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.
The documentation for this struct was generated from the following file:
- include/dnnl_types.h