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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 "luci/Pass/SparsifyTensorPass.h"
#include "Sparsifier.h"
#include <luci/IR/CircleNodes.h>
namespace luci
{
template <loco::DataType DT> void SparsifyTensorPass::sparsify_tensor(luci::CircleConst *cop)
{
using PRIMITIVE_DTYPE = typename loco::DataTypeImpl<DT>::Type;
std::vector<int32_t> dense_tensor_shape(cop->rank());
for (uint32_t d = 0; d < cop->rank(); d++)
{
dense_tensor_shape.at(d) = cop->dim(d).value();
}
Sparsifier<PRIMITIVE_DTYPE> sparsifier(dense_tensor_shape, _traversal_order, _format, _block_size,
_block_map);
// get dense tensor data
uint32_t dense_tensor_data_size = cop->size<DT>();
std::vector<PRIMITIVE_DTYPE> dense_tensor_data(dense_tensor_data_size);
for (uint32_t i = 0; i < dense_tensor_data_size; i++)
{
dense_tensor_data.at(i) = cop->at<DT>(i);
}
// sparsify
sparsifier.DenseToSparse(dense_tensor_data.data());
// get sparse tensor data
std::vector<PRIMITIVE_DTYPE> sparse_tensor_data = sparsifier.GetData();
uint32_t sparse_tensor_data_size = sparse_tensor_data.size();
cop->size<DT>(sparse_tensor_data_size);
for (uint32_t i = 0; i < sparse_tensor_data_size; i++)
{
cop->at<DT>(i) = sparse_tensor_data.at(i);
}
// make sparsity parameter
auto sparsityparam = std::make_unique<SparsityParam>();
sparsityparam->traversal_order = _traversal_order;
sparsityparam->block_map = _block_map;
// get dimension meta data
const auto dim_metadata = sparsifier.GetDimMetadata();
for (uint32_t idx = 0; idx < _format.size(); idx++)
{
if (_format.at(idx) == DimensionType::DENSE)
{
sparsityparam->dim_metadata.emplace_back(DimensionType::DENSE,
dim_metadata.at(idx * 2).at(0));
}
// TODO Set SparseIndexVectorType according to its data range
else if (_format.at(idx) == DimensionType::SPARSE_CSR)
{
sparsityparam->dim_metadata.emplace_back(
DimensionType::SPARSE_CSR, /* dense size */ 0,
/* array_segments */
SparseIndexVector{SparseIndexVectorType::U16, dim_metadata.at(idx * 2)},
/* array_indices */
SparseIndexVector{SparseIndexVectorType::U16, dim_metadata.at(idx * 2 + 1)});
}
}
for (uint32_t i = 0; i < _block_size.size(); i++)
{
assert(_block_size.at(i) == dim_metadata.at((_format.size() + i) * 2).at(0));
sparsityparam->dim_metadata.emplace_back(DimensionType::DENSE, _block_size.at(i));
}
cop->sparsityparam(std::move(sparsityparam));
}
bool SparsifyTensorPass::run(loco::Graph *g)
{
bool changed = false;
for (auto node : loco::active_nodes(loco::output_nodes(g)))
{
auto cop = dynamic_cast<luci::CircleConst *>(node);
if (not cop)
continue;
if (cop->name() != _tensor_name)
continue;
switch (cop->dtype())
{
case loco::DataType::S32:
sparsify_tensor<loco::DataType::S32>(cop);
break;
case loco::DataType::S8:
sparsify_tensor<loco::DataType::S8>(cop);
break;
case loco::DataType::FLOAT32:
sparsify_tensor<loco::DataType::FLOAT32>(cop);
break;
default:
throw std::runtime_error("SparsifyTensorPass: Unsupported dtype.");
}
changed = true;
}
return changed;
}
template void SparsifyTensorPass::sparsify_tensor<loco::DataType::S32>(luci::CircleConst *cop);
template void SparsifyTensorPass::sparsify_tensor<loco::DataType::S8>(luci::CircleConst *cop);
template void SparsifyTensorPass::sparsify_tensor<loco::DataType::FLOAT32>(luci::CircleConst *cop);
} // namespace luci
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