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initial commit.

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Files changed (8) hide show
  1. .gitignore +9 -0
  2. app.py +158 -0
  3. assets/lilylet-tokenizer.json +3667 -0
  4. lilyscript/__init__.py +5 -0
  5. lilyscript/generator.py +218 -0
  6. lilyscript/postprocess.py +69 -0
  7. lilyscript/tokenizer.py +60 -0
  8. requirements.txt +3 -0
.gitignore ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ # Large int8 ONNX weights — loaded from a local dir for now (LFS / HF hub later).
2
+ models/*.onnx
3
+
4
+ # Generated outputs written at runtime.
5
+ outputs/
6
+
7
+ __pycache__/
8
+ *.pyc
9
+ .DS_Store
app.py ADDED
@@ -0,0 +1,158 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """LilyScript — a symbolic-music AIGC app built on the LilyletNotaGen model.
2
+
3
+ Left column:
4
+ (1) generation parameter panel (2) streaming run log
5
+ (3) .lyl file list (session outputs + built-in examples) (4) editable lyl editor
6
+ Right column:
7
+ sheet-music panel (placeholder for now; later a lyl->MEI->Verovio SVG renderer
8
+ reusing the lilylet-live-editor pipeline).
9
+
10
+ Generation streams patch-by-patch: raw decoded text (with `[r:x/y]` stream
11
+ markers) goes to the run log, while the measure-segmented postprocessed text
12
+ fills the editor. The backend is the int8 + two-level KV-cache ONNX generator
13
+ (see lilyscript/generator.py); models load from a local dir for now.
14
+ """
15
+
16
+ import os
17
+ import time
18
+
19
+ import gradio as gr
20
+
21
+ from lilyscript.generator import StreamingLilyletGenerator
22
+
23
+ HERE = os.path.dirname(os.path.abspath(__file__))
24
+ # TODO: swap for huggingface_hub.snapshot_download(repo_id=...) to pull the int8
25
+ # ONNX weights from the hub instead of a local dir.
26
+ MODEL_DIR = os.environ.get('LILYSCRIPT_MODEL_DIR', os.path.join(HERE, 'models'))
27
+ ASSET_DIR = os.path.join(HERE, 'assets')
28
+ EXAMPLES_DIR = os.path.join(HERE, 'examples')
29
+ OUTPUT_DIR = os.path.join(HERE, 'outputs')
30
+
31
+ EXAMPLE_PREFIX = '\U0001F4C4 ' # 📄 examples
32
+ OUTPUT_PREFIX = '✨ ' # ✨ session outputs
33
+
34
+ _GEN = None
35
+
36
+
37
+ def get_generator ():
38
+ '''Lazily build the (heavy) ONNX generator on first use.'''
39
+ global _GEN
40
+ if _GEN is None:
41
+ _GEN = StreamingLilyletGenerator(MODEL_DIR, ASSET_DIR)
42
+ return _GEN
43
+
44
+
45
+ def load_examples ():
46
+ '''Read built-in example .lyl files into a {label: text} dict.'''
47
+ store = {}
48
+ if os.path.isdir(EXAMPLES_DIR):
49
+ for name in sorted(os.listdir(EXAMPLES_DIR)):
50
+ if name.endswith('.lyl'):
51
+ with open(os.path.join(EXAMPLES_DIR, name), encoding='utf-8') as f:
52
+ store[EXAMPLE_PREFIX + name] = f.read()
53
+ return store
54
+
55
+
56
+ def run_generation (prompt, measures, temperature, top_k, top_p, max_patches, seed, store):
57
+ '''Streaming generate callback. Yields updates for (log, editor, file_list, store).
58
+
59
+ store: {label: lyl_text} dict held in gr.State; the produced document is added
60
+ to it under a timestamped label once generation finishes.
61
+ '''
62
+ gen = get_generator()
63
+ meas = int(measures) if measures and int(measures) > 0 else None
64
+ store = dict(store or {})
65
+
66
+ raw = pretty = ''
67
+ for raw, pretty, done in gen.generate_stream(
68
+ prompt_text=prompt or '', max_patches=int(max_patches), temperature=float(temperature),
69
+ top_k=int(top_k), top_p=float(top_p), measures=meas, seed=int(seed)):
70
+ # stream: update log + editor, keep file list / store unchanged
71
+ yield raw, pretty, gr.update(), store
72
+
73
+ # finished: persist the document, refresh the file list, select the new entry
74
+ label = OUTPUT_PREFIX + time.strftime('%H%M%S') + ('_m%d' % meas if meas else '') + '_s%d' % int(seed)
75
+ store[label] = pretty
76
+ os.makedirs(OUTPUT_DIR, exist_ok=True)
77
+ with open(os.path.join(OUTPUT_DIR, label.replace(OUTPUT_PREFIX, '') + '.lyl'), 'w', encoding='utf-8') as f:
78
+ f.write(pretty)
79
+ yield raw, pretty, gr.update(choices=list(store.keys()), value=label), store
80
+
81
+
82
+ def load_file (label, store):
83
+ '''File-list selection -> load that document into the editor.'''
84
+ return (store or {}).get(label, '')
85
+
86
+
87
+ SHEET_PLACEHOLDER = '''
88
+ <div style="height:100%;min-height:600px;display:flex;align-items:center;
89
+ justify-content:center;border:1px dashed #c9c9c9;border-radius:8px;
90
+ color:#999;font-family:sans-serif;text-align:center;">
91
+ <div>
92
+ <div style="font-size:42px;margin-bottom:8px;">&#127932;</div>
93
+ <div>Sheet-music preview</div>
94
+ <div style="font-size:12px;margin-top:4px;">
95
+ (coming soon: Lilylet &rarr; MEI &rarr; Verovio)
96
+ </div>
97
+ </div>
98
+ </div>
99
+ '''
100
+
101
+
102
+ def build_ui ():
103
+ examples = load_examples()
104
+
105
+ with gr.Blocks(title='LilyScript') as demo:
106
+ gr.Markdown('## 🎼 LilyScript — symbolic music generation with Lilylet')
107
+ store = gr.State(examples)
108
+
109
+ with gr.Row(equal_height=True):
110
+ # ---------------- LEFT ----------------
111
+ with gr.Column(scale=5):
112
+ # top row: (1) params | (2) run log
113
+ with gr.Row(equal_height=True):
114
+ with gr.Group():
115
+ gr.Markdown('**① Parameters**')
116
+ prompt = gr.Textbox(label='Metadata prompt', lines=3, value='',
117
+ placeholder='[composer "..."]\n[genre "..."]\n(optional)')
118
+ measures = gr.Number(label='Measures (0 = let model decide)', value=8, precision=0)
119
+ with gr.Row():
120
+ temperature = gr.Slider(0.0, 2.0, value=1.0, step=0.05, label='temperature')
121
+ top_p = gr.Slider(0.0, 1.0, value=0.9, step=0.01, label='top_p')
122
+ with gr.Row():
123
+ top_k = gr.Number(label='top_k (0 = off)', value=0, precision=0)
124
+ max_patches = gr.Number(label='max patches', value=256, precision=0)
125
+ seed = gr.Number(label='seed', value=0, precision=0)
126
+ with gr.Row():
127
+ gen_btn = gr.Button('Generate', variant='primary')
128
+ stop_btn = gr.Button('Stop', variant='stop')
129
+
130
+ log = gr.Textbox(label='② Run log', lines=16, max_lines=16,
131
+ autoscroll=True, interactive=False)
132
+
133
+ # bottom row: (3) file list | (4) editor
134
+ with gr.Row(equal_height=True):
135
+ file_list = gr.Radio(label='③ Files', choices=list(examples.keys()),
136
+ value=None, interactive=True)
137
+ editor = gr.Code(label='④ Lilylet editor', language=None, lines=18,
138
+ interactive=True)
139
+
140
+ # ---------------- RIGHT ----------------
141
+ with gr.Column(scale=6):
142
+ gr.Markdown('**Sheet music**')
143
+ gr.HTML(SHEET_PLACEHOLDER)
144
+
145
+ # ---- wiring ----
146
+ gen_event = gen_btn.click(
147
+ run_generation,
148
+ inputs=[prompt, measures, temperature, top_k, top_p, max_patches, seed, store],
149
+ outputs=[log, editor, file_list, store],
150
+ )
151
+ stop_btn.click(None, None, None, cancels=[gen_event])
152
+ file_list.select(load_file, inputs=[file_list, store], outputs=[editor])
153
+
154
+ return demo
155
+
156
+
157
+ if __name__ == '__main__':
158
+ build_ui().queue().launch(theme=gr.themes.Soft())
assets/lilylet-tokenizer.json ADDED
@@ -0,0 +1,3667 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "version": 1,
3
+ "type": "byte-level-bpe",
4
+ "source": {
5
+ "corpusDir": "tests/output/notagenx-from-abc",
6
+ "serializer": "source/lilylet/serializer.ts",
7
+ "frequencyTsv": "tests/output/bpe-tokenizer/token-frequencies.tsv"
8
+ },
9
+ "config": {
10
+ "vocabSize": 256,
11
+ "minFrequency": 2,
12
+ "allowMerges": false,
13
+ "byteFallback": true,
14
+ "unknownToken": "<unknown>",
15
+ "protectedTokensDoNotMerge": true,
16
+ "fixedTokensUseLongestMatch": true,
17
+ "asciiByteIdsUseByteValue": true
18
+ },
19
+ "specialTokens": [
20
+ {
21
+ "id": 0,
22
+ "token": "<pad>"
23
+ },
24
+ {
25
+ "id": 1,
26
+ "token": "<bos>"
27
+ },
28
+ {
29
+ "id": 2,
30
+ "token": "<eos>"
31
+ },
32
+ {
33
+ "id": 3,
34
+ "token": "<unknown>"
35
+ },
36
+ {
37
+ "id": 4,
38
+ "token": "<mask>"
39
+ },
40
+ {
41
+ "id": 5,
42
+ "token": "<sep>"
43
+ },
44
+ {
45
+ "id": 6,
46
+ "token": "<reserved_6>"
47
+ },
48
+ {
49
+ "id": 7,
50
+ "token": "<reserved_7>"
51
+ }
52
+ ],
53
+ "protectedTokens": [
54
+ "\\numericTimeSignature",
55
+ "\\shortfermata",
56
+ "\\sostenutoOff",
57
+ "\\sostenutoOn",
58
+ "\\stemNeutral",
59
+ "\\sustainOff",
60
+ "\\sustainOn",
61
+ "Allegretto",
62
+ "espressivo",
63
+ "instrument",
64
+ "\\arpeggio",
65
+ "\\stemDown",
66
+ "\\treCorde",
67
+ "\\unaCorda",
68
+ "Andantino",
69
+ "cantabile",
70
+ "sostenuto",
71
+ "\"treble\"",
72
+ "\\fermata",
73
+ "\\mordent",
74
+ "\\partial",
75
+ "composer",
76
+ "grazioso",
77
+ "Menuetto",
78
+ "moderato",
79
+ "Moderato",
80
+ "\\markup",
81
+ "\\ottava",
82
+ "\\repeat",
83
+ "\\stemUp",
84
+ "\\tuplet",
85
+ "agitato",
86
+ "Allegro",
87
+ "Andante",
88
+ "animato",
89
+ "espress",
90
+ "marcato",
91
+ "\"bass\"",
92
+ "\\grace",
93
+ "\\major",
94
+ "\\minor",
95
+ "\\prall",
96
+ "\\staff",
97
+ "\\tempo",
98
+ "\\times",
99
+ "\\trill",
100
+ "Adagio",
101
+ "legato",
102
+ "Presto",
103
+ "ritard",
104
+ "sempre",
105
+ "troppo",
106
+ "vivace",
107
+ "\\clef",
108
+ "\\rest",
109
+ "\\time",
110
+ "\\turn",
111
+ "assai",
112
+ "colla",
113
+ "corda",
114
+ "cresc",
115
+ "dolce",
116
+ "genre",
117
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lilyscript/__init__.py ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ from .generator import StreamingLilyletGenerator
2
+ from .postprocess import postprocess
3
+ from .tokenizer import LilyletTokenizer
4
+
5
+ __all__ = ['StreamingLilyletGenerator', 'postprocess', 'LilyletTokenizer']
lilyscript/generator.py ADDED
@@ -0,0 +1,218 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Standalone streaming Lilylet generator (int8 ONNX + two-level KV cache).
2
+
3
+ Torch-free adaptation of deep-starry's ORTGeneratorKV
4
+ (tests/bench_lilylet_int8_ort.py): the patch-level decoder runs incrementally
5
+ through `patch_kv_int8.onnx` (O(1) per step) and the token decoder inside each
6
+ patch through `token_kv_int8.onnx`, both via onnxruntime. The token embedding
7
+ table and model geometry are loaded from vendored assets (`wte.npy`,
8
+ `geometry.json`) instead of a torch model, and sampling is reimplemented in
9
+ numpy — so the only runtime deps are onnxruntime + numpy.
10
+
11
+ `generate_stream(...)` is a Python generator: it yields `(raw, pretty, done)`
12
+ after every patch, where `raw` is the accumulated decoded text (for the run
13
+ log) and `pretty = postprocess(raw)` (for the editor, segmented by measure).
14
+ """
15
+
16
+ import os
17
+ import json
18
+
19
+ import numpy as np
20
+ import onnxruntime as ort
21
+
22
+ from .postprocess import postprocess
23
+
24
+
25
+ def sample_next (logits, rng, temperature=1.0, top_k=0, top_p=1.0):
26
+ '''Sample one token id from a logits vector (numpy) with temperature/top-k/top-p.'''
27
+ logits = logits.astype(np.float64)
28
+ if temperature != 1.0:
29
+ logits = logits / max(temperature, 1e-6)
30
+ if top_k and top_k > 0:
31
+ k = min(top_k, logits.shape[-1])
32
+ kth = np.sort(logits)[-k]
33
+ logits = np.where(logits < kth, -np.inf, logits)
34
+ if top_p and top_p < 1.0:
35
+ order = np.argsort(logits)[::-1]
36
+ sorted_logits = logits[order]
37
+ probs = _softmax(sorted_logits)
38
+ cdf = np.cumsum(probs)
39
+ remove = cdf > top_p
40
+ remove[1:] = remove[:-1].copy()
41
+ remove[0] = False
42
+ sorted_logits = np.where(remove, -np.inf, sorted_logits)
43
+ logits = np.full_like(logits, -np.inf)
44
+ logits[order] = sorted_logits
45
+ probs = _softmax(logits)
46
+ return int(rng.choice(len(probs), p=probs))
47
+
48
+
49
+ def _softmax (x):
50
+ x = x - np.max(x)
51
+ e = np.exp(x)
52
+ return e / e.sum()
53
+
54
+
55
+ class StreamingLilyletGenerator:
56
+ '''Loads the int8 KV ONNX sessions + vendored assets and streams generation.'''
57
+
58
+ def __init__ (self, model_dir, asset_dir, threads=None):
59
+ from .tokenizer import LilyletTokenizer
60
+
61
+ geo = json.load(open(os.path.join(asset_dir, 'geometry.json')))
62
+ self.patch_size = geo['patch_size']
63
+ self.pad_id = geo['pad_id']
64
+ self.bos_id = geo['bos_id']
65
+ self.eos_id = geo['eos_id']
66
+ # patch-level KV geometry
67
+ self.n_layers = geo['patch']['n_layers']
68
+ self.n_kv = geo['patch']['n_kv_heads']
69
+ self.head_dim = geo['patch']['head_dim']
70
+ # token-level KV geometry
71
+ self.t_layers = geo['token']['n_layers']
72
+ self.t_kv = geo['token']['n_kv_heads']
73
+ self.t_head_dim = geo['token']['head_dim']
74
+
75
+ self.tokenizer = LilyletTokenizer(os.path.join(asset_dir, 'lilylet-tokenizer.json'))
76
+ self.wte = np.load(os.path.join(asset_dir, 'wte.npy')) # [vocab, hidden]
77
+
78
+ so = ort.SessionOptions()
79
+ if threads:
80
+ so.intra_op_num_threads = threads
81
+ self.patch_kv_sess = ort.InferenceSession(
82
+ os.path.join(model_dir, 'patch_kv_int8.onnx'), so, providers=['CPUExecutionProvider'])
83
+ self.token_kv_sess = ort.InferenceSession(
84
+ os.path.join(model_dir, 'token_kv_int8.onnx'), so, providers=['CPUExecutionProvider'])
85
+ self.patch_out_names = [o.name for o in self.patch_kv_sess.get_outputs()]
86
+ self.token_out_names = [o.name for o in self.token_kv_sess.get_outputs()]
87
+
88
+ # ---- text helpers (mirror LilyletPatchyGenerator.patch_to_text) ----
89
+
90
+ def patch_to_text (self, patch):
91
+ out = []
92
+ for tid in patch:
93
+ tid = int(tid)
94
+ if tid == self.eos_id:
95
+ break
96
+ if tid in (self.pad_id, self.bos_id):
97
+ continue
98
+ out.append(self.tokenizer.text_by_id.get(tid, ''))
99
+ return ''.join(out)
100
+
101
+ def patches_to_text (self, patches):
102
+ return ''.join(self.patch_to_text(p) for p in patches)
103
+
104
+ # ---- KV plumbing ----
105
+
106
+ def _empty_patch_past (self):
107
+ return [np.zeros((1, self.n_kv, 0, self.head_dim), dtype=np.float32) for _ in range(2 * self.n_layers)]
108
+
109
+ def _empty_token_past (self):
110
+ return [np.zeros((1, self.t_kv, 0, self.t_head_dim), dtype=np.float32) for _ in range(2 * self.t_layers)]
111
+
112
+ def _patch_kv_step (self, patch_rows, past):
113
+ '''Feed L new patches (list of patch_size-length id rows) + past KV.
114
+ Returns (last_hidden [hidden], new_past list).'''
115
+ feed = {'patches': np.asarray([patch_rows], dtype=np.int64)}
116
+ for i in range(self.n_layers):
117
+ feed[f'past_k_{i}'] = past[2 * i]
118
+ feed[f'past_v_{i}'] = past[2 * i + 1]
119
+ out = dict(zip(self.patch_out_names, self.patch_kv_sess.run(None, feed)))
120
+ new_past = []
121
+ for i in range(self.n_layers):
122
+ new_past.append(out[f'new_k_{i}'])
123
+ new_past.append(out[f'new_v_{i}'])
124
+ return out['hidden'][0, -1], new_past
125
+
126
+ def _token_kv_step (self, emb_np, past):
127
+ '''Feed L new token embeddings [1,L,hidden] + past KV. Returns (logits[-1], new_past).'''
128
+ feed = {'inputs_embeds': emb_np.astype(np.float32)}
129
+ for i in range(self.t_layers):
130
+ feed[f'past_k_{i}'] = past[2 * i]
131
+ feed[f'past_v_{i}'] = past[2 * i + 1]
132
+ out = dict(zip(self.token_out_names, self.token_kv_sess.run(None, feed)))
133
+ new_past = []
134
+ for i in range(self.t_layers):
135
+ new_past.append(out[f'new_k_{i}'])
136
+ new_past.append(out[f'new_v_{i}'])
137
+ return out['logits'][0, -1], new_past
138
+
139
+ def _generate_patch (self, last_hidden, rng, prefix_ids=None, temperature=1.0, top_k=0, top_p=1.0):
140
+ '''Token-level decode for one patch through the token-KV session: feed the
141
+ patch state, then bos+prefix embeddings, then sample until the patch is full.'''
142
+ generated = list(prefix_ids or [])
143
+ tokens = [self.bos_id] + list(prefix_ids or [])
144
+ past = self._empty_token_past()
145
+ enc = last_hidden.reshape(1, 1, -1).astype(np.float32)
146
+ logits, past = self._token_kv_step(enc, past)
147
+ for i in range(1, len(tokens)):
148
+ emb = self.wte[tokens[i]].reshape(1, 1, -1)
149
+ logits, past = self._token_kv_step(emb, past)
150
+ while len(generated) < self.patch_size:
151
+ nxt = sample_next(logits, rng, temperature=temperature, top_k=top_k, top_p=top_p)
152
+ generated.append(nxt)
153
+ if len(generated) < self.patch_size:
154
+ emb = self.wte[nxt].reshape(1, 1, -1)
155
+ logits, past = self._token_kv_step(emb, past)
156
+ return generated
157
+
158
+ def generate_stream (self, prompt_text='', max_patches=256, temperature=1.0, top_k=0,
159
+ top_p=0.9, measures=None, seed=0):
160
+ '''Autoregressive generation, yielding after every patch.
161
+
162
+ Yields (raw, pretty, done):
163
+ raw -- accumulated decoded patch text (with `[r:x/y]` markers), for the log
164
+ pretty -- postprocess(raw), measure-segmented, for the editor
165
+ done -- True on the final yield (EOS patch or max_patches reached)
166
+ '''
167
+ rng = np.random.default_rng(seed)
168
+
169
+ bos_patch = [self.bos_id] * (self.patch_size - 1) + [self.eos_id]
170
+ patches = [bos_patch]
171
+ if prompt_text:
172
+ for line in prompt_text.splitlines():
173
+ ids = self.tokenizer.encode(line + '\n')
174
+ for i in range(0, len(ids), self.patch_size):
175
+ chunk = ids[i:i + self.patch_size]
176
+ patches.append(chunk + [self.pad_id] * (self.patch_size - len(chunk)))
177
+
178
+ out_text = self.patches_to_text(patches[1:])
179
+ prime_ids = self.tokenizer.encode(f'[r:0/{measures}]') if measures is not None else None
180
+ primed = False
181
+
182
+ # prefill: run all seed patches through the patch-KV decoder in one call
183
+ past = self._empty_patch_past()
184
+ last, past = self._patch_kv_step(patches, past)
185
+
186
+ yield out_text, postprocess(out_text), False
187
+
188
+ for _ in range(max_patches):
189
+ patch_ids = self._generate_patch(last, rng, temperature=temperature, top_k=top_k, top_p=top_p)
190
+
191
+ # first time the model emits a stream patch, re-sample with the forced
192
+ # `[r:0/<measures>]` prefix so the body starts at the requested measure count.
193
+ if prime_ids is not None and not primed and self.patch_to_text(patch_ids).startswith('[r:'):
194
+ primed = True
195
+ patch_ids = self._generate_patch(last, rng, prefix_ids=prime_ids,
196
+ temperature=temperature, top_k=top_k, top_p=top_p)
197
+
198
+ # EOS patch -> done
199
+ if patch_ids[0] == self.bos_id and patch_ids[1] == self.eos_id:
200
+ break
201
+
202
+ out_text += self.patch_to_text(patch_ids)
203
+
204
+ # mask tokens after the first EOS inside the patch to PAD before caching
205
+ clean = list(patch_ids)
206
+ seen_eos = False
207
+ for j in range(len(clean)):
208
+ if seen_eos:
209
+ clean[j] = self.pad_id
210
+ if clean[j] == self.eos_id:
211
+ seen_eos = True
212
+
213
+ # advance the patch-level cache by the one new patch -> next hidden state
214
+ last, past = self._patch_kv_step([clean], past)
215
+
216
+ yield out_text, postprocess(out_text), False
217
+
218
+ yield out_text, postprocess(out_text), True
lilyscript/postprocess.py ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Lilylet output post-processing (vendored from deep-starry, torch-free).
2
+
3
+ Cleans raw generated Lilylet text for readability:
4
+ - move each `[r:x/y]` stream marker from the measure head to a trailing
5
+ comment `% r:x/y` at the measure end (the line ending in `|`),
6
+ - insert a blank line after the metadata block,
7
+ - insert a blank line at every measure boundary.
8
+
9
+ Because a blank line is inserted at each measure boundary, the postprocessed
10
+ text is naturally segmented measure-by-measure — which is what the editor pane
11
+ displays as generation streams in.
12
+
13
+ Source: deep-starry/starry/lilylet/patchyGenerator.py (postprocess / _STREAM_RE).
14
+ """
15
+
16
+ import re
17
+
18
+
19
+ # matches a stream marker like `[r:12/34]` (and a trailing partial `[r:12/` at EOF);
20
+ # group 1 captures the `x/y` payload.
21
+ _STREAM_RE = re.compile(r'\[r:(\d+/\d*)\]?')
22
+
23
+
24
+ def postprocess (text: str) -> str:
25
+ lines = [ln.rstrip() for ln in text.split('\n')]
26
+ out = []
27
+ meta_done = False
28
+ pending = None # marker payload waiting to be attached at the measure end
29
+
30
+ for ln in lines:
31
+ # extract the stream marker (sits at the measure head); remember its
32
+ # payload and strip the marker text from the line.
33
+ m = _STREAM_RE.search(ln)
34
+ if m:
35
+ # a still-pending marker means the previous measure had no barline
36
+ # (e.g. truncated); keep it as a standalone comment so it isn't lost.
37
+ if pending is not None:
38
+ out.append('% r:' + pending)
39
+ pending = m.group(1)
40
+ ln = _STREAM_RE.sub('', ln).rstrip()
41
+
42
+ is_meta = ln.startswith('[') and ln.endswith(']')
43
+ # blank line once the metadata block ends and the body begins
44
+ if not meta_done and out and not is_meta and ln:
45
+ out.append('')
46
+ meta_done = True
47
+
48
+ is_measure_end = ln.endswith('|')
49
+ if is_measure_end and pending is not None:
50
+ ln = ln + ' % r:' + pending
51
+ pending = None
52
+
53
+ out.append(ln)
54
+
55
+ # blank line after a measure-ending line
56
+ if meta_done and is_measure_end:
57
+ out.append('')
58
+
59
+ # any marker left pending at EOF -> attach as a trailing comment
60
+ if pending is not None:
61
+ out.append('% r:' + pending)
62
+
63
+ # collapse runs of blank lines, trim leading/trailing blanks
64
+ cleaned = []
65
+ for ln in out:
66
+ if ln == '' and (not cleaned or cleaned[-1] == ''):
67
+ continue
68
+ cleaned.append(ln)
69
+ return '\n'.join(cleaned).strip('\n') + '\n'
lilyscript/tokenizer.py ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Standalone Lilylet tokenizer (vendored from deep-starry, torch-free).
2
+
3
+ Trimmed to what inference needs: load the vocab artifact, expose the special
4
+ ids + the id->text table, and encode text to ids (longest protected-token match
5
+ first, then per-character, then byte-level fallback). The training-time unknown
6
+ tracking and the patchify/packing machinery are intentionally dropped.
7
+
8
+ Source: deep-starry/starry/lilylet/data/patchifier.py (LilyletTokenizer).
9
+ """
10
+
11
+ import json
12
+ from typing import List
13
+
14
+
15
+ class LilyletTokenizer:
16
+ def __init__ (self, tokenizer_path: str):
17
+ self.path = tokenizer_path
18
+ with open(tokenizer_path, 'r', encoding='utf-8') as f:
19
+ self.artifact = json.load(f)
20
+
21
+ self.vocab = self.artifact['vocab']
22
+ self.id_by_token = {entry['token']: entry['id'] for entry in self.vocab}
23
+ self.text_by_id = {entry['id']: entry.get('text', entry['token']) for entry in self.vocab}
24
+ self.unknown_id = self.id_by_token.get('<unknown>', 3)
25
+ self.pad_id = self.id_by_token.get('<pad>', 0)
26
+ self.bos_id = self.id_by_token.get('<bos>', 1)
27
+ self.eos_id = self.id_by_token.get('<eos>', 2)
28
+ self.vocab_size = max(entry['id'] for entry in self.vocab) + 1
29
+
30
+ fixed = [entry['token'] for entry in self.vocab if entry.get('type') == 'protected']
31
+ self.fixed_tokens = sorted(set(fixed), key=lambda token: (-len(token), token))
32
+
33
+ def encode (self, text: str) -> List[int]:
34
+ ids: List[int] = []
35
+ i = 0
36
+ while i < len(text):
37
+ matched = None
38
+ for token in self.fixed_tokens:
39
+ if text.startswith(token, i):
40
+ matched = token
41
+ break
42
+ if matched is not None:
43
+ ids.append(self.id_by_token[matched])
44
+ i += len(matched)
45
+ continue
46
+
47
+ char = text[i]
48
+ if char in self.id_by_token:
49
+ ids.append(self.id_by_token[char])
50
+ else:
51
+ emitted_unknown = False
52
+ for byte in char.encode('utf-8'):
53
+ if 0x08 <= byte <= 0x7f and byte in self.text_by_id:
54
+ ids.append(byte)
55
+ else:
56
+ emitted_unknown = True
57
+ if emitted_unknown:
58
+ ids.append(self.unknown_id)
59
+ i += 1
60
+ return ids
requirements.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ gradio==6.18.0
2
+ onnxruntime
3
+ numpy