5 Commits

Author	SHA1	Message	Date
lars	878e9ddca3	Delete docs/v0.3.0-design.md and strip all references to it ... The design doc and its followups doc are no longer needed as a live reference now that the v0.3.0 redesign is implemented — comments and docstrings across the codebase cited it extensively (file path, "design doc §X.Y", "decision N", or bare "§X.Y" section numbers) as design rationale. Removed docs/ and edited every citing comment/docstring to drop the now-dangling reference while keeping the substantive explanation next to it. CLAUDE.md's v0.3.0 roadmap bullet loses its trailing pointer to the deleted file. Verified: no remaining "docs/v0.3.0", "design doc", "decision N", or "§N.N" references (repo-wide grep); ruff and ty clean; full test suite on the heaviest-touched modules (network, sample, rollout, migration, config, train) passes. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-10 11:19:02 +02:00
lars	da7cde3ef9	v0.3.0 post-implementation audit: resolve all 9 tracked discrepancies ... Works through docs/v0.3.0-followups.md item by item, closing the gap between the design doc and the shipped v0.3.0-stage2-autoregressive code: 1. validate.py: 7-tuple batch unpacking, sample_stage1/sample_stage2 dispatch, stage-2 particle-type-class marginal. 2. Stage-prefixed --stage1-*/--stage2-* CLI flags for train/new-run. 3. Thread stage2_model.k_max through loader/transforms/dataset/pipeline/ train instead of the hardcoded K_MAX constant. 4. Mixed conditioning.particle.type / conditioning.material.type support end-to-end (data pipeline + dwarf warm-cache). 5. conditioning.share_stages = true: one shared ConditionEncoder instance across both stages. 6. stage2_model.generator = "ddpm" formally deferred into design doc §11.2 (was silently unimplemented). 7. giant predict/rollout: implement conditioning.*.type = "onehot" via the checkpoint's saved pdg_topn_map/mat_topn_map. 8. network.py's checkpoint-path model_config migration now fails loudly on non-zero legacy expert_hidden_dim/expert_n_blocks, matching config.py's TOML-load path (§4.2). 9. validate_config now rejects stage2_model.n_sec.mode = "truth" for a rollout-capable checkpoint (§9). Also cleared all pre-existing `ty check` noise (44 -> 0 diagnostics), mostly a test-helper dict-unpack pattern that made every unrelated constructor keyword look like a type error. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-07 16:12:58 +02:00
lars	93b19911f8	v0.3.0 step 6: sample.py/rollout.py AR generation + class->PDG decode ... - giant/sample.py: fix every sampler's call convention against Stage1Model/Stage2OneShot's actual forward signatures (was still calling model(x, t, cond_cont, cond_cat) positionally); add sample_secondaries_ar (free-running AR loop, unsnapped history feature) and sample_stage1/sample_stage2/resolve_n_sec dispatch helpers that read each stage's generator_kind/decoder off the model instance itself. - giant/particles.py: decode_topn_class (argmax + other_policy) and decode_embedding_nearest (L1-snap + distance) turn a secondary's "onehot"/"embedding" type prediction into a concrete PDG. - giant/rollout.py: decode_secondary_identity routes all three particle_type.target values to real mass/charge; per-stage generator dispatch (drops the single shared `mode` string, adds ddpm support); L1DistCollector accumulates the §11.3 embedding-distance diagnostic. - giant/cli.py: drop the onehot/embedding-target rejection gate (narrowed to the still-unimplemented conditioning.particle/material.type=onehot axis); fix the dead model_cfg.get("mode") bug in predict/rollout. - giant/analysis/: new type_embedding_l1_distance PlotSpec, wired through the rollout YAML sidecar (no live-model call needed, unlike router_gating -- the histogram is already pre-aggregated at rollout time). - Un-xfail every test that was blocked on this step (test_rollout.py, test_flow.py, test_wgan.py, test_phase2.py, test_router.py, test_validate.py); add test_sample.py, test_type_embedding_distance.py. Known follow-up: giant/validate.py still unpacks the training val-batch as a stale 6-tuple and doesn't use the new per-stage dispatch, so marginal validation during training degrades gracefully with a warning rather than working -- not in this step's scope. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-07 10:37:57 +02:00
lars	9ce55e5013	v0.3.0 step 2: network.py refactor to composable stage models ... Decomposes the ten permutation classes in giant/model/network.py into the reusable parts from docs/v0.3.0-design.md §5: ConditionEncoder (now independently configurable per particle/material axis), ContextAdapter, Trunk/MonolithicTrunk/RoutedTrunk/ExpertTrunk, and the stage classes Stage1Model/Stage2OneShot/CriticModel (Stage2Autoregressive stubbed, raises NotImplementedError until step 4/5). build_models/build_critics now return a dict keyed by stage and accept the new nested config shape, with routed WGAN reachable for the first time (the old --mode wgan --router rejection is gone) and stage2_model.router.tie_to_stage1 sharing a literal Router instance. A v0.2 checkpoint's flat model_config auto-migrates via _migrate_legacy_model_config + migrate_legacy_state_dict, preserving the n_sec_head's attachment to Stage1Model (legacy_owner="stage1", design doc §4.1). tests/test_migration_v02_v03.py proves this bit-identical against a frozen v0.2 snapshot (tests/legacy/network_v02_snapshot.py) for both flow and wgan, both conditioning modes. scripts/check_migration_v02_v03.py is the real-checkpoint counterpart for a portal machine with /ceph access. giant/model/schedule.py's flow-matching/DDPM loss helpers are updated to the new model-call convention (t as a keyword). giant/sample.py, giant/rollout.py, and giant/validate.py are not yet updated (deferred to design doc step 6) — their exercising tests are marked xfail with that reasoning rather than silently broken. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-06 10:55:29 +02:00
lars	06c9ad8e5f	Fix crashes in physical-property conditioning edge cases ... - 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>	2026-07-20 10:36:25 +02:00