transforms: pad legacy cond normalizers for pre-physical-conditioning checkpoints

Checkpoints trained before commit 68fb99b (physical-property
conditioning, COND_DIM 8->15) saved a COND_DIM_BASE-wide cond
normalizer, fit before build_cond_features grew the extra physical
columns. Any inference against such a checkpoint under current code
(predict/rollout/router_gating) crashed broadcasting a 15-wide
cond_cont against an 8-wide mean/std.

In "embedding" mode those physical columns are never read by
ConditionEncoder, so padding the missing entries with mean=0/std=1 is
a safe no-op. "physical" mode reads them directly, so a mismatch there
still raises instead of silently normalizing garbage.

giant/data/transforms.py

@@ -530,11 +530,44 @@ def build_cond_features(
     cond_cat = np.column_stack([pdg_idx, mat_idx])
 
     if cond_normalizer is not None:
-        cond_cont = cond_normalizer.transform(cond_cont)
+        cond_cont = _cond_normalizer_transform(cond_cont, cond_normalizer, conditioning)
 
     return cond_cont, cond_cat
 
 
+def _cond_normalizer_transform(
+    cond_cont: np.ndarray, cond_normalizer: "Normalizer", conditioning: str
+) -> np.ndarray:
+    """Apply ``cond_normalizer``, padding a legacy narrower normalizer if needed.
+
+    Checkpoints trained before physical-property conditioning (``COND_DIM``
+    8->15, ``giant/constants.py``) saved a ``COND_DIM_BASE``-wide (8) cond
+    normalizer, fit before ``build_cond_features`` grew the extra physical
+    columns. In "embedding" mode those columns are never read by
+    ``ConditionEncoder`` (``giant/model/network.py``), so padding the missing
+    entries with mean=0/std=1 is a safe no-op that keeps such checkpoints
+    usable under the current, always-``COND_DIM``-wide contract. In
+    "physical" mode the physical columns are load-bearing, so a mismatch
+    there is a real incompatibility, not something to paper over.
+    """
+    mean, std = cond_normalizer.mean, cond_normalizer.std
+    assert mean is not None and std is not None, "Normalizer not fitted"
+    width = cond_cont.shape[-1]
+    if mean.shape[-1] < width:
+        if conditioning != "embedding":
+            raise ValueError(
+                f"cond normalizer has {mean.shape[-1]} columns, expected "
+                f"{width}, and conditioning={conditioning!r} reads the "
+                "physical columns directly — this checkpoint predates "
+                "physical-property conditioning and can't be safely padded; "
+                "retrain it under the current code."
+            )
+        pad = width - mean.shape[-1]
+        mean = np.concatenate([mean, np.zeros(pad, dtype=mean.dtype)])
+        std = np.concatenate([std, np.ones(pad, dtype=std.dtype)])
+    return ((cond_cont - mean) / std).astype(np.float32)
+
+
 def build_features(
     data: dict[str, np.ndarray],
     pdg_map: dict[int, int],