lars
06c9ad8e5f
- make_seed_frontier only resolves particle mass/charge in "physical" mode, so "embedding"-mode rollouts no longer crash on a seed PDG code giant.particles can't resolve (the TERM_UNKNOWN_PDG gate now handles it). - nearest_known_pdg skips unresolvable candidate PDG codes instead of raising and killing the whole rollout/predict run. - predict/rollout fail with a clear message when a checkpoint predates the sec_phys normalizer, instead of a bare KeyError. - validate_marginals' phys_kl degrades to NaN (matching the energy_fraction_kl pattern) instead of crashing when a validated batch has zero secondaries on either side. - Correct CLAUDE.md's stale claim that the materials table is unfilled. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
54 lines
2.2 KiB
Python
54 lines
2.2 KiB
Python
import numpy as np
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import torch
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from giant.constants import COND_DIM, K_MAX, SEC_SLOT_DIM, X_DIM
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from giant.model.network import DenoisingMLP, SecondaryDecoder
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from giant.validate import validate_marginals
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def _tiny_models():
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s1 = DenoisingMLP(pdg_vocab=3, mat_vocab=2, hidden_dim=16, n_blocks=1)
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s2 = SecondaryDecoder(pdg_vocab=3, mat_vocab=2, hidden_dim=16, n_blocks=1)
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return s1.eval(), s2.eval()
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def _zero_secondaries_loader(B=4, n_batches=2):
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"""A val_loader whose every batch has n_sec=0 (real side) — matches the
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(cond_cont, cond_cat, target_s1, n_sec, sec_cont, proc_idx) tuple shape
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StreamingStepsDataset yields."""
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batches = []
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for _ in range(n_batches):
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cond_cont = torch.randn(B, COND_DIM)
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cond_cat = torch.zeros(B, 2, dtype=torch.long)
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x1 = torch.randn(B, X_DIM)
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n_sec = torch.zeros(B, dtype=torch.long)
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sec_cont = torch.zeros(B, K_MAX, SEC_SLOT_DIM)
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proc_idx = torch.zeros(B, dtype=torch.long)
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batches.append((cond_cont, cond_cat, x1, n_sec, sec_cont, proc_idx))
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return batches
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def test_validate_marginals_all_zero_secondaries_returns_nan_phys_kl(monkeypatch):
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"""If n_sec_pred collapses to 0 across the whole validated set (realistic
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during early/unstable training), phys_kl must degrade to NaN instead of
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crashing on the empty-array .min()/.max() reduction inside
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_histogram_kl -- a regression the old species/bincount code this
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replaced explicitly guarded against."""
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s1, s2 = _tiny_models()
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loader = _zero_secondaries_loader()
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# Force the Stage-1 n_sec head's prediction to 0 for every sample too, so
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# the generated side's valid-slot mask is also empty (real side is
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# already all n_sec=0 by construction of the fake loader above).
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def _fake_sample_flow(model, cond_cont, cond_cat, **kw):
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B = cond_cont.size(0)
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return torch.randn(B, X_DIM), torch.zeros(B, dtype=torch.long)
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monkeypatch.setattr("giant.validate.sample_flow", _fake_sample_flow)
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result = validate_marginals(s1, loader, sec_decoder=s2, n_batches=2)
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assert np.asarray(result["phys_real"]).shape == (0, 2)
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assert np.asarray(result["phys_generated"]).shape == (0, 2)
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assert np.isnan(np.asarray(result["phys_kl"])).all()
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