/* * 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 elementwise { constexpr uint32_t kNumInputs = 1; constexpr uint32_t kInputTensor = 0; constexpr uint32_t kNumOutputs = 1; constexpr uint32_t kOutputTensor = 0; namespace { template <typename T> inline bool compute(float func(float), const T* input, const Shape& shape, T* output) { const auto size = getNumberOfElements(shape); for (uint32_t i = 0; i < size; ++i) { output[i] = static_cast<T>(func(static_cast<float>(input[i]))); } return true; } bool execute(IOperationExecutionContext* context, float func(float)) { switch (context->getInputType(kInputTensor)) { case OperandType::TENSOR_FLOAT16: return compute(func, context->getInputBuffer<_Float16>(kInputTensor), context->getInputShape(kInputTensor), context->getOutputBuffer<_Float16>(kOutputTensor)); case OperandType::TENSOR_FLOAT32: return compute(func, context->getInputBuffer<float>(kInputTensor), context->getInputShape(kInputTensor), context->getOutputBuffer<float>(kOutputTensor)); default: NN_RET_CHECK_FAIL() << "Unsupported tensor type for elementwise operation"; } } } // namespace bool validate(const IOperationValidationContext* context) { NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs); NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); OperandType inputType = context->getInputType(kInputTensor); NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 || inputType == OperandType::TENSOR_FLOAT32) << "Unsupported tensor type for elementwise operation"; NN_RET_CHECK(validateInputTypes(context, {inputType})); NN_RET_CHECK(validateOutputTypes(context, {inputType})); return validateHalVersion(context, HalVersion::V1_2); } bool prepare(IOperationExecutionContext* context) { Shape input = context->getInputShape(kInputTensor); Shape output = context->getOutputShape(kOutputTensor); NN_RET_CHECK(SetShape(input, &output)); return context->setOutputShape(kOutputTensor, output); } bool executeAbs(IOperationExecutionContext* context) { return execute(context, std::abs); } bool executeExp(IOperationExecutionContext* context) { return execute(context, std::exp); } bool executeLog(IOperationExecutionContext* context) { return execute(context, std::log); } bool executeRsqrt(IOperationExecutionContext* context) { return execute(context, [](float x) { return 1.f / std::sqrt(x); }); } bool executeSin(IOperationExecutionContext* context) { return execute(context, std::sin); } bool executeSqrt(IOperationExecutionContext* context) { return execute(context, std::sqrt); } } // namespace elementwise NN_REGISTER_OPERATION(ABS, "ABS", elementwise::validate, elementwise::prepare, elementwise::executeAbs); NN_REGISTER_OPERATION(EXP, "EXP", elementwise::validate, elementwise::prepare, elementwise::executeExp); NN_REGISTER_OPERATION(LOG, "LOG", elementwise::validate, elementwise::prepare, elementwise::executeLog); NN_REGISTER_OPERATION(RSQRT, "RSQRT", elementwise::validate, elementwise::prepare, elementwise::executeRsqrt); NN_REGISTER_OPERATION(SIN, "SIN", elementwise::validate, elementwise::prepare, elementwise::executeSin); NN_REGISTER_OPERATION(SQRT, "SQRT", elementwise::validate, elementwise::prepare, elementwise::executeSqrt); } // namespace nn } // namespace android