Performance library for Deep Learning
1.96.0
Pooling

API Reference

## General

The pooling primitive performs forward or backward max or average pooling operation on 1D, 2D, or 3D spatial data.

### Forward

The pooling operation is defined by the following formulas. We show formulas only for 2D spatial data which are straightforward to generalize to cases of higher and lower dimensions. Variable names follow the standard Naming Conventions.

Max pooling:

$\dst(n, c, oh, ow) = \max\limits_{kh, kw} \left( \src(n, c, oh \cdot SH + kh \cdot (DH + 1) - PH_L, ow \cdot SW + kw \cdot (DW + 1) - PW_L) \right)$

Average pooling:

$\dst(n, c, oh, ow) = \frac{1}{DENOM} \sum\limits_{kh, kw} \src(n, c, oh \cdot SH + kh \cdot (DH + 1) - PH_L, ow \cdot SW + kw \cdot (DW + 1) - PW_L)$

Here output spatial dimensions are calculated similarly to how they are done in Convolution.

Average pooling supports two algorithms:

TODO: a picture would be nice here.

### Backward

The backward propagation computes $$\diffsrc(n, c, h, w)$$, based on $$\diffdst(n, c, h, w)$$ and (in case of max pooling) workspace.

## Execution Arguments

When executed, the inputs and outputs should be mapped to an execution argument index as specified by the following table.

Primitive input/output Execution argument index
$$\src$$ DNNL_ARG_SRC
$$\dst$$ DNNL_ARG_DST
workspace DNNL_ARG_WORKSPACE
$$\diffsrc$$ DNNL_ARG_DIFF_SRC
$$\diffdst$$ DNNL_ARG_DIFF_DST
$$binary post-op$$ DNNL_ARG_ATTR_MULTIPLE_POST_OP(binary_post_op_position) | DNNL_ARG_SRC_1

## Implementation Details

### General Notes

1. During training, max pooling requires a workspace on forward (dnnl_forward_training) and backward passes to save indices where a maximum was found. The workspace format is opaque, and the indices cannot be restored from it. However, one can use backward pooling to perform up-sampling (used in some detection topologies). The workspace can be created via workspace_desc() from the pooling primitive descriptor.
2. A user can use memory format tag dnnl_format_tag_any for dst memory descriptor when creating pooling forward propagation. The library would derive the appropriate format from the src memory descriptor. However, the src itself must be defined. Similarly, a user can use memory format tag dnnl_format_tag_any for the diff_src memory descriptor when creating pooling backward propagation.

### Data Type Support

The pooling primitive supports the following combinations of data types:

Propagation Source / Destination Acc
forward / backward f32, bf16 f32
forward f16 f16
forward s8, u8, s32 s32
forward inference s8, u8 / f32 f32
forward inference f32 / s8, u8 f32
Warning
There might be hardware and/or implementation specific restrictions. Check Implementation Limitations section below.

### Data Representation

#### Source, Destination, and Their Gradients

Like other CNN primitives, the pooling primitive expects data to be an $$N \times C \times W$$ tensor for the 1D spatial case, an $$N \times C \times H \times W$$ tensor for the 2D spatial case, and an $$N \times C \times D \times H \times W$$ tensor for the 3D spatial case.

The pooling primitive is optimized for the following memory formats:

Spatial Logical tensor Data type Implementations optimized for memory formats
1D NCW f32 dnnl_ncw (dnnl_abc), dnnl_nwc (dnnl_acb), optimized^
1D NCW s32, s8, u8 dnnl_nwc (dnnl_acb), optimized^
2D NCHW f32 dnnl_nchw (dnnl_abcd), dnnl_nhwc (dnnl_acdb), optimized^
2D NCHW s32, s8, u8 dnnl_nhwc (dnnl_acdb), optimized^
3D NCDHW f32 dnnl_ncdhw (dnnl_abcde), dnnl_ndhwc (dnnl_acdeb), optimized^
3D NCDHW s32, s8, u8 dnnl_ndhwc (dnnl_acdeb), optimized^

Here optimized^ means the format that comes out of any preceding compute-intensive primitive.

### Post-ops and Attributes

Propagation Type Operation Description Restrictions
Forward Post-op Binary Applies a Binary operation to the result General binary post-op restrictions

## Implementation Limitations

1. Refer to Data Types for limitations related to data types support.
2. CPU
• Different data types of source and destination in forward inference are not supported.

N/A

## Examples

Engine Name Com
CPU/GPU Pooling Primitive Example This C++ API example demonstrates how to create and execute a Pooling primitive in forward training propagation mode.