Files
giant/tests/test_workflow_tasks.py
T
lars a482b04761 Add the giant/workflow b2luigi task graph (gitea #83)
One workflow TOML now parameterises a whole experiment and `giant workflow
run <spec.toml>` turns it into a b2luigi DAG whose targets are files on
/ceph: nothing already produced is recomputed, every step waits for its
inputs, and HTCondor submission/polling is b2luigi's job.

- spec.py: workflow TOML -> frozen dataclasses with name-uniqueness and
  cross-reference validation, unknown keys rejected the way giant.config
  rejects them, and a short spec_hash per task that folds in its transitive
  parents — so an edited spec re-runs exactly the affected subtree.
- htcondor.py: the CPU/GPU submit settings. The GPU requirement strings
  (ProvidesEtpCeph + optional device/memory pins) are ported from the
  condor-gpu-train-rollout branch rather than rewritten.
- tasks.py: DatasetTask, WarmCacheTask, GeometryOracleTask, TrainEpochTask
  (one short GPU job per epoch, chained via --resume, which the training
  loop already supports unchanged), TrainTask (publishes best.pt/last.pt and
  a concatenated metrics.csv so downstream never sees the epoch fan-out),
  RolloutTask, AnalysisPrepTask, AnalysisComputeTask (one job per plot x
  chunk, walltime sized from run_meta.json at submit time), AnalysisRenderTask
  (always local — the only step importing plotstyle/LaTeX), WorkflowTask.
  Task bodies call the existing entry points; none of them reimplement
  anything.
- run.py + `giant workflow run`: settings wiring and the script b2luigi
  re-executes on workers. add_filename_to_cmd is off because b2luigi passes
  only the script's basename, and --spec is forwarded via
  task_cmd_additional_args so a worker resolves the identical task graph.

configs/workflow_example.toml is the documented starting point.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-26 12:36:59 +02:00

173 lines
6.7 KiB
Python

"""Workflow task graph: dependencies, output paths, condor settings (gitea #83)."""
import pytest
from giant.analysis.catalog import catalog_ids, get_spec as get_plot_spec
from giant.workflow import tasks
from giant.workflow.spec import parse_spec
CONDOR = {
"accounting_group": "cms",
"repo_dir": "/work/lbogner/giant",
"env_script": "/work/lbogner/giant/condor_env.sh",
}
RAW = {
"workflow": {"name": "wf", "result_dir": "/results/wf"},
"condor": CONDOR,
"dataset": {"steps": "/data/train", "reference": "/data/holdout"},
"train": [
{"name": "base", "epochs": 3, "gpu_memory_mb": 20000},
{"name": "router", "epochs": 2},
],
"rollout": [
{"name": "base", "train": "base"},
{"name": "router", "train": "router"},
],
"analysis": [{"name": "cmp", "rollouts": ["base", "router"], "chunks": 4}],
}
@pytest.fixture
def spec():
s = parse_spec(RAW)
tasks.set_spec(s)
return s
def _requires(task):
return list(task.requires() or [])
def test_epoch_chain_is_linear_and_rooted_at_warm_cache(spec):
h = spec.train_hash("base")
third = tasks.TrainEpochTask(name="base", spec_hash=h, milestone=3)
second = _requires(third)
assert [type(t) for t in second] == [tasks.TrainEpochTask]
assert second[0].milestone == 2
first = _requires(second[0])[0]
assert first.milestone == 1
root = _requires(first)
assert [type(t) for t in root] == [tasks.WarmCacheTask]
# the warm cache is keyed by its own hash, not the training's
assert root[0].spec_hash == spec.warm_cache_hash("base")
def test_epoch_task_outputs_last_pt_per_milestone(spec):
h = spec.train_hash("base")
path = tasks.TrainEpochTask(name="base", spec_hash=h, milestone=2).output().path
assert path == f"/results/wf/train_epoch/name=base/spec_hash={h}/epochs=2/last.pt"
def test_train_task_requires_final_epoch_and_publishes_canonical_outputs(spec):
h = spec.train_hash("base")
train = tasks.TrainTask(name="base", spec_hash=h)
(dep,) = _requires(train)
assert isinstance(dep, tasks.TrainEpochTask) and dep.milestone == 3
out = train.output()
assert set(out) == {"best.pt", "last.pt", "metrics.csv"}
assert out["best.pt"].path == f"/results/wf/train/name=base/spec_hash={h}/best.pt"
# local: it only copies files around, no reason to queue a job for it
assert train.batch_system == "local"
def test_rollout_requires_training_geometry_and_reference(spec):
ro = tasks.RolloutTask(name="base", spec_hash=spec.rollout_hash("base"))
deps = _requires(ro)
assert [type(d) for d in deps] == [tasks.TrainTask, tasks.GeometryOracleTask, tasks.DatasetTask]
assert deps[0].name == "base"
assert deps[2].path == "/data/holdout"
out = ro.output()
assert out["rollout.yaml"].path.endswith("rollout.yaml")
# the sidecar sits next to the parquet — the deterministic path
# `giant rollout --out` now produces
assert out["rollout.yaml"].path[: -len(".yaml")] == out["rollout.parquet"].path[: -len(".parquet")]
def test_analysis_prep_requires_every_named_rollout(spec):
prep = tasks.AnalysisPrepTask(name="cmp", spec_hash=spec.analysis_hash("cmp"))
deps = _requires(prep)
assert [d.name for d in deps] == ["base", "router"]
assert all(isinstance(d, tasks.RolloutTask) for d in deps)
assert prep.batch_system == "local"
def test_compute_job_enumeration_collapses_non_chunkable_specs(spec):
jobs = tasks.analysis_jobs(spec, "cmp")
non_chunkable = [i for i in catalog_ids() if not get_plot_spec(i).chunkable]
expected = (len(catalog_ids()) - len(non_chunkable)) * 4 + len(non_chunkable)
assert len(jobs) == expected
assert non_chunkable, "expected some chunkable=False specs in the catalog"
for spec_id in non_chunkable:
assert [c for i, c in jobs if i == spec_id] == [0]
def test_compute_output_matches_the_on_disk_contract(spec):
h = spec.analysis_hash("cmp")
task = tasks.AnalysisComputeTask(name="cmp", spec_hash=h, plot_id="event_mean_length", chunk=2)
assert task.output().path == (
f"/results/wf/analysis/name=cmp/spec_hash={h}/reduced_partial/event_mean_length__2.json"
)
(dep,) = _requires(task)
assert isinstance(dep, tasks.AnalysisPrepTask)
def test_render_requires_every_compute_job_and_runs_locally(spec):
render = tasks.AnalysisRenderTask(name="cmp", spec_hash=spec.analysis_hash("cmp"))
deps = _requires(render)
assert len(deps) == len(tasks.analysis_jobs(spec, "cmp"))
assert render.batch_system == "local" # the only step importing plotstyle/LaTeX
assert render.output().path.endswith("/plots/metadata.yaml")
def test_workflow_task_wraps_every_analysis(spec):
deps = _requires(tasks.WorkflowTask(workflow_name="wf"))
assert [(type(d), d.name) for d in deps] == [(tasks.AnalysisRenderTask, "cmp")]
def test_workflow_without_analysis_falls_back_to_rollouts():
raw = {k: v for k, v in RAW.items() if k != "analysis"}
tasks.set_spec(parse_spec(raw))
deps = _requires(tasks.WorkflowTask(workflow_name="wf"))
assert [type(d) for d in deps] == [tasks.RolloutTask, tasks.RolloutTask]
def test_gpu_settings_carry_remote_ceph_and_pins(spec):
settings = tasks.TrainEpochTask(name="base", spec_hash=spec.train_hash("base"), milestone=1).htcondor_settings
assert settings["+RemoteJob"] == "True"
assert settings["RequestGPUs"] == 1
assert "TARGET.ProvidesEtpCeph =?= True" in settings["requirements"]
assert "TARGET.GPUs_GlobalMemoryMb >= 20000" in settings["requirements"]
assert settings["accounting_group"] == "cms"
assert settings["docker_image"] == "mschnepf/slc7-condocker"
def test_cpu_settings_used_for_analysis_compute(spec):
task = tasks.AnalysisComputeTask(
name="cmp", spec_hash=spec.analysis_hash("cmp"), plot_id="event_mean_length", chunk=0
)
settings = task.htcondor_settings
assert settings["docker_image"] == "cverstege/alma9-gridjob"
assert "RequestGPUs" not in settings
# no run_meta.json yet (prep hasn't run), so no walltime is claimed
assert "+RequestWalltime" not in settings
def test_cpu_settings_local_files_use_provides_etp_resources():
raw = {**RAW, "condor": {**CONDOR, "remote": False}}
spec = parse_spec(raw)
tasks.set_spec(spec)
settings = tasks.GeometryOracleTask(spec_hash=spec.geometry_hash()).htcondor_settings
assert settings["requirements"] == "TARGET.ProvidesETPResources"
assert "+RemoteJob" not in settings
def test_missing_dataset_fails_immediately(spec):
with pytest.raises(FileNotFoundError, match="/ceph"):
tasks.DatasetTask(path="/data/train").complete()
def test_dataset_that_exists_is_complete(tmp_path, spec):
(tmp_path / "steps.parquet").write_text("")
assert tasks.DatasetTask(path=str(tmp_path / "steps.parquet")).complete()