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d31e0d1 | 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 | """Small causal readiness head over frozen visual, state, and text features."""
from __future__ import annotations
from typing import Any
import torch
from torch import nn
class ResidualCrossAttentionBlock(nn.Module):
def __init__(self, width: int, heads: int, ffn_width: int, dropout: float):
super().__init__()
self.query_norm = nn.LayerNorm(width)
self.visual_norm = nn.LayerNorm(width)
self.attention = nn.MultiheadAttention(
width, heads, dropout=dropout, batch_first=True
)
self.ffn_norm = nn.LayerNorm(width)
self.ffn = nn.Sequential(
nn.Linear(width, ffn_width),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(ffn_width, width),
nn.Dropout(dropout),
)
def forward(
self, query: torch.Tensor, visual: torch.Tensor, visual_mask: torch.Tensor
) -> torch.Tensor:
normalized_visual = self.visual_norm(visual)
update, _ = self.attention(
self.query_norm(query),
normalized_visual,
normalized_visual,
key_padding_mask=~visual_mask,
need_weights=False,
)
query = query + update
return query + self.ffn(self.ffn_norm(query))
class CommitReadinessHead(nn.Module):
"""Predict evidence for committing away from the active semantic phase."""
def __init__(
self,
*,
image_dim: int = 1152,
text_dim: int = 4096,
state_dim: int = 8,
width: int = 256,
state_transformer_layers: int = 1,
cross_attention_layers: int = 2,
attention_heads: int = 4,
ffn_width: int = 1024,
head_hidden_width: int = 128,
dropout: float = 0.1,
max_relative_age: int = 2048,
state_recent_length: int = 16,
):
super().__init__()
self.image_dim = int(image_dim)
self.text_dim = int(text_dim)
self.state_dim = int(state_dim)
self.width = int(width)
self.state_recent_length = int(state_recent_length)
self.visual_projection = nn.Linear(image_dim, width)
self.visual_role_embedding = nn.Embedding(4, width)
self.visual_view_embedding = nn.Embedding(2, width)
self.visual_spatial_embedding = nn.Embedding(16, width)
self.visual_age_embedding = nn.Embedding(max_relative_age + 1, width)
self.state_projection = nn.Linear(state_dim, width)
self.state_role_embedding = nn.Embedding(2, width)
self.state_time_embedding = nn.Embedding(state_recent_length, width)
state_layer = nn.TransformerEncoderLayer(
d_model=width,
nhead=attention_heads,
dim_feedforward=ffn_width,
dropout=dropout,
activation="gelu",
batch_first=True,
norm_first=True,
)
self.state_transformer = nn.TransformerEncoder(
state_layer, num_layers=state_transformer_layers, enable_nested_tensor=False
)
self.state_fusion = nn.Sequential(
nn.Linear(2 * width, width), nn.GELU(), nn.Dropout(dropout)
)
self.text_projection = nn.Linear(text_dim, width)
self.query = nn.Parameter(torch.empty(1, 1, width))
nn.init.normal_(self.query, std=0.02)
self.cross_attention = nn.ModuleList(
[
ResidualCrossAttentionBlock(width, attention_heads, ffn_width, dropout)
for _ in range(cross_attention_layers)
]
)
self.output_norm = nn.LayerNorm(width)
self.output = nn.Sequential(
nn.Linear(width, head_hidden_width),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(head_hidden_width, 1),
)
@classmethod
def from_config(cls, cfg: Any) -> "CommitReadinessHead":
return cls(
image_dim=int(cfg.features.image_dim),
text_dim=int(cfg.features.text_dim),
state_dim=int(cfg.features.state_dim),
width=int(cfg.model.width),
state_transformer_layers=int(cfg.model.state_transformer_layers),
cross_attention_layers=int(cfg.model.cross_attention_layers),
attention_heads=int(cfg.model.attention_heads),
ffn_width=int(cfg.model.ffn_width),
head_hidden_width=int(cfg.model.head_hidden_width),
dropout=float(cfg.model.dropout),
max_relative_age=int(cfg.features.max_relative_age),
state_recent_length=int(cfg.features.state_recent_length),
)
def forward(self, **features: torch.Tensor) -> dict[str, torch.Tensor]:
visual_tokens = features["visual_tokens"]
visual_mask = features["visual_mask"].bool()
batch, token_count, image_dim = visual_tokens.shape
if token_count != 608 or image_dim != self.image_dim:
raise ValueError(
f"Expected visual [B,608,{self.image_dim}], got {tuple(visual_tokens.shape)}"
)
if (
visual_mask.shape != (batch, token_count)
or not visual_mask.any(dim=1).all()
):
raise ValueError("Each sample must have at least one valid visual token")
visual = (
self.visual_projection(visual_tokens)
+ self.visual_role_embedding(features["visual_role_ids"].long())
+ self.visual_view_embedding(features["visual_view_ids"].long())
+ self.visual_spatial_embedding(features["visual_spatial_ids"].long())
+ self.visual_age_embedding(features["visual_age_ids"].long())
)
active_state = features["active_start_state"]
recent_states = features["recent_states"]
recent_mask = features["recent_state_mask"].bool()
if active_state.shape != (batch, self.state_dim):
raise ValueError(
f"Bad active-start state shape: {tuple(active_state.shape)}"
)
if recent_states.shape != (batch, self.state_recent_length, self.state_dim):
raise ValueError(f"Bad recent state shape: {tuple(recent_states.shape)}")
if (
recent_mask.shape != (batch, self.state_recent_length)
or not recent_mask.any(dim=1).all()
):
raise ValueError("Each sample must have at least one valid recent state")
active_summary = (
self.state_projection(active_state) + self.state_role_embedding.weight[0]
)
time_ids = torch.arange(self.state_recent_length, device=recent_states.device)
recent = (
self.state_projection(recent_states)
+ self.state_role_embedding.weight[1]
+ self.state_time_embedding(time_ids)[None]
)
recent = self.state_transformer(recent, src_key_padding_mask=~recent_mask)
recent_summary = (recent * recent_mask.unsqueeze(-1)).sum(
dim=1
) / recent_mask.sum(dim=1, keepdim=True).clamp_min(1)
state_summary = self.state_fusion(
torch.cat([active_summary, recent_summary], dim=-1)
)
text = features["text_embedding"]
if text.shape != (batch, self.text_dim):
raise ValueError(f"Bad text embedding shape: {tuple(text.shape)}")
text_summary = self.text_projection(text)
query = (
self.query.expand(batch, -1, -1)
+ state_summary[:, None]
+ text_summary[:, None]
)
for block in self.cross_attention:
query = block(query, visual, visual_mask)
commit_logit = self.output(self.output_norm(query[:, 0])).squeeze(-1)
return {
"commit_logit": commit_logit,
"commit_score": torch.sigmoid(commit_logit),
}
def trainable_parameter_count(model: nn.Module) -> int:
return sum(
parameter.numel() for parameter in model.parameters() if parameter.requires_grad
)
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