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Diffstat (limited to 'runtime/onert/backend/cpu/kernel/OperationUtils.h')
| -rw-r--r-- | runtime/onert/backend/cpu/kernel/OperationUtils.h | 162 |
1 files changed, 162 insertions, 0 deletions
diff --git a/runtime/onert/backend/cpu/kernel/OperationUtils.h b/runtime/onert/backend/cpu/kernel/OperationUtils.h new file mode 100644 index 000000000..be9b24f36 --- /dev/null +++ b/runtime/onert/backend/cpu/kernel/OperationUtils.h @@ -0,0 +1,162 @@ +/* + * 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 "../operand/Tensor.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 kernel +{ + +union DataPtr { + uint8_t *u8; + int8_t *i8; + uint32_t *u32; + int32_t *i32; + bool *b; + float *f; + void *v; +}; + +uint32_t getNumberOfDimensions(const operand::Tensor *tensor); + +uint32_t getNumberOfElements(const operand::Tensor *tensor); + +uint32_t getSizeOfDimension(const operand::Tensor *tensor, uint32_t dimensionIdx); + +inline nnfw::cker::Shape convertToExtendedCkerShape(const operand::Tensor *tensor) +{ + assert(tensor); + std::vector<int32_t> raw_shape; + raw_shape.resize(4); + + uint32_t src = 4 - tensor->num_dimensions(); + for (uint32_t i = 0; i < 4; ++i) + { + if (i < src) + { + raw_shape[i] = 1; + } + else + { + raw_shape[i] = tensor->dimension(i - src); + } + } + + return nnfw::cker::GetShape(raw_shape); +} + +inline nnfw::cker::Shape convertTensorToCkerShape(const operand::Tensor *tensor) +{ + assert(tensor); + assert(tensor->layout() == ir::Layout::NHWC); + std::vector<int32_t> raw_shape; + raw_shape.resize(tensor->num_dimensions()); + for (uint32_t i = 0; i < tensor->num_dimensions(); ++i) + { + raw_shape[i] = tensor->dimension(i); + } + + return nnfw::cker::GetShape(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"}; + } +} + +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 operand::Tensor *inputDescr, + const operand::Tensor *filterDescr, + const operand::Tensor *biasDescr, + const operand::Tensor *outputDescr, double *multiplier); + +void QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier, + int *left_shift); + +void CalculateActivationRangeFloat(ir::Activation activation, float *activation_min, + float *activation_max); + +void CalculateActivationRangeUint8(ir::Activation activation, const operand::Tensor *output, + int32_t *act_min, int32_t *act_max); + +bool HaveSameShapes(const operand::Tensor *input1, const operand::Tensor *input2); + +int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift); + +uint32_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions); + +nnfw::cker::PaddingType getPaddingType(ir::PaddingType ir_padding_type); + +} // namespace kernel +} // namespace cpu +} // namespace backend +} // namespace onert + +#endif // __NNFW_SUPPORT_NNAPI_OPERATION_UTILS_H__ |