Merge branch 'pytorch-transformers' into xlnet

pytorch_pretrained_bert/convert_transfo_xl_checkpoint_to_pytorch.py → pytorch_transformers/convert_transfo_xl_checkpoint_to_pytorch.py

@@ -23,14 +23,13 @@ from io import open
 
 import torch
 
-import pytorch_pretrained_bert.tokenization_transfo_xl as data_utils
-from pytorch_pretrained_bert.modeling_transfo_xl import (CONFIG_NAME,
+import pytorch_transformers.tokenization_transfo_xl as data_utils
+from pytorch_transformers.modeling_transfo_xl import (CONFIG_NAME,
                                                          WEIGHTS_NAME,
                                                          TransfoXLConfig,
                                                          TransfoXLLMHeadModel,
                                                          load_tf_weights_in_transfo_xl)
-from pytorch_pretrained_bert.tokenization_transfo_xl import (CORPUS_NAME,
-                                                             VOCAB_NAME)
+from pytorch_transformers.tokenization_transfo_xl import (CORPUS_NAME, VOCAB_FILES_NAMES)
 
 if sys.version_info[0] == 2:
     import cPickle as pickle
@@ -53,7 +52,7 @@ def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
         with open(transfo_xl_dataset_file, "rb") as fp:
             corpus = pickle.load(fp, encoding="latin1")
         # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
-        pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_NAME
+        pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
         print("Save vocabulary to {}".format(pytorch_vocab_dump_path))
         corpus_vocab_dict = corpus.vocab.__dict__
         torch.save(corpus_vocab_dict, pytorch_vocab_dump_path)

pytorch_pretrained_bert/convert_xlm_checkpoint_to_pytorch.py → pytorch_transformers/convert_xlm_checkpoint_to_pytorch.py

@@ -23,8 +23,8 @@ from io import open
 import torch
 import numpy
 
-from pytorch_pretrained_bert.modeling_xlm import (CONFIG_NAME, WEIGHTS_NAME, XLMConfig, XLMModel)
-from pytorch_pretrained_bert.tokenization_xlm import MERGES_NAME, VOCAB_NAME
+from pytorch_transformers.modeling_xlm import (CONFIG_NAME, WEIGHTS_NAME, XLMConfig, XLMModel)
+from pytorch_transformers.tokenization_xlm import VOCAB_FILES_NAMES
 
 
 def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
@@ -42,7 +42,7 @@ def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_p
     # Save pytorch-model
     pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
     pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME
-    pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_NAME
+    pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' +  VOCAB_FILES_NAMES['vocab_file']
 
     print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
     torch.save(model, pytorch_weights_dump_path)

pytorch_pretrained_bert/convert_xlnet_checkpoint_to_pytorch.py → pytorch_transformers/convert_xlnet_checkpoint_to_pytorch.py

@@ -22,7 +22,7 @@ import os
 import argparse
 import torch
 
-from pytorch_pretrained_bert.modeling_xlnet import (CONFIG_NAME, WEIGHTS_NAME,
+from pytorch_transformers.modeling_xlnet import (CONFIG_NAME, WEIGHTS_NAME,
                                                     XLNetConfig,
                                                     XLNetLMHeadModel, XLNetForQuestionAnswering,
                                                     XLNetForSequenceClassification,

pytorch_pretrained_bert/file_utils.py → pytorch_transformers/file_utils.py

@@ -29,7 +29,7 @@ except ImportError:
     torch_cache_home = os.path.expanduser(
         os.getenv('TORCH_HOME', os.path.join(
             os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')))
-default_cache_path = os.path.join(torch_cache_home, 'pytorch_pretrained_bert')
+default_cache_path = os.path.join(torch_cache_home, 'pytorch_transformers')
 
 try:
     from urllib.parse import urlparse

pytorch_pretrained_bert/modeling_bert.py → pytorch_transformers/modeling_bert.py

@@ -28,12 +28,11 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .file_utils import cached_path
-from .model_utils import WEIGHTS_NAME, CONFIG_NAME, PretrainedConfig, PreTrainedModel, prune_linear_layer
+from .modeling_utils import WEIGHTS_NAME, CONFIG_NAME, PretrainedConfig, PreTrainedModel, prune_linear_layer
 
 logger = logging.getLogger(__name__)
 
-PRETRAINED_MODEL_ARCHIVE_MAP = {
+BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin",
     'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-pytorch_model.bin",
     'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-pytorch_model.bin",
@@ -49,7 +48,7 @@ PRETRAINED_MODEL_ARCHIVE_MAP = {
     'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-pytorch_model.bin",
 }
 
-PRETRAINED_CONFIG_ARCHIVE_MAP = {
+BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",
     'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json",
     'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json",
@@ -545,7 +544,7 @@ class BertPreTrainedModel(PreTrainedModel):
         a simple interface for dowloading and loading pretrained models.
     """
     config_class = BertConfig
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
+    pretrained_model_archive_map = BERT_PRETRAINED_MODEL_ARCHIVE_MAP
     load_tf_weights = load_tf_weights_in_bert
     base_model_prefix = "bert"
 

pytorch_pretrained_bert/modeling_gpt2.py → pytorch_transformers/modeling_gpt2.py

@@ -30,16 +30,15 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .file_utils import cached_path
-from .model_utils import (Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig,
+from .modeling_utils import (Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig,
                           PreTrainedModel, prune_conv1d_layer, SequenceSummary)
 from .modeling_bert import BertLayerNorm as LayerNorm
 
 logger = logging.getLogger(__name__)
 
-PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
+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"}
-PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
+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"}
 
 def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
@@ -103,7 +102,7 @@ def gelu(x):
 class GPT2Config(PretrainedConfig):
     """Configuration class to store the configuration of a `GPT2Model`.
     """
-    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+    pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(
         self,
@@ -120,11 +119,13 @@ class GPT2Config(PretrainedConfig):
         layer_norm_epsilon=1e-5,
         initializer_range=0.02,
         predict_special_tokens=True,
+
+        num_labels=1,
         summary_type='token_ids',
         summary_use_proj=True,
-        summary_num_classes=1,
         summary_activation=None,
-        summary_dropout=0.1,
+        summary_proj_to_labels=True,
+        summary_first_dropout=0.1,
         **kwargs
     ):
         """Constructs GPT2Config.
@@ -170,11 +171,13 @@ class GPT2Config(PretrainedConfig):
             self.layer_norm_epsilon = layer_norm_epsilon
             self.initializer_range = initializer_range
             self.predict_special_tokens = predict_special_tokens
+
+            self.num_labels = num_labels
             self.summary_type = summary_type
             self.summary_use_proj = summary_use_proj
-            self.summary_num_classes = summary_num_classes
             self.summary_activation = summary_activation
-            self.summary_dropout = summary_dropout
+            self.summary_first_dropout = summary_first_dropout
+            self.summary_proj_to_labels = summary_proj_to_labels
         else:
             raise ValueError(
                 "First argument must be either a vocabulary size (int)"
@@ -358,7 +361,7 @@ class GPT2PreTrainedModel(PreTrainedModel):
         a simple interface for dowloading and loading pretrained models.
     """
     config_class = GPT2Config
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
+    pretrained_model_archive_map = GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
     load_tf_weights = load_tf_weights_in_gpt2
     base_model_prefix = "transformer"
 

pytorch_pretrained_bert/modeling_openai.py → pytorch_transformers/modeling_openai.py

@@ -30,15 +30,14 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .file_utils import cached_path
-from .model_utils import (Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig,
+from .modeling_utils import (Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig,
                              PreTrainedModel, prune_conv1d_layer, SequenceSummary)
 from .modeling_bert import BertLayerNorm as LayerNorm
 
 logger = logging.getLogger(__name__)
 
-PRETRAINED_MODEL_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-pytorch_model.bin"}
-PRETRAINED_CONFIG_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json"}
+OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-pytorch_model.bin"}
+OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json"}
 
 
 def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
@@ -130,7 +129,7 @@ ACT_FNS = {"relu": nn.ReLU, "swish": swish, "gelu": gelu}
 class OpenAIGPTConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `OpenAIGPTModel`.
     """
-    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+    pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(
         self,
@@ -148,11 +147,13 @@ class OpenAIGPTConfig(PretrainedConfig):
         layer_norm_epsilon=1e-5,
         initializer_range=0.02,
         predict_special_tokens=True,
+
+        num_labels=1,
         summary_type='token_ids',
         summary_use_proj=True,
-        summary_num_classes=1,
         summary_activation=None,
-        summary_dropout=0.1,
+        summary_proj_to_labels=True,
+        summary_first_dropout=0.1,
         **kwargs
     ):
         """Constructs OpenAIGPTConfig.
@@ -201,11 +202,13 @@ class OpenAIGPTConfig(PretrainedConfig):
             self.layer_norm_epsilon = layer_norm_epsilon
             self.initializer_range = initializer_range
             self.predict_special_tokens = predict_special_tokens
+
+            self.num_labels = num_labels
             self.summary_type = summary_type
             self.summary_use_proj = summary_use_proj
-            self.summary_num_classes = summary_num_classes
             self.summary_activation = summary_activation
-            self.summary_dropout = summary_dropout
+            self.summary_first_dropout = summary_first_dropout
+            self.summary_proj_to_labels = summary_proj_to_labels
         else:
             raise ValueError(
                 "First argument must be either a vocabulary size (int)"
@@ -384,7 +387,7 @@ class OpenAIGPTPreTrainedModel(PreTrainedModel):
         a simple interface for dowloading and loading pretrained models.
     """
     config_class = OpenAIGPTConfig
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
+    pretrained_model_archive_map = OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
     load_tf_weights = load_tf_weights_in_openai_gpt
     base_model_prefix = "transformer"
 

pytorch_pretrained_bert/modeling_transfo_xl.py → pytorch_transformers/modeling_transfo_xl.py

@@ -36,15 +36,14 @@ from torch.nn.parameter import Parameter
 
 from .modeling_bert import BertLayerNorm as LayerNorm
 from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax, sample_logits
-from .file_utils import cached_path
-from .model_utils import CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel
+from .modeling_utils import CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel
 
 logger = logging.getLogger(__name__)
 
-PRETRAINED_MODEL_ARCHIVE_MAP = {
+TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-pytorch_model.bin",
 }
-PRETRAINED_CONFIG_ARCHIVE_MAP = {
+TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-config.json",
 }
 
@@ -179,7 +178,7 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
 class TransfoXLConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `TransfoXLModel`.
     """
-    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+    pretrained_config_archive_map = TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(self,
                  vocab_size_or_config_json_file=267735,
@@ -838,7 +837,7 @@ class TransfoXLPreTrainedModel(PreTrainedModel):
         a simple interface for dowloading and loading pretrained models.
     """
     config_class = TransfoXLConfig
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
+    pretrained_model_archive_map = TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
     load_tf_weights = load_tf_weights_in_transfo_xl
     base_model_prefix = "transformer"
 

pytorch_pretrained_bert/model_utils.py → pytorch_transformers/modeling_utils.py

@@ -25,7 +25,7 @@ from io import open
 
 import torch
 from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss, functional as F
+from torch.nn import CrossEntropyLoss, functional as F
 
 from .file_utils import cached_path
 
@@ -169,6 +169,22 @@ class PreTrainedModel(nn.Module):
         model_to_prune = getattr(self, self.base_model_prefix, self)  # get the base model if needed
         model_to_prune._prune_heads(heads_to_prune)
 
+    def save_pretrained(self, save_directory):
+        """ Save a model with its configuration file to a directory, so that it
+            can be re-loaded using the `from_pretrained(save_directory)` class method.
+        """
+        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
+
+        # Only save the model it-self if we are using distributed training
+        model_to_save = self.module if hasattr(self, 'module') else self
+
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
+        output_config_file = os.path.join(save_directory, CONFIG_NAME)
+
+        torch.save(model_to_save.state_dict(), output_model_file)
+        model_to_save.config.to_json_file(output_config_file)
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
         """
@@ -193,7 +209,8 @@ class PreTrainedModel(nn.Module):
         """
         state_dict = kwargs.pop('state_dict', None)
         cache_dir = kwargs.pop('cache_dir', None)
-        from_tf = kwargs.pop('from_tf', None)
+        from_tf = kwargs.pop('from_tf', False)
+        output_loading_info = kwargs.pop('output_loading_info', False)
 
         # Load config
         config = cls.config_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
@@ -239,6 +256,21 @@ class PreTrainedModel(nn.Module):
             # Directly load from a TensorFlow checkpoint
             return cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
 
+        # Convert old format to new format if needed from a PyTorch state_dict
+        old_keys = []
+        new_keys = []
+        for key in state_dict.keys():
+            new_key = None
+            if 'gamma' in key:
+                new_key = key.replace('gamma', 'weight')
+            if 'beta' in key:
+                new_key = key.replace('beta', 'bias')
+            if new_key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+        for old_key, new_key in zip(old_keys, new_keys):
+            state_dict[new_key] = state_dict.pop(old_key)
+
         # Load from a PyTorch state_dict
         missing_keys = []
         unexpected_keys = []
@@ -279,6 +311,10 @@ class PreTrainedModel(nn.Module):
         if hasattr(model, 'tie_weights'):
             model.tie_weights()  # make sure word embedding weights are still tied
 
+        if output_loading_info:
+            loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs}
+            return model, loading_info
+
         return model
 
 
@@ -478,10 +514,10 @@ class SequenceSummary(nn.Module):
                 - 'token_ids' => supply a Tensor of classification token indices (GPT/GPT-2)
                 - 'attn' => Not implemented now, use multi-head attention
             summary_use_proj: Add a projection after the vector extraction
-            summary_num_classes: If > 0: the projection outputs to n classes (otherwise to hidden_size)
-            summary_activation:
-                'tanh' => add a tanh activation to the output
-                    None => no activation
+            summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
+            summary_activation: 'tanh' => add a tanh activation to the output, Other => no activation. Default 
+            summary_first_dropout: Add a dropout before the projection and activation
+            summary_last_dropout: Add a dropout after the projection and activation
     """
     def __init__(self, config):
         super(SequenceSummary, self).__init__()
@@ -495,8 +531,8 @@ class SequenceSummary(nn.Module):
 
         self.summary = nn.Identity()
         if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
-            if hasattr(config, 'summary_num_classes') and config.summary_num_classes > 0:
-                num_classes = config.summary_num_classes
+            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
+                num_classes = config.num_labels
             else:
                 num_classes = config.hidden_size
             self.summary = nn.Linear(config.hidden_size, num_classes)
@@ -505,7 +541,13 @@ class SequenceSummary(nn.Module):
         if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
             self.activation = nn.Tanh()
 
-        self.dropout = nn.Dropout(config.summary_dropout)
+        self.first_dropout = nn.Identity()
+        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
+            self.first_dropout = nn.Dropout(config.summary_first_dropout)
+
+        self.last_dropout = nn.Identity()
+        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
+            self.last_dropout = nn.Dropout(config.summary_last_dropout)
 
     def forward(self, hidden_states, token_ids=None):
         """ hidden_states: float Tensor in shape [bsz, seq_len, hidden_size], the hidden-states of the last layer.
@@ -531,9 +573,10 @@ class SequenceSummary(nn.Module):
         elif self.summary_type == 'attn':
             raise NotImplementedError
 
+        output = self.first_dropout(output)
         output = self.summary(output)
         output = self.activation(output)
-        output = self.dropout(output)
+        output = self.last_dropout(output)
 
         return output
 
@@ -598,9 +641,3 @@ def prune_layer(layer, index, dim=None):
         return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
     else:
         raise ValueError("Can't prune layer of class {}".format(layer.__class__))
-
-def clean_up_tokenization(out_string):
-    out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ','
-                    ).replace(" ' ", "'").replace(" n't", "n't").replace(" 'm", "'m").replace(" do not", " don't"
-                    ).replace(" 's", "'s").replace(" 've", "'ve").replace(" 're", "'re")
-    return out_string

pytorch_pretrained_bert/modeling_xlm.py → pytorch_transformers/modeling_xlm.py

@@ -14,18 +14,14 @@
 # limitations under the License.
 """ PyTorch XLM model.
 """
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import json
 import logging
 import math
-import os
 import sys
 from io import open
 
-import math
 import itertools
 import numpy as np
 
@@ -34,16 +30,15 @@ from torch import nn
 from torch.nn import functional as F
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .file_utils import cached_path
-from .model_utils import (CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel,
+from .modeling_utils import (PretrainedConfig, PreTrainedModel,
                              prune_linear_layer, SequenceSummary, SQuADHead)
 
 logger = logging.getLogger(__name__)
 
-PRETRAINED_MODEL_ARCHIVE_MAP = {
+XLM_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-pytorch_model.bin",
 }
-PRETRAINED_CONFIG_ARCHIVE_MAP = {
+XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
 }
 
@@ -51,7 +46,7 @@ PRETRAINED_CONFIG_ARCHIVE_MAP = {
 class XLMConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `XLMModel`.
     """
-    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+    pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(self,
                  vocab_size_or_config_json_file=30145,
@@ -79,10 +74,11 @@ class XLMConfig(PretrainedConfig):
 
                  finetuning_task=None,
                  num_labels=2,
-                 summary_type='last',
+                 summary_type='first',
                  summary_use_proj=True,
-                 summary_activation='tanh',
-                 summary_dropout=0.1,
+                 summary_activation=None,
+                 summary_proj_to_labels=True,
+                 summary_first_dropout=0.1,
                  start_n_top=5,
                  end_n_top=5,
                  **kwargs):
@@ -164,7 +160,8 @@ class XLMConfig(PretrainedConfig):
             self.summary_type = summary_type
             self.summary_use_proj = summary_use_proj
             self.summary_activation = summary_activation
-            self.summary_dropout = summary_dropout
+            self.summary_proj_to_labels = summary_proj_to_labels
+            self.summary_first_dropout = summary_first_dropout
             self.start_n_top = start_n_top
             self.end_n_top = end_n_top
         else:
@@ -204,7 +201,7 @@ def gelu(x):
     GELU activation
     https://arxiv.org/abs/1606.08415
     https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/model_pytorch.py#L14
-    https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/modeling.py
+    https://github.com/huggingface/pytorch-transformers/blob/master/modeling.py
     """
     # return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
     return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
@@ -357,7 +354,7 @@ class XLMPreTrainedModel(PreTrainedModel):
         a simple interface for dowloading and loading pretrained models.
     """
     config_class = XLMConfig
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
+    pretrained_model_archive_map = XLM_PRETRAINED_MODEL_ARCHIVE_MAP
     load_tf_weights = None
     base_model_prefix = "transformer"
 

pytorch_pretrained_bert/modeling_xlnet.py → pytorch_transformers/modeling_xlnet.py

@@ -31,17 +31,16 @@ from torch import nn
 from torch.nn import functional as F
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .file_utils import cached_path
-from .model_utils import (CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel,
+from .modeling_utils import (CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig, PreTrainedModel,
                              SequenceSummary, PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits)
 
 
 logger = logging.getLogger(__name__)
 
-PRETRAINED_MODEL_ARCHIVE_MAP = {
+XLNET_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-pytorch_model.bin",
 }
-PRETRAINED_CONFIG_ARCHIVE_MAP = {
+XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
 }
 
@@ -195,7 +194,7 @@ ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
 class XLNetConfig(PretrainedConfig):
     """Configuration class to store the configuration of a `XLNetModel`.
     """
-    pretrained_config_archive_map = PRETRAINED_CONFIG_ARCHIVE_MAP
+    pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
 
     def __init__(self,
                  vocab_size_or_config_json_file=32000,
@@ -227,7 +226,7 @@ class XLNetConfig(PretrainedConfig):
                  summary_type='last',
                  summary_use_proj=True,
                  summary_activation='tanh',
-                 summary_dropout=0.1,
+                 summary_last_dropout=0.1,
                  start_n_top=5,
                  end_n_top=5,
                  **kwargs):
@@ -314,7 +313,7 @@ class XLNetConfig(PretrainedConfig):
             self.summary_type = summary_type
             self.summary_use_proj = summary_use_proj
             self.summary_activation = summary_activation
-            self.summary_dropout = summary_dropout
+            self.summary_last_dropout = summary_last_dropout
             self.start_n_top = start_n_top
             self.end_n_top = end_n_top
         else:
@@ -593,7 +592,7 @@ class XLNetPreTrainedModel(PreTrainedModel):
         a simple interface for dowloading and loading pretrained models.
     """
     config_class = XLNetConfig
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
+    pretrained_model_archive_map = XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
     load_tf_weights = load_tf_weights_in_xlnet
     base_model_prefix = "transformer"
 

pytorch_pretrained_bert/tests/modeling_bert_test.py → pytorch_transformers/tests/modeling_bert_test.py

@@ -20,13 +20,13 @@ import unittest
 import shutil
 import pytest
 
-from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM,
+from pytorch_transformers import (BertConfig, BertModel, BertForMaskedLM,
                                      BertForNextSentencePrediction, BertForPreTraining,
                                      BertForQuestionAnswering, BertForSequenceClassification,
                                      BertForTokenClassification, BertForMultipleChoice)
-from pytorch_pretrained_bert.modeling_bert import PRETRAINED_MODEL_ARCHIVE_MAP
+from pytorch_transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 
-from .model_tests_commons import (create_and_check_commons, ConfigTester, ids_tensor)
+from .modeling_tests_commons import (create_and_check_commons, ConfigTester, ids_tensor)
 
 
 class BertModelTest(unittest.TestCase):
@@ -266,8 +266,8 @@ class BertModelTest(unittest.TestCase):
 
     @pytest.mark.slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/pytorch_pretrained_bert_test/"
-        for model_name in list(PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        cache_dir = "/tmp/pytorch_transformers_test/"
+        for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
             model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)
             self.assertIsNotNone(model)