| /* |
| * Copyright (C) 2017 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| #define LOG_TAG "Operations" |
| |
| #include <tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h> |
| #include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h> |
| #include <tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h> |
| #include <tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h> |
| #include <tensorflow/lite/kernels/internal/reference/reference_ops.h> |
| |
| #include <algorithm> |
| #include <limits> |
| #include <vector> |
| |
| #include "ActivationFunctor.h" |
| #include "CpuOperationUtils.h" |
| #include "OperationResolver.h" |
| #include "OperationsUtils.h" |
| #include "Tracing.h" |
| |
| namespace android { |
| namespace nn { |
| |
| namespace activation { |
| |
| constexpr uint32_t kNumInputs = 1; |
| constexpr uint32_t kInputTensor = 0; |
| |
| constexpr uint32_t kNumOutputs = 1; |
| constexpr uint32_t kOutputTensor = 0; |
| |
| namespace { |
| |
| template <typename T> |
| bool reluFloat(const T* inputData, const Shape& inputShape, T* outputData, const Shape& outputShape, |
| float reluMin = 0.f, float reluMax = std::numeric_limits<float>::max()) { |
| NNTRACE_COMP("reluX"); |
| int numElements = getNumberOfElements(inputShape); |
| for (int i = 0; i < numElements; i++, inputData++, outputData++) { |
| *outputData = static_cast<T>( |
| std::min(std::max(reluMin, static_cast<float>(*inputData)), reluMax)); |
| } |
| return true; |
| } |
| template bool reluFloat<float>(const float* inputData, const Shape& inputShape, float* outputData, |
| const Shape& outputShape, float reluMin, float reluMax); |
| template bool reluFloat<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| _Float16* outputData, const Shape& outputShape, float reluMin, |
| float reluMax); |
| |
| template <typename T> |
| bool relu1Float(const T* inputData, const Shape& inputShape, T* outputData, |
| const Shape& outputShape) { |
| return reluFloat(inputData, inputShape, outputData, outputShape, -1.f, 1.f); |
| } |
| template bool relu1Float<float>(const float* inputData, const Shape& inputShape, float* outputData, |
| const Shape& outputShape); |
| template bool relu1Float<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| _Float16* outputData, const Shape& outputShape); |
| |
| template <typename T> |
| bool relu6Float(const T* inputData, const Shape& inputShape, T* outputData, |
| const Shape& outputShape) { |
| return reluFloat(inputData, inputShape, outputData, outputShape, 0.f, 6.f); |
| } |
| template bool relu6Float<float>(const float* inputData, const Shape& inputShape, float* outputData, |
| const Shape& outputShape); |
| template bool relu6Float<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| _Float16* outputData, const Shape& outputShape); |
| |
| bool tanhFloat16(const _Float16* inputData, const Shape& inputShape, _Float16* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("tanhFloat16"); |
| int numElements = getNumberOfElements(inputShape); |
| for (int i = 0; i < numElements; i++, inputData++, outputData++) { |
| *outputData = static_cast<_Float16>(std::tanh(static_cast<float>(*inputData))); |
| } |
| return true; |
| } |
| |
| bool tanhFloat32(const float* inputData, const Shape& inputShape, float* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("tanhFloat32"); |
| int numElements = getNumberOfElements(inputShape); |
| for (int i = 0; i < numElements; i++, inputData++, outputData++) { |
| *outputData = std::tanh(*inputData); |
| } |
| return true; |
| } |
| |
| template <typename T> |
| bool logisticFloat(const T* inputData, const Shape& inputShape, T* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("logisticFloat"); |
| int numElements = getNumberOfElements(inputShape); |
| for (int i = 0; i < numElements; i++, inputData++, outputData++) { |
| *outputData = static_cast<T>(1.f / (1.f + std::exp(static_cast<float>(-*inputData)))); |
| } |
| return true; |
| } |
| template bool logisticFloat<float>(const float* inputData, const Shape& inputShape, |
| float* outputData, const Shape& outputShape); |
| template bool logisticFloat<_Float16>(const _Float16* inputData, const Shape& inputShape, |
| _Float16* outputData, const Shape& outputShape); |
| |
| template <ActivationFn activation> |
| inline bool reluXQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape) { |
| int numElements = getNumberOfElements(inputShape); |
| int32_t output_activation_min = 0; |
| int32_t output_activation_max = 0; |
| |
| CalculateActivationRangeUint8(activation, inputShape, &output_activation_min, |
| &output_activation_max); |
| |
| for (int i = 0; i < numElements; i++, inputData++, outputData++) { |
| *outputData = std::min((uint8_t)output_activation_max, |
| std::max((uint8_t)output_activation_min, *inputData)); |
| } |
| return true; |
| } |
| |
| bool reluQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("reluQuant8"); |
| return reluXQuant8<kActivationRelu>(inputData, inputShape, outputData, outputShape); |
| } |
| |
| bool relu1Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("relu1Quant8"); |
| return reluXQuant8<kActivationRelu1>(inputData, inputShape, outputData, outputShape); |
| } |
| |
| bool relu6Quant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("relu6Quant8"); |
| return reluXQuant8<kActivationRelu6>(inputData, inputShape, outputData, outputShape); |
| } |
| |
| bool tanhQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("tanhQuant8"); |
| if (outputShape.offset != 128 || outputShape.scale != 1.f / 128) { |
| LOG(ERROR) << "incorrect scale or offset for TANH output"; |
| return false; |
| } |
| |
| int numElements = getNumberOfElements(inputShape); |
| static constexpr int kInputIntegerBits = 4; |
| |
| const double input_real_multiplier = |
| inputShape.scale * static_cast<double>(1 << (31 - kInputIntegerBits)); |
| |
| int32_t input_multiplier = 0; |
| int32_t input_left_shift = 0; |
| if (!QuantizeMultiplierGreaterThanOne(input_real_multiplier, &input_multiplier, |
| &input_left_shift)) { |
| return false; |
| } |
| int32_t input_range_radius = CalculateInputRadius(kInputIntegerBits, input_left_shift); |
| |
| NNTRACE_COMP_SWITCH("optimized_ops::Tanh"); |
| tflite::optimized_ops::Tanh(inputData, convertShapeToTflshape(inputShape), inputShape.offset, |
| input_range_radius, input_multiplier, input_left_shift, outputData, |
| convertShapeToTflshape(outputShape)); |
| |
| return true; |
| } |
| |
| bool logisticQuant8(const uint8_t* inputData, const Shape& inputShape, uint8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("logisticQuant8"); |
| if (outputShape.offset != 0 || outputShape.scale != 1.f / 256) { |
| LOG(ERROR) << "incorrect scale / offset for output"; |
| return false; |
| } |
| |
| int numElements = getNumberOfElements(inputShape); |
| static constexpr int kInputIntegerBits = 4; |
| |
| const double input_real_multiplier = |
| inputShape.scale * static_cast<double>(1 << (31 - kInputIntegerBits)); |
| |
| int32_t input_multiplier = 0; |
| int32_t input_left_shift = 0; |
| if (!QuantizeMultiplierGreaterThanOne(input_real_multiplier, &input_multiplier, |
| &input_left_shift)) { |
| return false; |
| } |
| int32_t input_range_radius = CalculateInputRadius(kInputIntegerBits, input_left_shift); |
| |
| NNTRACE_COMP_SWITCH("optimized_ops::Logistic"); |
| tflite::optimized_ops::Logistic( |
| inputData, convertShapeToTflshape(inputShape), inputShape.offset, input_range_radius, |
| input_multiplier, input_left_shift, outputData, convertShapeToTflshape(outputShape)); |
| |
| return true; |
| } |
| |
| template <ActivationFn activation> |
| inline bool reluXQuant8Signed(const int8_t* inputData, const Shape& inputShape, int8_t* outputData, |
| const Shape& outputShape) { |
| int numElements = getNumberOfElements(inputShape); |
| int32_t output_activation_min = 0; |
| int32_t output_activation_max = 0; |
| |
| CalculateActivationRangeInt8(activation, inputShape, &output_activation_min, |
| &output_activation_max); |
| |
| for (int i = 0; i < numElements; i++, inputData++, outputData++) { |
| *outputData = std::min((int8_t)output_activation_max, |
| std::max((int8_t)output_activation_min, *inputData)); |
| } |
| return true; |
| } |
| |
| bool reluQuant8Signed(const int8_t* inputData, const Shape& inputShape, int8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("reluQuant8"); |
| return reluXQuant8Signed<kActivationRelu>(inputData, inputShape, outputData, outputShape); |
| } |
| |
| bool relu1Quant8Signed(const int8_t* inputData, const Shape& inputShape, int8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("relu1Quant8"); |
| return reluXQuant8Signed<kActivationRelu1>(inputData, inputShape, outputData, outputShape); |
| } |
| |
| bool relu6Quant8Signed(const int8_t* inputData, const Shape& inputShape, int8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_COMP("relu6Quant8"); |
| return reluXQuant8Signed<kActivationRelu6>(inputData, inputShape, outputData, outputShape); |
| } |
| |
| bool tanhQuant8Signed(const int8_t* inputData, const Shape& inputShape, int8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("tanhQuant8Signed"); |
| if (outputShape.offset != 0 || outputShape.scale != 1.f / 128) { |
| LOG(ERROR) << "incorrect scale or offset for TANH output"; |
| return false; |
| } |
| |
| int numElements = getNumberOfElements(inputShape); |
| static constexpr int kInputIntegerBits = 4; |
| |
| const double input_real_multiplier = |
| inputShape.scale * static_cast<double>(1 << (31 - kInputIntegerBits)); |
| |
| int32_t input_multiplier = 0; |
| int32_t input_left_shift = 0; |
| if (!QuantizeMultiplierGreaterThanOne(input_real_multiplier, &input_multiplier, |
| &input_left_shift)) { |
| return false; |
| } |
| int32_t input_range_radius = CalculateInputRadius(kInputIntegerBits, input_left_shift); |
| |
| NNTRACE_COMP_SWITCH("reference_integer_ops::Tanh"); |
| tflite::reference_integer_ops::Tanh(inputShape.offset, input_range_radius, input_multiplier, |
| input_left_shift, numElements, inputData, outputData); |
| |
| return true; |
| } |
| |
| bool logisticQuant8Signed(const int8_t* inputData, const Shape& inputShape, int8_t* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("logisticQuant8Signed"); |
| if (outputShape.offset != -128 || outputShape.scale != 1.f / 256) { |
| LOG(ERROR) << "incorrect scale / offset for output"; |
| return false; |
| } |
| |
| int numElements = getNumberOfElements(inputShape); |
| static constexpr int kInputIntegerBits = 4; |
| |
| const double input_real_multiplier = |
| inputShape.scale * static_cast<double>(1 << (31 - kInputIntegerBits)); |
| |
| int32_t input_multiplier = 0; |
| int32_t input_left_shift = 0; |
| if (!QuantizeMultiplierGreaterThanOne(input_real_multiplier, &input_multiplier, |
| &input_left_shift)) { |
| return false; |
| } |
| int32_t input_range_radius = CalculateInputRadius(kInputIntegerBits, input_left_shift); |
| |
| NNTRACE_COMP_SWITCH("reference_integer_ops::Logistic"); |
| tflite::reference_integer_ops::Logistic(inputShape.offset, input_range_radius, input_multiplier, |
| input_left_shift, numElements, inputData, outputData); |
| |
| return true; |
| } |
| |
| void DownScaleInt32ToInt16Multiplier(int32_t multiplier_int32, int16_t* multiplier_int16) { |
| TFLITE_DCHECK_GE(multiplier_int32, 0); |
| static constexpr int32_t kRoundingOffset = 1 << 15; |
| if (multiplier_int32 >= std::numeric_limits<int32_t>::max() - kRoundingOffset) { |
| *multiplier_int16 = std::numeric_limits<int16_t>::max(); |
| return; |
| } |
| const int32_t result = (multiplier_int32 + kRoundingOffset) >> 16; |
| TFLITE_DCHECK_LE(result << 16, multiplier_int32 + kRoundingOffset); |
| TFLITE_DCHECK_GT(result << 16, multiplier_int32 - kRoundingOffset); |
| *multiplier_int16 = result; |
| TFLITE_DCHECK_EQ(*multiplier_int16, result); |
| } |
| |
| template <typename T> |
| bool hardSwishQuant(const T* inputData, const Shape& inputShape, T* outputData, |
| const Shape& outputShape) { |
| tflite::HardSwishParams params; |
| params.input_zero_point = inputShape.offset; |
| params.output_zero_point = outputShape.offset; |
| const float input_scale = inputShape.scale; |
| const float hires_input_scale = (1.0f / 128.0f) * input_scale; |
| const float reluish_scale = 3.0f / 32768.0f; |
| const float output_scale = outputShape.scale; |
| |
| const float output_multiplier = hires_input_scale / output_scale; |
| |
| int32_t output_multiplier_fixedpoint_int32; |
| NN_RET_CHECK(QuantizeMultiplier(output_multiplier, &output_multiplier_fixedpoint_int32, |
| ¶ms.output_multiplier_exponent)); |
| DownScaleInt32ToInt16Multiplier(output_multiplier_fixedpoint_int32, |
| ¶ms.output_multiplier_fixedpoint_int16); |
| NN_RET_CHECK(params.output_multiplier_exponent <= 0); |
| |
| const float reluish_multiplier = hires_input_scale / reluish_scale; |
| int32_t reluish_multiplier_fixedpoint_int32; |
| NN_RET_CHECK(QuantizeMultiplier(reluish_multiplier, &reluish_multiplier_fixedpoint_int32, |
| ¶ms.reluish_multiplier_exponent)); |
| DownScaleInt32ToInt16Multiplier(reluish_multiplier_fixedpoint_int32, |
| ¶ms.reluish_multiplier_fixedpoint_int16); |
| |
| tflite::reference_ops::HardSwish(params, convertShapeToTflshape(inputShape), inputData, |
| convertShapeToTflshape(outputShape), outputData); |
| return true; |
| } |
| |
| } // namespace |
| |
| Result<Version> validate(OperationType opType, const IOperationValidationContext* context) { |
| NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs); |
| NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); |
| auto inputType = context->getInputType(kInputTensor); |
| auto minSupportedVersion = Version::ANDROID_OC_MR1; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| minSupportedVersion = Version::ANDROID_OC_MR1; |
| } else if (inputType == OperandType::TENSOR_FLOAT16) { |
| minSupportedVersion = Version::ANDROID_Q; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| if (opType == OperationType::TANH) { |
| minSupportedVersion = Version::ANDROID_Q; |
| } else { |
| minSupportedVersion = Version::ANDROID_OC_MR1; |
| } |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| minSupportedVersion = Version::ANDROID_R; |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << opType; |
| } |
| const Shape& input = context->getInputShape(kInputTensor); |
| if (hasKnownRank(input)) { |
| NN_RET_CHECK_LE(getNumberOfDimensions(input), 4); |
| } |
| NN_RET_CHECK(validateInputTypes(context, {inputType})); |
| NN_RET_CHECK(validateOutputTypes(context, {inputType})); |
| return minSupportedVersion; |
| } |
| |
| Result<Version> validateHardSwish(const IOperationValidationContext* context) { |
| NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs); |
| NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); |
| auto inputType = context->getInputType(kInputTensor); |
| auto minSupportedVersion = Version::ANDROID_OC_MR1; |
| if (inputType == OperandType::TENSOR_FLOAT16 || inputType == OperandType::TENSOR_FLOAT32 || |
| inputType == OperandType::TENSOR_QUANT8_ASYMM || |
| inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| minSupportedVersion = Version::ANDROID_R; |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation ELU"; |
| } |
| NN_RET_CHECK(validateInputTypes(context, {inputType})); |
| NN_RET_CHECK(validateOutputTypes(context, {inputType})); |
| return minSupportedVersion; |
| } |
| |
| bool prepare(OperationType opType, IOperationExecutionContext* context) { |
| Shape input = context->getInputShape(kInputTensor); |
| if (opType != OperationType::HARD_SWISH) { |
| NN_RET_CHECK_LE(getNumberOfDimensions(input), 4); |
| } |
| Shape output = input; |
| if (input.type == OperandType::TENSOR_QUANT8_ASYMM || |
| input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| bool isSigned = input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED; |
| switch (opType) { |
| case OperationType::HARD_SWISH: { |
| auto outputShape = context->getOutputShape(kOutputTensor); |
| output.scale = outputShape.scale; |
| output.offset = outputShape.offset; |
| } break; |
| case OperationType::RELU: |
| case OperationType::RELU1: |
| case OperationType::RELU6: |
| break; |
| case OperationType::LOGISTIC: |
| output.scale = 1.f / 256; |
| output.offset = isSigned ? -128 : 0; |
| break; |
| case OperationType::TANH: |
| output.scale = 1.f / 128; |
| output.offset = isSigned ? 0 : 128; |
| break; |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported operation type"; |
| } |
| } |
| return context->setOutputShape(kOutputTensor, output); |
| } |
| |
| bool executeRelu(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT16: |
| return reluFloat(context->getInputBuffer<_Float16>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<_Float16>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_FLOAT32: |
| return reluFloat(context->getInputBuffer<float>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<float>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return reluQuant8(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return reluQuant8Signed(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation RELU"; |
| } |
| } |
| |
| bool executeRelu1(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT16: |
| return relu1Float(context->getInputBuffer<_Float16>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<_Float16>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_FLOAT32: |
| return relu1Float(context->getInputBuffer<float>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<float>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return relu1Quant8(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return relu1Quant8Signed(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation RELU1"; |
| } |
| } |
| |
| bool executeRelu6(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT16: |
| return relu6Float(context->getInputBuffer<_Float16>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<_Float16>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_FLOAT32: |
| return relu6Float(context->getInputBuffer<float>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<float>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return relu6Quant8(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return relu6Quant8Signed(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation RELU6"; |
| } |
| } |
| |
| bool executeLogistic(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT16: |
| return logisticFloat(context->getInputBuffer<_Float16>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<_Float16>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_FLOAT32: |
| return logisticFloat(context->getInputBuffer<float>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<float>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return logisticQuant8(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return logisticQuant8Signed(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation LOGISTIC"; |
| } |
| } |
| |
| bool executeTanh(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT16: |
| return tanhFloat16(context->getInputBuffer<_Float16>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<_Float16>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_FLOAT32: |
| return tanhFloat32(context->getInputBuffer<float>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<float>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return tanhQuant8(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return tanhQuant8Signed(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation TANH"; |
| } |
| } |
| |
| bool executeHardSwish(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT16: { |
| const Shape& inputShape = context->getInputShape(kInputTensor); |
| const Shape& outputShape = context->getOutputShape(kOutputTensor); |
| std::vector<float> inputFloat(getNumberOfElements(inputShape)); |
| std::vector<float> outputFloat(getNumberOfElements(outputShape)); |
| convertFloat16ToFloat32(context->getInputBuffer<_Float16>(kInputTensor), &inputFloat); |
| tflite::reference_ops::HardSwish(convertShapeToTflshape(inputShape), inputFloat.data(), |
| convertShapeToTflshape(outputShape), |
| outputFloat.data()); |
| convertFloat32ToFloat16(outputFloat, context->getOutputBuffer<_Float16>(kOutputTensor)); |
| return true; |
| } |
| case OperandType::TENSOR_FLOAT32: { |
| tflite::reference_ops::HardSwish( |
| convertShapeToTflshape(context->getInputShape(kInputTensor)), |
| context->getInputBuffer<float>(kInputTensor), |
| convertShapeToTflshape(context->getOutputShape(kOutputTensor)), |
| context->getOutputBuffer<float>(kOutputTensor)); |
| return true; |
| } |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return hardSwishQuant(context->getInputBuffer<uint8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<uint8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return hardSwishQuant(context->getInputBuffer<int8_t>(kInputTensor), |
| context->getInputShape(kInputTensor), |
| context->getOutputBuffer<int8_t>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation TANH"; |
| } |
| } |
| |
| } // namespace activation |
| |
| using std::placeholders::_1; |
| NN_REGISTER_OPERATION(RELU, "RELU", std::bind(activation::validate, OperationType::RELU, _1), |
| std::bind(activation::prepare, OperationType::RELU, _1), |
| activation::executeRelu, .allowZeroSizedInput = true); |
| NN_REGISTER_OPERATION(RELU1, "RELU1", std::bind(activation::validate, OperationType::RELU1, _1), |
| std::bind(activation::prepare, OperationType::RELU1, _1), |
| activation::executeRelu1, .allowZeroSizedInput = true); |
| NN_REGISTER_OPERATION(RELU6, "RELU6", std::bind(activation::validate, OperationType::RELU6, _1), |
| std::bind(activation::prepare, OperationType::RELU6, _1), |
| activation::executeRelu6, .allowZeroSizedInput = true); |
| NN_REGISTER_OPERATION(LOGISTIC, "LOGISTIC", |
| std::bind(activation::validate, OperationType::LOGISTIC, _1), |
| std::bind(activation::prepare, OperationType::LOGISTIC, _1), |
| activation::executeLogistic, .allowZeroSizedInput = true); |
| NN_REGISTER_OPERATION(TANH, "TANH", std::bind(activation::validate, OperationType::TANH, _1), |
| std::bind(activation::prepare, OperationType::TANH, _1), |
| activation::executeTanh, .allowZeroSizedInput = true); |
| NN_REGISTER_OPERATION(HARD_SWISH, "HARD_SWISH", activation::validateHardSwish, |
| std::bind(activation::prepare, OperationType::HARD_SWISH, _1), |
| activation::executeHardSwish, .allowZeroSizedInput = true); |
| |
| } // namespace nn |
| } // namespace android |
