onert::exec::train::TrainableExecutor Class Reference

#include <TrainableExecutor.h>

Collaboration diagram for onert::exec::train::TrainableExecutor:

Public Member Functions
	TrainableExecutor (std::unique_ptr< compiler::train::LoweredTrainableGraph > lowered_graph, backend::train::TrainableBackendContexts &&backend_contexts, const compiler::train::TensorRegistries &tensor_regs, compiler::train::TrainableCodeMap &&code_map, const std::vector< ir::OperationIndex > &forward_order, const std::vector< ir::OperationIndex > &backward_order, const util::TracingCtx *tracing_ctx, const ir::train::LossInfo &training_info)
	Construct a new TrainableExecutor object.
const ir::Graph &	graph () const final
	Returns graph object.
void	execute (const std::vector< backend::IPortableTensor * > &inputs, const std::vector< backend::IPortableTensor * > &outputs, const ExecutionOptions &options) override
	Execute with given input/output tensors.
uint32_t	inputSize () const override
	Get input size.
uint32_t	outputSize () const override
	Get output size.
const ir::OperandInfo &	inputInfo (uint32_t index) const override
	Get input info at index.
const ir::OperandInfo &	outputInfo (uint32_t index) const override
	Get output info at index.
ir::Layout	inputLayout (uint32_t index) const override
	Get input layout at index.
ir::Layout	outputLayout (uint32_t index) const override
	Get output layout at index.
void	forward (const std::vector< backend::IPortableTensor * > &inputs, const std::vector< backend::IPortableTensor * > &outputs, const ExecutionOptions &options, bool training)
void	backward (const ExecutionOptions &options, uint32_t training_step)
void	setIndexedRanks (std::shared_ptr< ir::OperationIndexMap< int64_t > > ranks) final
	Set an ordering on operations.
void	addObserver (std::unique_ptr< IExecutionObserver > ref)
float	getLoss (const ir::IOIndex &pred_io_ind) const
void	iterateTrainableTensors (const std::function< void(const ir::OperandIndex &, const backend::train::ITrainableTensor *)> &fn) const
backend::train::TrainableBackendContexts &	getBackendContexts ()
const ExecutionOptions &	currentOptions () const override
	Return current execution configuration.
Public Member Functions inherited from onert::exec::IExecutor
	IExecutor ()=default
	Construct a new IExecutor object.
virtual	~IExecutor ()=default
	Destroy the IExecutor object.

Detailed Description

Definition at line 39 of file TrainableExecutor.h.

Constructor & Destructor Documentation

TrainableExecutor()

onert::exec::train::TrainableExecutor::TrainableExecutor	(	std::unique_ptr< compiler::train::LoweredTrainableGraph >	lowered_graph,
		backend::train::TrainableBackendContexts &&	backend_contexts,
		const compiler::train::TensorRegistries &	tensor_regs,
		compiler::train::TrainableCodeMap &&	code_map,
		const std::vector< ir::OperationIndex > &	forward_order,
		const std::vector< ir::OperationIndex > &	backward_order,
		const util::TracingCtx *	tracing_ctx,
		const ir::train::LossInfo &	training_info
	)

Construct a new TrainableExecutor object.

Parameters

lowered_graph	LoweredTrainableGraph object
tensor_builders	Tensor builders that are currently used
code_map	ir::Operation and its code map

Definition at line 31 of file TrainableExecutor.cc.

  : _code_map{std::move(code_map)}, _forward_order{std::move(forward_order)},
    _backward_order{std::move(backward_order)}, _lowered_graph{std::move(lowered_graph)},
    _backend_contexts{std::move(backend_contexts)},
    _trainable_graph{_lowered_graph->trainable_graph()}, _tensor_regs{std::move(tensor_regs)},
    _mutex(), _tracing_ctx(tracing_ctx), _loss_info(loss_info)
{
  auto build_tensor_list = [&](const auto &ind_seq, auto &tensors) {
    assert(tensors.empty());
    for (auto &&ind : ind_seq)
    {
      backend::ITensor *tensor = _tensor_regs.getITensor(ind);
      assert(tensor != nullptr);
      auto io_tensor = nnfw::misc::polymorphic_downcast<backend::builtin::IOTensor *>(tensor);
      tensors.push_back(io_tensor);
    }
  };
  build_tensor_list(_trainable_graph.getInputs(), _input_tensors);
  build_tensor_list(_trainable_graph.getOutputs(), _output_tensors);
}

References onert::ir::train::TrainableGraph::getInputs(), onert::compiler::train::TensorRegistries::getITensor(), and onert::ir::train::TrainableGraph::getOutputs().

Member Function Documentation

addObserver()

void onert::exec::train::TrainableExecutor::addObserver ( std::unique_ptr< IExecutionObserver >  ref )

inline

Definition at line 98 of file TrainableExecutor.h.

{ _observers.add(std::move(ref)); };

References onert::exec::ExecObservers::add().

backward()

void onert::exec::train::TrainableExecutor::backward	(	const ExecutionOptions &	options,
		uint32_t	training_step
	)

Definition at line 137 of file TrainableExecutor.cc.

{
  // For thread-safe, use mutex
  // TODO: if all used backends on this executor are thread-safe,
  //       do not need to use mutex (otherwise, use mutex)
  std::lock_guard<std::mutex> lock(_mutex);
  _current_options = options;
 
  // Create observee
  ExecutionObservee subject(_observers, options);
 
  backwardImpl(subject, training_step);
}

Referenced by onert::exec::train::TrainableExecutors::train().

currentOptions()

const ExecutionOptions & onert::exec::train::TrainableExecutor::currentOptions ( ) const

inlineoverridevirtual

Return current execution configuration.

Returns

Current execution configuration

Implements onert::exec::IExecutor.

Definition at line 108 of file TrainableExecutor.h.

{ return _current_options; }

execute()

void onert::exec::train::TrainableExecutor::execute ( const std::vector< backend::IPortableTensor * > &  inputs, const std::vector< backend::IPortableTensor * > &  outputs, const ExecutionOptions &  options  )

inlineoverridevirtual

Execute with given input/output tensors.

Input and output tensors must be given.

Parameters

[in]	inputs	Tensors that are passed as inputs
[in]	outputs	Tensors that are passed as outputs
[in]	options	Execution options

Implements onert::exec::IExecutor.

Definition at line 59 of file TrainableExecutor.h.

  {
    forward(inputs, outputs, options, false);
  }

References forward().

forward()

void onert::exec::train::TrainableExecutor::forward	(	const std::vector< backend::IPortableTensor * > &	inputs,
		const std::vector< backend::IPortableTensor * > &	outputs,
		const ExecutionOptions &	options,
		bool	training
	)

Definition at line 59 of file TrainableExecutor.cc.

{
  // For thread-safe, use mutex
  // TODO: if all used backends on this executor are thread-safe,
  //       do not need to use mutex (otherwise, use mutex)
  std::lock_guard<std::mutex> lock(_mutex);
  _current_options = options;
 
  assert(_input_tensors.size() == inputs.size());
  for (uint32_t i = 0; i < _input_tensors.size(); ++i)
  {
    auto tensor = _input_tensors[i];
    const auto input = inputs[i];
    assert(input->buffer() != nullptr || input->get_info().total_size() == 0);
    assert(tensor != nullptr);
    tensor->setTensor(input);
  }
 
  // Set output(s)
  assert(_output_tensors.size() == outputs.size());
  for (uint32_t i = 0; i < _output_tensors.size(); ++i)
  {
    auto tensor = _output_tensors[i];
    const auto output = outputs[i];
    // Output may not be used on training, so don't check optional
    assert(tensor != nullptr);
    tensor->setTensor(output);
  }
 
  // Create observee
  ExecutionObservee subject(_observers, options);
 
  forwardImpl(subject, training);
 
  // TODO Update output(s) desc if desc has dynamic input
}

Referenced by execute().

getBackendContexts()

backend::train::TrainableBackendContexts & onert::exec::train::TrainableExecutor::getBackendContexts ( )

inline

Definition at line 106 of file TrainableExecutor.h.

{ return _backend_contexts; }

getLoss()

float onert::exec::train::TrainableExecutor::getLoss

(

const ir::IOIndex &

pred_io_ind

)

const

Definition at line 198 of file TrainableExecutor.cc.

{
  const auto &loss_ind = _trainable_graph.getLossIndex(pred_io_ind);
  if (loss_ind.undefined())
    throw std::runtime_error{"Loss " + std::to_string(loss_ind.value()) + " is not defined."};
  backend::ITensor *tensor = _tensor_regs.getITensor(loss_ind);
  long double sum = 0;
  for (uint64_t i = 0; i < tensor->getShape().num_elements(); ++i)
  {
    sum += reinterpret_cast<float *>(tensor->buffer())[i];
  }
  if (_loss_info.reduction_type == ir::train::LossReductionType::SumOverBatchSize)
  {
    sum /= tensor->getShape().num_elements();
  }
  return static_cast<float>(sum);
}

References onert::compiler::train::TensorRegistries::getITensor(), onert::ir::train::TrainableGraph::getLossIndex(), onert::ir::train::LossInfo::reduction_type, and onert::ir::train::SumOverBatchSize.

Referenced by onert::exec::train::TrainableExecutors::getLoss().

graph()

const ir::Graph & onert::exec::train::TrainableExecutor::graph ( ) const

inlinefinalvirtual

Returns graph object.

Returns

Graph object

Implements onert::exec::IExecutor.

Definition at line 57 of file TrainableExecutor.h.

{ return _trainable_graph.graph(); }

References onert::ir::train::TrainableGraph::graph().

Referenced by TopologicalSortHelper.TopologicalSortHelper::add_edge(), and TopologicalSortHelper.TopologicalSortHelper::sort_util().

inputInfo()

const ir::OperandInfo & onert::exec::train::TrainableExecutor::inputInfo ( uint32_t  index ) const

inlineoverridevirtual

Get input info at index.

Parameters

[in]

index

Index of input

Returns

Input operand info

Implements onert::exec::IExecutor.

Definition at line 70 of file TrainableExecutor.h.

  {
    return _input_tensors[index]->get_info();
  }

Referenced by onert::exec::train::TrainableExecutors::inputInfo().

inputLayout()

ir::Layout onert::exec::train::TrainableExecutor::inputLayout ( uint32_t  index ) const

inlineoverridevirtual

Get input layout at index.

Parameters

[in]

index

Index of input

Returns

Input operand layout

Implements onert::exec::IExecutor.

Definition at line 80 of file TrainableExecutor.h.

{ return _input_tensors[index]->layout(); }

inputSize()

uint32_t onert::exec::train::TrainableExecutor::inputSize ( ) const

inlineoverridevirtual

Get input size.

Returns

Input size

Implements onert::exec::IExecutor.

Definition at line 66 of file TrainableExecutor.h.

{ return _input_tensors.size(); }

Referenced by onert::exec::train::TrainableExecutors::inputSize().

iterateTrainableTensors()

void onert::exec::train::TrainableExecutor::iterateTrainableTensors

(

const std::function< void(const ir::OperandIndex &, const backend::train::ITrainableTensor *)> &

fn

)

const

Definition at line 216 of file TrainableExecutor.cc.

{
  _tensor_regs.iterateTrainableTensors(fn);
}

References onert::compiler::train::TensorRegistries::iterateTrainableTensors().

Referenced by onert::exec::train::TrainableExecutors::iterateTrainableTensors().

outputInfo()

const ir::OperandInfo & onert::exec::train::TrainableExecutor::outputInfo ( uint32_t  index ) const

inlineoverridevirtual

Get output info at index.

Parameters

[in]

index

Index of output

Returns

Output operand info

Implements onert::exec::IExecutor.

Definition at line 75 of file TrainableExecutor.h.

  {
    return _output_tensors[index]->get_info();
  }

Referenced by onert::exec::train::TrainableExecutors::outputInfo().

outputLayout()

ir::Layout onert::exec::train::TrainableExecutor::outputLayout ( uint32_t  index ) const

inlineoverridevirtual

Get output layout at index.

Parameters

[in]

index

Index of output

Returns

Output operand layout

Implements onert::exec::IExecutor.

Definition at line 82 of file TrainableExecutor.h.

  {
    return _output_tensors[index]->layout();
  }

outputSize()

uint32_t onert::exec::train::TrainableExecutor::outputSize ( ) const

inlineoverridevirtual

Get output size.

Returns

Output size

Implements onert::exec::IExecutor.

Definition at line 68 of file TrainableExecutor.h.

{ return _output_tensors.size(); }

Referenced by onert::exec::train::TrainableExecutors::outputSize().

setIndexedRanks()

void onert::exec::train::TrainableExecutor::setIndexedRanks ( std::shared_ptr< ir::OperationIndexMap< int64_t > >  )

inlinefinalvirtual

Set an ordering on operations.

Parameters

[in]

ranks

The table encoding the ordering

Implements onert::exec::IExecutor.

Definition at line 93 of file TrainableExecutor.h.

  {
    _indexed_ranks = std::move(ranks);
  };

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

• runtime/onert/core/src/exec/train/TrainableExecutor.h
• runtime/onert/core/src/exec/train/TrainableExecutor.cc