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/*
* 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 neg {
constexpr char kOperationName[] = "NEG";
constexpr uint32_t kNumInputs = 1;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
using namespace hal;
template <typename T>
inline bool compute(const T* input, const Shape& shape, T* output) {
const auto size = getNumberOfElements(shape);
for (uint32_t i = 0; i < size; ++i) {
output[i] = -input[i];
}
return true;
}
} // 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 || inputType == OperandType::TENSOR_INT32)
<< "Unsupported tensor type for operation " << kOperationName;
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 execute(IOperationExecutionContext* context) {
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT16:
return compute(context->getInputBuffer<_Float16>(kInputTensor),
context->getInputShape(kInputTensor),
context->getOutputBuffer<_Float16>(kOutputTensor));
case OperandType::TENSOR_FLOAT32:
return compute(context->getInputBuffer<float>(kInputTensor),
context->getInputShape(kInputTensor),
context->getOutputBuffer<float>(kOutputTensor));
case OperandType::TENSOR_INT32:
return compute(context->getInputBuffer<int32_t>(kInputTensor),
context->getInputShape(kInputTensor),
context->getOutputBuffer<int32_t>(kOutputTensor));
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace neg
NN_REGISTER_OPERATION(NEG, neg::kOperationName, neg::validate, neg::prepare, neg::execute);
} // namespace nn
} // namespace android
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