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>
- 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>
Builds the shared top-N-plus-other PDG/material maps (pooling both primary
and secondary occurrences for PDG, directly targeting the meeting's
species-collapse failure mode) and wires up conditioning.{particle,material}
= "onehot" plus stage2_model.particle_type.target in ("onehot", "embedding")
end-to-end: setup-cache persistence, Stage2OneShot's type_head (flow/ddpm)
vs. folded+ST-Gumbel-relaxed adversarial slice (wgan), and the corresponding
CE/MSE training losses. particle_type.target = "physical" stays byte-for-byte
unchanged, keeping the v0.2 migration shim's bit-identical guarantee intact.
giant predict/rollout fail loudly on a onehot/embedding checkpoint until
full decode support lands in step 6.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Removes unused imports and an ambiguous variable name, narrows
Optional types before use so ty's flow analysis is satisfied, swaps
sum() over polars expressions for pl.sum_horizontal to avoid the
Literal[0] fallback type, and converts numpy bin edges to plain lists
before passing to matplotlib's hist (whose stub only accepts
Sequence[float]). Also applies ruff format across the repo, which had
drifted out of sync with the formatter.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
When no --out is given, predictions are written to a UUID-named parquet
(/ceph central store for ceph inputs, sibling directory otherwise) and a
YAML reference file is created next to the checkpoint recording the output
path, dataset, checkpoint, and timestamp. Adds pyyaml as a core dependency
and unit tests for the two new helper functions.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
lars