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