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perf(cake_moe_grouped_gemm): run the split k512 wgrad tail whole on the least-loaded clusters on sm_107a - #8258
The Cake ragged BF16 grouped GEMM for MoE expert training has two variants. Both use FP32 accumulation. Each variant has forward (Y = X W^T), data-gradient (dX = G W) and weight-gradient (dW = G^T X, BF16 or FP32 output) programs. The deterministic variant, flashinfer.experimental.cake_moe_grouped_det_gemm, is bitwise reproducible from run to run. Callers select the non-deterministic variant, flashinfer.experimental.cake.cake_moe_grouped_gemm, with deterministic=False. The facade flashinfer.experimental.cake.grouped_mm also reaches it, but the facade default stays deterministic.
This PR changes only the sm_107a (Rubin) k512 wgrad programs of the non-deterministic variant. These are the weight-gradient programs on its unordered K=512 path: the half-release programs and the k256-tail programs, each with BF16 and FP32 output. The sm_100a and sm_103a programs, the forward and data-gradient programs, the deterministic variant and the Python API are unchanged. These programs split the K range of the last output tiles (the "tail") across several CTAs. Each CTA adds its FP32 partial tile into a shared FP32 sum with atomic reductions. The last CTA to arrive on a tile (a per-tile ticket) loads the sum, clears it and writes the output tile. Now ("slack placement" of the tail), one epilogue warp ranks the clusters by the load of their full-wave tiles while the first tiles run. If the less-loaded clusters have room (each such cluster stays within the largest load plus one tail share), each tail tile goes whole to one of them. That launch has no tail sums, no ticket and no finish, because one cluster stores the whole tile. If they do not have room, the split tail runs as before. The half-release programs also take the planned walk, as the sm_100a / sm_103a planned programs already do. FP32 accumulation is unchanged. As before for this variant, the summation order of a tail tile can differ.
This PR's own effect is the "vs previous non-deterministic Cake" column. The geomeans vs the previous kernel are listed below, with the lowest and highest row in parentheses:
ckpt ES owner 1.004 (0.976-1.018), ckpt ES unsorted 1.001 (0.993-1.019)
The largest single-row gains are dW down E4 M136064 skew 1.025, dW down E32 M131072 random 1.023, dW gate_up E4 M112576 1-empty asc 1.022 and dW gate_up E4 M136064 skew 1.020. Regressions, reported as is: 36 of the 100 rows are below 1.0 vs previous, all at 0.976 or above. The lowest are ckpt ES gate_up E8 M131072 bal 0.976, ckpt gate_up E16 M131072 bal 0.986 and ckpt down E4 M112576 bal 0.987. The lowest dW row is gate_up E4 M136064 1-empty 0.991.
Against the deterministic variant, dW owner rows are at parity (geomean 1.005, rows 0.988-1.017). The dW unsorted geomean is 1.080 (1.007-1.269), and ckpt is 1.001 (owner) / 1.020 (unsorted). Against the non-Cake SOTA, the geomeans (owner / unsorted) are dW 1.028 / 1.045, ckpt 1.221 / 1.242 and ckpt ES 1.218 / 1.239. 12 dW rows are below 1 vs SOTA (0.967-0.999), all with 4 experts; on those rows the SOTA is the per-expert cuBLAS loop.
The ckpt ES rows are faster than the deterministic variant (geomean 1.355 owner / 1.366 unsorted) because the earlier PR #8114 skips the forward recompute under checkpoint early stop, not because of this PR.
Baselines
deterministic Cake: cake_moe_grouped_det_gemm on main.
previous non-deterministic Cake: cake_moe_grouped_gemm on main (before this PR).
non-Cake SOTA: per row, the fastest supported of torch._grouped_mm (PyTorch 2.14.1+cu130), a per-expert cuBLAS loop (torch.mm per group) and the cuDNN grouped GEMM. It is the strongest applicable one because it is the fastest of the three on that row. Key in the SOTA cell: t = torch._grouped_mm, c = per-expert cuBLAS loop, d = cuDNN.
Coverage limits: cuDNN has no K-offset (weight gradient) form. The cuBLAS loop has no autograd form, so checkpointed rows compare with torch._grouped_mm. torch._grouped_mm requires the output dtype of mat_a, so FP32 dW rows compare with the cuBLAS loop.
Operations and shapes
dW: weight gradient, BF16 output unless the shape says fp32.
ckpt: one grouped GEMM in a non-reentrant checkpoint, forward + backward (dX and dW), early stop off (2 x fwd + bwd); ckpt ES: early stop on.
BF16; gate_up N=4096 K=6144, down N=6144 K=2048. E4 M112576 bal = E=4 experts, M=112576 total rows, balanced; skew / 1-empty = skewed / one empty group (descending); skew asc / 1-empty asc = the same sizes ascending; random = lognormal sizes (CV 0.35, seed 0, drawn order); fp32 = FP32 dW output.
Row class: unsorted = skew asc, 1-empty asc and random rows; owner = all other rows.
Only dW and checkpointed rows are listed, because they are the rows that run the changed programs.
Speedup = baseline latency / new latency, the median of the per-group paired ratios over 6 groups per row; the per-row table gives [min, max] over the groups. Their spread over the 100 rows, (max - min) / median, is median 3.85 %, p90 6.93 % vs deterministic Cake; median 3.67 %, p90 7.29 % vs previous non-deterministic Cake; and median 3.30 %, p90 8.04 % vs non-Cake SOTA.
Geometric mean of the per-row speedups (from unrounded ratios); in the deterministic Cake column the parentheses give the lowest and highest row.
GPU
operation
rows
count
vs deterministic Cake
vs non-Cake SOTA
vs previous non-deterministic Cake
Rubin (sm_107a)
dW
owner
22
1.005 (0.988-1.017)
1.028
1.006
Rubin (sm_107a)
dW
unsorted
14
1.080 (1.007-1.269)
1.045
1.010
Rubin (sm_107a)
ckpt
owner
18
1.001 (0.989-1.016)
1.221
0.998
Rubin (sm_107a)
ckpt
unsorted
14
1.020 (1.006-1.051)
1.242
1.002
Rubin (sm_107a)
ckpt ES
owner
18
1.355 (1.341-1.379)
1.218
1.004
Rubin (sm_107a)
ckpt ES
unsorted
14
1.366 (1.339-1.406)
1.239
1.001
Rubin (sm_107a)
NVIDIA Graphics Device (sm_107a, 208 SMs), CUDA 13.4, driver 615.71.10, torch 2.14.1+cu130 (CUDA 13.0); CUPTI GPU time of one call, cold L2, eager; median of paired groups in rotated order
GPU / architecture
workload / shape / mode
operation
deterministic Cake latency (ms)
non-Cake SOTA latency (ms)
previous non-deterministic Cake latency (ms)
new non-deterministic Cake latency (ms)
speedup vs deterministic Cake
speedup vs non-Cake SOTA
speedup vs previous non-deterministic Cake
sm_107a
gate_up E4 M112576 bal
dW
1.8425
1.9277 t
1.8364
1.8166
1.009 [0.998, 1.042]
1.061 [1.056, 1.102]
1.009 [0.995, 1.037]
sm_107a
gate_up E4 M112576 skew
dW
1.8833
1.8287 c
1.8760
1.8631
1.012 [1.000, 1.018]
0.980 [0.975, 1.052]
1.011 [1.001, 1.038]
sm_107a
gate_up E4 M112576 1-empty
dW
1.8985
1.8662 c
1.8856
1.8792
1.009 [1.000, 1.025]
0.998 [0.975, 1.026]
1.005 [0.985, 1.037]
sm_107a
gate_up E4 M112576 bal fp32
dW
1.8945
1.9170 c
1.8602
1.8753
1.009 [0.989, 1.022]
1.017 [0.982, 1.070]
0.998 [0.987, 1.002]
sm_107a
gate_up E4 M136064 bal
dW
2.2193
2.2121 c
2.2119
2.1998
1.009 [0.991, 1.021]
1.004 [0.989, 1.015]
1.005 [0.985, 1.018]
sm_107a
gate_up E4 M136064 skew
dW
2.2775
2.2369 c
2.2756
2.2408
1.013 [1.001, 1.038]
0.995 [0.979, 1.029]
1.020 [0.998, 1.029]
sm_107a
gate_up E4 M136064 1-empty
dW
2.2831
2.2275 c
2.2916
2.3087
0.988 [0.962, 1.008]
0.967 [0.953, 0.985]
0.991 [0.964, 1.031]
sm_107a
gate_up E4 M136064 bal fp32
dW
2.2868
2.2553 c
2.2507
2.2728
1.013 [0.983, 1.022]
0.994 [0.983, 1.092]
0.996 [0.976, 1.006]
sm_107a
gate_up E8 M131072 bal
dW
2.0970
2.1620 c
2.1161
2.1059
0.993 [0.981, 1.017]
1.027 [1.011, 1.040]
0.999 [0.993, 1.042]
sm_107a
gate_up E16 M131072 bal
dW
2.0916
2.2206 c
2.1083
2.0933
1.001 [0.974, 1.027]
1.063 [1.039, 1.085]
1.007 [0.982, 1.053]
sm_107a
gate_up E32 M131072 bal
dW
2.1792
2.3361 t
2.2122
2.1736
1.000 [0.983, 1.004]
1.067 [1.050, 1.086]
1.012 [0.990, 1.022]
sm_107a
gate_up E4 M112576 skew asc
dW
1.9502
1.8488 c
1.8861
1.8693
1.045 [1.028, 1.058]
0.987 [0.975, 1.007]
1.012 [1.001, 1.034]
sm_107a
gate_up E4 M112576 1-empty asc
dW
2.0168
1.8718 c
1.9023
1.8748
1.077 [1.048, 1.098]
1.005 [0.966, 1.022]
1.022 [0.991, 1.039]
sm_107a
gate_up E4 M136064 skew asc
dW
2.3577
2.2627 c
2.2888
2.2521
1.051 [1.007, 1.059]
1.007 [0.948, 1.086]
1.017 [0.976, 1.061]
sm_107a
gate_up E4 M136064 1-empty asc
dW
2.4067
2.2447 c
2.2816
2.2555
1.068 [1.062, 1.084]
0.995 [0.982, 1.013]
1.012 [0.990, 1.031]
sm_107a
gate_up E8 M131072 random
dW
2.2579
2.1792 c
2.1811
2.1952
1.032 [0.989, 1.075]
1.007 [0.992, 1.024]
0.994 [0.975, 1.011]
sm_107a
gate_up E16 M131072 random
dW
2.2048
2.2531 c
2.1572
2.1483
1.023 [1.014, 1.032]
1.049 [1.037, 1.069]
0.999 [0.994, 1.010]
sm_107a
gate_up E32 M131072 random
dW
2.2026
2.3912 t
2.2115
2.1700
1.012 [0.996, 1.025]
1.102 [1.095, 1.119]
1.019 [1.005, 1.037]
sm_107a
down E4 M112576 bal
dW
0.9316
1.0123 t
0.9351
0.9314
1.002 [0.994, 1.003]
1.084 [1.079, 1.166]
1.001 [0.996, 1.027]
sm_107a
down E4 M112576 skew
dW
0.9334
1.0133 c
0.9399
0.9208
1.017 [1.006, 1.025]
1.099 [1.095, 1.114]
1.019 [1.011, 1.024]
sm_107a
down E4 M112576 1-empty
dW
0.9352
0.9250 c
0.9328
0.9294
1.005 [1.003, 1.009]
0.996 [0.965, 1.073]
1.003 [1.002, 1.074]
sm_107a
down E4 M112576 bal fp32
dW
0.9491
0.9438 c
0.9457
0.9520
0.997 [0.938, 1.002]
0.998 [0.967, 1.011]
0.993 [0.947, 1.020]
sm_107a
down E4 M136064 bal
dW
1.1236
1.2126 t
1.1337
1.1256
1.001 [0.991, 1.010]
1.077 [1.074, 1.090]
1.005 [0.995, 1.017]
sm_107a
down E4 M136064 skew
dW
1.1279
1.1116 c
1.1405
1.1120
1.015 [1.014, 1.026]
1.002 [0.991, 1.081]
1.025 [1.015, 1.054]
sm_107a
down E4 M136064 1-empty
dW
1.1194
1.1154 c
1.1298
1.1246
1.001 [0.967, 1.043]
0.988 [0.979, 1.012]
1.004 [0.984, 1.020]
sm_107a
down E4 M136064 bal fp32
dW
1.1412
1.2196 c
1.1617
1.1469
0.998 [0.936, 1.024]
1.047 [0.996, 1.080]
1.001 [0.971, 1.038]
sm_107a
down E8 M131072 bal
dW
1.0907
1.1050 c
1.0845
1.0774
1.016 [0.999, 1.029]
1.027 [0.990, 1.092]
1.007 [0.984, 1.018]
sm_107a
down E16 M131072 bal
dW
1.0748
1.1388 c
1.0861
1.0774
0.994 [0.988, 1.022]
1.065 [1.038, 1.141]
1.003 [1.000, 1.035]
sm_107a
down E32 M131072 bal
dW
1.0791
1.1694 t
1.1109
1.0760
1.000 [0.968, 1.031]
1.084 [1.053, 1.112]
1.015 [0.993, 1.059]
sm_107a
down E4 M112576 skew asc
dW
1.0298
0.9346 c
0.9450
0.9340
1.100 [1.093, 1.113]
1.000 [0.992, 1.008]
1.011 [1.005, 1.016]
sm_107a
down E4 M112576 1-empty asc
dW
1.1668
0.9185 c
0.9335
0.9193
1.269 [1.221, 1.274]
0.999 [0.952, 1.002]
1.016 [0.977, 1.025]
sm_107a
down E4 M136064 skew asc
dW
1.2273
1.2508 c
1.1408
1.1232
1.094 [1.077, 1.100]
1.108 [0.982, 1.210]
1.017 [1.009, 1.023]
sm_107a
down E4 M136064 1-empty asc
dW
1.4408
1.1219 c
1.1397
1.1395
1.265 [1.256, 1.289]
0.986 [0.982, 0.994]
1.001 [0.993, 1.017]
sm_107a
down E8 M131072 random
dW
1.1030
1.2578 t
1.0921
1.0970
1.007 [1.002, 1.041]
1.149 [1.137, 1.157]
0.999 [0.979, 1.038]
sm_107a
down E16 M131072 random
dW
1.1770
1.2321 c
1.0970
1.0948
1.075 [1.072, 1.082]
1.124 [1.109, 1.172]
1.001 [0.994, 1.018]
sm_107a
down E32 M131072 random
dW
1.1430
1.2331 t
1.1236
1.0972
1.045 [1.019, 1.056]
1.133 [1.109, 1.155]
1.023 [0.986, 1.052]
sm_107a
gate_up E4 M112576 bal
ckpt
7.3652
9.6266 t
7.3118
7.3784
0.992 [0.960, 1.033]
1.309 [1.261, 1.314]
0.992 [0.963, 1.000]
sm_107a
gate_up E4 M112576 skew
ckpt
7.4451
9.4346 t
7.3735
7.3557
1.012 [0.995, 1.060]
1.282 [1.262, 1.303]
1.005 [0.995, 1.016]
sm_107a
gate_up E4 M112576 1-empty
ckpt
7.5456
9.7487 t
7.5041
7.5479
1.003 [0.992, 1.039]
1.291 [1.282, 1.323]
0.997 [0.979, 1.017]
sm_107a
gate_up E4 M136064 bal
ckpt
8.9174
11.6901 t
8.8176
8.8623
1.000 [0.990, 1.022]
1.317 [1.306, 1.329]
0.993 [0.975, 1.005]
sm_107a
gate_up E4 M136064 skew
ckpt
9.0026
11.6655 t
8.9509
9.0094
0.998 [0.993, 1.016]
1.295 [1.281, 1.307]
0.993 [0.988, 1.008]
sm_107a
gate_up E4 M136064 1-empty
ckpt
9.1178
11.9656 t
9.2132
9.1921
0.995 [0.971, 1.022]
1.302 [1.275, 1.316]
0.997 [0.987, 1.035]
sm_107a
gate_up E8 M131072 bal
ckpt
8.6391
10.0269 t
8.6701
8.5851
1.005 [0.983, 1.018]
1.170 [1.142, 1.180]
1.001 [0.993, 1.017]
sm_107a
gate_up E16 M131072 bal
ckpt
8.6646
9.5876 t
8.6368
8.6612
0.992 [0.969, 1.015]
1.101 [1.061, 1.127]
0.986 [0.965, 1.017]
sm_107a
gate_up E32 M131072 bal
ckpt
8.9755
9.5452 t
8.9335
8.8644
1.016 [0.986, 1.029]
1.075 [1.038, 1.104]
1.012 [0.988, 1.027]
sm_107a
gate_up E4 M112576 skew asc
ckpt
7.4697
9.5710 t
7.3609
7.3337
1.021 [1.001, 1.034]
1.305 [1.289, 1.316]
1.005 [0.991, 1.052]
sm_107a
gate_up E4 M112576 1-empty asc
ckpt
7.4571
9.7599 t
7.4368
7.3295
1.018 [1.014, 1.028]
1.331 [1.314, 1.340]
1.016 [0.992, 1.028]
sm_107a
gate_up E4 M136064 skew asc
ckpt
9.3352
12.1523 t
9.2977
9.1938
1.017 [0.985, 1.038]
1.316 [1.288, 1.328]
1.000 [0.991, 1.026]
sm_107a
gate_up E4 M136064 1-empty asc
ckpt
9.1113
11.8924 t
8.9628
8.8987
1.015 [0.991, 1.046]
1.337 [1.308, 1.352]
1.001 [0.992, 1.016]
sm_107a
gate_up E8 M131072 random
ckpt
8.7548
10.4145 t
8.7272
8.6179
1.014 [1.002, 1.058]
1.212 [1.195, 1.228]
1.008 [0.990, 1.022]
sm_107a
gate_up E16 M131072 random
ckpt
9.1860
10.5722 t
8.9773
9.1001
1.006 [0.959, 1.032]
1.160 [1.141, 1.187]
0.992 [0.961, 1.003]
sm_107a
gate_up E32 M131072 random
ckpt
8.9620
10.3142 t
8.8692
8.8926
1.011 [0.975, 1.018]
1.165 [1.100, 1.177]
0.996 [0.956, 1.005]
sm_107a
down E4 M112576 bal
ckpt
3.7223
4.5917 t
3.6987
3.7497
0.999 [0.970, 1.012]
1.225 [1.197, 1.243]
0.987 [0.958, 1.030]
sm_107a
down E4 M112576 skew
ckpt
3.7011
4.5849 t
3.7304
3.6769
1.004 [0.983, 1.041]
1.250 [1.215, 1.265]
1.004 [0.992, 1.033]
sm_107a
down E4 M112576 1-empty
ckpt
3.6984
4.7196 t
3.7464
3.7218
0.990 [0.967, 1.038]
1.271 [1.232, 1.300]
0.999 [0.976, 1.036]
sm_107a
down E4 M136064 bal
ckpt
4.4461
5.5319 t
4.4590
4.4407
1.003 [0.997, 1.020]
1.245 [1.239, 1.258]
1.004 [0.984, 1.017]
sm_107a
down E4 M136064 skew
ckpt
4.4840
5.7237 t
4.5026
4.5066
0.989 [0.955, 1.004]
1.271 [1.220, 1.277]
0.995 [0.958, 1.008]
sm_107a
down E4 M136064 1-empty
ckpt
4.5948
5.8212 t
4.5483
4.5732
1.004 [0.990, 1.033]
1.271 [1.258, 1.298]
0.995 [0.973, 1.024]
sm_107a
down E8 M131072 bal
ckpt
4.2912
4.9126 t
4.2774
4.2921
1.000 [0.983, 1.022]
1.141 [1.134, 1.164]
0.996 [0.978, 1.056]
sm_107a
down E16 M131072 bal
ckpt
4.3838
4.7841 t
4.3669
4.3335
1.013 [1.003, 1.039]
1.107 [1.097, 1.115]
1.009 [1.001, 1.038]
sm_107a
down E32 M131072 bal
ckpt
4.4268
4.8681 t
4.4235
4.4320
0.997 [0.993, 1.017]
1.098 [1.081, 1.114]
1.001 [0.994, 1.015]
sm_107a
down E4 M112576 skew asc
ckpt
3.7520
4.6339 t
3.7052
3.6491
1.028 [0.989, 1.045]
1.272 [1.214, 1.278]
1.017 [0.973, 1.064]
sm_107a
down E4 M112576 1-empty asc
ckpt
3.8728
4.7313 t
3.7223
3.7227
1.038 [1.032, 1.085]
1.267 [1.262, 1.323]
0.998 [0.993, 1.031]
sm_107a
down E4 M136064 skew asc
ckpt
4.5578
5.7276 t
4.4785
4.4583
1.021 [1.016, 1.039]
1.283 [1.274, 1.302]
1.003 [1.002, 1.014]
sm_107a
down E4 M136064 1-empty asc
ckpt
4.6512
5.8964 t
4.4162
4.4283
1.051 [1.039, 1.090]
1.332 [1.326, 1.346]
0.997 [0.985, 1.017]
sm_107a
down E8 M131072 random
ckpt
4.4376
5.2231 t
4.4297
4.3644
1.015 [0.973, 1.036]
1.191 [1.149, 1.208]
1.005 [0.963, 1.047]
sm_107a
down E16 M131072 random
ckpt
4.5176
5.1041 t
4.4499
4.4603
1.013 [1.006, 1.030]
1.145 [1.141, 1.155]
0.999 [0.993, 1.020]
sm_107a
down E32 M131072 random
ckpt
4.5983
5.0542 t
4.5147
4.5451
1.012 [1.006, 1.023]
1.111 [1.105, 1.148]
0.996 [0.989, 1.004]
sm_107a
gate_up E4 M112576 bal
ckpt ES
7.6970
7.1768 t
5.6368
5.6419
1.359 [1.334, 1.397]
1.272 [1.259, 1.280]
0.998 [0.980, 1.021]
sm_107a
gate_up E4 M112576 skew
ckpt ES
7.4217
6.9819 t
5.5796
5.5314
1.342 [1.323, 1.363]
1.266 [1.248, 1.271]
1.007 [0.996, 1.019]
sm_107a
gate_up E4 M112576 1-empty
ckpt ES
7.4015
7.1260 t
5.5182
5.4785
1.354 [1.324, 1.362]
1.300 [1.292, 1.309]
1.007 [0.998, 1.024]
sm_107a
gate_up E4 M136064 bal
ckpt ES
9.2574
8.6708 t
6.9146
6.8522
1.356 [1.315, 1.390]
1.264 [1.248, 1.289]
1.014 [1.001, 1.019]
sm_107a
gate_up E4 M136064 skew
ckpt ES
9.0413
8.5866 t
6.7743
6.7321
1.350 [1.327, 1.369]
1.275 [1.254, 1.289]
1.007 [0.984, 1.059]
sm_107a
gate_up E4 M136064 1-empty
ckpt ES
9.0810
8.6640 t
6.7278
6.7482
1.350 [1.319, 1.371]
1.287 [1.279, 1.297]
1.001 [0.984, 1.014]
sm_107a
gate_up E8 M131072 bal
ckpt ES
8.9141
7.5018 t
6.4589
6.6432
1.347 [1.298, 1.368]
1.143 [1.113, 1.180]
0.976 [0.944, 1.052]
sm_107a
gate_up E16 M131072 bal
ckpt ES
8.6387
7.1561 t
6.4411
6.4168
1.347 [1.314, 1.385]
1.113 [1.089, 1.139]
1.004 [0.994, 1.024]
sm_107a
gate_up E32 M131072 bal
ckpt ES
8.6734
6.8432 t
6.4842
6.4977
1.341 [1.304, 1.353]
1.050 [1.032, 1.115]
1.003 [0.970, 1.028]
sm_107a
gate_up E4 M112576 skew asc
ckpt ES
7.7073
7.1693 t
5.7194
5.6144
1.364 [1.327, 1.422]
1.277 [1.224, 1.302]
1.019 [0.986, 1.039]
sm_107a
gate_up E4 M112576 1-empty asc
ckpt ES
7.6136
7.1777 t
5.5592
5.5809
1.364 [1.298, 1.430]
1.289 [1.243, 1.298]
0.999 [0.964, 1.053]
sm_107a
gate_up E4 M136064 skew asc
ckpt ES
9.1248
8.6933 t
6.8108
6.7338
1.362 [1.329, 1.381]
1.294 [1.273, 1.302]
1.014 [0.989, 1.024]
sm_107a
gate_up E4 M136064 1-empty asc
ckpt ES
9.3027
8.9709 t
6.9052
6.9584
1.350 [1.301, 1.367]
1.288 [1.262, 1.315]
0.993 [0.987, 1.019]
sm_107a
gate_up E8 M131072 random
ckpt ES
8.6963
7.7283 t
6.4986
6.4811
1.340 [1.330, 1.360]
1.193 [1.181, 1.211]
1.003 [0.990, 1.022]
sm_107a
gate_up E16 M131072 random
ckpt ES
8.7390
7.5550 t
6.5294
6.5265
1.339 [1.323, 1.382]
1.162 [1.137, 1.175]
1.006 [0.976, 1.015]
sm_107a
gate_up E32 M131072 random
ckpt ES
9.1773
7.8851 t
6.7587
6.7305
1.342 [1.311, 1.389]
1.173 [1.123, 1.230]
1.000 [0.960, 1.057]
sm_107a
down E4 M112576 bal
ckpt ES
3.7168
3.4814 t
2.7749
2.7478
1.356 [1.336, 1.361]
1.267 [1.258, 1.273]
1.009 [0.996, 1.024]
sm_107a
down E4 M112576 skew
ckpt ES
3.6939
3.4206 t
2.7214
2.6982
1.367 [1.308, 1.426]
1.268 [1.222, 1.280]
1.003 [0.976, 1.017]
sm_107a
down E4 M112576 1-empty
ckpt ES
3.6949
3.5074 t
2.7604
2.7309
1.356 [1.339, 1.377]
1.283 [1.268, 1.301]
1.018 [0.991, 1.049]
sm_107a
down E4 M136064 bal
ckpt ES
4.5870
4.1937 t
3.3909
3.3805
1.362 [1.302, 1.397]
1.241 [1.216, 1.265]
1.007 [0.976, 1.025]
sm_107a
down E4 M136064 skew
ckpt ES
4.5458
4.2479 t
3.3483
3.3193
1.379 [1.338, 1.429]
1.280 [1.256, 1.293]
1.008 [0.989, 1.052]
sm_107a
down E4 M136064 1-empty
ckpt ES
4.5383
4.2788 t
3.3073
3.3115
1.366 [1.346, 1.375]
1.292 [1.278, 1.309]
0.996 [0.986, 1.055]
sm_107a
down E8 M131072 bal
ckpt ES
4.3555
3.7702 t
3.2415
3.2364
1.350 [1.317, 1.363]
1.167 [1.152, 1.181]
0.996 [0.994, 1.025]
sm_107a
down E16 M131072 bal
ckpt ES
4.3528
3.5837 t
3.2232
3.2145
1.353 [1.334, 1.381]
1.113 [1.072, 1.128]
1.006 [0.955, 1.016]
sm_107a
down E32 M131072 bal
ckpt ES
4.4005
3.5430 t
3.2544
3.2348
1.365 [1.348, 1.379]
1.095 [1.084, 1.107]
1.007 [0.998, 1.012]
sm_107a
down E4 M112576 skew asc
ckpt ES
3.7772
3.4692 t
2.7302
2.7448
1.372 [1.312, 1.382]
1.263 [1.211, 1.266]
0.993 [0.969, 0.997]
sm_107a
down E4 M112576 1-empty asc
ckpt ES
3.7808
3.4958 t
2.7044
2.7214
1.394 [1.348, 1.438]
1.285 [1.240, 1.306]
0.993 [0.964, 0.999]
sm_107a
down E4 M136064 skew asc
ckpt ES
4.6462
4.3129 t
3.3949
3.3836
1.379 [1.342, 1.397]
1.274 [1.244, 1.284]
1.008 [0.976, 1.027]
sm_107a
down E4 M136064 1-empty asc
ckpt ES
4.6442
4.3782 t
3.3103
3.3053
1.406 [1.392, 1.416]
1.324 [1.318, 1.332]
1.004 [0.991, 1.007]
sm_107a
down E8 M131072 random
ckpt ES
4.3471
3.8795 t
3.1831
3.2083
1.359 [1.345, 1.367]
1.209 [1.201, 1.218]
0.994 [0.989, 1.045]
sm_107a
down E16 M131072 random
ckpt ES
4.5435
3.9160 t
3.3100
3.3195
1.371 [1.357, 1.384]
1.181 [1.170, 1.188]
0.999 [0.985, 1.005]
sm_107a
down E32 M131072 random
ckpt ES
4.5741
3.7983 t
3.3156
3.3037
1.379 [1.357, 1.430]
1.145 [1.127, 1.151]
0.996 [0.987, 1.018]
The family's other Cake variant (deterministic, flashinfer.experimental.cake_moe_grouped_det_gemm) does not change; both variants ship on sm_100a (B200), sm_103a (GB300) and sm_107a (Rubin); this PR changes only sm_107a programs.
Summary by CodeRabbit
New Features
On supported hardware, grouped GEMM can place whole tail tiles on less-loaded clusters when conditions allow; otherwise, it retains the existing split-tail handling.
Updated build availability information for select 512-wide weight-gradient configurations.
The SM107a k512 weight-gradient kernel now selects between whole-item slack-tail placement and the existing split-tail traversal. The JIT registry and build manifest add updated program records, and the package documentation describes the supported routes.
Changes: Cake k512 tail scheduling
Layer / File(s)
Summary
Publish tail placement state flashinfer/experimental/cake/cake_moe_grouped_gemm/csrc/cake_moe_grouped_gemm/cake_moe_grouped_gemm_594235460aac7658daab_kernel.cu
The kernel adds shared memory and a readiness barrier for tail mode and owner state. A selector evaluates cluster loads and publishes slack-tail placement when its conditions pass.
Consume tail placement state flashinfer/experimental/cake/cake_moe_grouped_gemm/csrc/cake_moe_grouped_gemm/cake_moe_grouped_gemm_594235460aac7658daab_kernel.cu
The load, MMA, and epilogue loops use the published state. Slack-tail mode assigns whole tail items to clusters; the fallback retains split-tail traversal.
Wire generated routes and document support flashinfer/experimental/cake/cake_moe_grouped_gemm/cake_jit.py, flashinfer/experimental/cake/cake_moe_grouped_gemm/build.json, flashinfer/experimental/cake/cake_moe_grouped_gemm/README.md, docs/api/cake_moe_grouped_gemm.rst
The k512 routes bind updated SM107a program records and generated library entries. The README and API feature table describe slack-tail support.
sequenceDiagram
participant TailSelector
participant SharedTailState
participant LoadRole
participant MMARole
participant EpilogueRole
TailSelector->>SharedTailState: Publish mode and owner
TailSelector->>SharedTailState: Signal ks_ready
LoadRole->>SharedTailState: Wait for ks_ready and read mode and owner
MMARole->>SharedTailState: Wait for ks_ready and read mode and owner
EpilogueRole->>SharedTailState: Wait for ks_ready and read mode and owner
Loading
Possibly related issues
flashinfer-ai/flashinfer#7268 — Covers k512 and split-K tail builds; this change updates the SM107a k512 weight-gradient tail path.
flashinfer-ai/flashinfer#7213 — Tracks the Cake grouped GEMM family audit and k512 tail-build work; this change updates k512 tail behavior.
flashinfer-ai/flashinfer#8070 — Requests non-deterministic grouped GEMM support including wgrad; this change updates wgrad tail scheduling but does not establish the broader requested support.
Suggested reviewers:aleozlx
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Minor · Update the numerical contract for slack placement. · cake_moe_grouped_gemm.rst:42-44
docs/api/cake_moe_grouped_gemm.rst:42-44 Minor · Functional Correctness · Quick win
Update the numerical contract for slack placement.
This paragraph says every k512 tail tile is split and reduced through sums. On SM107, slack placement can instead process each tail tile whole. Describe that case and retain splitting as the fallback. (raw.githubusercontent.com)
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Review comment at @docs/api/cake_moe_grouped_gemm.rst around lines 42 - 44:
Update the numerical contract in the paragraph describing 512-wide k tiles to
state that SM107 slack placement may process each tail tile whole, while
splitting tail tiles into k-step units and reducing their fp32 sums remains the
fallback. Preserve the existing description of accumulation and final rounding
for the fallback.
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Review comments at @docs/api/cake_moe_grouped_gemm.rst:
- Around line 42-44: Update the numerical contract in the paragraph describing
512-wide k tiles to state that SM107 slack placement may process each tail tile
whole, while splitting tail tiles into k-step units and reducing their fp32 sums
remains the fallback. Preserve the existing description of accumulation and
final rounding for the fallback.
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The Cake ragged BF16 grouped GEMM for MoE expert training has two variants. Both use FP32 accumulation. Each variant has forward (
Y = X W^T), data-gradient (dX = G W) and weight-gradient (dW = G^T X, BF16 or FP32 output) programs. The deterministic variant,flashinfer.experimental.cake_moe_grouped_det_gemm, is bitwise reproducible from run to run. Callers select the non-deterministic variant,flashinfer.experimental.cake.cake_moe_grouped_gemm, withdeterministic=False. The facadeflashinfer.experimental.cake.grouped_mmalso reaches it, but the facade default stays deterministic.This PR changes only the sm_107a (Rubin) k512 wgrad programs of the non-deterministic variant. These are the weight-gradient programs on its unordered K=512 path: the half-release programs and the k256-tail programs, each with BF16 and FP32 output. The sm_100a and sm_103a programs, the forward and data-gradient programs, the deterministic variant and the Python API are unchanged. These programs split the K range of the last output tiles (the "tail") across several CTAs. Each CTA adds its FP32 partial tile into a shared FP32 sum with atomic reductions. The last CTA to arrive on a tile (a per-tile ticket) loads the sum, clears it and writes the output tile. Now ("slack placement" of the tail), one epilogue warp ranks the clusters by the load of their full-wave tiles while the first tiles run. If the less-loaded clusters have room (each such cluster stays within the largest load plus one tail share), each tail tile goes whole to one of them. That launch has no tail sums, no ticket and no finish, because one cluster stores the whole tile. If they do not have room, the split tail runs as before. The half-release programs also take the planned walk, as the sm_100a / sm_103a planned programs already do. FP32 accumulation is unchanged. As before for this variant, the summation order of a tail tile can differ.
This PR's own effect is the "vs previous non-deterministic Cake" column. The geomeans vs the previous kernel are listed below, with the lowest and highest row in parentheses:
The largest single-row gains are dW down E4 M136064 skew 1.025, dW down E32 M131072 random 1.023, dW gate_up E4 M112576 1-empty asc 1.022 and dW gate_up E4 M136064 skew 1.020. Regressions, reported as is: 36 of the 100 rows are below 1.0 vs previous, all at 0.976 or above. The lowest are ckpt ES gate_up E8 M131072 bal 0.976, ckpt gate_up E16 M131072 bal 0.986 and ckpt down E4 M112576 bal 0.987. The lowest dW row is gate_up E4 M136064 1-empty 0.991.
Against the deterministic variant, dW owner rows are at parity (geomean 1.005, rows 0.988-1.017). The dW unsorted geomean is 1.080 (1.007-1.269), and ckpt is 1.001 (owner) / 1.020 (unsorted). Against the non-Cake SOTA, the geomeans (owner / unsorted) are dW 1.028 / 1.045, ckpt 1.221 / 1.242 and ckpt ES 1.218 / 1.239. 12 dW rows are below 1 vs SOTA (0.967-0.999), all with 4 experts; on those rows the SOTA is the per-expert cuBLAS loop.
The ckpt ES rows are faster than the deterministic variant (geomean 1.355 owner / 1.366 unsorted) because the earlier PR #8114 skips the forward recompute under checkpoint early stop, not because of this PR.
Baselines
cake_moe_grouped_det_gemmon main.cake_moe_grouped_gemmon main (before this PR).torch._grouped_mm(PyTorch 2.14.1+cu130), a per-expert cuBLAS loop (torch.mmper group) and the cuDNN grouped GEMM. It is the strongest applicable one because it is the fastest of the three on that row. Key in the SOTA cell: t =torch._grouped_mm, c = per-expert cuBLAS loop, d = cuDNN.torch._grouped_mm.torch._grouped_mmrequires the output dtype ofmat_a, so FP32 dW rows compare with the cuBLAS loop.Operations and shapes
Speedup = baseline latency / new latency, the median of the per-group paired ratios over 6 groups per row; the per-row table gives [min, max] over the groups. Their spread over the 100 rows, (max - min) / median, is median 3.85 %, p90 6.93 % vs deterministic Cake; median 3.67 %, p90 7.29 % vs previous non-deterministic Cake; and median 3.30 %, p90 8.04 % vs non-Cake SOTA.
Geometric mean of the per-row speedups (from unrounded ratios); in the deterministic Cake column the parentheses give the lowest and highest row.
Rubin (sm_107a)
NVIDIA Graphics Device (sm_107a, 208 SMs), CUDA 13.4, driver 615.71.10, torch 2.14.1+cu130 (CUDA 13.0); CUPTI GPU time of one call, cold L2, eager; median of paired groups in rotated order
The family's other Cake variant (deterministic,
flashinfer.experimental.cake_moe_grouped_det_gemm) does not change; both variants ship on sm_100a (B200), sm_103a (GB300) and sm_107a (Rubin); this PR changes only sm_107a programs.Summary by CodeRabbit