add fallback path for apex used in modeling.py

pytorch_pretrained_bert/modeling.py

@@ -31,10 +31,6 @@ import shutil
 import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss
-try:
-    from apex.normalization.fused_layer_norm import FusedLayerNorm
-except ImportError:
-    raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this.")
 
 from .file_utils import cached_path
 
@@ -157,22 +153,24 @@ class BertConfig(object):
         """Serializes this instance to a JSON string."""
         return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
+try:
+    from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
+except ImportError:
+    print("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
 
-class BertLayerNorm(nn.Module):
-    def __init__(self, config, variance_epsilon=1e-12):
-        """Construct a layernorm module in the TF style (epsilon inside the square root).
-        """
-        super(BertLayerNorm, self).__init__()
-        self.gamma = nn.Parameter(torch.ones(config.hidden_size))
-        self.beta = nn.Parameter(torch.zeros(config.hidden_size))
-        self.variance_epsilon = variance_epsilon
-
-    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.gamma * x + self.beta
-
+        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
 
 class BertEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings.
@@ -185,7 +183,7 @@ class BertEmbeddings(nn.Module):
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
-        self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, input_ids, token_type_ids=None):
@@ -260,7 +258,7 @@ class BertSelfOutput(nn.Module):
     def __init__(self, config):
         super(BertSelfOutput, self).__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -299,7 +297,7 @@ class BertOutput(nn.Module):
     def __init__(self, config):
         super(BertOutput, self).__init__()
         self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, hidden_states, input_tensor):
@@ -361,7 +359,7 @@ class BertPredictionHeadTransform(nn.Module):
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
         self.transform_act_fn = ACT2FN[config.hidden_act] \
             if isinstance(config.hidden_act, str) else config.hidden_act
-        self.LayerNorm = FusedLayerNorm(config.hidden_size, eps=1e-12)
+        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
 
     def forward(self, hidden_states):
         hidden_states = self.dense(hidden_states)
@@ -443,12 +441,9 @@ class PreTrainedBertModel(nn.Module):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, FusedLayerNorm):
+        elif isinstance(module, BertLayerNorm):
             module.bias.data.normal_(mean=0.0, std=self.config.initializer_range)
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, BertLayerNorm):
-            module.beta.data.normal_(mean=0.0, std=self.config.initializer_range)
-            module.gamma.data.normal_(mean=0.0, std=self.config.initializer_range)
         if isinstance(module, nn.Linear) and module.bias is not None:
             module.bias.data.zero_()