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ADAPTER_CONFIG = {
"adapter_id": "003",
"name": "DualShuntAdapter-G",
"t5": {
"model": "google/flan-t5-base",
"hidden_size": 768,
},
"clip": {
"model": "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
"hidden_size": 1280,
},
"bottleneck": 640,
"heads": 20,
"tau_init": 0.1,
"max_guidance": 10.0,
"proj_layers": 2,
"layer_norm": True,
"dropout": 0.1,
"use_dropout": True,
"use_proj_stack": True,
"assert_input_dims": True,
"routing": {
"type": "cross_attention",
"enable_causal_mask": False,
"bidirectional": True
},
"version": "v0.3.2",
"description": "Final Dual Shunt Adapter with projection stack, dropout, and stacked residual refinement pocket."
}
import torch
import torch.nn as nn
import torch.nn.functional as F
# ─── Residual Pocket Block ───────────────────────────────────
class BottleneckResBlock(nn.Module):
def __init__(self, dim, kernel=3, dropout=0.1):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.conv = nn.Conv1d(dim, dim, kernel_size=kernel, padding=kernel // 2, groups=1)
self.proj = nn.Sequential(
nn.Linear(dim, dim * 2),
nn.GELU(),
nn.Linear(dim * 2, dim),
nn.Dropout(dropout)
)
def forward(self, x):
residual = x
x = self.norm(x)
x = x.transpose(1, 2)
x = self.conv(x).transpose(1, 2)
return residual + self.proj(x)
# ─── Two Stream Shunt Adapter ──────────────────────────────────────
class TwoStreamShuntAdapter(nn.Module):
def __init__(self, config: dict):
super().__init__()
self.config = config
self.t5_dim = config["t5"]["hidden_size"]
self.clip_dim = config["clip"]["hidden_size"]
self.bneck = config["bottleneck"]
self.heads = config["heads"]
self.tau_init = config["tau_init"]
self.max_guidance = config["max_guidance"]
use_norm = config.get("layer_norm", True)
use_do = config.get("use_dropout", True)
do_p = config.get("dropout", 0.1)
proj_depth = config.get("proj_layers", 2)
def build_projection(input_dim, output_dim):
layers = []
last_dim = input_dim
if use_norm:
layers.append(nn.LayerNorm(last_dim))
for i in range(proj_depth):
next_dim = self.bneck * (2 if i == 0 and proj_depth > 1 else 1)
layers.append(nn.Linear(last_dim, next_dim))
layers.append(nn.GELU())
if use_do:
layers.append(nn.Dropout(do_p))
last_dim = next_dim
layers.append(nn.Linear(last_dim, output_dim))
return nn.Sequential(*layers)
# Projections
self.proj_t5 = build_projection(self.t5_dim, self.bneck)
self.proj_clip = build_projection(self.clip_dim, self.bneck)
# Attention
self.cross_t2c = nn.MultiheadAttention(self.bneck, self.heads, batch_first=True, dropout=do_p)
self.cross_c2t = nn.MultiheadAttention(self.bneck, self.heads, batch_first=True, dropout=do_p)
self.tau = nn.Parameter(torch.full((self.heads, 1, 1), self.tau_init))
# Residual Pocket
self.pocket_blocks = nn.Sequential(
BottleneckResBlock(self.bneck, dropout=do_p),
BottleneckResBlock(self.bneck, dropout=do_p)
)
# Fuse
self.fuse = nn.Sequential(
nn.LayerNorm(2 * self.bneck),
nn.Linear(2 * self.bneck, self.bneck * 2),
nn.GELU(),
nn.Linear(self.bneck * 2, self.bneck)
)
# Output Projections
self.anchor_proj = build_projection(self.bneck, self.clip_dim)
self.delta_proj = build_projection(self.bneck, self.clip_dim)
self.logsig_proj = build_projection(self.bneck, self.clip_dim)
self.gate_proj = nn.Sequential(
nn.LayerNorm(self.bneck),
nn.Linear(self.bneck, self.bneck),
nn.GELU(),
nn.Linear(self.bneck, 1),
nn.Tanh(),
nn.Sigmoid()
)
self.guidance_proj = nn.Sequential(
nn.LayerNorm(self.bneck),
nn.Linear(self.bneck, 1),
nn.Sigmoid()
)
def forward(self, t5_seq: torch.Tensor, clip_seq: torch.Tensor):
if self.config.get("assert_input_dims", True):
assert t5_seq.size(-1) == self.t5_dim
assert clip_seq.size(-1) == self.clip_dim
t5_b = self.proj_t5(t5_seq)
clip_b = self.proj_clip(clip_seq)
t2c, attn_t2c = self.cross_t2c(t5_b, clip_b, clip_b, need_weights=True, average_attn_weights=False)
c2t, attn_c2t = self.cross_c2t(clip_b, t5_b, t5_b, need_weights=True, average_attn_weights=False)
pocket = self.pocket_blocks(t2c)
pocket_mean = pocket.mean(1, keepdim=True).expand(-1, clip_b.size(1), -1)
h = self.fuse(torch.cat([pocket_mean, c2t], dim=-1))
anchor = self.anchor_proj(h)
delta = self.delta_proj(h) * self.gate_proj(h)
log_sigma = self.logsig_proj(h)
g_tok = self.guidance_proj(h).squeeze(-1)
g_pred = g_tok.mean(1, keepdim=True) * self.max_guidance
return anchor, delta, log_sigma, attn_t2c, attn_c2t, self.tau, g_pred, self.gate_proj(h)
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