modeling_tf_openai.py
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modeling_tf_openai.py
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	# coding=utf-8
	# 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.
	""" TF 2.0 OpenAI GPT model."""

	from dataclasses import dataclass
	from typing import Optional, Tuple

	import tensorflow as tf

	from ...activations_tf import get_tf_activation
	from ...file_utils import (
	ModelOutput,
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)
	from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput, TFSequenceClassifierOutput
	from ...modeling_tf_utils import (
	TFCausalLanguageModelingLoss,
	TFConv1D,
	TFPreTrainedModel,
	TFSequenceClassificationLoss,
	TFSequenceSummary,
	TFSharedEmbeddings,
	get_initializer,
	input_processing,
	keras_serializable,
	shape_list,
	)
	from ...utils import logging
	from .configuration_openai import OpenAIGPTConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "openai-gpt"
	_CONFIG_FOR_DOC = "OpenAIGPTConfig"
	_TOKENIZER_FOR_DOC = "OpenAIGPTTokenizer"

	TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"openai-gpt",
	# See all OpenAI GPT models at https://huggingface.co/models?filter=openai-gpt
	]


	class TFAttention(tf.keras.layers.Layer):
	def __init__(self, nx, n_ctx, config, scale=False, **kwargs):
	super().__init__(**kwargs)

	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
	), f"Hidden dimension {n_state} not dividable by number of heads {config.n_head}"
	self.n_ctx = n_ctx
	self.n_head = config.n_head
	self.split_size = n_state
	self.scale = scale
	self.output_attentions = config.output_attentions

	self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name="c_attn")
	self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_proj")
	self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop)
	self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop)
	self.pruned_heads = set()

	def prune_heads(self, heads):
	pass

	@staticmethod
	def causal_attention_mask(nd, ns):
	"""
	1's in the lower triangle, counting from the lower right corner. Same as tf.matrix_band_part(tf.ones([nd, ns]),
	-1, ns-nd), but doesn't produce garbage on TPUs.
	"""
	i = tf.range(nd)[:, None]
	j = tf.range(ns)
	m = i >= j - ns + nd
	return m

	def _attn(self, q, k, v, attention_mask, head_mask, output_attentions, training=False):
	# q, k, v have shape [batch, heads, sequence, features]
	w = tf.matmul(q, k, transpose_b=True)
	if self.scale:
	dk = tf.cast(shape_list(k)[-1], dtype=w.dtype) # scale attention_scores
	w = w / tf.math.sqrt(dk)

	# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
	_, _, nd, ns = shape_list(w)
	b = tf.cast(self.causal_attention_mask(nd, ns), dtype=w.dtype)
	b = tf.reshape(b, [1, 1, nd, ns])
	w = w * b - 1e4 * (1 - b)

	if attention_mask is not None:
	# Apply the attention mask
	attention_mask = tf.cast(attention_mask, dtype=w.dtype)
	w = w + attention_mask

	w = tf.nn.softmax(w, axis=-1)
	w = self.attn_dropout(w, training=training)

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

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

	def merge_heads(self, x):
	x = tf.transpose(x, [0, 2, 1, 3])
	x_shape = shape_list(x)
	new_x_shape = x_shape[:-2] + [x_shape[-2] * x_shape[-1]]
	return tf.reshape(x, new_x_shape)

	def split_heads(self, x):
	x_shape = shape_list(x)
	new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head]
	x = tf.reshape(x, new_x_shape)
	return tf.transpose(x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features)

	def call(self, x, attention_mask, head_mask, output_attentions, training=False):
	x = self.c_attn(x)
	query, key, value = tf.split(x, 3, axis=2)
	query = self.split_heads(query)
	key = self.split_heads(key)
	value = self.split_heads(value)

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

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

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


	class TFMLP(tf.keras.layers.Layer):
	def __init__(self, n_state, config, **kwargs):
	super().__init__(**kwargs)
	nx = config.n_embd
	self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
	self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name="c_proj")
	self.act = get_tf_activation("gelu")
	self.dropout = tf.keras.layers.Dropout(config.resid_pdrop)

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


	class TFBlock(tf.keras.layers.Layer):
	def __init__(self, n_ctx, config, scale=False, **kwargs):
	super().__init__(**kwargs)
	nx = config.n_embd
	self.attn = TFAttention(nx, n_ctx, config, scale, name="attn")
	self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
	self.mlp = TFMLP(4 * nx, config, name="mlp")
	self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2")

	def call(self, x, attention_mask, head_mask, output_attentions, training=False):
	output_attn = self.attn(x, attention_mask, head_mask, output_attentions, training=training)
	a = output_attn[0] # output_attn: a, (attentions)

	n = self.ln_1(x + a)
	m = self.mlp(n, training=training)
	h = self.ln_2(n + m)

	outputs = [h] + output_attn[1:]
	return outputs # x, (attentions)


	@keras_serializable
	class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
	config_class = OpenAIGPTConfig

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

	self.config = config
	self.output_hidden_states = config.output_hidden_states
	self.output_attentions = config.output_attentions
	self.return_dict = config.use_return_dict
	self.num_hidden_layers = config.n_layer
	self.vocab_size = config.vocab_size
	self.n_embd = config.n_embd
	self.n_positions = config.n_positions
	self.initializer_range = config.initializer_range

	self.tokens_embed = TFSharedEmbeddings(
	config.vocab_size, config.n_embd, initializer_range=config.initializer_range, name="tokens_embed"
	)
	self.drop = tf.keras.layers.Dropout(config.embd_pdrop)
	self.h = [TFBlock(config.n_ctx, config, scale=True, name="h_._{}".format(i)) for i in range(config.n_layer)]

	def build(self, input_shape):
	with tf.name_scope("positions_embed"):
	self.positions_embed = self.add_weight(
	name="embeddings",
	shape=[self.n_positions, self.n_embd],
	initializer=get_initializer(self.initializer_range),
	)

	super().build(input_shape)

	def get_input_embeddings(self):
	return self.tokens_embed

	def set_input_embeddings(self, value):
	self.tokens_embed.weight = value
	self.tokens_embed.vocab_size = shape_list(value)[0]

	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}
	"""
	raise NotImplementedError

	def call(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	training=False,
	**kwargs,
	):
	inputs = input_processing(
	func=self.call,
	config=self.config,
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	training=training,
	kwargs_call=kwargs,
	)

	if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
	raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
	elif inputs["input_ids"] is not None:
	input_shape = shape_list(inputs["input_ids"])
	inputs["input_ids"] = tf.reshape(inputs["input_ids"], [-1, input_shape[-1]])
	elif inputs["inputs_embeds"] is not None:
	input_shape = shape_list(inputs["inputs_embeds"])[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if inputs["position_ids"] is None:
	inputs["position_ids"] = tf.expand_dims(tf.range(input_shape[-1]), axis=0)

	if inputs["attention_mask"] is not None:
	# 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.
	inputs["attention_mask"] = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, 1, input_shape[1]))

	# 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.

	one_cst = tf.constant(1.0)
	inputs["attention_mask"] = tf.cast(inputs["attention_mask"], dtype=one_cst.dtype)
	inputs["attention_mask"] = tf.multiply(
	tf.subtract(one_cst, inputs["attention_mask"]), tf.constant(-10000.0)
	)
	else:
	inputs["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
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	if inputs["head_mask"] is not None:
	raise NotImplementedError
	else:
	inputs["head_mask"] = [None] * self.num_hidden_layers
	# head_mask = tf.constant([0] * self.num_hidden_layers)

	inputs["position_ids"] = tf.reshape(inputs["position_ids"], [-1, shape_list(inputs["position_ids"])[-1]])

	if inputs["inputs_embeds"] is None:
	inputs["inputs_embeds"] = self.tokens_embed(inputs["input_ids"], mode="embedding")
	position_embeds = tf.gather(self.positions_embed, inputs["position_ids"])
	if inputs["token_type_ids"] is not None:
	inputs["token_type_ids"] = tf.reshape(
	inputs["token_type_ids"], [-1, shape_list(inputs["token_type_ids"])[-1]]
	)
	token_type_embeds = self.tokens_embed(inputs["token_type_ids"], mode="embedding")
	else:
	token_type_embeds = 0
	hidden_states = inputs["inputs_embeds"] + position_embeds + token_type_embeds
	hidden_states = self.drop(hidden_states, training=inputs["training"])

	output_shape = input_shape + [shape_list(hidden_states)[-1]]

	all_attentions = () if inputs["output_attentions"] else None
	all_hidden_states = () if inputs["output_hidden_states"] else None
	for i, block in enumerate(self.h):
	if inputs["output_hidden_states"]:
	all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)

	outputs = block(
	hidden_states,
	inputs["attention_mask"],
	inputs["head_mask"][i],
	inputs["output_attentions"],
	training=inputs["training"],
	)
	hidden_states = outputs[0]
	if inputs["output_attentions"]:
	all_attentions = all_attentions + (outputs[1],)

	hidden_states = tf.reshape(hidden_states, output_shape)
	# Add last hidden state
	if inputs["output_hidden_states"]:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if inputs["output_attentions"]:
	# let the number of heads free (-1) so we can extract attention even after head pruning
	attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
	all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)

	if not inputs["return_dict"]:
	return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)

	return TFBaseModelOutput(
	last_hidden_state=hidden_states,
	hidden_states=all_hidden_states,
	attentions=all_attentions,
	)


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

	config_class = OpenAIGPTConfig
	base_model_prefix = "transformer"

	@tf.function(
	input_signature=[
	{
	"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
	"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
	}
	]
	)
	def serving(self, inputs):
	output = self.call(inputs)

	return self.serving_output(output)


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

	Args:
	logits (:obj:`tf.Tensor` 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:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
	Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
	hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
	Tuple of :obj:`tf.Tensor` (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(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`tf.Tensor` (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.
	"""

	logits: tf.Tensor = None
	mc_logits: tf.Tensor = None
	hidden_states: Optional[Tuple[tf.Tensor]] = None
	attentions: Optional[Tuple[tf.Tensor]] = None


	OPENAI_GPT_START_DOCSTRING = r"""

	This model inherits from :class:`~transformers.TFPreTrainedModel`. 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 `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
	it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
	and behavior.

	.. note::

	TF 2.0 models accepts two formats as inputs:

	- having all inputs as keyword arguments (like PyTorch models), or
	- having all inputs as a list, tuple or dict in the first positional arguments.

	This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
	the tensors in the first argument of the model call function: :obj:`model(inputs)`.

	If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
	the first positional argument :

	- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
	- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
	:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
	- a dictionary with one or several input Tensors associated to the input names given in the docstring:
	:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`


	Parameters:
	config (:class:`~transformers.OpenAIGPTConfig`): 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.
	"""

	OPENAI_GPT_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary.

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

	`What are input IDs? <../glossary.html#input-ids>`__
	attention_mask (:obj:`tf.Tensor` or :obj:`Numpy array` 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:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_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:`tf.Tensor` or :obj:`Numpy array` 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:`tf.Tensor` or :obj:`Numpy array` 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:`tf.Tensor` or :obj:`Numpy array` 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.
	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. This argument can be used only in eager mode, in graph mode the value in the
	config will be used instead.
	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. This argument can be used only in eager mode, in graph mode the value in the config will be
	used instead.
	return_dict (:obj:`bool`, `optional`):
	Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
	argument can be used in eager mode, in graph mode the value will always be set to True.
	training (:obj:`bool`, `optional`, defaults to :obj:`False`):
	Whether or not to use the model in training mode (some modules like dropout modules have different
	behaviors between training and evaluation).
	"""


	@add_start_docstrings(
	"The bare OpenAI GPT transformer model outputting raw hidden-states without any specific head on top.",
	OPENAI_GPT_START_DOCSTRING,
	)
	class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
	def __init__(self, config, inputs, *kwargs):
	super().__init__(config, inputs, *kwargs)
	self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")

	@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=TFBaseModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def call(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	training=False,
	**kwargs,
	):
	inputs = input_processing(
	func=self.call,
	config=self.config,
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	training=training,
	kwargs_call=kwargs,
	)
	outputs = self.transformer(
	input_ids=inputs["input_ids"],
	attention_mask=inputs["attention_mask"],
	token_type_ids=inputs["token_type_ids"],
	position_ids=inputs["position_ids"],
	head_mask=inputs["head_mask"],
	inputs_embeds=inputs["inputs_embeds"],
	output_attentions=inputs["output_attentions"],
	output_hidden_states=inputs["output_hidden_states"],
	return_dict=inputs["return_dict"],
	training=inputs["training"],
	)
	return outputs

	# Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
	def serving_output(self, output):
	hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
	attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

	return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)


	@add_start_docstrings(
	"""
	OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
	embeddings).
	""",
	OPENAI_GPT_START_DOCSTRING,
	)
	class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelingLoss):
	def __init__(self, config, inputs, *kwargs):
	super().__init__(config, inputs, *kwargs)
	self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")

	def get_output_embeddings(self):
	return self.get_input_embeddings()

	def set_output_embeddings(self, value):
	self.set_input_embeddings(value)

	@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=TFCausalLMOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def call(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	labels=None,
	training=False,
	**kwargs,
	):
	r"""
	labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
	config.vocab_size - 1]``.
	"""
	inputs = input_processing(
	func=self.call,
	config=self.config,
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	labels=labels,
	training=training,
	kwargs_call=kwargs,
	)
	transformer_outputs = self.transformer(
	input_ids=inputs["input_ids"],
	attention_mask=inputs["attention_mask"],
	token_type_ids=inputs["token_type_ids"],
	position_ids=inputs["position_ids"],
	head_mask=inputs["head_mask"],
	inputs_embeds=inputs["inputs_embeds"],
	output_attentions=inputs["output_attentions"],
	output_hidden_states=inputs["output_hidden_states"],
	return_dict=inputs["return_dict"],
	training=inputs["training"],
	)
	hidden_states = transformer_outputs[0]

	logits = self.transformer.tokens_embed(hidden_states, mode="linear")

	loss = None
	if inputs["labels"] is not None:
	# shift labels to the left and cut last logit token
	logits = logits[:, :-1]
	labels = inputs["labels"][:, 1:]
	loss = self.compute_loss(labels, logits)

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

	return TFCausalLMOutput(
	loss=loss,
	logits=logits,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)

	# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output
	def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
	hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
	attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

	return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)


	@add_start_docstrings(
	"""
	OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
	RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
	input embeddings, the classification head takes as input the input of a specified classification token index in the
	input sequence).
	""",
	OPENAI_GPT_START_DOCSTRING,
	)
	class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
	def __init__(self, config, inputs, *kwargs):
	super().__init__(config, inputs, *kwargs)
	config.num_labels = 1
	self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
	self.multiple_choice_head = TFSequenceSummary(
	config, initializer_range=config.initializer_range, name="multiple_choice_head"
	)

	@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=TFOpenAIGPTDoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
	def call(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	mc_token_ids=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	training=False,
	**kwargs,
	):
	r"""
	mc_token_ids (:obj:`tf.Tensor` or :obj:`Numpy array` 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]``.

	Return:

	Examples::

	>>> import tensorflow as tf
	>>> from transformers import OpenAIGPTTokenizer, TFOpenAIGPTDoubleHeadsModel

	>>> tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
	>>> model = TFOpenAIGPTDoubleHeadsModel.from_pretrained('openai-gpt')

	>>> # Add a [CLS] to the vocabulary (we should train it also!)
	>>> tokenizer.add_special_tokens({'cls_token': '[CLS]'})
	>>> model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings with the new vocabulary size
	>>> print(tokenizer.cls_token_id, len(tokenizer)) # The newly token the last token of the vocabulary

	>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
	>>> encoding = tokenizer(choices, return_tensors="tf")
	>>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
	>>> inputs["mc_token_ids"]= tf.constant([inputs["input_ids"].shape[-1] - 1, inputs["input_ids"].shape[-1] - 1])[None, :] # Batch size 1
	>>> outputs = model(inputs)
	>>> lm_prediction_scores, mc_prediction_scores = outputs[:2]
	"""

	inputs = input_processing(
	func=self.call,
	config=self.config,
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	mc_token_ids=mc_token_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	training=training,
	kwargs_call=kwargs,
	)

	if inputs["input_ids"] is not None:
	input_shapes = shape_list(inputs["input_ids"])
	else:
	input_shapes = shape_list(inputs["inputs_embeds"])[:-1]

	seq_length = input_shapes[-1]
	flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
	flat_attention_mask = (
	tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
	)
	flat_token_type_ids = (
	tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
	)
	flat_position_ids = (
	tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] is not None else None
	)
	transformer_outputs = self.transformer(
	flat_input_ids,
	flat_attention_mask,
	flat_token_type_ids,
	flat_position_ids,
	inputs["head_mask"],
	inputs["inputs_embeds"],
	inputs["output_attentions"],
	inputs["output_hidden_states"],
	return_dict=inputs["return_dict"],
	training=inputs["training"],
	)
	hidden_states = transformer_outputs[0]
	hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])
	lm_logits = self.transformer.tokens_embed(hidden_states, mode="linear")
	mc_logits = self.multiple_choice_head(hidden_states, inputs["mc_token_ids"], training=inputs["training"])
	mc_logits = tf.squeeze(mc_logits, axis=-1)

	if not inputs["return_dict"]:
	return (lm_logits, mc_logits) + transformer_outputs[1:]

	return TFOpenAIGPTDoubleHeadsModelOutput(
	logits=lm_logits,
	mc_logits=mc_logits,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)

	@tf.function(
	input_signature=[
	{
	"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
	"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
	"mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
	}
	]
	)
	def serving(self, inputs):
	output = self.call(inputs)

	return self.serving_output(output)

	def serving_output(self, output):
	hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
	attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

	return TFOpenAIGPTDoubleHeadsModelOutput(
	logits=output.logits, mc_logits=output.mc_logits, hidden_states=hs, attentions=attns
	)


	@add_start_docstrings(
	"""
	The OpenAI GPT Model transformer with a sequence classification head on top (linear layer).

	:class:`~transformers.TFOpenAIGPTForSequenceClassification` uses the last token in order to do the classification,
	as other causal models (e.g. GPT-2) do.

	Since it does classification on the last token, it requires to know the position of the last token. If a
	:obj:`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
	row. If no :obj:`pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot
	guess the padding tokens when :obj:`inputs_embeds` are passed instead of :obj:`input_ids`, it does the same (take
	the last value in each row of the batch).
	""",
	OPENAI_GPT_START_DOCSTRING,
	)
	class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenceClassificationLoss):
	def __init__(self, config, inputs, *kwargs):
	super().__init__(config, inputs, *kwargs)
	self.num_labels = config.num_labels
	self.score = tf.keras.layers.Dense(
	config.num_labels,
	kernel_initializer=get_initializer(config.initializer_range),
	name="score",
	use_bias=False,
	)
	self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")

	@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=TFSequenceClassifierOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def call(
	self,
	input_ids=None,
	attention_mask=None,
	token_type_ids=None,
	position_ids=None,
	head_mask=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	labels=None,
	training=False,
	**kwargs,
	):
	r"""
	labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
	Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
	config.vocab_size - 1]``.
	"""
	inputs = input_processing(
	func=self.call,
	config=self.config,
	input_ids=input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	labels=labels,
	training=training,
	kwargs_call=kwargs,
	)

	transformer_outputs = self.transformer(
	input_ids=inputs["input_ids"],
	attention_mask=inputs["attention_mask"],
	token_type_ids=inputs["token_type_ids"],
	position_ids=inputs["position_ids"],
	head_mask=inputs["head_mask"],
	inputs_embeds=inputs["inputs_embeds"],
	output_attentions=inputs["output_attentions"],
	output_hidden_states=inputs["output_hidden_states"],
	return_dict=inputs["return_dict"],
	training=inputs["training"],
	)

	hidden_states = transformer_outputs[0]
	logits = self.score(hidden_states)
	in_logits = None
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if inputs["input_ids"] is not None:
	sequence_lengths = (
	tf.reduce_sum(
	tf.cast(
	tf.math.not_equal(inputs["input_ids"], self.config.pad_token_id),
	dtype=inputs["input_ids"].dtype,
	),
	-1,
	keepdims=False,
	)
	- 1
	)
	in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=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.`"
	)
	loss = None

	if inputs["labels"] is not None:
	if input_ids is not None:
	batch_size, sequence_length = shape_list(inputs["input_ids"])[:2]
	else:
	batch_size, sequence_length = shape_list(inputs["inputs_embeds"])[: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 not tf.is_tensor(sequence_lengths):
	in_logits = logits[0:batch_size, sequence_lengths]

	loss = self.compute_loss(
	tf.reshape(inputs["labels"], [-1, 1]), tf.reshape(in_logits, [-1, self.num_labels])
	)

	pooled_logits = in_logits if in_logits is not None else logits

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

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

	# Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
	def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
	hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
	attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None

	return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)

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