"""
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'))