added conversion script

convert_tf_checkpoint.py

@@ -0,0 +1,82 @@
+# coding=utf-8
+"""Convert BERT checkpoint."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import argparse
+import tensorflow as tf
+import torch
+
+from .modeling_pytorch import BertConfig, BertModel
+
+parser = argparse.ArgumentParser()
+
+## Required parameters
+parser.add_argument("--tf_checkpoint_path",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "Path the TensorFlow checkpoint path.")
+parser.add_argument("--bert_config_file",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "The config json file corresponding to the pre-trained BERT model. \n"
+                        "This specifies the model architecture.")
+parser.add_argument("--pytorch_dump_path",
+                    default = None,
+                    type = str,
+                    required = True,
+                    help = "Path to the output PyTorch model.")
+
+args = parser.parse_args()
+
+def convert():
+    # Load weights from TF model
+    path = args.tf_checkpoint_path
+    print("Converting TensorFlow checkpoint from {}".format(path))
+
+    init_vars = tf.train.list_variables(path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        print("Loading {} with shape {}".format(name, shape))
+        array = tf.train.load_variable(path, name)
+        print("Numpy array shape {}".format(array.shape))
+        names.append(name)
+        arrays.append(array)
+
+    # Initialise PyTorch model and fill weights-in
+    config = BertConfig.from_json_file(args.bert_config_file)
+    model = BertModel(config)
+    for name, array in zip(names, arrays):
+        name = name[5:]  # skip "bert/"
+        assert name[-2:] == ":0"
+        name = name[:-2]
+        name = name.split('/')
+        pointer = model
+        for m_name in name:
+            if re.fullmatch(r'[A-Za-z]+\d+', m_name):
+                l = re.split(r'(\d+)', m_name)
+            else:
+                l = [m_name]
+            pointer = getattr(pointer, l[0])
+            if len(l) >= 2:
+                num = int(l[1])
+                pointer = pointer[num]
+        try:
+            assert pointer.shape == array.shape
+        except AssertionError as e:
+            e.args += (pointer.shape, array.shape)
+            raise
+        pointer.data = torch.from_numpy(array)
+
+    # Save pytorch-model
+    torch.save(model.state_dict(), args.pytorch_dump_path)
+
+if __name__ == "__main__":
+    convert()
+    return None

modeling_pytorch.py

@@ -119,7 +119,7 @@ class BERTLayerNorm(nn.Module):
         self.variance_epsilon = variance_epsilon
 
     def forward(self, x):
-        # TODO check it's identical to TF implementation in details
+        # TODO check it's identical to TF implementation in details (epsilon and axes)
         u = x.mean(-1, keepdim=True)
         s = (x - u).pow(2).mean(-1, keepdim=True)
         x = (x - u) / torch.sqrt(s + self.variance_epsilon)
@@ -128,9 +128,7 @@ class BERTLayerNorm(nn.Module):
     #   inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)
 
 class BERTEmbeddings(nn.Module):
-    def __init__(self, embedding_size, vocab_size,
-                 token_type_vocab_size, max_position_embeddings,
-                 config):
+    def __init__(self, config):
 
         self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size)
 
@@ -323,27 +321,32 @@ class BERTEncoder(nn.Module):
         Return:
             float Tensor of shape [batch_size, seq_length, hidden_size]
         """
+        all_encoder_layers = []
         for layer_module in self.layer:
             hidden_states = layer_module(hidden_states, attention_mask)
-        return hidden_states
+            all_encoder_layers.append(hidden_states)
+        return all_encoder_layers
 
 
 class BERTPooler(nn.Module):
     def __init__(self, config):
         super(BERTPooler, self).__init__()
-        layer = BERTLayer(n_ctx, cfg, scale=True)
-        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])    
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
 
-    def forward(self, hidden_states, attention_mask):
+    def forward(self, hidden_states):
         """
         Args:
             hidden_states: float Tensor of shape [batch_size, seq_length, hidden_size]
         Return:
-            float Tensor of shape [batch_size, seq_length, hidden_size]
+            float Tensor of shape [batch_size, hidden_size]
         """
-        for layer_module in self.layer:
-            hidden_states = layer_module(hidden_states, attention_mask)
-        return hidden_states
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token. We assume that this has been pre-trained
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
 
 
 class BertModel(nn.Module):
@@ -381,14 +384,6 @@ class BertModel(nn.Module):
                 is invalid.
         """
         super(BertModel).__init__()
-        config = copy.deepcopy(config)
-        if not is_training:
-            config.hidden_dropout_prob = 0.0
-            config.attention_probs_dropout_prob = 0.0
-
-        batch_size = input_ids.size(0)
-        seq_length = input_ids.size(1)
-
         self.embeddings = BERTEmbeddings(config)
         self.encoder = BERTEncoder(config)
         self.pooler = BERTPooler(config)
@@ -396,4 +391,6 @@ class BertModel(nn.Module):
     def forward(self, input_ids, token_type_ids, attention_mask):
         embedding_output = self.embeddings(input_ids, token_type_ids)
         all_encoder_layers = self.encoder(embedding_output, attention_mask)
-        return all_encoder_layers
+        sequence_output = all_encoder_layers[-1]
+        pooled_output = self.pooler(sequence_output)
+        return all_encoder_layers, pooled_output