modeling_tf_gpt2.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-2 model. """


import logging

import numpy as np
import tensorflow as tf

from .configuration_gpt2 import GPT2Config
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import (
    TFConv1D,
    TFMainLayer,
    TFPreTrainedModel,
    TFSequenceSummary,
    TFSharedEmbeddings,
    get_initializer,
    shape_list,
)


logger = logging.getLogger(__name__)

TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-tf_model.h5",
    "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-tf_model.h5",
    "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-tf_model.h5",
    "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-tf_model.h5",
}


def gelu(x):
    """Gaussian Error Linear Unit.
    This is a smoother version of the RELU.
    Original paper: https://arxiv.org/abs/1606.08415
    Args:
        x: float Tensor to perform activation.
    Returns:
        `x` with the GELU activation applied.
    """
    cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
    return x * cdf


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

        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.n_ctx = n_ctx
        self.n_head = config.n_head
        self.split_size = n_state
        self.scale = scale

        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, dtype):
        """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 tf.cast(m, dtype)

    def _attn(self, inputs, training=False):
        q, k, v, attention_mask, head_mask = inputs
        # 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], tf.float32)  # 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 = 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
            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 self.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, inputs, training=False):
        x, layer_past, attention_mask, head_mask = inputs

        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)
        if layer_past is not None:
            past_key, past_value = tf.unstack(layer_past, axis=1)
            key = tf.concat([past_key, key], axis=-2)
            value = tf.concat([past_value, value], axis=-2)
        present = tf.stack([key, value], axis=1)

        attn_outputs = self._attn([query, key, value, attention_mask, head_mask], 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, present] + attn_outputs[1:]
        return outputs  # a, present, (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 = 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.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
        self.attn = TFAttention(nx, n_ctx, config, scale, name="attn")
        self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2")
        self.mlp = TFMLP(4 * nx, config, name="mlp")

    def call(self, inputs, training=False):
        x, layer_past, attention_mask, head_mask = inputs

        a = self.ln_1(x)
        output_attn = self.attn([a, layer_past, attention_mask, head_mask], training=training)
        a = output_attn[0]  # output_attn: a, present, (attentions)
        x = x + a

        m = self.ln_2(x)
        m = self.mlp(m, training=training)
        x = x + m

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


class TFGPT2MainLayer(TFMainLayer):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions
        self.num_hidden_layers = config.n_layer
        self.vocab_size = config.vocab_size
        self.n_embd = config.n_embd

        self.wte = TFSharedEmbeddings(
            config.vocab_size, config.hidden_size, initializer_range=config.initializer_range, name="wte"
        )
        self.wpe = tf.keras.layers.Embedding(
            config.n_positions,
            config.n_embd,
            embeddings_initializer=get_initializer(config.initializer_range),
            name="wpe",
        )
        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)]
        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f")

    def get_input_embeddings(self):
        return self.wte

    def _resize_token_embeddings(self, new_num_tokens):
        raise NotImplementedError

    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,
        inputs,
        past=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        training=False,
    ):
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            past = inputs[1] if len(inputs) > 1 else past
            attention_mask = inputs[2] if len(inputs) > 2 else attention_mask
            token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
            position_ids = inputs[4] if len(inputs) > 4 else position_ids
            head_mask = inputs[5] if len(inputs) > 5 else head_mask
            inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
            assert len(inputs) <= 7, "Too many inputs."
        elif isinstance(inputs, dict):
            input_ids = inputs.get("input_ids")
            past = inputs.get("past", past)
            attention_mask = inputs.get("attention_mask", attention_mask)
            token_type_ids = inputs.get("token_type_ids", token_type_ids)
            position_ids = inputs.get("position_ids", position_ids)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            assert len(inputs) <= 7, "Too many inputs."
        else:
            input_ids = inputs

        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 past is None:
            past_length = 0
            past = [None] * len(self.h)
        else:
            past_length = shape_list(past[0][0])[-2]
        if position_ids is None:
            position_ids = tf.range(past_length, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]

        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 = attention_mask[:, tf.newaxis, tf.newaxis, :]

            # 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 = tf.cast(attention_mask, tf.float32)
            attention_mask = (1.0 - attention_mask) * -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:
            inputs_embeds = self.wte(input_ids, mode="embedding")
        position_embeds = self.wpe(position_ids)
        if token_type_ids is not None:
            token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
            token_type_embeds = self.wte(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]]

        presents = ()
        all_attentions = []
        all_hidden_states = ()
        for i, (block, layer_past) in enumerate(zip(self.h, past)):
            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)

            outputs = block([hidden_states, layer_past, attention_mask, head_mask[i]], training=training)

            hidden_states, present = outputs[:2]
            presents = presents + (present,)

            if self.output_attentions:
                all_attentions.append(outputs[2])

        hidden_states = self.ln_f(hidden_states)

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

        outputs = (hidden_states, presents)
        if self.output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if self.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)
            outputs = outputs + (all_attentions,)
        return outputs  # last hidden state, presents, (all hidden_states), (attentions)


class TFGPT2PreTrainedModel(TFPreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = GPT2Config
    pretrained_model_archive_map = TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "transformer"


GPT2_START_DOCSTRING = r"""

    .. 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 :obj:`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 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.GPT2Config`): 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:`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.GPT2Tokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            (see `past` output below). Can be used to speed up sequential decoding. The token 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:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token

            `What are token type IDs? <../glossary.html#token-type-ids>`_
        position_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        head_mask (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        input_embeds (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
            Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
            (if set to :obj:`False`) for evaluation.
"""


@add_start_docstrings(
    "The bare GPT2 Model transformer outputing raw hidden-states without any specific head on top.",
    GPT2_START_DOCSTRING,
)
class TFGPT2Model(TFGPT2PreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFGPT2MainLayer(config, name="transformer")

    @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Return:
        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
        last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the last layer of the model.
        past (:obj:`List[tf.Tensor]` 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).
            Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        hidden_states (:obj:`tuple(tf.Tensor)` `optional`, returned 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 ``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.

    Examples::

        import tensorflow as tf
        from transformers import GPT2Tokenizer, TFGPT2Model

        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
        model = TFGPT2Model.from_pretrained('gpt2')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  # Batch size 1
        outputs = model(input_ids)
        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

    """
        outputs = self.transformer(inputs, **kwargs)
        return outputs


@add_start_docstrings(
    """The GPT2 Model transformer with a language modeling head on top
    (linear layer with weights tied to the input embeddings). """,
    GPT2_START_DOCSTRING,
)
class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFGPT2MainLayer(config, name="transformer")

    def get_output_embeddings(self):
        return self.transformer.wte

    def prepare_inputs_for_generation(self, inputs, past, **kwargs):
        # only last token for inputs_ids if past is defined in kwargs
        if past:
            inputs = tf.expand_dims(inputs[:, -1], -1)

        return {"inputs": inputs, "past": past}

    @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Return:
        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
        prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past (:obj:`List[tf.Tensor]` 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).
            Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned 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 ``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.

    Examples::

        import tensorflow as tf
        from transformers import GPT2Tokenizer, TFGPT2LMHeadModel

        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
        model = TFGPT2LMHeadModel.from_pretrained('gpt2')

        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  # Batch size 1
        outputs = model(input_ids)
        logits = outputs[0]

        """
        transformer_outputs = self.transformer(inputs, **kwargs)
        hidden_states = transformer_outputs[0]

        lm_logits = self.transformer.wte(hidden_states, mode="linear")

        outputs = (lm_logits,) + transformer_outputs[1:]

        return outputs  # lm_logits, presents, (all hidden_states), (attentions)


@add_start_docstrings(
    """The GPT2 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).
""",
    GPT2_START_DOCSTRING,
)
class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        config.num_labels = 1
        self.transformer = TFGPT2MainLayer(config, name="transformer")
        self.multiple_choice_head = TFSequenceSummary(
            config, initializer_range=config.initializer_range, name="multiple_choice_head"
        )

    def get_output_embeddings(self):
        return self.transformer.wte

    @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
    def call(
        self,
        inputs,
        past=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        mc_token_ids=None,
        training=False,
    ):
        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:
        :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
        lm_prediction_scores (: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_prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`):
            Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
        past (:obj:`List[tf.Tensor]` 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).
            Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned 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 ``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.


    Examples::

        # For example purposes. Not runnable.
        import tensorflow as tf
        from transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel

        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
        model = TFGPT2DoubleHeadsModel.from_pretrained('gpt2')

        # Add a [CLS] to the vocabulary (we should train it also!)
        # This option is currently not implemented in TF 2.0
        raise NotImplementedError
        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]"]
        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 = tf.constant(encoded_choices)[None, :]  # Batch size: 1, number of choices: 2
        mc_token_ids = tf.constant([cls_token_location])  # Batch size: 1

        outputs = model(input_ids, mc_token_ids=mc_token_ids)
        lm_prediction_scores, mc_prediction_scores = outputs[:2]

        """
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            past = inputs[1] if len(inputs) > 1 else past
            attention_mask = inputs[2] if len(inputs) > 2 else attention_mask
            token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
            position_ids = inputs[4] if len(inputs) > 4 else position_ids
            head_mask = inputs[5] if len(inputs) > 5 else head_mask
            inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
            mc_token_ids = inputs[7] if len(inputs) > 7 else mc_token_ids
            assert len(inputs) <= 8, "Too many inputs."
        elif isinstance(inputs, dict):
            input_ids = inputs.get("input_ids")
            past = inputs.get("past", past)
            attention_mask = inputs.get("attention_mask", attention_mask)
            token_type_ids = inputs.get("token_type_ids", token_type_ids)
            position_ids = inputs.get("position_ids", position_ids)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            mc_token_ids = inputs.get("mc_token_ids", mc_token_ids)
            assert len(inputs) <= 8, "Too many inputs."
        else:
            input_ids = inputs

        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

        flat_inputs = [
            flat_input_ids,
            past,
            flat_attention_mask,
            flat_token_type_ids,
            flat_position_ids,
            head_mask,
            inputs_embeds,
        ]

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

        hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])

        lm_logits = self.transformer.wte(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)

        outputs = (lm_logits, mc_logits) + transformer_outputs[1:]

        return outputs  # lm logits, mc logits, presents, (all hidden_states), (attentions)