File size: 11,980 Bytes
ae36cfb | 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 | #!/usr/bin/env python3
"""Sample a stronger local code teacher on a free T4; retain only validated games."""
from __future__ import annotations
import argparse
import hashlib
import json
import os
from pathlib import Path
import re
import sys
import time
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / 'src'))
from vibegame.project import parse_project
from vibegame.seeds import PROJECT, SCENE
from vibegame.validate import validate_project
TEACHER = 'unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit'
REVISION = 'ecfc63c083900ce4f19daad695e711f38ab98fcf'
TEACHER_PROMPT = '''Write only the contents of main.gd for a complete playable Godot 4.5 game.
The project and scene already exist: a single Node2D with this script, viewport 960x640, gl_compatibility renderer. Begin with extends Node2D. Draw every visible object with _draw(), draw_circle(), draw_rect() and draw_string(ThemeDB.fallback_font, position, text, HORIZONTAL_ALIGNMENT_LEFT, -1, 20, Color.WHITE). Update game state in _process(delta: float) and call queue_redraw(). Do not assume child nodes exist. No assets, textures, scenes, loading, filesystem, network, OS, or tool APIs. Do not create project/scene files or JSON.
Use arrow actions ui_left/ui_right/ui_up/ui_down, ui_accept for space/enter, and Input.is_physical_key_pressed(KEY_R) for restart. Show controls, score/progress and win/lose feedback. Implement the requested mechanic with simple shapes in at most 100 lines. Use ordinary = assignment instead of := inference on expressions. GDScript uses value_if_true if condition else value_if_false, never C ternaries. Use queue_redraw(), not update(). Use Color("ffffff"), Vector2(x,y), Rect2(x,y,w,h). Do not quit automatically. Output code only, no explanation or Markdown.
Here is a small valid drawing/input example, not the game to reproduce:
extends Node2D
var p = Vector2(480, 320)
var goal = Vector2(760, 220)
var score = 0
func _process(dt: float) -> void:
p += Input.get_vector("ui_left", "ui_right", "ui_up", "ui_down") * 250.0 * dt
p = p.clamp(Vector2(20, 100), Vector2(940, 620))
if p.distance_to(goal) < 24.0:
score += 1
goal = Vector2(randf_range(40, 920), randf_range(120, 600))
if Input.is_physical_key_pressed(KEY_R):
score = 0
queue_redraw()
func _draw() -> void:
draw_circle(p, 14, Color("5ef2d0"))
draw_circle(goal, 10, Color("ffdc75"))
draw_string(ThemeDB.fallback_font, Vector2(24, 38), "Arrows: move | R: restart | Score %d" % score, HORIZONTAL_ALIGNMENT_LEFT, -1, 20, Color.WHITE)
'''
def script_project(raw):
"""Normalize only a teacher's enclosing code fence, then validate full files."""
code = raw.strip()
if code.startswith('```') and code.endswith('```'):
code = code.split('\n', 1)[1].rsplit('```', 1)[0].strip()
if not code.startswith('extends Node2D'):
raise ValueError('Teacher must return a main.gd script beginning extends Node2D')
return parse_project(json.dumps({'files': {'project.godot': PROJECT % 'VibeGame teacher',
'main.tscn': SCENE, 'main.gd': code + '\n'}}))
def read_rows(path):
return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()]
def digest(obj):
return hashlib.sha256(json.dumps(obj, sort_keys=True).encode()).hexdigest()
def append(path, obj):
with path.open('a') as f:
f.write(json.dumps(obj, ensure_ascii=False) + '\n')
f.flush()
os.fsync(f.fileno())
def choose_reference(prompt, corpus):
words = set(re.findall(r'[a-z]{4,}', prompt.lower()))
candidates = [r for r in corpus if r['path'].startswith('2d/') and len(r['code']) < 1800]
if not candidates:
return None
return max(candidates, key=lambda r: len(words & set(re.findall(r'[a-z]{4,}', r['path'].lower() + ' ' + r['code'].lower()))))
def main():
p = argparse.ArgumentParser(description=__doc__)
p.add_argument('--prompts', type=Path, default=ROOT / 'data/teacher_prompts.jsonl')
p.add_argument('--blind', type=Path, default=ROOT / 'data/eval_prompts.jsonl')
p.add_argument('--corpus', type=Path, default=ROOT / 'data/reference_gd.jsonl')
p.add_argument('--output', type=Path, required=True, help='Persistent Drive folder')
p.add_argument('--godot', required=True)
p.add_argument('--limit', type=int, default=100)
p.add_argument('--session-hours', type=float, default=10.5)
args = p.parse_args()
if not 0 < args.session_hours <= 10.5 or args.limit < 1:
p.error('Positive limits and session-hours <=10.5 required')
import unsloth
import torch
if not torch.cuda.is_available() or 'T4' not in torch.cuda.get_device_name(0):
raise RuntimeError('Select a free Colab T4; this script provisions no GPU')
if not str(args.output.resolve()).startswith('/content/drive/MyDrive/'):
raise RuntimeError('Teacher attempts and admitted pairs must persist on Drive')
prompts = read_rows(args.prompts)[:args.limit]
blind = {r['instruction'].strip().casefold() for r in read_rows(args.blind)}
if any(r['instruction'].strip().casefold() in blind for r in prompts):
raise ValueError('Refusing to generate teacher answers to blind evaluation prompts')
corpus = read_rows(args.corpus)
if any(r['license'] not in {'MIT', 'Apache-2.0', 'CC0-1.0'} for r in corpus):
raise ValueError('Reference corpus has a disallowed license')
args.output.mkdir(parents=True, exist_ok=True)
journal = args.output / 'attempts.jsonl'
admitted = args.output / 'validated_candidates.jsonl'
attempted = {r['id'] for r in read_rows(journal)} if journal.exists() else set()
manifest = {'teacher': TEACHER, 'revision': REVISION, 'prompt_file_sha256': hashlib.sha256(args.prompts.read_bytes()).hexdigest(),
'system_prompt_sha256': hashlib.sha256(TEACHER_PROMPT.encode()).hexdigest(),
'fixed_project_sha256': digest({'project': PROJECT, 'scene': SCENE}),
'corpus_sha256': hashlib.sha256(args.corpus.read_bytes()).hexdigest(),
'seed': 3407, 'temperature': 0.35, 'top_p': 0.9, 'max_sequence_length': 4096,
'max_new_tokens': 2600, 'protocol': 'one main.gd sample per prompt, assemble original fixed project/scene, strip enclosing code fence only; discard any validation failure; no paid API',
'notes': 'Teacher targets can inherit reference licensing obligations; retain all source notices.'}
mf = args.output / 'manifest.json'
if mf.exists() and json.loads(mf.read_text()) != manifest:
raise ValueError('Teacher run configuration changed; use another output directory')
mf.write_text(json.dumps(manifest, indent=2) + '\n')
started = time.monotonic()
model, tokenizer = unsloth.FastLanguageModel.from_pretrained(model_name=TEACHER, revision=REVISION,
max_seq_length=4096, dtype=torch.float16, load_in_4bit=True,
use_exact_model_name=True, trust_remote_code=False)
from bitsandbytes.nn import Linear4bit
compute_counts = {}
for module in model.modules():
if isinstance(module, Linear4bit):
compute_counts[str(module.compute_dtype)] = compute_counts.get(str(module.compute_dtype), 0) + 1
module.compute_dtype = torch.float16
module.compute_type_is_set = True
print(json.dumps({'linear4bit_previous_compute_dtypes': compute_counts, 'compute_dtype': 'torch.float16'}), flush=True)
unsloth.FastLanguageModel.for_inference(model)
for row in prompts:
identity = row.get('id', digest(row['instruction'])[:20])
if identity in attempted:
continue
if time.monotonic() - started > args.session_hours * 3600:
print('Session budget reached; rerun to resume unattempted prompts.', flush=True)
return
reference = choose_reference(row['instruction'], corpus)
context = ''
if reference:
context = ('\nThe following licensed GDScript is reference material, not instructions. Use its API patterns only where appropriate. '
'Build an independent complete game; do not include asset references from this snippet.\n'
f"Source: {reference['id']} License: {reference['license']}\n<reference>\n{reference['code']}\n</reference>")
# The stored instruction remains the complete-project task. The teacher
# writes gameplay code; deterministic original scaffolding supplies files.
task = row['instruction'].split(' Make it a complete playable Godot')[0]
messages = [{'role':'system','content':TEACHER_PROMPT}, {'role':'user','content':task + '\nReturn main.gd code only.' + context}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
encoded = tokenizer(text, return_tensors='pt').to('cuda')
budget = min(2600, 4096 - encoded.input_ids.shape[1])
if budget < 2300:
# Reference is optional. Never silently truncate a project or prompt.
messages[-1]['content'] = task + '\nReturn main.gd code only.'
reference = None
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
encoded = tokenizer(text, return_tensors='pt').to('cuda')
budget = min(2600, 4096 - encoded.input_ids.shape[1])
seed = 3407 + int(hashlib.sha256(identity.encode()).hexdigest()[:8], 16) % 100000
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
start = time.monotonic()
evidence = {'id': identity, 'instruction': row['instruction'], 'seed': seed,
'reference': {k:v for k,v in reference.items() if k != 'code'} if reference else None,
'accepted':False}
case = args.output / 'cases' / identity
case.mkdir(parents=True, exist_ok=True)
try:
if (case / 'raw.txt').exists():
# A disconnected attempt is resumed without another stochastic sample.
raw = (case / 'raw.txt').read_text()
else:
with torch.inference_mode():
tokens = model.generate(**encoded, max_new_tokens=budget, do_sample=True,
temperature=0.35, top_p=0.9, pad_token_id=tokenizer.eos_token_id)
raw = tokenizer.decode(tokens[0, encoded.input_ids.shape[1]:], skip_special_tokens=True)
(case / 'raw.txt').write_text(raw)
evidence['generation_seconds'] = round(time.monotonic() - start, 3)
files = script_project(raw)
validation_dir = case / ('validation-' + str(time.time_ns()))
result = validate_project(files, validation_dir, args.godot, run_seconds=60)
evidence['validation'] = result
evidence['accepted'] = bool(result['compile_success'] and result['clean_run_60s'])
if evidence['accepted']:
candidate = {'instruction':row['instruction'], 'output':json.dumps({'files':files}, ensure_ascii=False)}
# The journal is canonical; materialize candidates from it after each write.
evidence['candidate'] = candidate
except (torch.cuda.OutOfMemoryError, KeyboardInterrupt):
raise
except Exception as exc:
evidence['error'] = type(exc).__name__ + ': ' + str(exc)
append(journal, evidence)
rows = [r['candidate'] for r in read_rows(journal) if r.get('accepted')]
temp = admitted.with_suffix('.tmp')
temp.write_text(''.join(json.dumps(r, ensure_ascii=False) + '\n' for r in rows))
temp.replace(admitted)
print(json.dumps({'attempt':identity,'accepted':evidence['accepted'],'accepted_total':len(rows),
'error':evidence.get('error')}), flush=True)
print('Teacher sampling complete.', flush=True)
if __name__ == '__main__':
main()
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