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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
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 .longformer import LongformerSelfAttention
from transformers.models.bart.modeling_bart import BartConfig, BartForConditionalGeneration, BartEncoder, BartLearnedPositionalEmbedding, BartEncoderLayer, BartDecoder, BartModel, _expand_mask
from transformers.modeling_outputs import BaseModelOutput
class LongformerEncoderDecoderForConditionalGeneration(BartForConditionalGeneration):
def __init__(self, config):
super(BartForConditionalGeneration, self).__init__(config)
self.model = LongBartModel(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 BartSelfAttention instead
else:
for i, layer in enumerate(self.model.encoder.layers):
layer.self_attn = LongformerSelfAttentionForBart(config, layer_id=i)
# Initialize weights and apply final processing
self.post_init()
class LongformerEncoderDecoderConfig(BartConfig):
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 ['tvm', 'sliding_chunks', 'n2']
class LongformerSelfAttentionForBart(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.bart.modeling_bart
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_bart.BartEncoder and BartEncoderLayer (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: Bart encoder expects shape (seq_len, bsz, embed_dim), no transpose needed
attn_output = self.output(outputs[0])
# new return in BartAttention 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 LongBartEncoder(BartEncoder):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BartEncoderLayer`].
Args:
config: BartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BartConfig, 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
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BartLearnedPositionalEmbedding(
self.max_source_positions,
embed_dim,
)
self.layers = nn.ModuleList([LongBartEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
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 [`BartTokenizer`]. 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_ids = input_ids.view(-1, input_ids.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)
embed_pos = embed_pos.to(inputs_embeds.device)
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],)
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 LongBartModel(BartModel):
def __init__(self, config: BartConfig):
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 = LongBartEncoder(config, self.shared)
self.decoder = BartDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
class LongBartEncoderLayer(BartEncoderLayer):
def __init__(self, config: BartConfig):
super().__init__(config)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
longformer_attention_mask: torch.Tensor,
layer_head_mask: torch.FloatTensor,
output_attentions: bool = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
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.
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 bart self attention: use special mask
if isinstance(self.self_attn, LongformerSelfAttentionForBart):
attention_mask = longformer_attention_mask
residual = 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 = 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
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = 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
hidden_states = self.final_layer_norm(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
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