wehub-resource-sync
140 lines
7.0 KiB
Python
140 lines
7.0 KiB
Python
"""
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GRPO / RLVR on GSM8K (DeepSeek-R1 style), from scratch -- no critic, group-relative
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advantages, verifiable reward.
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Per iteration: for each prompt sample a group of G completions, score each with the GSM8K
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verifier, compute group-relative advantages, and update with a token-level clipped
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surrogate + KL-to-reference penalty. An arithmetic warm-up curriculum runs first so the
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policy gets non-zero reward variance before facing full GSM8K.
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PYTHONPATH=. python scripts/train_grpo.py
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PYTHONPATH=. torchrun --standalone --nproc_per_node=2 scripts/train_grpo.py
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"""
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from __future__ import annotations
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import time
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import torch
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from config.post_training_config import GRPOConfig
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from data_loader.prompt_dataset import get_prompt_iterator
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from src.post_training.cli import parse_config_with_json
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from src.post_training.chat_template import decode, encode_prompt
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from src.post_training.distributed import ddp_setup, ddp_wrap, cleanup, reduce_scalar
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from src.post_training.evaluation import gsm8k_accuracy, load_gsm8k_eval
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from src.post_training.grpo import group_advantages, grpo_loss
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from src.post_training.logging_utils import MetricsLogger
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from src.post_training.optim import configure_optimizer
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from src.post_training.rewards import reward_gsm8k
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from src.post_training.rollout import compute_logprobs, rollout_prompts
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from src.post_training.utils import (
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amp_autocast, load_backbone_from_ckpt, make_frozen_copy, save_stage_ckpt, set_seed, unwrap,
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)
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def seq_lengths_from_mask(response_mask, prompt_lens):
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N, T = response_mask.shape
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pos = torch.arange(T, device=response_mask.device)
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last = torch.where(response_mask, pos[None, :], torch.full_like(response_mask, -1, dtype=torch.long)).max(dim=1).values
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return torch.where(last >= 0, last + 1, prompt_lens)
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def main():
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cfg, _ = parse_config_with_json(GRPOConfig, "configs/grpo.json")
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ctx = ddp_setup(cfg.device)
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set_seed(cfg.seed + ctx.rank)
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policy = load_backbone_from_ckpt(cfg, cfg.sft_ckpt, ctx.device)
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ref = make_frozen_copy(policy, device=ctx.device)
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policy_ddp = ddp_wrap(policy, ctx)
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optimizer = configure_optimizer(unwrap(policy_ddp), cfg.lr, weight_decay=0.0)
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eval_set = None
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logger = MetricsLogger("grpo", cfg.log_dir, use_wandb=cfg.use_wandb, wandb_project=cfg.wandb_project) if ctx.is_main else None
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if ctx.is_main:
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print(f"GRPO from {cfg.sft_ckpt} | group_size={cfg.group_size} | world={ctx.world_size}")
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warm_it = get_prompt_iterator(cfg.curriculum_path, cfg.prompts_per_iter, rank=ctx.rank,
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world_size=ctx.world_size, seed=cfg.seed)
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main_it = get_prompt_iterator(cfg.prompt_path, cfg.prompts_per_iter, rank=ctx.rank,
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world_size=ctx.world_size, seed=cfg.seed)
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G = cfg.group_size
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for it in range(cfg.iterations):
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rows = next(warm_it if it < cfg.curriculum_iters else main_it)
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# Replicate each prompt G times, group-contiguously.
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base_prompts = [encode_prompt([{"role": "user", "content": r["prompt"]}]) for r in rows]
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prompts = [p for p in base_prompts for _ in range(G)]
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golds = [r.get("gold") for r in rows for _ in range(G)]
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policy.eval()
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with amp_autocast(cfg.amp_dtype, ctx.device):
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seqs, rmask, plens = rollout_prompts(policy, prompts, cfg.rollout_len, device=ctx.device,
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temperature=cfg.temperature, top_p=cfg.top_p if cfg.top_p < 1 else None)
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resp = rmask[:, 1:]
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seq_lens = seq_lengths_from_mask(rmask, plens)
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responses = [decode(seqs[i, plens[i]:seq_lens[i]].tolist()) for i in range(len(prompts))]
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rewards = torch.tensor([reward_gsm8k(responses[i], golds[i]) for i in range(len(prompts))],
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device=ctx.device, dtype=torch.float32)
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adv = group_advantages(rewards, G)
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with torch.no_grad(), amp_autocast(cfg.amp_dtype, ctx.device):
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old_logp, _ = compute_logprobs(policy, seqs, rmask, temperature=cfg.temperature, requires_grad=False)
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ref_logp, _ = compute_logprobs(ref, seqs, rmask, temperature=cfg.temperature, requires_grad=False)
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old_logp, ref_logp = old_logp.float(), ref_logp.float()
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policy.train()
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N = seqs.size(0)
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agg = {"loss": 0.0, "kl": 0.0, "clipfrac": 0.0, "n": 0}
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for _ in range(cfg.grpo_epochs):
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perm = torch.randperm(N, device=ctx.device)
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for s in range(0, N, max(1, G)): # minibatch ~ one group's worth
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mb = perm[s:s + max(1, G)]
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with amp_autocast(cfg.amp_dtype, ctx.device):
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new_logp, _ = compute_logprobs(policy_ddp, seqs[mb], rmask[mb], temperature=cfg.temperature, requires_grad=True)
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loss, st = grpo_loss(new_logp.float(), old_logp[mb], ref_logp[mb], adv[mb], resp[mb],
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clip=cfg.clip, kl_coef=cfg.kl_coef)
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optimizer.zero_grad(set_to_none=True)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(policy_ddp.parameters(), cfg.grad_clip)
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optimizer.step()
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agg["loss"] += loss.item(); agg["kl"] += st["kl"]; agg["clipfrac"] += st["clipfrac"]; agg["n"] += 1
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mean_reward = reduce_scalar(rewards.mean().item(), ctx)
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# Fraction of groups with non-zero reward spread (informative groups).
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grp_std = rewards.view(-1, G).std(dim=1)
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informative = reduce_scalar((grp_std > 1e-6).float().mean().item(), ctx)
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resp_len = reduce_scalar(resp.float().sum(1).mean().item(), ctx)
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n = max(1, agg["n"])
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if ctx.is_main and it % 5 == 0:
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phase = "warmup" if it < cfg.curriculum_iters else "gsm8k"
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print(f"iter {it}[{phase}] | reward {mean_reward:.3f} | informative {informative:.2f} | "
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f"loss {agg['loss']/n:.4f} | KL {agg['kl']/n:.4f} | clipfrac {agg['clipfrac']/n:.3f} | resp_len {resp_len:.0f}")
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if logger:
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logger.log(it, {"reward": mean_reward, "informative_groups": informative,
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"loss": agg["loss"]/n, "kl": agg["kl"]/n, "resp_len": resp_len})
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if ctx.is_main and it > 0 and it % cfg.eval_every == 0:
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if eval_set is None:
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eval_set = load_gsm8k_eval("test", limit=200)
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res = gsm8k_accuracy(unwrap(policy_ddp), eval_set, device=ctx.device, max_new_tokens=cfg.rollout_len)
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print(f" [eval] iter {it} | GSM8K test acc {res['accuracy']:.3f} ({res['correct']}/{res['n']})")
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if logger:
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logger.log(it, {"gsm8k_acc": res["accuracy"]})
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if ctx.is_main and it > 0 and it % cfg.save_every == 0:
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save_stage_ckpt(cfg.out_ckpt, unwrap(policy_ddp), optimizer, stage="grpo", cfg=cfg, step=it,
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metrics={"reward": mean_reward})
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if ctx.is_main:
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save_stage_ckpt(cfg.out_ckpt, unwrap(policy_ddp), optimizer, stage="grpo", cfg=cfg, step=cfg.iterations, metrics={})
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print(f"Done GRPO -> {cfg.out_ckpt}")
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if logger:
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logger.close()
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cleanup(ctx)
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if __name__ == "__main__":
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main()
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