Merge branch 'master' into generative-finetuning

README.md

@@ -76,7 +76,7 @@ import torch
 from pytorch_transformers import *
 
 # PyTorch-Transformers has a unified API
-# for 6 transformer architectures and 27 pretrained weights.
+# for 7 transformer architectures and 30 pretrained weights.
 #          Model          | Tokenizer          | Pretrained weights shortcut
 MODELS = [(BertModel,       BertTokenizer,      'bert-base-uncased'),
           (OpenAIGPTModel,  OpenAIGPTTokenizer, 'openai-gpt'),
@@ -328,7 +328,7 @@ Breaking change in the `from_pretrained()`method:
 
 1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
 
-2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead which can break derived model classes build based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/pytorch-transformers/pull/866) by forwarding the the model `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuratoin class attributes.
+2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead which can break derived model classes build based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/pytorch-transformers/pull/866) by forwarding the the model `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
 
 Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.
 
@@ -393,8 +393,8 @@ for batch in train_data:
     loss = model(batch)
     loss.backward()
     torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
-    scheduler.step()
     optimizer.step()
+    scheduler.step()
     optimizer.zero_grad()
 ```
 

docs/source/examples.rst

@@ -68,7 +68,9 @@ GLUE results on dev set
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 We get the following results on the dev set of GLUE benchmark with an uncased BERT base
-model. All experiments were run on a P100 GPU with a batch size of 32.
+model (`bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train batch size of 24. Some of 
+these tasks have a small dataset and training can lead to high variance in the results between different runs.
+We report the median on 5 runs (with different seeds) for each of the metrics.
 
 .. list-table::
    :header-rows: 1
@@ -78,31 +80,31 @@ model. All experiments were run on a P100 GPU with a batch size of 32.
      - Result
    * - CoLA
      - Matthew's corr.
-     - 57.29
+     - 55.75
    * - SST-2
      - accuracy
-     - 93.00
+     - 92.09
    * - MRPC
      - F1/accuracy
-     - 88.85/83.82
+     - 90.48/86.27
    * - STS-B
      - Pearson/Spearman corr.
-     - 89.70/89.37
+     - 89.03/88.64
    * - QQP
      - accuracy/F1
-     - 90.72/87.41
+     - 90.92/87.72
    * - MNLI
      - matched acc./mismatched acc.
-     - 83.95/84.39
+     - 83.74/84.06
    * - QNLI
      - accuracy
-     - 89.04
+     - 91.07
    * - RTE
      - accuracy
-     - 61.01
+     - 68.59
    * - WNLI
      - accuracy
-     - 53.52
+     - 43.66
 
 
 Some of these results are significantly different from the ones reported on the test set

docs/source/pretrained_models.rst

@@ -62,6 +62,9 @@ Here is the full list of the currently provided pretrained models together with
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``gpt2-medium``                                            | | 24-layer, 1024-hidden, 16-heads, 345M parameters.                                                                                   |
 |                   |                                                            | | OpenAI's Medium-sized GPT-2 English model                                                                                           |
+|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
+|                   | ``gpt2-large``                                             | | 36-layer, 1280-hidden, 20-heads, 774M parameters.                                                                                   |
+|                   |                                                            | | OpenAI's Large-sized GPT-2 English model                                                                                            |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | Transformer-XL    | ``transfo-xl-wt103``                                       | | 18-layer, 1024-hidden, 16-heads, 257M parameters.                                                                                   |
 |                   |                                                            | | English model trained on wikitext-103                                                                                               |
@@ -72,16 +75,16 @@ Here is the full list of the currently provided pretrained models together with
 |                   | ``xlnet-large-cased``                                      | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                   |                                                            | | XLNet Large English model                                                                                                           |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| XLM               | ``xlm-mlm-en-2048``                                        | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
+| XLM               | ``xlm-mlm-en-2048``                                        | | 12-layer, 2048-hidden, 16-heads                                                                                                     |
 |                   |                                                            | | XLM English model                                                                                                                   |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-ende-1024``                                      | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
+|                   | ``xlm-mlm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
 |                   |                                                            | | XLM English-German Multi-language model                                                                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-enfr-1024``                                      | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
+|                   | ``xlm-mlm-enfr-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
 |                   |                                                            | | XLM English-French Multi-language model                                                                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-mlm-enro-1024``                                      | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
+|                   | ``xlm-mlm-enro-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
 |                   |                                                            | | XLM English-Romanian Multi-language model                                                                                           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``xlm-mlm-xnli15-1024``                                    | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
@@ -93,7 +96,7 @@ Here is the full list of the currently provided pretrained models together with
 |                   | ``xlm-clm-enfr-1024``                                      | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
 |                   |                                                            | | XLM English model trained with CLM (Causal Language Modeling)                                                                       |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``xlm-clm-ende-1024``                                      | | 12-layer, 1024-hidden, 8-heads                                                                                                      |
+|                   | ``xlm-clm-ende-1024``                                      | | 6-layer, 1024-hidden, 8-heads                                                                                                       |
 |                   |                                                            | | XLM English-German Multi-language model trained with CLM (Causal Language Modeling)                                                 |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | RoBERTa           | ``roberta-base``                                           | | 12-layer, 768-hidden, 12-heads, 125M parameters                                                                                     |

examples/lm_finetuning/finetune_on_pregenerated.py

@@ -314,15 +314,16 @@ def main():
                 mean_loss = tr_loss * args.gradient_accumulation_steps / nb_tr_steps
                 pbar.set_postfix_str(f"Loss: {mean_loss:.5f}")
                 if (step + 1) % args.gradient_accumulation_steps == 0:
-                    scheduler.step()  # Update learning rate schedule
                     optimizer.step()
+                    scheduler.step()  # Update learning rate schedule
                     optimizer.zero_grad()
                     global_step += 1
 
     # Save a trained model
-    if  n_gpu > 1 and torch.distributed.get_rank() == 0  or n_gpu <=1 :
+    if args.local_rank == -1 or torch.distributed.get_rank() == 0:
         logging.info("** ** * Saving fine-tuned model ** ** * ")
-        model.save_pretrained(args.output_dir)
+        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+        model_to_save.save_pretrained(args.output_dir)
         tokenizer.save_pretrained(args.output_dir)
 
 

examples/lm_finetuning/simple_lm_finetuning.py

@@ -507,7 +507,7 @@ def main():
 
     if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
         raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
-    if not os.path.exists(args.output_dir) and ( n_gpu > 1 and torch.distributed.get_rank() == 0  or n_gpu <=1 ):
+    if not os.path.exists(args.output_dir) and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         os.makedirs(args.output_dir)
 
     tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
@@ -602,15 +602,16 @@ def main():
                 nb_tr_examples += input_ids.size(0)
                 nb_tr_steps += 1
                 if (step + 1) % args.gradient_accumulation_steps == 0:
-                    scheduler.step()  # Update learning rate schedule
                     optimizer.step()
+                    scheduler.step()  # Update learning rate schedule
                     optimizer.zero_grad()
                     global_step += 1
 
         # Save a trained model
-        if args.do_train and ( n_gpu > 1 and torch.distributed.get_rank() == 0  or n_gpu <=1):
+        if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
             logger.info("** ** * Saving fine - tuned model ** ** * ")
-            model.save_pretrained(args.output_dir)
+            model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+            model_to_save.save_pretrained(args.output_dir)
             tokenizer.save_pretrained(args.output_dir)
 
 

examples/run_bertology.py

@@ -211,10 +211,12 @@ def prune_heads(args, model, eval_dataloader, head_mask):
 
 def main():
     parser = argparse.ArgumentParser()
+    ## Required parameters
     parser.add_argument("--data_dir", default=None, type=str, required=True,
                         help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
-    parser.add_argument("--model_name", default=None, type=str, required=True,
-                        help="Bert/XLNet/XLM pre-trained model selected in the list: " + ", ".join(ALL_MODELS))
+    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
+                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(
+                            ALL_MODELS))
     parser.add_argument("--task_name", default=None, type=str, required=True,
                         help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
     parser.add_argument("--output_dir", default=None, type=str, required=True,
@@ -222,9 +224,9 @@ def main():
 
     ## Other parameters
     parser.add_argument("--config_name", default="", type=str,
-                        help="Pretrained config name or path if not the same as model_name")
+                        help="Pretrained config name or path if not the same as model_name_or_path")
     parser.add_argument("--tokenizer_name", default="", type=str,
-                        help="Pretrained tokenizer name or path if not the same as model_name")
+                        help="Pretrained tokenizer name or path if not the same as model_name_or_path")
     parser.add_argument("--cache_dir", default="", type=str,
                         help="Where do you want to store the pre-trained models downloaded from s3")
     parser.add_argument("--data_subset", type=int, default=-1,
@@ -297,15 +299,15 @@ def main():
 
     args.model_type = ""
     for key in MODEL_CLASSES:
-        if key in args.model_name.lower():
+        if key in args.model_name_or_path.lower():
             args.model_type = key  # take the first match in model types
             break
     config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name,
+    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
                                           num_labels=num_labels, finetuning_task=args.task_name,
                                           output_attentions=True)
-    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name)
-    model = model_class.from_pretrained(args.model_name, from_tf=bool('.ckpt' in args.model_name), config=config)
+    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path)
+    model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
 
     if args.local_rank == 0:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

examples/run_glue.py

@@ -279,7 +279,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
             sep_token=tokenizer.sep_token,
             sep_token_extra=bool(args.model_type in ['roberta']),           # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
             pad_on_left=bool(args.model_type in ['xlnet']),                 # pad on the left for xlnet
-            pad_token=tokenizer.encoder[tokenizer.pad_token] if args.model_type in ['roberta'] else tokenizer.vocab[tokenizer.pad_token],
+            pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
             pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
         )
         if args.local_rank in [-1, 0]:
@@ -467,7 +467,7 @@ def main():
 
         # Load a trained model and vocabulary that you have fine-tuned
         model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         model.to(args.device)
 
 

examples/run_squad.py

@@ -481,7 +481,7 @@ def main():
 
 
     # Save the trained model and the tokenizer
-    if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
         # Create output directory if needed
         if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
             os.makedirs(args.output_dir)
@@ -498,7 +498,7 @@ def main():
 
         # Load a trained model and vocabulary that you have fine-tuned
         model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         model.to(args.device)
 
 

examples/utils_glue.py

@@ -422,12 +422,14 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
             tokens_b = tokenizer.tokenize(example.text_b)
             # Modifies `tokens_a` and `tokens_b` in place so that the total
             # length is less than the specified length.
-            # Account for [CLS], [SEP], [SEP] with "- 3"
-            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+            # Account for [CLS], [SEP], [SEP] with "- 3". " -4" for RoBERTa.
+            special_tokens_count = 4 if sep_token_extra else 3
+            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count)
         else:
-            # Account for [CLS] and [SEP] with "- 2"
-            if len(tokens_a) > max_seq_length - 2:
-                tokens_a = tokens_a[:(max_seq_length - 2)]
+            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
+            special_tokens_count = 3 if sep_token_extra else 2
+            if len(tokens_a) > max_seq_length - special_tokens_count:
+                tokens_a = tokens_a[:(max_seq_length - special_tokens_count)]
 
         # The convention in BERT is:
         # (a) For sequence pairs:

pytorch_transformers/convert_gpt2_checkpoint_to_pytorch.py

@@ -35,7 +35,7 @@ def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, p
     if gpt2_config_file == "":
         config = GPT2Config()
     else:
-        config = GPT2Config(gpt2_config_file)
+        config = GPT2Config.from_json_file(gpt2_config_file)
     model = GPT2Model(config)
 
     # Load weights from numpy

pytorch_transformers/convert_openai_checkpoint_to_pytorch.py

@@ -35,7 +35,7 @@ def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_c
     if openai_config_file == "":
         config = OpenAIGPTConfig()
     else:
-        config = OpenAIGPTConfig(openai_config_file)
+        config = OpenAIGPTConfig.from_json_file(openai_config_file)
     model = OpenAIGPTModel(config)
 
     # Load weights from numpy

pytorch_transformers/convert_transfo_xl_checkpoint_to_pytorch.py

@@ -75,7 +75,7 @@ def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
         if transfo_xl_config_file == "":
             config = TransfoXLConfig()
         else:
-            config = TransfoXLConfig(transfo_xl_config_file)
+            config = TransfoXLConfig.from_json_file(transfo_xl_config_file)
         print("Building PyTorch model from configuration: {}".format(str(config)))
         model = TransfoXLLMHeadModel(config)
 

pytorch_transformers/file_utils.py

@@ -17,8 +17,9 @@ from hashlib import sha256
 from io import open
 
 import boto3
-import requests
+from botocore.config import Config
 from botocore.exceptions import ClientError
+import requests
 from tqdm import tqdm
 
 try:
@@ -93,12 +94,15 @@ def filename_to_url(filename, cache_dir=None):
     return url, etag
 
 
-def cached_path(url_or_filename, cache_dir=None):
+def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None):
     """
     Given something that might be a URL (or might be a local path),
     determine which. If it's a URL, download the file and cache it, and
     return the path to the cached file. If it's already a local path,
     make sure the file exists and then return the path.
+    Args:
+        cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
+        force_download: if True, re-dowload the file even if it's already cached in the cache dir.
     """
     if cache_dir is None:
         cache_dir = PYTORCH_TRANSFORMERS_CACHE
@@ -111,7 +115,7 @@ def cached_path(url_or_filename, cache_dir=None):
 
     if parsed.scheme in ('http', 'https', 's3'):
         # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir)
+        return get_from_cache(url_or_filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
     elif os.path.exists(url_or_filename):
         # File, and it exists.
         return url_or_filename
@@ -156,24 +160,24 @@ def s3_request(func):
 
 
 @s3_request
-def s3_etag(url):
+def s3_etag(url, proxies=None):
     """Check ETag on S3 object."""
-    s3_resource = boto3.resource("s3")
+    s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
     bucket_name, s3_path = split_s3_path(url)
     s3_object = s3_resource.Object(bucket_name, s3_path)
     return s3_object.e_tag
 
 
 @s3_request
-def s3_get(url, temp_file):
+def s3_get(url, temp_file, proxies=None):
     """Pull a file directly from S3."""
-    s3_resource = boto3.resource("s3")
+    s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
     bucket_name, s3_path = split_s3_path(url)
     s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
 
 
-def http_get(url, temp_file):
-    req = requests.get(url, stream=True)
+def http_get(url, temp_file, proxies=None):
+    req = requests.get(url, stream=True, proxies=proxies)
     content_length = req.headers.get('Content-Length')
     total = int(content_length) if content_length is not None else None
     progress = tqdm(unit="B", total=total)
@@ -184,7 +188,7 @@ def http_get(url, temp_file):
     progress.close()
 
 
-def get_from_cache(url, cache_dir=None):
+def get_from_cache(url, cache_dir=None, force_download=False, proxies=None):
     """
     Given a URL, look for the corresponding dataset in the local cache.
     If it's not there, download it. Then return the path to the cached file.
@@ -201,10 +205,10 @@ def get_from_cache(url, cache_dir=None):
 
     # Get eTag to add to filename, if it exists.
     if url.startswith("s3://"):
-        etag = s3_etag(url)
+        etag = s3_etag(url, proxies=proxies)
     else:
         try:
-            response = requests.head(url, allow_redirects=True)
+            response = requests.head(url, allow_redirects=True, proxies=proxies)
             if response.status_code != 200:
                 etag = None
             else:
@@ -227,17 +231,17 @@ def get_from_cache(url, cache_dir=None):
         if matching_files:
             cache_path = os.path.join(cache_dir, matching_files[-1])
 
-    if not os.path.exists(cache_path):
+    if not os.path.exists(cache_path) or force_download:
         # Download to temporary file, then copy to cache dir once finished.
         # Otherwise you get corrupt cache entries if the download gets interrupted.
         with tempfile.NamedTemporaryFile() as temp_file:
-            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
+            logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
 
             # GET file object
             if url.startswith("s3://"):
-                s3_get(url, temp_file)
+                s3_get(url, temp_file, proxies=proxies)
             else:
-                http_get(url, temp_file)
+                http_get(url, temp_file, proxies=proxies)
 
             # we are copying the file before closing it, so flush to avoid truncation
             temp_file.flush()

pytorch_transformers/modeling_bert.py

@@ -577,7 +577,9 @@ BERT_START_DOCSTRING = r"""    The BERT model was proposed in
         https://pytorch.org/docs/stable/nn.html#module
 
     Parameters:
-        config (:class:`~pytorch_transformers.BertConfig`): Model configuration class with all the parameters of the model.
+        config (:class:`~pytorch_transformers.BertConfig`): Model configuration class with all the parameters of the model. 
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 BERT_INPUTS_DOCSTRING = r"""
@@ -597,7 +599,10 @@ BERT_INPUTS_DOCSTRING = r"""
                 ``tokens:         [CLS] the dog is hairy . [SEP]``
                 
                 ``token_type_ids:   0   0   0   0  0     0   0``
-    
+
+            Bert is a model with absolute position embeddings so it's usually advised to pad the inputs on
+            the right rather than the left.
+
             Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
             See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.

pytorch_transformers/modeling_gpt2.py

@@ -38,9 +38,11 @@ from .modeling_bert import BertLayerNorm as LayerNorm
 logger = logging.getLogger(__name__)
 
 GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
-                                     "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin"}
+                                     "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin",
+                                     "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-pytorch_model.bin"}
 GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
-                                      "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json"}
+                                      "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
+                                      "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json"}
 
 def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
     """ Load tf checkpoints in a pytorch model
@@ -383,11 +385,15 @@ GPT2_START_DOCSTRING = r"""    OpenAI GPT-2 model was proposed in
 
     Parameters:
         config (:class:`~pytorch_transformers.GPT2Config`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 GPT2_INPUTS_DOCSTRING = r"""    Inputs:
         **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
+            GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
+            the right rather than the left.
             Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
             See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
@@ -612,7 +618,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
 @add_start_docstrings("""The GPT2 Model transformer with a language modeling and a multiple-choice classification
 head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers.
 The language modeling head has its weights tied to the input embeddings,
-the classification head takes as input the input of a specified classification token index in the intput sequence).
+the classification head takes as input the input of a specified classification token index in the input sequence).
 """, GPT2_START_DOCSTRING)
 class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
     r"""    Inputs:

pytorch_transformers/modeling_openai.py

@@ -397,11 +397,15 @@ OPENAI_GPT_START_DOCSTRING = r"""    OpenAI GPT model was proposed in
 
     Parameters:
         config (:class:`~pytorch_transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 OPENAI_GPT_INPUTS_DOCSTRING = r"""    Inputs:
         **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
+            GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
+            the right rather than the left.
             Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
             See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
@@ -602,7 +606,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
 @add_start_docstrings("""OpenAI GPT Model transformer with a language modeling and a multiple-choice classification
 head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers.
 The language modeling head has its weights tied to the input embeddings,
-the classification head takes as input the input of a specified classification token index in the intput sequence).
+the classification head takes as input the input of a specified classification token index in the input sequence).
 """, OPENAI_GPT_START_DOCSTRING)
 class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
     r"""    Inputs:

pytorch_transformers/modeling_roberta.py

@@ -90,7 +90,8 @@ ROBERTA_START_DOCSTRING = r"""    The RoBERTa model was proposed in
 
     Parameters:
         config (:class:`~pytorch_transformers.RobertaConfig`): Model configuration class with all the parameters of the 
-            model.
+            model. Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 ROBERTA_INPUTS_DOCSTRING = r"""
@@ -109,6 +110,10 @@ ROBERTA_INPUTS_DOCSTRING = r"""
 
             Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with 
             the ``add_special_tokens`` parameter set to ``True``.
+
+            RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
+            the right rather than the left.
+
             See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:

pytorch_transformers/modeling_transfo_xl.py

@@ -928,12 +928,16 @@ TRANSFO_XL_START_DOCSTRING = r"""    The Transformer-XL model was proposed in
 
     Parameters:
         config (:class:`~pytorch_transformers.TransfoXLConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 TRANSFO_XL_INPUTS_DOCSTRING = r"""
     Inputs:
         **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
+            Transformer-XL is a model with relative position embeddings so you can either pad the inputs on
+            the right or on the left.
             Indices can be obtained using :class:`pytorch_transformers.TransfoXLTokenizer`.
             See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.

pytorch_transformers/modeling_utils.py

@@ -71,6 +71,10 @@ class PretrainedConfig(object):
     r""" Base class for all configuration classes.
         Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations.
 
+        Note:
+            A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights.
+            It only affects the model's configuration.
+
         Class attributes (overridden by derived classes):
             - ``pretrained_config_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained model configurations as values.
 
@@ -121,6 +125,13 @@ class PretrainedConfig(object):
                 - The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
                 - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
 
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
+
+            proxies: (`optional`) dict, default None:
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+                The proxies are used on each request.
+
             return_unused_kwargs: (`optional`) bool:
 
                 - If False, then this function returns just the final configuration object.
@@ -142,6 +153,8 @@ class PretrainedConfig(object):
 
         """
         cache_dir = kwargs.pop('cache_dir', None)
+        force_download = kwargs.pop('force_download', False)
+        proxies = kwargs.pop('proxies', None)
         return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
 
         if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
@@ -152,7 +165,7 @@ class PretrainedConfig(object):
             config_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
-            resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
+            resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
         except EnvironmentError:
             if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
                 logger.error(
@@ -396,6 +409,13 @@ class PreTrainedModel(nn.Module):
                 Path to a directory in which a downloaded pre-trained model
                 configuration should be cached if the standard cache should not be used.
 
+            force_download: (`optional`) boolean, default False:
+                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
+
+            proxies: (`optional`) dict, default None:
+                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
+                The proxies are used on each request.
+
             output_loading_info: (`optional`) boolean:
                 Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
 
@@ -420,6 +440,8 @@ class PreTrainedModel(nn.Module):
         state_dict = kwargs.pop('state_dict', None)
         cache_dir = kwargs.pop('cache_dir', None)
         from_tf = kwargs.pop('from_tf', False)
+        force_download = kwargs.pop('force_download', False)
+        proxies = kwargs.pop('proxies', None)
         output_loading_info = kwargs.pop('output_loading_info', False)
 
         # Load config
@@ -427,6 +449,7 @@ class PreTrainedModel(nn.Module):
             config, model_kwargs = cls.config_class.from_pretrained(
                 pretrained_model_name_or_path, *model_args,
                 cache_dir=cache_dir, return_unused_kwargs=True,
+                force_download=force_download,
                 **kwargs
             )
         else:
@@ -449,7 +472,7 @@ class PreTrainedModel(nn.Module):
                 archive_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
-            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
+            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
         except EnvironmentError:
             if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                 logger.error(

pytorch_transformers/modeling_xlm.py

@@ -416,12 +416,18 @@ XLM_START_DOCSTRING = r"""    The XLM model was proposed in
 
     Parameters:
         config (:class:`~pytorch_transformers.XLMConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the configuration.
+            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 XLM_INPUTS_DOCSTRING = r"""
     Inputs:
         **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
+
+            XLM is a model with absolute position embeddings so it's usually advised to pad the inputs on
+            the right rather than the left.
+
             Indices can be obtained using :class:`pytorch_transformers.XLMTokenizer`.
             See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
@@ -434,8 +440,10 @@ XLM_INPUTS_DOCSTRING = r"""
             Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
         **langs**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             A parallel sequence of tokens to be used to indicate the language of each token in the input.
-            Indices are selected in the pre-trained language vocabulary,
-            i.e. in the range ``[0, config.n_langs - 1[``.
+            Indices are languages ids which can be obtained from the language names by using two conversion mappings
+            provided in the configuration of the model (only provided for multilingual models).
+            More precisely, the `language name -> language id` mapping is in `model.config.lang2id` (dict str -> int) and
+            the `language id -> language name` mapping is `model.config.id2lang` (dict int -> str).
         **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``: