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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 "HalInterfaces.h"
#include "IndexedShapeWrapper.h"
#include "OperationResolver.h"
#include "Tracing.h"
#include <algorithm>
#include <cmath>
namespace android {
namespace nn {
namespace quantize {
constexpr uint32_t kNumInputs = 1;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
using namespace hal;
template <typename T>
bool quantizeToQuant8(const T* inputData, uint8_t* outputData, const Shape& outputShape) {
NNTRACE_COMP("quantizeToQuant8");
uint32_t size = getNumberOfElements(outputShape);
for (uint32_t i = 0; i < size; ++i) {
outputData[i] = static_cast<uint8_t>(std::max<float>(
0.0f, std::min<float>(255.0f, outputShape.offset + std::round(inputData[i] /
outputShape.scale))));
}
return true;
}
template <typename T>
bool quantizeToQuant8Signed(const T* inputData, int8_t* outputData, const Shape& outputShape) {
NNTRACE_COMP("quantizeToQuant8Signed");
uint32_t size = getNumberOfElements(outputShape);
for (uint32_t i = 0; i < size; ++i) {
outputData[i] = static_cast<int8_t>(std::max<float>(
-128.0f,
std::min<float>(127.0f, outputShape.offset +
std::round(inputData[i] / outputShape.scale))));
}
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);
NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
inputType == OperandType::TENSOR_FLOAT32)
<< "Unsupported input operand type for QUANTIZE op: " << toString(inputType);
NN_RET_CHECK(outputType == OperandType::TENSOR_QUANT8_ASYMM ||
outputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
<< "Unsupported output operand type for QUANTIZE op: " << toString(outputType);
if (outputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
return validateHalVersion(context, HalVersion::V1_3);
} else {
return validateHalVersion(context, HalVersion::V1_2);
}
}
bool prepare(IOperationExecutionContext* context) {
const Shape& input = context->getInputShape(kInputTensor);
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);
if (inputType == OperandType::TENSOR_FLOAT32) {
if (outputType == OperandType::TENSOR_QUANT8_ASYMM) {
return quantizeToQuant8<float>(context->getInputBuffer<float>(kInputTensor),
context->getOutputBuffer<uint8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
} else if (outputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
return quantizeToQuant8Signed<float>(context->getInputBuffer<float>(kInputTensor),
context->getOutputBuffer<int8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
}
} else if (inputType == OperandType::TENSOR_FLOAT16) {
if (outputType == OperandType::TENSOR_QUANT8_ASYMM) {
return quantizeToQuant8<_Float16>(context->getInputBuffer<_Float16>(kInputTensor),
context->getOutputBuffer<uint8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
} else if (outputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
return quantizeToQuant8Signed<_Float16>(context->getInputBuffer<_Float16>(kInputTensor),
context->getOutputBuffer<int8_t>(kOutputTensor),
context->getOutputShape(kOutputTensor));
}
}
NN_RET_CHECK_FAIL() << "Unsupported tensor types combination for QUANTIZE op. (input type: "
<< toString(inputType)
<< " output type: " << toString(context->getOutputType(kOutputTensor))
<< ")";
}
} // namespace quantize
NN_REGISTER_OPERATION(QUANTIZE, "QUANTIZE", quantize::validate, quantize::prepare,
quantize::execute, .allowZeroSizedInput = true);
} // namespace nn
} // namespace android
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