Clip raw predicted log_mass in decode_secondaries (gitea #54)

decode_secondaries inverted a secondary's raw predicted log_mass with
inv_log_transform (exp(y) - eps) unclipped. log_mass is a raw regression
output, not itself the result of log_transform, so it isn't guaranteed to
land in the range that round-trips cleanly: too negative and exp(y)
undershoots eps, making the result go slightly negative; too positive and
exp(y) overflows float32 to inf. Either one crashes the next rollout step,
since a track descended from that secondary feeds its mass back in as
conditioning, and log_transform raises on a non-finite input.

Clip log_mass to [log(_EPS), _LOG_MASS_MAX] before inverting, guaranteeing a
finite, non-negative mass. _LOG_MASS_MAX=80.0 matches the value from the
stale fix/rollout-negative-secondary-mass branch (comfortably below
float32's ~88.7 overflow point, far beyond any physical particle mass a
converged model would predict) — that branch had already implemented this
fix but forked before gitea #35/#36 and couldn't be merged as-is, so this
reimplements it fresh against current master and leaves the stale branch
untouched.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

giant/data/transforms.py

@@ -14,6 +14,14 @@ _EPS = 1e-8
 # the conservation it slightly softens is physically negligible (~0.001%).
 _SIMPLEX_FLOOR = 1e-5
 
+# Upper clip for a raw predicted log_mass before inv_log_transform: exp(y)
+# must stay well inside float32 range (~3.4e38, i.e. y < ~88.7) or it
+# overflows to inf, which — like the negative-mass case below — blows up the
+# next log_transform call once that mass is fed back in as conditioning.
+# 80.0 leaves comfortable headroom while still being far beyond any physical
+# particle mass a converged model would ever predict.
+_LOG_MASS_MAX = 80.0
+
 
 def log_transform(x: np.ndarray, eps: float = _EPS) -> np.ndarray:
     x = np.asarray(x, dtype=np.float32)
@@ -685,9 +693,18 @@ def decode_secondaries(
     log_mass = sec_cont[:, :, 4]  # (N, K)
     charge = sec_cont[:, :, 5]  # (N, K)
 
-    # mass is non-negative by construction (inv_log_transform of a real
-    # number is always > 0); clip to 0 for padded/invalid slots rather than
-    # leaving a spurious small positive floor from the log inverse.
+    # log_mass is a raw model prediction, not itself the output of
+    # log_transform, so it can land far outside the range that round-trips
+    # cleanly through inv_log_transform: too negative and exp(log_mass)
+    # undershoots _EPS, making inv_log_transform go slightly negative; too
+    # positive and exp(log_mass) overflows float32 to inf. Either one then
+    # blows up the next log_transform call on this track's mass once it's
+    # fed back in as conditioning for a further rollout step
+    # (giant/rollout.py -> build_cond_features -> _physical_cond_columns).
+    # Clip to a range whose inverse is guaranteed finite and >= 0 before
+    # that can happen; clip to 0 separately for padded/invalid slots rather
+    # than leaving a spurious small positive floor.
+    log_mass = np.clip(log_mass, np.log(_EPS), _LOG_MASS_MAX)
     sec_mass = np.where(sec_valid, inv_log_transform(log_mass), 0.0).astype(np.float32)
     sec_charge = np.where(sec_valid, charge, 0.0).astype(np.float32)