| |
| """Generate WSB candidates with pinned Hugging Face releases; never reuse audio.""" |
| import argparse |
| import hashlib |
| import importlib.metadata |
| import json |
| import platform |
| from pathlib import Path |
|
|
| MODEL = "m-a-p/YuE2-3B" |
| MODEL_REVISION = "1a96eca688d6ae5d7f0feb88573fec89920fcd19" |
| VAE = "m-a-p/YuE2-Vae-legacy" |
| VAE_REVISION = "b54118f0fc462f08999d1ec07e88817f4ee3f770" |
| MODEL_SHA = "1d55c42c1a9875c34f5d736e15078449992b044e807ce2a138e6cf289a1e59e9" |
| VAE_SHA = "b6d283628913bb41145ba99e2314eef613905ee95f690eb70e8212d5f4965044" |
|
|
|
|
| def sha(path): |
| h = hashlib.sha256() |
| with Path(path).open("rb") as stream: |
| for block in iter(lambda: stream.read(8 << 20), b""): |
| h.update(block) |
| return h.hexdigest() |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--manifest", type=Path, default=Path(__file__).resolve().parents[1] / "benchmark/reproduction_manifest.jsonl") |
| parser.add_argument("--output", type=Path, required=True) |
| parser.add_argument("--candidates", type=int, choices=(2, 8), default=8) |
| parser.add_argument("--backend", choices=("torch", "torch-eager", "vllm"), default="torch") |
| parser.add_argument("--shard-index", type=int, default=0) |
| parser.add_argument("--num-shards", type=int, default=1) |
| parser.add_argument("--prompt-index", type=int, action="append", help="Optional diagnostic subset; repeat for multiple row indices") |
| parser.add_argument("--cache-dir", type=Path) |
| args = parser.parse_args() |
| if not 0 <= args.shard_index < args.num_shards: |
| parser.error("Require 0 <= shard-index < num-shards") |
| if args.prompt_index is not None and (len(set(args.prompt_index)) != len(args.prompt_index) |
| or any(not 0 <= i < 192 for i in args.prompt_index)): |
| parser.error("Prompt indices must be distinct integers from 0 to 191") |
| if args.output.exists() and any(args.output.iterdir()): |
| parser.error("Use a new output directory so previous candidates cannot be reused or overwritten") |
| all_rows = [json.loads(line) for line in args.manifest.read_text(encoding="utf-8").splitlines() if line.strip()] |
| assert len(all_rows) == 192 and [r["prompt_index"] for r in all_rows] == list(range(192)) |
| bases = [831001, 831019, 831037, 831061, 831083, 831109, 831127, 831149] |
| for row in all_rows: |
| assert row["candidate_ar_seeds"] == [s + row["prompt_index"] for s in bases] |
| assert row["candidate_ar_seeds"] == row["candidate_nar_seeds"] |
| rows = [r for r in all_rows if r["prompt_index"] % args.num_shards == args.shard_index] |
| if args.prompt_index is not None: |
| rows = [r for r in rows if r["prompt_index"] in args.prompt_index] |
| assert rows |
| import torch |
| from yue2 import YuE2Pipeline |
| from yue2.protocol import SongRequest |
| from yue2.storage import verify_result |
| assert importlib.metadata.version("yue2-infer") == "0.1.3" |
| args.output.mkdir(parents=True, exist_ok=True) |
| evaluation_inputs = [] |
| for row in rows: |
| for candidate_index, seed in enumerate(row["candidate_ar_seeds"][:args.candidates]): |
| ident = f"wsb_{row['prompt_index']:03d}_r{candidate_index}_s{seed}" |
| evaluation_inputs.append({"prompt_index": row["prompt_index"], |
| "candidate_index": candidate_index, "seed": seed, |
| "path": f"songs/{ident}"}) |
| (args.output / "evaluation_inputs.jsonl").write_text( |
| "".join(json.dumps(row) + "\n" for row in evaluation_inputs)) |
| report = {"model": MODEL, "model_revision": MODEL_REVISION, "vae": VAE, |
| "vae_revision": VAE_REVISION, "backend": args.backend, |
| "manifest_sha256": sha(args.manifest), "python": platform.python_version(), |
| "torch": torch.__version__, "gpu": torch.cuda.get_device_name(0), |
| "shard_index": args.shard_index, "num_shards": args.num_shards, |
| "prompt_indices": [r["prompt_index"] for r in rows], |
| "expected_candidates": len(rows) * args.candidates, "results": [], "complete": False} |
| with YuE2Pipeline.from_pretrained(MODEL, revision=MODEL_REVISION, vae=VAE, |
| vae_revision=VAE_REVISION, cache_dir=args.cache_dir, |
| backend=args.backend, device="cuda:0", memory_budget_gib=24) as pipe: |
| assert sha(pipe.model_dir / "model.safetensors") == MODEL_SHA |
| assert sha(pipe.vae_dir / "model.safetensors") == VAE_SHA |
| report["generation_config"] = pipe.generation_config.to_dict() |
| for row in rows: |
| for candidate_index, seed in enumerate(row["candidate_ar_seeds"][:args.candidates]): |
| ident = f"wsb_{row['prompt_index']:03d}_r{candidate_index}_s{seed}" |
| request = dict(id=ident, style=row["style"], lyrics=row["lyrics"], |
| cot="full", cfg_scale=1.0, seed=seed) |
| assert SongRequest(**request).text() == row["generation_prompt"] |
| result = {"id": ident, "prompt_index": row["prompt_index"], |
| "candidate_index": candidate_index, "seed": seed} |
| try: |
| song = pipe(**request) |
| song.save_artifacts(args.output / "songs" / ident) |
| receipt = verify_result(args.output / "songs" / ident) |
| result.update(status="complete", identity=receipt["identity"], |
| truncated=receipt["truncated"], audio_seconds=receipt["audio_seconds"]) |
| del song |
| except Exception as exc: |
| result.update(status="failed", error=f"{type(exc).__name__}: {exc}") |
| report["results"].append(result) |
| (args.output / "generation_report.json").write_text(json.dumps(report, indent=2) + "\n") |
| print(json.dumps(result), flush=True) |
| report["complete"] = (len(report["results"]) == report["expected_candidates"] |
| and all(r["status"] == "complete" for r in report["results"])) |
| (args.output / "generation_report.json").write_text(json.dumps(report, indent=2) + "\n") |
| return 0 if report["complete"] else 1 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
