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Convolution
Inner Product
Matrix Multiplication
RNN
Batch Normalization
Binary
Concat
Eltwise
Group Normalization
General
Execution Arguments
Implementation Details
Implementation Limitations
Performance Tips
Examples
Layer Normalization
Local Response Normalization (LRN)
Pooling
PReLU
Resampling
Shuffle
Softmax
Sum
Reorder
Reduction
Abs
AbsBackward
Add
AvgPool
AvgPoolBackward
BatchNormForwardTraining
BatchNormInference
BatchNormTrainingBackward
BiasAdd
BiasAddBackward
Clamp
ClampBackward
Concat
Convolution
ConvolutionBackwardData
ConvolutionBackwardWeights
ConvTranspose
ConvTransposeBackwardData
ConvTransposeBackwardWeights
Dequantize
Divide
DynamicDequantize
DynamicQuantize
Elu
EluBackward
End
Exp
GELU
GELUBackward
HardSigmoid
HardSigmoidBackward
HardSwish
HardSwishBackward
Interpolate
InterpolateBackward
LayerNorm
LayerNormBackward
LeakyReLU
Log
LogSoftmax
LogSoftmaxBackward
MatMul
Maximum
MaxPool
MaxPoolBackward
Minimum
Mish
MishBackward
Multiply
Pow
PReLU
PReLUBackward
Quantize
Reciprocal
ReduceL1
ReduceL2
ReduceMax
ReduceMean
ReduceMin
ReduceProd
ReduceSum
ReLU
ReLUBackward
Reorder
Round
Select
Sigmoid
SigmoidBackward
SoftMax
SoftMaxBackward
SoftPlus
SoftPlusBackward
Sqrt
SqrtBackward
Square
SquaredDifference
StaticReshape
StaticTranspose
Subtract
Tanh
TanhBackward
TypeCast
Wildcard
enum dnnl_alg_kind_t
enum dnnl_normalization_flags_t
enum dnnl_primitive_kind_t
enum dnnl_prop_kind_t
enum dnnl_query_t
enum dnnl::normalization_flags
enum dnnl::query
struct dnnl_exec_arg_t
struct dnnl_primitive
struct dnnl_primitive_desc
struct dnnl::primitive
struct dnnl::primitive_desc
struct dnnl::primitive_desc_base
enum dnnl_rnn_direction_t
enum dnnl_rnn_flags_t
enum dnnl::rnn_direction
enum dnnl::rnn_flags
struct dnnl::augru_backward
struct dnnl::augru_forward
struct dnnl::gru_backward
struct dnnl::gru_forward
struct dnnl::lbr_augru_backward
struct dnnl::lbr_augru_forward
struct dnnl::lbr_gru_backward
struct dnnl::lbr_gru_forward
struct dnnl::lstm_backward
struct dnnl::lstm_forward
struct dnnl::rnn_primitive_desc_base
struct dnnl::vanilla_rnn_backward
struct dnnl::vanilla_rnn_forward
Visible to Intel only — GUID: GUID-3317F400-03FB-4A7C-B0E7-5737C0CC3089
Group Normalization
General
The group normalization primitive performs a forward or backward group normalization operation on tensors with numbers of dimensions equal to 3 or more.
Forward
The group 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. Variable names follow the standard Naming Conventions.
where
,
,
are optional scale and shift for a channel (see dnnl_use_scale and dnnl_use_shift flags),
are mean and variance for a group of channels in a batch (see dnnl_use_global_stats flag), and
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:
,
.
The 
and 
tensors are considered learnable.
NOTE:
- The group 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 group normalization primitive, running mean and variance
and 
can be computed as
Difference Between Forward Training and Forward Inference
If mean and variance are computed at runtime (i.e., dnnl_use_global_stats is not set), they become outputs for the propagation kind dnnl_forward_training (because they would be required during the backward propagation) and are not exposed for the propagation kind dnnl_forward_inference.
Backward
The backward propagation computes 
, 
, and 
based on 
, 
, 
, 
, 
, and 
.
The tensors marked with an asterisk are used only when the primitive is configured to use and (i.e., dnnl_use_scale or dnnl_use_shift are set).
Execution Arguments
Depending on the flags and propagation kind, the group normalization primitive requires different inputs and outputs. For clarity, a summary is shown below.
Flags |
||||
|---|---|---|---|---|
Inputs : |
Inputs : |
Inputs : |
Same as for dnnl_backward |
|
Inputs : |
Inputs : |
Inputs : |
Same as for dnnl_backward |
|
Inputs : |
Inputs : |
Inputs : |
Not supported |
|
Inputs : |
Inputs : |
Inputs : |
Not supported |
|
Inputs : |
Inputs : |
Inputs : |
Not supported |
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 |
|---|---|
|
DNNL_ARG_SRC |
|
DNNL_ARG_SCALE |
|
DNNL_ARG_SHIFT |
mean ( |
DNNL_ARG_MEAN |
variance ( |
DNNL_ARG_VARIANCE |
|
DNNL_ARG_DST |
|
DNNL_ARG_DIFF_DST |
|
DNNL_ARG_DIFF_SRC |
|
DNNL_ARG_DIFF_SCALE |
|
DNNL_ARG_DIFF_SHIFT |
Implementation Details
General Notes
The different flavors of the primitive are partially controlled by the flags parameter that is passed to the primitive descriptor creation function (e.g., dnnl::group_normalization_forward::primitive_desc()). Multiple flags can be set using the bitwise OR operator (|).
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 dnnl_use_global_stats flag. For the backward propagation, the mean and variance are always input parameters.
Both forward and backward propagation support in-place operations, meaning that
can be used as input and output for forward propagation, and 
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 the original , hence the corresponding forward propagation should not be performed in-place.
Data Type Support
The operation supports the following combinations of data types:
Propagation |
Source / Destination |
Mean / Variance / ScaleShift |
|---|---|---|
forward / backward |
f32, bf16, f16 |
f32 |
forward |
s8 |
f32 |
WARNING:
There might be hardware- or implementation-specific restrictions. Check the Implementation Limitations section below.
Data Representation
Mean and Variance
The mean (
) and variance (
) are separate 2D tensors of size 
.
The format of the corresponding memory object must be dnnl_nc (dnnl_ab).
Scale and Shift
If dnnl_use_scale or dnnl_use_shift are used, the scale (
) and shift (
) are separate 1D tensors of shape 
.
The format of the corresponding memory object must be dnnl_x (dnnl_a).
Source, Destination, and Their Gradients
The group normalization primitive expects data to be 
tensor.
The group normalization primitive is optimized for the following memory formats:
Spatial |
Logical tensor |
Implementations optimized for memory formats |
|---|---|---|
1D |
NCW |
|
2D |
NCHW |
|
3D |
NCDHW |
dnnl_ncdhw ( dnnl_abcde ), dnnl_ndhwc ( dnnl_acdeb ) |
Post-Ops and Attributes
Attributes enable you to modify the behavior of the group normalization primitive. The following attributes are supported by the group normalization primitive:
Propagation |
Type |
Operation |
Description |
Restrictions |
|---|---|---|---|---|
forward |
attribute |
Scales the corresponding tensor by the given scale factor(s). |
Supported only for int8 group normalization and one scale per tensor is supported. |
Implementation Limitations
Refer to Data Types for limitations related to data types support.
GPU is not supported.
Performance Tips
Mixing different formats for inputs and outputs is functionally supported but leads to highly suboptimal performance.
Use in-place operations whenever possible (see caveats in General Notes).
Examples
Group Normalization Primitive Example
This C++ API example demonstrates how to create and execute a Group Normalization primitive in forward training propagation mode.
Key optimizations included in this example:
In-place primitive execution;
Source memory format for an optimized primitive implementation;