Spaces:
Sleeping
Sleeping
File size: 63,025 Bytes
3e936b2 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 | from wan.modules.attention import attention
from wan.modules.model import WanRMSNorm, rope_apply, WanLayerNorm, WAN_CROSSATTENTION_CLASSES, rope_params, MLPProj, sinusoidal_embedding_1d
from torch.nn.attention.flex_attention import create_block_mask, flex_attention
from diffusers.configuration_utils import ConfigMixin, register_to_config
from torch.nn.attention.flex_attention import BlockMask
from diffusers.models.modeling_utils import ModelMixin
import os
import torch.nn as nn
import torch
import math
import torch.distributed as dist
flex_attention = torch.compile(flex_attention, dynamic=False, mode='max-autotune-no-cudagraphs')
def causal_rope_apply(x, grid_sizes, freqs, start_frame=0):
n, c = (x.size(2), x.size(3) // 2)
freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
output = []
for i, (f, h, w) in enumerate(grid_sizes.tolist()):
if f == 0:
output.append(x[i])
continue
seq_len = f * h * w
x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2))
freqs_i = torch.cat([freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1), freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)], dim=-1).reshape(seq_len, 1, -1)
x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
x_i = torch.cat([x_i, x[i, seq_len:]])
output.append(x_i)
return torch.stack(output).type_as(x)
class CausalWanSelfAttention(nn.Module):
def __init__(self, dim, num_heads, local_attn_size=-1, sink_size=0, qk_norm=True, eps=1e-06):
assert dim % num_heads == 0
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.local_attn_size = local_attn_size
self.sink_size = sink_size
self.qk_norm = qk_norm
self.eps = eps
if not isinstance(local_attn_size, int) and hasattr(local_attn_size, '__iter__'):
values = list(local_attn_size)
else:
values = [int(local_attn_size)]
non_neg_vals = [int(v) for v in values if int(v) != -1]
max_local = max(non_neg_vals) if len(non_neg_vals) > 0 else -1
self.max_attention_size = 32760 if max_local == -1 else max_local * 1560
self.q = nn.Linear(dim, dim)
self.k = nn.Linear(dim, dim)
self.v = nn.Linear(dim, dim)
self.o = nn.Linear(dim, dim)
self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
self.dr_rope = False
self.tri_rope_cont = False
self.tri_rope_pmax = 21
self.relative_rope = False
self.relative_rope_pmax = 21
self.num_frame_per_block_attr = 3
self._layer_id = -1
@staticmethod
def _compute_relative_positions(current_start_frame, B, R, N_Q, N_S, pmax, num_frame_per_block):
is_bulk_forward = B > num_frame_per_block
q_last_pos = min(current_start_frame + B - 1, pmax - 1)
q_start_pos = q_last_pos - B + 1
local_end_pos = q_last_pos
local_start_pos = local_end_pos - R + 1 if R > 0 else q_last_pos
if is_bulk_forward or N_Q == 0:
use_memory = False
mem_start_pos = -1
mem_end_pos = -1
else:
use_memory = True
mem_end_pos = local_start_pos - 1
mem_start_pos = mem_end_pos - N_Q + 1
return dict(is_bulk_forward=is_bulk_forward, use_memory=use_memory, q_start=q_start_pos, q_last=q_last_pos, local_start=local_start_pos, local_end=local_end_pos, mem_start=mem_start_pos, mem_end=mem_end_pos, sink_start=0)
def forward(self, x, seq_lens, grid_sizes, freqs, block_mask, kv_cache=None, current_start=0, cache_start=None, sink_recache_after_switch=False, memory_kv=None, capture_sink_qkv=False):
b, s, n, d = (*x.shape[:2], self.num_heads, self.head_dim)
if cache_start is None:
cache_start = current_start
def qkv_fn(x):
q = self.norm_q(self.q(x)).view(b, s, n, d)
k = self.norm_k(self.k(x)).view(b, s, n, d)
v = self.v(x).view(b, s, n, d)
return (q, k, v)
q, k, v = qkv_fn(x)
_sink_qkv_captured = None
if capture_sink_qkv and kv_cache is not None:
sink_tokens = self.sink_size * math.prod(grid_sizes[0][1:]).item()
if sink_tokens > 0 and s >= sink_tokens:
_sink_qkv_captured = (q[:, :sink_tokens].clone(), k[:, :sink_tokens].clone(), v[:, :sink_tokens].clone())
if kv_cache is None:
is_tf = s == seq_lens[0].item() * 2
if is_tf:
q_chunk = torch.chunk(q, 2, dim=1)
k_chunk = torch.chunk(k, 2, dim=1)
roped_query = []
roped_key = []
for ii in range(2):
rq = rope_apply(q_chunk[ii], grid_sizes, freqs).type_as(v)
rk = rope_apply(k_chunk[ii], grid_sizes, freqs).type_as(v)
roped_query.append(rq)
roped_key.append(rk)
roped_query = torch.cat(roped_query, dim=1)
roped_key = torch.cat(roped_key, dim=1)
padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
padded_roped_query = torch.cat([roped_query, torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], device=q.device, dtype=v.dtype)], dim=1)
padded_roped_key = torch.cat([roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], device=k.device, dtype=v.dtype)], dim=1)
padded_v = torch.cat([v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], device=v.device, dtype=v.dtype)], dim=1)
x = flex_attention(query=padded_roped_query.transpose(2, 1), key=padded_roped_key.transpose(2, 1), value=padded_v.transpose(2, 1), block_mask=block_mask)[:, :, :-padded_length].transpose(2, 1)
else:
roped_query = rope_apply(q, grid_sizes, freqs).type_as(v)
roped_key = rope_apply(k, grid_sizes, freqs).type_as(v)
padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
padded_roped_query = torch.cat([roped_query, torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], device=q.device, dtype=v.dtype)], dim=1)
padded_roped_key = torch.cat([roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], device=k.device, dtype=v.dtype)], dim=1)
padded_v = torch.cat([v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], device=v.device, dtype=v.dtype)], dim=1)
x = flex_attention(query=padded_roped_query.transpose(2, 1), key=padded_roped_key.transpose(2, 1), value=padded_v.transpose(2, 1), block_mask=block_mask)[:, :, :-padded_length].transpose(2, 1)
else:
frame_seqlen = math.prod(grid_sizes[0][1:]).item()
current_start_frame = current_start // frame_seqlen
_pre_rope_cache = self.dr_rope or self.tri_rope_cont or self.relative_rope
if not _pre_rope_cache:
roped_query = causal_rope_apply(q, grid_sizes, freqs, start_frame=current_start_frame).type_as(v)
roped_key = causal_rope_apply(k, grid_sizes, freqs, start_frame=current_start_frame).type_as(v)
current_end = current_start + roped_query.shape[1]
else:
current_end = current_start + q.shape[1]
k_for_cache = k if _pre_rope_cache else roped_key
sink_tokens = self.sink_size * frame_seqlen
kv_cache_size = kv_cache['k'].shape[1]
num_new_tokens = q.shape[1]
cache_update_info = None
is_recompute = current_end <= kv_cache['global_end_index'].item() and current_start > 0
if self.local_attn_size != -1 and current_end > kv_cache['global_end_index'].item() and (num_new_tokens + kv_cache['local_end_index'].item() > kv_cache_size):
num_evicted_tokens = num_new_tokens + kv_cache['local_end_index'].item() - kv_cache_size
num_rolled_tokens = kv_cache['local_end_index'].item() - num_evicted_tokens - sink_tokens
local_end_index = kv_cache['local_end_index'].item() + current_end - kv_cache['global_end_index'].item() - num_evicted_tokens
local_start_index = local_end_index - num_new_tokens
temp_k = kv_cache['k'].clone()
temp_v = kv_cache['v'].clone()
temp_k[:, sink_tokens:sink_tokens + num_rolled_tokens] = temp_k[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
temp_v[:, sink_tokens:sink_tokens + num_rolled_tokens] = temp_v[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
roped_offset = max(0, write_start_index - local_start_index)
write_len = max(0, local_end_index - write_start_index)
if write_len > 0:
temp_k[:, write_start_index:local_end_index] = k_for_cache[:, roped_offset:roped_offset + write_len]
temp_v[:, write_start_index:local_end_index] = v[:, roped_offset:roped_offset + write_len]
cache_update_info = {'action': 'roll_and_insert', 'sink_tokens': sink_tokens, 'num_rolled_tokens': num_rolled_tokens, 'num_evicted_tokens': num_evicted_tokens, 'local_start_index': local_start_index, 'local_end_index': local_end_index, 'write_start_index': write_start_index, 'write_end_index': local_end_index, 'new_k': k_for_cache[:, roped_offset:roped_offset + write_len], 'new_v': v[:, roped_offset:roped_offset + write_len], 'current_end': current_end, 'is_recompute': is_recompute}
else:
local_end_index = kv_cache['local_end_index'].item() + current_end - kv_cache['global_end_index'].item()
local_start_index = local_end_index - num_new_tokens
temp_k = kv_cache['k'].clone()
temp_v = kv_cache['v'].clone()
write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
if sink_recache_after_switch:
write_start_index = local_start_index
roped_offset = max(0, write_start_index - local_start_index)
write_len = max(0, local_end_index - write_start_index)
if write_len > 0:
temp_k[:, write_start_index:local_end_index] = k_for_cache[:, roped_offset:roped_offset + write_len]
temp_v[:, write_start_index:local_end_index] = v[:, roped_offset:roped_offset + write_len]
cache_update_info = {'action': 'direct_insert', 'local_start_index': local_start_index, 'local_end_index': local_end_index, 'write_start_index': write_start_index, 'write_end_index': local_end_index, 'new_k': k_for_cache[:, roped_offset:roped_offset + write_len], 'new_v': v[:, roped_offset:roped_offset + write_len], 'current_end': current_end, 'is_recompute': is_recompute}
if sink_tokens > 0:
local_budget = self.max_attention_size - sink_tokens
k_sink_stored = temp_k[:, :sink_tokens]
v_sink = temp_v[:, :sink_tokens]
num_new_frames_tri = num_new_tokens // frame_seqlen if frame_seqlen > 0 else 0
current_end_frame_tri = current_start_frame + num_new_frames_tri
if self.tri_rope_cont:
N_S = self.sink_size
N_L_max = (self.max_attention_size - N_S * frame_seqlen) // frame_seqlen
N_Q_active = memory_kv[0].shape[1] // frame_seqlen if memory_kv is not None else 0
delta_cum = max(0, current_start_frame - (N_S + N_L_max))
delta_target = self.tri_rope_pmax - (N_S + N_Q_active + N_L_max)
delta_eff = max(0, min(delta_cum, delta_target))
N_S_cache_tri = min(current_end_frame_tri, N_S)
tri_sink_start = delta_eff
tri_mem_start = delta_eff + N_S_cache_tri
tri_local_start = delta_eff + N_S_cache_tri + N_Q_active
CausalWanSelfAttention._delta_sum = getattr(CausalWanSelfAttention, '_delta_sum', 0) + int(delta_eff)
CausalWanSelfAttention._delta_count = getattr(CausalWanSelfAttention, '_delta_count', 0) + 1
if delta_target > 0:
CausalWanSelfAttention._delta_at_cap = getattr(CausalWanSelfAttention, '_delta_at_cap', 0) + int(delta_eff == delta_target)
else:
CausalWanSelfAttention._delta_at_cap = getattr(CausalWanSelfAttention, '_delta_at_cap', 0)
if delta_eff > 0 and (not getattr(CausalWanSelfAttention, '_tri_delta_logged', False)):
CausalWanSelfAttention._tri_delta_logged = True
if delta_target > 0 and delta_eff == delta_target and (not getattr(CausalWanSelfAttention, '_tri_cap_logged', False)):
CausalWanSelfAttention._tri_cap_logged = True
else:
delta_eff = 0
N_S_cache_tri = self.sink_size
N_Q_active = 0
tri_sink_start = 0
tri_mem_start = 0
tri_local_start = 0
if self.relative_rope:
N_Q_rr = memory_kv[0].shape[1] // frame_seqlen if memory_kv is not None else 0
N_S_cache_rr = min(current_end_frame_tri, self.sink_size)
if self.tri_rope_cont:
sink_grid = grid_sizes.clone()
sink_grid[:, 0] = N_S_cache_tri
k_sink = causal_rope_apply(k_sink_stored, sink_grid, freqs, start_frame=tri_sink_start).type_as(v)
elif self.dr_rope:
sink_grid = grid_sizes.clone()
sink_grid[:, 0] = self.sink_size
k_sink = causal_rope_apply(k_sink_stored, sink_grid, freqs, start_frame=0).type_as(v)
elif self.relative_rope:
sink_grid = grid_sizes.clone()
sink_grid[:, 0] = N_S_cache_rr
k_sink = causal_rope_apply(k_sink_stored, sink_grid, freqs, start_frame=0).type_as(v)
else:
k_sink = k_sink_stored
if local_budget > 0:
local_start_for_window = max(sink_tokens, local_end_index - local_budget)
k_local_stored = temp_k[:, local_start_for_window:local_end_index]
v_local = temp_v[:, local_start_for_window:local_end_index]
R_active = max(0, k_local_stored.shape[1] // frame_seqlen) if frame_seqlen > 0 else 0
num_new_frames = num_new_tokens // frame_seqlen if frame_seqlen > 0 else 0
if self.tri_rope_cont:
if R_active > 0:
local_grid = grid_sizes.clone()
local_grid[:, 0] = R_active
k_local = causal_rope_apply(k_local_stored, local_grid, freqs, start_frame=tri_local_start).type_as(v)
else:
k_local = k_local_stored
tri_q_start = tri_local_start + R_active - num_new_frames
q_slot_start = local_start_index // frame_seqlen
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=tri_q_start).type_as(v)
elif self.dr_rope:
local_start_rope = local_start_for_window // frame_seqlen
q_start_rope = local_start_index // frame_seqlen
if R_active > 0:
local_grid = grid_sizes.clone()
local_grid[:, 0] = R_active
k_local = causal_rope_apply(k_local_stored, local_grid, freqs, start_frame=local_start_rope).type_as(v)
else:
k_local = k_local_stored
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=q_start_rope).type_as(v)
elif self.relative_rope:
rr_pos = CausalWanSelfAttention._compute_relative_positions(current_start_frame=current_start_frame, B=num_new_frames, R=R_active, N_Q=N_Q_rr, N_S=self.sink_size, pmax=self.relative_rope_pmax, num_frame_per_block=self.num_frame_per_block_attr)
_diag_rr = f"[diag-rr] layer={self._layer_id} cur_start_f={current_start_frame} B={num_new_frames} R={R_active} N_Q={N_Q_rr} N_S={self.sink_size} pmax={self.relative_rope_pmax} B_f={self.num_frame_per_block_attr} is_bulk={rr_pos['is_bulk_forward']} use_mem={rr_pos['use_memory']} sink_s={rr_pos['sink_start']} mem_s={rr_pos['mem_start']} mem_e={rr_pos['mem_end']} local_s={rr_pos['local_start']} local_e={rr_pos['local_end']} q_s={rr_pos['q_start']} q_l={rr_pos['q_last']}"
assert rr_pos['q_last'] <= self.relative_rope_pmax - 1, f'RelRope overflow: q_last > pmax-1 | {_diag_rr}'
assert rr_pos['q_start'] >= 0, f'RelRope underflow: q_start < 0 | {_diag_rr}'
if rr_pos['use_memory']:
assert rr_pos['mem_start'] >= self.sink_size, f'RelRope memory overlaps sink: mem_start < sink_size | {_diag_rr}'
assert rr_pos['mem_end'] + 1 == rr_pos['local_start'], f'RelRope memory-local not contiguous | {_diag_rr}'
if R_active > 0 and (not rr_pos['is_bulk_forward']):
assert rr_pos['q_last'] == rr_pos['local_end'], f'RelRope Q-local tail mismatch: q_last != local_end | {_diag_rr}'
if R_active > 0:
local_grid = grid_sizes.clone()
local_grid[:, 0] = R_active
k_local = causal_rope_apply(k_local_stored, local_grid, freqs, start_frame=rr_pos['local_start']).type_as(v)
else:
k_local = k_local_stored
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=rr_pos['q_start']).type_as(v)
CausalWanSelfAttention._rr_q_last_sum = getattr(CausalWanSelfAttention, '_rr_q_last_sum', 0) + int(rr_pos['q_last'])
CausalWanSelfAttention._rr_total_count = getattr(CausalWanSelfAttention, '_rr_total_count', 0) + 1
if rr_pos['is_bulk_forward']:
CausalWanSelfAttention._rr_bulk_count = getattr(CausalWanSelfAttention, '_rr_bulk_count', 0) + 1
if rr_pos['q_last'] == self.relative_rope_pmax - 1 and (not rr_pos['is_bulk_forward']):
CausalWanSelfAttention._rr_long_count = getattr(CausalWanSelfAttention, '_rr_long_count', 0) + 1
if not rr_pos['is_bulk_forward'] and rr_pos['q_last'] == self.relative_rope_pmax - 1 and (not getattr(CausalWanSelfAttention, '_rr_long_logged', False)):
mem_str = f"mem[{rr_pos['mem_start']}..{rr_pos['mem_end']}]" if rr_pos['use_memory'] else 'mem=(inactive)'
CausalWanSelfAttention._rr_long_logged = True
if rr_pos['is_bulk_forward'] and (not getattr(CausalWanSelfAttention, '_rr_bulk_logged', False)):
CausalWanSelfAttention._rr_bulk_logged = True
else:
k_local = k_local_stored
_rr_skip_memory = self.relative_rope and rr_pos['is_bulk_forward']
if memory_kv is not None and (not _rr_skip_memory):
mem_k, mem_v = memory_kv
Q_frames = mem_k.shape[1] // frame_seqlen if frame_seqlen > 0 else 3
if self.tri_rope_cont:
mem_start = tri_mem_start
elif self.dr_rope:
mem_start = 0
elif self.relative_rope:
mem_start = rr_pos['mem_start']
else:
W_frames = k_local.shape[1] // frame_seqlen if frame_seqlen > 0 else 0
oldest_recent_frame = current_start_frame + num_new_frames - W_frames
mem_start = max(0, oldest_recent_frame - Q_frames)
mem_grid = grid_sizes.clone()
mem_grid[:, 0] = Q_frames
mem_k_roped = causal_rope_apply(mem_k, mem_grid, freqs, start_frame=mem_start)
k_cat = torch.cat([k_sink, mem_k_roped, k_local], dim=1)
v_cat = torch.cat([v_sink, mem_v, v_local], dim=1)
_prof_n_mem = mem_k_roped.shape[1]
if self.tri_rope_cont:
_diag = f'[diag] layer={self._layer_id} cur_start_f={current_start_frame} cur_end_f={current_end_frame_tri} num_new_f={num_new_frames} num_new_tok={num_new_tokens} δ_cum={delta_cum} δ_tgt={delta_target} δ_eff={delta_eff} N_S={N_S} N_L_max={N_L_max} N_Q_act={N_Q_active} Q_frames(local)={Q_frames} N_S_cache={N_S_cache_tri} sink_s={tri_sink_start} mem_s={tri_mem_start} loc_s={tri_local_start} q_s={tri_q_start} q_slot_s={q_slot_start} R_active={R_active} local_end_idx={local_end_index} local_start_idx={local_start_index} loc_start_for_win={local_start_for_window} mem_k_shape1={memory_kv[0].shape[1]} self.local_attn={self.local_attn_size} max_attn={self.max_attention_size}'
assert tri_sink_start + N_S_cache_tri == tri_mem_start, f'TriRope-13 sink-mem gap: {tri_sink_start + N_S_cache_tri} != {tri_mem_start} | {_diag}'
assert tri_mem_start + Q_frames == tri_local_start, f'TriRope-13 mem-local gap: {tri_mem_start + Q_frames} != {tri_local_start} | {_diag}'
assert tri_q_start + num_new_frames <= self.tri_rope_pmax, f'TriRope-13 overflow: q_end={tri_q_start + num_new_frames} > pmax={self.tri_rope_pmax} | {_diag}'
assert tri_sink_start >= 0, f'TriRope-13 underflow: sink_start={tri_sink_start} < 0 | {_diag}'
else:
k_cat = torch.cat([k_sink, k_local], dim=1)
v_cat = torch.cat([v_sink, v_local], dim=1)
_prof_n_mem = 0
if self.tri_rope_cont:
_diag_nm = f'[diag-no-mem] layer={self._layer_id} cur_start_f={current_start_frame} cur_end_f={current_end_frame_tri} num_new_f={num_new_frames} num_new_tok={num_new_tokens} δ_cum={delta_cum} δ_tgt={delta_target} δ_eff={delta_eff} N_S={N_S} N_L_max={N_L_max} N_Q_act={N_Q_active} N_S_cache={N_S_cache_tri} sink_s={tri_sink_start} mem_s={tri_mem_start} loc_s={tri_local_start} q_s={tri_q_start} q_slot_s={q_slot_start} R_active={R_active} local_end_idx={local_end_index} local_start_idx={local_start_index} loc_start_for_win={local_start_for_window} self.local_attn={self.local_attn_size} max_attn={self.max_attention_size}'
expected_local_start = tri_sink_start + N_S_cache_tri
assert expected_local_start == tri_local_start, f'TriRope-13 sink-local gap (no mem): {expected_local_start} != {tri_local_start} | {_diag_nm}'
assert tri_q_start + num_new_frames <= self.tri_rope_pmax, f'TriRope-13 overflow (no mem): q_end={tri_q_start + num_new_frames} > pmax={self.tri_rope_pmax} | {_diag_nm}'
else:
num_new_frames = num_new_tokens // frame_seqlen if frame_seqlen > 0 else 0
if self.tri_rope_cont:
q_slot_start = local_start_index // frame_seqlen
tri_q_start = tri_sink_start + N_S_cache_tri - num_new_frames
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=tri_q_start).type_as(v)
elif self.dr_rope:
q_start_rope = local_start_index // frame_seqlen
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=q_start_rope).type_as(v)
elif self.relative_rope:
rr_pos = CausalWanSelfAttention._compute_relative_positions(current_start_frame=current_start_frame, B=num_new_frames, R=0, N_Q=N_Q_rr, N_S=self.sink_size, pmax=self.relative_rope_pmax, num_frame_per_block=self.num_frame_per_block_attr)
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=rr_pos['q_start']).type_as(v)
k_cat = k_sink
v_cat = v_sink
_prof_n_mem = 0
if getattr(self, '_profile_attn', False):
with torch.no_grad():
_ns = sink_tokens
_nm = _prof_n_mem
_nr = k_cat.shape[1] - _ns - _nm
_ql = roped_query.shape[1]
_si = torch.linspace(0, _ql - 1, min(64, _ql), dtype=torch.long, device=roped_query.device)
_qs = roped_query[:, _si]
_sc = torch.einsum('bqhd,bkhd->bhqk', _qs.float(), k_cat.float()) * self.head_dim ** (-0.5)
_w = torch.softmax(_sc, dim=-1)
_wh_s = _w[:, :, :, :_ns].sum(-1).mean(dim=(0, 2))
_wh_m = _w[:, :, :, _ns:_ns + _nm].sum(-1).mean(dim=(0, 2)) if _nm > 0 else torch.zeros(self.num_heads, device=_w.device)
_wh_r = _w[:, :, :, _ns + _nm:].sum(-1).mean(dim=(0, 2))
if not hasattr(self, '_attn_profile_log'):
self._attn_profile_log = []
self._attn_profile_log.append({'sink': round(_wh_s.mean().item(), 5), 'memory': round(_wh_m.mean().item(), 5), 'recent': round(_wh_r.mean().item(), 5), 'per_head_memory': [round(x, 5) for x in _wh_m.tolist()], 'n_sink': _ns, 'n_mem': _nm, 'n_recent': _nr})
x = attention(roped_query, k_cat, v_cat)
else:
window_start = max(0, local_end_index - self.max_attention_size)
temp_k_window = temp_k[:, window_start:local_end_index]
R_active = max(0, temp_k_window.shape[1] // frame_seqlen) if frame_seqlen > 0 else 0
num_new_frames = num_new_tokens // frame_seqlen if frame_seqlen > 0 else 0
if self.tri_rope_cont:
N_L_max_nosink = self.max_attention_size // frame_seqlen if frame_seqlen > 0 else 0
delta_cum_ns = max(0, current_start_frame - N_L_max_nosink)
delta_target_ns = max(0, self.tri_rope_pmax - N_L_max_nosink)
delta_eff_ns = max(0, min(delta_cum_ns, delta_target_ns))
local_start_rope = delta_eff_ns
q_start_rope = delta_eff_ns + max(0, R_active - num_new_frames)
if R_active > 0:
local_grid = grid_sizes.clone()
local_grid[:, 0] = R_active
k_win = causal_rope_apply(temp_k_window, local_grid, freqs, start_frame=local_start_rope).type_as(v)
else:
k_win = temp_k_window
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=q_start_rope).type_as(v)
elif self.dr_rope:
local_start_rope = window_start // frame_seqlen
q_start_rope = local_start_index // frame_seqlen
if R_active > 0:
local_grid = grid_sizes.clone()
local_grid[:, 0] = R_active
k_win = causal_rope_apply(temp_k_window, local_grid, freqs, start_frame=local_start_rope).type_as(v)
else:
k_win = temp_k_window
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=q_start_rope).type_as(v)
elif self.relative_rope:
rr_pos = CausalWanSelfAttention._compute_relative_positions(current_start_frame=current_start_frame, B=num_new_frames, R=R_active, N_Q=0, N_S=0, pmax=self.relative_rope_pmax, num_frame_per_block=self.num_frame_per_block_attr)
if R_active > 0:
local_grid = grid_sizes.clone()
local_grid[:, 0] = R_active
k_win = causal_rope_apply(temp_k_window, local_grid, freqs, start_frame=rr_pos['local_start']).type_as(v)
else:
k_win = temp_k_window
q_grid = grid_sizes.clone()
q_grid[:, 0] = num_new_frames
roped_query = causal_rope_apply(q, q_grid, freqs, start_frame=rr_pos['q_start']).type_as(v)
else:
k_win = temp_k_window
x = attention(roped_query, k_win, temp_v[:, window_start:local_end_index])
x = x.flatten(2)
x = self.o(x)
if kv_cache is not None:
cache_info = (current_end, local_end_index, cache_update_info)
if _sink_qkv_captured is not None:
return (x, cache_info, _sink_qkv_captured)
return (x, cache_info)
else:
return x
class CausalWanAttentionBlock(nn.Module):
def __init__(self, cross_attn_type, dim, ffn_dim, num_heads, local_attn_size=-1, sink_size=0, qk_norm=True, cross_attn_norm=False, eps=1e-06):
super().__init__()
self.dim = dim
self.ffn_dim = ffn_dim
self.num_heads = num_heads
self.local_attn_size = local_attn_size
self.qk_norm = qk_norm
self.cross_attn_norm = cross_attn_norm
self.eps = eps
self.norm1 = WanLayerNorm(dim, eps)
self.self_attn = CausalWanSelfAttention(dim, num_heads, local_attn_size, sink_size, qk_norm, eps)
self.norm3 = WanLayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, num_heads, (-1, -1), qk_norm, eps)
self.norm2 = WanLayerNorm(dim, eps)
self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim))
self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim ** 0.5)
def forward(self, x, e, seq_lens, grid_sizes, freqs, context, context_lens, block_mask, kv_cache=None, crossattn_cache=None, current_start=0, cache_start=None, sink_recache_after_switch=False, memory_kv=None, capture_sink_qkv=False):
num_frames, frame_seqlen = (e.shape[1], x.shape[1] // e.shape[1])
e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2)
self_attn_result = self.self_attn((self.norm1(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0]).flatten(1, 2), seq_lens, grid_sizes, freqs, block_mask, kv_cache, current_start, cache_start, sink_recache_after_switch, memory_kv=memory_kv, capture_sink_qkv=capture_sink_qkv)
sink_qkv_data = None
if kv_cache is not None:
if isinstance(self_attn_result, tuple) and len(self_attn_result) == 3:
y, cache_update_info, sink_qkv_data = self_attn_result
else:
y, cache_update_info = self_attn_result
else:
y = self_attn_result
cache_update_info = None
x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[2]).flatten(1, 2)
def cross_attn_ffn(x, context, context_lens, e, crossattn_cache=None):
x = x + self.cross_attn(self.norm3(x), context, context_lens, crossattn_cache=crossattn_cache)
y = self.ffn((self.norm2(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[4]) + e[3]).flatten(1, 2))
x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[5]).flatten(1, 2)
return x
x = cross_attn_ffn(x, context, context_lens, e, crossattn_cache)
if cache_update_info is not None:
if sink_qkv_data is not None:
return (x, cache_update_info, sink_qkv_data)
return (x, cache_update_info)
else:
return x
class CausalHead(nn.Module):
def __init__(self, dim, out_dim, patch_size, eps=1e-06):
super().__init__()
self.dim = dim
self.out_dim = out_dim
self.patch_size = patch_size
self.eps = eps
out_dim = math.prod(patch_size) * out_dim
self.norm = WanLayerNorm(dim, eps)
self.head = nn.Linear(dim, out_dim)
self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim ** 0.5)
def forward(self, x, e):
num_frames, frame_seqlen = (e.shape[1], x.shape[1] // e.shape[1])
e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2)
x = self.head(self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0])
return x
class CausalWanModel(ModelMixin, ConfigMixin):
ignore_for_config = ['patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim']
_no_split_modules = ['WanAttentionBlock']
_supports_gradient_checkpointing = True
@register_to_config
def __init__(self, model_type='t2v', patch_size=(1, 2, 2), text_len=512, in_dim=16, dim=2048, ffn_dim=8192, freq_dim=256, text_dim=4096, out_dim=16, num_heads=16, num_layers=32, local_attn_size=-1, sink_size=0, qk_norm=True, cross_attn_norm=True, eps=1e-06, dr_rope=False, tri_rope_cont=False, tri_rope_pmax=21, relative_rope=False, relative_rope_pmax=21):
super().__init__()
assert model_type in ['t2v', 'i2v']
self.model_type = model_type
self.patch_size = patch_size
self.text_len = text_len
self.in_dim = in_dim
self.dim = dim
self.ffn_dim = ffn_dim
self.freq_dim = freq_dim
self.text_dim = text_dim
self.out_dim = out_dim
self.num_heads = num_heads
self.num_layers = num_layers
self.local_attn_size = local_attn_size
self.qk_norm = qk_norm
self.cross_attn_norm = cross_attn_norm
self.eps = eps
self.dr_rope = dr_rope
self.tri_rope_cont = tri_rope_cont
self.tri_rope_pmax = tri_rope_pmax
self.relative_rope = relative_rope
self.relative_rope_pmax = relative_rope_pmax
self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
self.text_embedding = nn.Sequential(nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim))
self.time_embedding = nn.Sequential(nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
self.blocks = nn.ModuleList([CausalWanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, local_attn_size, sink_size, qk_norm, cross_attn_norm, eps) for _ in range(num_layers)])
for _blk_i, blk in enumerate(self.blocks):
blk.self_attn.dr_rope = self.dr_rope
blk.self_attn.tri_rope_cont = self.tri_rope_cont
blk.self_attn.tri_rope_pmax = self.tri_rope_pmax
blk.self_attn.relative_rope = self.relative_rope
blk.self_attn.relative_rope_pmax = self.relative_rope_pmax
blk.self_attn.num_frame_per_block_attr = 3
blk.self_attn._layer_id = _blk_i
if self.tri_rope_cont:
first_attn = self.blocks[0].self_attn
sink_s = first_attn.sink_size
max_attn = first_attn.max_attention_size
W_est = max(0, (max_attn - sink_s * 1560) // 1560)
Q_max_est = 6
B_f_assume = 3
span = B_f_assume + W_est + Q_max_est + sink_s
assert span <= tri_rope_pmax, f'tri_rope_cont invariant violated (init smoke check): B_f≤{B_f_assume} + W({W_est}) + Q≤{Q_max_est} + sink({sink_s}) = {span} > pmax({tri_rope_pmax})'
if self.relative_rope:
first_attn = self.blocks[0].self_attn
sink_s = first_attn.sink_size
N_L = first_attn.max_attention_size // 1560 - sink_s
N_Q_est = 3
pmax_m1 = self.relative_rope_pmax - 1
self.head = CausalHead(dim, out_dim, patch_size, eps)
assert dim % num_heads == 0 and dim // num_heads % 2 == 0
d = dim // num_heads
self.freqs = torch.cat([rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6))], dim=1)
if model_type == 'i2v':
self.img_emb = MLPProj(1280, dim)
self.init_weights()
self.gradient_checkpointing = False
self.block_mask = None
self._num_frame_per_block = 1
self.independent_first_frame = False
object.__setattr__(self, 'query_memory_encoder', None)
object.__setattr__(self, 'sink_memory', None)
object.__setattr__(self, '_ei_prev_window_start', None)
@property
def num_frame_per_block(self):
return self._num_frame_per_block
@num_frame_per_block.setter
def num_frame_per_block(self, value):
self._num_frame_per_block = int(value)
for blk in self.blocks:
blk.self_attn.num_frame_per_block_attr = int(value)
def setup_memory_encoder(self, memory_kwargs):
from model.query_memory import QueryMemoryEncoder
from types import SimpleNamespace
if isinstance(memory_kwargs, dict):
memory_kwargs = SimpleNamespace(**memory_kwargs)
self.query_memory_encoder = QueryMemoryEncoder(memory_kwargs)
def setup_sink_memory(self, memory_kwargs):
from model.sink_memory import SinkMemory
if isinstance(memory_kwargs, dict):
from types import SimpleNamespace
memory_kwargs = SimpleNamespace(**memory_kwargs)
num_blocks = len(self.blocks)
num_heads = self.blocks[0].self_attn.num_heads
head_dim = self.blocks[0].self_attn.head_dim
tokens_per_frame = getattr(memory_kwargs, 'tokens_per_frame', 1560)
sink_size = self.blocks[0].self_attn.sink_size
hidden_dim = self.blocks[0].dim
sm = SinkMemory(num_blocks, num_heads, head_dim, tokens_per_frame, sink_size, hidden_dim)
object.__setattr__(self, 'sink_memory', sm)
def _set_gradient_checkpointing(self, module, value=False):
self.gradient_checkpointing = value
@staticmethod
def _prepare_blockwise_causal_attn_mask(device: torch.device | str, num_frames: int=21, frame_seqlen: int=1560, num_frame_per_block=1, local_attn_size=-1) -> BlockMask:
total_length = num_frames * frame_seqlen
padded_length = math.ceil(total_length / 128) * 128 - total_length
ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
frame_indices = torch.arange(start=0, end=total_length, step=frame_seqlen * num_frame_per_block, device=device)
for tmp in frame_indices:
ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + frame_seqlen * num_frame_per_block
def attention_mask(b, h, q_idx, kv_idx):
if local_attn_size == -1:
return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
else:
return (kv_idx < ends[q_idx]) & (kv_idx >= ends[q_idx] - local_attn_size * frame_seqlen) | (q_idx == kv_idx)
block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, KV_LEN=total_length + padded_length, _compile=False, device=device)
import torch.distributed as dist
return block_mask
@staticmethod
def _prepare_teacher_forcing_mask(device: torch.device | str, num_frames: int=21, frame_seqlen: int=1560, num_frame_per_block=1) -> BlockMask:
total_length = num_frames * frame_seqlen * 2
padded_length = math.ceil(total_length / 128) * 128 - total_length
clean_ends = num_frames * frame_seqlen
context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
noise_context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
noise_context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
noise_noise_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
noise_noise_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
attention_block_size = frame_seqlen * num_frame_per_block
frame_indices = torch.arange(start=0, end=num_frames * frame_seqlen, step=attention_block_size, device=device, dtype=torch.long)
for start in frame_indices:
context_ends[start:start + attention_block_size] = start + attention_block_size
noisy_image_start_list = torch.arange(num_frames * frame_seqlen, total_length, step=attention_block_size, device=device, dtype=torch.long)
noisy_image_end_list = noisy_image_start_list + attention_block_size
for block_index, (start, end) in enumerate(zip(noisy_image_start_list, noisy_image_end_list)):
noise_noise_starts[start:end] = start
noise_noise_ends[start:end] = end
noise_context_ends[start:end] = block_index * attention_block_size
def attention_mask(b, h, q_idx, kv_idx):
clean_mask = (q_idx < clean_ends) & (kv_idx < context_ends[q_idx])
C1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx])
C2 = (kv_idx < noise_context_ends[q_idx]) & (kv_idx >= noise_context_starts[q_idx])
noise_mask = (q_idx >= clean_ends) & (C1 | C2)
eye_mask = q_idx == kv_idx
return eye_mask | clean_mask | noise_mask
block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, KV_LEN=total_length + padded_length, _compile=False, device=device)
return block_mask
@staticmethod
def _prepare_blockwise_causal_attn_mask_i2v(device: torch.device | str, num_frames: int=21, frame_seqlen: int=1560, num_frame_per_block=4, local_attn_size=-1) -> BlockMask:
total_length = num_frames * frame_seqlen
padded_length = math.ceil(total_length / 128) * 128 - total_length
ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
ends[:frame_seqlen] = frame_seqlen
frame_indices = torch.arange(start=frame_seqlen, end=total_length, step=frame_seqlen * num_frame_per_block, device=device)
for idx, tmp in enumerate(frame_indices):
ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + frame_seqlen * num_frame_per_block
def attention_mask(b, h, q_idx, kv_idx):
if local_attn_size == -1:
return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
else:
return (kv_idx < ends[q_idx]) & (kv_idx >= ends[q_idx] - local_attn_size * frame_seqlen) | (q_idx == kv_idx)
block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, KV_LEN=total_length + padded_length, _compile=False, device=device)
return block_mask
def _apply_cache_updates(self, kv_cache, cache_update_infos):
for block_index, (current_end, local_end_index, update_info) in cache_update_infos:
if update_info is not None:
cache = kv_cache[block_index]
if update_info['action'] == 'roll_and_insert':
sink_tokens = update_info['sink_tokens']
num_rolled_tokens = update_info['num_rolled_tokens']
num_evicted_tokens = update_info['num_evicted_tokens']
local_start_index = update_info['local_start_index']
local_end_index = update_info['local_end_index']
write_start_index = update_info.get('write_start_index', local_start_index)
write_end_index = update_info.get('write_end_index', local_end_index)
new_k = update_info['new_k']
new_v = update_info['new_v']
cache['k'][:, sink_tokens:sink_tokens + num_rolled_tokens] = cache['k'][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
cache['v'][:, sink_tokens:sink_tokens + num_rolled_tokens] = cache['v'][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
if write_end_index > write_start_index and new_k.shape[1] == write_end_index - write_start_index:
cache['k'][:, write_start_index:write_end_index] = new_k
cache['v'][:, write_start_index:write_end_index] = new_v
elif update_info['action'] == 'direct_insert':
local_start_index = update_info['local_start_index']
local_end_index = update_info['local_end_index']
write_start_index = update_info.get('write_start_index', local_start_index)
write_end_index = update_info.get('write_end_index', local_end_index)
new_k = update_info['new_k']
new_v = update_info['new_v']
if write_end_index > write_start_index and new_k.shape[1] == write_end_index - write_start_index:
cache['k'][:, write_start_index:write_end_index] = new_k
cache['v'][:, write_start_index:write_end_index] = new_v
is_recompute = False if update_info is None else update_info.get('is_recompute', False)
if not is_recompute:
kv_cache[block_index]['global_end_index'].fill_(current_end)
kv_cache[block_index]['local_end_index'].fill_(local_end_index)
def _forward_inference(self, x, t, context, seq_len, clip_fea=None, y=None, kv_cache: dict=None, crossattn_cache: dict=None, current_start: int=0, cache_start: int=0, sink_recache_after_switch=False):
if self.model_type == 'i2v':
assert clip_fea is not None and y is not None
device = self.patch_embedding.weight.device
if self.freqs.device != device:
self.freqs = self.freqs.to(device)
if y is not None:
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
grid_sizes = torch.stack([torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
x = [u.flatten(2).transpose(1, 2) for u in x]
seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
assert seq_lens.max() <= seq_len
x = torch.cat(x)
e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x))
e0 = self.time_projection(e).unflatten(1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
context_lens = None
context = self.text_embedding(torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context]))
if clip_fea is not None:
context_clip = self.img_emb(clip_fea)
context = torch.concat([context_clip, context], dim=1)
if kv_cache is not None:
first_global_end = kv_cache[0]['global_end_index'].item() if kv_cache else 0
if first_global_end == 0 and current_start == 0:
B = x[0].shape[0] if isinstance(x, list) else x.shape[0]
if self.query_memory_encoder is not None:
self.query_memory_encoder.reset(batch_size=B, device=x[0].device if isinstance(x, list) else x.device, dtype=torch.bfloat16)
if self.sink_memory is not None:
self.sink_memory.reset()
self._ei_prev_window_start = None
sm = self.sink_memory
if sm is not None and (not sm.initialized) and (kv_cache is not None):
sink_tok = self.blocks[0].self_attn.sink_size * 1560
if current_start == 0 and sink_tok > 0:
sm._pending_sink_hidden = x[:, :sink_tok].clone().detach()
elif current_start > 0 and sm.sink_hidden is None:
pending_hidden = getattr(sm, '_pending_sink_hidden', None)
if pending_hidden is not None:
sm.initialize_hidden(pending_hidden)
sm._pending_sink_hidden = None
enc = self.query_memory_encoder
num_blocks = len(self.blocks)
if sm is not None and sm.has_history:
memory_kv_list = [sm.get_kv(i) for i in range(num_blocks)]
elif enc is not None and enc.has_history:
if enc.num_query_groups > 1:
blocks_per_group = num_blocks // enc.num_query_groups
group_kvs = [enc.get_kv(group_index=g) for g in range(enc.num_query_groups)]
memory_kv_list = [group_kvs[min(i // blocks_per_group, enc.num_query_groups - 1)] for i in range(num_blocks)]
else:
kv = enc.get_kv()
memory_kv_list = [kv] * num_blocks
else:
memory_kv_list = [None] * num_blocks
kwargs = dict(e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.freqs, context=context, context_lens=context_lens, block_mask=self.block_mask, sink_recache_after_switch=sink_recache_after_switch, memory_kv=None)
def create_custom_forward(module):
def custom_forward(*inputs, **kwargs):
return module(*inputs, **kwargs)
return custom_forward
_need_sink_capture = sm is not None and (not sm.initialized) and (kv_cache is not None) and (current_start == 0)
cache_update_info = None
cache_update_infos = []
for block_index, block in enumerate(self.blocks):
kwargs['memory_kv'] = memory_kv_list[block_index]
kwargs['capture_sink_qkv'] = _need_sink_capture
if torch.is_grad_enabled() and self.gradient_checkpointing:
kwargs.update({'kv_cache': kv_cache[block_index], 'current_start': current_start, 'cache_start': cache_start})
result = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, **kwargs, use_reentrant=False)
if kv_cache is not None and isinstance(result, tuple):
if len(result) == 3:
x, block_cache_update_info, sink_qkv = result
if not hasattr(sm, '_pending_sink_captures'):
sm._pending_sink_captures = {}
sm._pending_sink_captures[block_index] = sink_qkv
else:
x, block_cache_update_info = result
cache_update_infos.append((block_index, block_cache_update_info))
cache_update_info = block_cache_update_info[:2]
else:
x = result
else:
kwargs.update({'kv_cache': kv_cache[block_index], 'crossattn_cache': crossattn_cache[block_index], 'current_start': current_start, 'cache_start': cache_start})
result = block(x, **kwargs)
if kv_cache is not None and isinstance(result, tuple):
if len(result) == 3:
x, block_cache_update_info, sink_qkv = result
if not hasattr(sm, '_pending_sink_captures'):
sm._pending_sink_captures = {}
sm._pending_sink_captures[block_index] = sink_qkv
else:
x, block_cache_update_info = result
cache_update_infos.append((block_index, block_cache_update_info))
cache_update_info = block_cache_update_info[:2]
else:
x = result
if kv_cache is not None and (not dist.is_initialized() or dist.get_rank() == 0):
enc = getattr(self, 'query_memory_encoder', None)
if kv_cache is not None and cache_update_infos:
_has_memory = self.query_memory_encoder is not None or self.sink_memory is not None
if _has_memory and cache_update_infos:
last_block_idx = cache_update_infos[-1][0]
last_update_info = cache_update_infos[-1][1]
local_end_idx = last_update_info[1]
update_dict = last_update_info[2] if len(last_update_info) > 2 else None
sink_tok = self.blocks[0].self_attn.sink_size * 1560
sink_frames = self.blocks[0].self_attn.sink_size
frame_seqlen = 1560
max_attn = self.blocks[0].self_attn.max_attention_size
recent_window_frames = (max_attn - sink_tok) // frame_seqlen
num_new_frames = (cache_update_infos[-1][1][0] - current_start) // frame_seqlen
current_end_frame = current_start // frame_seqlen + num_new_frames
oldest_recent_frame = max(sink_frames, current_end_frame - recent_window_frames)
prev_oldest = getattr(self, '_ei_prev_window_start', None)
if prev_oldest is None:
prev_oldest = sink_frames
num_exited_frames = max(0, oldest_recent_frame - prev_oldest)
self._ei_prev_window_start = oldest_recent_frame
sm = self.sink_memory
_pending = getattr(sm, '_pending_sink_captures', {}) if sm is not None else {}
if sm is not None and (not sm.initialized) and (current_start > 0) and _pending:
mem_grid = grid_sizes.clone()
mem_grid[:, 0] = sm.sink_size
for blk_idx in range(len(self.blocks)):
if blk_idx in _pending:
q_pre, k_pre, v_sink = _pending[blk_idx]
q_roped = causal_rope_apply(q_pre, mem_grid, self.freqs, start_frame=0)
sm.initialize_block(blk_idx, q_roped, k_pre, v_sink)
sm._pending_sink_captures = {}
if num_exited_frames > 0:
num_exited_tokens = num_exited_frames * frame_seqlen
if update_dict is not None and update_dict.get('action') == 'roll_and_insert':
capture_start = sink_tok
else:
capture_start = prev_oldest * frame_seqlen
if self.query_memory_encoder is not None:
cache = kv_cache[last_block_idx]
exited_k = cache['k'][:, capture_start:capture_start + num_exited_tokens].clone()
exited_v = cache['v'][:, capture_start:capture_start + num_exited_tokens].clone()
sink_k = cache['k'][:, :sink_tok].clone() if sink_tok > 0 else None
sink_v = cache['v'][:, :sink_tok].clone() if sink_tok > 0 else None
self.query_memory_encoder.update(exited_k, exited_v, sink_k, sink_v)
if sm is not None and sm.initialized:
evicted_kv_all = []
for blk_idx in range(len(self.blocks)):
cache_blk = kv_cache[blk_idx]
ek = cache_blk['k'][:, capture_start:capture_start + num_exited_tokens].clone()
ev = cache_blk['v'][:, capture_start:capture_start + num_exited_tokens].clone()
evicted_kv_all.append((ek, ev))
sm.update(self.blocks, evicted_kv_all, e0, context, context_lens, self.freqs, grid_sizes, evicted_k_is_pre_rope=self.dr_rope or self.tri_rope_cont or self.relative_rope)
self._apply_cache_updates(kv_cache, cache_update_infos)
x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))
x = self.unpatchify(x, grid_sizes)
return torch.stack(x)
def _forward_train(self, x, t, context, seq_len, clean_x=None, aug_t=None, clip_fea=None, y=None):
pass
raise NotImplementedError()
if self.model_type == 'i2v':
assert clip_fea is not None and y is not None
device = self.patch_embedding.weight.device
if self.freqs.device != device:
self.freqs = self.freqs.to(device)
if self.block_mask is None:
if clean_x is not None:
if self.independent_first_frame:
raise NotImplementedError()
else:
self.block_mask = self._prepare_teacher_forcing_mask(device, num_frames=x.shape[2], frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]), num_frame_per_block=self.num_frame_per_block)
elif self.independent_first_frame:
self.block_mask = self._prepare_blockwise_causal_attn_mask_i2v(device, num_frames=x.shape[2], frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]), num_frame_per_block=self.num_frame_per_block, local_attn_size=self.local_attn_size)
else:
self.block_mask = self._prepare_blockwise_causal_attn_mask(device, num_frames=x.shape[2], frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]), num_frame_per_block=self.num_frame_per_block, local_attn_size=self.local_attn_size)
if y is not None:
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
grid_sizes = torch.stack([torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
x = [u.flatten(2).transpose(1, 2) for u in x]
seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
assert seq_lens.max() <= seq_len
x = torch.cat([torch.cat([u, u.new_zeros(1, seq_lens[0] - u.size(1), u.size(2))], dim=1) for u in x])
e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x))
e0 = self.time_projection(e).unflatten(1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
context_lens = None
context = self.text_embedding(torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context]))
if clip_fea is not None:
context_clip = self.img_emb(clip_fea)
context = torch.concat([context_clip, context], dim=1)
if clean_x is not None:
clean_x = [self.patch_embedding(u.unsqueeze(0)) for u in clean_x]
clean_x = [u.flatten(2).transpose(1, 2) for u in clean_x]
seq_lens_clean = torch.tensor([u.size(1) for u in clean_x], dtype=torch.long)
assert seq_lens_clean.max() <= seq_len
clean_x = torch.cat([torch.cat([u, u.new_zeros(1, seq_lens_clean[0] - u.size(1), u.size(2))], dim=1) for u in clean_x])
x = torch.cat([clean_x, x], dim=1)
if aug_t is None:
aug_t = torch.zeros_like(t)
e_clean = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, aug_t.flatten()).type_as(x))
e0_clean = self.time_projection(e_clean).unflatten(1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
e0 = torch.cat([e0_clean, e0], dim=1)
kwargs = dict(e=e0, seq_lens=seq_lens, grid_sizes=grid_sizes, freqs=self.freqs, context=context, context_lens=context_lens, block_mask=self.block_mask)
def create_custom_forward(module):
def custom_forward(*inputs, **kwargs):
return module(*inputs, **kwargs)
return custom_forward
for block in self.blocks:
if torch.is_grad_enabled() and self.gradient_checkpointing:
x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, **kwargs, use_reentrant=False)
else:
x = block(x, **kwargs)
if clean_x is not None:
x = x[:, x.shape[1] // 2:]
x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))
x = self.unpatchify(x, grid_sizes)
return torch.stack(x)
def forward(self, *args, **kwargs):
if kwargs.get('kv_cache', None) is not None:
return self._forward_inference(*args, **kwargs)
else:
return self._forward_train(*args, **kwargs)
def unpatchify(self, x, grid_sizes):
c = self.out_dim
out = []
for u, v in zip(x, grid_sizes.tolist()):
u = u[:math.prod(v)].view(*v, *self.patch_size, c)
u = torch.einsum('fhwpqrc->cfphqwr', u)
u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
out.append(u)
return out
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
for m in self.text_embedding.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=0.02)
for m in self.time_embedding.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=0.02)
nn.init.zeros_(self.head.head.weight)
|