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/*
* Copyright (C) 2019 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.
*/
#include "OperationsUtils.h"
#define LOG_TAG "Operations"
#include "IndexedShapeWrapper.h"
#include "OperationResolver.h"
namespace android {
namespace nn {
namespace dequantize {
constexpr uint32_t kNumInputs = 1;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
template <typename InputType, typename OutputType>
bool compute(const InputType* inputData, const Shape& inputShape, OutputType* outputData) {
const int numElements = getNumberOfElements(inputShape);
const int32_t zeroPoint = inputShape.offset;
const float scale = inputShape.scale;
for (int i = 0; i < numElements; ++i) {
const int32_t value = inputData[i];
outputData[i] = static_cast<OutputType>(scale * (value - zeroPoint));
}
return true;
}
template <typename OutputType>
bool computePerChannel(const int8_t* inputData, const Shape& inputShape, OutputType* outputData) {
// First we calculate a stride which is the number of elements we need to
// skip to change an index along a dimension with different quantization
// scales.
const int channelDim =
std::get<Operand::SymmPerChannelQuantParams>(inputShape.extraParams).channelDim;
int stride = 1;
for (int i = getNumberOfDimensions(inputShape) - 1; i > channelDim; --i) {
stride *= getSizeOfDimension(inputShape, i);
}
const int numElements = getNumberOfElements(inputShape);
const int32_t zeroPoint = inputShape.offset;
for (int i = 0; i < numElements; ++i) {
// To get current index along the quantized dimension we calculate how
// many even |strides| we looped through and take this number modulo the
// size of the dimension (so that we don't have an overflow if the
// channelDim is not 0).
const int scaleIndex = (i / stride) % getSizeOfDimension(inputShape, channelDim);
const float scale = std::get<Operand::SymmPerChannelQuantParams>(inputShape.extraParams)
.scales[scaleIndex];
const int32_t value = inputData[i];
outputData[i] = static_cast<OutputType>(scale * (value - zeroPoint));
}
return true;
}
} // namespace
bool validate(const IOperationValidationContext* context) {
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
const OperandType inputType = context->getInputType(kInputTensor);
const OperandType outputType = context->getOutputType(kOutputTensor);
const Shape& input = context->getInputShape(kInputTensor);
if (hasKnownRank(input)) {
NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
}
if (inputType == OperandType::TENSOR_QUANT8_ASYMM &&
outputType == OperandType::TENSOR_FLOAT32) {
return validateVersion(context, Version::ANDROID_OC_MR1);
}
NN_RET_CHECK(inputType == OperandType::TENSOR_QUANT8_ASYMM ||
inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED ||
inputType == OperandType::TENSOR_QUANT8_SYMM ||
inputType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)
<< "Unsupported input operand type for DEQUANTIZE op: " << inputType;
NN_RET_CHECK(outputType == OperandType::TENSOR_FLOAT16 ||
outputType == OperandType::TENSOR_FLOAT32)
<< "Unsupported output operand type for DEQUANTIZE op: " << outputType;
return validateVersion(context, Version::ANDROID_Q);
}
bool prepare(IOperationExecutionContext* context) {
const Shape& input = context->getInputShape(kInputTensor);
NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
Shape output = context->getOutputShape(kOutputTensor);
output.dimensions = input.dimensions;
return context->setOutputShape(kOutputTensor, output);
}
bool execute(IOperationExecutionContext* context) {
// Bypass execution in the case of zero-sized input.
if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
const OperandType inputType = context->getInputType(kInputTensor);
const OperandType outputType = context->getOutputType(kOutputTensor);
const Shape& inputShape = context->getInputShape(kInputTensor);
if (inputType == OperandType::TENSOR_QUANT8_ASYMM) {
const uint8_t* inputBuffer = context->getInputBuffer<uint8_t>(kInputTensor);
if (outputType == OperandType::TENSOR_FLOAT16) {
return compute(inputBuffer, inputShape,
context->getOutputBuffer<_Float16>(kOutputTensor));
} else if (outputType == OperandType::TENSOR_FLOAT32) {
return compute(inputBuffer, inputShape, context->getOutputBuffer<float>(kOutputTensor));
}
} else if (inputType == OperandType::TENSOR_QUANT8_SYMM) {
const int8_t* inputBuffer = context->getInputBuffer<int8_t>(kInputTensor);
if (outputType == OperandType::TENSOR_FLOAT16) {
return compute(inputBuffer, inputShape,
context->getOutputBuffer<_Float16>(kOutputTensor));
} else if (outputType == OperandType::TENSOR_FLOAT32) {
return compute(inputBuffer, inputShape, context->getOutputBuffer<float>(kOutputTensor));
}
} else if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
const int8_t* inputBuffer = context->getInputBuffer<int8_t>(kInputTensor);
if (outputType == OperandType::TENSOR_FLOAT16) {
return compute(inputBuffer, inputShape,
context->getOutputBuffer<_Float16>(kOutputTensor));
} else if (outputType == OperandType::TENSOR_FLOAT32) {
return compute(inputBuffer, inputShape, context->getOutputBuffer<float>(kOutputTensor));
}
} else if (inputType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
const int8_t* inputBuffer = context->getInputBuffer<int8_t>(kInputTensor);
if (outputType == OperandType::TENSOR_FLOAT16) {
return computePerChannel(inputBuffer, inputShape,
context->getOutputBuffer<_Float16>(kOutputTensor));
} else if (outputType == OperandType::TENSOR_FLOAT32) {
return computePerChannel(inputBuffer, inputShape,
context->getOutputBuffer<float>(kOutputTensor));
}
}
NN_RET_CHECK_FAIL() << "Unsupported tensor types combination for dequantize op. (input type: "
<< inputType << " output type: " << outputType << ")";
}
} // namespace dequantize
NN_REGISTER_OPERATION(DEQUANTIZE, "DEQUANTIZE", dequantize::validate, dequantize::prepare,
dequantize::execute, .allowZeroSizedInput = true);
} // namespace nn
} // namespace android
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