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b4ca4a0 | 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 | """Decoder capacity/routing variants for strict local-wrist Stereo-ACT.
Both variants retain frozen DINOv3 + DeFM and the 30x40 RGB->depth
cross_relbias front end from StereoACT. They use only current local wrist
RGB-D tokens and local qpos; no task/agent ID, language, peer, or global view.
"""
from __future__ import annotations
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from train_stereo_act import StereoACT
class _Expert(nn.Module):
def __init__(self, d_model, ffn_dim, dropout):
super().__init__()
self.net = nn.Sequential(nn.Linear(d_model, ffn_dim), nn.GELU(), nn.Dropout(dropout),
nn.Linear(ffn_dim, d_model), nn.Dropout(dropout))
def forward(self, x): return self.net(x)
class Top2SparseMoE(nn.Module):
"""Four FFN experts; each local decoder token selects exactly top-2."""
def __init__(self, d_model, ffn_dim, experts=4, dropout=.1):
super().__init__(); self.experts_n = experts
self.router = nn.Linear(d_model, experts, bias=False)
self.experts = nn.ModuleList([_Expert(d_model, ffn_dim, dropout) for _ in range(experts)])
def forward(self, x):
shape, flat = x.shape, x.reshape(-1, x.shape[-1])
logits = self.router(flat)
top_logits, top_ids = logits.topk(2, dim=-1)
gates = top_logits.softmax(-1)
out = torch.zeros_like(flat)
for expert_id, expert in enumerate(self.experts):
chosen = (top_ids == expert_id).nonzero(as_tuple=False)
if chosen.numel() == 0: continue
token_ids, slots = chosen[:, 0], chosen[:, 1]
y = expert(flat.index_select(0, token_ids))
out.index_add_(0, token_ids, y * gates[token_ids, slots].unsqueeze(-1))
# Switch-style differentiable importance/load balancing; its minimum is one.
importance = logits.softmax(-1).mean(0)
load = torch.bincount(top_ids.reshape(-1), minlength=self.experts_n).to(flat.dtype) / (2.0 * flat.shape[0])
aux = self.experts_n * (importance * load).sum()
return out.reshape(shape), aux
class MoEDecoderLayer(nn.Module):
def __init__(self, d_model, heads=8, ffn_dim=None, dropout=.1, experts=4):
super().__init__(); ffn_dim = ffn_dim or 4*d_model
self.self_attn = nn.MultiheadAttention(d_model, heads, dropout=dropout, batch_first=True)
self.cross_attn = nn.MultiheadAttention(d_model, heads, dropout=dropout, batch_first=True)
self.norm1, self.norm2, self.norm3 = nn.LayerNorm(d_model), nn.LayerNorm(d_model), nn.LayerNorm(d_model)
self.drop1, self.drop2 = nn.Dropout(dropout), nn.Dropout(dropout)
self.moe = Top2SparseMoE(d_model, ffn_dim, experts=experts, dropout=dropout)
def forward(self, x, memory):
x = x + self.drop1(self.self_attn(self.norm1(x), self.norm1(x), self.norm1(x), need_weights=False)[0])
x = x + self.drop2(self.cross_attn(self.norm2(x), memory, memory, need_weights=False)[0])
ff, aux = self.moe(self.norm3(x)); return x + ff, aux
class MoEDecoder(nn.Module):
def __init__(self, d_model, layers=7, experts=4, dropout=.1):
super().__init__(); self.layers = nn.ModuleList([MoEDecoderLayer(d_model, dropout=dropout, experts=experts) for _ in range(layers)])
def forward(self, x, memory):
aux = x.new_zeros(())
for layer in self.layers:
x, value = layer(x, memory); aux = aux + value
return x, aux / len(self.layers)
class StereoFFNMoE(StereoACT):
"""Stereo front end + top-2 four-expert FFN replacement in every decoder block."""
def __init__(self, *args, experts=4, **kwargs):
super().__init__(*args, **kwargs)
self.experts_n = experts
self.decoder = MoEDecoder(self.query.shape[-1], layers=len(self.decoder.layers), experts=experts)
def forward(self, image, depth_mm, qpos, actions=None):
x = self._rgbd_tokens(image, depth_mm); state = self.state(qpos).unsqueeze(1)
if actions is not None:
h = self.posterior(self.action(actions) + self.pos)
mu, logvar = self.latent(h.mean(1)).chunk(2, -1); z = mu + torch.randn_like(mu) * torch.exp(.5 * logvar)
else:
mu = logvar = None; z = torch.zeros((image.shape[0], self.z_proj.in_features), device=image.device)
memory = torch.cat((state, self.z_proj(z).unsqueeze(1), x), dim=1)
decoded, aux = self.decoder(self.query.expand(image.shape[0], -1, -1), memory)
return self.out(decoded), mu, logvar, aux
class RoleCrossAdapter(nn.Module):
"""Small role-specific cross-attention from one action query to current observation tokens."""
def __init__(self, d_model, rank=32):
super().__init__()
self.q = nn.Linear(d_model, rank, bias=False); self.k = nn.Linear(d_model, rank, bias=False)
self.v = nn.Linear(d_model, rank, bias=False); self.out = nn.Linear(rank, d_model, bias=False)
self.rank = rank
def forward(self, query, observation):
scores = torch.matmul(self.q(query), self.k(observation).transpose(-1, -2)) / math.sqrt(self.rank)
return self.out(torch.matmul(scores.softmax(-1), self.v(observation)))
class ARCADecoderLayer(nn.Module):
def __init__(self, d_model, roles=4, rank=32, heads=8, dropout=.1):
super().__init__(); ffn = 4*d_model
self.self_attn = nn.MultiheadAttention(d_model, heads, dropout=dropout, batch_first=True)
self.cross_attn = nn.MultiheadAttention(d_model, heads, dropout=dropout, batch_first=True)
self.norm1, self.norm2, self.norm3 = nn.LayerNorm(d_model), nn.LayerNorm(d_model), nn.LayerNorm(d_model)
self.drop1, self.drop2 = nn.Dropout(dropout), nn.Dropout(dropout)
self.ff = _Expert(d_model, ffn, dropout)
self.adapters = nn.ModuleList([RoleCrossAdapter(d_model, rank) for _ in range(roles)])
def forward(self, x, memory, observation, gates):
x = x + self.drop1(self.self_attn(self.norm1(x), self.norm1(x), self.norm1(x), need_weights=False)[0])
h = self.norm2(x)
base = self.cross_attn(h, memory, memory, need_weights=False)[0]
role = torch.zeros_like(base)
for role_id, adapter in enumerate(self.adapters):
role = role + gates[..., role_id:role_id+1] * adapter(h, observation)
x = x + self.drop2(base + role)
return x + self.ff(self.norm3(x))
class ARCADecoder(nn.Module):
def __init__(self, d_model, layers=7, roles=4, rank=32, dropout=.1):
super().__init__(); self.layers = nn.ModuleList([ARCADecoderLayer(d_model, roles, rank, dropout=dropout) for _ in range(layers)])
def forward(self, x, memory, observation, gates):
for layer in self.layers: x = layer(x, memory, observation, gates)
return x
class StereoARCA(StereoACT):
"""Action-role conditioned observation cross-attention inside every decoder layer."""
def __init__(self, *args, roles=4, role_rank=32, **kwargs):
super().__init__(*args, **kwargs)
d = self.query.shape[-1]; self.roles_n, self.role_rank = roles, role_rank
self.decoder = ARCADecoder(d, layers=len(self.decoder.layers), roles=roles, rank=role_rank)
self.route_state, self.route_observation = nn.Linear(d, d, bias=False), nn.Linear(d, d, bias=False)
self.route_mlp = nn.Sequential(nn.LayerNorm(d), nn.Linear(d, d), nn.GELU(), nn.Linear(d, d, bias=False))
self.role_prototypes = nn.Parameter(torch.randn(roles, d) * .02)
def _route(self, state, observation, batch):
# This deliberately excludes ACT's posterior z: z is zero at deployment.
q = self.query.expand(batch, -1, -1)
context = self.route_state(state) + self.route_observation(observation.mean(1))
features = self.route_mlp(q + context.unsqueeze(1))
logits = torch.matmul(features, self.role_prototypes.t()) / math.sqrt(features.shape[-1])
values, ids = logits.topk(2, dim=-1); gates = torch.zeros_like(logits).scatter_(-1, ids, values.softmax(-1).to(logits.dtype))
importance = logits.softmax(-1).mean((0, 1))
load = (gates.gt(0).to(logits.dtype).mean((0, 1)) / 2.0)
aux = self.roles_n * (importance * load).sum()
return gates, aux
def forward(self, image, depth_mm, qpos, actions=None):
x = self._rgbd_tokens(image, depth_mm); state_vec = self.state(qpos); state = state_vec.unsqueeze(1)
gates, aux = self._route(state_vec, x, image.shape[0])
if actions is not None:
h = self.posterior(self.action(actions) + self.pos)
mu, logvar = self.latent(h.mean(1)).chunk(2, -1); z = mu + torch.randn_like(mu) * torch.exp(.5 * logvar)
else:
mu = logvar = None; z = torch.zeros((image.shape[0], self.z_proj.in_features), device=image.device)
memory = torch.cat((state, self.z_proj(z).unsqueeze(1), x), dim=1)
decoded = self.decoder(self.query.expand(image.shape[0], -1, -1), memory, x, gates)
return self.out(decoded), mu, logvar, aux
class StereoSyncARCA(StereoARCA):
"""Stereo-ARCA with a training-only synchronized action-stage teacher.
At inference ``phase_target`` is never provided. Each local policy predicts
the phase from its own current wrist RGB-D tokens and qpos, then conditions
action-query routing on that *predicted* soft phase. The optional target is
used only for the CE loss in the trainer.
"""
def __init__(self, *args, phases=8, **kwargs):
super().__init__(*args, **kwargs)
d = self.query.shape[-1]
self.phases_n = phases
self.phase_head = nn.Sequential(nn.LayerNorm(d), nn.Linear(d, d), nn.GELU(), nn.Linear(d, phases))
self.phase_embed = nn.Parameter(torch.randn(phases, d) * .02)
def _sync_route(self, state, observation, phase_probs, batch):
q = self.query.expand(batch, -1, -1)
context = self.route_state(state) + self.route_observation(observation.mean(1))
phase_context = torch.matmul(phase_probs, self.phase_embed)
features = self.route_mlp(q + (context + phase_context).unsqueeze(1))
logits = torch.matmul(features, self.role_prototypes.t()) / math.sqrt(features.shape[-1])
values, ids = logits.topk(2, dim=-1)
gates = torch.zeros_like(logits).scatter_(-1, ids, values.softmax(-1).to(logits.dtype))
importance = logits.softmax(-1).mean((0, 1))
load = gates.gt(0).to(logits.dtype).mean((0, 1)) / 2.0
aux = self.roles_n * (importance * load).sum()
return gates, aux
def forward(self, image, depth_mm, qpos, actions=None):
x = self._rgbd_tokens(image, depth_mm)
state_vec = self.state(qpos)
local_context = self.route_state(state_vec) + self.route_observation(x.mean(1))
phase_logits = self.phase_head(local_context)
phase_probs = phase_logits.softmax(-1)
gates, aux = self._sync_route(state_vec, x, phase_probs, image.shape[0])
state = state_vec.unsqueeze(1)
if actions is not None:
h = self.posterior(self.action(actions) + self.pos)
mu, logvar = self.latent(h.mean(1)).chunk(2, -1)
z = mu + torch.randn_like(mu) * torch.exp(.5 * logvar)
else:
mu = logvar = None
z = torch.zeros((image.shape[0], self.z_proj.in_features), device=image.device)
memory = torch.cat((state, self.z_proj(z).unsqueeze(1), x), dim=1)
decoded = self.decoder(self.query.expand(image.shape[0], -1, -1), memory, x, gates)
return self.out(decoded), mu, logvar, aux, phase_logits, gates
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