gpt2-linear-xl-sharded-bf16 / modeling_gpt2l.py

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	# Copyright 2018 The OpenAI Team Authors and 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 OpenAI GPT-2 model."""

	import os
	import warnings
	from dataclasses import dataclass
	from typing import List, Optional, Tuple

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

	from transformers.activations import ACT2FN
	from .configuration_gpt2l import GPT2LConfig
	from transformers.file_utils import (
	ModelOutput,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	)
	from transformers.modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	# CausalLMOutputWithPastAndCrossAttentions,
	CausalLMOutputWithPast,
	SequenceClassifierOutputWithPast,
	)
	from transformers.modeling_utils import (
	Conv1D,
	PreTrainedModel,
	SequenceSummary,
	find_pruneable_heads_and_indices,
	prune_conv1d_layer,
	)
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "GPT2LConfig"
	_TOKENIZER_FOR_DOC = "GPT2Tokenizer"

	GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"gpt2",
	"gpt2-medium",
	"gpt2-large",
	"gpt2-xl",
	"distilgpt2",
	# See all GPT-2 models at https://huggingface.co/models?filter=gpt2
	]



	class Attention(nn.Module):
	def __init__(self, nx, n_ctx, config, scale=False, is_cross_attention=False):
	super().__init__()

	n_state = nx # in Attention: n_state=768 (nx=n_embd)
	# [switch nx => n_state from Block to Attention to keep identical to TF implem]
	assert n_state % config.n_head == 0
	self.register_buffer(
	"bias", torch.tril(torch.ones((n_ctx, n_ctx), dtype=torch.uint8)).view(1, 1, n_ctx, n_ctx)
	)
	self.register_buffer("masked_bias", torch.tensor(-1e4))
	self.n_head = config.n_head
	self.split_size = n_state
	self.scale = scale
	self.is_cross_attention = is_cross_attention
	if self.is_cross_attention:
	# self.c_attn = Conv1D(2 * n_state, nx)
	# self.q_attn = Conv1D(n_state, nx)
	self.c_attn = nn.Linear(nx, 2 * n_state)
	self.q_attn = nn.Linear(nx, n_state)
	else:
	self.c_attn = nn.Linear(nx, 3 * n_state)
	# self.c_proj = Conv1D(n_state, nx)
	self.c_proj = nn.Linear(nx, n_state)
	self.attn_dropout = nn.Dropout(config.attn_pdrop)
	self.resid_dropout = nn.Dropout(config.resid_pdrop)
	self.pruned_heads = set()

	def prune_heads(self, heads):
	if len(heads) == 0:
	return
	heads, index = find_pruneable_heads_and_indices(
	heads, self.n_head, self.split_size // self.n_head, self.pruned_heads
	)
	index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])

	# Prune conv1d layers
	self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
	self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)

	# Update hyper params
	self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
	self.n_head = self.n_head - len(heads)
	self.pruned_heads = self.pruned_heads.union(heads)

	def _attn(self, q, k, v, attention_mask=None, head_mask=None, output_attentions=False):
	w = torch.matmul(q, k)
	if self.scale:
	w = w / (float(v.size(-1)) ** 0.5)
	nd, ns = w.size(-2), w.size(-1)

	if not self.is_cross_attention:
	# if only "normal" attention layer implements causal mask
	mask = self.bias[:, :, ns - nd : ns, :ns]
	w = torch.where(mask.bool(), w, self.masked_bias.to(w.dtype))

	if attention_mask is not None:
	# Apply the attention mask
	w = w + attention_mask

	w = nn.Softmax(dim=-1)(w)
	w = self.attn_dropout(w)

	# Mask heads if we want to
	if head_mask is not None:
	w = w * head_mask

	outputs = [torch.matmul(w, v)]
	if output_attentions:
	outputs.append(w)
	return outputs

	def merge_heads(self, x):
	x = x.permute(0, 2, 1, 3).contiguous()
	new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
	return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states

	def split_heads(self, x, k=False):
	new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
	x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
	if k:
	return x.permute(0, 2, 3, 1) # (batch, head, head_features, seq_length)
	else:
	return x.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)

	def forward(
	self,
	hidden_states,
	layer_past=None,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	use_cache=False,
	output_attentions=False,
	):
	if encoder_hidden_states is not None:
	assert hasattr(
	self, "q_attn"
	), "If class is used as cross attention, the weights `q_attn` have to be defined. Please make sure to instantiate class with `Attention(..., is_cross_attention=True)`."
	query = self.q_attn(hidden_states)
	key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
	attention_mask = encoder_attention_mask
	else:
	query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)

	query = self.split_heads(query)
	key = self.split_heads(key, k=True)
	value = self.split_heads(value)
	if layer_past is not None:
	past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1] # transpose back cf below
	key = torch.cat((past_key, key), dim=-1)
	value = torch.cat((past_value, value), dim=-2)

	if use_cache is True:
	present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking
	else:
	present = (None,)

	attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions)
	a = attn_outputs[0]

	a = self.merge_heads(a)
	a = self.c_proj(a)
	a = self.resid_dropout(a)

	outputs = [a, present] + attn_outputs[1:]
	return outputs # a, present, (attentions)


	class MLP(nn.Module):
	def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
	super().__init__()
	nx = config.n_embd
	# self.c_fc = Conv1D(n_state, nx)
	# self.c_proj = Conv1D(nx, n_state)
	self.c_fc = nn.Linear(nx, n_state)
	self.c_proj = nn.Linear(n_state, nx)
	self.act = ACT2FN[config.activation_function]
	self.dropout = nn.Dropout(config.resid_pdrop)

	def forward(self, x):
	h = self.act(self.c_fc(x))
	h2 = self.c_proj(h)
	return self.dropout(h2)


	class Block(nn.Module):
	def __init__(self, n_ctx, config, scale=False):
	super().__init__()
	hidden_size = config.n_embd
	inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
	self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
	self.attn = Attention(hidden_size, n_ctx, config, scale)
	self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
	if config.add_cross_attention:
	self.crossattention = Attention(hidden_size, n_ctx, config, scale, is_cross_attention=True)
	self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
	self.mlp = MLP(inner_dim, config)

	def forward(
	self,
	hidden_states,
	layer_past=None,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	use_cache=False,
	output_attentions=False,
	):
	attn_outputs = self.attn(
	self.ln_1(hidden_states),
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
	outputs = attn_outputs[1:]
	# residual connection
	hidden_states = attn_output + hidden_states

	if encoder_hidden_states is not None:
	# add one self-attention block for cross-attention
	assert hasattr(
	self, "crossattention"
	), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
	cross_attn_outputs = self.crossattention(
	self.ln_cross_attn(hidden_states),
	attention_mask=attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_attentions=output_attentions,
	)
	attn_output = cross_attn_outputs[0]
	# residual connection
	hidden_states = hidden_states + attn_output
	outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights

	feed_forward_hidden_states = self.mlp(self.ln_2(hidden_states))
	# residual connection
	hidden_states = hidden_states + feed_forward_hidden_states

	outputs = [hidden_states] + outputs
	return outputs # hidden_states, present, (attentions, cross_attentions)


	class GPT2LPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""

	config_class = GPT2LConfig
	base_model_prefix = "transformer"

	def __init__(self, inputs, *kwargs):
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, (nn.Linear, nn.Embedding, Conv1D)):
	# Slightly different from the TF version which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)


	class GPT2LDoubleHeadsModelOutput(ModelOutput):
	"""
	Base class for outputs of models predicting if two sentences are consecutive or not.

	Args:
	loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``labels`` is provided):
	Language modeling loss.
	mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`mc_labels` is provided):
	Multiple choice classification loss.
	logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices, sequence_length, config.vocab_size)`):
	Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
	mc_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
	Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
	past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
	List of :obj:`torch.FloatTensor` of length :obj:`config.n_layers`, with each tensor of shape :obj:`(2,
	batch_size, num_heads, sequence_length, embed_size_per_head)`).

	Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
	:obj:`past_key_values` input) to speed up sequential decoding.
	hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or 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 ``output_attentions=True`` is passed or 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.
	"""

	loss: Optional[torch.FloatTensor] = None
	mc_loss: Optional[torch.FloatTensor] = None
	logits: torch.FloatTensor = None
	mc_logits: torch.FloatTensor = None
	past_key_values: Optional[List[torch.FloatTensor]] = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None



	GPT2L_START_DOCSTRING = r"""

	This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
	methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
	pruning heads etc.)

	This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
	subclass. 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.GPT2LConfig`): 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.
	"""

	GPT2_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
	:obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
	``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states). Indices of input
	sequence tokens in the vocabulary.

	If :obj:`past_key_values` is used, only ``input_ids`` that do not have their past calculated should be
	passed as ``input_ids``.

	Indices can be obtained using :class:`~transformers.GPT2Tokenizer`. See
	:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
	details.

	`What are input IDs? <../glossary.html#input-ids>`__
	past_key_values (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
	Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
	:obj:`past_key_values` output below). Can be used to speed up sequential decoding. The ``input_ids`` which
	have their past given to this model should not be passed as ``input_ids`` as they have already been
	computed.
	attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	`What are attention masks? <../glossary.html#attention-mask>`__
	token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`, `optional`):
	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`):
	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`):
	Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
	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 :obj:`input_ids` indices into associated
	vectors than the model's internal embedding lookup matrix.

	If :obj:`past_key_values` is used, optionally only the last :obj:`inputs_embeds` have to be input (see
	:obj:`past_key_values`).
	use_cache (:obj:`bool`, `optional`):
	If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
	decoding (see :obj:`past_key_values`).
	output_attentions (:obj:`bool`, `optional`):
	Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
	tensors for more detail.
	output_hidden_states (:obj:`bool`, `optional`):
	Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
	more detail.
	return_dict (:obj:`bool`, `optional`):
	Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
	"""


	class GPT2LModel(GPT2LPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)

	self.wte = nn.Embedding(config.vocab_size, config.n_embd)
	self.wpe = nn.Embedding(config.n_positions, config.n_embd)
	self.drop = nn.Dropout(config.embd_pdrop)
	self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
	self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)

	self.init_weights()

	def get_input_embeddings(self):
	return self.wte

	def set_input_embeddings(self, new_embeddings):
	self.wte = new_embeddings

	def _prune_heads(self, heads_to_prune):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
	"""
	for layer, heads in heads_to_prune.items():
	self.h[layer].attn.prune_heads(heads)

	def forward(
	self,
	input_ids=None,
	past_key_values=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs,
	):
	if "past" in kwargs:
	warnings.warn(
	"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
	FutureWarning,
	)
	past_key_values = kwargs.pop("past")
	assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
	elif input_ids is not None:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	batch_size = input_ids.shape[0]
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	batch_size = inputs_embeds.shape[0]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if token_type_ids is not None:
	token_type_ids = token_type_ids.view(-1, input_shape[-1])
	if position_ids is not None:
	position_ids = position_ids.view(-1, input_shape[-1])

	if past_key_values is None:
	past_length = 0
	past_key_values = [None] * len(self.h)
	else:
	past_length = past_key_values[0][0].size(-2)
	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
	position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

	# Attention mask.
	if attention_mask is not None:
	assert batch_size > 0, "batch_size has to be defined and > 0"
	attention_mask = attention_mask.view(batch_size, -1)
	# We create a 3D attention mask from a 2D tensor mask.
	# Sizes are [batch_size, 1, 1, to_seq_length]
	# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
	# this attention mask is more simple than the triangular masking of causal attention
	# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
	attention_mask = attention_mask[:, None, None, :]

	# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
	# masked positions, this operation will create a tensor which is 0.0 for
	# positions we want to attend and -10000.0 for masked positions.
	# Since we are adding it to the raw scores before the softmax, this is
	# effectively the same as removing these entirely.
	attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
	attention_mask = (1.0 - attention_mask) * -10000.0

	# If a 2D ou 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.config.add_cross_attention and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_attention_mask = None

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# head_mask has shape n_layer x batch x n_heads x N x N
	head_mask = self.get_head_mask(head_mask, self.config.n_layer)

	if inputs_embeds is None:
	inputs_embeds = self.wte(input_ids)
	position_embeds = self.wpe(position_ids)
	hidden_states = inputs_embeds + position_embeds

	if token_type_ids is not None:
	token_type_embeds = self.wte(token_type_ids)
	hidden_states = hidden_states + token_type_embeds

	hidden_states = self.drop(hidden_states)

	output_shape = input_shape + (hidden_states.size(-1),)

	presents = () if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
	all_hidden_states = () if output_hidden_states else None
	for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)

	if getattr(self.config, "gradient_checkpointing", False):

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# checkpointing only works with tuple returns, not with lists
	return tuple(output for output in module(*inputs, use_cache, output_attentions))

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(block),
	hidden_states,
	layer_past,
	attention_mask,
	head_mask[i],
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	outputs = block(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask[i],
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	hidden_states, present = outputs[:2]
	if use_cache is True:
	presents = presents + (present,)

	if output_attentions:
	all_self_attentions = all_self_attentions + (outputs[2],)
	if self.config.add_cross_attention:
	all_cross_attentions = all_cross_attentions + (outputs[3],)

	hidden_states = self.ln_f(hidden_states)

	hidden_states = hidden_states.view(*output_shape)
	# Add last hidden state
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)

	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	class GPT2LLMHeadModel(GPT2LPreTrainedModel):
	authorized_missing_keys = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.transformer = GPT2LModel(config)
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

	self.init_weights()

	def get_output_embeddings(self):
	return self.lm_head

	def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
	# only last token for inputs_ids if past is defined in kwargs
	if past:
	input_ids = input_ids[:, -1].unsqueeze(-1)

	attention_mask = kwargs.get("attention_mask", None)
	position_ids = kwargs.get("position_ids", None)

	if attention_mask is not None and position_ids is None:
	# create position_ids on the fly for batch generation
	position_ids = attention_mask.long().cumsum(-1) - 1
	position_ids.masked_fill_(attention_mask == 0, 1)
	if past:
	position_ids = position_ids[:, -1].unsqueeze(-1)
	else:
	position_ids = None
	return {
	"input_ids": input_ids,
	"past_key_values": past,
	"use_cache": kwargs.get("use_cache"),
	"position_ids": position_ids,
	"attention_mask": attention_mask,
	}


	def forward(
	self,
	input_ids=None,
	past_key_values=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	labels=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
	``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
	``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
	"""
	if "past" in kwargs:
	warnings.warn(
	"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
	FutureWarning,
	)
	past_key_values = kwargs.pop("past")
	assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]

	lm_logits = self.lm_head(hidden_states)

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

	if not return_dict:
	output = (lm_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return CausalLMOutputWithPast(
	loss=loss,
	logits=lm_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	# cross_attentions=transformer_outputs.cross_attentions,
	)

	class GPT2LDoubleHeadsModel(GPT2LPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	config.num_labels = 1
	self.transformer = GPT2LModel(config)
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
	self.multiple_choice_head = SequenceSummary(config)

	self.init_weights()

	def get_output_embeddings(self):
	return self.lm_head

	def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
	# only last token for inputs_ids if past is defined in kwargs
	if past:
	input_ids = input_ids[:, -1].unsqueeze(-1)

	return {
	"input_ids": input_ids,
	"past_key_values": past,
	"use_cache": kwargs.get("use_cache"),
	}

	def forward(
	self,
	input_ids=None,
	past_key_values=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	mc_token_ids=None,
	labels=None,
	mc_labels=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs,
	):
	r"""
	mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input):
	Index of the classification token in each input sequence. Selected in the range ``[0, input_ids.size(-1) -
	1[``.
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
	``labels = input_ids`` Indices are selected in ``[-1, 0, ..., config.vocab_size]`` All labels set to
	``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
	mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`):
	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)
	kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
	Used to hide legacy arguments that have been deprecated.

	Return:

	Example::

	>>> import torch
	>>> from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel

	>>> tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
	>>> model = GPT2DoubleHeadsModel.from_pretrained('gpt2, return_dict=True)

	>>> # Add a [CLS] to the vocabulary (we should train it also!)
	>>> num_added_tokens = tokenizer.add_special_tokens({'cls_token': '[CLS]'})

	>>> embedding_layer = model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings with the new vocabulary size

	>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
	>>> encoded_choices = [tokenizer.encode(s) for s in choices]
	>>> cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]

	>>> input_ids = torch.tensor(encoded_choices).unsqueeze(0) # Batch size: 1, number of choices: 2
	>>> mc_token_ids = torch.tensor([cls_token_location]) # Batch size: 1

	>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
	>>> lm_logits = outputs.lm_logits
	>>> mc_logits = outputs.mc_logits

	"""
	if "lm_labels" in kwargs:
	warnings.warn(
	"The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
	FutureWarning,
	)
	labels = kwargs.pop("lm_labels")
	if "past" in kwargs:
	warnings.warn(
	"The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
	FutureWarning,
	)
	past_key_values = kwargs.pop("past")
	assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = transformer_outputs[0]

	lm_logits = self.lm_head(hidden_states)
	mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)

	mc_loss = None
	if mc_labels is not None:
	loss_fct = CrossEntropyLoss()
	mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
	lm_loss = None
	if labels is not None:
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	loss_fct = CrossEntropyLoss()
	lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

	if not return_dict:
	output = (lm_logits, mc_logits) + transformer_outputs[1:]
	if mc_loss is not None:
	output = (mc_loss,) + output
	return ((lm_loss,) + output) if lm_loss is not None else output

	return GPT2DoubleHeadsModelOutput(
	loss=lm_loss,
	mc_loss=mc_loss,
	logits=lm_logits,
	mc_logits=mc_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)


	class GPT2LForSequenceClassification(GPT2LPreTrainedModel):
	authorized_missing_keys = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.transformer = GPT2LModel(config)
	self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)

	self.init_weights()
	def forward(
	self,
	input_ids=None,
	past_key_values=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	labels=None,
	use_cache=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
	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).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]
	logits = self.score(hidden_states)

	if input_ids is not None:
	batch_size, sequence_length = input_ids.shape[:2]
	else:
	batch_size, sequence_length = inputs_embeds.shape[:2]

	assert (
	self.config.pad_token_id is not None or batch_size == 1
	), "Cannot handle batch sizes > 1 if no padding token is defined."
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
	else:
	sequence_lengths = -1
	logger.warning(
	f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
	f"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
	)

	pooled_logits = logits[range(batch_size), sequence_lengths]

	loss = None
	if labels is not None:
	if self.num_labels == 1:
	# We are doing regression
	loss_fct = MSELoss()
	loss = loss_fct(pooled_logits.view(-1), labels.to(self.dtype).view(-1))
	else:
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))

	if not return_dict:
	output = (pooled_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return SequenceClassifierOutputWithPast(
	loss=loss,
	logits=pooled_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)