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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 "PermutationEliminationPass.h"
#include "backend/controlflow/Config.h"
#include "util/logging.h"
namespace onert
{
namespace compiler
{
namespace pass
{
void PermutationEliminationPass::callback(const ir::OperationIndex &ind, ir::Operation &node)
{
_op_ind = ind;
node.accept(*this);
};
void PermutationEliminationPass::visit(const ir::operation::Permute &node)
{
auto in_operand = node.getInputs().at(0);
auto out_operand = node.getOutputs().at(0);
// Check if two tensors are both portable if not, we can't eliminate the node
{
auto in_def_factor = _lowered_graph.getLowerInfo(in_operand)->def_factors().getOnlyElement();
auto out_def_factor = _lowered_graph.getLowerInfo(out_operand)->def_factors().getOnlyElement();
auto in_config = in_def_factor.backend()->config();
auto out_config = out_def_factor.backend()->config();
// FIXME Supporting dynamic tensor does not exactly mean those are portable.
// It may need to have another config option for checking if each uses `IPortableTensor`.
if (!(in_config->supportDynamicTensor() && out_config->supportDynamicTensor()))
return;
}
if (_graph.getOutputs().contains(out_operand))
{
// If the input is a const, we cannot remove it since we cannot put the constant data in the
// output buffer during prepare phase.
auto permute_input = node.getInputs().at(0);
if (_graph.operands().at(permute_input).isConstant())
return;
// If the input is a model input, we cannot remove it since our API lets users to set different
// buffers for inputs and outputs even though one tensor is both at the same time.
auto permute_output = node.getOutputs().at(0);
if (_graph.getInputs().contains(permute_input) && _graph.getOutputs().contains(permute_output))
return;
// Likewise, if copying between outputs to outputs, keep it.
if (_graph.getOutputs().contains(permute_input) && _graph.getOutputs().contains(permute_output))
return;
// Exceptional case : When the output operand is a model output
// In this case we keep the output and remove the input
auto &out_operand_obj = _graph.operands().at(out_operand);
assert(out_operand_obj.getDef() == _op_ind);
out_operand_obj.unsetDef();
_lowered_graph.op_seqs().iterate([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) {
if (!op_seq.getOutputs().contains(in_operand))
return;
// Update OpSequence/ir::Operation edges and ir::Operand edges
op_seq.replaceOutputs(in_operand, out_operand);
for (auto op : op_seq.operations())
{
auto &operation_obj = _graph.operations().at(op);
if (operation_obj.getOutputs().contains(in_operand))
{
operation_obj.replaceOutputs(in_operand, out_operand);
out_operand_obj.setDef(op);
}
}
});
// Remove Permute operation, enclosing OpSequence and the operand
{
_graph.removeOperand(in_operand);
auto op_seq_ind = _lowered_graph.op_seqs().getOperation(_op_ind);
// Assumes enclosing OpSequence contatins just this Permute operation
assert(_lowered_graph.op_seqs().at(op_seq_ind).size() == 1);
_lowered_graph.op_seqs().remove(op_seq_ind);
_graph.operations().remove(_op_ind);
}
_lowered_graph.op_seqs().iterate([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) {
if (!op_seq.getInputs().contains(in_operand))
return;
op_seq.replaceInputs(in_operand, out_operand);
for (auto op : op_seq.operations())
{
auto &operation_obj = _graph.operations().at(op);
if (operation_obj.getInputs().contains(in_operand))
{
operation_obj.replaceInputs(in_operand, out_operand);
out_operand_obj.insertUse(op);
}
}
});
VERBOSE(removePermute) << "Permute Op removed, node index : " << _op_ind << std::endl;
VERBOSE(removePermute) << " - Input (removed) ir::Operand : " << in_operand << std::endl;
VERBOSE(removePermute) << " - Output(kept) ir::Operand : " << out_operand << std::endl;
}
else
{
// Otherwise keep the input and remove the output
auto &in_operand_obj = _graph.operands().at(in_operand);
in_operand_obj.removeUse(_op_ind);
// Make OpSequences(that use the output) use the input
_lowered_graph.op_seqs().iterate([&](const ir::OpSequenceIndex &, ir::OpSequence &op_seq) {
if (!op_seq.getInputs().contains(out_operand))
return;
op_seq.replaceInputs(out_operand, in_operand);
for (auto op : op_seq.operations())
{
auto &operation_obj = _graph.operations().at(op);
if (operation_obj.getInputs().contains(out_operand))
{
operation_obj.replaceInputs(out_operand, in_operand);
in_operand_obj.insertUse(op);
}
}
});
// Remove Permute operation, enclosing OpSequence and the operand
{
_graph.removeOperand(out_operand);
auto op_seq_ind = _lowered_graph.op_seqs().getOperation(_op_ind);
// Assumes enclosing OpSequence contatins just this Permute operation
assert(_lowered_graph.op_seqs().at(op_seq_ind).size() == 1);
_lowered_graph.op_seqs().remove(op_seq_ind);
_graph.operations().remove(_op_ind);
}
VERBOSE(removePermute) << "Permute Op removed, node index : " << _op_ind << std::endl;
VERBOSE(removePermute) << " - Input (kept) ir::Operand : " << in_operand << std::endl;
VERBOSE(removePermute) << " - Output(removed) ir::Operand : " << out_operand << std::endl;
}
}
} // namespace pass
} // namespace compiler
} // namespace onert
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