Merge branch 'master' into reorder_arguments

pytorch_transformers/configuration_utils.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Configuration base class and utilities."""
+
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import copy
+import json
+import logging
+import os
+from io import open
+
+from .file_utils import cached_path, CONFIG_NAME
+
+logger = logging.getLogger(__name__)
+
+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.
+
+        Parameters:
+            ``finetuning_task``: string, default `None`. Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
+            ``num_labels``: integer, default `2`. Number of classes to use when the model is a classification model (sequences/tokens)
+            ``output_attentions``: boolean, default `False`. Should the model returns attentions weights.
+            ``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
+            ``torchscript``: string, default `False`. Is the model used with Torchscript.
+    """
+    pretrained_config_archive_map = {}
+
+    def __init__(self, **kwargs):
+        self.finetuning_task = kwargs.pop('finetuning_task', None)
+        self.num_labels = kwargs.pop('num_labels', 2)
+        self.output_attentions = kwargs.pop('output_attentions', False)
+        self.output_hidden_states = kwargs.pop('output_hidden_states', False)
+        self.torchscript = kwargs.pop('torchscript', False)
+        self.pruned_heads = kwargs.pop('pruned_heads', {})
+
+    def save_pretrained(self, save_directory):
+        """ Save a configuration object to the directory `save_directory`, so that it
+            can be re-loaded using the :func:`~pytorch_transformers.PretrainedConfig.from_pretrained` class method.
+        """
+        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
+
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_config_file = os.path.join(save_directory, CONFIG_NAME)
+
+        self.to_json_file(output_config_file)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        r""" Instantiate a :class:`~pytorch_transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
+
+        Parameters:
+            pretrained_model_name_or_path: either:
+
+                - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
+                - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
+
+            cache_dir: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+
+            kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
+
+                - 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.
+                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
+
+        Examples::
+
+            # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
+            # derived class: BertConfig
+            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
+            config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
+            config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
+            config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
+            assert config.output_attention == True
+            config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
+                                                               foo=False, return_unused_kwargs=True)
+            assert config.output_attention == True
+            assert unused_kwargs == {'foo': False}
+
+        """
+        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:
+            config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
+        elif os.path.isdir(pretrained_model_name_or_path):
+            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+        else:
+            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, force_download=force_download, proxies=proxies)
+        except EnvironmentError as e:
+            if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
+                logger.error(
+                    "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
+                        config_file))
+            else:
+                logger.error(
+                    "Model name '{}' was not found in model name list ({}). "
+                    "We assumed '{}' was a path or url but couldn't find any file "
+                    "associated to this path or url.".format(
+                        pretrained_model_name_or_path,
+                        ', '.join(cls.pretrained_config_archive_map.keys()),
+                        config_file))
+            raise e
+        if resolved_config_file == config_file:
+            logger.info("loading configuration file {}".format(config_file))
+        else:
+            logger.info("loading configuration file {} from cache at {}".format(
+                config_file, resolved_config_file))
+
+        # Load config
+        config = cls.from_json_file(resolved_config_file)
+
+        if hasattr(config, 'pruned_heads'):
+            config.pruned_heads = dict((int(key), set(value)) for key, value in config.pruned_heads.items())
+
+        # Update config with kwargs if needed
+        to_remove = []
+        for key, value in kwargs.items():
+            if hasattr(config, key):
+                setattr(config, key, value)
+                to_remove.append(key)
+        for key in to_remove:
+            kwargs.pop(key, None)
+
+        logger.info("Model config %s", config)
+        if return_unused_kwargs:
+            return config, kwargs
+        else:
+            return config
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `Config` from a Python dictionary of parameters."""
+        config = cls(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            config.__dict__[key] = value
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __eq__(self, other):
+        return self.__dict__ == other.__dict__
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())

pytorch_transformers/configuration_xlm.py

+# coding=utf-8
+# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" XLM configuration """
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",
+    'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",
+    'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",
+    'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",
+    'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",
+    'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",
+    'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",
+    'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",
+    'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",
+    'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",
+}
+
+
+class XLMConfig(PretrainedConfig):
+    """Configuration class to store the configuration of a `XLMModel`.
+
+    Args:
+        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLMModel`.
+        d_model: Size of the encoder layers and the pooler layer.
+        n_layer: Number of hidden layers in the Transformer encoder.
+        n_head: Number of attention heads for each attention layer in
+            the Transformer encoder.
+        d_inner: The size of the "intermediate" (i.e., feed-forward)
+            layer in the Transformer encoder.
+        ff_activation: The non-linear activation function (function or string) in the
+            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+        untie_r: untie relative position biases
+        attn_type: 'bi' for XLM, 'uni' for Transformer-XL
+
+        dropout: The dropout probabilitiy for all fully connected
+            layers in the embeddings, encoder, and pooler.
+        dropatt: The dropout ratio for the attention
+            probabilities.
+        max_position_embeddings: The maximum sequence length that this model might
+            ever be used with. Typically set this to something large just in case
+            (e.g., 512 or 1024 or 2048).
+        initializer_range: The sttdev of the truncated_normal_initializer for
+            initializing all weight matrices.
+        layer_norm_eps: The epsilon used by LayerNorm.
+
+        dropout: float, dropout rate.
+        dropatt: float, dropout rate on attention probabilities.
+        init: str, the initialization scheme, either "normal" or "uniform".
+        init_range: float, initialize the parameters with a uniform distribution
+            in [-init_range, init_range]. Only effective when init="uniform".
+        init_std: float, initialize the parameters with a normal distribution
+            with mean 0 and stddev init_std. Only effective when init="normal".
+        mem_len: int, the number of tokens to cache.
+        reuse_len: int, the number of tokens in the currect batch to be cached
+            and reused in the future.
+        bi_data: bool, whether to use bidirectional input pipeline.
+            Usually set to True during pretraining and False during finetuning.
+        clamp_len: int, clamp all relative distances larger than clamp_len.
+            -1 means no clamping.
+        same_length: bool, whether to use the same attention length for each token.
+    """
+    pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=30145,
+                 emb_dim=2048,
+                 n_layers=12,
+                 n_heads=16,
+                 dropout=0.1,
+                 attention_dropout=0.1,
+                 gelu_activation=True,
+                 sinusoidal_embeddings=False,
+                 causal=False,
+                 asm=False,
+                 n_langs=1,
+                 use_lang_emb=True,
+                 max_position_embeddings=512,
+                 embed_init_std=2048 ** -0.5,
+                 layer_norm_eps=1e-12,
+                 init_std=0.02,
+                 bos_index=0,
+                 eos_index=1,
+                 pad_index=2,
+                 unk_index=3,
+                 mask_index=5,
+                 is_encoder=True,
+
+                 finetuning_task=None,
+                 num_labels=2,
+                 summary_type='first',
+                 summary_use_proj=True,
+                 summary_activation=None,
+                 summary_proj_to_labels=True,
+                 summary_first_dropout=0.1,
+                 start_n_top=5,
+                 end_n_top=5,
+                 **kwargs):
+        """Constructs XLMConfig.
+        """
+        super(XLMConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "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.n_words = vocab_size_or_config_json_file
+            self.emb_dim = emb_dim
+            self.n_layers = n_layers
+            self.n_heads = n_heads
+            self.dropout = dropout
+            self.attention_dropout = attention_dropout
+            self.gelu_activation = gelu_activation
+            self.sinusoidal_embeddings = sinusoidal_embeddings
+            self.causal = causal
+            self.asm = asm
+            self.n_langs = n_langs
+            self.use_lang_emb = use_lang_emb
+            self.layer_norm_eps = layer_norm_eps
+            self.bos_index = bos_index
+            self.eos_index = eos_index
+            self.pad_index = pad_index
+            self.unk_index = unk_index
+            self.mask_index = mask_index
+            self.is_encoder = is_encoder
+            self.max_position_embeddings = max_position_embeddings
+            self.embed_init_std = embed_init_std
+            self.init_std = init_std
+            self.finetuning_task = finetuning_task
+            self.num_labels = num_labels
+            self.summary_type = summary_type
+            self.summary_use_proj = summary_use_proj
+            self.summary_activation = summary_activation
+            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:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+
+    @property
+    def vocab_size(self):
+        return self.n_words
+
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self.n_words = value
+
+    @property
+    def hidden_size(self):
+        return self.emb_dim
+
+    @property
+    def num_attention_heads(self):
+        return self.n_heads
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layers

pytorch_transformers/configuration_xlnet.py

+# coding=utf-8
+# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" XLNet configuration """
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+from io import open
+
+from .configuration_utils import PretrainedConfig
+
+logger = logging.getLogger(__name__)
+
+XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
+    'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
+}
+
+
+class XLNetConfig(PretrainedConfig):
+    """Configuration class to store the configuration of a ``XLNetModel``.
+
+    Args:
+        vocab_size_or_config_json_file: Vocabulary size of ``inputs_ids`` in ``XLNetModel``.
+        d_model: Size of the encoder layers and the pooler layer.
+        n_layer: Number of hidden layers in the Transformer encoder.
+        n_head: Number of attention heads for each attention layer in
+            the Transformer encoder.
+        d_inner: The size of the "intermediate" (i.e., feed-forward)
+            layer in the Transformer encoder.
+        ff_activation: The non-linear activation function (function or string) in the
+            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
+        untie_r: untie relative position biases
+        attn_type: 'bi' for XLNet, 'uni' for Transformer-XL
+
+        dropout: The dropout probabilitiy for all fully connected
+            layers in the embeddings, encoder, and pooler.
+        dropatt: The dropout ratio for the attention
+            probabilities.
+        initializer_range: The sttdev of the truncated_normal_initializer for
+            initializing all weight matrices.
+        layer_norm_eps: The epsilon used by LayerNorm.
+
+        dropout: float, dropout rate.
+        dropatt: float, dropout rate on attention probabilities.
+        init: str, the initialization scheme, either "normal" or "uniform".
+        init_range: float, initialize the parameters with a uniform distribution
+            in [-init_range, init_range]. Only effective when init="uniform".
+        init_std: float, initialize the parameters with a normal distribution
+            with mean 0 and stddev init_std. Only effective when init="normal".
+        mem_len: int, the number of tokens to cache.
+        reuse_len: int, the number of tokens in the currect batch to be cached
+            and reused in the future.
+        bi_data: bool, whether to use bidirectional input pipeline.
+            Usually set to True during pretraining and False during finetuning.
+        clamp_len: int, clamp all relative distances larger than clamp_len.
+            -1 means no clamping.
+        same_length: bool, whether to use the same attention length for each token.
+        finetuning_task: name of the glue task on which the model was fine-tuned if any
+    """
+    pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=32000,
+                 d_model=1024,
+                 n_layer=24,
+                 n_head=16,
+                 d_inner=4096,
+                 ff_activation="gelu",
+                 untie_r=True,
+                 attn_type="bi",
+
+                 initializer_range=0.02,
+                 layer_norm_eps=1e-12,
+
+                 dropout=0.1,
+                 mem_len=None,
+                 reuse_len=None,
+                 bi_data=False,
+                 clamp_len=-1,
+                 same_length=False,
+
+                 finetuning_task=None,
+                 num_labels=2,
+                 summary_type='last',
+                 summary_use_proj=True,
+                 summary_activation='tanh',
+                 summary_last_dropout=0.1,
+                 start_n_top=5,
+                 end_n_top=5,
+                 **kwargs):
+        """Constructs XLNetConfig.
+        """
+        super(XLNetConfig, self).__init__(**kwargs)
+
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "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.n_token = vocab_size_or_config_json_file
+            self.d_model = d_model
+            self.n_layer = n_layer
+            self.n_head = n_head
+            assert d_model % n_head == 0
+            self.d_head = d_model // n_head
+            self.ff_activation = ff_activation
+            self.d_inner = d_inner
+            self.untie_r = untie_r
+            self.attn_type = attn_type
+
+            self.initializer_range = initializer_range
+            self.layer_norm_eps = layer_norm_eps
+
+            self.dropout = dropout
+            self.mem_len = mem_len
+            self.reuse_len = reuse_len
+            self.bi_data = bi_data
+            self.clamp_len = clamp_len
+            self.same_length = same_length
+
+            self.finetuning_task = finetuning_task
+            self.num_labels = num_labels
+            self.summary_type = summary_type
+            self.summary_use_proj = summary_use_proj
+            self.summary_activation = summary_activation
+            self.summary_last_dropout = summary_last_dropout
+            self.start_n_top = start_n_top
+            self.end_n_top = end_n_top
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+
+    @property
+    def max_position_embeddings(self):
+        return -1
+
+    @property
+    def vocab_size(self):
+        return self.n_token
+
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self.n_token = value
+
+    @property
+    def hidden_size(self):
+        return self.d_model
+
+    @property
+    def num_attention_heads(self):
+        return self.n_head
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layer

pytorch_transformers/convert_gpt2_checkpoint_to_pytorch.py

@@ -21,7 +21,7 @@ from io import open
 
 import torch
 
-from pytorch_transformers.modeling_gpt2 import (CONFIG_NAME, WEIGHTS_NAME,
+from pytorch_transformers import (CONFIG_NAME, WEIGHTS_NAME,
                                                      GPT2Config,
                                                      GPT2Model,
                                                      load_tf_weights_in_gpt2)

pytorch_transformers/convert_openai_checkpoint_to_pytorch.py

@@ -21,7 +21,7 @@ from io import open
 
 import torch
 
-from pytorch_transformers.modeling_openai import (CONFIG_NAME, WEIGHTS_NAME,
+from pytorch_transformers import (CONFIG_NAME, WEIGHTS_NAME,
                                                      OpenAIGPTConfig,
                                                      OpenAIGPTModel,
                                                      load_tf_weights_in_openai_gpt)

pytorch_transformers/convert_pytorch_checkpoint_to_tf.py

@@ -20,7 +20,7 @@ import argparse
 import torch
 import numpy as np
 import tensorflow as tf
-from pytorch_transformers.modeling import BertModel
+from pytorch_transformers import BertModel
 
 
 def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str, model_name:str):

pytorch_transformers/convert_roberta_checkpoint_to_pytorch.py

@@ -23,12 +23,12 @@ import torch
 
 from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
 from fairseq.modules import TransformerSentenceEncoderLayer
-from pytorch_transformers.modeling_bert import (BertConfig, BertEncoder,
+from pytorch_transformers import (BertConfig, BertEncoder,
                                                 BertIntermediate, BertLayer,
                                                 BertModel, BertOutput,
                                                 BertSelfAttention,
                                                 BertSelfOutput)
-from pytorch_transformers.modeling_roberta import (RobertaEmbeddings,
+from pytorch_transformers import (RobertaEmbeddings,
                                                    RobertaForMaskedLM,
                                                    RobertaForSequenceClassification,
                                                    RobertaModel)

pytorch_transformers/convert_tf_checkpoint_to_pytorch.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import argparse
 import torch
 
-from pytorch_transformers.modeling_bert import BertConfig, BertForPreTraining, load_tf_weights_in_bert
+from pytorch_transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
 
 import logging
 logging.basicConfig(level=logging.INFO)

pytorch_transformers/convert_transfo_xl_checkpoint_to_pytorch.py

@@ -26,7 +26,7 @@ import torch
 import pytorch_transformers.tokenization_transfo_xl as data_utils
 
 from pytorch_transformers import CONFIG_NAME, WEIGHTS_NAME
-from pytorch_transformers.modeling_transfo_xl import (TransfoXLConfig, TransfoXLLMHeadModel,
+from pytorch_transformers import (TransfoXLConfig, TransfoXLLMHeadModel,
                                                       load_tf_weights_in_transfo_xl)
 from pytorch_transformers.tokenization_transfo_xl import (CORPUS_NAME, VOCAB_FILES_NAMES)
 

pytorch_transformers/convert_xlm_checkpoint_to_pytorch.py

@@ -23,7 +23,7 @@ from io import open
 import torch
 import numpy
 
-from pytorch_transformers.modeling_utils import CONFIG_NAME, WEIGHTS_NAME
+from pytorch_transformers import CONFIG_NAME, WEIGHTS_NAME
 from pytorch_transformers.tokenization_xlm import VOCAB_FILES_NAMES
 
 import logging

pytorch_transformers/convert_xlnet_checkpoint_to_pytorch.py

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

pytorch_transformers/file_utils.py

@@ -9,6 +9,7 @@ import sys
 import json
 import logging
 import os
+import six
 import shutil
 import tempfile
 import fnmatch
@@ -47,8 +48,35 @@ except (AttributeError, ImportError):
 
 PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
 
+WEIGHTS_NAME = "pytorch_model.bin"
+TF_WEIGHTS_NAME = 'model.ckpt'
+CONFIG_NAME = "config.json"
+
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
+if not six.PY2:
+    def add_start_docstrings(*docstr):
+        def docstring_decorator(fn):
+            fn.__doc__ = ''.join(docstr) + fn.__doc__
+            return fn
+        return docstring_decorator
+
+    def add_end_docstrings(*docstr):
+        def docstring_decorator(fn):
+            fn.__doc__ = fn.__doc__ + ''.join(docstr)
+            return fn
+        return docstring_decorator
+else:
+    # Not possible to update class docstrings on python2
+    def add_start_docstrings(*docstr):
+        def docstring_decorator(fn):
+            return fn
+        return docstring_decorator
+
+    def add_end_docstrings(*docstr):
+        def docstring_decorator(fn):
+            return fn
+        return docstring_decorator
 
 def url_to_filename(url, etag=None):
     """

pytorch_transformers/modeling_auto.py

@@ -18,123 +18,20 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import logging
 
-from .modeling_bert import BertConfig, BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
-from .modeling_openai import OpenAIGPTConfig, OpenAIGPTModel, OpenAIGPTLMHeadModel
-from .modeling_gpt2 import GPT2Config, GPT2Model, GPT2LMHeadModel
-from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel
-from .modeling_xlnet import XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
-from .modeling_xlm import XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
-from .modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
-from .modeling_distilbert import DistilBertConfig, DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
+from .modeling_bert import BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
+from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel
+from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel
+from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel
+from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
+from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
+from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
+from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
 
-from .modeling_utils import PreTrainedModel, SequenceSummary, add_start_docstrings
+from .modeling_utils import PreTrainedModel, SequenceSummary
 
-logger = logging.getLogger(__name__)
-
-
-class AutoConfig(object):
-    r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class
-        that will be instantiated as one of the configuration classes of the library
-        when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
-        class method.
-
-        The `from_pretrained()` method take care of returning the correct model class instance
-        using pattern matching on the `pretrained_model_name_or_path` string.
-
-        The base model class to instantiate is selected as the first pattern matching
-        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `distilbert`: DistilBertConfig (DistilBERT model)
-            - contains `bert`: BertConfig (Bert model)
-            - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
-            - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
-            - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
-            - contains `xlnet`: XLNetConfig (XLNet model)
-            - contains `xlm`: XLMConfig (XLM model)
-            - contains `roberta`: RobertaConfig (RoBERTa model)
-
-        This class cannot be instantiated using `__init__()` (throw an error).
-    """
-    def __init__(self):
-        raise EnvironmentError("AutoConfig is designed to be instantiated "
-            "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method.")
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        r""" Instantiate a one of the configuration classes of the library
-        from a pre-trained model configuration.
-
-        The configuration class to instantiate is selected as the first pattern matching
-        in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `distilbert`: DistilBertConfig (DistilBERT model)
-            - contains `bert`: BertConfig (Bert model)
-            - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
-            - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
-            - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
-            - contains `xlnet`: XLNetConfig (XLNet model)
-            - contains `xlm`: XLMConfig (XLM model)
-            - contains `roberta`: RobertaConfig (RoBERTa model)
-
-        Params:
-            pretrained_model_name_or_path: either:
-
-                - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
-                - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
-
-            cache_dir: (`optional`) string:
-                Path to a directory in which a downloaded pre-trained model
-                configuration should be cached if the standard cache should not be used.
-
-            kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
-
-                - 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.
+from .file_utils import add_start_docstrings
 
-            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.
-                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
-
-        Examples::
-
-            config = AutoConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
-            config = AutoConfig.from_pretrained('./test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
-            config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
-            config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
-            assert config.output_attention == True
-            config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
-                                                               foo=False, return_unused_kwargs=True)
-            assert config.output_attention == True
-            assert unused_kwargs == {'foo': False}
-
-        """
-        if 'distilbert' in pretrained_model_name_or_path:
-            return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'roberta' in pretrained_model_name_or_path:
-            return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'bert' in pretrained_model_name_or_path:
-            return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'openai-gpt' in pretrained_model_name_or_path:
-            return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'gpt2' in pretrained_model_name_or_path:
-            return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'transfo-xl' in pretrained_model_name_or_path:
-            return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'xlnet' in pretrained_model_name_or_path:
-            return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'xlm' in pretrained_model_name_or_path:
-            return XLMConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        raise ValueError("Unrecognized model identifier in {}. Should contains one of "
-                         "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
-                         "'xlm', 'roberta'".format(pretrained_model_name_or_path))
+logger = logging.getLogger(__name__)
 
 
 class AutoModel(object):

pytorch_transformers/modeling_bert.py

@@ -28,8 +28,9 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, PretrainedConfig, PreTrainedModel,
-                             prune_linear_layer, add_start_docstrings)
+from .modeling_utils import PreTrainedModel, prune_linear_layer
+from .configuration_bert import BertConfig
+from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 
@@ -49,23 +50,6 @@ BERT_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",
 }
 
-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",
-    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json",
-    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json",
-    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json",
-    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json",
-    'bert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json",
-    'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json",
-    'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json",
-    'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json",
-    'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json",
-    'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
-}
-
-
 def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
     """ Load tf checkpoints in a pytorch model.
     """
@@ -149,77 +133,6 @@ def swish(x):
 ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
 
 
-class BertConfig(PretrainedConfig):
-    r"""
-        :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a
-        `BertModel`.
-
-
-        Arguments:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `BertModel`.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-            layer_norm_eps: The epsilon used by LayerNorm.
-    """
-    pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-    def __init__(self,
-                 vocab_size_or_config_json_file=30522,
-                 hidden_size=768,
-                 num_hidden_layers=12,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 max_position_embeddings=512,
-                 type_vocab_size=2,
-                 initializer_range=0.02,
-                 layer_norm_eps=1e-12,
-                 **kwargs):
-        super(BertConfig, self).__init__(**kwargs)
-        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
-                        and isinstance(vocab_size_or_config_json_file, unicode)):
-            with open(vocab_size_or_config_json_file, "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.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.hidden_act = hidden_act
-            self.intermediate_size = intermediate_size
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.type_vocab_size = type_vocab_size
-            self.initializer_range = initializer_range
-            self.layer_norm_eps = layer_norm_eps
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             " or the path to a pretrained model config file (str)")
-
-
-
 try:
     from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
 except (ImportError, AttributeError) as e:

pytorch_transformers/modeling_distilbert.py

@@ -31,7 +31,9 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from pytorch_transformers.modeling_utils import PretrainedConfig, PreTrainedModel, add_start_docstrings, prune_linear_layer
+from .modeling_utils import PreTrainedModel, prune_linear_layer
+from .configuration_distilbert import DistilBertConfig
+from .file_utils import add_start_docstrings
 
 import logging
 logger = logging.getLogger(__name__)
@@ -42,69 +44,6 @@ DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin"
 }
 
-DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
-    'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json"
-}
-
-
-class DistilBertConfig(PretrainedConfig):
-    pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-    def __init__(self,
-                 vocab_size_or_config_json_file=30522,
-                 max_position_embeddings=512,
-                 sinusoidal_pos_embds=True,
-                 n_layers=6,
-                 n_heads=12,
-                 dim=768,
-                 hidden_dim=4*768,
-                 dropout=0.1,
-                 attention_dropout=0.1,
-                 activation='gelu',
-                 initializer_range=0.02,
-                 tie_weights_=True,
-                 qa_dropout=0.1,
-                 seq_classif_dropout=0.2,
-                 **kwargs):
-        super(DistilBertConfig, self).__init__(**kwargs)
-
-        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
-                        and isinstance(vocab_size_or_config_json_file, unicode)):
-            with open(vocab_size_or_config_json_file, "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_position_embeddings = max_position_embeddings
-            self.sinusoidal_pos_embds = sinusoidal_pos_embds
-            self.n_layers = n_layers
-            self.n_heads = n_heads
-            self.dim = dim
-            self.hidden_dim = hidden_dim
-            self.dropout = dropout
-            self.attention_dropout = attention_dropout
-            self.activation = activation
-            self.initializer_range = initializer_range
-            self.tie_weights_ = tie_weights_
-            self.qa_dropout = qa_dropout
-            self.seq_classif_dropout = seq_classif_dropout
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             " or the path to a pretrained model config file (str)")
-    @property
-    def hidden_size(self):
-        return self.dim
-
-    @property
-    def num_attention_heads(self):
-        return self.n_heads
-
-    @property
-    def num_hidden_layers(self):
-        return self.n_layers
-
 
 ### UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE ###
 def gelu(x):

pytorch_transformers/modeling_gpt2.py

@@ -30,19 +30,15 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .modeling_utils import (Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig,
-                             PreTrainedModel, prune_conv1d_layer, SequenceSummary,
-                             add_start_docstrings)
-from .modeling_bert import BertLayerNorm as LayerNorm
+from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
+from .configuration_gpt2 import GPT2Config
+from .file_utils import add_start_docstrings
 
 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-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-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
@@ -102,120 +98,6 @@ def gelu(x):
     return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
 
 
-class GPT2Config(PretrainedConfig):
-    """Configuration class to store the configuration of a `GPT2Model`.
-
-    Args:
-        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
-        n_positions: Number of positional embeddings.
-        n_ctx: Size of the causal mask (usually same as n_positions).
-        n_embd: Dimensionality of the embeddings and hidden states.
-        n_layer: Number of hidden layers in the Transformer encoder.
-        n_head: Number of attention heads for each attention layer in
-            the Transformer encoder.
-        layer_norm_epsilon: epsilon to use in the layer norm layers
-        resid_pdrop: The dropout probabilitiy for all fully connected
-            layers in the embeddings, encoder, and pooler.
-        attn_pdrop: The dropout ratio for the attention
-            probabilities.
-        embd_pdrop: The dropout ratio for the embeddings.
-        initializer_range: The sttdev of the truncated_normal_initializer for
-            initializing all weight matrices.
-    """
-    pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-    def __init__(
-        self,
-        vocab_size_or_config_json_file=50257,
-        n_positions=1024,
-        n_ctx=1024,
-        n_embd=768,
-        n_layer=12,
-        n_head=12,
-        resid_pdrop=0.1,
-        embd_pdrop=0.1,
-        attn_pdrop=0.1,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-
-        num_labels=1,
-        summary_type='cls_index',
-        summary_use_proj=True,
-        summary_activation=None,
-        summary_proj_to_labels=True,
-        summary_first_dropout=0.1,
-        **kwargs
-    ):
-        """Constructs GPT2Config.
-
-        Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
-            n_positions: Number of positional embeddings.
-            n_ctx: Size of the causal mask (usually same as n_positions).
-            n_embd: Dimensionality of the embeddings and hidden states.
-            n_layer: Number of hidden layers in the Transformer encoder.
-            n_head: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            layer_norm_epsilon: epsilon to use in the layer norm layers
-            resid_pdrop: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attn_pdrop: The dropout ratio for the attention
-                probabilities.
-            embd_pdrop: The dropout ratio for the embeddings.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-        """
-        super(GPT2Config, self).__init__(**kwargs)
-
-        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
-                        and isinstance(vocab_size_or_config_json_file, unicode)):
-            with open(vocab_size_or_config_json_file, "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.n_ctx = n_ctx
-            self.n_positions = n_positions
-            self.n_embd = n_embd
-            self.n_layer = n_layer
-            self.n_head = n_head
-            self.resid_pdrop = resid_pdrop
-            self.embd_pdrop = embd_pdrop
-            self.attn_pdrop = attn_pdrop
-            self.layer_norm_epsilon = layer_norm_epsilon
-            self.initializer_range = initializer_range
-
-            self.num_labels = num_labels
-            self.summary_type = summary_type
-            self.summary_use_proj = summary_use_proj
-            self.summary_activation = summary_activation
-            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)"
-                "or the path to a pretrained model config file (str)"
-            )
-
-    @property
-    def max_position_embeddings(self):
-        return self.n_positions
-
-    @property
-    def hidden_size(self):
-        return self.n_embd
-
-    @property
-    def num_attention_heads(self):
-        return self.n_head
-
-    @property
-    def num_hidden_layers(self):
-        return self.n_layer
-
-
-
 class Attention(nn.Module):
     def __init__(self, nx, n_ctx, config, scale=False):
         super(Attention, self).__init__()
@@ -336,9 +218,9 @@ class Block(nn.Module):
     def __init__(self, n_ctx, config, scale=False):
         super(Block, self).__init__()
         nx = config.n_embd
-        self.ln_1 = LayerNorm(nx, eps=config.layer_norm_epsilon)
+        self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
         self.attn = Attention(nx, n_ctx, config, scale)
-        self.ln_2 = LayerNorm(nx, eps=config.layer_norm_epsilon)
+        self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
         self.mlp = MLP(4 * nx, config)
 
     def forward(self, x, layer_past=None, attention_mask=None, head_mask=None):
@@ -377,7 +259,7 @@ class GPT2PreTrainedModel(PreTrainedModel):
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
                 module.bias.data.zero_()
-        elif isinstance(module, LayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
 
@@ -469,7 +351,7 @@ class GPT2Model(GPT2PreTrainedModel):
         self.wpe = nn.Embedding(config.n_positions, config.n_embd)
         self.drop = nn.Dropout(config.embd_pdrop)
         self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
-        self.ln_f = LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
 
         self.init_weights()
 

pytorch_transformers/modeling_openai.py

@@ -30,15 +30,13 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .modeling_utils import (Conv1D, CONFIG_NAME, WEIGHTS_NAME, PretrainedConfig,
-                             PreTrainedModel, prune_conv1d_layer, SequenceSummary,
-                             add_start_docstrings)
-from .modeling_bert import BertLayerNorm as LayerNorm
+from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
+from .configuration_openai import OpenAIGPTConfig
+from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 
 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):
@@ -127,111 +125,6 @@ def swish(x):
 ACT_FNS = {"relu": nn.ReLU, "swish": swish, "gelu": gelu}
 
 
-class OpenAIGPTConfig(PretrainedConfig):
-    """
-    Configuration class to store the configuration of a `OpenAIGPTModel`.
-
-    Args:
-        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `OpenAIGPTModel` or a configuration json file.
-        n_special: The number of special tokens to learn during fine-tuning ('[SEP]', '[CLF]', ...)
-        n_positions: Number of positional embeddings.
-        n_ctx: Size of the causal mask (usually same as n_positions).
-        n_embd: Dimensionality of the embeddings and hidden states.
-        n_layer: Number of hidden layers in the Transformer encoder.
-        n_head: Number of attention heads for each attention layer in
-            the Transformer encoder.
-        afn: The non-linear activation function (function or string) in the
-            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-        resid_pdrop: The dropout probabilitiy for all fully connected
-            layers in the embeddings, encoder, and pooler.
-        attn_pdrop: The dropout ratio for the attention
-            probabilities.
-        embd_pdrop: The dropout ratio for the embeddings.
-        layer_norm_epsilon: epsilon to use in the layer norm layers
-        initializer_range: The sttdev of the truncated_normal_initializer for
-            initializing all weight matrices.
-        predict_special_tokens: should we predict special tokens (when the model has a LM head)
-    """
-    pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-    def __init__(
-        self,
-        vocab_size_or_config_json_file=40478,
-        n_positions=512,
-        n_ctx=512,
-        n_embd=768,
-        n_layer=12,
-        n_head=12,
-        afn="gelu",
-        resid_pdrop=0.1,
-        embd_pdrop=0.1,
-        attn_pdrop=0.1,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-        predict_special_tokens=True,
-
-        num_labels=1,
-        summary_type='cls_index',
-        summary_use_proj=True,
-        summary_activation=None,
-        summary_proj_to_labels=True,
-        summary_first_dropout=0.1,
-        **kwargs
-    ):
-        """Constructs OpenAIGPTConfig.
-        """
-        super(OpenAIGPTConfig, self).__init__(**kwargs)
-
-        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
-                        and isinstance(vocab_size_or_config_json_file, unicode)):
-            with open(vocab_size_or_config_json_file, "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.n_ctx = n_ctx
-            self.n_positions = n_positions
-            self.n_embd = n_embd
-            self.n_layer = n_layer
-            self.n_head = n_head
-            self.afn = afn
-            self.resid_pdrop = resid_pdrop
-            self.embd_pdrop = embd_pdrop
-            self.attn_pdrop = attn_pdrop
-            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_activation = summary_activation
-            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)"
-                "or the path to a pretrained model config file (str)"
-            )
-
-    @property
-    def max_position_embeddings(self):
-        return self.n_positions
-
-    @property
-    def hidden_size(self):
-        return self.n_embd
-
-    @property
-    def num_attention_heads(self):
-        return self.n_head
-
-    @property
-    def num_hidden_layers(self):
-        return self.n_layer
-
-
 class Attention(nn.Module):
     def __init__(self, nx, n_ctx, config, scale=False):
         super(Attention, self).__init__()
@@ -346,9 +239,9 @@ class Block(nn.Module):
         super(Block, self).__init__()
         nx = config.n_embd
         self.attn = Attention(nx, n_ctx, config, scale)
-        self.ln_1 = LayerNorm(nx, eps=config.layer_norm_epsilon)
+        self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
         self.mlp = MLP(4 * nx, config)
-        self.ln_2 = LayerNorm(nx, eps=config.layer_norm_epsilon)
+        self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
 
     def forward(self, x, attention_mask=None, head_mask=None):
         attn_outputs = self.attn(x, attention_mask=attention_mask, head_mask=head_mask)
@@ -380,7 +273,7 @@ class OpenAIGPTPreTrainedModel(PreTrainedModel):
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
                 module.bias.data.zero_()
-        elif isinstance(module, LayerNorm):
+        elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)
 

pytorch_transformers/modeling_roberta.py

@@ -22,14 +22,11 @@ import logging
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from pytorch_transformers.modeling_bert import (BertConfig, BertEmbeddings,
-                                                BertLayerNorm, BertModel,
-                                                BertPreTrainedModel, gelu)
-
-from pytorch_transformers.modeling_utils import add_start_docstrings
+from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
+from .configuration_roberta import RobertaConfig
+from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 
@@ -39,13 +36,6 @@ ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
 }
 
-ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json",
-    'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json",
-    'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-config.json",
-}
-
-
 class RobertaEmbeddings(BertEmbeddings):
     """
     Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
@@ -66,10 +56,6 @@ class RobertaEmbeddings(BertEmbeddings):
                                                       position_ids=position_ids)
 
 
-class RobertaConfig(BertConfig):
-    pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-
 ROBERTA_START_DOCSTRING = r"""    The RoBERTa model was proposed in
     `RoBERTa: A Robustly Optimized BERT Pretraining Approach`_
     by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,

pytorch_transformers/modeling_transfo_xl.py

@@ -34,18 +34,16 @@ import torch.nn.functional as F
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .modeling_bert import BertLayerNorm as LayerNorm
+from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
+from .configuration_transfo_xl import TransfoXLConfig
 from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax, sample_logits
-from .modeling_utils import (PretrainedConfig, PreTrainedModel, add_start_docstrings)
+from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 
 TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-pytorch_model.bin",
 }
-TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-config.json",
-}
 
 def build_tf_to_pytorch_map(model, config):
     """ A map of modules from TF to PyTorch.
@@ -175,143 +173,6 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
     return model
 
 
-class TransfoXLConfig(PretrainedConfig):
-    """Configuration class to store the configuration of a `TransfoXLModel`.
-
-        Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `TransfoXLModel` or a configuration json file.
-            cutoffs: cutoffs for the adaptive softmax
-            d_model: Dimensionality of the model's hidden states.
-            d_embed: Dimensionality of the embeddings
-            d_head: Dimensionality of the model's heads.
-            div_val: divident value for adapative input and softmax
-            pre_lnorm: apply LayerNorm to the input instead of the output
-            d_inner: Inner dimension in FF
-            n_layer: Number of hidden layers in the Transformer encoder.
-            n_head: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            tgt_len: number of tokens to predict
-            ext_len: length of the extended context
-            mem_len: length of the retained previous heads
-            same_length: use the same attn length for all tokens
-            proj_share_all_but_first: True to share all but first projs, False not to share.
-            attn_type: attention type. 0 for Transformer-XL, 1 for Shaw et al, 2 for Vaswani et al, 3 for Al Rfou et al.
-            clamp_len: use the same pos embeddings after clamp_len
-            sample_softmax: number of samples in sampled softmax
-            adaptive: use adaptive softmax
-            tie_weight: tie the word embedding and softmax weights
-            dropout: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            dropatt: The dropout ratio for the attention probabilities.
-            untie_r: untie relative position biases
-            embd_pdrop: The dropout ratio for the embeddings.
-            init: parameter initializer to use
-            init_range: parameters initialized by U(-init_range, init_range).
-            proj_init_std: parameters initialized by N(0, init_std)
-            init_std: parameters initialized by N(0, init_std)
-    """
-    pretrained_config_archive_map = TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-    def __init__(self,
-                 vocab_size_or_config_json_file=267735,
-                 cutoffs=[20000, 40000, 200000],
-                 d_model=1024,
-                 d_embed=1024,
-                 n_head=16,
-                 d_head=64,
-                 d_inner=4096,
-                 div_val=4,
-                 pre_lnorm=False,
-                 n_layer=18,
-                 tgt_len=128,
-                 ext_len=0,
-                 mem_len=1600,
-                 clamp_len=1000,
-                 same_length=True,
-                 proj_share_all_but_first=True,
-                 attn_type=0,
-                 sample_softmax=-1,
-                 adaptive=True,
-                 tie_weight=True,
-                 dropout=0.1,
-                 dropatt=0.0,
-                 untie_r=True,
-                 init="normal",
-                 init_range=0.01,
-                 proj_init_std=0.01,
-                 init_std=0.02,
-                 **kwargs):
-        """Constructs TransfoXLConfig.
-        """
-        super(TransfoXLConfig, self).__init__(**kwargs)
-
-        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
-                        and isinstance(vocab_size_or_config_json_file, unicode)):
-            with open(vocab_size_or_config_json_file, "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.n_token = vocab_size_or_config_json_file
-            self.cutoffs = []
-            self.cutoffs.extend(cutoffs)
-            self.tie_weight = tie_weight
-            if proj_share_all_but_first:
-                self.tie_projs = [False] + [True] * len(self.cutoffs)
-            else:
-                self.tie_projs = [False] + [False] * len(self.cutoffs)
-            self.d_model = d_model
-            self.d_embed = d_embed
-            self.d_head = d_head
-            self.d_inner = d_inner
-            self.div_val = div_val
-            self.pre_lnorm = pre_lnorm
-            self.n_layer = n_layer
-            self.n_head = n_head
-            self.tgt_len = tgt_len
-            self.ext_len = ext_len
-            self.mem_len = mem_len
-            self.same_length = same_length
-            self.attn_type = attn_type
-            self.clamp_len = clamp_len
-            self.sample_softmax = sample_softmax
-            self.adaptive = adaptive
-            self.dropout = dropout
-            self.dropatt = dropatt
-            self.untie_r = untie_r
-            self.init = init
-            self.init_range = init_range
-            self.proj_init_std = proj_init_std
-            self.init_std = init_std
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             " or the path to a pretrained model config file (str)")
-
-    @property
-    def max_position_embeddings(self):
-        return self.tgt_len + self.ext_len + self.mem_len
-
-    @property
-    def vocab_size(self):
-        return self.n_token
-
-    @vocab_size.setter
-    def vocab_size(self, value):
-        self.n_token = value
-
-    @property
-    def hidden_size(self):
-        return self.d_model
-
-    @property
-    def num_attention_heads(self):
-        return self.n_head
-
-    @property
-    def num_hidden_layers(self):
-        return self.n_layer
-
-
 class PositionalEmbedding(nn.Module):
     def __init__(self, demb):
         super(PositionalEmbedding, self).__init__()
@@ -347,7 +208,7 @@ class PositionwiseFF(nn.Module):
             nn.Dropout(dropout),
         )
 
-        self.layer_norm = LayerNorm(d_model)
+        self.layer_norm = nn.LayerNorm(d_model)
 
         self.pre_lnorm = pre_lnorm
 
@@ -387,7 +248,7 @@ class MultiHeadAttn(nn.Module):
         self.dropatt = nn.Dropout(dropatt)
         self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
 
-        self.layer_norm = LayerNorm(d_model)
+        self.layer_norm = nn.LayerNorm(d_model)
 
         self.scale = 1 / (d_head ** 0.5)
 
@@ -477,7 +338,7 @@ class RelMultiHeadAttn(nn.Module):
         self.dropatt = nn.Dropout(dropatt)
         self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
 
-        self.layer_norm = LayerNorm(d_model)
+        self.layer_norm = nn.LayerNorm(d_model)
 
         self.scale = 1 / (d_head ** 0.5)
 
@@ -1135,7 +996,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
         mlen = mems[0].size(0) if mems is not None else 0
         klen = mlen + qlen
         if self.same_length:
-            all_ones = word_emb.new_ones(qlen, klen)
+            all_ones = word_emb.new_ones((qlen, klen), dtype=torch.uint8)
             mask_len = klen - self.mem_len
             if mask_len > 0:
                 mask_shift_len = qlen - mask_len
@@ -1145,7 +1006,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
                     + torch.tril(all_ones, -mask_shift_len))[:, :, None] # -1
         else:
             dec_attn_mask = torch.triu(
-                word_emb.new_ones(qlen, klen), diagonal=1+mlen)[:,:,None]
+                word_emb.new_ones((qlen, klen), dtype=torch.uint8), diagonal=1+mlen)[:,:,None]
 
         hids = []
         attentions = []

pytorch_transformers/modeling_utils.py

@@ -30,14 +30,11 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 from torch.nn import functional as F
 
-from .file_utils import cached_path
+from .configuration_utils import PretrainedConfig
+from .file_utils import cached_path, WEIGHTS_NAME, TF_WEIGHTS_NAME
 
 logger = logging.getLogger(__name__)
 
-CONFIG_NAME = "config.json"
-WEIGHTS_NAME = "pytorch_model.bin"
-TF_WEIGHTS_NAME = 'model.ckpt'
-
 
 try:
     from torch.nn import Identity
@@ -52,209 +49,6 @@ except ImportError:
         def forward(self, input):
             return input
 
-
-if not six.PY2:
-    def add_start_docstrings(*docstr):
-        def docstring_decorator(fn):
-            fn.__doc__ = ''.join(docstr) + fn.__doc__
-            return fn
-        return docstring_decorator
-
-    def add_end_docstrings(*docstr):
-        def docstring_decorator(fn):
-            fn.__doc__ = fn.__doc__ + ''.join(docstr)
-            return fn
-        return docstring_decorator
-else:
-    # Not possible to update class docstrings on python2
-    def add_start_docstrings(*docstr):
-        def docstring_decorator(fn):
-            return fn
-        return docstring_decorator
-
-    def add_end_docstrings(*docstr):
-        def docstring_decorator(fn):
-            return fn
-        return docstring_decorator
-
-
-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.
-
-        Parameters:
-            ``finetuning_task``: string, default `None`. Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
-            ``num_labels``: integer, default `2`. Number of classes to use when the model is a classification model (sequences/tokens)
-            ``output_attentions``: boolean, default `False`. Should the model returns attentions weights.
-            ``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
-            ``torchscript``: string, default `False`. Is the model used with Torchscript.
-    """
-    pretrained_config_archive_map = {}
-
-    def __init__(self, **kwargs):
-        self.finetuning_task = kwargs.pop('finetuning_task', None)
-        self.num_labels = kwargs.pop('num_labels', 2)
-        self.output_attentions = kwargs.pop('output_attentions', False)
-        self.output_hidden_states = kwargs.pop('output_hidden_states', False)
-        self.torchscript = kwargs.pop('torchscript', False)
-        self.pruned_heads = kwargs.pop('pruned_heads', {})
-
-    def save_pretrained(self, save_directory):
-        """ Save a configuration object to the directory `save_directory`, so that it
-            can be re-loaded using the :func:`~pytorch_transformers.PretrainedConfig.from_pretrained` class method.
-        """
-        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
-
-        # If we save using the predefined names, we can load using `from_pretrained`
-        output_config_file = os.path.join(save_directory, CONFIG_NAME)
-
-        self.to_json_file(output_config_file)
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        r""" Instantiate a :class:`~pytorch_transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
-
-        Parameters:
-            pretrained_model_name_or_path: either:
-
-                - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
-                - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
-
-            cache_dir: (`optional`) string:
-                Path to a directory in which a downloaded pre-trained model
-                configuration should be cached if the standard cache should not be used.
-
-            kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
-
-                - 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.
-                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
-
-        Examples::
-
-            # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
-            # derived class: BertConfig
-            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
-            config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
-            config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
-            config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
-            assert config.output_attention == True
-            config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
-                                                               foo=False, return_unused_kwargs=True)
-            assert config.output_attention == True
-            assert unused_kwargs == {'foo': False}
-
-        """
-        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:
-            config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
-        elif os.path.isdir(pretrained_model_name_or_path):
-            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
-        else:
-            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, force_download=force_download, proxies=proxies)
-        except EnvironmentError as e:
-            if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
-                logger.error(
-                    "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
-                        config_file))
-            else:
-                logger.error(
-                    "Model name '{}' was not found in model name list ({}). "
-                    "We assumed '{}' was a path or url but couldn't find any file "
-                    "associated to this path or url.".format(
-                        pretrained_model_name_or_path,
-                        ', '.join(cls.pretrained_config_archive_map.keys()),
-                        config_file))
-            raise e
-        if resolved_config_file == config_file:
-            logger.info("loading configuration file {}".format(config_file))
-        else:
-            logger.info("loading configuration file {} from cache at {}".format(
-                config_file, resolved_config_file))
-
-        # Load config
-        config = cls.from_json_file(resolved_config_file)
-
-        if hasattr(config, 'pruned_heads'):
-            config.pruned_heads = dict((int(key), set(value)) for key, value in config.pruned_heads.items())
-
-        # Update config with kwargs if needed
-        to_remove = []
-        for key, value in kwargs.items():
-            if hasattr(config, key):
-                setattr(config, key, value)
-                to_remove.append(key)
-        for key in to_remove:
-            kwargs.pop(key, None)
-
-        logger.info("Model config %s", config)
-        if return_unused_kwargs:
-            return config, kwargs
-        else:
-            return config
-
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `Config` from a Python dictionary of parameters."""
-        config = cls(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `BertConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding='utf-8') as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __eq__(self, other):
-        return self.__dict__ == other.__dict__
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path):
-        """ Save this instance to a json file."""
-        with open(json_file_path, "w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())
-
-
 class PreTrainedModel(nn.Module):
     r""" Base class for all models.