models/roberta.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.
"""PyTorch RoBERTa model. """


import logging

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss

from .configuration_roberta import RobertaConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
from .modeling_utils import create_position_ids_from_input_ids


logger = logging.getLogger(__name__)

ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
	"roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
	"roberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
	"roberta-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
	"distilroberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-pytorch_model.bin",
	"roberta-base-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-pytorch_model.bin",
	"roberta-large-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-pytorch_model.bin",
}


class RobertaEmbeddings(BertEmbeddings):
	"""
	Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
	"""

	def __init__(self, config):
		super().__init__(config)
		self.padding_idx = 1
		self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
		self.position_embeddings = nn.Embedding(
			config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
		)

	def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
		if position_ids is None:
			if input_ids is not None:
				# Create the position ids from the input token ids. Any padded tokens remain padded.
				position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
			else:
				position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)

		return super().forward(
			input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds
		)

	def create_position_ids_from_inputs_embeds(self, inputs_embeds):
		""" We are provided embeddings directly. We cannot infer which are padded so just generate
		sequential position ids.
		:param torch.Tensor inputs_embeds:
		:return torch.Tensor:
		"""
		input_shape = inputs_embeds.size()[:-1]
		sequence_length = input_shape[1]

		position_ids = torch.arange(
			self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
		)
		return position_ids.unsqueeze(0).expand(input_shape)


ROBERTA_START_DOCSTRING = r"""
	This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
	usage and behavior.
	Parameters:
		config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the
			model. Initializing with a config file does not load the weights associated with the model, only the configuration.
			Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

ROBERTA_INPUTS_DOCSTRING = r"""
	Args:
		input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
			Indices of input sequence tokens in the vocabulary.
			Indices can be obtained using :class:`transformers.RobertaTokenizer`.
			See :func:`transformers.PreTrainedTokenizer.encode` and
			:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
			`What are input IDs? <../glossary.html#input-ids>`__
		attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			Mask to avoid performing attention on padding token indices.
			Mask values selected in ``[0, 1]``:
			``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
			`What are attention masks? <../glossary.html#attention-mask>`__
		token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			Segment token indices to indicate first and second portions of the inputs.
			Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
			corresponds to a `sentence B` token
			`What are token type IDs? <../glossary.html#token-type-ids>`_
		position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			Indices of positions of each input sequence tokens in the position embeddings.
			Selected in the range ``[0, config.max_position_embeddings - 1]``.
			`What are position IDs? <../glossary.html#position-ids>`_
		head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
			Mask to nullify selected heads of the self-attention modules.
			Mask values selected in ``[0, 1]``:
			:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
		inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
			Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
			This is useful if you want more control over how to convert `input_ids` indices into associated vectors
			than the model's internal embedding lookup matrix.
"""


@add_start_docstrings(
	"The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
	ROBERTA_START_DOCSTRING,
)
class RobertaModel(BertModel):
	"""
	This class overrides :class:`~transformers.BertModel`. Please check the
	superclass for the appropriate documentation alongside usage examples.
	"""

	config_class = RobertaConfig
	pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
	base_model_prefix = "roberta"

	def __init__(self, config):
		super().__init__(config)

		self.embeddings = RobertaEmbeddings(config)
		self.init_weights()

	def get_input_embeddings(self):
		return self.embeddings.word_embeddings

	def set_input_embeddings(self, value):
		self.embeddings.word_embeddings = value


@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
class RobertaForMaskedLM(BertPreTrainedModel):
	config_class = RobertaConfig
	pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
	base_model_prefix = "roberta"

	def __init__(self, config):
		super().__init__(config)

		self.roberta = RobertaModel(config)
		self.lm_head = RobertaLMHead(config)

		self.init_weights()

	def get_output_embeddings(self):
		return self.lm_head.decoder

	@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
	def forward(
		self,
		input_ids=None,
		attention_mask=None,
		token_type_ids=None,
		position_ids=None,
		head_mask=None,
		inputs_embeds=None,
		masked_lm_labels=None,
	):
		r"""
		masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			Labels for computing the masked language modeling loss.
			Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
			Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
			in ``[0, ..., config.vocab_size]``
	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
		masked_lm_loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
			Masked language modeling loss.
		prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.
			Hidden-states of the model at the output of each layer plus the initial embedding outputs.
		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
			:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
			Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
			heads.
	Examples::
		from transformers import RobertaTokenizer, RobertaForMaskedLM
		import torch
		tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
		model = RobertaForMaskedLM.from_pretrained('roberta-base')
		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids, masked_lm_labels=input_ids)
		loss, prediction_scores = outputs[:2]
		"""
		outputs = self.roberta(
			input_ids,
			attention_mask=attention_mask,
			token_type_ids=token_type_ids,
			position_ids=position_ids,
			head_mask=head_mask,
			inputs_embeds=inputs_embeds,
		)
		sequence_output = outputs[0]
		prediction_scores = self.lm_head(sequence_output)

		outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here

		if masked_lm_labels is not None:
			loss_fct = CrossEntropyLoss()
			masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
			outputs = (masked_lm_loss,) + outputs

		return outputs  # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)


class RobertaLMHead(nn.Module):
	"""Roberta Head for masked language modeling."""

	def __init__(self, config):
		super().__init__()
		self.dense = nn.Linear(config.hidden_size, config.hidden_size)
		self.layer_norm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)

		self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
		self.bias = nn.Parameter(torch.zeros(config.vocab_size))

		# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
		self.decoder.bias = self.bias

	def forward(self, features, **kwargs):
		x = self.dense(features)
		x = gelu(x)
		x = self.layer_norm(x)

		# project back to size of vocabulary with bias
		x = self.decoder(x)

		return x


@add_start_docstrings(
	"""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
	on top of the pooled output) e.g. for GLUE tasks. """,
	ROBERTA_START_DOCSTRING,
)
class RobertaForSequenceClassification(BertPreTrainedModel):
	config_class = RobertaConfig
	pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
	base_model_prefix = "roberta"

	def __init__(self, config):

		print(config)


		super().__init__(config)
		self.num_labels = config.num_labels

		self.roberta = RobertaModel(config)
		self.classifier_ER = RobertaClassificationHead(config)
		self.classifier_IP = RobertaClassificationHead(config)
		self.classifier_EX = RobertaClassificationHead(config)

	@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
	def forward(
		self,
		input_ids=None,
		attention_mask=None,
		token_type_ids=None,
		position_ids=None,
		head_mask=None,
		inputs_embeds=None,
		labels_ER=None,
		labels_IP=None,
		labels_EX=None,
		lambda_ER=0.1,
		lambda_IP=0.4,
		lambda_EX=0.5
	):
		r"""
		labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
			Labels for computing the sequence classification/regression loss.
			Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
			If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
			If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
		loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
			Classification (or regression if config.num_labels==1) loss.
		logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
			Classification (or regression if config.num_labels==1) scores (before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.
			Hidden-states of the model at the output of each layer plus the initial embedding outputs.
		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
			:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
			Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
			heads.
	Examples::
		from transformers import RobertaTokenizer, RobertaForSequenceClassification
		import torch
		tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
		model = RobertaForSequenceClassification.from_pretrained('roberta-base')
		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
		labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids, labels=labels)
		loss, logits = outputs[:2]
		"""
		
		outputs = self.roberta(
			input_ids,
			attention_mask=attention_mask,
			token_type_ids=token_type_ids,
			position_ids=position_ids,
			head_mask=head_mask,
			inputs_embeds=inputs_embeds,
		)

		sequence_output = outputs[0]


		logits_ER = self.classifier_ER(sequence_output)
		logits_IP = self.classifier_IP(sequence_output)
		logits_EX = self.classifier_EX(sequence_output)


		outputs = (logits_ER, logits_IP, logits_EX) + outputs[2:]


		if labels_ER is not None:
			loss_fct = CrossEntropyLoss()
			# loss_fct_IP = CrossEntropyLoss()
			# loss_fct_EX = CrossEntropyLoss()
			
			
			loss_ER = loss_fct(logits_ER.view(-1, self.num_labels), labels_ER.view(-1))
			loss_IP = loss_fct(logits_IP.view(-1, self.num_labels), labels_IP.view(-1))
			loss_EX = loss_fct(logits_EX.view(-1, self.num_labels), labels_EX.view(-1))
			

			loss = (lambda_ER * loss_ER) + (lambda_IP * loss_IP) + (lambda_EX * loss_EX)
			
			outputs = (loss,) + outputs
			
			
			# if self.num_labels == 1:
			# 	#  We are doing regression
			# 	loss_fct = MSELoss()
			# 	loss = loss_fct(logits.view(-1), labels.view(-1))
			# else:
			# 	loss_fct = CrossEntropyLoss()
			# 	loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
			# outputs = (loss,) + outputs

		return outputs  # (loss), logits, (hidden_states), (attentions)


@add_start_docstrings(
	"""Roberta Model with a multiple choice classification head on top (a linear layer on top of
	the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
	ROBERTA_START_DOCSTRING,
)
class RobertaForMultipleChoice(BertPreTrainedModel):
	config_class = RobertaConfig
	pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
	base_model_prefix = "roberta"

	def __init__(self, config):
		super().__init__(config)

		self.roberta = RobertaModel(config)
		self.dropout = nn.Dropout(config.hidden_dropout_prob)
		self.classifier = nn.Linear(config.hidden_size, 1)

		self.init_weights()

	@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
	def forward(
		self,
		input_ids=None,
		token_type_ids=None,
		attention_mask=None,
		labels=None,
		position_ids=None,
		head_mask=None,
		inputs_embeds=None,
	):
		r"""
		labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
			Labels for computing the multiple choice classification loss.
			Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
			of the input tensors. (see `input_ids` above)
	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
		loss (:obj:`torch.FloatTensor`` of shape ``(1,)`, `optional`, returned when :obj:`labels` is provided):
			Classification loss.
		classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
			`num_choices` is the second dimension of the input tensors. (see `input_ids` above).
			Classification scores (before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.
			Hidden-states of the model at the output of each layer plus the initial embedding outputs.
		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
			:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
			Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
			heads.
	Examples::
		from transformers import RobertaTokenizer, RobertaForMultipleChoice
		import torch
		tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
		model = RobertaForMultipleChoice.from_pretrained('roberta-base')
		choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
		input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
		labels = torch.tensor(1).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids, labels=labels)
		loss, classification_scores = outputs[:2]
		"""
		num_choices = input_ids.shape[1]

		flat_input_ids = input_ids.view(-1, input_ids.size(-1))
		flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
		flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
		flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
		outputs = self.roberta(
			flat_input_ids,
			position_ids=flat_position_ids,
			token_type_ids=flat_token_type_ids,
			attention_mask=flat_attention_mask,
			head_mask=head_mask,
		)
		pooled_output = outputs[1]

		pooled_output = self.dropout(pooled_output)
		logits = self.classifier(pooled_output)
		reshaped_logits = logits.view(-1, num_choices)

		outputs = (reshaped_logits,) + outputs[2:]  # add hidden states and attention if they are here

		if labels is not None:
			loss_fct = CrossEntropyLoss()
			loss = loss_fct(reshaped_logits, labels)
			outputs = (loss,) + outputs

		return outputs  # (loss), reshaped_logits, (hidden_states), (attentions)


@add_start_docstrings(
	"""Roberta Model with a token classification head on top (a linear layer on top of
	the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
	ROBERTA_START_DOCSTRING,
)
class RobertaForTokenClassification(BertPreTrainedModel):
	config_class = RobertaConfig
	pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
	base_model_prefix = "roberta"

	def __init__(self, config):
		super().__init__(config)
		self.num_labels = config.num_labels

		self.roberta = RobertaModel(config)
		self.dropout = nn.Dropout(config.hidden_dropout_prob)
		self.classifier = nn.Linear(config.hidden_size, config.num_labels)

		self.init_weights()

	@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
	def forward(
		self,
		input_ids=None,
		attention_mask=None,
		token_type_ids=None,
		position_ids=None,
		head_mask=None,
		inputs_embeds=None,
		labels=None,
	):
		r"""
		labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
			Labels for computing the token classification loss.
			Indices should be in ``[0, ..., config.num_labels - 1]``.
	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
		loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided) :
			Classification loss.
		scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`)
			Classification scores (before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.
			Hidden-states of the model at the output of each layer plus the initial embedding outputs.
		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
			:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
			Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
			heads.
	Examples::
		from transformers import RobertaTokenizer, RobertaForTokenClassification
		import torch
		tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
		model = RobertaForTokenClassification.from_pretrained('roberta-base')
		input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
		labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
		outputs = model(input_ids, labels=labels)
		loss, scores = outputs[:2]
		"""

		outputs = self.roberta(
			input_ids,
			attention_mask=attention_mask,
			token_type_ids=token_type_ids,
			position_ids=position_ids,
			head_mask=head_mask,
			inputs_embeds=inputs_embeds,
		)

		sequence_output = outputs[0]

		sequence_output = self.dropout(sequence_output)
		logits = self.classifier(sequence_output)

		outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here

		if labels is not None:
			loss_fct = CrossEntropyLoss()
			# Only keep active parts of the loss
			if attention_mask is not None:
				active_loss = attention_mask.view(-1) == 1
				active_logits = logits.view(-1, self.num_labels)
				active_labels = torch.where(
					active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
				)
				loss = loss_fct(active_logits, active_labels)
			else:
				loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
			outputs = (loss,) + outputs

		return outputs  # (loss), scores, (hidden_states), (attentions)


class RobertaClassificationHead(nn.Module):
	"""Head for sentence-level classification tasks."""

	def __init__(self, config):
		super().__init__()
		self.dense = nn.Linear(config.hidden_size, 128)
		self.dropout = nn.Dropout(config.hidden_dropout_prob)
		self.out_proj = nn.Linear(128, config.num_labels)

	def forward(self, features, **kwargs):
		x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
		x = self.dropout(x)
		x = self.dense(x)
		x = torch.tanh(x)
		x = self.dropout(x)
		x = self.out_proj(x)
		return x


@add_start_docstrings(
	"""Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
	the hidden-states output to compute `span start logits` and `span end logits`). """,
	ROBERTA_START_DOCSTRING,
)
class RobertaForQuestionAnswering(BertPreTrainedModel):
	config_class = RobertaConfig
	pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
	base_model_prefix = "roberta"

	def __init__(self, config):
		super().__init__(config)
		self.num_labels = config.num_labels

		self.roberta = RobertaModel(config)
		self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

		self.init_weights()

	@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING)
	def forward(
		self,
		input_ids,
		attention_mask=None,
		token_type_ids=None,
		position_ids=None,
		head_mask=None,
		inputs_embeds=None,
		start_positions=None,
		end_positions=None,
	):
		r"""
		start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
			Labels for 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`).
			Position outside of the sequence are not taken into account for computing the loss.
		end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
			Labels for position (index) of the end of the labelled span for computing the token classification loss.
			Positions are clamped to the length of the sequence (`sequence_length`).
			Position outside of the sequence are not taken into account for computing the loss.
	Returns:
		:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
		loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
			Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
		start_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
			Span-start scores (before SoftMax).
		end_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length,)`):
			Span-end scores (before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.
			Hidden-states of the model at the output of each layer plus the initial embedding outputs.
		attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
			Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
			:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
			Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
			heads.
	Examples::
		# The checkpoint roberta-large is not fine-tuned for question answering. Please see the
		# examples/run_squad.py example to see how to fine-tune a model to a question answering task.
		from transformers import RobertaTokenizer, RobertaForQuestionAnswering
		import torch
		tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
		model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
		question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
		input_ids = tokenizer.encode(question, text)
		start_scores, end_scores = model(torch.tensor([input_ids]))
		all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
		answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
		"""

		outputs = self.roberta(
			input_ids,
			attention_mask=attention_mask,
			token_type_ids=token_type_ids,
			position_ids=position_ids,
			head_mask=head_mask,
			inputs_embeds=inputs_embeds,
		)

		sequence_output = outputs[0]

		logits = self.qa_outputs(sequence_output)
		start_logits, end_logits = logits.split(1, dim=-1)
		start_logits = start_logits.squeeze(-1)
		end_logits = end_logits.squeeze(-1)

		outputs = (start_logits, end_logits,) + outputs[2:]
		if start_positions is not None and end_positions is not None:
			# If we are on multi-GPU, split add a dimension
			if len(start_positions.size()) > 1:
				start_positions = start_positions.squeeze(-1)
			if len(end_positions.size()) > 1:
				end_positions = end_positions.squeeze(-1)
			# sometimes the start/end positions are outside our model inputs, we ignore these terms
			ignored_index = start_logits.size(1)
			start_positions.clamp_(0, ignored_index)
			end_positions.clamp_(0, ignored_index)

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

		return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)