File size: 8,933 Bytes
90884df | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | """Replay pinned short/multichunk Phase-0 latents through the frozen vocoder."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
import shutil
import tempfile
import torch
from music3lab.manifests import (
atomic_write_bytes,
canonical_json_bytes,
semantic_digest,
)
from music3lab.vocoder import (
build_replay_manifest,
load_frozen_vocoder,
load_phase0_vocoder_oracle,
module_state_sha256,
probe_latent_gradient,
publish_replay_bundle,
publish_replay_session_root,
replay_vocoder_oracle,
secure_replay_output_parent,
verify_replay_bundle,
verify_replay_session_tree,
)
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--snapshot", type=Path, required=True)
parser.add_argument("--base-manifest", type=Path, required=True)
parser.add_argument("--diffusers-root", type=Path, required=True)
parser.add_argument("--phase0-artifacts", type=Path, required=True)
parser.add_argument("--output-root", type=Path, required=True)
parser.add_argument("--device", default="cuda")
return parser
def main() -> int:
args = build_parser().parse_args()
device = torch.device(args.device)
if device.type != "cuda" or not torch.cuda.is_available():
raise RuntimeError(
"oracle replay requires the explicitly authorized CUDA GPU"
)
device_name = torch.cuda.get_device_name(device)
if "H100" not in device_name:
raise RuntimeError(f"oracle replay requires H100, observed {device_name}")
device_capability = tuple(torch.cuda.get_device_capability(device))
cuda_runtime = torch.version.cuda
if not cuda_runtime:
raise RuntimeError("PyTorch does not expose a CUDA runtime identity")
output_root = Path(
os.path.abspath(os.fspath(args.output_root.expanduser()))
)
if os.path.lexists(output_root):
raise FileExistsError(f"replay output already exists: {output_root}")
output_root.parent.mkdir(parents=True, exist_ok=True)
secure_replay_output_parent(output_root.parent)
oracles = {
kind: load_phase0_vocoder_oracle(args.phase0_artifacts, kind)
for kind in ("short", "multi")
}
adapter = load_frozen_vocoder(
snapshot=args.snapshot,
base_manifest=args.base_manifest,
diffusers_root=args.diffusers_root,
)
if adapter.report.project_git_dirty:
raise RuntimeError("oracle replay refuses dirty project source")
pending = []
summaries = []
for mode, dtype in (
("fp32_reference", torch.float32),
("bf16_pipeline", torch.bfloat16),
):
adapter.to(device=device, dtype=dtype)
torch.cuda.reset_peak_memory_stats(device)
mode_weight_sha256 = module_state_sha256(adapter.model)
gradient_probe = probe_latent_gradient(
adapter,
oracles["short"].latents[0],
device=device,
dtype=dtype,
)
torch.cuda.synchronize(device)
if gradient_probe.weight_state_sha256_before != mode_weight_sha256:
raise RuntimeError("gradient probe started from different weights")
for kind, oracle in oracles.items():
with torch.inference_mode():
result = replay_vocoder_oracle(
adapter,
oracle,
device=device,
dtype=dtype,
)
torch.cuda.synchronize(device)
peak_allocated = torch.cuda.max_memory_allocated(device)
peak_reserved = torch.cuda.max_memory_reserved(device)
manifest, audio_bytes, wav_bytes = build_replay_manifest(
mode=mode,
adapter=adapter,
oracle=oracle,
result=result,
weight_state_sha256_before=mode_weight_sha256,
gradient_probe=gradient_probe,
device_name=device_name,
device_capability=device_capability,
cuda_runtime=cuda_runtime,
peak_cuda_allocated_bytes=peak_allocated,
peak_cuda_reserved_bytes=peak_reserved,
)
if mode == "bf16_pipeline" and not manifest.exact_oracle_audio:
raise RuntimeError(
f"BF16 official replay differs for {kind}; stopping"
)
relative = Path(kind) / mode
pending.append((relative, manifest, audio_bytes, wav_bytes))
summary = {
"oracle": kind,
"mode": mode,
"output": str(output_root / relative),
"manifest_semantic_digest": manifest.semantic_digest,
"output_content_sha256": manifest.output_content_sha256,
"expected_content_sha256": manifest.expected_content_sha256,
"output_wav_sha256": manifest.output_wav_artifact.sha256,
"exact_oracle_audio": manifest.exact_oracle_audio,
"max_abs_error": manifest.max_abs_error,
"mean_abs_error": manifest.mean_abs_error,
"weight_state_sha256": manifest.weight_state_sha256_after,
"latent_gradient_sha256": (
manifest.gradient_probe.gradient_content_sha256
),
"peak_cuda_allocated_bytes": peak_allocated,
"peak_cuda_reserved_bytes": peak_reserved,
}
print(json.dumps(summary, sort_keys=True), flush=True)
summaries.append(summary)
del result
if module_state_sha256(adapter.model) != mode_weight_sha256:
raise RuntimeError("vocoder weights changed across replay mode")
temporary_root = Path(
tempfile.mkdtemp(
prefix=f".{output_root.name}.",
suffix=".tmp",
dir=output_root.parent,
)
)
published = False
try:
verified_summaries = []
for relative, manifest, audio_bytes, wav_bytes in pending:
leaf = temporary_root / relative
publish_replay_bundle(
leaf,
manifest=manifest,
audio_bytes=audio_bytes,
wav_bytes=wav_bytes,
)
verified = verify_replay_bundle(
leaf,
expected_adapter_semantic_digest=adapter.report.semantic_digest,
expected_oracle_semantic_digest=manifest.oracle_semantic_digest,
)
verified_summaries.append(
{
"path": relative.as_posix(),
"manifest_file_sha256": verified.manifest_file_sha256,
"manifest_semantic_digest": manifest.semantic_digest,
"audio_file_sha256": manifest.output_artifact.sha256,
"wav_file_sha256": manifest.output_wav_artifact.sha256,
"audio_content_sha256": manifest.output_content_sha256,
}
)
session_payload = {
"schema_version": "music3lab.vocoder-replay-session.v2",
"status": "PASS",
"adapter_report": adapter.report.model_dump(mode="json"),
"device": str(device),
"device_name": device_name,
"device_capability": list(device_capability),
"cuda_runtime": cuda_runtime,
"replays": verified_summaries,
}
session_payload["semantic_digest"] = semantic_digest(session_payload)
atomic_write_bytes(
temporary_root / "session.json",
canonical_json_bytes(session_payload),
mode=0o644,
)
publish_replay_session_root(temporary_root, output_root)
published = True
finally:
if not published and temporary_root.exists():
shutil.rmtree(temporary_root)
for relative, manifest, _audio_bytes, _wav_bytes in pending:
verify_replay_bundle(
output_root / relative,
expected_adapter_semantic_digest=adapter.report.semantic_digest,
expected_oracle_semantic_digest=manifest.oracle_semantic_digest,
)
verify_replay_session_tree(output_root)
print(
json.dumps(
{
"status": "PASS",
"adapter_semantic_digest": adapter.report.semantic_digest,
"project_git_commit": adapter.report.project_git_commit,
"project_source_sha256": adapter.report.project_source_sha256,
"session_semantic_digest": session_payload["semantic_digest"],
"output_root": str(output_root),
"replays": summaries,
},
sort_keys=True,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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