nnfw::cker::functor Namespace Reference

Data Structures
struct	BroadcastTo
struct	FillFunctor
struct	FillFunctor< Eigen::ThreadPoolDevice, T >
struct	FillPhiloxRandom
struct	FillPhiloxRandom< CPUDevice, Distribution >
struct	FillPhiloxRandomTask
struct	FillPhiloxRandomTask< Distribution, false >
struct	FillPhiloxRandomTask< Distribution, true >
struct	InflateFunctor
struct	ReduceFunctor
struct	ReduceOuterDimensions
struct	SetZeroFunctor
struct	StrideFunctor

Typedefs
typedef Eigen::ThreadPoolDevice	CPUDevice

Functions
void	biasReductionHelper (float *input_backprop_buffer, const Shape &input_backprop_shape, float *bias_grad_buffer, const Shape &bias_grad_shape)

Typedef Documentation

CPUDevice

typedef Eigen::ThreadPoolDevice nnfw::cker::functor::CPUDevice

Definition at line 37 of file RandomOp.h.

Function Documentation

biasReductionHelper()

void nnfw::cker::functor::biasReductionHelper	(	float *	input_backprop_buffer,
		const Shape &	input_backprop_shape,
		float *	bias_grad_buffer,
		const Shape &	bias_grad_shape
	)

Definition at line 193 of file redux_functor.h.

{
  assert(input_backprop_buffer);
  assert(bias_grad_buffer);
 
  const nnfw::cker::functor::ReduceOuterDimensions<Eigen::ThreadPoolDevice, float, float, float,
                                                   Eigen::internal::scalar_sum_op<float>>
    redux;
 
  const Tensor input_backprop_t{input_backprop_shape, static_cast<void *>(input_backprop_buffer)};
 
  Tensor bias_grad_t{bias_grad_shape, bias_grad_buffer};
 
  int outer = 1;
  for (int i = 0; i < input_backprop_shape.DimensionsCount() - 1; ++i)
    outer *= input_backprop_shape.Dims(i);
  int inner = input_backprop_shape.Dims(input_backprop_shape.DimensionsCount() - 1);
 
  redux(*eigen_support::GetThreadPoolDevice(), Eigen::DSizes<Eigen::Index, 2>{outer, inner},
        input_backprop_t, &bias_grad_t);
}

References nnfw::cker::Shape::DimensionsCount(), nnfw::cker::Shape::Dims(), and nnfw::cker::eigen_support::GetThreadPoolDevice().

Referenced by onert::backend::train::ops::biasGrad().