From e13465fb8bbabe3bbd528761818403aa5d2e128e Mon Sep 17 00:00:00 2001 From: David Pollack Date: Fri, 23 Aug 2019 12:12:12 +0200 Subject: [PATCH] change layernorm code to pytorch's native layer norm --- pytorch_transformers/modeling_bert.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/pytorch_transformers/modeling_bert.py b/pytorch_transformers/modeling_bert.py index 7b34b3fd90..8bf281feb9 100644 --- a/pytorch_transformers/modeling_bert.py +++ b/pytorch_transformers/modeling_bert.py @@ -224,20 +224,7 @@ try: from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm except (ImportError, AttributeError) as e: logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .") - class BertLayerNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-12): - """Construct a layernorm module in the TF style (epsilon inside the square root). - """ - super(BertLayerNorm, self).__init__() - self.weight = nn.Parameter(torch.ones(hidden_size)) - self.bias = nn.Parameter(torch.zeros(hidden_size)) - self.variance_epsilon = eps - - def forward(self, x): - u = x.mean(-1, keepdim=True) - s = (x - u).pow(2).mean(-1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.variance_epsilon) - return self.weight * x + self.bias + BertLayerNorm = torch.nn.LayerNorm class BertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.