File size: 6,281 Bytes
e5818d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | #!/usr/bin/env python3
"""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())
|