/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "Operations"
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#include "HalInterfaces.h"
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#include "IndexedShapeWrapper.h"
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#include "OperationResolver.h"
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#include "OperationsUtils.h"
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#include "Tracing.h"
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#include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
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namespace android {
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namespace nn {
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namespace prelu {
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constexpr char kOperationName[] = "PRELU";
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constexpr uint32_t kNumInputs = 2;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kAlphaTensor = 1;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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template <typename T>
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inline bool eval(const std::function<T(const T&, const T&)>& func, const T* aData,
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const Shape& aShape, const T* bData, const Shape& bShape, T* outputData,
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const Shape& outputShape) {
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IndexedShapeWrapper aShapeIndexed(aShape);
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IndexedShapeWrapper bShapeIndexed(bShape);
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IndexedShapeWrapper outputShapeIndexed(outputShape);
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std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
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bool lastIndex = false;
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do {
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uint32_t outputFlatIndex;
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NN_RET_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
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uint32_t aFlatIndex;
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NN_RET_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
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uint32_t bFlatIndex;
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NN_RET_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
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outputData[outputFlatIndex] = func(aData[aFlatIndex], bData[bFlatIndex]);
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NN_RET_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
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} while (!lastIndex);
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return true;
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}
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bool evalQuant8(const uint8_t* aData, const Shape& aShape, const uint8_t* bData,
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const Shape& bShape, uint8_t* outputData, const Shape& outputShape) {
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const int32_t input_offset = -aShape.offset;
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const int32_t alpha_offset = -bShape.offset;
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const int32_t output_offset = outputShape.offset;
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const double input_product_scale = aShape.scale * bShape.scale;
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const double real_multiplier_pos = aShape.scale / outputShape.scale;
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const double real_multiplier_neg = input_product_scale / outputShape.scale;
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int32_t output_multiplier_pos, output_shift_pos;
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int32_t output_multiplier_neg, output_shift_neg;
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tflite::QuantizeMultiplier(real_multiplier_pos, &output_multiplier_pos, &output_shift_pos);
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tflite::QuantizeMultiplier(real_multiplier_neg, &output_multiplier_neg, &output_shift_neg);
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return eval<uint8_t>(
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[&](const uint8_t& val1, const uint8_t& val2) -> uint8_t {
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const int32_t input = input_offset + static_cast<int32_t>(val1);
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int32_t output_val;
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if (input >= 0) {
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output_val =
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output_offset + tflite::MultiplyByQuantizedMultiplier(
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input, output_multiplier_pos, output_shift_pos);
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} else {
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const int32_t alpha = alpha_offset + static_cast<int32_t>(val2);
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output_val = output_offset +
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tflite::MultiplyByQuantizedMultiplier(
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input * alpha, output_multiplier_neg, output_shift_neg);
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}
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output_val = std::max(0, std::min(255, output_val));
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return static_cast<uint8_t>(output_val);
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},
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aData, aShape, bData, bShape, outputData, outputShape);
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}
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bool validate(const IOperationValidationContext* context) {
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NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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auto inputType = context->getInputType(kInputTensor);
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NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
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inputType == OperandType::TENSOR_FLOAT32 ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM)
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<< "Unsupported tensor type for operation " << kOperationName;
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NN_RET_CHECK(validateInputTypes(context, {inputType, inputType}));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return validateHalVersion(context, HalVersion::V1_2);
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape input = context->getInputShape(kInputTensor);
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Shape alpha = context->getInputShape(kAlphaTensor);
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NN_RET_CHECK(input.type == alpha.type);
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Shape output = context->getOutputShape(kOutputTensor);
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NN_RET_CHECK(calculateBroadcastedShape(input, alpha, &output));
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return context->setOutputShape(kOutputTensor, output);
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}
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bool execute(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return eval<_Float16>(
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[](const _Float16& val1, const _Float16& val2) -> _Float16 {
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return val1 >= 0.0f ? val1 : val1 * val2;
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},
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context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<_Float16>(kAlphaTensor),
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context->getInputShape(kAlphaTensor),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return eval<float>(
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[](const float& val1, const float& val2) -> float {
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return val1 >= 0.0f ? val1 : val1 * val2;
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},
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context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<float>(kAlphaTensor),
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context->getInputShape(kAlphaTensor),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM: {
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return evalQuant8(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<uint8_t>(kAlphaTensor),
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context->getInputShape(kAlphaTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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}
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} // namespace prelu
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NN_REGISTER_OPERATION(PRELU, prelu::kOperationName, prelu::validate, prelu::prepare,
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prelu::execute);
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} // namespace nn
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} // namespace android
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