/* * 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