Files
giant/tests
lars c4b12b5e7a
CI / Format (ruff format) (push) Successful in 28s
CI / Lint (ruff check) (push) Successful in 29s
CI / Sync project version with tag (push) Has been skipped
CI / Lint (ruff check) (pull_request) Successful in 40s
CI / Type check (ty) (push) Successful in 45s
CI / Format (ruff format) (pull_request) Successful in 33s
CI / Sync project version with tag (pull_request) Has been skipped
CI / Type check (ty) (pull_request) Successful in 36s
CI / Tests (pull_request) Successful in 3m52s
CI / Tests (push) Successful in 4m5s
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
..