Add b2luigi dependency and workflow prerequisites (gitea #83)

Groundwork for the b2luigi pipeline orchestration in gitea #83, split out so the workflow package itself lands as a self-contained change: - new `workflow` optional-dependency extra (b2luigi, which pulls luigi + tenacity), included in `dev`. - deterministic rollout/predict sidecar path: with an explicit `--out`, the YAML goes to `out.with_suffix(".yaml")` instead of a uuid-named file under the checkpoint directory, so a workflow task can declare it as a target. The uuid behaviour is kept for the no-`--out` case, leaving ad-hoc runs and the /ceph predictions convention untouched. - epoch-aware shuffle seeding in StreamingStepsDataset (`seed` + `set_epoch`, the DistributedSampler convention). Shuffling previously drew from the global numpy state, which `run_train_job` reseeds from `train.seed` at process start — so a one-epoch-per-job chain would have replayed the same batch order every epoch. Seeding from `(seed, epoch, worker_id)` makes epoch k's order identical whether it runs inside one long `giant train` or as its own resumed job. The val-split seed is untouched. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-26 11:43:36 +02:00
parent b66574877b
commit 72c3b9968b
6 changed files with 318 additions and 9 deletions
@@ -191,8 +191,17 @@ def _write_prediction_ref(
    out: Path,
    dataset_path: Path,
    comment: str | None = None,
+    explicit_out: bool = False,
 ) -> Path:
-    """Write a YAML sidecar in the checkpoint directory and return its path."""
+    """Write the YAML sidecar and return its path.
+
+    With an explicit ``--out`` the sidecar sits next to the output file as
+    ``out.with_suffix(".yaml")`` — a *deterministic* path, which is what lets
+    a workflow task (``giant/workflow/tasks.py``) declare it as a target.
+    Without one, the historic uuid-named file under the checkpoint directory
+    is kept, so ad-hoc runs and the ``/ceph`` predictions convention are
+    unaffected.
+    """
    ref = {
        "prediction_id": pred_uuid,
        "output": str(out),
@@ -202,7 +211,7 @@ def _write_prediction_ref(
    }
    if comment is not None:
        ref["comment"] = comment
-    ref_path = checkpoint.parent / f"{pred_uuid}.yaml"
+    ref_path = out.with_suffix(".yaml") if explicit_out else checkpoint.parent / f"{pred_uuid}.yaml"
    ref_path.write_text(yaml.dump(ref, default_flow_style=False, sort_keys=False))
    return ref_path

@@ -1076,6 +1085,7 @@ def predict(
    bs = batch_size_value

    # --- Output path ---
+    explicit_out = out is not None
    out, dataset_path, pred_uuid = _resolve_prediction_output(data, out)
    out.parent.mkdir(parents=True, exist_ok=True)
    typer.echo(f"output: {out}")
@@ -1295,7 +1305,7 @@ def predict(
    if writer is not None:
        writer.close()

-    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path, comment)
+    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path, comment, explicit_out=explicit_out)
    typer.echo(f"reference: {ref_path}")

    if skipped:
@@ -1443,6 +1453,7 @@ def rollout(
    seeds = _seed_from_data(files, n_events)
    typer.echo(f"seeded {len(seeds['event_id']):,} shower(s)")

+    explicit_out = out is not None
    out, dataset_path, pred_uuid = _resolve_prediction_output(data, out)
    out.parent.mkdir(parents=True, exist_ok=True)

@@ -1503,7 +1514,7 @@ def rollout(

    l1_summary = l1_dist_collector.summary()

-    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path)
+    ref_path = _write_prediction_ref(checkpoint, pred_uuid, out, dataset_path, explicit_out=explicit_out)
    ref = yaml.safe_load(ref_path.read_text())
    ref.update(
        {
@@ -97,6 +97,7 @@ class StreamingStepsDataset(IterableDataset):
        mat_topn_map: dict[str, int] | None = None,
        sec_type_class_map: dict | None = None,
        k_max: int = K_MAX,
+        seed: int = 0,
    ) -> None:
        self.files = list(files)
        self._offsets = {path: event_id_offset(i) for i, path in enumerate(self.files)}
@@ -117,16 +118,35 @@ class StreamingStepsDataset(IterableDataset):
        self.mat_topn_map = mat_topn_map
        self.sec_type_class_map = sec_type_class_map
        self.k_max = k_max
+        self.seed = seed
+        self.epoch = 0
+        self._rng = np.random.default_rng()
+
+    def set_epoch(self, epoch: int) -> None:
+        """Select the shuffle stream for `epoch` (the DistributedSampler convention).
+
+        The training loop calls this at the top of every epoch. Shuffling is
+        seeded from `(seed, epoch, worker_id)` rather than the global numpy
+        state so epoch *k*'s batch order is the same whether it runs as epoch
+        *k* of one long `giant train`, or as its own resumed job in a
+        per-epoch workflow chain (`giant/workflow/tasks.py:TrainEpochTask`).
+        Workers are re-forked from this object each epoch (no
+        `persistent_workers`), so setting it here reaches them.
+        """
+        self.epoch = int(epoch)

    def __iter__(self):
        worker_info = torch.utils.data.get_worker_info()
        files = self.files
+        worker_id = worker_info.id if worker_info is not None else 0
        if worker_info is not None:
-            files = files[worker_info.id :: worker_info.num_workers]
+            files = files[worker_id :: worker_info.num_workers]
+
+        self._rng = np.random.default_rng([self.seed, self.epoch, worker_id])

        if self.shuffle:
            files = list(files)
-            np.random.default_rng().shuffle(files)
+            self._rng.shuffle(files)

        buf_cont: list[np.ndarray] = []
        buf_cat: list[np.ndarray] = []
@@ -222,7 +242,7 @@ class StreamingStepsDataset(IterableDataset):
        styp = np.concatenate(buf_type)

        if self.shuffle:
-            idx = np.random.permutation(len(cont))
+            idx = self._rng.permutation(len(cont))
            cont, cat, tgt = cont[idx], cat[idx], tgt[idx]
            nsec, sec, proc, styp = nsec[idx], sec[idx], proc[idx], styp[idx]

@@ -435,6 +435,7 @@ def run_train_job(
        mat_topn_map=cond_mat_topn,
        sec_type_class_map=sec_type_class_map,
        k_max=k_max,
+        seed=t["seed"],
    )
    val_ds = StreamingStepsDataset(
        files=files,
@@ -184,6 +184,13 @@ def train(
            if device.type == "cuda":
                torch.cuda.reset_peak_memory_stats(device)
            collector.start_epoch(epoch)
+            # Epoch-aware shuffle stream (see StreamingStepsDataset.set_epoch):
+            # keeps epoch k's batch order identical whether it runs here or as
+            # its own resumed per-epoch job in a b2luigi workflow.
+            # (tests hand `train` a plain list of batches, which has neither)
+            set_epoch = getattr(getattr(train_loader, "dataset", None), "set_epoch", None)
+            if callable(set_epoch):
+                set_epoch(epoch)
            for trainer in trainers.values():
                trainer.train_mode()

@@ -28,7 +28,7 @@ dev = [
    "ty>=0.0.50,<0.1",
    "bump-my-version>=1.2,<2",
    "git-cliff>=2,<3",
-    "giant[convert,analysis,geometry,wandb]",
+    "giant[convert,analysis,geometry,wandb,workflow]",
 ]
 geometry = [
    "scikit-learn>=1.4,<2",
@@ -49,6 +49,11 @@ analysis = [
    # `giant analyze render` step imports it; compute workers never do.
    "plotstyle>=1.0.0",
 ]
+# b2luigi pulls luigi + tenacity; the only sanctioned way to chain a
+# multi-step pipeline (see giant/workflow/).
+workflow = [
+    "b2luigi>=1.0,<2",
+]

 [project.scripts]
 giant = "giant.cli:app"
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 ]

+[[package]]
+name = "b2luigi"
+version = "1.2.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cachetools" },
+    { name = "colorama" },
+    { name = "gitpython" },
+    { name = "jinja2" },
+    { name = "luigi" },
+    { name = "parse" },
+    { name = "setuptools" },
+    { name = "tenacity" },
+    { name = "webdavclient3" },
+]
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+[[package]]
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+]
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 ]

+[[package]]
+name = "docutils"
+version = "0.23"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/39/a4/5180d9afc57e8fca05601dd652bdff19604c218814037fe90ffc7625a50a/docutils-0.23.tar.gz", hash = "sha256:746f5060322511280a1e50eb76846ed6bf2342984b2ac04dc42caa1a8d78799e", size = 2303823, upload-time = "2026-05-27T17:41:06.934Z" }
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@@ -728,10 +766,14 @@ geometry = [
 wandb = [
    { name = "wandb" },
 ]
+workflow = [
+    { name = "b2luigi" },
+]

 [package.metadata]
 requires-dist = [
    { name = "awkward", marker = "extra == 'convert'", specifier = ">=2.6,<3" },
+    { name = "b2luigi", marker = "extra == 'workflow'", specifier = ">=1.0,<2" },
    { name = "bump-my-version", marker = "extra == 'dev'", specifier = ">=1.2,<2" },
    { name = "giant", extras = ["convert", "analysis", "geometry", "wandb"], marker = "extra == 'dev'" },
    { name = "git-cliff", marker = "extra == 'dev'", specifier = ">=2,<3" },
@@ -757,7 +799,7 @@ requires-dist = [
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    { name = "wandb", marker = "extra == 'wandb'", specifier = ">=0.16,<1" },
 ]
-provides-extras = ["cpu", "cuda", "dev", "geometry", "wandb", "convert", "analysis"]
+provides-extras = ["cpu", "cuda", "dev", "geometry", "wandb", "convert", "analysis", "workflow"]

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