lars
d656cf3109
CI / Format (ruff format) (push) Successful in 26s
CI / Lint (ruff check) (push) Successful in 28s
CI / Sync project version with tag (push) Has been skipped
CI / Type check (ty) (push) Successful in 24s
CI / Tests (push) Successful in 58s
CI / Format (ruff format) (pull_request) Successful in 28s
CI / Lint (ruff check) (pull_request) Successful in 28s
CI / Sync project version with tag (pull_request) Has been skipped
CI / Type check (ty) (pull_request) Successful in 28s
CI / Tests (pull_request) Successful in 59s
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.
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
"""dwarf warm-cache — precompute `giant train`'s setup-stage sidecar ahead of time.
|
|
|
|
Thin wrapper around `giant.pipeline.run_setup_stage` so a dataset's vocab
|
|
maps, event-id split index, and normalizer stats can be warmed once — e.g.
|
|
right after `dwarf convert`, or before kicking off a `dwarf hparam-scan`
|
|
sweep — without needing to also start training. See giant/data/setup_cache.py
|
|
for the sidecar itself.
|
|
"""
|
|
|
|
from pathlib import Path
|
|
|
|
from giant.pipeline import run_setup_stage
|
|
|
|
|
|
def run_warm_setup_cache(
|
|
data: str,
|
|
val_fraction: float = 0.1,
|
|
seed: int = 0,
|
|
conditioning: str = "physical",
|
|
router_enabled: bool = False,
|
|
router_type: str = "energy",
|
|
n_experts: int = 4,
|
|
rebuild: bool = False,
|
|
echo=print,
|
|
) -> None:
|
|
"""Populate (or refresh) the setup cache sidecar for `data`.
|
|
|
|
`val_fraction`/`seed`/`conditioning` select the normalizer cache entry
|
|
(`giant.data.setup_cache.normalizer_key`) — pass the same values a later
|
|
`giant train` invocation will use so it hits this warmed entry.
|
|
`router_enabled`/`router_type`/`n_experts` only matter for
|
|
`router_type == "process"` (warms that `n_experts`'s process map); the
|
|
energy-router quantile summary is always collected regardless, so a
|
|
later `--router-type energy` run never needs to rescan just to seed
|
|
centers.
|
|
"""
|
|
router_cfg = {
|
|
"enabled": router_enabled,
|
|
"type": router_type,
|
|
"n_experts": n_experts,
|
|
}
|
|
run_setup_stage(
|
|
Path(data),
|
|
val_fraction=val_fraction,
|
|
seed=seed,
|
|
conditioning=conditioning,
|
|
router_cfg=router_cfg,
|
|
cache_setup=True,
|
|
rebuild_setup_cache=rebuild,
|
|
echo=echo,
|
|
)
|
|
echo("setup cache warmed.")
|