onert::compiler::train::pass::LossInsertionPass Class Reference

#include <LossInsertionPass.h>

Collaboration diagram for onert::compiler::train::pass::LossInsertionPass:

Public Member Functions
	LossInsertionPass (ir::train::TrainableGraph &trainable_graph, const ir::train::TrainingInfo *training_info, const ir::SubgraphIndex &subg_index)
std::string	id () final
void	run () final
Public Member Functions inherited from onert::compiler::train::pass::Pass
	Pass (ir::train::TrainableGraph &trainable_graph, const ir::train::TrainingInfo *training_info)
virtual	~Pass ()=default
Public Member Functions inherited from onert::compiler::pass::IPass
virtual	~IPass ()=default

Additional Inherited Members
Protected Attributes inherited from onert::compiler::train::pass::Pass
ir::train::TrainableGraph &	_trainable_graph
const ir::train::TrainingInfo *	_training_info

Detailed Description

Definition at line 33 of file LossInsertionPass.h.

Constructor & Destructor Documentation

LossInsertionPass()

onert::compiler::train::pass::LossInsertionPass::LossInsertionPass ( ir::train::TrainableGraph &  trainable_graph, const ir::train::TrainingInfo *  training_info, const ir::SubgraphIndex &  subg_index  )

inline

Definition at line 36 of file LossInsertionPass.h.

    : Pass{trainable_graph, training_info}, _subg_index{subg_index}
  {
  }

Member Function Documentation

id()

std::string onert::compiler::train::pass::LossInsertionPass::id ( )

inlinefinalvirtual

Implements onert::compiler::pass::IPass.

Definition at line 44 of file LossInsertionPass.h.

{ return "LossInsertionPass"; }

run()

void onert::compiler::train::pass::LossInsertionPass::run ( )

finalvirtual

Implements onert::compiler::pass::IPass.

Definition at line 32 of file LossInsertionPass.cc.

{
  const auto &loss_info = _training_info->lossInfo();
 
  if (_trainable_graph.getOutputs().size() != 1)
  {
    throw std::runtime_error("LossInsertionPass: Not supported multiple outputs");
  }
 
  // TODO Consider SparseCategoricalCrossentropy y_true shape
  //      SparseCategoricalCrossentropy loss has a different y_true shape than y_pred.
 
  // TODO Implement Loop [0, getOutputs().size())
  //      index: a loop index
  const auto index = 0;
  const auto &y_pred_index = _trainable_graph.getOutputs().at(index);
  const auto &y_pred = _trainable_graph.operands().at(y_pred_index);
  auto y_true_index = _trainable_graph.addOperand(y_pred.shape(), y_pred.typeInfo());
  ir::OperandIndexSequence inputs{y_pred_index, y_true_index};
 
  ir::Shape output_shape;
  if (loss_info.reduction_type == ir::train::LossReductionType::Sum ||
      loss_info.reduction_type == ir::train::LossReductionType::SumOverBatchSize)
  {
    output_shape = ir::Shape{1};
  }
  else
  {
    throw std::runtime_error("LossInsertionPass: Not supported reduction type");
  }
 
  const ir::TypeInfo float_op(ir::DataType::FLOAT32);
  auto output_index = _trainable_graph.addOperand(output_shape, float_op);
  ir::OperandIndexSequence outputs{output_index};
 
  // The y_pred node information may be required in some loss layers (e.g.,
  // CategoricalCrossEntropy(SoftmaxCrossEntropy));
  const auto &y_pred_node = _trainable_graph.operations().at(y_pred.getDef());
  const auto y_pred_op_code = y_pred_node.opcode();
 
  auto loss_op = std::make_unique<ir::operation::Loss>(inputs, outputs);
  auto trainable_loss_op =
    std::make_unique<ir::train::operation::Loss>(*loss_op, loss_info, y_pred_op_code);
  trainable_loss_op->enableBackward();
 
  _trainable_graph.addOperation(std::move(trainable_loss_op));
 
  _trainable_graph.addInput(y_true_index);
 
  // TODO Add loss as many as output size
  _trainable_graph.addLoss(output_index, ir::IOIndex{index});
}

References onert::compiler::train::pass::Pass::_trainable_graph, onert::compiler::train::pass::Pass::_training_info, onert::ir::train::TrainableGraph::addInput(), onert::ir::train::TrainableGraph::addLoss(), onert::ir::train::TrainableGraph::addOperand(), onert::ir::train::TrainableGraph::addOperation(), onert::util::ObjectManager< Index, Object >::at(), onert::ir::OperandIndexSequence::at(), onert::ir::train::TrainableGraph::getOutputs(), onert::ir::train::TrainingInfo::lossInfo(), onert::ir::train::TrainableGraph::operands(), onert::ir::train::TrainableGraph::operations(), output_shape, onert::ir::OperandIndexSequence::size(), onert::ir::train::Sum, and onert::ir::train::SumOverBatchSize.

Referenced by package.infer.session::inference().

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The documentation for this class was generated from the following files:

• runtime/onert/core/src/compiler/train/pass/LossInsertionPass.h
• runtime/onert/core/src/compiler/train/pass/LossInsertionPass.cc