feat(cake_kda): with grad, add non-deterministic Cake KDA context-parallel (KCP) autograd backend by yyihuang · Pull Request #8218 · flashinfer-ai/flashinfer

yyihuang · 2026-11-01T15:51:40Z

Cake KDA (Kimi Delta Attention) chunked training for packed variable-length sequences at head dim 128 has four variants: deterministic and non-deterministic, each in a single-rank form and a context-parallel (KCP) form. This PR adds the non-deterministic KCP package, flashinfer.experimental.cake_kda_train_kcp, and its facade flashinfer.cake_kda_train_kcp. Every facade entry (chunk_kda_kcp, chunk_kda_fwd_kcp, chunk_kda_bwd_kcp and the workspace helpers) takes deterministic: bool = True. True runs the deterministic KCP package flashinfer.experimental.cake_kda_det_train_kcp, and False runs this one. The package's own entries accept only deterministic=False. Each rank runs the chunk stages of the non-deterministic single-rank package from #8191 (flashinfer.experimental.cake.cake_kda_train) on its own shard. The KCP state walks, merge and exchanges come from the deterministic package. The PR also adds an opt-in lean local arena for the KCP backward, set with local_arena="lean" and lean_passes=2 on the workspace. The tables below use the default arena. They compare this package with two baselines on the same workload: the deterministic Cake KCP package and the strongest non-Cake implementation, flash-linear-attention (FLA).

Workload: one packed batch of 4 documents (42024, 57573, 73122 and 88673 tokens, T = 261392), split over the ranks of a torch.distributed process group, at H = 4, 16 and 64. q, k and v are BF16 [T, H, 128], with q and k L2-normalized. The gate g is FP32 [T, H, 128], pre-activated, in (-5, 0). beta is BF16 [T, H]. The timed step is the KCP KDA step alone, without the short convolution. On one node, the context-parallel sizes are CP8, CP4 and CP2 on the 8-GPU B200 and B300 nodes, and CP4 and CP2 on the 4-GPU R200 node. Across nodes, NVIDIA GB300 (sm_103a, 152 SMs, 4 GPUs per node) runs CP8 on 2 nodes and CP16 on 4 nodes of one NVL72 rack, with one process per GPU. NCCL carries every collective over the rack's multi-node NVLink. CP16 uses T = 261376 (42021, 57569, 73118 and 88668 tokens), because FLA's zigzag partition needs T to be a multiple of 2W = 32.

Token layouts, for W ranks: contiguous gives rank r part r of W equal parts. zigzag (global) gives rank r parts r and 2W-1-r of 2W equal parts. zigzag_per_document applies the zigzag split inside every document. Ops: fwd is the forward with autograd recording. bwd is the backward of a forward that runs untimed before it. ckpt is one torch.utils.checkpoint(use_reentrant=False) step: forward, recompute and backward, which is 2 x forward + backward.

Baselines: deterministic Cake is flashinfer.experimental.cake_kda_det_train_kcp on this PR's base, which is bitwise identical from run to run. It uses its own partition and context, and so does the new package. The strongest non-Cake implementation ("SOTA" in the tables) is FLA 0.6.0's context-parallel KDA. It is fla.ops.kda.chunk_kda with cp_context=fla.ops.cp.build_cp_context(cu_seqlens, group, layout) (Triton) and safe_gate=True, which is FLA's path for gates bounded below. It runs on the same pre-activated operands, with scale=None and no in-kernel L2 norm, gate or beta sigmoid. FLA's contiguous and zigzag layouts are equal-token partitions. Here they give every rank the same tokens, in the same order, as the Cake shard. FLA has no per-document zigzag layout, so the zigzag_per_document rows show unsupported in the SOTA columns. We chose FLA because it is the reference open-source KDA implementation and the only non-Cake KDA training kernel with context parallelism that we know of. Speedup = baseline latency / new latency, so a value above 1 means the new package is faster.

Protocol: benchmarks/bench_cake_kda_train_kcp.py with arms det nd control fla_cp, in eager execution. Timing uses CUDA events. Each iteration runs after an L2 flush, a device synchronize and a barrier, and counts the slowest rank. Per op, every arm gets one untimed call, followed by 3 groups. In every group the arms run in the palindromic order det, nd, control, fla_cp, fla_cp, control, nd, det, and every visit takes 8 timed iterations. Each cell is the median over the groups of each group's median. control is a second visit of the new package. Its ratio to the new package measures the protocol's own noise, and the acceptance band is 0.98-1.02. drift is the change of the new package's median between its first and last group, with a limit of 2 %. A † after the op marks a row where the drift or the control ratio is outside its limit. Software: torch 2.14.1+cu130 (CUDA runtime 13.0), FLA 0.6.0, and Cake kernels built with CUDA 13.4.

Against the deterministic Cake KCP package, the new package is at parity on every architecture. No row of the 270 is below 0.98. The lowest is B300 CP8 contiguous H16 fwd at 0.9801, and it is a † row. Parity is expected for two reasons. The KCP state walks, merge and exchanges are the deterministic package's. The rank-local chunk stages come from #8191, and on one GPU that package is itself at parity with the deterministic package (speedup vs det 0.989-1.018 in #8191). Against FLA 0.6.0's context-parallel KDA, the new package is faster on every row that FLA supports. The SOTA ranges exclude the zigzag_per_document rows, because FLA has no per-document zigzag layout.

GPU / architecture	rows	speedup vs det	speedup vs det, geometric mean	speedup vs SOTA	† rows
B200 (`sm_100a`)	81	0.985-1.015	1.001	1.84-8.14x	7
B300 (`sm_103a`)	81	0.980-1.052	1.000	1.87-8.34x	3
R200 (`sm_107a`)	54	0.998-1.028	1.003	2.04-9.99x	3
GB300 (`sm_103a`, 2 and 4 nodes)	54	0.987-1.032	1.003	1.79-4.10x	11

NVIDIA B200 (`sm_100a`, 148 SMs, 8 GPUs)

All B200 rows come from one 8-GPU node. A 4-GPU run of the CP4 and CP2 configurations on another node of the same partition, with the same build, benchmark and protocol, gave speedup vs det 0.976-1.019 and speedup vs SOTA 1.84-8.19x.

GPU / architecture	workload (shape, dtype, layout)	op	deterministic Cake (ms)	strongest non-Cake (ms)	new non-deterministic Cake (ms)	speedup vs det	speedup vs SOTA
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd †	0.953	5.414	0.940	1.0138	5.7591
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd †	1.778	7.497	1.773	1.0025	4.2279
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt †	4.135	17.760	4.192	0.9863	4.2362
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd	1.602	6.618	1.605	0.9982	4.1234
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	3.333	10.435	3.329	1.0010	3.1344
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.833	23.187	5.857	0.9960	3.9588
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	4.693	11.962	4.705	0.9976	2.5426
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	10.370	24.522	10.344	1.0025	2.3707
B200 `sm_100a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	19.124	47.920	19.174	0.9974	2.4992
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd †	0.926	5.011	0.929	0.9966	5.3947
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd	1.902	6.365	1.877	1.0132	3.3907
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.369	16.107	4.362	1.0017	3.6928
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	1.718	6.518	1.719	0.9993	3.7906
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	3.408	9.732	3.401	1.0020	2.8614
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.971	22.121	5.972	0.9998	3.7043
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	4.528	12.261	4.532	0.9991	2.7057
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	10.125	24.765	10.060	1.0065	2.4618
B200 `sm_100a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	18.823	48.698	18.630	1.0104	2.6140
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	0.917	unsupported	0.905	1.0133	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	1.668	unsupported	1.664	1.0023	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	3.959	unsupported	3.920	1.0099	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	1.657	unsupported	1.659	0.9988	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	3.373	unsupported	3.365	1.0024	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.842	unsupported	5.832	1.0017	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	4.595	unsupported	4.599	0.9992	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	10.444	unsupported	10.356	1.0085	n/a
B200 `sm_100a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	19.322	unsupported	19.386	0.9967	n/a
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd	1.116	6.655	1.119	0.9977	5.9498
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd	2.274	14.420	2.257	1.0075	6.3884
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.537	26.206	4.545	0.9982	5.7659
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd	2.464	8.849	2.468	0.9982	3.5854
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	5.422	20.168	5.424	0.9996	3.7183
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	9.520	36.346	9.515	1.0005	3.8198
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	8.455	17.477	8.439	1.0019	2.0709
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	20.002	47.838	20.142	0.9931	2.3750
B200 `sm_100a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	37.081	82.357	37.468	0.9897	2.1981
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	1.137	9.243	1.136	1.0011	8.1352
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd	2.218	9.558	2.216	1.0009	4.3140
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.528	27.988	4.531	0.9993	6.1768
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	2.741	11.782	2.747	0.9979	4.2895
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	5.597	15.787	5.599	0.9996	2.8193
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	10.249	39.113	10.253	0.9996	3.8149
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	8.689	23.201	8.679	1.0012	2.6732
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd †	20.026	43.555	20.221	0.9903	2.1539
B200 `sm_100a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt †	37.546	89.427	37.958	0.9892	2.3560
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	1.087	unsupported	1.085	1.0021	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	2.160	unsupported	2.152	1.0034	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.399	unsupported	4.395	1.0009	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	2.614	unsupported	2.616	0.9992	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	5.557	unsupported	5.554	1.0005	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	9.943	unsupported	9.943	1.0000	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	8.369	unsupported	8.371	0.9998	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	20.007	unsupported	20.156	0.9926	n/a
B200 `sm_100a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	37.343	unsupported	37.390	0.9987	n/a
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd	1.641	7.981	1.642	0.9995	4.8608
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd	3.332	14.932	3.324	1.0024	4.4922
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt †	6.030	29.539	6.041	0.9982	4.8896
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd	4.366	11.918	4.373	0.9986	2.7256
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	9.814	26.266	9.833	0.9981	2.6713
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	18.166	48.773	17.898	1.0150	2.7251
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	16.023	29.325	15.950	1.0046	1.8386
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	39.513	77.373	39.168	1.0088	1.9754
B200 `sm_100a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	72.964	135.322	72.670	1.0040	1.8621
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	1.650	9.830	1.653	0.9987	5.9481
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd	3.351	16.618	3.327	1.0072	4.9954
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	5.868	35.161	5.864	1.0006	5.9958
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	4.648	14.481	4.651	0.9993	3.1133
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	10.073	28.711	10.043	1.0029	2.8587
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	18.665	55.932	18.546	1.0064	3.0158
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	16.281	33.376	16.144	1.0085	2.0673
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	39.409	83.868	39.073	1.0086	2.1464
B200 `sm_100a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	73.375	149.759	73.095	1.0038	2.0488
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	1.577	unsupported	1.573	1.0024	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	3.205	unsupported	3.199	1.0019	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	5.602	unsupported	5.604	0.9997	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	4.553	unsupported	4.552	1.0003	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	10.068	unsupported	10.025	1.0043	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	18.397	unsupported	18.671	0.9853	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	16.345	unsupported	16.524	0.9892	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	39.405	unsupported	39.190	1.0055	n/a
B200 `sm_100a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	73.715	unsupported	73.584	1.0018	n/a

NVIDIA B300 (`sm_103a`, 148 SMs, 8 GPUs)

GPU / architecture	workload (shape, dtype, layout)	op	deterministic Cake (ms)	strongest non-Cake (ms)	new non-deterministic Cake (ms)	speedup vs det	speedup vs SOTA
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd	0.965	5.315	0.963	1.0021	5.5211
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd †	1.805	7.271	1.801	1.0022	4.0370
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.230	17.341	4.215	1.0037	4.1145
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd †	1.640	6.403	1.673	0.9801	3.8262
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	3.188	10.082	3.186	1.0005	3.1644
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.713	22.426	5.707	1.0010	3.9295
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	4.557	11.625	4.548	1.0020	2.5563
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	9.609	23.410	9.614	0.9995	2.4350
B300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	17.851	46.195	17.860	0.9995	2.5865
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	0.924	4.974	0.923	1.0013	5.3878
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd	1.687	6.226	1.691	0.9973	3.6811
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.018	15.859	4.077	0.9855	3.8898
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	1.695	6.255	1.699	0.9979	3.6815
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	3.258	9.486	3.253	1.0014	2.9158
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.807	21.469	5.824	0.9972	3.6867
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	4.335	11.905	4.341	0.9986	2.7423
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	9.414	23.672	9.415	0.9999	2.5144
B300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	17.265	46.899	17.273	0.9995	2.7151
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	0.968	unsupported	0.962	1.0065	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	1.777	unsupported	1.781	0.9978	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.540	unsupported	4.315	1.0521	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	1.652	unsupported	1.650	1.0014	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	3.212	unsupported	3.215	0.9993	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.743	unsupported	5.750	0.9989	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	4.437	unsupported	4.440	0.9994	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	9.578	unsupported	9.580	0.9997	n/a
B300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	17.620	unsupported	17.621	1.0000	n/a
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd	1.094	6.469	1.097	0.9975	5.8982
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd	2.157	14.135	2.161	0.9981	6.5423
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.424	25.627	4.435	0.9974	5.7783
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd	2.426	8.357	2.426	1.0001	3.4445
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	5.135	19.577	5.138	0.9996	3.8105
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	9.129	34.847	9.130	0.9999	3.8170
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	8.095	17.009	8.098	0.9997	2.1005
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	17.738	45.604	17.747	0.9995	2.5698
B300 `sm_103a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	33.108	79.130	33.138	0.9991	2.3879
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	1.107	9.257	1.110	0.9981	8.3432
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd †	2.125	9.250	2.128	0.9985	4.3465
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.456	28.043	4.451	1.0010	6.3006
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	2.680	11.622	2.679	1.0002	4.3376
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	5.283	15.201	5.284	0.9998	2.8771
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	9.782	38.180	9.770	1.0012	3.9079
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	8.341	22.576	8.344	0.9997	2.7058
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	17.895	41.595	17.900	0.9997	2.3237
B300 `sm_103a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	33.583	86.139	33.577	1.0002	2.5654
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	1.060	unsupported	1.067	0.9932	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	2.046	unsupported	2.043	1.0014	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.323	unsupported	4.302	1.0048	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	2.559	unsupported	2.564	0.9982	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	5.253	unsupported	5.260	0.9986	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	9.500	unsupported	9.506	0.9994	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	7.998	unsupported	8.003	0.9994	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	17.769	unsupported	17.762	1.0004	n/a
B300 `sm_103a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	32.965	unsupported	32.984	0.9994	n/a
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd	1.608	7.777	1.608	1.0000	4.8368
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd	3.117	14.377	3.115	1.0008	4.6156
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt	5.654	28.464	5.645	1.0016	5.0423
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd	4.253	11.365	4.265	0.9972	2.6646
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	9.187	24.993	9.191	0.9996	2.7193
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	16.851	46.371	16.857	0.9996	2.7509
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	15.261	28.548	15.261	1.0000	1.8706
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	34.365	73.660	34.441	0.9978	2.1387
B300 `sm_103a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	64.340	130.342	64.430	0.9986	2.0230
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	1.599	9.592	1.605	0.9963	5.9779
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd	3.102	16.009	3.103	0.9995	5.1585
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	5.666	34.122	5.658	1.0015	6.0307
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	4.495	13.665	4.501	0.9988	3.0360
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	9.415	27.840	9.424	0.9990	2.9542
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	17.546	53.461	17.553	0.9996	3.0457
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	15.441	32.523	15.440	1.0001	2.1064
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	34.467	80.058	34.491	0.9993	2.3211
B300 `sm_103a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	64.586	144.134	64.788	0.9969	2.2247
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	1.532	unsupported	1.534	0.9985	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	3.075	unsupported	3.074	1.0006	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	5.478	unsupported	5.463	1.0028	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	4.406	unsupported	4.414	0.9983	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	9.377	unsupported	9.376	1.0002	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	17.353	unsupported	17.364	0.9994	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	15.539	unsupported	15.549	0.9994	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	34.445	unsupported	34.464	0.9994	n/a
B300 `sm_103a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	64.845	unsupported	64.894	0.9993	n/a

NVIDIA R200 (`sm_107a`, 212 SMs, 4 GPUs)

The H4 and H16 rows come from a second run of the same protocol, because in the first run 7 of those 12 configurations had the drift or the control ratio outside its limit. The H64 rows come from the first run.

GPU / architecture	workload (shape, dtype, layout)	op	deterministic Cake (ms)	strongest non-Cake (ms)	new non-deterministic Cake (ms)	speedup vs det	speedup vs SOTA
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd	0.747	5.431	0.745	1.0034	7.2912
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd	1.308	11.263	1.300	1.0068	8.6668
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt	3.005	21.119	2.959	1.0155	7.1380
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd †	1.503	6.824	1.500	1.0022	4.5501
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	2.988	15.024	2.980	1.0030	5.0424
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.598	27.340	5.601	0.9994	4.8811
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	4.552	11.323	4.557	0.9989	2.4848
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	10.593	28.342	10.561	1.0031	2.6837
R200 `sm_107a`	CP4 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	18.929	49.521	18.907	1.0012	2.6192
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	0.756	7.540	0.755	1.0018	9.9882
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd †	1.220	7.533	1.218	1.0019	6.1873
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	2.834	22.863	2.815	1.0069	8.1231
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	1.596	8.775	1.598	0.9985	5.4915
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	3.070	10.773	3.043	1.0086	3.5399
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.832	28.281	5.833	0.9998	4.8484
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	4.955	14.322	4.954	1.0003	2.8911
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	10.893	24.462	10.884	1.0008	2.2475
R200 `sm_107a`	CP4 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	20.065	52.725	20.041	1.0012	2.6309
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	0.713	unsupported	0.708	1.0079	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	1.175	unsupported	1.168	1.0062	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	2.764	unsupported	2.736	1.0101	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	1.567	unsupported	1.563	1.0024	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	3.049	unsupported	3.050	0.9996	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.820	unsupported	5.804	1.0027	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	4.591	unsupported	4.588	1.0005	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	10.608	unsupported	10.596	1.0011	n/a
R200 `sm_107a`	CP4 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	19.095	unsupported	19.080	1.0008	n/a
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd	0.994	6.475	0.992	1.0018	6.5287
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd	1.855	11.208	1.848	1.0037	6.0635
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt	3.465	23.139	3.456	1.0025	6.6954
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd	2.590	8.636	2.588	1.0006	3.3368
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	5.425	17.372	5.417	1.0015	3.2071
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	10.206	33.422	10.206	1.0000	3.2749
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	8.502	17.300	8.496	1.0006	2.0363
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	19.949	42.913	19.920	1.0015	2.1543
R200 `sm_107a`	CP2 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	36.229	75.970	36.161	1.0019	2.1009
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	0.991	7.983	0.989	1.0025	8.0724
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd	1.844	12.922	1.835	1.0046	7.0407
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	3.464	28.302	3.470	0.9983	8.1571
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	2.700	10.117	2.689	1.0041	3.7632
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	5.659	18.998	5.507	1.0277	3.4499
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	10.514	37.740	10.505	1.0008	3.5925
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	8.632	19.375	8.629	1.0003	2.2455
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	20.068	45.893	20.045	1.0012	2.2896
R200 `sm_107a`	CP2 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	36.421	83.746	36.390	1.0009	2.3013
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	0.954	unsupported	0.952	1.0020	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd	1.739	unsupported	1.737	1.0014	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt	3.286	unsupported	3.270	1.0049	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	2.629	unsupported	2.632	0.9991	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd †	5.501	unsupported	5.488	1.0022	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	10.358	unsupported	10.365	0.9994	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	8.727	unsupported	8.724	1.0003	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	20.113	unsupported	20.068	1.0022	n/a
R200 `sm_107a`	CP2 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	36.833	unsupported	36.788	1.0012	n/a

NVIDIA GB300 (`sm_103a`, 152 SMs, 2 and 4 nodes of one NVL72 rack)

A rerun of the configurations marked † did not bring them within the limits, so the table keeps the first run. The CP8 contiguous H4 and H16 rows ran while a test job used the rack's other two nodes.

GPU / architecture	workload (shape, dtype, layout)	op	deterministic Cake (ms)	strongest non-Cake (ms)	new non-deterministic Cake (ms)	speedup vs det	speedup vs SOTA
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=4, BF16 packed	fwd	1.422	2.994	1.378	1.0315	2.1722
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=4, BF16 packed	bwd	1.723	3.678	1.708	1.0084	2.1530
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=4, BF16 packed	ckpt	4.944	8.965	4.994	0.9899	1.7949
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=16, BF16 packed	fwd	1.439	3.471	1.424	1.0111	2.4381
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=16, BF16 packed	bwd	2.107	5.080	2.093	1.0067	2.4270
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=16, BF16 packed	ckpt	5.406	11.644	5.341	1.0121	2.1802
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=64, BF16 packed	fwd	2.574	6.078	2.571	1.0014	2.3643
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=64, BF16 packed	bwd	4.935	11.703	4.950	0.9970	2.3642
GB300 `sm_103a`	CP16 contiguous, 4 docs, T=261376, H=64, BF16 packed	ckpt	9.156	23.554	9.185	0.9968	2.5643
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=4, BF16 packed	fwd	1.298	3.043	1.315	0.9867	2.3139
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=4, BF16 packed	bwd	1.677	3.272	1.642	1.0215	1.9929
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=4, BF16 packed	ckpt †	4.973	9.036	4.886	1.0179	1.8493
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=16, BF16 packed	fwd †	1.328	3.500	1.323	1.0043	2.6465
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=16, BF16 packed	bwd †	2.051	4.750	2.035	1.0082	2.3343
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=16, BF16 packed	ckpt †	5.146	11.523	5.099	1.0093	2.2601
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=64, BF16 packed	fwd	2.468	6.772	2.467	1.0004	2.7451
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=64, BF16 packed	bwd	4.874	12.300	4.882	0.9983	2.5194
GB300 `sm_103a`	CP16 zigzag, 4 docs, T=261376, H=64, BF16 packed	ckpt	8.961	25.346	8.961	1.0000	2.8284
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=4, BF16 packed	fwd †	1.295	unsupported	1.293	1.0020	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=4, BF16 packed	bwd	1.627	unsupported	1.620	1.0044	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=4, BF16 packed	ckpt †	4.727	unsupported	4.706	1.0045	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=16, BF16 packed	fwd	1.344	unsupported	1.333	1.0077	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=16, BF16 packed	bwd	2.068	unsupported	2.074	0.9973	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=16, BF16 packed	ckpt †	5.093	unsupported	5.092	1.0002	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=64, BF16 packed	fwd	2.636	unsupported	2.638	0.9990	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=64, BF16 packed	bwd	5.136	unsupported	5.143	0.9985	n/a
GB300 `sm_103a`	CP16 zigzag per document, 4 docs, T=261376, H=64, BF16 packed	ckpt	9.612	unsupported	9.604	1.0008	n/a
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	fwd †	1.296	5.307	1.293	1.0019	4.1037
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	bwd †	1.697	6.814	1.703	0.9960	3.9999
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.709	16.958	4.688	1.0045	3.6174
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	fwd	1.677	6.384	1.669	1.0046	3.8248
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	bwd	2.920	9.696	2.919	1.0003	3.3220
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.944	22.179	5.944	1.0000	3.7314
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	fwd	4.451	11.330	4.454	0.9993	2.5435
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	bwd	9.035	22.577	9.029	1.0006	2.5004
GB300 `sm_103a`	CP8 contiguous, 4 docs, T=261392, H=64, BF16 packed	ckpt	17.075	44.916	17.079	0.9998	2.6299
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	fwd	1.301	4.982	1.304	0.9980	3.8206
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	bwd	1.740	5.792	1.724	1.0091	3.3591
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=4, BF16 packed	ckpt	4.751	15.359	4.750	1.0003	3.2337
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	fwd	1.762	6.178	1.762	1.0001	3.5073
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	bwd	3.045	8.999	3.037	1.0025	2.9630
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=16, BF16 packed	ckpt	6.056	20.866	6.019	1.0063	3.4669
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	fwd	4.320	11.752	4.313	1.0016	2.7247
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	bwd	8.874	22.949	8.870	1.0005	2.5874
GB300 `sm_103a`	CP8 zigzag, 4 docs, T=261392, H=64, BF16 packed	ckpt	16.700	45.891	16.698	1.0002	2.7483
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	fwd	1.304	unsupported	1.295	1.0069	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	bwd †	1.686	unsupported	1.692	0.9966	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=4, BF16 packed	ckpt †	4.728	unsupported	4.707	1.0044	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	fwd	1.717	unsupported	1.712	1.0032	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	bwd	3.010	unsupported	3.002	1.0026	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=16, BF16 packed	ckpt	5.997	unsupported	6.005	0.9987	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	fwd	4.374	unsupported	4.373	1.0002	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	bwd	9.099	unsupported	9.111	0.9987	n/a
GB300 `sm_103a`	CP8 zigzag per document, 4 docs, T=261392, H=64, BF16 packed	ckpt	17.080	unsupported	17.089	0.9995	n/a

The other Cake KDA variants are the deterministic single-rank package (flashinfer.experimental.cake_kda_det_train, facade flashinfer.cake_kda_det_train) and its KCP form (flashinfer.experimental.cake_kda_det_train_kcp, facade flashinfer.cake_kda_det_train_kcp), both on main, plus the non-deterministic single-rank package (#8191) and its activation-checkpoint hand-off (#8209), and the family covers sm_100a, sm_103a and sm_107a.

Summary by CodeRabbit

New Features
- Added an experimental context-parallel KDA training API with selectable deterministic and non-deterministic execution, supporting forward and backward operations.
- Added support for compatible GPU architectures, along with build-time kernel loading and workspace configuration.
- Added a distributed benchmark for comparing execution modes and reporting timing, memory, and numerical results.
Documentation
- Added usage guidance, supported configurations, and operational limits for the experimental API.
Tests
- Added coverage for API routing, compatibility, workspace behavior, uploads, and benchmark/build configuration.

coderabbitai · 2026-11-01T15:51:48Z

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Files selected for processing: 1

flashinfer/experimental/cake_kda_train_kcp/README.md

Files skipped from review as they are similar to previous changes: 1

flashinfer/experimental/cake_kda_train_kcp/README.md

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Walkthrough

This pull request adds a non-deterministic Cake KDA context-parallel training package and a facade that selects deterministic or non-deterministic execution. It also adds CUDA kernels, build and workspace support, a distributed benchmark, documentation, repository wiring, and validation tests.

Changes: KCP backend

Layer / File(s)	Summary
Public API and workspace integration `flashinfer/cake_kda_train_kcp.py`, `flashinfer/experimental/cake_kda_train_kcp/*`, `flashinfer/experimental/cake/cake_kda_train/cake_backend.py`	Adds mode selection and routing, lazy exports, cross-package partition handling, workspace layout registration, and backend documentation.
Build manifests and kernel dispatch `flashinfer/experimental/cake_kda_train_kcp/build*.json`, `flashinfer/experimental/cake_kda_train_kcp/cake_build.py`, `flashinfer/experimental/cake_kda_train_kcp/cake_kcp_jit.py`	Adds architecture-specific manifests, source checks, prebuilt and JIT loading, kernel records, launch selection, and test-only records.
CUDA kernels and FFI launchers `flashinfer/experimental/cake_kda_train_kcp/csrc/*`	Adds TVM-FFI input checks, tensor-map setup, launch bindings, and CUDA kernels for training and related stages.
Distributed benchmark `benchmarks/bench_cake_kda_train_kcp.py`	Adds deterministic and non-deterministic benchmark arms, optional FLA arms, timing and memory measurements, numerical comparisons, control checks, and JSON reporting.
Validation suites `tests/experimental/test_cake_kda_train_kcp_*.py`	Adds API, build, FSDP, workspace, upload-ordering, graph-replay, resource-release, and distributed parity tests.
Repository wiring `.github/*`, `pyproject.toml`, `scripts/check_cake_build_manifests.py`, `tests/test_helpers/cake_package.py`	Registers ownership, labeling, package data, manifest checks, and test package mapping.

Estimated code review effort: 5 (Critical) | ~90 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Caller
  participant Facade as flashinfer.cake_kda_train_kcp
  participant Package as cake_kda_train_kcp
  participant JIT as cake_kcp_jit
  participant Binding as CUDA FFI binding
  Caller->>Facade: Call KCP entry point with deterministic=False
  Facade->>Package: Route inputs and workspace
  Package->>JIT: Select kernel record and launch plan
  JIT->>Binding: Load module and launch kernel
  Binding-->>Package: Return from kernel launch
  Package-->>Facade: Return outputs and saved state
  Facade-->>Caller: Return training result

Possibly related issues

[Feature][CAKE] Non-deterministic packed KDA with gradient, with non-KCP and KCP options #8064: Requests a non-deterministic packed KDA implementation with KCP and non-KCP options; this PR adds the non-deterministic KCP package and dispatch facade.
[Feature][CAKE] Improve CAKE KDA KCP training performance and memory usage at CP8–CP64 #7815: Covers KCP performance, workspace pressure, communication, and checkpoint costs; this PR adds KCP benchmarking and lean backward arena support.

Suggested reviewers: aleozlx

Merge Risk: Minimal · previous review f479e

No actionable issue is identified in the selected documentation change; it is mergeable after normal checks.

Pre-merge checks: 3 · 2

Failed checks: 2 warnings

Check name	Status	Explanation	Resolution
Description check		The description is detailed and directly related to the pull request, but it omits required template sections. It does not provide the Related Issues section, checklist completion, or the Experimental…	Reformat the description using the repository template. Add the Related Issues section, complete the pre-commit and test checklists, and complete the Experimental Track section with the tracking issue, owner, reason for the experimental pat…
Docstring Coverage		Docstring coverage is 34.23% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 3 passed

Check name	Status	Explanation
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Title check		The title names the main change: adding a non-deterministic Cake KDA context-parallel (KCP) autograd backend. The changeset adds the package flashinfer.experimental.cake_kda_train_kcp, its facade rout…

Full details: Title check

Explanation

The title names the main change: adding a non-deterministic Cake KDA context-parallel (KCP) autograd backend. The changeset adds the package flashinfer.experimental.cake_kda_train_kcp, its facade routing via deterministic=False in flashinfer/cake_kda_train_kcp.py, and the supporting kernels, bindings, build manifests, and tests. The title is a single sentence, but the leading 'with grad' phrase is slightly awkward and does not name the facade or the deterministic-selection flag.

Full details: Description check

Explanation

The description is detailed and directly related to the pull request, but it omits required template sections. It does not provide the Related Issues section, checklist completion, or the Experimental Track declaration with a tracking issue, owner and graduation plan, and experimental test targets.

Resolution

Reformat the description using the repository template. Add the Related Issues section, complete the pre-commit and test checklists, and complete the Experimental Track section with the tracking issue, owner, reason for the experimental path, graduation plan, and valid experimental-tests targets under tests/experimental/.

Full details: Docstring Coverage

Explanation

Docstring coverage is 34.23% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 444 functions across 44 files. (1 skipped: 1 unsupported.)

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

Finishing Touches

Generate unit tests (beta)

Create a new PR

Autofix · Keep fixing CodeRabbit findings and required CI, and resolving merge conflicts

yyihuang · 2026-11-01T15:51:56Z

@flashinfer-bot run tests/experimental/test_cake_kda_train.py tests/experimental/test_cake_kda_train_api.py tests/experimental/test_cake_kda_train_kcp.py tests/experimental/test_cake_kda_train_kcp_api.py tests/experimental/test_cake_kda_train_kcp_build.py tests/experimental/test_cake_kda_train_kcp_upload.py tests/experimental/test_cake_kda_train_kcp_fsdp_walk.py tests/experimental/test_cake_kda_train_kcp_shared_ws.py tests/experimental/test_cake_kda_train_kcp_lean.py tests/experimental/test_cake_kda_train_kcp_dist.py tests/experimental/test_cake_kda_det_train_kcp_api.py

yyihuang · 2026-11-01T19:47:02Z

@flashinfer-bot run tests/experimental/test_cake_kda_train.py tests/experimental/test_cake_kda_train_api.py tests/experimental/test_cake_kda_train_kcp.py tests/experimental/test_cake_kda_train_kcp_api.py tests/experimental/test_cake_kda_train_kcp_build.py tests/experimental/test_cake_kda_train_kcp_upload.py tests/experimental/test_cake_kda_train_kcp_fsdp_walk.py tests/experimental/test_cake_kda_train_kcp_shared_ws.py tests/experimental/test_cake_kda_train_kcp_lean.py tests/experimental/test_cake_kda_train_kcp_dist.py tests/experimental/test_cake_kda_det_train_kcp_api.py

flashinfer-bot

CODEOWNERS gap attestation (not a code review).

A code owner of a changed path has approved this PR:

@yzh119 -> benchmarks/bench_cake_kda_train_kcp.py
@yzh119 -> flashinfer/cake_kda_train_kcp.py
@yzh119 -> flashinfer/experimental/cake/cake_kda_train/cake_backend.py

The following files match only the catch-all rule; this approval covers that technical gap under the one-approval policy. GitHub still requires each owned path's own owner.

flashinfer/experimental/cake_kda_train_kcp/build.json
flashinfer/experimental/cake_kda_train_kcp/build.test.json
flashinfer/experimental/cake_kda_train_kcp/cake_build.py
flashinfer/experimental/cake_kda_train_kcp/cake_kcp_jit.py
flashinfer/experimental/cake_kda_train_kcp/cake_kcp_plan.py
flashinfer/experimental/cake_kda_train_kcp/cake_kcp.py
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_ffi_checks.h
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1ac972f0cb7cc7b13cf7_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1ac972f0cb7cc7b13cf7_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1c66573e87150245374b_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1c66573e87150245374b_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_32b1aa657b082f2f4cb7_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_32b1aa657b082f2f4cb7_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_3808ea4d567b82ea21a4_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_3808ea4d567b82ea21a4_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_5a20cd12bb62bb6f3ac5_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_5a20cd12bb62bb6f3ac5_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_64ba2583f28f7d4c0102_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_64ba2583f28f7d4c0102_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_6fac4410ba86c48541c4_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_6fac4410ba86c48541c4_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_7658fcc297c6fd906d53_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_7658fcc297c6fd906d53_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_89f5ff59f03eb170e3bf_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_89f5ff59f03eb170e3bf_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9a3f306bacf8c8a1547d_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9a3f306bacf8c8a1547d_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9cfe05bce78410f1304b_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9cfe05bce78410f1304b_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_afe02fb600500ba3556f_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_afe02fb600500ba3556f_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1477ed31fad32a5c2c9_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1477ed31fad32a5c2c9_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1a95a8ee8110e47558e_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1a95a8ee8110e47558e_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c6958a0a4ed5cb55a9a1_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c6958a0a4ed5cb55a9a1_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c811c1b52ed680d0a515_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c811c1b52ed680d0a515_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_f227dd21f1f3cb464b29_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_f227dd21f1f3cb464b29_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/.clang-format
flashinfer/experimental/cake_kda_train_kcp/.gitignore
flashinfer/experimental/cake_kda_train_kcp/init.py
flashinfer/experimental/cake_kda_train_kcp/README.md
.github/CODEOWNERS
.github/labeler.yml
pyproject.toml
scripts/check_cake_build_manifests.py
tests/experimental/test_cake_kda_train_kcp_api.py
tests/experimental/test_cake_kda_train_kcp_build.py
tests/experimental/test_cake_kda_train_kcp_dist.py
tests/experimental/test_cake_kda_train_kcp_fsdp_walk.py
tests/experimental/test_cake_kda_train_kcp_lean.py
tests/experimental/test_cake_kda_train_kcp.py
tests/experimental/test_cake_kda_train_kcp_shared_ws.py
tests/experimental/test_cake_kda_train_kcp_upload.py
tests/test_helpers/cake_package.py

Head: 1430b48

coderabbitai

Actionable comments posted: 1

Fix comments: Fix CodeRabbit comments on this PR

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Inline comments:
Review comments at @flashinfer/experimental/cake_kda_train_kcp/README.md:
- Around line 425-429: Update the generated-source file list in the README:
change the context-parallel program count to sixteen and include `kcp_fwd_o`
among the `_kcp` programs; describe only `kcp_dav` as using the suffix-free
naming pattern.

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Reviewing files that changed from the base of the PR and between 91a3ea8 and 1430b48.

Files selected for processing: 61

.github/CODEOWNERS
.github/labeler.yml
benchmarks/bench_cake_kda_train_kcp.py
flashinfer/cake_kda_train_kcp.py
flashinfer/experimental/cake/cake_kda_train/cake_backend.py
flashinfer/experimental/cake_kda_train_kcp/.gitignore
flashinfer/experimental/cake_kda_train_kcp/README.md
flashinfer/experimental/cake_kda_train_kcp/__init__.py
flashinfer/experimental/cake_kda_train_kcp/build.json
flashinfer/experimental/cake_kda_train_kcp/build.test.json
flashinfer/experimental/cake_kda_train_kcp/cake_build.py
flashinfer/experimental/cake_kda_train_kcp/cake_kcp.py
flashinfer/experimental/cake_kda_train_kcp/cake_kcp_jit.py
flashinfer/experimental/cake_kda_train_kcp/cake_kcp_plan.py
flashinfer/experimental/cake_kda_train_kcp/csrc/.clang-format
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_ffi_checks.h
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1ac972f0cb7cc7b13cf7_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1ac972f0cb7cc7b13cf7_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1c66573e87150245374b_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1c66573e87150245374b_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_32b1aa657b082f2f4cb7_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_32b1aa657b082f2f4cb7_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_3808ea4d567b82ea21a4_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_3808ea4d567b82ea21a4_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_5a20cd12bb62bb6f3ac5_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_5a20cd12bb62bb6f3ac5_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_64ba2583f28f7d4c0102_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_64ba2583f28f7d4c0102_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_6fac4410ba86c48541c4_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_6fac4410ba86c48541c4_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_7658fcc297c6fd906d53_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_7658fcc297c6fd906d53_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_89f5ff59f03eb170e3bf_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_89f5ff59f03eb170e3bf_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9a3f306bacf8c8a1547d_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9a3f306bacf8c8a1547d_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9cfe05bce78410f1304b_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_9cfe05bce78410f1304b_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_afe02fb600500ba3556f_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_afe02fb600500ba3556f_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1477ed31fad32a5c2c9_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1477ed31fad32a5c2c9_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1a95a8ee8110e47558e_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1a95a8ee8110e47558e_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c6958a0a4ed5cb55a9a1_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c6958a0a4ed5cb55a9a1_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c811c1b52ed680d0a515_kcp_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_c811c1b52ed680d0a515_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_f227dd21f1f3cb464b29_binding.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_f227dd21f1f3cb464b29_kernel.cu
pyproject.toml
scripts/check_cake_build_manifests.py
tests/experimental/test_cake_kda_train_kcp.py
tests/experimental/test_cake_kda_train_kcp_api.py
tests/experimental/test_cake_kda_train_kcp_build.py
tests/experimental/test_cake_kda_train_kcp_dist.py
tests/experimental/test_cake_kda_train_kcp_fsdp_walk.py
tests/experimental/test_cake_kda_train_kcp_lean.py
tests/experimental/test_cake_kda_train_kcp_shared_ws.py
tests/experimental/test_cake_kda_train_kcp_upload.py
tests/test_helpers/cake_package.py

Files not fully reviewed because their changes were unavailable: 9

flashinfer/experimental/cake_kda_train_kcp/cake_kcp_plan.py
flashinfer/experimental/cake_kda_train_kcp/cake_kcp.py
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_1ac972f0cb7cc7b13cf7_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_64ba2583f28f7d4c0102_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1477ed31fad32a5c2c9_kcp_kernel.cu
flashinfer/experimental/cake_kda_train_kcp/csrc/cake_kda_train_b1a95a8ee8110e47558e_kcp_kernel.cu
tests/experimental/test_cake_kda_train_kcp.py
tests/experimental/test_cake_kda_train_kcp_dist.py
tests/experimental/test_cake_kda_train_kcp_shared_ws.py

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yyihuang · 2026-11-01T20:42:03Z

@flashinfer-bot run tests/experimental/test_cake_kda_train.py tests/experimental/test_cake_kda_train_api.py tests/experimental/test_cake_kda_train_kcp.py tests/experimental/test_cake_kda_train_kcp_api.py tests/experimental/test_cake_kda_train_kcp_build.py tests/experimental/test_cake_kda_train_kcp_upload.py tests/experimental/test_cake_kda_train_kcp_fsdp_walk.py tests/experimental/test_cake_kda_train_kcp_shared_ws.py tests/experimental/test_cake_kda_train_kcp_lean.py tests/experimental/test_cake_kda_train_kcp_dist.py tests/experimental/test_cake_kda_det_train_kcp_api.py

…ning package flashinfer.experimental.cake_kda_train_kcp is the deterministic=False counterpart of flashinfer.experimental.cake_kda_det_train_kcp: packed KDA (Kimi Delta Attention) chunked training forward and backward with the packed batch split over the ranks of a torch.distributed process group (KCP), for sm_100a, sm_103a and sm_107a. The facade flashinfer.cake_kda_train_kcp (partition_kcp, build_context_kcp, chunk_kda_kcp, chunk_kda_fwd_kcp, chunk_kda_bwd_kcp, kda_workspace_bytes_kcp, kda_saved_bytes_kcp, kda_comm_bytes_kcp) takes deterministic: bool = True. True runs the deterministic package unchanged; False runs this package; False under torch.use_deterministic_algorithms(True) runs the deterministic package with one warning per process. The package's own entries raise ValueError unless deterministic is False. The KCP programs (the state walks, the transfer walk, the affine merge, kcp_fwd_o, kcp_dav) and the exchanges are copies of the deterministic package's under this package's library prefix; kcp_fwd_o takes its (chunk, head) tiles from Cluster Launch Control as the deterministic package's does. Each rank runs the chunk stages of flashinfer.experimental.cake.cake_kda_train at its own shape, so the backward runs that package's S1 pairs on the rows its registry lists: out, dq, dk, dv and dbeta are bitwise the deterministic package's and dg differs from it by FP32 rounding only. build.json, build.test.json and csrc/ are generated; csrc/.clang-format keeps the formatter off the generated units.

…d (local_arena="lean", lean_passes=2) The deterministic package's lean local arena, mirrored: WorkspaceKcp(local_arena="lean", lean_passes=None) runs the KCP backward on a lean layout of the rank's local arena (3460 B per internal token-head; the replay arena takes 4488 B): kcp_dav's dA_qk goes to a dead sink, and the fused gradient, GLM intra and the gate epilogue run once per range of ceil(NC_pad / passes) chunks on per-pass buffers, their item bounds read from a small tail-count table allocated with the arena. lean_passes (1 to 8, two by default) sizes the arena for that many tail passes. "replay" stays the default. The same kernels address the same bytes as in one pass over the replay layout. The single-rank package flashinfer.experimental.cake.cake_kda_train gains the route-layout registry (_register_route_slots) its _layout reads, and KdaWorkspace gains layouts=, which sizes the arena for exactly the given route layouts; it registers no layout of its own, so its layouts and byte counts do not change. kda_workspace_bytes_kcp (plan and facade) takes local_arena and lean_passes. The lean layout serves the rows whose replay fused-gradient record stores the whole dA_qk itself: here dqkg_int_davq and its S1 forms dqkg_int_davq_v24 and dqkg_int_davq_nohint_v24, which read the same buffers but q. The rows of 9 to 15 heads (dqkg_int) keep the replay layout.

…against the deterministic package tests/experimental/test_cake_kda_train_kcp*.py: the deterministic KCP package's suites with every entry called with deterministic=False: the plan, partitions, tables and byte queries on the host, the FP64 fake backend, the chunk-table parity on a GPU (test_cake_kda_train_kcp.py); the registry and the generated manifests (_build); the facade, the mode keyword and the import isolation (_api); host synchronization (_upload); the FSDP input walk (_fsdp_walk); one workspace shared by several packs (_shared_ws); and the multi-GPU rows against the FP64 reference and the deterministic package (_dist, W = 2, 4 and 8). test_cake_kda_train_kcp_lean.py mirrors the deterministic lean-arena suite: host rows for the layout's bytes, lifetimes and per-pass packing, the single-rank layouts and bytes unchanged with and without the registered layout, the registration rules, the fused-gradient records the layout relies on, the value dicts of every launched record and the byte prediction; GPU rows bitwise against the replay arena (H 1 to 64, ragged packs, both boundary modes, recompute handles, graph replay, the overlap schedule, NaN-filled arena and per-pass buffers, steady calls without allocation or blocking) and multi-GPU rows (W = 2, 4 and 8). benchmarks/bench_cake_kda_train_kcp.py: the KCP causal convolution and KDA step of this package against the deterministic package, a control arm, the single-rank kernels on each rank's shard and flash-linear-attention's chunk_kda (when it is installed), one process per rank with the slowest rank's time counting; forward, backward, forward + backward and the checkpointed step in palindromic groups.

…train_kcp Package data (csrc, build.json and build.test.json), the build-manifest checker's package row, the test package table key cake_kda_train_kcp, CODEOWNERS and the labeler entry.

…t's CPUs The multi-GPU rows of the dist, lean and shared-workspace suites spawn one process per rank. Each process started as many CPU threads as the host has, so on a 56-CPU slice four ranks ran four times that many while rank 0 computed the FP64 reference and the other ranks waited in a collective: the s2048_5_3000_1-h4-w4-zigzag_per_document row took 813 s and the waiting ranks hit the process group's ten-minute timeout. Each spawned rank now calls torch.set_num_threads(CPUs of the process // W) first; the same row takes 22 s.

…ne arm flash-linear-attention 0.6.0 ships a context-parallel chunk_kda (cp_context from fla.ops.cp.build_cp_context; layouts contiguous and zigzag over equal token parts). The new optional arm fla_cp runs it on FLA's own partition of the same packed batch, which holds the same tokens as the Cake partition when the parts divide the batch; with the convolution it runs the main arms' KCP convolution first. It joins the palindrome of the main arms, its out is compared with the deterministic arm's, and it is dropped with the reason in fla_cp_error for a layout without an FLA counterpart (zigzag_per_document), a batch the parts do not divide or a missing fla.ops.cp. An untimed step on one-rank groups compiles FLA's rank-specific kernels on every rank before the first exchange. fla and fla_cp now call FLA with safe_gate=True, its path for gates bounded below (the operands' gates lie in (-5, 0)); --no-fla-safe-gate selects its general path. fla_cp runs only when named in --arms.

…le list kcp_fwd_o carries the _kcp suffix, so the package has sixteen _kcp programs and one suffix-free program (kcp_dav).

yyihuang · 2026-11-01T21:14:07Z

@flashinfer-bot run tests/experimental/test_cake_kda_train.py tests/experimental/test_cake_kda_train_api.py tests/experimental/test_cake_kda_train_kcp.py tests/experimental/test_cake_kda_train_kcp_api.py tests/experimental/test_cake_kda_train_kcp_build.py tests/experimental/test_cake_kda_train_kcp_upload.py tests/experimental/test_cake_kda_train_kcp_fsdp_walk.py tests/experimental/test_cake_kda_train_kcp_shared_ws.py tests/experimental/test_cake_kda_train_kcp_lean.py tests/experimental/test_cake_kda_train_kcp_dist.py tests/experimental/test_cake_kda_det_train_kcp_api.py

yyihuang added run-ci op: linear attention KDA, mamba, GDN, etc. review filtering. labels Nov 1, 2026

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yyihuang force-pushed the cake-kda-train-kcp-nondet branch from 9277ff3 to 1430b48 Compare November 1, 2026 19:45

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yyihuang marked this pull request as ready for review November 1, 2026 20:18

yyihuang mentioned this pull request Nov 1, 2026

[CAKE] Linear Attention & State Space Kernel Progress Tracker #6595

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yyihuang requested review from Anerudhan, Jingfan-Sun, YangXu1990uiuc, aleozlx, bkryu, flashinfer-bot, jhjpark, jimmyzho, kahyunnam and yzh119 as code owners November 1, 2026 20:18

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Comment thread flashinfer/experimental/cake_kda_train_kcp/README.md Resolved

flashinfer-bot added run-ci and removed run-ci labels Nov 1, 2026

yyihuang changed the title ~~feat(cake_kda): with grad, add non-deterministic Cake KDA context-parallel (KCP) training backend~~ feat(cake_kda): with grad, add non-deterministic Cake KDA context-parallel (KCP) autograd backend Nov 1, 2026

averyhNV added 6 commits November 1, 2026 13:13

chore(cake_kda_train_kcp): register flashinfer.experimental.cake_kda_…

f8cb4c4

…train_kcp Package data (csrc, build.json and build.test.json), the build-manifest checker's package row, the test package table key cake_kda_train_kcp, CODEOWNERS and the labeler entry.

docs(cake_kda_train_kcp): correct the program counts in the README fi…

73a0f83

…le list kcp_fwd_o carries the _kcp suffix, so the package has sixteen _kcp programs and one suffix-free program (kcp_dav).

yyihuang force-pushed the cake-kda-train-kcp-nondet branch from f479ea5 to 73a0f83 Compare November 1, 2026 21:14

yyihuang enabled auto-merge (squash) November 1, 2026 21:14

flashinfer-bot added run-ci and removed run-ci labels Nov 1, 2026

yyihuang merged commit 06c5bdc into flashinfer-ai:main Nov 1, 2026
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[Feature][CAKE] Non-deterministic packed KDA with gradient, with non-KCP and KCP options #8064

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feat(cake_kda): with grad, add non-deterministic Cake KDA context-parallel (KCP) autograd backend - #8218

feat(cake_kda): with grad, add non-deterministic Cake KDA context-parallel (KCP) autograd backend#8218
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NVIDIA B200 (sm_100a, 148 SMs, 8 GPUs)

NVIDIA B300 (sm_103a, 148 SMs, 8 GPUs)

NVIDIA R200 (sm_107a, 212 SMs, 4 GPUs)

NVIDIA GB300 (sm_103a, 152 SMs, 2 and 4 nodes of one NVL72 rack)

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NVIDIA B200 (`sm_100a`, 148 SMs, 8 GPUs)

NVIDIA B300 (`sm_103a`, 148 SMs, 8 GPUs)

NVIDIA R200 (`sm_107a`, 212 SMs, 4 GPUs)

NVIDIA GB300 (`sm_103a`, 152 SMs, 2 and 4 nodes of one NVL72 rack)

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