dnnl_memory_desc_t Struct Reference

Memory descriptor. More...

#include <dnnl_types.h>

Collaboration diagram for dnnl_memory_desc_t:

Public Attributes
int	ndims
	Number of dimensions.
dnnl_dims_t	dims
	Dimensions in the following order: More...
dnnl_data_type_t	data_type
	Data type of the tensor elements.
dnnl_dims_t	padded_dims
	Size of the data including padding in each dimension.
dnnl_dims_t	padded_offsets
	Per-dimension offset from the padding to actual data, the top-level tensor with offsets applied must lie within the padding area. More...
dnnl_dim_t	offset0
	Offset from memory origin to the current block, non-zero only in a description of a memory sub-block. More...
dnnl_format_kind_t	format_kind
	Memory format kind.
dnnl_blocking_desc_t	blocking
	Description of the data layout for memory formats that use blocking. More...
dnnl_wino_desc_t	wino_desc
	Tensor of weights for integer 8bit winograd convolution.
dnnl_rnn_packed_desc_t	rnn_packed_desc
	Tensor of packed weights for RNN.

Detailed Description

Memory descriptor.

The description is based on a number of dimensions, dimensions themselves, plus information about elements type and memory format. Additionally, contains format-specific descriptions of the data layout.

Examples:

cnn_inference_f32.c, cpu_cnn_training_f32.c, and cross_engine_reorder.c.

Member Data Documentation

dims

dnnl_dims_t dnnl_memory_desc_t::dims

Dimensions in the following order:

• CNN data tensors: mini-batch, channel, spatial ({N, C, [[D,] H,] W})
• CNN weight tensors: group (optional), output channel, input channel, spatial ({[G,] O, I, [[D,] H,] W})
• RNN data tensors: time, mini-batch, channels ({T, N, C}) or layers, directions, states, mini-batch, channels ({L, D, S, N, C})
• RNN weight tensor: layers, directions, input channel, gates, output channels ({L, D, I, G, O}).

Note

The order of dimensions does not depend on the memory format, so whether the data is laid out in dnnl_nchw or dnnl_nhwc the dims for 4D CN data tensor would be {N, C, H, W}.

Examples:

cpu_matmul_quantization.cpp.

padded_offsets

dnnl_dims_t dnnl_memory_desc_t::padded_offsets

Per-dimension offset from the padding to actual data, the top-level tensor with offsets applied must lie within the padding area.

offset0

dnnl_dim_t dnnl_memory_desc_t::offset0

Offset from memory origin to the current block, non-zero only in a description of a memory sub-block.

blocking

dnnl_blocking_desc_t dnnl_memory_desc_t::blocking

Description of the data layout for memory formats that use blocking.

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The documentation for this struct was generated from the following file:

• include/oneapi/dnnl/dnnl_types.h