Python module

rms_norm

Normalization layer.

`DistributedRMSNorm`

class max.nn.norm.rms_norm.DistributedRMSNorm(*args, devices, **kwargs)

class max.nn.norm.rms_norm.RMSNorm(dim, dtype, eps=1e-06, weight_offset=0.0, multiply_before_cast=True)

Computes the Root Mean Square normalization on inputs.

Parameters:

dim (int ) – Size of last dimension of the expected input.
eps (float ) – Value added to denominator for numerical stability.
weight_offset (float ) – Constant offset added to the learned weights at runtime. For Gemma-style RMSNorm, this should be set to 1.0.
multiply_before_cast (bool ) – True if we multiply the inputs by the learned weights before casting to the input type (Gemma3-style). False if we cast the inputs to the input type first, then multiply by the learned weights (Llama-style).
dtype (DType )

class max.nn.norm.rms_norm.RMSNormV1(weight, eps=1e-06, weight_offset=0.0, multiply_before_cast=True)

Computes the Root Mean Square normalization on inputs.

Deprecated: Use RMSNorm instead.

Parameters:

weight (Value [ TensorType ] | TensorValue | Shape | Dim | int | float | integer | floating | ndarray )
eps (float )
weight_offset (float )
multiply_before_cast (bool )

eps*: float* = 1e-06

multiply_before_cast*: bool* = True

weight*: Value[TensorType] | TensorValue | Shape | Dim | int | float | integer | floating | ndarray*

weight_offset*: float* = 0.0