diff options
Diffstat (limited to 'libs/support/tflite/src/nnapi_delegate.cpp')
| -rw-r--r-- | libs/support/tflite/src/nnapi_delegate.cpp | 720 |
1 files changed, 0 insertions, 720 deletions
diff --git a/libs/support/tflite/src/nnapi_delegate.cpp b/libs/support/tflite/src/nnapi_delegate.cpp deleted file mode 100644 index 1eada4bca..000000000 --- a/libs/support/tflite/src/nnapi_delegate.cpp +++ /dev/null @@ -1,720 +0,0 @@ -/* Copyright (c) 2018 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. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This code is derived from the following file (in TensorFlow) -// 'externals/tensorflow/tensorflow/contrib/lite/nnapi_delegate.cc' -#include "support/tflite/nnapi_delegate.h" -#include <fcntl.h> -#include <sys/mman.h> -#include <sys/stat.h> -#include <sys/types.h> -#include "tensorflow/contrib/lite/builtin_op_data.h" -#include "tensorflow/contrib/lite/error_reporter.h" -#include "tensorflow/contrib/lite/model.h" -#include "NeuralNetworksShim.h" -#include "NeuralNetworksExShim.h" - -#ifdef __ANDROID__ -#include <sys/system_properties.h> -#endif - -namespace nnfw -{ - -// TODO(aselle): FATAL leaves resources hanging. -void FATAL(const char* format, ...) { - va_list args; - va_start(args, format); - vfprintf(stderr, format, args); - va_end(args); - fflush(stderr); - exit(1); -} - -// TODO(aselle): Change the error model to use status codes. -#define CHECK_TFLITE_SUCCESS(x) \ - if (x != kTfLiteOk) { \ - FATAL("Aborting since tflite returned failure."); \ - } - -#define CHECK_NN(x) \ - if (x != ANEURALNETWORKS_NO_ERROR) { \ - FATAL("Aborting since tflite returned failure."); \ - } - -namespace { - -int32_t GetAndroidSdkVersion() { -#ifdef __ANDROID__ - const char* sdkProp = "ro.build.version.sdk"; - char sdkVersion[PROP_VALUE_MAX]; - int length = __system_property_get(sdkProp, sdkVersion); - if (length != 0) { - for (int i = 0; i < length; ++i) { - int digit = sdkVersion[i] - '0'; - if (digit < 0 || digit > 9) { - // Non-numeric SDK version, assume it's higher then expected; - return 0xFFFF; - } - } - return atoi(sdkVersion); - } - FATAL("No %s prop", sdkProp); -#endif // __ANDROID__ - return 0; -} - -static const int32_t kAndroidSdkVersion = GetAndroidSdkVersion(); - -} // namespace - -NNAPIAllocation::NNAPIAllocation(const char* filename, - ::tflite::ErrorReporter* error_reporter) - : MMAPAllocation(filename, error_reporter) { - if (mmapped_buffer_ != MAP_FAILED) - CHECK_NN(ANeuralNetworksMemory_createFromFd(buffer_size_bytes_, PROT_READ, - mmap_fd_, 0, &handle_)); -} - -NNAPIAllocation::~NNAPIAllocation() { - if (handle_) { - ANeuralNetworksMemory_free(handle_); - } -} - -NNAPIDelegate::~NNAPIDelegate() { - if (nn_compiled_model_) { - ANeuralNetworksCompilation_free(nn_compiled_model_); - nn_compiled_model_ = nullptr; - } - if (nn_model_) { - ANeuralNetworksModel_free(nn_model_); - nn_model_ = nullptr; - // TODO(aselle): Is this thread-safe and callable multiple times? - } - // ANeuralNetworksShutdown(); -} - -// Adds the tensors of the interpreter to the NN API model. -// Returns the number of operands added. -uint32_t addTensorOperands(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, - const std::vector<uint32_t>& skip_list) { - uint32_t next_id = 0; - for (size_t i = 0; i < interpreter->tensors_size(); i++) { - // skip temporaries tensors. - bool shouldSkip = false; - for (auto skip_idx : skip_list) { - if (i == skip_idx) { - shouldSkip = true; - break; - } - } - if (shouldSkip) continue; - - int32_t nn_type = 0; - // NNAPI requires 32-bit float scale to be zero, tflite doesn't care - float scale = 0.0f; - int32_t zeroPoint = 0; - TfLiteTensor* tensor = interpreter->tensor(i); - switch (tensor->type) { - case kTfLiteNoType: - // Tensors added during initialization of Ops don't have a type yet and - // should not be registered with the NNAPI. - continue; - case kTfLiteFloat32: - nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; - break; - case kTfLiteUInt8: - nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; - scale = tensor->params.scale; - // FIXME The next line is a workaround because currently zero scale is - // passed down from TF - // Lite. Note that the latest NeuralNetworks.h (see - // https://android.googlesource.com/platform/frameworks/ml/+/master/nn/runtime/include/NeuralNetworks.h) - // requires scale to be greater than zero. Remove this workaround - // when the scale - // value is correctly passed. - scale = (scale == 0.0f) ? 1.0f : scale; - zeroPoint = tensor->params.zero_point; - break; - case kTfLiteInt32: - nn_type = ANEURALNETWORKS_TENSOR_INT32; - scale = tensor->params.scale; - zeroPoint = tensor->params.zero_point; - break; - default: - FATAL("Unsupported type."); - } - // TODO(aselle): Note, many of these are intermediate results. Do I need - // to ever specify these sizes. I am currently below doing setValue - // on all of them, but I shouldn't in the future. - // Answer(jeanluc): If all the operators can set the dimension correctly, - // you won't need to. - ANeuralNetworksOperandType operand_type{ - nn_type, static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), scale, zeroPoint}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - // TODO(aselle): Based on Michael's suggestion, limiting this to read - // only memory - if (tensor->allocation_type == kTfLiteMmapRo) { - if (const NNAPIAllocation* alloc = dynamic_cast<const NNAPIAllocation*>( - static_cast<const ::tflite::Allocation*>(tensor->allocation))) { - CHECK_NN(ANeuralNetworksModel_setOperandValueFromMemory( - nn_model, next_id, alloc->memory(), alloc->offset(tensor->data.raw), - tensor->bytes)); - } else { - CHECK_NN(ANeuralNetworksModel_setOperandValue( - nn_model, next_id, tensor->data.raw, tensor->bytes)); - } - } else if (tensor->bytes == 0) { - // These size 0 tensors are optional tensors reserved. - CHECK_NN( - ANeuralNetworksModel_setOperandValue(nn_model, next_id, nullptr, 0)); - } - - ++next_id; - } - return next_id; -} - -// Adds the operations and their parameters to the NN API model. -// 'next-id' is the operand ID of the next operand of the model. -void AddOpsAndParams(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, uint32_t next_id, - std::vector<int>* model_state_inputs, - std::vector<int>* model_state_outputs) { - for (size_t i = 0; i < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - const TfLiteRegistration& registration = node_and_registration->second; - tflite::BuiltinOperator builtin = - static_cast<tflite::BuiltinOperator>(registration.builtin_code); - - // Add the parameters. - std::vector<uint32_t> augmented_inputs( - node.inputs->data, node.inputs->data + node.inputs->size); - std::vector<uint32_t> augmented_outputs( - node.outputs->data, node.outputs->data + node.outputs->size); - - auto add_scalar_int32 = [&nn_model, &augmented_inputs, - &next_id](int value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_INT32}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(int32_t))) - augmented_inputs.push_back(next_id++); - }; - - auto add_scalar_float32 = [&nn_model, &augmented_inputs, - &next_id](float value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_FLOAT32}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(float))) - augmented_inputs.push_back(next_id++); - }; - - // Handle state tensors of RNN, LSTM, SVDF. - // For each state_out tensor, a corresponding state_in operand needs to be - // created for NNAPI. - auto duplicate_state_tensor_float32 = - [interpreter, &nn_model, &next_id, &augmented_inputs, - &model_state_inputs, &model_state_outputs](int tensor_id) { - const TfLiteTensor* tensor = interpreter->tensor(tensor_id); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), - tensor->params.scale, tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_inputs.push_back(next_id); - model_state_inputs->push_back(next_id); - model_state_outputs->push_back(tensor_id); - next_id++; - }; - - auto add_add_params = [&add_scalar_int32]() { add_scalar_int32(0); }; - - auto add_pooling_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLitePoolParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->filter_width); - add_scalar_int32(builtin->filter_height); - add_scalar_int32(builtin->activation); - }; - - auto add_convolution_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->activation); - }; - - auto add_depthwise_conv_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteDepthwiseConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->depth_multiplier); - add_scalar_int32(builtin->activation); - }; - - auto add_fully_connected_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteFullyConnectedParams*>(data); - add_scalar_int32(builtin->activation); - }; - - auto add_concatenation_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteConcatenationParams*>(data); - add_scalar_int32(builtin->axis); - if (builtin->activation != kTfLiteActNone) { - FATAL("Concatenation does not support fused activation in NNAPI"); - } - }; - - auto add_softmax_params = [&add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteSoftmaxParams*>(data); - add_scalar_float32(builtin->beta); - }; - - auto add_space_to_depth_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSpaceToDepthParams*>(data); - add_scalar_int32(builtin->block_size); - }; - - auto add_lstm_params = [&add_scalar_int32, - &add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteLSTMParams*>(data); - add_scalar_int32(builtin->activation); - add_scalar_float32(builtin->cell_clip); - add_scalar_float32(builtin->proj_clip); - }; - - // LSTM in NNAPI requires scratch tensor as an output operand. - auto add_lstm_scratch_tensor_float32 = [interpreter, &node, &nn_model, - &next_id, &augmented_outputs]() { - int scratch_buffer_index = node.temporaries->data[0]; - const TfLiteTensor* tensor = interpreter->tensor(scratch_buffer_index); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), tensor->params.scale, - tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_outputs.insert(augmented_outputs.begin(), next_id++); - }; - - auto add_mean_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteMeanParams*>(data); - add_scalar_int32(builtin->keep_dims); - }; - - auto add_svdf_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSVDFParams*>(data); - add_scalar_int32(builtin->rank); - add_scalar_int32(builtin->activation); - }; - - auto add_rnn_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteRNNParams*>(data); - add_scalar_int32(builtin->activation); - }; - - // Handle optional input tensors. - auto add_optional_tensors = [&nn_model, &augmented_inputs, - &next_id](int nn_type) { - for (size_t idx = 0; idx < augmented_inputs.size(); idx++) { - if (augmented_inputs[idx] == kOptionalTensor) { - const std::vector<uint32_t> dim = {0, 0}; - ANeuralNetworksOperandType operand_type{nn_type, 2, dim.data(), 0, 0}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, - nullptr, 0)) - augmented_inputs[idx] = next_id++; - } - } - }; - - int nnapi_version = 10; -#include "nnapi_delegate_ex_AddOpsAndParams_lambda.inc" - - ANeuralNetworksOperationType nn_op_type; - - switch (builtin) { - case tflite::BuiltinOperator_ADD: - nn_op_type = ANEURALNETWORKS_ADD; - add_add_params(); - break; - case tflite::BuiltinOperator_MUL: - nn_op_type = ANEURALNETWORKS_MUL; - add_add_params(); - break; - case tflite::BuiltinOperator_AVERAGE_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_AVERAGE_POOL_2D; - break; - case tflite::BuiltinOperator_MAX_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_MAX_POOL_2D; - break; - case tflite::BuiltinOperator_L2_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_L2_POOL_2D; - break; - case tflite::BuiltinOperator_CONV_2D: - add_convolution_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_CONV_2D; - break; - case tflite::BuiltinOperator_RELU: - nn_op_type = ANEURALNETWORKS_RELU; - break; - case tflite::BuiltinOperator_RELU_N1_TO_1: - nn_op_type = ANEURALNETWORKS_RELU1; - break; - case tflite::BuiltinOperator_RELU6: - nn_op_type = ANEURALNETWORKS_RELU6; - break; - case tflite::BuiltinOperator_TANH: - nn_op_type = ANEURALNETWORKS_TANH; - break; - case tflite::BuiltinOperator_FLOOR: - nn_op_type = ANEURALNETWORKS_FLOOR; - break; - case tflite::BuiltinOperator_LOGISTIC: - nn_op_type = ANEURALNETWORKS_LOGISTIC; - break; - case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: - add_depthwise_conv_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_DEPTHWISE_CONV_2D; - break; - case tflite::BuiltinOperator_CONCATENATION: - add_concatenation_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_CONCATENATION; - break; - case tflite::BuiltinOperator_SOFTMAX: - add_softmax_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SOFTMAX; - break; - case tflite::BuiltinOperator_FULLY_CONNECTED: - add_fully_connected_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_FULLY_CONNECTED; - break; - case tflite::BuiltinOperator_RESHAPE: - nn_op_type = ANEURALNETWORKS_RESHAPE; - // add_reshape_params(node.builtin_data); - break; - case tflite::BuiltinOperator_RESIZE_BILINEAR: - add_resize_bilinear_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RESIZE_BILINEAR; - break; - case tflite::BuiltinOperator_SPACE_TO_DEPTH: - add_space_to_depth_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SPACE_TO_DEPTH; - break; - case tflite::BuiltinOperator_LSTM: { - duplicate_state_tensor_float32( - node.outputs->data[/*kOutputStateTensor*/ 0]); - duplicate_state_tensor_float32( - node.outputs->data[/*kCellStateTensor*/ 1]); - add_lstm_params(node.builtin_data); - add_lstm_scratch_tensor_float32(); - add_optional_tensors(ANEURALNETWORKS_TENSOR_FLOAT32); - nn_op_type = ANEURALNETWORKS_LSTM; - break; - } - case tflite::BuiltinOperator_DEQUANTIZE: - nn_op_type = ANEURALNETWORKS_DEQUANTIZE; - break; - case tflite::BuiltinOperator_SVDF: { - duplicate_state_tensor_float32(node.outputs->data[/*kStateTensor*/ 0]); - add_svdf_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SVDF; - break; - } - case tflite::BuiltinOperator_RNN: { - duplicate_state_tensor_float32( - node.outputs->data[/*kHiddenStateTensor*/ 0]); - add_rnn_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RNN; - break; - } - case tflite::BuiltinOperator_EMBEDDING_LOOKUP: - nn_op_type = ANEURALNETWORKS_EMBEDDING_LOOKUP; - break; - case tflite::BuiltinOperator_PAD: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_PAD; - break; - case tflite::BuiltinOperator_MEAN: - nnapi_version = 11; // require NNAPI 1.1 - add_mean_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_MEAN; - break; - case tflite::BuiltinOperator_DIV: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_DIV; - add_add_params(); - break; - case tflite::BuiltinOperator_SUB: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_SUB; - add_add_params(); - break; - case tflite::BuiltinOperator_STRIDED_SLICE: - add_strided_slice_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_STRIDED_SLICE; - break; - case tflite::BuiltinOperator_CAST: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_CAST_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_TOPK_V2: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TOPK_V2_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_GATHER: - add_gather_ex_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_GATHER_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_SPLIT: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SPLIT_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_TRANSPOSE: - nn_op_type = ANEURALNETWORKS_TRANSPOSE; - // param is almost same as reshape - break; - case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: - case tflite::BuiltinOperator_LSH_PROJECTION: - case tflite::BuiltinOperator_HASHTABLE_LOOKUP: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_L2_NORMALIZATION: - case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: - case tflite::BuiltinOperator_PADV2: - case tflite::BuiltinOperator_CALL: - case tflite::BuiltinOperator_SKIP_GRAM: - case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: - case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: - case tflite::BuiltinOperator_SQUEEZE: - case tflite::BuiltinOperator_EXP: - case tflite::BuiltinOperator_LOG_SOFTMAX: - case tflite::BuiltinOperator_DELEGATE: - case tflite::BuiltinOperator_PRELU: - case tflite::BuiltinOperator_MAXIMUM: - case tflite::BuiltinOperator_MINIMUM: - case tflite::BuiltinOperator_ARG_MAX: - case tflite::BuiltinOperator_GREATER: - case tflite::BuiltinOperator_GREATER_EQUAL: - case tflite::BuiltinOperator_LESS: - case tflite::BuiltinOperator_LESS_EQUAL: - case tflite::BuiltinOperator_NEG: - case tflite::BuiltinOperator_SELECT: - case tflite::BuiltinOperator_SLICE: - case tflite::BuiltinOperator_SIN: - case tflite::BuiltinOperator_TRANSPOSE_CONV: - case tflite::BuiltinOperator_SPARSE_TO_DENSE: - FATAL("Op code %d is currently not delegated to NNAPI", builtin); - nn_op_type = -1; // set to invalid - break; - case tflite::BuiltinOperator_CUSTOM: - std::string custom_name(registration.custom_name); - if (custom_name.compare("TensorFlowMax") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TENSORFLOW_MAX_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - else if (custom_name.compare("RSQRT") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_RSQRT_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - else if (custom_name.compare("SquaredDifference") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SQUARED_DIFFERENCE_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - - FATAL("Custom operations are not supported when using NNAPI."); - nn_op_type = -1; // set to invalid - break; - } - - //if (nnapi_version == 11 && kAndroidSdkVersion < 28) { - // FATAL("Op %d needs NNAPI1.1", builtin); - //} - - // Add the operation. - CHECK_NN(ANeuralNetworksModel_addOperation( - nn_model, nn_op_type, static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<uint32_t*>(augmented_outputs.data()))); - } -} - -TfLiteStatus NNAPIDelegate::BuildGraph(::tflite::Interpreter* interpreter) { - // TODO(aselle): This is not correct. need to handle resize invalidation. - if (nn_model_ && nn_compiled_model_) return kTfLiteOk; - - if (!nn_model_) { - CHECK_NN(ANeuralNetworksModel_create(&nn_model_)); - - // Find all the temporary tensors and put them in a skip_list. - std::vector<uint32_t> skip_list; - for (size_t i = 0; i < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - if (node.temporaries != nullptr) { - for (int j = 0; j < node.temporaries->size; j++) { - skip_list.push_back(static_cast<uint32_t>(node.temporaries->data[j])); - } - } - } - - uint32_t next_id = addTensorOperands(interpreter, nn_model_, skip_list); - AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, - &model_states_outputs_); - - std::vector<int> augmented_inputs = interpreter->inputs(); - std::vector<int> augmented_outputs = interpreter->outputs(); - - // All state tensors input/output need to be treated as model input/output. - augmented_inputs.insert(augmented_inputs.end(), - model_states_inputs_.begin(), - model_states_inputs_.end()); - augmented_outputs.insert(augmented_outputs.end(), - model_states_outputs_.begin(), - model_states_outputs_.end()); - - CHECK_NN(ANeuralNetworksModel_identifyInputsAndOutputs( - nn_model_, static_cast<uint32_t>(augmented_inputs.size()), - reinterpret_cast<const uint32_t*>(augmented_inputs.data()), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<const uint32_t*>(augmented_outputs.data()))); - CHECK_NN(ANeuralNetworksModel_finish(nn_model_)); - } - if (!nn_compiled_model_) { - CHECK_NN(ANeuralNetworksCompilation_create(nn_model_, &nn_compiled_model_)); - CHECK_NN(ANeuralNetworksCompilation_finish(nn_compiled_model_)); - } - return kTfLiteOk; -} - -TfLiteStatus NNAPIDelegate::Invoke(::tflite::Interpreter* interpreter) { - if (!nn_model_) { - TF_LITE_ENSURE_STATUS(BuildGraph(interpreter)); - } - - ANeuralNetworksExecution* execution = nullptr; - CHECK_NN(ANeuralNetworksExecution_create(nn_compiled_model_, &execution)); - - // Currently perform deep copy of input buffer - for (size_t i = 0; i < interpreter->inputs().size(); i++) { - int input = interpreter->inputs()[i]; - // TODO(aselle): Is this what we want or do we want input instead? - // TODO(aselle): This should be called setInputValue maybe to be cons. - TfLiteTensor* tensor = interpreter->tensor(input); - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - - // Tell nn api where to place final data. - for (size_t i = 0; i < interpreter->outputs().size(); i++) { - int output = interpreter->outputs()[i]; - TfLiteTensor* tensor = interpreter->tensor(output); - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - - // The state_out of previous invocation need to be mapped to state_in of - // current invocation. - for (size_t i = 0; i < model_states_outputs_.size(); i++) { - int state_tensor_idx = model_states_outputs_[i]; - TfLiteTensor* tensor = interpreter->tensor(state_tensor_idx); - // Here we are using a deep copy for state_in tensors so that we are not - // reading and writing into the same buffer during a invocation. - // TODO(miaowang): using double shared buffer to minimize the copies. - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i + interpreter->inputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - // Tell NNAPI where to output the state_out. - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i + interpreter->outputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - } - - // Currently use blocking compute. - ANeuralNetworksEvent* event = nullptr; - CHECK_NN(ANeuralNetworksExecution_startCompute(execution, &event)); - CHECK_NN(ANeuralNetworksEvent_wait(event)); - ANeuralNetworksEvent_free(event); - ANeuralNetworksExecution_free(execution); - -#if 0 - printf("From the NN API:\n"); - TfLiteTensor* tensor = interpreter->tensor(interpreter->outputs()[0]); - if (float* data = - interpreter->typed_tensor<float>(interpreter->outputs()[0])) { - size_t num = tensor->bytes / sizeof(float); - for (float* p = data; p < data + num; p++) { - printf(" %f", *p); - } - printf("\n"); - } -#endif - - return kTfLiteOk; -} - -} // namespace nnfw - -// clang-format on |