Pooling

Pooling#

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

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 Conventions.

Forward#

Max pooling:

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

Average pooling:

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

Here output spatial dimensions are calculated similarly to how they are done for Convolution and Deconvolution.

Average pooling supports two algorithms:

pooling_avg_include_padding, in which case \(DENOM = KH \cdot KW\),
pooling_avg_exclude_padding, in which case \(DENOM\) equals to the size of overlap between an averaging window and images.

Difference Between Forward Training and Forward Inference#

Max pooling requires a workspace for the forward_training propagation kind, and does not require it for forward_inference (see details below).

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`

Operation Details#

During training, max pooling requires a workspace on forward (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 dnnl::pooling_forward::primitive_desc::workspace_desc().
A user can use memory 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 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.

Note

Here we abbreviate data types names for readability. For example, dnnl::memory::data_type::f32 is abbreviated to f32.

Propagation	Source / Destination	Accumulation data type (used for average pooling only)
forward / backward	`f32`, `bf16`	`f32`
forward	`f16`	`f16`
forward	`s8`, `u8`, `s32`	`s32`

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	`ncw` (`abc`), `nwc` (`acb`), optimized^
1D	NCW	s32, s8, u8	`nwc` (`acb`), optimized^
2D	NCHW	f32	`nchw` (`abcd`), `nhwc` (`acdb`), optimized^
2D	NCHW	s32, s8, u8	`nhwc` (`acdb`), optimized^
3D	NCDHW	f32	`ncdhw` (`abcde`), `ndhwc` (`acdeb`), optimized^
3D	NCDHW	s32, s8, u8	`ndhwc` (`acdeb`), optimized^

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

Post-ops and Attributes#

The pooling primitive does not support any post-ops or attributes.

API#

struct pooling_forward : public dnnl::primitive #

Pooling forward propagation primitive.

Public Functions

pooling_forward()#: Default constructor. Produces an empty object.

pooling_forward(const primitive_desc &pd)#

Constructs a pooling forward propagation primitive.

Parameters:: pd – Primitive descriptor for a pooling forward propagation primitive.

struct primitive_desc : public dnnl::primitive_desc #

Primitive descriptor for a pooling forward propagation primitive.

Public Functions

primitive_desc() = default#: Default constructor. Produces an empty object.

primitive_desc(const engine &aengine, prop_kind aprop_kind, algorithm aalgorithm, const memory::desc &src_desc, const memory::desc &dst_desc, const memory::dims &strides, const memory::dims &kernel, const memory::dims &dilation, const memory::dims &padding_l, const memory::dims &padding_r, const primitive_attr &attr = default_attr(), bool allow_empty = false)#

Constructs a primitive descriptor for pooling forward propagation primitive.

Arrays strides, kernel, dilation, padding_l and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

aengine – Engine to use.
aprop_kind – Propagation kind. Possible values are dnnl::prop_kind::forward_training, and dnnl::prop_kind::forward_inference.
aalgorithm – Pooling algorithm kind: either dnnl::algorithm::pooling_max, dnnl::algorithm::pooling_avg_include_padding, or dnnl::algorithm::pooling_avg_exclude_padding.
src_desc – Source memory descriptor.
dst_desc – Destination memory descriptor.
strides – Vector of strides for spatial dimension.
kernel – Vector of kernel spatial dimensions.
dilation – Array of dilations for spatial dimension.
padding_l – Vector of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r – Vector of padding values for high indices for each spatial dimension ([[back,] bottom,] right).
attr – Primitive attributes to use. Attributes are optional and default to empty attributes.
allow_empty – A flag signifying whether construction is allowed to fail without throwing an exception. In this case an empty object will be produced. This flag is optional and defaults to false.

memory::desc src_desc() const#

Returns a source memory descriptor.

Returns:: Source memory descriptor.
Returns:: A zero memory descriptor if the primitive does not have a source parameter.

memory::desc dst_desc() const#

Returns a destination memory descriptor.

Returns:: Destination memory descriptor.
Returns:: A zero memory descriptor if the primitive does not have a destination parameter.

memory::desc workspace_desc() const#

Returns the workspace memory descriptor.

Returns:: Workspace memory descriptor.
Returns:: A zero memory descriptor if the primitive does not require workspace parameter.

algorithm get_algorithm() const#

Returns an algorithm kind.

Returns:: An algorithm kind.
Returns:: dnnl::algorithm::undef if the primitive does not have an algorithm parameter.

prop_kind get_prop_kind() const#

Returns a propagation kind.

Returns:: A propagation kind.
Returns:: dnnl::prop_kind::undef if the primitive does not have a propagation parameter.

memory::dims get_strides() const#

Returns strides.

Returns:: Strides.
Returns:: An empty dnnl::memory::dims if the primitive does not have a strides parameter.

memory::dims get_kernel() const#

Returns a pooling kernel parameter.

Returns:: A pooling kernel parameter.
Returns:: An empty dnnl::memory::dims if the primitive does not have a pooling kernel parameter.

memory::dims get_dilations() const#

Returns dilations.

Returns:: Dilations.
Returns:: An empty dnnl::memory::dims if the primitive does not have a dilations parameter.

memory::dims get_padding_l() const#

Returns a left padding.

Returns:: A left padding.
Returns:: An empty dnnl::memory::dims if the primitive does not have a left padding parameter.

memory::dims get_padding_r() const#

Returns a right padding.

Returns:: A right padding.
Returns:: An empty dnnl::memory::dims if the primitive does not have a right padding parameter.

struct pooling_backward : public dnnl::primitive #

Pooling backward propagation primitive.

Public Functions

pooling_backward()#: Default constructor. Produces an empty object.

pooling_backward(const primitive_desc &pd)#

Constructs a pooling backward propagation primitive.

Parameters:: pd – Primitive descriptor for a pooling backward propagation primitive.

struct primitive_desc : public dnnl::primitive_desc #

Primitive descriptor for a pooling backward propagation primitive.

Public Functions

primitive_desc() = default#: Default constructor. Produces an empty object.

primitive_desc(const engine &aengine, algorithm aalgorithm, const memory::desc &diff_src_desc, const memory::desc &diff_dst_desc, const memory::dims &strides, const memory::dims &kernel, const memory::dims &dilation, const memory::dims &padding_l, const memory::dims &padding_r, const pooling_forward::primitive_desc &hint_fwd_pd, const primitive_attr &attr = default_attr(), bool allow_empty = false)#

Constructs a primitive descriptor for a pooling backward propagation primitive.

Arrays strides, kernel, dilation, padding_l and padding_r contain values for spatial dimensions only and hence must have the same number of elements as there are spatial dimensions. The order of values is the same as in the tensor: depth (for 3D tensors), height (for 3D and 2D tensors), and width.

Parameters:

aengine – Engine to use.
aalgorithm – Pooling algorithm kind: either dnnl::algorithm::pooling_max, dnnl::algorithm::pooling_avg_include_padding, or dnnl::algorithm::pooling_avg_exclude_padding.
diff_src_desc – Diff source memory descriptor.
diff_dst_desc – Diff destination memory descriptor.
strides – Vector of strides for spatial dimension.
kernel – Vector of kernel spatial dimensions.
dilation – Array of dilations for spatial dimension.
padding_l – Vector of padding values for low indices for each spatial dimension ([[front,] top,] left).
padding_r – Vector of padding values for high indices for each spatial dimension ([[back,] bottom,] right).
hint_fwd_pd – Primitive descriptor for a pooling forward propagation primitive. It is used as a hint for deciding which memory format to use.
attr – Primitive attributes to use. Attributes are optional and default to empty attributes.
allow_empty – A flag signifying whether construction is allowed to fail without throwing an exception. In this case an empty object will be produced. This flag is optional and defaults to false.

memory::desc diff_src_desc() const#

Returns a source memory descriptor.

Returns:: Source memory descriptor.
Returns:: A zero memory descriptor if the primitive does not have a source parameter.

memory::desc diff_dst_desc() const#

Returns a diff destination memory descriptor.

Returns:: Diff destination memory descriptor.
Returns:: A zero memory descriptor if the primitive does not have a diff destination parameter.

memory::desc workspace_desc() const#

Returns the workspace memory descriptor.

Returns:: Workspace memory descriptor.
Returns:: A zero memory descriptor if the primitive does not require workspace parameter.

algorithm get_algorithm() const#

Returns an algorithm kind.

Returns:: An algorithm kind.
Returns:: dnnl::algorithm::undef if the primitive does not have an algorithm parameter.

prop_kind get_prop_kind() const#

Returns a propagation kind.

Returns:: A propagation kind.
Returns:: dnnl::prop_kind::undef if the primitive does not have a propagation parameter.

memory::dims get_strides() const#

Returns strides.

Returns:: Strides.
Returns:: An empty dnnl::memory::dims if the primitive does not have a strides parameter.

memory::dims get_kernel() const#

Returns a pooling kernel parameter.

Returns:: A pooling kernel parameter.
Returns:: An empty dnnl::memory::dims if the primitive does not have a pooling kernel parameter.

memory::dims get_dilations() const#

Returns dilations.

Returns:: Dilations.
Returns:: An empty dnnl::memory::dims if the primitive does not have a dilations parameter.

memory::dims get_padding_l() const#

Returns a left padding.

Returns:: A left padding.
Returns:: An empty dnnl::memory::dims if the primitive does not have a left padding parameter.

memory::dims get_padding_r() const#

Returns a right padding.

Returns:: A right padding.
Returns:: An empty dnnl::memory::dims if the primitive does not have a right padding parameter.

Pooling

Contents

Pooling#

Forward#

Difference Between Forward Training and Forward Inference#

Backward#

Execution Arguments#

Operation Details#

Data Type Support#

Data Representation#

Source, Destination, and Their Gradients#

Post-ops and Attributes#

API#