135 lines
6.0 KiB
C++
135 lines
6.0 KiB
C++
/*
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* Copyright (C) 2019 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 "Dequantize.h"
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#include "IndexedShapeWrapper.h"
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#include "OperationResolver.h"
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#include "OperationsExecutionUtils.h"
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namespace android {
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namespace nn {
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namespace dequantize {
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namespace {
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template <typename InputType, typename OutputType>
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bool compute(const InputType* inputData, const Shape& inputShape, OutputType* outputData) {
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const int numElements = getNumberOfElements(inputShape);
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const int32_t zeroPoint = inputShape.offset;
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const float scale = inputShape.scale;
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for (int i = 0; i < numElements; ++i) {
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const int32_t value = inputData[i];
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// This dequantization formula also appears in Elementwise.cpp.
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outputData[i] = static_cast<OutputType>(scale * (value - zeroPoint));
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}
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return true;
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}
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template <typename OutputType>
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bool computePerChannel(const int8_t* inputData, const Shape& inputShape, OutputType* outputData) {
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// First we calculate a stride which is the number of elements we need to
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// skip to change an index along a dimension with different quantization
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// scales.
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const int channelDim =
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std::get<Operand::SymmPerChannelQuantParams>(inputShape.extraParams).channelDim;
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int stride = 1;
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for (int i = getNumberOfDimensions(inputShape) - 1; i > channelDim; --i) {
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stride *= getSizeOfDimension(inputShape, i);
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}
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const int numElements = getNumberOfElements(inputShape);
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const int32_t zeroPoint = inputShape.offset;
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for (int i = 0; i < numElements; ++i) {
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// To get current index along the quantized dimension we calculate how
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// many even |strides| we looped through and take this number modulo the
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// size of the dimension (so that we don't have an overflow if the
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// channelDim is not 0).
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const int scaleIndex = (i / stride) % getSizeOfDimension(inputShape, channelDim);
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const float scale = std::get<Operand::SymmPerChannelQuantParams>(inputShape.extraParams)
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.scales[scaleIndex];
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const int32_t value = inputData[i];
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outputData[i] = static_cast<OutputType>(scale * (value - zeroPoint));
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}
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return true;
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}
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} // namespace
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bool prepare(IOperationExecutionContext* context) {
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const Shape& input = context->getInputShape(kInputTensor);
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NN_RET_CHECK_LE(getNumberOfDimensions(input), 4u);
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Shape output = context->getOutputShape(kOutputTensor);
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output.dimensions = input.dimensions;
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return context->setOutputShape(kOutputTensor, output);
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}
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bool execute(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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const OperandType inputType = context->getInputType(kInputTensor);
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const OperandType outputType = context->getOutputType(kOutputTensor);
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const Shape& inputShape = context->getInputShape(kInputTensor);
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if (inputType == OperandType::TENSOR_QUANT8_ASYMM) {
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const uint8_t* inputBuffer = context->getInputBuffer<uint8_t>(kInputTensor);
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if (outputType == OperandType::TENSOR_FLOAT16) {
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return compute(inputBuffer, inputShape,
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context->getOutputBuffer<_Float16>(kOutputTensor));
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} else if (outputType == OperandType::TENSOR_FLOAT32) {
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return compute(inputBuffer, inputShape, context->getOutputBuffer<float>(kOutputTensor));
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}
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} else if (inputType == OperandType::TENSOR_QUANT8_SYMM) {
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const int8_t* inputBuffer = context->getInputBuffer<int8_t>(kInputTensor);
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if (outputType == OperandType::TENSOR_FLOAT16) {
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return compute(inputBuffer, inputShape,
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context->getOutputBuffer<_Float16>(kOutputTensor));
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} else if (outputType == OperandType::TENSOR_FLOAT32) {
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return compute(inputBuffer, inputShape, context->getOutputBuffer<float>(kOutputTensor));
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}
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} else if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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const int8_t* inputBuffer = context->getInputBuffer<int8_t>(kInputTensor);
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if (outputType == OperandType::TENSOR_FLOAT16) {
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return compute(inputBuffer, inputShape,
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context->getOutputBuffer<_Float16>(kOutputTensor));
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} else if (outputType == OperandType::TENSOR_FLOAT32) {
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return compute(inputBuffer, inputShape, context->getOutputBuffer<float>(kOutputTensor));
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}
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} else if (inputType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
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const int8_t* inputBuffer = context->getInputBuffer<int8_t>(kInputTensor);
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if (outputType == OperandType::TENSOR_FLOAT16) {
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return computePerChannel(inputBuffer, inputShape,
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context->getOutputBuffer<_Float16>(kOutputTensor));
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} else if (outputType == OperandType::TENSOR_FLOAT32) {
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return computePerChannel(inputBuffer, inputShape,
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context->getOutputBuffer<float>(kOutputTensor));
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}
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}
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NN_RET_CHECK_FAIL() << "Unsupported tensor types combination for dequantize op. (input type: "
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<< inputType << " output type: " << outputType << ")";
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}
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} // namespace dequantize
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NN_REGISTER_OPERATION_DEFAULT_VALIDATION(DEQUANTIZE, dequantize::prepare, dequantize::execute,
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.allowZeroSizedInput = true);
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} // namespace nn
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} // namespace android
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