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e534973 | 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 | import argparse, copy, sys, time
import numpy as np, torch
sys.path.insert(0, "sim")
torch.set_num_threads(2)
from episode import load_config
from rl_env import VecChessEnv
from rl_policy import FlowRL
from rl_train import run_episodes, gae, ppo_update, summarize
if __name__ == "__main__":
cfg = load_config()
pol = FlowRL("models/baseline", cfg["dataset"]["instruction"], 0.2, 0.002)
venv = VecChessEnv(10, cfg)
t0 = time.time()
eps = run_episodes(pol, venv, 7_700_003, list(range(20)), noise=True, early_stop=True, check_expert=False, record=True)
print(f"20 episodes on 10 envs: {time.time()-t0:.1f}s, parts {dict((k, round(v, 1)) for k, v in run_episodes.timing.items())}", flush=True)
venv.close()
gae(eps, 0.99, 0.95)
dec = [d for e in eps for d in e["decisions"]]
for d in dec:
d["adv"] = float(np.random.randn())
start = {n: p.detach().clone() for n, p in pol.model.named_parameters() if p.requires_grad}
for lr in (3e-7, 1e-6):
with torch.no_grad():
for n, p in pol.model.named_parameters():
if n in start:
p.copy_(start[n])
opt = torch.optim.AdamW([dict(params=pol.policy_params(), lr=lr), dict(params=list(pol.critic.parameters()), lr=3e-4)])
ns = argparse.Namespace(epochs=1, minibatch=16, clip=0.2, vf_coef=0.5, max_grad_norm=1.0, target_kl=1e9)
t0 = time.time()
out = ppo_update(pol, opt, dec, ns, policy_on=True)
with torch.no_grad():
new, _ = pol.evaluate(None, torch.stack([d["chain"] for d in dec[:32]]).to(pol.device),
prefix=pol.stack_prefix([d["prefix"] for d in dec[:32]]))
old = torch.stack([d["logp"] for d in dec[:32]]).to(pol.device)
lr_ = (new - old)
kl_el = ((lr_.exp() - 1) - lr_).mean().item()
per_step = lr_.abs().mean(dim=(0, 2, 3)).cpu().numpy().round(3)
print(f"lr {lr}: {len(dec)} decisions, one epoch in {time.time()-t0:.1f}s; during: {out}; after the epoch: per-element KL {kl_el:.4f}, "
f"mean |log-ratio| per denoising step {per_step}", flush=True)
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