Spaces:
Sleeping
Sleeping
File size: 12,001 Bytes
e1ccef5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 |
import torch
import torch.nn as nn
import numpy as np
from math import sqrt
from utils.masking import TriangularCausalMask, ProbMask
from reformer_pytorch import LSHSelfAttention
from einops import rearrange, repeat
class DSAttention(nn.Module):
'''De-stationary Attention'''
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(DSAttention, self).__init__()
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
B, L, H, E = queries.shape
_, S, _, D = values.shape
scale = self.scale or 1. / sqrt(E)
tau = 1.0 if tau is None else tau.unsqueeze(
1).unsqueeze(1) # B x 1 x 1 x 1
delta = 0.0 if delta is None else delta.unsqueeze(
1).unsqueeze(1) # B x 1 x 1 x S
# De-stationary Attention, rescaling pre-softmax score with learned de-stationary factors
scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta
if self.mask_flag:
if attn_mask is None:
attn_mask = TriangularCausalMask(B, L, device=queries.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
A = self.dropout(torch.softmax(scale * scores, dim=-1))
V = torch.einsum("bhls,bshd->blhd", A, values)
if self.output_attention:
return V.contiguous(), A
else:
return V.contiguous(), None
class FullAttention(nn.Module):
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(FullAttention, self).__init__()
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
B, L, H, E = queries.shape
_, S, _, D = values.shape
scale = self.scale or 1. / sqrt(E)
scores = torch.einsum("blhe,bshe->bhls", queries, keys)
if self.mask_flag:
if attn_mask is None:
attn_mask = TriangularCausalMask(B, L, device=queries.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
A = self.dropout(torch.softmax(scale * scores, dim=-1))
V = torch.einsum("bhls,bshd->blhd", A, values)
if self.output_attention:
return V.contiguous(), A
else:
return V.contiguous(), None
class ProbAttention(nn.Module):
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
super(ProbAttention, self).__init__()
self.factor = factor
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
def _prob_QK(self, Q, K, sample_k, n_top): # n_top: c*ln(L_q)
# Q [B, H, L, D]
B, H, L_K, E = K.shape
_, _, L_Q, _ = Q.shape
# calculate the sampled Q_K
K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
# real U = U_part(factor*ln(L_k))*L_q
index_sample = torch.randint(L_K, (L_Q, sample_k))
K_sample = K_expand[:, :, torch.arange(
L_Q).unsqueeze(1), index_sample, :]
Q_K_sample = torch.matmul(
Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze()
# find the Top_k query with sparisty measurement
M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
M_top = M.topk(n_top, sorted=False)[1]
# use the reduced Q to calculate Q_K
Q_reduce = Q[torch.arange(B)[:, None, None],
torch.arange(H)[None, :, None],
M_top, :] # factor*ln(L_q)
Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1)) # factor*ln(L_q)*L_k
return Q_K, M_top
def _get_initial_context(self, V, L_Q):
B, H, L_V, D = V.shape
if not self.mask_flag:
# V_sum = V.sum(dim=-2)
V_sum = V.mean(dim=-2)
contex = V_sum.unsqueeze(-2).expand(B, H,
L_Q, V_sum.shape[-1]).clone()
else: # use mask
# requires that L_Q == L_V, i.e. for self-attention only
assert (L_Q == L_V)
contex = V.cumsum(dim=-2)
return contex
def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
B, H, L_V, D = V.shape
if self.mask_flag:
attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
scores.masked_fill_(attn_mask.mask, -np.inf)
attn = torch.softmax(scores, dim=-1) # nn.Softmax(dim=-1)(scores)
context_in[torch.arange(B)[:, None, None],
torch.arange(H)[None, :, None],
index, :] = torch.matmul(attn, V).type_as(context_in)
if self.output_attention:
attns = (torch.ones([B, H, L_V, L_V]) /
L_V).type_as(attn).to(attn.device)
attns[torch.arange(B)[:, None, None], torch.arange(H)[
None, :, None], index, :] = attn
return context_in, attns
else:
return context_in, None
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
B, L_Q, H, D = queries.shape
_, L_K, _, _ = keys.shape
queries = queries.transpose(2, 1)
keys = keys.transpose(2, 1)
values = values.transpose(2, 1)
U_part = self.factor * \
np.ceil(np.log(L_K)).astype('int').item() # c*ln(L_k)
u = self.factor * \
np.ceil(np.log(L_Q)).astype('int').item() # c*ln(L_q)
U_part = U_part if U_part < L_K else L_K
u = u if u < L_Q else L_Q
scores_top, index = self._prob_QK(
queries, keys, sample_k=U_part, n_top=u)
# add scale factor
scale = self.scale or 1. / sqrt(D)
if scale is not None:
scores_top = scores_top * scale
# get the context
context = self._get_initial_context(values, L_Q)
# update the context with selected top_k queries
context, attn = self._update_context(
context, values, scores_top, index, L_Q, attn_mask)
return context.contiguous(), attn
class AttentionLayer(nn.Module):
def __init__(self, attention, d_model, n_heads, d_keys=None,
d_values=None):
super(AttentionLayer, self).__init__()
d_keys = d_keys or (d_model // n_heads)
d_values = d_values or (d_model // n_heads)
self.inner_attention = attention
self.query_projection = nn.Linear(d_model, d_keys * n_heads)
self.key_projection = nn.Linear(d_model, d_keys * n_heads)
self.value_projection = nn.Linear(d_model, d_values * n_heads)
self.out_projection = nn.Linear(d_values * n_heads, d_model)
self.n_heads = n_heads
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
B, L, _ = queries.shape
_, S, _ = keys.shape
H = self.n_heads
queries = self.query_projection(queries).view(B, L, H, -1)
keys = self.key_projection(keys).view(B, S, H, -1)
values = self.value_projection(values).view(B, S, H, -1)
out, attn = self.inner_attention(
queries,
keys,
values,
attn_mask,
tau=tau,
delta=delta
)
out = out.view(B, L, -1)
return self.out_projection(out), attn
class ReformerLayer(nn.Module):
def __init__(self, attention, d_model, n_heads, d_keys=None,
d_values=None, causal=False, bucket_size=4, n_hashes=4):
super().__init__()
self.bucket_size = bucket_size
self.attn = LSHSelfAttention(
dim=d_model,
heads=n_heads,
bucket_size=bucket_size,
n_hashes=n_hashes,
causal=causal
)
def fit_length(self, queries):
# inside reformer: assert N % (bucket_size * 2) == 0
B, N, C = queries.shape
if N % (self.bucket_size * 2) == 0:
return queries
else:
# fill the time series
fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2))
return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1)
def forward(self, queries, keys, values, attn_mask, tau, delta):
# in Reformer: defalut queries=keys
B, N, C = queries.shape
queries = self.attn(self.fit_length(queries))[:, :N, :]
return queries, None
class TwoStageAttentionLayer(nn.Module):
'''
The Two Stage Attention (TSA) Layer
input/output shape: [batch_size, Data_dim(D), Seg_num(L), d_model]
'''
def __init__(self, configs,
seg_num, factor, d_model, n_heads, d_ff=None, dropout=0.1):
super(TwoStageAttentionLayer, self).__init__()
d_ff = d_ff or 4 * d_model
self.time_attention = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=False), d_model, n_heads)
self.dim_sender = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=False), d_model, n_heads)
self.dim_receiver = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=False), d_model, n_heads)
self.router = nn.Parameter(torch.randn(seg_num, factor, d_model))
self.dropout = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.norm4 = nn.LayerNorm(d_model)
self.MLP1 = nn.Sequential(nn.Linear(d_model, d_ff),
nn.GELU(),
nn.Linear(d_ff, d_model))
self.MLP2 = nn.Sequential(nn.Linear(d_model, d_ff),
nn.GELU(),
nn.Linear(d_ff, d_model))
def forward(self, x, attn_mask=None, tau=None, delta=None):
# Cross Time Stage: Directly apply MSA to each dimension
batch = x.shape[0]
time_in = rearrange(x, 'b ts_d seg_num d_model -> (b ts_d) seg_num d_model')
time_enc, attn = self.time_attention(
time_in, time_in, time_in, attn_mask=None, tau=None, delta=None
)
dim_in = time_in + self.dropout(time_enc)
dim_in = self.norm1(dim_in)
dim_in = dim_in + self.dropout(self.MLP1(dim_in))
dim_in = self.norm2(dim_in)
# Cross Dimension Stage: use a small set of learnable vectors to aggregate and distribute messages to build the D-to-D connection
dim_send = rearrange(dim_in, '(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model', b=batch)
batch_router = repeat(self.router, 'seg_num factor d_model -> (repeat seg_num) factor d_model', repeat=batch)
dim_buffer, attn = self.dim_sender(batch_router, dim_send, dim_send, attn_mask=None, tau=None, delta=None)
dim_receive, attn = self.dim_receiver(dim_send, dim_buffer, dim_buffer, attn_mask=None, tau=None, delta=None)
dim_enc = dim_send + self.dropout(dim_receive)
dim_enc = self.norm3(dim_enc)
dim_enc = dim_enc + self.dropout(self.MLP2(dim_enc))
dim_enc = self.norm4(dim_enc)
final_out = rearrange(dim_enc, '(b seg_num) ts_d d_model -> b ts_d seg_num d_model', b=batch)
return final_out
|