perf+exp: 轮次34——复用cum_xf进库(22项指标逐位不变)、批量认证压最坏尾部(80→37ms)、FP16/BF16实测无收益
- 撤回一条自己的假收益：先前"合并几何查询→24.5ms"不成立。①第一版把 xf= 传给含5行的 gaps， 只有第0行走完宽相位，认证点全部退回截顶40.0（少干活）；②真实顺序 特征→推理→候选→认证， 认证点依赖网络输出，故"特征间隙与认证点合并一次调用"实现上做不到 - 可实现并已落地：gm_groups(..., xf=) 复用 features 已算的 cum_xf，B=1 交错实测 P50 −3.4ms； 256题重跑 22 项指标与轮次33 base 逐项相同 - 新增 --cert_batch（默认0不变）：K条候选并成1次 gaps 调用。合成夹具(B=2)断言选择/轨迹/证书逐位一致， 变异检查（换回错误索引）2项测试立即失败；256题重跑同样 22 项指标逐位相同 实测交换：典型 33.5→37.6ms，最坏（前面候选被拒）80.2→37.1ms，P99 64.7→48.3ms - FP16/BF16 实测不比 FP32 快（推理 7.68→8.31→8.58ms），整周期不变而 P99 更差，动作偏差 0.005°/0.038° → 小批量下不是算力受限，半精度不划算，推理保持 FP32 - 仍不达 20ms：下一刀是流水线复用（下次规划的特征间隙＝本次已认证的末点），可整块省掉特征 ~13ms - 测试 32→34 项
This commit is contained in:
@@ -0,0 +1,71 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
|
||||
"fb_fallback": 0.0,
|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
"safety_try": 0,
|
||||
"sel_w": 0.1,
|
||||
"dls_safety": 1,
|
||||
"fb_clear": 0.0,
|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": null,
|
||||
"out": "docs/experiment_round34_cert_batch_check.json",
|
||||
"trace": null,
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"inc_cap": 0,
|
||||
"gap_margin": 0.0,
|
||||
"cert_batch": 1,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.57421875,
|
||||
"collision_free_success": 0.57421875,
|
||||
"time_ok_1p3": 0.390625,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.4449527263641357,
|
||||
"time_mean_s": 2.3076188564300537,
|
||||
"brakes_mean": 19.25,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.20997506380081177,
|
||||
"err_fail_med": 229.68746948242188,
|
||||
"err_success_med": 0.18899640440940857,
|
||||
"dls_share": 0.625,
|
||||
"time_budget": "teacher×2(≥3 s)",
|
||||
"speed_ratio_p50": 1.6600004434585571,
|
||||
"speed_ratio_p95": 1.6600035429000854,
|
||||
"speed_ratio_max": 1.660003662109375,
|
||||
"over_vmax_rate": 1.0,
|
||||
"accel_ratio_p95": 1.5835107564926147,
|
||||
"accel_ratio_max": 1.6600004434585571
|
||||
},
|
||||
"simulation_wall_s": 306.9490996999957
|
||||
}
|
||||
@@ -0,0 +1,629 @@
|
||||
{
|
||||
"device": "NVIDIA GeForce RTX 3090",
|
||||
"B": 1,
|
||||
"K": 8,
|
||||
"steps": 1,
|
||||
"reps": 90,
|
||||
"design": "\u540c\u4e00\u6279 4 \u4e2a\u53d8\u4f53\u9010\u8f6e\u4ea4\u66ff\uff08\u5947\u6570\u8f6e\u53cd\u5e8f\uff09\uff0c\u6d88\u9664 GPU \u65f6\u949f\u6f02\u79fb\uff1b\u539f\u59cb\u8017\u65f6\u5168\u90e8\u843d\u76d8",
|
||||
"equivalence": {
|
||||
"features_reuse_bitwise_equal": true,
|
||||
"gm_groups_vs_full_table_max_abs_mm": 0.0,
|
||||
"cert_matrix_bitwise_equal_at_same_cap": true,
|
||||
"shared_xf_cert_bitwise_equal_at_cap1": true,
|
||||
"max_abs_shared_diff_mm": 0.0,
|
||||
"max_abs_cert_diff_mm": 0.0,
|
||||
"decision_equal_by_threshold": {
|
||||
"thr_0.0": true,
|
||||
"thr_0.5": true,
|
||||
"thr_1.0": true
|
||||
},
|
||||
"per_point_min_cur_mm": [
|
||||
8.833421,
|
||||
8.849185,
|
||||
8.859405,
|
||||
8.83829
|
||||
],
|
||||
"per_point_min_fused_mm": [
|
||||
8.833421,
|
||||
8.849185,
|
||||
8.859405,
|
||||
8.83829
|
||||
]
|
||||
},
|
||||
"results": {
|
||||
"current_discrete": {
|
||||
"p50": 32.932,
|
||||
"p95": 42.589,
|
||||
"p99": 55.568,
|
||||
"max": 55.568,
|
||||
"hz_p50": 30.4,
|
||||
"raw_ms": [
|
||||
30.458,
|
||||
30.624,
|
||||
30.789,
|
||||
31.17,
|
||||
31.229,
|
||||
31.283,
|
||||
31.371,
|
||||
31.394,
|
||||
31.454,
|
||||
31.473,
|
||||
31.564,
|
||||
31.58,
|
||||
31.719,
|
||||
31.772,
|
||||
31.78,
|
||||
31.785,
|
||||
31.859,
|
||||
31.875,
|
||||
31.906,
|
||||
31.973,
|
||||
31.979,
|
||||
32.002,
|
||||
32.017,
|
||||
32.063,
|
||||
32.099,
|
||||
32.113,
|
||||
32.123,
|
||||
32.127,
|
||||
32.13,
|
||||
32.137,
|
||||
32.191,
|
||||
32.286,
|
||||
32.308,
|
||||
32.324,
|
||||
32.33,
|
||||
32.388,
|
||||
32.48,
|
||||
32.508,
|
||||
32.564,
|
||||
32.63,
|
||||
32.64,
|
||||
32.702,
|
||||
32.78,
|
||||
32.822,
|
||||
32.834,
|
||||
32.932,
|
||||
32.964,
|
||||
32.975,
|
||||
32.996,
|
||||
33.071,
|
||||
33.163,
|
||||
33.191,
|
||||
33.192,
|
||||
33.402,
|
||||
33.477,
|
||||
33.509,
|
||||
33.583,
|
||||
33.956,
|
||||
34.014,
|
||||
34.04,
|
||||
34.047,
|
||||
34.047,
|
||||
34.154,
|
||||
34.302,
|
||||
34.595,
|
||||
34.658,
|
||||
34.943,
|
||||
35.296,
|
||||
35.522,
|
||||
35.67,
|
||||
35.757,
|
||||
35.828,
|
||||
35.859,
|
||||
36.459,
|
||||
36.503,
|
||||
36.562,
|
||||
36.956,
|
||||
37.047,
|
||||
37.067,
|
||||
37.142,
|
||||
37.442,
|
||||
38.632,
|
||||
39.285,
|
||||
41.269,
|
||||
41.932,
|
||||
42.589,
|
||||
43.199,
|
||||
43.329,
|
||||
54.407,
|
||||
55.568
|
||||
]
|
||||
},
|
||||
"fused_discrete": {
|
||||
"p50": 24.515,
|
||||
"p95": 30.02,
|
||||
"p99": 42.085,
|
||||
"max": 42.085,
|
||||
"hz_p50": 40.8,
|
||||
"raw_ms": [
|
||||
22.146,
|
||||
22.266,
|
||||
22.409,
|
||||
22.653,
|
||||
22.66,
|
||||
22.695,
|
||||
22.76,
|
||||
22.827,
|
||||
22.848,
|
||||
23.001,
|
||||
23.033,
|
||||
23.087,
|
||||
23.249,
|
||||
23.36,
|
||||
23.488,
|
||||
23.573,
|
||||
23.607,
|
||||
23.625,
|
||||
23.649,
|
||||
23.651,
|
||||
23.652,
|
||||
23.652,
|
||||
23.656,
|
||||
23.676,
|
||||
23.716,
|
||||
23.737,
|
||||
23.745,
|
||||
23.795,
|
||||
23.871,
|
||||
23.883,
|
||||
23.92,
|
||||
23.922,
|
||||
23.945,
|
||||
23.959,
|
||||
24.046,
|
||||
24.13,
|
||||
24.184,
|
||||
24.282,
|
||||
24.291,
|
||||
24.313,
|
||||
24.318,
|
||||
24.319,
|
||||
24.32,
|
||||
24.34,
|
||||
24.399,
|
||||
24.515,
|
||||
24.532,
|
||||
24.541,
|
||||
24.552,
|
||||
24.592,
|
||||
24.618,
|
||||
24.731,
|
||||
24.741,
|
||||
24.745,
|
||||
24.766,
|
||||
24.807,
|
||||
24.813,
|
||||
24.898,
|
||||
25.066,
|
||||
25.088,
|
||||
25.137,
|
||||
25.18,
|
||||
25.223,
|
||||
25.376,
|
||||
25.474,
|
||||
25.603,
|
||||
25.68,
|
||||
25.744,
|
||||
25.82,
|
||||
25.936,
|
||||
25.95,
|
||||
25.953,
|
||||
26.002,
|
||||
26.174,
|
||||
26.312,
|
||||
26.88,
|
||||
27.036,
|
||||
27.209,
|
||||
27.329,
|
||||
27.424,
|
||||
27.715,
|
||||
28.796,
|
||||
28.824,
|
||||
29.427,
|
||||
29.662,
|
||||
30.02,
|
||||
31.515,
|
||||
31.839,
|
||||
36.18,
|
||||
42.085
|
||||
]
|
||||
},
|
||||
"shared_xf_discrete": {
|
||||
"p50": 26.864,
|
||||
"p95": 34.544,
|
||||
"p99": 67.243,
|
||||
"max": 67.243,
|
||||
"hz_p50": 37.2,
|
||||
"raw_ms": [
|
||||
24.433,
|
||||
24.441,
|
||||
24.585,
|
||||
24.777,
|
||||
24.862,
|
||||
24.935,
|
||||
25.054,
|
||||
25.08,
|
||||
25.105,
|
||||
25.193,
|
||||
25.313,
|
||||
25.44,
|
||||
25.466,
|
||||
25.483,
|
||||
25.516,
|
||||
25.554,
|
||||
25.577,
|
||||
25.689,
|
||||
25.702,
|
||||
25.708,
|
||||
25.854,
|
||||
25.896,
|
||||
25.898,
|
||||
26.011,
|
||||
26.097,
|
||||
26.175,
|
||||
26.255,
|
||||
26.289,
|
||||
26.297,
|
||||
26.306,
|
||||
26.328,
|
||||
26.336,
|
||||
26.346,
|
||||
26.375,
|
||||
26.397,
|
||||
26.456,
|
||||
26.502,
|
||||
26.523,
|
||||
26.563,
|
||||
26.613,
|
||||
26.634,
|
||||
26.658,
|
||||
26.708,
|
||||
26.752,
|
||||
26.798,
|
||||
26.864,
|
||||
26.869,
|
||||
26.874,
|
||||
27.315,
|
||||
27.341,
|
||||
27.668,
|
||||
27.67,
|
||||
27.732,
|
||||
27.774,
|
||||
27.812,
|
||||
27.84,
|
||||
27.882,
|
||||
27.921,
|
||||
28.24,
|
||||
28.302,
|
||||
28.421,
|
||||
28.694,
|
||||
28.722,
|
||||
28.739,
|
||||
28.817,
|
||||
28.914,
|
||||
28.944,
|
||||
28.998,
|
||||
29.12,
|
||||
29.122,
|
||||
29.259,
|
||||
29.292,
|
||||
29.334,
|
||||
29.658,
|
||||
29.847,
|
||||
30.181,
|
||||
30.206,
|
||||
30.292,
|
||||
30.492,
|
||||
30.782,
|
||||
31.168,
|
||||
31.222,
|
||||
31.607,
|
||||
31.614,
|
||||
32.187,
|
||||
34.544,
|
||||
35.274,
|
||||
35.565,
|
||||
35.915,
|
||||
67.243
|
||||
]
|
||||
},
|
||||
"current_cert_n8": {
|
||||
"p50": 42.624,
|
||||
"p95": 49.434,
|
||||
"p99": 57.771,
|
||||
"max": 57.771,
|
||||
"hz_p50": 23.5,
|
||||
"raw_ms": [
|
||||
38.546,
|
||||
38.666,
|
||||
39.152,
|
||||
39.265,
|
||||
39.344,
|
||||
39.441,
|
||||
39.584,
|
||||
39.626,
|
||||
39.738,
|
||||
39.841,
|
||||
40.228,
|
||||
40.306,
|
||||
40.514,
|
||||
40.805,
|
||||
40.838,
|
||||
40.875,
|
||||
40.884,
|
||||
40.907,
|
||||
40.952,
|
||||
40.971,
|
||||
41.185,
|
||||
41.193,
|
||||
41.193,
|
||||
41.277,
|
||||
41.279,
|
||||
41.422,
|
||||
41.448,
|
||||
41.463,
|
||||
41.465,
|
||||
41.473,
|
||||
41.504,
|
||||
41.551,
|
||||
41.684,
|
||||
41.733,
|
||||
41.841,
|
||||
41.947,
|
||||
41.967,
|
||||
42.011,
|
||||
42.077,
|
||||
42.241,
|
||||
42.249,
|
||||
42.269,
|
||||
42.301,
|
||||
42.314,
|
||||
42.388,
|
||||
42.624,
|
||||
42.661,
|
||||
42.767,
|
||||
42.773,
|
||||
42.837,
|
||||
42.865,
|
||||
42.87,
|
||||
42.913,
|
||||
43.343,
|
||||
43.35,
|
||||
43.356,
|
||||
43.376,
|
||||
43.501,
|
||||
43.695,
|
||||
43.708,
|
||||
43.842,
|
||||
43.923,
|
||||
43.972,
|
||||
44.243,
|
||||
44.476,
|
||||
44.536,
|
||||
44.648,
|
||||
44.745,
|
||||
44.894,
|
||||
45.034,
|
||||
45.144,
|
||||
45.236,
|
||||
45.697,
|
||||
45.859,
|
||||
45.944,
|
||||
46.039,
|
||||
46.126,
|
||||
46.467,
|
||||
46.575,
|
||||
46.887,
|
||||
46.983,
|
||||
47.234,
|
||||
47.621,
|
||||
47.685,
|
||||
48.547,
|
||||
49.434,
|
||||
49.683,
|
||||
50.646,
|
||||
54.754,
|
||||
57.771
|
||||
]
|
||||
},
|
||||
"fused_cert_n8": {
|
||||
"p50": 24.826,
|
||||
"p95": 31.721,
|
||||
"p99": 49.398,
|
||||
"max": 49.398,
|
||||
"hz_p50": 40.3,
|
||||
"raw_ms": [
|
||||
22.127,
|
||||
22.553,
|
||||
22.598,
|
||||
22.624,
|
||||
22.789,
|
||||
22.798,
|
||||
22.878,
|
||||
22.983,
|
||||
23.002,
|
||||
23.134,
|
||||
23.192,
|
||||
23.273,
|
||||
23.323,
|
||||
23.345,
|
||||
23.377,
|
||||
23.445,
|
||||
23.466,
|
||||
23.549,
|
||||
23.611,
|
||||
23.613,
|
||||
23.861,
|
||||
23.868,
|
||||
23.885,
|
||||
23.885,
|
||||
23.906,
|
||||
23.918,
|
||||
23.919,
|
||||
23.933,
|
||||
23.947,
|
||||
24.052,
|
||||
24.064,
|
||||
24.067,
|
||||
24.08,
|
||||
24.103,
|
||||
24.13,
|
||||
24.164,
|
||||
24.249,
|
||||
24.301,
|
||||
24.429,
|
||||
24.515,
|
||||
24.603,
|
||||
24.609,
|
||||
24.7,
|
||||
24.748,
|
||||
24.812,
|
||||
24.826,
|
||||
24.855,
|
||||
24.858,
|
||||
24.954,
|
||||
24.99,
|
||||
25.052,
|
||||
25.097,
|
||||
25.195,
|
||||
25.209,
|
||||
25.241,
|
||||
25.252,
|
||||
25.264,
|
||||
25.298,
|
||||
25.63,
|
||||
25.659,
|
||||
25.767,
|
||||
25.804,
|
||||
25.928,
|
||||
25.976,
|
||||
26.494,
|
||||
26.871,
|
||||
26.886,
|
||||
26.988,
|
||||
26.995,
|
||||
27.004,
|
||||
27.307,
|
||||
27.322,
|
||||
27.352,
|
||||
27.937,
|
||||
28.144,
|
||||
28.165,
|
||||
28.216,
|
||||
28.288,
|
||||
28.331,
|
||||
29.37,
|
||||
29.611,
|
||||
30.6,
|
||||
30.704,
|
||||
31.026,
|
||||
31.552,
|
||||
31.721,
|
||||
32.875,
|
||||
34.001,
|
||||
40.223,
|
||||
49.398
|
||||
]
|
||||
},
|
||||
"shared_xf_cert_n8": {
|
||||
"p50": 36.185,
|
||||
"p95": 41.136,
|
||||
"p99": 64.287,
|
||||
"max": 64.287,
|
||||
"hz_p50": 27.6,
|
||||
"raw_ms": [
|
||||
33.051,
|
||||
33.153,
|
||||
33.201,
|
||||
33.406,
|
||||
33.819,
|
||||
33.845,
|
||||
33.94,
|
||||
34.083,
|
||||
34.145,
|
||||
34.235,
|
||||
34.3,
|
||||
34.372,
|
||||
34.605,
|
||||
34.697,
|
||||
34.714,
|
||||
34.755,
|
||||
34.817,
|
||||
34.855,
|
||||
34.925,
|
||||
34.957,
|
||||
34.963,
|
||||
34.967,
|
||||
34.986,
|
||||
35.167,
|
||||
35.239,
|
||||
35.314,
|
||||
35.325,
|
||||
35.402,
|
||||
35.444,
|
||||
35.518,
|
||||
35.544,
|
||||
35.582,
|
||||
35.594,
|
||||
35.662,
|
||||
35.675,
|
||||
35.724,
|
||||
35.775,
|
||||
35.874,
|
||||
35.908,
|
||||
35.929,
|
||||
35.965,
|
||||
36.019,
|
||||
36.04,
|
||||
36.126,
|
||||
36.164,
|
||||
36.185,
|
||||
36.2,
|
||||
36.259,
|
||||
36.263,
|
||||
36.266,
|
||||
36.301,
|
||||
36.324,
|
||||
36.383,
|
||||
36.393,
|
||||
36.609,
|
||||
36.667,
|
||||
36.696,
|
||||
36.779,
|
||||
36.799,
|
||||
36.836,
|
||||
36.881,
|
||||
36.978,
|
||||
37.001,
|
||||
37.033,
|
||||
37.043,
|
||||
37.078,
|
||||
37.183,
|
||||
37.206,
|
||||
37.278,
|
||||
37.3,
|
||||
37.411,
|
||||
37.485,
|
||||
37.722,
|
||||
37.923,
|
||||
37.965,
|
||||
38.197,
|
||||
38.588,
|
||||
38.871,
|
||||
39.082,
|
||||
39.099,
|
||||
39.135,
|
||||
39.351,
|
||||
39.505,
|
||||
40.204,
|
||||
40.892,
|
||||
41.136,
|
||||
41.449,
|
||||
42.611,
|
||||
44.687,
|
||||
64.287
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,630 @@
|
||||
{
|
||||
"device": "NVIDIA GeForce RTX 3090",
|
||||
"B": 1,
|
||||
"K": 8,
|
||||
"commit_n": 2,
|
||||
"reps": 80,
|
||||
"design": "5 \u4e2a\u53d8\u4f53\u9010\u8f6e\u4ea4\u66ff\uff08\u5947\u6570\u8f6e\u53cd\u5e8f\uff09\uff1b\u53ea\u6d4b\u53ef\u5b9e\u73b0\u7684\u53e3\u5f84\uff1a\u7279\u5f81/\u63a8\u7406/\u8ba4\u8bc1\u6309\u771f\u5b9e\u987a\u5e8f",
|
||||
"equivalence": {
|
||||
"batch_cert_matrix_equal_to_loop": true,
|
||||
"features_xf_reuse_bitwise_equal": true
|
||||
},
|
||||
"results": {
|
||||
"A_today": {
|
||||
"p50": 33.494,
|
||||
"p95": 46.143,
|
||||
"p99": 64.693,
|
||||
"max": 64.693,
|
||||
"raw_ms": [
|
||||
28.984,
|
||||
29.251,
|
||||
29.361,
|
||||
29.549,
|
||||
29.899,
|
||||
29.944,
|
||||
30.181,
|
||||
30.605,
|
||||
30.624,
|
||||
30.744,
|
||||
30.815,
|
||||
30.838,
|
||||
30.862,
|
||||
30.9,
|
||||
31.024,
|
||||
31.092,
|
||||
31.115,
|
||||
31.262,
|
||||
31.433,
|
||||
31.463,
|
||||
31.52,
|
||||
31.572,
|
||||
31.642,
|
||||
31.741,
|
||||
31.871,
|
||||
31.902,
|
||||
31.924,
|
||||
31.933,
|
||||
31.976,
|
||||
32.001,
|
||||
32.03,
|
||||
32.06,
|
||||
32.285,
|
||||
32.384,
|
||||
32.624,
|
||||
32.825,
|
||||
32.919,
|
||||
33.063,
|
||||
33.168,
|
||||
33.197,
|
||||
33.494,
|
||||
33.589,
|
||||
33.624,
|
||||
33.632,
|
||||
33.666,
|
||||
33.708,
|
||||
33.727,
|
||||
33.735,
|
||||
33.754,
|
||||
33.773,
|
||||
33.869,
|
||||
33.907,
|
||||
34.116,
|
||||
34.219,
|
||||
34.314,
|
||||
34.337,
|
||||
34.897,
|
||||
34.957,
|
||||
35.436,
|
||||
35.469,
|
||||
35.999,
|
||||
36.106,
|
||||
36.523,
|
||||
36.559,
|
||||
36.846,
|
||||
36.866,
|
||||
36.922,
|
||||
37.354,
|
||||
37.375,
|
||||
37.581,
|
||||
37.933,
|
||||
38.026,
|
||||
40.541,
|
||||
40.882,
|
||||
44.161,
|
||||
44.233,
|
||||
46.143,
|
||||
54.047,
|
||||
58.36,
|
||||
64.693
|
||||
]
|
||||
},
|
||||
"B_xf_reuse": {
|
||||
"p50": 30.122,
|
||||
"p95": 42.349,
|
||||
"p99": 66.368,
|
||||
"max": 66.368,
|
||||
"raw_ms": [
|
||||
26.698,
|
||||
26.958,
|
||||
26.984,
|
||||
27.007,
|
||||
27.018,
|
||||
27.158,
|
||||
27.308,
|
||||
27.369,
|
||||
27.987,
|
||||
28.066,
|
||||
28.219,
|
||||
28.288,
|
||||
28.362,
|
||||
28.517,
|
||||
28.537,
|
||||
28.539,
|
||||
28.593,
|
||||
28.611,
|
||||
28.638,
|
||||
28.735,
|
||||
28.828,
|
||||
28.871,
|
||||
28.922,
|
||||
28.945,
|
||||
28.978,
|
||||
29.088,
|
||||
29.202,
|
||||
29.226,
|
||||
29.226,
|
||||
29.31,
|
||||
29.497,
|
||||
29.506,
|
||||
29.531,
|
||||
29.549,
|
||||
29.649,
|
||||
29.875,
|
||||
29.978,
|
||||
29.992,
|
||||
30.025,
|
||||
30.102,
|
||||
30.122,
|
||||
30.243,
|
||||
30.245,
|
||||
30.263,
|
||||
30.407,
|
||||
30.578,
|
||||
30.665,
|
||||
30.717,
|
||||
30.863,
|
||||
30.884,
|
||||
30.889,
|
||||
30.95,
|
||||
31.023,
|
||||
31.314,
|
||||
31.525,
|
||||
31.537,
|
||||
31.543,
|
||||
31.549,
|
||||
31.62,
|
||||
32.011,
|
||||
32.098,
|
||||
32.168,
|
||||
32.188,
|
||||
32.22,
|
||||
32.578,
|
||||
32.818,
|
||||
32.851,
|
||||
32.929,
|
||||
32.991,
|
||||
33.76,
|
||||
33.805,
|
||||
33.831,
|
||||
33.94,
|
||||
34.124,
|
||||
34.891,
|
||||
39.001,
|
||||
42.349,
|
||||
43.636,
|
||||
62.045,
|
||||
66.368
|
||||
]
|
||||
},
|
||||
"C_batch_cert": {
|
||||
"p50": 38.048,
|
||||
"p95": 50.171,
|
||||
"p99": 59.86,
|
||||
"max": 59.86,
|
||||
"raw_ms": [
|
||||
33.625,
|
||||
33.736,
|
||||
33.848,
|
||||
34.235,
|
||||
34.49,
|
||||
34.917,
|
||||
34.918,
|
||||
35.108,
|
||||
35.407,
|
||||
35.919,
|
||||
36.023,
|
||||
36.035,
|
||||
36.087,
|
||||
36.263,
|
||||
36.318,
|
||||
36.531,
|
||||
36.627,
|
||||
36.638,
|
||||
36.722,
|
||||
36.778,
|
||||
36.791,
|
||||
36.801,
|
||||
36.816,
|
||||
36.859,
|
||||
36.97,
|
||||
36.998,
|
||||
37.029,
|
||||
37.055,
|
||||
37.057,
|
||||
37.093,
|
||||
37.208,
|
||||
37.318,
|
||||
37.408,
|
||||
37.438,
|
||||
37.463,
|
||||
37.501,
|
||||
37.503,
|
||||
37.605,
|
||||
37.63,
|
||||
37.984,
|
||||
38.048,
|
||||
38.283,
|
||||
38.347,
|
||||
38.524,
|
||||
38.547,
|
||||
38.667,
|
||||
38.816,
|
||||
38.995,
|
||||
39.028,
|
||||
39.34,
|
||||
39.617,
|
||||
39.679,
|
||||
39.845,
|
||||
39.852,
|
||||
39.871,
|
||||
39.894,
|
||||
39.923,
|
||||
40.195,
|
||||
40.514,
|
||||
41.421,
|
||||
41.684,
|
||||
41.804,
|
||||
41.879,
|
||||
41.933,
|
||||
42.011,
|
||||
42.127,
|
||||
42.483,
|
||||
42.551,
|
||||
42.656,
|
||||
43.038,
|
||||
43.248,
|
||||
43.269,
|
||||
45.68,
|
||||
46.514,
|
||||
46.755,
|
||||
48.909,
|
||||
50.171,
|
||||
50.906,
|
||||
55.33,
|
||||
59.86
|
||||
]
|
||||
},
|
||||
"D_both": {
|
||||
"p50": 34.87,
|
||||
"p95": 39.811,
|
||||
"p99": 48.258,
|
||||
"max": 48.258,
|
||||
"raw_ms": [
|
||||
30.044,
|
||||
30.934,
|
||||
31.005,
|
||||
31.007,
|
||||
31.064,
|
||||
31.492,
|
||||
31.675,
|
||||
31.758,
|
||||
32.114,
|
||||
32.153,
|
||||
32.338,
|
||||
32.374,
|
||||
32.442,
|
||||
32.494,
|
||||
32.681,
|
||||
32.882,
|
||||
32.942,
|
||||
32.991,
|
||||
33.167,
|
||||
33.18,
|
||||
33.302,
|
||||
33.305,
|
||||
33.342,
|
||||
33.449,
|
||||
33.58,
|
||||
33.694,
|
||||
33.739,
|
||||
33.784,
|
||||
33.832,
|
||||
33.835,
|
||||
33.866,
|
||||
33.961,
|
||||
34.037,
|
||||
34.135,
|
||||
34.315,
|
||||
34.395,
|
||||
34.465,
|
||||
34.556,
|
||||
34.656,
|
||||
34.819,
|
||||
34.87,
|
||||
34.98,
|
||||
35.047,
|
||||
35.176,
|
||||
35.19,
|
||||
35.37,
|
||||
35.461,
|
||||
35.528,
|
||||
35.557,
|
||||
35.59,
|
||||
35.643,
|
||||
35.716,
|
||||
35.742,
|
||||
35.804,
|
||||
35.991,
|
||||
36.018,
|
||||
36.063,
|
||||
36.105,
|
||||
36.293,
|
||||
36.312,
|
||||
36.326,
|
||||
36.537,
|
||||
36.541,
|
||||
36.602,
|
||||
36.717,
|
||||
36.934,
|
||||
36.945,
|
||||
37.088,
|
||||
37.141,
|
||||
37.46,
|
||||
37.561,
|
||||
37.608,
|
||||
37.765,
|
||||
37.904,
|
||||
37.974,
|
||||
39.253,
|
||||
39.811,
|
||||
40.076,
|
||||
46.504,
|
||||
48.258
|
||||
]
|
||||
},
|
||||
"E_both_cont_n8": {
|
||||
"p50": 43.725,
|
||||
"p95": 51.084,
|
||||
"p99": 65.681,
|
||||
"max": 65.681,
|
||||
"raw_ms": [
|
||||
37.733,
|
||||
39.087,
|
||||
39.154,
|
||||
39.238,
|
||||
39.36,
|
||||
39.454,
|
||||
39.514,
|
||||
40.167,
|
||||
40.44,
|
||||
40.837,
|
||||
40.86,
|
||||
40.868,
|
||||
41.237,
|
||||
41.276,
|
||||
41.346,
|
||||
41.408,
|
||||
41.551,
|
||||
41.815,
|
||||
41.824,
|
||||
41.912,
|
||||
41.932,
|
||||
41.973,
|
||||
42.074,
|
||||
42.296,
|
||||
42.341,
|
||||
42.365,
|
||||
42.435,
|
||||
42.505,
|
||||
42.592,
|
||||
42.598,
|
||||
42.637,
|
||||
42.85,
|
||||
43.045,
|
||||
43.048,
|
||||
43.18,
|
||||
43.209,
|
||||
43.359,
|
||||
43.361,
|
||||
43.373,
|
||||
43.411,
|
||||
43.725,
|
||||
43.728,
|
||||
43.787,
|
||||
43.833,
|
||||
43.985,
|
||||
44.133,
|
||||
44.185,
|
||||
44.401,
|
||||
44.458,
|
||||
44.585,
|
||||
44.689,
|
||||
44.819,
|
||||
44.841,
|
||||
44.86,
|
||||
45.081,
|
||||
45.087,
|
||||
45.55,
|
||||
45.701,
|
||||
45.939,
|
||||
46.06,
|
||||
46.128,
|
||||
46.264,
|
||||
46.368,
|
||||
46.43,
|
||||
46.957,
|
||||
47.145,
|
||||
47.231,
|
||||
47.256,
|
||||
47.463,
|
||||
47.637,
|
||||
47.819,
|
||||
47.863,
|
||||
48.382,
|
||||
48.87,
|
||||
49.676,
|
||||
50.884,
|
||||
51.084,
|
||||
51.17,
|
||||
53.859,
|
||||
65.681
|
||||
]
|
||||
},
|
||||
"F_worst_loop8": {
|
||||
"p50": 80.224,
|
||||
"p95": 88.436,
|
||||
"p99": 89.553,
|
||||
"max": 89.553,
|
||||
"raw_ms": [
|
||||
67.204,
|
||||
70.008,
|
||||
70.436,
|
||||
71.432,
|
||||
72.454,
|
||||
72.503,
|
||||
73.006,
|
||||
73.12,
|
||||
73.492,
|
||||
73.971,
|
||||
74.203,
|
||||
74.358,
|
||||
74.756,
|
||||
74.816,
|
||||
74.893,
|
||||
75.536,
|
||||
75.98,
|
||||
76.049,
|
||||
76.164,
|
||||
76.25,
|
||||
76.547,
|
||||
76.669,
|
||||
76.843,
|
||||
77.268,
|
||||
77.534,
|
||||
77.658,
|
||||
77.872,
|
||||
78.149,
|
||||
78.343,
|
||||
78.377,
|
||||
78.445,
|
||||
78.483,
|
||||
78.86,
|
||||
78.863,
|
||||
78.905,
|
||||
79.048,
|
||||
79.315,
|
||||
79.857,
|
||||
80.035,
|
||||
80.041,
|
||||
80.224,
|
||||
80.602,
|
||||
80.644,
|
||||
80.677,
|
||||
80.848,
|
||||
81.061,
|
||||
81.094,
|
||||
81.155,
|
||||
81.243,
|
||||
81.384,
|
||||
81.628,
|
||||
81.679,
|
||||
81.75,
|
||||
82.117,
|
||||
82.148,
|
||||
82.363,
|
||||
82.444,
|
||||
82.511,
|
||||
82.651,
|
||||
82.828,
|
||||
82.921,
|
||||
83.371,
|
||||
83.592,
|
||||
83.894,
|
||||
83.926,
|
||||
84.386,
|
||||
84.519,
|
||||
84.608,
|
||||
85.06,
|
||||
85.078,
|
||||
85.094,
|
||||
85.26,
|
||||
87.212,
|
||||
87.744,
|
||||
87.847,
|
||||
88.098,
|
||||
88.436,
|
||||
88.461,
|
||||
88.919,
|
||||
89.553
|
||||
]
|
||||
},
|
||||
"G_worst_batch": {
|
||||
"p50": 37.139,
|
||||
"p95": 46.06,
|
||||
"p99": 52.215,
|
||||
"max": 52.215,
|
||||
"raw_ms": [
|
||||
32.718,
|
||||
33.447,
|
||||
33.569,
|
||||
33.692,
|
||||
33.81,
|
||||
34.236,
|
||||
34.28,
|
||||
34.475,
|
||||
34.496,
|
||||
34.746,
|
||||
35.19,
|
||||
35.392,
|
||||
35.435,
|
||||
35.564,
|
||||
35.569,
|
||||
35.669,
|
||||
35.693,
|
||||
35.734,
|
||||
35.741,
|
||||
35.812,
|
||||
35.828,
|
||||
35.934,
|
||||
36.015,
|
||||
36.036,
|
||||
36.097,
|
||||
36.144,
|
||||
36.197,
|
||||
36.205,
|
||||
36.487,
|
||||
36.637,
|
||||
36.71,
|
||||
36.775,
|
||||
36.79,
|
||||
36.83,
|
||||
36.831,
|
||||
36.885,
|
||||
36.894,
|
||||
36.903,
|
||||
36.947,
|
||||
37.122,
|
||||
37.139,
|
||||
37.154,
|
||||
37.198,
|
||||
37.225,
|
||||
37.249,
|
||||
37.478,
|
||||
37.615,
|
||||
37.617,
|
||||
38.114,
|
||||
38.164,
|
||||
38.288,
|
||||
38.637,
|
||||
38.651,
|
||||
38.724,
|
||||
38.813,
|
||||
39.108,
|
||||
39.15,
|
||||
39.848,
|
||||
39.866,
|
||||
39.946,
|
||||
39.969,
|
||||
39.992,
|
||||
40.04,
|
||||
40.28,
|
||||
40.35,
|
||||
40.697,
|
||||
40.727,
|
||||
40.893,
|
||||
40.972,
|
||||
41.029,
|
||||
41.138,
|
||||
41.479,
|
||||
41.51,
|
||||
41.711,
|
||||
45.036,
|
||||
45.784,
|
||||
46.06,
|
||||
48.597,
|
||||
50.186,
|
||||
52.215
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
|
||||
"fb_fallback": 0.0,
|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
"safety_try": 0,
|
||||
"sel_w": 0.1,
|
||||
"dls_safety": 1,
|
||||
"fb_clear": 0.0,
|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": null,
|
||||
"out": "docs/experiment_round34_xf_reuse_check.json",
|
||||
"trace": null,
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"inc_cap": 0,
|
||||
"gap_margin": 0.0,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.57421875,
|
||||
"collision_free_success": 0.57421875,
|
||||
"time_ok_1p3": 0.390625,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.4449527263641357,
|
||||
"time_mean_s": 2.3076188564300537,
|
||||
"brakes_mean": 19.25,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.20997506380081177,
|
||||
"err_fail_med": 229.68746948242188,
|
||||
"err_success_med": 0.18899640440940857,
|
||||
"dls_share": 0.625,
|
||||
"time_budget": "teacher×2(≥3 s)",
|
||||
"speed_ratio_p50": 1.6600004434585571,
|
||||
"speed_ratio_p95": 1.6600035429000854,
|
||||
"speed_ratio_max": 1.660003662109375,
|
||||
"over_vmax_rate": 1.0,
|
||||
"accel_ratio_p95": 1.5835107564926147,
|
||||
"accel_ratio_max": 1.6600004434585571
|
||||
},
|
||||
"simulation_wall_s": 317.96790459999465
|
||||
}
|
||||
@@ -694,6 +694,33 @@
|
||||
- **50 Hz 结论（当前状态）**：真实配方一个规划周期 **36.7 ms（P99 49.2）≈ 27 Hz**，仍不满足"P99<20 ms"；且必须记住这是在**不限幅**（可超速 1.66×）的策略分布下测的——限幅后的到达率代价见上表，两者不能混着谈。
|
||||
- f32 与 f64 的间隙在 3 个点上完全相同（最大偏差 0.0 mm、无符号翻转），但 **3 个点不足以支持"认证可以改用 float32"**，需按真实轨迹批量对比才能下结论。
|
||||
|
||||
## 轮次 34：GPU 周期的可实现优化——复用 FK 与压认证最坏情况（exp: `exp/dagger-deploy`）
|
||||
|
||||
- **起因与一次自我撤回**：轮次 33 记下"合并几何查询看能不能进 20 ms"后，先按 `docs/experiment_round34_fused_cycle.json` 测出"整周期 41.9→24.5 ms"。这个数**作废**，两个独立原因：
|
||||
1. 第一版的"合并"把 `xf=` 传给了包含 5 行的 `gaps` 调用，而 `gaps` 的 `xf` 是按行对应的 → 只有第 0 行走完宽相位，认证点 4 行全部退回截顶值 40.0 mm，即**少干了活**（表现为认证矩阵差 31.17 mm）。修好后同一 cap 下矩阵逐位一致（差 0.0 mm）。
|
||||
2. 更根本：真实管线顺序是 特征 → 推理 → 候选 → 认证，**认证点依赖网络输出**，所以"特征用的 q 间隙与认证点间隙合成同一次调用"在实现上做不到。该文件保留作记录，但不能当作可实现收益。
|
||||
- **只测可实现的口径**（`docs/experiment_round34_geom_opt.json`；B=1、K=8、commit 2；7 个变体逐轮交替、奇数轮反序，80 轮；等价性两项都 `True`：批量认证矩阵与逐条循环逐位一致、features 复用 xf 前后逐位一致）：
|
||||
|
||||
| 变体 | 含义 | P50 | P95 | P99 |
|
||||
|---|---|---|---|---|
|
||||
| A_today | 现口径 | 33.49 | 46.14 | 64.69 |
|
||||
| B_xf_reuse | `gm_groups` 复用 features 已算的 `cum_xf` | **30.12** | 42.35 | 66.37 |
|
||||
| C_batch_cert | K 条候选的认证点并成 1 次 gaps | 38.05 | 50.17 | 59.86 |
|
||||
| D_both | B + C | 34.87 | 39.81 | 48.26 |
|
||||
| E_both_cont_n8 | B + C + 连续认证 n=8 | 43.73 | 51.08 | 65.68 |
|
||||
| F_worst_loop8 | **最坏情况**：所有候选被拒（今天逐条串行 K 次调用） | 80.22 | 88.44 | 89.55 |
|
||||
| G_worst_batch | 同一最坏情况但并成 1 次调用 | **37.14** | 46.06 | 52.22 |
|
||||
|
||||
- **复用 FK 是无条件收益**：省一次 `cum_xf`（约 3.4–3.7 ms），特征输出逐位一致，已直接实现进 `ml/common/kin_torch.py` 的 `gm_groups(..., xf=)` 与 `ml/common/features.py`（默认行为不变，新参数仅在调用方已算过 xf 时传）。
|
||||
- **批量认证是"典型换尾部"**：常见路径（第一条候选就通过）贵 4.6 ms；但今天一旦前面的候选被拒就要串行调用，实测最坏 **80.2 ms（12.5 Hz）→ 37.1 ms**，P99 64.7→48.3 ms（D vs A）。硬实时看的是最坏情况，所以这条值得做，但要按"典型略变慢、尾部大幅变稳"来汇报，不能只报 P50。
|
||||
- **仍未到 20 ms**：即便 D 档 P50 34.9 ms、P99 48.3 ms。连续认证挂在 D 上是 43.7 ms（E）。所以 GPU 侧下一步只能是更结构性的：认证降 float32（需先批量量化 f32/f64 判据差异，不能凭 3 个点下结论）、把宽相位做成可增量复用的核、或 `torch.compile`/CUDA Graph 把碎 kernel 合批。
|
||||
- **等价性闸门（纯效率改动不许改变任何结果）**：
|
||||
- 复用 FK：同一 256 题、同 seed、同配方重跑（`docs/experiment_round34_xf_reuse_check.json`），**22 个指标逐项与轮次 33 base 完全相同**（含 success 0.57421875、time_ok 0.390625、brakes 19.25、speed_ratio_max 1.660003662 等）。整批墙钟 296.3 → 318.0 s，是两次独立运行的差，**不能当作开销结论**：省下的那次 `cum_xf` 只在 B=1 交错实测里量到 −3.4 ms，批量评估里一次调用已覆盖 256 行，摊薄后不足以抵消运行间波动。
|
||||
- 批量认证（`--cert_batch 1`，默认 0 不变）：同一 256 题重跑 **22 项指标与 base 逐项相同**（`docs/experiment_round34_cert_batch_check.json`，success 0.57421875、time_ok 0.390625、speed_ratio_max 1.660003662 等全部一致），整批墙钟 296.3 → 306.9 s（批量口径把 8 次串行调用换成 1 次 K×256 点的大调用，均值略涨符合预期，与 B=1 交错实测方向一致）。合成夹具里也断言两条路径选到同一条候选、同一前缀长度、逐位相同的轨迹与证书。
|
||||
- **测试 32→34 项**：`CertBatchEquivalenceTest` 两条（候选 0 前缀不完整 → 选候选 1；所有候选第 2 步都不安全 → 两条路径都停在第 1 步）。写夹具时踩到一个自己的坑：桩里掩码写成 `q[:, :1]` 再 `[:, None]` 变成 3 维，广播出 (4,4,49) 才报"size 16"，是**测试桩错**不是库错——先证形状再改代码。
|
||||
|
||||
## 分支清理（2026-10-20）
|
||||
|
||||
## 分支清理（2026-10-20）
|
||||
|
||||
- `feat/fb-mix`（8e5431c）：反馈混合方案的中间分支，**已验证是 `exp/dagger-deploy` 的祖先**（所有提交都在后者历史里），本地/远端分支删除，内容保留在 exp/dagger-deploy。
|
||||
|
||||
@@ -52,7 +52,7 @@ def features(kin, q, q_prev, T, R, prefix, prefix_len, sub, sub_flag, gm=None, x
|
||||
out.append(sd)
|
||||
out.append(sub_flag[:, None].to(dt))
|
||||
if gm is None:
|
||||
out.append(kin.gm_groups(q, GP, GAP_CAP) / GAP_CAP) # 2026-10-20：只算特征用到的 8 组
|
||||
out.append(kin.gm_groups(q, GP, GAP_CAP, xf=xf) / GAP_CAP) # 2026-10-20：只算特征用到的 8 组；xf 复用不重复算 FK
|
||||
gm = None
|
||||
if gm is not None:
|
||||
out.append(torch.stack([gm[:, a * 7 + b].clamp(max=GAP_CAP) / GAP_CAP for a, b in GP], -1))
|
||||
|
||||
@@ -235,9 +235,10 @@ class Kin:
|
||||
if not pairs: return {'gm': gm.view(B, 49)}
|
||||
return {'gm': gm.view(B, 49), 'b': rep_b, 'i': pi, 'j': pj, 'gap': gap}
|
||||
|
||||
def gm_groups(self, q, gp, cap=40.0):
|
||||
def gm_groups(self, q, gp, cap=40.0, xf=None):
|
||||
"""只算指定组对 gp=[(a,b),...] 的最小间隙 [B,len(gp)]（特征用，避免 49 组全量）。
|
||||
数值与 gaps(q,cap)['gm'][:, a*7+b] 一致；组对在碰撞粗对里不存在时返回 cap。"""
|
||||
数值与 gaps(q,cap)['gm'][:, a*7+b] 一致；组对在碰撞粗对里不存在时返回 cap。
|
||||
xf 可传入调用者已算好的 cum_xf(q)，避免同一姿态重复算 FK（特征路径每周期省一次）。"""
|
||||
key = tuple(gp)
|
||||
if getattr(self, '_gp_key', None) != key:
|
||||
m = torch.full((49,), -1, dtype=torch.long, device=q.device)
|
||||
@@ -247,7 +248,7 @@ class Kin:
|
||||
sel = torch.isin(cg, torch.tensor([min(a, b) * 7 + max(a, b) for a, b in gp], device=q.device))
|
||||
self._gp = (m, sel, sel.nonzero(as_tuple=True)[0]); self._gp_key = key
|
||||
self._gp49, sel, idx = self._gp
|
||||
xf = self.cum_xf(q); Rs, Ts = xf; B = q.shape[0]
|
||||
xf = xf if xf is not None else self.cum_xf(q); Rs, Ts = xf; B = q.shape[0]
|
||||
with torch.no_grad():
|
||||
cc = torch.einsum('bkij,kj->bki', Rs[:, self.cl_g], self.cl_c.to(q.dtype)) + Ts[:, self.cl_g]
|
||||
d2 = (cc[:, self.cp_a[idx]] - cc[:, self.cp_b[idx]]).pow(2).sum(-1)
|
||||
|
||||
+2
-1
@@ -92,6 +92,7 @@ def main():
|
||||
ap.add_argument('--partial', type=int, default=1)
|
||||
ap.add_argument('--inc_cap', type=int, default=0, help='1=反馈叠加后再按每轴 VMAX·DT 限幅')
|
||||
ap.add_argument('--gap_margin', type=float, default=0.0, help='安全层要求的间隙余量 mm（>0 时宽相位 cap 自动抬到 margin+0.5）')
|
||||
ap.add_argument('--cert_batch', type=int, default=0, help='1=一次 gaps 调用覆盖全部候选（压最坏情况，典型路径略贵）')
|
||||
ap.add_argument('--max_sec', type=float, default=None)
|
||||
ap.add_argument('--seed', type=int, default=0)
|
||||
ap.add_argument('--dls', default='analytic', choices=['analytic', 'fd'])
|
||||
@@ -118,7 +119,7 @@ def main():
|
||||
safety_partial=(a.commit if (a.safety and a.partial) else 0), commit_n=a.commit,
|
||||
select=a.select, replan=a.replan, fb_gain=a.fb, fb_scale=a.fb_scale, fb_near=a.fb_near, fb_fallback=a.fb_fallback, fb_ki=a.ki, fb_decay=a.kdecay, fb_imax=a.kimax,
|
||||
fb_every=a.fb_every, safety_try=a.safety_try, sel_w=a.sel_w, dls_safety=a.dls_safety, fb_clear=a.fb_clear, fb_cmin=a.fb_cmin, sub_refresh=a.sub_refresh, fb_block_gain=a.fb_block_gain,
|
||||
inc_cap=a.inc_cap, gap_margin=a.gap_margin, return_traj=bool(a.trace))
|
||||
inc_cap=a.inc_cap, gap_margin=a.gap_margin, cert_batch=a.cert_batch, return_traj=bool(a.trace))
|
||||
wall = time.perf_counter() - t0
|
||||
result = {**meta, 'metrics': r, 'simulation_wall_s': wall}
|
||||
print(json.dumps(json_finite(result), ensure_ascii=False, allow_nan=False), flush=True)
|
||||
|
||||
+31
-16
@@ -163,7 +163,7 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
qp0=None, steps=1, z_scale=1.0, select='consist', oracle=None, replan='r3', sub_rule='cross',
|
||||
fb_gain=0.0, fb_scale=0.0, fb_near=0.2, fb_fallback=0.0, safety_partial=0, commit_n=3, collect=None,
|
||||
fb_exec=0.0, exec_safety=0, fb_ki=0.0, fb_decay=0.9, fb_imax=0.5, fb_every=1, safety_try=0, sel_w=0.1, dls_safety=0, fb_clear=0.0, fb_cmin=0.3, sub_refresh=0, fb_block_gain=1.0,
|
||||
inc_cap=0, gap_margin=0.0, progress=None):
|
||||
inc_cap=0, gap_margin=0.0, cert_batch=0, progress=None):
|
||||
"""use_sub=False：不用子目标（sf=0）；sub_override=(subs [B,M,6] 绝对关节角, n [B] 个数 或 need [B] bool)：冷启动时用给定途经点代替子目标头；
|
||||
qp0 [B,6]：起点的上一帧（默认 = q0，即静止起步）。steps：MeanFlow 采样段数；z_scale：噪声缩放；
|
||||
select：consist（部署口径：冷启动价值前瞻、之后与上一条已提交计划的一致性）/ value（每次都价值前瞻）/ first（不选，取第 0 条）。
|
||||
@@ -329,22 +329,37 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
|
||||
maxlen = torch.zeros(B, dtype=torch.long, device=device)
|
||||
chosen = torch.zeros(B, commit_n, 6, device=device); chosen_k = pick.clone()
|
||||
ntry = min(K_eff, safety_try) if safety_try > 0 else K_eff
|
||||
for kk in range(ntry):
|
||||
ck = inc[ar, order[:, kk], r:r + commit_n]
|
||||
pts, mids = execution_points(kin, qe, ck)
|
||||
allq = torch.cat([pts, mids], 1).reshape(-1, 6)
|
||||
gaps6 = kin.gaps(allq, gcap)['gm'].min(-1).values.view(B, 2 * commit_n)
|
||||
ok6 = gaps6 > gap_margin
|
||||
okstep = ok6[:, :commit_n] & ok6[:, commit_n:]
|
||||
plen = torch.cumprod(okstep.long(), 1).sum(1)
|
||||
take = plen > maxlen
|
||||
chosen = torch.where(take[:, None, None], ck, chosen); chosen_k = torch.where(take, order[:, kk], chosen_k)
|
||||
if cert_batch: # 一次 gaps 覆盖全部候选：典型路径略贵，但把「前面候选被拒→串行调用 K 次」的最坏情况封成 1 次
|
||||
cks = inc[ar[:, None], order[:, :ntry], r:r + commit_n].reshape(B * ntry, commit_n, 6)
|
||||
pts, mids = execution_points(kin, qe.repeat_interleave(ntry, 0), cks)
|
||||
gaps6 = kin.gaps(torch.cat([pts, mids], 1).reshape(-1, 6), gcap)['gm'].min(-1).values.view(B, ntry, 2 * commit_n)
|
||||
plen = torch.cumprod(((gaps6 > gap_margin)[:, :, :commit_n] &
|
||||
(gaps6 > gap_margin)[:, :, commit_n:]).long(), -1).sum(-1)
|
||||
maxlen = plen.max(-1).values
|
||||
best = plen.argmax(-1) # 与串行同口径：严格更大者胜 ⇒ 首个最大值
|
||||
chosen = cks.view(B, ntry, commit_n, 6)[ar, best]
|
||||
chosen_k = order[ar, best]
|
||||
if return_traj:
|
||||
cert_pts = torch.where(take[:, None, None], pts, cert_pts)
|
||||
cert_mids = torch.where(take[:, None, None], mids, cert_mids)
|
||||
cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
|
||||
maxlen = torch.maximum(maxlen, plen)
|
||||
if B == 1 and int(maxlen[0]) >= commit_n: break # 实时单题：首个全安全候选即停（安全层 16 点→2-4 点）
|
||||
cert_pts = pts.view(B, ntry, commit_n, 6)[ar, best]
|
||||
cert_mids = mids.view(B, ntry, commit_n, 6)[ar, best]
|
||||
cert_gaps = gaps6[ar, best]
|
||||
else:
|
||||
for kk in range(ntry):
|
||||
ck = inc[ar, order[:, kk], r:r + commit_n]
|
||||
pts, mids = execution_points(kin, qe, ck)
|
||||
allq = torch.cat([pts, mids], 1).reshape(-1, 6)
|
||||
gaps6 = kin.gaps(allq, gcap)['gm'].min(-1).values.view(B, 2 * commit_n)
|
||||
ok6 = gaps6 > gap_margin
|
||||
okstep = ok6[:, :commit_n] & ok6[:, commit_n:]
|
||||
plen = torch.cumprod(okstep.long(), 1).sum(1)
|
||||
take = plen > maxlen
|
||||
chosen = torch.where(take[:, None, None], ck, chosen); chosen_k = torch.where(take, order[:, kk], chosen_k)
|
||||
if return_traj:
|
||||
cert_pts = torch.where(take[:, None, None], pts, cert_pts)
|
||||
cert_mids = torch.where(take[:, None, None], mids, cert_mids)
|
||||
cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
|
||||
maxlen = torch.maximum(maxlen, plen)
|
||||
if B == 1 and int(maxlen[0]) >= commit_n: break # 实时单题：首个全安全候选即停（安全层 16 点→2-4 点）
|
||||
msk = (torch.arange(commit_n, device=device)[None] < maxlen[:, None])
|
||||
commit = torch.where(msk[..., None], chosen, torch.zeros_like(chosen))
|
||||
if return_traj: certified = msk.clone()
|
||||
|
||||
@@ -620,5 +620,85 @@ class SpeedAndMarginTest(unittest.TestCase):
|
||||
self.assertTrue((per['plans']['gaps'] > 0.5).all().item())
|
||||
|
||||
|
||||
class FeatureGeometryReuseTest(unittest.TestCase):
|
||||
"""#16 第①条：gm_groups 复用调用者已算好的 cum_xf，省一次 FK，但结果必须与原口径逐位一致。"""
|
||||
def test_gm_groups_with_supplied_xf_is_bitwise_identical(self):
|
||||
from ml.common.features import features, GP, GAP_CAP
|
||||
kin = Kin()
|
||||
q = torch.tensor([[-129.9, 46.68, 43.49, -97.46, -85.1, -139.7],
|
||||
[10.0, -20.0, 30.0, -40.0, 50.0, -60.0]])
|
||||
xf = kin.cum_xf(q)
|
||||
torch.testing.assert_close(kin.gm_groups(q, GP, GAP_CAP, xf=xf), kin.gm_groups(q, GP, GAP_CAP),
|
||||
rtol=0, atol=0)
|
||||
idx = [a * 7 + b for a, b in GP]
|
||||
table = kin.gaps(q, GAP_CAP, xf)['gm'][:, idx]
|
||||
torch.testing.assert_close(kin.gm_groups(q, GP, GAP_CAP), table, rtol=0, atol=0)
|
||||
z = torch.zeros(len(q), 6)
|
||||
f = features(kin, q, z, q[:, :3], torch.zeros(len(q), 9), z[:, None, :].expand(len(q), 3, 6),
|
||||
torch.zeros(len(q)), z, torch.zeros(len(q)))
|
||||
cols = 12 + 3 + 6 + 3 + 6 + 3 + 6 + 3 + 3 + 2 + 18 + 3 + 6 + 1 # 间隙块之前的特征数 = 75
|
||||
self.assertEqual(cols + 8, 83)
|
||||
torch.testing.assert_close(f[:, cols:cols + 8], table / GAP_CAP, rtol=0, atol=0)
|
||||
|
||||
|
||||
class CertBatchEquivalenceTest(unittest.TestCase):
|
||||
"""#16 第②条：把 K 条候选的认证并成一次 gaps 调用，选择结果必须与串行循环逐位一致。"""
|
||||
class StubModel:
|
||||
act_scale = 1.0
|
||||
|
||||
def __init__(self, act):
|
||||
self.act = act
|
||||
|
||||
def sample_k(self, feat, K, z, steps=1):
|
||||
B = feat.shape[0]
|
||||
return (self.act.expand(B, K, 16, 6).clone(), torch.zeros(B, device=feat.device),
|
||||
torch.zeros(B, 13, device=feat.device))
|
||||
|
||||
def run_both(self, acts, reject_first):
|
||||
class StepKin:
|
||||
def clamp_box(self, q):
|
||||
return q
|
||||
|
||||
def gaps(self, q, cap):
|
||||
bad = (q[:, 0] > 0.5) if reject_first else torch.zeros(len(q), dtype=torch.bool, device=q.device)
|
||||
return {'gm': torch.where(bad[:, None], -torch.ones(len(q), 49, device=q.device),
|
||||
torch.full((len(q), 49), 1.0, device=q.device))}
|
||||
|
||||
acts = torch.tensor(acts, dtype=torch.float32) # [K,16,6] 动作（±1 归一化）
|
||||
model = self.StubModel(acts)
|
||||
tasks = [{'q0': [0] * 6, 'T': [10, 0, 0]}] * 2 # B=2：批量索引形状必须在多行下也成立
|
||||
out = []
|
||||
for batched in (0, 1):
|
||||
with patch('ml.sim.closed_loop.features', return_value=torch.zeros(len(tasks), 83)), \
|
||||
patch('ml.sim.closed_loop.goal_err',
|
||||
side_effect=lambda kin, q, T, R: (torch.full((q.shape[0],), 10.0), torch.zeros(q.shape[0]))):
|
||||
_, per = rollout(model, StepKin(), tasks, 'cpu', K=acts.shape[0], safety=True, max_sec=0.04,
|
||||
return_traj=True, use_sub=False, select='first', replan='empty', commit_n=2,
|
||||
safety_partial=1, cert_batch=batched)
|
||||
out.append(per)
|
||||
return out
|
||||
|
||||
def test_batched_certification_selects_the_same_candidate(self):
|
||||
# 候选 0 第 2 步撞上 q0>0.5 的障碍 ⇒ 只有前缀 1 步；候选 1 全程安全 ⇒ 应被选中
|
||||
acts = [[[0.2, 0, 0, 0, 0, 0], [0.5, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14,
|
||||
[[0.1, 0, 0, 0, 0, 0], [0.1, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14]
|
||||
seq, batched = self.run_both(acts, reject_first=True)
|
||||
torch.testing.assert_close(batched['traj'], seq['traj'], rtol=0, atol=0)
|
||||
torch.testing.assert_close(batched['plans']['commit'], seq['plans']['commit'], rtol=0, atol=0)
|
||||
torch.testing.assert_close(batched['plans']['certified'], seq['plans']['certified'], rtol=0, atol=0)
|
||||
self.assertEqual(batched['brakes'].tolist(), seq['brakes'].tolist())
|
||||
self.assertAlmostEqual(seq['traj'][0, -1, 0].item(), 0.24, places=5) # 候选 1 的两步 0.1×STEP=0.12°
|
||||
|
||||
def test_batched_certification_keeps_partial_prefix_choice(self):
|
||||
# 所有候选都只走得了第 1 步 ⇒ 两条路径都取第 1 条候选、前缀长度 1
|
||||
acts = [[[0.2, 0, 0, 0, 0, 0], [0.5, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14,
|
||||
[[0.3, 0, 0, 0, 0, 0], [0.6, 0, 0, 0, 0, 0]] + [[0, 0, 0, 0, 0, 0]] * 14]
|
||||
seq, batched = self.run_both(acts, reject_first=True)
|
||||
torch.testing.assert_close(batched['traj'], seq['traj'], rtol=0, atol=0)
|
||||
torch.testing.assert_close(batched['plans']['commit'], seq['plans']['commit'], rtol=0, atol=0)
|
||||
self.assertAlmostEqual(seq['traj'][0, 1, 0].item(), 0.24, places=5)
|
||||
self.assertAlmostEqual(seq['traj'][0, 2, 0].item(), 0.24, places=5) # 第 2 步被拒后停在原地
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
Reference in New Issue
Block a user