Pulled forward from the not-yet-mergeable condor-gpu-train-rollout branch:
`new-run` resolves CLI hyperparameter overrides into a full config.toml and
run dir (reusing the existing default_out_dir_name collision-avoidance and a
newly factored-out router-override helper shared with `train`), so a run can
be prepared and reviewed before `giant train` actually kicks off. Also
brings README up to date with the model/CLI as it actually stands
(physical/embedding conditioning, WGAN/MoE-router modes, giant analyze,
W&B, setup-stage caching), which had drifted back to describing the
Phase-1 proof-of-concept.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Lets the vocab maps, event-id split index, and normalizer stats be
warmed once for a dataset (right after `dwarf convert`, or before a
`dwarf hparam-scan` sweep) without needing to also start training.
Extracts the setup-stage logic out of giant/pipeline.py:run_train_job
into a standalone run_setup_stage() (returning a SetupStageResult),
reused by both run_train_job and the new dwarf command's
scripts/warm_setup_cache.py — a behavior-preserving refactor, covered
by the existing test_pipeline.py suite.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Records both identified contributors to the 2026-07-22 router divergence
(missing lambda_balance and the uniform-linspace center init) and that
the latter is now fixed, with next steps covering both for the retrain.
The 10-expert EnergyRouter checkpoint (lambda_balance=0.0) diverges badly
from Geant4 on rollout (step granularity, secondary species, shower
shape), and the router_gating diagnostic shows the experts heavily
overlap rather than partitioning the pre-step energy domain. Recorded as
"needs retraining with a different router config" rather than an
abandoned direction. Full analysis in the knowledge base at
experiments/giant-router-energy-rollout-validation.md.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Add a second parallelism axis to giant analyze: each plot's data can now
be split into a configurable number of event_id-disjoint chunks, each
computed as its own HTCondor job, bounding per-job walltime and scan cost
on large rollout/reference files instead of one job re-scanning the
whole file per plot.
Every PlotSpec now splits into compute_partial (runs per (plot, chunk)
job against a chunk-filtered Bundle) and finalize (merges chunks -
elementwise sum for fixed-edge histograms/species shares, concatenate
-then-recompute for specs that derive edges or mean/std from the full
per-event/per-secondary array). Router diagnostics stay chunkable=False
and always run as a single job. giant analyze render now joins every
plot's chunk partials (merge_all) before rendering, transparently.
New: --chunks on `analyze prep`/`analyze submit`, --chunk on
`analyze compute-one`, and a new `analyze merge-one` command.
Adds a Compute environment section (laptop/desktop vs. shared portal
machines vs. condor workers) and corrects the roadmap: the WGAN-GP and
MoE routing-trunk tracks are actually implemented (untested and
under-testing respectively), not "not yet built" as previously stated.
Also notes the in-progress condor-gpu-train-rollout branch.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
`giant analyze prep` / `submit` now take the `giant rollout` YAML sidecar as
their only positional input instead of explicit --rollout/--reference/--out-dir.
The YAML's `output`/`dataset` keys name the rollout parquet and its seed file
(the reference truth), and the rest of the sidecar (checkpoint, geometry oracle,
cutoffs) flows into every plot's gallery metadata.
prep derives its own run directory next to the rollout parquet
(<...>/analysis_<id>/) holding shared.json, run_meta.json, reduced/, plots/.
compute-one and render now take just --run-dir / a run-dir argument and read the
resolved paths + metadata from run_meta.json, so the condor wrapper no longer
threads file paths. open_side scans a directory of reference shards via glob.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the monolithic giant/analysis.py (predict-local + RolloutVsTruth
diagnostics) with a lean giant/analysis/ package that compares one
autoregressive `giant rollout` for a checkpoint against a held-out
miniCaloSim reference file, and generates publication-styled plots in
parallel on HTCondor.
Rollout output and a raw reference file share a world-frame physical
column subset under identical names, so the old ALR/local-frame decode
machinery is gone — everything is world-frame mm/MeV.
- sources.py: canonical LazyFrames, synthetic-termination-row filtering,
the secondary view (rollout generation>0 tracks vs reference sec_*_list).
- reduce.py: streaming primitives — a single hist1d group_by pass, per-event
scalars, edep-weighted depth/transverse profiles, species share, leakage.
- context.py/grouping.py: prep resolves fixed bin edges + energy/pdg/material
group sets once into shared.json, so each compute job is one pass, no range
scan (histogram efficiency).
- catalog.py: declarative PlotSpec registry — marginals x {overall,energy,pdg,
material}, per-event totals, shower profiles, species/leakage, secondaries.
- render.py: the only plotstyle/LaTeX importer; PDFs + gallery metadata.
- condor.py + `giant analyze` CLI (prep/compute-one/list/render/submit):
one job per plot, compute/render split (workers polars-only, no LaTeX).
Styling via ETPlot's plotstyle (added to the analysis extra). New tests cover
the reduce primitives, catalog id uniqueness + compute, condor submit, and a
guarded render smoke test. Delete the two predict-diagnostics notebooks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- make_seed_frontier only resolves particle mass/charge in "physical"
mode, so "embedding"-mode rollouts no longer crash on a seed PDG code
giant.particles can't resolve (the TERM_UNKNOWN_PDG gate now handles it).
- nearest_known_pdg skips unresolvable candidate PDG codes instead of
raising and killing the whole rollout/predict run.
- predict/rollout fail with a clear message when a checkpoint predates
the sec_phys normalizer, instead of a bare KeyError.
- validate_marginals' phys_kl degrades to NaN (matching the
energy_fraction_kl pattern) instead of crashing when a validated batch
has zero secondaries on either side.
- Correct CLAUDE.md's stale claim that the materials table is unfilled.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Adds model.conditioning = "physical" | "embedding": physical mode routes
particle mass/charge and material Z_eff/A_eff/density/X0/lambda_int through
small MLPs to replace the learned PDG/material embedding tables, so the
surrogate generalizes to PDG codes/materials outside the training vocab
instead of memorizing it. "embedding" stays available as the comparison
baseline (old checkpoints without the key default to it).
Stage 2 now regresses a secondary's mass/charge directly against a fixed
physics-derived target instead of a learned/snapped embedding, and uses no
snapping at inference — the model's raw predicted (mass, charge) is the
secondary's physical identity, including for its own further rollout steps.
A separate reporting-only nearest-known-PDG lookup (never fed back into the
model) populates output pdg columns / the embedding-mode rollout fallback.
giant/materials.py's table is populated with Geant4's own built-in NIST
constants (Z_eff, A_eff, density, X0, lambda_int), extracted directly from
the Geant4 11.4.1 build vendored in minicalosim via G4NistManager rather
than hand-typed literature values. G4_LYSO is left unfilled: confirmed (both
by runtime lookup and by searching minicalosim's history) that it's never
actually a constructed Geant4 material there, only documentation/UI color-map
text.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Merged in every non-analysis change from the MoE-prototype branch (routing,
training, data pipeline, streaming rollout output), keeping this branch's
lean streaming giant/analysis.py and rebuilding the rollout-vs-truth feature
natively on it instead of resurrecting the old numpy SampleCollection path.
- Add RolloutVsTruth, accepted anywhere Tier 1-3 functions take a predict-parquet
source: decodes a giant rollout file and a held-out truth file into
RAW_TARGET_NAMES space via a polars port of the forward local-frame rotation,
fully streaming (no SampleCollection, no eager materialization).
- Add compute_rollout_vs_truth_observables_pl for Tier 4, reusing
EventObservables (now backed by independent real_table/gen_table to support
unequal rollout/truth event counts) so every existing shower-observable plot
function works unchanged for both one-step and full-rollout comparisons.
- Update analysis/rollout_validation.ipynb to the new API and CLAUDE.md's
architecture description; add test coverage for the new source type.
- Fix a pre-existing return-type mismatch in giant.rollout.rollout() (found by
`ty check`): the on_chunk summary-dict branch didn't match the declared
dict[str, np.ndarray] return type, now expressed as a RolloutSummary TypedDict.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Bring the docs in line with the current two-stage code: energy ALR
simplex output, 8D conditioning (n_sec/e_sec now predicted, not given),
the SecondaryDecoder stage, and the shower rollout + geometry oracle.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Closes the loop from single-step prediction into full showers:
- giant/geometry.py + `dwarf build-geometry-oracle`: learn position ->
(material, layer_id) from data (KNN/SVM) to supply the conditioning the
surrogate does not predict; flag detector escape by NN distance.
- giant/rollout.py: breadth-first batched frontier that steps all active
tracks, spawns secondaries as new tracks, and terminates on energy cutoff,
per-track max steps, escape, or natural end. Energy is deposited locally on
every stop except escape (leakage), so showers conserve energy exactly.
- `giant rollout` CLI: seed from real events (argmax pre_E), load checkpoint,
write a world-frame steps parquet + YAML sidecar.
- giant/analysis.py: compute_rollout_observables + plot_rollout_* for
single-sided longitudinal/transverse/total-energy shower profiles;
analysis/export_rollout_observables.py driver.
- scikit-learn added as an optional `geometry` extra (lazy-imported).
- Tests: tests/test_geometry.py, tests/test_rollout.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the five separately-hyphenated uv entry points (steps-to-parquet,
steps-to-parquet-parallel, migrate-geant-steps, bump-dataset-version,
create-root-files) plus the unregistered hparam_scan.py with one `dwarf`
command exposing convert/migrate/bump-gen/bump-schema/status/
update-manifest/create-manifest/make-root/hparam-scan as subcommands.
Each scripts/*.py module now only holds argparse-free business logic;
scripts/dwarf.py wires it up with Typer, matching giant/cli.py's style.
`dwarf convert` merges the old serial/parallel conversion scripts behind
a --jobs flag (default 1: sequential with plain -o; >1: dataset-layout
fan-out via subprocess).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The Typer-based giant/cli.py train command now has full feature
parity (dropout, warmup-epochs, validate-steps, shorthand flags),
making the standalone argparse script redundant.
Pins torch to 2.3.x via mutually-exclusive cpu/cuda uv extras (newer
torch requires newer NVIDIA drivers), and adds upper bounds to the
other dependencies based on current PyPI releases.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
step_length already encodes |post_pos - pre_pos| by definition, so a raw
post_pos target would duplicate that magnitude and could drift inconsistent
with step_length during sampling. Instead add travel_dir, a unit vector
(local frame) giving only the direction of pre_pos->post_pos; post_pos is
reconstructed at inference as pre_pos + step_length * travel_dir, keeping
the two self-consistent. Target grows from 6D to 9D.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
lars