BackendContext.cc

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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.
 */
 
#include "BackendContext.h"
 
#include "TensorBuilder.h"
#include "KernelGenerator.h"
#include "util/logging.h"
#include "ir/Index.h"
#include "ir/OperandIndexMap.h"
#include "ir/OperandIndexSequence.h"
#include "backend/basic/BackendContextHelpers.h"
 
namespace onert
{
namespace backend
{
namespace xnnpack
{
 
ITensorRegistry *BackendContext::genTensors() { return basic::genTensors(*this); }
 
FunctionMap BackendContext::genKernels()
{
  FunctionMap ret;
 
  for (auto &&op_ind : _data.op_order)
  {
    auto fn_seq = kernel_gen->generate(op_ind);
    ret.emplace(op_ind, std::move(fn_seq));
  }
 
  basic::initConsts(*this);
 
  // NOTE For memory optimization, we want to free some operand data
  const_cast<ir::Graph &>(*_data.graph)
    .operands()
    .iterate([&](const ir::OperandIndex &, ir::Operand &obj) { obj.releaseData(); });
 
  for (auto &&it : ret)
  {
    auto &fn_seq = it.second;
    fn_seq->iterate([&](exec::IFunction &ifunc) { ifunc.prepare(); });
  }
 
  return ret;
}
 
} // namespace xnnpack
} // namespace backend
} // namespace onert