cbb
c2732fb3cd
- train.py：evaluate 改 bs=64，结束后 empty_cache()。WDDM 下 15.9 GiB 的评估峰值用完不退， 整卡顶到 96% 后驱动把训练张量换出（实测 Local 18.8 GB / Shared 1.9 GB），吞吐从 5.5k 掉到 444 samples/s、8000 步 eval 卡死 68 分钟；改后峰值 5.32 GiB、eval 106 s、吞吐 6.4–7.0k。 - pstate/pipeline：新增 --fresh-from=<阶段>：只把该阶段及之后的产物移进 ml/archive/（不删）， 与参数失效取并集；配合 FROM 实现"只重训某一阶段，之后全部重做"（沙盒实测选 train 只归档 ml/runs/base）。 - monitor：运行方式加「从指定阶段重训」+ 起始阶段下拉；TARGET_SAMPLES 默认改 200 万； 「最好 X%」改标最好那次 eval 的步数（原来错标当前步，18,000 步显示成 0.2% 的出处）。 连同工作区已有的停止补扫 sweep_nt（按命令行补杀 git-bash 下逃逸的孙进程）一并提交。 - eval/diag_tail.py：闭环末位误差 / 是否进过 5 mm 窗口的逐题诊断（手动运行，不进流水线）。
140 lines
8.4 KiB
Python
140 lines
8.4 KiB
Python
"""预训练（方案 §6）：MeanFlow 一步头 + FK 损失 + 平滑 + 价值头 + 子目标头。
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python -m ml.train --samples ml/data/samples --out ml/runs/base [--config ml/configs/base.json]
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吞吐摸底：python -m ml.train --samples ml/data/samples --out ml/runs/bench_base --steps 2000 --no-eval
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通过线（方案 R10）：全程 60000 步 ≤ 3.5 h，即 samples_per_s ≥ 5000（3090 实测 6.2 k/s ≈ 2.8 h）；日志 eta_h 为剩余小时数。
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每 eval_every 步做一次闭环验证（ml/sim/closed_loop.py，部署节奏），按 success 存 best.pt；连续 patience_evals 次不升即停。"""
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import argparse, json, math, os, sys, time
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import torch
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from ml.common.kin_torch import Kin
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from ml.common.features import STEP, D2R
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from ml.common.ckpt import Saver, atomic_save, rng_state, set_rng_state
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from ml.common import vram
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from ml.data import Samples, load_tasks
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from ml.model.policy import build, wta_loss
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from ml.model.meanflow import meanflow_loss, one_step
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from ml.sim.closed_loop import evaluate
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def fk_points(kin, q, idx):
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"""选定胶囊端点 + TCP 的世界坐标 [B, 2*len(idx)+1, 3]"""
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xf = kin.cum_xf(q)
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a, b = kin.caps_world(q, xf)
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return torch.cat([a[:, idx], b[:, idx], kin.fk(q, xf)[:, None]], 1)
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def q_from_feat(X):
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s, c = X[:, 0:12:2], X[:, 1:12:2]
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return torch.atan2(s, c) / D2R
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument('--samples', nargs='+', default=['ml/data/samples']); ap.add_argument('--out', default='ml/runs/base')
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ap.add_argument('--config', default='ml/configs/base.json'); ap.add_argument('--steps', type=int, default=None)
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ap.add_argument('--resume', default=None); ap.add_argument('--no-eval', action='store_true')
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ap.add_argument('--stop-at', type=int, default=0, help='自检用：跑到第 N 步按停止请求处理（存 last.pt、rc 143）')
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ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu'); ap.add_argument('--seed', type=int, default=0)
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a = ap.parse_args()
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cfg = json.load(open(a.config)); tc = cfg['train']
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if a.steps: tc['steps'] = a.steps
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os.makedirs(a.out, exist_ok=True)
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torch.manual_seed(a.seed)
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dev = a.device
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vram.limit()
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kin = Kin(device=dev)
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data = Samples(a.samples, device=dev, split='train')
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val_tasks = [t for t in load_tasks(a.samples) if t['split'] == 'val'][:tc['eval_tasks']]
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print(f'train samples {data.n}, val tasks {len(val_tasks)}, device {dev}', flush=True)
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model = build(cfg).to(dev)
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print(f'params {sum(p.numel() for p in model.parameters()) / 1e6:.2f} M', flush=True)
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opt = torch.optim.AdamW(model.parameters(), lr=tc['lr'], betas=tuple(tc['betas']), weight_decay=tc['wd'])
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step0, best, bad_evals = 0, -1.0, 0
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if a.resume:
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ck = torch.load(a.resume, map_location=dev); model.load_state_dict(ck['model'])
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if 'opt' in ck: opt.load_state_dict(ck['opt'])
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step0, best, bad_evals = ck.get('step', 0), ck.get('best', -1.0), ck.get('bad_evals', 0)
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if set_rng_state(ck.get('rng')): print('续训：随机数状态已恢复（batch 顺序与不断点一致）', flush=True)
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print(f'续训：从第 {step0} 步继续（共 {tc["steps"]} 步），当前最好 success {best:.4f}', flush=True)
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total = tc['steps']
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lr_at = lambda s: tc['lr'] * (s / tc['warmup'] if s < tc['warmup'] else 0.5 * (1 + math.cos(math.pi * (s - tc['warmup']) / max(1, total - tc['warmup']))))
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amp = dev.startswith('cuda') and tc.get('amp') == 'bf16'
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stepv = torch.tensor(STEP, device=dev)
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fk_idx = torch.linspace(0, kin.cap_a.shape[0] - 1, tc['fk_caps'], device=dev).long()
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log = open(os.path.join(a.out, 'log.jsonl'), 'a' if a.resume else 'w') # 新训练覆盖旧日志，续训才追加
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if not val_tasks and not a.no_eval:
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print('没有 val 任务（样本太少），本次跳过闭环验证', flush=True); a.no_eval = True
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t0, seen, diverged = time.time(), 0, False
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saver = Saver(tc.get('ckpt_min', 10)) # 2026-10-18：每 10 分钟 + 收到停止请求时存 last.pt（原子写）
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def save(name, step, extra=None):
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d = {'model': model.state_dict(), 'opt': opt.state_dict(), 'cfg': cfg, 'step': step, 'best': best, 'bad_evals': bad_evals, 'fp': kin.fp}
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if name == 'last.pt': d['rng'] = rng_state()
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if extra: d.update(extra)
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atomic_save(d, os.path.join(a.out, name))
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if name == 'last.pt': saver.mark()
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step = step0 - 1 # 续训时已训满：循环一次不跑，下面照常收尾
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for step in range(step0, total):
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for gpar in opt.param_groups: gpar['lr'] = lr_at(step)
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X, Y, V, SG = data.batch(tc['batch'])
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model.train()
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with torch.autocast('cuda', dtype=torch.bfloat16, enabled=amp):
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mem = model.encode(X)
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if model.head == 'wta':
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l_mf, xh = wta_loss(model, mem, Y); mf_raw = l_mf.detach(); xh = xh.float()
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else:
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l_mf, mf_raw = meanflow_loss(model, mem, Y, p_neq=tc['p_neq'])
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e = torch.randn_like(Y)
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xh = one_step(model, mem, e).float()
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v, s = model.heads(mem)
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l_val = (v.float() - V).abs().mean(); l_sub = (s.float() - SG).abs().mean()
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d2 = xh[:, 2:] - 2 * xh[:, 1:-1] + xh[:, :-2]
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l_sm = d2.pow(2).mean()
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l_fk = torch.zeros((), device=dev)
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if tc['w_fk'] > 0:
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q = q_from_feat(X)
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ks = torch.tensor([3, 7, 11, 15], device=dev)
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qh = q[:, None] + torch.cumsum(xh.clamp(-1, 1) * stepv, 1)[:, ks]
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qt = q[:, None] + torch.cumsum(Y * stepv, 1)[:, ks]
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B4 = qh.shape[0] * 4
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ph = fk_points(kin, qh.reshape(B4, 6), fk_idx); pt = fk_points(kin, qt.reshape(B4, 6), fk_idx)
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l_fk = ((ph - pt).norm(dim=-1) / 10).mean()
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loss = tc['w_mf'] * l_mf + tc['w_fk'] * l_fk + tc['w_smooth'] * l_sm + tc['w_value'] * l_val + tc['w_sub'] * l_sub
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opt.zero_grad(set_to_none=True)
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loss.backward()
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gn = torch.nn.utils.clip_grad_norm_(model.parameters(), tc['grad_clip'])
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if not torch.isfinite(loss):
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print('非有限损失 → 训练发散（M5 门禁：触发 WTA 回退条件）', flush=True); save('diverged.pt', step); diverged = True; break
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opt.step(); seen += X.shape[0]
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if saver.stop() or (a.stop_at and step + 1 >= a.stop_at):
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save('last.pt', step + 1); log.close()
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print(f'收到停止请求：已存档 last.pt（第 {step + 1} 步），下次运行自动续训', flush=True); sys.exit(143)
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if saver.due(): save('last.pt', step + 1)
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if step % tc['log_every'] == 0:
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el = time.time() - t0
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rec = {'step': step, 'total': total, 't': time.time(), 'loss': loss.item(), 'mf': mf_raw.item(), 'fk_cm': l_fk.item(), 'smooth': l_sm.item(),
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'value': l_val.item(), 'sub': l_sub.item(), 'gnorm': gn.item(), 'lr': lr_at(step), 'samples_per_s': seen / max(el, 1e-6),
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'eta_h': (total - step - 1) * tc['batch'] / max(seen / max(el, 1e-6), 1e-6) / 3600, 'vram': vram.peak_gib()}
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print(json.dumps(rec), flush=True); log.write(json.dumps(rec) + '\n'); log.flush()
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if not a.no_eval and (step + 1) % tc['eval_every'] == 0:
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model.eval()
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# bs=64 + 归还缓存池：WDDM 卡满时 15.9 GiB 的评估峰值会被驱动换出到内存，eval 从 2 分钟变 1 小时
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res, _ = evaluate(model, kin, val_tasks, dev, bs=64)
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torch.cuda.empty_cache()
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res['step'] = step + 1; res['t'] = time.time()
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print('EVAL', json.dumps(res), flush=True); log.write(json.dumps({'eval': res}) + '\n'); log.flush()
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save('last.pt', step + 1)
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if res['success'] > best + 1e-4:
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best = res['success']; bad_evals = 0; save('best.pt', step + 1, {'eval': res})
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else:
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bad_evals += 1
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if bad_evals >= tc['patience_evals']: print('闭环到达率不再上升，停止', flush=True); break
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if diverged: # 发散的权重不能覆盖 last.pt / best.pt：last.pt 保留发散前最后一次存档，M5 门禁会因此判不过 → 自动 WTA
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print('训练发散，last.pt / best.pt 保留发散前的存档', flush=True); return
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save('last.pt', step + 1)
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if a.no_eval: save('best.pt', step + 1)
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print(f'done, best success {best:.4f}, {(time.time() - t0) / 3600:.2f} h', flush=True)
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if __name__ == '__main__':
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main()
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