Also clip the positive tail of raw predicted log_mass in rollout

The previous commit clipped log_mass's negative tail (undershooting
_EPS made mass go slightly negative). The mirror case also crashes
rollout: a sufficiently large raw predicted log_mass overflows
exp() in float32, giving mass = inf, which then fails the same
downstream log_transform finiteness check when that mass is fed back
in as conditioning for a further step. Bound the upper tail too, at a
value comfortably below float32's overflow point.

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

giant/data/transforms.py

@@ -10,6 +10,14 @@ _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)
@@ -630,15 +638,17 @@ def decode_secondaries(
             )
 
     # log_mass is a raw model prediction, not itself the output of
-    # log_transform, so exp(log_mass) can undershoot _EPS and make
-    # inv_log_transform(log_mass) = exp(log_mass) - _EPS go slightly
-    # negative. That negative mass 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 log_mass so its inverse is guaranteed
-    # >= 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), None)
+    # 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 log_mass 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)
 

tests/test_phase2.py

@@ -566,3 +566,27 @@ def test_decode_secondaries_extreme_negative_log_mass_stays_nonnegative():
     # stay well above -eps so log_transform(mass) stays finite.
     assert sec_mass[0, 0] > -1e-8
     log_transform(sec_mass[0, 0])
+
+
+def test_decode_secondaries_extreme_positive_log_mass_stays_finite():
+    from giant.data.transforms import decode_secondaries, log_transform
+
+    N = 1
+    e_sec = np.array([5.0], dtype=np.float32)
+    n_sec = np.array([1])
+    pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32)
+
+    sec_cont = np.zeros((N, K_MAX, 6), dtype=np.float32)
+    sec_cont[0, 0, 0] = 10.0  # stick logit -> ~all of e_sec
+    sec_cont[0, 0, 1:4] = [0, 0, 1]
+    sec_cont[0, 0, 4] = 200.0  # raw model prediction: extremely positive log_mass
+    sec_cont[0, 0, 5] = 1.0
+
+    _, _, sec_mass, _, _ = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir)
+
+    # Mirror image of the extreme-negative case above: exp(log_mass)
+    # overflows float32 to inf for an unclipped raw prediction this large,
+    # which then crashes the next log_transform call the same way a
+    # negative mass would.
+    assert np.isfinite(sec_mass[0, 0])
+    log_transform(sec_mass[0, 0])