Datasets:
Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
reinforcement-learning
policy-optimization
klpo
exact-moment-replay
score-centering
stratified-sampling
License:
| """Optional single-process Transformers reference; live Hub/GPU run not included. | |
| Install .[hf]. Provide an explicit model identifier and immutable revision. | |
| No remote repository Python code, shell tools, or model-written rewards run. | |
| """ | |
| import argparse | |
| import json | |
| from pathlib import Path | |
| import torch | |
| from celestis_rl.hf_reference import collect_language_rollout, language_rollout_loss | |
| def main(): | |
| p=argparse.ArgumentParser(description=__doc__) | |
| p.add_argument("--model",required=True);p.add_argument("--revision",required=True) | |
| p.add_argument("--data",default="data/exact_match_prompts.jsonl") | |
| p.add_argument("--output",default="runs/hf_reference") | |
| p.add_argument("--steps",type=int,default=10);p.add_argument("--replay-epochs",type=int,default=2) | |
| p.add_argument("--batch-size",type=int,default=2);p.add_argument("--horizon",type=int,default=16) | |
| p.add_argument("--budget",type=int,default=32);p.add_argument("--temperature",type=float,default=1.) | |
| p.add_argument("--learning-rate",type=float,default=1e-6);p.add_argument("--beta",type=float,default=.1) | |
| p.add_argument("--device",default="cpu");p.add_argument("--seed",type=int,default=7) | |
| p.add_argument("--local-files-only",action="store_true") | |
| args=p.parse_args() | |
| for k in ("steps","replay_epochs","batch_size","horizon","budget"): | |
| if getattr(args,k)<1: p.error(f"{k} must be positive") | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| torch.manual_seed(args.seed) | |
| tokenizer=AutoTokenizer.from_pretrained(args.model,revision=args.revision,trust_remote_code=False, | |
| local_files_only=args.local_files_only) | |
| model=AutoModelForCausalLM.from_pretrained(args.model,revision=args.revision,trust_remote_code=False, | |
| use_safetensors=True,local_files_only=args.local_files_only).to(args.device).eval() | |
| model.config.use_cache=False | |
| optimizer=torch.optim.AdamW(model.parameters(),lr=args.learning_rate) | |
| policy_rng=torch.Generator(device=args.device).manual_seed(args.seed+10_001) | |
| auxiliary_rng=torch.Generator(device=args.device).manual_seed(args.seed+20_003) | |
| examples=[json.loads(line) for line in Path(args.data).read_text(encoding="utf-8").splitlines() if line.strip()] | |
| if not examples or any(not isinstance(e.get("prompt"),str) or not isinstance(e.get("answer"),str) for e in examples): | |
| raise ValueError("JSONL requires prompt and answer strings") | |
| out=Path(args.output);out.mkdir(parents=True,exist_ok=True) | |
| (out/"run_config.json").write_text(json.dumps(vars(args),indent=2)+"\n",encoding="utf-8") | |
| # Full model/Adam memory can be large. No hidden LoRA, quantization or mixed precision. | |
| with (out/"metrics.jsonl").open("w",encoding="utf-8") as log: | |
| for step in range(args.steps): | |
| batch=[] | |
| for j in range(args.batch_size): | |
| item=examples[(step*args.batch_size+j)%len(examples)] | |
| prompt=tokenizer(item["prompt"],return_tensors="pt").input_ids[0] | |
| answer=item["answer"].strip() | |
| def reward_fn(ids,answer=answer): | |
| return float(tokenizer.decode(ids,skip_special_tokens=True).strip()==answer) | |
| batch.append(collect_language_rollout(model,prompt,horizon=args.horizon, | |
| eos_token_id=tokenizer.eos_token_id,temperature=args.temperature,generator=policy_rng, | |
| reward_fn=reward_fn,sampler_version=f"step-{step}",device=args.device)) | |
| for epoch in range(args.replay_epochs): | |
| optimizer.zero_grad(set_to_none=True);loss_sum=0. | |
| for record in batch: | |
| loss,_=language_rollout_loss(model,record,beta=args.beta,budget=args.budget, | |
| generator=auxiliary_rng,device=args.device) | |
| (loss/len(batch)).backward();loss_sum+=float(loss.detach())/len(batch) | |
| if any(p.grad is not None and not torch.isfinite(p.grad).all() for p in model.parameters()): | |
| raise FloatingPointError("nonfinite gradient: update aborted") | |
| optimizer.step() | |
| row={"step":step,"epoch":epoch,"surrogate":loss_sum, | |
| "rollout_reward_mean":sum(r.reward for r in batch)/len(batch), | |
| "terminations":[r.termination for r in batch],"q_bytes":sum(r.q.numel()*8 for r in batch)} | |
| log.write(json.dumps(row,allow_nan=False)+"\n");log.flush();print(json.dumps(row)) | |
| model.save_pretrained(out/"model",safe_serialization=True) | |
| tokenizer.save_pretrained(out/"model") | |
| print("Saved model and metrics. This training run alone does not establish superiority.") | |
| if __name__=="__main__":main() | |