Merge branch 'master' into pr/2189

examples/run_generation.py

@@ -247,7 +247,11 @@ def main():
         out = out[:, len(context_tokens):].tolist()
         for o in out:
             text = tokenizer.decode(o, clean_up_tokenization_spaces=True)
-            text = text[: text.find(args.stop_token) if args.stop_token else None]
+            if args.stop_token:
+                index =  text.find(args.stop_token)
+                if index == -1:
+                    index = None
+                text = text[:index]
 
             print(text)
 

examples/run_squad.py

@@ -223,7 +223,7 @@ def evaluate(args, model, tokenizer, prefix=""):
     eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
     # multi-gpu evaluate
-    if args.n_gpu > 1:
+    if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
         model = torch.nn.DataParallel(model)
 
     # Eval!

examples/summarization/configuration_bertabs.py

@@ -33,6 +33,8 @@ class BertAbsConfig(PretrainedConfig):
     r""" Class to store the configuration of the BertAbs model.
 
     Arguments:
+        vocab_size: int
+            Number of tokens in the vocabulary.
         max_pos: int
             The maximum sequence length that this model will be used with.
         enc_layer: int
@@ -65,7 +67,7 @@ class BertAbsConfig(PretrainedConfig):
 
     def __init__(
         self,
-        vocab_size_or_config_json_file=30522,
+        vocab_size=30522,
         max_pos=512,
         enc_layers=6,
         enc_hidden_size=512,
@@ -81,39 +83,17 @@ class BertAbsConfig(PretrainedConfig):
     ):
         super(BertAbsConfig, self).__init__(**kwargs)
 
-        if self._input_is_path_to_json(vocab_size_or_config_json_file):
-            path_to_json = vocab_size_or_config_json_file
-            with open(path_to_json, "r", encoding="utf-8") as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.max_pos = max_pos
+        self.vocab_size = vocab_size
+        self.max_pos = max_pos
 
-            self.enc_layers = enc_layers
-            self.enc_hidden_size = enc_hidden_size
-            self.enc_heads = enc_heads
-            self.enc_ff_size = enc_ff_size
-            self.enc_dropout = enc_dropout
+        self.enc_layers = enc_layers
+        self.enc_hidden_size = enc_hidden_size
+        self.enc_heads = enc_heads
+        self.enc_ff_size = enc_ff_size
+        self.enc_dropout = enc_dropout
 
-            self.dec_layers = dec_layers
-            self.dec_hidden_size = dec_hidden_size
-            self.dec_heads = dec_heads
-            self.dec_ff_size = dec_ff_size
-            self.dec_dropout = dec_dropout
-        else:
-            raise ValueError(
-                "First argument must be either a vocabulary size (int)"
-                "or the path to a pretrained model config file (str)"
-            )
-
-    def _input_is_path_to_json(self, first_argument):
-        """ Checks whether the first argument passed to config
-        is the path to a JSON file that contains the config.
-        """
-        is_python_2 = sys.version_info[0] == 2
-        if is_python_2:
-            return isinstance(first_argument, unicode)
-        else:
-            return isinstance(first_argument, str)
+        self.dec_layers = dec_layers
+        self.dec_hidden_size = dec_hidden_size
+        self.dec_heads = dec_heads
+        self.dec_ff_size = dec_ff_size
+        self.dec_dropout = dec_dropout