Allow multi-section YuE2 lyrics and force non-flash attention on Windows.

Co-authored-by: Cursor <cursoragent@cursor.com>

scripts/yue2-host.mjs

@@ -10,9 +10,7 @@ export function validateYue2Request(body) {
   if (!/^[a-zA-Z0-9-]{12,80}$/.test(body.id || '')) throw new Error('Invalid job ID.')
   const tags = String(body.tags || '').trim()
   const lyrics = String(body.lyrics || '').trim()
-  if (!tags || tags.length > 2000 || !lyrics || lyrics.length > 8000) throw new Error('Genre tags and one lyric section are required.')
-  const sections = [...lyrics.matchAll(/\[([^\]]+)\]\s*([^\[]*)/gs)].filter(m => m[2].trim())
-  if (sections.length !== 1) throw new Error('YuE2 requires one non-empty lyric section.')
+  if (!tags || tags.length > 2000 || !lyrics || lyrics.length > 8000) throw new Error('Genre tags and non-empty lyrics are required.')
   return { id: body.id, duration, seed: body.seed, tags, lyrics }
 }
 

scripts/yue2-worker.py

@@ -18,14 +18,47 @@ def emit(**event):
 
 
 def normalize_lyrics(text):
-    # Same one-section rule as YuEGP field validation.
+    # YuE2 accepts multiple [Verse]/[Chorus] headings. Only wrap when none exist.
     import re
-    sections = re.findall(r'\[([^\]]+)\]\s*([^\[]*)', text.strip(), re.S)
-    sections = [(name, words.strip()) for name, words in sections if words.strip()]
-    if len(sections) != 1:
-        raise ValueError('YuE2 requires one non-empty lyric section. Combine the lyrics under one heading.')
-    name, words = sections[0]
-    return '[' + re.sub(r'\W+', '', name).lower() + ']\n' + words + '\n\n'
+    text = str(text or '').strip()
+    if not text:
+        raise ValueError('YuE2 requires non-empty lyrics.')
+    if re.search(r'\[[^\]]+\]', text):
+        return text if text.endswith('\n') else text + '\n'
+    return '[song]\n' + text + '\n\n'
+
+
+def resolve_attention_backend(torch_mod):
+    """Pick a non-flash attention path for the Windows CUDA wheel on the 5080."""
+    flash = False
+    try:
+        check = getattr(torch_mod.backends.cuda, 'is_flash_attention_available', None)
+        flash = bool(check()) if callable(check) else False
+    except Exception:
+        flash = False
+    # Cognito/YuE2-Windows: when flash is unavailable, fall back to cudnn.
+    # This host never enables flash: the torch wheel exposes the op without USE_FLASH_ATTENTION.
+    if sys.platform == 'win32' or not flash:
+        if torch_mod.cuda.is_available() and torch_mod.backends.cudnn.is_available():
+            return 'cudnn', 'torch'
+        return 'sdpa', 'torch-eager'
+    if torch_mod.cuda.is_available() and torch_mod.backends.cudnn.is_available():
+        return 'cudnn', 'torch'
+    return 'sdpa', 'torch-eager'
+
+
+def patch_graph_attention(attention_backend):
+    from yue2.cuda_graph import GraphAR
+    original = GraphAR.__init__
+
+    def init(self, model, prefixes, max_tokens, *, capture=True, attention_backend='auto', fuse_projections=False):
+        if attention_backend in ('auto', 'flash'):
+            attention_backend = patch_graph_attention.forced
+        return original(self, model, prefixes, max_tokens, capture=capture,
+                        attention_backend=attention_backend, fuse_projections=fuse_projections)
+
+    patch_graph_attention.forced = attention_backend
+    GraphAR.__init__ = init
 
 
 def main():
@@ -56,16 +89,19 @@ def main():
     os.chdir(root)
     if str(root) not in sys.path:
         sys.path.insert(0, str(root))
-    emit(stage='loading', progress=1, message='Loading YuE2', model=model, vae=vae, duration=duration)
     import torch
     import soundfile as sf
+    attention_backend, pipeline_backend = resolve_attention_backend(torch)
+    emit(stage='loading', progress=1, message='Loading YuE2', model=model, vae=vae,
+         duration=duration, attention_backend=attention_backend, pipeline_backend=pipeline_backend)
     from yue2 import YuE2Pipeline
     if not torch.cuda.is_available():
         raise RuntimeError('YuE2 requires a CUDA GPU; CPU fallback is disabled.')
+    patch_graph_attention(attention_backend)
     pipe_kwargs = dict(style=style, lyrics=lyrics, cot='full', seed=seed)
     # Duration is kept for library metadata and validation. Upstream one-shot
     # requests do not take a seconds field; song length follows the plan.
-    with YuE2Pipeline.from_pretrained(model, vae=vae, device='cuda') as pipe:
+    with YuE2Pipeline.from_pretrained(model, vae=vae, device='cuda', backend=pipeline_backend) as pipe:
         emit(stage='plan', message='Planning melody and chords', progress=5)
         plan = pipe.plan(**pipe_kwargs)
         emit(stage='semantic', message='Generating semantic tokens', progress=25)