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284 lines
13 KiB
284 lines
13 KiB
/*
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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 <functional>
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#include <vector>
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#include "IndexedShapeWrapper.h"
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#include "OperationResolver.h"
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#include "OperationsUtils.h"
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namespace android {
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namespace nn {
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namespace comparisons {
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constexpr uint32_t kNumInputs = 2;
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constexpr uint32_t kInputTensor1 = 0;
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constexpr uint32_t kInputTensor2 = 1;
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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 DataType, typename ComparisonType>
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bool compute(const std::function<bool(ComparisonType, ComparisonType)>& func, const DataType* aData,
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const Shape& aShape, const DataType* bData, const Shape& bShape, bool8* outputData,
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const Shape& outputShape) {
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IndexedShapeWrapper aShapeIndexed(aShape);
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IndexedShapeWrapper bShapeIndexed(bShape);
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IndexedShapeWrapper outputShapeIndexed(outputShape);
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std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
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bool lastIndex = false;
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do {
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uint32_t outputFlatIndex;
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NN_RET_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
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uint32_t aFlatIndex;
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NN_RET_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
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uint32_t bFlatIndex;
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NN_RET_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
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if (aShape.type == OperandType::TENSOR_QUANT8_ASYMM ||
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aShape.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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const float realA = (aData[aFlatIndex] - aShape.offset) * aShape.scale;
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const float realB = (bData[bFlatIndex] - bShape.offset) * bShape.scale;
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outputData[outputFlatIndex] = func(realA, realB);
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} else {
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outputData[outputFlatIndex] = func(aData[aFlatIndex], bData[bFlatIndex]);
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}
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NN_RET_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
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} while (!lastIndex);
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return true;
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}
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template <typename DataType, typename ComparisonType>
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bool executeLessTyped(IOperationExecutionContext* context) {
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return compute<DataType, ComparisonType>(
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std::less<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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template <typename DataType, typename ComparisonType>
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bool executeLessEqualTyped(IOperationExecutionContext* context) {
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return compute<DataType, ComparisonType>(
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std::less_equal<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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template <typename DataType, typename ComparisonType>
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bool executeEqualTyped(IOperationExecutionContext* context) {
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return compute<DataType, ComparisonType>(
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std::equal_to<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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template <typename DataType, typename ComparisonType>
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bool executeNotEqualTyped(IOperationExecutionContext* context) {
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return compute<DataType, ComparisonType>(
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std::not_equal_to<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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template <typename DataType, typename ComparisonType>
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bool executeGreaterEqualTyped(IOperationExecutionContext* context) {
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return compute<DataType, ComparisonType>(
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std::greater_equal<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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template <typename DataType, typename ComparisonType>
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bool executeGreaterTyped(IOperationExecutionContext* context) {
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return compute<DataType, ComparisonType>(
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std::greater<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
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context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
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context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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} // namespace
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Result<Version> 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(kInputTensor1);
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NN_RET_CHECK(
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inputType == OperandType::TENSOR_BOOL8 || inputType == OperandType::TENSOR_FLOAT16 ||
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inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_INT32 ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
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<< "Unsupported input operand type for comparison op: " << inputType;
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NN_RET_CHECK(validateInputTypes(context, {inputType, inputType}));
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NN_RET_CHECK(validateOutputTypes(context, {OperandType::TENSOR_BOOL8}));
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if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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return Version::ANDROID_R;
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} else {
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return Version::ANDROID_Q;
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}
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape input1 = context->getInputShape(kInputTensor1);
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Shape input2 = context->getInputShape(kInputTensor2);
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Shape output = context->getOutputShape(kOutputTensor);
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NN_RET_CHECK(calculateBroadcastedShape(input1, input2, &output));
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return context->setOutputShape(kOutputTensor, output);
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}
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bool executeLess(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return executeLessTyped<_Float16, _Float16>(context);
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case OperandType::TENSOR_FLOAT32:
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return executeLessTyped<float, float>(context);
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case OperandType::TENSOR_INT32:
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return executeLessTyped<int32_t, int32_t>(context);
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case OperandType::TENSOR_QUANT8_ASYMM:
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return executeLessTyped<uint8_t, float>(context);
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return executeLessTyped<int8_t, float>(context);
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case OperandType::TENSOR_BOOL8:
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return executeLessTyped<bool8, bool8>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
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}
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}
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bool executeLessEqual(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return executeLessEqualTyped<_Float16, _Float16>(context);
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case OperandType::TENSOR_FLOAT32:
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return executeLessEqualTyped<float, float>(context);
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case OperandType::TENSOR_INT32:
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return executeLessEqualTyped<int32_t, int32_t>(context);
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case OperandType::TENSOR_QUANT8_ASYMM:
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return executeLessEqualTyped<uint8_t, float>(context);
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return executeLessEqualTyped<int8_t, float>(context);
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case OperandType::TENSOR_BOOL8:
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return executeLessEqualTyped<bool8, bool8>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
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}
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}
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bool executeEqual(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return executeEqualTyped<_Float16, _Float16>(context);
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case OperandType::TENSOR_FLOAT32:
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return executeEqualTyped<float, float>(context);
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case OperandType::TENSOR_INT32:
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return executeEqualTyped<int32_t, int32_t>(context);
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case OperandType::TENSOR_QUANT8_ASYMM:
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return executeEqualTyped<uint8_t, float>(context);
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return executeEqualTyped<int8_t, float>(context);
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case OperandType::TENSOR_BOOL8:
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return executeEqualTyped<bool8, bool8>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
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}
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}
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bool executeNotEqual(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return executeNotEqualTyped<_Float16, _Float16>(context);
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case OperandType::TENSOR_FLOAT32:
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return executeNotEqualTyped<float, float>(context);
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case OperandType::TENSOR_INT32:
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return executeNotEqualTyped<int32_t, int32_t>(context);
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case OperandType::TENSOR_QUANT8_ASYMM:
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return executeNotEqualTyped<uint8_t, float>(context);
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return executeNotEqualTyped<int8_t, float>(context);
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case OperandType::TENSOR_BOOL8:
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return executeNotEqualTyped<bool8, bool8>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
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}
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}
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bool executeGreaterEqual(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return executeGreaterEqualTyped<_Float16, _Float16>(context);
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case OperandType::TENSOR_FLOAT32:
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return executeGreaterEqualTyped<float, float>(context);
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case OperandType::TENSOR_INT32:
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return executeGreaterEqualTyped<int32_t, int32_t>(context);
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case OperandType::TENSOR_QUANT8_ASYMM:
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return executeGreaterEqualTyped<uint8_t, float>(context);
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return executeGreaterEqualTyped<int8_t, float>(context);
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case OperandType::TENSOR_BOOL8:
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return executeGreaterEqualTyped<bool8, bool8>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
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}
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}
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bool executeGreater(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return executeGreaterTyped<_Float16, _Float16>(context);
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case OperandType::TENSOR_FLOAT32:
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return executeGreaterTyped<float, float>(context);
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case OperandType::TENSOR_INT32:
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return executeGreaterTyped<int32_t, int32_t>(context);
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case OperandType::TENSOR_QUANT8_ASYMM:
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return executeGreaterTyped<uint8_t, float>(context);
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return executeGreaterTyped<int8_t, float>(context);
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case OperandType::TENSOR_BOOL8:
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return executeGreaterTyped<bool8, bool8>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
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}
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}
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} // namespace comparisons
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NN_REGISTER_OPERATION(LESS, "LESS", comparisons::validate, comparisons::prepare,
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comparisons::executeLess);
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NN_REGISTER_OPERATION(LESS_EQUAL, "LESS_EQUAL", comparisons::validate, comparisons::prepare,
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comparisons::executeLessEqual);
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NN_REGISTER_OPERATION(EQUAL, "EQUAL", comparisons::validate, comparisons::prepare,
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comparisons::executeEqual);
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NN_REGISTER_OPERATION(NOT_EQUAL, "NOT_EQUAL", comparisons::validate, comparisons::prepare,
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comparisons::executeNotEqual);
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NN_REGISTER_OPERATION(GREATER_EQUAL, "GREATER_EQUAL", comparisons::validate, comparisons::prepare,
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comparisons::executeGreaterEqual);
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NN_REGISTER_OPERATION(GREATER, "GREATER", comparisons::validate, comparisons::prepare,
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comparisons::executeGreater);
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} // namespace nn
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} // namespace android
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