/*
* 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 "MaximumMinimum.h"
#include "IndexedShapeWrapper.h"
#include "OperationsUtils.h"
#include "Tracing.h"
namespace android {
namespace nn {
namespace maximum_minimum {
namespace {
template <typename T>
bool evalGeneric(const T* aData, const Shape& aShape, const T* bData, const Shape& bShape,
bool isMinimum, T* 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_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
uint32_t aFlatIndex;
NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
uint32_t bFlatIndex;
NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
outputData[outputFlatIndex] = isMinimum ? std::min(aData[aFlatIndex], bData[bFlatIndex])
: std::max(aData[aFlatIndex], bData[bFlatIndex]);
NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
} while (!lastIndex);
return true;
}
bool evalQuant8(const uint8_t* aData, const Shape& aShape, const uint8_t* bData,
const Shape& bShape, bool isMinimum, uint8_t* 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_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
uint32_t aFlatIndex;
NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
uint32_t bFlatIndex;
NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
uint8_t aValue = requantize(aData[aFlatIndex], aShape, outputShape);
uint8_t bValue = requantize(bData[bFlatIndex], bShape, outputShape);
outputData[outputFlatIndex] =
isMinimum ? std::min(aValue, bValue) : std::max(aValue, bValue);
NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
} while (!lastIndex);
return true;
}
} // namespace
bool prepare(const Shape& in1, const Shape& in2, Shape* out) {
NN_CHECK(in1.type == in2.type);
return calculateBroadcastedShape(in1, in2, out);
}
bool eval(const void* in1, const Shape& shape1, const void* in2, const Shape& shape2,
bool isMinimum, void* output, const Shape& outputShape) {
NNTRACE_COMP("maximum_minimum::eval");
switch (shape1.type) {
case OperandType::TENSOR_FLOAT16: {
return evalGeneric(reinterpret_cast<const _Float16*>(in1), shape1,
reinterpret_cast<const _Float16*>(in2), shape2, isMinimum,
reinterpret_cast<_Float16*>(output), outputShape);
}
case OperandType::TENSOR_FLOAT32: {
return evalGeneric(reinterpret_cast<const float*>(in1), shape1,
reinterpret_cast<const float*>(in2), shape2, isMinimum,
reinterpret_cast<float*>(output), outputShape);
}
case OperandType::TENSOR_INT32: {
return evalGeneric(reinterpret_cast<const int32_t*>(in1), shape1,
reinterpret_cast<const int32_t*>(in2), shape2, isMinimum,
reinterpret_cast<int32_t*>(output), outputShape);
}
case OperandType::TENSOR_QUANT8_ASYMM: {
return evalQuant8(reinterpret_cast<const uint8_t*>(in1), shape1,
reinterpret_cast<const uint8_t*>(in2), shape2, isMinimum,
reinterpret_cast<uint8_t*>(output), outputShape);
}
default: {
LOG(ERROR) << "Unsupported data type: " << toString(shape1.type);
return false;
}
}
}
} // namespace maximum_minimum
} // namespace nn
} // namespace android