custom-decoder-ats / longformer_enc_dec_custom.py

mBART + fine-tuned benjamin/gerpt2

3519726 about 1 year ago

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	"""

	This code is in part adapted from AllenAI's Longformer:
	https://github.com/allenai/longformer/
	and in part adapted from:
	https://github.com/huggingface/transformers

	Author: Annette Rios (rios@cl.uzh.ch)

	"""
	from typing import List, Optional, Tuple, Dict, Union
	from torch import nn, Tensor, zeros
	import torch
	import math
	import random
	from transformers.models.mbart.modeling_mbart import MBartConfig, MBartForConditionalGeneration, MBartEncoder, MBartLearnedPositionalEmbedding, MBartEncoderLayer, MBartDecoder, MBartModel, _expand_mask
	from transformers.modeling_outputs import BaseModelOutput,Seq2SeqModelOutput
	from transformers.configuration_utils import PretrainedConfig
	from transformers import GPT2Model, GPT2Config, AutoModelForCausalLM,AutoConfig
	from transformers.activations import ACT2FN

	import torch.nn.functional as F
	from transformers.models.roberta.modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM

	from functools import lru_cache
	import os.path


	class MLongformerEncoderDecoderForConditionalGenerationCustom(MBartForConditionalGeneration):
	def __init__(self, config):
	super(MBartForConditionalGeneration, self).__init__(config)
	self.decoder_config = GPT2Config.from_dict(config.decoder_config)
	self.decoder_config.add_cross_attention=True
	self.config.eos_token_id = self.decoder_config.eos_token_id
	#self.config.bos_token_id = 0

	self.model = LongMBartModelCustom(config)
	#self.register_buffer("final_logits_bias", torch.zeros((1, self.decoder_config.vocab_size)))

	if self.config.from_mbart:
	self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
	self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
	else:
	self.lm_head = nn.Linear(self.decoder_config.n_embd, self.decoder_config.vocab_size, bias=False)
	self.register_buffer("final_logits_bias", torch.zeros((1, self.decoder_config.vocab_size)))

	self.model.decoder = GPT2Model(self.decoder_config)
	if config.attention_mode == 'n2':
	pass # do nothing, use MBartSelfAttention instead
	else:
	for i, layer in enumerate(self.model.encoder.layers):
	layer.self_attn = LongformerSelfAttentionForMBart(config, layer_id=i)
	# Initialize weights and apply final processing
	self.post_init()

	def post_init(self):
	super().post_init()
	if not self.config.from_mbart:
	self.lm_head = nn.Linear(self.decoder_config.n_embd, self.decoder_config.vocab_size, bias=False)

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, (MBartDecoder)):
	module.gradient_checkpointing = value
	self.model.decoder._set_gradient_checkpointing(module, value=value)

	@classmethod
	def from_encoder_decoder_pretrained(
	cls,
	mbart_pretrained_model_name_or_path: str = None,
	decoder_pretrained_model_name_or_path: str = None,
	*model_args,
	**kwargs
	) -> MBartForConditionalGeneration:
	config = MLongformerEncoderDecoderConfigCustom.from_pretrained(mbart_pretrained_model_name_or_path)
	config.from_mbart = True
	config.tie_word_embeddings = False
	config.decoder_config = GPT2Config.from_pretrained(decoder_pretrained_model_name_or_path).to_dict()

	mbart = super().from_pretrained(mbart_pretrained_model_name_or_path, config=config)
	decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, add_cross_attention=True)

	mbart.model.decoder = decoder.transformer
	mbart.lm_head = decoder.lm_head
	mbart.register_buffer("final_logits_bias", torch.zeros((1, decoder.config.vocab_size)))

	#reinit cross attention layers
	mbart.model.enc_to_dec_proj.apply(mbart.model._init_weights)
	for layer in mbart.model.decoder.h:
	layer.crossattention.c_attn.apply(mbart.model.decoder._init_weights)

	del mbart.model.shared
	return mbart


	class MLongformerEncoderDecoderConfigCustom(MBartConfig):
	def __init__(self, attention_window: List[int] = None, attention_dilation: List[int] = None,
	autoregressive: bool = False, attention_mode: str = 'sliding_chunks',
	gradient_checkpointing: bool = False, **kwargs):
	"""
	Args:
	attention_window: list of attention window sizes of length = number of layers.
	window size = number of attention locations on each side.
	For an affective window size of 512, use `attention_window=[256]*num_layers`
	which is 256 on each side.
	attention_dilation: list of attention dilation of length = number of layers.
	attention dilation of `1` means no dilation.
	autoregressive: do autoregressive attention or have attention of both sides
	attention_mode: 'n2' for regular n^2 self-attention, 'tvm' for TVM implemenation of Longformer
	selfattention, 'sliding_chunks' for another implementation of Longformer selfattention
	"""
	super().__init__(**kwargs)
	self.from_mbart = False
	self.attention_window = attention_window
	self.attention_dilation = attention_dilation
	self.autoregressive = autoregressive
	self.attention_mode = attention_mode
	self.gradient_checkpointing = gradient_checkpointing
	assert self.attention_mode in ['sliding_chunks', 'n2']


	class LongMBartModelCustom(MBartModel):
	def __init__(self, config: MBartConfig):
	super().__init__(config)
	del self.shared
	decoder_config = GPT2Config.from_dict(config.decoder_config)

	padding_idx, vocab_size = config.pad_token_id, config.vocab_size
	if self.config.from_mbart:
	self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

	self.encoder = LongMBartEncoder(config)
	self.enc_to_dec_proj = torch.nn.Linear(config.d_model, decoder_config.n_embd)
	self.act = ACT2FN[decoder_config.activation_function]
	self.decoder = GPT2Model(decoder_config)

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

	def get_input_embeddings(self):
	return self.encoder.embed_tokens

	def set_input_embeddings(self, value):
	self.encoder.embed_tokens = value

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = 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

	# different to other models, MBart automatically creates decoder_input_ids from
	# input_ids if no decoder_input_ids are provided
	if decoder_input_ids is None and decoder_inputs_embeds is None:
	decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)

	#print("input_ids: ", input_ids)
	#print("input_embeds: ", inputs_embeds)
	#print("decoder_input_ids: ", decoder_input_ids.shape)
	#print("attention_mask: ",attention_mask.shape)

	if encoder_outputs is None:
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
	elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
	encoder_outputs = BaseModelOutput(
	last_hidden_state=encoder_outputs[0],
	hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
	attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
	)

	encoder_hidden_states = encoder_outputs[0]

	#remove uneccessary padding spaces
	non_empty_mask = attention_mask.abs().sum(dim=0).bool()
	encoder_hidden_states = encoder_hidden_states[:,non_empty_mask]
	encoder_attention_mask = attention_mask[:,non_empty_mask]

	#to remove global attention tokens (2)
	encoder_attention_mask = torch.clamp(encoder_attention_mask, min=0, max=1)

	encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
	encoder_hidden_states = self.act(encoder_hidden_states)
	encoder_hidden_states = torch.nn.Dropout(p=0.1)(encoder_hidden_states)

	# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	head_mask=decoder_head_mask,
	#cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return decoder_outputs + encoder_outputs

	return Seq2SeqModelOutput(
	last_hidden_state=decoder_outputs.last_hidden_state,
	past_key_values=decoder_outputs.past_key_values,
	decoder_hidden_states=decoder_outputs.hidden_states,
	decoder_attentions=decoder_outputs.attentions,
	cross_attentions=decoder_outputs.cross_attentions,
	encoder_last_hidden_state=encoder_outputs.last_hidden_state,
	encoder_hidden_states=encoder_outputs.hidden_states,
	encoder_attentions=encoder_outputs.attentions,
	)

	class MLongformerEncoderDecoderForConditionalGeneration(MBartForConditionalGeneration):
	def __init__(self, config):
	super(MBartForConditionalGeneration, self).__init__(config)

	self.model = LongMBartModel(config)
	self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
	self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
	#print(self)

	if config.attention_mode == 'n2':
	pass # do nothing, use MBartSelfAttention instead
	else:
	for i, layer in enumerate(self.model.encoder.layers):
	layer.self_attn = LongformerSelfAttentionForMBart(config, layer_id=i)
	# Initialize weights and apply final processing
	self.post_init()


	class MLongformerEncoderDecoderConfig(MBartConfig):
	def __init__(self, attention_window: List[int] = None, attention_dilation: List[int] = None,
	autoregressive: bool = False, attention_mode: str = 'sliding_chunks',
	gradient_checkpointing: bool = False, **kwargs):
	"""
	Args:
	attention_window: list of attention window sizes of length = number of layers.
	window size = number of attention locations on each side.
	For an affective window size of 512, use `attention_window=[256]*num_layers`
	which is 256 on each side.
	attention_dilation: list of attention dilation of length = number of layers.
	attention dilation of `1` means no dilation.
	autoregressive: do autoregressive attention or have attention of both sides
	attention_mode: 'n2' for regular n^2 self-attention, 'tvm' for TVM implemenation of Longformer
	selfattention, 'sliding_chunks' for another implementation of Longformer selfattention
	"""
	super().__init__(**kwargs)
	self.attention_window = attention_window
	self.attention_dilation = attention_dilation
	self.autoregressive = autoregressive
	self.attention_mode = attention_mode
	self.gradient_checkpointing = gradient_checkpointing
	assert self.attention_mode in ['sliding_chunks', 'n2']

	class LongformerSelfAttentionForMBart(nn.Module):
	def __init__(self, config, layer_id):
	super().__init__()
	self.embed_dim = config.d_model
	self.longformer_self_attn = LongformerSelfAttention(config, layer_id=layer_id)
	self.output = nn.Linear(self.embed_dim, self.embed_dim)

	def forward(
	self,
	hidden_states: Tensor, # shape (batch_size, q_len, model_size)
	key_value_states: Optional[Tensor] = None, # cross-attention in transformers.models.mbart.modeling_mbart
	past_key_value: Optional[Tuple[Tensor]] = None, # only for decoder
	attention_mask: Optional[Tensor] = None, # shape (batch_size, k_len) -> changed in transformers.models.modeling_mbart.MBartEncoder and MBartEncoderLayer (new mask uses bool -> global attention positions are lost, need to use the inverted orignal mask
	layer_head_mask: Optional[Tensor] = None, # head dropout?
	output_attentions: bool = False
	) -> Tuple[Tensor, Optional[Tensor]]:

	bsz, tgt_len, embed_dim = hidden_states.size()
	assert embed_dim == self.embed_dim
	assert list(hidden_states.size()) == [bsz, tgt_len, embed_dim]

	outputs = self.longformer_self_attn(
	hidden_states,
	attention_mask=attention_mask * -1, # shape (batch_size, 1, 1, key_len)
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	output_attentions=output_attentions,
	)

	## new: MBart encoder expects shape (seq_len, bsz, embed_dim), no transpose needed
	attn_output = self.output(outputs[0])
	# new return in MBartAttention has attn_output, attn_weights_reshaped, past_key_value (only for decoder), need to return 3 values (None for past_key_value)
	return (attn_output, outputs[1:] ,None) if len(outputs) == 2 else (attn_output, None, None)


	class LongMBartEncoder(MBartEncoder):
	"""
	Transformer encoder consisting of config.encoder_layers self attention layers. Each layer is a
	[`MBartEncoderLayer`].

	Args:
	config: MBartConfig
	embed_tokens (nn.Embedding): output embedding
	"""

	def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = None):
	super().__init__(config)

	self.dropout = config.dropout
	self.layerdrop = config.encoder_layerdrop

	embed_dim = config.d_model
	self.padding_idx = config.pad_token_id
	self.max_source_positions = config.max_encoder_position_embeddings
	self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

	if embed_tokens is not None:
	self.embed_tokens = embed_tokens
	else:
	self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)

	self.embed_positions = MBartLearnedPositionalEmbedding(
	self.max_source_positions,
	embed_dim,
	)
	self.layers = nn.ModuleList([LongMBartEncoderLayer(config) for _ in range(config.encoder_layers)])
	self.layernorm_embedding = nn.LayerNorm(embed_dim)
	self.layer_norm = nn.LayerNorm(config.d_model)

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

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutput]:
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert `input_ids` indices into associated vectors
	than the model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	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
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# retrieve input_ids and inputs_embeds
	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 = input_ids
	input_shape = input.shape
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input = inputs_embeds[:, :, -1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	embed_pos = self.embed_positions(input)

	hidden_states = inputs_embeds + embed_pos
	hidden_states = self.layernorm_embedding(hidden_states)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

	# expand attention_mask
	longformer_attention_mask = None
	if attention_mask is not None:
	# need to return original, inverted mask for longformer attention, else value for global attention (=2 in given mask, will be -1) is lost
	longformer_attention_mask = 1 - attention_mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)


	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	# check if head_mask has a correct number of layers specified if desired
	if head_mask is not None:
	if head_mask.size()[0] != len(self.layers):
	raise ValueError(
	f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
	f" {head_mask.size()[0]}."
	)
	for idx, encoder_layer in enumerate(self.layers):
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	dropout_probability = random.uniform(0, 1)
	if self.training and (dropout_probability < self.layerdrop): # skip the layer
	layer_outputs = (None, None)
	else:
	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(encoder_layer),
	hidden_states,
	attention_mask,
	longformer_attention_mask,
	(head_mask[idx] if head_mask is not None else None),
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	longformer_attention_mask,
	layer_head_mask=(head_mask[idx] if head_mask is not None else None),
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	hidden_states = self.layer_norm(hidden_states)
	#print("Encoder output: ",hidden_states.shape)

	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
	return BaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
	)


	class LongMBartModel(MBartModel):
	def __init__(self, config: MBartConfig):
	super().__init__(config)

	padding_idx, vocab_size = config.pad_token_id, config.vocab_size
	self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

	self.encoder = LongMBartEncoder(config, self.shared)
	self.decoder = MBartDecoder(config, self.shared)

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

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Seq2SeqModelOutput, Tuple[torch.FloatTensor]]:
	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

	# different to other models, MBart automatically creates decoder_input_ids from
	# input_ids if no decoder_input_ids are provided
	if decoder_input_ids is None and decoder_inputs_embeds is None:
	decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)

	if encoder_outputs is None:
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
	elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
	encoder_outputs = BaseModelOutput(
	last_hidden_state=encoder_outputs[0],
	hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
	attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
	)

	# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	encoder_hidden_states=encoder_outputs[0],
	encoder_attention_mask=attention_mask,
	head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return decoder_outputs + encoder_outputs

	return Seq2SeqModelOutput(
	last_hidden_state=decoder_outputs.last_hidden_state,
	past_key_values=decoder_outputs.past_key_values,
	decoder_hidden_states=decoder_outputs.hidden_states,
	decoder_attentions=decoder_outputs.attentions,
	cross_attentions=decoder_outputs.cross_attentions,
	encoder_last_hidden_state=encoder_outputs.last_hidden_state,
	encoder_hidden_states=encoder_outputs.hidden_states,
	encoder_attentions=encoder_outputs.attentions,
	)

	class LongMBartEncoderLayer(MBartEncoderLayer):
	def __init__(self, config: MBartConfig):
	super().__init__(config)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	longformer_attention_mask: torch.Tensor,
	layer_head_mask: torch.Tensor,
	output_attentions: bool = False,
	) -> torch.Tensor:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape (seq_len, batch, embed_dim)
	attention_mask (`torch.FloatTensor`): attention mask of size
	(batch, 1, tgt_len, src_len) where padding elements are indicated by very large negative values.
	longformer_attention_mask (:obj:`torch.FloatTensor`): attention mask of size
	`(batch, src_len)` where 0=local, -1=global, 1=padding.
	layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
	(encoder_attention_heads,).
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	# if longformer attention instead of mbart self attention: use special mask
	if isinstance(self.self_attn, LongformerSelfAttentionForMBart):
	attention_mask = longformer_attention_mask
	residual = hidden_states
	hidden_states = self.self_attn_layer_norm(hidden_states)
	hidden_states, attn_weights, _ = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
	hidden_states = self.fc2(hidden_states)
	hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states

	if hidden_states.dtype == torch.float16 and (
	torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
	):
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs

	class Longformer(RobertaModel):
	def __init__(self, config):
	super(Longformer, self).__init__(config)
	if config.attention_mode == 'n2':
	pass # do nothing, use BertSelfAttention instead
	else:
	for i, layer in enumerate(self.encoder.layer):
	layer.attention.self = LongformerSelfAttention(config, layer_id=i)


	class LongformerForMaskedLM(RobertaForMaskedLM):
	def __init__(self, config):
	super(LongformerForMaskedLM, self).__init__(config)
	if config.attention_mode == 'n2':
	pass # do nothing, use BertSelfAttention instead
	else:
	for i, layer in enumerate(self.roberta.encoder.layer):
	layer.attention.self = LongformerSelfAttention(config, layer_id=i)


	class LongformerConfig(RobertaConfig):
	def __init__(self, attention_window: List[int] = None, attention_dilation: List[int] = None,
	autoregressive: bool = False, attention_mode: str = 'sliding_chunks', **kwargs):
	"""
	Args:
	attention_window: list of attention window sizes of length = number of layers.
	window size = number of attention locations on each side.
	For an affective window size of 512, use `attention_window=[256]*num_layers`
	which is 256 on each side.
	attention_dilation: list of attention dilation of length = number of layers.
	attention dilation of `1` means no dilation.
	autoregressive: do autoregressive attention or have attention of both sides
	attention_mode: 'n2' for regular n^2 self-attention, 'tvm' for TVM implemenation of Longformer
	selfattention, 'sliding_chunks' for another implementation of Longformer selfattention
	"""
	super().__init__(**kwargs)
	self.attention_window = attention_window
	self.attention_dilation = attention_dilation
	self.autoregressive = autoregressive
	self.attention_mode = attention_mode
	assert self.attention_mode in ['sliding_chunks', 'n2', 'sliding_chunks_no_overlap']


	class LongformerSelfAttention(nn.Module):
	def __init__(self, config, layer_id):
	super(LongformerSelfAttention, self).__init__()
	if config.hidden_size % config.num_attention_heads != 0:
	raise ValueError(
	"The hidden size (%d) is not a multiple of the number of attention "
	"heads (%d)" % (config.hidden_size, config.num_attention_heads))
	self.num_heads = config.num_attention_heads
	self.head_dim = int(config.hidden_size / config.num_attention_heads)
	self.embed_dim = config.hidden_size

	self.query = nn.Linear(config.hidden_size, self.embed_dim)
	self.key = nn.Linear(config.hidden_size, self.embed_dim)
	self.value = nn.Linear(config.hidden_size, self.embed_dim)

	self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
	self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
	self.value_global = nn.Linear(config.hidden_size, self.embed_dim)

	self.dropout = config.attention_probs_dropout_prob

	self.layer_id = layer_id
	self.attention_window = config.attention_window[self.layer_id]
	self.attention_dilation = config.attention_dilation[self.layer_id]
	self.attention_mode = config.attention_mode
	self.autoregressive = config.autoregressive
	assert self.attention_window > 0
	assert self.attention_dilation > 0
	assert self.attention_mode in ['sliding_chunks', 'sliding_chunks_no_overlap']
	if self.attention_mode in ['sliding_chunks', 'sliding_chunks_no_overlap']:
	assert not self.autoregressive # not supported
	assert self.attention_dilation == 1 # dilation is not supported

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	output_attentions=False,
	):
	'''
	The `attention_mask` is changed in `BertModel.forward` from 0, 1, 2 to
	-ve: no attention
	0: local attention
	+ve: global attention
	'''
	assert encoder_hidden_states is None, "`encoder_hidden_states` is not supported and should be None"
	assert encoder_attention_mask is None, "`encoder_attention_mask` is not supported and should be None"

	if attention_mask is not None:
	key_padding_mask = attention_mask < 0
	extra_attention_mask = attention_mask > 0
	remove_from_windowed_attention_mask = attention_mask != 0

	num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
	max_num_extra_indices_per_batch = num_extra_indices_per_batch.max()
	if max_num_extra_indices_per_batch <= 0:
	extra_attention_mask = None
	else:
	# To support the case of variable number of global attention in the rows of a batch,
	# we use the following three selection masks to select global attention embeddings
	# in a 3d tensor and pad it to `max_num_extra_indices_per_batch`
	# 1) selecting embeddings that correspond to global attention
	extra_attention_mask_nonzeros = extra_attention_mask.nonzero(as_tuple=True)
	zero_to_max_range = torch.arange(0, max_num_extra_indices_per_batch,
	device=num_extra_indices_per_batch.device)
	# mask indicating which values are actually going to be padding
	selection_padding_mask = zero_to_max_range < num_extra_indices_per_batch.unsqueeze(dim=-1)
	# 2) location of the non-padding values in the selected global attention
	selection_padding_mask_nonzeros = selection_padding_mask.nonzero(as_tuple=True)
	# 3) location of the padding values in the selected global attention
	selection_padding_mask_zeros = (selection_padding_mask == 0).nonzero(as_tuple=True)
	else:
	remove_from_windowed_attention_mask = None
	extra_attention_mask = None
	key_padding_mask = None

	hidden_states = hidden_states.transpose(0, 1)
	seq_len, bsz, embed_dim = hidden_states.size()
	assert embed_dim == self.embed_dim
	q = self.query(hidden_states)
	k = self.key(hidden_states)
	v = self.value(hidden_states)
	q /= math.sqrt(self.head_dim)

	q = q.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
	k = k.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
	# attn_weights = (bsz, seq_len, num_heads, window*2+1)
	if self.attention_mode == "sliding_chunks":
	attn_weights = sliding_chunks_matmul_qk(q, k, self.attention_window, padding_value=0)
	elif self.attention_mode == "sliding_chunks_no_overlap":
	attn_weights = sliding_chunks_no_overlap_matmul_qk(q, k, self.attention_window, padding_value=0)
	else:
	raise False
	mask_invalid_locations(attn_weights, self.attention_window, self.attention_dilation, False)
	if remove_from_windowed_attention_mask is not None:
	# This implementation is fast and takes very little memory because num_heads x hidden_size = 1
	# from (bsz x seq_len) to (bsz x seq_len x num_heads x hidden_size)
	remove_from_windowed_attention_mask = remove_from_windowed_attention_mask.unsqueeze(dim=-1).unsqueeze(dim=-1)
	# cast to float/half then replace 1's with -inf
	float_mask = remove_from_windowed_attention_mask.type_as(q).masked_fill(remove_from_windowed_attention_mask, -10000.0)
	repeat_size = 1 if isinstance(self.attention_dilation, int) else len(self.attention_dilation)
	float_mask = float_mask.repeat(1, 1, repeat_size, 1)
	ones = float_mask.new_ones(size=float_mask.size()) # tensor of ones
	# diagonal mask with zeros everywhere and -inf inplace of padding
	if self.attention_mode == "sliding_chunks":
	d_mask = sliding_chunks_matmul_qk(ones, float_mask, self.attention_window, padding_value=0)
	elif self.attention_mode == "sliding_chunks_no_overlap":
	d_mask = sliding_chunks_no_overlap_matmul_qk(ones, float_mask, self.attention_window, padding_value=0)

	attn_weights += d_mask
	assert list(attn_weights.size())[:3] == [bsz, seq_len, self.num_heads]
	assert attn_weights.size(dim=3) in [self.attention_window * 2 + 1, self.attention_window * 3]

	# the extra attention
	if extra_attention_mask is not None:
	selected_k = k.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
	selected_k[selection_padding_mask_nonzeros] = k[extra_attention_mask_nonzeros]
	# (bsz, seq_len, num_heads, max_num_extra_indices_per_batch)
	selected_attn_weights = torch.einsum('blhd,bshd->blhs', (q, selected_k))
	selected_attn_weights[selection_padding_mask_zeros[0], :, :, selection_padding_mask_zeros[1]] = -10000
	# concat to attn_weights
	# (bsz, seq_len, num_heads, extra attention count + 2*window+1)
	attn_weights = torch.cat((selected_attn_weights, attn_weights), dim=-1)
	attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32) # use fp32 for numerical stability
	if key_padding_mask is not None:
	# softmax sometimes inserts NaN if all positions are masked, replace them with 0
	attn_weights_float = torch.masked_fill(attn_weights_float, key_padding_mask.unsqueeze(-1).unsqueeze(-1), 0.0)
	attn_weights = attn_weights_float.type_as(attn_weights)
	attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
	v = v.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
	attn = 0
	if extra_attention_mask is not None:
	selected_attn_probs = attn_probs.narrow(-1, 0, max_num_extra_indices_per_batch)
	selected_v = v.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
	selected_v[selection_padding_mask_nonzeros] = v[extra_attention_mask_nonzeros]
	# use `matmul` because `einsum` crashes sometimes with fp16
	# attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
	attn = torch.matmul(selected_attn_probs.transpose(1, 2), selected_v.transpose(1, 2).type_as(selected_attn_probs)).transpose(1, 2)
	attn_probs = attn_probs.narrow(-1, max_num_extra_indices_per_batch, attn_probs.size(-1) - max_num_extra_indices_per_batch).contiguous()

	if self.attention_mode == "sliding_chunks":
	attn += sliding_chunks_matmul_pv(attn_probs, v, self.attention_window)
	elif self.attention_mode == "sliding_chunks_no_overlap":
	attn += sliding_chunks_no_overlap_matmul_pv(attn_probs, v, self.attention_window)
	else:
	raise False

	attn = attn.type_as(hidden_states)
	assert list(attn.size()) == [bsz, seq_len, self.num_heads, self.head_dim]
	attn = attn.transpose(0, 1).reshape(seq_len, bsz, embed_dim).contiguous()

	# For this case, we'll just recompute the attention for these indices
	# and overwrite the attn tensor. TODO: remove the redundant computation
	if extra_attention_mask is not None:
	selected_hidden_states = hidden_states.new_zeros(max_num_extra_indices_per_batch, bsz, embed_dim)
	selected_hidden_states[selection_padding_mask_nonzeros[::-1]] = hidden_states[extra_attention_mask_nonzeros[::-1]]

	q = self.query_global(selected_hidden_states)
	k = self.key_global(hidden_states)
	v = self.value_global(hidden_states)
	q /= math.sqrt(self.head_dim)

	q = q.contiguous().view(max_num_extra_indices_per_batch, bsz * self.num_heads, self.head_dim).transpose(0, 1) # (bsz*self.num_heads, max_num_extra_indices_per_batch, head_dim)
	k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) # bsz * self.num_heads, seq_len, head_dim)
	v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1) # bsz * self.num_heads, seq_len, head_dim)
	attn_weights = torch.bmm(q, k.transpose(1, 2))
	assert list(attn_weights.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len]

	attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
	attn_weights[selection_padding_mask_zeros[0], :, selection_padding_mask_zeros[1], :] = -10000.0
	if key_padding_mask is not None:
	attn_weights = attn_weights.masked_fill(
	key_padding_mask.unsqueeze(1).unsqueeze(2),
	-10000.0,
	)
	attn_weights = attn_weights.view(bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len)
	attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32) # use fp32 for numerical stability
	attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
	selected_attn = torch.bmm(attn_probs, v)
	assert list(selected_attn.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, self.head_dim]

	selected_attn_4d = selected_attn.view(bsz, self.num_heads, max_num_extra_indices_per_batch, self.head_dim)
	nonzero_selected_attn = selected_attn_4d[selection_padding_mask_nonzeros[0], :, selection_padding_mask_nonzeros[1]]
	attn[extra_attention_mask_nonzeros[::-1]] = nonzero_selected_attn.view(len(selection_padding_mask_nonzeros[0]), -1).type_as(hidden_states)

	context_layer = attn.transpose(0, 1) # attn shape: (seq_len, bsz, embed_dim), context_layer shape: (bsz, seq_len, embed_dim)
	if output_attentions:
	if extra_attention_mask is not None:
	# With global attention, return global attention probabilities only
	# batch_size x num_heads x max_num_global_attention_tokens x sequence_length
	# which is the attention weights from tokens with global attention to all tokens
	# It doesn't not return local attention
	# In case of variable number of global attantion in the rows of a batch,
	# attn_weights are padded with -10000.0 attention scores
	attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
	else:
	# without global attention, return local attention probabilities
	# batch_size x num_heads x sequence_length x window_size
	# which is the attention weights of every token attending to its neighbours
	attn_weights = attn_weights.permute(0, 2, 1, 3)
	outputs = (context_layer, attn_weights) if output_attentions else (context_layer,)
	return outputs

	def _skew(x, direction, padding_value):
	'''Convert diagonals into columns (or columns into diagonals depending on `direction`'''
	x_padded = F.pad(x, direction, value=padding_value)
	x_padded = x_padded.view(*x_padded.size()[:-2], x_padded.size(-1), x_padded.size(-2))
	return x_padded


	def _skew2(x, padding_value):
	'''shift every row 1 step to right converting columns into diagonals'''
	# X = B x C x M x L
	B, C, M, L = x.size()
	x = F.pad(x, (0, M + 1), value=padding_value) # B x C x M x (L+M+1)
	x = x.view(B, C, -1) # B x C x ML+MM+M
	x = x[:, :, :-M] # B x C x ML+MM
	x = x.view(B, C, M, M + L) # B x C, M x L+M
	x = x[:, :, :, :-1]
	return x


	def _chunk(x, w):
	'''convert into overlapping chunkings. Chunk size = 2w, overlap size = w'''

	# non-overlapping chunks of size = 2w
	x = x.view(x.size(0), x.size(1) // (w * 2), w * 2, x.size(2))

	# use `as_strided` to make the chunks overlap with an overlap size = w
	chunk_size = list(x.size())
	chunk_size[1] = chunk_size[1] * 2 - 1

	chunk_stride = list(x.stride())
	chunk_stride[1] = chunk_stride[1] // 2
	return x.as_strided(size=chunk_size, stride=chunk_stride)


	def sliding_chunks_matmul_qk(q: torch.Tensor, k: torch.Tensor, w: int, padding_value: float):
	'''Matrix multiplicatio of query x key tensors using with a sliding window attention pattern.
	This implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer)
	with an overlap of size w'''
	bsz, seqlen, num_heads, head_dim = q.size()
	assert seqlen % (w * 2) == 0
	assert q.size() == k.size()

	chunks_count = seqlen // w - 1

	# group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size w * 2
	q = q.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)
	k = k.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)

	chunk_q = _chunk(q, w)
	chunk_k = _chunk(k, w)

	# matrix multipication
	# bcxd: bsz*num_heads x chunks x 2w x head_dim
	# bcyd: bsz*num_heads x chunks x 2w x head_dim
	# bcxy: bsz*num_heads x chunks x 2w x 2w
	chunk_attn = torch.einsum('bcxd,bcyd->bcxy', (chunk_q, chunk_k)) # multiply

	# convert diagonals into columns
	diagonal_chunk_attn = _skew(chunk_attn, direction=(0, 0, 0, 1), padding_value=padding_value)

	# allocate space for the overall attention matrix where the chunks are compined. The last dimension
	# has (w * 2 + 1) columns. The first (w) columns are the w lower triangles (attention from a word to
	# w previous words). The following column is attention score from each word to itself, then
	# followed by w columns for the upper triangle.

	diagonal_attn = diagonal_chunk_attn.new_empty((bsz * num_heads, chunks_count + 1, w, w * 2 + 1))

	# copy parts from diagonal_chunk_attn into the compined matrix of attentions
	# - copying the main diagonal and the upper triangle
	diagonal_attn[:, :-1, :, w:] = diagonal_chunk_attn[:, :, :w, :w + 1]
	diagonal_attn[:, -1, :, w:] = diagonal_chunk_attn[:, -1, w:, :w + 1]
	# - copying the lower triangle
	diagonal_attn[:, 1:, :, :w] = diagonal_chunk_attn[:, :, - (w + 1):-1, w + 1:]
	diagonal_attn[:, 0, 1:w, 1:w] = diagonal_chunk_attn[:, 0, :w - 1, 1 - w:]

	# separate bsz and num_heads dimensions again
	diagonal_attn = diagonal_attn.view(bsz, num_heads, seqlen, 2 * w + 1).transpose(2, 1)

	mask_invalid_locations(diagonal_attn, w, 1, False)
	return diagonal_attn


	def sliding_chunks_matmul_pv(prob: torch.Tensor, v: torch.Tensor, w: int):
	'''Same as sliding_chunks_matmul_qk but for prob and value tensors. It is expecting the same output
	format from sliding_chunks_matmul_qk'''
	bsz, seqlen, num_heads, head_dim = v.size()
	assert seqlen % (w * 2) == 0
	assert prob.size()[:3] == v.size()[:3]
	assert prob.size(3) == 2 * w + 1
	chunks_count = seqlen // w - 1
	# group bsz and num_heads dimensions into one, then chunk seqlen into chunks of size 2w
	chunk_prob = prob.transpose(1, 2).reshape(bsz * num_heads, seqlen // w, w, 2 * w + 1)

	# group bsz and num_heads dimensions into one
	v = v.transpose(1, 2).reshape(bsz * num_heads, seqlen, head_dim)

	# pad seqlen with w at the beginning of the sequence and another w at the end
	padded_v = F.pad(v, (0, 0, w, w), value=-1)

	# chunk padded_v into chunks of size 3w and an overlap of size w
	chunk_v_size = (bsz * num_heads, chunks_count + 1, 3 * w, head_dim)
	chunk_v_stride = padded_v.stride()
	chunk_v_stride = chunk_v_stride[0], w * chunk_v_stride[1], chunk_v_stride[1], chunk_v_stride[2]
	chunk_v = padded_v.as_strided(size=chunk_v_size, stride=chunk_v_stride)

	skewed_prob = _skew2(chunk_prob, padding_value=0)

	context = torch.einsum('bcwd,bcdh->bcwh', (skewed_prob, chunk_v))
	return context.view(bsz, num_heads, seqlen, head_dim).transpose(1, 2)


	def pad_to_window_size(input_ids: torch.Tensor, attention_mask: torch.Tensor,
	one_sided_window_size: int, pad_token_id: int):
	'''A helper function to pad tokens and mask to work with the sliding_chunks implementation of Longformer selfattention.
	Input:
	input_ids = torch.Tensor(bsz x seqlen): ids of wordpieces
	attention_mask = torch.Tensor(bsz x seqlen): attention mask
	one_sided_window_size = int: window size on one side of each token
	pad_token_id = int: tokenizer.pad_token_id
	Returns
	(input_ids, attention_mask) padded to length divisible by 2 * one_sided_window_size
	'''
	w = int(2 * one_sided_window_size)
	seqlen = input_ids.size(1)
	padding_len = (w - seqlen % w) % w
	input_ids = F.pad(input_ids, (0, padding_len), value=pad_token_id)
	attention_mask = F.pad(attention_mask, (0, padding_len), value=False) # no attention on the padding tokens
	return input_ids, attention_mask


	# ========= "sliding_chunks_no_overlap": alternative implemenation of the sliding window attention =========
	# This implementation uses non-overlapping chunks (or blocks) of size `w` with number of local attention = 3xw
	# To make this implemenation comparable to "sliding_chunks" set w such that
	# w_of_sliding_chunks_no_overlap = w_of_sliding_chunks * 2 / 3
	# For example,
	# w_of_sliding_chunks = 256 (this is one sided. Total attention size = 512)
	# w_of_sliding_chunks_no_overlap = 170 (Total attention size = 510)
	# Performance:
	# - Speed: 30% faster than "sliding_chunks"
	# - Memory: 95% of the memory usage of "sliding_chunks"
	# The windows are asymmetric where number of attention on each side of a token ranges between w to 2w
	# while "sliding_chunks" has a symmetric window around each token.


	def sliding_chunks_no_overlap_matmul_qk(q: torch.Tensor, k: torch.Tensor, w: int, padding_value: float):
	bsz, seqlen, num_heads, head_dim = q.size()
	assert seqlen % w == 0
	assert q.size() == k.size()
	# chunk seqlen into non-overlapping chunks of size w
	chunk_q = q.view(bsz, seqlen // w, w, num_heads, head_dim)
	chunk_k = k.view(bsz, seqlen // w, w, num_heads, head_dim)
	chunk_k_expanded = torch.stack((
	F.pad(chunk_k[:, :-1], (0, 0, 0, 0, 0, 0, 1, 0), value=0.0),
	chunk_k,
	F.pad(chunk_k[:, 1:], (0, 0, 0, 0, 0, 0, 0, 1), value=0.0),
	), dim=-1)
	diagonal_attn = torch.einsum('bcxhd,bcyhde->bcxhey', (chunk_q, chunk_k_expanded)) # multiply
	return diagonal_attn.reshape(bsz, seqlen, num_heads, 3 * w)


	def sliding_chunks_no_overlap_matmul_pv(prob: torch.Tensor, v: torch.Tensor, w: int):
	bsz, seqlen, num_heads, head_dim = v.size()
	chunk_prob = prob.view(bsz, seqlen // w, w, num_heads, 3, w)
	chunk_v = v.view(bsz, seqlen // w, w, num_heads, head_dim)
	chunk_v_extended = torch.stack((
	F.pad(chunk_v[:, :-1], (0, 0, 0, 0, 0, 0, 1, 0), value=0.0),
	chunk_v,
	F.pad(chunk_v[:, 1:], (0, 0, 0, 0, 0, 0, 0, 1), value=0.0),
	), dim=-1)
	context = torch.einsum('bcwhpd,bcdhep->bcwhe', (chunk_prob, chunk_v_extended))
	return context.reshape(bsz, seqlen, num_heads, head_dim)

	def _get_invalid_locations_mask_fixed_dilation(seq_len: int, w: int, d: int):
	diagonals_list = []
	for j in range(-d * w, d, d):
	diagonal_mask = torch.zeros(seq_len, device='cpu', dtype=torch.uint8)
	diagonal_mask[:-j] = 1
	diagonals_list.append(diagonal_mask)
	return torch.stack(diagonals_list, dim=-1)

	@lru_cache()
	def _get_invalid_locations_mask(w: int, d: Union[torch.Tensor,int], autoregressive: bool, device: str):
	if isinstance(d, int):
	affected_seq_len = w * d
	mask = _get_invalid_locations_mask_fixed_dilation(affected_seq_len, w, d)
	mask = mask[None, :, None, :]
	else:
	affected_seq_len = w * d.max()
	head_masks = []
	d_list = d.cpu().numpy().tolist()
	for d in d_list:
	one_head_mask = _get_invalid_locations_mask_fixed_dilation(affected_seq_len, w, d)
	head_masks.append(one_head_mask)
	mask = torch.stack(head_masks, dim=-2)
	mask = mask[None, :, :, :]

	ending_mask = None if autoregressive else mask.flip(dims=(1, 3)).bool().to(device)
	return affected_seq_len, mask.bool().to(device), ending_mask

	def mask_invalid_locations(input_tensor: torch.Tensor, w: int, d: Union[torch.Tensor, int], autoregressive: bool) -> torch.Tensor:
	affected_seq_len, beginning_mask, ending_mask = _get_invalid_locations_mask(w, d, autoregressive, input_tensor.device)
	seq_len = input_tensor.size(1)
	beginning_input = input_tensor[:, :affected_seq_len, :, :w+1]
	beginning_mask = beginning_mask[:, :seq_len].expand(beginning_input.size())
	beginning_input.masked_fill_(beginning_mask, -float('inf'))
	if not autoregressive:
	ending_input = input_tensor[:, -affected_seq_len:, :, -(w+1):]
	ending_mask = ending_mask[:, -seq_len:].expand(ending_input.size())
	ending_input.masked_fill_(ending_mask, -float('inf'))