File size: 16,616 Bytes
340c8dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This code is adapted from AllenAI's Longformer:
https://github.com/allenai/longformer/
"""
from typing import List
import math
import torch
from torch import nn
import torch.nn.functional as F
from .diagonaled_mm_tvm import diagonaled_mm as diagonaled_mm_tvm, mask_invalid_locations
from .sliding_chunks import sliding_chunks_matmul_qk, sliding_chunks_matmul_pv
from .sliding_chunks import sliding_chunks_no_overlap_matmul_qk, sliding_chunks_no_overlap_matmul_pv
from transformers.models.roberta.modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM
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 ['tvm', '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 ['tvm', '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 == 'tvm':
q = q.float().contiguous()
k = k.float().contiguous()
attn_weights = diagonaled_mm_tvm(q, k, self.attention_window, self.attention_dilation, False, 0, False)
elif 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 == 'tvm':
d_mask = diagonaled_mm_tvm(ones, float_mask, self.attention_window, self.attention_dilation, False, 0, False)
elif 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 == 'tvm':
v = v.float().contiguous()
attn += diagonaled_mm_tvm(attn_probs, v, self.attention_window, self.attention_dilation, True, 0, False)
elif 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
|