6 Commits

Author	SHA1	Message	Date
lars	c4b12b5e7a	Pass ConditioningAxisConfig/ParticleTypeConfig themselves instead of raw dicts (gitea #38) ... build_models/build_critics parsed model_config into frozen dataclasses (ConditioningConfig, Stage2ModelConfig, ...) but then threw the parsed sub-objects away and passed the original raw dicts (conditioning["particle"], s2_spec.particle_type.to_dict()) down into ConditionEncoder/StageModel/etc, which re-read them with their own hardcoded .get(key, default) fallbacks — each an independent copy of a fact the dataclass already stated once. Worst instance: giant/training/trainers.py:236 converted an already-parsed ParticleTypeConfig back into a dict for no reason. Threads ConditioningAxisConfig (particle_cfg/material_cfg) and ParticleTypeConfig (particle_type_cfg) as the actual dataclass instances through every signature that used to type them dict: ConditionEncoder, StageModel/CriticModel, resolve_type_n_classes/stage2_type_dim/ stage2_trunk_sec_dim, giant/model/builders.py, giant/sample.py, giant/training/stage2_inputs.py, giant/training/trainers.py (StageSpec/ StageTrainer), giant/pipeline.py, giant/rollout.py, giant/validate.py — so ty now catches a misspelled field instead of it silently falling back. No config-schema change: config.toml/checkpoint model_config keep the same nested-dict shape; only what happens after the existing X.from_dict(...) parse changes. User-confirmed scope decision: both axes (particle_cfg/material_cfg and particle_type_cfg), not just the more heavily-duplicated particle_type_cfg axis, and not stopping at the two most literal parse-then-discard round trips — matching the issue's own proposal. Preserved-default decision: StageModel's particle_type_cfg=None sentinel (hit only by direct/test construction — build_models always passes an explicit particle_type) still resolves to ParticleTypeConfig(target= "physical"), not ParticleTypeConfig()'s own target="onehot" config-file default — switching it would have silently grown an unused, gradient-less type_head on every test that constructs Stage2OneShot/Stage2Autoregressive without particle_type_cfg=, breaking their "every param has a grad" checks. New tests in tests/test_network.py: ConditionEncoder/StageModel store the exact ConditioningAxisConfig/ParticleTypeConfig instance passed in (identity, not just equality) — no internal dict round-trip — and build_models's output carries real dataclass instances end to end, not the plain dicts it produced before this fix. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-14 15:03:55 +02:00
lars	55332db67a	Bump ruff line-length to 120 and reformat ... Rejoins lines that only wrapped because they exceeded the old 88-char limit; ruff check and the full test suite (725 passed) are unaffected.	2026-08-12 13:33:09 +02:00
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	200c6d243b	v0.3.0 step 7: AttentionHistory (KV-cached) + scheduled/never teacher forcing ... AttentionHistory (giant/model/network.py) adds causal self-attention over the emitted-secondary prefix as the alternative to MarkovHistory, with a parallel forward() for training and an init_cache()/step() KV-cache path for sample.py's per-slot AR inference loop, wired into Stage2Autoregressive via history="attention". giant/train.py adds _stage2_tf_prob and _assemble_stage2_ar_inputs_scheduled, mixing ground-truth history with a detached sample_secondaries_ar self-sample per slot so teacher_forcing="scheduled"/"never" close the train/inference gap teacher_forcing="always" always avoided; wired into both stage-2 AR trainers. config.py's validate_config no longer rejects these two previously unimplemented schema values. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-07 13:15:56 +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