fix and generate docs for FusedRMSNorm

apex/normalization/fused_layer_norm.py

@@ -303,19 +303,19 @@ class FusedRMSNorm(torch.nn.Module):
     Currently only runs on cuda() tensors.
 
     .. math::
-        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+        y = \frac{x}{\mathrm{RMS}[x]} * \gamma
 
-    The mean and standard-deviation are calculated separately over the last
+    The root-mean-square is calculated separately over the last
     certain number dimensions which have to be of the shape specified by
     :attr:`normalized_shape`.
-    :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of
+    :math:`\gamma` is a learnable affine transform parameter of
     :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``.
 
     .. note::
         Unlike Batch Normalization and Instance Normalization, which applies
         scalar scale and bias for each entire channel/plane with the
-        :attr:`affine` option, Layer Normalization applies per-element scale and
-        bias with :attr:`elementwise_affine`.
+        :attr:`affine` option, RMS Normalization applies per-element scale
+        with :attr:`elementwise_affine`.
 
     This layer uses statistics computed from input data in both training and
     evaluation modes.
@@ -353,7 +353,7 @@ class FusedRMSNorm(torch.nn.Module):
         >>> # Activating the module
         >>> output = m(input)
 
-    .. _`Layer Normalization`: https://arxiv.org/abs/1607.06450
+    .. _`Root Mean Square Layer Normalization`: https://arxiv.org/pdf/1910.07467.pdf
     """
 
     def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):

docs/source/layernorm.rst

@@ -12,3 +12,6 @@ apex.normalization.fused_layer_norm
 
 .. autoclass:: FusedLayerNorm
     :members:
+
+.. autoclass:: FusedRMSNorm
+    :members: