File size: 42,799 Bytes
bb5b74a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from transformers.modeling_utils import PreTrainedModel
flash_attn_available = True
try:
from flash_attn import flash_attn_varlen_func
except ImportError:
flash_attn_available = False
from .configuration_monkeyocrv2_vitae import MonkeyOCRv2ViTAEEncoderConfig
# ---------------------------------------------------------------------------
# 2D-RoPE helpers
# ---------------------------------------------------------------------------
def rotate_half(x):
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
orig_dtype = tensor.dtype
tensor = tensor.float()
cos = freqs.cos()
sin = freqs.sin()
cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
output = (tensor * cos) + (rotate_half(tensor) * sin)
return output.to(orig_dtype)
class VisionRotaryEmbedding(nn.Module):
def __init__(self, dim: int, theta: float = 10000.0) -> None:
super().__init__()
if dim % 2 != 0:
raise ValueError("2D-RoPE requires an even per-axis rotary dimension.")
inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, seqlen: int) -> torch.Tensor:
seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
freqs = torch.outer(seq, self.inv_freq)
return freqs
# ---------------------------------------------------------------------------
# Norms
# ---------------------------------------------------------------------------
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self._norm(x.float()).type_as(x) * self.weight
def _norm(self, x: torch.Tensor) -> torch.Tensor:
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
class RMSNorm2d(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.bias = nn.Parameter(torch.zeros(dim))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
orig_dtype = x.dtype
x = x.float()
x = x * torch.rsqrt(x.pow(2).mean(1, keepdim=True) + self.eps)
x = x * self.weight[:, None, None] + self.bias[:, None, None]
return x.to(orig_dtype)
# ---------------------------------------------------------------------------
# Attention
# ---------------------------------------------------------------------------
class VisionAttentionSdpa(nn.Module):
def __init__(self, dim: int, num_heads: int, bias: bool = False):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=bias)
self.proj = nn.Linear(dim, dim, bias=bias)
def forward(self, x: torch.Tensor, cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor = None) -> torch.Tensor:
S = x.shape[0]
q, k, v = self.qkv(x).reshape(S, 3, self.num_heads, self.head_dim).permute(1, 0, 2, 3).unbind(0)
if rotary_pos_emb is not None:
q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
mask = torch.zeros([1, S, S], device=q.device, dtype=torch.bool)
for i in range(1, len(cu_seqlens)):
mask[..., cu_seqlens[i - 1]:cu_seqlens[i], cu_seqlens[i - 1]:cu_seqlens[i]] = True
q = q.transpose(0, 1).unsqueeze(0)
k = k.transpose(0, 1).unsqueeze(0)
v = v.transpose(0, 1).unsqueeze(0)
if mask.stride(-1) != 1:
mask = torch.empty_like(mask, memory_format=torch.contiguous_format).copy_(mask)
out = F.scaled_dot_product_attention(q, k, v, mask, dropout_p=0.0)
out = out.squeeze(0).transpose(0, 1).reshape(S, -1)
return self.proj(out)
class VisionFlashAttention2(nn.Module):
def __init__(self, dim: int, num_heads: int, is_causal: bool = False, bias: bool = False):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.is_causal = is_causal
self.qkv = nn.Linear(dim, dim * 3, bias=bias)
self.proj = nn.Linear(dim, dim, bias=bias)
def forward(self, x: torch.Tensor, cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor = None) -> torch.Tensor:
S = x.shape[0]
q, k, v = self.qkv(x).reshape(S, 3, self.num_heads, self.head_dim).permute(1, 0, 2, 3).unbind(0)
if rotary_pos_emb is not None:
q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
out = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
causal=self.is_causal)
out = out.reshape(S, -1)
return self.proj(out)
class VisionAttentionEager(nn.Module):
def __init__(self, dim: int, num_heads: int, bias: bool = False):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=bias)
self.proj = nn.Linear(dim, dim, bias=bias)
def forward(self, x: torch.Tensor, cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor = None) -> torch.Tensor:
S = x.shape[0]
q, k, v = self.qkv(x).reshape(S, 3, self.num_heads, self.head_dim).permute(1, 0, 2, 3).unbind(0)
if rotary_pos_emb is not None:
q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
mask = torch.full([1, S, S], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype)
for i in range(1, len(cu_seqlens)):
mask[..., cu_seqlens[i - 1]:cu_seqlens[i], cu_seqlens[i - 1]:cu_seqlens[i]] = 0
q = q.transpose(0, 1); k = k.transpose(0, 1); v = v.transpose(0, 1)
w = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) + mask
w = F.softmax(w, dim=-1, dtype=torch.float32).to(q.dtype)
out = torch.matmul(w, v).transpose(0, 1).reshape(S, -1)
return self.proj(out)
def _build_attn(dim: int, num_heads: int, attn_impl: str, is_causal: bool, bias: bool) -> nn.Module:
if attn_impl == "flash_attention_2":
if flash_attn_available:
return VisionFlashAttention2(dim, num_heads, is_causal=is_causal, bias=bias)
attn_impl = "sdpa"
if attn_impl == "sdpa":
return VisionAttentionSdpa(dim, num_heads, bias=bias)
return VisionAttentionEager(dim, num_heads, bias=bias)
# ---------------------------------------------------------------------------
# MLP (standard 2-layer GELU, matching official ViTAEv2)
# ---------------------------------------------------------------------------
class Mlp(nn.Module):
def __init__(self, dim: int, hidden_dim: int, bias: bool = False):
super().__init__()
self.fc1 = nn.Linear(dim, hidden_dim, bias=bias)
self.fc2 = nn.Linear(hidden_dim, dim, bias=bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.fc2(F.gelu(self.fc1(x)))
class SwiGLU(nn.Module):
def __init__(self, dim: int, hidden_dim: int, bias: bool = False):
super().__init__()
self.gate_proj = nn.Linear(dim, hidden_dim, bias=bias)
self.up_proj = nn.Linear(dim, hidden_dim, bias=bias)
self.down_proj = nn.Linear(hidden_dim, dim, bias=bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
# ---------------------------------------------------------------------------
# Window-attention helpers
# ---------------------------------------------------------------------------
def window_partition(x: torch.Tensor, window_size: int):
"""x: [B, H, W, C] → [nW*B, ws, ws, C]"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
return x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
def window_reverse(windows: torch.Tensor, window_size: int, H: int, W: int) -> torch.Tensor:
"""windows: [nW*B, ws, ws, C] → [B, H, W, C]"""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
return x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
# ---------------------------------------------------------------------------
# NormalCell with 2D-RoPE
# ---------------------------------------------------------------------------
class NormalCell2DRoPE(nn.Module):
"""ViTAEv2 NormalCell with windowed or global attention + 2D-RoPE + PCM."""
def __init__(self, dim: int, num_heads: int, mlp_ratio: float = 4.0,
window_size: int = 7, window_attn: bool = True,
bias: bool = False, norm_eps: float = 1e-6,
attn_impl: str = "sdpa", is_causal: bool = False,
pcm_group: int = None):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
if self.head_dim % 4 != 0:
raise ValueError("2D-RoPE requires attention head_dim divisible by 4.")
self.window_attn = window_attn
self.window_size = window_size
self.norm1 = RMSNorm(dim, eps=norm_eps)
self.norm2 = RMSNorm(dim, eps=norm_eps)
self.qkv = nn.Linear(dim, dim * 3, bias=bias)
self.proj = nn.Linear(dim, dim, bias=bias)
hidden_dim = int(dim * mlp_ratio)
swiglu_hidden_dim = math.ceil((2 * hidden_dim) / 3)
self.mlp = SwiGLU(dim, swiglu_hidden_dim, bias=bias)
g = pcm_group if pcm_group is not None else max(1, dim // 64)
self.pcm = nn.Sequential(
nn.Conv2d(dim, hidden_dim, 3, 1, 1, groups=g),
RMSNorm2d(hidden_dim, eps=norm_eps),
nn.SiLU(inplace=True),
nn.Conv2d(hidden_dim, dim, 3, 1, 1, groups=g),
RMSNorm2d(dim, eps=norm_eps),
nn.SiLU(inplace=True),
nn.Conv2d(dim, dim, 3, 1, 1, groups=g),
)
self._attn_impl = attn_impl
self._is_causal = is_causal
half_d = self.head_dim // 2
ws = window_size
h_ids = torch.arange(ws).unsqueeze(1).expand(ws, ws).reshape(-1)
w_ids = torch.arange(ws).unsqueeze(0).expand(ws, ws).reshape(-1)
theta = 1.0 / (10000.0 ** (torch.arange(0, half_d, 2, dtype=torch.float32) / half_d))
freq_h = torch.outer(h_ids.float(), theta) # [ws*ws, half_d//2]
freq_w = torch.outer(w_ids.float(), theta)
freqs = torch.stack([freq_h, freq_w], dim=2).reshape(ws * ws, half_d) # [ws*ws, half_d]
cos_emb = freqs.cos().repeat(1, 2)[None, None] # [1, 1, ws*ws, head_dim]
sin_emb = freqs.sin().repeat(1, 2)[None, None]
self.register_buffer("_local_rope_cos", cos_emb, persistent=False)
self.register_buffer("_local_rope_sin", sin_emb, persistent=False)
def _apply_local_rope_window(self, q: torch.Tensor, ws: int) -> torch.Tensor:
q_f = q.float()
q_out = q_f * self._local_rope_cos + rotate_half(q_f) * self._local_rope_sin
return q_out.to(q.dtype)
def _window_attn(self, x: torch.Tensor, H: int, W: int) -> torch.Tensor:
ws = self.window_size
pad_h = (ws - H % ws) % ws
pad_w = (ws - W % ws) % ws
x2d = x.view(H, W, self.dim).unsqueeze(0)
if pad_h + pad_w > 0:
x2d = x2d.permute(0, 3, 1, 2)
x2d = F.pad(x2d, (0, pad_w, 0, pad_h))
x2d = x2d.permute(0, 2, 3, 1)
Hp, Wp = H + pad_h, W + pad_w
wins = window_partition(x2d, ws) # [nW, ws, ws, dim]
nWB = wins.shape[0]
wins = wins.view(nWB, ws * ws, self.dim)
q, k, v = self.qkv(wins).reshape(nWB, ws * ws, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4).unbind(0)
q = self._apply_local_rope_window(q, ws)
k = self._apply_local_rope_window(k, ws)
attn = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
attn = F.softmax(attn, dim=-1, dtype=torch.float32).to(q.dtype)
out = torch.matmul(attn, v).transpose(1, 2).reshape(nWB, ws * ws, self.dim)
out = self.proj(out)
out = out.view(nWB, ws, ws, self.dim)
out = window_reverse(out, ws, Hp, Wp)
if pad_h + pad_w > 0:
out = out[:, :H, :W, :].contiguous()
return out.view(-1, self.dim)
def _global_attn(self, x: torch.Tensor, cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor) -> torch.Tensor:
S = x.shape[0]
q, k, v = self.qkv(x).reshape(S, 3, self.num_heads, self.head_dim).permute(1, 0, 2, 3).unbind(0)
if rotary_pos_emb is not None:
q = apply_rotary_pos_emb_vision(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_vision(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
if flash_attn_available and self._attn_impl == "flash_attention_2":
max_sl = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
out = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_sl, max_sl, causal=False)
else:
mask = torch.zeros([1, S, S], device=q.device, dtype=torch.bool)
for i in range(1, len(cu_seqlens)):
mask[..., cu_seqlens[i - 1]:cu_seqlens[i], cu_seqlens[i - 1]:cu_seqlens[i]] = True
qt = q.transpose(0, 1).unsqueeze(0)
kt = k.transpose(0, 1).unsqueeze(0)
vt = v.transpose(0, 1).unsqueeze(0)
if mask.stride(-1) != 1:
mask = torch.empty_like(mask, memory_format=torch.contiguous_format).copy_(mask)
out = F.scaled_dot_product_attention(qt, kt, vt, mask, dropout_p=0.0)
out = out.squeeze(0).transpose(0, 1)
out = out.reshape(S, -1)
return self.proj(out)
def _pcm_single(self, x_flat: torch.Tensor, H: int, W: int) -> torch.Tensor:
x2d = x_flat.view(1, H, W, self.dim).permute(0, 3, 1, 2) # [1, dim, H, W]
return self.pcm(x2d).permute(0, 2, 3, 1).reshape(H * W, self.dim)
def _pcm(self, x: torch.Tensor, cu_seqlens: torch.Tensor, grid_hw: torch.Tensor) -> torch.Tensor:
seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
outs, token_idx = [], 0
for img_idx, sl in enumerate(seqlens):
H, W = int(grid_hw[img_idx, 0]), int(grid_hw[img_idx, 1])
outs.append(self._pcm_single(x[token_idx: token_idx + sl], H, W))
token_idx += sl
return torch.cat(outs, dim=0)
def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor, grid_hw: torch.Tensor = None) -> torch.Tensor:
shortcut = hidden_states
x_norm = self.norm1(hidden_states)
if self.window_attn and grid_hw is not None:
seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
attn_outs, pcm_outs = [], []
token_idx = 0
for img_idx, sl in enumerate(seqlens):
H, W = int(grid_hw[img_idx, 0]), int(grid_hw[img_idx, 1])
attn_outs.append(self._window_attn(x_norm[token_idx: token_idx + sl], H, W))
pcm_outs.append(self._pcm_single(shortcut[token_idx: token_idx + sl], H, W))
token_idx += sl
attn_out = torch.cat(attn_outs, dim=0)
pcm_out = torch.cat(pcm_outs, dim=0)
else:
attn_out = self._global_attn(x_norm, cu_seqlens, rotary_pos_emb)
pcm_out = self._pcm(shortcut, cu_seqlens, grid_hw) if grid_hw is not None else 0.0
hidden_states = shortcut + attn_out + pcm_out
hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
return hidden_states
# ---------------------------------------------------------------------------
# ReductionCell
# ---------------------------------------------------------------------------
class ReductionCell(nn.Module):
"""
ViTAEv2 ReductionCell: downsamples tokens via PRM (4 dilation branches) +
single Transformer attention block + 3-conv PCM shortcut.
RC_tokens_type in ViTAEv2-S: RC0&RC1 -> window attention (W), RC2&RC3 -> global attention (F).
RC_group in ViTAEv2-S: [1, 16, 32, 64] for RC0, RC1, RC2, RC3.
"""
def __init__(self, in_dim: int, out_dim: int,
num_heads: int = 1,
window_attn: bool = True,
window_size: int = 7,
pcm_group: int = 1,
prm_embed_dim: int = 64,
downsample_ratio: int = 2,
kernel_size: int = 3,
norm_eps: float = 1e-6,
bias: bool = False,
attn_impl: str = "sdpa"):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.num_heads = num_heads
self.head_dim = out_dim // num_heads
self.window_attn = window_attn
self.window_size = window_size
self.prm_embed_dim = prm_embed_dim
self.downsample_ratio = downsample_ratio
self.kernel_size = kernel_size
self._attn_impl = attn_impl
dilations = [1, 2, 3, 4]
self.prm_convs = nn.ModuleList()
for d in dilations:
pad = math.ceil(((kernel_size - 1) * d + 1 - downsample_ratio) / 2)
self.prm_convs.append(nn.Sequential(
nn.Conv2d(in_dim, prm_embed_dim, kernel_size=kernel_size, stride=downsample_ratio, padding=pad, dilation=d),
nn.GELU(),
))
self.prm_proj = nn.Linear(prm_embed_dim * 4, out_dim, bias=bias)
pcm_strides = []
residual = downsample_ratio // 2
for _ in range(3):
pcm_strides.append((residual > 0) + 1)
residual //= 2
assert residual == 0
g = pcm_group
self.pcm = nn.Sequential(
nn.Conv2d(in_dim, prm_embed_dim, 3, stride=pcm_strides[0], padding=1, groups=g),
RMSNorm2d(prm_embed_dim, eps=norm_eps),
nn.SiLU(inplace=True),
nn.Conv2d(prm_embed_dim, prm_embed_dim, 3, stride=pcm_strides[1], padding=1, groups=g),
RMSNorm2d(prm_embed_dim, eps=norm_eps),
nn.SiLU(inplace=True),
nn.Conv2d(prm_embed_dim, out_dim, 3, stride=pcm_strides[2], padding=1, groups=g),
)
self.norm_attn = RMSNorm(out_dim, eps=norm_eps)
self.qkv = nn.Linear(out_dim, out_dim * 3, bias=bias)
self.attn_proj = nn.Linear(out_dim, out_dim, bias=bias)
self.norm_ffn = RMSNorm(out_dim, eps=norm_eps)
self.mlp = Mlp(out_dim, out_dim, bias=bias)
def _window_attn_rc(self, y: torch.Tensor, H: int, W: int) -> torch.Tensor:
"""Window self-attention on flat tokens [H*W, out_dim]. Returns [H*W, out_dim]."""
ws = self.window_size
y2d = y.view(1, H, W, self.out_dim)
pad_h = (ws - H % ws) % ws
pad_w = (ws - W % ws) % ws
if pad_h + pad_w > 0:
y2d = F.pad(y2d.permute(0, 3, 1, 2), (0, pad_w, 0, pad_h)).permute(0, 2, 3, 1)
Hp, Wp = H + pad_h, W + pad_w
wins = window_partition(y2d, ws) # [nW, ws, ws, out_dim]
nW = wins.shape[0]
wins_flat = wins.view(nW, ws * ws, self.out_dim)
q, k, v = self.qkv(wins_flat).reshape(nW, ws * ws, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4).unbind(0)
attn = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
attn = F.softmax(attn, dim=-1, dtype=torch.float32).to(q.dtype)
out = torch.matmul(attn, v).transpose(1, 2).reshape(nW, ws * ws, self.out_dim)
out = self.attn_proj(out)
out = window_reverse(out.view(nW, ws, ws, self.out_dim), ws, Hp, Wp)
if pad_h + pad_w > 0:
out = out[:, :H, :W, :].contiguous()
return out.view(H * W, self.out_dim)
def _global_attn_rc(self, y: torch.Tensor, cu_seqlens: torch.Tensor,
attn_impl: str = "sdpa") -> torch.Tensor:
"""Global attention over packed tokens using cu_seqlens. Returns same shape."""
S = y.shape[0]
q, k, v = self.qkv(y).reshape(S, 3, self.num_heads, self.head_dim).permute(1, 0, 2, 3).unbind(0)
if flash_attn_available and attn_impl == "flash_attention_2":
max_sl = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
out = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_sl, max_sl, causal=False)
else:
mask = torch.zeros([1, S, S], device=q.device, dtype=torch.bool)
for i in range(1, len(cu_seqlens)):
mask[..., cu_seqlens[i - 1]:cu_seqlens[i], cu_seqlens[i - 1]:cu_seqlens[i]] = True
qt = q.transpose(0, 1).unsqueeze(0)
kt = k.transpose(0, 1).unsqueeze(0)
vt = v.transpose(0, 1).unsqueeze(0)
if mask.stride(-1) != 1:
mask = torch.empty_like(mask, memory_format=torch.contiguous_format).copy_(mask)
out = F.scaled_dot_product_attention(qt, kt, vt, mask, dropout_p=0.0)
out = out.squeeze(0).transpose(0, 1)
return self.attn_proj(out.reshape(S, self.out_dim))
def forward(self, x: torch.Tensor, cu_seqlens: torch.Tensor, grid_hw: torch.Tensor):
"""
x: [total_tokens, in_dim], cu_seqlens: [N+1], grid_hw: [N, 2]
Returns: (x_out [total_new, out_dim], new_cu_seqlens [N+1], new_grid_hw [N, 2])
"""
seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
prm_list, pcm_list, new_hw_list = [], [], []
token_idx = 0
for img_idx, sl in enumerate(seqlens):
H, W = int(grid_hw[img_idx, 0]), int(grid_hw[img_idx, 1])
tok = x[token_idx: token_idx + sl]
x2d = tok.view(1, H, W, self.in_dim).permute(0, 3, 1, 2) # [1, in_dim, H, W]
ys = [conv(x2d) for conv in self.prm_convs] # each [1, prm_embed_dim, H//2, W//2]
Hn, Wn = ys[0].shape[2], ys[0].shape[3]
y = torch.cat(ys, dim=1) # [1, prm_embed_dim*4, Hn, Wn]
y = y.permute(0, 2, 3, 1).reshape(Hn * Wn, self.prm_embed_dim * 4)
prm_list.append(self.prm_proj(y)) # [Hn*Wn, out_dim]
pcm_list.append(self.pcm(x2d).permute(0, 2, 3, 1).reshape(Hn * Wn, self.out_dim))
new_hw_list.append((Hn, Wn))
token_idx += sl
prm_out = torch.cat(prm_list, dim=0)
pcm_out = torch.cat(pcm_list, dim=0)
new_lens = torch.tensor([h * w for h, w in new_hw_list], dtype=torch.long, device=x.device)
new_cu_seqlens = F.pad(new_lens.cumsum(0, dtype=torch.int32), (1, 0), value=0)
new_grid_hw = torch.tensor(new_hw_list, dtype=torch.long, device=x.device)
y = prm_out + pcm_out
y_norm = self.norm_attn(y)
if self.window_attn:
attn_outs = []
idx = 0
for Hn, Wn in new_hw_list:
sl_n = Hn * Wn
attn_outs.append(self._window_attn_rc(y_norm[idx: idx + sl_n], Hn, Wn))
idx += sl_n
attn_out = torch.cat(attn_outs, dim=0)
else:
attn_out = self._global_attn_rc(y_norm, new_cu_seqlens, self._attn_impl)
y = y + attn_out
y = y + self.mlp(self.norm_ffn(y))
return y, new_cu_seqlens, new_grid_hw
class ConvStem(nn.Module):
def __init__(self, num_channels: int = 3, out_dim: int = 64,
patch_size: int = 32, temporal_patch_size: int = 1):
super().__init__()
self.num_channels = num_channels
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
inter = out_dim // 2
self.conv1 = nn.Sequential(
nn.Conv2d(num_channels, inter, 3, stride=2, padding=1, bias=False),
RMSNorm2d(inter),
nn.GELU(),
)
self.conv2 = nn.Sequential(
nn.Conv2d(inter, out_dim, 3, stride=2, padding=1, bias=False),
RMSNorm2d(out_dim),
nn.GELU(),
)
self.proj = nn.Conv2d(out_dim, out_dim, 3, stride=1, padding=1)
self.norm = RMSNorm(out_dim)
def forward(self, pixel_values: torch.Tensor, grid_thw: torch.Tensor):
"""
pixel_values: [total_tiles, C * temporal * ph * pw] — flat from Qwen2VLImageProcessor
OR [total_tiles, C, ph, pw] — already spatial
grid_thw: [N, 3] (t, h_tiles, w_tiles)
Returns:
x: [total_tokens, out_dim] packed
cu_seqlens: [N+1]
grid_hw: [N, 2] (H_feat, W_feat) after 4x downsampling
"""
# Reshape flat pixel_values [N, C*T*ph*pw] → [N, C, ph, pw]
if pixel_values.ndim == 2:
C = self.num_channels
T = self.temporal_patch_size
hw = int(math.sqrt(pixel_values.shape[1] / (C * T)))
pixel_values = pixel_values.view(-1, C, T, hw, hw)[:, :, 0] # [N, C, ph, pw]
N = grid_thw.shape[0]
outputs = []
hw_list = []
token_idx = 0
for img_idx in range(N):
t, h, w = int(grid_thw[img_idx, 0]), int(grid_thw[img_idx, 1]), int(grid_thw[img_idx, 2])
num_tiles = t * h * w
tiles = pixel_values[token_idx: token_idx + num_tiles] # [h*w, C, ph, pw]
token_idx += num_tiles
ph = tiles.shape[-2]
pw = tiles.shape[-1]
img = tiles.view(h, w, -1, ph, pw).permute(2, 0, 3, 1, 4).reshape(-1, h * ph, w * pw).unsqueeze(0)
out = self.conv1(img) # [1, inter, H/2, W/2]
out = self.conv2(out) # [1, dim, H/4, W/4]
out = self.proj(out) # [1, dim, H/4, W/4]
Hn, Wn = out.shape[2], out.shape[3]
out = out.squeeze(0).permute(1, 2, 0).reshape(Hn * Wn, -1) # [Hn*Wn, dim]
out = self.norm(out)
outputs.append(out)
hw_list.append((Hn, Wn))
x = torch.cat(outputs, dim=0)
grid_hw = torch.tensor(hw_list, dtype=torch.long, device=x.device)
lens = torch.tensor([h * w for h, w in hw_list], dtype=torch.long, device=x.device)
cu_seqlens = F.pad(lens.cumsum(0, dtype=torch.int32), (1, 0), value=0)
return x, cu_seqlens, grid_hw
class StemReductionCell(nn.Module):
"""
First-stage reduction cell replacing ConvStem: reads raw pixel tiles and applies
4x PRM (kernel=7, dilations=[1,2,3,4], stride=4) + PCM +
window-attention block. Matches the official ViTAEv2-S RC0 (downsample_ratio=4).
Forward interface is identical to the old ConvStem:
forward(pixel_values, grid_thw) -> (packed_tokens, cu_seqlens, grid_hw)
"""
def __init__(self, num_channels: int = 3, out_dim: int = 64, num_heads: int = 1,
window_size: int = 7, prm_embed_dim: int = 64,
downsample_ratio: int = 4, kernel_size: int = 7, pcm_group: int = 1,
patch_size: int = 32, temporal_patch_size: int = 1,
norm_eps: float = 1e-6, bias: bool = False, attn_impl: str = "sdpa"):
super().__init__()
self.num_channels = num_channels
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
self.out_dim = out_dim
self.num_heads = num_heads
self.head_dim = out_dim // num_heads
self.window_size = window_size
self.prm_embed_dim = prm_embed_dim
self.downsample_ratio = downsample_ratio
self.kernel_size = kernel_size
self._attn_impl = attn_impl
in_dim = num_channels
dilations = [1, 2, 3, 4]
self.prm_convs = nn.ModuleList()
for d in dilations:
pad = math.ceil(((kernel_size - 1) * d + 1 - downsample_ratio) / 2)
self.prm_convs.append(nn.Sequential(
nn.Conv2d(in_dim, prm_embed_dim, kernel_size=kernel_size,
stride=downsample_ratio, padding=pad, dilation=d),
nn.GELU(),
))
self.prm_proj = nn.Linear(prm_embed_dim * 4, out_dim, bias=bias)
pcm_strides = []
residual = downsample_ratio // 2
for _ in range(3):
pcm_strides.append((residual > 0) + 1)
residual //= 2
assert residual == 0
inter = prm_embed_dim
self.pcm = nn.Sequential(
nn.Conv2d(in_dim, inter, 3, stride=pcm_strides[0], padding=1, groups=pcm_group),
RMSNorm2d(inter, eps=norm_eps),
nn.SiLU(inplace=True),
nn.Conv2d(inter, inter, 3, stride=pcm_strides[1], padding=1, groups=pcm_group),
RMSNorm2d(inter, eps=norm_eps),
nn.SiLU(inplace=True),
nn.Conv2d(inter, out_dim, 3, stride=pcm_strides[2], padding=1, groups=pcm_group),
)
self.norm_attn = RMSNorm(out_dim, eps=norm_eps)
self.qkv = nn.Linear(out_dim, out_dim * 3, bias=bias)
self.attn_proj = nn.Linear(out_dim, out_dim, bias=bias)
self.norm_ffn = RMSNorm(out_dim, eps=norm_eps)
self.mlp = Mlp(out_dim, out_dim, bias=bias)
def _window_attn(self, y: torch.Tensor, H: int, W: int) -> torch.Tensor:
"""Window self-attention on flat tokens [H*W, out_dim]."""
ws = self.window_size
y2d = y.view(1, H, W, self.out_dim)
pad_h = (ws - H % ws) % ws
pad_w = (ws - W % ws) % ws
if pad_h + pad_w > 0:
y2d = F.pad(y2d.permute(0, 3, 1, 2), (0, pad_w, 0, pad_h)).permute(0, 2, 3, 1)
Hp, Wp = H + pad_h, W + pad_w
wins = window_partition(y2d, ws) # [nW, ws, ws, out_dim]
nW = wins.shape[0]
wins_flat = wins.view(nW, ws * ws, self.out_dim)
q, k, v = (self.qkv(wins_flat)
.reshape(nW, ws * ws, 3, self.num_heads, self.head_dim)
.permute(2, 0, 3, 1, 4).unbind(0))
attn = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
attn = F.softmax(attn, dim=-1, dtype=torch.float32).to(q.dtype)
out = torch.matmul(attn, v).transpose(1, 2).reshape(nW, ws * ws, self.out_dim)
out = self.attn_proj(out)
out = window_reverse(out.view(nW, ws, ws, self.out_dim), ws, Hp, Wp)
if pad_h + pad_w > 0:
out = out[:, :H, :W, :].contiguous()
return out.view(H * W, self.out_dim)
def forward(self, pixel_values: torch.Tensor, grid_thw: torch.Tensor):
"""
pixel_values : [total_tiles, C, ph, pw] or [total_tiles, C*T*ph*pw]
grid_thw : [N, 3] (t, h_tiles, w_tiles)
Returns : (x [total_tokens, out_dim], cu_seqlens [N+1], grid_hw [N, 2])
"""
# Reshape flat tiles if preprocessor returned [N, C*T*ph*pw]
if pixel_values.ndim == 2:
C = self.num_channels
T = self.temporal_patch_size
hw_px = int(math.sqrt(pixel_values.shape[1] / (C * T)))
pixel_values = pixel_values.view(-1, C, T, hw_px, hw_px)[:, :, 0]
N = grid_thw.shape[0]
prm_list, pcm_list, hw_list = [], [], []
tile_idx = 0
for img_idx in range(N):
t, h, w = (int(grid_thw[img_idx, 0]),
int(grid_thw[img_idx, 1]),
int(grid_thw[img_idx, 2]))
num_tiles = t * h * w
tiles = pixel_values[tile_idx: tile_idx + num_tiles] # [h*w, C, ph, pw]
tile_idx += num_tiles
ph, pw = tiles.shape[-2], tiles.shape[-1]
img = (tiles.view(h, w, -1, ph, pw)
.permute(2, 0, 3, 1, 4)
.reshape(-1, h * ph, w * pw)
.unsqueeze(0))
ys = [conv(img) for conv in self.prm_convs]
Hn, Wn = ys[0].shape[2], ys[0].shape[3]
y_cat = torch.cat(ys, dim=1) # [1, emb*4, Hn, Wn]
y_flat = y_cat.permute(0, 2, 3, 1).reshape(Hn * Wn, self.prm_embed_dim * 4)
prm_list.append(self.prm_proj(y_flat)) # [Hn*Wn, out_dim]
pcm_list.append(
self.pcm(img).permute(0, 2, 3, 1).reshape(Hn * Wn, self.out_dim)
)
hw_list.append((Hn, Wn))
prm_out = torch.cat(prm_list, dim=0) # [total_tokens, out_dim]
pcm_out = torch.cat(pcm_list, dim=0)
new_lens = torch.tensor([h * w for h, w in hw_list],
dtype=torch.long, device=prm_out.device)
cu_seqlens = F.pad(new_lens.cumsum(0, dtype=torch.int32), (1, 0), value=0)
grid_hw = torch.tensor(hw_list, dtype=torch.long, device=prm_out.device)
x = prm_out + pcm_out
x_norm = self.norm_attn(x)
attn_outs, idx = [], 0
for Hn, Wn in hw_list:
sl = Hn * Wn
attn_outs.append(self._window_attn(x_norm[idx: idx + sl], Hn, Wn))
idx += sl
x = x + torch.cat(attn_outs, dim=0)
x = x + self.mlp(self.norm_ffn(x))
return x, cu_seqlens, grid_hw
# ---------------------------------------------------------------------------
# 2D-RoPE for global attention
# ---------------------------------------------------------------------------
def compute_2d_rope(grid_hw: torch.Tensor, rotary_emb: VisionRotaryEmbedding):
emb_device = rotary_emb.inv_freq.device
pos_ids_list = []
for h, w in grid_hw.tolist():
h, w = int(h), int(w)
hpos = torch.arange(h, device=emb_device).unsqueeze(1).expand(h, w).flatten()
wpos = torch.arange(w, device=emb_device).unsqueeze(0).expand(h, w).flatten()
pos_ids_list.append(torch.stack([hpos, wpos], dim=-1))
pos_ids = torch.cat(pos_ids_list, dim=0)
max_size = grid_hw.max().item()
full_emb = rotary_emb(int(max_size))
emb = full_emb[pos_ids]
return torch.stack([emb[:, 0], emb[:, 1]], dim=2).reshape(emb.shape[0], -1)
# ---------------------------------------------------------------------------
# PatchMerger
# ---------------------------------------------------------------------------
class PatchMerger(nn.Module):
def __init__(self, dim: int, context_dim: int, init_std: float = 0.02):
super().__init__()
self.norm = RMSNorm(context_dim)
self.mlp = nn.Sequential(
nn.Linear(context_dim, context_dim),
nn.GELU(),
nn.Linear(context_dim, dim),
)
nn.init.normal_(self.mlp[0].weight, std=init_std)
nn.init.zeros_(self.mlp[0].bias)
nn.init.normal_(self.mlp[2].weight, std=init_std)
nn.init.zeros_(self.mlp[2].bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.mlp(self.norm(x))
# ---------------------------------------------------------------------------
# Main ViTAEv2 Vision Transformer
# ---------------------------------------------------------------------------
class MonkeyOCRv2ViTAEVisionTransformer(PreTrainedModel):
_supports_flash_attn = True
_supports_sdpa = True
_no_split_modules = ["StemReductionCell", "ReductionCell", "NormalCell2DRoPE"]
config_class = MonkeyOCRv2ViTAEEncoderConfig
def __init__(self, config: MonkeyOCRv2ViTAEEncoderConfig) -> None:
super().__init__(config)
self.config = config
sd = config.stage_dims # [64, 128, 256, 512] (ViTAEv2-S)
sdp = config.stage_depths # [2, 2, 8, 2]
sh = config.stage_heads # [1, 2, 4, 8] (head_dim=64 at all stages)
dsr = getattr(config, 'downsample_ratios', [4, 2, 2, 2])
ksz = getattr(config, 'kernel_sizes', [7, 3, 3, 3])
rct = getattr(config, 'rc_tokens_type', ["window", "window", "transformer", "transformer"])
nct = getattr(config, 'nc_tokens_type', ["window", "window", "transformer", "transformer"])
ncg = getattr(config, 'nc_groups', [1, 32, 64, 128])
rcg = list(getattr(config, 'rc_groups', [1, 16, 32, 64]))
rcpem = list(getattr(config, 'rc_embed_dims', [64, 64, 128, 256]))
rch = list(getattr(config, 'rc_heads', [1, 1, 2, 4]))
if len(rcg) == 3:
rcg = [1] + rcg
if len(rch) == 3:
rch = [1] + rch
if len(rcpem) == 3:
rcpem = [getattr(config, 'prm_embed_dim', 64)] + rcpem
ws = config.window_size
mr = config.mlp_ratio
bias = config.use_bias
eps = config.rms_norm_eps
attn_impl = config.attn_implementation
is_causal = config.is_causal
self.stem = StemReductionCell(
num_channels=config.num_channels,
out_dim=sd[0],
num_heads=rch[0],
window_size=ws,
prm_embed_dim=rcpem[0],
downsample_ratio=dsr[0],
kernel_size=ksz[0],
pcm_group=rcg[0],
patch_size=config.patch_size,
temporal_patch_size=config.temporal_patch_size,
norm_eps=eps,
bias=bias,
attn_impl=attn_impl,
)
self.stage1 = nn.ModuleList([
NormalCell2DRoPE(sd[0], sh[0], mr, ws, nct[0] == "window", bias, eps, attn_impl, is_causal, ncg[0])
for _ in range(sdp[0])])
self.rc1 = ReductionCell(sd[0], sd[1], num_heads=rch[1], window_attn=rct[1] == "window", window_size=ws, pcm_group=rcg[1], prm_embed_dim=rcpem[1], downsample_ratio=dsr[1], kernel_size=ksz[1], norm_eps=eps, bias=bias, attn_impl=attn_impl)
self.stage2 = nn.ModuleList([
NormalCell2DRoPE(sd[1], sh[1], mr, ws, nct[1] == "window", bias, eps, attn_impl, is_causal, ncg[1])
for _ in range(sdp[1])])
self.rc2 = ReductionCell(sd[1], sd[2], num_heads=rch[2], window_attn=rct[2] == "window", window_size=ws, pcm_group=rcg[2], prm_embed_dim=rcpem[2], downsample_ratio=dsr[2], kernel_size=ksz[2], norm_eps=eps, bias=bias, attn_impl=attn_impl)
head_dim_s3 = sd[2] // sh[2]
self.rotary_pos_emb_s3 = VisionRotaryEmbedding(head_dim_s3 // 2)
self.stage3 = nn.ModuleList([
NormalCell2DRoPE(sd[2], sh[2], mr, ws, nct[2] == "window", bias, eps, attn_impl, is_causal, ncg[2])
for _ in range(sdp[2])])
self.rc3 = ReductionCell(sd[2], sd[3], num_heads=rch[3], window_attn=rct[3] == "window", window_size=ws, pcm_group=rcg[3], prm_embed_dim=rcpem[3], downsample_ratio=dsr[3], kernel_size=ksz[3], norm_eps=eps, bias=bias, attn_impl=attn_impl)
head_dim_s4 = sd[3] // sh[3]
self.rotary_pos_emb = VisionRotaryEmbedding(head_dim_s4 // 2)
self.stage4 = nn.ModuleList([
NormalCell2DRoPE(sd[3], sh[3], mr, ws, nct[3] == "window", bias, eps, attn_impl, is_causal, ncg[3])
for _ in range(sdp[3])])
if config.post_norm:
self.post_trunk_norm = RMSNorm(sd[3], eps=eps)
self.gradient_checkpointing = False
self._gradient_checkpointing_func = torch.utils.checkpoint.checkpoint
self.apply(self._init_weights)
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Conv2d):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (RMSNorm, RMSNorm2d)):
module.weight.data.fill_(1.0)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.zero_()
@property
def dtype(self):
return self.stem.prm_proj.weight.dtype
@property
def device(self):
return self.stem.prm_proj.weight.device
def _run_stage(self, cells, x, cu_seqlens, rotary_pos_emb, grid_hw):
for cell in cells:
if self.gradient_checkpointing and self.training:
x = self._gradient_checkpointing_func(cell.__call__, x, cu_seqlens, rotary_pos_emb, grid_hw)
else:
x = cell(x, cu_seqlens, rotary_pos_emb, grid_hw)
return x
def forward(self, pixel_values: torch.Tensor, grid_thw: torch.Tensor) -> tuple:
pixel_values = pixel_values.to(dtype=self.dtype)
all_hidden_states =[]
all_grid_hw = []
# --- Stage 1 (1/4) ---
x, cu_seqlens, grid_hw = self.stem(pixel_values, grid_thw)
x = self._run_stage(self.stage1, x, cu_seqlens, None, grid_hw)
all_hidden_states.append(x.clone())
all_grid_hw.append(grid_hw.clone())
# --- Stage 2 (1/8) ---
x, cu_seqlens, grid_hw = self.rc1(x, cu_seqlens, grid_hw)
x = self._run_stage(self.stage2, x, cu_seqlens, None, grid_hw)
all_hidden_states.append(x.clone())
all_grid_hw.append(grid_hw.clone())
# --- Stage 3 (1/16) → global attention + 2D-RoPE ---
x, cu_seqlens, grid_hw = self.rc2(x, cu_seqlens, grid_hw)
rotary_pos_emb_s3 = compute_2d_rope(grid_hw, self.rotary_pos_emb_s3)
x = self._run_stage(self.stage3, x, cu_seqlens, rotary_pos_emb_s3, grid_hw)
all_hidden_states.append(x.clone())
all_grid_hw.append(grid_hw.clone())
# --- Stage 4 (1/32) → global attention ---
x, cu_seqlens, grid_hw = self.rc3(x, cu_seqlens, grid_hw)
rotary_pos_emb = compute_2d_rope(grid_hw, self.rotary_pos_emb)
x = self._run_stage(self.stage4, x, cu_seqlens, rotary_pos_emb, grid_hw)
if self.config.post_norm:
x = self.post_trunk_norm(x)
all_hidden_states.append(x)
all_grid_hw.append(grid_hw)
return all_hidden_states, all_grid_hw
|