Fix negative secondary mass crashing log_transform during rollout
decode_secondaries() applied inv_log_transform() to the model's raw predicted log_mass directly. Since that value isn't itself the output of log_transform, exp(log_mass) can undershoot _EPS, making inv_log_transform(log_mass) = exp(log_mass) - _EPS go slightly negative. Once that secondary spawns a track and its mass is fed back in as conditioning for a further rollout step, log_transform(mass) computes log(mass + eps) with mass <= -eps, producing a non-finite value and raising. Clip log_mass to log(_EPS) before inverting so the resulting mass is guaranteed >= 0 (matching the invariant the surrounding comment already assumed, but didn't enforce). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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@@ -540,3 +540,29 @@ def test_decode_secondaries_mass_charge_round_trip_with_normalizer():
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)
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assert sec_mass[0, 0] == pytest.approx(938.27208943, abs=1e-2)
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assert sec_charge[0, 0] == pytest.approx(1.0, abs=1e-4)
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def test_decode_secondaries_extreme_negative_log_mass_stays_nonnegative():
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from giant.data.transforms import decode_secondaries, log_transform
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N = 1
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e_sec = np.array([5.0], dtype=np.float32)
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n_sec = np.array([1])
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pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32)
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sec_cont = np.zeros((N, K_MAX, 6), dtype=np.float32)
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sec_cont[0, 0, 0] = 10.0 # stick logit -> ~all of e_sec
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sec_cont[0, 0, 1:4] = [0, 0, 1]
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sec_cont[0, 0, 4] = -50.0 # raw model prediction: extremely negative log_mass
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sec_cont[0, 0, 5] = 1.0
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_, _, sec_mass, _, _ = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir)
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# A raw model prediction isn't itself the output of log_transform, so
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# naively applying inv_log_transform can undershoot zero (see
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# decode_secondaries) — which then crashes the next log_transform call
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# once this mass is fed back in as conditioning during rollout. The
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# float32 residual from clipping can land a hair below zero, but must
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# stay well above -eps so log_transform(mass) stays finite.
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assert sec_mass[0, 0] > -1e-8
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log_transform(sec_mass[0, 0])
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