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/*
* Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2019 The TensorFlow Authors. 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 "kernels/While.h"
#include "kernels/Utils.h"
#include <cstring>
namespace luci_interpreter
{
namespace kernels
{
namespace
{
void copy(const std::vector<const Tensor *> &src, const std::vector<Tensor *> &dst)
{
for (size_t i = 0; i < src.size(); ++i)
{
LUCI_INTERPRETER_CHECK(dst[i]->element_type() == src[i]->element_type());
dst[i]->resize(src[i]->shape());
const int32_t num_elements = src[i]->shape().num_elements();
const std::size_t element_size = getDataTypeSize(src[i]->element_type());
std::memcpy(dst[i]->data<void>(), src[i]->data<void>(), num_elements * element_size);
}
}
void copy(const std::vector<Tensor *> &src, const std::vector<Tensor *> &dst)
{
std::vector<const Tensor *> const_src;
for (const auto &t : src)
const_src.push_back(t);
copy(const_src, dst);
}
// TODO: Think about how allocate memory for output in main graph
void configureTensorsAllocations(const std::vector<Tensor *> &tensors, RuntimeGraph *run_graph)
{
for (auto tensor : tensors)
run_graph->configureAllocations(tensor);
}
} // namespace
While::While(std::vector<const Tensor *> inputs, std::vector<Tensor *> outputs,
RuntimeGraph *cond_graph, RuntimeGraph *body_graph)
: Kernel(std::move(inputs), std::move(outputs)), _cond_graph(cond_graph), _body_graph(body_graph)
{
}
void While::configure()
{
LUCI_INTERPRETER_CHECK(_body_graph->getInputTensors().size() == getInputTensors().size());
LUCI_INTERPRETER_CHECK(_body_graph->getOutputTensors().size() == getOutputTensors().size());
LUCI_INTERPRETER_CHECK(_body_graph->getOutputTensors().size() == getInputTensors().size());
LUCI_INTERPRETER_CHECK(_cond_graph->getInputTensors().size() == getInputTensors().size());
const auto &cond_outputs = _cond_graph->getOutputTensors();
LUCI_INTERPRETER_CHECK(cond_outputs.size() == 1)
LUCI_INTERPRETER_CHECK(cond_outputs[0]->element_type() == DataType::BOOL);
}
/**
* @note Dynamic shape such as {1, 0, 8} may fail in tensor->data()
*/
void While::execute() const
{
const auto &cond_inputs = _cond_graph->getInputTensors();
const auto &cond_outputs = _cond_graph->getOutputTensors();
configureTensorsAllocations(cond_inputs, _cond_graph);
copy(getInputTensors(), cond_inputs);
const auto &body_inputs = _body_graph->getInputTensors();
const auto &body_outputs = _body_graph->getOutputTensors();
configureTensorsAllocations(body_inputs, _body_graph);
while (true)
{
_cond_graph->execute();
bool cond_value = cond_outputs[0]->data<bool>()[0];
if (!cond_value)
break;
copy(cond_inputs, body_inputs);
_body_graph->execute();
copy(body_outputs, cond_inputs);
}
copy(cond_inputs, getOutputTensors());
}
} // namespace kernels
} // namespace luci_interpreter
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