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"""R3 battery evaluation: do model-internal relations align across modalities?
Compares, at the replayed true view correspondence, the cross-modal
alignment of relation fields read from inside the frozen models
(cross-phrase attention per layer group, in-context state cosines) against
the isolated-encoding cosine baseline (matched rho 0.128, z 46.6).
Spearman is rank-based, so monotone attention transforms are immaterial.
View truth is evaluation-only.
"""
from __future__ import annotations
import argparse
import json
import torch
import torch.nn.functional as F
from .common import write_json
from .vg_view_probe import offdiag, replay_view_permutations, spearman
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--vg-dir", default="artifacts/vg_5k")
parser.add_argument("--cache-dir", default="/tmp/yurenh2-worldalign-vg-hf")
parser.add_argument("--seed", type=int, default=20260728)
parser.add_argument("--views", type=int, default=16)
parser.add_argument("--text-attn", default="artifacts/manifold_gate/attn_text_full.pt")
parser.add_argument(
"--vision-attn", default="artifacts/manifold_gate/attn_vision_full.pt"
)
parser.add_argument("--nodes", type=int, default=0)
parser.add_argument(
"--output", default="artifacts/manifold_gate/attention_probe.json"
)
return parser.parse_args()
def channel_fields(state: dict, side: str) -> dict[str, torch.Tensor]:
"""Named [N, V, V] relation fields for one side."""
attention = state["attention_channels"].double()
fields = {
f"{side}_attn_g{group}": attention[:, group]
for group in range(attention.shape[1])
}
context = F.normalize(state["context_states"].double(), dim=-1)
fields[f"{side}_ctx_cos"] = context @ context.transpose(-2, -1)
return fields
def main() -> None:
args = parse_args()
mappings = replay_view_permutations(args)
text_state = torch.load(args.text_attn, map_location="cpu", weights_only=False)
vision_state = torch.load(args.vision_attn, map_location="cpu", weights_only=False)
text_index = {node: i for i, node in enumerate(text_state["node_ids"])}
vision_index = {node: i for i, node in enumerate(vision_state["node_ids"])}
baseline_vision = torch.load(
f"{args.vg_dir}/vision_features.pt", map_location="cpu", weights_only=False
)
baseline_text = torch.load(
f"{args.vg_dir}/text_features.pt", map_location="cpu", weights_only=False
)
baseline_vision_index = {
node: i for i, node in enumerate(baseline_vision["node_ids"])
}
baseline_text_index = {node: i for i, node in enumerate(baseline_text["node_ids"])}
text_fields = channel_fields(text_state, "text")
vision_fields = channel_fields(vision_state, "vision")
node_ids = sorted(mappings)
if args.nodes:
node_ids = node_ids[: args.nodes]
pairs = [
(t_name, v_name) for t_name in text_fields for v_name in vision_fields
]
matched: dict[tuple[str, str], list[float]] = {pair: [] for pair in pairs}
shuffled: dict[tuple[str, str], list[float]] = {pair: [] for pair in pairs}
baseline_matched: list[float] = []
baseline_shuffled: list[float] = []
generator = torch.Generator().manual_seed(args.seed)
for node in node_ids:
mapping = mappings[node]
text_row = text_index[mapping["text_node_id"]]
vision_row = vision_index[node]
text_to_vision = torch.tensor(mapping["text_to_vision"])
vision_to_text = torch.empty_like(text_to_vision)
vision_to_text[text_to_vision] = torch.arange(len(text_to_vision))
shuffle = torch.randperm(args.views, generator=generator)
for t_name, v_name in pairs:
text_field = text_fields[t_name][text_row]
aligned = text_field[vision_to_text][:, vision_to_text]
visual_field = vision_fields[v_name][vision_row]
matched[(t_name, v_name)].append(
spearman(offdiag(visual_field), offdiag(aligned))
)
scrambled = text_field[shuffle][:, shuffle]
shuffled[(t_name, v_name)].append(
spearman(offdiag(visual_field), offdiag(scrambled))
)
crops = F.normalize(
baseline_vision["region_features"][
baseline_vision_index[node]
].double(),
dim=-1,
)
phrases = F.normalize(
baseline_text["region_features"][
baseline_text_index[mapping["text_node_id"]]
].double(),
dim=-1,
)
crop_field = crops @ crops.T
phrase_field = (phrases @ phrases.T)[vision_to_text][:, vision_to_text]
baseline_matched.append(spearman(offdiag(crop_field), offdiag(phrase_field)))
scrambled = (phrases @ phrases.T)[shuffle][:, shuffle]
baseline_shuffled.append(spearman(offdiag(crop_field), offdiag(scrambled)))
def summarize(matched_values: list[float], shuffled_values: list[float]) -> dict:
m = torch.tensor(matched_values)
s = torch.tensor(shuffled_values)
return {
"matched_mean": float(m.mean()),
"shuffled_mean": float(s.mean()),
"gap_z": float(
(m.mean() - s.mean())
/ (m - s).std().clamp_min(1e-12)
* len(m) ** 0.5
),
}
report = {
"protocol": (
"Replayed view truth, evaluation-only. Each cell is the "
"within-node cross-modal Spearman of relation fields at the "
"true view correspondence, against a shuffled-view control."
),
"nodes": len(node_ids),
"baseline_isolated_cosine": summarize(baseline_matched, baseline_shuffled),
"channels": {
f"{t} x {v}": summarize(matched[(t, v)], shuffled[(t, v)])
for t, v in pairs
},
}
write_json(args.output, report)
best = max(
report["channels"].items(), key=lambda item: item[1]["matched_mean"]
)
print(
json.dumps(
{
"baseline": report["baseline_isolated_cosine"],
"best_channel": {best[0]: best[1]},
"nodes": len(node_ids),
}
)
)
print(f"Wrote {args.output}")
if __name__ == "__main__":
main()
|
