Batch Normalization#

The batch normalization primitive performs a forward or backward batch normalization operation on tensors with number of dimensions equal to 2 or more. Variable names follow the standard Conventions.

The batch normalization 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.

The different flavors of the primitive are controlled by the flags parameter that is passed to the primitive descriptor initialization function like dnnl::batch_normalization_forward::primitive_desc. Multiple flags can be combined using the bitwise OR operator (|).

Forward#

\[\dst(n, c, h, w) = \gamma(c) \cdot \frac{\src(n, c, h, w) - \mu(c)} {\sqrt{\sigma^2(c) + \varepsilon}} + \beta(c),\]

where

\(\gamma(c)\) and \(\beta(c)\) are optional scale and shift for a channel (controlled using the use_scale and use_shift flags),
\(\mu(c)\) and \(\sigma^2(c)\) are mean and variance for a channel (controlled using the use_global_stats flag), and
\(\varepsilon\) is a constant to improve numerical stability.

Mean and variance are computed at runtime or provided by a user. When mean and variance are computed at runtime, the following formulas are used:

\(\mu(c) = \frac{1}{NHW} \sum\limits_{nhw} \src(n, c, h, w)_{}\),
\(\sigma^2(c) = \frac{1}{NHW} \sum\limits_{nhw} {}_{} (\src(n, c, h, w) - \mu(c))^2\).

The \(\gamma(c)\) and \(\beta(c)\) tensors are considered learnable.

In the training mode, the primitive also optionally supports fusion with ReLU activation with zero negative slope applied to the result (see fuse_norm_relu flag).

Note

The batch normalization primitive computes population mean and variance and not the sample or unbiased versions that are typically used to compute running mean and variance. * Using the mean and variance computed by the batch normalization primitive, running mean and variance \(\hat\mu_i\) and \(\hat\sigma^2_i\) where \(i\) is iteration number, can be computed as:

\[\begin{split}\hat\mu_{i+1} = \alpha \cdot \hat\mu_i + (1 - \alpha) \cdot \mu, \\ \hat\sigma^2_{i+1} = \alpha \cdot \hat\sigma^2_i + (1 - \alpha) \cdot \sigma^2.\end{split}\]

Difference Between Forward Training and Forward Inference#

If mean and variance are computed at runtime (i.e., use_global_stats is not set), they become outputs for the propagation kind forward_training (because they would be required during the backward propagation) and are not exposed for the propagation kind forward_inference.
If batch normalization is created with ReLU fusion (i.e., fuse_norm_relu is set), for the propagation kind forward_training the primitive would produce a workspace memory as one extra output. This memory is required to compute the backward propagation. When the primitive is executed with propagation kind forward_inference, the workspace is not produced. Behavior would be the same as creating a batch normalization primitive with ReLU as a post-op (see section below).

Backward#

The backward propagation computes \(\diffsrc(n, c, h, w)\), \(\diffgamma(c)^*\), and \(\diffbeta(c)^*\) based on \(\diffdst(n, c, h, w)\), \(\src(n, c, h, w)\), \(\mu(c)\), \(\sigma^2(c)\), \(\gamma(c) ^*\), and \(\beta(c) ^*\).

The tensors marked with an asterisk are used only when the primitive is configured to use \(\gamma(c)\) and \(\beta(c)\) (i.e., use_scale and use_shift are set).

Execution Arguments#

Depending on the flags and propagation kind, the batch normalization primitive requires different inputs and outputs. For clarity, a summary is shown below.

	`forward_inference`	`forward_training`	`backward`	`backward_data`
`none`	In: \(\src\); Out: \(\dst\)	In: \(\src\); Out: \(\dst\), \(\mu\), \(\sigma^2\)	In: \(\diffdst\), \(\src\), \(\mu\), \(\sigma^2\); Out: \(\diffsrc\)	Same as for `backward`
`use_global_stats`	In: \(\src\), \(\mu\), \(\sigma^2\); Out: \(\dst\)	In: \(\src\), \(\mu\), \(\sigma^2\); Out: \(\dst\)	In: \(\diffdst\), \(\src\), \(\mu\), \(\sigma^2\); Out: \(\diffsrc\)	Same as for `backward`
`use_scale`	In: \(\src\), \(\gamma\); Out: \(\dst\)	In: \(\src\), \(\gamma\); Out: \(\dst\), \(\mu\), \(\sigma^2\)	In: \(\diffdst\), \(\src\), \(\mu\), \(\sigma^2\), \(\gamma\); Out: \(\diffsrc\), \(\diffgamma\)	Not supported
`use_shift`	In: \(\src\), \(\beta\); Out: \(\dst\)	In: \(\src\), \(\beta\); Out: \(\dst\), \(\mu\), \(\sigma^2\)	In: \(\diffdst\), \(\src\), \(\mu\), \(\sigma^2\), \(\beta\); Out: \(\diffsrc\), \(\diffbeta\)	Not supported
`use_scale` \| `use_shift`	In: \(\src\), \(\gamma\), \(\beta\); Out: \(\dst\)	In: \(\src\), \(\gamma\), \(\beta\); Out: \(\dst\), \(\mu\), \(\sigma^2\)	In: \(\diffdst\), \(\src\), \(\mu\), \(\sigma^2\), \(\gamma\), \(\beta\); Out: \(\diffsrc\), \(\diffgamma\), \(\diffbeta\)	Not supported
`use_global_stats` \| `use_scale` \| `use_shift`	In: \(\src\), \(\mu\), \(\sigma^2\), \(\gamma\), \(\beta\); Out: \(\dst\)	In: \(\src\), \(\mu\), \(\sigma^2\), \(\gamma\), \(\beta\); Out: \(\dst\)	In: \(\diffdst\), \(\src\), \(\mu\), \(\sigma^2\), \(\gamma\), \(\beta\); Out: \(\diffsrc\), \(\diffgamma\), \(\diffbeta\)	Not supported
`flags` \| `fuse_norm_relu`	In: same as with `flags`; Out: same as with `flags`	In: same as with `flags`; Out: same as with `flags`, workspace	In: same as with `flags`, workspace; Out: same as with `flags`	Same as for `backward` if `flags` do not contain `use_scale` or `use_shift`; not supported otherwise

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`
\(\gamma\)	`DNNL_ARG_SCALE`
\(\beta\)	`DNNL_ARG_SHIFT`
mean (\(\mu\))	`DNNL_ARG_MEAN`
variance (\(\sigma\))	`DNNL_ARG_VARIANCE`
\(\dst\)	`DNNL_ARG_DST`
workspace	`DNNL_ARG_WORKSPACE`
\(\diffdst\)	`DNNL_ARG_DIFF_DST`
\(\diffsrc\)	`DNNL_ARG_DIFF_SRC`
\(\diffgamma\)	`DNNL_ARG_DIFF_SCALE`
\(\diffbeta\)	`DNNL_ARG_DIFF_SHIFT`

Operation Details#

For forward propagation, the mean and variance might be either computed at runtime (in which case they are outputs of the primitive) or provided by a user (in which case they are inputs). In the latter case, a user must set the use_global_stats flag. For the backward propagation, the mean and variance are always input parameters.
The memory format and data type for src and dst are assumed to be the same, and in the API they are typically referred to as data (e.g., see data_desc in dnnl::batch_normalization_forward::primitive_desc). The same is true for diff_src and diff_dst. The corresponding memory descriptors are referred to as diff_data_desc.
Both forward and backward propagation support in-place operations, meaning that \(\src\) can be used as input and output for forward propagation, and \(\diffdst\) can be used as input and output for backward propagation. In case of an in-place operation, the original data will be overwritten. Note, however, that backward propagation requires original \(\src\), hence the corresponding forward propagation should not be performed in-place.
As mentioned above, the batch normalization primitive can be fused with ReLU activation even in the training mode. In this case, on the forward propagation the primitive has one additional output, workspace, that should be passed during the backward propagation.

Data Types Support#

The operation 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	Mean / Variance / Scale / Shift
forward / backward	`f32`, `bf16`	`f32`
forward	`f16`	`f32`
forward	`s8`	`f32`

Data Representation#

Source, Destination, and Their Gradients#

Like other CNN primitives, the batch normalization primitive expects data to be \(N \times C \times SP_n \times \cdots \times SP_0\) tensor.

The batch normalization primitive is optimized for the following memory formats:

Spatial	Logical tensor	Implementations optimized for memory formats
0D	NC	`nc` (`ab`)
1D	NCW	`ncw` (`abc`), `nwc` (`acb`), optimized
2D	NCHW	`nchw` (`abcd`), `nhwc` (`acdb`), optimized
3D	NCDHW	`ncdhw` (`abcde`), `ndhwc` (`acdeb`), optimized

Here optimized means the format chosen by the preceding compute-intensive primitive.

Statistics Tensors#

The mean (\(\mu\)) and variance (\(\sigma^2\)) are separate 1D tensors of size \(C\).

The format of the corresponding memory object must be x (a).

If used, the scale (\(\gamma\)) and shift (\(\beta\)) are combined in a single 2D tensor of shape \(2 \times C\).

The format of the corresponding memory object must be nc (ab).

Post-ops and Attributes#

Propagation	Type	Operation	Description
forward	post-op	eltwise	Applies an eltwise operation to the output.

Note

Using ReLU as a post-op does not produce additional output in the workspace that is required to compute backward propagation correctly. Hence, one should use the fuse_norm_relu flag for training.

API#

struct batch_normalization_forward : public dnnl::primitive #

Batch normalization forward propagation primitive.

Public Functions

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

batch_normalization_forward(const primitive_desc &pd)#

Constructs a batch normalization forward propagation primitive.

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

struct primitive_desc : public dnnl::primitive_desc #

Primitive descriptor for a batch normalization forward propagation primitive.

Public Functions

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

primitive_desc(const engine &aengine, prop_kind aprop_kind, const memory::desc &src_desc, const memory::desc &dst_desc, float epsilon, normalization_flags flags, const primitive_attr &attr = default_attr(), bool allow_empty = false)#

Constructs a primitive descriptor for a batch normalization forward propagation primitive.

Note

In-place operation is supported: the dst can refer to the same memory as the src.

Parameters:

aengine – Engine to use.
aprop_kind – Propagation kind. Possible values are dnnl::prop_kind::forward_training and dnnl::prop_kind::forward_inference.
src_desc – Source memory descriptor.
dst_desc – Destination memory descriptor.
epsilon – Batch normalization epsilon parameter.
flags – Batch normalization flags (dnnl::normalization_flags).
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 weights_desc() const#

Returns a weights memory descriptor.

Returns:: Weights memory descriptor.
Returns:: A zero memory descriptor if the primitive does not have a weights 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.

memory::desc mean_desc() const#

Returns memory descriptor for mean.

Returns:: Memory descriptor for mean.

memory::desc variance_desc() const#

Returns memory descriptor for variance.

Returns:: Memory descriptor for variance.

dnnl::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.

float get_epsilon() const#

Returns an epsilon.

Returns:: An epsilon.
Returns:: Zero if the primitive does not have an epsilon parameter.

normalization_flags get_flags() const#

Returns normalization flags.

Returns:: Normalization flags.

struct batch_normalization_backward : public dnnl::primitive #

Batch normalization backward propagation primitive.

Public Functions

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

batch_normalization_backward(const primitive_desc &pd)#

Constructs a batch normalization backward propagation primitive.

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

struct primitive_desc : public dnnl::primitive_desc #

Primitive descriptor for a batch normalization backward propagation primitive.

Public Functions

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

primitive_desc(const engine &aengine, prop_kind aprop_kind, const memory::desc &diff_src_desc, const memory::desc &diff_dst_desc, const memory::desc &src_desc, float epsilon, normalization_flags flags, const batch_normalization_forward::primitive_desc &hint_fwd_pd, const primitive_attr &attr = default_attr(), bool allow_empty = false)#

Constructs a primitive descriptor for a batch normalization backward propagation primitive.

Parameters:

aengine – Engine to use.
aprop_kind – Propagation kind. Possible values are dnnl::prop_kind::backward_data and dnnl::prop_kind::backward (diffs for all parameters are computed in this case).
diff_src_desc – Diff source memory descriptor.
diff_dst_desc – Diff destination memory descriptor.
src_desc – Source memory descriptor.
epsilon – Batch normalization epsilon parameter.
flags – Batch normalization flags (dnnl::normalization_flags).
hint_fwd_pd – Primitive descriptor for a batch normalization 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 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 weights_desc() const#

Returns a weights memory descriptor.

Returns:: Weights memory descriptor.
Returns:: A zero memory descriptor if the primitive does not have a weights 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 diff_src_desc() const#

Returns a diff source memory descriptor.

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

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 diff_weights_desc()#

Returns a diff weights memory descriptor.

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

memory::desc mean_desc() const#

Returns memory descriptor for mean.

Returns:: Memory descriptor for mean.

memory::desc variance_desc() const#

Returns memory descriptor for variance.

Returns:: Memory descriptor for variance.

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.

dnnl::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.

float get_epsilon() const#

Returns an epsilon.

Returns:: An epsilon.
Returns:: Zero if the primitive does not have an epsilon parameter.

normalization_flags get_flags() const#

Returns normalization flags.

Returns:: Normalization flags.

Batch Normalization

Contents

Batch Normalization#

Forward#

Difference Between Forward Training and Forward Inference#

Backward#

Execution Arguments#

Operation Details#

Data Types Support#

Data Representation#

Source, Destination, and Their Gradients#

Statistics Tensors#

Post-ops and Attributes#

API#