lars
93b19911f8
CI / Format (ruff format) (push) Successful in 36s
CI / Lint (ruff check) (push) Successful in 38s
CI / Sync project version with tag (push) Has been skipped
CI / Type check (ty) (push) Failing after 45s
CI / Lint (ruff check) (pull_request) Successful in 41s
CI / Tests (push) Has been skipped
CI / Format (ruff format) (pull_request) Successful in 35s
CI / Sync project version with tag (pull_request) Has been skipped
CI / Type check (ty) (pull_request) Failing after 37s
CI / Tests (pull_request) Has been skipped
- 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>