormat data_format) - { - fbb_.AddElement<int8_t>(SubGraph::VT_DATA_FORMAT, static_cast<int8_t>(data_format), 0); - } - explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SubGraphBuilder &operator=(const SubGraphBuilder &); - flatbuffers::Offset<SubGraph> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SubGraph>(end); - return o; - } -}; - -inline flatbuffers::Offset<SubGraph> CreateSubGraph( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators = 0, - flatbuffers::Offset<flatbuffers::String> name = 0, - DataFormat data_format = DataFormat_CHANNELS_LAST) -{ - SubGraphBuilder builder_(_fbb); - builder_.add_name(name); - builder_.add_operators(operators); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_tensors(tensors); - builder_.add_data_format(data_format); - return builder_.Finish(); -} - -inline flatbuffers::Offset<SubGraph> -CreateSubGraphDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<flatbuffers::Offset<Tensor>> *tensors = nullptr, - const std::vector<int32_t> *inputs = nullptr, - const std::vector<int32_t> *outputs = nullptr, - const std::vector<flatbuffers::Offset<Operator>> *operators = nullptr, - const char *name = nullptr, DataFormat data_format = DataFormat_CHANNELS_LAST) -{ - return circle::CreateSubGraph( - _fbb, tensors ? _fbb.CreateVector<flatbuffers::Offset<Tensor>>(*tensors) : 0, - inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0, - outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0, - operators ? _fbb.CreateVector<flatbuffers::Offset<Operator>>(*operators) : 0, - name ? _fbb.CreateString(name) : 0, data_format); -} - -struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_DATA = 4 - }; - const flatbuffers::Vector<uint8_t> *data() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_DATA); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_DATA) && - verifier.VerifyVector(data()) && verifier.EndTable(); - } -}; - -struct BufferBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_data(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data) - { - fbb_.AddOffset(Buffer::VT_DATA, data); - } - explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BufferBuilder &operator=(const BufferBuilder &); - flatbuffers::Offset<Buffer> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Buffer>(end); - return o; - } -}; - -inline flatbuffers::Offset<Buffer> -CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data = 0) -{ - BufferBuilder builder_(_fbb); - builder_.add_data(data); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Buffer> CreateBufferDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint8_t> *data = nullptr) -{ - return circle::CreateBuffer(_fbb, data ? _fbb.CreateVector<uint8_t>(*data) : 0); -} - -struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NAME = 4, - VT_BUFFER = 6 - }; - const flatbuffers::String *name() const - { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - uint32_t buffer() const { return GetField<uint32_t>(VT_BUFFER, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && VerifyField<uint32_t>(verifier, VT_BUFFER) && - verifier.EndTable(); - } -}; - -struct MetadataBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset<flatbuffers::String> name) - { - fbb_.AddOffset(Metadata::VT_NAME, name); - } - void add_buffer(uint32_t buffer) { fbb_.AddElement<uint32_t>(Metadata::VT_BUFFER, buffer, 0); } - explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MetadataBuilder &operator=(const MetadataBuilder &); - flatbuffers::Offset<Metadata> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Metadata>(end); - return o; - } -}; - -inline flatbuffers::Offset<Metadata> -CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::String> name = 0, uint32_t buffer = 0) -{ - MetadataBuilder builder_(_fbb); - builder_.add_buffer(buffer); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Metadata> CreateMetadataDirect(flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - uint32_t buffer = 0) -{ - return circle::CreateMetadata(_fbb, name ? _fbb.CreateString(name) : 0, buffer); -} - -struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VERSION = 4, - VT_OPERATOR_CODES = 6, - VT_SUBGRAPHS = 8, - VT_DESCRIPTION = 10, - VT_BUFFERS = 12, - VT_METADATA_BUFFER = 14, - VT_METADATA = 16 - }; - uint32_t version() const { return GetField<uint32_t>(VT_VERSION, 0); } - const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *operator_codes() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *>( - VT_OPERATOR_CODES); - } - const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *subgraphs() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *>(VT_SUBGRAPHS); - } - const flatbuffers::String *description() const - { - return GetPointer<const flatbuffers::String *>(VT_DESCRIPTION); - } - const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *buffers() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *>(VT_BUFFERS); - } - const flatbuffers::Vector<int32_t> *metadata_buffer() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_METADATA_BUFFER); - } - const flatbuffers::Vector<flatbuffers::Offset<Metadata>> *metadata() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Metadata>> *>(VT_METADATA); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_VERSION) && - VerifyOffset(verifier, VT_OPERATOR_CODES) && verifier.VerifyVector(operator_codes()) && - verifier.VerifyVectorOfTables(operator_codes()) && - VerifyOffset(verifier, VT_SUBGRAPHS) && verifier.VerifyVector(subgraphs()) && - verifier.VerifyVectorOfTables(subgraphs()) && VerifyOffset(verifier, VT_DESCRIPTION) && - verifier.VerifyString(description()) && VerifyOffset(verifier, VT_BUFFERS) && - verifier.VerifyVector(buffers()) && verifier.VerifyVectorOfTables(buffers()) && - VerifyOffset(verifier, VT_METADATA_BUFFER) && verifier.VerifyVector(metadata_buffer()) && - VerifyOffset(verifier, VT_METADATA) && verifier.VerifyVector(metadata()) && - verifier.VerifyVectorOfTables(metadata()) && verifier.EndTable(); - } -}; - -struct ModelBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_version(uint32_t version) { fbb_.AddElement<uint32_t>(Model::VT_VERSION, version, 0); } - void add_operator_codes( - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes) - { - fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes); - } - void - add_subgraphs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs) - { - fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs); - } - void add_description(flatbuffers::Offset<flatbuffers::String> description) - { - fbb_.AddOffset(Model::VT_DESCRIPTION, description); - } - void add_buffers(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers) - { - fbb_.AddOffset(Model::VT_BUFFERS, buffers); - } - void add_metadata_buffer(flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer) - { - fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer); - } - void - add_metadata(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Metadata>>> metadata) - { - fbb_.AddOffset(Model::VT_METADATA, metadata); - } - explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ModelBuilder &operator=(const ModelBuilder &); - flatbuffers::Offset<Model> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Model>(end); - return o; - } -}; - -inline flatbuffers::Offset<Model> CreateModel( - flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs = 0, - flatbuffers::Offset<flatbuffers::String> description = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Metadata>>> metadata = 0) -{ - ModelBuilder builder_(_fbb); - builder_.add_metadata(metadata); - builder_.add_metadata_buffer(metadata_buffer); - builder_.add_buffers(buffers); - builder_.add_description(description); - builder_.add_subgraphs(subgraphs); - builder_.add_operator_codes(operator_codes); - builder_.add_version(version); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Model> -CreateModelDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0, - const std::vector<flatbuffers::Offset<OperatorCode>> *operator_codes = nullptr, - const std::vector<flatbuffers::Offset<SubGraph>> *subgraphs = nullptr, - const char *description = nullptr, - const std::vector<flatbuffers::Offset<Buffer>> *buffers = nullptr, - const std::vector<int32_t> *metadata_buffer = nullptr, - const std::vector<flatbuffers::Offset<Metadata>> *metadata = nullptr) -{ - return circle::CreateModel( - _fbb, version, - operator_codes ? _fbb.CreateVector<flatbuffers::Offset<OperatorCode>>(*operator_codes) : 0, - subgraphs ? _fbb.CreateVector<flatbuffers::Offset<SubGraph>>(*subgraphs) : 0, - description ? _fbb.CreateString(description) : 0, - buffers ? _fbb.CreateVector<flatbuffers::Offset<Buffer>>(*buffers) : 0, - metadata_buffer ? _fbb.CreateVector<int32_t>(*metadata_buffer) : 0, - metadata ? _fbb.CreateVector<flatbuffers::Offset<Metadata>>(*metadata) : 0); -} - -inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, - QuantizationDetails type) -{ - switch (type) - { - case QuantizationDetails_NONE: - { - return true; - } - case QuantizationDetails_CustomQuantization: - { - auto ptr = reinterpret_cast<const CustomQuantization *>(obj); - return verifier.VerifyTable(ptr); - } - default: - return false; - } -} - -inline bool -VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types) -{ - if (!values || !types) - return !values && !types; - if (values->size() != types->size()) - return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) - { - if (!VerifyQuantizationDetails(verifier, values->Get(i), - types->GetEnum<QuantizationDetails>(i))) - { - return false; - } - } - return true; -} - -inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, - SparseIndexVector type) -{ - switch (type) - { - case SparseIndexVector_NONE: - { - return true; - } - case SparseIndexVector_Int32Vector: - { - auto ptr = reinterpret_cast<const Int32Vector *>(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint16Vector: - { - auto ptr = reinterpret_cast<const Uint16Vector *>(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint8Vector: - { - auto ptr = reinterpret_cast<const Uint8Vector *>(obj); - return verifier.VerifyTable(ptr); - } - default: - return false; - } -} - -inline bool -VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types) -{ - if (!values || !types) - return !values && !types; - if (values->size() != types->size()) - return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) - { - if (!VerifySparseIndexVector(verifier, values->Get(i), types->GetEnum<SparseIndexVector>(i))) - { - return false; - } - } - return true; -} - -inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, - BuiltinOptions type) -{ - switch (type) - { - case BuiltinOptions_NONE: - { - return true; - } - case BuiltinOptions_Conv2DOptions: - { - auto ptr = reinterpret_cast<const Conv2DOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthwiseConv2DOptions: - { - auto ptr = reinterpret_cast<const DepthwiseConv2DOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatEmbeddingsOptions: - { - auto ptr = reinterpret_cast<const ConcatEmbeddingsOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSHProjectionOptions: - { - auto ptr = reinterpret_cast<const LSHProjectionOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_Pool2DOptions: - { - auto ptr = reinterpret_cast<const Pool2DOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SVDFOptions: - { - auto ptr = reinterpret_cast<const SVDFOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RNNOptions: - { - auto ptr = reinterpret_cast<const RNNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FullyConnectedOptions: - { - auto ptr = reinterpret_cast<const FullyConnectedOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SoftmaxOptions: - { - auto ptr = reinterpret_cast<const SoftmaxOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatenationOptions: - { - auto ptr = reinterpret_cast<const ConcatenationOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddOptions: - { - auto ptr = reinterpret_cast<const AddOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_L2NormOptions: - { - auto ptr = reinterpret_cast<const L2NormOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LocalResponseNormalizationOptions: - { - auto ptr = reinterpret_cast<const LocalResponseNormalizationOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSTMOptions: - { - auto ptr = reinterpret_cast<const LSTMOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeBilinearOptions: - { - auto ptr = reinterpret_cast<const ResizeBilinearOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CallOptions: - { - auto ptr = reinterpret_cast<const CallOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReshapeOptions: - { - auto ptr = reinterpret_cast<const ReshapeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SkipGramOptions: - { - auto ptr = reinterpret_cast<const SkipGramOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToDepthOptions: - { - auto ptr = reinterpret_cast<const SpaceToDepthOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: - { - auto ptr = reinterpret_cast<const EmbeddingLookupSparseOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MulOptions: - { - auto ptr = reinterpret_cast<const MulOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadOptions: - { - auto ptr = reinterpret_cast<const PadOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherOptions: - { - auto ptr = reinterpret_cast<const GatherOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchToSpaceNDOptions: - { - auto ptr = reinterpret_cast<const BatchToSpaceNDOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToBatchNDOptions: - { - auto ptr = reinterpret_cast<const SpaceToBatchNDOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeOptions: - { - auto ptr = reinterpret_cast<const TransposeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReducerOptions: - { - auto ptr = reinterpret_cast<const ReducerOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SubOptions: - { - auto ptr = reinterpret_cast<const SubOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DivOptions: - { - auto ptr = reinterpret_cast<const DivOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SqueezeOptions: - { - auto ptr = reinterpret_cast<const SqueezeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SequenceRNNOptions: - { - auto ptr = reinterpret_cast<const SequenceRNNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_StridedSliceOptions: - { - auto ptr = reinterpret_cast<const StridedSliceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpOptions: - { - auto ptr = reinterpret_cast<const ExpOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TopKV2Options: - { - auto ptr = reinterpret_cast<const TopKV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitOptions: - { - auto ptr = reinterpret_cast<const SplitOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogSoftmaxOptions: - { - auto ptr = reinterpret_cast<const LogSoftmaxOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CastOptions: - { - auto ptr = reinterpret_cast<const CastOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DequantizeOptions: - { - auto ptr = reinterpret_cast<const DequantizeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MaximumMinimumOptions: - { - auto ptr = reinterpret_cast<const MaximumMinimumOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMaxOptions: - { - auto ptr = reinterpret_cast<const ArgMaxOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessOptions: - { - auto ptr = reinterpret_cast<const LessOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NegOptions: - { - auto ptr = reinterpret_cast<const NegOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadV2Options: - { - auto ptr = reinterpret_cast<const PadV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterOptions: - { - auto ptr = reinterpret_cast<const GreaterOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterEqualOptions: - { - auto ptr = reinterpret_cast<const GreaterEqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessEqualOptions: - { - auto ptr = reinterpret_cast<const LessEqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectOptions: - { - auto ptr = reinterpret_cast<const SelectOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SliceOptions: - { - auto ptr = reinterpret_cast<const SliceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeConvOptions: - { - auto ptr = reinterpret_cast<const TransposeConvOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SparseToDenseOptions: - { - auto ptr = reinterpret_cast<const SparseToDenseOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TileOptions: - { - auto ptr = reinterpret_cast<const TileOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpandDimsOptions: - { - auto ptr = reinterpret_cast<const ExpandDimsOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EqualOptions: - { - auto ptr = reinterpret_cast<const EqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NotEqualOptions: - { - auto ptr = reinterpret_cast<const NotEqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ShapeOptions: - { - auto ptr = reinterpret_cast<const ShapeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PowOptions: - { - auto ptr = reinterpret_cast<const PowOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMinOptions: - { - auto ptr = reinterpret_cast<const ArgMinOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FakeQuantOptions: - { - auto ptr = reinterpret_cast<const FakeQuantOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PackOptions: - { - auto ptr = reinterpret_cast<const PackOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalOrOptions: - { - auto ptr = reinterpret_cast<const LogicalOrOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_OneHotOptions: - { - auto ptr = reinterpret_cast<const OneHotOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalAndOptions: - { - auto ptr = reinterpret_cast<const LogicalAndOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalNotOptions: - { - auto ptr = reinterpret_cast<const LogicalNotOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnpackOptions: - { - auto ptr = reinterpret_cast<const UnpackOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorDivOptions: - { - auto ptr = reinterpret_cast<const FloorDivOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquareOptions: - { - auto ptr = reinterpret_cast<const SquareOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ZerosLikeOptions: - { - auto ptr = reinterpret_cast<const ZerosLikeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FillOptions: - { - auto ptr = reinterpret_cast<const FillOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: - { - auto ptr = reinterpret_cast<const BidirectionalSequenceLSTMOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: - { - auto ptr = reinterpret_cast<const BidirectionalSequenceRNNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: - { - auto ptr = reinterpret_cast<const UnidirectionalSequenceLSTMOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorModOptions: - { - auto ptr = reinterpret_cast<const FloorModOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RangeOptions: - { - auto ptr = reinterpret_cast<const RangeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeNearestNeighborOptions: - { - auto ptr = reinterpret_cast<const ResizeNearestNeighborOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LeakyReluOptions: - { - auto ptr = reinterpret_cast<const LeakyReluOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquaredDifferenceOptions: - { - auto ptr = reinterpret_cast<const SquaredDifferenceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MirrorPadOptions: - { - auto ptr = reinterpret_cast<const MirrorPadOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AbsOptions: - { - auto ptr = reinterpret_cast<const AbsOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitVOptions: - { - auto ptr = reinterpret_cast<const SplitVOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UniqueOptions: - { - auto ptr = reinterpret_cast<const UniqueOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseV2Options: - { - auto ptr = reinterpret_cast<const ReverseV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddNOptions: - { - auto ptr = reinterpret_cast<const AddNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherNdOptions: - { - auto ptr = reinterpret_cast<const GatherNdOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CosOptions: - { - auto ptr = reinterpret_cast<const CosOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhereOptions: - { - auto ptr = reinterpret_cast<const WhereOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RankOptions: - { - auto ptr = reinterpret_cast<const RankOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseSequenceOptions: - { - auto ptr = reinterpret_cast<const ReverseSequenceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixDiagOptions: - { - auto ptr = reinterpret_cast<const MatrixDiagOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_QuantizeOptions: - { - auto ptr = reinterpret_cast<const QuantizeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixSetDiagOptions: - { - auto ptr = reinterpret_cast<const MatrixSetDiagOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HardSwishOptions: - { - auto ptr = reinterpret_cast<const HardSwishOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_IfOptions: - { - auto ptr = reinterpret_cast<const IfOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhileOptions: - { - auto ptr = reinterpret_cast<const WhileOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthToSpaceOptions: - { - auto ptr = reinterpret_cast<const DepthToSpaceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV4Options: - { - auto ptr = reinterpret_cast<const NonMaxSuppressionV4Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV5Options: - { - auto ptr = reinterpret_cast<const NonMaxSuppressionV5Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ScatterNdOptions: - { - auto ptr = reinterpret_cast<const ScatterNdOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectV2Options: - { - auto ptr = reinterpret_cast<const SelectV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DensifyOptions: - { - auto ptr = reinterpret_cast<const DensifyOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SegmentSumOptions: - { - auto ptr = reinterpret_cast<const SegmentSumOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchMatMulOptions: - { - auto ptr = reinterpret_cast<const BatchMatMulOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BCQGatherOptions: - { - auto ptr = reinterpret_cast<const BCQGatherOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BCQFullyConnectedOptions: - { - auto ptr = reinterpret_cast<const BCQFullyConnectedOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_InstanceNormOptions: - { - auto ptr = reinterpret_cast<const InstanceNormOptions *>(obj); - return verifier.VerifyTable(ptr); - } - default: - return false; - } -} - -inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types) -{ - if (!values || !types) - return !values && !types; - if (values->size() != types->size()) - return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) - { - if (!VerifyBuiltinOptions(verifier, values->Get(i), types->GetEnum<BuiltinOptions>(i))) - { - return false; - } - } - return true; -} - -inline const circle::Model *GetModel(const void *buf) -{ - return flatbuffers::GetRoot<circle::Model>(buf); -} - -inline const circle::Model *GetSizePrefixedModel(const void *buf) -{ - return flatbuffers::GetSizePrefixedRoot<circle::Model>(buf); -} - -inline const char *ModelIdentifier() { return "CIR0"; } - -inline bool ModelBufferHasIdentifier(const void *buf) -{ - return flatbuffers::BufferHasIdentifier(buf, ModelIdentifier()); -} - -inline bool VerifyModelBuffer(flatbuffers::Verifier &verifier) -{ - return verifier.VerifyBuffer<circle::Model>(ModelIdentifier()); -} - -inline bool VerifySizePrefixedModelBuffer(flatbuffers::Verifier &verifier) -{ - return verifier.VerifySizePrefixedBuffer<circle::Model>(ModelIdentifier()); -} - -inline const char *ModelExtension() { return "circle"; } - -inline void FinishModelBuffer(flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset<circle::Model> root) -{ - fbb.Finish(root, ModelIdentifier()); -} - -inline void FinishSizePrefixedModelBuffer(flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset<circle::Model> root) -{ - fbb.FinishSizePrefixed(root, ModelIdentifier()); -} - -} // namespace circle - -#endif // FLATBUFFERS_GENERATED_CIRCLESCHEMA_CIRCLE_H_ diff --git a/runtime/onert/frontend/nnapi/CMakeLists.txt b/runtime/onert/frontend/nnapi/CMakeLists.txt deleted file mode 100644 index dafd84ccf..000000000 --- a/runtime/onert/frontend/nnapi/CMakeLists.txt +++ /dev/null @@ -1,27 +0,0 @@ -file(GLOB_RECURSE SOURCES_FRONTEND "*.cc") -file(GLOB_RECURSE TESTS_FRONTEND "*.test.cc") -list(REMOVE_ITEM SOURCES_FRONTEND ${TESTS_FRONTEND}) - -set(LIB_ONERT onert) - -add_library(${LIB_ONERT} SHARED ${SOURCES_FRONTEND}) -target_link_libraries(${LIB_ONERT} PUBLIC nnfw-nnapi-header) -target_link_libraries(${LIB_ONERT} PUBLIC onert_core) # TODO Link PRIVATE onert_core -target_link_libraries(${LIB_ONERT} PRIVATE nnfw_common) -target_link_libraries(${LIB_ONERT} PRIVATE nnfw_coverage) - -set_target_properties(${LIB_ONERT} PROPERTIES OUTPUT_NAME neuralnetworks) - -install(TARGETS ${LIB_ONERT} DESTINATION lib) - -if(NOT ENABLE_TEST) - return() -endif(NOT ENABLE_TEST) - -add_executable(test_onert_frontend_nnapi ${TESTS_FRONTEND}) - -target_link_libraries(test_onert_frontend_nnapi PRIVATE ${LIB_ONERT} dl) -target_link_libraries(test_onert_frontend_nnapi PRIVATE gtest) -target_link_libraries(test_onert_frontend_nnapi PRIVATE gtest_main) - -install(TARGETS test_onert_frontend_nnapi DESTINATION unittest_standalone) diff --git a/runtime/onert/frontend/nnapi/compilation.cc b/runtime/onert/frontend/nnapi/compilation.cc deleted file mode 100644 index 871c040ef..000000000 --- a/runtime/onert/frontend/nnapi/compilation.cc +++ /dev/null @@ -1,106 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <NeuralNetworks.h> - -#include <new> - -#include "wrapper/ANeuralNetworksModel.h" -#include "wrapper/ANeuralNetworksCompilation.h" -#include "util/logging.h" - -// -// NNAPI Implementation -// -int ANeuralNetworksCompilation_create(ANeuralNetworksModel *model, - ANeuralNetworksCompilation **compilation) -{ - if ((model == nullptr) || (compilation == nullptr)) - { - VERBOSE(NNAPI::Compilation) << "create: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (!model->isFinished()) - { - VERBOSE(NNAPI::Compilation) << "create: Model define is not finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - *compilation = new (std::nothrow) ANeuralNetworksCompilation(model); - if (*compilation == nullptr) - { - VERBOSE(NNAPI::Compilation) << "create: ail to create compilation object" << std::endl; - return ANEURALNETWORKS_OUT_OF_MEMORY; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksCompilation_finish(ANeuralNetworksCompilation *compilation) -{ - if (compilation == nullptr) - { - VERBOSE(NNAPI::Compilation) << "finish: Incorrect null pointer parameter" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (compilation->state() != ::onert::compiler::State::CREATED) - { - VERBOSE(NNAPI::Compilation) << "finish: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - if (!compilation->finish()) - { - VERBOSE(NNAPI::Compilation) << "finish: Fail to compile" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksCompilation_free(ANeuralNetworksCompilation *compilation) -{ - delete compilation; -} - -int ANeuralNetworksCompilation_setPreference(ANeuralNetworksCompilation *compilation, - int32_t preference) -{ - if (compilation == nullptr) - { - VERBOSE(NNAPI::Compilation) << "setPreference: Incorrect null pointer parameter" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (compilation->state() != ::onert::compiler::State::CREATED) - { - VERBOSE(NNAPI::Compilation) << "setPreference: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - const PreferenceCode FIRST_PREFERENCE_CODE = ANEURALNETWORKS_PREFER_LOW_POWER; - const PreferenceCode LAST_PREFERENCE_CODE = ANEURALNETWORKS_PREFER_SUSTAINED_SPEED; - if ((preference < FIRST_PREFERENCE_CODE) || (preference > LAST_PREFERENCE_CODE)) - { - VERBOSE(NNAPI::Compilation) << "setPreference: Incorrect preference code" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - // NYI: nothing to set - return ANEURALNETWORKS_NO_ERROR; -} diff --git a/runtime/onert/frontend/nnapi/event.cc b/runtime/onert/frontend/nnapi/event.cc deleted file mode 100644 index 593b74e90..000000000 --- a/runtime/onert/frontend/nnapi/event.cc +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <NeuralNetworks.h> - -#include "wrapper/ANeuralNetworksEvent.h" - -int ANeuralNetworksEvent_wait(ANeuralNetworksEvent *event) -{ - if (event == nullptr) - { - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (!event->waitFinish()) - { - return ANEURALNETWORKS_BAD_STATE; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksEvent_free(ANeuralNetworksEvent *event) { delete event; } diff --git a/runtime/onert/frontend/nnapi/execution.cc b/runtime/onert/frontend/nnapi/execution.cc deleted file mode 100644 index 56ca5ef00..000000000 --- a/runtime/onert/frontend/nnapi/execution.cc +++ /dev/null @@ -1,504 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <NeuralNetworks.h> - -#include <new> - -#include "wrapper/ANeuralNetworksCompilation.h" -#include "wrapper/ANeuralNetworksExecution.h" -#include "wrapper/ANeuralNetworksMemory.h" -#include "wrapper/ANeuralNetworksEvent.h" -#include "wrapper/NNAPIConvert.h" -#include "util/logging.h" - -// -// NNAPI Implementation -// -int ANeuralNetworksExecution_create(ANeuralNetworksCompilation *compilation, - ANeuralNetworksExecution **execution) -{ - if ((compilation == nullptr) || (execution == nullptr)) - { - VERBOSE(NNAPI::Execution) << "create: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - std::shared_ptr<onert::exec::ExecutorMap> executors; - - compilation->publish(executors); - - if (executors == nullptr) - { - VERBOSE(NNAPI::Execution) << "create: Never compiled yet" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - *execution = new (std::nothrow) ANeuralNetworksExecution{executors}; - if (*execution == nullptr) - { - VERBOSE(NNAPI::Execution) << "create: Fail to create execution object" << std::endl; - return ANEURALNETWORKS_OUT_OF_MEMORY; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -// NOTE Handle optional input -// Unspecified shape on model build -// Optional and omitted input on execution: skip input setting (workaround for LSTM) -// Optional but not omitted input on execution: cannot handle -// Normal input on execution: cannot handle -// Fully specified shape on model build -// Optional input on execution: cannot handle -// Normal input: handle normally -int ANeuralNetworksExecution_setInput(ANeuralNetworksExecution *execution, int32_t index, - const ANeuralNetworksOperandType *type, const void *buffer, - size_t length) -{ - // Don't check type - // Comment about ANeuralNetworksOperandType in NeuralNetworks.h: - // If the input or output is optional and omitted then it need not have a fully specified tensor - // operand type - if ((execution == nullptr) || ((buffer == nullptr) && (length != 0))) - { - VERBOSE(NNAPI::Execution) << "setInput: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if ((buffer != nullptr) && (length == 0)) - { - VERBOSE(NNAPI::Execution) << "setInput: Zero length input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - const auto operand_index = execution->getInputOperandIndex(index); - if (!operand_index.valid()) - { - VERBOSE(NNAPI::Execution) << "setInput: Invalid input index" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - // Omitted optional input - // LSTM operation's some inputs can be optional input - // Transpose operation's permutation input can be optional input - if ((buffer == nullptr) && (length == 0)) - { - uint32_t dims[1] = {0}; - ANeuralNetworksOperandType compared_shape; - compared_shape.dimensionCount = 1; - compared_shape.dimensions = dims; - if (execution->hasUnspecifiedDims(operand_index)) - { - return ANEURALNETWORKS_NO_ERROR; - } - else if (type == nullptr && execution->IsOptionalInput(operand_index)) - { - if (!execution->setOptionalInput(index, type, buffer, length)) - { - VERBOSE(NNAPI::Execution) << "setInput: Fail to set optional input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - return ANEURALNETWORKS_NO_ERROR; - } - // TODO Changes the condition to check zero sized - else if (execution->compareShape(&compared_shape, operand_index)) - { - if (!execution->setInput(index, type, buffer, length)) - { - VERBOSE(NNAPI::Execution) << "setInput: Fail to set input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - return ANEURALNETWORKS_NO_ERROR; - } - else - { - VERBOSE(NNAPI::Execution) << "setInput: Cannot handle fully-specified shape on model build " - "but omitted input on execution" - << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - if (type != nullptr) - { - if (!execution->compareDataType(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setInput: Data type mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->compareShape(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setInput: Shape mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (NNAPIConvert::calculateSizeFromType(type) != length) - { - VERBOSE(NNAPI::Execution) << "setInput: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - else - { - if (execution->hasUnspecifiedDims(operand_index)) - { - VERBOSE(NNAPI::Execution) << "setInput: Unspecified dimension value" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (execution->getOperandSize(operand_index) != length) - { - VERBOSE(NNAPI::Execution) << "setInput: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - if (!execution->setInput(index, type, buffer, length)) - { - VERBOSE(NNAPI::Execution) << "setInput: Fail to set input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_setOutput(ANeuralNetworksExecution *execution, int32_t index, - const ANeuralNetworksOperandType *type, void *buffer, - size_t length) -{ - // Don't check type - // Comment about ANeuralNetworksOperandType in NeuralNetworks.h: - // If the input or output is optional and omitted then it need not have a fully specified tensor - // operand type - if ((execution == nullptr) || ((buffer == nullptr) && (length != 0))) - { - VERBOSE(NNAPI::Execution) << "setOutput: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if ((buffer != nullptr) && (length == 0)) - { - VERBOSE(NNAPI::Execution) << "setOutput: Zero length output" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - // Handle optional output - if (buffer == nullptr) - { - return ANEURALNETWORKS_NO_ERROR; - } - - const auto operand_index = execution->getOutputOperandIndex(index); - if (!operand_index.valid()) - { - VERBOSE(NNAPI::Execution) << "setOutput: Invalid output index" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (type != nullptr) - { - if (!execution->compareDataType(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setOutput: Data type mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->compareShape(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setOutput: Shape mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (NNAPIConvert::calculateSizeFromType(type) != length) - { - VERBOSE(NNAPI::Execution) << "setOutput: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - else - { - if (execution->hasUnspecifiedDims(operand_index)) - { - VERBOSE(NNAPI::Execution) << "setOutput: Unspecified dimension value" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (execution->getOperandSize(operand_index) != length) - { - VERBOSE(NNAPI::Execution) << "setOutput: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - if (!execution->setOutput(index, type, buffer, length)) - { - VERBOSE(NNAPI::Execution) << "setOutput: Fail to set output" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_startCompute(ANeuralNetworksExecution *execution, - ANeuralNetworksEvent **event) -{ - if ((execution == nullptr) || (event == nullptr)) - { - VERBOSE(NNAPI::Execution) << "startCompute: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - // TODO: Handle event - auto instance = execution->instance(); - *event = new (std::nothrow) ANeuralNetworksEvent{instance}; - if (*event == nullptr) - { - VERBOSE(NNAPI::Execution) << "startCompute: Fail to create event" << std::endl; - return ANEURALNETWORKS_OUT_OF_MEMORY; - } - - if (!execution->startExecute()) - { - VERBOSE(NNAPI::Execution) << "startCompute: Fail to start execution" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_compute(ANeuralNetworksExecution *execution) -{ - if (execution == nullptr) - { - VERBOSE(NNAPI::Execution) << "Compute: Incorrect null pointer parameter" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (!execution->execute()) - { - VERBOSE(NNAPI::Execution) << "Compute: Fail to execution" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksExecution_free(ANeuralNetworksExecution *execution) { delete execution; } - -int ANeuralNetworksExecution_setInputFromMemory(ANeuralNetworksExecution *execution, int32_t index, - const ANeuralNetworksOperandType *type, - const ANeuralNetworksMemory *memory, size_t offset, - size_t length) -{ - if ((execution == nullptr) || (memory == nullptr)) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Incorrect null pointer parameter(s)" - << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (length == 0) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Zero length input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - const auto operand_index = execution->getInputOperandIndex(index); - if (!operand_index.valid()) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid input index" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (type != nullptr) - { - if (!execution->compareDataType(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Data type mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->compareShape(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Shape mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (NNAPIConvert::calculateSizeFromType(type) != length) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - else - { - if (execution->hasUnspecifiedDims(operand_index)) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Unspecified dimension value" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (execution->getOperandSize(operand_index) != length) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - if (!memory->vaildAccess(offset, length)) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Invalid memory access" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->setInput(index, type, reinterpret_cast<const void *>(memory->base() + offset), - length)) - { - VERBOSE(NNAPI::Execution) << "setInputFromMemory: Fail to set input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_setOutputFromMemory(ANeuralNetworksExecution *execution, int32_t index, - const ANeuralNetworksOperandType *type, - const ANeuralNetworksMemory *memory, size_t offset, - size_t length) -{ - if ((execution == nullptr) || (memory == nullptr)) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Incorrect null pointer parameter(s)" - << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (length == 0) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Zero length input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - const auto operand_index = execution->getOutputOperandIndex(index); - if (!operand_index.valid()) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid output index" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (type != nullptr) - { - if (!execution->compareDataType(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Data type mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->compareShape(type, operand_index)) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Shape mismatch" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (NNAPIConvert::calculateSizeFromType(type) != length) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - else - { - if (execution->hasUnspecifiedDims(operand_index)) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Unspecified dimension value" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (execution->getOperandSize(operand_index) != length) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - if (!memory->vaildAccess(offset, length)) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Invalid memory access" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->setOutput(index, type, reinterpret_cast<void *>(memory->base() + offset), length)) - { - VERBOSE(NNAPI::Execution) << "setOutputFromMemory: Fail to set input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_getOutputOperandRank(ANeuralNetworksExecution *execution, - int32_t index, uint32_t *rank) -{ - if ((execution == nullptr) || (rank == nullptr)) - { - VERBOSE(NNAPI::Execution) << "getOutputOperandRank: Incorrect null pointer parameter(s)" - << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - const auto operand_index = execution->getOutputOperandIndex(index); - if (!operand_index.valid()) - { - VERBOSE(NNAPI::Execution) << "getOutputOperandRank: Invalid output index" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->getOutputOperandRank(index, rank)) - { - VERBOSE(NNAPI::Execution) << "getOutputOperandRank: Fail to get rank" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksExecution_getOutputOperandDimensions(ANeuralNetworksExecution *execution, - int32_t index, uint32_t *dimensions) -{ - if ((execution == nullptr) || (dimensions == nullptr)) - { - VERBOSE(NNAPI::Execution) << "getOutputOperandDimensions: Incorrect null pointer parameter(s)" - << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - const auto operand_index = execution->getOutputOperandIndex(index); - if (!operand_index.valid()) - { - VERBOSE(NNAPI::Execution) << "getOutputOperandDimensions: Invalid output index" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!execution->getOutputOperandDimensions(index, dimensions)) - { - VERBOSE(NNAPI::Execution) << "getOutputOperandDimensions: Fail to get rank" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - return ANEURALNETWORKS_NO_ERROR; -} diff --git a/runtime/onert/frontend/nnapi/memory.cc b/runtime/onert/frontend/nnapi/memory.cc deleted file mode 100644 index 6e568a926..000000000 --- a/runtime/onert/frontend/nnapi/memory.cc +++ /dev/null @@ -1,42 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <NeuralNetworks.h> -#include <sys/mman.h> -#include <new> -#include <memory> - -#include <memory> -#include "wrapper/ANeuralNetworksMemory.h" - -int ANeuralNetworksMemory_createFromFd(size_t size, int protect, int fd, size_t offset, - ANeuralNetworksMemory **memory) -{ - if (memory == nullptr) - { - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - *memory = new (std::nothrow) ANeuralNetworksMemory{size, protect, fd, offset}; - if (*memory == nullptr) - { - return ANEURALNETWORKS_OUT_OF_MEMORY; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksMemory_free(ANeuralNetworksMemory *memory) { delete memory; } diff --git a/runtime/onert/frontend/nnapi/model.cc b/runtime/onert/frontend/nnapi/model.cc deleted file mode 100644 index 8c7bd1789..000000000 --- a/runtime/onert/frontend/nnapi/model.cc +++ /dev/null @@ -1,416 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <NeuralNetworks.h> -#include <NeuralNetworksEx.h> - -#include <new> - -#include "wrapper/ANeuralNetworksModel.h" -#include "wrapper/ANeuralNetworksMemory.h" -#include "util/logging.h" - -int ANeuralNetworksModel_create(ANeuralNetworksModel **model) -{ - if (model == nullptr) - { - VERBOSE(NNAPI::Model) << "create: Incorrect null pointer parameter" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - *model = new (std::nothrow) ANeuralNetworksModel{}; - if (*model == nullptr) - { - VERBOSE(NNAPI::Model) << "create: Fail to create model object" << std::endl; - return ANEURALNETWORKS_OUT_OF_MEMORY; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -void ANeuralNetworksModel_free(ANeuralNetworksModel *model) { delete model; } - -int ANeuralNetworksModel_addOperand(ANeuralNetworksModel *model, - const ANeuralNetworksOperandType *type) -{ - if ((model == nullptr) || (type == nullptr)) - { - VERBOSE(NNAPI::Model) << "addOperand: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "addOperand: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - // scale and zeroPoint should be zero for scalars and non-fixed point tensors - // Quantized: - // scale: a 32 bit floating point value greater than zero - // zeroPoint: a 32 bit integer, in range [0, 255] - if (type->type == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) - { - if (!(type->scale > 0.0f)) - { - VERBOSE(NNAPI::Model) << "addOperand: Incorrect scale value for quantization" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if ((type->zeroPoint < 0) || (type->zeroPoint > 255)) - { - VERBOSE(NNAPI::Model) << "addOperand: Incorrect zeroPoint value for quantization" - << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - // NOTE Validation of scale and zeroPoint would be skipped for a while. - // We do not know whether scalar type can have scale and zeroPoint. - // To pass ValidationTest and GeneratedTest, this validation code - // would not be implemented until we can define this issue clearly. - // - // scale and zeroPoint should be zero for scalars and non-fixed point tensors - // else if ((type->scale != 0.0f) || (type->zeroPoint != 0)) - // { - // return ANEURALNETWORKS_BAD_DATA; - // } - - // dimensionCount should be zero for scalars - if ((type->dimensionCount != 0) && - ((type->type == ANEURALNETWORKS_FLOAT32) || (type->type == ANEURALNETWORKS_INT32) || - (type->type == ANEURALNETWORKS_UINT32))) - { - VERBOSE(NNAPI::Model) << "addOperand: Incorrect data type" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!model->addOperand(type)) - { - VERBOSE(NNAPI::Model) << "addOperand: Fail to add operand" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValue(ANeuralNetworksModel *model, int32_t index, - const void *buffer, size_t length) -{ - const bool optional_operand = ((buffer == nullptr) && (length == 0)); - - if ((model == nullptr) || ((buffer == nullptr) && (length != 0))) - { - VERBOSE(NNAPI::Model) << "setOperandValue: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "setOperandValue: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - // Negative index value is not allowed - if (index < 0) - { - VERBOSE(NNAPI::Model) << "setOperandValue: Invalid index value (negative)" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - // NOTE OperandIndex uses uint32_t as its underlying type as various NNAPI - // functions such as ANeuralNetworksModel_addOperation use uint32_t to represent operand - // index - // ANeuralNetworksModel_setOperandValue, however, uses int32_t to represent operand index. - // - // Below, static_cast<uint32_t>(...) is introduced to eliminate compiler warning. - uint32_t ind = static_cast<uint32_t>(index); - - if (!model->isExistOperand(ind)) - { - VERBOSE(NNAPI::Model) << "setOperandValue: Invalid index value (not exist)" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!optional_operand && (model->operandSize(ind) != length)) - { - VERBOSE(NNAPI::Model) << "setOperandValue: Invalid data length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (model->isUsageSet(ind)) - { - VERBOSE(NNAPI::Model) << "setOperandValue: Already set operand" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - // NNAPI spec in NeuralNetworks.h - // For values of length greater than ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES, - // the application is responsible for not changing the content of this region - // until all executions using this model have completed - bool copy_value = false; - if (length <= ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES) - { - copy_value = true; - } - - if (!model->setOperandValue(ind, buffer, length, optional_operand, copy_value)) - { - VERBOSE(NNAPI::Model) << "setOperandValue: Fail to set operand value" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_setOperandValueFromMemory(ANeuralNetworksModel *model, int32_t index, - const ANeuralNetworksMemory *memory, - size_t offset, size_t length) -{ - if ((model == nullptr) || (memory == nullptr)) - { - VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Incorrect null pointer parameter(s)" - << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - // Negative index value is not allowed - if (index < 0) - { - VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Invalid index value (negative)" - << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - // NOTE OperandIndex uses uint32_t as its underlying type as various NNAPI - // functions such as ANeuralNetworksModel_addOperation use uint32_t to represent operand - // index - // ANeuralNetworksModel_setOperandValue, however, uses int32_t to represent operand index. - // - // Below, static_cast<uint32_t>(...) is introduced to eliminate compiler warning. - uint32_t ind = static_cast<uint32_t>(index); - - if (!model->isExistOperand(ind)) - { - VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Invalid index value (not exist)" - << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if ((model->operandSize(ind) != length) || (memory->size() < (offset + length))) - { - VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Invalid data length" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (model->isUsageSet(ind)) - { - VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Already set operand" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!model->setOperandValue(ind, memory->base() + offset, length)) - { - VERBOSE(NNAPI::Model) << "setOperandValueFromMemory: Fail to set operand value" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_addOperation(ANeuralNetworksModel *model, - ANeuralNetworksOperationType type, uint32_t inputCount, - const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) -{ - if ((model == nullptr) || (inputs == nullptr) || (outputs == nullptr)) - { - VERBOSE(NNAPI::Model) << "addOperation: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "addOperation: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - const ANeuralNetworksOperationType FIRST_OPERATION = ANEURALNETWORKS_ADD; - const ANeuralNetworksOperationType LAST_OPERATION = ANEURALNETWORKS_RESIZE_NEAREST_NEIGHBOR; - if ((type < FIRST_OPERATION) || (type > LAST_OPERATION)) - { - return ANEURALNETWORKS_BAD_DATA; - } - - for (uint32_t i = 0; i < outputCount; i++) - { - if (model->isUsageSet(outputs[i])) - { - VERBOSE(NNAPI::Model) << "addOperation: Already set output operand" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - if (!model->addOperation(type, inputCount, inputs, outputCount, outputs)) - { - VERBOSE(NNAPI::Model) << "addOperation: Fail to add operation" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_addOperationEx(ANeuralNetworksModel *model, - ANeuralNetworksOperationTypeEx type, uint32_t inputCount, - const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) -{ - if ((model == nullptr) || (inputs == nullptr) || (outputs == nullptr)) - { - VERBOSE(NNAPI::Model) << "addOperation: Incorrect null pointer parameter(s)" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "addOperation: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - const ANeuralNetworksOperationTypeEx FIRST_OPERATION = ANEURALNETWORKS_CAST_EX; - const ANeuralNetworksOperationTypeEx LAST_OPERATION = ANEURALNETWORKS_SPLIT_V_EX; - if ((type < FIRST_OPERATION) || (type > LAST_OPERATION)) - { - VERBOSE(NNAPI::Model) << "addOperation: Invalid operation type" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - for (uint32_t i = 0; i < outputCount; i++) - { - if (model->isUsageSet(outputs[i])) - { - VERBOSE(NNAPI::Model) << "addOperation: Already set output operand" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - if (!model->addOperationEx(type, inputCount, inputs, outputCount, outputs)) - { - VERBOSE(NNAPI::Model) << "addOperation: Fail to add operation" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_identifyInputsAndOutputs(ANeuralNetworksModel *model, uint32_t inputCount, - const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) -{ - if ((model == nullptr) || (inputs == nullptr) || (outputs == nullptr)) - { - VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Incorrect null pointer parameter(s)" - << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - for (uint32_t n = 0; n < inputCount; ++n) - { - uint32_t ind = inputs[n]; - if (model->isUsageSet(ind)) - { - VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Already set input operand" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!model->addModelInput(ind)) - { - VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Fail to add input" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - for (uint32_t n = 0; n < outputCount; ++n) - { - uint32_t ind = outputs[n]; - - if (!model->isOperationOutput(ind)) - { - VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Need to set output operand" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - - if (!model->addModelOutput(ind)) - { - VERBOSE(NNAPI::Model) << "identifyInputsAndOutputs: Fail to add output" << std::endl; - return ANEURALNETWORKS_BAD_DATA; - } - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_finish(ANeuralNetworksModel *model) -{ - if (model == nullptr) - { - VERBOSE(NNAPI::Model) << "finish: Incorrect null pointer parameter" << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "finish: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - if (!model->finish()) - { - VERBOSE(NNAPI::Model) << "finish: Fail to generate internal graph" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - return ANEURALNETWORKS_NO_ERROR; -} - -int ANeuralNetworksModel_relaxComputationFloat32toFloat16(ANeuralNetworksModel *model, bool allow) -{ - if (model == nullptr) - { - VERBOSE(NNAPI::Model) << "relaxComputationFloat32toFloat16: Incorrect null pointer parameter" - << std::endl; - return ANEURALNETWORKS_UNEXPECTED_NULL; - } - - if (model->isFinished()) - { - VERBOSE(NNAPI::Model) << "relaxComputationFloat32toFloat16: Already finished" << std::endl; - return ANEURALNETWORKS_BAD_STATE; - } - - model->allowFloat32toFloat16(allow); - - return ANEURALNETWORKS_NO_ERROR; -} diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksCompilation.cc b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksCompilation.cc deleted file mode 100644 index 81cd38f4f..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksCompilation.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ANeuralNetworksCompilation.h" - -#include "util/logging.h" - -// TODO Support multiple subgraphs -ANeuralNetworksCompilation::ANeuralNetworksCompilation(const ANeuralNetworksModel *model) noexcept - : _subgraphs{model->getSubGraphs()}, _compiler{new onert::compiler::Compiler{_subgraphs}} -{ - if (model->allowedToFp16()) - { - _compiler->enableToFp16(); - } -} - -bool ANeuralNetworksCompilation::finish() noexcept -{ - try - { - _executors = _compiler->compile(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksCompilation.h b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksCompilation.h deleted file mode 100644 index 5f0650b9a..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksCompilation.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __COMPILATION_H__ -#define __COMPILATION_H__ - -#include "ANeuralNetworksModel.h" - -#include "compiler/Compiler.h" -#include "ir/Graph.h" -#include "ir/Subgraphs.h" -#include "exec/IExecutor.h" - -struct ANeuralNetworksCompilation -{ -public: - ANeuralNetworksCompilation(const ANeuralNetworksModel *model) noexcept; - -public: - bool finish() noexcept; - - onert::compiler::State state(void) noexcept { return _compiler->state(); } - void publish(std::shared_ptr<onert::exec::ExecutorMap> &executors) noexcept - { - executors = _executors; - } - -private: - std::shared_ptr<onert::ir::Subgraphs> _subgraphs; - std::shared_ptr<onert::compiler::Compiler> _compiler; - std::shared_ptr<onert::exec::ExecutorMap> _executors; -}; - -#endif diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksEvent.cc b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksEvent.cc deleted file mode 100644 index 2bea729be..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksEvent.cc +++ /dev/null @@ -1,42 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ANeuralNetworksEvent.h" - -#include "exec/Execution.h" -#include "util/logging.h" - -ANeuralNetworksEvent::ANeuralNetworksEvent(const std::shared_ptr<onert::exec::Execution> &execution) - : _execution{execution} -{ - // DO NOTHING -} - -bool ANeuralNetworksEvent::waitFinish(void) noexcept -{ - try - { - _execution->waitFinish(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksEvent.h b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksEvent.h deleted file mode 100644 index 7b462d3d6..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksEvent.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __EVENT_H__ -#define __EVENT_H__ - -#include <NeuralNetworks.h> - -#include <memory> - -namespace onert -{ -namespace exec -{ -class Execution; -} // namespace exec -} // namespace onert - -struct ANeuralNetworksEvent -{ -public: - ANeuralNetworksEvent(const std::shared_ptr<onert::exec::Execution> &execution); - -public: - bool waitFinish(void) noexcept; - -private: - const std::shared_ptr<onert::exec::Execution> _execution; -}; - -#endif diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc deleted file mode 100644 index 6114b74b0..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.cc +++ /dev/null @@ -1,335 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ANeuralNetworksExecution.h" -#include "NNAPIConvert.h" -#include "util/logging.h" - -const onert::ir::OperandIndex ANeuralNetworksExecution::getInputOperandIndex(int32_t index) noexcept -{ - if (index < 0) - { - // Negative index: return invalid index - return onert::ir::OperandIndex{}; - } - - uint32_t cast_index = static_cast<uint32_t>(index); - if (cast_index >= _execution->primary_subgraph().getInputs().size()) - { - // Return invalid index - return onert::ir::OperandIndex{}; - } - - onert::ir::IOIndex input_index{cast_index}; - const auto operand_index = _execution->primary_subgraph().getInputs().at(input_index); - return operand_index; -} - -const onert::ir::OperandIndex -ANeuralNetworksExecution::getOutputOperandIndex(int32_t index) noexcept -{ - if (index < 0) - { - // Negative index: return invalid index - return onert::ir::OperandIndex{}; - } - - uint32_t cast_index = static_cast<uint32_t>(index); - if (cast_index >= _execution->primary_subgraph().getOutputs().size()) - { - // Return invalid index - return onert::ir::OperandIndex{}; - } - - onert::ir::IOIndex output_index{cast_index}; - const auto operand_index = _execution->primary_subgraph().getOutputs().at(output_index); - return operand_index; -} - -bool ANeuralNetworksExecution::compareDataType(const ANeuralNetworksOperandType *type, - const onert::ir::OperandIndex index) noexcept -{ - try - { - const auto operand_type = _execution->primary_subgraph().operands().at(index).typeInfo(); - const auto typeInfo = NNAPIConvert::getTypeInfo(type); - - if (operand_type != typeInfo) - { - // Data type mismatch - return false; - } - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksExecution::compareShape(const ANeuralNetworksOperandType *type, - const onert::ir::OperandIndex index) noexcept -{ - // Passed shape should be specified - if (hasUnspecifiedDims(index)) - { - return false; - } - - const auto &operand_shape = _execution->primary_subgraph().operands().at(index).shape(); - const auto &shape_from_type = NNAPIConvert::getShape(type); - - return operand_shape == shape_from_type; -} - -bool ANeuralNetworksExecution::IsOptionalInput(const onert::ir::OperandIndex index) noexcept -{ - const auto &operand_shape = _execution->primary_subgraph().operands().at(index).shape(); - for (int32_t i = 0; i < operand_shape.rank(); ++i) - { - if (operand_shape.dim(i) != 0) - return false; - } - return true; -} - -bool ANeuralNetworksExecution::hasUnspecifiedDims(const onert::ir::OperandIndex index) noexcept -{ - const auto operand_shape = _execution->primary_subgraph().operands().at(index).shape(); - - return operand_shape.hasUnspecifiedDims(); -} - -size_t ANeuralNetworksExecution::getOperandSize(const onert::ir::OperandIndex index) noexcept -{ - try - { - return _execution->primary_subgraph().operands().at(index).operandSize(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return 0; - } -} - -bool ANeuralNetworksExecution::setInput(uint32_t index, const ANeuralNetworksOperandType *type, - const void *buffer, size_t length) noexcept -{ - try - { - onert::ir::IOIndex input_index{index}; - const auto operand_index = getInputOperandIndex(index); - - const auto type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo(); - const auto shape = (type != nullptr) - ? NNAPIConvert::getShape(type) - : _execution->primary_subgraph().operands().at(operand_index).shape(); - - // NOTE The nnapi does not provide setting io_layout and not support changing layout. In other - // words, we can assume that io_layout from nnapi always is the same as layout of the used - // model. - // TODO Set layout of model - _execution->setInput(input_index, type_info, shape, buffer, length, onert::ir::Layout::NHWC); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksExecution::setOptionalInput(uint32_t index, - const ANeuralNetworksOperandType *type, - const void *buffer, size_t length) noexcept -{ - assert(type == nullptr); - assert(buffer == nullptr); - assert(length == 0); - try - { - onert::ir::IOIndex input_index{index}; - const auto operand_index = getInputOperandIndex(index); - - const auto type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo(); - const auto shape = (type != nullptr) - ? NNAPIConvert::getShape(type) - : _execution->primary_subgraph().operands().at(operand_index).shape(); - - // ANeuralNetworksExecution::setInput() uses only shape information - ANeuralNetworksOperandType optional_input_type; - optional_input_type.dimensionCount = shape.rank(); - std::vector<uint32_t> dims(optional_input_type.dimensionCount); - for (uint32_t i = 0; i < optional_input_type.dimensionCount; ++i) - { - dims.at(i) = shape.dim(i); - } - optional_input_type.dimensions = dims.data(); - - return setInput(index, &optional_input_type, buffer, length); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksExecution::setOutput(uint32_t index, const ANeuralNetworksOperandType *type, - void *buffer, size_t length) noexcept -{ - try - { - onert::ir::IOIndex output_index{index}; - const auto operand_index = getOutputOperandIndex(index); - - const auto type_info = _execution->primary_subgraph().operands().at(operand_index).typeInfo(); - const auto shape = (type != nullptr) - ? NNAPIConvert::getShape(type) - : _execution->primary_subgraph().operands().at(operand_index).shape(); - - // NOTE The nnapi does not provide setting io_layout and not support changing layout. In other - // words, we can assume that io_layout from nnapi always is the same as layout of the used - // model. - // TODO Set layout of model - _execution->setOutput(output_index, type_info, shape, buffer, length, onert::ir::Layout::NHWC); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksExecution::startExecute(void) noexcept -{ - try - { - _execution->startExecute(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksExecution::execute(void) noexcept -{ - try - { - _execution->execute(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -const std::shared_ptr<onert::exec::Execution> ANeuralNetworksExecution::instance(void) noexcept -{ - return _execution; -} - -bool ANeuralNetworksExecution::getOutputOperandRank(uint32_t index, uint32_t *rank) noexcept -{ - try - { - onert::ir::IOIndex output_index{index}; - - // Check execution is finished - if (!_execution->isFinished()) - { - return false; - } - - const auto shape = _execution->getOutputShape(output_index); - if (shape.hasUnspecifiedDims()) - { - throw std::runtime_error{"Internal error: Output tensor has unspecified dims"}; - } - - *rank = shape.rank(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksExecution::getOutputOperandDimensions(uint32_t index, uint32_t *dimensions) -{ - try - { - onert::ir::IOIndex output_index{index}; - - // Check execution is finished - if (!_execution->isFinished()) - { - return false; - } - - const auto shape = _execution->getOutputShape(output_index); - if (shape.hasUnspecifiedDims()) - { - throw std::runtime_error{"Internal error: Output tensor has unspecified dims"}; - } - - for (int i = 0; i < shape.rank(); i++) - { - auto dim = shape.dim(i); - - if (dim <= 0) - { - throw std::runtime_error{"Invalid dimension value"}; - } - - dimensions[i] = static_cast<uint32_t>(dim); - } - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.h b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.h deleted file mode 100644 index 1f4b868f6..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksExecution.h +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __EXECUTION_H__ -#define __EXECUTION_H__ - -#include <NeuralNetworks.h> - -#include <memory> - -#include "exec/Execution.h" - -struct ANeuralNetworksExecution -{ -public: - ANeuralNetworksExecution(const std::shared_ptr<onert::exec::ExecutorMap> &executors) - : _execution{std::make_shared<onert::exec::Execution>(executors)} - { - // DO NOTHING - } - -public: - bool setInput(uint32_t index, const ANeuralNetworksOperandType *type, const void *buffer, - size_t length) noexcept; - bool setOptionalInput(uint32_t index, const ANeuralNetworksOperandType *type, const void *buffer, - size_t length) noexcept; - bool setOutput(uint32_t index, const ANeuralNetworksOperandType *type, void *buffer, - size_t length) noexcept; - bool startExecute(void) noexcept; - bool execute(void) noexcept; - - const onert::ir::OperandIndex getInputOperandIndex(int32_t index) noexcept; - const onert::ir::OperandIndex getOutputOperandIndex(int32_t index) noexcept; - bool compareDataType(const ANeuralNetworksOperandType *type, - const onert::ir::OperandIndex index) noexcept; - bool compareShape(const ANeuralNetworksOperandType *type, - const onert::ir::OperandIndex index) noexcept; - bool IsOptionalInput(const onert::ir::OperandIndex index) noexcept; - bool hasUnspecifiedDims(const onert::ir::OperandIndex index) noexcept; - size_t getOperandSize(const onert::ir::OperandIndex index) noexcept; - const std::shared_ptr<onert::exec::Execution> instance(void) noexcept; - - /** - * @brief Get output operand's rank - * @param[in] index Output index - * @param[out] rank Output operand's rank - * @return @c true if success to get rank, otherwise @c false - */ - bool getOutputOperandRank(uint32_t index, uint32_t *rank) noexcept; - /** - * @brief Get dimensions of the output operand - * @param[in] index Output index - * @param[out] dimensions Output operand's dimensions - * @return @c true if success to get rank, otherwise @c false - * @note This must be called after execution is finished to get resolved output shape - * unspecified in model - */ - bool getOutputOperandDimensions(uint32_t index, uint32_t *dimensions); - -private: - std::shared_ptr<onert::exec::Execution> _execution; -}; - -#endif diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksMemory.cc b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksMemory.cc deleted file mode 100644 index 9cc100585..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksMemory.cc +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <NeuralNetworks.h> -#include <sys/mman.h> - -#include "ANeuralNetworksMemory.h" - -// -// ANeuralNetworksMemory -// -ANeuralNetworksMemory::ANeuralNetworksMemory(size_t size, int protect, int fd, size_t offset) -{ - _base = reinterpret_cast<uint8_t *>(mmap(nullptr, size, protect, MAP_PRIVATE, fd, offset)); - _size = size; -} - -ANeuralNetworksMemory::~ANeuralNetworksMemory() { munmap(reinterpret_cast<void *>(_base), _size); } - -bool ANeuralNetworksMemory::vaildAccess(size_t offset, size_t length) const -{ - if ((offset >= _size) || (length > _size)) - { - return false; - } - - if ((offset + length) >= _size) - { - return false; - } - - return true; -} diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksMemory.h b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksMemory.h deleted file mode 100644 index 48a1bc5fc..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksMemory.h +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __MEMORY_H__ -#define __MEMORY_H__ - -#include <cstdint> - -struct ANeuralNetworksMemory -{ -public: - ANeuralNetworksMemory(size_t size, int protect, int fd, size_t offset); - ~ANeuralNetworksMemory(); - -public: - size_t size(void) const { return _size; } - uint8_t *base(void) { return _base; } - uint8_t *base(void) const { return _base; } - bool vaildAccess(size_t offset, size_t length) const; - -private: - size_t _size; - uint8_t *_base; -}; - -#endif // __MEMORY_H__ diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.cc b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.cc deleted file mode 100644 index 97b820aea..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.cc +++ /dev/null @@ -1,287 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ANeuralNetworksModel.h" -#include "OperationFactory.h" -#include "NNAPIConvert.h" - -#include "ir/Operations.Include.h" -#include "util/logging.h" - -#include <memory> - -// -// ANeuralNetworksModel -// -ANeuralNetworksModel::ANeuralNetworksModel() noexcept - : _optional_operands{}, _operand_usages{}, _allowFloat32toFloat16{false} -{ - _graph = std::make_shared<onert::ir::Graph>(); -} - -bool ANeuralNetworksModel::addOperand(const ANeuralNetworksOperandType *type) noexcept -{ - try - { - const auto shape = NNAPIConvert::getShape(type); - const auto typeInfo = NNAPIConvert::getTypeInfo(type); - _graph->addOperand(shape, typeInfo); - _operand_usages.emplace_back(OperandUsage::NOT_DEFINED); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksModel::setOperandValue(uint32_t index, const void *buffer, size_t length, - bool optional, bool copy) noexcept -{ - const onert::ir::OperandIndex ind{index}; - - try - { - _operand_usages[index] = OperandUsage::CONSTANT; - - // Remain operands.at(ind).data()->base() as nullptr for optional operand - // This will be filled when model finished - if (optional) - { - setOptionalOperand(ind); - } - - using onert::ir::CachedData; - using onert::ir::ExternalData; - if (copy) - { - _graph->operands().at(ind).data( - std::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(buffer), length)); - } - else - { - _graph->operands().at(ind).data( - std::make_unique<ExternalData>(reinterpret_cast<const uint8_t *>(buffer), length)); - } - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksModel::addOperation(ANeuralNetworksOperationType type, uint32_t inputCount, - const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) noexcept -{ - try - { - for (uint32_t i = 0; i < outputCount; i++) - { - _operand_usages[outputs[i]] = OperandUsage::OPERATION_OUTPUT; - } - - auto &factory = OperationFactory::get(); - OperationFactory::Param param{inputCount, inputs, outputCount, outputs}; - - auto node = factory.create(type, param, _graph->operands()); - _graph->addOperation(std::unique_ptr<onert::ir::Operation>{node}); - - // TODO Move these codes to delegate.cpp - if (type == ANEURALNETWORKS_FULLY_CONNECTED) - { - const auto &input_operand = - _graph->operands().at(node->getInputs().at(onert::ir::operation::FullyConnected::INPUT)); - auto &weights_operand = - _graph->operands().at(node->getInputs().at(onert::ir::operation::FullyConnected::WEIGHT)); - if (input_operand.typeInfo().type() == onert::ir::DataType::FLOAT32 && - weights_operand.typeInfo().type() == onert::ir::DataType::QUANT_UINT8_ASYMM) - { - weights_operand.type(onert::ir::DataType::QUANT_INT8_SYMM); - } - } - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -bool ANeuralNetworksModel::addOperationEx(ANeuralNetworksOperationTypeEx type, uint32_t inputCount, - const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) noexcept -{ - try - { - for (uint32_t i = 0; i < outputCount; i++) - { - _operand_usages[outputs[i]] = OperandUsage::OPERATION_OUTPUT; - } - - auto &factory = OperationFactory::get(); - OperationFactory::Param param{inputCount, inputs, outputCount, outputs}; - - auto node = factory.create(type, param, _graph->operands()); - _graph->addOperation(std::unique_ptr<onert::ir::Operation>{node}); - } - catch (const std::exception &e) - { - return false; - } - return true; -} - -bool ANeuralNetworksModel::addModelInput(uint32_t index) noexcept -{ - try - { - _operand_usages[index] = OperandUsage::MODEL_INPUT; - - const onert::ir::OperandIndex ind{index}; - _graph->addInput(ind); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} -bool ANeuralNetworksModel::addModelOutput(uint32_t index) noexcept -{ - try - { - const onert::ir::OperandIndex ind{index}; - - // Duplicated output is not allowed - if (_graph->getOutputs().contains(ind)) - { - return false; - } - - _graph->addOutput(ind); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << std::endl; - - return false; - } - - return true; -} - -void ANeuralNetworksModel::allowFloat32toFloat16(bool allow) noexcept -{ - _allowFloat32toFloat16 = allow; -} - -bool ANeuralNetworksModel::finish() noexcept -{ - try - { - fillOptionalOperand(); - - _graph->finishBuilding(); - - _operand_usages.clear(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << '\n'; - - return false; - } - - return true; -} - -bool ANeuralNetworksModel::isFinished() noexcept { return !_graph->isBuildingPhase(); } - -bool ANeuralNetworksModel::isExistOperand(uint32_t index) noexcept -{ - return _graph->operands().exist(onert::ir::OperandIndex{index}); -} - -size_t ANeuralNetworksModel::operandSize(uint32_t index) noexcept -{ - try - { - return _graph->operands().at(onert::ir::OperandIndex{index}).operandSize(); - } - catch (const std::exception &e) - { - VERBOSE(EXCEPTION) << e.what() << '\n'; - - return 0; - } -} - -bool ANeuralNetworksModel::isUsageSet(uint32_t index) noexcept -{ - return (_operand_usages[index] != OperandUsage::NOT_DEFINED); -} - -bool ANeuralNetworksModel::isOperationOutput(uint32_t index) noexcept -{ - return (_operand_usages[index] == OperandUsage::OPERATION_OUTPUT); -} - -void ANeuralNetworksModel::setOptionalOperand(const onert::ir::OperandIndex idx) -{ - _optional_operands.insert(idx); -} - -void ANeuralNetworksModel::fillOptionalOperand(void) -{ - _graph->operations().iterate([&](const onert::ir::OperationIndex &, onert::ir::Operation &node) { - for (auto input : node.getInputs()) - { - // TODO fill default value for optional operands - if (_optional_operands.find(input) != _optional_operands.end()) - { - throw std::runtime_error{"Optional operand is not supported yet"}; - } - } - }); -} - -std::shared_ptr<onert::ir::Subgraphs> ANeuralNetworksModel::getSubGraphs() const -{ - auto all_subgs = std::make_shared<onert::ir::Subgraphs>(); - - all_subgs->push(onert::ir::SubgraphIndex{0}, _graph); - // TODO Find all child subgraphs and copy them to all_subgs - // Must find the same subgraph by using to compare pointer of subgraphs and set subgraph's index - // to operands of control flow operations - // Must clean all child subgraphs's pointer to prevent memory leak in case of that graph has - // subgraph itself recursively - - return all_subgs; -} diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.h b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.h deleted file mode 100644 index df6c97c44..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __MODEL_H__ -#define __MODEL_H__ - -#include <unordered_set> -#include <NeuralNetworks.h> -#include <NeuralNetworksEx.h> - -#include "ir/Graph.h" -#include "ir/Subgraphs.h" - -struct ANeuralNetworksModel -{ -public: - enum class OperandUsage - { - NOT_DEFINED = 0, - MODEL_INPUT, - CONSTANT, - OPERATION_OUTPUT, - }; - -public: - ANeuralNetworksModel() noexcept; - -public: - bool addOperand(const ANeuralNetworksOperandType *type) noexcept; - bool setOperandValue(uint32_t index, const void *buffer, size_t length, bool optional = false, - bool copy = false) noexcept; - bool addOperation(ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t *inputs, - uint32_t outputCount, const uint32_t *outputs) noexcept; - bool addOperationEx(ANeuralNetworksOperationTypeEx type, uint32_t inputCount, - const uint32_t *inputs, uint32_t outputCount, - const uint32_t *outputs) noexcept; - bool addModelInput(uint32_t index) noexcept; - bool addModelOutput(uint32_t index) noexcept; - void allowFloat32toFloat16(bool allow) noexcept; - bool allowedToFp16() const noexcept { return _allowFloat32toFloat16; } - bool finish() noexcept; - - onert::ir::Graph &deref(void) { return *_graph; } - bool isFinished() noexcept; - bool isExistOperand(uint32_t index) noexcept; - size_t operandSize(uint32_t index) noexcept; - bool isUsageSet(uint32_t index) noexcept; - bool isOperationOutput(uint32_t index) noexcept; - std::shared_ptr<onert::ir::Subgraphs> getSubGraphs() const; - -private: - void setOptionalOperand(const onert::ir::OperandIndex idx); - void fillOptionalOperand(void); - -private: - std::shared_ptr<onert::ir::Graph> _graph; - std::unordered_set<onert::ir::OperandIndex> _optional_operands; - std::vector<OperandUsage> _operand_usages; - bool _allowFloat32toFloat16; -}; - -#endif // __MODEL_H__ diff --git a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.test.cc b/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.test.cc deleted file mode 100644 index bb42f2b08..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/ANeuralNetworksModel.test.cc +++ /dev/null @@ -1,25 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ANeuralNetworksModel.h" - -TEST(MODEL, neg_model_build) -{ - ANeuralNetworksModel model; - ASSERT_FALSE(model.isFinished()); -} diff --git a/runtime/onert/frontend/nnapi/wrapper/NNAPIConvert.cc b/runtime/onert/frontend/nnapi/wrapper/NNAPIConvert.cc deleted file mode 100644 index 63d4e3c09..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/NNAPIConvert.cc +++ /dev/null @@ -1,100 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "NNAPIConvert.h" - -#include <numeric> - -using namespace onert::ir; - -DataType NNAPIConvert::getDataType(OperandCode type) -{ - switch (type) - { - case ANEURALNETWORKS_FLOAT32: - case ANEURALNETWORKS_TENSOR_FLOAT32: - return DataType::FLOAT32; - case ANEURALNETWORKS_INT32: - case ANEURALNETWORKS_TENSOR_INT32: - return DataType::INT32; - case ANEURALNETWORKS_UINT32: - return DataType::UINT32; - case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM: - return DataType::QUANT_UINT8_ASYMM; - case ANEURALNETWORKS_TENSOR_QUANT8_SYMM: - return DataType::QUANT_INT8_SYMM; - case ANEURALNETWORKS_BOOL: - case ANEURALNETWORKS_TENSOR_BOOL8: - return DataType::BOOL8; - default: - throw std::runtime_error("Unsupported type"); - } -} - -TypeInfo NNAPIConvert::getTypeInfo(const ANeuralNetworksOperandType *type) -{ - return TypeInfo(getDataType((OperandCode)(type->type)), type->scale, type->zeroPoint); -} - -Shape NNAPIConvert::getShape(const ANeuralNetworksOperandType *type) -{ - Shape shape(type->dimensionCount); - - for (uint32_t axis = 0; axis < type->dimensionCount; ++axis) - { - shape.dim(axis) = type->dimensions[axis]; - } - - return shape; -} - -size_t NNAPIConvert::calculateSizeFromType(const ANeuralNetworksOperandType *type) -{ - auto shape = getShape(type); - auto data_type = getDataType((OperandCode)(type->type)); - - return shape.num_elements() * sizeOfDataType(data_type); -} - -Activation NNAPIConvert::getFusedActivation(FuseCode act) -{ - switch (act) - { - case ANEURALNETWORKS_FUSED_NONE: - return Activation::NONE; - case ANEURALNETWORKS_FUSED_RELU: - return Activation::RELU; - case ANEURALNETWORKS_FUSED_RELU1: - return Activation::RELU1; - case ANEURALNETWORKS_FUSED_RELU6: - return Activation::RELU6; - default: - throw std::runtime_error("Unsupported activation type"); - } -} - -PaddingType NNAPIConvert::getPaddingType(PaddingCode type) -{ - switch (type) - { - case ANEURALNETWORKS_PADDING_SAME: - return PaddingType::SAME; - case ANEURALNETWORKS_PADDING_VALID: - return PaddingType::VALID; - default: - throw std::runtime_error("Unsupported type"); - } -} diff --git a/runtime/onert/frontend/nnapi/wrapper/NNAPIConvert.h b/runtime/onert/frontend/nnapi/wrapper/NNAPIConvert.h deleted file mode 100644 index 4fd985e6e..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/NNAPIConvert.h +++ /dev/null @@ -1,79 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file NNAPIConvert.h - * @brief This file contains convereter(s)\n - * from NNAPI frontend's struct to onert's internal struct - */ -#ifndef __ONERT_NNAPI_CONVERT_H__ -#define __ONERT_NNAPI_CONVERT_H__ - -#include <NeuralNetworks.h> - -#include <ir/TypeInfo.h> -#include <ir/Shape.h> -#include <ir/Padding.h> -#include <ir/InternalType.h> - -class NNAPIConvert -{ - -public: - /** - * @brief Convert data type from NNAPI to internal data type - * @param[in] type NNAPI's data type - * @return onert's internal data type - */ - static onert::ir::DataType getDataType(OperandCode type); - - /** - * @brief Convert operand type info from NNAPI to interanl operand type info - * @param[in] type NNAPI's operand type - * @return onert's internal operand type info - */ - static onert::ir::TypeInfo getTypeInfo(const ANeuralNetworksOperandType *type); - - /** - * @brief Convert operand shape info from NNAPI to internal operand shape - * @param[in] type NNAPI's operand type - * @return onert's internal operand shape - */ - static onert::ir::Shape getShape(const ANeuralNetworksOperandType *type); - - /** - * @brief Calcaulate operand size from NNAPI type - * @param[in] type NNAPI's operand type - * @return Operand size - */ - static size_t calculateSizeFromType(const ANeuralNetworksOperandType *type); - - /** - * @brief Convert NNAPI FuseCode to internal activation type - * @param[in] act NNAPI's FuseCode type - * @return onert's internal activation type - */ - static onert::ir::Activation getFusedActivation(FuseCode act); - - /** - * @brief Convert NNAPI PaddingCode to internal padding type - * @param[in] type NNAPI's PaddingCode type - * @return onert's internal padding type - */ - static onert::ir::PaddingType getPaddingType(PaddingCode type); -}; - -#endif // __ONERT_NNAPI_CONVERT_H__ diff --git a/runtime/onert/frontend/nnapi/wrapper/OperationFactory.cc b/runtime/onert/frontend/nnapi/wrapper/OperationFactory.cc deleted file mode 100644 index e6c38f5f8..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/OperationFactory.cc +++ /dev/null @@ -1,1914 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "OperationFactory.h" -#include "NNAPIConvert.h" - -#include <ir/Operations.Include.h> -#include <string.h> - -namespace -{ -using namespace onert::ir; - -void replaceDataType(Operands &operands, const OperandIndex &index, const DataType type) -{ - assert(operands.exist(index)); - operands.at(index).type(type); -} - -ExplicitPadding makeExplicitPadding(Operands &operands, const OperandIndex &left_index, - const OperandIndex &right_index, const OperandIndex &top_index, - const OperandIndex &bottom_index) -{ - auto left = operands.at(left_index).asScalar<int32_t>(); - auto right = operands.at(right_index).asScalar<int32_t>(); - auto top = operands.at(top_index).asScalar<int32_t>(); - auto bottom = operands.at(bottom_index).asScalar<int32_t>(); - - if (left < 0 || right < 0 || top < 0 || bottom < 0) - { - throw std::runtime_error{"Cannot handle negative explicit padding value"}; - } - - ExplicitPadding param; - param.left = static_cast<uint32_t>(left); - param.right = static_cast<uint32_t>(right); - param.top = static_cast<uint32_t>(top); - param.bottom = static_cast<uint32_t>(bottom); - - return param; -} - -Stride makeStride(Operands &operands, const OperandIndex &horizontal_index, - const OperandIndex &vertical_index) -{ - auto horizontal = operands.at(horizontal_index).asScalar<int32_t>(); - auto vertical = operands.at(vertical_index).asScalar<int32_t>(); - - if (vertical < 0 || horizontal < 0) - { - throw std::runtime_error{"Cannot handle negative stride value"}; - } - - Stride stride; - stride.horizontal = static_cast<uint32_t>(horizontal); - stride.vertical = static_cast<uint32_t>(vertical); - - return stride; -} - -uint32_t getUint32Scalar(Operands &operands, const OperandIndex index) -{ - auto int32_value = operands.at(index).asScalar<int32_t>(); - if (int32_value < 0) - { - throw std::runtime_error{"Cannot handle negative value"}; - } - - return static_cast<uint32_t>(int32_value); -} - -OperationFactory::Generator -getElementwiseActivationGenerator(const onert::ir::operation::ElementwiseActivation::Type op_type, - float alpha = 0.f, float beta = 0.f) -{ - return [op_type, alpha, beta](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 1); - assert(init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - - OperandIndexSequence inputs{init_param.inputs[0]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::ElementwiseActivation::Param param; - param.op_type = op_type; - param.alpha = alpha; - param.beta = beta; - - return new operation::ElementwiseActivation{inputs, outputs, param}; - }; -} - -OperationFactory::Generator getElementwiseBinaryGenerator( - const onert::ir::operation::ElementwiseBinary::ElementwiseBinaryType op_type) -{ - return [op_type](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 2); - assert(init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> Lefthand side operand - // 1 -> Righthand side operand - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::ElementwiseBinary::Param param; - param.op_type = op_type; - - return new operation::ElementwiseBinary{inputs, outputs, param}; - }; -} - -OperationFactory::Generator -getElementwiseUnaryGenerator(const onert::ir::operation::ElementwiseUnary::Type op_type) -{ - return [op_type](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 1); - assert(init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - - OperandIndexSequence inputs{init_param.inputs[0]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::ElementwiseUnary::Param param; - param.op_type = op_type; - - if (op_type == operation::ElementwiseUnary::Type::CAST) - { - // NNAPI uses QUANT_UINT8_ASYMM to represent UINT8 type for ANEURALNETWORKS_CAST's - // input/output - if (operands.at(inputs.at(0)).typeInfo().type() == DataType::QUANT_UINT8_ASYMM) - { - replaceDataType(operands, inputs.at(0), DataType::UINT8); - } - if (operands.at(outputs.at(0)).typeInfo().type() == DataType::QUANT_UINT8_ASYMM) - { - replaceDataType(operands, outputs.at(0), DataType::UINT8); - } - } - - return new operation::ElementwiseUnary{inputs, outputs, param}; - }; -} - -OperationFactory::Generator -getBinaryArithmeticGenerator(const onert::ir::operation::BinaryArithmetic::ArithmeticType op_type) -{ - return [op_type](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 3); - assert(init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> Lefthand side operand - // 1 -> Righthand side operand - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::BinaryArithmetic::Param param; - param.arithmetic_type = op_type; - const auto activation_index = OperandIndex{init_param.inputs[2]}; - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - - return new operation::BinaryArithmetic{inputs, outputs, param}; - }; -} - -OperationFactory::Generator -getPool2DGenerator(const onert::ir::operation::Pool2D::PoolType pool_type) -{ - return [pool_type](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 7 || init_param.input_count == 10); - assert(init_param.output_count == 1); - - // In common - // 0 -> IFM Tensor Index - OperandIndexSequence inputs{init_param.inputs[0]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::Pool2D::Param param; - param.op_type = pool_type; - if (init_param.input_count == 7) // support implicit padding - { - // Each input should be interpreted as follows: - // - // 1 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index - // 2 -> Horizontal (over width) Stride Index - // 3 -> Vertial (over height) Stride Index - // 4 -> Filter Width Index - // 5 -> Filter Height Index - // 6 -> FuseCode (activation) Index - - const auto padding_index = OperandIndex{init_param.inputs[1]}; - const auto hstride_index = OperandIndex{init_param.inputs[2]}; - const auto vstride_index = OperandIndex{init_param.inputs[3]}; - const auto kw_index = OperandIndex{init_param.inputs[4]}; - const auto kh_index = OperandIndex{init_param.inputs[5]}; - const auto activation_index = OperandIndex{init_param.inputs[6]}; - - param.padding.type = - NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>()); - param.stride = makeStride(operands, hstride_index, vstride_index); - param.kw = getUint32Scalar(operands, kw_index); - param.kh = operands.at(kh_index).asScalar<uint32_t>(); - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - } - else // support explicit padding - { - // Each input should be interpreted as follows: - // - // 1 -> Padding_left index - // 2 -> Padding_right index - // 3 -> Padding_top index - // 4 -> Padding_bottom index - // 5 -> Horizontal (over width) Stride Index - // 6 -> Vertial (over height) Stride Index - // 7 -> Filter Width Index - // 8 -> Filter Height Index - // 9 -> FuseCode (activation) Index - - const auto padding_left_index = OperandIndex{init_param.inputs[1]}; - const auto padding_right_index = OperandIndex{init_param.inputs[2]}; - const auto padding_top_index = OperandIndex{init_param.inputs[3]}; - const auto padding_bottom_index = OperandIndex{init_param.inputs[4]}; - const auto hstride_index = OperandIndex{init_param.inputs[5]}; - const auto vstride_index = OperandIndex{init_param.inputs[6]}; - const auto kw_index = OperandIndex{init_param.inputs[7]}; - const auto kh_index = OperandIndex{init_param.inputs[8]}; - const auto activation_index = OperandIndex{init_param.inputs[9]}; - - param.padding.type = PaddingType::EXPLICIT; - param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index, - padding_top_index, padding_bottom_index); - param.stride = makeStride(operands, hstride_index, vstride_index); - param.kw = getUint32Scalar(operands, kw_index); - param.kh = getUint32Scalar(operands, kh_index); - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - } - return new operation::Pool2D{inputs, outputs, param}; - }; -} - -OperationFactory::Generator -getReduceGenerator(const onert::ir::operation::Reduce::ReduceType reduce_type) -{ - return [reduce_type](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 3); - assert(init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Reduced Axes Tensor Index - // 2 -> keep_dims Index - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::Reduce::Param param; - param.reduce_type = reduce_type; - param.keep_dims = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int8_t>() != 0; - - return new operation::Reduce{inputs, outputs, param}; - }; -} - -template <typename T> -Operation *CreateSimpleUnaryOp(const OperationFactory::Param &init_param, Operands &) -{ - assert(init_param.input_count == 1 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - OperandIndexSequence inputs{init_param.inputs[0]}; - - return new T{inputs, outputs}; -} - -// A generator function for binary ops with no params -template <typename T> -Operation *createSimpleBinaryOp(const OperationFactory::Param &init_param, Operands &) -{ - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - return new T{inputs, outputs}; -} - -OperationFactory::Generator getComparisonGenerator(operation::Comparison::ComparisonType type) -{ - return [type](const OperationFactory::Param &init_param, Operands &) -> Operation * { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input0 Tensor Index - // 1 -> input1 Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::Comparison::Param param; - param.comparison_type = type; - - return new operation::Comparison{inputs, outputs, param}; - }; -} - -} // namespace - -OperationFactory &OperationFactory::get() -{ - static OperationFactory factory; - return factory; -} - -OperationFactory::OperationFactory() -{ - // Each input should be interpreted as follows: - // 0 -> Input Tensor Index - // 1 -> Block size Index - _map[ANEURALNETWORKS_BATCH_TO_SPACE_ND] = createSimpleBinaryOp<operation::BatchToSpaceND>; - - _map[ANEURALNETWORKS_DEPTHWISE_CONV_2D] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert((init_param.input_count == 8 || init_param.input_count == 11) && - init_param.output_count == 1); - - // In common - // 0 -> IFM Tensor Index - // 1 -> Kernel Tensor Index - // 2 -> Bias Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::DepthwiseConv2D::Param param; - if (init_param.input_count == 8) - { - // Imlicit Padding case - // Each input should be interpreted as follows: - // - // 3 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index - // 4 -> Stride (width) Index - // 5 -> Stride (height) INdex - // 6 -> Depthwise multiplier - // 7 -> Activation Index - - const auto padding_index = OperandIndex{init_param.inputs[3]}; - const auto hstride_index = OperandIndex{init_param.inputs[4]}; - const auto vstride_index = OperandIndex{init_param.inputs[5]}; - const auto multiplier_index = OperandIndex{init_param.inputs[6]}; - const auto activation_index = OperandIndex{init_param.inputs[7]}; - - param.padding.type = - NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>()); - param.stride = makeStride(operands, hstride_index, vstride_index); - param.multiplier = getUint32Scalar(operands, multiplier_index); - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - } - else - { - // Explicit Padding case - // Each input should be interpreted as follows: - // - // 3 -> Padding On the Left - // 4 -> Padding On the Right - // 5 -> Padding On the Top - // 6 -> Padding On the Bottom - // 7 -> Stride (width) Index - // 8 -> Stride (height) Index - // 9 -> Depthwise multiplier - // 10-> Activation Index - - const auto padding_left_index = OperandIndex{init_param.inputs[3]}; - const auto padding_right_index = OperandIndex{init_param.inputs[4]}; - const auto padding_top_index = OperandIndex{init_param.inputs[5]}; - const auto padding_bottom_index = OperandIndex{init_param.inputs[6]}; - const auto hstride_index = OperandIndex{init_param.inputs[7]}; - const auto vstride_index = OperandIndex{init_param.inputs[8]}; - const auto multiplier_index = OperandIndex{init_param.inputs[9]}; - const auto activation_index = OperandIndex{init_param.inputs[10]}; - - param.padding.type = PaddingType::EXPLICIT; - param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index, - padding_top_index, padding_bottom_index); - param.stride = makeStride(operands, hstride_index, vstride_index); - param.multiplier = getUint32Scalar(operands, multiplier_index); - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - } - - return new operation::DepthwiseConv2D{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_MAX_POOL_2D] = getPool2DGenerator(operation::Pool2D::PoolType::MAX); - - _map[ANEURALNETWORKS_AVERAGE_POOL_2D] = getPool2DGenerator(operation::Pool2D::PoolType::AVG); - - _map[ANEURALNETWORKS_CONCATENATION] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count >= 2); // At least one one input tensor and axis - assert(init_param.output_count == 1); - - // When there are N + 1 inputs, each input should be interpreted as follows: - // - // [0, N) -> Input tensors - // N -> Axis - // - - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count - 1; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::Concat::Param param; - const OperandIndex axis_index{init_param.inputs[init_param.input_count - 1]}; - param.axis = operands.at(axis_index).asScalar<int32_t>(); - - return new operation::Concat{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_RESHAPE] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> A tensor, specifying the tensor to be reshaped. - // 1 -> A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32, defining the shape of the output - // tensor - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::Reshape::Param param{}; - - return new operation::Reshape{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_FULLY_CONNECTED] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 4 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> A tensor, specifying the input. - // 1 -> A 2-D tensor, specifying the weights - // 2 -> A 1-D tensor, specifying the bias - // 3 -> An INT32 value, and has to be one of the FuseCode values - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::FullyConnected::Param param; - const auto activation_index = OperandIndex{init_param.inputs[3]}; - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - - return new operation::FullyConnected{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_SOFTMAX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> A 2-D or 4-D tensor, specifying the tensor to be reshaped. - // 1 -> FLOAT32 value, specifying the positive scaling factor for the exponent, beta. - - OperandIndexSequence inputs{init_param.inputs[0]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - const auto beta_index = OperandIndex{init_param.inputs[1]}; - - operation::Softmax::Param param; - param.beta = operands.at(beta_index).asScalar<float>(); - - return new operation::Softmax{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_CAST] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::CAST); - - // ANEURALNETWORKS_CAST_EX is deprecated - // TODO Remove ANEURALNETWORKS_CAST_EX - _map[ANEURALNETWORKS_CAST_EX] = _map[ANEURALNETWORKS_CAST]; - - _map[ANEURALNETWORKS_CONV_2D] = [](const OperationFactory::Param &init_param, - Operands &operands) { - using operation::Conv2D; - - // inputCount is either 7 or 10 acccording to NN API specification. - // - Padding is implicit when inputCount is 7 - // - Padding is explicit when inputCount is 10 - assert(init_param.input_count == 7 || init_param.input_count == 10 || - init_param.input_count == 13); - assert(init_param.output_count == 1); - - // 0 -> IFM Tensor Index - // 1 -> Kernel Tensor Index - // 2 -> Bias Tensor Index - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - Conv2D::Param param; - if (init_param.input_count == 7) // support implicit padding - { - // Each input should be interpreted as follows: - // - // 3 -> Padding Code (ANEURALNETWORKS_PADDING_SAME or ANEURALNETWORKS_PADDING_VALID) Index - // 4 -> Stride (width) Index - // 5 -> Stride (height) INdex - // 6 -> Activation Index - - const auto padding_index = OperandIndex{init_param.inputs[3]}; - const auto hstride_index = OperandIndex{init_param.inputs[4]}; - const auto vstride_index = OperandIndex{init_param.inputs[5]}; - const auto activation_index = OperandIndex{init_param.inputs[6]}; - - param.padding.type = - NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>()); - param.stride = makeStride(operands, hstride_index, vstride_index); - - param.dilation.width_factor = 1; - param.dilation.height_factor = 1; - - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - } - else if (init_param.input_count == 10) // support explicit padding - { - // Each input should be interpreted as follows: - // - // 3 -> Padding_left index - // 4 -> Padding_right index - // 5 -> Padding_top index - // 6 -> Padding_bottom index - // 7 -> Stride (width) Index - // 8 -> Stride (height) INdex - // 9 -> Activation Index - - const auto padding_left_index = OperandIndex{init_param.inputs[3]}; - const auto padding_right_index = OperandIndex{init_param.inputs[4]}; - const auto padding_top_index = OperandIndex{init_param.inputs[5]}; - const auto padding_bottom_index = OperandIndex{init_param.inputs[6]}; - const auto hstride_index = OperandIndex{init_param.inputs[7]}; - const auto vstride_index = OperandIndex{init_param.inputs[8]}; - const auto activation_index = OperandIndex{init_param.inputs[9]}; - - param.padding.type = PaddingType::EXPLICIT; - param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index, - padding_top_index, padding_bottom_index); - param.stride = makeStride(operands, hstride_index, vstride_index); - - param.dilation.width_factor = 1; - param.dilation.height_factor = 1; - - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - } - else if (init_param.input_count == 13) // support dilation - { - // Each input should be interpreted as follows: - // - // 3 -> Padding_left Index - // 4 -> Padding_right Index - // 5 -> Padding_top Index - // 6 -> Padding_bottom Index - // 7 -> Stride (width) Index - // 8 -> Stride (height) Index - // 9 -> Activation Index - // 11 -> Dilation (width_factor) Index - // 12 -> Dilation (height_factor) INdex - - const auto padding_left_index = OperandIndex{init_param.inputs[3]}; - const auto padding_right_index = OperandIndex{init_param.inputs[4]}; - const auto padding_top_index = OperandIndex{init_param.inputs[5]}; - const auto padding_bottom_index = OperandIndex{init_param.inputs[6]}; - const auto hstride_index = OperandIndex{init_param.inputs[7]}; - const auto vstride_index = OperandIndex{init_param.inputs[8]}; - const auto activation_index = OperandIndex{init_param.inputs[9]}; - const auto width_factor_index = OperandIndex{init_param.inputs[11]}; - const auto height_factor_index = OperandIndex{init_param.inputs[12]}; - - param.padding.type = PaddingType::EXPLICIT; - param.padding.param = makeExplicitPadding(operands, padding_left_index, padding_right_index, - padding_top_index, padding_bottom_index); - param.stride = makeStride(operands, hstride_index, vstride_index); - - auto width_factor = operands.at(width_factor_index).asScalar<int32_t>(); - auto height_factor = operands.at(height_factor_index).asScalar<int32_t>(); - - param.dilation.width_factor = width_factor; - param.dilation.height_factor = height_factor; - - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - } - else - { - throw std::runtime_error{"Conv2D: unsupported input operand count"}; - } - - return new Conv2D{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_ADD] = - getBinaryArithmeticGenerator(onert::ir::operation::BinaryArithmetic::ArithmeticType::ADD); - - _map[ANEURALNETWORKS_ADDV2_EX] = _map[ANEURALNETWORKS_ADD]; - - _map[ANEURALNETWORKS_REDUCE_SUM] = - getReduceGenerator(onert::ir::operation::Reduce::ReduceType::SUM); - - // ANEURALNETWORKS_REDUCE_SUM_EX is deprecated - // TODO Remove ANEURALNETWORKS_REDUCE_SUM_EX - _map[ANEURALNETWORKS_REDUCE_SUM_EX] = _map[ANEURALNETWORKS_REDUCE_SUM]; - - _map[ANEURALNETWORKS_SUB] = - getBinaryArithmeticGenerator(onert::ir::operation::BinaryArithmetic::ArithmeticType::SUB); - - _map[ANEURALNETWORKS_SLICE] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Begins Tensor Index - // 2 -> Sizes Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - - return new operation::Slice{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_STRIDED_SLICE] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 7 && init_param.output_count == 1); - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2], - init_param.inputs[3]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 1 -> A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the starts of - // the dimensions of the input tensor to be sliced. The length must be - // of rank(input0). - // 2 -> A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the ends of - // the dimensions of the input tensor to be sliced. The length must be - // of rank(input0). - // 3 -> A 1-D Tensor of {@link ANEURALNETWORKS_TENSOR_INT32}, the strides of - // the dimensions of the input tensor to be sliced. The length must be - // of rank(input0). - // 4 -> An {@link ANEURALNETWORKS_INT32} scalar, begin_mask. If the ith bit - // of begin_mask is set, begin[i] is ignored and the fullest possible - // range in that dimension is used instead. - // 5 -> An {@link ANEURALNETWORKS_INT32} scalar, end_mask. If the ith bit of - // end_mask is set, end[i] is ignored and the fullest possible range in - // that dimension is used instead. - // 6 -> An {@link ANEURALNETWORKS_INT32} scalar, shrink_axis_mask. An int32 - // mask. If the ith bit of shrink_axis_mask is set, it implies that the - // ith specification shrinks the dimensionality by 1. A slice of size 1 - // starting from begin[i] in the dimension must be preserved. - - operation::StridedSlice::Param param; - - param.begin_mask = operands.at(OperandIndex{init_param.inputs[4]}).asScalar<std::int32_t>(); - param.end_mask = operands.at(OperandIndex{init_param.inputs[5]}).asScalar<std::int32_t>(); - param.shrink_axis_mask = - operands.at(OperandIndex{init_param.inputs[6]}).asScalar<std::int32_t>(); - - return new operation::StridedSlice{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_TRANSPOSE] = createSimpleBinaryOp<operation::Transpose>; - - _map[ANEURALNETWORKS_MUL] = - getBinaryArithmeticGenerator(onert::ir::operation::BinaryArithmetic::ArithmeticType::MUL); - - _map[ANEURALNETWORKS_SQUEEZE] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 1 || init_param.input_count == 2); - assert(init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> An n-D tensor, the tensor to be squeezed. - // 1 -> An optional 1-D tensor of ANEURALNETWORKS_TENSOR_INT32. The dimensions to squeeze. - // If specified only squeezes the dimensions listed. Otherwise, squeezes all dimensions. - // The dimension index starts at 0. An error must be reported if squeezing a dimension that - // is not 1. - - // Add mandatory input index - OperandIndexSequence inputs{init_param.inputs[0]}; - - // Add dims index if specified - operation::Squeeze::Param param{}; - if (init_param.input_count == 2) - { - auto squeeze_dims_idx = OperandIndex{init_param.inputs[1]}; - assert(operands.at(squeeze_dims_idx).shape().rank() == 1); - assert(operands.at(squeeze_dims_idx).shape().dim(0) >= 0); - assert(static_cast<uint32_t>(operands.at(squeeze_dims_idx).shape().dim(0)) <= - sizeof(param.dims)); - param.ndim = operands.at(squeeze_dims_idx).shape().dim(0); - if (param.ndim > 0) - { - assert(operands.at(squeeze_dims_idx).data()); - memcpy(param.dims, operands.at(squeeze_dims_idx).data()->base(), - param.ndim * sizeof(param.dims[0])); - } - } - - return new operation::Squeeze{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_TANH] = getElementwiseActivationGenerator( - onert::ir::operation::ElementwiseActivation::Type::TANH, 1.f, 1.f); - - _map[ANEURALNETWORKS_LOG] = getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::LOG); - - _map[ANEURALNETWORKS_LOGISTIC] = getElementwiseActivationGenerator( - onert::ir::operation::ElementwiseActivation::Type::LOGISTIC); - - _map[ANEURALNETWORKS_DIV] = - getBinaryArithmeticGenerator(onert::ir::operation::BinaryArithmetic::ArithmeticType::DIV); - - _map[ANEURALNETWORKS_EXP] = getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::EXP); - - // ANEURALNETWORKS_EXP_EX is deprecated - // TODO Remove ANEURALNETWORKS_EXP_EX - _map[ANEURALNETWORKS_EXP_EX] = _map[ANEURALNETWORKS_EXP]; - - // Each input should be interpreted as follows: - // 0 -> Input Tensor Index - // 1 -> Axis Tensor Index - _map[ANEURALNETWORKS_EXPAND_DIMS] = createSimpleBinaryOp<operation::ExpandDims>; - - _map[ANEURALNETWORKS_GREATER] = - getComparisonGenerator(operation::Comparison::ComparisonType::Greater); - _map[ANEURALNETWORKS_GREATER_EQUAL] = - getComparisonGenerator(operation::Comparison::ComparisonType::GreaterEqual); - _map[ANEURALNETWORKS_LESS] = getComparisonGenerator(operation::Comparison::ComparisonType::Less); - _map[ANEURALNETWORKS_LESS_EQUAL] = - getComparisonGenerator(operation::Comparison::ComparisonType::LessEqual); - _map[ANEURALNETWORKS_NOT_EQUAL] = - getComparisonGenerator(operation::Comparison::ComparisonType::NotEqual); - _map[ANEURALNETWORKS_EQUAL] = - getComparisonGenerator(operation::Comparison::ComparisonType::Equal); - - // ANEURALNETWORKS_GREATER_EQUAL_EX is deprecated - // TODO Remove ANEURALNETWORKS_GREATER_EQUAL_EX - _map[ANEURALNETWORKS_GREATER_EQUAL_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input0 Tensor Index - // 1 -> input1 Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::Comparison::Param param; - param.comparison_type = operation::Comparison::ComparisonType::GreaterEqual; - - // Output operand type must be boolean - replaceDataType(operands, outputs.at(0), DataType::BOOL8); - - return new operation::Comparison{inputs, outputs, param}; - }; - - // ANEURALNETWORKS_LESS_EX is deprecated - // TODO Remove ANEURALNETWORKS_LESS_EX - _map[ANEURALNETWORKS_LESS_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input0 Tensor Index - // 1 -> input1 Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::Comparison::Param param; - param.comparison_type = operation::Comparison::ComparisonType::Less; - - // Output operand type must be boolean - replaceDataType(operands, outputs.at(0), DataType::BOOL8); - - return new operation::Comparison{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_REDUCE_ALL] = - getReduceGenerator(onert::ir::operation::Reduce::ReduceType::ALL); - - _map[ANEURALNETWORKS_REDUCE_ANY] = - getReduceGenerator(onert::ir::operation::Reduce::ReduceType::ANY); - - _map[ANEURALNETWORKS_REDUCE_MAX] = - getReduceGenerator(onert::ir::operation::Reduce::ReduceType::MAX); - - // ANEURALNETWORKS_REDUCE_MAX_EX is deprecated - // TODO Remove ANEURALNETWORKS_REDUCE_MAX_EX - _map[ANEURALNETWORKS_REDUCE_MAX_EX] = _map[ANEURALNETWORKS_REDUCE_MAX]; - - // ANEURALNETWORKS_NOT_EQUAL_EX is deprecated - // TODO Remove ANEURALNETWORKS_NOT_EQUAL_EX - _map[ANEURALNETWORKS_NOT_EQUAL_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input1 Tensor Index - // 1 -> input2 Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::Comparison::Param param; - param.comparison_type = operation::Comparison::ComparisonType::NotEqual; - - // Output operand type must be boolean - replaceDataType(operands, outputs.at(0), DataType::BOOL8); - - return new operation::Comparison{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_LOGICAL_AND] = getElementwiseBinaryGenerator( - operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_AND); - - // ANEURALNETWORKS_LOGICAL_AND_EX is deprecated - // TODO Remove ANEURALNETWORKS_LOGICAL_AND_EX - _map[ANEURALNETWORKS_LOGICAL_AND_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input0 Tensor Index - // 1 -> input1 Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - // This operation's operands must be boolean type. - replaceDataType(operands, inputs.at(0), DataType::BOOL8); - replaceDataType(operands, inputs.at(1), DataType::BOOL8); - replaceDataType(operands, outputs.at(0), DataType::BOOL8); - - operation::ElementwiseBinary::Param param; - param.op_type = operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_AND; - - return new operation::ElementwiseBinary{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_RSQRT] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::RSQRT); - - _map[ANEURALNETWORKS_SELECT] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Condition Tensor Index - // 1 -> Input X(true) Tensor Index - // 2 -> Input Y(false) Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - - return new operation::Select{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_SELECT_V2_EX] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Condition Tensor Index - // 1 -> Input X(true) Tensor Index - // 2 -> Input Y(false) Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - - return new operation::Select{inputs, outputs}; - }; - - // ANEURALNETWORKS_RSQRT_EX is deprecated - // TODO Remove ANEURALNETWORKS_RSQRT_EX - _map[ANEURALNETWORKS_RSQRT_EX] = _map[ANEURALNETWORKS_RSQRT]; - - _map[ANEURALNETWORKS_RELU] = - getElementwiseActivationGenerator(onert::ir::operation::ElementwiseActivation::Type::RELU, - onert::ir::operation::ElementwiseActivation::infinity, 0); - - _map[ANEURALNETWORKS_RESIZE_BILINEAR] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> IFM Index - // 1 -> Height Index - // 2 -> Width Index - OperandIndexSequence inputs{init_param.inputs[0]}; - - operation::ResizeBilinear::Param param; - param.height_out = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<int32_t>(); - param.width_out = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>(); - param.align_corners = false; - param.half_pixel_centers = false; - return new operation::ResizeBilinear{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_RESIZE_NEAREST_NEIGHBOR] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert((init_param.input_count == 3 || init_param.input_count == 4) && - init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> IFM Index - // 1 -> Height Index - // 2 -> Width Index - OperandIndexSequence inputs{init_param.inputs[0]}; - - operation::ResizeNearestNeighbor::Param param; - param.height_out = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<int32_t>(); - param.width_out = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>(); - param.align_corners = false; - // The layout input is not supported yet - return new operation::ResizeNearestNeighbor{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_RELU1] = getElementwiseActivationGenerator( - onert::ir::operation::ElementwiseActivation::Type::RELU, 1.f, -1.f); - - _map[ANEURALNETWORKS_RELU6] = getElementwiseActivationGenerator( - onert::ir::operation::ElementwiseActivation::Type::RELU, 6.f, 0.f); - - _map[ANEURALNETWORKS_REVERSE_EX] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Axis Tensor Index - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - return new operation::Reverse{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_RNN] = [](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 6 && init_param.output_count == 2); - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Weights Tensor Index - // 2 -> Recurrent Weights Tensor Index - // 3 -> Bias Tensor Index - // 4 -> Hidden state (in) Index - // 5 -> Activation Index - - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count - 1; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - OperandIndexSequence outputs; - for (uint32_t n = 0; n < init_param.output_count; ++n) - { - outputs.append(OperandIndex{init_param.outputs[n]}); - } - - operation::RNN::Param param; - const auto activation_index = OperandIndex{init_param.inputs[5]}; - param.activation = - NNAPIConvert::getFusedActivation(operands.at(activation_index).asScalar<FuseCode>()); - - return new operation::RNN{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_FLOOR] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::FLOOR); - - _map[ANEURALNETWORKS_SPACE_TO_BATCH_ND] = [](const OperationFactory::Param &init_param, - Operands &) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Block size Index - // 2 -> Paddings Index - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - - return new operation::SpaceToBatchND{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_SPACE_TO_DEPTH] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Block size Index - OperandIndexSequence inputs{init_param.inputs[0]}; - - operation::SpaceToDepth::Param param; - param.block_size = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>(); - - return new operation::SpaceToDepth{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_L2_POOL_2D] = getPool2DGenerator(operation::Pool2D::PoolType::L2); - - _map[ANEURALNETWORKS_EMBEDDING_LOOKUP] = [](const OperationFactory::Param &init_param, - Operands &) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Lookups Index - // 1 -> Values Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - return new operation::EmbeddingLookup{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_L2_NORMALIZATION] = [](const OperationFactory::Param &init_param, - Operands &) { - assert(init_param.input_count == 1 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // 0 -> input Tensor Index - OperandIndexSequence inputs{init_param.inputs[0]}; - - return new operation::L2Normalization{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_HASHTABLE_LOOKUP] = [](const OperationFactory::Param &init_param, - Operands &) { - assert(init_param.input_count == 3 && init_param.output_count == 2); - - // Each output should be interpreted as follows: - // - // 0 -> Output Index - // 1 -> Hits Index - OperandIndexSequence outputs{init_param.outputs[0], init_param.outputs[1]}; - - // Each input should be interpreted as follows: - // - // 0 -> Lookups Index - // 1 -> Keys Index - // 2 -> Values Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - - return new operation::HashtableLookup{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_PRELU] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input Tensor Index - // 1 -> alpha Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - return new operation::PReLU{inputs, outputs}; - }; - - // ANEURALNETWORKS_PRELU_EX is deprecated - // TODO Remove ANEURALNETWORKS_PRELU_EX - _map[ANEURALNETWORKS_PRELU_EX] = _map[ANEURALNETWORKS_PRELU]; - - _map[ANEURALNETWORKS_TRANSPOSE_CONV_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 6 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Output Shape Index - // 1 -> Weights Index - // 2 -> Input Tensor Index - // 3 -> Padding Type - // 4 -> Stride width - // 5 -> Stride height - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - - operation::TransposeConv::Param param; - - const auto padding_index = OperandIndex{init_param.inputs[3]}; - const auto hstride_index = OperandIndex{init_param.inputs[4]}; - const auto vstride_index = OperandIndex{init_param.inputs[5]}; - - param.padding.type = - NNAPIConvert::getPaddingType(operands.at(padding_index).asScalar<PaddingCode>()); - param.stride = makeStride(operands, hstride_index, vstride_index); - - return new operation::TransposeConv{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_SQRT] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::SQRT); - - // ANEURALNETWORKS_SQRT_EX is deprecated - // TODO Remove ANEURALNETWORKS_SQRT_EX - _map[ANEURALNETWORKS_SQRT_EX] = _map[ANEURALNETWORKS_SQRT]; - - _map[ANEURALNETWORKS_LOGICAL_OR] = getElementwiseBinaryGenerator( - operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR); - - // ANEURALNETWORKS_LOGICAL_OR_EX is deprecated - // TODO Remove ANEURALNETWORKS_LOGICAL_OR_EX - _map[ANEURALNETWORKS_LOGICAL_OR_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input0 Tensor Index - // 1 -> input1 Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - // This operation's operands must be boolean type. - replaceDataType(operands, inputs.at(0), DataType::BOOL8); - replaceDataType(operands, inputs.at(1), DataType::BOOL8); - replaceDataType(operands, outputs.at(0), DataType::BOOL8); - - operation::ElementwiseBinary::Param param; - param.op_type = operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR; - - return new operation::ElementwiseBinary{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_LOGICAL_NOT] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::LOGICAL_NOT); - - // ANEURALNETWORKS_LOGICAL_NOT_EX is deprecated - // TODO Remove ANEURALNETWORKS_LOGICAL_NOT_EX - _map[ANEURALNETWORKS_LOGICAL_NOT_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 1 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input Tensor Index - OperandIndexSequence inputs{init_param.inputs[0]}; - - // This operation's operands must be boolean type. - replaceDataType(operands, inputs.at(0), DataType::BOOL8); - replaceDataType(operands, outputs.at(0), DataType::BOOL8); - - operation::ElementwiseUnary::Param param; - param.op_type = operation::ElementwiseUnary::Type::LOGICAL_NOT; - - return new operation::ElementwiseUnary{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_LSTM] = [](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 23 && init_param.output_count == 4); - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Input to Input Tensor Index - // 2 -> Input to Forget Tensor Index - // 3 -> Input to Cell Tensor Index - // 4 -> Input to Output Tensor Index - // 5 -> Recurrent to Input Weights Tensor Index - // 6 -> Recurrent to Forget Weights Tensor Index - // 7 -> Recurrent to Cell Weights Tensor Index - // 8 -> Recurrent to Output Weights Tensor Index - // 9 -> Cell to Input Weights Tensor Index - // 10 -> Cell to Forget Weights Tensor Index - // 11 -> Cell to Output Weights Tensor Index - // 12 -> Input Gate Bias Tensor Index - // 13 -> Forget Gate Bias Tensor Index - // 14 -> Cell Bias Tensor Index - // 15 -> Output Gate Bias Tensor Index - // 16 -> Projection Weights Tensor Index - // 17 -> Projection Bias Tensor Index - // 18 -> Output State In Tensor Index - // 19 -> Cell State In Tensor Index - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count - 3; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - - // Each output should be interpreted as follows: - // - // 0 -> Scratch Buffer Tensor Index - // 1 -> Output State Out Tensor Index - // 2 -> Cell State Out Tensor Index - // 3 -> Output Tensor Index - OperandIndexSequence outputs; - for (uint32_t n = 0; n < init_param.output_count; ++n) - { - outputs.append(OperandIndex{init_param.outputs[n]}); - } - - operation::LSTM::Param param; - const auto activation_index = OperandIndex{init_param.inputs[20]}; - switch (operands.at(activation_index).asScalar<int32_t>()) - { - case 0: - param.activation = Activation::NONE; - break; - case 1: - param.activation = Activation::RELU; - break; - case 2: - param.activation = Activation::RELU1; - break; - case 3: - param.activation = Activation::RELU6; - break; - case 4: - param.activation = Activation::TANH; - break; - case 6: - param.activation = Activation::SIGMOID; - break; - default: - throw std::runtime_error("Unsupported activation type"); - break; - } - param.cell_threshold = operands.at(OperandIndex{init_param.inputs[21]}).asScalar<float>(); - param.projection_threshold = operands.at(OperandIndex{init_param.inputs[22]}).asScalar<float>(); - // This is initialization to prevent warning or error by static code analyzer. LSTM operation - // does not need time_major - param.time_major = false; - - return new operation::LSTM{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_LSTM] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert((init_param.input_count >= 24 || init_param.input_count <= 28) && - (init_param.output_count >= 1 && init_param.output_count <= 3)); - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Input to Input Tensor Index - // 2 -> Input to Forget Tensor Index - // 3 -> Input to Cell Tensor Index - // 4 -> Input to Output Tensor Index - // 5 -> Recurrent to Input Weights Tensor Index - // 6 -> Recurrent to Forget Weights Tensor Index - // 7 -> Recurrent to Cell Weights Tensor Index - // 8 -> Recurrent to Output Weights Tensor Index - // 9 -> Cell to Input Weights Tensor Index - // 10 -> Cell to Forget Weights Tensor Index - // 11 -> Cell to Output Weights Tensor Index - // 12 -> Input Gate Bias Tensor Index - // 13 -> Forget Gate Bias Tensor Index - // 14 -> Cell Bias Tensor Index - // 15 -> Output Gate Bias Tensor Index - // 16 -> Projection Weights Tensor Index - // 17 -> Projection Bias Tensor Index - // 18 -> Output State In Tensor Index - // 19 -> Cell State In Tensor Index - assert(init_param.input_count - 3 > 20); - OperandIndexSequence inputs; - for (uint32_t n = 0; n < 20; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - - // 24 -> Input Layer Normalization Weights Tensor Index - // 25 -> Forget Layer Normalization Weights Tensor Index - // 26 -> Cell Layer Normalization Weights Tensor Index - // 27 -> Output Layer Normalization Weights Tensor Index - if (init_param.input_count > 24) - { - for (uint32_t n = 24; n < 28; ++n) - { - if (init_param.input_count > n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - } - } - - // Each output should be interpreted as follows: - // - // 0 -> Output Tensor Index -> 3 - // 1 -> Output State Out Tensor Index - // 2 -> Cell State Out Tensor Index - const OperandIndex scratch_buffer_index; - OperandIndex output_state_index = - init_param.output_count >= 2 ? OperandIndex{init_param.outputs[1]} : OperandIndex(); - OperandIndex cell_state_index = - init_param.output_count >= 3 ? OperandIndex{init_param.outputs[2]} : OperandIndex(); - const OperandIndex output_index = OperandIndex{init_param.outputs[0]}; - OperandIndexSequence outputs{scratch_buffer_index, output_state_index, cell_state_index, - output_index}; - - operation::LSTM::Param param; - const auto activation_index = OperandIndex{init_param.inputs[20]}; - switch (operands.at(activation_index).asScalar<int32_t>()) - { - case 0: - param.activation = Activation::NONE; - break; - case 1: - param.activation = Activation::RELU; - break; - case 2: - param.activation = Activation::RELU1; - break; - case 3: - param.activation = Activation::RELU6; - break; - case 4: - param.activation = Activation::TANH; - break; - case 6: - param.activation = Activation::SIGMOID; - break; - default: - throw std::runtime_error("Unsupported activation type"); - break; - } - param.cell_threshold = operands.at(OperandIndex{init_param.inputs[21]}).asScalar<float>(); - param.projection_threshold = operands.at(OperandIndex{init_param.inputs[22]}).asScalar<float>(); - param.time_major = operands.at(OperandIndex{init_param.inputs[23]}).asScalar<bool>(); - - return new operation::LSTM{inputs, outputs, param}; - }; - - // ANEURALNETWORKS_EQUAL_EX is deprecated - // TODO Remove ANEURALNETWORKS_EQUAL_EX - _map[ANEURALNETWORKS_EQUAL_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input0 Tensor Index - // 1 -> input1 Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::Comparison::Param param; - param.comparison_type = operation::Comparison::ComparisonType::Equal; - - // Output operand type must be boolean - replaceDataType(operands, outputs.at(0), DataType::BOOL8); - - return new operation::Comparison{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_SQUARED_DIFFERENCE_EX] = [](const OperationFactory::Param &init_param, - Operands &) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> LHS Tensor Index - // 1 -> RHS Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - return new operation::SquaredDifference{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_TOPK_V2] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 2); - - // Each output should be interpreted as follows: - // - // 0 -> Index for Output Values - // 1 -> Index for Output Indices - OperandIndexSequence outputs{init_param.outputs[0], init_param.outputs[1]}; - - // Each input should be interpreted as follows: - // - // 0 -> Index for Input Data - // 1 -> Index for K - OperandIndexSequence inputs{init_param.inputs[0]}; - - operation::TopKV2::Param param; - param.k = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>(); - - return new operation::TopKV2{inputs, outputs, param}; - }; - - // ANEURALNETWORKS_CAST_EX is deprecated - // TODO Remove ANEURALNETWORKS_CAST_EX - _map[ANEURALNETWORKS_TOPK_V2_EX] = _map[ANEURALNETWORKS_TOPK_V2]; - - _map[ANEURALNETWORKS_GATHER] = [](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> input Tensor Index - // 1 -> axis Index - // 2 -> indices Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[2]}; - - operation::Gather::Param param; - param.axis = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<int32_t>(); - - return new operation::Gather{inputs, outputs, param}; - }; - - // ANEURALNETWORKS_GATHER_EX is deprecated - // TODO Remove ANEURALNETWORKS_GATHER_EX - _map[ANEURALNETWORKS_GATHER_EX] = _map[ANEURALNETWORKS_GATHER]; - - _map[ANEURALNETWORKS_NEG] = getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::NEG); - - // ANEURALNETWORKS_NEG_EX is deprecated - // TODO Remove ANEURALNETWORKS_NEG_EX - _map[ANEURALNETWORKS_NEG_EX] = _map[ANEURALNETWORKS_NEG]; - - _map[ANEURALNETWORKS_ABS] = getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::ABS); - - // ANEURALNETWORKS_ABS_EX is deprecated - // TODO Remove ANEURALNETWORKS_ABS_EX - _map[ANEURALNETWORKS_ABS_EX] = _map[ANEURALNETWORKS_ABS]; - - _map[ANEURALNETWORKS_ARGMAX] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Axis Tensor Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::ArgMax::Param param; - // NNAPI ARGMAX output type is always int32 - param.output_type = DataType::INT32; - - return new operation::ArgMax{inputs, outputs, param}; - }; - - // ANEURALNETWORKS_ARGMAX_EX is deprecated - // TODO Remove ANEURALNETWORKS_ARGMAX_EX - _map[ANEURALNETWORKS_ARGMAX_EX] = _map[ANEURALNETWORKS_ARGMAX]; - - _map[ANEURALNETWORKS_DEQUANTIZE] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::DEQUANTIZE); - - _map[ANEURALNETWORKS_MEAN] = [](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> ifm Tensor Index - // 1 -> axis Tensor Index - // 2 -> keep_dims Index - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::Reduce::Param param; - param.reduce_type = operation::Reduce::ReduceType::MEAN; - param.keep_dims = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<int32_t>() != 0; - - return new operation::Reduce{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 5 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - OperandIndexSequence inputs{init_param.inputs[0]}; - - operation::LocalResponseNormalization::Param param; - param.radius = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>(); - param.bias = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<float>(); - param.alpha = operands.at(OperandIndex{init_param.inputs[3]}).asScalar<float>(); - param.beta = operands.at(OperandIndex{init_param.inputs[4]}).asScalar<float>(); - - return new operation::LocalResponseNormalization{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_DEPTH_TO_SPACE] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Input Tensor Index - // 1 -> Block size Index - OperandIndexSequence inputs{init_param.inputs[0]}; - - operation::DepthToSpace::Param param; - param.block_size = operands.at(OperandIndex{init_param.inputs[1]}).asScalar<std::int32_t>(); - - return new operation::DepthToSpace{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_PACK_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count >= 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count - 2; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - - operation::Pack::Param param; - const auto num_index = OperandIndex{init_param.inputs[init_param.input_count - 2]}; - const auto axis_index = OperandIndex{init_param.inputs[init_param.input_count - 1]}; - param.num = operands.at(num_index).asScalar<int32_t>(); - param.axis = operands.at(axis_index).asScalar<int32_t>(); - - return new operation::Pack{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_REDUCE_MIN] = - getReduceGenerator(onert::ir::operation::Reduce::ReduceType::MIN); - - // ANEURALNETWORKS_REDUCE_MIN_EX is deprecated - // TODO Remove ANEURALNETWORKS_REDUCE_MIN_EX - _map[ANEURALNETWORKS_REDUCE_MIN_EX] = _map[ANEURALNETWORKS_REDUCE_MIN]; - - _map[ANEURALNETWORKS_SPLIT] = [](const OperationFactory::Param &init_param, Operands &operands) { - assert(init_param.input_count == 3); - assert(init_param.output_count >= 1); // At least one output tensor and axis - - OperandIndexSequence inputs{init_param.inputs[1], init_param.inputs[0]}; - OperandIndexSequence outputs; - for (uint32_t n = 0; n < init_param.output_count; ++n) - { - outputs.append(OperandIndex{init_param.outputs[n]}); - } - - operation::Split::Param param; - param.num_splits = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<std::int32_t>(); - - return new operation::Split{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_SPLIT_V_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 4); - assert(init_param.output_count >= 1); // At least one output tensor and axis - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - OperandIndexSequence outputs; - for (uint32_t n = 0; n < init_param.output_count; ++n) - { - outputs.append(OperandIndex{init_param.outputs[n]}); - } - - operation::SplitV::Param param; - param.num_splits = operands.at(OperandIndex{init_param.inputs[3]}).asScalar<std::int32_t>(); - return new operation::SplitV{inputs, outputs, param}; - }; - - // ANEURALNETWORKS_SPLIT_EX is deprecated - // TODO Remove ANEURALNETWORKS_SPLIT_EX - _map[ANEURALNETWORKS_SPLIT_EX] = _map[ANEURALNETWORKS_SPLIT]; - - _map[ANEURALNETWORKS_UNPACK_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 3 && init_param.output_count >= 1); - - OperandIndexSequence inputs{init_param.inputs[0]}; - OperandIndexSequence outputs; - for (uint32_t n = 0; n < init_param.output_count; ++n) - { - outputs.append(OperandIndex{init_param.outputs[n]}); - } - - operation::Unpack::Param param; - const auto num_index = OperandIndex{init_param.inputs[1]}; - const auto axis_index = OperandIndex{init_param.inputs[2]}; - param.num = operands.at(num_index).asScalar<int32_t>(); - param.axis = operands.at(axis_index).asScalar<int32_t>(); - - return new operation::Unpack{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_PAD] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count >= 2 && init_param.input_count <= 3 && - init_param.output_count >= 1); - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - if (init_param.input_count == 3) - { - inputs.append(OperandIndex{init_param.inputs[2]}); - } - OperandIndexSequence outputs{init_param.outputs[0]}; - - return new operation::Pad{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_PAD_V2] = _map[ANEURALNETWORKS_PAD]; - - _map[ANEURALNETWORKS_MINIMUM] = - getElementwiseBinaryGenerator(operation::ElementwiseBinary::ElementwiseBinaryType::MIN); - - _map[ANEURALNETWORKS_MAXIMUM] = - getElementwiseBinaryGenerator(operation::ElementwiseBinary::ElementwiseBinaryType::MAX); - - _map[ANEURALNETWORKS_ONE_HOT_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 5); - assert(init_param.output_count == 1); - // Each input should be interpreted as follows: - // - // 0 -> indices tensor - // 1 -> depth tensor - // 2 -> on_value tensor - // 3 -> off_value tensor - // 4 -> axis scalar - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count - 1; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::OneHot::Param param; - param.axis = operands.at(OperandIndex{init_param.inputs[4]}).asScalar<std::int32_t>(); - - return new operation::OneHot{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_COS_EX] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::COS); - - _map[ANEURALNETWORKS_SIN] = getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::SIN); - - _map[ANEURALNETWORKS_SHAPE_EX] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 1 && init_param.output_count == 1); - - OperandIndexSequence inputs{init_param.inputs[0]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - return new operation::Shape{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_REDUCE_PROD] = - getReduceGenerator(onert::ir::operation::Reduce::ReduceType::PROD); - - _map[ANEURALNETWORKS_ROUND_EX] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::ROUND); - - _map[ANEURALNETWORKS_RANGE_EX] = [](const OperationFactory::Param &init_param, Operands &) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // 0 -> start Tensor Index - // 1 -> limit Tensor Index - // 2 -> delta Tensor Index - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - - return new operation::Range{inputs, outputs}; - }; - - // Each input should be interpreted as follows: - // 0 -> LHS Tensor Index - // 1 -> RHS Tensor Index - _map[ANEURALNETWORKS_POW] = createSimpleBinaryOp<operation::Pow>; - - // Each input should be interpreted as follows: - // 0 -> A tensor, specifying the input. - // 1 -> A 1-D tensor, specifying the value - _map[ANEURALNETWORKS_FILL_EX] = createSimpleBinaryOp<operation::Fill>; - - _map[ANEURALNETWORKS_ZEROS_LIKE_EX] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::ZEROS_LIKE); - // Each input should be interpreted as follows: - // 0 -> Input Tensor Index - // 1 -> Multiple Tensor Index - _map[ANEURALNETWORKS_TILE] = createSimpleBinaryOp<operation::Tile>; - - _map[ANEURALNETWORKS_MATRIX_BAND_PART_EX] = [](const OperationFactory::Param &init_param, - Operands &) { - assert(init_param.input_count == 3); - assert(init_param.output_count == 1); - // Each input should be interpreted as follows: - // - // 0 -> A tensor, input - // 1 -> A 0-D tensor, number of lower diagnonals to keep - // 2 -> A 0-D tensor, number of upper diagnonals to keep - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1], init_param.inputs[2]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - return new operation::MatrixBandPart{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_BATCH_MATMUL_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 4 && init_param.output_count == 1); - - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Lhs Tensor Index - // 1 -> Rhs Tensor Index - // 2 -> adj_x boolean scalar Index - // 3 -> adj_y boolean scalar Index - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - operation::BatchMatMul::Param param; - param.adj_x = operands.at(OperandIndex{init_param.inputs[2]}).asScalar<bool>(); - param.adj_y = operands.at(OperandIndex{init_param.inputs[3]}).asScalar<bool>(); - - return new operation::BatchMatMul{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_EINSUM_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - // Each input should be interpreted as follows: - // - // 0....n - 1 -> n Input Tensors Index - // n -> equation - assert(init_param.input_count >= 1 && init_param.output_count == 1); - - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count - 1; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::Einsum::Param param; - const OperandIndex equation_index{init_param.inputs[init_param.input_count - 1]}; - std::vector<char> equation_vector = operands.at(equation_index).asVector<char>(); - param.equation = std::string(equation_vector.begin(), equation_vector.end()); - - return new operation::Einsum{inputs, outputs, param}; - }; - - // 0 -> Input Tensor Index - // 1 -> int32, int64, An 1-D int tensor Index - _map[ANEURALNETWORKS_BROADCAST_TO_EX] = createSimpleBinaryOp<operation::BroadcastTo>; - - _map[ANEURALNETWORKS_STATELESS_RANDOM_UNIFORM_EX] = [](const OperationFactory::Param &init_param, - Operands &) { - assert(init_param.input_count == 2 && init_param.output_count == 1); - OperandIndexSequence outputs{init_param.outputs[0]}; - - // Each input should be interpreted as follows: - // - // 0 -> Shape Tensor Index - // 1 -> int32, int64, An 1-D int tensor Index - - OperandIndexSequence inputs{init_param.inputs[0], init_param.inputs[1]}; - - return new operation::StatelessRandomUniform{inputs, outputs}; - }; - - _map[ANEURALNETWORKS_FUSED_BATCH_NORM_V3_EX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - // Each input should be interpreted as follows: - // - // 0....4 -> 5 Input Tensors Index - // n-2 -> is_training - // n-1 -> data_format - // n -> epsilon - - assert(init_param.input_count == 8 && init_param.output_count == 1); - - OperandIndexSequence inputs; - for (uint32_t n = 0; n < init_param.input_count - 3; ++n) - { - inputs.append(OperandIndex{init_param.inputs[n]}); - } - OperandIndexSequence outputs{init_param.outputs[0]}; - - operation::FusedBatchNorm::Param param; - const OperandIndex is_training_index{init_param.inputs[init_param.input_count - 3]}; - param.is_training = operands.at(is_training_index).asScalar<bool>(); - - const OperandIndex data_format_index{init_param.inputs[init_param.input_count - 2]}; - std::vector<char> data_format_vector = operands.at(data_format_index).asVector<char>(); - param.data_format = std::string(data_format_vector.begin(), data_format_vector.end()); - - const OperandIndex epsilon_index{init_param.inputs[init_param.input_count - 1]}; - param.epsilon = operands.at(epsilon_index).asScalar<float>(); - return new operation::FusedBatchNorm{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_LOG_SOFTMAX] = [](const OperationFactory::Param &init_param, - Operands &operands) { - assert(init_param.input_count == 3 && init_param.output_count == 1); - - // Each input should be interpreted as follows: - // - // 0 -> A tensor specifying the input logits. - // 1 -> A scalar, specifying the positive scaling factor for the exponent, beta. - // 2 -> An scalar specifying the axis to reduce across. - - OperandIndexSequence inputs{init_param.inputs[0]}; - OperandIndexSequence outputs{init_param.outputs[0]}; - - const auto beta_index = OperandIndex{init_param.inputs[1]}; - const auto axis_index = OperandIndex{init_param.inputs[2]}; - - operation::LogSoftmax::Param param; - param.beta = operands.at(beta_index).asScalar<float>(); - param.axis = operands.at(axis_index).asScalar<int>(); - - return new operation::LogSoftmax{inputs, outputs, param}; - }; - - _map[ANEURALNETWORKS_QUANTIZE] = - getElementwiseUnaryGenerator(operation::ElementwiseUnary::Type::QUANTIZE); -} - -Operation *OperationFactory::create(ANeuralNetworksOperationType type, - const OperationFactory::Param ¶m, Operands &operands) -{ - auto it = _map.find(type); - if (it == _map.end()) - { - throw std::runtime_error("Unsupported operation type: " + std::to_string(type)); - } - return it->second(param, operands); -} diff --git a/runtime/onert/frontend/nnapi/wrapper/OperationFactory.h b/runtime/onert/frontend/nnapi/wrapper/OperationFactory.h deleted file mode 100644 index 367cf74db..000000000 --- a/runtime/onert/frontend/nnapi/wrapper/OperationFactory.h +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __OPERATION_FACTORY_H__ -#define __OPERATION_FACTORY_H__ - -#include <unordered_map> - -#include "ir/Operands.h" -#include "ir/Operation.h" -#include "NeuralNetworks.h" -#include "NeuralNetworksEx.h" - -/** - * @brief A class to create a onert operation object from NN API input parameters - */ -class OperationFactory -{ -public: - struct Param - { - uint32_t input_count; - const uint32_t *inputs; - uint32_t output_count; - const uint32_t *outputs; - }; - -public: - using Generator = - std::function<onert::ir::Operation *(const OperationFactory::Param &, onert::ir::Operands &)>; - -public: - static OperationFactory &get(); - -private: - OperationFactory(); - -public: - onert::ir::Operation *create(ANeuralNetworksOperationType, const OperationFactory::Param ¶m, - onert::ir::Operands &operands); - // TODO add "register" method for separating registration, possibly supporting custom-ops - -private: - std::unordered_map<ANeuralNetworksOperationType, Generator> _map; -}; - -#endif // __OPERATION_FACTORY_H__ diff --git a/runtime/onert/frontend/tflite/CMakeLists.txt b/runtime/onert/frontend/tflite/CMakeLists.txt deleted file mode 100644 index 604a9e4cb..000000000 --- a/runtime/onert/frontend/tflite/CMakeLists.txt +++ /dev/null @@ -1,14 +0,0 @@ -if(NOT BUILD_TFLITE_LOADER) - return() -endif(NOT BUILD_TFLITE_LOADER) - -set(TFLITE_LOADER_SOURCES src/tflite_loader.cc) - -add_library(tflite_loader SHARED ${TFLITE_LOADER_SOURCES}) - -target_include_directories(tflite_loader PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) - -target_link_libraries(tflite_loader PRIVATE onert_core) -target_link_libraries(tflite_loader PRIVATE base_loader nnfw_common nnfw_coverage) - -install(TARGETS tflite_loader DESTINATION lib) diff --git a/runtime/onert/frontend/tflite/include/tflite_loader.h b/runtime/onert/frontend/tflite/include/tflite_loader.h deleted file mode 100644 index 743c05f9e..000000000 --- a/runtime/onert/frontend/tflite/include/tflite_loader.h +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __TFLITE_TFLITE_LOADER_H__ -#define __TFLITE_TFLITE_LOADER_H__ - -#include "ir/Graph.h" - -#include <memory> - -namespace onert -{ -namespace tflite_loader -{ - -std::unique_ptr<ir::Subgraphs> loadModel(const char *filename); - -} // namespace tflite_loader -} // namespace onert - -#endif // __TFLITE_TFLITE_LOADER_H__ diff --git a/runtime/onert/frontend/tflite/src/tflite_loader.cc b/runtime/onert/frontend/tflite/src/tflite_loader.cc deleted file mode 100644 index fe4295ada..000000000 --- a/runtime/onert/frontend/tflite/src/tflite_loader.cc +++ /dev/null @@ -1,126 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "tflite_loader.h" -#include "base_loader.h" -#include "tflite_schema_generated.h" - -namespace onert -{ -namespace tflite_loader -{ - -namespace -{ - -struct LoaderDomain -{ - using Verifier = flatbuffers::Verifier; - using ActivationFunctionType = onert_tflite::ActivationFunctionType; - using Buffer = onert_tflite::Buffer; - using BuiltinOperator = onert_tflite::BuiltinOperator; - using CustomOptionsFormat = onert_tflite::CustomOptionsFormat; - using Model = onert_tflite::Model; - using Operator = onert_tflite::Operator; - using Padding = onert_tflite::Padding; - using Pool2DOptions = onert_tflite::Pool2DOptions; - using Tensor = onert_tflite::Tensor; - using TensorType = onert_tflite::TensorType; - using SubGraph = onert_tflite::SubGraph; - using DimensionType = onert_tflite::DimensionType; - using SparseIndexVector = onert_tflite::SparseIndexVector; - - static const char *EnumNameBuiltinOperator(BuiltinOperator e) - { - return onert_tflite::EnumNameBuiltinOperator(e); - } - static const char *EnumNameActivationFunctionType(ActivationFunctionType e) - { - return onert_tflite::EnumNameActivationFunctionType(e); - } - static const char *EnumNameTensorType(TensorType e) - { - return onert_tflite::EnumNameTensorType(e); - } - static const Model *GetModel(const void *buf) { return onert_tflite::GetModel(buf); } - static bool VerifyModelBuffer(Verifier &verifier) - { - return onert_tflite::VerifyModelBuffer(verifier); - } -}; - -class TFLiteLoader final : public base_loader::BaseLoader<LoaderDomain> -{ -public: - using BaseLoader::BaseLoader; - - bool allowOptionalInputTensor(BuiltinOperator op) override - { - switch (op) - { - case BuiltinOperator::BuiltinOperator_FULLY_CONNECTED: - return true; - default: - return false; - } - } - -private: - std::unique_ptr<ir::Graph> loadSubgraph(const onert_tflite::SubGraph *tflite_subg) override - { - auto subg = std::make_unique<ir::Graph>(); - // Load tensors - _tensor_to_operand.resize(tflite_subg->tensors()->size()); - for (flatbuffers::uoffset_t i = 0; i < tflite_subg->tensors()->size(); ++i) - { - _tensor_to_operand[i] = loadOperand(tflite_subg->tensors()->Get(i), *subg); - } - // Set inputs - for (const std::int32_t input_ind : *tflite_subg->inputs()) - { - subg->addInput(tensorIdxToOperandIdx(input_ind), - _tensor_names.at(_tensor_to_operand[input_ind])); - } - // Set outputs - for (const std::int32_t output_ind : *tflite_subg->outputs()) - { - subg->addOutput(tensorIdxToOperandIdx(output_ind), - _tensor_names.at(_tensor_to_operand[output_ind])); - } - // Create operations - for (const auto *op : *tflite_subg->operators()) - { - loadOperation(op, *subg); - } - - subg->finishBuilding(); - - return subg; - } -}; - -} // namespace - -std::unique_ptr<ir::Subgraphs> loadModel(const char *filename) -{ - auto subgraphs = std::make_unique<ir::Subgraphs>(); - TFLiteLoader loader(subgraphs); - loader.loadFromFile(filename); - return subgraphs; -} - -} // namespace tflite_loader -} // namespace onert diff --git a/runtime/onert/frontend/tflite/src/tflite_schema_generated.h b/runtime/onert/frontend/tflite/src/tflite_schema_generated.h deleted file mode 100644 index c6e9147cd..000000000 --- a/runtime/onert/frontend/tflite/src/tflite_schema_generated.h +++ /dev/null @@ -1,9553 +0,0 @@ -/* - * Copyright (c) 2019-2020 Samsung Electronics Co., Ltd. All Rights Reserved - * Copyright 2018 The TensorFlow Authors. All Rights Reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -// automatically generated by the FlatBuffers compiler, do not modify - -#ifndef FLATBUFFERS_GENERATED_TFLITESCHEMA_ONERT_TFLITE_H_ -#define FLATBUFFERS_GENERATED_TFLITESCHEMA_ONERT_TFLITE_H_ - -#include "flatbuffers/flatbuffers.h" - -namespace onert_tflite -{ - -struct CustomQuantization; - -struct QuantizationParameters; - -struct Int32Vector; - -struct Uint16Vector; - -struct Uint8Vector; - -struct DimensionMetadata; - -struct SparsityParameters; - -struct Tensor; - -struct Conv2DOptions; - -struct Pool2DOptions; - -struct DepthwiseConv2DOptions; - -struct ConcatEmbeddingsOptions; - -struct LSHProjectionOptions; - -struct SVDFOptions; - -struct RNNOptions; - -struct SequenceRNNOptions; - -struct BidirectionalSequenceRNNOptions; - -struct FullyConnectedOptions; - -struct SoftmaxOptions; - -struct ConcatenationOptions; - -struct AddOptions; - -struct MulOptions; - -struct L2NormOptions; - -struct LocalResponseNormalizationOptions; - -struct LSTMOptions; - -struct UnidirectionalSequenceLSTMOptions; - -struct BidirectionalSequenceLSTMOptions; - -struct ResizeBilinearOptions; - -struct ResizeNearestNeighborOptions; - -struct CallOptions; - -struct PadOptions; - -struct PadV2Options; - -struct ReshapeOptions; - -struct SpaceToBatchNDOptions; - -struct BatchToSpaceNDOptions; - -struct SkipGramOptions; - -struct SpaceToDepthOptions; - -struct DepthToSpaceOptions; - -struct SubOptions; - -struct DivOptions; - -struct TopKV2Options; - -struct EmbeddingLookupSparseOptions; - -struct GatherOptions; - -struct TransposeOptions; - -struct ExpOptions; - -struct CosOptions; - -struct ReducerOptions; - -struct SqueezeOptions; - -struct SplitOptions; - -struct SplitVOptions; - -struct StridedSliceOptions; - -struct LogSoftmaxOptions; - -struct CastOptions; - -struct DequantizeOptions; - -struct MaximumMinimumOptions; - -struct TileOptions; - -struct ArgMaxOptions; - -struct ArgMinOptions; - -struct GreaterOptions; - -struct GreaterEqualOptions; - -struct LessOptions; - -struct LessEqualOptions; - -struct NegOptions; - -struct SelectOptions; - -struct SliceOptions; - -struct TransposeConvOptions; - -struct ExpandDimsOptions; - -struct SparseToDenseOptions; - -struct EqualOptions; - -struct NotEqualOptions; - -struct ShapeOptions; - -struct RankOptions; - -struct PowOptions; - -struct FakeQuantOptions; - -struct PackOptions; - -struct LogicalOrOptions; - -struct OneHotOptions; - -struct AbsOptions; - -struct HardSwishOptions; - -struct LogicalAndOptions; - -struct LogicalNotOptions; - -struct UnpackOptions; - -struct FloorDivOptions; - -struct SquareOptions; - -struct ZerosLikeOptions; - -struct FillOptions; - -struct FloorModOptions; - -struct RangeOptions; - -struct LeakyReluOptions; - -struct SquaredDifferenceOptions; - -struct MirrorPadOptions; - -struct UniqueOptions; - -struct ReverseV2Options; - -struct AddNOptions; - -struct GatherNdOptions; - -struct WhereOptions; - -struct ReverseSequenceOptions; - -struct MatrixDiagOptions; - -struct QuantizeOptions; - -struct MatrixSetDiagOptions; - -struct IfOptions; - -struct WhileOptions; - -struct NonMaxSuppressionV4Options; - -struct NonMaxSuppressionV5Options; - -struct ScatterNdOptions; - -struct SelectV2Options; - -struct DensifyOptions; - -struct SegmentSumOptions; - -struct BatchMatMulOptions; - -struct OperatorCode; - -struct Operator; - -struct SubGraph; - -struct Buffer; - -struct Metadata; - -struct Model; - -enum TensorType -{ - TensorType_FLOAT32 = 0, - TensorType_FLOAT16 = 1, - TensorType_INT32 = 2, - TensorType_UINT8 = 3, - TensorType_INT64 = 4, - TensorType_STRING = 5, - TensorType_BOOL = 6, - TensorType_INT16 = 7, - TensorType_COMPLEX64 = 8, - TensorType_INT8 = 9, - TensorType_FLOAT64 = 10, - TensorType_MIN = TensorType_FLOAT32, - TensorType_MAX = TensorType_FLOAT64 -}; - -inline const TensorType (&EnumValuesTensorType())[11] -{ - static const TensorType values[] = {TensorType_FLOAT32, TensorType_FLOAT16, TensorType_INT32, - TensorType_UINT8, TensorType_INT64, TensorType_STRING, - TensorType_BOOL, TensorType_INT16, TensorType_COMPLEX64, - TensorType_INT8, TensorType_FLOAT64}; - return values; -} - -inline const char *const *EnumNamesTensorType() -{ - static const char *const names[] = {"FLOAT32", "FLOAT16", "INT32", "UINT8", - "INT64", "STRING", "BOOL", "INT16", - "COMPLEX64", "INT8", "FLOAT64", nullptr}; - return names; -} - -inline const char *EnumNameTensorType(TensorType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesTensorType()[index]; -} - -enum QuantizationDetails -{ - QuantizationDetails_NONE = 0, - QuantizationDetails_CustomQuantization = 1, - QuantizationDetails_MIN = QuantizationDetails_NONE, - QuantizationDetails_MAX = QuantizationDetails_CustomQuantization -}; - -inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2] -{ - static const QuantizationDetails values[] = {QuantizationDetails_NONE, - QuantizationDetails_CustomQuantization}; - return values; -} - -inline const char *const *EnumNamesQuantizationDetails() -{ - static const char *const names[] = {"NONE", "CustomQuantization", nullptr}; - return names; -} - -inline const char *EnumNameQuantizationDetails(QuantizationDetails e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesQuantizationDetails()[index]; -} - -template <typename T> struct QuantizationDetailsTraits -{ - static const QuantizationDetails enum_value = QuantizationDetails_NONE; -}; - -template <> struct QuantizationDetailsTraits<CustomQuantization> -{ - static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; -}; - -bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, - QuantizationDetails type); -bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types); - -enum DimensionType -{ - DimensionType_DENSE = 0, - DimensionType_SPARSE_CSR = 1, - DimensionType_MIN = DimensionType_DENSE, - DimensionType_MAX = DimensionType_SPARSE_CSR -}; - -inline const DimensionType (&EnumValuesDimensionType())[2] -{ - static const DimensionType values[] = {DimensionType_DENSE, DimensionType_SPARSE_CSR}; - return values; -} - -inline const char *const *EnumNamesDimensionType() -{ - static const char *const names[] = {"DENSE", "SPARSE_CSR", nullptr}; - return names; -} - -inline const char *EnumNameDimensionType(DimensionType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesDimensionType()[index]; -} - -enum SparseIndexVector -{ - SparseIndexVector_NONE = 0, - SparseIndexVector_Int32Vector = 1, - SparseIndexVector_Uint16Vector = 2, - SparseIndexVector_Uint8Vector = 3, - SparseIndexVector_MIN = SparseIndexVector_NONE, - SparseIndexVector_MAX = SparseIndexVector_Uint8Vector -}; - -inline const SparseIndexVector (&EnumValuesSparseIndexVector())[4] -{ - static const SparseIndexVector values[] = {SparseIndexVector_NONE, SparseIndexVector_Int32Vector, - SparseIndexVector_Uint16Vector, - SparseIndexVector_Uint8Vector}; - return values; -} - -inline const char *const *EnumNamesSparseIndexVector() -{ - static const char *const names[] = {"NONE", "Int32Vector", "Uint16Vector", "Uint8Vector", - nullptr}; - return names; -} - -inline const char *EnumNameSparseIndexVector(SparseIndexVector e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesSparseIndexVector()[index]; -} - -template <typename T> struct SparseIndexVectorTraits -{ - static const SparseIndexVector enum_value = SparseIndexVector_NONE; -}; - -template <> struct SparseIndexVectorTraits<Int32Vector> -{ - static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; -}; - -template <> struct SparseIndexVectorTraits<Uint16Vector> -{ - static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; -}; - -template <> struct SparseIndexVectorTraits<Uint8Vector> -{ - static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; -}; - -bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, - SparseIndexVector type); -bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types); - -enum BuiltinOperator -{ - BuiltinOperator_ADD = 0, - BuiltinOperator_AVERAGE_POOL_2D = 1, - BuiltinOperator_CONCATENATION = 2, - BuiltinOperator_CONV_2D = 3, - BuiltinOperator_DEPTHWISE_CONV_2D = 4, - BuiltinOperator_DEPTH_TO_SPACE = 5, - BuiltinOperator_DEQUANTIZE = 6, - BuiltinOperator_EMBEDDING_LOOKUP = 7, - BuiltinOperator_FLOOR = 8, - BuiltinOperator_FULLY_CONNECTED = 9, - BuiltinOperator_HASHTABLE_LOOKUP = 10, - BuiltinOperator_L2_NORMALIZATION = 11, - BuiltinOperator_L2_POOL_2D = 12, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13, - BuiltinOperator_LOGISTIC = 14, - BuiltinOperator_LSH_PROJECTION = 15, - BuiltinOperator_LSTM = 16, - BuiltinOperator_MAX_POOL_2D = 17, - BuiltinOperator_MUL = 18, - BuiltinOperator_RELU = 19, - BuiltinOperator_RELU_N1_TO_1 = 20, - BuiltinOperator_RELU6 = 21, - BuiltinOperator_RESHAPE = 22, - BuiltinOperator_RESIZE_BILINEAR = 23, - BuiltinOperator_RNN = 24, - BuiltinOperator_SOFTMAX = 25, - BuiltinOperator_SPACE_TO_DEPTH = 26, - BuiltinOperator_SVDF = 27, - BuiltinOperator_TANH = 28, - BuiltinOperator_CONCAT_EMBEDDINGS = 29, - BuiltinOperator_SKIP_GRAM = 30, - BuiltinOperator_CALL = 31, - BuiltinOperator_CUSTOM = 32, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33, - BuiltinOperator_PAD = 34, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35, - BuiltinOperator_GATHER = 36, - BuiltinOperator_BATCH_TO_SPACE_ND = 37, - BuiltinOperator_SPACE_TO_BATCH_ND = 38, - BuiltinOperator_TRANSPOSE = 39, - BuiltinOperator_MEAN = 40, - BuiltinOperator_SUB = 41, - BuiltinOperator_DIV = 42, - BuiltinOperator_SQUEEZE = 43, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, - BuiltinOperator_STRIDED_SLICE = 45, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, - BuiltinOperator_EXP = 47, - BuiltinOperator_TOPK_V2 = 48, - BuiltinOperator_SPLIT = 49, - BuiltinOperator_LOG_SOFTMAX = 50, - BuiltinOperator_DELEGATE = 51, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, - BuiltinOperator_CAST = 53, - BuiltinOperator_PRELU = 54, - BuiltinOperator_MAXIMUM = 55, - BuiltinOperator_ARG_MAX = 56, - BuiltinOperator_MINIMUM = 57, - BuiltinOperator_LESS = 58, - BuiltinOperator_NEG = 59, - BuiltinOperator_PADV2 = 60, - BuiltinOperator_GREATER = 61, - BuiltinOperator_GREATER_EQUAL = 62, - BuiltinOperator_LESS_EQUAL = 63, - BuiltinOperator_SELECT = 64, - BuiltinOperator_SLICE = 65, - BuiltinOperator_SIN = 66, - BuiltinOperator_TRANSPOSE_CONV = 67, - BuiltinOperator_SPARSE_TO_DENSE = 68, - BuiltinOperator_TILE = 69, - BuiltinOperator_EXPAND_DIMS = 70, - BuiltinOperator_EQUAL = 71, - BuiltinOperator_NOT_EQUAL = 72, - BuiltinOperator_LOG = 73, - BuiltinOperator_SUM = 74, - BuiltinOperator_SQRT = 75, - BuiltinOperator_RSQRT = 76, - BuiltinOperator_SHAPE = 77, - BuiltinOperator_POW = 78, - BuiltinOperator_ARG_MIN = 79, - BuiltinOperator_FAKE_QUANT = 80, - BuiltinOperator_REDUCE_PROD = 81, - BuiltinOperator_REDUCE_MAX = 82, - BuiltinOperator_PACK = 83, - BuiltinOperator_LOGICAL_OR = 84, - BuiltinOperator_ONE_HOT = 85, - BuiltinOperator_LOGICAL_AND = 86, - BuiltinOperator_LOGICAL_NOT = 87, - BuiltinOperator_UNPACK = 88, - BuiltinOperator_REDUCE_MIN = 89, - BuiltinOperator_FLOOR_DIV = 90, - BuiltinOperator_REDUCE_ANY = 91, - BuiltinOperator_SQUARE = 92, - BuiltinOperator_ZEROS_LIKE = 93, - BuiltinOperator_FILL = 94, - BuiltinOperator_FLOOR_MOD = 95, - BuiltinOperator_RANGE = 96, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97, - BuiltinOperator_LEAKY_RELU = 98, - BuiltinOperator_SQUARED_DIFFERENCE = 99, - BuiltinOperator_MIRROR_PAD = 100, - BuiltinOperator_ABS = 101, - BuiltinOperator_SPLIT_V = 102, - BuiltinOperator_UNIQUE = 103, - BuiltinOperator_CEIL = 104, - BuiltinOperator_REVERSE_V2 = 105, - BuiltinOperator_ADD_N = 106, - BuiltinOperator_GATHER_ND = 107, - BuiltinOperator_COS = 108, - BuiltinOperator_WHERE = 109, - BuiltinOperator_RANK = 110, - BuiltinOperator_ELU = 111, - BuiltinOperator_REVERSE_SEQUENCE = 112, - BuiltinOperator_MATRIX_DIAG = 113, - BuiltinOperator_QUANTIZE = 114, - BuiltinOperator_MATRIX_SET_DIAG = 115, - BuiltinOperator_ROUND = 116, - BuiltinOperator_HARD_SWISH = 117, - BuiltinOperator_IF = 118, - BuiltinOperator_WHILE = 119, - BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120, - BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121, - BuiltinOperator_SCATTER_ND = 122, - BuiltinOperator_SELECT_V2 = 123, - BuiltinOperator_DENSIFY = 124, - BuiltinOperator_SEGMENT_SUM = 125, - BuiltinOperator_BATCH_MATMUL = 126, - BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_BATCH_MATMUL -}; - -inline const BuiltinOperator (&EnumValuesBuiltinOperator())[127] -{ - static const BuiltinOperator values[] = {BuiltinOperator_ADD, - BuiltinOperator_AVERAGE_POOL_2D, - BuiltinOperator_CONCATENATION, - BuiltinOperator_CONV_2D, - BuiltinOperator_DEPTHWISE_CONV_2D, - BuiltinOperator_DEPTH_TO_SPACE, - BuiltinOperator_DEQUANTIZE, - BuiltinOperator_EMBEDDING_LOOKUP, - BuiltinOperator_FLOOR, - BuiltinOperator_FULLY_CONNECTED, - BuiltinOperator_HASHTABLE_LOOKUP, - BuiltinOperator_L2_NORMALIZATION, - BuiltinOperator_L2_POOL_2D, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, - BuiltinOperator_LOGISTIC, - BuiltinOperator_LSH_PROJECTION, - BuiltinOperator_LSTM, - BuiltinOperator_MAX_POOL_2D, - BuiltinOperator_MUL, - BuiltinOperator_RELU, - BuiltinOperator_RELU_N1_TO_1, - BuiltinOperator_RELU6, - BuiltinOperator_RESHAPE, - BuiltinOperator_RESIZE_BILINEAR, - BuiltinOperator_RNN, - BuiltinOperator_SOFTMAX, - BuiltinOperator_SPACE_TO_DEPTH, - BuiltinOperator_SVDF, - BuiltinOperator_TANH, - BuiltinOperator_CONCAT_EMBEDDINGS, - BuiltinOperator_SKIP_GRAM, - BuiltinOperator_CALL, - BuiltinOperator_CUSTOM, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, - BuiltinOperator_PAD, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_GATHER, - BuiltinOperator_BATCH_TO_SPACE_ND, - BuiltinOperator_SPACE_TO_BATCH_ND, - BuiltinOperator_TRANSPOSE, - BuiltinOperator_MEAN, - BuiltinOperator_SUB, - BuiltinOperator_DIV, - BuiltinOperator_SQUEEZE, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_STRIDED_SLICE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_EXP, - BuiltinOperator_TOPK_V2, - BuiltinOperator_SPLIT, - BuiltinOperator_LOG_SOFTMAX, - BuiltinOperator_DELEGATE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_CAST, - BuiltinOperator_PRELU, - BuiltinOperator_MAXIMUM, - BuiltinOperator_ARG_MAX, - BuiltinOperator_MINIMUM, - BuiltinOperator_LESS, - BuiltinOperator_NEG, - BuiltinOperator_PADV2, - BuiltinOperator_GREATER, - BuiltinOperator_GREATER_EQUAL, - BuiltinOperator_LESS_EQUAL, - BuiltinOperator_SELECT, - BuiltinOperator_SLICE, - BuiltinOperator_SIN, - BuiltinOperator_TRANSPOSE_CONV, - BuiltinOperator_SPARSE_TO_DENSE, - BuiltinOperator_TILE, - BuiltinOperator_EXPAND_DIMS, - BuiltinOperator_EQUAL, - BuiltinOperator_NOT_EQUAL, - BuiltinOperator_LOG, - BuiltinOperator_SUM, - BuiltinOperator_SQRT, - BuiltinOperator_RSQRT, - BuiltinOperator_SHAPE, - BuiltinOperator_POW, - BuiltinOperator_ARG_MIN, - BuiltinOperator_FAKE_QUANT, - BuiltinOperator_REDUCE_PROD, - BuiltinOperator_REDUCE_MAX, - BuiltinOperator_PACK, - BuiltinOperator_LOGICAL_OR, - BuiltinOperator_ONE_HOT, - BuiltinOperator_LOGICAL_AND, - BuiltinOperator_LOGICAL_NOT, - BuiltinOperator_UNPACK, - BuiltinOperator_REDUCE_MIN, - BuiltinOperator_FLOOR_DIV, - BuiltinOperator_REDUCE_ANY, - BuiltinOperator_SQUARE, - BuiltinOperator_ZEROS_LIKE, - BuiltinOperator_FILL, - BuiltinOperator_FLOOR_MOD, - BuiltinOperator_RANGE, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - BuiltinOperator_LEAKY_RELU, - BuiltinOperator_SQUARED_DIFFERENCE, - BuiltinOperator_MIRROR_PAD, - BuiltinOperator_ABS, - BuiltinOperator_SPLIT_V, - BuiltinOperator_UNIQUE, - BuiltinOperator_CEIL, - BuiltinOperator_REVERSE_V2, - BuiltinOperator_ADD_N, - BuiltinOperator_GATHER_ND, - BuiltinOperator_COS, - BuiltinOperator_WHERE, - BuiltinOperator_RANK, - BuiltinOperator_ELU, - BuiltinOperator_REVERSE_SEQUENCE, - BuiltinOperator_MATRIX_DIAG, - BuiltinOperator_QUANTIZE, - BuiltinOperator_MATRIX_SET_DIAG, - BuiltinOperator_ROUND, - BuiltinOperator_HARD_SWISH, - BuiltinOperator_IF, - BuiltinOperator_WHILE, - BuiltinOperator_NON_MAX_SUPPRESSION_V4, - BuiltinOperator_NON_MAX_SUPPRESSION_V5, - BuiltinOperator_SCATTER_ND, - BuiltinOperator_SELECT_V2, - BuiltinOperator_DENSIFY, - BuiltinOperator_SEGMENT_SUM, - BuiltinOperator_BATCH_MATMUL}; - return values; -} - -inline const char *const *EnumNamesBuiltinOperator() -{ - static const char *const names[] = {"ADD", - "AVERAGE_POOL_2D", - "CONCATENATION", - "CONV_2D", - "DEPTHWISE_CONV_2D", - "DEPTH_TO_SPACE", - "DEQUANTIZE", - "EMBEDDING_LOOKUP", - "FLOOR", - "FULLY_CONNECTED", - "HASHTABLE_LOOKUP", - "L2_NORMALIZATION", - "L2_POOL_2D", - "LOCAL_RESPONSE_NORMALIZATION", - "LOGISTIC", - "LSH_PROJECTION", - "LSTM", - "MAX_POOL_2D", - "MUL", - "RELU", - "RELU_N1_TO_1", - "RELU6", - "RESHAPE", - "RESIZE_BILINEAR", - "RNN", - "SOFTMAX", - "SPACE_TO_DEPTH", - "SVDF", - "TANH", - "CONCAT_EMBEDDINGS", - "SKIP_GRAM", - "CALL", - "CUSTOM", - "EMBEDDING_LOOKUP_SPARSE", - "PAD", - "UNIDIRECTIONAL_SEQUENCE_RNN", - "GATHER", - "BATCH_TO_SPACE_ND", - "SPACE_TO_BATCH_ND", - "TRANSPOSE", - "MEAN", - "SUB", - "DIV", - "SQUEEZE", - "UNIDIRECTIONAL_SEQUENCE_LSTM", - "STRIDED_SLICE", - "BIDIRECTIONAL_SEQUENCE_RNN", - "EXP", - "TOPK_V2", - "SPLIT", - "LOG_SOFTMAX", - "DELEGATE", - "BIDIRECTIONAL_SEQUENCE_LSTM", - "CAST", - "PRELU", - "MAXIMUM", - "ARG_MAX", - "MINIMUM", - "LESS", - "NEG", - "PADV2", - "GREATER", - "GREATER_EQUAL", - "LESS_EQUAL", - "SELECT", - "SLICE", - "SIN", - "TRANSPOSE_CONV", - "SPARSE_TO_DENSE", - "TILE", - "EXPAND_DIMS", - "EQUAL", - "NOT_EQUAL", - "LOG", - "SUM", - "SQRT", - "RSQRT", - "SHAPE", - "POW", - "ARG_MIN", - "FAKE_QUANT", - "REDUCE_PROD", - "REDUCE_MAX", - "PACK", - "LOGICAL_OR", - "ONE_HOT", - "LOGICAL_AND", - "LOGICAL_NOT", - "UNPACK", - "REDUCE_MIN", - "FLOOR_DIV", - "REDUCE_ANY", - "SQUARE", - "ZEROS_LIKE", - "FILL", - "FLOOR_MOD", - "RANGE", - "RESIZE_NEAREST_NEIGHBOR", - "LEAKY_RELU", - "SQUARED_DIFFERENCE", - "MIRROR_PAD", - "ABS", - "SPLIT_V", - "UNIQUE", - "CEIL", - "REVERSE_V2", - "ADD_N", - "GATHER_ND", - "COS", - "WHERE", - "RANK", - "ELU", - "REVERSE_SEQUENCE", - "MATRIX_DIAG", - "QUANTIZE", - "MATRIX_SET_DIAG", - "ROUND", - "HARD_SWISH", - "IF", - "WHILE", - "NON_MAX_SUPPRESSION_V4", - "NON_MAX_SUPPRESSION_V5", - "SCATTER_ND", - "SELECT_V2", - "DENSIFY", - "SEGMENT_SUM", - "BATCH_MATMUL", - nullptr}; - return names; -} - -inline const char *EnumNameBuiltinOperator(BuiltinOperator e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesBuiltinOperator()[index]; -} - -enum BuiltinOptions -{ - BuiltinOptions_NONE = 0, - BuiltinOptions_Conv2DOptions = 1, - BuiltinOptions_DepthwiseConv2DOptions = 2, - BuiltinOptions_ConcatEmbeddingsOptions = 3, - BuiltinOptions_LSHProjectionOptions = 4, - BuiltinOptions_Pool2DOptions = 5, - BuiltinOptions_SVDFOptions = 6, - BuiltinOptions_RNNOptions = 7, - BuiltinOptions_FullyConnectedOptions = 8, - BuiltinOptions_SoftmaxOptions = 9, - BuiltinOptions_ConcatenationOptions = 10, - BuiltinOptions_AddOptions = 11, - BuiltinOptions_L2NormOptions = 12, - BuiltinOptions_LocalResponseNormalizationOptions = 13, - BuiltinOptions_LSTMOptions = 14, - BuiltinOptions_ResizeBilinearOptions = 15, - BuiltinOptions_CallOptions = 16, - BuiltinOptions_ReshapeOptions = 17, - BuiltinOptions_SkipGramOptions = 18, - BuiltinOptions_SpaceToDepthOptions = 19, - BuiltinOptions_EmbeddingLookupSparseOptions = 20, - BuiltinOptions_MulOptions = 21, - BuiltinOptions_PadOptions = 22, - BuiltinOptions_GatherOptions = 23, - BuiltinOptions_BatchToSpaceNDOptions = 24, - BuiltinOptions_SpaceToBatchNDOptions = 25, - BuiltinOptions_TransposeOptions = 26, - BuiltinOptions_ReducerOptions = 27, - BuiltinOptions_SubOptions = 28, - BuiltinOptions_DivOptions = 29, - BuiltinOptions_SqueezeOptions = 30, - BuiltinOptions_SequenceRNNOptions = 31, - BuiltinOptions_StridedSliceOptions = 32, - BuiltinOptions_ExpOptions = 33, - BuiltinOptions_TopKV2Options = 34, - BuiltinOptions_SplitOptions = 35, - BuiltinOptions_LogSoftmaxOptions = 36, - BuiltinOptions_CastOptions = 37, - BuiltinOptions_DequantizeOptions = 38, - BuiltinOptions_MaximumMinimumOptions = 39, - BuiltinOptions_ArgMaxOptions = 40, - BuiltinOptions_LessOptions = 41, - BuiltinOptions_NegOptions = 42, - BuiltinOptions_PadV2Options = 43, - BuiltinOptions_GreaterOptions = 44, - BuiltinOptions_GreaterEqualOptions = 45, - BuiltinOptions_LessEqualOptions = 46, - BuiltinOptions_SelectOptions = 47, - BuiltinOptions_SliceOptions = 48, - BuiltinOptions_TransposeConvOptions = 49, - BuiltinOptions_SparseToDenseOptions = 50, - BuiltinOptions_TileOptions = 51, - BuiltinOptions_ExpandDimsOptions = 52, - BuiltinOptions_EqualOptions = 53, - BuiltinOptions_NotEqualOptions = 54, - BuiltinOptions_ShapeOptions = 55, - BuiltinOptions_PowOptions = 56, - BuiltinOptions_ArgMinOptions = 57, - BuiltinOptions_FakeQuantOptions = 58, - BuiltinOptions_PackOptions = 59, - BuiltinOptions_LogicalOrOptions = 60, - BuiltinOptions_OneHotOptions = 61, - BuiltinOptions_LogicalAndOptions = 62, - BuiltinOptions_LogicalNotOptions = 63, - BuiltinOptions_UnpackOptions = 64, - BuiltinOptions_FloorDivOptions = 65, - BuiltinOptions_SquareOptions = 66, - BuiltinOptions_ZerosLikeOptions = 67, - BuiltinOptions_FillOptions = 68, - BuiltinOptions_BidirectionalSequenceLSTMOptions = 69, - BuiltinOptions_BidirectionalSequenceRNNOptions = 70, - BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71, - BuiltinOptions_FloorModOptions = 72, - BuiltinOptions_RangeOptions = 73, - BuiltinOptions_ResizeNearestNeighborOptions = 74, - BuiltinOptions_LeakyReluOptions = 75, - BuiltinOptions_SquaredDifferenceOptions = 76, - BuiltinOptions_MirrorPadOptions = 77, - BuiltinOptions_AbsOptions = 78, - BuiltinOptions_SplitVOptions = 79, - BuiltinOptions_UniqueOptions = 80, - BuiltinOptions_ReverseV2Options = 81, - BuiltinOptions_AddNOptions = 82, - BuiltinOptions_GatherNdOptions = 83, - BuiltinOptions_CosOptions = 84, - BuiltinOptions_WhereOptions = 85, - BuiltinOptions_RankOptions = 86, - BuiltinOptions_ReverseSequenceOptions = 87, - BuiltinOptions_MatrixDiagOptions = 88, - BuiltinOptions_QuantizeOptions = 89, - BuiltinOptions_MatrixSetDiagOptions = 90, - BuiltinOptions_HardSwishOptions = 91, - BuiltinOptions_IfOptions = 92, - BuiltinOptions_WhileOptions = 93, - BuiltinOptions_DepthToSpaceOptions = 94, - BuiltinOptions_NonMaxSuppressionV4Options = 95, - BuiltinOptions_NonMaxSuppressionV5Options = 96, - BuiltinOptions_ScatterNdOptions = 97, - BuiltinOptions_SelectV2Options = 98, - BuiltinOptions_DensifyOptions = 99, - BuiltinOptions_SegmentSumOptions = 100, - BuiltinOptions_BatchMatMulOptions = 101, - BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_BatchMatMulOptions -}; - -inline const BuiltinOptions (&EnumValuesBuiltinOptions())[102] -{ - static const BuiltinOptions values[] = {BuiltinOptions_NONE, - BuiltinOptions_Conv2DOptions, - BuiltinOptions_DepthwiseConv2DOptions, - BuiltinOptions_ConcatEmbeddingsOptions, - BuiltinOptions_LSHProjectionOptions, - BuiltinOptions_Pool2DOptions, - BuiltinOptions_SVDFOptions, - BuiltinOptions_RNNOptions, - BuiltinOptions_FullyConnectedOptions, - BuiltinOptions_SoftmaxOptions, - BuiltinOptions_ConcatenationOptions, - BuiltinOptions_AddOptions, - BuiltinOptions_L2NormOptions, - BuiltinOptions_LocalResponseNormalizationOptions, - BuiltinOptions_LSTMOptions, - BuiltinOptions_ResizeBilinearOptions, - BuiltinOptions_CallOptions, - BuiltinOptions_ReshapeOptions, - BuiltinOptions_SkipGramOptions, - BuiltinOptions_SpaceToDepthOptions, - BuiltinOptions_EmbeddingLookupSparseOptions, - BuiltinOptions_MulOptions, - BuiltinOptions_PadOptions, - BuiltinOptions_GatherOptions, - BuiltinOptions_BatchToSpaceNDOptions, - BuiltinOptions_SpaceToBatchNDOptions, - BuiltinOptions_TransposeOptions, - BuiltinOptions_ReducerOptions, - BuiltinOptions_SubOptions, - BuiltinOptions_DivOptions, - BuiltinOptions_SqueezeOptions, - BuiltinOptions_SequenceRNNOptions, - BuiltinOptions_StridedSliceOptions, - BuiltinOptions_ExpOptions, - BuiltinOptions_TopKV2Options, - BuiltinOptions_SplitOptions, - BuiltinOptions_LogSoftmaxOptions, - BuiltinOptions_CastOptions, - BuiltinOptions_DequantizeOptions, - BuiltinOptions_MaximumMinimumOptions, - BuiltinOptions_ArgMaxOptions, - BuiltinOptions_LessOptions, - BuiltinOptions_NegOptions, - BuiltinOptions_PadV2Options, - BuiltinOptions_GreaterOptions, - BuiltinOptions_GreaterEqualOptions, - BuiltinOptions_LessEqualOptions, - BuiltinOptions_SelectOptions, - BuiltinOptions_SliceOptions, - BuiltinOptions_TransposeConvOptions, - BuiltinOptions_SparseToDenseOptions, - BuiltinOptions_TileOptions, - BuiltinOptions_ExpandDimsOptions, - BuiltinOptions_EqualOptions, - BuiltinOptions_NotEqualOptions, - BuiltinOptions_ShapeOptions, - BuiltinOptions_PowOptions, - BuiltinOptions_ArgMinOptions, - BuiltinOptions_FakeQuantOptions, - BuiltinOptions_PackOptions, - BuiltinOptions_LogicalOrOptions, - BuiltinOptions_OneHotOptions, - BuiltinOptions_LogicalAndOptions, - BuiltinOptions_LogicalNotOptions, - BuiltinOptions_UnpackOptions, - BuiltinOptions_FloorDivOptions, - BuiltinOptions_SquareOptions, - BuiltinOptions_ZerosLikeOptions, - BuiltinOptions_FillOptions, - BuiltinOptions_BidirectionalSequenceLSTMOptions, - BuiltinOptions_BidirectionalSequenceRNNOptions, - BuiltinOptions_UnidirectionalSequenceLSTMOptions, - BuiltinOptions_FloorModOptions, - BuiltinOptions_RangeOptions, - BuiltinOptions_ResizeNearestNeighborOptions, - BuiltinOptions_LeakyReluOptions, - BuiltinOptions_SquaredDifferenceOptions, - BuiltinOptions_MirrorPadOptions, - BuiltinOptions_AbsOptions, - BuiltinOptions_SplitVOptions, - BuiltinOptions_UniqueOptions, - BuiltinOptions_ReverseV2Options, - BuiltinOptions_AddNOptions, - BuiltinOptions_GatherNdOptions, - BuiltinOptions_CosOptions, - BuiltinOptions_WhereOptions, - BuiltinOptions_RankOptions, - BuiltinOptions_ReverseSequenceOptions, - BuiltinOptions_MatrixDiagOptions, - BuiltinOptions_QuantizeOptions, - BuiltinOptions_MatrixSetDiagOptions, - BuiltinOptions_HardSwishOptions, - BuiltinOptions_IfOptions, - BuiltinOptions_WhileOptions, - BuiltinOptions_DepthToSpaceOptions, - BuiltinOptions_NonMaxSuppressionV4Options, - BuiltinOptions_NonMaxSuppressionV5Options, - BuiltinOptions_ScatterNdOptions, - BuiltinOptions_SelectV2Options, - BuiltinOptions_DensifyOptions, - BuiltinOptions_SegmentSumOptions, - BuiltinOptions_BatchMatMulOptions}; - return values; -} - -inline const char *const *EnumNamesBuiltinOptions() -{ - static const char *const names[] = {"NONE", - "Conv2DOptions", - "DepthwiseConv2DOptions", - "ConcatEmbeddingsOptions", - "LSHProjectionOptions", - "Pool2DOptions", - "SVDFOptions", - "RNNOptions", - "FullyConnectedOptions", - "SoftmaxOptions", - "ConcatenationOptions", - "AddOptions", - "L2NormOptions", - "LocalResponseNormalizationOptions", - "LSTMOptions", - "ResizeBilinearOptions", - "CallOptions", - "ReshapeOptions", - "SkipGramOptions", - "SpaceToDepthOptions", - "EmbeddingLookupSparseOptions", - "MulOptions", - "PadOptions", - "GatherOptions", - "BatchToSpaceNDOptions", - "SpaceToBatchNDOptions", - "TransposeOptions", - "ReducerOptions", - "SubOptions", - "DivOptions", - "SqueezeOptions", - "SequenceRNNOptions", - "StridedSliceOptions", - "ExpOptions", - "TopKV2Options", - "SplitOptions", - "LogSoftmaxOptions", - "CastOptions", - "DequantizeOptions", - "MaximumMinimumOptions", - "ArgMaxOptions", - "LessOptions", - "NegOptions", - "PadV2Options", - "GreaterOptions", - "GreaterEqualOptions", - "LessEqualOptions", - "SelectOptions", - "SliceOptions", - "TransposeConvOptions", - "SparseToDenseOptions", - "TileOptions", - "ExpandDimsOptions", - "EqualOptions", - "NotEqualOptions", - "ShapeOptions", - "PowOptions", - "ArgMinOptions", - "FakeQuantOptions", - "PackOptions", - "LogicalOrOptions", - "OneHotOptions", - "LogicalAndOptions", - "LogicalNotOptions", - "UnpackOptions", - "FloorDivOptions", - "SquareOptions", - "ZerosLikeOptions", - "FillOptions", - "BidirectionalSequenceLSTMOptions", - "BidirectionalSequenceRNNOptions", - "UnidirectionalSequenceLSTMOptions", - "FloorModOptions", - "RangeOptions", - "ResizeNearestNeighborOptions", - "LeakyReluOptions", - "SquaredDifferenceOptions", - "MirrorPadOptions", - "AbsOptions", - "SplitVOptions", - "UniqueOptions", - "ReverseV2Options", - "AddNOptions", - "GatherNdOptions", - "CosOptions", - "WhereOptions", - "RankOptions", - "ReverseSequenceOptions", - "MatrixDiagOptions", - "QuantizeOptions", - "MatrixSetDiagOptions", - "HardSwishOptions", - "IfOptions", - "WhileOptions", - "DepthToSpaceOptions", - "NonMaxSuppressionV4Options", - "NonMaxSuppressionV5Options", - "ScatterNdOptions", - "SelectV2Options", - "DensifyOptions", - "SegmentSumOptions", - "BatchMatMulOptions", - nullptr}; - return names; -} - -inline const char *EnumNameBuiltinOptions(BuiltinOptions e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesBuiltinOptions()[index]; -} - -template <typename T> struct BuiltinOptionsTraits -{ - static const BuiltinOptions enum_value = BuiltinOptions_NONE; -}; - -template <> struct BuiltinOptionsTraits<Conv2DOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; -}; - -template <> struct BuiltinOptionsTraits<DepthwiseConv2DOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; -}; - -template <> struct BuiltinOptionsTraits<ConcatEmbeddingsOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; -}; - -template <> struct BuiltinOptionsTraits<LSHProjectionOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; -}; - -template <> struct BuiltinOptionsTraits<Pool2DOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; -}; - -template <> struct BuiltinOptionsTraits<SVDFOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; -}; - -template <> struct BuiltinOptionsTraits<RNNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; -}; - -template <> struct BuiltinOptionsTraits<FullyConnectedOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; -}; - -template <> struct BuiltinOptionsTraits<SoftmaxOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; -}; - -template <> struct BuiltinOptionsTraits<ConcatenationOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; -}; - -template <> struct BuiltinOptionsTraits<AddOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; -}; - -template <> struct BuiltinOptionsTraits<L2NormOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; -}; - -template <> struct BuiltinOptionsTraits<LocalResponseNormalizationOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; -}; - -template <> struct BuiltinOptionsTraits<LSTMOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; -}; - -template <> struct BuiltinOptionsTraits<ResizeBilinearOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; -}; - -template <> struct BuiltinOptionsTraits<CallOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; -}; - -template <> struct BuiltinOptionsTraits<ReshapeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; -}; - -template <> struct BuiltinOptionsTraits<SkipGramOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; -}; - -template <> struct BuiltinOptionsTraits<SpaceToDepthOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; -}; - -template <> struct BuiltinOptionsTraits<EmbeddingLookupSparseOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; -}; - -template <> struct BuiltinOptionsTraits<MulOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; -}; - -template <> struct BuiltinOptionsTraits<PadOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; -}; - -template <> struct BuiltinOptionsTraits<GatherOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; -}; - -template <> struct BuiltinOptionsTraits<BatchToSpaceNDOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; -}; - -template <> struct BuiltinOptionsTraits<SpaceToBatchNDOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; -}; - -template <> struct BuiltinOptionsTraits<TransposeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; -}; - -template <> struct BuiltinOptionsTraits<ReducerOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; -}; - -template <> struct BuiltinOptionsTraits<SubOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; -}; - -template <> struct BuiltinOptionsTraits<DivOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; -}; - -template <> struct BuiltinOptionsTraits<SqueezeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; -}; - -template <> struct BuiltinOptionsTraits<SequenceRNNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; -}; - -template <> struct BuiltinOptionsTraits<StridedSliceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; -}; - -template <> struct BuiltinOptionsTraits<ExpOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; -}; - -template <> struct BuiltinOptionsTraits<TopKV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; -}; - -template <> struct BuiltinOptionsTraits<SplitOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; -}; - -template <> struct BuiltinOptionsTraits<LogSoftmaxOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; -}; - -template <> struct BuiltinOptionsTraits<CastOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; -}; - -template <> struct BuiltinOptionsTraits<DequantizeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; -}; - -template <> struct BuiltinOptionsTraits<MaximumMinimumOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; -}; - -template <> struct BuiltinOptionsTraits<ArgMaxOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; -}; - -template <> struct BuiltinOptionsTraits<LessOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; -}; - -template <> struct BuiltinOptionsTraits<NegOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; -}; - -template <> struct BuiltinOptionsTraits<PadV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; -}; - -template <> struct BuiltinOptionsTraits<GreaterOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; -}; - -template <> struct BuiltinOptionsTraits<GreaterEqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; -}; - -template <> struct BuiltinOptionsTraits<LessEqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; -}; - -template <> struct BuiltinOptionsTraits<SelectOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; -}; - -template <> struct BuiltinOptionsTraits<SliceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; -}; - -template <> struct BuiltinOptionsTraits<TransposeConvOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; -}; - -template <> struct BuiltinOptionsTraits<SparseToDenseOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; -}; - -template <> struct BuiltinOptionsTraits<TileOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; -}; - -template <> struct BuiltinOptionsTraits<ExpandDimsOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; -}; - -template <> struct BuiltinOptionsTraits<EqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; -}; - -template <> struct BuiltinOptionsTraits<NotEqualOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; -}; - -template <> struct BuiltinOptionsTraits<ShapeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; -}; - -template <> struct BuiltinOptionsTraits<PowOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; -}; - -template <> struct BuiltinOptionsTraits<ArgMinOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; -}; - -template <> struct BuiltinOptionsTraits<FakeQuantOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; -}; - -template <> struct BuiltinOptionsTraits<PackOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; -}; - -template <> struct BuiltinOptionsTraits<LogicalOrOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; -}; - -template <> struct BuiltinOptionsTraits<OneHotOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; -}; - -template <> struct BuiltinOptionsTraits<LogicalAndOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; -}; - -template <> struct BuiltinOptionsTraits<LogicalNotOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; -}; - -template <> struct BuiltinOptionsTraits<UnpackOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; -}; - -template <> struct BuiltinOptionsTraits<FloorDivOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; -}; - -template <> struct BuiltinOptionsTraits<SquareOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; -}; - -template <> struct BuiltinOptionsTraits<ZerosLikeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; -}; - -template <> struct BuiltinOptionsTraits<FillOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; -}; - -template <> struct BuiltinOptionsTraits<BidirectionalSequenceLSTMOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; -}; - -template <> struct BuiltinOptionsTraits<BidirectionalSequenceRNNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; -}; - -template <> struct BuiltinOptionsTraits<UnidirectionalSequenceLSTMOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; -}; - -template <> struct BuiltinOptionsTraits<FloorModOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; -}; - -template <> struct BuiltinOptionsTraits<RangeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; -}; - -template <> struct BuiltinOptionsTraits<ResizeNearestNeighborOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; -}; - -template <> struct BuiltinOptionsTraits<LeakyReluOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; -}; - -template <> struct BuiltinOptionsTraits<SquaredDifferenceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; -}; - -template <> struct BuiltinOptionsTraits<MirrorPadOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; -}; - -template <> struct BuiltinOptionsTraits<AbsOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; -}; - -template <> struct BuiltinOptionsTraits<SplitVOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; -}; - -template <> struct BuiltinOptionsTraits<UniqueOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; -}; - -template <> struct BuiltinOptionsTraits<ReverseV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; -}; - -template <> struct BuiltinOptionsTraits<AddNOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; -}; - -template <> struct BuiltinOptionsTraits<GatherNdOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; -}; - -template <> struct BuiltinOptionsTraits<CosOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; -}; - -template <> struct BuiltinOptionsTraits<WhereOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; -}; - -template <> struct BuiltinOptionsTraits<RankOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; -}; - -template <> struct BuiltinOptionsTraits<ReverseSequenceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; -}; - -template <> struct BuiltinOptionsTraits<MatrixDiagOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; -}; - -template <> struct BuiltinOptionsTraits<QuantizeOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; -}; - -template <> struct BuiltinOptionsTraits<MatrixSetDiagOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; -}; - -template <> struct BuiltinOptionsTraits<HardSwishOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; -}; - -template <> struct BuiltinOptionsTraits<IfOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; -}; - -template <> struct BuiltinOptionsTraits<WhileOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; -}; - -template <> struct BuiltinOptionsTraits<DepthToSpaceOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; -}; - -template <> struct BuiltinOptionsTraits<NonMaxSuppressionV4Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; -}; - -template <> struct BuiltinOptionsTraits<NonMaxSuppressionV5Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; -}; - -template <> struct BuiltinOptionsTraits<ScatterNdOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; -}; - -template <> struct BuiltinOptionsTraits<SelectV2Options> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; -}; - -template <> struct BuiltinOptionsTraits<DensifyOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; -}; - -template <> struct BuiltinOptionsTraits<SegmentSumOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; -}; - -template <> struct BuiltinOptionsTraits<BatchMatMulOptions> -{ - static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; -}; - -bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); -bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types); - -enum Padding -{ - Padding_SAME = 0, - Padding_VALID = 1, - Padding_MIN = Padding_SAME, - Padding_MAX = Padding_VALID -}; - -inline const Padding (&EnumValuesPadding())[2] -{ - static const Padding values[] = {Padding_SAME, Padding_VALID}; - return values; -} - -inline const char *const *EnumNamesPadding() -{ - static const char *const names[] = {"SAME", "VALID", nullptr}; - return names; -} - -inline const char *EnumNamePadding(Padding e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesPadding()[index]; -} - -enum ActivationFunctionType -{ - ActivationFunctionType_NONE = 0, - ActivationFunctionType_RELU = 1, - ActivationFunctionType_RELU_N1_TO_1 = 2, - ActivationFunctionType_RELU6 = 3, - ActivationFunctionType_TANH = 4, - ActivationFunctionType_SIGN_BIT = 5, - ActivationFunctionType_MIN = ActivationFunctionType_NONE, - ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT -}; - -inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6] -{ - static const ActivationFunctionType values[] = { - ActivationFunctionType_NONE, ActivationFunctionType_RELU, - ActivationFunctionType_RELU_N1_TO_1, ActivationFunctionType_RELU6, - ActivationFunctionType_TANH, ActivationFunctionType_SIGN_BIT}; - return values; -} - -inline const char *const *EnumNamesActivationFunctionType() -{ - static const char *const names[] = {"NONE", "RELU", "RELU_N1_TO_1", "RELU6", - "TANH", "SIGN_BIT", nullptr}; - return names; -} - -inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesActivationFunctionType()[index]; -} - -enum LSHProjectionType -{ - LSHProjectionType_UNKNOWN = 0, - LSHProjectionType_SPARSE = 1, - LSHProjectionType_DENSE = 2, - LSHProjectionType_MIN = LSHProjectionType_UNKNOWN, - LSHProjectionType_MAX = LSHProjectionType_DENSE -}; - -inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3] -{ - static const LSHProjectionType values[] = {LSHProjectionType_UNKNOWN, LSHProjectionType_SPARSE, - LSHProjectionType_DENSE}; - return values; -} - -inline const char *const *EnumNamesLSHProjectionType() -{ - static const char *const names[] = {"UNKNOWN", "SPARSE", "DENSE", nullptr}; - return names; -} - -inline const char *EnumNameLSHProjectionType(LSHProjectionType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesLSHProjectionType()[index]; -} - -enum FullyConnectedOptionsWeightsFormat -{ - FullyConnectedOptionsWeightsFormat_DEFAULT = 0, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1, - FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 -}; - -inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] -{ - static const FullyConnectedOptionsWeightsFormat values[] = { - FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8}; - return values; -} - -inline const char *const *EnumNamesFullyConnectedOptionsWeightsFormat() -{ - static const char *const names[] = {"DEFAULT", "SHUFFLED4x16INT8", nullptr}; - return names; -} - -inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesFullyConnectedOptionsWeightsFormat()[index]; -} - -enum LSTMKernelType -{ - LSTMKernelType_FULL = 0, - LSTMKernelType_BASIC = 1, - LSTMKernelType_MIN = LSTMKernelType_FULL, - LSTMKernelType_MAX = LSTMKernelType_BASIC -}; - -inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2] -{ - static const LSTMKernelType values[] = {LSTMKernelType_FULL, LSTMKernelType_BASIC}; - return values; -} - -inline const char *const *EnumNamesLSTMKernelType() -{ - static const char *const names[] = {"FULL", "BASIC", nullptr}; - return names; -} - -inline const char *EnumNameLSTMKernelType(LSTMKernelType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesLSTMKernelType()[index]; -} - -enum CombinerType -{ - CombinerType_SUM = 0, - CombinerType_MEAN = 1, - CombinerType_SQRTN = 2, - CombinerType_MIN = CombinerType_SUM, - CombinerType_MAX = CombinerType_SQRTN -}; - -inline const CombinerType (&EnumValuesCombinerType())[3] -{ - static const CombinerType values[] = {CombinerType_SUM, CombinerType_MEAN, CombinerType_SQRTN}; - return values; -} - -inline const char *const *EnumNamesCombinerType() -{ - static const char *const names[] = {"SUM", "MEAN", "SQRTN", nullptr}; - return names; -} - -inline const char *EnumNameCombinerType(CombinerType e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesCombinerType()[index]; -} - -enum MirrorPadMode -{ - MirrorPadMode_REFLECT = 0, - MirrorPadMode_SYMMETRIC = 1, - MirrorPadMode_MIN = MirrorPadMode_REFLECT, - MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC -}; - -inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2] -{ - static const MirrorPadMode values[] = {MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC}; - return values; -} - -inline const char *const *EnumNamesMirrorPadMode() -{ - static const char *const names[] = {"REFLECT", "SYMMETRIC", nullptr}; - return names; -} - -inline const char *EnumNameMirrorPadMode(MirrorPadMode e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesMirrorPadMode()[index]; -} - -enum CustomOptionsFormat -{ - CustomOptionsFormat_FLEXBUFFERS = 0, - CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS, - CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS -}; - -inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] -{ - static const CustomOptionsFormat values[] = {CustomOptionsFormat_FLEXBUFFERS}; - return values; -} - -inline const char *const *EnumNamesCustomOptionsFormat() -{ - static const char *const names[] = {"FLEXBUFFERS", nullptr}; - return names; -} - -inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) -{ - const size_t index = static_cast<int>(e); - return EnumNamesCustomOptionsFormat()[index]; -} - -struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_CUSTOM = 4 - }; - const flatbuffers::Vector<uint8_t> *custom() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_CUSTOM) && - verifier.VerifyVector(custom()) && verifier.EndTable(); - } -}; - -struct CustomQuantizationBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_custom(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom) - { - fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom); - } - explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CustomQuantizationBuilder &operator=(const CustomQuantizationBuilder &); - flatbuffers::Offset<CustomQuantization> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CustomQuantization>(end); - return o; - } -}; - -inline flatbuffers::Offset<CustomQuantization> -CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom = 0) -{ - CustomQuantizationBuilder builder_(_fbb); - builder_.add_custom(custom); - return builder_.Finish(); -} - -inline flatbuffers::Offset<CustomQuantization> -CreateCustomQuantizationDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint8_t> *custom = nullptr) -{ - return onert_tflite::CreateCustomQuantization(_fbb, - custom ? _fbb.CreateVector<uint8_t>(*custom) : 0); -} - -struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_MIN = 4, - VT_MAX = 6, - VT_SCALE = 8, - VT_ZERO_POINT = 10, - VT_DETAILS_TYPE = 12, - VT_DETAILS = 14, - VT_QUANTIZED_DIMENSION = 16 - }; - const flatbuffers::Vector<float> *min() const - { - return GetPointer<const flatbuffers::Vector<float> *>(VT_MIN); - } - const flatbuffers::Vector<float> *max() const - { - return GetPointer<const flatbuffers::Vector<float> *>(VT_MAX); - } - const flatbuffers::Vector<float> *scale() const - { - return GetPointer<const flatbuffers::Vector<float> *>(VT_SCALE); - } - const flatbuffers::Vector<int64_t> *zero_point() const - { - return GetPointer<const flatbuffers::Vector<int64_t> *>(VT_ZERO_POINT); - } - QuantizationDetails details_type() const - { - return static_cast<QuantizationDetails>(GetField<uint8_t>(VT_DETAILS_TYPE, 0)); - } - const void *details() const { return GetPointer<const void *>(VT_DETAILS); } - template <typename T> const T *details_as() const; - const CustomQuantization *details_as_CustomQuantization() const - { - return details_type() == QuantizationDetails_CustomQuantization - ? static_cast<const CustomQuantization *>(details()) - : nullptr; - } - int32_t quantized_dimension() const { return GetField<int32_t>(VT_QUANTIZED_DIMENSION, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_MIN) && - verifier.VerifyVector(min()) && VerifyOffset(verifier, VT_MAX) && - verifier.VerifyVector(max()) && VerifyOffset(verifier, VT_SCALE) && - verifier.VerifyVector(scale()) && VerifyOffset(verifier, VT_ZERO_POINT) && - verifier.VerifyVector(zero_point()) && VerifyField<uint8_t>(verifier, VT_DETAILS_TYPE) && - VerifyOffset(verifier, VT_DETAILS) && - VerifyQuantizationDetails(verifier, details(), details_type()) && - VerifyField<int32_t>(verifier, VT_QUANTIZED_DIMENSION) && verifier.EndTable(); - } -}; - -template <> -inline const CustomQuantization *QuantizationParameters::details_as<CustomQuantization>() const -{ - return details_as_CustomQuantization(); -} - -struct QuantizationParametersBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(flatbuffers::Offset<flatbuffers::Vector<float>> min) - { - fbb_.AddOffset(QuantizationParameters::VT_MIN, min); - } - void add_max(flatbuffers::Offset<flatbuffers::Vector<float>> max) - { - fbb_.AddOffset(QuantizationParameters::VT_MAX, max); - } - void add_scale(flatbuffers::Offset<flatbuffers::Vector<float>> scale) - { - fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale); - } - void add_zero_point(flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point) - { - fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point); - } - void add_details_type(QuantizationDetails details_type) - { - fbb_.AddElement<uint8_t>(QuantizationParameters::VT_DETAILS_TYPE, - static_cast<uint8_t>(details_type), 0); - } - void add_details(flatbuffers::Offset<void> details) - { - fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details); - } - void add_quantized_dimension(int32_t quantized_dimension) - { - fbb_.AddElement<int32_t>(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, - 0); - } - explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - QuantizationParametersBuilder &operator=(const QuantizationParametersBuilder &); - flatbuffers::Offset<QuantizationParameters> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<QuantizationParameters>(end); - return o; - } -}; - -inline flatbuffers::Offset<QuantizationParameters> -CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<float>> min = 0, - flatbuffers::Offset<flatbuffers::Vector<float>> max = 0, - flatbuffers::Offset<flatbuffers::Vector<float>> scale = 0, - flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point = 0, - QuantizationDetails details_type = QuantizationDetails_NONE, - flatbuffers::Offset<void> details = 0, int32_t quantized_dimension = 0) -{ - QuantizationParametersBuilder builder_(_fbb); - builder_.add_quantized_dimension(quantized_dimension); - builder_.add_details(details); - builder_.add_zero_point(zero_point); - builder_.add_scale(scale); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_details_type(details_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, const std::vector<float> *min = nullptr, - const std::vector<float> *max = nullptr, const std::vector<float> *scale = nullptr, - const std::vector<int64_t> *zero_point = nullptr, - QuantizationDetails details_type = QuantizationDetails_NONE, - flatbuffers::Offset<void> details = 0, int32_t quantized_dimension = 0) -{ - return onert_tflite::CreateQuantizationParameters( - _fbb, min ? _fbb.CreateVector<float>(*min) : 0, max ? _fbb.CreateVector<float>(*max) : 0, - scale ? _fbb.CreateVector<float>(*scale) : 0, - zero_point ? _fbb.CreateVector<int64_t>(*zero_point) : 0, details_type, details, - quantized_dimension); -} - -struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES = 4 - }; - const flatbuffers::Vector<int32_t> *values() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && verifier.EndTable(); - } -}; - -struct Int32VectorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset<flatbuffers::Vector<int32_t>> values) - { - fbb_.AddOffset(Int32Vector::VT_VALUES, values); - } - explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Int32VectorBuilder &operator=(const Int32VectorBuilder &); - flatbuffers::Offset<Int32Vector> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Int32Vector>(end); - return o; - } -}; - -inline flatbuffers::Offset<Int32Vector> -CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> values = 0) -{ - Int32VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Int32Vector> -CreateInt32VectorDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *values = nullptr) -{ - return onert_tflite::CreateInt32Vector(_fbb, values ? _fbb.CreateVector<int32_t>(*values) : 0); -} - -struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES = 4 - }; - const flatbuffers::Vector<uint16_t> *values() const - { - return GetPointer<const flatbuffers::Vector<uint16_t> *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && verifier.EndTable(); - } -}; - -struct Uint16VectorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values) - { - fbb_.AddOffset(Uint16Vector::VT_VALUES, values); - } - explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Uint16VectorBuilder &operator=(const Uint16VectorBuilder &); - flatbuffers::Offset<Uint16Vector> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Uint16Vector>(end); - return o; - } -}; - -inline flatbuffers::Offset<Uint16Vector> -CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values = 0) -{ - Uint16VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Uint16Vector> -CreateUint16VectorDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint16_t> *values = nullptr) -{ - return onert_tflite::CreateUint16Vector(_fbb, values ? _fbb.CreateVector<uint16_t>(*values) : 0); -} - -struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES = 4 - }; - const flatbuffers::Vector<uint8_t> *values() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && verifier.EndTable(); - } -}; - -struct Uint8VectorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values) - { - fbb_.AddOffset(Uint8Vector::VT_VALUES, values); - } - explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Uint8VectorBuilder &operator=(const Uint8VectorBuilder &); - flatbuffers::Offset<Uint8Vector> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Uint8Vector>(end); - return o; - } -}; - -inline flatbuffers::Offset<Uint8Vector> -CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values = 0) -{ - Uint8VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Uint8Vector> -CreateUint8VectorDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint8_t> *values = nullptr) -{ - return onert_tflite::CreateUint8Vector(_fbb, values ? _fbb.CreateVector<uint8_t>(*values) : 0); -} - -struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FORMAT = 4, - VT_DENSE_SIZE = 6, - VT_ARRAY_SEGMENTS_TYPE = 8, - VT_ARRAY_SEGMENTS = 10, - VT_ARRAY_INDICES_TYPE = 12, - VT_ARRAY_INDICES = 14 - }; - DimensionType format() const - { - return static_cast<DimensionType>(GetField<int8_t>(VT_FORMAT, 0)); - } - int32_t dense_size() const { return GetField<int32_t>(VT_DENSE_SIZE, 0); } - SparseIndexVector array_segments_type() const - { - return static_cast<SparseIndexVector>(GetField<uint8_t>(VT_ARRAY_SEGMENTS_TYPE, 0)); - } - const void *array_segments() const { return GetPointer<const void *>(VT_ARRAY_SEGMENTS); } - template <typename T> const T *array_segments_as() const; - const Int32Vector *array_segments_as_Int32Vector() const - { - return array_segments_type() == SparseIndexVector_Int32Vector - ? static_cast<const Int32Vector *>(array_segments()) - : nullptr; - } - const Uint16Vector *array_segments_as_Uint16Vector() const - { - return array_segments_type() == SparseIndexVector_Uint16Vector - ? static_cast<const Uint16Vector *>(array_segments()) - : nullptr; - } - const Uint8Vector *array_segments_as_Uint8Vector() const - { - return array_segments_type() == SparseIndexVector_Uint8Vector - ? static_cast<const Uint8Vector *>(array_segments()) - : nullptr; - } - SparseIndexVector array_indices_type() const - { - return static_cast<SparseIndexVector>(GetField<uint8_t>(VT_ARRAY_INDICES_TYPE, 0)); - } - const void *array_indices() const { return GetPointer<const void *>(VT_ARRAY_INDICES); } - template <typename T> const T *array_indices_as() const; - const Int32Vector *array_indices_as_Int32Vector() const - { - return array_indices_type() == SparseIndexVector_Int32Vector - ? static_cast<const Int32Vector *>(array_indices()) - : nullptr; - } - const Uint16Vector *array_indices_as_Uint16Vector() const - { - return array_indices_type() == SparseIndexVector_Uint16Vector - ? static_cast<const Uint16Vector *>(array_indices()) - : nullptr; - } - const Uint8Vector *array_indices_as_Uint8Vector() const - { - return array_indices_type() == SparseIndexVector_Uint8Vector - ? static_cast<const Uint8Vector *>(array_indices()) - : nullptr; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_FORMAT) && - VerifyField<int32_t>(verifier, VT_DENSE_SIZE) && - VerifyField<uint8_t>(verifier, VT_ARRAY_SEGMENTS_TYPE) && - VerifyOffset(verifier, VT_ARRAY_SEGMENTS) && - VerifySparseIndexVector(verifier, array_segments(), array_segments_type()) && - VerifyField<uint8_t>(verifier, VT_ARRAY_INDICES_TYPE) && - VerifyOffset(verifier, VT_ARRAY_INDICES) && - VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) && - verifier.EndTable(); - } -}; - -template <> inline const Int32Vector *DimensionMetadata::array_segments_as<Int32Vector>() const -{ - return array_segments_as_Int32Vector(); -} - -template <> inline const Uint16Vector *DimensionMetadata::array_segments_as<Uint16Vector>() const -{ - return array_segments_as_Uint16Vector(); -} - -template <> inline const Uint8Vector *DimensionMetadata::array_segments_as<Uint8Vector>() const -{ - return array_segments_as_Uint8Vector(); -} - -template <> inline const Int32Vector *DimensionMetadata::array_indices_as<Int32Vector>() const -{ - return array_indices_as_Int32Vector(); -} - -template <> inline const Uint16Vector *DimensionMetadata::array_indices_as<Uint16Vector>() const -{ - return array_indices_as_Uint16Vector(); -} - -template <> inline const Uint8Vector *DimensionMetadata::array_indices_as<Uint8Vector>() const -{ - return array_indices_as_Uint8Vector(); -} - -struct DimensionMetadataBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_format(DimensionType format) - { - fbb_.AddElement<int8_t>(DimensionMetadata::VT_FORMAT, static_cast<int8_t>(format), 0); - } - void add_dense_size(int32_t dense_size) - { - fbb_.AddElement<int32_t>(DimensionMetadata::VT_DENSE_SIZE, dense_size, 0); - } - void add_array_segments_type(SparseIndexVector array_segments_type) - { - fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, - static_cast<uint8_t>(array_segments_type), 0); - } - void add_array_segments(flatbuffers::Offset<void> array_segments) - { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments); - } - void add_array_indices_type(SparseIndexVector array_indices_type) - { - fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_INDICES_TYPE, - static_cast<uint8_t>(array_indices_type), 0); - } - void add_array_indices(flatbuffers::Offset<void> array_indices) - { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices); - } - explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DimensionMetadataBuilder &operator=(const DimensionMetadataBuilder &); - flatbuffers::Offset<DimensionMetadata> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DimensionMetadata>(end); - return o; - } -}; - -inline flatbuffers::Offset<DimensionMetadata> -CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, - DimensionType format = DimensionType_DENSE, int32_t dense_size = 0, - SparseIndexVector array_segments_type = SparseIndexVector_NONE, - flatbuffers::Offset<void> array_segments = 0, - SparseIndexVector array_indices_type = SparseIndexVector_NONE, - flatbuffers::Offset<void> array_indices = 0) -{ - DimensionMetadataBuilder builder_(_fbb); - builder_.add_array_indices(array_indices); - builder_.add_array_segments(array_segments); - builder_.add_dense_size(dense_size); - builder_.add_array_indices_type(array_indices_type); - builder_.add_array_segments_type(array_segments_type); - builder_.add_format(format); - return builder_.Finish(); -} - -struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TRAVERSAL_ORDER = 4, - VT_BLOCK_MAP = 6, - VT_DIM_METADATA = 8 - }; - const flatbuffers::Vector<int32_t> *traversal_order() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_TRAVERSAL_ORDER); - } - const flatbuffers::Vector<int32_t> *block_map() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_BLOCK_MAP); - } - const flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>> *dim_metadata() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>> *>( - VT_DIM_METADATA); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_TRAVERSAL_ORDER) && - verifier.VerifyVector(traversal_order()) && VerifyOffset(verifier, VT_BLOCK_MAP) && - verifier.VerifyVector(block_map()) && VerifyOffset(verifier, VT_DIM_METADATA) && - verifier.VerifyVector(dim_metadata()) && verifier.VerifyVectorOfTables(dim_metadata()) && - verifier.EndTable(); - } -}; - -struct SparsityParametersBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_traversal_order(flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order) - { - fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order); - } - void add_block_map(flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map) - { - fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map); - } - void add_dim_metadata( - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>>> dim_metadata) - { - fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata); - } - explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SparsityParametersBuilder &operator=(const SparsityParametersBuilder &); - flatbuffers::Offset<SparsityParameters> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SparsityParameters>(end); - return o; - } -}; - -inline flatbuffers::Offset<SparsityParameters> CreateSparsityParameters( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DimensionMetadata>>> dim_metadata = - 0) -{ - SparsityParametersBuilder builder_(_fbb); - builder_.add_dim_metadata(dim_metadata); - builder_.add_block_map(block_map); - builder_.add_traversal_order(traversal_order); - return builder_.Finish(); -} - -inline flatbuffers::Offset<SparsityParameters> CreateSparsityParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, const std::vector<int32_t> *traversal_order = nullptr, - const std::vector<int32_t> *block_map = nullptr, - const std::vector<flatbuffers::Offset<DimensionMetadata>> *dim_metadata = nullptr) -{ - return onert_tflite::CreateSparsityParameters( - _fbb, traversal_order ? _fbb.CreateVector<int32_t>(*traversal_order) : 0, - block_map ? _fbb.CreateVector<int32_t>(*block_map) : 0, - dim_metadata ? _fbb.CreateVector<flatbuffers::Offset<DimensionMetadata>>(*dim_metadata) : 0); -} - -struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SHAPE = 4, - VT_TYPE = 6, - VT_BUFFER = 8, - VT_NAME = 10, - VT_QUANTIZATION = 12, - VT_IS_VARIABLE = 14, - VT_SPARSITY = 16, - VT_SHAPE_SIGNATURE = 18 - }; - const flatbuffers::Vector<int32_t> *shape() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE); - } - TensorType type() const { return static_cast<TensorType>(GetField<int8_t>(VT_TYPE, 0)); } - uint32_t buffer() const { return GetField<uint32_t>(VT_BUFFER, 0); } - const flatbuffers::String *name() const - { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - const QuantizationParameters *quantization() const - { - return GetPointer<const QuantizationParameters *>(VT_QUANTIZATION); - } - bool is_variable() const { return GetField<uint8_t>(VT_IS_VARIABLE, 0) != 0; } - const SparsityParameters *sparsity() const - { - return GetPointer<const SparsityParameters *>(VT_SPARSITY); - } - const flatbuffers::Vector<int32_t> *shape_signature() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE_SIGNATURE); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SHAPE) && - verifier.VerifyVector(shape()) && VerifyField<int8_t>(verifier, VT_TYPE) && - VerifyField<uint32_t>(verifier, VT_BUFFER) && VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && VerifyOffset(verifier, VT_QUANTIZATION) && - verifier.VerifyTable(quantization()) && VerifyField<uint8_t>(verifier, VT_IS_VARIABLE) && - VerifyOffset(verifier, VT_SPARSITY) && verifier.VerifyTable(sparsity()) && - VerifyOffset(verifier, VT_SHAPE_SIGNATURE) && verifier.VerifyVector(shape_signature()) && - verifier.EndTable(); - } -}; - -struct TensorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) - { - fbb_.AddOffset(Tensor::VT_SHAPE, shape); - } - void add_type(TensorType type) - { - fbb_.AddElement<int8_t>(Tensor::VT_TYPE, static_cast<int8_t>(type), 0); - } - void add_buffer(uint32_t buffer) { fbb_.AddElement<uint32_t>(Tensor::VT_BUFFER, buffer, 0); } - void add_name(flatbuffers::Offset<flatbuffers::String> name) - { - fbb_.AddOffset(Tensor::VT_NAME, name); - } - void add_quantization(flatbuffers::Offset<QuantizationParameters> quantization) - { - fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); - } - void add_is_variable(bool is_variable) - { - fbb_.AddElement<uint8_t>(Tensor::VT_IS_VARIABLE, static_cast<uint8_t>(is_variable), 0); - } - void add_sparsity(flatbuffers::Offset<SparsityParameters> sparsity) - { - fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity); - } - void add_shape_signature(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature) - { - fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature); - } - explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TensorBuilder &operator=(const TensorBuilder &); - flatbuffers::Offset<Tensor> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Tensor>(end); - return o; - } -}; - -inline flatbuffers::Offset<Tensor> -CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0, - TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, - flatbuffers::Offset<flatbuffers::String> name = 0, - flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false, - flatbuffers::Offset<SparsityParameters> sparsity = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature = 0) -{ - TensorBuilder builder_(_fbb); - builder_.add_shape_signature(shape_signature); - builder_.add_sparsity(sparsity); - builder_.add_quantization(quantization); - builder_.add_name(name); - builder_.add_buffer(buffer); - builder_.add_shape(shape); - builder_.add_is_variable(is_variable); - builder_.add_type(type); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Tensor> CreateTensorDirect( - flatbuffers::FlatBufferBuilder &_fbb, const std::vector<int32_t> *shape = nullptr, - TensorType type = TensorType_FLOAT32, uint32_t buffer = 0, const char *name = nullptr, - flatbuffers::Offset<QuantizationParameters> quantization = 0, bool is_variable = false, - flatbuffers::Offset<SparsityParameters> sparsity = 0, - const std::vector<int32_t> *shape_signature = nullptr) -{ - return onert_tflite::CreateTensor( - _fbb, shape ? _fbb.CreateVector<int32_t>(*shape) : 0, type, buffer, - name ? _fbb.CreateString(name) : 0, quantization, is_variable, sparsity, - shape_signature ? _fbb.CreateVector<int32_t>(*shape_signature) : 0); -} - -struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FUSED_ACTIVATION_FUNCTION = 10, - VT_DILATION_W_FACTOR = 12, - VT_DILATION_H_FACTOR = 14 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); } - int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) && - VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable(); - } -}; - -struct Conv2DOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(Conv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) - { - fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Conv2DOptionsBuilder &operator=(const Conv2DOptionsBuilder &); - flatbuffers::Offset<Conv2DOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Conv2DOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<Conv2DOptions> -CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1) -{ - Conv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FILTER_WIDTH = 10, - VT_FILTER_HEIGHT = 12, - VT_FUSED_ACTIVATION_FUNCTION = 14 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - int32_t filter_width() const { return GetField<int32_t>(VT_FILTER_WIDTH, 0); } - int32_t filter_height() const { return GetField<int32_t>(VT_FILTER_HEIGHT, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && - VerifyField<int32_t>(verifier, VT_FILTER_WIDTH) && - VerifyField<int32_t>(verifier, VT_FILTER_HEIGHT) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct Pool2DOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(Pool2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_filter_width(int32_t filter_width) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0); - } - void add_filter_height(int32_t filter_height) - { - fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - Pool2DOptionsBuilder &operator=(const Pool2DOptionsBuilder &); - flatbuffers::Offset<Pool2DOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Pool2DOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<Pool2DOptions> -CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0, int32_t filter_width = 0, - int32_t filter_height = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - Pool2DOptionsBuilder builder_(_fbb); - builder_.add_filter_height(filter_height); - builder_.add_filter_width(filter_width); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_DEPTH_MULTIPLIER = 10, - VT_FUSED_ACTIVATION_FUNCTION = 12, - VT_DILATION_W_FACTOR = 14, - VT_DILATION_H_FACTOR = 16 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - int32_t depth_multiplier() const { return GetField<int32_t>(VT_DEPTH_MULTIPLIER, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { return GetField<int32_t>(VT_DILATION_W_FACTOR, 1); } - int32_t dilation_h_factor() const { return GetField<int32_t>(VT_DILATION_H_FACTOR, 1); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && - VerifyField<int32_t>(verifier, VT_DEPTH_MULTIPLIER) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR) && - VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR) && verifier.EndTable(); - } -}; - -struct DepthwiseConv2DOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_depth_multiplier(int32_t depth_multiplier) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) - { - fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DepthwiseConv2DOptionsBuilder &operator=(const DepthwiseConv2DOptionsBuilder &); - flatbuffers::Offset<DepthwiseConv2DOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DepthwiseConv2DOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, int32_t stride_w = 0, - int32_t stride_h = 0, int32_t depth_multiplier = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, int32_t dilation_h_factor = 1) -{ - DepthwiseConv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_depth_multiplier(depth_multiplier); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM_CHANNELS = 4, - VT_NUM_COLUMNS_PER_CHANNEL = 6, - VT_EMBEDDING_DIM_PER_CHANNEL = 8 - }; - int32_t num_channels() const { return GetField<int32_t>(VT_NUM_CHANNELS, 0); } - const flatbuffers::Vector<int32_t> *num_columns_per_channel() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NUM_COLUMNS_PER_CHANNEL); - } - const flatbuffers::Vector<int32_t> *embedding_dim_per_channel() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_EMBEDDING_DIM_PER_CHANNEL); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_CHANNELS) && - VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) && - verifier.VerifyVector(num_columns_per_channel()) && - VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) && - verifier.VerifyVector(embedding_dim_per_channel()) && verifier.EndTable(); - } -}; - -struct ConcatEmbeddingsOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_channels(int32_t num_channels) - { - fbb_.AddElement<int32_t>(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0); - } - void add_num_columns_per_channel( - flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel) - { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel); - } - void add_embedding_dim_per_channel( - flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel) - { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, - embedding_dim_per_channel); - } - explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ConcatEmbeddingsOptionsBuilder &operator=(const ConcatEmbeddingsOptionsBuilder &); - flatbuffers::Offset<ConcatEmbeddingsOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ConcatEmbeddingsOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel = 0) -{ - ConcatEmbeddingsOptionsBuilder builder_(_fbb); - builder_.add_embedding_dim_per_channel(embedding_dim_per_channel); - builder_.add_num_columns_per_channel(num_columns_per_channel); - builder_.add_num_channels(num_channels); - return builder_.Finish(); -} - -inline flatbuffers::Offset<ConcatEmbeddingsOptions> -CreateConcatEmbeddingsOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, int32_t num_channels = 0, - const std::vector<int32_t> *num_columns_per_channel = nullptr, - const std::vector<int32_t> *embedding_dim_per_channel = nullptr) -{ - return onert_tflite::CreateConcatEmbeddingsOptions( - _fbb, num_channels, - num_columns_per_channel ? _fbb.CreateVector<int32_t>(*num_columns_per_channel) : 0, - embedding_dim_per_channel ? _fbb.CreateVector<int32_t>(*embedding_dim_per_channel) : 0); -} - -struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TYPE = 4 - }; - LSHProjectionType type() const - { - return static_cast<LSHProjectionType>(GetField<int8_t>(VT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_TYPE) && - verifier.EndTable(); - } -}; - -struct LSHProjectionOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_type(LSHProjectionType type) - { - fbb_.AddElement<int8_t>(LSHProjectionOptions::VT_TYPE, static_cast<int8_t>(type), 0); - } - explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LSHProjectionOptionsBuilder &operator=(const LSHProjectionOptionsBuilder &); - flatbuffers::Offset<LSHProjectionOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LSHProjectionOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LSHProjectionOptions> -CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, - LSHProjectionType type = LSHProjectionType_UNKNOWN) -{ - LSHProjectionOptionsBuilder builder_(_fbb); - builder_.add_type(type); - return builder_.Finish(); -} - -struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_RANK = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - int32_t rank() const { return GetField<int32_t>(VT_RANK, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RANK) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct SVDFOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_rank(int32_t rank) { fbb_.AddElement<int32_t>(SVDFOptions::VT_RANK, rank, 0); } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SVDFOptionsBuilder &operator=(const SVDFOptionsBuilder &); - flatbuffers::Offset<SVDFOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SVDFOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SVDFOptions> -CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t rank = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) -{ - SVDFOptionsBuilder builder_(_fbb); - builder_.add_rank(rank); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 6 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct RNNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - RNNOptionsBuilder &operator=(const RNNOptionsBuilder &); - flatbuffers::Offset<RNNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<RNNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<RNNOptions> -CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) -{ - RNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct SequenceRNNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), - 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SequenceRNNOptionsBuilder &operator=(const SequenceRNNOptionsBuilder &); - flatbuffers::Offset<SequenceRNNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SequenceRNNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) -{ - SequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_MERGE_OUTPUTS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 - }; - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct BidirectionalSequenceRNNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, - static_cast<uint8_t>(time_major), 0); - } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_merge_outputs(bool merge_outputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS, - static_cast<uint8_t>(merge_outputs), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BidirectionalSequenceRNNOptionsBuilder &operator=(const BidirectionalSequenceRNNOptionsBuilder &); - flatbuffers::Offset<BidirectionalSequenceRNNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BidirectionalSequenceRNNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, bool time_major = false, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - bool merge_outputs = false, bool asymmetric_quantize_inputs = false) -{ - BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_WEIGHTS_FORMAT = 6, - VT_KEEP_NUM_DIMS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - FullyConnectedOptionsWeightsFormat weights_format() const - { - return static_cast<FullyConnectedOptionsWeightsFormat>(GetField<int8_t>(VT_WEIGHTS_FORMAT, 0)); - } - bool keep_num_dims() const { return GetField<uint8_t>(VT_KEEP_NUM_DIMS, 0) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<int8_t>(verifier, VT_WEIGHTS_FORMAT) && - VerifyField<uint8_t>(verifier, VT_KEEP_NUM_DIMS) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct FullyConnectedOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_weights_format(FullyConnectedOptionsWeightsFormat weights_format) - { - fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_WEIGHTS_FORMAT, - static_cast<int8_t>(weights_format), 0); - } - void add_keep_num_dims(bool keep_num_dims) - { - fbb_.AddElement<uint8_t>(FullyConnectedOptions::VT_KEEP_NUM_DIMS, - static_cast<uint8_t>(keep_num_dims), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FullyConnectedOptionsBuilder &operator=(const FullyConnectedOptionsBuilder &); - flatbuffers::Offset<FullyConnectedOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FullyConnectedOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions( - flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - FullyConnectedOptionsWeightsFormat weights_format = FullyConnectedOptionsWeightsFormat_DEFAULT, - bool keep_num_dims = false, bool asymmetric_quantize_inputs = false) -{ - FullyConnectedOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_keep_num_dims(keep_num_dims); - builder_.add_weights_format(weights_format); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BETA = 4 - }; - float beta() const { return GetField<float>(VT_BETA, 0.0f); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_BETA) && - verifier.EndTable(); - } -}; - -struct SoftmaxOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_beta(float beta) { fbb_.AddElement<float>(SoftmaxOptions::VT_BETA, beta, 0.0f); } - explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SoftmaxOptionsBuilder &operator=(const SoftmaxOptionsBuilder &); - flatbuffers::Offset<SoftmaxOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SoftmaxOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SoftmaxOptions> -CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, float beta = 0.0f) -{ - SoftmaxOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - return builder_.Finish(); -} - -struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_AXIS = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6 - }; - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct ConcatenationOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(ConcatenationOptions::VT_AXIS, axis, 0); } - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ConcatenationOptionsBuilder &operator=(const ConcatenationOptionsBuilder &); - flatbuffers::Offset<ConcatenationOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ConcatenationOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions( - flatbuffers::FlatBufferBuilder &_fbb, int32_t axis = 0, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - ConcatenationOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct AddOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - AddOptionsBuilder &operator=(const AddOptionsBuilder &); - flatbuffers::Offset<AddOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<AddOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<AddOptions> -CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - AddOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct MulOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MulOptionsBuilder &operator=(const MulOptionsBuilder &); - flatbuffers::Offset<MulOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MulOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MulOptions> -CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - MulOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct L2NormOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - L2NormOptionsBuilder &operator=(const L2NormOptionsBuilder &); - flatbuffers::Offset<L2NormOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<L2NormOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<L2NormOptions> -CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - L2NormOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_RADIUS = 4, - VT_BIAS = 6, - VT_ALPHA = 8, - VT_BETA = 10 - }; - int32_t radius() const { return GetField<int32_t>(VT_RADIUS, 0); } - float bias() const { return GetField<float>(VT_BIAS, 0.0f); } - float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); } - float beta() const { return GetField<float>(VT_BETA, 0.0f); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_RADIUS) && - VerifyField<float>(verifier, VT_BIAS) && VerifyField<float>(verifier, VT_ALPHA) && - VerifyField<float>(verifier, VT_BETA) && verifier.EndTable(); - } -}; - -struct LocalResponseNormalizationOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_radius(int32_t radius) - { - fbb_.AddElement<int32_t>(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0); - } - void add_bias(float bias) - { - fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f); - } - void add_alpha(float alpha) - { - fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f); - } - void add_beta(float beta) - { - fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f); - } - explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LocalResponseNormalizationOptionsBuilder & - operator=(const LocalResponseNormalizationOptionsBuilder &); - flatbuffers::Offset<LocalResponseNormalizationOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LocalResponseNormalizationOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LocalResponseNormalizationOptions> -CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t radius = 0, - float bias = 0.0f, float alpha = 0.0f, float beta = 0.0f) -{ - LocalResponseNormalizationOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - builder_.add_alpha(alpha); - builder_.add_bias(bias); - builder_.add_radius(radius); - return builder_.Finish(); -} - -struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_KERNEL_TYPE = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); } - float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); } - LSTMKernelType kernel_type() const - { - return static_cast<LSTMKernelType>(GetField<int8_t>(VT_KERNEL_TYPE, 0)); - } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<float>(verifier, VT_CELL_CLIP) && - VerifyField<float>(verifier, VT_PROJ_CLIP) && - VerifyField<int8_t>(verifier, VT_KERNEL_TYPE) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct LSTMOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) - { - fbb_.AddElement<float>(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) - { - fbb_.AddElement<float>(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_kernel_type(LSTMKernelType kernel_type) - { - fbb_.AddElement<int8_t>(LSTMOptions::VT_KERNEL_TYPE, static_cast<int8_t>(kernel_type), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LSTMOptionsBuilder &operator=(const LSTMOptionsBuilder &); - flatbuffers::Offset<LSTMOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LSTMOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LSTMOptions> -CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - float cell_clip = 0.0f, float proj_clip = 0.0f, - LSTMKernelType kernel_type = LSTMKernelType_FULL, - bool asymmetric_quantize_inputs = false) -{ - LSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_kernel_type(kernel_type); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_TIME_MAJOR = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); } - float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); } - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<float>(verifier, VT_CELL_CLIP) && - VerifyField<float>(verifier, VT_PROJ_CLIP) && - VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct UnidirectionalSequenceLSTMOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) - { - fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) - { - fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, - static_cast<uint8_t>(time_major), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - UnidirectionalSequenceLSTMOptionsBuilder & - operator=(const UnidirectionalSequenceLSTMOptionsBuilder &); - flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> -CreateUnidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - float cell_clip = 0.0f, float proj_clip = 0.0f, bool time_major = false, - bool asymmetric_quantize_inputs = false) -{ - UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_MERGE_OUTPUTS = 10, - VT_TIME_MAJOR = 12, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 14 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { return GetField<float>(VT_CELL_CLIP, 0.0f); } - float proj_clip() const { return GetField<float>(VT_PROJ_CLIP, 0.0f); } - bool merge_outputs() const { return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0; } - bool time_major() const { return GetField<uint8_t>(VT_TIME_MAJOR, 1) != 0; } - bool asymmetric_quantize_inputs() const - { - return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && - VerifyField<float>(verifier, VT_CELL_CLIP) && - VerifyField<float>(verifier, VT_PROJ_CLIP) && - VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS) && - VerifyField<uint8_t>(verifier, VT_TIME_MAJOR) && - VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS) && verifier.EndTable(); - } -}; - -struct BidirectionalSequenceLSTMOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) - { - fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) - { - fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_merge_outputs(bool merge_outputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS, - static_cast<uint8_t>(merge_outputs), 0); - } - void add_time_major(bool time_major) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, - static_cast<uint8_t>(time_major), 1); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) - { - fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, - static_cast<uint8_t>(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BidirectionalSequenceLSTMOptionsBuilder & - operator=(const BidirectionalSequenceLSTMOptionsBuilder &); - flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BidirectionalSequenceLSTMOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE, - float cell_clip = 0.0f, float proj_clip = 0.0f, bool merge_outputs = false, - bool time_major = true, bool asymmetric_quantize_inputs = false) -{ - BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ALIGN_CORNERS = 8, - VT_HALF_PIXEL_CENTERS = 10 - }; - bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; } - bool half_pixel_centers() const { return GetField<uint8_t>(VT_HALF_PIXEL_CENTERS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) && - VerifyField<uint8_t>(verifier, VT_HALF_PIXEL_CENTERS) && verifier.EndTable(); - } -}; - -struct ResizeBilinearOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) - { - fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_ALIGN_CORNERS, - static_cast<uint8_t>(align_corners), 0); - } - void add_half_pixel_centers(bool half_pixel_centers) - { - fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, - static_cast<uint8_t>(half_pixel_centers), 0); - } - explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ResizeBilinearOptionsBuilder &operator=(const ResizeBilinearOptionsBuilder &); - flatbuffers::Offset<ResizeBilinearOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ResizeBilinearOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ResizeBilinearOptions> -CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false, - bool half_pixel_centers = false) -{ - ResizeBilinearOptionsBuilder builder_(_fbb); - builder_.add_half_pixel_centers(half_pixel_centers); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ALIGN_CORNERS = 4 - }; - bool align_corners() const { return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS) && - verifier.EndTable(); - } -}; - -struct ResizeNearestNeighborOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) - { - fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, - static_cast<uint8_t>(align_corners), 0); - } - explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ResizeNearestNeighborOptionsBuilder &operator=(const ResizeNearestNeighborOptionsBuilder &); - flatbuffers::Offset<ResizeNearestNeighborOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ResizeNearestNeighborOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ResizeNearestNeighborOptions> -CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, bool align_corners = false) -{ - ResizeNearestNeighborOptionsBuilder builder_(_fbb); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SUBGRAPH = 4 - }; - uint32_t subgraph() const { return GetField<uint32_t>(VT_SUBGRAPH, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_SUBGRAPH) && - verifier.EndTable(); - } -}; - -struct CallOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_subgraph(uint32_t subgraph) - { - fbb_.AddElement<uint32_t>(CallOptions::VT_SUBGRAPH, subgraph, 0); - } - explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CallOptionsBuilder &operator=(const CallOptionsBuilder &); - flatbuffers::Offset<CallOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CallOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, - uint32_t subgraph = 0) -{ - CallOptionsBuilder builder_(_fbb); - builder_.add_subgraph(subgraph); - return builder_.Finish(); -} - -struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct PadOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PadOptionsBuilder &operator=(const PadOptionsBuilder &); - flatbuffers::Offset<PadOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PadOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - PadOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct PadV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PadV2OptionsBuilder &operator=(const PadV2OptionsBuilder &); - flatbuffers::Offset<PadV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PadV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - PadV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NEW_SHAPE = 4 - }; - const flatbuffers::Vector<int32_t> *new_shape() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NEW_SHAPE); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_NEW_SHAPE) && - verifier.VerifyVector(new_shape()) && verifier.EndTable(); - } -}; - -struct ReshapeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_new_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape) - { - fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape); - } - explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReshapeOptionsBuilder &operator=(const ReshapeOptionsBuilder &); - flatbuffers::Offset<ReshapeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReshapeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReshapeOptions> -CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape = 0) -{ - ReshapeOptionsBuilder builder_(_fbb); - builder_.add_new_shape(new_shape); - return builder_.Finish(); -} - -inline flatbuffers::Offset<ReshapeOptions> -CreateReshapeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *new_shape = nullptr) -{ - return onert_tflite::CreateReshapeOptions(_fbb, - new_shape ? _fbb.CreateVector<int32_t>(*new_shape) : 0); -} - -struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SpaceToBatchNDOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SpaceToBatchNDOptionsBuilder &operator=(const SpaceToBatchNDOptionsBuilder &); - flatbuffers::Offset<SpaceToBatchNDOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SpaceToBatchNDOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SpaceToBatchNDOptions> -CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SpaceToBatchNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct BatchToSpaceNDOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BatchToSpaceNDOptionsBuilder &operator=(const BatchToSpaceNDOptionsBuilder &); - flatbuffers::Offset<BatchToSpaceNDOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BatchToSpaceNDOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BatchToSpaceNDOptions> -CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - BatchToSpaceNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NGRAM_SIZE = 4, - VT_MAX_SKIP_SIZE = 6, - VT_INCLUDE_ALL_NGRAMS = 8 - }; - int32_t ngram_size() const { return GetField<int32_t>(VT_NGRAM_SIZE, 0); } - int32_t max_skip_size() const { return GetField<int32_t>(VT_MAX_SKIP_SIZE, 0); } - bool include_all_ngrams() const { return GetField<uint8_t>(VT_INCLUDE_ALL_NGRAMS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NGRAM_SIZE) && - VerifyField<int32_t>(verifier, VT_MAX_SKIP_SIZE) && - VerifyField<uint8_t>(verifier, VT_INCLUDE_ALL_NGRAMS) && verifier.EndTable(); - } -}; - -struct SkipGramOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_ngram_size(int32_t ngram_size) - { - fbb_.AddElement<int32_t>(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0); - } - void add_max_skip_size(int32_t max_skip_size) - { - fbb_.AddElement<int32_t>(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0); - } - void add_include_all_ngrams(bool include_all_ngrams) - { - fbb_.AddElement<uint8_t>(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, - static_cast<uint8_t>(include_all_ngrams), 0); - } - explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SkipGramOptionsBuilder &operator=(const SkipGramOptionsBuilder &); - flatbuffers::Offset<SkipGramOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SkipGramOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SkipGramOptions> -CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t ngram_size = 0, - int32_t max_skip_size = 0, bool include_all_ngrams = false) -{ - SkipGramOptionsBuilder builder_(_fbb); - builder_.add_max_skip_size(max_skip_size); - builder_.add_ngram_size(ngram_size); - builder_.add_include_all_ngrams(include_all_ngrams); - return builder_.Finish(); -} - -struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { return GetField<int32_t>(VT_BLOCK_SIZE, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BLOCK_SIZE) && - verifier.EndTable(); - } -}; - -struct SpaceToDepthOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) - { - fbb_.AddElement<int32_t>(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SpaceToDepthOptionsBuilder &operator=(const SpaceToDepthOptionsBuilder &); - flatbuffers::Offset<SpaceToDepthOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SpaceToDepthOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SpaceToDepthOptions> -CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t block_size = 0) -{ - SpaceToDepthOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { return GetField<int32_t>(VT_BLOCK_SIZE, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BLOCK_SIZE) && - verifier.EndTable(); - } -}; - -struct DepthToSpaceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) - { - fbb_.AddElement<int32_t>(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DepthToSpaceOptionsBuilder &operator=(const DepthToSpaceOptionsBuilder &); - flatbuffers::Offset<DepthToSpaceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DepthToSpaceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DepthToSpaceOptions> -CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t block_size = 0) -{ - DepthToSpaceOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct SubOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SubOptionsBuilder &operator=(const SubOptionsBuilder &); - flatbuffers::Offset<SubOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SubOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SubOptions> -CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - SubOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - ActivationFunctionType fused_activation_function() const - { - return static_cast<ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && - VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION) && verifier.EndTable(); - } -}; - -struct DivOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(ActivationFunctionType fused_activation_function) - { - fbb_.AddElement<int8_t>(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, - static_cast<int8_t>(fused_activation_function), 0); - } - explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DivOptionsBuilder &operator=(const DivOptionsBuilder &); - flatbuffers::Offset<DivOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DivOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DivOptions> -CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, - ActivationFunctionType fused_activation_function = ActivationFunctionType_NONE) -{ - DivOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct TopKV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TopKV2OptionsBuilder &operator=(const TopKV2OptionsBuilder &); - flatbuffers::Offset<TopKV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TopKV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - TopKV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_COMBINER = 4 - }; - CombinerType combiner() const - { - return static_cast<CombinerType>(GetField<int8_t>(VT_COMBINER, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_COMBINER) && - verifier.EndTable(); - } -}; - -struct EmbeddingLookupSparseOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_combiner(CombinerType combiner) - { - fbb_.AddElement<int8_t>(EmbeddingLookupSparseOptions::VT_COMBINER, - static_cast<int8_t>(combiner), 0); - } - explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - EmbeddingLookupSparseOptionsBuilder &operator=(const EmbeddingLookupSparseOptionsBuilder &); - flatbuffers::Offset<EmbeddingLookupSparseOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<EmbeddingLookupSparseOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<EmbeddingLookupSparseOptions> -CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, - CombinerType combiner = CombinerType_SUM) -{ - EmbeddingLookupSparseOptionsBuilder builder_(_fbb); - builder_.add_combiner(combiner); - return builder_.Finish(); -} - -struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_AXIS = 4 - }; - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) && - verifier.EndTable(); - } -}; - -struct GatherOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(GatherOptions::VT_AXIS, axis, 0); } - explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GatherOptionsBuilder &operator=(const GatherOptionsBuilder &); - flatbuffers::Offset<GatherOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GatherOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) -{ - GatherOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct TransposeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TransposeOptionsBuilder &operator=(const TransposeOptionsBuilder &); - flatbuffers::Offset<TransposeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TransposeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<TransposeOptions> -CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - TransposeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ExpOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ExpOptionsBuilder &operator=(const ExpOptionsBuilder &); - flatbuffers::Offset<ExpOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ExpOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ExpOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct CosOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CosOptionsBuilder &operator=(const CosOptionsBuilder &); - flatbuffers::Offset<CosOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CosOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<CosOptions> CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - CosOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_KEEP_DIMS = 4 - }; - bool keep_dims() const { return GetField<uint8_t>(VT_KEEP_DIMS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_KEEP_DIMS) && - verifier.EndTable(); - } -}; - -struct ReducerOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_keep_dims(bool keep_dims) - { - fbb_.AddElement<uint8_t>(ReducerOptions::VT_KEEP_DIMS, static_cast<uint8_t>(keep_dims), 0); - } - explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReducerOptionsBuilder &operator=(const ReducerOptionsBuilder &); - flatbuffers::Offset<ReducerOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReducerOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReducerOptions> -CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, bool keep_dims = false) -{ - ReducerOptionsBuilder builder_(_fbb); - builder_.add_keep_dims(keep_dims); - return builder_.Finish(); -} - -struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SQUEEZE_DIMS = 4 - }; - const flatbuffers::Vector<int32_t> *squeeze_dims() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SQUEEZE_DIMS); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_SQUEEZE_DIMS) && - verifier.VerifyVector(squeeze_dims()) && verifier.EndTable(); - } -}; - -struct SqueezeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_squeeze_dims(flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims) - { - fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims); - } - explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SqueezeOptionsBuilder &operator=(const SqueezeOptionsBuilder &); - flatbuffers::Offset<SqueezeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SqueezeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SqueezeOptions> -CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims = 0) -{ - SqueezeOptionsBuilder builder_(_fbb); - builder_.add_squeeze_dims(squeeze_dims); - return builder_.Finish(); -} - -inline flatbuffers::Offset<SqueezeOptions> -CreateSqueezeOptionsDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<int32_t> *squeeze_dims = nullptr) -{ - return onert_tflite::CreateSqueezeOptions( - _fbb, squeeze_dims ? _fbb.CreateVector<int32_t>(*squeeze_dims) : 0); -} - -struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) && - verifier.EndTable(); - } -}; - -struct SplitOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) - { - fbb_.AddElement<int32_t>(SplitOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SplitOptionsBuilder &operator=(const SplitOptionsBuilder &); - flatbuffers::Offset<SplitOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SplitOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) -{ - SplitOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { return GetField<int32_t>(VT_NUM_SPLITS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM_SPLITS) && - verifier.EndTable(); - } -}; - -struct SplitVOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) - { - fbb_.AddElement<int32_t>(SplitVOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SplitVOptionsBuilder &operator=(const SplitVOptionsBuilder &); - flatbuffers::Offset<SplitVOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SplitVOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) -{ - SplitVOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BEGIN_MASK = 4, - VT_END_MASK = 6, - VT_ELLIPSIS_MASK = 8, - VT_NEW_AXIS_MASK = 10, - VT_SHRINK_AXIS_MASK = 12 - }; - int32_t begin_mask() const { return GetField<int32_t>(VT_BEGIN_MASK, 0); } - int32_t end_mask() const { return GetField<int32_t>(VT_END_MASK, 0); } - int32_t ellipsis_mask() const { return GetField<int32_t>(VT_ELLIPSIS_MASK, 0); } - int32_t new_axis_mask() const { return GetField<int32_t>(VT_NEW_AXIS_MASK, 0); } - int32_t shrink_axis_mask() const { return GetField<int32_t>(VT_SHRINK_AXIS_MASK, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_BEGIN_MASK) && - VerifyField<int32_t>(verifier, VT_END_MASK) && - VerifyField<int32_t>(verifier, VT_ELLIPSIS_MASK) && - VerifyField<int32_t>(verifier, VT_NEW_AXIS_MASK) && - VerifyField<int32_t>(verifier, VT_SHRINK_AXIS_MASK) && verifier.EndTable(); - } -}; - -struct StridedSliceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_begin_mask(int32_t begin_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0); - } - void add_end_mask(int32_t end_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_END_MASK, end_mask, 0); - } - void add_ellipsis_mask(int32_t ellipsis_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0); - } - void add_new_axis_mask(int32_t new_axis_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0); - } - void add_shrink_axis_mask(int32_t shrink_axis_mask) - { - fbb_.AddElement<int32_t>(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); - } - explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - StridedSliceOptionsBuilder &operator=(const StridedSliceOptionsBuilder &); - flatbuffers::Offset<StridedSliceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<StridedSliceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<StridedSliceOptions> -CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t begin_mask = 0, - int32_t end_mask = 0, int32_t ellipsis_mask = 0, - int32_t new_axis_mask = 0, int32_t shrink_axis_mask = 0) -{ - StridedSliceOptionsBuilder builder_(_fbb); - builder_.add_shrink_axis_mask(shrink_axis_mask); - builder_.add_new_axis_mask(new_axis_mask); - builder_.add_ellipsis_mask(ellipsis_mask); - builder_.add_end_mask(end_mask); - builder_.add_begin_mask(begin_mask); - return builder_.Finish(); -} - -struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogSoftmaxOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogSoftmaxOptionsBuilder &operator=(const LogSoftmaxOptionsBuilder &); - flatbuffers::Offset<LogSoftmaxOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogSoftmaxOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogSoftmaxOptions> -CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogSoftmaxOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_IN_DATA_TYPE = 4, - VT_OUT_DATA_TYPE = 6 - }; - TensorType in_data_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_IN_DATA_TYPE, 0)); - } - TensorType out_data_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_OUT_DATA_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_IN_DATA_TYPE) && - VerifyField<int8_t>(verifier, VT_OUT_DATA_TYPE) && verifier.EndTable(); - } -}; - -struct CastOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_in_data_type(TensorType in_data_type) - { - fbb_.AddElement<int8_t>(CastOptions::VT_IN_DATA_TYPE, static_cast<int8_t>(in_data_type), 0); - } - void add_out_data_type(TensorType out_data_type) - { - fbb_.AddElement<int8_t>(CastOptions::VT_OUT_DATA_TYPE, static_cast<int8_t>(out_data_type), 0); - } - explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - CastOptionsBuilder &operator=(const CastOptionsBuilder &); - flatbuffers::Offset<CastOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<CastOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<CastOptions> -CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType in_data_type = TensorType_FLOAT32, - TensorType out_data_type = TensorType_FLOAT32) -{ - CastOptionsBuilder builder_(_fbb); - builder_.add_out_data_type(out_data_type); - builder_.add_in_data_type(in_data_type); - return builder_.Finish(); -} - -struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct DequantizeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DequantizeOptionsBuilder &operator=(const DequantizeOptionsBuilder &); - flatbuffers::Offset<DequantizeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DequantizeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DequantizeOptions> -CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - DequantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct MaximumMinimumOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MaximumMinimumOptionsBuilder &operator=(const MaximumMinimumOptionsBuilder &); - flatbuffers::Offset<MaximumMinimumOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MaximumMinimumOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MaximumMinimumOptions> -CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - MaximumMinimumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct TileOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TileOptionsBuilder &operator=(const TileOptionsBuilder &); - flatbuffers::Offset<TileOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TileOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - TileOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OUTPUT_TYPE = 4 - }; - TensorType output_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) && - verifier.EndTable(); - } -}; - -struct ArgMaxOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(TensorType output_type) - { - fbb_.AddElement<int8_t>(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0); - } - explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ArgMaxOptionsBuilder &operator=(const ArgMaxOptionsBuilder &); - flatbuffers::Offset<ArgMaxOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ArgMaxOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ArgMaxOptions> -CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType output_type = TensorType_FLOAT32) -{ - ArgMaxOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OUTPUT_TYPE = 4 - }; - TensorType output_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE) && - verifier.EndTable(); - } -}; - -struct ArgMinOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(TensorType output_type) - { - fbb_.AddElement<int8_t>(ArgMinOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0); - } - explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ArgMinOptionsBuilder &operator=(const ArgMinOptionsBuilder &); - flatbuffers::Offset<ArgMinOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ArgMinOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ArgMinOptions> -CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType output_type = TensorType_FLOAT32) -{ - ArgMinOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct GreaterOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GreaterOptionsBuilder &operator=(const GreaterOptionsBuilder &); - flatbuffers::Offset<GreaterOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GreaterOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GreaterOptions> -CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - GreaterOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct GreaterEqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GreaterEqualOptionsBuilder &operator=(const GreaterEqualOptionsBuilder &); - flatbuffers::Offset<GreaterEqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GreaterEqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GreaterEqualOptions> -CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - GreaterEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LessOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LessOptionsBuilder &operator=(const LessOptionsBuilder &); - flatbuffers::Offset<LessOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LessOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LessOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LessEqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LessEqualOptionsBuilder &operator=(const LessEqualOptionsBuilder &); - flatbuffers::Offset<LessEqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LessEqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LessEqualOptions> -CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LessEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NegOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NegOptionsBuilder &operator=(const NegOptionsBuilder &); - flatbuffers::Offset<NegOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NegOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - NegOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SelectOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SelectOptionsBuilder &operator=(const SelectOptionsBuilder &); - flatbuffers::Offset<SelectOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SelectOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SelectOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SliceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SliceOptionsBuilder &operator=(const SliceOptionsBuilder &); - flatbuffers::Offset<SliceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SliceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SliceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8 - }; - Padding padding() const { return static_cast<Padding>(GetField<int8_t>(VT_PADDING, 0)); } - int32_t stride_w() const { return GetField<int32_t>(VT_STRIDE_W, 0); } - int32_t stride_h() const { return GetField<int32_t>(VT_STRIDE_H, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_PADDING) && - VerifyField<int32_t>(verifier, VT_STRIDE_W) && - VerifyField<int32_t>(verifier, VT_STRIDE_H) && verifier.EndTable(); - } -}; - -struct TransposeConvOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(Padding padding) - { - fbb_.AddElement<int8_t>(TransposeConvOptions::VT_PADDING, static_cast<int8_t>(padding), 0); - } - void add_stride_w(int32_t stride_w) - { - fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) - { - fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_H, stride_h, 0); - } - explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - TransposeConvOptionsBuilder &operator=(const TransposeConvOptionsBuilder &); - flatbuffers::Offset<TransposeConvOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<TransposeConvOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<TransposeConvOptions> -CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, Padding padding = Padding_SAME, - int32_t stride_w = 0, int32_t stride_h = 0) -{ - TransposeConvOptionsBuilder builder_(_fbb); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_padding(padding); - return builder_.Finish(); -} - -struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ExpandDimsOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ExpandDimsOptionsBuilder &operator=(const ExpandDimsOptionsBuilder &); - flatbuffers::Offset<ExpandDimsOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ExpandDimsOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ExpandDimsOptions> -CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ExpandDimsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALIDATE_INDICES = 4 - }; - bool validate_indices() const { return GetField<uint8_t>(VT_VALIDATE_INDICES, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_VALIDATE_INDICES) && - verifier.EndTable(); - } -}; - -struct SparseToDenseOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_validate_indices(bool validate_indices) - { - fbb_.AddElement<uint8_t>(SparseToDenseOptions::VT_VALIDATE_INDICES, - static_cast<uint8_t>(validate_indices), 0); - } - explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SparseToDenseOptionsBuilder &operator=(const SparseToDenseOptionsBuilder &); - flatbuffers::Offset<SparseToDenseOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SparseToDenseOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SparseToDenseOptions> -CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, bool validate_indices = false) -{ - SparseToDenseOptionsBuilder builder_(_fbb); - builder_.add_validate_indices(validate_indices); - return builder_.Finish(); -} - -struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct EqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - EqualOptionsBuilder &operator=(const EqualOptionsBuilder &); - flatbuffers::Offset<EqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<EqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - EqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NotEqualOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NotEqualOptionsBuilder &operator=(const NotEqualOptionsBuilder &); - flatbuffers::Offset<NotEqualOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NotEqualOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<NotEqualOptions> -CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - NotEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OUT_TYPE = 4 - }; - TensorType out_type() const { return static_cast<TensorType>(GetField<int8_t>(VT_OUT_TYPE, 0)); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_OUT_TYPE) && - verifier.EndTable(); - } -}; - -struct ShapeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_out_type(TensorType out_type) - { - fbb_.AddElement<int8_t>(ShapeOptions::VT_OUT_TYPE, static_cast<int8_t>(out_type), 0); - } - explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ShapeOptionsBuilder &operator=(const ShapeOptionsBuilder &); - flatbuffers::Offset<ShapeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ShapeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ShapeOptions> -CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, TensorType out_type = TensorType_FLOAT32) -{ - ShapeOptionsBuilder builder_(_fbb); - builder_.add_out_type(out_type); - return builder_.Finish(); -} - -struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct RankOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - RankOptionsBuilder &operator=(const RankOptionsBuilder &); - flatbuffers::Offset<RankOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<RankOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<RankOptions> CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - RankOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct PowOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PowOptionsBuilder &operator=(const PowOptionsBuilder &); - flatbuffers::Offset<PowOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PowOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - PowOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_MIN = 4, - VT_MAX = 6, - VT_NUM_BITS = 8, - VT_NARROW_RANGE = 10 - }; - float min() const { return GetField<float>(VT_MIN, 0.0f); } - float max() const { return GetField<float>(VT_MAX, 0.0f); } - int32_t num_bits() const { return GetField<int32_t>(VT_NUM_BITS, 0); } - bool narrow_range() const { return GetField<uint8_t>(VT_NARROW_RANGE, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_MIN) && - VerifyField<float>(verifier, VT_MAX) && VerifyField<int32_t>(verifier, VT_NUM_BITS) && - VerifyField<uint8_t>(verifier, VT_NARROW_RANGE) && verifier.EndTable(); - } -}; - -struct FakeQuantOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(float min) { fbb_.AddElement<float>(FakeQuantOptions::VT_MIN, min, 0.0f); } - void add_max(float max) { fbb_.AddElement<float>(FakeQuantOptions::VT_MAX, max, 0.0f); } - void add_num_bits(int32_t num_bits) - { - fbb_.AddElement<int32_t>(FakeQuantOptions::VT_NUM_BITS, num_bits, 0); - } - void add_narrow_range(bool narrow_range) - { - fbb_.AddElement<uint8_t>(FakeQuantOptions::VT_NARROW_RANGE, static_cast<uint8_t>(narrow_range), - 0); - } - explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FakeQuantOptionsBuilder &operator=(const FakeQuantOptionsBuilder &); - flatbuffers::Offset<FakeQuantOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FakeQuantOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FakeQuantOptions> -CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, float min = 0.0f, float max = 0.0f, - int32_t num_bits = 0, bool narrow_range = false) -{ - FakeQuantOptionsBuilder builder_(_fbb); - builder_.add_num_bits(num_bits); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_narrow_range(narrow_range); - return builder_.Finish(); -} - -struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VALUES_COUNT = 4, - VT_AXIS = 6 - }; - int32_t values_count() const { return GetField<int32_t>(VT_VALUES_COUNT, 0); } - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_VALUES_COUNT) && - VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable(); - } -}; - -struct PackOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values_count(int32_t values_count) - { - fbb_.AddElement<int32_t>(PackOptions::VT_VALUES_COUNT, values_count, 0); - } - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(PackOptions::VT_AXIS, axis, 0); } - explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - PackOptionsBuilder &operator=(const PackOptionsBuilder &); - flatbuffers::Offset<PackOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<PackOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<PackOptions> -CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t values_count = 0, int32_t axis = 0) -{ - PackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_values_count(values_count); - return builder_.Finish(); -} - -struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogicalOrOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogicalOrOptionsBuilder &operator=(const LogicalOrOptionsBuilder &); - flatbuffers::Offset<LogicalOrOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogicalOrOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogicalOrOptions> -CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogicalOrOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_AXIS = 4 - }; - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_AXIS) && - verifier.EndTable(); - } -}; - -struct OneHotOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(OneHotOptions::VT_AXIS, axis, 0); } - explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - OneHotOptionsBuilder &operator=(const OneHotOptionsBuilder &); - flatbuffers::Offset<OneHotOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<OneHotOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) -{ - OneHotOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct AbsOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - AbsOptionsBuilder &operator=(const AbsOptionsBuilder &); - flatbuffers::Offset<AbsOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<AbsOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - AbsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct HardSwishOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - HardSwishOptionsBuilder &operator=(const HardSwishOptionsBuilder &); - flatbuffers::Offset<HardSwishOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<HardSwishOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<HardSwishOptions> -CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - HardSwishOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogicalAndOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogicalAndOptionsBuilder &operator=(const LogicalAndOptionsBuilder &); - flatbuffers::Offset<LogicalAndOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogicalAndOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogicalAndOptions> -CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogicalAndOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct LogicalNotOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LogicalNotOptionsBuilder &operator=(const LogicalNotOptionsBuilder &); - flatbuffers::Offset<LogicalNotOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LogicalNotOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LogicalNotOptions> -CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - LogicalNotOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NUM = 4, - VT_AXIS = 6 - }; - int32_t num() const { return GetField<int32_t>(VT_NUM, 0); } - int32_t axis() const { return GetField<int32_t>(VT_AXIS, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_NUM) && - VerifyField<int32_t>(verifier, VT_AXIS) && verifier.EndTable(); - } -}; - -struct UnpackOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num(int32_t num) { fbb_.AddElement<int32_t>(UnpackOptions::VT_NUM, num, 0); } - void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(UnpackOptions::VT_AXIS, axis, 0); } - explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - UnpackOptionsBuilder &operator=(const UnpackOptionsBuilder &); - flatbuffers::Offset<UnpackOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<UnpackOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t num = 0, int32_t axis = 0) -{ - UnpackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_num(num); - return builder_.Finish(); -} - -struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct FloorDivOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FloorDivOptionsBuilder &operator=(const FloorDivOptionsBuilder &); - flatbuffers::Offset<FloorDivOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FloorDivOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FloorDivOptions> -CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - FloorDivOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SquareOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SquareOptionsBuilder &operator=(const SquareOptionsBuilder &); - flatbuffers::Offset<SquareOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SquareOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SquareOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ZerosLikeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ZerosLikeOptionsBuilder &operator=(const ZerosLikeOptionsBuilder &); - flatbuffers::Offset<ZerosLikeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ZerosLikeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ZerosLikeOptions> -CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ZerosLikeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct FillOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FillOptionsBuilder &operator=(const FillOptionsBuilder &); - flatbuffers::Offset<FillOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FillOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - FillOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct FloorModOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - FloorModOptionsBuilder &operator=(const FloorModOptionsBuilder &); - flatbuffers::Offset<FloorModOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<FloorModOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<FloorModOptions> -CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - FloorModOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct RangeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - RangeOptionsBuilder &operator=(const RangeOptionsBuilder &); - flatbuffers::Offset<RangeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<RangeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - RangeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ALPHA = 4 - }; - float alpha() const { return GetField<float>(VT_ALPHA, 0.0f); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<float>(verifier, VT_ALPHA) && - verifier.EndTable(); - } -}; - -struct LeakyReluOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_alpha(float alpha) { fbb_.AddElement<float>(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); } - explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - LeakyReluOptionsBuilder &operator=(const LeakyReluOptionsBuilder &); - flatbuffers::Offset<LeakyReluOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<LeakyReluOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<LeakyReluOptions> -CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, float alpha = 0.0f) -{ - LeakyReluOptionsBuilder builder_(_fbb); - builder_.add_alpha(alpha); - return builder_.Finish(); -} - -struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SquaredDifferenceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SquaredDifferenceOptionsBuilder &operator=(const SquaredDifferenceOptionsBuilder &); - flatbuffers::Offset<SquaredDifferenceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SquaredDifferenceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SquaredDifferenceOptions> -CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SquaredDifferenceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_MODE = 4 - }; - MirrorPadMode mode() const { return static_cast<MirrorPadMode>(GetField<int8_t>(VT_MODE, 0)); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_MODE) && - verifier.EndTable(); - } -}; - -struct MirrorPadOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_mode(MirrorPadMode mode) - { - fbb_.AddElement<int8_t>(MirrorPadOptions::VT_MODE, static_cast<int8_t>(mode), 0); - } - explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MirrorPadOptionsBuilder &operator=(const MirrorPadOptionsBuilder &); - flatbuffers::Offset<MirrorPadOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MirrorPadOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MirrorPadOptions> -CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, - MirrorPadMode mode = MirrorPadMode_REFLECT) -{ - MirrorPadOptionsBuilder builder_(_fbb); - builder_.add_mode(mode); - return builder_.Finish(); -} - -struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_IDX_OUT_TYPE = 4 - }; - TensorType idx_out_type() const - { - return static_cast<TensorType>(GetField<int8_t>(VT_IDX_OUT_TYPE, 2)); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_IDX_OUT_TYPE) && - verifier.EndTable(); - } -}; - -struct UniqueOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_idx_out_type(TensorType idx_out_type) - { - fbb_.AddElement<int8_t>(UniqueOptions::VT_IDX_OUT_TYPE, static_cast<int8_t>(idx_out_type), 2); - } - explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - UniqueOptionsBuilder &operator=(const UniqueOptionsBuilder &); - flatbuffers::Offset<UniqueOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<UniqueOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<UniqueOptions> -CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, - TensorType idx_out_type = TensorType_INT32) -{ - UniqueOptionsBuilder builder_(_fbb); - builder_.add_idx_out_type(idx_out_type); - return builder_.Finish(); -} - -struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ReverseV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReverseV2OptionsBuilder &operator=(const ReverseV2OptionsBuilder &); - flatbuffers::Offset<ReverseV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReverseV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReverseV2Options> -CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - ReverseV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct AddNOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - AddNOptionsBuilder &operator=(const AddNOptionsBuilder &); - flatbuffers::Offset<AddNOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<AddNOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<AddNOptions> CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - AddNOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct GatherNdOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - GatherNdOptionsBuilder &operator=(const GatherNdOptionsBuilder &); - flatbuffers::Offset<GatherNdOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<GatherNdOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<GatherNdOptions> -CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - GatherNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct WhereOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - WhereOptionsBuilder &operator=(const WhereOptionsBuilder &); - flatbuffers::Offset<WhereOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<WhereOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<WhereOptions> CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - WhereOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_SEQ_DIM = 4, - VT_BATCH_DIM = 6 - }; - int32_t seq_dim() const { return GetField<int32_t>(VT_SEQ_DIM, 0); } - int32_t batch_dim() const { return GetField<int32_t>(VT_BATCH_DIM, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_SEQ_DIM) && - VerifyField<int32_t>(verifier, VT_BATCH_DIM) && verifier.EndTable(); - } -}; - -struct ReverseSequenceOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_seq_dim(int32_t seq_dim) - { - fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0); - } - void add_batch_dim(int32_t batch_dim) - { - fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0); - } - explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ReverseSequenceOptionsBuilder &operator=(const ReverseSequenceOptionsBuilder &); - flatbuffers::Offset<ReverseSequenceOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ReverseSequenceOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ReverseSequenceOptions> -CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, int32_t seq_dim = 0, - int32_t batch_dim = 0) -{ - ReverseSequenceOptionsBuilder builder_(_fbb); - builder_.add_batch_dim(batch_dim); - builder_.add_seq_dim(seq_dim); - return builder_.Finish(); -} - -struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct MatrixDiagOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MatrixDiagOptionsBuilder &operator=(const MatrixDiagOptionsBuilder &); - flatbuffers::Offset<MatrixDiagOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MatrixDiagOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MatrixDiagOptions> -CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - MatrixDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct QuantizeOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - QuantizeOptionsBuilder &operator=(const QuantizeOptionsBuilder &); - flatbuffers::Offset<QuantizeOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<QuantizeOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<QuantizeOptions> -CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - QuantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct MatrixSetDiagOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MatrixSetDiagOptionsBuilder &operator=(const MatrixSetDiagOptionsBuilder &); - flatbuffers::Offset<MatrixSetDiagOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<MatrixSetDiagOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<MatrixSetDiagOptions> -CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - MatrixSetDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_THEN_SUBGRAPH_INDEX = 4, - VT_ELSE_SUBGRAPH_INDEX = 6 - }; - int32_t then_subgraph_index() const { return GetField<int32_t>(VT_THEN_SUBGRAPH_INDEX, 0); } - int32_t else_subgraph_index() const { return GetField<int32_t>(VT_ELSE_SUBGRAPH_INDEX, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_THEN_SUBGRAPH_INDEX) && - VerifyField<int32_t>(verifier, VT_ELSE_SUBGRAPH_INDEX) && verifier.EndTable(); - } -}; - -struct IfOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_then_subgraph_index(int32_t then_subgraph_index) - { - fbb_.AddElement<int32_t>(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0); - } - void add_else_subgraph_index(int32_t else_subgraph_index) - { - fbb_.AddElement<int32_t>(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0); - } - explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - IfOptionsBuilder &operator=(const IfOptionsBuilder &); - flatbuffers::Offset<IfOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<IfOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<IfOptions> CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t then_subgraph_index = 0, - int32_t else_subgraph_index = 0) -{ - IfOptionsBuilder builder_(_fbb); - builder_.add_else_subgraph_index(else_subgraph_index); - builder_.add_then_subgraph_index(then_subgraph_index); - return builder_.Finish(); -} - -struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_COND_SUBGRAPH_INDEX = 4, - VT_BODY_SUBGRAPH_INDEX = 6 - }; - int32_t cond_subgraph_index() const { return GetField<int32_t>(VT_COND_SUBGRAPH_INDEX, 0); } - int32_t body_subgraph_index() const { return GetField<int32_t>(VT_BODY_SUBGRAPH_INDEX, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int32_t>(verifier, VT_COND_SUBGRAPH_INDEX) && - VerifyField<int32_t>(verifier, VT_BODY_SUBGRAPH_INDEX) && verifier.EndTable(); - } -}; - -struct WhileOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_cond_subgraph_index(int32_t cond_subgraph_index) - { - fbb_.AddElement<int32_t>(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); - } - void add_body_subgraph_index(int32_t body_subgraph_index) - { - fbb_.AddElement<int32_t>(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); - } - explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - WhileOptionsBuilder &operator=(const WhileOptionsBuilder &); - flatbuffers::Offset<WhileOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<WhileOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<WhileOptions> CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, - int32_t cond_subgraph_index = 0, - int32_t body_subgraph_index = 0) -{ - WhileOptionsBuilder builder_(_fbb); - builder_.add_body_subgraph_index(body_subgraph_index); - builder_.add_cond_subgraph_index(cond_subgraph_index); - return builder_.Finish(); -} - -struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NonMaxSuppressionV4OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NonMaxSuppressionV4OptionsBuilder &operator=(const NonMaxSuppressionV4OptionsBuilder &); - flatbuffers::Offset<NonMaxSuppressionV4Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NonMaxSuppressionV4Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<NonMaxSuppressionV4Options> -CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - NonMaxSuppressionV4OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct NonMaxSuppressionV5OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - NonMaxSuppressionV5OptionsBuilder &operator=(const NonMaxSuppressionV5OptionsBuilder &); - flatbuffers::Offset<NonMaxSuppressionV5Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<NonMaxSuppressionV5Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<NonMaxSuppressionV5Options> -CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - NonMaxSuppressionV5OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct ScatterNdOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ScatterNdOptionsBuilder &operator=(const ScatterNdOptionsBuilder &); - flatbuffers::Offset<ScatterNdOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<ScatterNdOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<ScatterNdOptions> -CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - ScatterNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SelectV2OptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SelectV2OptionsBuilder &operator=(const SelectV2OptionsBuilder &); - flatbuffers::Offset<SelectV2Options> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SelectV2Options>(end); - return o; - } -}; - -inline flatbuffers::Offset<SelectV2Options> -CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb) -{ - SelectV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct DensifyOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - DensifyOptionsBuilder &operator=(const DensifyOptionsBuilder &); - flatbuffers::Offset<DensifyOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<DensifyOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<DensifyOptions> -CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - DensifyOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && verifier.EndTable(); - } -}; - -struct SegmentSumOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SegmentSumOptionsBuilder &operator=(const SegmentSumOptionsBuilder &); - flatbuffers::Offset<SegmentSumOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SegmentSumOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<SegmentSumOptions> -CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb) -{ - SegmentSumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_ADJOINT_LHS = 4, - VT_ADJOINT_RHS = 6 - }; - bool adjoint_lhs() const { return GetField<uint8_t>(VT_ADJOINT_LHS, 0) != 0; } - bool adjoint_rhs() const { return GetField<uint8_t>(VT_ADJOINT_RHS, 0) != 0; } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint8_t>(verifier, VT_ADJOINT_LHS) && - VerifyField<uint8_t>(verifier, VT_ADJOINT_RHS) && verifier.EndTable(); - } -}; - -struct BatchMatMulOptionsBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_adjoint_lhs(bool adjoint_lhs) - { - fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ADJOINT_LHS, static_cast<uint8_t>(adjoint_lhs), - 0); - } - void add_adjoint_rhs(bool adjoint_rhs) - { - fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ADJOINT_RHS, static_cast<uint8_t>(adjoint_rhs), - 0); - } - explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BatchMatMulOptionsBuilder &operator=(const BatchMatMulOptionsBuilder &); - flatbuffers::Offset<BatchMatMulOptions> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<BatchMatMulOptions>(end); - return o; - } -}; - -inline flatbuffers::Offset<BatchMatMulOptions> -CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, bool adjoint_lhs = false, - bool adjoint_rhs = false) -{ - BatchMatMulOptionsBuilder builder_(_fbb); - builder_.add_adjoint_rhs(adjoint_rhs); - builder_.add_adjoint_lhs(adjoint_lhs); - return builder_.Finish(); -} - -struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_BUILTIN_CODE = 4, - VT_CUSTOM_CODE = 6, - VT_VERSION = 8 - }; - BuiltinOperator builtin_code() const - { - return static_cast<BuiltinOperator>(GetField<int8_t>(VT_BUILTIN_CODE, 0)); - } - const flatbuffers::String *custom_code() const - { - return GetPointer<const flatbuffers::String *>(VT_CUSTOM_CODE); - } - int32_t version() const { return GetField<int32_t>(VT_VERSION, 1); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<int8_t>(verifier, VT_BUILTIN_CODE) && - VerifyOffset(verifier, VT_CUSTOM_CODE) && verifier.VerifyString(custom_code()) && - VerifyField<int32_t>(verifier, VT_VERSION) && verifier.EndTable(); - } -}; - -struct OperatorCodeBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_builtin_code(BuiltinOperator builtin_code) - { - fbb_.AddElement<int8_t>(OperatorCode::VT_BUILTIN_CODE, static_cast<int8_t>(builtin_code), 0); - } - void add_custom_code(flatbuffers::Offset<flatbuffers::String> custom_code) - { - fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code); - } - void add_version(int32_t version) - { - fbb_.AddElement<int32_t>(OperatorCode::VT_VERSION, version, 1); - } - explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - OperatorCodeBuilder &operator=(const OperatorCodeBuilder &); - flatbuffers::Offset<OperatorCode> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<OperatorCode>(end); - return o; - } -}; - -inline flatbuffers::Offset<OperatorCode> -CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, - BuiltinOperator builtin_code = BuiltinOperator_ADD, - flatbuffers::Offset<flatbuffers::String> custom_code = 0, int32_t version = 1) -{ - OperatorCodeBuilder builder_(_fbb); - builder_.add_version(version); - builder_.add_custom_code(custom_code); - builder_.add_builtin_code(builtin_code); - return builder_.Finish(); -} - -inline flatbuffers::Offset<OperatorCode> -CreateOperatorCodeDirect(flatbuffers::FlatBufferBuilder &_fbb, - BuiltinOperator builtin_code = BuiltinOperator_ADD, - const char *custom_code = nullptr, int32_t version = 1) -{ - return onert_tflite::CreateOperatorCode( - _fbb, builtin_code, custom_code ? _fbb.CreateString(custom_code) : 0, version); -} - -struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_OPCODE_INDEX = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_BUILTIN_OPTIONS_TYPE = 10, - VT_BUILTIN_OPTIONS = 12, - VT_CUSTOM_OPTIONS = 14, - VT_CUSTOM_OPTIONS_FORMAT = 16, - VT_MUTATING_VARIABLE_INPUTS = 18, - VT_INTERMEDIATES = 20 - }; - uint32_t opcode_index() const { return GetField<uint32_t>(VT_OPCODE_INDEX, 0); } - const flatbuffers::Vector<int32_t> *inputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS); - } - const flatbuffers::Vector<int32_t> *outputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS); - } - BuiltinOptions builtin_options_type() const - { - return static_cast<BuiltinOptions>(GetField<uint8_t>(VT_BUILTIN_OPTIONS_TYPE, 0)); - } - const void *builtin_options() const { return GetPointer<const void *>(VT_BUILTIN_OPTIONS); } - template <typename T> const T *builtin_options_as() const; - const Conv2DOptions *builtin_options_as_Conv2DOptions() const - { - return builtin_options_type() == BuiltinOptions_Conv2DOptions - ? static_cast<const Conv2DOptions *>(builtin_options()) - : nullptr; - } - const DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const - { - return builtin_options_type() == BuiltinOptions_DepthwiseConv2DOptions - ? static_cast<const DepthwiseConv2DOptions *>(builtin_options()) - : nullptr; - } - const ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const - { - return builtin_options_type() == BuiltinOptions_ConcatEmbeddingsOptions - ? static_cast<const ConcatEmbeddingsOptions *>(builtin_options()) - : nullptr; - } - const LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const - { - return builtin_options_type() == BuiltinOptions_LSHProjectionOptions - ? static_cast<const LSHProjectionOptions *>(builtin_options()) - : nullptr; - } - const Pool2DOptions *builtin_options_as_Pool2DOptions() const - { - return builtin_options_type() == BuiltinOptions_Pool2DOptions - ? static_cast<const Pool2DOptions *>(builtin_options()) - : nullptr; - } - const SVDFOptions *builtin_options_as_SVDFOptions() const - { - return builtin_options_type() == BuiltinOptions_SVDFOptions - ? static_cast<const SVDFOptions *>(builtin_options()) - : nullptr; - } - const RNNOptions *builtin_options_as_RNNOptions() const - { - return builtin_options_type() == BuiltinOptions_RNNOptions - ? static_cast<const RNNOptions *>(builtin_options()) - : nullptr; - } - const FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const - { - return builtin_options_type() == BuiltinOptions_FullyConnectedOptions - ? static_cast<const FullyConnectedOptions *>(builtin_options()) - : nullptr; - } - const SoftmaxOptions *builtin_options_as_SoftmaxOptions() const - { - return builtin_options_type() == BuiltinOptions_SoftmaxOptions - ? static_cast<const SoftmaxOptions *>(builtin_options()) - : nullptr; - } - const ConcatenationOptions *builtin_options_as_ConcatenationOptions() const - { - return builtin_options_type() == BuiltinOptions_ConcatenationOptions - ? static_cast<const ConcatenationOptions *>(builtin_options()) - : nullptr; - } - const AddOptions *builtin_options_as_AddOptions() const - { - return builtin_options_type() == BuiltinOptions_AddOptions - ? static_cast<const AddOptions *>(builtin_options()) - : nullptr; - } - const L2NormOptions *builtin_options_as_L2NormOptions() const - { - return builtin_options_type() == BuiltinOptions_L2NormOptions - ? static_cast<const L2NormOptions *>(builtin_options()) - : nullptr; - } - const LocalResponseNormalizationOptions * - builtin_options_as_LocalResponseNormalizationOptions() const - { - return builtin_options_type() == BuiltinOptions_LocalResponseNormalizationOptions - ? static_cast<const LocalResponseNormalizationOptions *>(builtin_options()) - : nullptr; - } - const LSTMOptions *builtin_options_as_LSTMOptions() const - { - return builtin_options_type() == BuiltinOptions_LSTMOptions - ? static_cast<const LSTMOptions *>(builtin_options()) - : nullptr; - } - const ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const - { - return builtin_options_type() == BuiltinOptions_ResizeBilinearOptions - ? static_cast<const ResizeBilinearOptions *>(builtin_options()) - : nullptr; - } - const CallOptions *builtin_options_as_CallOptions() const - { - return builtin_options_type() == BuiltinOptions_CallOptions - ? static_cast<const CallOptions *>(builtin_options()) - : nullptr; - } - const ReshapeOptions *builtin_options_as_ReshapeOptions() const - { - return builtin_options_type() == BuiltinOptions_ReshapeOptions - ? static_cast<const ReshapeOptions *>(builtin_options()) - : nullptr; - } - const SkipGramOptions *builtin_options_as_SkipGramOptions() const - { - return builtin_options_type() == BuiltinOptions_SkipGramOptions - ? static_cast<const SkipGramOptions *>(builtin_options()) - : nullptr; - } - const SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const - { - return builtin_options_type() == BuiltinOptions_SpaceToDepthOptions - ? static_cast<const SpaceToDepthOptions *>(builtin_options()) - : nullptr; - } - const EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const - { - return builtin_options_type() == BuiltinOptions_EmbeddingLookupSparseOptions - ? static_cast<const EmbeddingLookupSparseOptions *>(builtin_options()) - : nullptr; - } - const MulOptions *builtin_options_as_MulOptions() const - { - return builtin_options_type() == BuiltinOptions_MulOptions - ? static_cast<const MulOptions *>(builtin_options()) - : nullptr; - } - const PadOptions *builtin_options_as_PadOptions() const - { - return builtin_options_type() == BuiltinOptions_PadOptions - ? static_cast<const PadOptions *>(builtin_options()) - : nullptr; - } - const GatherOptions *builtin_options_as_GatherOptions() const - { - return builtin_options_type() == BuiltinOptions_GatherOptions - ? static_cast<const GatherOptions *>(builtin_options()) - : nullptr; - } - const BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const - { - return builtin_options_type() == BuiltinOptions_BatchToSpaceNDOptions - ? static_cast<const BatchToSpaceNDOptions *>(builtin_options()) - : nullptr; - } - const SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const - { - return builtin_options_type() == BuiltinOptions_SpaceToBatchNDOptions - ? static_cast<const SpaceToBatchNDOptions *>(builtin_options()) - : nullptr; - } - const TransposeOptions *builtin_options_as_TransposeOptions() const - { - return builtin_options_type() == BuiltinOptions_TransposeOptions - ? static_cast<const TransposeOptions *>(builtin_options()) - : nullptr; - } - const ReducerOptions *builtin_options_as_ReducerOptions() const - { - return builtin_options_type() == BuiltinOptions_ReducerOptions - ? static_cast<const ReducerOptions *>(builtin_options()) - : nullptr; - } - const SubOptions *builtin_options_as_SubOptions() const - { - return builtin_options_type() == BuiltinOptions_SubOptions - ? static_cast<const SubOptions *>(builtin_options()) - : nullptr; - } - const DivOptions *builtin_options_as_DivOptions() const - { - return builtin_options_type() == BuiltinOptions_DivOptions - ? static_cast<const DivOptions *>(builtin_options()) - : nullptr; - } - const SqueezeOptions *builtin_options_as_SqueezeOptions() const - { - return builtin_options_type() == BuiltinOptions_SqueezeOptions - ? static_cast<const SqueezeOptions *>(builtin_options()) - : nullptr; - } - const SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const - { - return builtin_options_type() == BuiltinOptions_SequenceRNNOptions - ? static_cast<const SequenceRNNOptions *>(builtin_options()) - : nullptr; - } - const StridedSliceOptions *builtin_options_as_StridedSliceOptions() const - { - return builtin_options_type() == BuiltinOptions_StridedSliceOptions - ? static_cast<const StridedSliceOptions *>(builtin_options()) - : nullptr; - } - const ExpOptions *builtin_options_as_ExpOptions() const - { - return builtin_options_type() == BuiltinOptions_ExpOptions - ? static_cast<const ExpOptions *>(builtin_options()) - : nullptr; - } - const TopKV2Options *builtin_options_as_TopKV2Options() const - { - return builtin_options_type() == BuiltinOptions_TopKV2Options - ? static_cast<const TopKV2Options *>(builtin_options()) - : nullptr; - } - const SplitOptions *builtin_options_as_SplitOptions() const - { - return builtin_options_type() == BuiltinOptions_SplitOptions - ? static_cast<const SplitOptions *>(builtin_options()) - : nullptr; - } - const LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const - { - return builtin_options_type() == BuiltinOptions_LogSoftmaxOptions - ? static_cast<const LogSoftmaxOptions *>(builtin_options()) - : nullptr; - } - const CastOptions *builtin_options_as_CastOptions() const - { - return builtin_options_type() == BuiltinOptions_CastOptions - ? static_cast<const CastOptions *>(builtin_options()) - : nullptr; - } - const DequantizeOptions *builtin_options_as_DequantizeOptions() const - { - return builtin_options_type() == BuiltinOptions_DequantizeOptions - ? static_cast<const DequantizeOptions *>(builtin_options()) - : nullptr; - } - const MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const - { - return builtin_options_type() == BuiltinOptions_MaximumMinimumOptions - ? static_cast<const MaximumMinimumOptions *>(builtin_options()) - : nullptr; - } - const ArgMaxOptions *builtin_options_as_ArgMaxOptions() const - { - return builtin_options_type() == BuiltinOptions_ArgMaxOptions - ? static_cast<const ArgMaxOptions *>(builtin_options()) - : nullptr; - } - const LessOptions *builtin_options_as_LessOptions() const - { - return builtin_options_type() == BuiltinOptions_LessOptions - ? static_cast<const LessOptions *>(builtin_options()) - : nullptr; - } - const NegOptions *builtin_options_as_NegOptions() const - { - return builtin_options_type() == BuiltinOptions_NegOptions - ? static_cast<const NegOptions *>(builtin_options()) - : nullptr; - } - const PadV2Options *builtin_options_as_PadV2Options() const - { - return builtin_options_type() == BuiltinOptions_PadV2Options - ? static_cast<const PadV2Options *>(builtin_options()) - : nullptr; - } - const GreaterOptions *builtin_options_as_GreaterOptions() const - { - return builtin_options_type() == BuiltinOptions_GreaterOptions - ? static_cast<const GreaterOptions *>(builtin_options()) - : nullptr; - } - const GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const - { - return builtin_options_type() == BuiltinOptions_GreaterEqualOptions - ? static_cast<const GreaterEqualOptions *>(builtin_options()) - : nullptr; - } - const LessEqualOptions *builtin_options_as_LessEqualOptions() const - { - return builtin_options_type() == BuiltinOptions_LessEqualOptions - ? static_cast<const LessEqualOptions *>(builtin_options()) - : nullptr; - } - const SelectOptions *builtin_options_as_SelectOptions() const - { - return builtin_options_type() == BuiltinOptions_SelectOptions - ? static_cast<const SelectOptions *>(builtin_options()) - : nullptr; - } - const SliceOptions *builtin_options_as_SliceOptions() const - { - return builtin_options_type() == BuiltinOptions_SliceOptions - ? static_cast<const SliceOptions *>(builtin_options()) - : nullptr; - } - const TransposeConvOptions *builtin_options_as_TransposeConvOptions() const - { - return builtin_options_type() == BuiltinOptions_TransposeConvOptions - ? static_cast<const TransposeConvOptions *>(builtin_options()) - : nullptr; - } - const SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const - { - return builtin_options_type() == BuiltinOptions_SparseToDenseOptions - ? static_cast<const SparseToDenseOptions *>(builtin_options()) - : nullptr; - } - const TileOptions *builtin_options_as_TileOptions() const - { - return builtin_options_type() == BuiltinOptions_TileOptions - ? static_cast<const TileOptions *>(builtin_options()) - : nullptr; - } - const ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const - { - return builtin_options_type() == BuiltinOptions_ExpandDimsOptions - ? static_cast<const ExpandDimsOptions *>(builtin_options()) - : nullptr; - } - const EqualOptions *builtin_options_as_EqualOptions() const - { - return builtin_options_type() == BuiltinOptions_EqualOptions - ? static_cast<const EqualOptions *>(builtin_options()) - : nullptr; - } - const NotEqualOptions *builtin_options_as_NotEqualOptions() const - { - return builtin_options_type() == BuiltinOptions_NotEqualOptions - ? static_cast<const NotEqualOptions *>(builtin_options()) - : nullptr; - } - const ShapeOptions *builtin_options_as_ShapeOptions() const - { - return builtin_options_type() == BuiltinOptions_ShapeOptions - ? static_cast<const ShapeOptions *>(builtin_options()) - : nullptr; - } - const PowOptions *builtin_options_as_PowOptions() const - { - return builtin_options_type() == BuiltinOptions_PowOptions - ? static_cast<const PowOptions *>(builtin_options()) - : nullptr; - } - const ArgMinOptions *builtin_options_as_ArgMinOptions() const - { - return builtin_options_type() == BuiltinOptions_ArgMinOptions - ? static_cast<const ArgMinOptions *>(builtin_options()) - : nullptr; - } - const FakeQuantOptions *builtin_options_as_FakeQuantOptions() const - { - return builtin_options_type() == BuiltinOptions_FakeQuantOptions - ? static_cast<const FakeQuantOptions *>(builtin_options()) - : nullptr; - } - const PackOptions *builtin_options_as_PackOptions() const - { - return builtin_options_type() == BuiltinOptions_PackOptions - ? static_cast<const PackOptions *>(builtin_options()) - : nullptr; - } - const LogicalOrOptions *builtin_options_as_LogicalOrOptions() const - { - return builtin_options_type() == BuiltinOptions_LogicalOrOptions - ? static_cast<const LogicalOrOptions *>(builtin_options()) - : nullptr; - } - const OneHotOptions *builtin_options_as_OneHotOptions() const - { - return builtin_options_type() == BuiltinOptions_OneHotOptions - ? static_cast<const OneHotOptions *>(builtin_options()) - : nullptr; - } - const LogicalAndOptions *builtin_options_as_LogicalAndOptions() const - { - return builtin_options_type() == BuiltinOptions_LogicalAndOptions - ? static_cast<const LogicalAndOptions *>(builtin_options()) - : nullptr; - } - const LogicalNotOptions *builtin_options_as_LogicalNotOptions() const - { - return builtin_options_type() == BuiltinOptions_LogicalNotOptions - ? static_cast<const LogicalNotOptions *>(builtin_options()) - : nullptr; - } - const UnpackOptions *builtin_options_as_UnpackOptions() const - { - return builtin_options_type() == BuiltinOptions_UnpackOptions - ? static_cast<const UnpackOptions *>(builtin_options()) - : nullptr; - } - const FloorDivOptions *builtin_options_as_FloorDivOptions() const - { - return builtin_options_type() == BuiltinOptions_FloorDivOptions - ? static_cast<const FloorDivOptions *>(builtin_options()) - : nullptr; - } - const SquareOptions *builtin_options_as_SquareOptions() const - { - return builtin_options_type() == BuiltinOptions_SquareOptions - ? static_cast<const SquareOptions *>(builtin_options()) - : nullptr; - } - const ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const - { - return builtin_options_type() == BuiltinOptions_ZerosLikeOptions - ? static_cast<const ZerosLikeOptions *>(builtin_options()) - : nullptr; - } - const FillOptions *builtin_options_as_FillOptions() const - { - return builtin_options_type() == BuiltinOptions_FillOptions - ? static_cast<const FillOptions *>(builtin_options()) - : nullptr; - } - const BidirectionalSequenceLSTMOptions * - builtin_options_as_BidirectionalSequenceLSTMOptions() const - { - return builtin_options_type() == BuiltinOptions_BidirectionalSequenceLSTMOptions - ? static_cast<const BidirectionalSequenceLSTMOptions *>(builtin_options()) - : nullptr; - } - const BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const - { - return builtin_options_type() == BuiltinOptions_BidirectionalSequenceRNNOptions - ? static_cast<const BidirectionalSequenceRNNOptions *>(builtin_options()) - : nullptr; - } - const UnidirectionalSequenceLSTMOptions * - builtin_options_as_UnidirectionalSequenceLSTMOptions() const - { - return builtin_options_type() == BuiltinOptions_UnidirectionalSequenceLSTMOptions - ? static_cast<const UnidirectionalSequenceLSTMOptions *>(builtin_options()) - : nullptr; - } - const FloorModOptions *builtin_options_as_FloorModOptions() const - { - return builtin_options_type() == BuiltinOptions_FloorModOptions - ? static_cast<const FloorModOptions *>(builtin_options()) - : nullptr; - } - const RangeOptions *builtin_options_as_RangeOptions() const - { - return builtin_options_type() == BuiltinOptions_RangeOptions - ? static_cast<const RangeOptions *>(builtin_options()) - : nullptr; - } - const ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const - { - return builtin_options_type() == BuiltinOptions_ResizeNearestNeighborOptions - ? static_cast<const ResizeNearestNeighborOptions *>(builtin_options()) - : nullptr; - } - const LeakyReluOptions *builtin_options_as_LeakyReluOptions() const - { - return builtin_options_type() == BuiltinOptions_LeakyReluOptions - ? static_cast<const LeakyReluOptions *>(builtin_options()) - : nullptr; - } - const SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const - { - return builtin_options_type() == BuiltinOptions_SquaredDifferenceOptions - ? static_cast<const SquaredDifferenceOptions *>(builtin_options()) - : nullptr; - } - const MirrorPadOptions *builtin_options_as_MirrorPadOptions() const - { - return builtin_options_type() == BuiltinOptions_MirrorPadOptions - ? static_cast<const MirrorPadOptions *>(builtin_options()) - : nullptr; - } - const AbsOptions *builtin_options_as_AbsOptions() const - { - return builtin_options_type() == BuiltinOptions_AbsOptions - ? static_cast<const AbsOptions *>(builtin_options()) - : nullptr; - } - const SplitVOptions *builtin_options_as_SplitVOptions() const - { - return builtin_options_type() == BuiltinOptions_SplitVOptions - ? static_cast<const SplitVOptions *>(builtin_options()) - : nullptr; - } - const UniqueOptions *builtin_options_as_UniqueOptions() const - { - return builtin_options_type() == BuiltinOptions_UniqueOptions - ? static_cast<const UniqueOptions *>(builtin_options()) - : nullptr; - } - const ReverseV2Options *builtin_options_as_ReverseV2Options() const - { - return builtin_options_type() == BuiltinOptions_ReverseV2Options - ? static_cast<const ReverseV2Options *>(builtin_options()) - : nullptr; - } - const AddNOptions *builtin_options_as_AddNOptions() const - { - return builtin_options_type() == BuiltinOptions_AddNOptions - ? static_cast<const AddNOptions *>(builtin_options()) - : nullptr; - } - const GatherNdOptions *builtin_options_as_GatherNdOptions() const - { - return builtin_options_type() == BuiltinOptions_GatherNdOptions - ? static_cast<const GatherNdOptions *>(builtin_options()) - : nullptr; - } - const CosOptions *builtin_options_as_CosOptions() const - { - return builtin_options_type() == BuiltinOptions_CosOptions - ? static_cast<const CosOptions *>(builtin_options()) - : nullptr; - } - const WhereOptions *builtin_options_as_WhereOptions() const - { - return builtin_options_type() == BuiltinOptions_WhereOptions - ? static_cast<const WhereOptions *>(builtin_options()) - : nullptr; - } - const RankOptions *builtin_options_as_RankOptions() const - { - return builtin_options_type() == BuiltinOptions_RankOptions - ? static_cast<const RankOptions *>(builtin_options()) - : nullptr; - } - const ReverseSequenceOptions *builtin_options_as_ReverseSequenceOptions() const - { - return builtin_options_type() == BuiltinOptions_ReverseSequenceOptions - ? static_cast<const ReverseSequenceOptions *>(builtin_options()) - : nullptr; - } - const MatrixDiagOptions *builtin_options_as_MatrixDiagOptions() const - { - return builtin_options_type() == BuiltinOptions_MatrixDiagOptions - ? static_cast<const MatrixDiagOptions *>(builtin_options()) - : nullptr; - } - const QuantizeOptions *builtin_options_as_QuantizeOptions() const - { - return builtin_options_type() == BuiltinOptions_QuantizeOptions - ? static_cast<const QuantizeOptions *>(builtin_options()) - : nullptr; - } - const MatrixSetDiagOptions *builtin_options_as_MatrixSetDiagOptions() const - { - return builtin_options_type() == BuiltinOptions_MatrixSetDiagOptions - ? static_cast<const MatrixSetDiagOptions *>(builtin_options()) - : nullptr; - } - const HardSwishOptions *builtin_options_as_HardSwishOptions() const - { - return builtin_options_type() == BuiltinOptions_HardSwishOptions - ? static_cast<const HardSwishOptions *>(builtin_options()) - : nullptr; - } - const IfOptions *builtin_options_as_IfOptions() const - { - return builtin_options_type() == BuiltinOptions_IfOptions - ? static_cast<const IfOptions *>(builtin_options()) - : nullptr; - } - const WhileOptions *builtin_options_as_WhileOptions() const - { - return builtin_options_type() == BuiltinOptions_WhileOptions - ? static_cast<const WhileOptions *>(builtin_options()) - : nullptr; - } - const DepthToSpaceOptions *builtin_options_as_DepthToSpaceOptions() const - { - return builtin_options_type() == BuiltinOptions_DepthToSpaceOptions - ? static_cast<const DepthToSpaceOptions *>(builtin_options()) - : nullptr; - } - const NonMaxSuppressionV4Options *builtin_options_as_NonMaxSuppressionV4Options() const - { - return builtin_options_type() == BuiltinOptions_NonMaxSuppressionV4Options - ? static_cast<const NonMaxSuppressionV4Options *>(builtin_options()) - : nullptr; - } - const NonMaxSuppressionV5Options *builtin_options_as_NonMaxSuppressionV5Options() const - { - return builtin_options_type() == BuiltinOptions_NonMaxSuppressionV5Options - ? static_cast<const NonMaxSuppressionV5Options *>(builtin_options()) - : nullptr; - } - const ScatterNdOptions *builtin_options_as_ScatterNdOptions() const - { - return builtin_options_type() == BuiltinOptions_ScatterNdOptions - ? static_cast<const ScatterNdOptions *>(builtin_options()) - : nullptr; - } - const SelectV2Options *builtin_options_as_SelectV2Options() const - { - return builtin_options_type() == BuiltinOptions_SelectV2Options - ? static_cast<const SelectV2Options *>(builtin_options()) - : nullptr; - } - const DensifyOptions *builtin_options_as_DensifyOptions() const - { - return builtin_options_type() == BuiltinOptions_DensifyOptions - ? static_cast<const DensifyOptions *>(builtin_options()) - : nullptr; - } - const SegmentSumOptions *builtin_options_as_SegmentSumOptions() const - { - return builtin_options_type() == BuiltinOptions_SegmentSumOptions - ? static_cast<const SegmentSumOptions *>(builtin_options()) - : nullptr; - } - const BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const - { - return builtin_options_type() == BuiltinOptions_BatchMatMulOptions - ? static_cast<const BatchMatMulOptions *>(builtin_options()) - : nullptr; - } - const flatbuffers::Vector<uint8_t> *custom_options() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS); - } - CustomOptionsFormat custom_options_format() const - { - return static_cast<CustomOptionsFormat>(GetField<int8_t>(VT_CUSTOM_OPTIONS_FORMAT, 0)); - } - const flatbuffers::Vector<uint8_t> *mutating_variable_inputs() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_MUTATING_VARIABLE_INPUTS); - } - const flatbuffers::Vector<int32_t> *intermediates() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INTERMEDIATES); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_OPCODE_INDEX) && - VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) && - VerifyField<uint8_t>(verifier, VT_BUILTIN_OPTIONS_TYPE) && - VerifyOffset(verifier, VT_BUILTIN_OPTIONS) && - VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) && - VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && verifier.VerifyVector(custom_options()) && - VerifyField<int8_t>(verifier, VT_CUSTOM_OPTIONS_FORMAT) && - VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && - verifier.VerifyVector(mutating_variable_inputs()) && - VerifyOffset(verifier, VT_INTERMEDIATES) && verifier.VerifyVector(intermediates()) && - verifier.EndTable(); - } -}; - -template <> inline const Conv2DOptions *Operator::builtin_options_as<Conv2DOptions>() const -{ - return builtin_options_as_Conv2DOptions(); -} - -template <> -inline const DepthwiseConv2DOptions *Operator::builtin_options_as<DepthwiseConv2DOptions>() const -{ - return builtin_options_as_DepthwiseConv2DOptions(); -} - -template <> -inline const ConcatEmbeddingsOptions *Operator::builtin_options_as<ConcatEmbeddingsOptions>() const -{ - return builtin_options_as_ConcatEmbeddingsOptions(); -} - -template <> -inline const LSHProjectionOptions *Operator::builtin_options_as<LSHProjectionOptions>() const -{ - return builtin_options_as_LSHProjectionOptions(); -} - -template <> inline const Pool2DOptions *Operator::builtin_options_as<Pool2DOptions>() const -{ - return builtin_options_as_Pool2DOptions(); -} - -template <> inline const SVDFOptions *Operator::builtin_options_as<SVDFOptions>() const -{ - return builtin_options_as_SVDFOptions(); -} - -template <> inline const RNNOptions *Operator::builtin_options_as<RNNOptions>() const -{ - return builtin_options_as_RNNOptions(); -} - -template <> -inline const FullyConnectedOptions *Operator::builtin_options_as<FullyConnectedOptions>() const -{ - return builtin_options_as_FullyConnectedOptions(); -} - -template <> inline const SoftmaxOptions *Operator::builtin_options_as<SoftmaxOptions>() const -{ - return builtin_options_as_SoftmaxOptions(); -} - -template <> -inline const ConcatenationOptions *Operator::builtin_options_as<ConcatenationOptions>() const -{ - return builtin_options_as_ConcatenationOptions(); -} - -template <> inline const AddOptions *Operator::builtin_options_as<AddOptions>() const -{ - return builtin_options_as_AddOptions(); -} - -template <> inline const L2NormOptions *Operator::builtin_options_as<L2NormOptions>() const -{ - return builtin_options_as_L2NormOptions(); -} - -template <> -inline const LocalResponseNormalizationOptions * -Operator::builtin_options_as<LocalResponseNormalizationOptions>() const -{ - return builtin_options_as_LocalResponseNormalizationOptions(); -} - -template <> inline const LSTMOptions *Operator::builtin_options_as<LSTMOptions>() const -{ - return builtin_options_as_LSTMOptions(); -} - -template <> -inline const ResizeBilinearOptions *Operator::builtin_options_as<ResizeBilinearOptions>() const -{ - return builtin_options_as_ResizeBilinearOptions(); -} - -template <> inline const CallOptions *Operator::builtin_options_as<CallOptions>() const -{ - return builtin_options_as_CallOptions(); -} - -template <> inline const ReshapeOptions *Operator::builtin_options_as<ReshapeOptions>() const -{ - return builtin_options_as_ReshapeOptions(); -} - -template <> inline const SkipGramOptions *Operator::builtin_options_as<SkipGramOptions>() const -{ - return builtin_options_as_SkipGramOptions(); -} - -template <> -inline const SpaceToDepthOptions *Operator::builtin_options_as<SpaceToDepthOptions>() const -{ - return builtin_options_as_SpaceToDepthOptions(); -} - -template <> -inline const EmbeddingLookupSparseOptions * -Operator::builtin_options_as<EmbeddingLookupSparseOptions>() const -{ - return builtin_options_as_EmbeddingLookupSparseOptions(); -} - -template <> inline const MulOptions *Operator::builtin_options_as<MulOptions>() const -{ - return builtin_options_as_MulOptions(); -} - -template <> inline const PadOptions *Operator::builtin_options_as<PadOptions>() const -{ - return builtin_options_as_PadOptions(); -} - -template <> inline const GatherOptions *Operator::builtin_options_as<GatherOptions>() const -{ - return builtin_options_as_GatherOptions(); -} - -template <> -inline const BatchToSpaceNDOptions *Operator::builtin_options_as<BatchToSpaceNDOptions>() const -{ - return builtin_options_as_BatchToSpaceNDOptions(); -} - -template <> -inline const SpaceToBatchNDOptions *Operator::builtin_options_as<SpaceToBatchNDOptions>() const -{ - return builtin_options_as_SpaceToBatchNDOptions(); -} - -template <> inline const TransposeOptions *Operator::builtin_options_as<TransposeOptions>() const -{ - return builtin_options_as_TransposeOptions(); -} - -template <> inline const ReducerOptions *Operator::builtin_options_as<ReducerOptions>() const -{ - return builtin_options_as_ReducerOptions(); -} - -template <> inline const SubOptions *Operator::builtin_options_as<SubOptions>() const -{ - return builtin_options_as_SubOptions(); -} - -template <> inline const DivOptions *Operator::builtin_options_as<DivOptions>() const -{ - return builtin_options_as_DivOptions(); -} - -template <> inline const SqueezeOptions *Operator::builtin_options_as<SqueezeOptions>() const -{ - return builtin_options_as_SqueezeOptions(); -} - -template <> -inline const SequenceRNNOptions *Operator::builtin_options_as<SequenceRNNOptions>() const -{ - return builtin_options_as_SequenceRNNOptions(); -} - -template <> -inline const StridedSliceOptions *Operator::builtin_options_as<StridedSliceOptions>() const -{ - return builtin_options_as_StridedSliceOptions(); -} - -template <> inline const ExpOptions *Operator::builtin_options_as<ExpOptions>() const -{ - return builtin_options_as_ExpOptions(); -} - -template <> inline const TopKV2Options *Operator::builtin_options_as<TopKV2Options>() const -{ - return builtin_options_as_TopKV2Options(); -} - -template <> inline const SplitOptions *Operator::builtin_options_as<SplitOptions>() const -{ - return builtin_options_as_SplitOptions(); -} - -template <> inline const LogSoftmaxOptions *Operator::builtin_options_as<LogSoftmaxOptions>() const -{ - return builtin_options_as_LogSoftmaxOptions(); -} - -template <> inline const CastOptions *Operator::builtin_options_as<CastOptions>() const -{ - return builtin_options_as_CastOptions(); -} - -template <> inline const DequantizeOptions *Operator::builtin_options_as<DequantizeOptions>() const -{ - return builtin_options_as_DequantizeOptions(); -} - -template <> -inline const MaximumMinimumOptions *Operator::builtin_options_as<MaximumMinimumOptions>() const -{ - return builtin_options_as_MaximumMinimumOptions(); -} - -template <> inline const ArgMaxOptions *Operator::builtin_options_as<ArgMaxOptions>() const -{ - return builtin_options_as_ArgMaxOptions(); -} - -template <> inline const LessOptions *Operator::builtin_options_as<LessOptions>() const -{ - return builtin_options_as_LessOptions(); -} - -template <> inline const NegOptions *Operator::builtin_options_as<NegOptions>() const -{ - return builtin_options_as_NegOptions(); -} - -template <> inline const PadV2Options *Operator::builtin_options_as<PadV2Options>() const -{ - return builtin_options_as_PadV2Options(); -} - -template <> inline const GreaterOptions *Operator::builtin_options_as<GreaterOptions>() const -{ - return builtin_options_as_GreaterOptions(); -} - -template <> -inline const GreaterEqualOptions *Operator::builtin_options_as<GreaterEqualOptions>() const -{ - return builtin_options_as_GreaterEqualOptions(); -} - -template <> inline const LessEqualOptions *Operator::builtin_options_as<LessEqualOptions>() const -{ - return builtin_options_as_LessEqualOptions(); -} - -template <> inline const SelectOptions *Operator::builtin_options_as<SelectOptions>() const -{ - return builtin_options_as_SelectOptions(); -} - -template <> inline const SliceOptions *Operator::builtin_options_as<SliceOptions>() const -{ - return builtin_options_as_SliceOptions(); -} - -template <> -inline const TransposeConvOptions *Operator::builtin_options_as<TransposeConvOptions>() const -{ - return builtin_options_as_TransposeConvOptions(); -} - -template <> -inline const SparseToDenseOptions *Operator::builtin_options_as<SparseToDenseOptions>() const -{ - return builtin_options_as_SparseToDenseOptions(); -} - -template <> inline const TileOptions *Operator::builtin_options_as<TileOptions>() const -{ - return builtin_options_as_TileOptions(); -} - -template <> inline const ExpandDimsOptions *Operator::builtin_options_as<ExpandDimsOptions>() const -{ - return builtin_options_as_ExpandDimsOptions(); -} - -template <> inline const EqualOptions *Operator::builtin_options_as<EqualOptions>() const -{ - return builtin_options_as_EqualOptions(); -} - -template <> inline const NotEqualOptions *Operator::builtin_options_as<NotEqualOptions>() const -{ - return builtin_options_as_NotEqualOptions(); -} - -template <> inline const ShapeOptions *Operator::builtin_options_as<ShapeOptions>() const -{ - return builtin_options_as_ShapeOptions(); -} - -template <> inline const PowOptions *Operator::builtin_options_as<PowOptions>() const -{ - return builtin_options_as_PowOptions(); -} - -template <> inline const ArgMinOptions *Operator::builtin_options_as<ArgMinOptions>() const -{ - return builtin_options_as_ArgMinOptions(); -} - -template <> inline const FakeQuantOptions *Operator::builtin_options_as<FakeQuantOptions>() const -{ - return builtin_options_as_FakeQuantOptions(); -} - -template <> inline const PackOptions *Operator::builtin_options_as<PackOptions>() const -{ - return builtin_options_as_PackOptions(); -} - -template <> inline const LogicalOrOptions *Operator::builtin_options_as<LogicalOrOptions>() const -{ - return builtin_options_as_LogicalOrOptions(); -} - -template <> inline const OneHotOptions *Operator::builtin_options_as<OneHotOptions>() const -{ - return builtin_options_as_OneHotOptions(); -} - -template <> inline const LogicalAndOptions *Operator::builtin_options_as<LogicalAndOptions>() const -{ - return builtin_options_as_LogicalAndOptions(); -} - -template <> inline const LogicalNotOptions *Operator::builtin_options_as<LogicalNotOptions>() const -{ - return builtin_options_as_LogicalNotOptions(); -} - -template <> inline const UnpackOptions *Operator::builtin_options_as<UnpackOptions>() const -{ - return builtin_options_as_UnpackOptions(); -} - -template <> inline const FloorDivOptions *Operator::builtin_options_as<FloorDivOptions>() const -{ - return builtin_options_as_FloorDivOptions(); -} - -template <> inline const SquareOptions *Operator::builtin_options_as<SquareOptions>() const -{ - return builtin_options_as_SquareOptions(); -} - -template <> inline const ZerosLikeOptions *Operator::builtin_options_as<ZerosLikeOptions>() const -{ - return builtin_options_as_ZerosLikeOptions(); -} - -template <> inline const FillOptions *Operator::builtin_options_as<FillOptions>() const -{ - return builtin_options_as_FillOptions(); -} - -template <> -inline const BidirectionalSequenceLSTMOptions * -Operator::builtin_options_as<BidirectionalSequenceLSTMOptions>() const -{ - return builtin_options_as_BidirectionalSequenceLSTMOptions(); -} - -template <> -inline const BidirectionalSequenceRNNOptions * -Operator::builtin_options_as<BidirectionalSequenceRNNOptions>() const -{ - return builtin_options_as_BidirectionalSequenceRNNOptions(); -} - -template <> -inline const UnidirectionalSequenceLSTMOptions * -Operator::builtin_options_as<UnidirectionalSequenceLSTMOptions>() const -{ - return builtin_options_as_UnidirectionalSequenceLSTMOptions(); -} - -template <> inline const FloorModOptions *Operator::builtin_options_as<FloorModOptions>() const -{ - return builtin_options_as_FloorModOptions(); -} - -template <> inline const RangeOptions *Operator::builtin_options_as<RangeOptions>() const -{ - return builtin_options_as_RangeOptions(); -} - -template <> -inline const ResizeNearestNeighborOptions * -Operator::builtin_options_as<ResizeNearestNeighborOptions>() const -{ - return builtin_options_as_ResizeNearestNeighborOptions(); -} - -template <> inline const LeakyReluOptions *Operator::builtin_options_as<LeakyReluOptions>() const -{ - return builtin_options_as_LeakyReluOptions(); -} - -template <> -inline const SquaredDifferenceOptions * -Operator::builtin_options_as<SquaredDifferenceOptions>() const -{ - return builtin_options_as_SquaredDifferenceOptions(); -} - -template <> inline const MirrorPadOptions *Operator::builtin_options_as<MirrorPadOptions>() const -{ - return builtin_options_as_MirrorPadOptions(); -} - -template <> inline const AbsOptions *Operator::builtin_options_as<AbsOptions>() const -{ - return builtin_options_as_AbsOptions(); -} - -template <> inline const SplitVOptions *Operator::builtin_options_as<SplitVOptions>() const -{ - return builtin_options_as_SplitVOptions(); -} - -template <> inline const UniqueOptions *Operator::builtin_options_as<UniqueOptions>() const -{ - return builtin_options_as_UniqueOptions(); -} - -template <> inline const ReverseV2Options *Operator::builtin_options_as<ReverseV2Options>() const -{ - return builtin_options_as_ReverseV2Options(); -} - -template <> inline const AddNOptions *Operator::builtin_options_as<AddNOptions>() const -{ - return builtin_options_as_AddNOptions(); -} - -template <> inline const GatherNdOptions *Operator::builtin_options_as<GatherNdOptions>() const -{ - return builtin_options_as_GatherNdOptions(); -} - -template <> inline const CosOptions *Operator::builtin_options_as<CosOptions>() const -{ - return builtin_options_as_CosOptions(); -} - -template <> inline const WhereOptions *Operator::builtin_options_as<WhereOptions>() const -{ - return builtin_options_as_WhereOptions(); -} - -template <> inline const RankOptions *Operator::builtin_options_as<RankOptions>() const -{ - return builtin_options_as_RankOptions(); -} - -template <> -inline const ReverseSequenceOptions *Operator::builtin_options_as<ReverseSequenceOptions>() const -{ - return builtin_options_as_ReverseSequenceOptions(); -} - -template <> inline const MatrixDiagOptions *Operator::builtin_options_as<MatrixDiagOptions>() const -{ - return builtin_options_as_MatrixDiagOptions(); -} - -template <> inline const QuantizeOptions *Operator::builtin_options_as<QuantizeOptions>() const -{ - return builtin_options_as_QuantizeOptions(); -} - -template <> -inline const MatrixSetDiagOptions *Operator::builtin_options_as<MatrixSetDiagOptions>() const -{ - return builtin_options_as_MatrixSetDiagOptions(); -} - -template <> inline const HardSwishOptions *Operator::builtin_options_as<HardSwishOptions>() const -{ - return builtin_options_as_HardSwishOptions(); -} - -template <> inline const IfOptions *Operator::builtin_options_as<IfOptions>() const -{ - return builtin_options_as_IfOptions(); -} - -template <> inline const WhileOptions *Operator::builtin_options_as<WhileOptions>() const -{ - return builtin_options_as_WhileOptions(); -} - -template <> -inline const DepthToSpaceOptions *Operator::builtin_options_as<DepthToSpaceOptions>() const -{ - return builtin_options_as_DepthToSpaceOptions(); -} - -template <> -inline const NonMaxSuppressionV4Options * -Operator::builtin_options_as<NonMaxSuppressionV4Options>() const -{ - return builtin_options_as_NonMaxSuppressionV4Options(); -} - -template <> -inline const NonMaxSuppressionV5Options * -Operator::builtin_options_as<NonMaxSuppressionV5Options>() const -{ - return builtin_options_as_NonMaxSuppressionV5Options(); -} - -template <> inline const ScatterNdOptions *Operator::builtin_options_as<ScatterNdOptions>() const -{ - return builtin_options_as_ScatterNdOptions(); -} - -template <> inline const SelectV2Options *Operator::builtin_options_as<SelectV2Options>() const -{ - return builtin_options_as_SelectV2Options(); -} - -template <> inline const DensifyOptions *Operator::builtin_options_as<DensifyOptions>() const -{ - return builtin_options_as_DensifyOptions(); -} - -template <> inline const SegmentSumOptions *Operator::builtin_options_as<SegmentSumOptions>() const -{ - return builtin_options_as_SegmentSumOptions(); -} - -template <> -inline const BatchMatMulOptions *Operator::builtin_options_as<BatchMatMulOptions>() const -{ - return builtin_options_as_BatchMatMulOptions(); -} - -struct OperatorBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_opcode_index(uint32_t opcode_index) - { - fbb_.AddElement<uint32_t>(Operator::VT_OPCODE_INDEX, opcode_index, 0); - } - void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) - { - fbb_.AddOffset(Operator::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) - { - fbb_.AddOffset(Operator::VT_OUTPUTS, outputs); - } - void add_builtin_options_type(BuiltinOptions builtin_options_type) - { - fbb_.AddElement<uint8_t>(Operator::VT_BUILTIN_OPTIONS_TYPE, - static_cast<uint8_t>(builtin_options_type), 0); - } - void add_builtin_options(flatbuffers::Offset<void> builtin_options) - { - fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options); - } - void add_custom_options(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options) - { - fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options); - } - void add_custom_options_format(CustomOptionsFormat custom_options_format) - { - fbb_.AddElement<int8_t>(Operator::VT_CUSTOM_OPTIONS_FORMAT, - static_cast<int8_t>(custom_options_format), 0); - } - void add_mutating_variable_inputs( - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs) - { - fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); - } - void add_intermediates(flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates) - { - fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates); - } - explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - OperatorBuilder &operator=(const OperatorBuilder &); - flatbuffers::Offset<Operator> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Operator>(end); - return o; - } -}; - -inline flatbuffers::Offset<Operator> -CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0, - BuiltinOptions builtin_options_type = BuiltinOptions_NONE, - flatbuffers::Offset<void> builtin_options = 0, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options = 0, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates = 0) -{ - OperatorBuilder builder_(_fbb); - builder_.add_intermediates(intermediates); - builder_.add_mutating_variable_inputs(mutating_variable_inputs); - builder_.add_custom_options(custom_options); - builder_.add_builtin_options(builtin_options); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_opcode_index(opcode_index); - builder_.add_custom_options_format(custom_options_format); - builder_.add_builtin_options_type(builtin_options_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Operator> -CreateOperatorDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t opcode_index = 0, - const std::vector<int32_t> *inputs = nullptr, - const std::vector<int32_t> *outputs = nullptr, - BuiltinOptions builtin_options_type = BuiltinOptions_NONE, - flatbuffers::Offset<void> builtin_options = 0, - const std::vector<uint8_t> *custom_options = nullptr, - CustomOptionsFormat custom_options_format = CustomOptionsFormat_FLEXBUFFERS, - const std::vector<uint8_t> *mutating_variable_inputs = nullptr, - const std::vector<int32_t> *intermediates = nullptr) -{ - return onert_tflite::CreateOperator( - _fbb, opcode_index, inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0, - outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0, builtin_options_type, builtin_options, - custom_options ? _fbb.CreateVector<uint8_t>(*custom_options) : 0, custom_options_format, - mutating_variable_inputs ? _fbb.CreateVector<uint8_t>(*mutating_variable_inputs) : 0, - intermediates ? _fbb.CreateVector<int32_t>(*intermediates) : 0); -} - -struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_TENSORS = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_OPERATORS = 10, - VT_NAME = 12 - }; - const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *tensors() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Tensor>> *>(VT_TENSORS); - } - const flatbuffers::Vector<int32_t> *inputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS); - } - const flatbuffers::Vector<int32_t> *outputs() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS); - } - const flatbuffers::Vector<flatbuffers::Offset<Operator>> *operators() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Operator>> *>(VT_OPERATORS); - } - const flatbuffers::String *name() const - { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_TENSORS) && - verifier.VerifyVector(tensors()) && verifier.VerifyVectorOfTables(tensors()) && - VerifyOffset(verifier, VT_INPUTS) && verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && verifier.VerifyVector(outputs()) && - VerifyOffset(verifier, VT_OPERATORS) && verifier.VerifyVector(operators()) && - verifier.VerifyVectorOfTables(operators()) && VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && verifier.EndTable(); - } -}; - -struct SubGraphBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors) - { - fbb_.AddOffset(SubGraph::VT_TENSORS, tensors); - } - void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) - { - fbb_.AddOffset(SubGraph::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) - { - fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs); - } - void - add_operators(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators) - { - fbb_.AddOffset(SubGraph::VT_OPERATORS, operators); - } - void add_name(flatbuffers::Offset<flatbuffers::String> name) - { - fbb_.AddOffset(SubGraph::VT_NAME, name); - } - explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - SubGraphBuilder &operator=(const SubGraphBuilder &); - flatbuffers::Offset<SubGraph> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<SubGraph>(end); - return o; - } -}; - -inline flatbuffers::Offset<SubGraph> CreateSubGraph( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Tensor>>> tensors = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Operator>>> operators = 0, - flatbuffers::Offset<flatbuffers::String> name = 0) -{ - SubGraphBuilder builder_(_fbb); - builder_.add_name(name); - builder_.add_operators(operators); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_tensors(tensors); - return builder_.Finish(); -} - -inline flatbuffers::Offset<SubGraph> -CreateSubGraphDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<flatbuffers::Offset<Tensor>> *tensors = nullptr, - const std::vector<int32_t> *inputs = nullptr, - const std::vector<int32_t> *outputs = nullptr, - const std::vector<flatbuffers::Offset<Operator>> *operators = nullptr, - const char *name = nullptr) -{ - return onert_tflite::CreateSubGraph( - _fbb, tensors ? _fbb.CreateVector<flatbuffers::Offset<Tensor>>(*tensors) : 0, - inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0, - outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0, - operators ? _fbb.CreateVector<flatbuffers::Offset<Operator>>(*operators) : 0, - name ? _fbb.CreateString(name) : 0); -} - -struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_DATA = 4 - }; - const flatbuffers::Vector<uint8_t> *data() const - { - return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_DATA); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_DATA) && - verifier.VerifyVector(data()) && verifier.EndTable(); - } -}; - -struct BufferBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_data(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data) - { - fbb_.AddOffset(Buffer::VT_DATA, data); - } - explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - BufferBuilder &operator=(const BufferBuilder &); - flatbuffers::Offset<Buffer> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Buffer>(end); - return o; - } -}; - -inline flatbuffers::Offset<Buffer> -CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data = 0) -{ - BufferBuilder builder_(_fbb); - builder_.add_data(data); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Buffer> CreateBufferDirect(flatbuffers::FlatBufferBuilder &_fbb, - const std::vector<uint8_t> *data = nullptr) -{ - return onert_tflite::CreateBuffer(_fbb, data ? _fbb.CreateVector<uint8_t>(*data) : 0); -} - -struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_NAME = 4, - VT_BUFFER = 6 - }; - const flatbuffers::String *name() const - { - return GetPointer<const flatbuffers::String *>(VT_NAME); - } - uint32_t buffer() const { return GetField<uint32_t>(VT_BUFFER, 0); } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && VerifyField<uint32_t>(verifier, VT_BUFFER) && - verifier.EndTable(); - } -}; - -struct MetadataBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset<flatbuffers::String> name) - { - fbb_.AddOffset(Metadata::VT_NAME, name); - } - void add_buffer(uint32_t buffer) { fbb_.AddElement<uint32_t>(Metadata::VT_BUFFER, buffer, 0); } - explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - MetadataBuilder &operator=(const MetadataBuilder &); - flatbuffers::Offset<Metadata> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Metadata>(end); - return o; - } -}; - -inline flatbuffers::Offset<Metadata> -CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset<flatbuffers::String> name = 0, uint32_t buffer = 0) -{ - MetadataBuilder builder_(_fbb); - builder_.add_buffer(buffer); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Metadata> CreateMetadataDirect(flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - uint32_t buffer = 0) -{ - return onert_tflite::CreateMetadata(_fbb, name ? _fbb.CreateString(name) : 0, buffer); -} - -struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table -{ - enum - { - VT_VERSION = 4, - VT_OPERATOR_CODES = 6, - VT_SUBGRAPHS = 8, - VT_DESCRIPTION = 10, - VT_BUFFERS = 12, - VT_METADATA_BUFFER = 14, - VT_METADATA = 16 - }; - uint32_t version() const { return GetField<uint32_t>(VT_VERSION, 0); } - const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *operator_codes() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<OperatorCode>> *>( - VT_OPERATOR_CODES); - } - const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *subgraphs() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<SubGraph>> *>(VT_SUBGRAPHS); - } - const flatbuffers::String *description() const - { - return GetPointer<const flatbuffers::String *>(VT_DESCRIPTION); - } - const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *buffers() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Buffer>> *>(VT_BUFFERS); - } - const flatbuffers::Vector<int32_t> *metadata_buffer() const - { - return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_METADATA_BUFFER); - } - const flatbuffers::Vector<flatbuffers::Offset<Metadata>> *metadata() const - { - return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<Metadata>> *>(VT_METADATA); - } - bool Verify(flatbuffers::Verifier &verifier) const - { - return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_VERSION) && - VerifyOffset(verifier, VT_OPERATOR_CODES) && verifier.VerifyVector(operator_codes()) && - verifier.VerifyVectorOfTables(operator_codes()) && - VerifyOffset(verifier, VT_SUBGRAPHS) && verifier.VerifyVector(subgraphs()) && - verifier.VerifyVectorOfTables(subgraphs()) && VerifyOffset(verifier, VT_DESCRIPTION) && - verifier.VerifyString(description()) && VerifyOffset(verifier, VT_BUFFERS) && - verifier.VerifyVector(buffers()) && verifier.VerifyVectorOfTables(buffers()) && - VerifyOffset(verifier, VT_METADATA_BUFFER) && verifier.VerifyVector(metadata_buffer()) && - VerifyOffset(verifier, VT_METADATA) && verifier.VerifyVector(metadata()) && - verifier.VerifyVectorOfTables(metadata()) && verifier.EndTable(); - } -}; - -struct ModelBuilder -{ - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_version(uint32_t version) { fbb_.AddElement<uint32_t>(Model::VT_VERSION, version, 0); } - void add_operator_codes( - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes) - { - fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes); - } - void - add_subgraphs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs) - { - fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs); - } - void add_description(flatbuffers::Offset<flatbuffers::String> description) - { - fbb_.AddOffset(Model::VT_DESCRIPTION, description); - } - void add_buffers(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers) - { - fbb_.AddOffset(Model::VT_BUFFERS, buffers); - } - void add_metadata_buffer(flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer) - { - fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer); - } - void - add_metadata(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Metadata>>> metadata) - { - fbb_.AddOffset(Model::VT_METADATA, metadata); - } - explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) - { - start_ = fbb_.StartTable(); - } - ModelBuilder &operator=(const ModelBuilder &); - flatbuffers::Offset<Model> Finish() - { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset<Model>(end); - return o; - } -}; - -inline flatbuffers::Offset<Model> CreateModel( - flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<OperatorCode>>> operator_codes = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<SubGraph>>> subgraphs = 0, - flatbuffers::Offset<flatbuffers::String> description = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> buffers = 0, - flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer = 0, - flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Metadata>>> metadata = 0) -{ - ModelBuilder builder_(_fbb); - builder_.add_metadata(metadata); - builder_.add_metadata_buffer(metadata_buffer); - builder_.add_buffers(buffers); - builder_.add_description(description); - builder_.add_subgraphs(subgraphs); - builder_.add_operator_codes(operator_codes); - builder_.add_version(version); - return builder_.Finish(); -} - -inline flatbuffers::Offset<Model> -CreateModelDirect(flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0, - const std::vector<flatbuffers::Offset<OperatorCode>> *operator_codes = nullptr, - const std::vector<flatbuffers::Offset<SubGraph>> *subgraphs = nullptr, - const char *description = nullptr, - const std::vector<flatbuffers::Offset<Buffer>> *buffers = nullptr, - const std::vector<int32_t> *metadata_buffer = nullptr, - const std::vector<flatbuffers::Offset<Metadata>> *metadata = nullptr) -{ - return onert_tflite::CreateModel( - _fbb, version, - operator_codes ? _fbb.CreateVector<flatbuffers::Offset<OperatorCode>>(*operator_codes) : 0, - subgraphs ? _fbb.CreateVector<flatbuffers::Offset<SubGraph>>(*subgraphs) : 0, - description ? _fbb.CreateString(description) : 0, - buffers ? _fbb.CreateVector<flatbuffers::Offset<Buffer>>(*buffers) : 0, - metadata_buffer ? _fbb.CreateVector<int32_t>(*metadata_buffer) : 0, - metadata ? _fbb.CreateVector<flatbuffers::Offset<Metadata>>(*metadata) : 0); -} - -inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, - QuantizationDetails type) -{ - switch (type) - { - case QuantizationDetails_NONE: - { - return true; - } - case QuantizationDetails_CustomQuantization: - { - auto ptr = reinterpret_cast<const CustomQuantization *>(obj); - return verifier.VerifyTable(ptr); - } - default: - return false; - } -} - -inline bool -VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types) -{ - if (!values || !types) - return !values && !types; - if (values->size() != types->size()) - return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) - { - if (!VerifyQuantizationDetails(verifier, values->Get(i), - types->GetEnum<QuantizationDetails>(i))) - { - return false; - } - } - return true; -} - -inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, - SparseIndexVector type) -{ - switch (type) - { - case SparseIndexVector_NONE: - { - return true; - } - case SparseIndexVector_Int32Vector: - { - auto ptr = reinterpret_cast<const Int32Vector *>(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint16Vector: - { - auto ptr = reinterpret_cast<const Uint16Vector *>(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint8Vector: - { - auto ptr = reinterpret_cast<const Uint8Vector *>(obj); - return verifier.VerifyTable(ptr); - } - default: - return false; - } -} - -inline bool -VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types) -{ - if (!values || !types) - return !values && !types; - if (values->size() != types->size()) - return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) - { - if (!VerifySparseIndexVector(verifier, values->Get(i), types->GetEnum<SparseIndexVector>(i))) - { - return false; - } - } - return true; -} - -inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, - BuiltinOptions type) -{ - switch (type) - { - case BuiltinOptions_NONE: - { - return true; - } - case BuiltinOptions_Conv2DOptions: - { - auto ptr = reinterpret_cast<const Conv2DOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthwiseConv2DOptions: - { - auto ptr = reinterpret_cast<const DepthwiseConv2DOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatEmbeddingsOptions: - { - auto ptr = reinterpret_cast<const ConcatEmbeddingsOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSHProjectionOptions: - { - auto ptr = reinterpret_cast<const LSHProjectionOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_Pool2DOptions: - { - auto ptr = reinterpret_cast<const Pool2DOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SVDFOptions: - { - auto ptr = reinterpret_cast<const SVDFOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RNNOptions: - { - auto ptr = reinterpret_cast<const RNNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FullyConnectedOptions: - { - auto ptr = reinterpret_cast<const FullyConnectedOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SoftmaxOptions: - { - auto ptr = reinterpret_cast<const SoftmaxOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatenationOptions: - { - auto ptr = reinterpret_cast<const ConcatenationOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddOptions: - { - auto ptr = reinterpret_cast<const AddOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_L2NormOptions: - { - auto ptr = reinterpret_cast<const L2NormOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LocalResponseNormalizationOptions: - { - auto ptr = reinterpret_cast<const LocalResponseNormalizationOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSTMOptions: - { - auto ptr = reinterpret_cast<const LSTMOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeBilinearOptions: - { - auto ptr = reinterpret_cast<const ResizeBilinearOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CallOptions: - { - auto ptr = reinterpret_cast<const CallOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReshapeOptions: - { - auto ptr = reinterpret_cast<const ReshapeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SkipGramOptions: - { - auto ptr = reinterpret_cast<const SkipGramOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToDepthOptions: - { - auto ptr = reinterpret_cast<const SpaceToDepthOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: - { - auto ptr = reinterpret_cast<const EmbeddingLookupSparseOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MulOptions: - { - auto ptr = reinterpret_cast<const MulOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadOptions: - { - auto ptr = reinterpret_cast<const PadOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherOptions: - { - auto ptr = reinterpret_cast<const GatherOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchToSpaceNDOptions: - { - auto ptr = reinterpret_cast<const BatchToSpaceNDOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToBatchNDOptions: - { - auto ptr = reinterpret_cast<const SpaceToBatchNDOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeOptions: - { - auto ptr = reinterpret_cast<const TransposeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReducerOptions: - { - auto ptr = reinterpret_cast<const ReducerOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SubOptions: - { - auto ptr = reinterpret_cast<const SubOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DivOptions: - { - auto ptr = reinterpret_cast<const DivOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SqueezeOptions: - { - auto ptr = reinterpret_cast<const SqueezeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SequenceRNNOptions: - { - auto ptr = reinterpret_cast<const SequenceRNNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_StridedSliceOptions: - { - auto ptr = reinterpret_cast<const StridedSliceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpOptions: - { - auto ptr = reinterpret_cast<const ExpOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TopKV2Options: - { - auto ptr = reinterpret_cast<const TopKV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitOptions: - { - auto ptr = reinterpret_cast<const SplitOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogSoftmaxOptions: - { - auto ptr = reinterpret_cast<const LogSoftmaxOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CastOptions: - { - auto ptr = reinterpret_cast<const CastOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DequantizeOptions: - { - auto ptr = reinterpret_cast<const DequantizeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MaximumMinimumOptions: - { - auto ptr = reinterpret_cast<const MaximumMinimumOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMaxOptions: - { - auto ptr = reinterpret_cast<const ArgMaxOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessOptions: - { - auto ptr = reinterpret_cast<const LessOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NegOptions: - { - auto ptr = reinterpret_cast<const NegOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadV2Options: - { - auto ptr = reinterpret_cast<const PadV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterOptions: - { - auto ptr = reinterpret_cast<const GreaterOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterEqualOptions: - { - auto ptr = reinterpret_cast<const GreaterEqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessEqualOptions: - { - auto ptr = reinterpret_cast<const LessEqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectOptions: - { - auto ptr = reinterpret_cast<const SelectOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SliceOptions: - { - auto ptr = reinterpret_cast<const SliceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeConvOptions: - { - auto ptr = reinterpret_cast<const TransposeConvOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SparseToDenseOptions: - { - auto ptr = reinterpret_cast<const SparseToDenseOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TileOptions: - { - auto ptr = reinterpret_cast<const TileOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpandDimsOptions: - { - auto ptr = reinterpret_cast<const ExpandDimsOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EqualOptions: - { - auto ptr = reinterpret_cast<const EqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NotEqualOptions: - { - auto ptr = reinterpret_cast<const NotEqualOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ShapeOptions: - { - auto ptr = reinterpret_cast<const ShapeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PowOptions: - { - auto ptr = reinterpret_cast<const PowOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMinOptions: - { - auto ptr = reinterpret_cast<const ArgMinOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FakeQuantOptions: - { - auto ptr = reinterpret_cast<const FakeQuantOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PackOptions: - { - auto ptr = reinterpret_cast<const PackOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalOrOptions: - { - auto ptr = reinterpret_cast<const LogicalOrOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_OneHotOptions: - { - auto ptr = reinterpret_cast<const OneHotOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalAndOptions: - { - auto ptr = reinterpret_cast<const LogicalAndOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalNotOptions: - { - auto ptr = reinterpret_cast<const LogicalNotOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnpackOptions: - { - auto ptr = reinterpret_cast<const UnpackOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorDivOptions: - { - auto ptr = reinterpret_cast<const FloorDivOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquareOptions: - { - auto ptr = reinterpret_cast<const SquareOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ZerosLikeOptions: - { - auto ptr = reinterpret_cast<const ZerosLikeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FillOptions: - { - auto ptr = reinterpret_cast<const FillOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: - { - auto ptr = reinterpret_cast<const BidirectionalSequenceLSTMOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: - { - auto ptr = reinterpret_cast<const BidirectionalSequenceRNNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: - { - auto ptr = reinterpret_cast<const UnidirectionalSequenceLSTMOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorModOptions: - { - auto ptr = reinterpret_cast<const FloorModOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RangeOptions: - { - auto ptr = reinterpret_cast<const RangeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeNearestNeighborOptions: - { - auto ptr = reinterpret_cast<const ResizeNearestNeighborOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LeakyReluOptions: - { - auto ptr = reinterpret_cast<const LeakyReluOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquaredDifferenceOptions: - { - auto ptr = reinterpret_cast<const SquaredDifferenceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MirrorPadOptions: - { - auto ptr = reinterpret_cast<const MirrorPadOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AbsOptions: - { - auto ptr = reinterpret_cast<const AbsOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitVOptions: - { - auto ptr = reinterpret_cast<const SplitVOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UniqueOptions: - { - auto ptr = reinterpret_cast<const UniqueOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseV2Options: - { - auto ptr = reinterpret_cast<const ReverseV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddNOptions: - { - auto ptr = reinterpret_cast<const AddNOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherNdOptions: - { - auto ptr = reinterpret_cast<const GatherNdOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CosOptions: - { - auto ptr = reinterpret_cast<const CosOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhereOptions: - { - auto ptr = reinterpret_cast<const WhereOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RankOptions: - { - auto ptr = reinterpret_cast<const RankOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseSequenceOptions: - { - auto ptr = reinterpret_cast<const ReverseSequenceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixDiagOptions: - { - auto ptr = reinterpret_cast<const MatrixDiagOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_QuantizeOptions: - { - auto ptr = reinterpret_cast<const QuantizeOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixSetDiagOptions: - { - auto ptr = reinterpret_cast<const MatrixSetDiagOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HardSwishOptions: - { - auto ptr = reinterpret_cast<const HardSwishOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_IfOptions: - { - auto ptr = reinterpret_cast<const IfOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhileOptions: - { - auto ptr = reinterpret_cast<const WhileOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthToSpaceOptions: - { - auto ptr = reinterpret_cast<const DepthToSpaceOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV4Options: - { - auto ptr = reinterpret_cast<const NonMaxSuppressionV4Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV5Options: - { - auto ptr = reinterpret_cast<const NonMaxSuppressionV5Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ScatterNdOptions: - { - auto ptr = reinterpret_cast<const ScatterNdOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectV2Options: - { - auto ptr = reinterpret_cast<const SelectV2Options *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DensifyOptions: - { - auto ptr = reinterpret_cast<const DensifyOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SegmentSumOptions: - { - auto ptr = reinterpret_cast<const SegmentSumOptions *>(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchMatMulOptions: - { - auto ptr = reinterpret_cast<const BatchMatMulOptions *>(obj); - return verifier.VerifyTable(ptr); - } - default: - return false; - } -} - -inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, - const flatbuffers::Vector<flatbuffers::Offset<void>> *values, - const flatbuffers::Vector<uint8_t> *types) -{ - if (!values || !types) - return !values && !types; - if (values->size() != types->size()) - return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) - { - if (!VerifyBuiltinOptions(verifier, values->Get(i), types->GetEnum<BuiltinOptions>(i))) - { - return false; - } - } - return true; -} - -inline const onert_tflite::Model *GetModel(const void *buf) -{ - return flatbuffers::GetRoot<onert_tflite::Model>(buf); -} - -inline const onert_tflite::Model *GetSizePrefixedModel(const void *buf) -{ - return flatbuffers::GetSizePrefixedRoot<onert_tflite::Model>(buf); -} - -inline const char *ModelIdentifier() { return "TFL3"; } - -inline bool ModelBufferHasIdentifier(const void *buf) -{ - return flatbuffers::BufferHasIdentifier(buf, ModelIdentifier()); -} - -inline bool VerifyModelBuffer(flatbuffers::Verifier &verifier) -{ - return verifier.VerifyBuffer<onert_tflite::Model>(ModelIdentifier()); -} - -inline bool VerifySizePrefixedModelBuffer(flatbuffers::Verifier &verifier) -{ - return verifier.VerifySizePrefixedBuffer<onert_tflite::Model>(ModelIdentifier()); -} - -inline const char *ModelExtension() { return "tflite"; } - -inline void FinishModelBuffer(flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset<onert_tflite::Model> root) -{ - fbb.Finish(root, ModelIdentifier()); -} - -inline void FinishSizePrefixedModelBuffer(flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset<onert_tflite::Model> root) -{ - fbb.FinishSizePrefixed(root, ModelIdentifier()); -} - -} // namespace onert_tflite - -#endif // FLATBUFFERS_GENERATED_TFLITESCHEMA_ONERT_TFLITE_H_ diff --git a/runtime/onert/frontend/tflite/tflite_schema-1.13.1.fbs b/runtime/onert/frontend/tflite/tflite_schema-1.13.1.fbs deleted file mode 100644 index ae6b5230f..000000000 --- a/runtime/onert/frontend/tflite/tflite_schema-1.13.1.fbs +++ /dev/null @@ -1,795 +0,0 @@ -// Copyright 2017 The TensorFlow Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// Revision History -// Version 0: Initial version. -// Version 1: Add subgraphs to schema. -// Version 2: Rename operators to conform to NN API. -// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers. - -// Change namespace to onert_tflite -namespace onert_tflite; - -// This corresponds to the version. -file_identifier "TFL3"; -// File extension of any written files. -file_extension "tflite"; - -// IMPORTANT: All new members of tables, enums and unions must be added at the -// end to ensure backwards compatibility. - -// The type of data stored in a tensor. -enum TensorType : byte { - FLOAT32 = 0, - FLOAT16 = 1, - INT32 = 2, - UINT8 = 3, - INT64 = 4, - STRING = 5, - BOOL = 6, - INT16 = 7, - COMPLEX64 = 8, - INT8 = 9, -} - -// Custom quantization parameters for experimenting with new quantization -// techniques. -table CustomQuantization { - custom:[ubyte] (force_align: 16); -} - -// Represents a specific quantization technique's parameters. -union QuantizationDetails { - CustomQuantization, -} - -// Parameters for converting a quantized tensor back to float. -table QuantizationParameters { - // These four parameters are the asymmetric linear quantization parameters. - // Given a quantized value q, the corresponding float value f should be: - // f = scale * (q - zero_point) - // For other quantization types, the QuantizationDetails below is used. - min:[float]; // For importing back into tensorflow. - max:[float]; // For importing back into tensorflow. - scale:[float]; // For dequantizing the tensor's values. - zero_point:[long]; - - // If this is not none, the quantization parameters above are ignored and the - // value of the QuantizationDetails union below should be used. - details:QuantizationDetails; -} - -table Tensor { - // The tensor shape. The meaning of each entry is operator-specific but - // builtin ops use: [batch size, height, width, number of channels] (That's - // Tensorflow's NHWC). - shape:[int]; - type:TensorType; - // An index that refers to the buffers table at the root of the model. Or, - // if there is no data buffer associated (i.e. intermediate results), then - // this is 0 (which refers to an always existent empty buffer). - // - // The data_buffer itself is an opaque container, with the assumption that the - // target device is little-endian. In addition, all builtin operators assume - // the memory is ordered such that if `shape` is [4, 3, 2], then index - // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k]. - buffer:uint; - name:string; // For debugging and importing back into tensorflow. - quantization:QuantizationParameters; // Optional. - - is_variable:bool = false; -} - -// A list of builtin operators. Builtin operators are slightly faster than custom -// ones, but not by much. Moreover, while custom operators accept an opaque -// object containing configuration parameters, builtins have a predetermined -// set of acceptable options. -enum BuiltinOperator : byte { - ADD = 0, - AVERAGE_POOL_2D = 1, - CONCATENATION = 2, - CONV_2D = 3, - DEPTHWISE_CONV_2D = 4, - // DEPTH_TO_SPACE = 5, - DEQUANTIZE = 6, - EMBEDDING_LOOKUP = 7, - FLOOR = 8, - FULLY_CONNECTED = 9, - HASHTABLE_LOOKUP = 10, - L2_NORMALIZATION = 11, - L2_POOL_2D = 12, - LOCAL_RESPONSE_NORMALIZATION = 13, - LOGISTIC = 14, - LSH_PROJECTION = 15, - LSTM = 16, - MAX_POOL_2D = 17, - MUL = 18, - RELU = 19, - // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed - // since different model developers use RELU1 in different ways. Never - // create another op called RELU1. - RELU_N1_TO_1 = 20, - RELU6 = 21, - RESHAPE = 22, - RESIZE_BILINEAR = 23, - RNN = 24, - SOFTMAX = 25, - SPACE_TO_DEPTH = 26, - SVDF = 27, - TANH = 28, - // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS - CONCAT_EMBEDDINGS = 29, - SKIP_GRAM = 30, - CALL = 31, - CUSTOM = 32, - EMBEDDING_LOOKUP_SPARSE = 33, - PAD = 34, - UNIDIRECTIONAL_SEQUENCE_RNN = 35, - GATHER = 36, - BATCH_TO_SPACE_ND = 37, - SPACE_TO_BATCH_ND = 38, - TRANSPOSE = 39, - MEAN = 40, - SUB = 41, - DIV = 42, - SQUEEZE = 43, - UNIDIRECTIONAL_SEQUENCE_LSTM = 44, - STRIDED_SLICE = 45, - BIDIRECTIONAL_SEQUENCE_RNN = 46, - EXP = 47, - TOPK_V2 = 48, - SPLIT = 49, - LOG_SOFTMAX = 50, - // DELEGATE is a special op type for the operations which are delegated to - // other backends. - // WARNING: Experimental interface, subject to change - DELEGATE = 51, - BIDIRECTIONAL_SEQUENCE_LSTM = 52, - CAST = 53, - PRELU = 54, - MAXIMUM = 55, - ARG_MAX = 56, - MINIMUM = 57, - LESS = 58, - NEG = 59, - PADV2 = 60, - GREATER = 61, - GREATER_EQUAL = 62, - LESS_EQUAL = 63, - SELECT = 64, - SLICE = 65, - SIN = 66, - TRANSPOSE_CONV = 67, - SPARSE_TO_DENSE = 68, - TILE = 69, - EXPAND_DIMS = 70, - EQUAL = 71, - NOT_EQUAL = 72, - LOG = 73, - SUM = 74, - SQRT = 75, - RSQRT = 76, - SHAPE = 77, - POW = 78, - ARG_MIN = 79, - FAKE_QUANT = 80, - REDUCE_PROD = 81, - REDUCE_MAX = 82, - PACK = 83, - LOGICAL_OR = 84, - ONE_HOT = 85, - LOGICAL_AND = 86, - LOGICAL_NOT = 87, - UNPACK = 88, - REDUCE_MIN = 89, - FLOOR_DIV = 90, - REDUCE_ANY = 91, - SQUARE = 92, - ZEROS_LIKE = 93, - FILL = 94, - FLOOR_MOD = 95, - RANGE = 96, - RESIZE_NEAREST_NEIGHBOR = 97, - LEAKY_RELU = 98, - SQUARED_DIFFERENCE = 99, - MIRROR_PAD = 100, - ABS = 101, - SPLIT_V = 102, -} - -// Options for the builtin operators. -union BuiltinOptions { - Conv2DOptions, - DepthwiseConv2DOptions, - ConcatEmbeddingsOptions, - LSHProjectionOptions, - Pool2DOptions, - SVDFOptions, - RNNOptions, - FullyConnectedOptions, - SoftmaxOptions, - ConcatenationOptions, - AddOptions, - L2NormOptions, - LocalResponseNormalizationOptions, - LSTMOptions, - ResizeBilinearOptions, - CallOptions, - ReshapeOptions, - SkipGramOptions, - SpaceToDepthOptions, - EmbeddingLookupSparseOptions, - MulOptions, - PadOptions, - GatherOptions, - BatchToSpaceNDOptions, - SpaceToBatchNDOptions, - TransposeOptions, - ReducerOptions, - SubOptions, - DivOptions, - SqueezeOptions, - SequenceRNNOptions, - StridedSliceOptions, - ExpOptions, - TopKV2Options, - SplitOptions, - LogSoftmaxOptions, - CastOptions, - DequantizeOptions, - MaximumMinimumOptions, - ArgMaxOptions, - LessOptions, - NegOptions, - PadV2Options, - GreaterOptions, - GreaterEqualOptions, - LessEqualOptions, - SelectOptions, - SliceOptions, - TransposeConvOptions, - SparseToDenseOptions, - TileOptions, - ExpandDimsOptions, - EqualOptions, - NotEqualOptions, - ShapeOptions, - PowOptions, - ArgMinOptions, - FakeQuantOptions, - PackOptions, - LogicalOrOptions, - OneHotOptions, - LogicalAndOptions, - LogicalNotOptions, - UnpackOptions, - FloorDivOptions, - SquareOptions, - ZerosLikeOptions, - FillOptions, - BidirectionalSequenceLSTMOptions, - BidirectionalSequenceRNNOptions, - UnidirectionalSequenceLSTMOptions, - FloorModOptions, - RangeOptions, - ResizeNearestNeighborOptions, - LeakyReluOptions, - SquaredDifferenceOptions, - MirrorPadOptions, - AbsOptions, - SplitVOptions, -} - -enum Padding : byte { SAME, VALID } - -enum ActivationFunctionType : byte { - NONE = 0, - RELU = 1, - RELU_N1_TO_1 = 2, - RELU6 = 3, - TANH = 4, - SIGN_BIT = 5, -} - -table Conv2DOptions { - padding:Padding; - stride_w:int; - stride_h:int; - fused_activation_function:ActivationFunctionType; - dilation_w_factor:int = 1; - dilation_h_factor:int = 1; -} - -table Pool2DOptions { - padding:Padding; - stride_w:int; - stride_h:int; - filter_width:int; - filter_height:int; - fused_activation_function:ActivationFunctionType; -} - -table DepthwiseConv2DOptions { - // Parameters for DepthwiseConv version 1 or above. - padding:Padding; - stride_w:int; - stride_h:int; - depth_multiplier:int; - fused_activation_function:ActivationFunctionType; - // Parameters for DepthwiseConv version 2 or above. - dilation_w_factor:int = 1; - dilation_h_factor:int = 1; -} - -table ConcatEmbeddingsOptions { - num_channels:int; - num_columns_per_channel:[int]; - embedding_dim_per_channel:[int]; // This could be inferred from parameters. -} - -enum LSHProjectionType: byte { - UNKNOWN = 0, - SPARSE = 1, - DENSE = 2, -} - -table LSHProjectionOptions { - type: LSHProjectionType; -} - -table SVDFOptions { - rank:int; - fused_activation_function:ActivationFunctionType; -} - -// An implementation of TensorFlow RNNCell. -table RNNOptions { - fused_activation_function:ActivationFunctionType; -} - -// An implementation of TensorFlow dynamic_rnn with RNNCell. -table SequenceRNNOptions { - time_major:bool; - fused_activation_function:ActivationFunctionType; -} - -// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell. -table BidirectionalSequenceRNNOptions { - time_major:bool; - fused_activation_function:ActivationFunctionType; - merge_outputs: bool; -} - -enum FullyConnectedOptionsWeightsFormat: byte { - DEFAULT = 0, - SHUFFLED4x16INT8 = 1, -} - -// An implementation of TensorFlow fully_connected (a.k.a Dense) layer. -table FullyConnectedOptions { - // Parameters for FullyConnected version 1 or above. - fused_activation_function:ActivationFunctionType; - - // Parameters for FullyConnected version 2 or above. - weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT; -} - -table SoftmaxOptions { - beta: float; -} - -// An implementation of TensorFlow concat. -table ConcatenationOptions { - axis:int; - fused_activation_function:ActivationFunctionType; -} - -table AddOptions { - fused_activation_function:ActivationFunctionType; -} - -table MulOptions { - fused_activation_function:ActivationFunctionType; -} - -table L2NormOptions { - fused_activation_function:ActivationFunctionType; -} - -table LocalResponseNormalizationOptions { - radius:int; - bias:float; - alpha:float; - beta:float; -} - -enum LSTMKernelType : byte { - // Full LSTM kernel which supports peephole and projection. - FULL = 0, - // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell. - BASIC = 1, -} - -// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell -table LSTMOptions { - // Parameters for LSTM version 1 or above. - fused_activation_function:ActivationFunctionType; - cell_clip: float; // Optional, 0.0 means no clipping - proj_clip: float; // Optional, 0.0 means no clipping - - // Parameters for LSTM version 2 or above. - // Basic kernel is only supported in version 2 or above. - kernel_type: LSTMKernelType = FULL; -} - -// An implementation of TensorFlow dynamic_rnn with LSTMCell. -table UnidirectionalSequenceLSTMOptions { - fused_activation_function:ActivationFunctionType; - cell_clip: float; // Optional, 0.0 means no clipping - proj_clip: float; // Optional, 0.0 means no clipping - - // If true then first dimension is sequence, otherwise batch. - time_major:bool; -} - -table BidirectionalSequenceLSTMOptions { - fused_activation_function:ActivationFunctionType; - cell_clip: float; // Optional, 0.0 means no clipping - proj_clip: float; // Optional, 0.0 means no clipping - - // If true, store the outputs of both directions into the first output. - merge_outputs: bool; -} - -table ResizeBilinearOptions { - new_height: int (deprecated); - new_width: int (deprecated); - align_corners: bool; -} - -table ResizeNearestNeighborOptions { - align_corners: bool; -} - -// A call operation options -table CallOptions { - // The subgraph index that needs to be called. - subgraph:uint; -} - -table PadOptions { -} - -table PadV2Options { -} - -table ReshapeOptions { - new_shape:[int]; -} - -table SpaceToBatchNDOptions { -} - -table BatchToSpaceNDOptions { -} - -table SkipGramOptions { - ngram_size: int; - max_skip_size: int; - include_all_ngrams: bool; -} - -table SpaceToDepthOptions { - block_size: int; -} - -table SubOptions { - fused_activation_function:ActivationFunctionType; -} - -table DivOptions { - fused_activation_function:ActivationFunctionType; -} - -table TopKV2Options { -} - -enum CombinerType : byte { - SUM = 0, - MEAN = 1, - SQRTN = 2, -} - -table EmbeddingLookupSparseOptions { - combiner:CombinerType; -} - -table GatherOptions { - axis: int; -} - -table TransposeOptions { -} - -table ExpOptions { -} - -table ReducerOptions { - keep_dims: bool; -} - -table SqueezeOptions { - squeeze_dims:[int]; -} - -table SplitOptions { - num_splits: int; -} - -table SplitVOptions { - num_splits: int; -} - -table StridedSliceOptions { - begin_mask: int; - end_mask: int; - ellipsis_mask: int; - new_axis_mask: int; - shrink_axis_mask: int; -} - -table LogSoftmaxOptions { -} - -table CastOptions { - in_data_type: TensorType; - out_data_type: TensorType; -} - -table DequantizeOptions { -} - -table MaximumMinimumOptions { -} - -table TileOptions { -} - -table ArgMaxOptions { - output_type : TensorType; -} - -table ArgMinOptions { - output_type : TensorType; -} - -table GreaterOptions { -} - -table GreaterEqualOptions { -} - -table LessOptions { -} - -table LessEqualOptions { -} - -table NegOptions { -} - -table SelectOptions { -} - -table SliceOptions { -} - -table TransposeConvOptions { - padding:Padding; - stride_w:int; - stride_h:int; -} - -table ExpandDimsOptions { -} - -table SparseToDenseOptions { - validate_indices:bool; -} - -table EqualOptions { -} - -table NotEqualOptions { -} - -table ShapeOptions { - // Optional output type of the operation (int32 or int64). Defaults to int32. - out_type : TensorType; -} - -table PowOptions { -} - -table FakeQuantOptions { - // Parameters supported by version 1: - min:float; - max:float; - num_bits:int; - - // Parameters supported by version 2: - narrow_range:bool; -} - -table PackOptions { - values_count:int; - axis:int; -} - -table LogicalOrOptions { -} - -table OneHotOptions { - axis:int; -} - -table AbsOptions { -} - - -table LogicalAndOptions { -} - -table LogicalNotOptions { -} - -table UnpackOptions { - num:int; - axis:int; -} - -table FloorDivOptions { -} - -table SquareOptions { -} - -table ZerosLikeOptions { -} - -table FillOptions { -} - -table FloorModOptions { -} - -table RangeOptions { -} - -table LeakyReluOptions { - alpha:float; -} - -table SquaredDifferenceOptions { -} - -enum MirrorPadMode : byte { - // Doesn't include borders. - REFLECT = 0, - // Includes borders. - SYMMETRIC = 1, -} - -table MirrorPadOptions { - mode:MirrorPadMode; -} - -// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a -// builtin, or a string if the operator is custom. -table OperatorCode { - builtin_code:BuiltinOperator; - custom_code:string; - - // The version of the operator. The version need to be bumped whenever new - // parameters are introduced into an op. - version:int = 1; -} - -enum CustomOptionsFormat : byte { - FLEXBUFFERS = 0, -} - -// An operator takes tensors as inputs and outputs. The type of operation being -// performed is determined by an index into the list of valid OperatorCodes, -// while the specifics of each operations is configured using builtin_options -// or custom_options. -table Operator { - // Index into the operator_codes array. Using an integer here avoids - // complicate map lookups. - opcode_index:uint; - - // Optional input and output tensors are indicated by -1. - inputs:[int]; - outputs:[int]; - - builtin_options:BuiltinOptions; - custom_options:[ubyte]; - custom_options_format:CustomOptionsFormat; - - // A list of booleans indicating the input tensors which are being mutated by - // this operator.(e.g. used by RNN and LSTM). - // For example, if the "inputs" array refers to 5 tensors and the second and - // fifth are mutable variables, then this list will contain - // [false, true, false, false, true]. - // - // If the list is empty, no variable is mutated in this operator. - // The list either has the same length as `inputs`, or is empty. - mutating_variable_inputs:[bool]; -} - -// The root type, defining a subgraph, which typically represents an entire -// model. -table SubGraph { - // A list of all tensors used in this subgraph. - tensors:[Tensor]; - - // Indices of the tensors that are inputs into this subgraph. Note this is - // the list of non-static tensors that feed into the subgraph for inference. - inputs:[int]; - - // Indices of the tensors that are outputs out of this subgraph. Note this is - // the list of output tensors that are considered the product of the - // subgraph's inference. - outputs:[int]; - - // All operators, in execution order. - operators:[Operator]; - - // Name of this subgraph (used for debugging). - name:string; -} - -// Table of raw data buffers (used for constant tensors). Referenced by tensors -// by index. The generous alignment accommodates mmap-friendly data structures. -table Buffer { - data:[ubyte] (force_align: 16); -} - -table Model { - // Version of the schema. - version:uint; - - // A list of all operator codes used in this model. This is - // kept in order because operators carry an index into this - // vector. - operator_codes:[OperatorCode]; - - // All the subgraphs of the model. The 0th is assumed to be the main - // model. - subgraphs:[SubGraph]; - - // A description of the model. - description:string; - - // Buffers of the model. - // Note the 0th entry of this array must be an empty buffer (sentinel). - // This is a convention so that tensors without a buffer can provide 0 as - // their buffer. - buffers:[Buffer]; - - // Metadata about the model. Indirects into the existings buffers list. - metadata_buffer:[int]; -} - -root_type Model; diff --git a/runtime/onert/frontend/tflite/tflite_schema.fbs b/runtime/onert/frontend/tflite/tflite_schema.fbs deleted file mode 100644 index 9bffb4f3c..000000000 --- a/runtime/onert/frontend/tflite/tflite_schema.fbs +++ /dev/null @@ -1,1095 +0,0 @@ -// Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved -// Copyright 2017 The TensorFlow Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// Revision History -// Version 0: Initial version. -// Version 1: Add subgraphs to schema. -// Version 2: Rename operators to conform to NN API. -// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers. - -// Change namespace to onert_tflite -namespace onert_tflite; - -// This corresponds to the version. -file_identifier "TFL3"; -// File extension of any written files. -file_extension "tflite"; - -// IMPORTANT: All new members of tables, enums and unions must be added at the -// end to ensure backwards compatibility. - -// The type of data stored in a tensor. -enum TensorType : byte { - FLOAT32 = 0, - FLOAT16 = 1, - INT32 = 2, - UINT8 = 3, - INT64 = 4, - STRING = 5, - BOOL = 6, - INT16 = 7, - COMPLEX64 = 8, - INT8 = 9, - FLOAT64 = 10, -} - -// Custom quantization parameters for experimenting with new quantization -// techniques. -table CustomQuantization { - custom:[ubyte] (force_align: 16); -} - -// Represents a specific quantization technique's parameters. -union QuantizationDetails { - CustomQuantization, -} - -// Parameters for converting a quantized tensor back to float. -table QuantizationParameters { - // These four parameters are the asymmetric linear quantization parameters. - // Given a quantized value q, the corresponding float value f should be: - // f = scale * (q - zero_point) - // For other quantization types, the QuantizationDetails below is used. - min:[float]; // For importing back into tensorflow. - max:[float]; // For importing back into tensorflow. - scale:[float]; // For dequantizing the tensor's values. - zero_point:[long]; - - // If this is not none, the other quantization parameters (i.e. min, max, - // scale, zero_point fields above) are ignored and the value of the - // QuantizationDetails union should be used. - details:QuantizationDetails; - - // Specifies the dimension of the Tensor's shape that the scales and - // zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1] - // with quantization params: - // scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1 - // will be quantized across the second dimension of t. - // t[:, 0, :, :] will have scale[0]=1.0, zero_point[0]=1 - // t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2 - // t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3 - quantized_dimension:int; -} - -// Sparse tensors. -// We use a modification of the TACO format. -// Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf -// -// To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1), -// potentially with a k-dimensional block (0 <= k <= n) with dims -// (dn, ..., dn+k-1), the format needs to specify: -// 1. In what order to traverse these dimensions. For example, to store a 2-D -// matrix in row major order, the traversal order would be (d0, d1), -// whereas to store it in column major order, the traversal order would be -// (d1, d0). If the 2-D matrix has a 2-D inner block, the traversal order -// could be (d0, d1, d2, d3). -// 2. How each block dimension in (dn, ..., dn+k-1) maps to the original -// tensor dimension in (d0, ..., dn-1). -// 3. In the traversal order defined above, the format (dense vs. sparse) and -// index metadata for each dimension. For a dense dimension, this is just -// the size of that dimension. For a sparse dimension, it's the same as -// the compressed index defined in the Compressed Sparse Row (CSR) format. -// (http://scipy-lectures.org/advanced/scipy_sparse/csr_matrix.html) - -// The storage type for a dimension. Currently we support: -// 1. DENSE: each coordinate in this dimension is stored implicitly. -// 2. SPARSE_CSR: only the coordinates with non-zero elements are stored. The -// compression technique is the same what CSR uses. -// More types like a sparse dimension with a different compression technique -// could be added to the list in the future. -enum DimensionType : byte { - DENSE = 0, - SPARSE_CSR = 1, -} - -table Int32Vector { - values:[int]; -} - -table Uint16Vector { - values:[ushort] (force_align: 4); -} - -table Uint8Vector { - values:[ubyte] (force_align: 4); -} - -// Variable-typed buffer to store the index metadata for a sparse dimension. -// The widest type is Int32 instead of UInt32 because tensor's shape is a int32 -// vector. We don't want the per-dimensional index to overflow that range. -union SparseIndexVector { - Int32Vector, - Uint16Vector, - Uint8Vector -} - -table DimensionMetadata { - // Whether a dimension is dense or sparse. - format:DimensionType; - // Index metadata used for a dimension. - // - If format is DimensionType.DENSE then we use the dense_size field to - // store the size of that dimension. Each index in that dimension is - // stored implicitly. - // - If format is DimensionType.SPARSE_CSR then we use array_segments and - // array_indices to encode that dimension. array_segments represents how - // to segment the indices array, each segment corresponds to one element - // in the previous dimension. array_indices represents the index of the - // non-zero elements within this dimension (as those in the CSR matrix - // format, where the first array is row pointers and the second array is - // column indices). - dense_size:int; - array_segments:SparseIndexVector; - array_indices:SparseIndexVector; -} - -// Parameters to encode a sparse TfLite tensor. -table SparsityParameters { - // The traversal order of the dimensions defined in the `shape` field of the - // conceptual dense tensor. For a n-dimensional tensors with dims (d0, d1, - // ..., dn-1), - // - if not block sparse, the traversal_order is just a permutation of (d0, - // ..., dn-1). For example, a 2-D matrix stored in row-major order would - // have traversal_order = (d0, d1). - // - if block sparse with a k-dimensional block (0 <= k <= n), the - // traversal_order has n + k elements. The first n elements are still a - // permutation of (d0, ..., dn-1). The lask k elements are a permutation - // of (dn, ..., dn+k-1), defining how to traverse a block internally. For - // example, a 2-D matrix with 2-D blocks, both stored in row-major order - // would have traversal_order = (d0, d1, d2, d3). - traversal_order:[int]; - // For an n-dimensional tensor with a k-dimensional block (0 <= k <= n), - // stores how a block dimension in (dn, ..., dn+k-1) maps to the original - // tensor dimension in (d0, ..., dn). - // It's stored in the order of (dn, ..., dn+k-1). - // If not block-sparse, this field is NULL. - block_map:[int]; - // In the traversal order defined above, the metadata needed for - // each dimension to locate the non-zero values in the original dense tensor. - // The size of the dim_metadata array = the size of the traversal_order array - // = n + k. - dim_metadata:[DimensionMetadata]; -} - -table Tensor { - // The tensor shape. The meaning of each entry is operator-specific but - // builtin ops use: [batch size, height, width, number of channels] (That's - // Tensorflow's NHWC). - shape:[int]; - type:TensorType; - // An index that refers to the buffers table at the root of the model. Or, - // if there is no data buffer associated (i.e. intermediate results), then - // this is 0 (which refers to an always existent empty buffer). - // - // The data_buffer itself is an opaque container, with the assumption that the - // target device is little-endian. In addition, all builtin operators assume - // the memory is ordered such that if `shape` is [4, 3, 2], then index - // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k]. - buffer:uint; - name:string; // For debugging and importing back into tensorflow. - quantization:QuantizationParameters; // Optional. - - is_variable:bool = false; - - // Parameters to encode a sparse tensor. See the example in - // tensorflow/lite/testdata/sparse_tensor.json. - sparsity:SparsityParameters; // Optional. - - // Encodes `shape` with unknown dimensions. Unknown dimensions are - // represented with -1. - shape_signature:[int]; // Optional. -} - -// A list of builtin operators. Builtin operators are slightly faster than custom -// ones, but not by much. Moreover, while custom operators accept an opaque -// object containing configuration parameters, builtins have a predetermined -// set of acceptable options. - -enum BuiltinOperator : byte { - ADD = 0, - AVERAGE_POOL_2D = 1, - CONCATENATION = 2, - CONV_2D = 3, - DEPTHWISE_CONV_2D = 4, - DEPTH_TO_SPACE = 5, - DEQUANTIZE = 6, - EMBEDDING_LOOKUP = 7, - FLOOR = 8, - FULLY_CONNECTED = 9, - HASHTABLE_LOOKUP = 10, - L2_NORMALIZATION = 11, - L2_POOL_2D = 12, - LOCAL_RESPONSE_NORMALIZATION = 13, - LOGISTIC = 14, - LSH_PROJECTION = 15, - LSTM = 16, - MAX_POOL_2D = 17, - MUL = 18, - RELU = 19, - // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed - // since different model developers use RELU1 in different ways. Never - // create another op called RELU1. - RELU_N1_TO_1 = 20, - RELU6 = 21, - RESHAPE = 22, - RESIZE_BILINEAR = 23, - RNN = 24, - SOFTMAX = 25, - SPACE_TO_DEPTH = 26, - SVDF = 27, - TANH = 28, - // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS - CONCAT_EMBEDDINGS = 29, - SKIP_GRAM = 30, - CALL = 31, - CUSTOM = 32, - EMBEDDING_LOOKUP_SPARSE = 33, - PAD = 34, - UNIDIRECTIONAL_SEQUENCE_RNN = 35, - GATHER = 36, - BATCH_TO_SPACE_ND = 37, - SPACE_TO_BATCH_ND = 38, - TRANSPOSE = 39, - MEAN = 40, - SUB = 41, - DIV = 42, - SQUEEZE = 43, - UNIDIRECTIONAL_SEQUENCE_LSTM = 44, - STRIDED_SLICE = 45, - BIDIRECTIONAL_SEQUENCE_RNN = 46, - EXP = 47, - TOPK_V2 = 48, - SPLIT = 49, - LOG_SOFTMAX = 50, - // DELEGATE is a special op type for the operations which are delegated to - // other backends. - // WARNING: Experimental interface, subject to change - DELEGATE = 51, - BIDIRECTIONAL_SEQUENCE_LSTM = 52, - CAST = 53, - PRELU = 54, - MAXIMUM = 55, - ARG_MAX = 56, - MINIMUM = 57, - LESS = 58, - NEG = 59, - PADV2 = 60, - GREATER = 61, - GREATER_EQUAL = 62, - LESS_EQUAL = 63, - SELECT = 64, - SLICE = 65, - SIN = 66, - TRANSPOSE_CONV = 67, - SPARSE_TO_DENSE = 68, - TILE = 69, - EXPAND_DIMS = 70, - EQUAL = 71, - NOT_EQUAL = 72, - LOG = 73, - SUM = 74, - SQRT = 75, - RSQRT = 76, - SHAPE = 77, - POW = 78, - ARG_MIN = 79, - FAKE_QUANT = 80, - REDUCE_PROD = 81, - REDUCE_MAX = 82, - PACK = 83, - LOGICAL_OR = 84, - ONE_HOT = 85, - LOGICAL_AND = 86, - LOGICAL_NOT = 87, - UNPACK = 88, - REDUCE_MIN = 89, - FLOOR_DIV = 90, - REDUCE_ANY = 91, - SQUARE = 92, - ZEROS_LIKE = 93, - FILL = 94, - FLOOR_MOD = 95, - RANGE = 96, - RESIZE_NEAREST_NEIGHBOR = 97, - LEAKY_RELU = 98, - SQUARED_DIFFERENCE = 99, - MIRROR_PAD = 100, - ABS = 101, - SPLIT_V = 102, - UNIQUE = 103, - CEIL = 104, - REVERSE_V2 = 105, - ADD_N = 106, - GATHER_ND = 107, - COS = 108, - WHERE = 109, - RANK = 110, - ELU = 111, - REVERSE_SEQUENCE = 112, - MATRIX_DIAG = 113, - QUANTIZE = 114, - MATRIX_SET_DIAG = 115, - ROUND = 116, - HARD_SWISH = 117, - IF = 118, - WHILE = 119, - NON_MAX_SUPPRESSION_V4 = 120, - NON_MAX_SUPPRESSION_V5 = 121, - SCATTER_ND = 122, - SELECT_V2 = 123, - DENSIFY = 124, - SEGMENT_SUM = 125, - BATCH_MATMUL = 126 -} - - -// Options for the builtin operators. -union BuiltinOptions { - Conv2DOptions, - DepthwiseConv2DOptions, - ConcatEmbeddingsOptions, - LSHProjectionOptions, - Pool2DOptions, - SVDFOptions, - RNNOptions, - FullyConnectedOptions, - SoftmaxOptions, - ConcatenationOptions, - AddOptions, - L2NormOptions, - LocalResponseNormalizationOptions, - LSTMOptions, - ResizeBilinearOptions, - CallOptions, - ReshapeOptions, - SkipGramOptions, - SpaceToDepthOptions, - EmbeddingLookupSparseOptions, - MulOptions, - PadOptions, - GatherOptions, - BatchToSpaceNDOptions, - SpaceToBatchNDOptions, - TransposeOptions, - ReducerOptions, - SubOptions, - DivOptions, - SqueezeOptions, - SequenceRNNOptions, - StridedSliceOptions, - ExpOptions, - TopKV2Options, - SplitOptions, - LogSoftmaxOptions, - CastOptions, - DequantizeOptions, - MaximumMinimumOptions, - ArgMaxOptions, - LessOptions, - NegOptions, - PadV2Options, - GreaterOptions, - GreaterEqualOptions, - LessEqualOptions, - SelectOptions, - SliceOptions, - TransposeConvOptions, - SparseToDenseOptions, - TileOptions, - ExpandDimsOptions, - EqualOptions, - NotEqualOptions, - ShapeOptions, - PowOptions, - ArgMinOptions, - FakeQuantOptions, - PackOptions, - LogicalOrOptions, - OneHotOptions, - LogicalAndOptions, - LogicalNotOptions, - UnpackOptions, - FloorDivOptions, - SquareOptions, - ZerosLikeOptions, - FillOptions, - BidirectionalSequenceLSTMOptions, - BidirectionalSequenceRNNOptions, - UnidirectionalSequenceLSTMOptions, - FloorModOptions, - RangeOptions, - ResizeNearestNeighborOptions, - LeakyReluOptions, - SquaredDifferenceOptions, - MirrorPadOptions, - AbsOptions, - SplitVOptions, - UniqueOptions, - ReverseV2Options, - AddNOptions, - GatherNdOptions, - CosOptions, - WhereOptions, - RankOptions, - ReverseSequenceOptions, - MatrixDiagOptions, - QuantizeOptions, - MatrixSetDiagOptions, - HardSwishOptions, - IfOptions, - WhileOptions, - DepthToSpaceOptions, - NonMaxSuppressionV4Options, - NonMaxSuppressionV5Options, - ScatterNdOptions, - SelectV2Options, - DensifyOptions, - SegmentSumOptions, - BatchMatMulOptions -} - -enum Padding : byte { SAME, VALID } - -enum ActivationFunctionType : byte { - NONE = 0, - RELU = 1, - RELU_N1_TO_1 = 2, - RELU6 = 3, - TANH = 4, - SIGN_BIT = 5, -} - -table Conv2DOptions { - padding:Padding; - stride_w:int; - stride_h:int; - fused_activation_function:ActivationFunctionType; - dilation_w_factor:int = 1; - dilation_h_factor:int = 1; -} - -table Pool2DOptions { - padding:Padding; - stride_w:int; - stride_h:int; - filter_width:int; - filter_height:int; - fused_activation_function:ActivationFunctionType; -} - -table DepthwiseConv2DOptions { - // Parameters for DepthwiseConv version 1 or above. - padding:Padding; - stride_w:int; - stride_h:int; - // `depth_multiplier` is redundant. It's used by CPU kernels in - // TensorFlow 2.0 or below, but ignored in versions above. - // See comments in lite/c/builtin_op_data.h for more details. - depth_multiplier:int; - fused_activation_function:ActivationFunctionType; - // Parameters for DepthwiseConv version 2 or above. - dilation_w_factor:int = 1; - dilation_h_factor:int = 1; -} - -table ConcatEmbeddingsOptions { - num_channels:int; - num_columns_per_channel:[int]; - embedding_dim_per_channel:[int]; // This could be inferred from parameters. -} - -enum LSHProjectionType: byte { - UNKNOWN = 0, - SPARSE = 1, - DENSE = 2, -} - -table LSHProjectionOptions { - type: LSHProjectionType; -} - -table SVDFOptions { - rank:int; - fused_activation_function:ActivationFunctionType; - // For weights-only quantization, use asymmetric quantization for non - // constant inputs at evaluation time. - asymmetric_quantize_inputs:bool; -} - -// An implementation of TensorFlow RNNCell. -table RNNOptions { - fused_activation_function:ActivationFunctionType; - asymmetric_quantize_inputs:bool; -} - -// An implementation of TensorFlow dynamic_rnn with RNNCell. -table SequenceRNNOptions { - time_major:bool; - fused_activation_function:ActivationFunctionType; - asymmetric_quantize_inputs:bool; -} - -// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell. -table BidirectionalSequenceRNNOptions { - time_major:bool; - fused_activation_function:ActivationFunctionType; - merge_outputs: bool; - asymmetric_quantize_inputs:bool; -} - -enum FullyConnectedOptionsWeightsFormat: byte { - DEFAULT = 0, - SHUFFLED4x16INT8 = 1, -} - -// An implementation of TensorFlow fully_connected (a.k.a Dense) layer. -table FullyConnectedOptions { - // Parameters for FullyConnected version 1 or above. - fused_activation_function:ActivationFunctionType; - - // Parameters for FullyConnected version 2 or above. - weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT; - - // Parameters for FullyConnected version 5 or above. - // If set to true, then the number of dimension is preserved. Furthermore, - // all but the last dimension of the input and output shapes will be equal. - keep_num_dims: bool; - - // Parameters for FullyConnected version 7 or above. - // If set to true, then weights-only op will use asymmetric quantization for - // inputs. - asymmetric_quantize_inputs: bool; -} - -table SoftmaxOptions { - beta: float; -} - -// An implementation of TensorFlow concat. -table ConcatenationOptions { - axis:int; - fused_activation_function:ActivationFunctionType; -} - -table AddOptions { - fused_activation_function:ActivationFunctionType; -} - -table MulOptions { - fused_activation_function:ActivationFunctionType; -} - -table L2NormOptions { - fused_activation_function:ActivationFunctionType; -} - -table LocalResponseNormalizationOptions { - radius:int; - bias:float; - alpha:float; - beta:float; -} - -enum LSTMKernelType : byte { - // Full LSTM kernel which supports peephole and projection. - FULL = 0, - // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell. - BASIC = 1, -} - -// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell -table LSTMOptions { - // Parameters for LSTM version 1 or above. - fused_activation_function:ActivationFunctionType; - cell_clip: float; // Optional, 0.0 means no clipping - proj_clip: float; // Optional, 0.0 means no clipping - - // Parameters for LSTM version 2 or above. - // Basic kernel is only supported in version 2 or above. - kernel_type: LSTMKernelType = FULL; - - // Parameters for LSTM version 4 or above. - asymmetric_quantize_inputs: bool; -} - -// An implementation of TensorFlow dynamic_rnn with LSTMCell. -table UnidirectionalSequenceLSTMOptions { - fused_activation_function:ActivationFunctionType; - cell_clip: float; // Optional, 0.0 means no clipping - proj_clip: float; // Optional, 0.0 means no clipping - - // If true then first dimension is sequence, otherwise batch. - time_major:bool; - - // Parameter for Unidirectional Sequence LSTM version 4. - asymmetric_quantize_inputs:bool; -} - -table BidirectionalSequenceLSTMOptions { - // Parameters supported by version 1: - fused_activation_function:ActivationFunctionType; - cell_clip: float; // Optional, 0.0 means no clipping - proj_clip: float; // Optional, 0.0 means no clipping - - // If true, store the outputs of both directions into the first output. - merge_outputs: bool; - - // Parameters supported by version 2: - // If true then first dimension is sequence, otherwise batch. - // Version 1 implementations assumed time_major to be true, so this default - // value should never change. - time_major: bool = true; - - // Parameters for version 3 or above. - asymmetric_quantize_inputs:bool; -} - -table ResizeBilinearOptions { - new_height: int (deprecated); - new_width: int (deprecated); - align_corners: bool; - half_pixel_centers: bool; -} - -table ResizeNearestNeighborOptions { - align_corners: bool; -} - -// A call operation options -table CallOptions { - // The subgraph index that needs to be called. - subgraph:uint; -} - -table PadOptions { -} - -table PadV2Options { -} - -table ReshapeOptions { - new_shape:[int]; -} - -table SpaceToBatchNDOptions { -} - -table BatchToSpaceNDOptions { -} - -table SkipGramOptions { - ngram_size: int; - max_skip_size: int; - include_all_ngrams: bool; -} - -table SpaceToDepthOptions { - block_size: int; -} - -table DepthToSpaceOptions { - block_size: int; -} - -table SubOptions { - fused_activation_function:ActivationFunctionType; -} - -table DivOptions { - fused_activation_function:ActivationFunctionType; -} - -table TopKV2Options { -} - -enum CombinerType : byte { - SUM = 0, - MEAN = 1, - SQRTN = 2, -} - -table EmbeddingLookupSparseOptions { - combiner:CombinerType; -} - -table GatherOptions { - axis: int; -} - -table TransposeOptions { -} - -table ExpOptions { -} - -table CosOptions { -} - -table ReducerOptions { - keep_dims: bool; -} - -table SqueezeOptions { - squeeze_dims:[int]; -} - -table SplitOptions { - num_splits: int; -} - -table SplitVOptions { - num_splits: int; -} - -table StridedSliceOptions { - begin_mask: int; - end_mask: int; - ellipsis_mask: int; - new_axis_mask: int; - shrink_axis_mask: int; -} - -table LogSoftmaxOptions { -} - -table CastOptions { - in_data_type: TensorType; - out_data_type: TensorType; -} - -table DequantizeOptions { -} - -table MaximumMinimumOptions { -} - -table TileOptions { -} - -table ArgMaxOptions { - output_type : TensorType; -} - -table ArgMinOptions { - output_type : TensorType; -} - -table GreaterOptions { -} - -table GreaterEqualOptions { -} - -table LessOptions { -} - -table LessEqualOptions { -} - -table NegOptions { -} - -table SelectOptions { -} - -table SliceOptions { -} - -table TransposeConvOptions { - padding:Padding; - stride_w:int; - stride_h:int; -} - -table ExpandDimsOptions { -} - -table SparseToDenseOptions { - validate_indices:bool; -} - -table EqualOptions { -} - -table NotEqualOptions { -} - -table ShapeOptions { - // Optional output type of the operation (int32 or int64). Defaults to int32. - out_type : TensorType; -} - -table RankOptions { -} - -table PowOptions { -} - -table FakeQuantOptions { - // Parameters supported by version 1: - min:float; - max:float; - num_bits:int; - - // Parameters supported by version 2: - narrow_range:bool; -} - -table PackOptions { - values_count:int; - axis:int; -} - -table LogicalOrOptions { -} - -table OneHotOptions { - axis:int; -} - -table AbsOptions { -} - - -table HardSwishOptions { -} - -table LogicalAndOptions { -} - -table LogicalNotOptions { -} - -table UnpackOptions { - num:int; - axis:int; -} - -table FloorDivOptions { -} - -table SquareOptions { -} - -table ZerosLikeOptions { -} - -table FillOptions { -} - -table FloorModOptions { -} - -table RangeOptions { -} - -table LeakyReluOptions { - alpha:float; -} - -table SquaredDifferenceOptions { -} - -enum MirrorPadMode : byte { - // Doesn't include borders. - REFLECT = 0, - // Includes borders. - SYMMETRIC = 1, -} - -table MirrorPadOptions { - mode:MirrorPadMode; -} - -table UniqueOptions { - idx_out_type:TensorType = INT32; -} - -table ReverseV2Options { -} - -table AddNOptions { -} - -table GatherNdOptions { -} - -table WhereOptions { -} - -table ReverseSequenceOptions { - seq_dim:int; - batch_dim:int = 0; -} - -table MatrixDiagOptions { -} - -table QuantizeOptions { -} - -table MatrixSetDiagOptions { -} - -table IfOptions { - then_subgraph_index:int; - else_subgraph_index:int; -} - -table WhileOptions { - cond_subgraph_index:int; - body_subgraph_index:int; -} - -table NonMaxSuppressionV4Options { -} - -table NonMaxSuppressionV5Options { -} - -table ScatterNdOptions { -} - -table SelectV2Options { -} - -table DensifyOptions { -} - -table SegmentSumOptions { -} - -table BatchMatMulOptions { - adjoint_lhs:bool; - adjoint_rhs:bool; -} - -// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a -// builtin, or a string if the operator is custom. -table OperatorCode { - builtin_code:BuiltinOperator; - custom_code:string; - - // The version of the operator. The version need to be bumped whenever new - // parameters are introduced into an op. - version:int = 1; -} - -enum CustomOptionsFormat : byte { - FLEXBUFFERS = 0, -} - -// An operator takes tensors as inputs and outputs. The type of operation being -// performed is determined by an index into the list of valid OperatorCodes, -// while the specifics of each operations is configured using builtin_options -// or custom_options. -table Operator { - // Index into the operator_codes array. Using an integer here avoids - // complicate map lookups. - opcode_index:uint; - - // Optional input are indicated by -1. - inputs:[int]; - outputs:[int]; - - builtin_options:BuiltinOptions; - custom_options:[ubyte]; - custom_options_format:CustomOptionsFormat; - - // A list of booleans indicating the input tensors which are being mutated by - // this operator.(e.g. used by RNN and LSTM). - // For example, if the "inputs" array refers to 5 tensors and the second and - // fifth are mutable variables, then this list will contain - // [false, true, false, false, true]. - // - // If the list is empty, no variable is mutated in this operator. - // The list either has the same length as `inputs`, or is empty. - mutating_variable_inputs:[bool]; - - // A list of indices to the subgraph's "tensors" that are internal to an Op. - // Internal tensors are those that do not flow in or out of the operation, - // but instead are part of internal computation. As such, the operation's - // implementation may manage its memory more efficiently. They are needed - // however (i.e. not just an implementation detail) since they are part of the - // computation, which may require relevant metadata such as quantization - // parameters. - intermediates:[int]; -} - -// The root type, defining a subgraph, which typically represents an entire -// model. -table SubGraph { - // A list of all tensors used in this subgraph. - tensors:[Tensor]; - - // Indices of the tensors that are inputs into this subgraph. Note this is - // the list of non-static tensors that feed into the subgraph for inference. - inputs:[int]; - - // Indices of the tensors that are outputs out of this subgraph. Note this is - // the list of output tensors that are considered the product of the - // subgraph's inference. - outputs:[int]; - - // All operators, in execution order. - operators:[Operator]; - - // Name of this subgraph (used for debugging). - name:string; -} - -// Table of raw data buffers (used for constant tensors). Referenced by tensors -// by index. The generous alignment accommodates mmap-friendly data structures. -table Buffer { - data:[ubyte] (force_align: 16); -} - -table Metadata { - // A human readable string to uniquely identify a Metadata. - name:string; - // An index to the buffers table. - buffer:uint; -} - -table Model { - // Version of the schema. - version:uint; - - // A list of all operator codes used in this model. This is - // kept in order because operators carry an index into this - // vector. - operator_codes:[OperatorCode]; - - // All the subgraphs of the model. The 0th is assumed to be the main - // model. - subgraphs:[SubGraph]; - - // A description of the model. - description:string; - - // Buffers of the model. - // Note the 0th entry of this array must be an empty buffer (sentinel). - // This is a convention so that tensors without a buffer can provide 0 as - // their buffer. - buffers:[Buffer]; - - // Metadata about the model. Indirects into the existings buffers list. - // Deprecated, prefer to use metadata field. - metadata_buffer:[int]; - - // Metadata about the model. - metadata:[Metadata]; -} - -root_type Model; diff --git a/runtime/onert/sample/CMakeLists.txt b/runtime/onert/sample/CMakeLists.txt deleted file mode 100644 index d853ba634..000000000 --- a/runtime/onert/sample/CMakeLists.txt +++ /dev/null @@ -1 +0,0 @@ -add_subdirectory(minimal) diff --git a/runtime/onert/sample/minimal/CMakeLists.txt b/runtime/onert/sample/minimal/CMakeLists.txt deleted file mode 100644 index e54223e3b..000000000 --- a/runtime/onert/sample/minimal/CMakeLists.txt +++ /dev/null @@ -1,10 +0,0 @@ -if(NOT BUILD_MINIMAL_SAMPLE) - return() -endif(NOT BUILD_MINIMAL_SAMPLE) - -list(APPEND MINIMAL_SRCS "src/minimal.cc") - -add_executable(onert-minimal-app ${MINIMAL_SRCS}) -target_link_libraries(onert-minimal-app nnfw-dev pthread dl) - -install(TARGETS onert-minimal-app DESTINATION bin) diff --git a/runtime/onert/sample/minimal/README.md b/runtime/onert/sample/minimal/README.md deleted file mode 100644 index fecad6fb2..000000000 --- a/runtime/onert/sample/minimal/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# minimal - -`minimal` is a simple driver to run `nnpackage` with nnfw API. - -It takes `nnpackage` as input. It uses **nnfwAPI** internally. - -It assumes model of float32 tensor type as an input. - -## Usage - -``` -$ ./minimal path_to_nnpackage_directory -``` diff --git a/runtime/onert/sample/minimal/src/minimal.cc b/runtime/onert/sample/minimal/src/minimal.cc deleted file mode 100644 index 0436b9368..000000000 --- a/runtime/onert/sample/minimal/src/minimal.cc +++ /dev/null @@ -1,71 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "nnfw.h" -#include <vector> -#include <iostream> - -uint64_t num_elems(const nnfw_tensorinfo *ti) -{ - uint64_t n = 1; - for (uint32_t i = 0; i < ti->rank; ++i) - { - n *= ti->dims[i]; - } - return n; -} - -int main(const int argc, char **argv) -{ - nnfw_session *session = nullptr; - nnfw_create_session(&session); - - // Loading nnpackage - nnfw_load_model_from_file(session, argv[1]); - - // Use acl_neon backend for CONV_2D and acl_cl for otherwise. - // Note that defalut backend is acl_cl - nnfw_set_op_backend(session, "CONV_2D", "acl_neon"); - - // Compile model - nnfw_prepare(session); - - // Prepare input. Here we just allocate dummy input arrays. - std::vector<float> input; - nnfw_tensorinfo ti; - nnfw_input_tensorinfo(session, 0, &ti); // get first input's info - uint32_t input_elements = num_elems(&ti); - input.resize(input_elements); - // TODO: Please add initialization for your input. - nnfw_set_input(session, 0, ti.dtype, input.data(), sizeof(float) * input_elements); - - // Prepare output - std::vector<float> output; - nnfw_output_tensorinfo(session, 0, &ti); // get first output's info - uint32_t output_elements = num_elems(&ti); - output.resize(output_elements); - nnfw_set_output(session, 0, ti.dtype, output.data(), sizeof(float) * output_elements); - - // Do inference - nnfw_run(session); - - // TODO: Please print or compare the output value in your way. - - nnfw_close_session(session); - - std::cout << "nnpackage " << argv[1] << " runs successfully." << std::endl; - return 0; -} diff --git a/runtime/onert/test/CMakeLists.txt b/runtime/onert/test/CMakeLists.txt deleted file mode 100644 index 38899976d..000000000 --- a/runtime/onert/test/CMakeLists.txt +++ /dev/null @@ -1,15 +0,0 @@ -set(TEST_ONERT test_onert) - -file(GLOB_RECURSE TESTS "*.cc") - -add_executable(${TEST_ONERT} ${TESTS}) - -target_include_directories(${TEST_ONERT} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../core/src) - -target_link_libraries(${TEST_ONERT} onert_core) -target_link_libraries(${TEST_ONERT} gtest) -target_link_libraries(${TEST_ONERT} gtest_main) -target_link_libraries(${TEST_ONERT} ${LIB_PTHREAD} dl) -add_test(${TEST_ONERT} ${TEST_ONERT}) - -install(TARGETS ${TEST_ONERT} DESTINATION unittest_standalone) diff --git a/runtime/onert/test/core/compiler/Scheduler.cc b/runtime/onert/test/core/compiler/Scheduler.cc deleted file mode 100644 index 50f3964db..000000000 --- a/runtime/onert/test/core/compiler/Scheduler.cc +++ /dev/null @@ -1,587 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <compiler/HEScheduler.h> -#include <exec/ExecTime.h> - -#include <ir/Shape.h> -#include <ir/InternalType.h> -#include <ir/TypeInfo.h> -#include <ir/DataType.h> - -#include <ir/operation/BinaryArithmetic.h> -#include <ir/operation/FullyConnected.h> - -#include <gtest/gtest.h> - -namespace -{ -using namespace onert; -using namespace ir; -using namespace backend; -using namespace operation; -using namespace exec; - -// -// Mock backends classes -// - -struct MockConfigCPU : public IConfig -{ - std::string id() override { return "cpu"; } - bool initialize() override { return true; }; - bool supportPermutation() override { return false; } - Layout supportLayout(const Operation &, Layout) override { return Layout::UNKNOWN; } - bool supportDynamicTensor() override { return false; } - bool supportFP16() override { return false; } -}; - -struct MockBackendCPU : public Backend -{ - std::shared_ptr<IConfig> config() const override { return std::make_shared<MockConfigCPU>(); } - std::unique_ptr<BackendContext> - newContext(const Graph &, const std::shared_ptr<custom::IKernelBuilder> &, bool) const override - { - return std::unique_ptr<BackendContext>( - new BackendContext{this, nullptr, nullptr, nullptr, nullptr}); - } -}; - -struct MockConfigGPU : public IConfig -{ - std::string id() override { return "gpu"; } - bool initialize() override { return true; }; - bool supportPermutation() override { return false; } - ir::Layout supportLayout(const ir::Operation &, ir::Layout) override - { - return ir::Layout::UNKNOWN; - } - bool supportDynamicTensor() override { return false; } - bool supportFP16() override { return false; } -}; - -struct MockBackendGPU : public Backend -{ - std::shared_ptr<IConfig> config() const override { return std::make_shared<MockConfigGPU>(); } - std::unique_ptr<BackendContext> - newContext(const Graph &, const std::shared_ptr<custom::IKernelBuilder> &, bool) const override - { - return std::unique_ptr<BackendContext>( - new BackendContext{this, nullptr, nullptr, nullptr, nullptr}); - } -}; - -struct MockConfigNPU : public IConfig -{ - std::string id() override { return "npu"; } - bool initialize() override { return true; }; - bool supportPermutation() override { return false; } - ir::Layout supportLayout(const ir::Operation &, ir::Layout) override - { - return ir::Layout::UNKNOWN; - } - bool supportDynamicTensor() override { return false; } - bool supportFP16() override { return false; } -}; - -struct MockBackendNPU : public Backend -{ - std::shared_ptr<IConfig> config() const override { return std::make_shared<MockConfigNPU>(); } - std::unique_ptr<BackendContext> - newContext(const Graph &, const std::shared_ptr<custom::IKernelBuilder> &, bool) const override - { - return std::unique_ptr<BackendContext>( - new BackendContext{this, nullptr, nullptr, nullptr, nullptr}); - } -}; - -// -// Constants -// - -const int OPERAND_ELEMS = 268203; -const int OPERAND_SIZE = OPERAND_ELEMS * 4; -const int OPERATION_SIZE = OPERAND_SIZE * 3; - -const std::string LINEAR("Linear"); -const std::string DATAFLOW("Dataflow"); -const std::string PARALLEL("Parallel"); - -// -// Helper functions -// - -// Set executor through environment variable -void setExecutor(const std::string &executor) { setenv("EXECUTOR", executor.c_str(), true); } - -// Set profiling mode through environment variable -void setProfilingMode(const bool value) { setenv("PROFILING_MODE", value ? "1" : "0", true); } - -// Calculate operation size by addition sizes of all input and output operands -uint32_t calcOpSize(const std::shared_ptr<Graph> &graph, const OperationIndex &op_idx) -{ - uint32_t size = 0; - const auto &op = graph->operations().at(op_idx); - for (const auto &ind : op.getInputs() + op.getOutputs()) - size += graph->operands().at(ind).info().total_size(); - return size; -} - -// Set execution operation time. This method is needed since ExecutionTime has only -// 'updateOperationExecTime' method. -void setOperationExecTime(ExecTime &et, const Backend *backend, const std::string &operation, - bool quant, uint32_t op_size, int64_t time) -{ - // You shouldn't set negative time with this method since nnfw JSON deserializer can't read it - assert(time > 0); - int64_t prev_time = et.getOperationExecTime(backend, operation, quant, op_size); - int64_t time_to_set = prev_time == ExecTime::NOT_FOUND ? time : 2 * time - prev_time; - et.updateOperationExecTime(backend, operation, quant, op_size, time_to_set); - assert(et.getOperationExecTime(backend, operation, quant, op_size) == time); -} - -// Set same execution time for all given backends/operations -void setOperationsExecutionTime(const std::vector<const Backend *> &backends, - const std::vector<std::string> &op_names, - const std::vector<uint32_t> &op_sizes, int64_t exec_time) -{ - assert(op_names.size() == op_sizes.size()); - ExecTime et(backends); - for (int i = 0; i < op_names.size(); ++i) - { - for (auto &backend : backends) - setOperationExecTime(et, backend, op_names[i], false, op_sizes[i], exec_time); - } - et.uploadOperationsExecTime(); -} - -// Set permute time from one backend to another. This method is needed since ExecutionTime has only -// 'updatePermuteTime' method. -void setPermutationTime(ExecTime &et, const Backend *from_backend, const Backend *to_backend, - bool quant, uint32_t op_size, int64_t time) -{ - // You shouldn't set negative time with this method since nnfw JSON deserializer can't read it - assert(time > 0); - int64_t prev_time = et.getPermuteTime(from_backend, to_backend, quant, op_size); - int64_t time_to_set = prev_time == ExecTime::NOT_FOUND ? time : 2 * time - prev_time; - et.updatePermuteTime(from_backend, to_backend, quant, op_size, time_to_set); - assert(et.getPermuteTime(from_backend, to_backend, quant, op_size) == time); -} - -// Set same permutation time between all given backends -void setPermutationsExecutionTime(const std::vector<const Backend *> &backends, - const int operand_size, const int64_t exec_time) -{ - ExecTime et(backends); - for (const auto &backend : backends) - { - for (auto &other_backend : backends) - { - if (backend == other_backend) - continue; - setPermutationTime(et, backend, other_backend, false, operand_size, exec_time); - } - } - et.uploadOperationsExecTime(); -} - -// -// Functions for creating graphs -// - -using OIS = OperandIndexSequence; - -template <typename NodeT, typename... Types> -OperationIndex create(std::shared_ptr<Graph> graph, Types &&... args) -{ - auto op = std::make_unique<NodeT>(std::forward<Types>(args)...); - auto op_idx = graph->addOperation(std::move(op)); - // For now in scheduler test all operations in tested graphs has same size (for simplicity) - assert(calcOpSize(graph, op_idx) == OPERATION_SIZE); - return op_idx; -} - -// Create straight graph: Add->Sub->Mul -std::shared_ptr<Graph> createStraightGraph() -{ - auto graph = std::make_shared<Graph>(); - const TypeInfo float_op(DataType::FLOAT32); - - // Create add node - auto add_lhs_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto add_rhs_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto add_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - BinaryArithmetic::Param add_op_params{BinaryArithmetic::ArithmeticType::ADD, Activation::NONE}; - create<BinaryArithmetic>(graph, OIS{add_lhs_idx, add_rhs_idx}, OIS{add_out_idx}, add_op_params); - - // Create sub node - auto sub_const_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto sub_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - BinaryArithmetic::Param sub_op_params{BinaryArithmetic::ArithmeticType::SUB, Activation::NONE}; - create<BinaryArithmetic>(graph, OIS{add_out_idx, sub_const_idx}, OIS{sub_out_idx}, sub_op_params); - - // Create mul node - auto mul_const_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto mul_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - BinaryArithmetic::Param mul_op_params{BinaryArithmetic::ArithmeticType::MUL, Activation::NONE}; - create<BinaryArithmetic>(graph, OIS{sub_out_idx, mul_const_idx}, OIS{mul_out_idx}, mul_op_params); - - graph->finishBuilding(); - return graph; -} - -/* Create branched graph: - * [Add] - * // \\ - * [Mul1] [FC2] - * || || - * [Mul2] [FC2] - * \\ // - * [Sub] - */ -std::shared_ptr<Graph> createBranchedGraph() -{ - auto graph = std::make_shared<Graph>(); - const TypeInfo float_op(DataType::FLOAT32); - - // Create add node - auto add_lhs_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto add_rhs_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto add_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - BinaryArithmetic::Param add_op_params{BinaryArithmetic::ArithmeticType::ADD, Activation::NONE}; - create<BinaryArithmetic>(graph, OIS{add_lhs_idx, add_rhs_idx}, OIS{add_out_idx}, add_op_params); - - // Create mul1 node - auto mul1_const_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto mul1_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - BinaryArithmetic::Param mul1_op_params{BinaryArithmetic::ArithmeticType::MUL, Activation::NONE}; - create<BinaryArithmetic>(graph, OIS{add_out_idx, mul1_const_idx}, OIS{mul1_out_idx}, - mul1_op_params); - - // Create mul2 node - auto mul2_const_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto mul2_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - BinaryArithmetic::Param mul2_op_params{BinaryArithmetic::ArithmeticType::MUL, Activation::NONE}; - create<BinaryArithmetic>(graph, OIS{mul1_out_idx, mul2_const_idx}, OIS{mul2_out_idx}, - mul2_op_params); - - // Create fc1 node - auto fc1_const_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto fc1_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - FullyConnected::Param fc1_op_params{Activation::NONE}; - create<FullyConnected>(graph, OIS{add_out_idx, fc1_const_idx}, OIS{fc1_out_idx}, fc1_op_params); - - // Create fc2 node - auto fc2_const_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - auto fc2_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - FullyConnected::Param fc2_op_params{Activation::NONE}; - create<FullyConnected>(graph, OIS{fc1_out_idx, fc2_const_idx}, OIS{fc2_out_idx}, fc2_op_params); - - // Create sub node - auto sub_out_idx = graph->addOperand(ir::Shape{OPERAND_ELEMS}, float_op); - BinaryArithmetic::Param sub_op_params{BinaryArithmetic::ArithmeticType::SUB, Activation::NONE}; - create<BinaryArithmetic>(graph, OIS{mul2_out_idx, fc2_out_idx}, OIS{sub_out_idx}, sub_op_params); - - graph->finishBuilding(); - return graph; -} - -// -// Tests setup/teardown -// - -// SetUp/TearDown methods runs before/after each test and performs actions common for each test -class SchedulerTest : public ::testing::Test -{ -protected: - void SetUp() override - { - // Initialize mock backends - _cpu_backend = new MockBackendCPU(); - _gpu_backend = new MockBackendGPU(); - _npu_backend = new MockBackendNPU(); - _mock_backends = {_cpu_backend, _gpu_backend, _npu_backend}; - - // Remove previous profile data if it exists - if (!remove("exec_time.json")) - { - // DO NOTHING (no profile data) - } - - // Remember original value of 'EXECUTOR' environment variable - char *executor = std::getenv("EXECUTOR"); - _original_executor = executor == nullptr ? "" : executor; - - // Remember original value of 'PROFILING_MODE' environment variable - char *profiling_mode = std::getenv("PROFILING_MODE"); - _original_profiling_mode = profiling_mode == nullptr ? "" : profiling_mode; - } - - void TearDown() override - { - delete _cpu_backend; - delete _gpu_backend; - delete _npu_backend; - EXPECT_EQ(remove("exec_time.json"), 0); - setenv("EXECUTOR", _original_executor.c_str(), true); - setenv("PROFILING_MODE", _original_profiling_mode.c_str(), true); - } - - backend::BackendContexts buildBackendContexts(const Graph &graph) - { - backend::BackendContexts contexts; - for (auto backend : _mock_backends) - { - contexts.emplace(backend, backend->newContext(graph, nullptr, false)); - } - return contexts; - } - - const MockBackendCPU *_cpu_backend{nullptr}; - const MockBackendGPU *_gpu_backend{nullptr}; - const MockBackendNPU *_npu_backend{nullptr}; - std::vector<const Backend *> _mock_backends; - - std::string _original_executor; - std::string _original_profiling_mode; -}; - -class SchedulerTestWithExecutorParam : public SchedulerTest, - public testing::WithParamInterface<std::string> -{ -}; - -// -// HEScheduler tests -// - -// Test scheduler behavior for straight graph with known execution time of all nodes and permutes. -TEST_P(SchedulerTestWithExecutorParam, straight_graph_known_exec_time) -{ - setExecutor(GetParam()); - - // Prepare graph - ir::Subgraphs subgs; - auto graph(createStraightGraph()); - subgs.push(ir::SubgraphIndex{0}, graph); - OperationIndex add_op_idx(0), sub_op_idx(1), mul_op_idx(2); - - // Set default execution and transfer time - setPermutationsExecutionTime(_mock_backends, OPERAND_SIZE, 1); - setOperationsExecutionTime(_mock_backends, {"Add", "Sub", "Mul"}, - {OPERATION_SIZE, OPERATION_SIZE, OPERATION_SIZE}, 1e4); - - // Test 1 - // Expected behaviour: scheduler assigns different backend to each node - { - // For each backend reduce execution time of one node - ExecTime et(_mock_backends); - setOperationExecTime(et, _cpu_backend, "Add", false, OPERATION_SIZE, 1); - setOperationExecTime(et, _gpu_backend, "Sub", false, OPERATION_SIZE, 1); - setOperationExecTime(et, _npu_backend, "Mul", false, OPERATION_SIZE, 1); - et.uploadOperationsExecTime(); - - // Test scheduler - auto backend_contexts = buildBackendContexts(*graph); - auto scheduler = compiler::HEScheduler(backend_contexts, - compiler::fetchCompilerOptionsFromGlobalConfig(subgs)); - const auto br = scheduler.schedule(*graph); - ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "cpu"); - ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "gpu"); - ASSERT_EQ(br->getBackend(mul_op_idx)->config()->id(), "npu"); - } - - // Test 2 - // Expected behaviour: scheduler assigns single backend to all nodes because of big transfer time - { - // Increase transfer time - setPermutationsExecutionTime(_mock_backends, OPERAND_SIZE, 1e5); - - // Test scheduler - auto backend_contexts = buildBackendContexts(*graph); - auto scheduler = compiler::HEScheduler(backend_contexts, - compiler::fetchCompilerOptionsFromGlobalConfig(subgs)); - const auto br = scheduler.schedule(*graph); - ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "cpu"); - ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "cpu"); - ASSERT_EQ(br->getBackend(mul_op_idx)->config()->id(), "cpu"); - } -} - -// Test scheduler behavior for branched graph with known execution time of all nodes and permutes -TEST_P(SchedulerTestWithExecutorParam, branched_graph_known_exec_time) -{ - const int64_t NPU_ET = 5000; - setExecutor(GetParam()); - - // Prepare graph - ir::Subgraphs subgs; - auto graph(createBranchedGraph()); - subgs.push(ir::SubgraphIndex{0}, graph); - OperationIndex add_op_idx(0), mul1_op_idx(1), mul2_op_idx(2), fc1_op_idx(3), fc2_op_idx(4), - sub_op_idx(5); - - // Set default execution and transfer time - setPermutationsExecutionTime(_mock_backends, OPERAND_SIZE, 1000); - setOperationsExecutionTime(_mock_backends, {"Add", "Sub", "Mul", "FullyConnected"}, - {OPERATION_SIZE, OPERATION_SIZE, OPERATION_SIZE, OPERATION_SIZE}, 1e4); - - // Test 1 - // Expected behaviour: for dataflow and linear executors scheduler assigns fastest backend to all - // nodes, in case of parallel executor scheduler assigns different backends to branches. - { - // Reduce execution time - ExecTime et(_mock_backends); - setOperationExecTime(et, _npu_backend, "Add", false, OPERATION_SIZE, NPU_ET); - setOperationExecTime(et, _npu_backend, "Mul", false, OPERATION_SIZE, NPU_ET); - setOperationExecTime(et, _npu_backend, "Sub", false, OPERATION_SIZE, NPU_ET); - setOperationExecTime(et, _npu_backend, "FullyConnected", false, OPERATION_SIZE, NPU_ET); - setOperationExecTime(et, _gpu_backend, "Mul", false, OPERATION_SIZE, NPU_ET + 1000); - setOperationExecTime(et, _gpu_backend, "FullyConnected", false, OPERATION_SIZE, NPU_ET + 1000); - et.uploadOperationsExecTime(); - - // Test scheduler - auto backend_contexts = buildBackendContexts(*graph); - auto scheduler = compiler::HEScheduler(backend_contexts, - compiler::fetchCompilerOptionsFromGlobalConfig(subgs)); - const auto br = scheduler.schedule(*graph); - - std::string branch1_expected_backend("npu"), branch2_expected_backend("npu"); - if (GetParam() == PARALLEL) - { - branch1_expected_backend = - br->getBackend(mul1_op_idx)->config()->id() == "npu" ? "npu" : "gpu"; - branch2_expected_backend = branch1_expected_backend == "npu" ? "gpu" : "npu"; - } - - ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(mul1_op_idx)->config()->id(), branch1_expected_backend); - ASSERT_EQ(br->getBackend(mul2_op_idx)->config()->id(), branch1_expected_backend); - ASSERT_EQ(br->getBackend(fc1_op_idx)->config()->id(), branch2_expected_backend); - ASSERT_EQ(br->getBackend(fc2_op_idx)->config()->id(), branch2_expected_backend); - ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "npu"); - } - - // Test 2 - // Expected behaviour: scheduler assigns single backend to all nodes - { - // Increase execution time for GPU backend - ExecTime et(_mock_backends); - /* for parallel executor: set a time, that is larger than sum_of_other_branches_nodes_cnt * - * npu_exec_time so that npu is prefered: the ith branch will wait for npu until it finishes the - * [0;i-1] branches nodes in DFS order. In each branch it goes deep intul doesn't encounter - * branching or scheduler assigns another backend to a node*/ - setOperationExecTime(et, _gpu_backend, "Mul", false, OPERATION_SIZE, NPU_ET * 3 + 1); - setOperationExecTime(et, _gpu_backend, "FullyConnected", false, OPERATION_SIZE, NPU_ET * 3 + 1); - et.uploadOperationsExecTime(); - - // Test scheduler - auto backend_contexts = buildBackendContexts(*graph); - auto scheduler = compiler::HEScheduler(backend_contexts, - compiler::fetchCompilerOptionsFromGlobalConfig(subgs)); - const auto br = scheduler.schedule(*graph); - ASSERT_EQ(br->getBackend(add_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(mul1_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(mul2_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(fc1_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(fc2_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "npu"); - } -} - -// SchedulerTestWithExecutorParam tests are parameterized with executor name and runs three times - -// one time for each executor -INSTANTIATE_TEST_CASE_P(AllExecutors, SchedulerTestWithExecutorParam, - testing::Values(LINEAR, DATAFLOW, PARALLEL)); - -// Test scheduler behavior for branched graph and enabled profiling mode -TEST_F(SchedulerTest, branched_graph_profiling_mode) -{ - const int ET = 1e5; - - // Turn on profiling mode - setProfilingMode(true); - setExecutor(DATAFLOW); - - // Prepare graph - ir::Subgraphs subgs; - auto graph(createBranchedGraph()); - subgs.push(ir::SubgraphIndex{0}, graph); - OperationIndex add_op_idx(0), mul1_op_idx(1), mul2_op_idx(2), fc1_op_idx(3), fc2_op_idx(4), - sub_op_idx(5); - - // Test 1 - // Expected behaviour: scheduler assigns backends to nodes with unknown execution time - { - // Set execution time for all backends/nodes except for cpu/Sub, npu/Mul, gpu/FC - ExecTime et(_mock_backends); - setOperationExecTime(et, _cpu_backend, "Add", false, OPERATION_SIZE, ET); - setOperationExecTime(et, _cpu_backend, "Mul", false, OPERATION_SIZE, ET + 1); - setOperationExecTime(et, _cpu_backend, "FullyConnected", false, OPERATION_SIZE, ET); - setOperationExecTime(et, _npu_backend, "Add", false, OPERATION_SIZE, ET); - setOperationExecTime(et, _npu_backend, "FullyConnected", false, OPERATION_SIZE, ET); - setOperationExecTime(et, _npu_backend, "Sub", false, OPERATION_SIZE, ET); - setOperationExecTime(et, _gpu_backend, "Add", false, OPERATION_SIZE, ET); - setOperationExecTime(et, _gpu_backend, "Mul", false, OPERATION_SIZE, ET + 1); - setOperationExecTime(et, _gpu_backend, "Sub", false, OPERATION_SIZE, ET); - et.uploadOperationsExecTime(); - - // Test scheduler - auto backend_contexts = buildBackendContexts(*graph); - auto scheduler = compiler::HEScheduler(backend_contexts, - compiler::fetchCompilerOptionsFromGlobalConfig(subgs)); - const auto br = scheduler.schedule(*graph); - ASSERT_EQ(br->getBackend(mul1_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(mul2_op_idx)->config()->id(), "npu"); - ASSERT_EQ(br->getBackend(fc1_op_idx)->config()->id(), "gpu"); - ASSERT_EQ(br->getBackend(fc2_op_idx)->config()->id(), "gpu"); - ASSERT_EQ(br->getBackend(sub_op_idx)->config()->id(), "cpu"); - } - - // Test 2 - // Expected behaviour: scheduler shuffling backends, so different backends are assigned to - // neighbor nodes - { - // Set execution time for rest backends/nodes (cpu/Sub, npu/Mul, gpu/FC) - ExecTime et(_mock_backends); - setOperationExecTime(et, _cpu_backend, "Sub", false, OPERATION_SIZE, ET); - setOperationExecTime(et, _npu_backend, "Mul", false, OPERATION_SIZE, ET + 1); - setOperationExecTime(et, _gpu_backend, "FullyConnected", false, OPERATION_SIZE, ET); - et.uploadOperationsExecTime(); - - // Test scheduler - auto backend_contexts = buildBackendContexts(*graph); - auto scheduler = compiler::HEScheduler(backend_contexts, - compiler::fetchCompilerOptionsFromGlobalConfig(subgs)); - const auto br = scheduler.schedule(*graph); - ASSERT_NE(br->getBackend(add_op_idx)->config()->id(), - br->getBackend(mul1_op_idx)->config()->id()); - ASSERT_NE(br->getBackend(add_op_idx)->config()->id(), - br->getBackend(fc1_op_idx)->config()->id()); - ASSERT_NE(br->getBackend(mul1_op_idx)->config()->id(), - br->getBackend(mul2_op_idx)->config()->id()); - ASSERT_NE(br->getBackend(fc1_op_idx)->config()->id(), - br->getBackend(fc2_op_idx)->config()->id()); - ASSERT_NE(br->getBackend(mul2_op_idx)->config()->id(), - br->getBackend(sub_op_idx)->config()->id()); - ASSERT_NE(br->getBackend(fc2_op_idx)->config()->id(), - br->getBackend(sub_op_idx)->config()->id()); - } -} - -// TODO: Add tests with unknown execution and permutation time - -} // unnamed namespace diff --git a/runtime/onert/test/core/exec/ExecInstance.cc b/runtime/onert/test/core/exec/ExecInstance.cc deleted file mode 100644 index 806b47ecc..000000000 --- a/runtime/onert/test/core/exec/ExecInstance.cc +++ /dev/null @@ -1,297 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> -#include <thread> - -#include "ir/Graph.h" -#include "compiler/Compiler.h" -#include "exec/Execution.h" -#include "ir/operation/BinaryArithmetic.h" - -namespace -{ - -using namespace onert::ir; - -class CompiledMockUpModel -{ -public: - CompiledMockUpModel() - { - // Model: two elementwise add operation - // model input: lhs, rhs1 - // model output: second add result (result2) - // constant: rhs2 - // result1 <= (lhs + rhs) - // result2 <= (result1 + rhs2) - // lhs, rhs1, rh2, result1, result2 shape: {1, 2, 2, 1} - // activation: none (constant) - graph = std::make_shared<Graph>(); - // 1st add operands (result1 <= lhs + rhs1) - Shape shape{1, 2, 2, 1}; - TypeInfo type{DataType::FLOAT32}; - static float rhs2_data[4] = {3, 1, -1, 5}; - auto operand_lhs = graph->addOperand(shape, type); - auto operand_rhs1 = graph->addOperand(shape, type); - auto operand_result1 = graph->addOperand(shape, type); - auto operand_rhs2 = graph->addOperand(shape, type); - auto operand_result2 = graph->addOperand(shape, type); - graph->operands() - .at(operand_rhs2) - .data(std::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(&rhs2_data), 16)); - // 2nd add operations (result2 <= result1 + rhs2) - operation::BinaryArithmetic::Param param1; - param1.arithmetic_type = operation::BinaryArithmetic::ArithmeticType::ADD; - param1.activation = Activation::NONE; - auto input_set1 = OperandIndexSequence{operand_lhs, operand_rhs1}; - auto output_set1 = OperandIndexSequence{operand_result1}; - graph->addOperation( - std::make_unique<operation::BinaryArithmetic>(input_set1, output_set1, param1)); - operation::BinaryArithmetic::Param param2; - param2.arithmetic_type = operation::BinaryArithmetic::ArithmeticType::ADD; - param2.activation = Activation::NONE; - auto input_set2 = OperandIndexSequence{operand_result1, operand_rhs2}; - auto output_set2 = OperandIndexSequence{operand_result2}; - graph->addOperation( - std::make_unique<operation::BinaryArithmetic>(input_set2, output_set2, param2)); - // Identify model inputs and outputs - graph->addInput(operand_lhs); - graph->addInput(operand_rhs1); - graph->addOutput(operand_result2); - graph->finishBuilding(); - - // Compile - auto subgs = std::make_shared<onert::ir::Subgraphs>(); - subgs->push(onert::ir::SubgraphIndex{0}, graph); - onert::compiler::Compiler compiler{subgs}; - executors = compiler.compile(); - } - -public: - std::shared_ptr<Graph> graph; - std::shared_ptr<onert::exec::ExecutorMap> executors; -}; - -TEST(ExecInstance, simple) -{ - auto mockup = CompiledMockUpModel(); - auto graph = mockup.graph; - auto executors = mockup.executors; - - auto input1 = IOIndex{0}; - auto input2 = IOIndex{1}; - auto output = IOIndex{0}; - - const float input1_buffer[4] = {1, 0, -1, -2}; - const float input2_buffer[4] = {1, -3, 2, -4}; - float output_buffer[4] = {}; - const float output_expected[4] = {5, -2, 0, -1}; - - onert::exec::Execution execution{executors}; - - execution.setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16); - execution.setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16); - execution.setOutput(output, reinterpret_cast<void *>(output_buffer), 16); - execution.execute(); - - for (auto i = 0; i < 4; i++) - { - EXPECT_EQ(output_buffer[i], output_expected[i]); - } -} - -TEST(ExecInstance, twoCompile) -{ - auto mockup = CompiledMockUpModel(); - auto graph = mockup.graph; - auto executors1 = mockup.executors; - onert::exec::Execution execution1{executors1}; - - auto input1 = IOIndex{0}; - auto input2 = IOIndex{1}; - auto output = IOIndex{0}; - - const float exe1_input1_buffer[4] = {1, 0, -1, -2}; - const float exe1_input2_buffer[4] = {1, -3, 2, -4}; - float exe1_output_buffer[4] = {}; - const float exe1_output_expected[4] = {5, -2, 0, -1}; - - execution1.setInput(input1, reinterpret_cast<const void *>(exe1_input1_buffer), 16); - execution1.setInput(input2, reinterpret_cast<const void *>(exe1_input2_buffer), 16); - execution1.setOutput(output, reinterpret_cast<void *>(exe1_output_buffer), 16); - - // Make new executor: compile again - auto subgs = std::make_shared<onert::ir::Subgraphs>(); - subgs->push(onert::ir::SubgraphIndex{0}, graph); - onert::compiler::Compiler compiler{subgs}; - std::shared_ptr<onert::exec::ExecutorMap> executors2 = compiler.compile(); - onert::exec::Execution execution2{executors2}; - - const float exe2_input1_buffer[4] = {2, 1, -2, 0}; - const float exe2_input2_buffer[4] = {-3, 3, 1, 2}; - float exe2_output_buffer[4] = {}; - const float exe2_output_expected[4] = {2, 5, -2, 7}; - - execution2.setInput(input1, reinterpret_cast<const void *>(exe2_input1_buffer), 16); - execution2.setInput(input2, reinterpret_cast<const void *>(exe2_input2_buffer), 16); - execution2.setOutput(output, reinterpret_cast<void *>(exe2_output_buffer), 16); - - execution1.execute(); - execution2.execute(); - - for (auto i = 0; i < 4; i++) - { - EXPECT_EQ(exe1_output_buffer[i], exe1_output_expected[i]); - EXPECT_EQ(exe2_output_buffer[i], exe2_output_expected[i]); - } -} - -// Support two initialized execution instance then ordered execution -TEST(ExecInstance, twoExecution) -{ - auto mockup = CompiledMockUpModel(); - auto executors = mockup.executors; - auto input1 = IOIndex{0}; - auto input2 = IOIndex{1}; - auto output1 = IOIndex{0}; - - const float exe1_input1_buffer[4] = {1, 0, -1, -2}; - const float exe1_input2_buffer[4] = {1, -3, 2, -4}; - float exe1_output_buffer[4] = {}; - const float exe1_output_expected[4] = {5, -2, 0, -1}; - const float exe2_output_expected[4] = {2, 5, -2, 7}; - - onert::exec::Execution execution1{executors}; - execution1.setInput(input1, reinterpret_cast<const void *>(exe1_input1_buffer), 16); - execution1.setInput(input2, reinterpret_cast<const void *>(exe1_input2_buffer), 16); - execution1.setOutput(output1, reinterpret_cast<void *>(exe1_output_buffer), 16); - - const float exe2_input1_buffer[4] = {2, 1, -2, 0}; - const float exe2_input2_buffer[4] = {-3, 3, 1, 2}; - float exe2_output_buffer[4] = {}; - - // Make new execution - onert::exec::Execution execution2{executors}; - execution2.setInput(input1, reinterpret_cast<const void *>(exe2_input1_buffer), 16); - execution2.setInput(input2, reinterpret_cast<const void *>(exe2_input2_buffer), 16); - execution2.setOutput(output1, reinterpret_cast<void *>(exe2_output_buffer), 16); - - execution1.execute(); - execution2.execute(); - - for (auto i = 0; i < 4; i++) - { - EXPECT_EQ(exe1_output_buffer[i], exe1_output_expected[i]); - EXPECT_EQ(exe2_output_buffer[i], exe2_output_expected[i]); - } -} - -class Inference -{ -public: - Inference(const float (&input1)[4], const float (&input2)[4], float (&output)[4], - std::shared_ptr<onert::exec::ExecutorMap> &executors) - : _input1{input1}, _input2{input2}, _output{output}, _executors{executors} - { - // DO NOTHING - } - - void inference(void) - { - auto input1 = IOIndex{0}; - auto input2 = IOIndex{1}; - auto output1 = IOIndex{0}; - - onert::exec::Execution execution{_executors}; - execution.setInput(input1, reinterpret_cast<const void *>(_input1), 16); - execution.setInput(input2, reinterpret_cast<const void *>(_input2), 16); - execution.setOutput(output1, reinterpret_cast<void *>(_output), 16); - - execution.execute(); - } - -private: - const float (&_input1)[4]; - const float (&_input2)[4]; - float (&_output)[4]; - std::shared_ptr<onert::exec::ExecutorMap> &_executors; -}; - -// Support multi-thread execution -TEST(ExecInstance, twoThreads) -{ - auto mockup = CompiledMockUpModel(); - auto executors = mockup.executors; - - const float exe1_input1_buffer[4] = {1, 0, -1, -2}; - const float exe1_input2_buffer[4] = {1, -3, 2, -4}; - float exe1_output_buffer[4] = {}; - const float exe1_output_expected[4] = {5, -2, 0, -1}; - - Inference execution1{exe1_input1_buffer, exe1_input2_buffer, exe1_output_buffer, executors}; - - const float exe2_input1_buffer[4] = {2, 1, -2, 0}; - const float exe2_input2_buffer[4] = {-3, 3, 1, 2}; - float exe2_output_buffer[4] = {}; - const float exe2_output_expected[4] = {2, 5, -2, 7}; - - Inference execution2{exe2_input1_buffer, exe2_input2_buffer, exe2_output_buffer, executors}; - - std::thread t1{&Inference::inference, &execution1}; - std::thread t2{&Inference::inference, &execution2}; - - t1.join(); - t2.join(); - - for (auto i = 0; i < 4; i++) - { - EXPECT_EQ(exe1_output_buffer[i], exe1_output_expected[i]); - EXPECT_EQ(exe2_output_buffer[i], exe2_output_expected[i]); - } -} - -// Support asynchronous execution -TEST(ExecInstance, async) -{ - auto mockup = CompiledMockUpModel(); - auto graph = mockup.graph; - auto executors = mockup.executors; - - auto input1 = IOIndex{0}; - auto input2 = IOIndex{1}; - auto output = IOIndex{0}; - - const float input1_buffer[4] = {1, 0, -1, -2}; - const float input2_buffer[4] = {1, -3, 2, -4}; - float output_buffer[4] = {}; - const float output_expected[4] = {5, -2, 0, -1}; - - onert::exec::Execution execution{executors}; - - execution.setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16); - execution.setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16); - execution.setOutput(output, reinterpret_cast<void *>(output_buffer), 16); - execution.startExecute(); - execution.waitFinish(); - - for (auto i = 0; i < 4; i++) - { - EXPECT_EQ(output_buffer[i], output_expected[i]); - } -} - -} // namespace diff --git a/runtime/onert/test/core/exec/ExecTime.test.cc b/runtime/onert/test/core/exec/ExecTime.test.cc deleted file mode 100644 index 8c2e34df8..000000000 --- a/runtime/onert/test/core/exec/ExecTime.test.cc +++ /dev/null @@ -1,105 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "exec/ExecTime.h" -#include "backend/IConfig.h" -#include "backend/Backend.h" -#include <gtest/gtest.h> -#include <string> - -namespace -{ -using namespace onert; -using namespace exec; -using namespace backend; - -struct MockConfig : public IConfig -{ - std::string id() override { return "b1"; } - bool initialize() override { return true; }; - bool supportPermutation() override { return false; } - ir::Layout supportLayout(const ir::Operation &, ir::Layout) override - { - return ir::Layout::UNKNOWN; - } - bool supportDynamicTensor() override { return false; } - bool supportFP16() override { return false; } -}; - -struct MockBackend : public ::onert::backend::Backend -{ - std::shared_ptr<onert::backend::IConfig> config() const override - { - return std::make_shared<MockConfig>(); - } - std::unique_ptr<BackendContext> newContext(const ir::Graph &, - const std::shared_ptr<custom::IKernelBuilder> &kb, - bool) const override - { - return nullptr; - } -}; - -TEST(ExecTime, roundtrip_ok) -{ - const auto *b = new MockBackend(); - std::vector<const Backend *> bs = {b}; - { - ExecTime et(bs); - et.updateOperationExecTime(b, "op1", true, 100, 100); - et.updateOperationExecTime(b, "op1", true, 200, 200); - et.updateOperationExecTime(b, "op1", false, 100, 888); - et.uploadOperationsExecTime(); - } - { - ExecTime et(bs); - auto time = et.getOperationExecTime(b, "op1", true, 100); - ASSERT_EQ(time, 100); - // Check interpolation - time = et.getOperationExecTime(b, "op1", true, 150); - ASSERT_EQ(time, 150); - time = et.getOperationExecTime(b, "op1", false, 100); - ASSERT_EQ(time, 888); - et.uploadOperationsExecTime(); - } - // clean up - EXPECT_EQ(remove("exec_time.json"), 0); -} - -TEST(ExecTime, structure) -{ - - const auto *b = new MockBackend(); - std::vector<const Backend *> bs = {b}; - { - ExecTime et(bs); - et.updateOperationExecTime(b, "op1", true, 100, 100); - et.updateOperationExecTime(b, "op1", true, 200, 200); - et.uploadOperationsExecTime(); - } - { - ExecTime et(bs); - auto time = et.getOperationExecTime(b, "op1", true, 100); - ASSERT_EQ(time, 100); - // Check interpolation - time = et.getOperationExecTime(b, "op1", true, 200); - ASSERT_EQ(time, 200); - et.uploadOperationsExecTime(); - } - // clean up - EXPECT_EQ(remove("exec_time.json"), 0); -} -} // unnamed namespace diff --git a/runtime/onert/test/core/interp/ExecManager.cc b/runtime/onert/test/core/interp/ExecManager.cc deleted file mode 100644 index 09190bc58..000000000 --- a/runtime/onert/test/core/interp/ExecManager.cc +++ /dev/null @@ -1,361 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include <memory> - -#include "ir/Graph.h" -#include "interp/InterpExecutor.h" -#include "exec/Execution.h" -#include "ir/operation/BinaryArithmetic.h" - -namespace -{ - -using namespace onert::ir; -using InterpExecutor = onert::interp::InterpExecutor; -using Execution = onert::exec::Execution; -using ExecutorMap = onert::exec::ExecutorMap; - -class InterpExecutorTest : public ::testing::Test -{ -protected: - virtual void SetUp() {} - void CreateSimpleModel() - { - // Model: one elementwise add operation - // model input: lhs, rhs - // model output: add result - // lhs, rhs, result shape: {1, 2, 2, 1} - // activation: none (constant) - _graph = std::make_unique<Graph>(); - - // Add operands - - Shape shape{1, 2, 2, 1}; - TypeInfo type{DataType::INT32}; - Shape shape_scalar(0); - TypeInfo type_scalar{DataType::INT32}; - - auto operand_lhs = _graph->addOperand(shape, type); - auto operand_rhs = _graph->addOperand(shape, type); - auto operand_result = _graph->addOperand(shape, type); - - // Add operations - - operation::BinaryArithmetic::Param param; - param.arithmetic_type = operation::BinaryArithmetic::ArithmeticType::ADD; - param.activation = Activation::NONE; - auto input_set = OperandIndexSequence{operand_lhs, operand_rhs}; - auto output_set = OperandIndexSequence{operand_result}; - _graph->addOperation( - std::make_unique<operation::BinaryArithmetic>(input_set, output_set, param)); - - // Identify model inputs and outputs - - _graph->getInputs().append(operand_lhs); - _graph->getInputs().append(operand_rhs); - _graph->getOutputs().append(operand_result); - - _graph->finishBuilding(); - - auto subgs = std::make_shared<onert::ir::Subgraphs>(); - subgs->push(onert::ir::SubgraphIndex{0}, _graph); - _graph->setSubgraphs(subgs); - - _executors = std::make_shared<ExecutorMap>(); - _executors->insert( - std::make_pair(onert::ir::SubgraphIndex{0}, std::make_unique<InterpExecutor>(*_graph))); - } - - void CreateTwoStepModel() - { - // Model: two elementwise add operation - // model input: lhs, rhs1 - // model output: second add result (result2) - // constant: rhs2 - // result1 <= (lhs + rhs) - // result2 <= (result1 + rhs2) - // lhs, rhs1, rh2, result1, result2 shape: {1, 2, 2, 1} - // activation: none (constant) - _graph = std::make_unique<Graph>(); - - // 1st add operands (result1 <= lhs + rhs1) - - Shape shape{1, 2, 2, 1}; - TypeInfo type{DataType::INT32}; - Shape shape_scalar(0); - TypeInfo type_scalar{DataType::INT32}; - - static int32_t rhs2_data[4] = {3, 1, -1, 5}; - - auto operand_lhs = _graph->addOperand(shape, type); - auto operand_rhs1 = _graph->addOperand(shape, type); - auto operand_result1 = _graph->addOperand(shape, type); - auto operand_rhs2 = _graph->addOperand(shape, type); - auto operand_result2 = _graph->addOperand(shape, type); - _graph->operands() - .at(operand_rhs2) - .data(std::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(&rhs2_data), 16)); - - // 2nd add operations (result2 <= result1 + rhs2) - - operation::BinaryArithmetic::Param param1; - param1.arithmetic_type = operation::BinaryArithmetic::ArithmeticType::ADD; - param1.activation = Activation::NONE; - auto input_set1 = OperandIndexSequence{operand_lhs, operand_rhs1}; - auto output_set1 = OperandIndexSequence{operand_result1}; - _graph->addOperation( - std::make_unique<operation::BinaryArithmetic>(input_set1, output_set1, param1)); - - operation::BinaryArithmetic::Param param2; - param2.arithmetic_type = operation::BinaryArithmetic::ArithmeticType::ADD; - param2.activation = Activation::NONE; - auto input_set2 = OperandIndexSequence{operand_result1, operand_rhs2}; - auto output_set2 = OperandIndexSequence{operand_result2}; - _graph->addOperation( - std::make_unique<operation::BinaryArithmetic>(input_set2, output_set2, param2)); - - // Identify model inputs and outputs - - _graph->getInputs().append(operand_lhs); - _graph->getInputs().append(operand_rhs1); - _graph->getOutputs().append(operand_result2); - - _graph->finishBuilding(); - - auto subgs = std::make_shared<onert::ir::Subgraphs>(); - subgs->push(onert::ir::SubgraphIndex{0}, _graph); - _graph->setSubgraphs(subgs); - - _executors = std::make_shared<ExecutorMap>(); - _executors->insert( - std::make_pair(onert::ir::SubgraphIndex{0}, std::make_unique<InterpExecutor>(*_graph))); - } - - void CreateUnspecifiedDimensionsModel() - { - // Model: one elementwise add operation - // model input: lhs, rhs - // model output: add result - // lhs, rhs, result shape: {1, unknown, 2, 1} - // activation: none (constant) - _graph = std::make_unique<Graph>(); - - // Add operands - - Shape shape{1, 0, 2, 1}; - TypeInfo type{DataType::INT32}; - Shape shape_scalar(0); - TypeInfo type_scalar{DataType::INT32}; - - auto operand_lhs = _graph->addOperand(shape, type); - auto operand_rhs = _graph->addOperand(shape, type); - - auto operand_activation = _graph->addOperand(shape_scalar, type_scalar); - _graph->operands() - .at(operand_activation) - .data( - std::make_unique<CachedData>(reinterpret_cast<const uint8_t *>(&_activation_value), 4)); - - auto operand_result = _graph->addOperand(shape, type); - - // Add operations - - operation::BinaryArithmetic::Param param; - param.arithmetic_type = operation::BinaryArithmetic::ArithmeticType::ADD; - param.activation = Activation::NONE; - auto input_set = OperandIndexSequence{operand_lhs, operand_rhs}; - auto output_set = OperandIndexSequence{operand_result}; - _graph->addOperation( - std::make_unique<operation::BinaryArithmetic>(input_set, output_set, param)); - - // Identify model inputs and outputs - - _graph->getInputs().append(operand_lhs); - _graph->getInputs().append(operand_rhs); - _graph->getOutputs().append(operand_result); - - _graph->finishBuilding(); - - auto subgs = std::make_shared<onert::ir::Subgraphs>(); - subgs->push(onert::ir::SubgraphIndex{0}, _graph); - _graph->setSubgraphs(subgs); - - _executors = std::make_shared<ExecutorMap>(); - _executors->insert( - std::make_pair(onert::ir::SubgraphIndex{0}, std::make_unique<InterpExecutor>(*_graph))); - } - - void createExecution() { _execution = std::make_unique<Execution>(_executors); } - - virtual void TearDown() { _executors = nullptr; } - - std::shared_ptr<Graph> _graph{nullptr}; - std::shared_ptr<ExecutorMap> _executors{nullptr}; - std::unique_ptr<Execution> _execution{nullptr}; - const int32_t _activation_value{0}; -}; - -TEST_F(InterpExecutorTest, create_empty) -{ - Graph graph; - graph.finishBuilding(); - auto executor = std::make_unique<InterpExecutor>(graph); - ASSERT_NE(executor, nullptr); -} - -TEST_F(InterpExecutorTest, create_simple) -{ - CreateSimpleModel(); - ASSERT_NE(_executors, nullptr); - ASSERT_NE(_executors->at(onert::ir::SubgraphIndex{0}), nullptr); -} - -TEST_F(InterpExecutorTest, setInput) -{ - CreateSimpleModel(); - createExecution(); - - auto input1 = IOIndex{0}; - const int32_t input1_buffer[4] = {1, 0, -1, -2}; - - EXPECT_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 4), - std::runtime_error); - EXPECT_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 12), - std::runtime_error); - EXPECT_NO_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16)); -} - -TEST_F(InterpExecutorTest, setOutput) -{ - CreateSimpleModel(); - createExecution(); - - auto output = IOIndex{0}; - auto output_idx = _graph->getOutputs().at(output); - - int32_t output_buffer[4] = {}; - - EXPECT_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 4), - std::runtime_error); - EXPECT_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 12), - std::runtime_error); - EXPECT_NO_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16)); -} - -TEST_F(InterpExecutorTest, setInputForUnspecifiedDimensions) -{ - CreateUnspecifiedDimensionsModel(); - createExecution(); - - auto input1 = IOIndex{0}; - const int32_t input1_buffer[4] = {1, 0, -1, -2}; - - TypeInfo operand_type{DataType::INT32}; - Shape operand_shape{1, 2, 2, 1}; - - EXPECT_THROW(_execution->setInput(input1, operand_type, operand_shape, - reinterpret_cast<const void *>(input1_buffer), 4), - std::runtime_error); - EXPECT_THROW(_execution->setInput(input1, operand_type, operand_shape, - reinterpret_cast<const void *>(input1_buffer), 12), - std::runtime_error); - EXPECT_NO_THROW(_execution->setInput(input1, operand_type, operand_shape, - reinterpret_cast<const void *>(input1_buffer), 16)); -} - -TEST_F(InterpExecutorTest, setOutputForUnspecifiedDimensions) -{ - CreateUnspecifiedDimensionsModel(); - createExecution(); - - auto output = IOIndex{0}; - auto output_idx = _graph->getOutputs().at(output); - - TypeInfo operand_type{DataType::INT32}; - Shape operand_shape{1, 2, 2, 1}; - - int32_t output_buffer[4] = {}; - - EXPECT_THROW(_execution->setOutput(output, operand_type, operand_shape, - reinterpret_cast<void *>(output_buffer), 4), - std::runtime_error); - EXPECT_THROW(_execution->setOutput(output, operand_type, operand_shape, - reinterpret_cast<void *>(output_buffer), 12), - std::runtime_error); - EXPECT_NO_THROW(_execution->setOutput(output, operand_type, operand_shape, - reinterpret_cast<void *>(output_buffer), 16)); -} - -TEST_F(InterpExecutorTest, execute) -{ - CreateSimpleModel(); - createExecution(); - - auto input1 = IOIndex{0}; - auto input2 = IOIndex{1}; - auto input1_idx = _graph->getInputs().at(input1); - auto input2_idx = _graph->getInputs().at(input2); - - const int32_t input1_buffer[4] = {1, 0, -1, -2}; - const int32_t input2_buffer[4] = {1, -3, 2, -4}; - - auto output = IOIndex{0}; - auto output_idx = _graph->getOutputs().at(output); - - int32_t output_buffer[4] = {}; - - EXPECT_NO_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16)); - EXPECT_NO_THROW(_execution->setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16)); - EXPECT_NO_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16)); - EXPECT_NO_THROW(_execution->execute()); - EXPECT_EQ(output_buffer[0], 2); - EXPECT_EQ(output_buffer[1], -3); - EXPECT_EQ(output_buffer[2], 1); - EXPECT_EQ(output_buffer[3], -6); -} - -TEST_F(InterpExecutorTest, executeTwoStep) -{ - CreateTwoStepModel(); - createExecution(); - - auto input1 = IOIndex{0}; - auto input2 = IOIndex{1}; - auto input1_idx = _graph->getInputs().at(input1); - auto input2_idx = _graph->getInputs().at(input2); - - const int32_t input1_buffer[4] = {1, 0, -1, -2}; - const int32_t input2_buffer[4] = {1, -3, 2, -4}; - - auto output = IOIndex{0}; - auto output_idx = _graph->getOutputs().at(output); - - int32_t output_buffer[4] = {}; - - EXPECT_NO_THROW(_execution->setInput(input1, reinterpret_cast<const void *>(input1_buffer), 16)); - EXPECT_NO_THROW(_execution->setInput(input2, reinterpret_cast<const void *>(input2_buffer), 16)); - EXPECT_NO_THROW(_execution->setOutput(output, reinterpret_cast<void *>(output_buffer), 16)); - EXPECT_NO_THROW(_execution->execute()); - EXPECT_EQ(output_buffer[0], 5); - EXPECT_EQ(output_buffer[1], -2); - EXPECT_EQ(output_buffer[2], 0); - EXPECT_EQ(output_buffer[3], -1); -} - -} // namespace diff --git a/runtime/onert/test/graph/Graph.cc b/runtime/onert/test/graph/Graph.cc deleted file mode 100644 index 34e9fe002..000000000 --- a/runtime/onert/test/graph/Graph.cc +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/Graph.h" - -TEST(Graph, inputs_and_outputs) -{ - onert::ir::Graph graph; - - onert::ir::OperandIndex index0{0u}; - onert::ir::OperandIndex index1{1u}; - - graph.addInput({index0}); - graph.addInput({index1}); - - onert::ir::OperandIndex index10{10u}; - onert::ir::OperandIndex index11{11u}; - onert::ir::OperandIndex index12{12u}; - - graph.addOutput({index10}); - graph.addOutput({index11}); - graph.addOutput({index12}); - - ASSERT_EQ(graph.getInputs().size(), 2); - ASSERT_EQ(graph.getOutputs().size(), 3); - - onert::ir::IOIndex io_index0{0}; - onert::ir::IOIndex io_index1{1}; - onert::ir::IOIndex io_index2{2}; - - ASSERT_EQ(graph.getInputs().at(io_index0), 0); - ASSERT_EQ(graph.getInputs().at(io_index1), 1); - - ASSERT_EQ(graph.getOutputs().at(io_index0), 10); - ASSERT_EQ(graph.getOutputs().at(io_index1), 11); - ASSERT_EQ(graph.getOutputs().at(io_index2), 12); -} diff --git a/runtime/onert/test/graph/Index.cc b/runtime/onert/test/graph/Index.cc deleted file mode 100644 index 2d110e326..000000000 --- a/runtime/onert/test/graph/Index.cc +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "util/Index.h" - -using Index = ::onert::util::Index<uint32_t, struct TestTag>; - -TEST(Index, neg_index_test) -{ - Index idx1{1u}; - Index idx2{2u}; - Index idx3{idx1}; - - ASSERT_EQ(idx1, 1); - ASSERT_EQ(idx1, 1u); - ASSERT_EQ(idx1.value(), 1u); - ASSERT_NE(idx1, idx2); - ASSERT_EQ(idx1, idx3); -} diff --git a/runtime/onert/test/graph/MockNode.h b/runtime/onert/test/graph/MockNode.h deleted file mode 100644 index 60b4719ed..000000000 --- a/runtime/onert/test/graph/MockNode.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ONERT_TEST_GRAPH_MOCK_NODE_H__ -#define __ONERT_TEST_GRAPH_MOCK_NODE_H__ - -#include "ir/Operation.h" -#include "ir/OperandIndexSequence.h" - -namespace onert_test -{ -namespace ir -{ - -class SimpleMock : public onert::ir::Operation -{ -public: - SimpleMock(const onert::ir::OperandIndexSequence &inputs, - const onert::ir::OperandIndexSequence &outputs) - : Operation{onert::ir::OperandConstraint::createAny()} - { - setInputs(inputs); - setOutputs(outputs); - } - -public: - void accept(onert::ir::OperationVisitor &) const override {} - onert::ir::OpCode opcode() const final { return onert::ir::OpCode::Invalid; } -}; - -} // namespace ir -} // namespace onert_test - -#endif // __ONERT_TEST_GRAPH_MOCK_NODE_H__ diff --git a/runtime/onert/test/graph/operand/IndexSet.cc b/runtime/onert/test/graph/operand/IndexSet.cc deleted file mode 100644 index 6ef425a2d..000000000 --- a/runtime/onert/test/graph/operand/IndexSet.cc +++ /dev/null @@ -1,52 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/OperandIndexSequence.h" - -using onert::ir::OperandIndex; -using onert::ir::OperandIndexSequence; - -TEST(graph_OperandIndexSequence, neg_append) -{ - OperandIndexSequence iset{0, 2, 4, 8}; - - ASSERT_EQ(iset.size(), 4); - - iset.append(OperandIndex{10}); - - ASSERT_EQ(iset.size(), 5); - - onert::ir::IOIndex index1{1}; - onert::ir::IOIndex index2{4}; - - ASSERT_EQ(iset.at(index1), 2); - ASSERT_EQ(iset.at(index2), 10); - - ASSERT_TRUE(iset.contains(OperandIndex{2})); - ASSERT_TRUE(iset.contains(OperandIndex{10})); - ASSERT_FALSE(iset.contains(OperandIndex{11})); -} - -TEST(graph_OperandIndexSequence, neg_replace) -{ - OperandIndexSequence iset{0, 1, 2, 3}; - - iset.replace(OperandIndex{1}, OperandIndex{9}); - ASSERT_FALSE(iset.contains(OperandIndex{1})); - ASSERT_TRUE(iset.contains(OperandIndex{9})); -} diff --git a/runtime/onert/test/graph/operand/LayoutSet.cc b/runtime/onert/test/graph/operand/LayoutSet.cc deleted file mode 100644 index ef965a41e..000000000 --- a/runtime/onert/test/graph/operand/LayoutSet.cc +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/LayoutSet.h" - -using onert::ir::Layout; -using onert::ir::LayoutSet; - -TEST(graph_operand_LayoutSet, neg_add_remove) -{ - LayoutSet set{Layout::NCHW}; - set.remove(Layout::NHWC); - ASSERT_EQ(set.size(), 1); - set.add(Layout::NHWC); - ASSERT_EQ(set.size(), 2); - set.remove(Layout::NHWC); - ASSERT_EQ(set.size(), 1); - set.remove(Layout::NCHW); - ASSERT_EQ(set.size(), 0); - set.remove(Layout::NCHW); - ASSERT_EQ(set.size(), 0); -} - -TEST(graph_operand_LayoutSet, set_operators) -{ - LayoutSet set1{Layout::NCHW}; - LayoutSet set2{Layout::NHWC}; - LayoutSet set3 = set1 | set2; - - ASSERT_EQ(set3.size(), 2); - - ASSERT_EQ((set3 - set1).size(), 1); - ASSERT_EQ((set3 - set1).contains(Layout::NHWC), true); - ASSERT_EQ((set3 - set2).size(), 1); - ASSERT_EQ((set3 - set2).contains(Layout::NCHW), true); - ASSERT_EQ((set3 - set3).size(), 0); - - ASSERT_EQ((set3 & set1).size(), 1); - ASSERT_EQ((set3 & set1).contains(Layout::NCHW), true); - ASSERT_EQ((set3 & set2).size(), 1); - ASSERT_EQ((set3 & set2).contains(Layout::NHWC), true); - ASSERT_EQ((set1 & set2).size(), 0); -} diff --git a/runtime/onert/test/graph/operand/Set.cc b/runtime/onert/test/graph/operand/Set.cc deleted file mode 100644 index ffee417b8..000000000 --- a/runtime/onert/test/graph/operand/Set.cc +++ /dev/null @@ -1,45 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/Operands.h" - -TEST(graph_operand_Set, neg_set_test) -{ - onert::ir::Operands set; - - onert::ir::Shape shape0{1, 2, 3}; - - onert::ir::Shape shape1(4); - shape1.dim(0) = 10; - shape1.dim(1) = 20; - shape1.dim(2) = 30; - shape1.dim(3) = 40; - - onert::ir::TypeInfo type{onert::ir::DataType::INT32}; - - set.emplace(shape0, type); - set.emplace(shape1, type); - - ASSERT_EQ(set.exist(onert::ir::OperandIndex{0u}), true); - ASSERT_EQ(set.exist(onert::ir::OperandIndex{1u}), true); - ASSERT_EQ(set.exist(onert::ir::OperandIndex{2u}), false); - - ASSERT_EQ(set.at(onert::ir::OperandIndex{0u}).shape().dim(0), 1); - ASSERT_EQ(set.at(onert::ir::OperandIndex{0u}).shape().dim(1), 2); - ASSERT_EQ(set.at(onert::ir::OperandIndex{0u}).shape().dim(2), 3); -} diff --git a/runtime/onert/test/graph/operand/UseDef.cc b/runtime/onert/test/graph/operand/UseDef.cc deleted file mode 100644 index a8686eb18..000000000 --- a/runtime/onert/test/graph/operand/UseDef.cc +++ /dev/null @@ -1,85 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/Graph.h" -#include "ir/verifier/Verifier.h" -#include <memory> -#include "../MockNode.h" - -#include <typeindex> - -namespace -{ - -using IndexSet = onert::ir::OperandIndexSequence; -using Mock = onert_test::ir::SimpleMock; - -} // namespace - -TEST(graph_operand_usedef, neg_usedef_test) -{ - onert::ir::Graph graph; - onert::ir::verifier::DAGChecker verifier; - - onert::ir::Shape shape(3); - onert::ir::TypeInfo type{onert::ir::DataType::INT32}; - - // Model Input/Output - auto input_operand = graph.addOperand(shape, type); - auto output_operand = graph.addOperand(shape, type); - - graph.addInput(input_operand); - graph.addOutput(output_operand); - - // MockNode1 - auto operand_index1 = graph.addOperand(shape, type); - auto mocknode_index1 = - graph.addOperation(std::make_unique<Mock>(IndexSet{input_operand}, IndexSet{operand_index1})); - - // MockNode2 - auto operand_index2 = graph.addOperand(shape, type); - auto mocknode_index2 = - graph.addOperation(std::make_unique<Mock>(IndexSet{input_operand}, IndexSet{operand_index2})); - - // MockNode3(two input) - auto multiinput_index = graph.addOperation( - std::make_unique<Mock>(IndexSet{operand_index1, operand_index2}, IndexSet{output_operand})); - - graph.finishBuilding(); - - ASSERT_TRUE(verifier.verify(graph)); - - // Check def - ASSERT_EQ(graph.operands().at(operand_index1).getDef(), mocknode_index1); - ASSERT_EQ(graph.operands().at(operand_index2).getDef(), mocknode_index2); - ASSERT_EQ(graph.operands().at(output_operand).getDef(), multiinput_index); - - ASSERT_NE(graph.operands().at(operand_index1).getDef(), mocknode_index2); - ASSERT_NE(graph.operands().at(operand_index1).getDef(), multiinput_index); - - // Check use - ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(mocknode_index1), true); - ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(mocknode_index2), true); - ASSERT_EQ(graph.operands().at(input_operand).getUses().contains(multiinput_index), false); - ASSERT_EQ(graph.operands().at(operand_index1).getUses().contains(multiinput_index), true); - ASSERT_EQ(graph.operands().at(operand_index2).getUses().contains(multiinput_index), true); - - ASSERT_EQ(graph.operands().at(input_operand).getUses().size(), 2); - ASSERT_EQ(graph.operands().at(operand_index1).getUses().size(), 1); - ASSERT_EQ(graph.operands().at(output_operand).getUses().size(), 0); -} diff --git a/runtime/onert/test/graph/operation/Set.cc b/runtime/onert/test/graph/operation/Set.cc deleted file mode 100644 index 088c44b8c..000000000 --- a/runtime/onert/test/graph/operation/Set.cc +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "../MockNode.h" -#include "ir/Operations.h" - -using onert::ir::Operation; -using onert::ir::OperationIndex; -using onert::ir::Operations; - -TEST(graph_operation_Set, operation_test) -{ - Operations ops; - ops.push(std::unique_ptr<Operation>(new onert_test::ir::SimpleMock({1, 2, 3, 4}, {5, 6, 7}))); - OperationIndex idx{0u}; - ASSERT_EQ(ops.at(idx).getInputs().size(), 4); - ASSERT_EQ(ops.at(idx).getOutputs().size(), 3); -} diff --git a/runtime/onert/test/graph/operation/SetIO.cc b/runtime/onert/test/graph/operation/SetIO.cc deleted file mode 100644 index 22068ff58..000000000 --- a/runtime/onert/test/graph/operation/SetIO.cc +++ /dev/null @@ -1,99 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/Graph.h" -#include "ir/Index.h" -#include "ir/OperandIndexSequence.h" -#include "ir/operation/Conv2D.h" -#include "ir/operation/Concat.h" - -#include <memory> - -#include <stdexcept> - -using Index = onert::ir::IOIndex; -using IndexSet = onert::ir::OperandIndexSequence; - -TEST(graph_operation_setIO, operation_setIO_conv) -{ - onert::ir::Graph graph; - - onert::ir::Shape shape{3}; - onert::ir::TypeInfo type{onert::ir::DataType::INT32}; - - // Add Conv - using Graph = onert::ir::operation::Conv2D; - - auto input_operand = graph.addOperand(shape, type); - auto kernel_operand = graph.addOperand(shape, type); - auto bias_operand = graph.addOperand(shape, type); - IndexSet inputs{input_operand, kernel_operand, bias_operand}; - - Graph::Param conv_params; - conv_params.padding.type = onert::ir::PaddingType::SAME; - conv_params.stride.horizontal = 1; - conv_params.stride.vertical = 1; - conv_params.activation = onert::ir::Activation::NONE; - - auto output_operand = graph.addOperand(shape, type).value(); - IndexSet outputs{output_operand}; - - auto conv = std::make_unique<Graph>(inputs, outputs, conv_params); - - ASSERT_NE(conv, nullptr); - ASSERT_EQ(conv->getInputs().at(Index{0}).value(), inputs.at(0).value()); - conv->setInputs({8, 9, 10}); - ASSERT_NE(conv->getInputs().at(Index{0}).value(), inputs.at(0).value()); - ASSERT_EQ(conv->getInputs().at(Index{0}).value(), 8); -} - -TEST(graph_operation_setIO, neg_operation_setIO_concat) -{ - onert::ir::Graph graph; - - onert::ir::Shape shape{3}; - - onert::ir::TypeInfo type{onert::ir::DataType::INT32}; - - using Graph = onert::ir::operation::Concat; - - // Add Concat - IndexSet inputs; - for (int i = 0; i < 6; ++i) - { - inputs.append(graph.addOperand(shape, type)); - } - - Graph::Param concat_params{0}; - - auto output_operand = graph.addOperand(shape, type).value(); - IndexSet outputs{output_operand}; - - auto concat = std::make_unique<Graph>(inputs, outputs, concat_params); - - ASSERT_NE(concat, nullptr); - ASSERT_EQ(concat->getInputs().size(), 6); - ASSERT_EQ(concat->getInputs().at(Index{0}).value(), inputs.at(0).value()); - - concat->setInputs({80, 6, 9, 11}); - ASSERT_EQ(concat->getInputs().size(), 4); - ASSERT_NE(concat->getInputs().at(Index{0}).value(), inputs.at(0).value()); - ASSERT_EQ(concat->getInputs().at(Index{0}).value(), 80); - ASSERT_EQ(concat->getInputs().at(Index{2}).value(), 9); - ASSERT_THROW(concat->getInputs().at(Index{5}), std::out_of_range); -} diff --git a/runtime/onert/test/graph/verifier/Verifier.cc b/runtime/onert/test/graph/verifier/Verifier.cc deleted file mode 100644 index 3bce2746c..000000000 --- a/runtime/onert/test/graph/verifier/Verifier.cc +++ /dev/null @@ -1,98 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/Operation.h" -#include "ir/Graph.h" -#include "ir/verifier/Verifier.h" -#include <memory> -#include "ir/Operand.h" -#include "../MockNode.h" - -using IndexSet = onert::ir::OperandIndexSequence; -using Mock = onert_test::ir::SimpleMock; - -TEST(Verifier, dag_checker) -{ - onert::ir::Graph graph; - - onert::ir::Shape shape{3}; - onert::ir::TypeInfo type{onert::ir::DataType::INT32}; - - auto operand1 = graph.addOperand(shape, type); - auto operand2 = graph.addOperand(shape, type); - - graph.addInput(operand1); - graph.addOutput(operand2); - - graph.addOperation(std::make_unique<Mock>(IndexSet{operand1}, IndexSet{operand2})); - - graph.finishBuilding(); - - onert::ir::verifier::DAGChecker verifier; - - ASSERT_TRUE(verifier.verify(graph)); -} - -TEST(Verifier, neg_edge_consistency_checker_1) -{ - onert::ir::Graph graph; - - onert::ir::Shape shape{3}; - onert::ir::TypeInfo type{onert::ir::DataType::INT32}; - - auto operand1 = graph.addOperand(shape, type); - auto operand2 = graph.addOperand(shape, type); - - graph.addInput(operand1); - graph.addOutput(operand2); - - auto mock_op = std::make_unique<Mock>(IndexSet{operand1}, IndexSet{operand2}); - auto op_ind = graph.addOperation(std::move(mock_op)); - - graph.finishBuilding(); - - graph.operands().at(operand1).removeUse(op_ind); // Manipulate the operand alone - - onert::ir::verifier::EdgeConsistencyChecker verifier; - ASSERT_FALSE(verifier.verify(graph)); -} - -TEST(Verifier, neg_edge_consistency_checker_2) -{ - onert::ir::Graph graph; - - onert::ir::Shape shape{3}; - onert::ir::TypeInfo type{onert::ir::DataType::INT32}; - - auto operand1 = graph.addOperand(shape, type); - auto operand2 = graph.addOperand(shape, type); - - graph.addInput(operand1); - graph.addOutput(operand2); - - auto mock_op = std::make_unique<Mock>(IndexSet{operand1}, IndexSet{operand2}); - auto mock_op_ptr = mock_op.get(); - auto op_ind = graph.addOperation(std::move(mock_op)); - - graph.finishBuilding(); - - mock_op_ptr->setInputs({operand2}); // Manipulate the operation alone - - onert::ir::verifier::EdgeConsistencyChecker verifier; - ASSERT_FALSE(verifier.verify(graph)); -} diff --git a/runtime/onert/test/ir/Shape.cc b/runtime/onert/test/ir/Shape.cc deleted file mode 100644 index c24aeda8d..000000000 --- a/runtime/onert/test/ir/Shape.cc +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <ir/Shape.h> - -#include <gtest/gtest.h> - -TEST(ShapeTest, basic_test) -{ - { - onert::ir::Shape shape(3); - - shape.dim(0) = 1; - shape.dim(1) = 2; - shape.dim(2) = 3; - - ASSERT_EQ(shape.rank(), 3); - ASSERT_EQ(shape.num_elements(), 6); - ASSERT_EQ(onert::ir::rankMaybeUnspecified(shape), false); - ASSERT_EQ(shape.hasUnspecifiedDims(), false); - } - { - onert::ir::Shape shape; // scalar or rank is unspecified - - ASSERT_EQ(shape.rank(), 0); - ASSERT_EQ(shape.num_elements(), 1); - ASSERT_EQ(onert::ir::rankMaybeUnspecified(shape), true); - ASSERT_EQ(shape.hasUnspecifiedDims(), false); - } -} - -TEST(ShapeTest, neg_basic_test) -{ - { - onert::ir::Shape shape(2); - - shape.dim(0) = 1; - shape.dim(1) = onert::ir::Shape::UNSPECIFIED_DIM; - - ASSERT_EQ(shape.rank(), 2); - ASSERT_EQ(onert::ir::rankMaybeUnspecified(shape), false); - ASSERT_EQ(shape.hasUnspecifiedDims(), true); - EXPECT_ANY_THROW(shape.num_elements()); - } -} diff --git a/runtime/onert/test/util/ObjectManager.cc b/runtime/onert/test/util/ObjectManager.cc deleted file mode 100644 index 5051bcfa6..000000000 --- a/runtime/onert/test/util/ObjectManager.cc +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "util/ObjectManager.h" -#include "util/Index.h" - -using namespace onert; - -struct TestTag; -using Index = typename util::Index<uint32_t, TestTag>; - -TEST(ObjectManager, emplace) -{ - util::ObjectManager<Index, int> man; - - auto index = man.emplace(100); - ASSERT_EQ(man.at(index), 100); -} - -TEST(ObjectManager, remove_1) -{ - util::ObjectManager<Index, int> man; - - Index index = man.emplace(100); - ASSERT_TRUE(man.exist(index)); - ASSERT_EQ(man.at(index), 100); - - man.remove(index); - ASSERT_FALSE(man.exist(index)); -} - -TEST(ObjectManager, remove_2) -{ - util::ObjectManager<Index, int> man; - - auto index0 = man.emplace(100); - auto index1 = man.emplace(200); - ASSERT_TRUE(man.exist(index0)); - ASSERT_EQ(man.at(index0), 100); - ASSERT_TRUE(man.exist(index1)); - ASSERT_EQ(man.at(index1), 200); - - man.remove(index0); - ASSERT_FALSE(man.exist(index0)); - ASSERT_TRUE(man.exist(index1)); - ASSERT_EQ(man.at(index1), 200); -} - -TEST(ObjectManager, push) -{ - util::ObjectManager<Index, int> man; - - auto index = man.push(std::unique_ptr<int>{new int{100}}); - ASSERT_EQ(man.at(index), 100); -} - -TEST(ObjectManager, const_iterate) -{ - util::ObjectManager<Index, int> man; - - auto index0 = man.emplace(100); - auto index1 = man.emplace(200); - auto index2 = man.emplace(300); - - int sum = 0; - man.iterate([&](const Index &index, const int &val) { sum += val; }); - ASSERT_EQ(sum, 600); -} - -TEST(ObjectManager, non_const_iterate) -{ - util::ObjectManager<Index, int> man; - - auto index0 = man.emplace(100); - auto index1 = man.emplace(200); - auto index2 = man.emplace(300); - - man.iterate([&](const Index &index, int &val) { val += 1; }); - ASSERT_EQ(man.at(index0), 101); - ASSERT_EQ(man.at(index1), 201); - ASSERT_EQ(man.at(index2), 301); -} diff --git a/runtime/onert/test/util/ShapeInference.cc b/runtime/onert/test/util/ShapeInference.cc deleted file mode 100644 index a5f0af5ee..000000000 --- a/runtime/onert/test/util/ShapeInference.cc +++ /dev/null @@ -1,544 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include <gtest/gtest.h> - -#include "ir/Layout.h" -#include "util/ShapeInference.h" - -using namespace onert::ir; - -TEST(ShapeInference, Elementwise) -{ - Shape lhs_shape{1, 299, 299, 3}; - Shape rhs_shape{3}; - auto infered_out_shape = onert::shape_inference::inferEltwiseShape(lhs_shape, rhs_shape); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.dim(0), 1); - ASSERT_EQ(infered_out_shape.dim(1), 299); - ASSERT_EQ(infered_out_shape.dim(2), 299); - ASSERT_EQ(infered_out_shape.dim(3), 3); -} - -TEST(ShapeInference, neg_Elementwise) -{ - Shape lhs_shape{1, 299, 299, 3}; - Shape rhs_shape{5, 3}; - ASSERT_THROW(onert::shape_inference::inferEltwiseShape(lhs_shape, rhs_shape), std::runtime_error); -} - -TEST(ShapeInference, Pool2DNodeSame) -{ - Shape in_shape{10, 6, 12, 20}; - Stride stride{3, 7}; - Padding padding{PaddingType::SAME}; - - operation::Pool2D::Param avg_pool_param{ - operation::Pool2D::PoolType::AVG, 3, 6, stride, padding, Activation::NONE}; - auto infered_out_shape = onert::shape_inference::inferPoolShape(in_shape, avg_pool_param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20); - - operation::Pool2D::Param max_pool_param{ - operation::Pool2D::PoolType::MAX, 3, 6, stride, padding, Activation::NONE}; - infered_out_shape = onert::shape_inference::inferPoolShape(in_shape, max_pool_param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20); -} - -TEST(ShapeInference, Pool2DNodeValid) -{ - Shape in_shape{10, 6, 12, 20}; - Stride stride{3, 7}; - Padding padding{PaddingType::VALID}; - - operation::Pool2D::Param avg_pool_param{ - operation::Pool2D::PoolType::AVG, 3, 6, stride, padding, Activation::NONE}; - auto infered_out_shape = onert::shape_inference::inferPoolShape(in_shape, avg_pool_param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20); - - operation::Pool2D::Param max_pool_param{ - operation::Pool2D::PoolType::MAX, 3, 6, stride, padding, Activation::NONE}; - infered_out_shape = onert::shape_inference::inferPoolShape(in_shape, max_pool_param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20); -} - -TEST(ShapeInference, Pool2DNodeExplicit) -{ - Shape in_shape{10, 3, 5, 20}; - - Stride stride{3, 7}; - Padding padding{4, 3, 2, 1}; - - operation::Pool2D::Param avg_pool_param{ - operation::Pool2D::PoolType::AVG, 3, 6, stride, padding, Activation::NONE}; - auto infered_out_shape = onert::shape_inference::inferPoolShape(in_shape, avg_pool_param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20); - - operation::Pool2D::Param max_pool_param{ - operation::Pool2D::PoolType::MAX, 3, 6, stride, padding, Activation::NONE}; - infered_out_shape = onert::shape_inference::inferPoolShape(in_shape, max_pool_param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20); -} - -TEST(ShapeInference, neg_Pool2DNode_InvalidStride) -{ - Shape in_shape{10, 6, 12, 20}; - Stride stride{0, 7}; - Padding padding{PaddingType::SAME}; - - operation::Pool2D::Param avg_pool_param{ - operation::Pool2D::PoolType::AVG, 3, 6, stride, padding, Activation::NONE}; - ASSERT_THROW(onert::shape_inference::inferPoolShape(in_shape, avg_pool_param), - std::runtime_error); -} - -TEST(ShapeInference, Conv2D) -{ - Shape in_shape{10, 6, 12, 20}; - Shape ker_shape{30, 3, 6, 20}; - - operation::Conv2D::Param param{Stride{3, 7}, Padding{PaddingType::VALID}, Activation::NONE, - Dilation{1, 1}}; - auto infered_out_shape = onert::shape_inference::inferConv2DShape(in_shape, ker_shape, param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30); - - param = operation::Conv2D::Param{Stride{3, 7}, Padding{PaddingType::SAME}, Activation::NONE, - Dilation{1, 1}}; - infered_out_shape = onert::shape_inference::inferConv2DShape(in_shape, ker_shape, param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30); - - param = - operation::Conv2D::Param{Stride{3, 7}, Padding{4, 3, 2, 1}, Activation::NONE, Dilation{1, 1}}; - infered_out_shape = onert::shape_inference::inferConv2DShape(in_shape, ker_shape, param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 3); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30); -} - -TEST(ShapeInference, neg_Conv2D_InvalidStride) -{ - Shape in_shape{10, 6, 12, 20}; - Shape ker_shape{30, 3, 6, 20}; - - operation::Conv2D::Param param{Stride{0, 0}, Padding{PaddingType::VALID}, Activation::NONE, - Dilation{1, 1}}; - ASSERT_THROW(onert::shape_inference::inferConv2DShape(in_shape, ker_shape, param), - std::runtime_error); -} - -TEST(ShapeInference, DepthwiseConv2D) -{ - Shape in_shape{10, 6, 12, 20}; - Shape ker_shape{1, 3, 6, 60}; - - operation::DepthwiseConv2D::Param param{Stride{3, 7}, Padding{PaddingType::VALID}, 3, - Activation::NONE}; - auto infered_out_shape = - onert::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60); - - param = operation::DepthwiseConv2D::Param{Stride{3, 7}, Padding{PaddingType::SAME}, 3, - Activation::NONE}; - infered_out_shape = onert::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60); - - param = operation::DepthwiseConv2D::Param{Stride{3, 7}, Padding{4, 3, 2, 1}, 3, Activation::NONE}; - infered_out_shape = onert::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param); - - ASSERT_EQ(infered_out_shape.rank(), 4); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 3); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2); - ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60); -} - -TEST(ShapeInference, neg_DepthwiseConv2D_InvalidSride) -{ - Shape in_shape{10, 6, 12, 20}; - Shape ker_shape{1, 3, 6, 60}; - - operation::DepthwiseConv2D::Param param{Stride{3, 0}, Padding{PaddingType::VALID}, 3, - Activation::NONE}; - ASSERT_THROW(onert::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param), - std::runtime_error); -} - -TEST(ShapeInference, Concat) -{ - { - Shape in1{10, 20, 30, 3, 50}; - Shape in2{10, 20, 30, 2, 50}; - Shape in3{10, 20, 30, 2, 50}; - - operation::Concat::Param param{3}; - auto infered_out_shape = onert::shape_inference::inferConcatShape({in1, in2, in3}, param); - - ASSERT_EQ(infered_out_shape.rank(), 5); - ASSERT_EQ(infered_out_shape.dim(0), 10); - ASSERT_EQ(infered_out_shape.dim(1), 20); - ASSERT_EQ(infered_out_shape.dim(2), 30); - ASSERT_EQ(infered_out_shape.dim(3), 7); - ASSERT_EQ(infered_out_shape.dim(4), 50); - } - { - // case 1. when axis < 0 - Shape in1{10, 20, 2}; - Shape in2{10, 20, 3}; - - operation::Concat::Param param{-1}; - auto infered_out_shape = onert::shape_inference::inferConcatShape({in1, in2}, param); - - ASSERT_EQ(infered_out_shape.rank(), 3); - ASSERT_EQ(infered_out_shape.dim(0), 10); - ASSERT_EQ(infered_out_shape.dim(1), 20); - ASSERT_EQ(infered_out_shape.dim(2), 5); - } - { - // case 2. when axis < 0 - Shape in1{2, 20, 2}; - Shape in2{3, 20, 2}; - - operation::Concat::Param param{-3}; - auto infered_out_shape = onert::shape_inference::inferConcatShape({in1, in2}, param); - - ASSERT_EQ(infered_out_shape.rank(), 3); - ASSERT_EQ(infered_out_shape.dim(0), 5); - ASSERT_EQ(infered_out_shape.dim(1), 20); - ASSERT_EQ(infered_out_shape.dim(2), 2); - } -} - -TEST(ShapeInference, neg_Concat) -{ - { - operation::Concat::Param param{2}; - Shape in1{10, 1, 3}; - Shape in2{10, 2, 4}; // dim[1] should be 1 but 2 - - EXPECT_ANY_THROW(onert::shape_inference::inferConcatShape({in1, in2}, param)); - } - { // wrong rank - operation::Concat::Param param{2}; - Shape in1{10, 2, 3, 4}; - Shape in2{10, 2, 4}; // rank should be 4 - - EXPECT_ANY_THROW(onert::shape_inference::inferConcatShape({in1, in2}, param)); - } -} - -TEST(ShapeInference, ExpandDims) -{ - Shape in_shape{30, 40}; - - auto check = [&](int32_t axis, Shape &expected) { - auto actual = onert::shape_inference::inferExpandDimsShape(in_shape, axis); - - ASSERT_EQ(actual.rank(), 3); - for (int32_t dim = 0; dim < expected.rank(); dim++) - ASSERT_EQ(actual.dim(dim), expected.dim(dim)); - }; - - { // boundary - int32_t axis = 0; - Shape expected{1, 30, 40}; - check(axis, expected); - } - { // boundary - int32_t axis = 2; - Shape expected{30, 40, 1}; - check(axis, expected); - } - { // inside - int32_t axis = 1; - Shape expected{30, 1, 40}; - check(axis, expected); - } - { // negative boundary - int32_t axis = -1; - Shape expected{30, 40, 1}; - check(axis, expected); - } - { // negative boundary - int32_t axis = -3; - Shape expected{1, 30, 40}; - check(axis, expected); - } -} - -TEST(ShapeInference, neg_ExpandDims) -{ - Shape in_shape{30, 40}; - - { // over boundary - int32_t axis = 3; - ASSERT_THROW(onert::shape_inference::inferExpandDimsShape(in_shape, axis), std::runtime_error); - } - { // over boundary - int32_t axis = -4; - ASSERT_THROW(onert::shape_inference::inferExpandDimsShape(in_shape, axis), std::runtime_error); - } -} - -TEST(ShapeInference, FullyConnected) -{ - Shape in_shape{3, 4, 5, 6}; - Shape ker_shape{3, 10}; - auto infered_out_shape = onert::shape_inference::inferFullyConnectedShape(in_shape, ker_shape); - - ASSERT_EQ(infered_out_shape.rank(), 2); - ASSERT_EQ(infered_out_shape.dim(0), 36); - ASSERT_EQ(infered_out_shape.dim(1), 3); -} - -TEST(ShapeInference, Transpose) -{ - auto check = [&](Shape &in_shape, std::vector<int> perm, Shape &expected) { - // pre-conditions - ASSERT_EQ(in_shape.rank(), perm.size()); - ASSERT_EQ(expected.rank(), perm.size()); - auto inferred_out_shape = - onert::shape_inference::inferTransposeShape(in_shape, perm.data(), perm.size()); - // post-conditions - ASSERT_EQ(inferred_out_shape.rank(), perm.size()); - for (int32_t dim = 0; dim < expected.rank(); dim++) - { - ASSERT_EQ(inferred_out_shape.dim(dim), expected.dim(dim)); - } - }; - // check for 2-D - { - Shape in_shape{2, 3}; - std::vector<int> perm = {1, 0}; - Shape expected{3, 2}; - // int32_t rank = 2; - check(in_shape, perm, expected); - } - // check for 3-D - { - Shape in_shape{1, 2, 3}; - std::vector<int> perm = {2, 0, 1}; - Shape expected{3, 1, 2}; - // int32_t rank = 3; - check(in_shape, perm, expected); - } - // check for 4-D - { - Shape in_shape{1, 2, 3, 4}; - std::vector<int> perm = {1, 3, 0, 2}; - Shape expected{2, 4, 1, 3}; - // int32_t rank = 4; - check(in_shape, perm, expected); - } -} - -TEST(ShapeInference, neg_Transpose) -{ - Shape in_shape{1, 2, 3}; - // Invalid parameter size - { - std::vector<int> perm = {2, 0, 1, 0}; - // int32_t rank = 3; - ASSERT_THROW(onert::shape_inference::inferTransposeShape(in_shape, perm.data(), perm.size()), - std::runtime_error); - } - // Invalid parameter value - { - std::vector<int> perm = {2, 0, 3}; - // int32_t rank = 3; - ASSERT_THROW(onert::shape_inference::inferTransposeShape(in_shape, perm.data(), perm.size()), - std::runtime_error); - } -} - -TEST(ShapeInference, Gather) -{ - auto check = [&](Shape &input, Shape &indices, Shape &expected, int32_t axis) { - int rank = input.rank(); - auto actual = onert::shape_inference::inferGatherShape(input, indices, axis, rank); - - ASSERT_EQ(actual.rank(), expected.rank()); - - for (int32_t dim = 0; dim < expected.rank(); dim++) - ASSERT_EQ(actual.dim(dim), expected.dim(dim)); - }; - - // check for 2-D, 3-D, axis 0 - { - Shape input{3, 4}; - Shape indices{1, 1, 2}; - int32_t axis = 0; - Shape expected{1, 1, 2, 4}; - check(input, indices, expected, axis); - } - - // check for 2-D, 3-D, axis 1 - { - Shape input{3, 4}; - Shape indices{1, 2, 1}; - int32_t axis = 1; - Shape expected{3, 1, 2, 1}; - check(input, indices, expected, axis); - } - - // check for 3-D, 2-D, axis 0 - { - Shape input{2, 3, 4}; - Shape indices{1, 2}; - int32_t axis = 0; - Shape expected{1, 2, 3, 4}; - check(input, indices, expected, axis); - } - - // check for 3-D, 2-D, axis 2 - { - Shape input{2, 3, 4}; - Shape indices{2, 1}; - int32_t axis = 2; - Shape expected{2, 3, 2, 1}; - check(input, indices, expected, axis); - } - - // check for 4D, axis 0 - { - Shape input{1, 2, 3, 4}; - Shape indices{2}; - int32_t axis = 0; - Shape expected{2, 2, 3, 4}; - check(input, indices, expected, axis); - } -} - -TEST(ShapeInference, BCQFullyConnected) -{ - auto check = [&](Shape &in_shape, Shape &cluster_shape, std::vector<int> cluster, - Shape &expected) { - auto actual = onert::shape_inference::inferBCQFullyConnectedShape(in_shape, cluster_shape, - cluster.data()); - ASSERT_EQ(actual.rank(), expected.rank()); - - for (int32_t dim = 0; dim < expected.rank(); dim++) - ASSERT_EQ(actual.dim(dim), expected.dim(dim)); - }; - - { - Shape in_shape{10, 1}; - Shape cluster_shape{3, 2}; - std::vector<int> cluster = {1, 10, 2, 10, 3, 10}; - - Shape expected{30, 1}; - check(in_shape, cluster_shape, cluster, expected); - } - - { - Shape in_shape{1, 1}; - Shape cluster_shape{1, 2}; - std::vector<int> cluster = {3, 50}; - - Shape expected{50, 1}; - check(in_shape, cluster_shape, cluster, expected); - } -} - -TEST(ShapeInference, BCQGather) -{ - auto check = [&](Shape &indices_shape, Shape &cluster_shape, std::vector<int> cluster, - uint32_t hidden_size, uint32_t axis, int rank, Shape &expected) { - operation::BCQGather::Param param{hidden_size, axis}; - auto actual = onert::shape_inference::inferBCQGatherShape(indices_shape, cluster_shape, - cluster.data(), rank, param); - ASSERT_EQ(actual.rank(), expected.rank()); - - for (int32_t dim = 0; dim < expected.rank(); dim++) - ASSERT_EQ(actual.dim(dim), expected.dim(dim)); - }; - - { - Shape indices_shape{5, 1}; - Shape cluster_shape{3, 2}; - std::vector<int> cluster = {1, 10, 2, 10, 3, 10}; - uint32_t hidden_size = 10; - uint32_t axis = 0; - int rank = 2; - - Shape expected{5, 1, 10}; - check(indices_shape, cluster_shape, cluster, hidden_size, axis, rank, expected); - } - - { - Shape indices_shape{5, 1}; - Shape cluster_shape{3, 2}; - std::vector<int> cluster = {1, 10, 2, 10, 3, 10}; - uint32_t hidden_size = 10; - uint32_t axis = 1; - int rank = 2; - - Shape expected{30, 5, 1}; - check(indices_shape, cluster_shape, cluster, hidden_size, axis, rank, expected); - } -} |