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/*
* Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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.
*/
#ifndef __ONERT_EXEC_TRAIN_TRAINABLE_EXECUTOR_H_
#define __ONERT_EXEC_TRAIN_TRAINABLE_EXECUTOR_H_
#include "exec/IExecutor.h"
#include "../ExecutionObservee.h"
#include "../../compiler/train/TensorRegistries.h"
#include "backend/train/TrainableBackendContext.h"
#include "compiler/train/TrainableCodeMap.h"
#include "compiler/train/LoweredTrainableGraph.h"
#include "ir/Index.h"
#include "util/TracingCtx.h"
namespace onert
{
namespace exec
{
namespace train
{
class TrainableExecutor : public IExecutor
{
public:
/**
* @brief Construct a new TrainableExecutor object
* @param lowered_graph LoweredTrainableGraph object
* @param tensor_builders Tensor builders that are currently used
* @param code_map @c ir::Operation and its code map
*/
TrainableExecutor(std::unique_ptr<compiler::train::LoweredTrainableGraph> lowered_graph,
backend::train::TrainableBackendContexts &&backend_contexts,
const compiler::train::TensorRegistries &tensor_regs,
compiler::train::TrainableCodeMap &&code_map,
const std::vector<ir::OperationIndex> &order,
const util::TracingCtx *tracing_ctx);
public:
const ir::Graph &graph() const final { return _trainable_graph.graph(); }
void execute(const IODescription &desc) override { forward(desc, false); };
void execute(const std::vector<backend::IPortableTensor *> &inputs,
const std::vector<backend::IPortableTensor *> &outputs) override;
void forward(const IODescription &desc, bool training);
void backward(const IODescription &desc, uint32_t training_step);
// Used only in Dataflow and Parallel Executors
void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>> ranks) final
{
_indexed_ranks = std::move(ranks);
};
void addObserver(std::unique_ptr<IExecutionObserver> ref) { _subject.add(std::move(ref)); };
const std::vector<backend::builtin::IOTensor *> &getInputTensors() const override
{
return _input_tensors;
}
const std::vector<backend::builtin::IOTensor *> &getOutputTensors() const override
{
return _output_tensors;
}
float getLoss(const ir::IOIndex &pred_io_ind) const;
backend::train::TrainableBackendContexts &getBackendContexts() { return _backend_contexts; }
private:
void forwardImpl(bool training);
void backwardImpl(uint32_t training_step);
private:
std::vector<compiler::train::TrainableCodeAndInfo> _code;
ExecutionObservee _subject;
std::shared_ptr<ir::OperationIndexMap<int64_t>> _indexed_ranks;
std::unique_ptr<compiler::train::LoweredTrainableGraph> _lowered_graph;
backend::train::TrainableBackendContexts _backend_contexts;
const ir::train::TrainableGraph &_trainable_graph;
compiler::train::TensorRegistries _tensor_regs;
std::vector<backend::builtin::IOTensor *> _input_tensors;
std::vector<backend::builtin::IOTensor *> _output_tensors;
std::mutex _mutex;
const util::TracingCtx *_tracing_ctx;
};
} // namespace train
} // namespace exec
} // namespace onert
#endif // __ONERT_EXEC_TRAIN_TRAINABLE_EXECUTOR_H_
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