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exp+fix: 轮次31——确定界连续认证抓到段内碰撞，但单段P50 22.9ms进不了20ms；自适应cap收益经交错复测撤回

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- Kin.cert_lever 用三角不等式给全姿态位移界（替代 JS 随机采样×1.05），界量级 5.93–48.86 mm/整步
- Kin.certify_segments 批量前沿二分+每根查询预算：48 固定段中预算32判安全37/碰撞1/未知拒10，
  第180题1.08–1.10s漏检段以5次查询判碰撞；未知只拒不放
- 修 seg_seg_dist 第二条退化成点不投影（模型内退化胶囊686受影响）；但7586个加密点的组最小间隙
  逐位不变、2000样本JS对拍仍 PASS → 不解释轮次30漏检、不改既有成绩
- 负结论：B=1 单段认证 P50 22.93/P90 58.43/P99 101.57 ms，432次中225次>20ms；
  自适应宽相位 cap 交错复测耗时无收益，先前1.6×为GPU顺序漂移，已在日志撤回
- 成本模型 L=ceil(log2(bound/(ga+gb)))、查询=2^L+1 与实测对齐（安全段2–17次，未知段33–65次）
- 新增8项段认证测试+1项回放测试（14→23项通过）；变异实测证明张量 max_depth 回绕会误放行
This commit is contained in:
cbb
2026-10-20 19:13:40 +08:00
parent 95da49193f
commit 6de085fd37
10 changed files with 5960 additions and 5 deletions
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tests/test_eval_cl.py
+137 -1
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@@ -9,8 +9,9 @@ from unittest.mock import patch
import torch
from ml.eval.eval_cl import json_finite, run
from ml.eval.diag_collision import replay_dense, replay_first_hits
from ml.eval.diag_collision import replay_certificates, replay_dense, replay_first_hits
from ml.sim.closed_loop import TeacherOracle, rollout
from ml.common.kin_torch import Kin, seg_seg_dist
class EvalMetricsTest(unittest.TestCase):
@@ -124,6 +125,30 @@ class EvalMetricsTest(unittest.TestCase):
self.assertGreater(row['min_gap64_mm'], 0)
self.assertEqual(row['steps'], 1)
def test_certificate_replay_samples_actual_movement_before_deadlines(self):
kin = SegmentCertificateTest.ThinObstacle()
kin.fp = 'probe'
traj = torch.zeros(3, 4, 6)
traj[:, :, 0] = torch.tensor([[0, 1, 1, 1], [0, -0.5, -1, -1], [0, 0, 1, 1]])
per = {'traj': traj, 'limit': torch.tensor([0.06, 0.02, 0.02]),
't_done': torch.tensor([float('nan'), 0.02, float('nan')]),
'plans': {'it': torch.arange(3), 'commit': torch.zeros(3, 3, 1, 6),
'certified': torch.ones(3, 3, 1, dtype=torch.bool), 'gaps': torch.full((3, 3, 2), 0.5)}}
with tempfile.TemporaryDirectory() as folder:
trace, output = Path(folder) / 'trace.pt', Path(folder) / 'audit.json'
torch.save({'kin_fp': 'probe', 'tasks': [{}] * 3, 'batches': [({}, per, 3)]}, trace)
with patch('ml.eval.diag_collision.Kin', return_value=kin), contextlib.redirect_stdout(io.StringIO()):
result = replay_certificates(trace, [0, 1, 2], out=output, repeats=1, extra_segments=[(0, 0)])
with self.assertRaisesRegex(ValueError, 'extra segment'):
replay_certificates(trace, [1], repeats=1, extra_segments=[(1, 1)])
self.assertEqual(json.loads(output.read_text())['dense_collision_segments'], 1)
self.assertEqual([(r['i'], r['start_it']) for r in result['rows']], [(0, 0), (1, 0)])
self.assertEqual(result['budgets']['32']['safe'], 1)
self.assertEqual(result['budgets']['32']['collision'], 1)
self.assertEqual(result['accepted_dense_collision_segments'], 0)
self.assertTrue(result['batch_single_match'])
self.assertEqual(result['timing']['calls'], 2)
class DlsSafetyTest(unittest.TestCase):
def test_rejected_refinement_retains_safe_candidate(self):
@@ -373,5 +398,116 @@ class ClampedPathSafetyTest(unittest.TestCase):
self.assertFalse(per['plans']['certified'].any().item())
class SegmentCertificateTest(unittest.TestCase):
class ThinObstacle(Kin):
def __init__(self):
self.dtype = torch.float64
self.lo, self.hi = torch.full((6,), -5.0), torch.full((6,), 5.0)
self.cont = torch.zeros(6, dtype=torch.bool)
self.cert_groups = torch.tensor([0])
self.cert_lever = torch.tensor([[1.0, 0, 0, 0, 0, 0]], dtype=self.dtype)
def gaps(self, q, cap):
return {'gm': ((q[:, :1] - 0.25).abs() - 0.05).clamp(max=cap)}
def test_bound_detects_hidden_collision_and_accepts_clear_segment(self):
a, b = torch.zeros(3, 6, dtype=torch.float64), torch.zeros(3, 6, dtype=torch.float64)
a[:, 0] = torch.tensor([0.0, -1.0, 0.25])
b[:, 0] = torch.tensor([1.0, 0.0, 0.25])
result = self.ThinObstacle().certify_segments(a, b)
self.assertEqual(result['safe'].tolist(), [False, True, False])
self.assertEqual(result['collision'].tolist(), [True, False, True])
self.assertEqual(result['queries'].tolist(), [4, 2, 2])
def test_budget_and_depth_exhaustion_do_not_accept_unknown_segment(self):
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
b[:, 0] = 1.0
kin = self.ThinObstacle()
for kwargs in ({'max_queries': 2}, {'max_depth': 0}):
with self.subTest(**kwargs):
result = kin.certify_segments(a, b, **kwargs)
self.assertFalse(result['safe'].item())
self.assertTrue(result['unresolved'].item())
self.assertEqual(result['queries'].item(), 2)
def test_budget_exhaustion_scales_with_gap_and_bound(self):
class ConstantGap(self.ThinObstacle):
def gaps(self, q, cap):
return {'gm': torch.full((q.shape[0], 1), 0.1, dtype=q.dtype, device=q.device)}
# 界取 1 mm/度：1° 段要证明无碰撞，必须细分到位移界 < 两端间隙之和(0.2 mm)。
kin = ConstantGap()
kin.cert_lever = torch.ones(1, 6, dtype=torch.float64)
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
b[:, 0] = 1.0
for budget, safe, queries in ((4, False, 3), (8, False, 5), (9, True, 9)):
with self.subTest(budget=budget):
result = kin.certify_segments(a, b, max_queries=budget)
self.assertEqual(bool(result['safe'].item()), safe)
self.assertEqual(int(result['queries'].item()), queries)
def test_tensor_budget_cannot_wrap_depth(self):
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
a[:, 0] = b[:, 0] = 0.25
for tensor in (torch.tensor(255, dtype=torch.uint8), torch.tensor(255)):
with self.subTest(dtype=tensor.dtype):
with self.assertRaisesRegex(ValueError, 'plain ints'):
self.ThinObstacle().certify_segments(a, b, max_depth=tensor)
def test_batch_matches_per_root_and_ignores_order(self):
kin = self.ThinObstacle()
a = torch.tensor([[0.0, 0, 0, 0, 0, 0], [0.25, 0, 0, 0, 0, 0], [-1.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
b = torch.tensor([[1.0, 0, 0, 0, 0, 0], [0.25, 0, 0, 0, 0, 0], [0.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
batch = kin.certify_segments(a, b, max_queries=16)
self.assertEqual(batch['collision'].tolist(), [True, True, False])
self.assertEqual(batch['safe'].tolist(), [False, False, True])
for perm in ([0, 1, 2], [2, 1, 0], [1, 0, 2]):
index = torch.tensor(perm)
moved = kin.certify_segments(a[index], b[index], max_queries=16)
for key in ('safe', 'collision', 'unresolved', 'queries'):
torch.testing.assert_close(moved[key], batch[key][index], rtol=0, atol=0)
def test_empty_batch_and_all_rejected_frontier(self):
kin = self.ThinObstacle()
empty = kin.certify_segments(torch.zeros(0, 6, dtype=torch.float64), torch.zeros(0, 6, dtype=torch.float64))
self.assertEqual(empty['safe'].shape, (0,))
a = torch.tensor([[0.25, 0, 0, 0, 0, 0], [6.0, 0, 0, 0, 0, 0]], dtype=torch.float64)
result = kin.certify_segments(a, a.clone(), max_queries=2)
self.assertEqual(result['safe'].tolist(), [False, False])
self.assertEqual(result['collision'].tolist(), [True, False])
self.assertEqual(result['outside_box'].tolist(), [False, True])
def test_nonfinite_and_outside_box_are_rejected(self):
a, b = torch.zeros(1, 6, dtype=torch.float64), torch.zeros(1, 6, dtype=torch.float64)
kin = self.ThinObstacle()
with patch.object(kin, 'gaps', side_effect=lambda q, cap: {'gm': torch.full((q.shape[0], 1), float('nan'))}):
result = kin.certify_segments(a, b)
self.assertFalse(result['safe'].item())
self.assertTrue(result['unresolved'].item())
for value in (float('inf'), -float('inf')):
with self.subTest(value=value):
with patch.object(kin, 'gaps',
side_effect=lambda q, cap, v=value: {'gm': torch.full((q.shape[0], 1), v)}):
result = kin.certify_segments(a, b)
self.assertFalse(result['safe'].item())
a[:, 0] = b[:, 0] = 6.0
result = kin.certify_segments(a, b)
self.assertFalse(result['safe'].item())
self.assertTrue(result['outside_box'].item())
def test_point_segment_distance_is_symmetric_and_differentiable(self):
p = torch.tensor([[0.0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], dtype=torch.float64, requires_grad=True)
q = torch.tensor([[2.0, 0, 0]] * 4, dtype=torch.float64)
point = torch.tensor([[1.0, 0, 0], [1, 3, 0], [4, 0, 0], [-1, 0, 0]], dtype=torch.float64)
forward = seg_seg_dist(p, q, point, point)
reverse = seg_seg_dist(point, point, p, q)
torch.testing.assert_close(forward, torch.tensor([0.0, 3, 2, 1], dtype=torch.float64), atol=1e-8, rtol=0)
torch.testing.assert_close(forward, reverse, atol=1e-8, rtol=0)
forward.sum().backward()
self.assertTrue(torch.isfinite(p.grad).all())
torch.testing.assert_close(seg_seg_dist(p.detach(), p.detach(), point, point),
(p.detach() - point).norm(dim=-1), atol=1e-8, rtol=0)
if __name__ == '__main__':
unittest.main()