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GRPOTrainer — Flow-GRPO fine-tuning of MoFlow + SRA on NBA.
Subclasses the SFT `Trainer` to reuse its eval / metric / checkpoint machinery
(`eval_dataloader`, `compute_*`, `sample_from_denoising_model`, `save_ckpt`),
and replaces `train()` with the GRPO loop:
rollout (stochastic SDE sampler, no_grad)
-> combined reward (accuracy + coherence)
-> group-relative advantage over the K modes
-> E inner PPO-clip epochs (recompute log-probs w/ grad) + KL to frozen ref
-> opt.step, EMA update
The model is kept in eval() mode throughout so the SRA backbone builds its pass-1
graph from y_0_prev (self-conditioning), never from GT (teacher forcing).
Evaluation uses the inherited *deterministic* sampler (sde_noise -> 0).
"""
import torch
from einops import rearrange
from trainer.denoising_model_trainers import Trainer
from utils.normalization import unnormalize_min_max, unnormalize_sqrt
from grpo.rewards import compute_reward_agentwise, group_advantage
class GRPOTrainer(Trainer):
def __init__(self, cfg, denoiser, ref_model, train_loader, test_loader,
logger=None, tb_log=None, grpo=None):
super().__init__(cfg, denoiser, train_loader, test_loader,
logger=logger, tb_log=tb_log)
g = grpo or {}
self.grpo_iters = int(g.get('iters', 2000))
self.rl_steps = g.get('rl_steps', None) # None -> cfg.sampling_steps
self.sde_noise = float(g.get('sde_noise', 0.1))
self.eta_min = float(g.get('eta_min', 1e-3))
self.inner_epochs = int(g.get('inner_epochs', 2))
self.clip_eps = float(g.get('clip_eps', 0.2))
self.kl_beta = float(g.get('kl_beta', 0.1))
self.logratio_clip = float(g.get('logratio_clip', 10.0))
self.eval_every = int(g.get('eval_every', 100))
self.adv_eps = float(g.get('adv_eps', 1e-4))
self.reward_kwargs = dict(
w_ade=float(g.get('w_ade', 1.0)),
w_fde=float(g.get('w_fde', 1.0)),
w_jade=float(g.get('w_jade', 0.5)),
w_jfde=float(g.get('w_jfde', 0.5)),
w_col=float(g.get('w_col', 1.0)),
w_kin=float(g.get('w_kin', 0.0)),
d_min=float(g.get('d_min', 0.4)),
a_max=float(g.get('a_max', 1.0)),
ball_idx=g.get('ball_idx', None),
)
# eval/checkpoint config
self.best_score = float('inf') # best ADE+FDE+JADE+JFDE (accuracy runs)
self.best_coll = float('inf')
self.ade_tol = float(g.get('ade_tol', 0.71)) # save best only if ADE_min(4s) <= this
self.coll_thresholds = (0.2, 0.3, 0.4)
self.coll_ball_idx = g.get('ball_idx', 10)
self.coll_eval_batches = int(g.get('coll_eval_batches', 5))
# frozen reference policy (KL anchor)
self.ref_model = ref_model.to(self.device)
self.ref_model.eval()
for p in self.ref_model.parameters():
p.requires_grad_(False)
self.model_grpo = self.accelerator.unwrap_model(self.denoiser)
# ------------------------------------------------------------------
def _pred_to_metric(self, pred_norm):
"""[B,K,A,Do] normalized -> [B,K,A,T,2] metric scale."""
B, K, A, _ = pred_norm.shape
pred = pred_norm.view(B, K, A, self.cfg.future_frames, 2)
if self.cfg.get('data_norm', None) == 'min_max':
return unnormalize_min_max(pred, self.cfg.fut_traj_min, self.cfg.fut_traj_max, -1, 1)
elif self.cfg.get('data_norm', None) == 'sqrt':
return unnormalize_sqrt(pred, self.sqrt_a_, self.sqrt_b_)
return pred
# ------------------------------------------------------------------
@torch.no_grad()
def eval_collision(self):
"""Deterministic player-player collision rate on the val set (a subset of
batches for speed). Returns {thresh: rate over all (scene,mode)}."""
self.denoiser.eval()
A, T = self.cfg.agents, self.cfg.future_frames
pm = ~torch.eye(A, dtype=torch.bool, device=self.device)
if self.coll_ball_idx is not None:
pm[self.coll_ball_idx, :] = False
pm[:, self.coll_ball_idx] = False
counts = {th: [0.0, 0.0] for th in self.coll_thresholds}
for i, data in enumerate(self.val_loader):
if i >= self.coll_eval_batches:
break
data = {k: v.to(self.device) for k, v in data.items()}
B = int(data['batch_size'])
pred, _, _, _, _ = self.sample_from_denoising_model(data) # [B*A,K,T,2] metric
K = pred.shape[1]
pred = pred.reshape(B, A, K, T, 2).permute(0, 2, 1, 3, 4) # [B,K,A,T,2]
init = data['past_traj_original_scale'][:, :, -1, 0:2]
abs_pred = pred + init[:, None, :, None, :]
d = (abs_pred.unsqueeze(3) - abs_pred.unsqueeze(2)).norm(dim=-1) # [B,K,A,A,T]
cp = d.min(dim=-1).values.masked_fill(~pm, 1e9).reshape(B, K, -1).min(-1).values # [B,K]
for th in self.coll_thresholds:
counts[th][0] += (cp < th).float().sum().item()
counts[th][1] += cp.numel()
return {th: counts[th][0] / max(counts[th][1], 1) for th in self.coll_thresholds}
def train(self):
self.logger.info('GRPO training start')
model, ref = self.model_grpo, self.ref_model
model.eval()
G = self.cfg.denoising_head_preds
for it in range(self.grpo_iters):
self.step = it
data = {k: v.to(self.device) for k, v in next(self.dl).items()}
# ---------------- rollout ----------------
with torch.no_grad():
roll = model.rollout(data, num_trajs=G, sde_noise=self.sde_noise,
eta_min=self.eta_min, sampling_steps=self.rl_steps)
# ---------------- reward + advantage (per-agent) ----------------
pred_metric = self._pred_to_metric(roll['final_action']) # [B,K,A,T,2]
gt_metric = data['fut_traj_original_scale'] # [B,A,T,2]
init_pos = data['past_traj_original_scale'][:, :, -1, 0:2] # [B,A,2]
reward, info = compute_reward_agentwise(pred_metric, gt_metric, init_pos, **self.reward_kwargs)
adv = group_advantage(reward, eps=self.adv_eps) # [B,K,A]
# ---------------- PPO-clip update ----------------
num_steps = len(roll['steps'])
last_stats = {}
for _ in range(self.inner_epochs):
self.opt.zero_grad()
pg_sum = kl_sum = ratio_sum = clipfrac_sum = 0.0
for step in roll['steps']:
logp_new, kl = model.recompute_logp_kl(
step, data, ref, self.sde_noise, self.eta_min)
logratio = (logp_new - step['logp_old']).clamp(
-self.logratio_clip, self.logratio_clip)
ratio = logratio.exp()
unclipped = ratio * adv
clipped = ratio.clamp(1 - self.clip_eps, 1 + self.clip_eps) * adv
pg = -torch.min(unclipped, clipped).mean()
kl_loss = kl.mean()
loss = (pg + self.kl_beta * kl_loss) / num_steps
self.accelerator.backward(loss)
pg_sum += pg.item() / num_steps
kl_sum += kl_loss.item() / num_steps
ratio_sum += ratio.mean().item() / num_steps
clipfrac_sum += ((ratio - 1.0).abs() > self.clip_eps).float().mean().item() / num_steps
self.accelerator.clip_grad_norm_(
self.denoiser.parameters(), self.cfg.OPTIMIZATION.GRAD_NORM_CLIP)
self.opt.step()
if self.accelerator.is_main_process:
self.ema.update()
last_stats = dict(pg=pg_sum, kl=kl_sum, ratio=ratio_sum, clipfrac=clipfrac_sum)
# ---------------- logging ----------------
if it % 10 == 0:
self.logger.info(
f'[GRPO {it}/{self.grpo_iters}] R={reward.mean().item():.4f} '
f'ADE*={info["ade_bestk"].item():.4f} JADE*={info["jade_bestk"].item():.4f} '
f'coll={info["collision"].mean().item():.4f} collrate={info["collision_rate"].item():.3f} '
f'| pg={last_stats["pg"]:.4f} kl={last_stats["kl"]:.5f} '
f'ratio={last_stats["ratio"]:.3f} clipfrac={last_stats["clipfrac"]:.3f}')
if self.tb_log is not None:
self.tb_log.add_scalar('grpo/reward', reward.mean().item(), it)
self.tb_log.add_scalar('grpo/ade_bestk', info['ade_bestk'].item(), it)
self.tb_log.add_scalar('grpo/jade_bestk', info['jade_bestk'].item(), it)
self.tb_log.add_scalar('grpo/collision', info['collision'].mean().item(), it)
self.tb_log.add_scalar('grpo/collision_rate', info['collision_rate'].item(), it)
self.tb_log.add_scalar('grpo/pg_loss', last_stats['pg'], it)
self.tb_log.add_scalar('grpo/kl', last_stats['kl'], it)
self.tb_log.add_scalar('grpo/ratio', last_stats['ratio'], it)
self.tb_log.add_scalar('grpo/clipfrac', last_stats['clipfrac'], it)
self.tb_log.add_scalar('grpo/lr', self.opt.param_groups[0]['lr'], it)
# ---------------- periodic eval (deterministic sampler) ----------------
if (it + 1) % self.eval_every == 0 and self.accelerator.is_main_process:
_, perf, n = self.eval_dataloader(testing_mode=False)
pj = self.last_perf_joint
ade4 = perf['ADE_min'][3] / n
fde4 = perf['FDE_min'][3] / n
jade4 = pj['JADE_min'][3] / n
jfde4 = pj['JFDE_min'][3] / n
score = ade4 + fde4 + jade4 + jfde4 # combined accuracy (lower=better)
self.logger.info(
f'[GRPO eval @ {it}] ADE={ade4:.5f} FDE={fde4:.5f} '
f'JADE={jade4:.5f} JFDE={jfde4:.5f} | sum={score:.5f} (best={self.best_score:.5f})')
if self.tb_log is not None:
self.tb_log.add_scalar('eval/ADE', ade4, it)
self.tb_log.add_scalar('eval/FDE', fde4, it)
self.tb_log.add_scalar('eval/JADE', jade4, it)
self.tb_log.add_scalar('eval/JFDE', jfde4, it)
if score < self.best_score:
self.best_score = score
self.logger.info(f' new best sum={score:.5f} '
f'(ADE={ade4:.5f} JADE={jade4:.5f}) -> checkpoint_best')
self.save_ckpt('checkpoint_best')
self.save_ckpt('checkpoint_last')
model.eval()
self.save_ckpt('checkpoint_last')
self.logger.info('GRPO training complete')
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