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giant/tests/test_condor.py
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analyze: chunk per-plot aggregation across HTCondor jobs
Add a second parallelism axis to giant analyze: each plot's data can now
be split into a configurable number of event_id-disjoint chunks, each
computed as its own HTCondor job, bounding per-job walltime and scan cost
on large rollout/reference files instead of one job re-scanning the
whole file per plot.

Every PlotSpec now splits into compute_partial (runs per (plot, chunk)
job against a chunk-filtered Bundle) and finalize (merges chunks -
elementwise sum for fixed-edge histograms/species shares, concatenate
-then-recompute for specs that derive edges or mean/std from the full
per-event/per-secondary array). Router diagnostics stay chunkable=False
and always run as a single job. giant analyze render now joins every
plot's chunk partials (merge_all) before rendering, transparently.

New: --chunks on `analyze prep`/`analyze submit`, --chunk on
`analyze compute-one`, and a new `analyze merge-one` command.
2026-07-27 09:24:19 +02:00

200 lines
7.0 KiB
Python

"""Tests for the rollout-YAML → run-directory flow, compute, and submit."""
from __future__ import annotations
from pathlib import Path
import pyarrow.parquet as pq
import pytest
import yaml
from giant.analysis import (
RunMeta,
SubmitConfig,
catalog_ids,
compute_one,
compute_reduced,
derive_run_dir,
load_rollout_yaml,
merge_one,
prep,
write_submit,
)
from giant.analysis.catalog import get_spec
from giant.analysis.condor import Context
from giant.analysis.reduced import Partial, Reduced
from giant.constants import PREDICT_COORD_METADATA_KEY, ROLLOUT_COORD_VALUE
from tests.test_analysis_reduce import _reference_frame, _rollout_frame
def _write_inputs(tmp_path: Path) -> Path:
"""Materialize rollout+reference parquet and a rollout YAML; return the YAML path."""
rollout = tmp_path / "rollout.parquet"
reference = tmp_path / "reference.parquet"
tbl = _rollout_frame().collect().to_arrow()
tbl = tbl.replace_schema_metadata({PREDICT_COORD_METADATA_KEY: ROLLOUT_COORD_VALUE})
pq.write_table(tbl, rollout)
_reference_frame().collect().write_parquet(reference)
yaml_path = tmp_path / "run.yaml"
yaml_path.write_text(
yaml.safe_dump(
{
"prediction_id": "abcd1234ef",
"output": str(rollout),
"dataset": str(reference),
"checkpoint": "/ckpt/best.pt",
"kind": "rollout",
"energy_cutoff": 0.1,
"steps": 10,
}
)
)
return yaml_path
def _prep(
rollout_yaml: Path, run_dir: str | Path | None = None, chunks: int = 1
) -> Path:
"""``prep`` with small test-sized context bins/sampling."""
return prep(
rollout_yaml,
run_dir,
n_chunks=chunks,
n_energy_bins=2,
n_marginal_bins=8,
top_k_pdg=3,
sample_rows=1000,
)
def test_load_rollout_yaml_requires_paths(tmp_path: Path):
bad = tmp_path / "bad.yaml"
bad.write_text(yaml.safe_dump({"output": "x.parquet"})) # no dataset
with pytest.raises(ValueError):
load_rollout_yaml(bad)
def test_derive_run_dir_next_to_rollout():
y = {"output": "/data/roll.parquet", "prediction_id": "abcd1234ef", "dataset": "d"}
assert derive_run_dir(y) == Path("/data/analysis_abcd1234")
assert derive_run_dir(y, "/somewhere") == Path("/somewhere")
def test_prep_lays_out_run_dir(tmp_path: Path):
yaml_path = _write_inputs(tmp_path)
run_dir = _prep(yaml_path)
assert run_dir == tmp_path / "analysis_abcd1234"
assert (run_dir / "shared.json").exists()
ctx = Context.load(run_dir / "shared.json")
assert set(ctx.var_ranges) == {"step_length", "edep", "delta_e", "post_E"}
meta = RunMeta.load(run_dir / "run_meta.json")
assert meta.reference.endswith("reference.parquet")
assert meta.plot_meta["checkpoint"] == "/ckpt/best.pt"
assert "best.pt" in meta.title
assert meta.n_chunks == 1
def test_compute_one_from_run_dir(tmp_path: Path):
run_dir = _prep(_write_inputs(tmp_path))
out = compute_one("marginal_edep", run_dir)
assert out == run_dir / "reduced_partial" / "marginal_edep__0.json"
partial = Partial.load(out)
assert partial.id == "marginal_edep" and partial.chunk == 0
assert "r" in partial.data and "t" in partial.data
def test_compute_reduced_explicit_paths(tmp_path: Path):
run_dir = _prep(_write_inputs(tmp_path))
meta = RunMeta.load(run_dir / "run_meta.json")
out = compute_reduced(
"marginal_step_length",
meta.rollout,
meta.reference,
run_dir / "shared.json",
tmp_path / "r.json",
)
assert Partial.load(out).id == "marginal_step_length"
def test_merge_one_produces_reduced(tmp_path: Path):
run_dir = _prep(_write_inputs(tmp_path))
compute_one("marginal_edep", run_dir)
out = merge_one("marginal_edep", run_dir)
assert out == run_dir / "reduced" / "marginal_edep.json"
reduced = Reduced.load(out)
assert reduced.id == "marginal_edep"
assert len(reduced.payload["rollout"]) == len(reduced.payload["edges"]) - 1
def test_merge_one_fails_loudly_on_missing_chunk(tmp_path: Path):
run_dir = _prep(_write_inputs(tmp_path), chunks=2)
compute_one("marginal_edep", run_dir, chunk_index=0) # chunk 1 never computed
with pytest.raises(FileNotFoundError, match="missing chunk"):
merge_one("marginal_edep", run_dir)
def test_chunked_compute_and_merge_matches_unchunked(tmp_path: Path):
(tmp_path / "a").mkdir()
(tmp_path / "b").mkdir()
unchunked_dir = _prep(_write_inputs(tmp_path / "a"))
compute_one("marginal_step_length", unchunked_dir)
unchunked = Reduced.load(merge_one("marginal_step_length", unchunked_dir))
chunked_dir = _prep(_write_inputs(tmp_path / "b"), chunks=2)
for k in range(2):
compute_one("marginal_step_length", chunked_dir, chunk_index=k)
chunked = Reduced.load(merge_one("marginal_step_length", chunked_dir))
assert chunked.payload == unchunked.payload
def test_compute_reduced_rejects_out_of_range_chunk(tmp_path: Path):
run_dir = _prep(_write_inputs(tmp_path)) # n_chunks=1 (default)
with pytest.raises(ValueError, match="out of range"):
compute_one("marginal_edep", run_dir, chunk_index=1)
def test_write_submit_description(tmp_path: Path):
run_dir = _prep(_write_inputs(tmp_path))
cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path)
txt = write_submit(cfg).read_text()
assert "universe = docker" in txt
assert "docker_image = mschnepf/slc7-condocker" in txt
assert "requirements = TARGET.ProvidesETPResources" in txt
assert "accounting_group = cms" in txt
assert "queue plotid,chunk from" in txt
jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()]
assert [i for i, _ in jobs] == catalog_ids()
assert all(k == "0" for _, k in jobs) # n_chunks=1 default
wrapper = run_dir / "run_compute.sh"
assert wrapper.exists() and (wrapper.stat().st_mode & 0o111)
body = wrapper.read_text()
assert "giant analyze compute-one --id" in body
assert "--chunk" in body and "--run-dir" in body
def test_write_submit_remote_flag(tmp_path: Path):
run_dir = _prep(_write_inputs(tmp_path))
cfg = SubmitConfig(
run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, remote=True
)
txt = write_submit(cfg).read_text()
assert "+RemoteJob = True" in txt
assert "ProvidesETPResources" not in txt
def test_write_submit_chunks_respect_chunkable(tmp_path: Path):
assert get_spec("router_gating").chunkable is False
run_dir = _prep(_write_inputs(tmp_path))
cfg = SubmitConfig(
run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4
)
write_submit(cfg)
jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()]
counts: dict[str, int] = {}
for spec_id, _ in jobs:
counts[spec_id] = counts.get(spec_id, 0) + 1
assert counts["marginal_edep"] == 4
assert counts["router_gating"] == 1 # chunkable=False, ignores n_chunks