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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 ruy
{
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_back(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 ruy
} // namespace backend
} // namespace onert
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