/* * 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" 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 = inputShape.extraParams.channelQuant().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 = inputShape.extraParams.channelQuant().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); if (inputType == OperandType::TENSOR_QUANT8_ASYMM && outputType == OperandType::TENSOR_FLOAT32) { return validateHalVersion(context, HalVersion::V1_0); } NN_RET_CHECK(inputType == OperandType::TENSOR_QUANT8_ASYMM || inputType == OperandType::TENSOR_QUANT8_SYMM || inputType == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) << "Unsupported input operand type for DEQUANTIZE op: " << toString(inputType); NN_RET_CHECK(outputType == OperandType::TENSOR_FLOAT16 || outputType == OperandType::TENSOR_FLOAT32) << "Unsupported output operand type for DEQUANTIZE op: " << toString(outputType); 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); 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_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: " << toString(inputType) << " output type: " << toString(outputType) << ")"; } } // namespace dequantize NN_REGISTER_OPERATION(DEQUANTIZE, "DEQUANTIZE", dequantize::validate, dequantize::prepare, dequantize::execute, .allowZeroSizedInput = true); } // namespace nn } // namespace android