3 Commits

Author	SHA1	Message	Date
lars	9112e845e0	v0.3.0 step 3: per-stage train.py trainers + pipeline.py/cli.py rewrite ... Replaces train.py's single global training loop with a StageTrainer hierarchy (FlowDDPMStageTrainer, WGANStageTrainer) — one per active stage, each owning its own optimizer/LR schedule/EMA and reading only the shared batch tuple (stage 2 always teacher-forces on the ground-truth x1_s1, so stages never need each other's output at train time). Supports every stage1/stage2 generator combination, including the design doc's headline mixed case (stage1=flow + stage2=wgan) and its reverse, plus stage1-only/stage2-only ablation runs, routed+gumbel stages, and checkpoint save/resume. metrics.csv/wandb logging are stage-prefixed. validate_marginals calls are guarded with a one-time warning and a Wasserstein-magnitude fallback for wgan best-checkpoint selection, since giant/sample.py still assumes stage1 always owns n_sec_head (decision 1 moved it to stage 2 by default) — deferred to design doc step 6, not silently papered over. pipeline.py's run_setup_stage/run_train_job now read the new nested config directly; the dangling resolve_expert_dims call and the --mode wgan --router rejection are both gone (routed WGAN works). cli.py's train/new-run build correctly-shaped config overrides (architecture flags -> stage1_model only per the approved decision; --mode/--n-critic/--gp-weight/--critic-lr broadcast to both stages, matching migrate_config's own precedent and avoiding a regression on the common --mode case); predict/rollout's dangling build_models tuple-unpack is fixed; new-run now tags config_version, fixing a bug where a re-loaded v0.3 config.toml would have been silently corrupted by migrate_config mistaking it for v0.2. config.py's validate_config rejects mixed particle/material conditioning types for now (ConditionEncoder supports it, the data pipeline in giant/data/transforms.py doesn't yet). analysis/render.py and router_gating.py handle both the new nested model_config shape and legacy flat checkpoints. scripts/warm_setup_cache.py updated for run_setup_stage's new signature. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-08-06 11:31:49 +02:00
lars	d656cf3109	Store a quantile grid instead of a raw reservoir sample in the setup cache ... NormalizerEntry.energy_reservoir_sample kept 100k raw energy values purely to seed EnergyRouter centers via np.quantile at load time, which alone accounted for most of the setup cache sidecar's ~2MB size (float32 values round-tripped through Python floats serialize at full double precision). Only a handful of quantile levels are ever read back, so collapse the sample to a fixed 1001-point quantile grid at save time and interpolate arbitrary levels from it at use time instead — about 100x smaller with negligible (<0.001) error on the levels that matter. Bumps the cache format version since old sidecars have no such grid to fall back on.	2026-07-30 13:32:46 +02:00
lars	471a81b5e7	Add dwarf warm-cache to precompute the setup-stage sidecar ... 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>	2026-07-30 10:53:31 +02:00