feat(m1): offline generation pipeline + CLI (T1.5)

- tsmm.data.gen_pipeline.gen_one_sample: composes attrs/series/anomaly/event/missing/instruct
  into one JSON-serializable sample (full schema)
- write_jsonl helper
- scripts/gen_synthetic.py: multiprocessing + tqdm CLI (--n/--out/--seed/--workers)
- regression: build_instruction('event') with empty events no longer raises (TDD RED first)
- tests/test_gen_pipeline.py (6 tests, RED->GREEN)

Exit criteria: python scripts/gen_synthetic.py --n 1000 -> 1000 samples, schema-OK,
all 6 categories balanced (~130-190 each).

scripts/gen_synthetic.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""Offline synthetic data generation (T1.5).
+
+Produces JSONL datasets by composing :mod:`tsmm.data` primitives in parallel:
+
+* ``data/align.jsonl``  — alignment-style samples (default: many)
+* ``data/sft.jsonl``    — Evol-Instruct rephrased (subset)
+* ``data/eval_synth.jsonl`` — held-out eval set (fixed seed)
+
+Usage::
+
+    python scripts/gen_synthetic.py --n 1000 --out data/align.jsonl --seed 0 --workers 8
+
+Each line is one JSON sample with keys: series, attributes, timestamps,
+events, instruction, answer, labels, category, segments.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from pathlib import Path
+
+from tqdm import tqdm
+
+from tsmm.data.gen_pipeline import gen_one_sample, write_jsonl
+from tsmm.data.instruct import evolve_instruction
+
+
+def _one(args):
+    """Worker: generate a single sample (top-level for pickling)."""
+    T, C, seed, anomaly_types, missing_rate, event_prob, do_evolve = args
+    s = gen_one_sample(
+        T=T, C=C, seed=seed,
+        anomaly_types=anomaly_types,
+        missing_rate=missing_rate,
+        event_prob=event_prob,
+    )
+    if do_evolve:
+        s["instruction"] = evolve_instruction(s["instruction"], seed=seed)
+    return s
+
+
+def run(
+    n: int,
+    out: str,
+    *,
+    seed: int = 0,
+    workers: int = max(1, (os.cpu_count() or 2) - 1),
+    T: int = 512,
+    C: int = 5,
+    anomaly_types=("spike", "level_shift", "variance_change", "drift"),
+    missing_rate: float = 0.05,
+    event_prob: float = 0.5,
+    do_evolve: bool = False,
+) -> int:
+    Path(out).parent.mkdir(parents=True, exist_ok=True)
+    work = [
+        (T, C, seed + i, anomaly_types, missing_rate, event_prob, do_evolve)
+        for i in range(n)
+    ]
+    samples = [None] * n
+    with ProcessPoolExecutor(max_workers=workers) as ex:
+        futures = {ex.submit(_one, w): i for i, w in enumerate(work)}
+        for fut in tqdm(as_completed(futures), total=n, desc=os.path.basename(out)):
+            i = futures[fut]
+            samples[i] = fut.result()
+    return write_jsonl(samples, out)
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(description="Generate synthetic TS-QA JSONL data.")
+    p.add_argument("--n", type=int, default=1000, help="number of samples")
+    p.add_argument("--out", type=str, default="data/align.jsonl", help="output JSONL path")
+    p.add_argument("--seed", type=int, default=0, help="base RNG seed")
+    p.add_argument("--workers", type=int, default=max(1, (os.cpu_count() or 2) - 1))
+    p.add_argument("--T", type=int, default=512, help="series length")
+    p.add_argument("--C", type=int, default=5, help="number of channels")
+    p.add_argument("--missing-rate", type=float, default=0.05)
+    p.add_argument("--event-prob", type=float, default=0.5)
+    p.add_argument("--evolve", action="store_true", help="apply Evol-Instruct rephrasing (for sft set)")
+    args = p.parse_args(argv)
+
+    n = run(
+        n=args.n,
+        out=args.out,
+        seed=args.seed,
+        workers=args.workers,
+        T=args.T,
+        C=args.C,
+        missing_rate=args.missing_rate,
+        event_prob=args.event_prob,
+        do_evolve=args.evolve,
+    )
+    print(f"wrote {n} samples -> {args.out}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())