#include <ConvolutionLayer.h>

Collaboration diagram for onert::backend::train::ops::ConvolutionLayer:

[legend]

Public Member Functions
	ConvolutionLayer ()

	~ConvolutionLayer ()

void	configureBackward (const IPortableTensor weights, IPortableTensor back_prop_input, IPortableTensor grad_weights, IPortableTensor grad_bias, const IPortableTensor *back_prop_output, const ir::Activation activation)

void	forward (bool training) override

void	backward () override

Public Member Functions inherited from onert::exec::train::ITrainableFunction
virtual	~ITrainableFunction ()=default

virtual std::optional< backend::train::LayerScopeTensors >	registerLayerScopeTensors ()

Public Member Functions inherited from onert::backend::cpu::ops::ConvolutionLayer
	ConvolutionLayer ()

	~ConvolutionLayer ()

void	configure (const IPortableTensor input, const IPortableTensor kernel, const IPortableTensor bias, ir::PaddingType _paddingType, const uint32_t paddingLeft, const uint32_t paddingRight, const uint32_t paddingTop, const uint32_t paddingBottom, const uint32_t strideWidth, const uint32_t strideHeight, const uint32_t dilationWidthFactor, const uint32_t dilationHeightFactor, const ir::Activation activation, IPortableTensor output, bool is_cachable_weights)

void	prepare () override

void	run () override

Public Member Functions inherited from onert::exec::IFunction
virtual	~IFunction ()=default

Additional Inherited Members
Protected Attributes inherited from onert::backend::cpu::ops::ConvolutionLayer
const IPortableTensor *	_input

const IPortableTensor *	_kernel

const IPortableTensor *	_bias

IPortableTensor *	_output

ir::PaddingType	_paddingType

uint32_t	_paddingLeft

uint32_t	_paddingTop

uint32_t	_paddingRight

uint32_t	_paddingBottom

uint32_t	_strideWidth

uint32_t	_strideHeight

uint32_t	_dilationWidthFactor

uint32_t	_dilationHeightFactor

ir::Activation	_activation

std::unique_ptr< nnfw::cker::Conv >	_conv_kernel

std::unique_ptr< nnfw::cker::ConvHybridTempArena >	_hybrid_arena

bool	_prepare

bool	_is_cachable_weights

bool	_is_hybrid

Detailed Description

Definition at line 34 of file ConvolutionLayer.h.

Constructor & Destructor Documentation

◆ ConvolutionLayer()

onert::backend::train::ops::ConvolutionLayer::ConvolutionLayer ( )

Definition at line 59 of file ConvolutionLayer.cc.

  : cpu::ops::ConvolutionLayer(), _grad_weights{nullptr}, _grad_bias{nullptr},
    _back_prop_input{nullptr}, _back_prop_output{nullptr}, _transposed_weights{nullptr}
{
  // DO NOTHING
}

◆ ~ConvolutionLayer()

onert::backend::train::ops::ConvolutionLayer::~ConvolutionLayer ( )

default

Member Function Documentation

◆ backward()

void onert::backend::train::ops::ConvolutionLayer::backward ( )

overridevirtual

Implements onert::exec::train::ITrainableFunction.

Definition at line 104 of file ConvolutionLayer.cc.

{
  const auto data_type = _back_prop_output->data_type();
  assert(data_type == _input->data_type());
  switch (data_type)
  {
    case OperandType::FLOAT32:
    {
      assert(data_type == _grad_bias->data_type());
      backwardFloat32();
      break;
    }
    default:
      throw std::runtime_error{"train ConvolutionLayer: unsupported data type"};
  }
}

References onert::backend::cpu::ops::ConvolutionLayer::_input, and onert::backend::IPortableTensor::data_type().

◆ configureBackward()

void onert::backend::train::ops::ConvolutionLayer::configureBackward	(	const IPortableTensor *	weights,
		IPortableTensor *	back_prop_input,
		IPortableTensor *	grad_weights,
		IPortableTensor *	grad_bias,
		const IPortableTensor *	back_prop_output,
		const ir::Activation	activation
	)

Definition at line 68 of file ConvolutionLayer.cc.

{
  _back_prop_input = back_prop_input;
  _grad_weights = grad_weights;
  _grad_bias = grad_bias;
  _back_prop_output = back_prop_output;
 
  if (_dilationHeightFactor != 1 || _dilationWidthFactor != 1)
    throw std::runtime_error("train ConvolutionLayer: Unsupported dilation yet");
 
  // TODO Optimize transposed tensors
  _transposed_weights = createTransposedWeights<Tensor>(weights);
  _transposed_weights->setBuffer(
    std::make_shared<basic::Allocator>(_transposed_weights->total_size()));
 
  _conv_back_prop_output = std::make_unique<BackPropTensor>(back_prop_output->get_info());
  _conv_back_prop_output->setBuffer(
    std::make_shared<basic::Allocator>(_conv_back_prop_output->total_size()));
 
  _transposed_grad_weights = createTransposedWeights<GradientTensor>(weights);
  _transposed_grad_weights->setBuffer(
    std::make_shared<basic::Allocator>(_transposed_grad_weights->total_size()));
 
  if (activation != ir::Activation::NONE)
  {
    _act_back_prop_output = std::make_unique<BackPropTensor>(_back_prop_output->get_info());
    _act_back_prop_output->setBuffer(
      std::make_shared<basic::Allocator>(_act_back_prop_output->total_size()));
  }
}