#include <BinaryArithmeticLayer.h>

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

[legend]

Public Member Functions
	BinaryArithmeticLayer ()

void	configureBackward (IPortableTensor back_prop_lhs, IPortableTensor back_prop_rhs, const IPortableTensor *back_prop_output, const ir::Activation activation, const ArithmeticType arithmetic_type)

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::BinaryArithmeticLayer
	BinaryArithmeticLayer ()

void	configure (const IPortableTensor lhs, const IPortableTensor rhs, IPortableTensor *output, const ir::Activation activation, const ArithmeticType arithmetic_type)

void	run () override

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

virtual void	prepare ()

Additional Inherited Members
Protected Attributes inherited from onert::backend::cpu::ops::BinaryArithmeticLayer
const IPortableTensor *	_lhs

const IPortableTensor *	_rhs

IPortableTensor *	_output

std::function< void(const IPortableTensor , const IPortableTensor , IPortableTensor *)>	_kernel

Detailed Description

Definition at line 37 of file BinaryArithmeticLayer.h.

Constructor & Destructor Documentation

◆ BinaryArithmeticLayer()

onert::backend::train::ops::BinaryArithmeticLayer::BinaryArithmeticLayer ( )

Definition at line 30 of file BinaryArithmeticLayer.cc.

  : cpu::ops::BinaryArithmeticLayer(), _back_prop_lhs{nullptr}, _back_prop_rhs{nullptr},
    _back_prop_output{nullptr}, _arithmetic_type{ArithmeticType::kAdd},
    _activation{ir::Activation::NONE}, _act_back_prop_output{nullptr}
{
  // DO NOTHING
}

Member Function Documentation

◆ backward()

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

overridevirtual

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

Definition at line 59 of file BinaryArithmeticLayer.cc.

{
  // Calculate gradient for activation
  if (_back_prop_output->data_type() != OperandType::FLOAT32)
    throw std::runtime_error{"Unsupported Data Type"};
 
  const IPortableTensor *backprop_act;
  try
  {
    backprop_act =
      backpropActivation(_activation, _output, _back_prop_output, _act_back_prop_output.get());
  }
  catch (const std::exception &e)
  {
    throw std::runtime_error{"train BinaryArithmeticLayer: " + std::string(e.what())};
  }
  assert(backprop_act != nullptr);
 
  nnfw::cker::train::BinaryArithmeticGrad(
    getShape(_lhs), getBuffer<float>(_lhs), getShape(_rhs), getBuffer<float>(_rhs),
    getShape(backprop_act), getBuffer<float>(backprop_act), getShape(_back_prop_lhs),
    getBuffer<float>(_back_prop_lhs), getShape(_back_prop_rhs), getBuffer<float>(_back_prop_rhs),
    static_cast<nnfw::cker::train::ArithmeticType>(_arithmetic_type));
}

References onert::backend::cpu::ops::BinaryArithmeticLayer::_lhs, onert::backend::cpu::ops::BinaryArithmeticLayer::_output, onert::backend::cpu::ops::BinaryArithmeticLayer::_rhs, onert::backend::train::ops::backpropActivation(), nnfw::cker::train::BinaryArithmeticGrad(), onert::backend::IPortableTensor::data_type(), and onert::backend::train::ops::getShape().

◆ configureBackward()

void onert::backend::train::ops::BinaryArithmeticLayer::configureBackward	(	IPortableTensor *	back_prop_lhs,
		IPortableTensor *	back_prop_rhs,
		const IPortableTensor *	back_prop_output,
		const ir::Activation	activation,
		const ArithmeticType	arithmetic_type
	)

Definition at line 38 of file BinaryArithmeticLayer.cc.

{
  _back_prop_lhs = back_prop_lhs;
  _back_prop_rhs = back_prop_rhs;
  _back_prop_output = back_prop_output;
  _arithmetic_type = arithmetic_type;
  _activation = activation;
 
  if (activation != ir::Activation::NONE)
  {
    _act_back_prop_output = std::make_unique<Tensor>(_output->get_info());
    _act_back_prop_output->setBuffer(std::make_shared<basic::Allocator>(_output->total_size()));
  }
}