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fix add_start_docstrings on python 2 (removed)

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thomwolf
2019-07-15 09:49:02 +02:00
parent 5bc3d0cc5b
commit 62b8eb43c1
3 changed files with 32 additions and 21 deletions
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16
pytorch_transformers/modeling_bert.py
View File

@@ -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`).

9
pytorch_transformers/modeling_gpt2.py
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@@ -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.

18
pytorch_transformers/modeling_utils.py
View File

@@ -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'
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):