Backend.h

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/*
 * Copyright (c) 2020 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_BACKEND_BUILTIN_BACKEND_H__
#define __ONERT_BACKEND_BUILTIN_BACKEND_H__
 
#include "BackendContext.h"
#include "Config.h"
#include "KernelGenerator.h"
#include "TensorBuilder.h"
#include "Tensor.h"
#include "train/BackendContext.h"
#include "train/KernelGenerator.h"
#include "train/TensorRegistry.h"
 
#include <backend/Backend.h>
#include <backend/train/ITrainableBackend.h>
 
#include <memory>
 
namespace onert::backend::builtin
{
 
class Backend : public ::onert::backend::Backend, public backend::train::ITrainableBackend
{
public:
  Backend() : _config{std::make_shared<Config>()} {}
 
  std::shared_ptr<IConfig> config() const override { return _config; }
 
  std::unique_ptr<onert::backend::BackendContext> newContext(ContextData &&data) const override
  {
    auto context = std::make_unique<BackendContext>(this, std::move(data));
    // ControlFlow backend may not build tensors for itself because the backend's operation uses
    // tensors of other baceknd instead
    // But the backend builds tensors in case of that the controlflow operation may have constant
    // input or that consecutive controflow operations exist. We have to make them not to be built
    // later
    // 1. Constant input
    //   These tensors cannot be dynamic tensor, so let's do it as follows:
    //   - always skip copying
    //   - if it is operation's input in child subgraph: register "use" as constant input of the
    //   operations in child subgraph
    //   - if it is child subgraph's output: register "use" as constant input of the operations
    //   using it
    // 2. Consecutive controflow operation's intermediate tensor
    //   These tensors can be dynamic tensor and this is complicated to support without copying. But
    //   there is no such case until now, let's support it later
    // TODO Remove TensorBuilder and ConstantInitializer
    // TODO Support Consecutive controflow operation's intermediate tensor
    auto tr = std::make_shared<TensorRegistry>();
    auto tb = std::make_shared<TensorBuilder>(tr);
    context->tensor_registry = tr;
    context->tensor_builder = tb;
    context->kernel_gen = std::make_shared<KernelGenerator>(
      *context->graph(), tb->dynamicTensorManager(), tr, context->external_context());
    return context;
  }
  std::unique_ptr<onert::backend::BackendContext> newContext(ContextData &&data) const override {…}
 
  std::unique_ptr<backend::train::TrainableBackendContext>
  newContext(backend::train::TrainableContextData &&tdata) const override
  {
    const auto &tgraph = *tdata.tgraph;
    auto tr = std::make_shared<train::TensorRegistry>();
    // TODO Create TensorBuilder if necessary
    auto tdata_ptr = std::make_unique<backend::train::TrainableContextData>(std::move(tdata));
    auto context = std::make_unique<train::BackendContext>(this, std::move(tdata_ptr), tr);
 
    context->kernel_gen =
      std::make_shared<train::KernelGenerator>(tgraph, tr, context->external_context());
    return context;
  }
  newContext(backend::train::TrainableContextData &&tdata) const override {…}
 
private:
  std::shared_ptr<IConfig> _config;
};
class Backend : public ::onert::backend::Backend, public backend::train::ITrainableBackend {…};
 
} // namespace onert::backend::builtin
namespace onert::backend::builtin {…}
 
#endif // __ONERT_BACKEND_BUILTIN_BACKEND_H__