modeling_utils.py 40.7 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
# 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.
"""PyTorch BERT model."""

18
19
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
20

21
22
import copy
import json
23
24
import logging
import os
thomwolf's avatar
thomwolf committed
25
from io import open
26

27
import six
28
29
import torch
from torch import nn
30
31
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
32

33
from .configuration_utils import PretrainedConfig
34
from .file_utils import cached_path, WEIGHTS_NAME, TF_WEIGHTS_NAME
35
36
37
38

logger = logging.getLogger(__name__)


thomwolf's avatar
thomwolf committed
39
40
41
42
43
44
45
46
47
48
49
50
51
try:
    from torch.nn import Identity
except ImportError:
    # Older PyTorch compatibility
    class Identity(nn.Module):
        r"""A placeholder identity operator that is argument-insensitive.
        """
        def __init__(self, *args, **kwargs):
            super(Identity, self).__init__()

        def forward(self, input):
            return input

52
class PreTrainedModel(nn.Module):
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
    r""" Base class for all models.

        :class:`~pytorch_transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
        as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.

        Class attributes (overridden by derived classes):
            - ``config_class``: a class derived from :class:`~pytorch_transformers.PretrainedConfig` to use as configuration class for this model architecture.
            - ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
            - ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:

                - ``model``: an instance of the relevant subclass of :class:`~pytorch_transformers.PreTrainedModel`,
                - ``config``: an instance of the relevant subclass of :class:`~pytorch_transformers.PretrainedConfig`,
                - ``path``: a path (string) to the TensorFlow checkpoint.

            - ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
68
    """
69
    config_class = None
70
71
72
73
74
75
76
77
78
79
80
81
82
    pretrained_model_archive_map = {}
    load_tf_weights = lambda model, config, path: None
    base_model_prefix = ""

    def __init__(self, config, *inputs, **kwargs):
        super(PreTrainedModel, self).__init__()
        if not isinstance(config, PretrainedConfig):
            raise ValueError(
                "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
                "To create a model from a pretrained model use "
                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
                    self.__class__.__name__, self.__class__.__name__
                ))
thomwolf's avatar
thomwolf committed
83
        # Save config in model
84
85
        self.config = config

thomwolf's avatar
thomwolf committed
86
87
88
89
90
91
    def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
        """ Build a resized Embedding Module from a provided token Embedding Module.
            Increasing the size will add newly initialized vectors at the end
            Reducing the size will remove vectors from the end

        Args:
thomwolf's avatar
thomwolf committed
92
93
            new_num_tokens: (`optional`) int
                New number of tokens in the embedding matrix.
thomwolf's avatar
thomwolf committed
94
95
96
                Increasing the size will add newly initialized vectors at the end
                Reducing the size will remove vectors from the end
                If not provided or None: return the provided token Embedding Module.
thomwolf's avatar
thomwolf committed
97
        Return: ``torch.nn.Embeddings``
thomwolf's avatar
thomwolf committed
98
99
100
101
102
            Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
        """
        if new_num_tokens is None:
            return old_embeddings

thomwolf's avatar
thomwolf committed
103
        old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
thomwolf's avatar
thomwolf committed
104
105
106
107
        if old_num_tokens == new_num_tokens:
            return old_embeddings

        # Build new embeddings
thomwolf's avatar
thomwolf committed
108
109
110
111
        new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
        new_embeddings.to(old_embeddings.weight.device)

        # initialize all new embeddings (in particular added tokens)
112
        self._init_weights(new_embeddings)
thomwolf's avatar
thomwolf committed
113
114
115
116
117
118
119

        # Copy word embeddings from the previous weights
        num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
        new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]

        return new_embeddings

thomwolf's avatar
thomwolf committed
120
121
122
123
124
125
126
127
    def _tie_or_clone_weights(self, first_module, second_module):
        """ Tie or clone module weights depending of weither we are using TorchScript or not
        """
        if self.config.torchscript:
            first_module.weight = nn.Parameter(second_module.weight.clone())
        else:
            first_module.weight = second_module.weight

LysandreJik's avatar
LysandreJik committed
128
        if hasattr(first_module, 'bias') and first_module.bias is not None:
129
130
131
132
133
134
135
            first_module.bias.data = torch.nn.functional.pad(
                first_module.bias.data,
                (0, first_module.weight.shape[0] - first_module.bias.shape[0]),
                'constant',
                0
            )

thomwolf's avatar
thomwolf committed
136
137
    def resize_token_embeddings(self, new_num_tokens=None):
        """ Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
138
        Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
thomwolf's avatar
thomwolf committed
139

140
141
142
143
144
        Arguments:

            new_num_tokens: (`optional`) int:
                New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. 
                If not provided or None: does nothing and just returns a pointer to the input tokens ``torch.nn.Embeddings`` Module of the model.
thomwolf's avatar
thomwolf committed
145

thomwolf's avatar
thomwolf committed
146
        Return: ``torch.nn.Embeddings``
147
            Pointer to the input tokens Embeddings Module of the model
thomwolf's avatar
thomwolf committed
148
149
        """
        base_model = getattr(self, self.base_model_prefix, self)  # get the base model if needed
thomwolf's avatar
thomwolf committed
150
151
152
        model_embeds = base_model._resize_token_embeddings(new_num_tokens)
        if new_num_tokens is None:
            return model_embeds
thomwolf's avatar
thomwolf committed
153
154
155
156
157
158
159
160
161

        # Update base model and current model config
        self.config.vocab_size = new_num_tokens
        base_model.vocab_size = new_num_tokens

        # Tie weights again if needed
        if hasattr(self, 'tie_weights'):
            self.tie_weights()

thomwolf's avatar
thomwolf committed
162
163
        return model_embeds

164
165
166
167
168
169
170
171
172
    def init_weights(self):
        """ Initialize and prunes weights if needed. """
        # Initialize weights
        self.apply(self._init_weights)

        # Prune heads if needed
        if self.config.pruned_heads:
            self.prune_heads(self.config.pruned_heads)

thomwolf's avatar
thomwolf committed
173
174
    def prune_heads(self, heads_to_prune):
        """ Prunes heads of the base model.
175
176
177
178

            Arguments:

                heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
179
                E.g. {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
thomwolf's avatar
thomwolf committed
180
        """
thomwolf's avatar
thomwolf committed
181
        base_model = getattr(self, self.base_model_prefix, self)  # get the base model if needed
182

183
        # save new sets of pruned heads as union of previously stored pruned heads and newly pruned heads
184
        for layer, heads in heads_to_prune.items():
185
186
187
188
            union_heads = set(self.config.pruned_heads.get(layer, [])) | set(heads)
            self.config.pruned_heads[layer] = list(union_heads)  # Unfortunately we have to store it as list for JSON

        base_model._prune_heads(heads_to_prune)
thomwolf's avatar
thomwolf committed
189

190
    def save_pretrained(self, save_directory):
191
192
        """ Save a model and its configuration file to a directory, so that it
            can be re-loaded using the `:func:`~pytorch_transformers.PreTrainedModel.from_pretrained`` class method.
193
194
195
196
197
198
        """
        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"

        # Only save the model it-self if we are using distributed training
        model_to_save = self.module if hasattr(self, 'module') else self

thomwolf's avatar
thomwolf committed
199
200
201
        # Save configuration file
        model_to_save.config.save_pretrained(save_directory)

202
203
204
205
206
        # If we save using the predefined names, we can load using `from_pretrained`
        output_model_file = os.path.join(save_directory, WEIGHTS_NAME)

        torch.save(model_to_save.state_dict(), output_model_file)

207
    @classmethod
208
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
209
210
        r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.

211
212
213
        The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated)
        To train the model, you should first set it back in training mode with ``model.train()``

214
215
216
217
218
        The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
        It is up to you to train those weights with a downstream fine-tuning task.

        The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.

219
220
221
222
223
224
        Parameters:
            pretrained_model_name_or_path: either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
thomwolf's avatar
thomwolf committed
225
                - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)
226
227
228
229
230
231
232
233
234
235
236
237
238

            model_args: (`optional`) Sequence of positional arguments:
                All remaning positional arguments will be passed to the underlying model's ``__init__`` method

            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
                Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:

                - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.

            state_dict: (`optional`) dict:
                an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
thomwolf's avatar
typos  
thomwolf committed
239
                This option can be used if you want to create a model from a pretrained configuration but load your own weights.
240
241
242
                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.

            cache_dir: (`optional`) string:
thomwolf's avatar
thomwolf committed
243
244
                Path to a directory in which a downloaded pre-trained model
                configuration should be cached if the standard cache should not be used.
245

246
247
248
            force_download: (`optional`) boolean, default False:
                Force to (re-)download the model weights and configuration files and override the cached versions if they exists.

249
250
251
252
            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.

253
            output_loading_info: (`optional`) boolean:
thomwolf's avatar
thomwolf committed
254
                Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
255
256
257
258
259
260
261
262

            kwargs: (`optional`) Remaining dictionary of keyword arguments:
                Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:

                - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.

        Examples::
thomwolf's avatar
thomwolf committed
263

thomwolf's avatar
thomwolf committed
264
265
266
267
268
269
270
            model = BertModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
            model = BertModel.from_pretrained('./test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
            model = BertModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
            assert model.config.output_attention == True
            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
            model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
thomwolf's avatar
thomwolf committed
271

272
        """
thomwolf's avatar
thomwolf committed
273
        config = kwargs.pop('config', None)
thomwolf's avatar
thomwolf committed
274
275
        state_dict = kwargs.pop('state_dict', None)
        cache_dir = kwargs.pop('cache_dir', None)
thomwolf's avatar
thomwolf committed
276
        from_tf = kwargs.pop('from_tf', False)
277
        force_download = kwargs.pop('force_download', False)
278
        proxies = kwargs.pop('proxies', None)
thomwolf's avatar
thomwolf committed
279
        output_loading_info = kwargs.pop('output_loading_info', False)
thomwolf's avatar
thomwolf committed
280
281

        # Load config
thomwolf's avatar
thomwolf committed
282
        if config is None:
283
284
            config, model_kwargs = cls.config_class.from_pretrained(
                pretrained_model_name_or_path, *model_args,
285
                cache_dir=cache_dir, return_unused_kwargs=True,
286
                force_download=force_download,
287
                **kwargs
288
289
290
            )
        else:
            model_kwargs = kwargs
291

thomwolf's avatar
thomwolf committed
292
        # Load model
thomwolf's avatar
thomwolf committed
293
        if pretrained_model_name_or_path is not None:
294
            if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
thomwolf's avatar
thomwolf committed
295
296
297
298
299
300
301
                archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path]
            elif os.path.isdir(pretrained_model_name_or_path):
                if from_tf:
                    # Directly load from a TensorFlow checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
                else:
                    archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
302
303
            elif os.path.isfile(pretrained_model_name_or_path):
                archive_file = pretrained_model_name_or_path
thomwolf's avatar
thomwolf committed
304
            else:
305
306
307
                assert from_tf, "Error finding file {}, no file or TF 1.X checkpoint found".format(pretrained_model_name_or_path)
                archive_file = pretrained_model_name_or_path + ".index"

thomwolf's avatar
thomwolf committed
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
            # redirect to the cache, if necessary
            try:
                resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
            except EnvironmentError as e:
                if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                    logger.error(
                        "Couldn't reach server at '{}' to download pretrained weights.".format(
                            archive_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_model_archive_map.keys()),
                            archive_file))
                raise e
            if resolved_archive_file == archive_file:
                logger.info("loading weights file {}".format(archive_file))
327
            else:
thomwolf's avatar
thomwolf committed
328
329
                logger.info("loading weights file {} from cache at {}".format(
                    archive_file, resolved_archive_file))
330
        else:
thomwolf's avatar
thomwolf committed
331
            resolved_archive_file = None
332
333

        # Instantiate model.
334
        model = cls(config, *model_args, **model_kwargs)
thomwolf's avatar
thomwolf committed
335

336
337
        if state_dict is None and not from_tf:
            state_dict = torch.load(resolved_archive_file, map_location='cpu')
338

339
340
341
        missing_keys = []
        unexpected_keys = []
        error_msgs = []
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403

        if from_tf:
            if resolved_archive_file.endswith('.index'):
                # Load from a TensorFlow 1.X checkpoint - provided by original authors
                model = cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
            else:
                # Load from our TensorFlow 2.0 checkpoints
                try:
                    from pytorch_transformers import load_tf2_checkpoint_in_pytorch_model
                    model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
                except ImportError as e:
                    logger.error("Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
                        "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.")
                    raise e
        else:
            # Convert old format to new format if needed from a PyTorch state_dict
            old_keys = []
            new_keys = []
            for key in state_dict.keys():
                new_key = None
                if 'gamma' in key:
                    new_key = key.replace('gamma', 'weight')
                if 'beta' in key:
                    new_key = key.replace('beta', 'bias')
                if new_key:
                    old_keys.append(key)
                    new_keys.append(new_key)
            for old_key, new_key in zip(old_keys, new_keys):
                state_dict[new_key] = state_dict.pop(old_key)

            # copy state_dict so _load_from_state_dict can modify it
            metadata = getattr(state_dict, '_metadata', None)
            state_dict = state_dict.copy()
            if metadata is not None:
                state_dict._metadata = metadata

            def load(module, prefix=''):
                local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
                module._load_from_state_dict(
                    state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
                for name, child in module._modules.items():
                    if child is not None:
                        load(child, prefix + name + '.')

            # Make sure we are able to load base models as well as derived models (with heads)
            start_prefix = ''
            model_to_load = model
            if not hasattr(model, cls.base_model_prefix) and any(s.startswith(cls.base_model_prefix) for s in state_dict.keys()):
                start_prefix = cls.base_model_prefix + '.'
            if hasattr(model, cls.base_model_prefix) and not any(s.startswith(cls.base_model_prefix) for s in state_dict.keys()):
                model_to_load = getattr(model, cls.base_model_prefix)

            load(model_to_load, prefix=start_prefix)
            if len(missing_keys) > 0:
                logger.info("Weights of {} not initialized from pretrained model: {}".format(
                    model.__class__.__name__, missing_keys))
            if len(unexpected_keys) > 0:
                logger.info("Weights from pretrained model not used in {}: {}".format(
                    model.__class__.__name__, unexpected_keys))
            if len(error_msgs) > 0:
                raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
                                model.__class__.__name__, "\n\t".join(error_msgs)))
404

thomwolf's avatar
thomwolf committed
405
        if hasattr(model, 'tie_weights'):
406
407
            model.tie_weights()  # make sure word embedding weights are still tied

408
409
410
        # Set model in evaluation mode to desactivate DropOut modules by default
        model.eval()

thomwolf's avatar
thomwolf committed
411
412
413
414
        if output_loading_info:
            loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs}
            return model, loading_info

415
416
417
        return model


thomwolf's avatar
thomwolf committed
418
419
class Conv1D(nn.Module):
    def __init__(self, nf, nx):
thomwolf's avatar
thomwolf committed
420
        """ Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
thomwolf's avatar
thomwolf committed
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
            Basically works like a Linear layer but the weights are transposed
        """
        super(Conv1D, self).__init__()
        self.nf = nf
        w = torch.empty(nx, nf)
        nn.init.normal_(w, std=0.02)
        self.weight = nn.Parameter(w)
        self.bias = nn.Parameter(torch.zeros(nf))

    def forward(self, x):
        size_out = x.size()[:-1] + (self.nf,)
        x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
        x = x.view(*size_out)
        return x


thomwolf's avatar
thomwolf committed
437
438
class PoolerStartLogits(nn.Module):
    """ Compute SQuAD start_logits from sequence hidden states. """
thomwolf's avatar
thomwolf committed
439
    def __init__(self, config):
thomwolf's avatar
thomwolf committed
440
441
442
443
444
        super(PoolerStartLogits, self).__init__()
        self.dense = nn.Linear(config.hidden_size, 1)

    def forward(self, hidden_states, p_mask=None):
        """ Args:
445
446
447
            **p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`
                invalid position mask such as query and special symbols (PAD, SEP, CLS)
                1.0 means token should be masked.
thomwolf's avatar
thomwolf committed
448
        """
thomwolf's avatar
thomwolf committed
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
        x = self.dense(hidden_states).squeeze(-1)

        if p_mask is not None:
            x = x * (1 - p_mask) - 1e30 * p_mask

        return x


class PoolerEndLogits(nn.Module):
    """ Compute SQuAD end_logits from sequence hidden states and start token hidden state.
    """
    def __init__(self, config):
        super(PoolerEndLogits, self).__init__()
        self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
        self.activation = nn.Tanh()
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dense_1 = nn.Linear(config.hidden_size, 1)

    def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):
        """ Args:
469
470
471
472
473
474
            One of ``start_states``, ``start_positions`` should be not None.
            If both are set, ``start_positions`` overrides ``start_states``.

            **start_states**: ``torch.LongTensor`` of shape identical to hidden_states
                hidden states of the first tokens for the labeled span.
            **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
475
                position of the first token for the labeled span:
476
477
478
            **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
                Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
                1.0 means token should be masked.
thomwolf's avatar
thomwolf committed
479
480
481
        """
        assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
        if start_positions is not None:
482
            slen, hsz = hidden_states.shape[-2:]
thomwolf's avatar
thomwolf committed
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
            start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
            start_states = hidden_states.gather(-2, start_positions) # shape (bsz, 1, hsz)
            start_states = start_states.expand(-1, slen, -1) # shape (bsz, slen, hsz)

        x = self.dense_0(torch.cat([hidden_states, start_states], dim=-1))
        x = self.activation(x)
        x = self.LayerNorm(x)
        x = self.dense_1(x).squeeze(-1)

        if p_mask is not None:
            x = x * (1 - p_mask) - 1e30 * p_mask

        return x


class PoolerAnswerClass(nn.Module):
    """ Compute SQuAD 2.0 answer class from classification and start tokens hidden states. """
    def __init__(self, config):
        super(PoolerAnswerClass, self).__init__()
        self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)
        self.activation = nn.Tanh()
        self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)

    def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None):
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
        """
        Args:
            One of ``start_states``, ``start_positions`` should be not None.
            If both are set, ``start_positions`` overrides ``start_states``.

            **start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``.
                hidden states of the first tokens for the labeled span.
            **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
                position of the first token for the labeled span.
            **cls_index**: torch.LongTensor of shape ``(batch_size,)``
                position of the CLS token. If None, take the last token.

            note(Original repo):
                no dependency on end_feature so that we can obtain one single `cls_logits`
                for each sample
thomwolf's avatar
thomwolf committed
522
        """
523
        hsz = hidden_states.shape[-1]
thomwolf's avatar
thomwolf committed
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
        assert start_states is not None or start_positions is not None, "One of start_states, start_positions should be not None"
        if start_positions is not None:
            start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
            start_states = hidden_states.gather(-2, start_positions).squeeze(-2) # shape (bsz, hsz)

        if cls_index is not None:
            cls_index = cls_index[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
            cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, hsz)
        else:
            cls_token_state = hidden_states[:, -1, :] # shape (bsz, hsz)

        x = self.dense_0(torch.cat([start_states, cls_token_state], dim=-1))
        x = self.activation(x)
        x = self.dense_1(x).squeeze(-1)

        return x


class SQuADHead(nn.Module):
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
    r""" A SQuAD head inspired by XLNet.

    Parameters:
        config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.

    Inputs:
        **hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``
            hidden states of sequence tokens
        **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
            position of the first token for the labeled span.
        **end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
            position of the last token for the labeled span.
        **cls_index**: torch.LongTensor of shape ``(batch_size,)``
            position of the CLS token. If None, take the last token.
        **is_impossible**: ``torch.LongTensor`` of shape ``(batch_size,)``
            Whether the question has a possible answer in the paragraph or not.
        **p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
            Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
            1.0 means token should be masked.

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned if both ``start_positions`` and ``end_positions`` are provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Classification loss as the sum of start token, end token (and is_impossible if provided) classification losses.
thomwolf's avatar
thomwolf committed
566
        **start_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
567
568
            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top)``
            Log probabilities for the top config.start_n_top start token possibilities (beam-search).
thomwolf's avatar
thomwolf committed
569
        **start_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
570
571
            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top)``
            Indices for the top config.start_n_top start token possibilities (beam-search).
thomwolf's avatar
thomwolf committed
572
        **end_top_log_probs**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
573
574
            ``torch.FloatTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
            Log probabilities for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
thomwolf's avatar
thomwolf committed
575
        **end_top_index**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
576
577
            ``torch.LongTensor`` of shape ``(batch_size, config.start_n_top * config.end_n_top)``
            Indices for the top ``config.start_n_top * config.end_n_top`` end token possibilities (beam-search).
thomwolf's avatar
thomwolf committed
578
        **cls_logits**: (`optional`, returned if ``start_positions`` or ``end_positions`` is not provided)
579
580
            ``torch.FloatTensor`` of shape ``(batch_size,)``
            Log probabilities for the ``is_impossible`` label of the answers.
thomwolf's avatar
thomwolf committed
581
582
583
584
585
586
587
588
589
590
591
592
593
594
    """
    def __init__(self, config):
        super(SQuADHead, self).__init__()
        self.start_n_top = config.start_n_top
        self.end_n_top = config.end_n_top

        self.start_logits = PoolerStartLogits(config)
        self.end_logits = PoolerEndLogits(config)
        self.answer_class = PoolerAnswerClass(config)

    def forward(self, hidden_states, start_positions=None, end_positions=None,
                cls_index=None, is_impossible=None, p_mask=None):
        outputs = ()

thomwolf's avatar
thomwolf committed
595
        start_logits = self.start_logits(hidden_states, p_mask=p_mask)
thomwolf's avatar
thomwolf committed
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618

        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, let's remove the dimension added by batch splitting
            for x in (start_positions, end_positions, cls_index, is_impossible):
                if x is not None and x.dim() > 1:
                    x.squeeze_(-1)

            # during training, compute the end logits based on the ground truth of the start position
            end_logits = self.end_logits(hidden_states, start_positions=start_positions, p_mask=p_mask)

            loss_fct = CrossEntropyLoss()
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

            if cls_index is not None and is_impossible is not None:
                # Predict answerability from the representation of CLS and START
                cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
                loss_fct_cls = nn.BCEWithLogitsLoss()
                cls_loss = loss_fct_cls(cls_logits, is_impossible)

                # note(zhiliny): by default multiply the loss by 0.5 so that the scale is comparable to start_loss and end_loss
                total_loss += cls_loss * 0.5
619
620

            outputs = (total_loss,) + outputs
thomwolf's avatar
thomwolf committed
621
622
623
624
625
626
627

        else:
            # during inference, compute the end logits based on beam search
            bsz, slen, hsz = hidden_states.size()
            start_log_probs = F.softmax(start_logits, dim=-1) # shape (bsz, slen)

            start_top_log_probs, start_top_index = torch.topk(start_log_probs, self.start_n_top, dim=-1) # shape (bsz, start_n_top)
628
629
            start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
            start_states = torch.gather(hidden_states, -2, start_top_index_exp) # shape (bsz, start_n_top, hsz)
thomwolf's avatar
thomwolf committed
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
            start_states = start_states.unsqueeze(1).expand(-1, slen, -1, -1) # shape (bsz, slen, start_n_top, hsz)

            hidden_states_expanded = hidden_states.unsqueeze(2).expand_as(start_states) # shape (bsz, slen, start_n_top, hsz)
            p_mask = p_mask.unsqueeze(-1) if p_mask is not None else None
            end_logits = self.end_logits(hidden_states_expanded, start_states=start_states, p_mask=p_mask)
            end_log_probs = F.softmax(end_logits, dim=1) # shape (bsz, slen, start_n_top)

            end_top_log_probs, end_top_index = torch.topk(end_log_probs, self.end_n_top, dim=1) # shape (bsz, end_n_top, start_n_top)
            end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
            end_top_index = end_top_index.view(-1, self.start_n_top * self.end_n_top)

            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)

            outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs

        # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
647
        # or (if labels are provided) (total_loss,)
thomwolf's avatar
thomwolf committed
648
649
650
651
        return outputs


class SequenceSummary(nn.Module):
thomwolf's avatar
thomwolf committed
652
    r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
thomwolf's avatar
thomwolf committed
653
654
655
656
657
        Args of the config class:
            summary_type:
                - 'last' => [default] take the last token hidden state (like XLNet)
                - 'first' => take the first token hidden state (like Bert)
                - 'mean' => take the mean of all tokens hidden states
thomwolf's avatar
thomwolf committed
658
                - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
thomwolf's avatar
thomwolf committed
659
660
                - 'attn' => Not implemented now, use multi-head attention
            summary_use_proj: Add a projection after the vector extraction
661
            summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
662
            summary_activation: 'tanh' => add a tanh activation to the output, Other => no activation. Default
663
664
            summary_first_dropout: Add a dropout before the projection and activation
            summary_last_dropout: Add a dropout after the projection and activation
thomwolf's avatar
thomwolf committed
665
666
    """
    def __init__(self, config):
thomwolf's avatar
thomwolf committed
667
668
669
        super(SequenceSummary, self).__init__()

        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
thomwolf's avatar
thomwolf committed
670
        if self.summary_type == 'attn':
thomwolf's avatar
thomwolf committed
671
672
673
674
675
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

thomwolf's avatar
thomwolf committed
676
        self.summary = Identity()
thomwolf's avatar
thomwolf committed
677
        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
678
679
            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
thomwolf's avatar
thomwolf committed
680
681
682
683
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

thomwolf's avatar
thomwolf committed
684
        self.activation = Identity()
thomwolf's avatar
thomwolf committed
685
686
687
        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
            self.activation = nn.Tanh()

thomwolf's avatar
thomwolf committed
688
        self.first_dropout = Identity()
689
690
691
        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

thomwolf's avatar
thomwolf committed
692
        self.last_dropout = Identity()
693
694
        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout)
thomwolf's avatar
thomwolf committed
695

thomwolf's avatar
thomwolf committed
696
    def forward(self, hidden_states, cls_index=None):
697
        """ hidden_states: float Tensor in shape [bsz, ..., seq_len, hidden_size], the hidden-states of the last layer.
thomwolf's avatar
thomwolf committed
698
            cls_index: [optional] position of the classification token if summary_type == 'cls_index',
thomwolf's avatar
thomwolf committed
699
                shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.
thomwolf's avatar
thomwolf committed
700
                if summary_type == 'cls_index' and cls_index is None:
thomwolf's avatar
thomwolf committed
701
702
703
704
705
706
707
708
                    we take the last token of the sequence as classification token
        """
        if self.summary_type == 'last':
            output = hidden_states[:, -1]
        elif self.summary_type == 'first':
            output = hidden_states[:, 0]
        elif self.summary_type == 'mean':
            output = hidden_states.mean(dim=1)
thomwolf's avatar
thomwolf committed
709
710
711
        elif self.summary_type == 'cls_index':
            if cls_index is None:
                cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2]-1, dtype=torch.long)
thomwolf's avatar
thomwolf committed
712
            else:
thomwolf's avatar
thomwolf committed
713
714
715
716
                cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
                cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),))
            # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
            output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
thomwolf's avatar
thomwolf committed
717
718
719
        elif self.summary_type == 'attn':
            raise NotImplementedError

720
        output = self.first_dropout(output)
thomwolf's avatar
thomwolf committed
721
722
        output = self.summary(output)
        output = self.activation(output)
723
        output = self.last_dropout(output)
thomwolf's avatar
thomwolf committed
724
725
726
727

        return output


728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
def prune_linear_layer(layer, index, dim=0):
    """ Prune a linear layer (a model parameters) to keep only entries in index.
        Return the pruned layer as a new layer with requires_grad=True.
        Used to remove heads.
    """
    index = index.to(layer.weight.device)
    W = layer.weight.index_select(dim, index).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            b = layer.bias.clone().detach()
        else:
            b = layer.bias[index].clone().detach()
    new_size = list(layer.weight.size())
    new_size[dim] = len(index)
    new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
    new_layer.weight.requires_grad = False
    new_layer.weight.copy_(W.contiguous())
    new_layer.weight.requires_grad = True
    if layer.bias is not None:
        new_layer.bias.requires_grad = False
        new_layer.bias.copy_(b.contiguous())
        new_layer.bias.requires_grad = True
    return new_layer


def prune_conv1d_layer(layer, index, dim=1):
    """ Prune a Conv1D layer (a model parameters) to keep only entries in index.
        A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
        Return the pruned layer as a new layer with requires_grad=True.
        Used to remove heads.
    """
    index = index.to(layer.weight.device)
    W = layer.weight.index_select(dim, index).clone().detach()
    if dim == 0:
        b = layer.bias.clone().detach()
    else:
        b = layer.bias[index].clone().detach()
    new_size = list(layer.weight.size())
    new_size[dim] = len(index)
    new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
    new_layer.weight.requires_grad = False
    new_layer.weight.copy_(W.contiguous())
    new_layer.weight.requires_grad = True
    new_layer.bias.requires_grad = False
    new_layer.bias.copy_(b.contiguous())
    new_layer.bias.requires_grad = True
    return new_layer
775
776
777
778
779
780
781
782
783
784
785
786
787


def prune_layer(layer, index, dim=None):
    """ Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index.
        Return the pruned layer as a new layer with requires_grad=True.
        Used to remove heads.
    """
    if isinstance(layer, nn.Linear):
        return prune_linear_layer(layer, index, dim=0 if dim is None else dim)
    elif isinstance(layer, Conv1D):
        return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)
    else:
        raise ValueError("Can't prune layer of class {}".format(layer.__class__))