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/*
* Copyright (c) 2019 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 "TensorReduceConverter.h"
#include "Dialect/IR/TFLNodes.h"
#include "Check.h"
#include <oops/InternalExn.h>
#include <loco.h>
#include <loco/Service/ShapeInference.h>
namespace
{
/**
* @brief Convert given TensorReduce as TFLMean
*
* <Before>
* In --- loco::TensorReduce --- Out(s)
*
* <After>
* In -------- locoex::TFLMean --- Out(s)
* /
* TFLConst ---
* (reduction indices)
*/
bool convert_as_mean(loco::TensorReduce *origin)
{
EXO_ASSERT(origin->func() == loco::ReduceFunc::Mean, "func should be Mean for this helper");
EXO_ASSERT(origin->input(), "TensorReduce has no input");
auto *graph = origin->graph();
// Make reduction indicies TFLConst node
auto reduction = graph->nodes()->create<locoex::TFLConst>();
{
auto input_rank = loco::shape_get(origin->input()).as<loco::TensorShape>().rank();
std::vector<int32_t> red_vec;
for (uint32_t axis = 0; axis < input_rank; ++axis)
if (origin->axes()->defined(axis))
red_vec.push_back(static_cast<int32_t>(axis));
const loco::DataType S32 = loco::DataType::S32;
reduction->dtype(S32);
reduction->rank(1);
reduction->dim(0) = red_vec.size();
reduction->size<S32>(red_vec.size());
for (uint32_t i = 0; i < red_vec.size(); ++i)
reduction->at<S32>(i) = red_vec.at(i);
}
// Make TFLMean node to replace
auto mean = graph->nodes()->create<locoex::TFLMean>();
mean->input(origin->input());
mean->reduction_indices(reduction);
mean->keep_dims(true); // Canonical TensorReduce always keep dimensions
// replace canonical node
loco::replace(origin).with(mean);
origin->input(nullptr);
return true;
}
} // namespace
namespace exo
{
bool TensorReduceConverter::convert(loco::TensorReduce *origin)
{
if (origin->func() == loco::ReduceFunc::Mean)
return convert_as_mean(origin);
else
INTERNAL_EXN_V("Unsupported ReduceFunc", oops::to_uint32(origin->func()));
}
} // namespace exo
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