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>
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>
Adds --mode wgan alongside flow/ddpm: both stages get a WGAN-GP
generator/critic pair (giant.model.wgan) instead of flow matching, so
inference is a single forward pass per stage rather than a 10-step ODE
integration — the fast-eval architecture noted in the roadmap.
predict/rollout auto-detect the mode from the checkpoint's model_config.
Best-checkpoint selection for wgan uses marginal-KL against the EMA
generators every epoch, since a critic loss isn't a monotone quality
signal. --router is not supported together with --mode wgan.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
lars