modeling_tf_openai.py

# 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, Union

import numpy as np
import tensorflow as tf

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


logger = logging.get_logger(__name__)

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

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, 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 implementation]
        assert (
            n_state % config.n_head == 0
        ), f"Hidden dimension {n_state} not dividable by number of heads {config.n_head}"
        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 = stable_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, config, scale=False, **kwargs):
        super().__init__(**kwargs)
        nx = config.n_embd
        self.attn = TFAttention(nx, 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.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, scale=True, name=f"h_._{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

    @unpack_inputs
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFBaseModelOutput]:
        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 = shape_list(input_ids)
            input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

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

        if 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.
            attention_mask = tf.reshape(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)
            attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
            attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0))
        else:
            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 head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.num_hidden_layers
            # head_mask = tf.constant([0] * self.num_hidden_layers)

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

        if inputs_embeds is None:
            # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound
            # indices on GPU, returning zeros instead. This is a dangerous silent behavior.
            tf.debugging.assert_less(
                input_ids,
                tf.cast(self.config.vocab_size, dtype=input_ids.dtype),
                message=(
                    "input_ids must be smaller than the embedding layer's input dimension (got"
                    f" {tf.math.reduce_max(input_ids)} >= {self.config.vocab_size})"
                ),
            )
            inputs_embeds = self.tokens_embed(input_ids, mode="embedding")
        position_embeds = tf.gather(self.positions_embed, position_ids)
        if token_type_ids is not None:
            token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
            # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound
            # indices on GPU, returning zeros instead. This is a dangerous silent behavior.
            tf.debugging.assert_less(
                token_type_ids,
                tf.cast(self.config.vocab_size, dtype=token_type_ids.dtype),
                message=(
                    "token_type_ids must be smaller than the embedding layer's input dimension (got"
                    f" {tf.math.reduce_max(token_type_ids)} >= {self.config.vocab_size})"
                ),
            )
            token_type_embeds = self.tokens_embed(token_type_ids, mode="embedding")
        else:
            token_type_embeds = 0
        hidden_states = inputs_embeds + position_embeds + token_type_embeds
        hidden_states = self.drop(hidden_states, training=training)

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

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

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

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

        if 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 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 (`tf.Tensor` of shape `(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 (`tf.Tensor` of shape `(batch_size, num_choices)`):
            Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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 [`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.

    <Tip>

    TensorFlow models and layers in `transformers` accept two formats as input:

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

    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:

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

    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!

    </Tip>

    Parameters:
        config ([`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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

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

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
            [`PreTrainedTokenizer.encode`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`tf.Tensor` or `Numpy array` of shape `(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#attention-mask)
        token_type_ids (`tf.Tensor` or `Numpy array` of shape `(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#token-type-ids)
        position_ids (`tf.Tensor` or `Numpy array` of shape `(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#position-ids)
        head_mask (`tf.Tensor` or `Numpy array` of shape `(num_heads,)` or `(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 (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `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.
        output_attentions (`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 (`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 (`bool`, *optional*):
            Whether or not to return a [`~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 (`bool`, *optional*, defaults to `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")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFBaseModelOutput]:
        outputs = self.transformer(
            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,
        )
        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")
        # OpenAIGPT does not have past caching features
        self.supports_xla_generation = False

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

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

    @unpack_inputs
    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFCausalLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFCausalLMOutput]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
            config.vocab_size - 1]`.
        """

        transformer_outputs = self.transformer(
            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,
        )
        hidden_states = transformer_outputs[0]

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

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

        if not 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,
        )

    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)

    def prepare_inputs_for_generation(self, inputs, **kwargs):
        return {"input_ids": inputs}


@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"
        )

    @unpack_inputs
    @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: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        mc_token_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFOpenAIGPTDoubleHeadsModelOutput]:
        r"""
        mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(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:

        ```python
        >>> import tensorflow as tf
        >>> from transformers import AutoTokenizer, TFOpenAIGPTDoubleHeadsModel

        >>> tokenizer = AutoTokenizer.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]
        ```"""

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

        seq_length = input_shapes[-1]
        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
        flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
        transformer_outputs = self.transformer(
            flat_input_ids,
            flat_attention_mask,
            flat_token_type_ids,
            flat_position_ids,
            head_mask,
            inputs_embeds,
            output_attentions,
            output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        hidden_states = transformer_outputs[0]
        hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])
        if return_dict and output_hidden_states:
            # We do this to match the slightly odd PT behaviour - the final hidden state is reshaped to rank 4 when the
            # input is rank 3, but all other hidden states remain at rank-3 (with the first 2 dims merged)
            all_hidden_states = transformer_outputs.hidden_states[:-1] + (hidden_states,)
        else:
            all_hidden_states = None
        lm_logits = self.transformer.tokens_embed(hidden_states, mode="linear")
        mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids, training=training)
        mc_logits = tf.squeeze(mc_logits, axis=-1)

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

        return TFOpenAIGPTDoubleHeadsModelOutput(
            logits=lm_logits,
            mc_logits=mc_logits,
            hidden_states=all_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).

    [`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
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `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 `inputs_embeds` are passed instead of `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")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        self,
        input_ids: Optional[TFModelInputType] = None,
        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
        head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFSequenceClassifierOutput]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
            config.vocab_size - 1]`.
        """
        transformer_outputs = self.transformer(
            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,
        )

        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 input_ids is not None:
                sequence_lengths = (
                    tf.reduce_sum(
                        tf.cast(
                            tf.math.not_equal(input_ids, self.config.pad_token_id),
                            dtype=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 "
                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                )
        loss = None

        if labels is not None:
            if input_ids is not None:
                batch_size, sequence_length = shape_list(input_ids)[:2]
            else:
                batch_size, sequence_length = shape_list(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.hf_compute_loss(tf.reshape(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 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)