/*
* 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 "OperationResolver.h"
#include "OperationsUtils.h"
#include "Tracing.h"
#include <cmath>
namespace android {
namespace nn {
namespace log_softmax {
constexpr char kOperationName[] = "LOG_SOFTMAX";
constexpr uint32_t kNumInputs = 3;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kInputBeta = 1;
constexpr uint32_t kInputAxis = 2;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
template <typename T>
inline bool compute(const T* input, const Shape& shape, T beta, uint32_t axis, T* output) {
const uint32_t outerSize = getNumberOfElements(shape, 0, axis);
const uint32_t axisSize = getSizeOfDimension(shape, axis);
const uint32_t innerSize = getNumberOfElements(shape, axis + 1, getNumberOfDimensions(shape));
for (uint32_t outer = 0; outer < outerSize; ++outer) {
for (uint32_t inner = 0; inner < innerSize; ++inner) {
// We subtract the maximum value from each element to ensure
// numerical stability, taking advantage of the following equality:
// exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C))
T maxValue = input[outer * axisSize * innerSize + inner];
for (uint32_t i = 1; i < axisSize; ++i) {
maxValue = std::max(maxValue, input[(outer * axisSize + i) * innerSize + inner]);
}
T sum = 0;
for (uint32_t i = 0; i < axisSize; ++i) {
sum += std::exp(static_cast<double>(
(input[(outer * axisSize + i) * innerSize + inner] - maxValue) * beta));
}
const T logSum = std::log(static_cast<double>(sum));
for (uint32_t i = 0; i < axisSize; ++i) {
output[(outer * axisSize + i) * innerSize + inner] =
(input[(outer * axisSize + i) * innerSize + inner] - maxValue) * beta -
logSum;
}
}
}
return true;
}
bool validate(const IOperationValidationContext* context) {
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
OperandType inputType = context->getInputType(kInputTensor);
std::vector<OperandType> inExpectedTypes;
std::vector<OperandType> outExpectedTypes;
if (inputType == OperandType::TENSOR_FLOAT32) {
inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::FLOAT32, OperandType::INT32};
outExpectedTypes = {OperandType::TENSOR_FLOAT32};
} else if (inputType == OperandType::TENSOR_FLOAT16) {
inExpectedTypes = {OperandType::TENSOR_FLOAT16, OperandType::FLOAT16, OperandType::INT32};
outExpectedTypes = {OperandType::TENSOR_FLOAT16};
} else {
LOG(ERROR) << "Unsupported input tensor type for operation " << kOperationName;
return false;
}
NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
NN_RET_CHECK(validateOutputTypes(context, outExpectedTypes));
return validateHalVersion(context, HalVersion::V1_2);
}
bool prepare(IOperationExecutionContext* context) {
return context->setOutputShape(kOutputTensor, context->getInputShape(kInputTensor));
}
bool execute(IOperationExecutionContext* context) {
int32_t axis = context->getInputValue<int32_t>(kInputAxis);
NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT16:
return compute(context->getInputBuffer<_Float16>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputValue<_Float16>(kInputBeta), axis,
context->getOutputBuffer<_Float16>(kOutputTensor));
case OperandType::TENSOR_FLOAT32:
return compute(context->getInputBuffer<float>(kInputTensor),
context->getInputShape(kInputTensor),
context->getInputValue<float>(kInputBeta), axis,
context->getOutputBuffer<float>(kOutputTensor));
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace log_softmax
NN_REGISTER_OPERATION(LOG_SOFTMAX, log_softmax::kOperationName, log_softmax::validate,
log_softmax::prepare, log_softmax::execute);
} // namespace nn
} // namespace android