| /* |
| * Copyright (c) 2021 Arm Limited. All rights reserved. |
| * 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; |
| } |