| """One deterministic generation per task, followed by isolated execution.""" |
| import argparse |
| import hashlib |
| import json |
| from pathlib import Path |
| import sys |
| from validators.dataset import load |
| from runners import get_runner,LANGUAGES |
| from runners.common import extract_code |
| from runners.sandbox import Sandbox |
| from metrics import aggregate |
| ROOT=Path(__file__).resolve().parent |
| PROMPT_VERSION='smolscript-completion-v1' |
|
|
| def prompt(row): |
| |
| fixture=row['metadata'].get('fixture','');base=row['metadata'].get('base_css','') |
| return ('Write code for the following task. Use the standard library only. Return code only.\n' |
| +row['prompt']+'\n'+row['output_format']+'\n'+(row['metadata'].get('input_domain') or '') |
| +('\nHTML fixture:\n'+fixture if fixture else '')+('\nExisting CSS:\n'+base if base else '')+'\nCode:\n') |
|
|
| def save(path,value): |
| path.parent.mkdir(parents=True,exist_ok=True);temp=path.with_suffix('.tmp');temp.write_text(json.dumps(value,indent=2,allow_nan=False)+'\n');temp.replace(path) |
|
|
| def main(): |
| p=argparse.ArgumentParser(description=__doc__);p.add_argument('--model');p.add_argument('--revision',default='main');p.add_argument('--output',type=Path,required=True) |
| p.add_argument('--language',nargs='+',choices=LANGUAGES);p.add_argument('--difficulty',nargs='+',choices=('easy','medium','moderate')) |
| p.add_argument('--resume',action='store_true');p.add_argument('--predictions',type=Path,help='JSONL with id and code; bypass model inference') |
| p.add_argument('--image',default='smolscript-runtime:0.1.0');p.add_argument('--engine',default='docker');p.add_argument('--max-new-tokens',type=int,default=384);p.add_argument('--device',default='auto');a=p.parse_args() |
| if not a.model and not a.predictions:p.error('--model or --predictions required') |
| if a.max_new_tokens<1:p.error('--max-new-tokens must be positive') |
| sandbox=Sandbox(a.image,a.engine) |
| rows=[r for r in load(ROOT/'data/smolscript.jsonl') if (not a.language or r['language'] in a.language) and (not a.difficulty or r['difficulty'] in a.difficulty)] |
| hidden={r['id']:r for r in load(ROOT/'private_tests/hidden.jsonl')} |
| files=['data/smolscript.jsonl','private_tests/hidden.jsonl','evaluate.py','metrics.py']+[str(p.relative_to(ROOT)) for p in sorted((ROOT/'runners').glob('*.py'))]+['sandbox/worker.py'] |
| fingerprint={f:hashlib.sha256((ROOT/f).read_bytes()).hexdigest() for f in files} |
| settings={'model':a.model,'requested_revision':a.revision,'language':a.language,'difficulty':a.difficulty, |
| 'max_new_tokens':a.max_new_tokens,'prompt_version':PROMPT_VERSION,'dataset':fingerprint,'sandbox':sandbox.identity, |
| 'predictions_sha':hashlib.sha256(a.predictions.read_bytes()).hexdigest() if a.predictions else None} |
| if a.output.exists() and not a.resume:p.error('Output exists; use --resume or a new path') |
| state=json.loads(a.output.read_text()) if a.output.exists() else {'settings':settings,'results':[],'complete':False} |
| if state['settings']!=settings:p.error('Resume settings mismatch') |
| if a.resume and not a.output.exists():p.error('Resume output missing') |
| |
| import fcntl |
| lock=a.output.with_suffix(a.output.suffix+'.lock');lock.parent.mkdir(parents=True,exist_ok=True) |
| with lock.open('a') as stream: |
| fcntl.flock(stream,fcntl.LOCK_EX|fcntl.LOCK_NB) |
| codes={} |
| if a.predictions:codes={r['id']:r['code'] for r in load(a.predictions)} |
| else: |
| import torch |
| from transformers import AutoTokenizer,AutoModelForCausalLM,AutoConfig |
| from huggingface_hub import snapshot_download |
| snapshot=snapshot_download(a.model,revision=state.get('resolved_revision') or a.revision) |
| revision=Path(snapshot).name |
| state['resolved_revision']=revision |
| tokenizer=AutoTokenizer.from_pretrained(snapshot) |
| model=AutoModelForCausalLM.from_pretrained(snapshot,torch_dtype='auto',device_map=a.device) |
| model.eval();torch.manual_seed(0) |
| import importlib.metadata |
| state['environment']={'python':sys.version,'packages':{n:importlib.metadata.version(n) for n in ('torch','transformers','huggingface-hub')},'device':str(model.device)} |
| done={r['id'] for r in state['results']} |
| if len(done)!=len(state['results']) or done-set(r['id'] for r in rows):raise ValueError('Corrupt resume IDs') |
| save(a.output,state) |
| for row in rows: |
| if row['id'] in done:continue |
| if state.get('pending',{}).get('id')==row['id']: |
| text=state['pending']['generation'] |
| elif a.predictions: |
| if row['id'] not in codes:raise ValueError('Missing prediction '+row['id']) |
| text=codes[row['id']] |
| else: |
| encoded=tokenizer(prompt(row),return_tensors='pt',return_token_type_ids=False).to(model.device) |
| native=getattr(model.config,'max_position_embeddings',getattr(model.config,'n_positions',4096)) |
| if encoded['input_ids'].shape[1]+a.max_new_tokens>native: |
| raise ValueError('Insufficient context for fixed generation settings: '+row['id']) |
| with torch.inference_mode():out=model.generate(**encoded,max_new_tokens=a.max_new_tokens,do_sample=False,num_beams=1,pad_token_id=tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id) |
| text=tokenizer.decode(out[0,encoded['input_ids'].shape[1]:],skip_special_tokens=True) |
| state['pending']={'id':row['id'],'generation':text};save(a.output,state) |
| try:code=extract_code(text) |
| except ValueError as e:result={'passed':False,'syntax_success':False,'tests':[],'error':str(e)} |
| else:result=get_runner(row['language']).evaluate(row,code,hidden[row['id']],sandbox) |
| state['results'].append({'id':row['id'],'language':row['language'],'difficulty':row['difficulty'],'generation':text,**result}) |
| state.pop('pending',None) |
| save(a.output,state);print(f"{len(state['results'])}/{len(rows)} {row['id']} {'PASS' if result['passed'] else 'FAIL'}",flush=True) |
| state['metrics']=aggregate(state['results'],[r['id'] for r in rows],full_run=not(a.language or a.difficulty));state['complete']=True;save(a.output,state) |
| print(json.dumps(state['metrics'],indent=2)) |
|
|
| if __name__=='__main__':main() |
|
|
