Is there any reason we used a different LayerNorm implementation?

[dpheap2222]

We have a custom defined LayerNorm

annotated-transformer/the_annotated_transformer.py

Lines 315 to 327 in debc9fd

	class LayerNorm(nn.Module):
	"Construct a layernorm module (See citation for details)."
	def __init__(self, features, eps=1e-6):
	super(LayerNorm, self).__init__()
	self.a_2 = nn.Parameter(torch.ones(features))
	self.b_2 = nn.Parameter(torch.zeros(features))
	self.eps = eps
	def forward(self, x):
	mean = x.mean(-1, keepdim=True)
	std = x.std(-1, keepdim=True)
	return self.a_2 * (x - mean) / (std + self.eps) + self.b_2

From the look of line 326, there is no specification of 'correction=0'. By default, this means 'correction=1', which applies a Bessel’s correction. Had we removed this correction, we could easily implement with PyTorch's native LayerNorm class. Is there any reason we opted for the custom route? Thank you.