/*
* Copyright (C) 2018 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 "HalInterfaces.h"
#include "IndexedShapeWrapper.h"
#include "OperationResolver.h"
#include "OperationsUtils.h"
#include "Tracing.h"
#include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
namespace android {
namespace nn {
namespace prelu {
constexpr char kOperationName[] = "PRELU";
constexpr uint32_t kNumInputs = 2;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kAlphaTensor = 1;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
template <typename T>
inline bool eval(const std::function<T(const T&, const T&)>& func, const T* aData,
const Shape& aShape, const T* bData, const Shape& bShape, T* outputData,
const Shape& outputShape) {
IndexedShapeWrapper aShapeIndexed(aShape);
IndexedShapeWrapper bShapeIndexed(bShape);
IndexedShapeWrapper outputShapeIndexed(outputShape);
std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
bool lastIndex = false;
do {
uint32_t outputFlatIndex;
NN_RET_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
uint32_t aFlatIndex;
NN_RET_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
uint32_t bFlatIndex;
NN_RET_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
outputData[outputFlatIndex] = func(aData[aFlatIndex], bData[bFlatIndex]);
NN_RET_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
} while (!lastIndex);
return true;
}
bool evalQuant8(const uint8_t* aData, const Shape& aShape, const uint8_t* bData,
const Shape& bShape, uint8_t* outputData, const Shape& outputShape) {
const int32_t input_offset = -aShape.offset;
const int32_t alpha_offset = -bShape.offset;
const int32_t output_offset = outputShape.offset;
const double input_product_scale = aShape.scale * bShape.scale;
const double real_multiplier_pos = aShape.scale / outputShape.scale;
const double real_multiplier_neg = input_product_scale / outputShape.scale;
int32_t output_multiplier_pos, output_shift_pos;
int32_t output_multiplier_neg, output_shift_neg;
tflite::QuantizeMultiplier(real_multiplier_pos, &output_multiplier_pos, &output_shift_pos);
tflite::QuantizeMultiplier(real_multiplier_neg, &output_multiplier_neg, &output_shift_neg);
return eval<uint8_t>(
[&](const uint8_t& val1, const uint8_t& val2) -> uint8_t {
const int32_t input = input_offset + static_cast<int32_t>(val1);
int32_t output_val;
if (input >= 0) {
output_val =
output_offset + tflite::MultiplyByQuantizedMultiplier(
input, output_multiplier_pos, output_shift_pos);
} else {
const int32_t alpha = alpha_offset + static_cast<int32_t>(val2);
output_val = output_offset +
tflite::MultiplyByQuantizedMultiplier(
input * alpha, output_multiplier_neg, output_shift_neg);
}
output_val = std::max(0, std::min(255, output_val));
return static_cast<uint8_t>(output_val);
},
aData, aShape, bData, bShape, outputData, outputShape);
}
bool validate(const IOperationValidationContext* context) {
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
auto inputType = context->getInputType(kInputTensor);
NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
inputType == OperandType::TENSOR_FLOAT32 ||
inputType == OperandType::TENSOR_QUANT8_ASYMM)
<< "Unsupported tensor type for operation " << kOperationName;
NN_RET_CHECK(validateInputTypes(context, {inputType, inputType}));
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
return validateHalVersion(context, HalVersion::V1_2);
}
bool prepare(IOperationExecutionContext* context) {
Shape input = context->getInputShape(kInputTensor);
Shape alpha = context->getInputShape(kAlphaTensor);
NN_RET_CHECK(input.type == alpha.type);
Shape output = context->getOutputShape(kOutputTensor);
NN_RET_CHECK(calculateBroadcastedShape(input, alpha, &output));
return context->setOutputShape(kOutputTensor, output);
}
bool execute(IOperationExecutionContext* context) {
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT16:
return eval<_Float16>(
[](const _Float16& val1, const _Float16& val2) -> _Float16 {
return val1 >= 0.0f ? val1 : val1 * val2;
},
context->getInputBuffer<_Float16>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<_Float16>(kAlphaTensor),
context->getInputShape(kAlphaTensor),
context->getOutputBuffer<_Float16>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_FLOAT32:
return eval<float>(
[](const float& val1, const float& val2) -> float {
return val1 >= 0.0f ? val1 : val1 * val2;
},
context->getInputBuffer<float>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<float>(kAlphaTensor),
context->getInputShape(kAlphaTensor),
context->getOutputBuffer<float>(kOutputTensor),
context->getOutputShape(kOutputTensor));
case OperandType::TENSOR_QUANT8_ASYMM: {
return evalQuant8(context->getInputBuffer<uint8_t>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputBuffer<uint8_t>(kAlphaTensor),
context->getInputShape(kAlphaTensor),
context->getOutputBuffer<uint8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
}
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace prelu
NN_REGISTER_OPERATION(PRELU, prelu::kOperationName, prelu::validate, prelu::prepare,
prelu::execute);
} // namespace nn
} // namespace android