test2.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.
"""PyTorch OpenAI GPT-2 model."""

import os
import numpy as np
from dataclasses import dataclass
from typing import Optional, Tuple

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

from transformers.activations import ACT2FN
from transformers.file_utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    replace_return_docstrings,
)
from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import (
    Conv1D,
    PreTrainedModel,
    SequenceSummary,
    find_pruneable_heads_and_indices,
    prune_conv1d_layer,
)
from transformers.utils import logging
from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
from transformers.models.gpt2.configuration_gpt2 import GPT2Config
import torch.nn.functional as F

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "gpt2"
_CONFIG_FOR_DOC = "GPT2Config"
_TOKENIZER_FOR_DOC = "GPT2Tokenizer"

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


def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
    """Load tf checkpoints in a pytorch model"""
    try:
        import re

        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(gpt2_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array.squeeze())

    for name, array in zip(names, arrays):
        name = name[6:]  # skip "model/"
        name = name.split("/")
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+\d+", m_name):
                scope_names = re.split(r"(\d+)", m_name)
            else:
                scope_names = [m_name]
            if scope_names[0] == "w" or scope_names[0] == "g":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "b":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "wpe" or scope_names[0] == "wte":
                pointer = getattr(pointer, scope_names[0])
                pointer = getattr(pointer, "weight")
            else:
                pointer = getattr(pointer, scope_names[0])
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        try:
            assert (
                pointer.shape == array.shape
            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model


class GPT2Attention(nn.Module):
    def __init__(self, config, is_cross_attention=False):
        super().__init__()

        max_positions = config.max_position_embeddings
        self.register_buffer(
            "bias",
            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
                1, 1, max_positions, max_positions
            ),
        )
        self.register_buffer("masked_bias", torch.tensor(-1e4))

        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        self.split_size = self.embed_dim
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
            )

        self.scale_attn_weights = config.scale_attn_weights
        self.is_cross_attention = is_cross_attention

        if self.is_cross_attention:
            self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
            self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
        else:
            self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
        self.c_proj = Conv1D(self.embed_dim, self.embed_dim)

        self.attn_dropout = nn.Dropout(config.attn_pdrop)
        self.resid_dropout = nn.Dropout(config.resid_pdrop)

        self.pruned_heads = set()

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

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

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

    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
        attn_weights = torch.matmul(query, key.transpose(-1, -2))

        if self.scale_attn_weights:
            attn_weights = attn_weights / (float(value.size(-1)) ** 0.5)

        if not self.is_cross_attention:
            # if only "normal" attention layer implements causal mask
            query_length, key_length = query.size(-2), key.size(-2)
            causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool()
            attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))

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

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

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

        attn_output = torch.matmul(attn_weights, value)

        return attn_output, attn_weights

    def _split_heads(self, tensor, num_heads, attn_head_size):
        """
        Splits hidden_size dim into attn_head_size and num_heads
        """
        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
        tensor = tensor.view(*new_shape)
        return tensor.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)

    def _merge_heads(self, tensor, num_heads, attn_head_size):
        """
        Merges attn_head_size dim and num_attn_heads dim into hidden_size
        """
        tensor = tensor.permute(0, 2, 1, 3).contiguous()
        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
        return tensor.view(new_shape)

    def forward(
        self,
        hidden_states,
        layer_past=None,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        use_cache=False,
        output_attentions=False,
    ):
        if encoder_hidden_states is not None:
            if not hasattr(self, "q_attn"):
                raise ValueError(
                    "If class is used as cross attention, the weights `q_attn` have to be defined. "
                    "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
                )

            query = self.q_attn(hidden_states)
            key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
            attention_mask = encoder_attention_mask
        else:
            query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)

        query = self._split_heads(query, self.num_heads, self.head_dim)
        key = self._split_heads(key, self.num_heads, self.head_dim)
        value = self._split_heads(value, self.num_heads, self.head_dim)

        if layer_past is not None:
            past_key, past_value = layer_past
            key = torch.cat((past_key, key), dim=-2)
            value = torch.cat((past_value, value), dim=-2)

        if use_cache is True:
            present = (key, value)
        else:
            present = None

        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

        attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
        attn_output = self.c_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)

        outputs = (attn_output, present)
        if output_attentions:
            outputs += (attn_weights,)

        return outputs  # a, present, (attentions)


class GPT2MLP(nn.Module):
    def __init__(self, intermediate_size, config):
        super().__init__()
        embed_dim = config.hidden_size
        self.c_fc = Conv1D(intermediate_size, embed_dim)
        self.c_proj = Conv1D(embed_dim, intermediate_size)
        self.act = ACT2FN[config.activation_function]
        self.dropout = nn.Dropout(config.resid_pdrop)

    def forward(self, hidden_states):
        hidden_states = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.c_proj(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class GPT2Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = GPT2Attention(config)
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        if config.add_cross_attention:
            self.crossattention = GPT2Attention(config, is_cross_attention=True)
            self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        self.mlp = GPT2MLP(inner_dim, config)

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

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

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

        if use_cache:
            outputs = (hidden_states,) + outputs
        else:
            outputs = (hidden_states,) + outputs[1:]

        return outputs  # hidden_states, present, (attentions, cross_attentions)


class GPT2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = GPT2Config
    load_tf_weights = load_tf_weights_in_gpt2
    base_model_prefix = "transformer"
    is_parallelizable = True

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

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



class GPT2DoubleHeadsModelOutput(ModelOutput):
    """
    Base class for outputs of models predicting if two sentences are consecutive or not.

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

            Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
            :obj:`past_key_values` input) to speed up sequential decoding.
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
            sequence_length, sequence_length)`.

            GPT2Attentions weights after the attention softmax, used to compute the weighted average in the
            self-attention heads.
    """

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



GPT2_START_DOCSTRING = r"""

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

    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
    general usage and behavior.

    Parameters:
        config (:class:`~transformers.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:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
            :obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
            ``past_key_values[0][0].shape[-2]`` (``sequence_length`` of input past key value states). Indices of input
            sequence tokens in the vocabulary.

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

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

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

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

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`, `optional`):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
            1]``:

            - 0 corresponds to a `sentence A` token,
            - 1 corresponds to a `sentence B` token.

            `What are token type IDs? <../glossary.html#token-type-ids>`_
        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
            config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:

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

        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
            vectors than the model's internal embedding lookup matrix.

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

    Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
    it will evenly distribute blocks across all devices.

    Args:
        device_map (:obj:`Dict[int, list]`, optional, defaults to None):
            A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
            automatically mapped to the first device (for esoteric reasons). That means that the first device should
            have fewer attention modules mapped to it than other devices. For reference, the gpt2 models have the
            following number of attention modules:

                - gpt2: 12
                - gpt2-medium: 24
                - gpt2-large: 36
                - gpt2-xl: 48

    Example::

            # Here is an example of a device map on a machine with 4 GPUs using gpt2-xl, which has a total of 48 attention modules:
            model = GPT2LMHeadModel.from_pretrained('gpt2-xl')
            device_map = {0: [0, 1, 2, 3, 4, 5, 6, 7, 8],

                          1: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
                          2: [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],
                          3: [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]}
            model.parallelize(device_map)
"""
DEPARALLELIZE_DOCSTRING = r"""
    Moves the model to cpu from a model parallel state.

    Example::

        # On a 4 GPU machine with gpt2-large:
        model = GPT2LMHeadModel.from_pretrained('gpt2-large')
        device_map = {0: [0, 1, 2, 3, 4, 5, 6, 7],

                    1: [8, 9, 10, 11, 12, 13, 14, 15],
                    2: [16, 17, 18, 19, 20, 21, 22, 23],
                    3: [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]}
        model.parallelize(device_map) # Splits the model across several devices
        model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
"""



class GPT2Model(GPT2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.embed_dim = config.hidden_size

        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)

        self.drop = nn.Dropout(config.embd_pdrop)
        self.h = nn.ModuleList([GPT2Block(config) for _ in range(config.num_hidden_layers)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

        self.init_weights()

        # Model parallel
        self.model_parallel = False
        self.device_map = None


    def parallelize(self, device_map=None):
        # Check validity of device_map
        self.device_map = (
            get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map
        )
        assert_device_map(self.device_map, len(self.h))
        self.model_parallel = False
        self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
        self.last_device = "cuda:" + str(max(self.device_map.keys()))
        self.wte = self.wte.to(self.first_device)
        self.wpe = self.wpe.to(self.first_device)
        # Load onto devices
        for k, v in self.device_map.items():
            for block in v:
                cuda_device = "cuda:" + str(k)
                self.h[block] = self.h[block].to(cuda_device)
        # ln_f to last
        self.ln_f = self.ln_f.to(self.last_device)



    def deparallelize(self):
        self.model_parallel = False
        self.device_map = None
        self.first_device = "cpu"
        self.last_device = "cpu"
        self.wte = self.wte.to("cpu")
        self.wpe = self.wpe.to("cpu")
        for index in range(len(self.h)):
            self.h[index] = self.h[index].to("cpu")
        self.ln_f = self.ln_f.to("cpu")
        torch.cuda.empty_cache()


    def get_input_embeddings(self):
        return self.wte

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

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


    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        past_key_values=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        device = input_ids.device if input_ids is not None else inputs_embeds.device

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

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            past_length = past_key_values[0][0].size(-2)
        if position_ids is None:
            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

        # GPT2Attention mask.
        if attention_mask is not None:
            assert batch_size > 0, "batch_size has to be defined and > 0"
            attention_mask = attention_mask.view(batch_size, -1)
            # We create a 3D attention mask from a 2D tensor mask.
            # Sizes are [batch_size, 1, 1, to_seq_length]
            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
            # this attention mask is more simple than the triangular masking of causal attention
            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
            attention_mask = attention_mask[:, None, None, :]

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

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

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

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

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

        hidden_states = self.drop(hidden_states)

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

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

            # Model parallel
            if self.model_parallel:
                torch.cuda.set_device(hidden_states.device)
                # Ensure layer_past is on same device as hidden_states (might not be correct)
                if layer_past is not None:
                    layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
                # Ensure that attention_mask is always on the same device as hidden_states
                if attention_mask is not None:
                    attention_mask = attention_mask.to(hidden_states.device)
                if isinstance(head_mask, torch.Tensor):
                    head_mask = head_mask.to(hidden_states.device)
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

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

                if use_cache:
                    logger.warning(
                        "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
                        "`use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, use_cache, output_attentions)

                    return custom_forward

                outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    None,
                    attention_mask,
                    head_mask[i],
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                outputs = block(
                    hidden_states,
                    layer_past=layer_past,
                    attention_mask=attention_mask,
                    head_mask=head_mask[i],
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
                if self.config.add_cross_attention:
                    all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)

            # Model Parallel: If it's the last layer for that device, put things on the next device
            if self.model_parallel:
                for k, v in self.device_map.items():
                    if i == v[-1] and "cuda:" + str(k) != self.last_device:
                        hidden_states = hidden_states.to("cuda:" + str(k + 1))

        hidden_states = self.ln_f(hidden_states)

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

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

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



def tok_to_ent(tok2ent):
    if tok2ent == 'mean':
        return MeanPooling
    elif tok2ent == 'mean_max':
        return MeanMaxPooling
    else:
        raise NotImplementedError

def mean_pooling(input, mask):
    mean_pooled = input.sum(dim=1) / mask.sum(dim=1, keepdim=True)
    return mean_pooled

class MeanPooling(nn.Module):
    def __init__(self):
        super(MeanPooling, self).__init__()

    def forward(self, doc_state, entity_mapping, entity_lens):
        entity_states = entity_mapping.unsqueeze(3) * doc_state.unsqueeze(1)  # N x E x L x d
        mean_pooled = torch.sum(entity_states, dim=2) / entity_lens.unsqueeze(2)
        return mean_pooled


class MeanMaxPooling(nn.Module):
    def __init__(self):
        super(MeanMaxPooling, self).__init__()

    def forward(self, doc_state, entity_mapping, entity_lens):
        """
        :param doc_state:  N x L x d
        :param entity_mapping:  N x E x L
        :param entity_lens:  N x E
        :return: N x E x 2d
        """
        test1 = entity_mapping
        test2 = doc_state
        entity_states = entity_mapping.unsqueeze(3) * doc_state.unsqueeze(1)  # N x E x L x d
        max_pooled = torch.max(entity_states, dim=2)[0]
        mean_pooled = torch.sum(entity_states, dim=2) / entity_lens.unsqueeze(2)
        output = torch.cat([max_pooled, mean_pooled], dim=2)  # N x E x 2d
        return output
def get_weights(size, gain=1.414):
    weights = nn.Parameter(torch.zeros(size=size))
    nn.init.xavier_uniform_(weights, gain=gain)
    return weights

###########GatNet###########
def get_act(act):
    if act.startswith('lrelu'):
        return nn.LeakyReLU(float(act.split(':')[1]))
    elif act == 'relu':
        return nn.ReLU()
    else:
        raise NotImplementedError

class GATSelfAttention(nn.Module):
    def __init__(self, in_dim, out_dim, config, layer_id=0, head_id=0):
        """ One head GAT """
        super(GATSelfAttention, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.dropout = 0.5
        self.q_attn = True
        self.query_dim = in_dim
        self.n_type = 2

        # Case study
        self.layer_id = layer_id
        self.head_id = head_id
        self.step = 0

        self.W_type = nn.ParameterList()
        self.a_type = nn.ParameterList()
        self.qattn_W1 = nn.ParameterList()
        self.qattn_W2 = nn.ParameterList()
        for i in range(self.n_type):
            self.W_type.append(get_weights((in_dim, out_dim)))
            self.a_type.append(get_weights((out_dim * 2, 1)))

            if True:
                q_dim = 768
                self.qattn_W1.append(get_weights((q_dim, out_dim * 2)))
                self.qattn_W2.append(get_weights((out_dim * 2, out_dim * 2)))

        self.act = get_act('lrelu:0.2')

    def forward(self, input_state, adj, entity_mask, adj_mask=None, query_vec=None):
        zero_vec = torch.zeros_like(adj)
        scores = 0

        for i in range(self.n_type):
            h = torch.matmul(input_state, self.W_type[i])
            h = F.dropout(h, self.dropout, self.training)
            N, E, d = h.shape

            a_input = torch.cat([h.repeat(1, 1, E).view(N, E * E, -1), h.repeat(1, E, 1)], dim=-1)
            a_input = a_input.view(-1, E, E, 2*d)

            if self.q_attn:
                q_gate = F.relu(torch.matmul(query_vec, self.qattn_W1[i]))
                q_gate = torch.sigmoid(torch.matmul(q_gate, self.qattn_W2[i]))
                a_input = a_input * q_gate[:, None, None, :]
                score = self.act(torch.matmul(a_input, self.a_type[i]).squeeze(3))
            else:
                score = self.act(torch.matmul(a_input, self.a_type[i]).squeeze(3))
            scores += torch.where(adj == i+1, score, zero_vec)

        zero_vec = -9e15 * torch.ones_like(scores)
        scores = torch.where(adj > 0, scores, zero_vec)

        # Ahead Alloc
        if adj_mask is not None:
            h = h * adj_mask

        coefs = F.softmax(scores, dim=2)  # N * E * E
        h = coefs.unsqueeze(3) * h.unsqueeze(2)  # N * E * E * d
        h = torch.sum(h, dim=1)
        return h


class AttentionLayer(nn.Module):
    def __init__(self, in_dim, hid_dim, n_head, config, layer_id=0):
        super(AttentionLayer, self).__init__()
        assert hid_dim % n_head == 0
        self.dropout = 0.5

        self.attn_funcs = nn.ModuleList()
        for i in range(n_head):
            self.attn_funcs.append(  # in_dim =
                GATSelfAttention(in_dim=in_dim, out_dim=hid_dim // n_head, config=config, layer_id=layer_id, head_id=i))

        if in_dim != hid_dim:
            self.align_dim = nn.Linear(in_dim, hid_dim)
            nn.init.xavier_uniform_(self.align_dim.weight, gain=1.414)
        else:
            self.align_dim = lambda x: x

    def forward(self, input, adj, entity_mask, adj_mask=None, query_vec=None):
        hidden_list = []
        for attn in self.attn_funcs:
            h = attn(input, adj, entity_mask, adj_mask=adj_mask, query_vec=query_vec)
            hidden_list.append(h)

        h = torch.cat(hidden_list, dim=-1)
        h = F.dropout(h, self.dropout, training=self.training)
        h = F.relu(h)
        return h


###########GatNet###########


###########BiAttention###########


class BiAttention(nn.Module):
    def __init__(self, input_dim, memory_dim, hid_dim, dropout):
        super(BiAttention, self).__init__()
        self.dropout = dropout
        self.input_linear_1 = nn.Linear(input_dim, 1, bias=False)
        self.memory_linear_1 = nn.Linear(memory_dim, 1, bias=False)

        self.input_linear_2 = nn.Linear(input_dim, hid_dim, bias=True)
        self.memory_linear_2 = nn.Linear(memory_dim, hid_dim, bias=True)

        self.dot_scale = np.sqrt(input_dim)

    def forward(self, input, memory, mask):
        """
        :param input: context_encoding N * Ld * d
        :param memory: query_encoding N * Lm * d
        :param mask: query_mask N * Lm
        :return:
        """
        bsz, input_len, memory_len = input.size(0), input.size(1), memory.size(1)

        input = F.dropout(input, self.dropout, training=self.training)  # N x Ld x d
        memory = F.dropout(memory, self.dropout, training=self.training)  # N x Lm x d

        input_dot = self.input_linear_1(input)  # N x Ld x 1
        memory_dot = self.memory_linear_1(memory)  # N x 1 x Lm
        memory_dot = memory_dot.view(bsz, 1, memory_len)
        # N * Ld * Lm
        cross_dot = torch.bmm(input, memory.permute(0, 2, 1).contiguous()) / self.dot_scale
        # [f1, f2]^T [w1, w2] + <f1 * w3, f2>
        # (N * Ld * 1) + (N * 1 * Lm) + (N * Ld * Lm)
        att = input_dot + memory_dot + cross_dot  # N x Ld x Lm
        # N * Ld * Lm
        att = att - 1e30 * (1 - mask[:, None])

        input = self.input_linear_2(input)   # N x Ld x hid_dim
        memory = self.memory_linear_2(memory)   # N x Lm x hid_dim

        weight_one = F.softmax(att, dim=-1)   # N x Ld x Lm
        output_one = torch.bmm(weight_one, memory)   # N x Ld x hid_dim
        weight_two = F.softmax(att.max(dim=-1)[0], dim=-1).view(bsz, 1, input_len)   # N x 1 x Ld
        output_two = torch.bmm(weight_two, input)  # N x 1 x hid_dim
        # N x Ld x hid_dim + N x Ld x hid_dim + N x Ld x hid_dim + N x Ld x hid_dim
        return torch.cat([input, output_one, input*output_one, output_two*output_one], dim=-1), memory

###########BiAttention###########

class ListModule(torch.nn.Module):
    """
    抽象层列表类
    """

    def __init__(self, *args):
        """
        模型初始化
        """
        super(ListModule, self).__init__()
        idx = 0
        for module in args:
            self.add_module(str(idx), module)
            idx += 1

    def __getitem__(self, idx):
        """
        获取层索引
        """
        if idx < 0 or idx >= len(self._modules):
            raise IndexError('index {} is out of range'.format(idx))
        it = iter(self._modules.values())
        for i in range(idx):
            next(it)
        return next(it)

    def __iter__(self):
        """
        迭代
        """
        return iter(self._modules.values())

    def __len__(self):
        """
        层数
        """
        return len(self._modules)

class GPT2LMHeadModel(GPT2PreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"attn.bias", r"lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.transformer = GPT2Model(config)
        self.tok2ent = tok_to_ent("mean_max")()
        self.ans_weight = get_weights((768, 1536))
        self.temp = np.sqrt(768 * 1536)
        self.gat = [AttentionLayer(1536, 768, 2, config)]
        self.gat.append(AttentionLayer(768, 768, 2, config))
        self.gat = ListModule(*self.gat)

        self.answer_update_layer = BiAttention(768, 768, 768, 0.3)
        self.answer_linear_layer = nn.Linear(768 * 4, 768)
        #self.entity_linear_layer = nn.Linear(768 * 4, 768)
        self.W_f = nn.Parameter(torch.Tensor(768, 768))
        self.U_f = nn.Parameter(torch.Tensor(768, 768))
        self.init_weights()
        self.block =GPT2Block(config)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

        self.init_weights()

        # Model parallel
        self.model_parallel = False
        self.device_map = None


    def parallelize(self, device_map=None):
        self.device_map = (
            get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
            if device_map is None
            else device_map
        )
        assert_device_map(self.device_map, len(self.transformer.h))
        self.transformer.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.transformer.first_device)
        self.model_parallel = False



    def deparallelize(self):
        self.transformer.deparallelize()
        self.transformer = self.transformer.to("cpu")
        self.lm_head = self.lm_head.to("cpu")
        self.model_parallel = False
        torch.cuda.empty_cache()


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

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
        token_type_ids = kwargs.get("token_type_ids", None)
        # only last token for inputs_ids if past is defined in kwargs
        if past:
            input_ids = input_ids[:, -1].unsqueeze(-1)
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

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

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


    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=CausalLMOutputWithCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids = None,
        generate_inputs =None,
        past_key_values=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
            ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        device = torch.device("cuda")
        batch = input_ids
        context_len_idxs = torch.as_tensor(batch.context_len_idxs, dtype=torch.int64)
        if labels is None:
            #context_len_idxs =generate_inputs[:, :404, :]
            context_len_idxs = generate_inputs
            context_len_idxs = torch.as_tensor(context_len_idxs, dtype=torch.int64)


        #context_len_idxs = torch.tensor(batch.context_len_idxs).to(torch.int64).clone().detach()
        context_len_idxs = context_len_idxs.to(device)
        entity_mapping = torch.as_tensor(batch.entity_mapping, dtype=torch.float)
        entity_mapping = entity_mapping.to(device)
        entity_length = torch.as_tensor(batch.entity_length, dtype=torch.float)
        entity_length = entity_length.to(device)
        answer_mapping = torch.as_tensor(batch.answer_mapping, dtype=torch.float)
        answer_mapping = answer_mapping.to(device)
        entity_mask =torch.as_tensor(batch.entity_mask, dtype=torch.float)
        entity_mask = entity_mask.to(device)
        adj = torch.as_tensor(batch.adj, dtype=torch.float)
        adj = adj.to(device)

        transformer_outputs = self.transformer(context_len_idxs)
        sequence_output = transformer_outputs[0]

        if self.model_parallel:
            torch.cuda.set_device(self.transformer.first_device)
            sequence_output = sequence_output.to(self.lm_head.weight.device)

        sequence_output = sequence_output.squeeze()
        # sep_len = len(sep_token)
        doc_state = sequence_output[:, :384, :]
        ans_state = sequence_output[:, 384:404, :]
        #entity_mapping = batch.entity_mapping


        entity_len = list(entity_mapping.size())
        doc_state_temp = doc_state[:, :entity_len[2], :]
        entity_state = self.tok2ent(doc_state_temp, entity_mapping, entity_length)

        ans_len = list(answer_mapping.size())
        ans_state_temp = ans_state[:, :ans_len[1], :]


        ans_vec = mean_pooling(ans_state_temp, answer_mapping)
        entity_mask = entity_mask


        answer = torch.matmul(ans_vec, self.ans_weight)
        answer_scores = torch.bmm(entity_state, answer.unsqueeze(2)) / self.temp
        # entity_mask = entity_mask.unsqueeze(0)

        softmask = answer_scores * entity_mask.unsqueeze(2)  # N x E x 1  BCELossWithLogits
        adj_mask = torch.sigmoid(softmask)
        # adj_mask = adj_mask.unsqueeze(1)
        batch.adj = adj.squeeze()
        ###
        # 使用2层gat
        for layer in self.gat:
            entity_state = layer(entity_state, adj, entity_mask, adj_mask=adj_mask, query_vec=ans_vec)
        #entity_state = self.gat(entity_state, adj, entity_mask, adj_mask=adj_mask, query_vec=ans_vec)



        ###
        ###########
        # 李老师让写的更新实体与问句的注意力
        ans_state_temp, _ = self.answer_update_layer(ans_state_temp, entity_state, entity_mask)
        #answer_len = list(ans_state_temp.size())
        #answer_mask = torch.ones(answer_len[0], answer_len[1])
        #answer_mask = answer_mask.to(device)
        #entity_state, _ = self.answer_update_layer(entity_state, ans_state_temp, answer_mask)
        #entity_state = self.entity_linear_layer(entity_state)
        #answer_linear_layer
        #entity_state = self.entity_linear_layer(entity_state)

        entity_mapping = torch.transpose(entity_mapping, 2, 1)
        # entity_mapping = torch.sum(entity_mapping, dim=2)
        # entity_mapping = entity_mapping.expand_as(entity_state)
        ###########
        if entity_mapping.shape[2] != entity_state.shape[1]:
            print("not same shpae")
            entity_state = entity_state[:, :entity_mapping.shape[2], :]

        new_entity_state = torch.bmm(entity_mapping, entity_state)

        new_entity_state_len = list(new_entity_state.size())

        embedding = "concat"
        if embedding == "replace":
            test_sequence = torch.where(new_entity_state > 0, new_entity_state,
                                        sequence_output[:, 0:new_entity_state_len[1], :])
            sequence_output[:, 0:new_entity_state_len[1], :] = test_sequence
        if embedding == "sum":
            sequence_output[:, 0:new_entity_state_len[1], :] = sequence_output[:, 0:new_entity_state_len[1],
                                                               :] + new_entity_state
        if embedding == "concat":
            sequence_output[:, 0:new_entity_state_len[1], :] = sequence_output[:, 0:new_entity_state_len[1],
                                                               :] @ self.W_f + new_entity_state @ self.U_f


        ans_state_update = self.answer_linear_layer(ans_state_temp)
        sequence_output[:, 384:384 + ans_len[1], :] = ans_state_update
            #sequence_output = sequence_output.unsqueeze(dim=0)


        hidden_states = self.block(sequence_output)
        hidden_states = hidden_states[0]
        lm_logits = self.lm_head(hidden_states)

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

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

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


    @staticmethod
    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
        """
        This function is used to re-order the :obj:`past_key_values` cache if
        :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
        called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
        """
        return tuple(
            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
            for layer_past in past
        )


class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        config.num_labels = 1
        self.transformer = GPT2Model(config)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.multiple_choice_head = SequenceSummary(config)

        self.init_weights()

        # Model parallel
        self.model_parallel = True
        self.device_map = None

    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        self.device_map = (
            get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
            if device_map is None
            else device_map
        )
        assert_device_map(self.device_map, len(self.transformer.h))
        self.transformer.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.transformer.first_device)
        self.multiple_choice_head = self.multiple_choice_head.to(self.transformer.first_device)
        self.model_parallel = True

    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        self.transformer.deparallelize()
        self.transformer = self.transformer.to("cpu")
        self.lm_head = self.lm_head.to("cpu")
        self.multiple_choice_head = self.multiple_choice_head.to("cpu")
        self.model_parallel = False
        torch.cuda.empty_cache()

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
        token_type_ids = kwargs.get("token_type_ids", None)
        # only last token for inputs_ids if past is defined in kwargs
        if past:
            input_ids = input_ids[:, -1].unsqueeze(-1)
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

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

        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past:
                position_ids = position_ids[:, -1].unsqueeze(-1)
        else:
            position_ids = None

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


    @replace_return_docstrings(output_type=GPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids=None,
        past_key_values=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        mc_token_ids=None,
        labels=None,
        mc_labels=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        **kwargs,
    ):
        r"""
        mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input):
            Index of the classification token in each input sequence. Selected in the range ``[0, input_ids.size(-1) -
            1[``.
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size - 1]`` All labels set to
            ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size - 1]``
        mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`):
            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
            num_choices]`` where `num_choices` is the size of the second dimension of the input tensors. (see
            `input_ids` above)

        Return:

        Example::

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

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

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

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

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

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

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

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        hidden_states = transformer_outputs[0]

        # Set device for model parallelism
        if self.model_parallel:
            torch.cuda.set_device(self.transformer.first_device)
            hidden_states = hidden_states.to(self.lm_head.weight.device)

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

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

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

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


    @staticmethod
    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
        """
        This function is used to re-order the :obj:`past_key_values` cache if
        :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
        called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
        """
        return tuple(
            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
            for layer_past in past
        )




class GPT2ForSequenceClassification(GPT2PreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = GPT2Model(config)
        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)

        self.init_weights()

        # Model parallel
        self.model_parallel = False
        self.device_map = None


    @add_code_sample_docstrings(
        tokenizer_class=_TOKENIZER_FOR_DOC,
        checkpoint="microsoft/DialogRPT-updown",
        output_type=SequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids=None,
        past_key_values=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        labels=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

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

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

        pooled_logits = logits[range(batch_size), sequence_lengths]

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

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

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