fix add_start_docstrings on python 2 (removed)

pytorch_transformers/modeling_bert.py

@@ -646,7 +646,7 @@ BERT_INPUTS_DOCSTRING = r"""
 @add_start_docstrings("The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
                       BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
 class BertModel(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
         **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
             Sequence of hidden-states at the last layer of the model.
@@ -742,7 +742,7 @@ class BertModel(BertPreTrainedModel):
     a `masked language modeling` head and a `next sentence prediction (classification)` head. """,
     BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
 class BertForPreTraining(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
         **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Labels for computing the masked language modeling loss.
             Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
@@ -818,7 +818,7 @@ class BertForPreTraining(BertPreTrainedModel):
 @add_start_docstrings("""Bert Model transformer BERT model with a `language modeling` head on top. """,
     BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
 class BertForMaskedLM(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
         **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Labels for computing the masked language modeling loss.
             Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
@@ -883,7 +883,7 @@ class BertForMaskedLM(BertPreTrainedModel):
 @add_start_docstrings("""Bert Model transformer BERT model with a `next sentence prediction (classification)` head on top. """,
     BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
 class BertForNextSentencePrediction(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
         **next_sentence_label**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
             Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see ``input_ids`` docstring)
             Indices should be in ``[0, 1]``.
@@ -941,7 +941,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
     the pooled output) e.g. for GLUE tasks. """,
     BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
 class BertForSequenceClassification(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
         **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
             Labels for computing the sequence classification/regression loss.
             Indices should be in ``[0, ..., config.num_labels]``.
@@ -1009,7 +1009,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
     the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
     BERT_START_DOCSTRING)
 class BertForMultipleChoice(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
     Inputs:
         **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
@@ -1115,7 +1115,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
     the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
     BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
 class BertForTokenClassification(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
         **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Labels for computing the token classification loss.
             Indices should be in ``[0, ..., config.num_labels]``.
@@ -1182,7 +1182,7 @@ class BertForTokenClassification(BertPreTrainedModel):
     the hidden-states output to compute `span start logits` and `span end logits`). """,
     BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
 class BertForQuestionAnswering(BertPreTrainedModel):
-    __doc__ = r"""
+    r"""
         **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
             Position (index) of the start of the labelled span for computing the token classification loss.
             Positions are clamped to the length of the sequence (`sequence_length`).

pytorch_transformers/modeling_gpt2.py

@@ -31,7 +31,8 @@ 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)
+                             PreTrainedModel, prune_conv1d_layer, SequenceSummary,
+                             add_start_docstrings)
 from .modeling_bert import BertLayerNorm as LayerNorm
 
 logger = logging.getLogger(__name__)
@@ -414,7 +415,7 @@ GPT2_INPUTS_DOCTRING = r"""    Inputs:
 @add_start_docstrings("The bare GPT2 Model transformer outputing raw hidden-states without any specific head on top.",
                       GPT2_START_DOCSTRING, GPT2_INPUTS_DOCTRING)
 class GPT2Model(GPT2PreTrainedModel):
-    __doc__ = r"""
+    r"""
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
         **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
             Sequence of hidden-states at the last layer of the model.
@@ -539,7 +540,7 @@ class GPT2Model(GPT2PreTrainedModel):
 @add_start_docstrings("""The GPT2 Model transformer with a language modeling head on top
 (linear layer with weights tied to the input embeddings). """, GPT2_START_DOCSTRING, GPT2_INPUTS_DOCTRING)
 class GPT2LMHeadModel(GPT2PreTrainedModel):
-    __doc__ = r"""
+    r"""
         **lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             Labels for language modeling.
             Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
@@ -615,7 +616,7 @@ The language modeling head has its weights tied to the input embeddings,
 the classification head takes as input the input of a specified classification token index in the intput sequence).
 """, GPT2_START_DOCSTRING)
 class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
-    __doc__ = r"""    Inputs:
+    r"""    Inputs:
         **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
             The second dimension of the input (`num_choices`) indicates the number of choices to score.

pytorch_transformers/modeling_utils.py

@@ -15,17 +15,20 @@
 # limitations under the License.
 """PyTorch BERT model."""
 
-from __future__ import absolute_import, division, print_function, unicode_literals
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
 
+import copy
+import json
 import logging
 import os
-import json
-import copy
 from io import open
 
+import six
 import torch
 from torch import nn
-from torch.nn import CrossEntropyLoss, functional as F
+from torch.nn import CrossEntropyLoss
+from torch.nn import functional as F
 
 from .file_utils import cached_path
 
@@ -36,11 +39,18 @@ WEIGHTS_NAME = "pytorch_model.bin"
 TF_WEIGHTS_NAME = 'model.ckpt'
 
 
-def add_start_docstrings(*docstr):
-    def docstring_decorator(fn):
-        fn.__doc__ = ''.join(docstr) + fn.__doc__
-        return fn
-    return docstring_decorator
+if not six.PY2:
+    def add_start_docstrings(*docstr):
+        def docstring_decorator(fn):
+            fn.__doc__ = ''.join(docstr) + fn.__doc__
+            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
 
 
 class PretrainedConfig(object):