m2-bert-80M-32k-retrieval / bert_layers.py

Dan Fu

Making automodel work better

a2ccdc5 4 months ago

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	# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
	# Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
	# Copyright (c) 2022, Tri Dao.
	# Copyright (c) 2023, MosaicML.
	# Copyright (c) 2023, Dan Fu and Simran Arora.

	import copy
	import logging
	import math
	import os
	import sys
	import warnings
	from typing import List, Optional, Tuple, Union
	from functools import partial

	# Add folder root to path to allow us to use relative imports regardless of what directory the script is run from
	# sys.path.append(os.path.dirname(os.path.realpath(__file__)))

	from .bert_padding import (index_first_axis,
	index_put_first_axis, pad_input,
	unpad_input, unpad_input_only)
	import torch
	import torch.nn as nn
	from einops import rearrange
	from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import (MaskedLMOutput,
	SequenceClassifierOutput)
	from transformers.models.bert.modeling_bert import BertPreTrainedModel

	from .blockdiag_linear import BlockdiagLinear
	from .configuration_bert import BertConfig
	from .monarch_mixer_sequence_mixer import MonarchMixerSequenceMixing

	logger = logging.getLogger(__name__)

	torch.backends.cuda.matmul.allow_tf32 = True
	torch.backends.cudnn.allow_tf32 = True

	class BertEmbeddings(nn.Module):
	"""Construct the embeddings for words, ignoring position.

	There are no positional embeddings since we use ALiBi and token_type
	embeddings.

	This module is modeled after the Hugging Face BERT's
	:class:`~transformers.model.bert.modeling_bert.BertEmbeddings`, but is
	modified as part of Mosaic BERT's ALiBi implementation. The key change is
	that position embeddings are removed. Position information instead comes
	from attention biases that scale linearly with the position distance
	between query and key tokens.

	This module ignores the `position_ids` input to the `forward` method.
	"""

	def __init__(self, config):
	super().__init__()
	self.word_embeddings = nn.Embedding(config.vocab_size,
	config.hidden_size,
	padding_idx=config.pad_token_id)
	# ALiBi doesn't use position embeddings
	if config.use_positional_encodings:
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
	self.use_positional_encodings = config.use_positional_encodings
	self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
	config.hidden_size)

	# self.LayerNorm is not snake-cased to stick with TensorFlow model
	# variable name and be able to load any TensorFlow checkpoint file
	self.LayerNorm = nn.LayerNorm(config.hidden_size,
	eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	if config.use_positional_encodings:
	self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
	self.register_buffer('token_type_ids',
	torch.zeros(config.max_position_embeddings,
	dtype=torch.long),
	persistent=False)

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	past_key_values_length: int = 0,
	return_position_encodings: bool = False,
	) -> torch.Tensor:
	if (input_ids is not None) == (inputs_embeds is not None):
	raise ValueError('Must specify either input_ids or input_embeds!')
	if input_ids is not None:
	input_shape = input_ids.size()
	else:
	assert inputs_embeds is not None # just for type checking
	input_shape = inputs_embeds.size()[:-1]

	seq_length = input_shape[1]

	if position_ids is None:
	if self.use_positional_encodings:
	position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]

	# Setting the token_type_ids to the registered buffer in constructor
	# where it is all zeros, which usually occurs when it's auto-generated;
	# registered buffer helps users when tracing the model without passing
	# token_type_ids, solves issue #5664
	if token_type_ids is None:
	if hasattr(self, 'token_type_ids'):
	assert isinstance(self.token_type_ids, torch.LongTensor)
	buffered_token_type_ids = self.token_type_ids[:, :seq_length]
	buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
	input_shape[0], seq_length)
	token_type_ids = buffered_token_type_ids_expanded # type: ignore
	else:
	token_type_ids = torch.zeros(input_shape, # type: ignore
	dtype=torch.long,
	device=self.word_embeddings.device) # type: ignore # yapf: disable

	if inputs_embeds is None:
	inputs_embeds = self.word_embeddings(input_ids)
	token_type_embeddings = self.token_type_embeddings(token_type_ids)

	embeddings = inputs_embeds + token_type_embeddings
	if self.use_positional_encodings:
	position_embeddings = self.position_embeddings(position_ids)
	embeddings += position_embeddings
	embeddings = self.LayerNorm(embeddings)
	embeddings = self.dropout(embeddings)
	if return_position_encodings:
	return embeddings, position_embeddings
	else:
	return embeddings

	class BertMLP(nn.Module):
	"""Applies the FFN at the end of each BERT layer."""

	def __init__(self, config):
	super().__init__()
	self.config = config

	if self.config.use_monarch_mlp:
	linear_cls = partial(BlockdiagLinear, nblocks=self.config.monarch_mlp_nblocks)
	else:
	linear_cls = nn.Linear

	self.gated_layers = linear_cls(config.hidden_size,
	config.intermediate_size,
	bias=False)
	self.act = nn.GELU(approximate='none')
	self.wo = linear_cls(config.intermediate_size, config.hidden_size)

	self.layernorm = nn.LayerNorm(config.hidden_size,
	eps=config.layer_norm_eps)

	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	"""Compute new hidden states from current hidden states.

	Args:
	hidden_states (torch.Tensor): The (unpadded) hidden states from
	the attention layer [nnz, dim].
	"""

	residual_connection = hidden_states
	hidden_states = self.gated_layers(hidden_states)
	hidden_states = self.act(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.wo(hidden_states)
	hidden_states = self.layernorm(hidden_states + residual_connection)
	return hidden_states


	class BertGatedLinearUnitMLP(nn.Module):
	"""Applies the FFN at the end of each BERT layer with a Gated Linear Unit"""

	def __init__(self, config):
	super().__init__()
	self.config = config

	self.is_padded = True

	if self.config.use_monarch_mlp:
	linear_cls = partial(BlockdiagLinear, nblocks=self.config.monarch_mlp_nblocks)
	else:
	linear_cls = nn.Linear
	self.gated_layers = linear_cls(
	config.hidden_size,
	config.intermediate_size * 2,
	bias=False
	)
	self.act = nn.GELU(approximate='none')
	self.wo = linear_cls(config.intermediate_size, config.hidden_size)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)
	self.layernorm = nn.LayerNorm(config.hidden_size,
	eps=config.layer_norm_eps)


	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	"""Compute new hidden states from current hidden states.

	Args:
	hidden_states (torch.Tensor): The (unpadded) hidden states from
	the attention layer [nnz, dim].
	"""

	residual_connection = hidden_states
	# compute the activation
	hidden_states = self.gated_layers(hidden_states)

	if self.is_padded:
	gated = hidden_states[:, :, :self.config.intermediate_size]
	non_gated = hidden_states[:, :, self.config.intermediate_size:]
	else:
	gated = hidden_states[:, :self.config.intermediate_size]
	non_gated = hidden_states[:, self.config.intermediate_size:]

	hidden_states = self.act(gated) * non_gated
	hidden_states = self.dropout(hidden_states)
	# multiply by the second matrix
	hidden_states = self.wo(hidden_states)
	# add the residual connection and post-LN
	hidden_states = self.layernorm(hidden_states + residual_connection)

	return hidden_states


	class BertLayer(nn.Module):
	"""BERT layer, which includes Sequence Mixing (e.g. Hyena) and State Mixing (e.g. MLP)."""

	def __init__(self, config):
	super(BertLayer, self).__init__()

	mm_cls = MonarchMixerSequenceMixing
	self.attention = mm_cls(
	config.hidden_size,
	l_max=config.long_conv_l_max,
	hyena_kernel_lr=config.long_conv_kernel_learning_rate,
	bidirectional=config.bidirectional,

	hyena_lr_pos_emb=config.hyena_lr_pos_emb,
	hyena_w=config.hyena_w,
	hyena_w_mod=config.hyena_w_mod,
	hyena_wd=config.hyena_wd,
	hyena_emb_dim=config.hyena_emb_dim,
	hyena_filter_dropout=config.hyena_filter_dropout,
	hyena_filter_order=config.hyena_filter_order,
	residual_long_conv=config.residual_long_conv,
	hyena_training_additions=config.hyena_training_additions,
	)

	if config.use_glu_mlp:
	self.mlp = BertGatedLinearUnitMLP(config)
	else:
	self.mlp = BertMLP(config)

	def forward(
	self,
	hidden_states: torch.Tensor,
	cu_seqlens: torch.Tensor,
	seqlen: int,
	subset_idx: Optional[torch.Tensor] = None,
	indices: Optional[torch.Tensor] = None,
	attn_mask: Optional[torch.Tensor] = None,
	bias: Optional[torch.Tensor] = None,
	) -> torch.Tensor:
	"""Forward pass for a BERT layer, including both attention and MLP.

	Args:
	hidden_states: (total_nnz, dim)
	cu_seqlens: (batch + 1,)
	seqlen: int
	subset_idx: () set of indices whose values we care about at the end of the layer
	(e.g., the masked tokens, if this is the final layer).
	indices: None or (total_nnz,)
	attn_mask: None or (batch, max_seqlen_in_batch)
	bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
	"""

	attention_output = self.attention(hidden_states)
	if type(attention_output) == tuple:
	attention_output, _ = attention_output

	layer_output = self.mlp(attention_output)

	return layer_output


	class BertEncoder(nn.Module):
	"""A stack of BERT layers providing the backbone of BERT.

	Compared to the analogous Hugging Face BERT module, this module handles unpadding to reduce unnecessary computation
	at padded tokens, and pre-computes attention biases to implement ALiBi.
	"""

	def __init__(self, config):
	super().__init__()
	layer = BertLayer(config)
	self.layer = nn.ModuleList(
	[copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])

	self.num_attention_heads = config.num_attention_heads

	def rebuild_alibi_tensor(self,
	size: int,
	device: Optional[Union[torch.device, str]] = None):
	# Alibi
	# Following https://github.com/ofirpress/attention_with_linear_biases/issues/5 (Implementation 1)
	# In the causal case, you can exploit the fact that softmax is invariant to a uniform translation
	# of the logits, which makes the math work out after applying causal masking. If no causal masking
	# will be applied, it is necessary to construct the diagonal mask.
	n_heads = self.num_attention_heads

	def _get_alibi_head_slopes(n_heads: int) -> List[float]:

	def get_slopes_power_of_2(n_heads: int) -> List[float]:
	start = (2(-2-(math.log2(n_heads) - 3)))
	ratio = start
	return [start * ratio**i for i in range(n_heads)]

	# In the paper, they only train models that have 2^a heads for some a. This function
	# has some good properties that only occur when the input is a power of 2. To
	# maintain that even when the number of heads is not a power of 2, we use a
	# workaround.
	if math.log2(n_heads).is_integer():
	return get_slopes_power_of_2(n_heads)

	closest_power_of_2 = 2**math.floor(math.log2(n_heads))
	slopes_a = get_slopes_power_of_2(closest_power_of_2)
	slopes_b = _get_alibi_head_slopes(2 * closest_power_of_2)
	slopes_b = slopes_b[0::2][:n_heads - closest_power_of_2]
	return slopes_a + slopes_b

	context_position = torch.arange(size, device=device)[:, None]
	memory_position = torch.arange(size, device=device)[None, :]
	relative_position = torch.abs(memory_position - context_position)
	# [n_heads, max_token_length, max_token_length]
	relative_position = relative_position.unsqueeze(0).expand(
	n_heads, -1, -1)
	slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device)
	alibi = slopes.unsqueeze(1).unsqueeze(1) * -relative_position
	# [1, n_heads, max_token_length, max_token_length]
	alibi = alibi.unsqueeze(0)
	assert alibi.shape == torch.Size([1, n_heads, size, size])

	self._current_alibi_size = size
	self.alibi = alibi

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	output_all_encoded_layers: Optional[bool] = True,
	subset_mask: Optional[torch.Tensor] = None,
	position_encodings: Optional[torch.Tensor] = None,
	) -> List[torch.Tensor]:

	extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
	extended_attention_mask = extended_attention_mask.to(
	dtype=next(self.parameters()).dtype) # fp16 compatibility
	extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
	attention_mask_bool = attention_mask.bool()
	batch, seqlen = hidden_states.shape[:2]

	cu_seqlens = None
	indices = None
	alibi_attn_mask = None

	all_encoder_layers = []
	for layer_module in self.layer:
	hidden_states = layer_module(hidden_states,
	cu_seqlens,
	seqlen,
	None,
	indices,
	attn_mask=attention_mask,
	bias=alibi_attn_mask
	)
	if position_encodings is not None:
	hidden_states = hidden_states + position_encodings
	if output_all_encoded_layers:
	all_encoder_layers.append(hidden_states)
	if subset_mask is not None:
	hidden_states = hidden_states[subset_mask]

	if not output_all_encoded_layers:
	all_encoder_layers.append(hidden_states)
	return all_encoder_layers


	class BertPooler(nn.Module):

	def __init__(self, config):
	super(BertPooler, self).__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.activation = nn.Tanh()
	self.pool_all = config.pool_all

	def forward(self,
	hidden_states: torch.Tensor,
	pool: Optional[bool] = True,
	mask= None) -> torch.Tensor:
	# We "pool" the model by simply taking the hidden state corresponding
	# to the first token.
	if not self.pool_all:
	first_token_tensor = hidden_states[:, 0] if pool else hidden_states
	pooled_output = self.dense(first_token_tensor)
	pooled_output = self.activation(pooled_output)
	else:
	# mean pool everything that isn't masked out
	denom = torch.sum(mask, dim=1, keepdim=True)
	mean_tensor = torch.sum((hidden_states) * mask.unsqueeze(-1), dim = 1) / denom
	pooled_output = self.dense(mean_tensor)
	pooled_output = self.activation(pooled_output)
	return pooled_output


	class BertPredictionHeadTransform(nn.Module):

	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	if isinstance(config.hidden_act, str):
	self.transform_act_fn = ACT2FN[config.hidden_act]
	else:
	self.transform_act_fn = config.hidden_act
	self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.transform_act_fn(hidden_states)
	hidden_states = self.LayerNorm(hidden_states)
	return hidden_states


	class BertModel(BertPreTrainedModel):
	"""Overall BERT model.

	Args:
	config: a BertConfig class instance with the configuration to build a new model

	Inputs:
	`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
	with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
	`extract_features.py`, `run_classifier.py` and `run_squad.py`)
	`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
	types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
	a `sentence B` token (see BERT paper for more details).
	`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
	selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
	input sequence length in the current batch. It's the mask that we typically use for attention when
	a batch has varying length sentences.
	`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.

	Outputs: Tuple of (encoded_layers, pooled_output)
	`encoded_layers`: controlled by `output_all_encoded_layers` argument:
	- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
	of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
	encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
	- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
	to the last attention block of shape [batch_size, sequence_length, hidden_size],
	`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
	classifier pretrained on top of the hidden state associated to the first character of the
	input (`CLS`) to train on the Next-Sentence task (see BERT's paper).

	Example usage:
	```python
	# Already been converted into WordPiece token ids
	input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
	input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
	token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
	config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
	num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
	model = BertModel(config=config)
	all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
	```
	"""

	config_class = BertConfig

	def __init__(self, config, add_pooling_layer=True):
	super(BertModel, self).__init__(config)
	self.embeddings = BertEmbeddings(config)
	self.encoder = BertEncoder(config)

	self.pooler = BertPooler(config) if add_pooling_layer else None
	self.post_init()


	def get_input_embeddings(self):
	return self.embeddings.word_embeddings

	def set_input_embeddings(self, value):
	self.embeddings.word_embeddings = value

	def forward(
	self,
	input_ids: torch.Tensor,
	token_type_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	output_all_encoded_layers: Optional[bool] = False,
	masked_tokens_mask: Optional[torch.Tensor] = None,
	**kwargs
	) -> Tuple[Union[List[torch.Tensor], torch.Tensor], Optional[torch.Tensor]]:
	if attention_mask is None:
	attention_mask = torch.ones_like(input_ids)
	if token_type_ids is None:
	token_type_ids = torch.zeros_like(input_ids)

	embedding_output = self.embeddings(
	input_ids,
	token_type_ids,
	position_ids
	)
	position_encodings = None

	subset_mask = []
	first_col_mask = []

	if masked_tokens_mask is None:
	subset_mask = None
	else:
	first_col_mask = torch.zeros_like(masked_tokens_mask)
	first_col_mask[:, 0] = True
	subset_mask = masked_tokens_mask \| first_col_mask

	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask,
	output_all_encoded_layers=output_all_encoded_layers,
	subset_mask=subset_mask,
	position_encodings=position_encodings)
	if masked_tokens_mask is None:
	sequence_output = encoder_outputs[-1]
	pooled_output = self.pooler(
	sequence_output, mask = attention_mask) if self.pooler is not None else None
	else:
	# TD [2022-03-01]: the indexing here is very tricky.
	attention_mask_bool = attention_mask.bool()
	subset_idx = subset_mask[attention_mask_bool] # type: ignore
	sequence_output = encoder_outputs[-1][
	masked_tokens_mask[attention_mask_bool][subset_idx]]
	if self.pooler is not None:
	pool_input = encoder_outputs[-1][
	first_col_mask[attention_mask_bool][subset_idx]]
	pooled_output = self.pooler(pool_input, pool=False, mask = attention_mask)
	else:
	pooled_output = None

	if not output_all_encoded_layers:
	encoder_outputs = sequence_output

	if self.pooler is not None:
	return encoder_outputs, pooled_output

	return encoder_outputs, None


	###################
	# Bert Heads
	###################
	class BertLMPredictionHead(nn.Module):

	def __init__(self, config, bert_model_embedding_weights):
	super().__init__()
	self.transform = BertPredictionHeadTransform(config)
	# The output weights are the same as the input embeddings, but there is
	# an output-only bias for each token.
	self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
	bert_model_embedding_weights.size(0))
	self.decoder.weight = bert_model_embedding_weights

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.transform(hidden_states)
	hidden_states = self.decoder(hidden_states)
	return hidden_states


	class BertOnlyMLMHead(nn.Module):

	def __init__(self, config, bert_model_embedding_weights):
	super().__init__()
	self.predictions = BertLMPredictionHead(config,
	bert_model_embedding_weights)

	def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
	prediction_scores = self.predictions(sequence_output)
	return prediction_scores


	class BertOnlyNSPHead(nn.Module):

	def __init__(self, config):
	super().__init__()
	self.seq_relationship = nn.Linear(config.hidden_size, 2)

	def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
	seq_relationship_score = self.seq_relationship(pooled_output)
	return seq_relationship_score


	#######################
	# Construct Bert model
	#######################
	class BertForMaskedLM(BertPreTrainedModel):

	config_class = BertConfig

	def __init__(self, config):
	super().__init__(config)

	if config.is_decoder:
	warnings.warn(
	'If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for '
	'bi-directional self-attention.')

	self.bert = BertModel(config, add_pooling_layer=False)
	self.cls = BertOnlyMLMHead(config,
	self.bert.embeddings.word_embeddings.weight)

	# Initialize weights and apply final processing
	self.post_init()

	@classmethod
	def from_composer(cls,
	pretrained_checkpoint,
	state_dict=None,
	cache_dir=None,
	from_tf=False,
	config=None,
	*inputs,
	**kwargs):
	"""Load from pre-trained."""
	model = cls(config, inputs, *kwargs)
	if from_tf:
	raise ValueError(
	'TensorFlow is not supported.')

	state_dict = torch.load(pretrained_checkpoint)
	# If the state_dict was saved after wrapping with `composer.HuggingFaceModel`, it takes on the `model` prefix
	consume_prefix_in_state_dict_if_present(state_dict, prefix='model.')
	missing_keys, unexpected_keys = model.load_state_dict(state_dict,
	strict=False)

	if len(missing_keys) > 0:
	logger.warning(
	f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
	)
	if len(unexpected_keys) > 0:
	logger.warning(
	f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}"
	)

	return model

	def get_output_embeddings(self):
	return self.cls.predictions.decoder

	def set_output_embeddings(self, new_embeddings):
	self.cls.predictions.decoder = new_embeddings

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
	# labels should be a `torch.LongTensor` of shape
	# `(batch_size, sequence_length)`. These are used for computing the
	# masked language modeling loss.
	#
	# Indices should be in `[-100, 0, ..., config.vocab_size]` (see
	# `input_ids` docstring) Tokens with indices set to `-100` are ignored
	# (masked), the loss is only computed for the tokens with labels in `[0,
	# ..., config.vocab_size]`
	#
	# Prediction scores are only computed for masked tokens and the (bs,
	# seqlen) dimensions are flattened
	if (input_ids is not None) == (inputs_embeds is not None):
	raise ValueError('Must specify either input_ids or input_embeds!')

	if labels is None:
	masked_tokens_mask = None
	else:
	masked_tokens_mask = labels > 0

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

	outputs = self.bert(
	input_ids,
	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,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	masked_tokens_mask=masked_tokens_mask,
	)

	if torch.isnan(outputs[0]).any():
	print("NaNs in outputs.")
	raise ValueError()

	#print("MLM Outputs")
	#print(outputs[0].shape)

	pooled_output = outputs[0]

	last_hidden_state_formatted = outputs[0][:,0,:].view(-1, self.config.hidden_size)
	return {"sentence_embedding": last_hidden_state_formatted}

	def prepare_inputs_for_generation(self, input_ids: torch.Tensor,
	attention_mask: torch.Tensor,
	**model_kwargs):
	input_shape = input_ids.shape
	effective_batch_size = input_shape[0]

	# add a dummy token
	if self.config.pad_token_id is None:
	raise ValueError('The PAD token should be defined for generation')

	attention_mask = torch.cat([
	attention_mask,
	attention_mask.new_zeros((attention_mask.shape[0], 1))
	], dim=-1)
	dummy_token = torch.full((effective_batch_size, 1),
	self.config.pad_token_id,
	dtype=torch.long,
	device=input_ids.device)
	input_ids = torch.cat([input_ids, dummy_token], dim=1)

	return {'input_ids': input_ids, 'attention_mask': attention_mask}


	class BertForSequenceClassification(BertPreTrainedModel):
	"""Bert Model transformer with a sequence classification/regression head.

	This head is just a linear layer on top of the pooled output. Used for,
	e.g., GLUE tasks.
	"""

	config_class = BertConfig

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.config = config

	self.bert = BertModel(config)
	classifier_dropout = (config.classifier_dropout
	if config.classifier_dropout is not None else
	config.hidden_dropout_prob)
	self.dropout = nn.Dropout(classifier_dropout)
	self.classifier = nn.Linear(config.hidden_size, config.num_labels)

	# Initialize weights and apply final processing
	self.post_init()

	@classmethod
	def from_composer(cls,
	pretrained_checkpoint,
	state_dict=None,
	cache_dir=None,
	from_tf=False,
	config=None,
	*inputs,
	**kwargs):
	"""Load from pre-trained."""
	model = cls(config, inputs, *kwargs)
	if from_tf:
	raise ValueError(
	'TensorFlow is not supported.')

	state_dict = torch.load(pretrained_checkpoint)
	# If the state_dict was saved after wrapping with `composer.HuggingFaceModel`, it takes on the `model` prefix
	consume_prefix_in_state_dict_if_present(state_dict, prefix='model.')
	missing_keys, unexpected_keys = model.load_state_dict(state_dict,
	strict=False)

	if len(missing_keys) > 0:
	logger.warning(
	f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
	)
	if len(unexpected_keys) > 0:
	logger.warning(
	f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}"
	)

	return model

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
	# labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	# Labels for computing the sequence classification/regression loss.
	# Indices should be in `[0, ..., config.num_labels - 1]`.
	# If `config.num_labels == 1` a regression loss is computed
	# (mean-square loss). If `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

	outputs = self.bert(
	input_ids,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = outputs[1]

	pooled_output = self.dropout(pooled_output)
	logits = self.classifier(pooled_output)

	loss = None
	if labels is not None:
	# Compute loss
	if self.config.problem_type is None:
	if self.num_labels == 1:
	self.config.problem_type = 'regression'
	elif self.num_labels > 1 and (labels.dtype == torch.long or
	labels.dtype == torch.int):
	self.config.problem_type = 'single_label_classification'
	else:
	self.config.problem_type = 'multi_label_classification'

	if self.config.problem_type == 'regression':
	loss_fct = nn.MSELoss()
	if self.num_labels == 1:
	loss = loss_fct(logits.squeeze(), labels.squeeze())
	else:
	loss = loss_fct(logits, labels)
	elif self.config.problem_type == 'single_label_classification':
	loss_fct = nn.CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.num_labels),
	labels.view(-1))
	elif self.config.problem_type == 'multi_label_classification':
	loss_fct = nn.BCEWithLogitsLoss()
	loss = loss_fct(logits, labels)

	if not return_dict:
	output = (logits,) + outputs[2:]
	return ((loss,) + output) if loss is not None else output

	return SequenceClassifierOutput(
	loss=loss,
	logits=logits,
	hidden_states=None,
	attentions=None,
	)

	class BertForTextEncoding(BertPreTrainedModel):

	config_class = BertConfig

	def __init__(self, config):
	super().__init__(config)

	if config.is_decoder:
	warnings.warn(
	'If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for '
	'bi-directional self-attention.')

	self.bert = BertModel(config, add_pooling_layer=True)

	# Initialize weights and apply final processing
	self.post_init()

	@classmethod
	def from_composer(cls,
	pretrained_checkpoint,
	state_dict=None,
	cache_dir=None,
	from_tf=False,
	config=None,
	*inputs,
	**kwargs):
	"""Load from pre-trained."""
	model = cls(config, inputs, *kwargs)
	if from_tf:
	raise ValueError(
	'TensorFlow is not supported.')

	state_dict = torch.load(pretrained_checkpoint)
	# If the state_dict was saved after wrapping with `composer.HuggingFaceModel`, it takes on the `model` prefix
	consume_prefix_in_state_dict_if_present(state_dict, prefix='model.')
	missing_keys, unexpected_keys = model.load_state_dict(state_dict,
	strict=False)

	if len(missing_keys) > 0:
	logger.warning(
	f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
	)
	if len(unexpected_keys) > 0:
	logger.warning(
	f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}"
	)

	return model

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:

	if (input_ids is not None) == (inputs_embeds is not None):
	raise ValueError('Must specify either input_ids or input_embeds!')

	if labels is None:
	masked_tokens_mask = None
	else:
	masked_tokens_mask = labels > 0

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

	outputs = self.bert(
	input_ids,
	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,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	masked_tokens_mask=masked_tokens_mask,
	)

	pooled_output = outputs[1]

	return {"sentence_embedding": pooled_output}

	def prepare_inputs_for_generation(self, input_ids: torch.Tensor,
	attention_mask: torch.Tensor,
	**model_kwargs):
	input_shape = input_ids.shape
	effective_batch_size = input_shape[0]

	# add a dummy token
	if self.config.pad_token_id is None:
	raise ValueError('The PAD token should be defined for generation')

	attention_mask = torch.cat([
	attention_mask,
	attention_mask.new_zeros((attention_mask.shape[0], 1))
	], dim=-1)
	dummy_token = torch.full((effective_batch_size, 1),
	self.config.pad_token_id,
	dtype=torch.long,
	device=input_ids.device)
	input_ids = torch.cat([input_ids, dummy_token], dim=1)

	return {'input_ids': input_ids, 'attention_mask': attention_mask}