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c645013 | 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 | """Structured Safety Adapter (SSA) with binary presence supervision."""
from __future__ import annotations
import math
from dataclasses import dataclass
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from .capabilities import ALL_SAFETY_HEADS, SafetyCapabilities
# These indices are the canonical Falcon-X annotation order. The paper's
# "main charge" is represented by the dataset label ``explosive``.
COMPONENT_TYPES = ("detonator", "explosive", "battery")
LINK_PAIRS = ((2, 0), (2, 1), (0, 1))
SAFETY_TOKEN_TYPES = (
"scene_risk",
"presence_detonator",
"presence_explosive",
"presence_battery",
"link_battery_detonator",
"link_battery_explosive",
"link_detonator_explosive",
)
@dataclass
class SafetyOutput:
slots: Tensor
attention: Tensor
presence_logits: Tensor
presence_probabilities: Tensor
link_logits: Tensor
link_probabilities: Tensor
risk_logit: Tensor
risk_probability: Tensor
tokens: Tensor
capabilities: SafetyCapabilities = SafetyCapabilities()
def prediction(self, batch_index: int = 0) -> dict:
"""Serialize unavailable heads as null, never plausible-looking numbers."""
return {
"risk": float(self.risk_probability[batch_index].detach().cpu())
if self.capabilities.risk
else None,
"presence": [
float(value.detach().cpu()) if enabled else None
for value, enabled in zip(
self.presence_probabilities[batch_index],
self.capabilities.presence,
strict=True,
)
],
"links": [
float(value.detach().cpu()) if enabled else None
for value, enabled in zip(
self.link_probabilities[batch_index], self.capabilities.links, strict=True
)
],
"capabilities": self.capabilities.as_dict(),
}
@property
def scalar_values(self) -> Tensor:
"""Values in the seven-token paper order: risk, presence, links."""
return torch.cat(
(
self.risk_probability.unsqueeze(-1),
self.presence_probabilities,
self.link_probabilities,
),
dim=-1,
)
class StructuredSafetyAdapter(nn.Module):
"""Map a variable proposal set to three typed component slots and seven tokens."""
def __init__(
self,
model_dim: int,
token_dim: int,
*,
head_hidden_dim: int | None = None,
capabilities: SafetyCapabilities = ALL_SAFETY_HEADS,
) -> None:
super().__init__()
hidden_dim = head_hidden_dim or model_dim
self.model_dim = model_dim
self.token_dim = token_dim
self.capabilities = capabilities
self.component_queries = nn.Parameter(torch.empty(3, model_dim))
nn.init.normal_(self.component_queries, std=0.02)
self.presence_head = nn.Linear(model_dim, 1)
self.link_heads = nn.ModuleList(
[
nn.Sequential(
nn.Linear(2 * model_dim, hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, 1),
)
for _ in LINK_PAIRS
]
)
self.risk_head = nn.Sequential(
nn.Linear(3 * model_dim + len(LINK_PAIRS), hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, 1),
)
self.value_projection = nn.Linear(1, token_dim)
self.type_embedding = nn.Embedding(len(SAFETY_TOKEN_TYPES), token_dim)
self.freeze_unavailable_heads()
def freeze_unavailable_heads(self) -> None:
"""Reapply after a training-stage change has enabled the shared adapter."""
modules = [
(self.presence_head, any(self.capabilities.presence)),
(self.risk_head, self.capabilities.risk),
]
modules.extend(zip(self.link_heads, self.capabilities.links, strict=True))
for module, enabled in modules:
if not enabled:
for parameter in module.parameters():
parameter.requires_grad = False
def _component_slots(
self,
region_embeddings: Tensor,
valid: Tensor,
) -> tuple[Tensor, Tensor]:
batch, proposals, channels = region_embeddings.shape
if channels != self.model_dim:
raise ValueError(f"expected region dimension {self.model_dim}, received {channels}")
if valid.shape != (batch, proposals):
raise ValueError("valid must have shape [batch, proposals]")
if proposals == 0:
slots = region_embeddings.new_zeros((batch, 3, channels))
attention = region_embeddings.new_zeros((batch, 3, 0))
return slots, attention
scores = torch.einsum("cd,bnd->bcn", self.component_queries, region_embeddings)
scores = scores / math.sqrt(float(channels))
expanded_valid = valid[:, None, :].to(dtype=torch.bool)
scores = scores.masked_fill(~expanded_valid, torch.finfo(scores.dtype).min)
attention = scores.softmax(dim=-1) * expanded_valid.to(dtype=scores.dtype)
attention = attention / attention.sum(dim=-1, keepdim=True).clamp_min(1e-12)
slots = torch.einsum("bcn,bnd->bcd", attention, region_embeddings)
return slots, attention
def forward(
self,
region_embeddings: Tensor,
valid: Tensor | None = None,
) -> SafetyOutput:
if region_embeddings.ndim != 3:
raise ValueError("region_embeddings must have shape [batch, proposals, channels]")
if valid is None:
valid = torch.ones(
region_embeddings.shape[:2],
dtype=torch.bool,
device=region_embeddings.device,
)
else:
valid = valid.to(device=region_embeddings.device, dtype=torch.bool)
slots, attention = self._component_slots(region_embeddings, valid)
presence_logits = (
self.presence_head(slots).squeeze(-1)
if any(self.capabilities.presence)
else slots.new_zeros(slots.shape[:2])
)
link_logits = torch.stack(
[
head(torch.cat((slots[:, left], slots[:, right]), dim=-1)).squeeze(-1)
if enabled
else slots.new_zeros(slots.shape[0])
for head, (left, right), enabled in zip(
self.link_heads, LINK_PAIRS, self.capabilities.links, strict=True
)
],
dim=-1,
)
link_probabilities = link_logits.sigmoid() * slots.new_tensor(self.capabilities.links)
# Risk is explicitly conditioned on every slot and the three inferred links.
risk_input = torch.cat((slots.flatten(start_dim=1), link_probabilities), dim=-1)
risk_logit = (
self.risk_head(risk_input).squeeze(-1)
if self.capabilities.risk
else slots.new_zeros(slots.shape[0])
)
risk_probability = risk_logit.sigmoid() * int(self.capabilities.risk)
presence_probabilities = presence_logits.sigmoid() * slots.new_tensor(
self.capabilities.presence
)
scalar_values = torch.cat(
(
risk_probability.unsqueeze(-1),
presence_probabilities,
link_probabilities,
),
dim=-1,
)
type_ids = torch.tensor(
self.capabilities.token_indices, device=slots.device, dtype=torch.long
)
tokens = self.value_projection(scalar_values[:, type_ids].unsqueeze(-1))
tokens = tokens + self.type_embedding(type_ids).unsqueeze(0)
return SafetyOutput(
slots=slots,
attention=attention,
presence_logits=presence_logits,
presence_probabilities=presence_probabilities,
link_logits=link_logits,
link_probabilities=link_probabilities,
risk_logit=risk_logit,
risk_probability=risk_probability,
tokens=tokens,
capabilities=self.capabilities,
)
@staticmethod
def loss(
output: SafetyOutput,
*,
risk: Tensor,
presence: Tensor,
links: Tensor,
) -> dict[str, Tensor]:
"""Stage-2/3 objective: presence BCE with logits and risk/link mean L1."""
risk_target = risk.to(output.risk_probability).reshape_as(output.risk_probability)
presence_logits = output.presence_logits.float()
presence_target = presence.to(
device=presence_logits.device, dtype=torch.float32
).reshape_as(presence_logits)
link_target = links.to(output.link_probabilities).reshape_as(output.link_probabilities)
if not output.capabilities.risk:
risk_target = torch.full_like(risk_target, float("nan"))
presence_target = presence_target.masked_fill(
~torch.tensor(output.capabilities.presence, device=presence_target.device), float("nan")
)
link_target = link_target.masked_fill(
~torch.tensor(output.capabilities.links, device=link_target.device), float("nan")
)
def finite_l1(prediction: Tensor, target: Tensor) -> Tensor:
finite = torch.isfinite(target)
if finite.any():
return F.l1_loss(prediction[finite], target[finite])
# Keep a differentiable zero when a sample has no annotated links.
return prediction.sum() * 0.0
risk_loss = finite_l1(output.risk_probability, risk_target)
observed_presence = torch.isfinite(presence_target)
if observed_presence.any():
presence_loss = F.binary_cross_entropy_with_logits(
presence_logits[observed_presence], presence_target[observed_presence]
)
else:
presence_loss = presence_logits.sum() * 0.0
link_loss = finite_l1(output.link_probabilities, link_target)
return {
"risk": risk_loss,
"presence": presence_loss,
"links": link_loss,
}
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