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Python class

GroupNorm

`GroupNorm`

class max.experimental.nn.norm.GroupNorm(num_groups, num_channels, eps=1e-05, affine=True)

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Bases: Module

Group normalization block.

Divides channels into groups and computes normalization stats per group. Follows the implementation pattern from PyTorch’s group_norm.

This implementation uses Tensor instead of Weight, which automatically handles dtype matching with input tensors, eliminating the need for dtype workarounds.

Example:

from max.experimental.nn import GroupNorm
from max.experimental.tensor import Tensor

norm = GroupNorm(num_groups=32, num_channels=128)
x = Tensor.ones([1, 128, 32, 32])
result = norm(x)

Initialize GroupNorm module.

Parameters:

num_groups (int) – Number of groups to separate the channels into
num_channels (int) – Number of input channels
eps (float) – Small constant added to denominator for numerical stability. Default: 1e-5
affine (bool) – If True, apply learnable affine transform parameters. Default: True

`bias`

bias: Tensor | None

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The bias tensor with shape [num_channels] (None if affine=False).

`eps`

eps: float

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Small constant added to denominator for numerical stability.

`forward()`

forward(x)

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Apply group normalization to input tensor.

Parameters:: x (Tensor) – Input tensor of shape [N, C, …] where C is number of channels
Returns:: Normalized tensor of same shape as input
Return type:: Tensor

`num_channels`

num_channels: int

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Number of input channels.

`num_groups`

num_groups: int

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Number of groups to separate the channels into.

`weight`

weight: Tensor | None

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The weight tensor with shape [num_channels] (None if affine=False).

GroupNorm​

bias​

eps​

forward()​

num_channels​

num_groups​

weight​