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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 <functional>
#include <vector>
#include "IndexedShapeWrapper.h"
#include "OperationResolver.h"
#include "OperationsUtils.h"
namespace android {
namespace nn {
namespace logical {
constexpr uint32_t kNumInputs = 2;
constexpr uint32_t kInputTensor1 = 0;
constexpr uint32_t kInputTensor2 = 1;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
bool compute(const std::function<bool(bool, bool)>& func, const bool8* aData, const Shape& aShape,
const bool8* bData, const Shape& bShape, bool8* outputData, const Shape& outputShape) {
IndexedShapeWrapper aShapeIndexed(aShape);
IndexedShapeWrapper bShapeIndexed(bShape);
IndexedShapeWrapper outputShapeIndexed(outputShape);
std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
bool lastIndex = false;
do {
uint32_t outputFlatIndex;
NN_RET_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
uint32_t aFlatIndex;
NN_RET_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
uint32_t bFlatIndex;
NN_RET_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
outputData[outputFlatIndex] = func(aData[aFlatIndex], bData[bFlatIndex]);
NN_RET_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
} while (!lastIndex);
return true;
}
} // namespace
Result<Version> validate(const IOperationValidationContext* context) {
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
OperandType inputType = context->getInputType(kInputTensor1);
NN_RET_CHECK(inputType == OperandType::TENSOR_BOOL8)
<< "Unsupported tensor type for a logical operation";
NN_RET_CHECK(validateInputTypes(context, {inputType, inputType}));
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
return Version::ANDROID_Q;
}
bool prepare(IOperationExecutionContext* context) {
Shape input1 = context->getInputShape(kInputTensor1);
Shape input2 = context->getInputShape(kInputTensor2);
Shape output = context->getOutputShape(kOutputTensor);
NN_RET_CHECK(calculateBroadcastedShape(input1, input2, &output));
return context->setOutputShape(kOutputTensor, output);
}
bool executeAnd(IOperationExecutionContext* context) {
return compute(
std::logical_and<bool>(), context->getInputBuffer<bool8>(kInputTensor1),
context->getInputShape(kInputTensor1), context->getInputBuffer<bool8>(kInputTensor2),
context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
context->getOutputShape(kOutputTensor));
}
bool executeOr(IOperationExecutionContext* context) {
return compute(
std::logical_or<bool>(), context->getInputBuffer<bool8>(kInputTensor1),
context->getInputShape(kInputTensor1), context->getInputBuffer<bool8>(kInputTensor2),
context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
context->getOutputShape(kOutputTensor));
}
} // namespace logical
NN_REGISTER_OPERATION(LOGICAL_AND, "LOGICAL_AND", logical::validate, logical::prepare,
logical::executeAnd);
NN_REGISTER_OPERATION(LOGICAL_OR, "LOGICAL_OR", logical::validate, logical::prepare,
logical::executeOr);
} // namespace nn
} // namespace android