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3217f9a 2574ad2 3217f9a | 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 | """MoE-ViT: MoE++ (zero/copy/constant experts) on a pretrained DINOv3 ViT stem.
Replaces the MoR recursive core with a plain stacked-transformer core whose
FFNs are MoE++ layers (arXiv:2410.07348):
dense MLP (d_ff=1536) -> 4 FFN experts (d_ff=384) + 1 zero + 1 copy
+ 2 constant experts, top_k=2 per token
Routing is per patch token, so easy image regions land on zero/copy/constant
specialists (near-zero compute) while hard regions route to real FFN experts -
adaptive compute at the finest granularity, with no recursion machinery.
The frozen stem and the study-level pooling/head mirror MoR-ViT so the two
models share the same ``features()`` / ``forward()`` contract and can be
compared on identical benchmarks.
"""
from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
import timm
HAS_TIMM = True
except ImportError: # pragma: no cover - optional dependency
HAS_TIMM = False
class SwiGLU(nn.Module):
def __init__(self, d_model: int, d_ff: int):
super().__init__()
self.w1 = nn.Linear(d_model, d_ff, bias=False)
self.w2 = nn.Linear(d_ff, d_model, bias=False)
self.w3 = nn.Linear(d_model, d_ff, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class ConstantExpert(nn.Module):
"""MoE++ constant expert: alpha1*x + alpha2*v (negligible params)."""
def __init__(self, d_model: int):
super().__init__()
self.v = nn.Parameter(torch.zeros(d_model))
self.wc = nn.Linear(d_model, 2, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
alpha = F.softmax(self.wc(x), dim=-1)
return (alpha[..., 0:1] * x + alpha[..., 1:2] * self.v.to(dtype=x.dtype)).to(dtype=x.dtype)
class MoEFFN(nn.Module):
"""Sparse MoE FFN with MoE++ zero / copy / constant experts.
Index layout: [FFN | zero | copy | constant]. Routing is softmax top-k
with renormalisation; only tokens selected for a real FFN expert are
computed (``index_select``), so zero-compute routing is real skipping.
Returns the load-balance aux loss and pre-softmax gate logits.
"""
def __init__(
self,
d_model: int,
d_ff: int,
num_ffn: int = 4,
top_k: int = 2,
n_zero: int = 1,
n_copy: int = 1,
n_const: int = 2,
tau: float = 0.75,
gating_residual: bool = True,
gate_ctx: bool = False,
):
super().__init__()
self.num_ffn = num_ffn
self.top_k = min(top_k, num_ffn + n_zero + n_copy + n_const)
self.n_zero = max(0, n_zero)
self.n_copy = max(0, n_copy)
self.n_const = max(0, n_const)
self.n_zc = self.n_zero + self.n_copy + self.n_const
self.total_experts = num_ffn + self.n_zc
self.last_counts: torch.Tensor | None = None
self.tau = float(tau)
self.gate_ctx = gate_ctx
self.copy_start = num_ffn + self.n_zero
self.const_start = self.copy_start + self.n_copy
self.router = nn.Linear(d_model, self.total_experts, bias=False)
self.experts = nn.ModuleList([SwiGLU(d_model, d_ff) for _ in range(num_ffn)])
self.const_experts = nn.ModuleList([ConstantExpert(d_model) for _ in range(self.n_const)])
self.gating_residual = None
if gating_residual:
self.gating_residual = nn.Linear(self.total_experts, self.total_experts, bias=False)
nn.init.zeros_(self.gating_residual.weight)
self.ctx_proj = None
if gate_ctx:
self.ctx_proj = nn.Linear(d_model, self.total_experts, bias=False)
nn.init.zeros_(self.ctx_proj.weight)
eta = torch.ones(self.total_experts)
if self.n_zc > 0:
eta[num_ffn:] = self.tau
self.register_buffer("eta", eta, persistent=False)
def forward(
self,
x: torch.Tensor,
prev_gate: torch.Tensor | None = None,
ctx: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Returns (output, aux_loss, gate_logits, gate_weights)."""
B, T, D = x.shape
x_2d = x.reshape(-1, D)
N = x_2d.size(0)
gate_logits = self.router(x_2d)
if self.ctx_proj is not None and ctx is not None:
gate_logits = gate_logits + self.ctx_proj(ctx).repeat_interleave(T, dim=0)
if self.gating_residual is not None and prev_gate is not None:
if prev_gate.dim() == 3:
prev_gate = prev_gate.reshape(-1, prev_gate.size(-1))
if prev_gate.size(0) == N and prev_gate.size(-1) == self.total_experts:
gate_logits = gate_logits + self.gating_residual(prev_gate)
gate_weights = F.softmax(gate_logits, dim=-1)
top_weights, top_indices = gate_weights.topk(self.top_k, dim=-1)
top_weights = top_weights / top_weights.sum(dim=-1, keepdim=True).clamp(min=1e-9)
route_weights = top_weights.to(dtype=x_2d.dtype)
flat_expert = top_indices.reshape(-1)
flat_token = (
torch.arange(N, device=x.device)
.unsqueeze(1)
.expand(-1, self.top_k)
.reshape(-1)
)
flat_weight = route_weights.reshape(-1, 1)
order = torch.argsort(flat_expert, stable=True)
sorted_tokens = flat_token[order]
sorted_weights = flat_weight[order]
sorted_x = x_2d.index_select(0, sorted_tokens)
counts = torch.bincount(
flat_expert, minlength=self.total_experts
).to(torch.int64)
offsets = torch.cat(
[torch.zeros(1, dtype=torch.int64, device=x.device),
torch.cumsum(counts, 0)[:-1]]
)
output = torch.zeros_like(x_2d)
for expert_idx in range(self.num_ffn):
lo = int(offsets[expert_idx])
hi = lo + int(counts[expert_idx])
if lo == hi:
continue
tids = sorted_tokens[lo:hi]
w = sorted_weights[lo:hi]
output.index_add_(
0, tids,
(self.experts[expert_idx](sorted_x[lo:hi]) * w).to(dtype=output.dtype),
)
if self.n_zc > 0:
for i in range(self.n_copy):
expert_idx = self.copy_start + i
lo = int(offsets[expert_idx])
hi = lo + int(counts[expert_idx])
if lo == hi:
continue
tids = sorted_tokens[lo:hi]
w = sorted_weights[lo:hi]
output.index_add_(
0, tids, (sorted_x[lo:hi] * w).to(dtype=output.dtype)
)
for i in range(self.n_const):
expert_idx = self.const_start + i
lo = int(offsets[expert_idx])
hi = lo + int(counts[expert_idx])
if lo == hi:
continue
tids = sorted_tokens[lo:hi]
w = sorted_weights[lo:hi]
output.index_add_(
0, tids,
(self.const_experts[i](sorted_x[lo:hi]) * w).to(dtype=output.dtype),
)
counts = torch.zeros(self.total_experts, device=x.device, dtype=gate_weights.dtype)
counts.scatter_add_(0, flat_expert, torch.ones_like(flat_expert, dtype=gate_weights.dtype))
self.last_counts = counts.detach()
fractions = counts / counts.sum().clamp(min=1e-12)
avg_prob = gate_weights.mean(dim=0)
if self.n_zc > 0:
aux_loss = self.total_experts * (self.eta.to(dtype=gate_weights.dtype) * fractions * avg_prob).sum()
else:
aux_loss = self.total_experts * (fractions * avg_prob).sum()
return (output.view(B, T, D), aux_loss, gate_logits,
gate_weights.view(B, T, self.total_experts))
class MoEAttentionBlock(nn.Module):
"""Pre-norm attention + MoE++ FFN. Stores the FFN gate maps for the
spatial-smoothness (TV) loss."""
def __init__(
self,
dim: int,
n_heads: int = 6,
d_ff: int = 384,
num_ffn: int = 4,
top_k: int = 2,
n_zero: int = 1,
n_copy: int = 1,
n_const: int = 2,
tau: float = 0.75,
gating_residual: bool = True,
gate_ctx: bool = False,
):
super().__init__()
self.n_heads = n_heads
self.head_dim = dim // n_heads
self.q = nn.Linear(dim, dim)
self.k = nn.Linear(dim, dim)
self.v = nn.Linear(dim, dim)
self.proj = nn.Linear(dim, dim)
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.moe = MoEFFN(
dim, d_ff, num_ffn=num_ffn, top_k=top_k,
n_zero=n_zero, n_copy=n_copy, n_const=n_const,
tau=tau, gating_residual=gating_residual, gate_ctx=gate_ctx,
)
def forward(
self, x: torch.Tensor, prev_gate: torch.Tensor | None = None,
ctx: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
S, T, D = x.shape
H = self.n_heads
xn = self.norm1(x)
q = self.q(xn).reshape(S, T, H, self.head_dim).transpose(1, 2)
k = self.k(xn).reshape(S, T, H, self.head_dim).transpose(1, 2)
v = self.v(xn).reshape(S, T, H, self.head_dim).transpose(1, 2)
attn = (q @ k.transpose(-1, -2) / (self.head_dim ** 0.5)).softmax(dim=-1)
out = (attn @ v).transpose(1, 2).reshape(S, T, D)
x = x + self.proj(out)
y, aux, gate_logits, gate_weights = self.moe(
self.norm2(x), prev_gate=prev_gate, ctx=ctx)
x = x + y
return x, aux, gate_logits, gate_weights
class SliceAttentionPool(nn.Module):
"""Weight slices by learned relevance before aggregating into a study vector."""
def __init__(self, dim: int):
super().__init__()
self.query = nn.Parameter(torch.randn(dim))
self.scale = dim ** -0.5
def forward(self, x: torch.Tensor) -> torch.Tensor:
w = F.softmax(x @ self.query * self.scale, dim=0)
return (w.unsqueeze(1) * x).sum(0)
class PerTargetPool(nn.Module):
"""Per-finding attention over slices: each of n_classes findings owns a
query that weights the slices most relevant to it -> [n_classes, D]."""
def __init__(self, dim: int, n_classes: int):
super().__init__()
self.queries = nn.Parameter(torch.randn(n_classes, dim) * dim ** -0.5)
self.scale = dim ** -0.5
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: [S, D] slice vectors -> [n_classes, S] attention weights
w = F.softmax(x @ self.queries.T * self.scale, dim=0)
return (x.unsqueeze(0) * w.T.unsqueeze(-1)).sum(1)
class PerTargetHead(nn.Module):
"""Shared MLP then per-target projection (12 findings, each its own weight)."""
def __init__(self, dim: int, n_classes: int, hidden: int = 256):
super().__init__()
self.mlp = nn.Sequential(nn.Linear(dim, hidden), nn.GELU())
self.w = nn.Parameter(torch.randn(n_classes, hidden) * hidden ** -0.5)
self.b = nn.Parameter(torch.zeros(n_classes))
def forward(self, ctx: torch.Tensor) -> torch.Tensor:
# ctx: [n_classes, D] -> logits [n_classes]
h = self.mlp(ctx)
return (h * self.w).sum(-1) + self.b
class MoEViT(nn.Module):
"""Pretrained DINOv3 stem + stacked MoE++ core with per-token routing."""
def __init__(
self,
*,
stem_name: str = "vit_small_patch16_dinov3",
stem: nn.Module | None = None,
pretrained: bool = True,
freeze_stem: bool = True,
core_blocks: int = 2,
n_heads: int = 6,
d_ff: int = 384,
num_ffn: int = 4,
top_k: int = 2,
n_zero: int = 1,
n_copy: int = 1,
n_const: int = 2,
tau: float = 0.75,
gating_residual: bool = True,
n_classes: int = 12,
gate_ctx: bool = False,
per_target: bool = False,
):
super().__init__()
if stem is None:
if not HAS_TIMM:
raise ImportError("timm is required for a pretrained stem")
stem = timm.create_model(stem_name, pretrained=pretrained, num_classes=0)
self.num_ffn = num_ffn
self.top_k = top_k
self.gate_ctx = gate_ctx
self.per_target = per_target
self.stem = stem
stem_dim = self.stem.embed_dim
self.core_dim = 384
self.input_proj = nn.Linear(stem_dim, self.core_dim) if stem_dim != 384 else None
dim = self.core_dim
self.n_prefix = int(getattr(self.stem, "num_prefix_tokens", 1))
if freeze_stem:
for p in self.stem.parameters():
p.requires_grad_(False)
self._ffn_maps: list[torch.Tensor] = []
self.blocks = nn.ModuleList(
[
MoEAttentionBlock(
dim, n_heads=n_heads, d_ff=d_ff, num_ffn=num_ffn, top_k=top_k,
n_zero=n_zero, n_copy=n_copy, n_const=n_const,
tau=tau, gating_residual=gating_residual, gate_ctx=gate_ctx,
)
for _ in range(core_blocks)
]
)
self.exit_norm = nn.LayerNorm(dim)
if per_target:
self.pool = PerTargetPool(dim, n_classes)
self.head = PerTargetHead(dim, n_classes)
else:
self.pool = SliceAttentionPool(dim)
self.head = nn.Sequential(
nn.Linear(dim, dim * 2),
nn.GELU(),
nn.Linear(dim * 2, n_classes),
)
def moe_aux_loss(self) -> torch.Tensor:
total = None
for b in self.blocks:
if not hasattr(b, "_aux"):
continue
total = b._aux if total is None else total + b._aux
if total is None:
return torch.tensor(0.0, device=next(self.parameters()).device)
return total / len(self.blocks)
def routing_tv_loss(self) -> torch.Tensor:
"""Total-variation penalty on the per-patch FFN-mass maps.
Knee findings occupy contiguous regions; scattered routing is a bug
signal. The map is the probability mass routed to real FFN experts,
reshaped to the patch grid.
"""
if not self._ffn_maps:
return torch.tensor(0.0, device=next(self.parameters()).device)
total = None
for p in self._ffn_maps:
P = p.size(1)
h = int(round(P ** 0.5))
if h * h != P:
continue
g = p.reshape(p.size(0), h, h)
tv = (g[:, 1:, :] - g[:, :-1, :]).abs().mean() + (
g[:, :, 1:] - g[:, :, :-1]
).abs().mean()
total = tv if total is None else total + tv
if total is None:
return torch.tensor(0.0, device=next(self.parameters()).device)
return total / len(self._ffn_maps)
def features(
self, x: torch.Tensor, pool: str = "patchmean"
) -> torch.Tensor:
"""Per-slice features (no head): [S, D] from [S, 3, H, W] slices."""
tokens = self.stem.forward_features(x) # [S, T, D]
return self.features_from_tokens(tokens, pool)
def features_from_tokens(
self, tokens: torch.Tensor, pool: str = "patchmean"
) -> torch.Tensor:
"""Features from cached stem tokens (feat cache path): [S, D]."""
tokens = tokens.float() # core internals are f32; bf16 cache upcasts exactly
if self.input_proj is not None:
with torch.autocast("cuda", enabled=False):
tokens = self.input_proj(tokens).float() # keep f32; autocast would make it bf16
self._ffn_maps = []
prev_gate = None
ctx = tokens.mean(dim=1) if self.gate_ctx else None # [B, D] per-slice context
for b in self.blocks:
tokens, aux, gate_logits, gate_weights = b(
tokens, prev_gate=prev_gate, ctx=ctx)
b._aux = aux
self._ffn_maps.append(
gate_weights[:, self.n_prefix:, : self.num_ffn].sum(dim=-1)
)
prev_gate = gate_logits
tokens = self.exit_norm(tokens)
if pool == "cls":
return tokens[:, 0]
return tokens[:, self.n_prefix:].mean(1)
def forward(
self, x: torch.Tensor
) -> tuple[torch.Tensor, list[float]]:
"""Forward one study: [S, 3, H, W] slices -> (logits [n_classes], aux_stats)."""
slice_feats = self.features(x) # [S, D]
study = self.pool(slice_feats) # [D] or [n_classes, D]
logits = self.head(study)
if logits.dim() == 2:
logits = logits.squeeze(-1)
return logits, [self.moe_aux_loss().item()] |