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

import os
import re
from dataclasses import dataclass
from typing import Optional, Tuple

import pandas as pd
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import GPT2Config
from transformers.activations import ACT2FN
from transformers.file_utils import (ModelOutput, add_code_sample_docstrings, add_start_docstrings,
                                     add_start_docstrings_to_model_forward)
from transformers.modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	)
from transformers.modeling_utils import (
	Conv1D,
	PreTrainedModel,
	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 utils import STEFunction, bimodal_normal, hardmax, rescale_norm

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


@dataclass
class ProbingViaPromptingOutputs(ModelOutput):
	"""
	Base class for causal language model (or autoregressive) outputs.

	Args:
		loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
			Language modeling loss (for next-token prediction).
		logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
			Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
		hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
			Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
			of shape :obj:`(batch_size, sequence_length, hidden_size)`.

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

			Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
			heads.
		cross_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)`.

			Cross attentions weights after the attention softmax, used to compute the weighted average in the
			cross-attention heads.
		past_key_values (:obj:`tuple(tuple(torch.FloatTensor))`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):
			Tuple of :obj:`torch.FloatTensor` tuples of length :obj:`config.n_layers`, with each tuple containing the
			cached key, value states of the self-attention and the cross-attention layers if model is used in
			encoder-decoder setting. Only relevant if ``config.is_decoder = True``.

			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.
	"""

	loss: Optional[torch.FloatTensor] = None
	accuracy: Optional[torch.FloatTensor] = None
	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
	cross_attentions: Optional[Tuple[torch.FloatTensor]] = None


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()

		self.head_mask = None

		if hasattr(config, "saturated"):
			self.saturated: bool = config.saturated
		else:
			self.saturated: bool = False

	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):
		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

		if not self.saturated:
			attn_weights = nn.Softmax(dim=-1)(attn_weights)
		else:
			attn_weights = hardmax(attn_weights)

		attn_weights = self.attn_dropout(attn_weights)

		# Mask heads if we want to
		if self.head_mask is not None:
			head_mask = self.head_mask.to(attn_weights.device)
			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)

		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)

	def apply_masks(self, head_mask):
		self.head_mask = head_mask

	def get_masks(self):
		if self.head_mask is not None:
			return self.head_mask.flatten()
		else:
			return None


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)

	def apply_masks(self, head_mask):
		self.attn.apply_masks(head_mask)

	def get_masks(self):
		return self.attn.get_masks()


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."""
		try:
			if self.config.mod_randomized:
				if isinstance(module, (nn.Linear, Conv1D)):
					module.weight.data.normal_(mean=self.config.init_mean, std=self.config.init_std)
					if module.bias is not None:
						module.bias.data.zero_()
				elif isinstance(module, nn.Embedding):
					module.weight.data.normal_(mean=self.config.init_mean, std=self.config.init_std)
					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)
			else:
				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)
		except:
			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)


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()
"""


@add_start_docstrings(
	"The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
	GPT2_START_DOCSTRING,
	)
class GPT2Model(GPT2PreTrainedModel):
	_keys_to_ignore_on_load_missing = ["attn.masked_bias"]

	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)

		if hasattr(self.config, "agg_mod_rand") and self.config.agg_mod_rand:
			self.init_weights_agg_mod()
		elif hasattr(self.config, "fine_mod_rand") and self.config.fine_mod_rand:
			self.init_weights_fine_mod()
		elif hasattr(self.config, "norm_mod_rand") and self.config.norm_mod_rand:
			self.init_weights_norm_mod()
		else:
			self.init_weights()

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

	def init_weights_agg_mod(self):
		"""
		Initialize the weights with custom values head by head.
		"""
		print("Execute init_weights_agg_mod()")
		MATCH_RULE: str = r"h\.\d{1,2}"  # Match prefix "h.1" format
		MEAN_COL_NAME: str = "mean"
		STD_COL_NAME: str = "std"
		weights_df = pd.read_csv("tables/gpt_module_agg_stat.csv")

		for name, module in self.named_modules():
			matched = re.match(MATCH_RULE, name)
			if matched:
				agg_name = matched.group(0)
				head_mean = weights_df[weights_df['module_name'] == agg_name][MEAN_COL_NAME].values[0]
				head_std = weights_df[weights_df['module_name'] == agg_name][STD_COL_NAME].values[0]
				# print("Module: {}, Abs Mean: {}, Abs Std: {}".format(agg_name, head_mean, head_std))
				if isinstance(module, (nn.Linear, Conv1D)):
					bimodal_normal(x=module.weight.data, mu=head_mean, sigma=head_std)
					if module.bias is not None:
						bimodal_normal(x=module.bias.data, mu=head_mean, sigma=head_std)
				elif isinstance(module, nn.LayerNorm):
					bimodal_normal(x=module.weight.data, mu=head_mean, sigma=head_std)
					bimodal_normal(x=module.bias.data, mu=head_mean, sigma=head_std)
			else:
				if name == 'wte' or name == 'wpe':
					if name == 'wte':
						mean = weights_df[weights_df['module_name'] == 'wte.weight'][MEAN_COL_NAME].values[0]
						std = weights_df[weights_df['module_name'] == 'wte.weight'][STD_COL_NAME].values[0]
					elif name == 'wpe':
						mean = weights_df[weights_df['module_name'] == 'wpe.weight'][MEAN_COL_NAME].values[0]
						std = weights_df[weights_df['module_name'] == 'wpe.weight'][STD_COL_NAME].values[0]
					else:
						raise ValueError("Unknown module name: {}".format(name))
					bimodal_normal(x=module.weight.data, mu=mean, sigma=std)
					if module.padding_idx is not None:
						module.weight.data[module.padding_idx].zero_()
				elif name == 'ln_f':
					bias_mean = weights_df[weights_df['module_name'] == 'ln_f.bias'][MEAN_COL_NAME].values[0]
					bias_std = weights_df[weights_df['module_name'] == 'ln_f.bias'][STD_COL_NAME].values[0]
					bimodal_normal(x=module.bias.data, mu=bias_mean, sigma=bias_std)

					# NOTE: ln_f.weight is not symmetric, so we don't flip the sign
					weight_mean = weights_df[weights_df['module_name'] == 'ln_f.weight'][MEAN_COL_NAME].values[0]
					weight_std = weights_df[weights_df['module_name'] == 'ln_f.weight'][STD_COL_NAME].values[0]
					module.weight.data.normal_(mean=weight_mean, std=weight_std)

	def init_weights_fine_mod(self):
		"""
		Initialize the weights with custom values module by module.
		"""
		print("Execute init_weights_fine_mod()")
		MEAN_COL_NAME: str = "mean"
		STD_COL_NAME: str = "std"
		weights_df = pd.read_csv("tables/gpt_module_stat.csv")

		for name, module in self.named_modules():
			if name == 'wte' or name == 'wpe':
				if name == 'wte':
					mean = weights_df[weights_df['module_name'] == 'wte.weight'][MEAN_COL_NAME].values[0]
					std = weights_df[weights_df['module_name'] == 'wte.weight'][STD_COL_NAME].values[0]
				elif name == 'wpe':
					mean = weights_df[weights_df['module_name'] == 'wpe.weight'][MEAN_COL_NAME].values[0]
					std = weights_df[weights_df['module_name'] == 'wpe.weight'][STD_COL_NAME].values[0]
				else:
					raise ValueError("Unknown module name: {}".format(name))
				bimodal_normal(x=module.weight.data, mu=mean, sigma=std)
				if module.padding_idx is not None:
					module.weight.data[module.padding_idx].zero_()
			elif name == 'ln_f':
				bias_mean = weights_df[weights_df['module_name'] == 'ln_f.bias'][MEAN_COL_NAME].values[0]
				bias_std = weights_df[weights_df['module_name'] == 'ln_f.bias'][STD_COL_NAME].values[0]
				bimodal_normal(x=module.bias.data, mu=bias_mean, sigma=bias_std)

				# NOTE: Weights of LayerNorm are not symmetric, so we don't flip the sign
				weight_mean = weights_df[weights_df['module_name'] == 'ln_f.weight'][MEAN_COL_NAME].values[0]
				weight_std = weights_df[weights_df['module_name'] == 'ln_f.weight'][STD_COL_NAME].values[0]
				module.weight.data.normal_(mean=weight_mean, std=weight_std)
			else:
				weight_name = name + ".weight"
				bias_name = name + ".bias"
				if isinstance(module, (nn.Linear, Conv1D)):
					weight_mean = weights_df[weights_df['module_name'] == weight_name][MEAN_COL_NAME].values[0]
					weight_std = weights_df[weights_df['module_name'] == weight_name][STD_COL_NAME].values[0]
					bimodal_normal(x=module.weight.data, mu=weight_mean, sigma=weight_std)
					if module.bias is not None:
						bias_mean = weights_df[weights_df['module_name'] == bias_name][MEAN_COL_NAME].values[0]
						bias_std = weights_df[weights_df['module_name'] == bias_name][STD_COL_NAME].values[0]
						bimodal_normal(x=module.bias.data, mu=bias_mean, sigma=bias_std)
				elif isinstance(module, nn.LayerNorm):
					# NOTE: Weights of LayerNorm are not symmetric, so we don't flip the sign
					weight_mean = weights_df[weights_df['module_name'] == weight_name][MEAN_COL_NAME].values[0]
					weight_std = weights_df[weights_df['module_name'] == weight_name][STD_COL_NAME].values[0]
					module.weight.data.normal_(mean=weight_mean, std=weight_std)

					bias_mean = weights_df[weights_df['module_name'] == bias_name][MEAN_COL_NAME].values[0]
					bias_std = weights_df[weights_df['module_name'] == bias_name][STD_COL_NAME].values[0]
					bimodal_normal(x=module.bias.data, mu=bias_mean, sigma=bias_std)

	def init_weights_norm_mod(self):
		print("Execute init_weights_norm_mod()")
		norm_df = pd.read_csv("tables/gpt_module_norm.csv")

		for name, module in self.named_modules():
			if name == 'wte' or name == 'wpe':
				if name == 'wte':
					norm = norm_df[norm_df['module_name'] == 'wte.weight']["norm"].values[0]
				elif name == 'wpe':
					norm = norm_df[norm_df['module_name'] == 'wpe.weight']["norm"].values[0]
				else:
					raise ValueError("Unknown module name: {}".format(name))
				module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
				module.weight.data = rescale_norm(module.weight.data, norm)
				if module.padding_idx is not None:
					module.weight.data[module.padding_idx].zero_()
			elif name == 'ln_f':
				weight_norm = norm_df[norm_df['module_name'] == 'ln_f.weight']["norm"].values[0]
				bias_norm = norm_df[norm_df['module_name'] == 'ln_f.bias']["norm"].values[0]
				module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
				module.bias.data.normal_(mean=0.0, std=self.config.initializer_range)
				module.weight.data = rescale_norm(module.weight.data, weight_norm)
				module.bias.data = rescale_norm(module.bias.data, bias_norm)
			else:
				weight_name = name + ".weight"
				bias_name = name + ".bias"
				if isinstance(module, (nn.Linear, Conv1D)):
					weight_norm = norm_df[norm_df['module_name'] == weight_name]["norm"].values[0]
					module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
					module.weight.data = rescale_norm(module.weight.data, weight_norm)
					if module.bias is not None:
						bias_norm = norm_df[norm_df['module_name'] == bias_name]["norm"].values[0]
						module.bias.data.normal_(mean=0.0, std=self.config.initializer_range)
						module.bias.data = rescale_norm(module.bias.data, bias_norm)
				elif isinstance(module, nn.LayerNorm):
					weight_norm = norm_df[norm_df['module_name'] == weight_name]["norm"].values[0]
					bias_norm = norm_df[norm_df['module_name'] == bias_name]["norm"].values[0]
					module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
					module.bias.data.normal_(mean=0.0, std=self.config.initializer_range)
					module.weight.data = rescale_norm(module.weight.data, weight_norm)
					module.bias.data = rescale_norm(module.bias.data, bias_norm)

	@add_start_docstrings(PARALLELIZE_DOCSTRING)
	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 = True
		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)

	@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
	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)

	def apply_masks(self, head_mask):
		for layer, module in enumerate(self.h):
			module.apply_masks(head_mask[layer])

	def get_masks(self):
		return [module.get_masks() for module in self.h]

	@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
		processor_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:
			if batch_size <= 0:
				raise ValueError("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, all_cross_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,
			)


@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 GatedGPT2LMHeadModel(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.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

		self.w = nn.Parameter(torch.empty([config.num_hidden_layers, config.num_attention_heads]))
		nn.init.xavier_uniform(self.w)
		self.num_of_heads = None
		self.use_dsp = False

		self.num_labels = config.num_labels
		self.eval_acc = False

	@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.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.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_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
	@add_code_sample_docstrings(
		processor_class=_TOKENIZER_FOR_DOC,
		checkpoint=_CHECKPOINT_FOR_DOC,
		output_type=ProbingViaPromptingOutputs,
		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,
			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

		if self.use_dsp:
			head_mask = STEFunction.apply(self.w.view(-1), self.num_of_heads).view_as(self.w)
			self.apply_masks(head_mask)

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

		# 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)

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

			if self.eval_acc:
				# Token prediction
				# Take out the tokens for prediction
				# There is only one token per example that has labels not equal to -100
				indices = labels != -100
				selected_labels = labels[indices]
				# print('selected_labels', selected_labels)
				num_tokens = lm_logits.shape[-1]
				correct_labels = selected_labels - num_tokens + self.num_labels  # convert to 0-based to match with pred_labels
				# print('correct_labels', correct_labels)
				selected_logits = lm_logits[:, :-1, :][
					indices[:, 1:]]  # we need to shift one position backward for logits
				pred_labels = selected_logits[:, -self.num_labels:].argmax(-1)  # predict only from candidate tokens
				# print('pred_labels', pred_labels)
				accuracy = (correct_labels == pred_labels).float()
			else:
				accuracy = None

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

		return ProbingViaPromptingOutputs(
			loss=loss,
			accuracy=accuracy,
			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
			)

	def apply_masks(self, head_mask):
		head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
		self.transformer.apply_masks(head_mask)

	def get_masks(self):
		return torch.stack(self.transformer.get_masks())

	def apply_dsp(self, num_of_heads):
		self.num_of_heads = num_of_heads
		self.use_dsp = True