wehub-resource-sync
168 lines
8.6 KiB
Python
168 lines
8.6 KiB
Python
"""
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PPO RLHF on GSM8K (the classic InstructGPT recipe), from scratch.
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Per iteration: roll out completions with the current policy, score them (verifiable GSM8K
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reward, or a trained reward model), add a per-token KL-to-reference penalty, compute GAE
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advantages with the shared value head, then run several clipped-surrogate update epochs.
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Reports mean reward, KL, value loss, clip fraction, and held-out GSM8K accuracy.
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PYTHONPATH=. python scripts/train_ppo.py --reward_source verifier
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PYTHONPATH=. torchrun --standalone --nproc_per_node=2 scripts/train_ppo.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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import torch.nn.functional as F
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from config.post_training_config import PPOConfig
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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 EOT_ID, 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.logging_utils import MetricsLogger
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from src.post_training.optim import configure_optimizer
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from src.post_training.ppo import compute_gae, whiten, ppo_policy_loss, ppo_value_loss, approx_kl
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from src.post_training.reward_model import load_reward_model
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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, masked_mean, save_stage_ckpt, set_seed, unwrap,
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)
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from src.post_training.value_head import TransformerWithValueHead
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def actor_logp_values(actor, seqs, temperature):
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"""One forward through the actor-critic -> (logp, values) in the action frame (B,T-1)."""
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logits, values = actor(seqs)
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logits = logits[:, :-1, :]
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logp_all = F.log_softmax(logits.float() / max(temperature, 1e-6), dim=-1)
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logp = logp_all.gather(-1, seqs[:, 1:, None]).squeeze(-1)
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return logp, values[:, :-1]
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def seq_lengths_from_mask(response_mask, prompt_lens):
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"""Real token count per row = last response position + 1 (or prompt_len if no response)."""
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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(PPOConfig, "configs/ppo.json")
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ctx = ddp_setup(cfg.device)
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set_seed(cfg.seed + ctx.rank)
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backbone = load_backbone_from_ckpt(cfg, cfg.sft_ckpt, ctx.device)
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ref = make_frozen_copy(backbone, device=ctx.device)
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actor = TransformerWithValueHead(backbone).to(ctx.device)
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actor_ddp = ddp_wrap(actor, ctx)
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optimizer = configure_optimizer(unwrap(actor_ddp), cfg.lr, weight_decay=0.0)
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rm = load_reward_model(cfg, cfg.reward_ckpt, ctx.device) if cfg.reward_source == "rm" else None
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eval_set = None # loaded lazily on first eval to keep startup/smoke runs offline
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logger = MetricsLogger("ppo", 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"PPO from {cfg.sft_ckpt} | reward={cfg.reward_source} | world={ctx.world_size}")
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prompt_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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for it in range(cfg.iterations):
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rows = next(prompt_it)
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prompts = [encode_prompt([{"role": "user", "content": r["prompt"]}]) for r in rows]
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golds = [r.get("gold") for r in rows]
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# --- rollout ---
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actor.eval()
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with amp_autocast(cfg.amp_dtype, ctx.device):
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seqs, rmask, plens = rollout_prompts(actor, 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:] # action-frame response mask (N, T-1)
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# --- score ---
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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(rows))]
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if cfg.reward_source == "rm":
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with torch.no_grad(), amp_autocast(cfg.amp_dtype, ctx.device):
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task_r = rm(seqs, seq_lengths=seq_lens).float().tolist()
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else:
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task_r = [reward_gsm8k(responses[i], golds[i]) for i in range(len(rows))]
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# --- per-token rewards: KL penalty everywhere + task reward at last response token ---
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with torch.no_grad(), amp_autocast(cfg.amp_dtype, ctx.device):
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old_logp, old_values = actor_logp_values(actor, seqs, cfg.temperature)
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ref_logp, _ = compute_logprobs(ref, seqs, rmask, temperature=cfg.temperature, requires_grad=False)
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old_logp, old_values, ref_logp = old_logp.float(), old_values.float(), ref_logp.float()
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rewards = -cfg.kl_coef * (old_logp - ref_logp) * resp.float()
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last_idx = seq_lens - 2 # action index of the last response token
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last_idx = last_idx.clamp(min=0)
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task_t = torch.tensor(task_r, device=ctx.device, dtype=torch.float32)
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rewards[torch.arange(len(rows), device=ctx.device), last_idx] += task_t
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values_next = torch.cat([old_values[:, 1:], torch.zeros_like(old_values[:, :1])], dim=1)
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adv, returns = compute_gae(rewards, old_values, values_next, resp, gamma=cfg.gamma, lam=cfg.gae_lambda)
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adv = whiten(adv, resp)
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# --- clipped PPO update epochs over minibatches of rollout rows ---
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actor.train()
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N = seqs.size(0)
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stats = {"policy_loss": 0.0, "value_loss": 0.0, "clipfrac": 0.0, "kl": 0.0, "n": 0}
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for _ in range(cfg.ppo_epochs):
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perm = torch.randperm(N, device=ctx.device)
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for s in range(0, N, cfg.minibatch_size):
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mb = perm[s:s + cfg.minibatch_size]
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with amp_autocast(cfg.amp_dtype, ctx.device):
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new_logp, new_values = actor_logp_values(actor_ddp, seqs[mb], cfg.temperature)
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m = resp[mb]
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p_loss, clipf = ppo_policy_loss(new_logp.float(), old_logp[mb], adv[mb], m, clip=cfg.clip)
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v_loss = ppo_value_loss(new_values.float(), old_values[mb], returns[mb], m, vf_clip=cfg.vf_clip)
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loss = p_loss + cfg.vf_coef * v_loss
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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_(actor_ddp.parameters(), cfg.grad_clip)
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optimizer.step()
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stats["policy_loss"] += p_loss.item(); stats["value_loss"] += v_loss.item()
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stats["clipfrac"] += clipf.item()
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stats["kl"] += approx_kl(new_logp.float(), old_logp[mb], m).item(); stats["n"] += 1
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mean_reward = reduce_scalar(float(sum(task_r) / max(1, len(task_r))), ctx)
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kl_ref = reduce_scalar(masked_mean(old_logp - ref_logp, resp).item(), ctx)
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resp_len = reduce_scalar(resp.float().sum(1).mean().item(), ctx)
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n = max(1, stats["n"])
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if ctx.is_main and it % 5 == 0:
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print(f"iter {it} | reward {mean_reward:.3f} | KL_ref {kl_ref:.3f} | "
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f"ploss {stats['policy_loss']/n:.4f} | vloss {stats['value_loss']/n:.4f} | "
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f"clipfrac {stats['clipfrac']/n:.3f} | resp_len {resp_len:.0f}")
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if logger:
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logger.log(it, {"reward": mean_reward, "kl_ref": kl_ref, "policy_loss": stats["policy_loss"]/n,
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"value_loss": stats["value_loss"]/n, "clipfrac": stats["clipfrac"]/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(actor_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(actor_ddp).transformer, optimizer, stage="ppo", 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(actor_ddp).transformer, optimizer, stage="ppo", cfg=cfg, step=cfg.iterations, metrics={})
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print(f"Done PPO -> {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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