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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>
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
"""dwarf warm-cache — precompute `giant train`'s setup-stage sidecar ahead of time.
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Thin wrapper around `giant.pipeline.run_setup_stage` so a dataset's vocab
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maps, event-id split index, and normalizer stats can be warmed once — e.g.
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right after `dwarf convert`, or before kicking off a `dwarf hparam-scan`
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sweep — without needing to also start training. See giant/data/setup_cache.py
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for the sidecar itself.
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"""
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from pathlib import Path
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from giant.constants import K_MAX
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from giant.pipeline import run_setup_stage
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def run_warm_setup_cache(
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data: str,
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val_fraction: float = 0.1,
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seed: int = 0,
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particle_conditioning: str = "physical",
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material_conditioning: str = "physical",
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router_enabled: bool = False,
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router_type: str = "energy",
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n_experts: int = 4,
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rebuild: bool = False,
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echo=print,
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) -> None:
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"""Populate (or refresh) the setup cache sidecar for `data`.
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`val_fraction`/`seed`/`particle_conditioning`/`material_conditioning`
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select the normalizer cache entry
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(`giant.data.setup_cache.normalizer_key`) — pass the same values a later
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`giant train` invocation will use so it hits this warmed entry. The two
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conditioning axes are independent (docs/v0.3.0-design.md §3.1) and may
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differ. `router_enabled`/`router_type`/`n_experts` only matter for
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`router_type == "process"` (warms that `n_experts`'s process map); the
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energy-router quantile summary is always collected regardless, so a
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later `--router-type energy` run never needs to rescan just to seed
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centers.
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"""
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router_cfg = {
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"enabled": router_enabled,
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"type": router_type,
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"n_experts": n_experts,
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}
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# A minimal v0.3 cfg — only the keys run_setup_stage actually reads
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# (conditioning.{particle,material}.type, stage{1,2}_model.router). This
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# CLI only ever configures one router (matching today's single
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# --router-type flag), so it's placed on stage1_model; stage2_model's
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# stays disabled.
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cfg = {
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"conditioning": {
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"particle": {"type": particle_conditioning},
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"material": {"type": material_conditioning},
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},
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"stage1_model": {"router": router_cfg},
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"stage2_model": {"router": {"enabled": False}, "k_max": K_MAX},
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}
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run_setup_stage(
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Path(data),
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val_fraction=val_fraction,
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seed=seed,
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cfg=cfg,
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cache_setup=True,
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rebuild_setup_cache=rebuild,
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echo=echo,
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)
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echo("setup cache warmed.")
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