perf+exp(closed_loop): 解析雅可比 DLS（消除 float32 差分数值噪声）——commit3 到达 36.3→46.5%，commit2 49.2%（n=512），DLS n1 9.3→7.1ms
- Kin.jac 几何雅可比，与 autograd 逐项一致（FD 自身 rel 误差 3.58%） - dls_steps(analytic=True) 默认；每步少 6 次批量 FK - 新增 ml/eval/eval_cl.py（部署口径小规模评估）与 check_dls.py（对拍+延迟） - 积分反馈 fb_ki 脚手架（默认 0，未启用）
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"""DLS 解析雅可比对拍 + 延迟（python -m ml.eval.check_dls）"""
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import time
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import torch
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from ml.common.kin_torch import Kin
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from ml.sim.closed_loop import dls_steps
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def main():
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dev = 'cuda' if torch.cuda.is_available() else 'cpu'
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kin = Kin(device=dev)
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g = torch.Generator(device=dev); g.manual_seed(0)
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B = 64
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lo, hi = kin.lo + 20, kin.hi - 20
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q = lo + (hi - lo) * torch.rand(B, 6, device=dev, generator=g)
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xf = kin.cum_xf(q)
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p = kin.fk(q, xf)
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T = p + 6 * torch.randn(B, 3, device=dev, generator=g)
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R = torch.zeros(B, 9, device=dev)
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a = dls_steps(kin, q, T, R, n=3, analytic=True)
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b = dls_steps(kin, q, T, R, n=3, analytic=False)
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d = (a - b).abs()
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print('pos-only max|Δdq| %.6f° mean %.6f° rel %.4f' % (d.max().item(), d.mean().item(), (d.norm() / b.norm()).item()))
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# 带姿态目标：绕随机轴 ±8°
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axis = torch.randn(B, 3, device=dev, generator=g); axis = axis / axis.norm(dim=-1, keepdim=True)
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ang = (torch.rand(B, 1, device=dev, generator=g) * 16 - 8) * torch.pi / 180
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Kx = torch.zeros(B, 3, 3, device=dev)
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Kx[:, 0, 1] = -axis[:, 2]; Kx[:, 0, 2] = axis[:, 1]; Kx[:, 1, 0] = axis[:, 2]
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Kx[:, 1, 2] = -axis[:, 0]; Kx[:, 2, 0] = -axis[:, 1]; Kx[:, 2, 1] = axis[:, 0]
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I = torch.eye(3, device=dev)
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Rd = I + torch.sin(ang)[..., None] * Kx + (1 - torch.cos(ang))[..., None] * (Kx @ Kx)
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Rt = kin.orient(q, xf) @ Rd
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R9 = Rt.reshape(B, 9)
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a = dls_steps(kin, q, T, R9, n=3, analytic=True)
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b = dls_steps(kin, q, T, R9, n=3, analytic=False)
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d = (a - b).abs()
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print('pos+rot max|Δdq| %.6f° mean %.6f° rel %.4f' % (d.max().item(), d.mean().item(), (d.norm() / b.norm()).item()))
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for n_ in (1, 3):
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for Bn in (1, 256):
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qq = q[:Bn]; TT = T[:Bn]; RR = R9[:Bn]
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for an in (False, True):
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for _ in range(3):
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dls_steps(kin, qq, TT, RR, n=n_, analytic=an)
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torch.cuda.synchronize() if dev == 'cuda' else None
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t0 = time.perf_counter()
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for _ in range(20):
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dls_steps(kin, qq, TT, RR, n=n_, analytic=an)
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torch.cuda.synchronize() if dev == 'cuda' else None
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print('n=%d B=%3d analytic=%d %.2f ms' % (n_, Bn, an, (time.perf_counter() - t0) / 20 * 1e3))
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if __name__ == '__main__':
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main()
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"""小规模闭环评估（部署口径）：
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python -m ml.eval.eval_cl --n 256 --commit 3 --fb 1.8 --partial [--ki 0.1 --select consist]
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输出：到达 / 碰撞 / ratio2s / 刹停 / 精修接管 / 墙钟。直接调 rollout，参数与部署一致。"""
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import argparse, functools, json, time
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import torch
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from ml.common.ckpt import load_policy
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from ml.common.kin_torch import Kin
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from ml.data import load_tasks
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from ml.sim.closed_loop import rollout
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import ml.sim.closed_loop as clmod
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def run(model, kin, tasks, device, bs=256, **kw):
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agg, per = {}, []
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for i in range(0, len(tasks), bs):
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r, p = rollout(model, kin, tasks[i:i + bs], device, **kw)
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per.append((r, p, len(tasks[i:i + bs])))
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n = sum(w for _, _, w in per)
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for r, _, w in per:
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for k, v in r.items():
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if k in ('n', 'time_budget') or v is None: continue
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agg[k] = agg.get(k, 0.0) + v * w
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out = {k: v / n for k, v in agg.items()}; out['n'] = n
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out['goal_ratio_2s'] = torch.cat([p['goal_ratio_2s'] for _, p, _ in per]).median().item()
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out['err_fail_med'] = torch.cat([p['err'] for _, p, _ in per])[~torch.cat([p['ok'] for _, p, _ in per])].median().item()
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out['dls_share'] = torch.cat([p['dls_it'] for _, p, _ in per]).ge(0).float().mean().item()
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out['time_budget'] = per[0][0]['time_budget']
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return out
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument('--ckpt', default='ml/runs/base_dag2_dag1/best.pt')
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ap.add_argument('--samples', default='ml/data/samples')
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ap.add_argument('--tasks', default=None)
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ap.add_argument('--split', default='val')
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ap.add_argument('--n', type=int, default=256)
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ap.add_argument('--K', type=int, default=8)
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ap.add_argument('--commit', type=int, default=3)
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ap.add_argument('--fb', type=float, default=1.8)
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ap.add_argument('--ki', type=float, default=0.0)
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ap.add_argument('--kdecay', type=float, default=0.9)
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ap.add_argument('--kimax', type=float, default=0.5)
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ap.add_argument('--select', default='consist')
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ap.add_argument('--replan', default='r3')
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ap.add_argument('--safety', type=int, default=1, help='1=安全层+部分前缀；0=关')
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ap.add_argument('--partial', type=int, default=1)
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ap.add_argument('--max_sec', type=float, default=None)
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ap.add_argument('--seed', type=int, default=0)
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ap.add_argument('--dls', default='analytic', choices=['analytic', 'fd'])
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ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
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a = ap.parse_args()
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if a.dls == 'fd':
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clmod.dls_steps = functools.partial(clmod.dls_steps, analytic=False)
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model, ck = load_policy(a.ckpt, a.device)
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kin = Kin(device=a.device)
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assert ck.get('fp') in (None, kin.fp)
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tasks = ([json.loads(l) for l in open(a.tasks, encoding='utf-8') if l.strip()] if a.tasks
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else [t for t in load_tasks([a.samples]) if t['split'] == a.split])
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tasks = tasks[:a.n]
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t0 = time.perf_counter()
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r = run(model, kin, tasks, a.device, K=a.K, safety=bool(a.safety), seed=a.seed, max_sec=a.max_sec,
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safety_partial=(a.commit if (a.safety and a.partial) else 0), commit_n=a.commit,
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select=a.select, replan=a.replan, fb_gain=a.fb, fb_ki=a.ki, fb_decay=a.kdecay, fb_imax=a.kimax)
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wall = time.perf_counter() - t0
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print('ckpt=%s n=%d K=%d commit=%d fb=%.2f ki=%.2f select=%s replan=%s safety=%d' %
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(a.ckpt, r['n'], a.K, a.commit, a.fb, a.ki, a.select, a.replan, a.safety))
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print('success %.3f time_ok_1p3 %.3f coll %.3f ratio2s %.3f brakes %.1f dls_share %.3f err_fail %.2f wall %.1fs (%.0f ms/题)'
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% (r['success'], r['time_ok_1p3'] or 0, r['collision_rate'], r['goal_ratio_2s'], r['brakes_mean'],
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r['dls_share'], r['err_fail_med'], wall, wall / max(1, len(tasks)) * 1000))
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if __name__ == '__main__':
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main()
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