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>
Replace giant/analysis.py's dual numpy-SampleCollection + polars paths with a
single polars-streaming implementation that produces the validation notebook's
plots directly from a `giant predict --coord local` parquet, sized for files
larger than RAM.
- Drop the numpy SampleCollection path (load_predicted_local, marginal_table,
correlation_matrices, direction_alignment, constraint_report, plot_kl_bars)
and the rollout observables; the 5 remaining plotters now take a parquet
path / LazyFrame and stream internally.
- Rewrite compute_event_observables_pl to aggregate in parallel streaming
polars (post-pos reconstruction as expressions) instead of a serial
pyarrow-batch + numpy loop, fixing a pre-existing OOM (holistic median +
323M-row join in the bin-edge sizing). Medians are approximated from a
streaming log-bin histogram with within-bin interpolation.
- Keep every full-file scan narrow (few columns): on a file larger than RAM,
peak mmap memory, not scan count, is the binding constraint. Marginals run
one dim at a time (~15GB peak) rather than a combined all-dims pass (OOM).
- Update analysis/validation.ipynb to the path-based API; delete the
analysis/export_*.py and compare_ode_steps_*.py one-off scripts.
- Rewrite tests/test_analysis.py around parquet fixtures with an inline numpy
oracle; add correlation/streaming-plotter and approx-median coverage.
Verified end-to-end on the 32GB predict file: full notebook completes at
~25GB peak (no OOM); event rollup runs at ~13 cores.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
lars