onert::backend::basic::train Namespace Reference

Data Structures
class	TrainableTensor

Functions
template<typename TensorBuilder >
ITensorRegistry *	genTensors (backend::train::TrainableBackendContext &ctx, const std::shared_ptr< TensorBuilder > &tensor_builder)

Function Documentation

genTensors()

template<typename TensorBuilder >

ITensorRegistry * onert::backend::basic::train::genTensors	(	backend::train::TrainableBackendContext &	ctx,
		const std::shared_ptr< TensorBuilder > &	tensor_builder
	)

Definition at line 28 of file TrainableBackendContextHelpers.h.

{
  const auto &tgraph = *ctx.trainable_graph();
 
  tgraph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) {
    if (ctx.external_operands().contains(ind))
      return;
    tensor_builder->registerTensorInfo(ind, obj.info());
  });
 
  // For the executors that does not have fixed linear execution order:
  // To make tensors never be deallocated, this is a workaround to use static memory planner
  tgraph.operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &) {
    if (tensor_builder->isRegistered(ind))
      tensor_builder->notifyFirstUse(ind);
  });
 
  tensor_builder->allocate();
 
  return ctx.tensor_registry().get();
}

References onert::backend::train::TrainableBackendContext::external_operands(), onert::util::ObjectManager< Index, Object >::iterate(), onert::ir::train::TrainableGraph::operands(), onert::backend::train::TrainableBackendContext::tensor_registry(), and onert::backend::train::TrainableBackendContext::trainable_graph().