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/*
* SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
* SPDX-License-Identifier: Apache-2.0
*
* 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 "RNNoiseModel.hpp"
#include "log_macros.h"
const tflite::MicroOpResolver& arm::app::RNNoiseModel::GetOpResolver()
{
return this->m_opResolver;
}
bool arm::app::RNNoiseModel::EnlistOperations()
{
this->m_opResolver.AddUnpack();
this->m_opResolver.AddFullyConnected();
this->m_opResolver.AddSplit();
this->m_opResolver.AddSplitV();
this->m_opResolver.AddAdd();
this->m_opResolver.AddLogistic();
this->m_opResolver.AddMul();
this->m_opResolver.AddSub();
this->m_opResolver.AddTanh();
this->m_opResolver.AddPack();
this->m_opResolver.AddReshape();
this->m_opResolver.AddQuantize();
this->m_opResolver.AddConcatenation();
this->m_opResolver.AddRelu();
if (kTfLiteOk == this->m_opResolver.AddEthosU()) {
info("Added %s support to op resolver\n",
tflite::GetString_ETHOSU());
} else {
printf_err("Failed to add Arm NPU support to op resolver.");
return false;
}
return true;
}
bool arm::app::RNNoiseModel::RunInference()
{
return Model::RunInference();
}
void arm::app::RNNoiseModel::ResetGruState()
{
for (auto& stateMapping: this->m_gruStateMap) {
TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.second);
auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor);
/* Initial value of states is 0, but this is affected by quantization zero point. */
auto quantParams = arm::app::GetTensorQuantParams(inputGruStateTensor);
memset(inputGruState, quantParams.offset, inputGruStateTensor->bytes);
}
}
bool arm::app::RNNoiseModel::CopyGruStates()
{
std::vector<std::pair<size_t, std::vector<int8_t>>> tempOutGruStates;
/* Saving output states before copying them to input states to avoid output states modification in the tensor.
* tflu shares input and output tensors memory, thus writing to input tensor can change output tensor values. */
for (auto& stateMapping: this->m_gruStateMap) {
TfLiteTensor* outputGruStateTensor = this->GetOutputTensor(stateMapping.first);
std::vector<int8_t> tempOutGruState(outputGruStateTensor->bytes);
auto* outGruState = tflite::GetTensorData<int8_t>(outputGruStateTensor);
memcpy(tempOutGruState.data(), outGruState, outputGruStateTensor->bytes);
/* Index of the input tensor and the data to copy. */
tempOutGruStates.emplace_back(stateMapping.second, std::move(tempOutGruState));
}
/* Updating input GRU states with saved GRU output states. */
for (auto& stateMapping: tempOutGruStates) {
auto outputGruStateTensorData = stateMapping.second;
TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.first);
if (outputGruStateTensorData.size() != inputGruStateTensor->bytes) {
printf_err("Unexpected number of bytes for GRU state mapping. Input = %zuz, output = %zuz.\n",
inputGruStateTensor->bytes,
outputGruStateTensorData.size());
return false;
}
auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor);
auto* outGruState = outputGruStateTensorData.data();
memcpy(inputGruState, outGruState, inputGruStateTensor->bytes);
}
return true;
}
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