use the latest layernorm without bias in pytorch if available

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'x-transformers',
   packages = find_packages(exclude=['examples']),
-  version = '1.27.0',
+  version = '1.27.1',
   license='MIT',
   description = 'X-Transformers - Pytorch',
   author = 'Phil Wang',

x_transformers/x_transformers.py

@@ -1,6 +1,7 @@
 import math
 from random import random
 from typing import Dict
+from packaging import version
 
 import torch
 from torch import nn, einsum, Tensor
@@ -502,7 +503,6 @@ class LayerNorm(nn.Module):
     def __init__(self, dim):
         """
         bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
-        latest pytorch actually has a way to turn this off in nn.LayerNorm
         """
         super().__init__()
         self.gamma = nn.Parameter(torch.ones(dim))
@@ -511,6 +511,9 @@ def __init__(self, dim):
     def forward(self, x):
         return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)
 
+if version.parse(torch.__version__) >= version.parse('2.1.0'):
+    LayerNorm = partial(nn.LayerNorm, bias = False)
+
 class RMSNorm(nn.Module):
     def __init__(self, dim):
         super().__init__()