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wassname
98ceb38815
launch.py imports `app` from app.py, which registers app.py's @local_entrypoint `main`; launch.py defining its own `main` raised InvalidError(Duplicate local entrypoint). So launch.py had never actually run -- the earlier vanilla verify was via app.py directly. Invoke: modal run modal/launch.py::fanout [--only N]. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
92 lines
4.3 KiB
Python
92 lines
4.3 KiB
Python
"""Fan out the n=3 v2-eval keynote set as parallel Modal containers.
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15 runs = 5 arms x seeds {42,41,43}, argv mirrors the local pueue runs (same
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preset/steps/eval cadence), so each Modal run == its local twin and the two
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environments cross-replicate. All on the v2 train/test token-gap eval (the
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mounted src/ carries the current committed code).
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id 1 = the canary: seed-42 vanilla (needs only the shared teacher pool, no
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pairset/direction inputs). Fire it alone first, then fan out once it's green:
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.venv/bin/modal run modal/launch.py::fanout --only 1 # canary -> clean v2 FINAL EVAL
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.venv/bin/modal run modal/launch.py::fanout # all 15
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Each container writes out/runs/<ts>_<tag>/per_mode_deploy.json to the Volume;
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this entrypoint pulls the full run dir + log back into out/runs/ locally.
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"""
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from __future__ import annotations
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import json
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import subprocess
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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from app import app, train # noqa: E402 (same dir; registers the functions)
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VOL = "vgrout-cache"
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# `modal` isn't on PATH; the CLI binary sits next to the interpreter running
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# this entrypoint (.venv/bin/python -> .venv/bin/modal).
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MODAL_BIN = str(Path(sys.executable).with_name("modal"))
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def _pull_dir(remote: str, local_parent: Path):
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"""modal recreates the remote leaf dir UNDER the target, so pass the parent."""
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local_parent.mkdir(parents=True, exist_ok=True)
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subprocess.run([MODAL_BIN, "volume", "get", "--force", VOL, remote, str(local_parent)], check=False)
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def _pull_file(remote: str, local: Path):
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local.parent.mkdir(parents=True, exist_ok=True)
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subprocess.run([MODAL_BIN, "volume", "get", "--force", VOL, remote, str(local)], check=False)
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# The keynote arms (A2 method+baseline, A3 ablation). Flags after the seed;
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# tag stems match the local pueue runs so Modal and local artifacts line up.
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COMMON = "fast --eval-ablate-every=10 --steps=60"
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ARMS: dict[str, str] = {
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"vanilla": "--intervention=none",
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"routeV_real": "--intervention=routeV --vhack-pairs-path=out/pairsets/prog_wide.json --vhack-refresh-every=5",
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"routeV_pertok_real": "--intervention=routeV --vhack-pairs-path=out/pairsets/prog_wide.json --vhack-refresh-every=5 --routeV-per-token",
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"routeV_randomV157": "--intervention=routeV --vhack-pairs-path=out/pairsets/prog_wide.json --vhack-refresh-every=5 --routeV-random-v-seed=157",
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"routeV_placebo_vampire": "--intervention=routeV --vhack-pairs-path=out/pairsets/null_vampire.json --vhack-refresh-every=5",
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}
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# seed 42 first so id 1 == the canary (vanilla s42, the seed missing locally).
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SEEDS = [42, 41, 43]
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JOBS: dict[int, str] = {}
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_jid = 1
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for _s in SEEDS:
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for _arm, _flags in ARMS.items():
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JOBS[_jid] = f"{COMMON} --seed={_s} {_flags} --out-tag=_{_arm}_s{_s}"
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_jid += 1
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@app.local_entrypoint()
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def fanout(only: str = ""): # NOT `main`: app.py (imported above) already owns that entrypoint name
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ids = [int(x) for x in only.split(",")] if only else sorted(JOBS)
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print(f"[launch] spawning {len(ids)} jobs: {ids}")
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# spawn = non-blocking; all run concurrently (subject to your Modal limits).
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handles = {jid: train.spawn(JOBS[jid]) for jid in ids}
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# Mirror the Volume layout locally so downloaded runs sit where train.py would
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# have written them (out/runs/<stem>/, logs/<stem>.log).
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repo = Path(__file__).parent.parent
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results = {}
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for jid, h in handles.items():
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try:
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res = h.get() # blocks until this container finishes
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results[jid] = {"ok": True, **res}
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# Pull the FULL run dir (ckpts, rollouts, per_mode_deploy.json) + the log.
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_pull_dir(res["run_dir"], repo / "out" / "runs") # recreates <stem>/ under out/runs
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_pull_file(res["log"], repo / res["log"])
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print(f"[ok] job {jid}: {res['wall_s']/60:.1f} min -> {res['run_dir']} ({len(res['files'])} files)")
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except Exception as e:
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results[jid] = {"ok": False, "error": repr(e)}
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print(f"[FAIL] job {jid}: {e!r}")
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out_dir = Path(__file__).parent / "results"
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out_dir.mkdir(exist_ok=True)
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(out_dir / "_summary.json").write_text(json.dumps(results, indent=2, default=str))
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n_ok = sum(r["ok"] for r in results.values())
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print(f"[launch] {n_ok}/{len(ids)} ok. artifacts in {out_dir}")
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