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feat(cake_gqa_train): packed-varlen HD256 GQA training attention for SM100 / SM103 / SM107 - #7774
Cake's HD256 dense GQA training attention covers the forward and first-order backward passes. It runs on inputs packed by cu_seqlens, with a bottom-right-aligned causal mask per segment so query and key lengths can differ. The kernel has one data-type variant: BF16 inputs and outputs with FP32 accumulation. Its backward writes dK/dV in one of three forms: FP32 window gradients, an FP32 add into caller-owned destination rows through an int32 row map, or BF16. This PR delivers that variant, which is the first Cake version of the kernel, as the experimental flashinfer.gqa_train API (varlen_gqa_attention, varlen_gqa_attention_fwd, varlen_gqa_attention_bwd). The baseline is FlashAttention-4 (CuTe DSL) HD256 varlen forward and backward (flash-attn-4 4.0.0b38.dev9 built from FlashAttention PR 2968, head f4015896), plus the FP32 index_add_ workaround from #7073 that adds its BF16 window gradients into the destination rows. Against that baseline, the geometric-mean speedup over the 9 shapes below is 1.029x. Total time improves by 1.020x, and every shape is at least 1.003x faster. issue1-issue3 are the three packed layouts of #7073 (context-parallel zigzag, CP 8 rank 0). zz_cp{N}_r{R}_{T} is rank R of an N-way context-parallel zigzag split of T tokens packed from several documents of irregular lengths (zz_cp1 is a plain packed batch).
NVIDIA GB200 (sm_100a), CUDA 13.4: one training step of one attention layer under non-reentrant activation checkpointing (2 x forward + backward, including initialization), median of 10 steps; Cake vs FlashAttention-4 + FP32 index_add_ on the same BF16 inputs; H_q = 24, H_kv = 4, head dim 256.
Shape
T_q
T_k
Segments
Cake ms
FlashAttention-4 ms
Speedup
issue1
30,193
256,933
12
60.403
62.759
1.039x
issue2
32,737
278,362
6
131.835
133.262
1.011x
issue3
32,388
275,373
4
192.519
197.685
1.027x
zz_cp8_r3_120000
15,000
127,512
10
23.621
24.609
1.042x
zz_cp4_r1_96000
24,000
108,003
6
36.799
37.561
1.021x
zz_cp2_r0_40000
20,000
50,001
4
34.511
34.908
1.011x
zz_cp8_r7_300000
37,499
318,788
16
149.242
149.684
1.003x
zz_cp16_r0_420000
26,249
433,126
6
237.453
242.881
1.023x
zz_cp1_r0_16461
16,461
24,693
8
4.527
4.915
1.086x
Cake ships no other data-type or layout variant of this kernel, and it is prebuilt for sm_100a, sm_103a and sm_107a.
Summary by CodeRabbit
New Features
Added an experimental packed variable-length causal GQA attention training backend for BF16 inputs with head dimension 256 on supported NVIDIA architectures.
Added forward, backward, and autograd APIs, with reusable workspace, optional log-sum-exp output, and flexible gradient accumulation options.
Added a benchmark comparing training stages with FlashAttention CuTe.
Documentation
Added guidance on usage, supported hardware, numerical behavior, and limitations.
Tests
Added coverage for API behavior, output and gradient accuracy, workspace reuse, and supported-device constraints.
…SM100 / SM103 with grad
A standalone Cake package (cake-build/1) for causal GQA attention over packed
variable-length segments with head dimension 256: forward with the natural-log
LSE, and a backward whose dK / dV are FP32 sums over the query heads of each key
head, returned as BF16, as FP32 window gradients, or added in FP32 into
caller-owned destinations through a row map. Causal alignment is bottom-right
per segment; T_q, T_k, the segment count and every length are run-time values
read on the device only.
- flashinfer/experimental/cake_gqa_train: registry and host plan (cake_jit),
loader, workspace, launches and the autograd Function (cake_backend), README.
Autograd saves (q, k, v, out, lse, cu_seqlens_q, cu_seqlens_k) through
save_for_backward only; a dkv_acc callable supplies the FP32 destinations at
backward time; the workspace is held by weak reference.
- flashinfer/gqa_train.py: experimental entry points (deferred import).
- Tests: FP64-reference numerics, document isolation, empty and keyless rows,
repeated destination rows, dynamic shapes on one workspace, non-reentrant
checkpointing, no device synchronization, a resident side-stream kernel.
- benchmarks/bench_cake_gqa_train.py: 2 x forward + backward against
FlashAttention CuTe on context-parallel zigzag layouts.
Co-Authored-By: Claude <noreply@anthropic.com>
…sizes the backward grid
- cake_jit.py: ARCHES and ARCH_BY_CAPABILITY gain sm_107a (10, 7); every
record serves sm_100a / sm_103a / sm_107a; the forward is one program for
all three (record `fwd`); the backward main grid is (2 N, H_kv, B), a CTA
pair per 128-key block of the longest segment, per key head and segment.
- cake_backend.py / gqa_train.py: optional host keyword `max_seqlen_k` on the
backward and the autograd entry points (saved on ctx as a scalar); None
bounds the longest segment by T_k (always correct, more CTAs that retire at
once). It is never read from the device.
- tests: the grid test covers both bounds; the FP32 forms pass the exact
longest key segment, the BF16 form passes None; SM107 is a target.
- cake_jit.py: the postprocess grid is one CTA per 64 packed rows (its
program's rows_per_cta), the preprocess one per 32.
- tests: dQ of two runs (atomic FP32 adds in a scheduling order) is compared
to one BF16 step plus an absolute floor of 2^-16 of its largest magnitude
for the FP32 reordering error; O, LSE, dK and dV stay bitwise. The loader
byte check reads the other package from the repository, so the suite runs
with flashinfer blocked.
- benchmark: the Cake arm passes max_seqlen_k as the FlashAttention arm does.
Co-Authored-By: Claude <noreply@anthropic.com>
Six programs (forward; backward preprocess; backward main with FP32 window,
FP32 row-map add and BF16 dK / dV; dQ postprocess), each one device unit and
one tvm-ffi binding for sm_100a, sm_103a and sm_107a; build.json (18
entries), the pinned loader and the checks header.
Co-Authored-By: Claude <noreply@anthropic.com>
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Walkthrough
Adds an experimental packed variable-length causal GQA training backend for BF16 inputs with head dimension 256 on SM100, SM103, and SM107. The change includes CUDA kernels, Python APIs, build and loading support, tests, documentation, and a benchmark against FlashAttention CuTe.
Changes: Cake GQA training backend
Layer / File(s)
Summary
Registry and build integration flashinfer/experimental/cake_gqa_train/*, pyproject.toml, scripts/check_cake_build_manifests.py, tests/test_helpers/cake_package.py
Adds architecture-specific kernel manifests, JIT and prebuilt library loading, scratch and grid planning, package metadata, registry integration, and backend documentation.
Python API and backend execution flashinfer/experimental/cake_gqa_train/cake_backend.py, flashinfer/gqa_train.py
Adds forward, backward, and autograd APIs; operand validation; reusable workspaces; BF16 or FP32 dK/dV outputs; and optional FP32 accumulation through gradient destinations and row maps.
CUDA kernels and FFI launchers flashinfer/experimental/cake_gqa_train/csrc/*
Adds preprocessing, forward, backward, and postprocessing kernels, plus FFI checks, TMA map validation, and CUDA launch bindings.
Validation and behavioral tests tests/experimental/test_cake_gqa_train.py, tests/experimental/test_cake_gqa_train_api.py
Adds loader and registry checks, numerical and gradient comparisons, and tests for workspaces, checkpointing, device and stream behavior, timing, validation, and the public API.
Training benchmark benchmarks/bench_cake_gqa_train.py
Adds Cake and FlashAttention CuTe measurements for forward, replay, backward, and full-step execution across packed layouts.
The earlier concerns about launching on the operand device's stream and about the warning text omitting SM107 are fixed. No concrete merge-blocking risk remains in the reviewed changes.
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The title clearly and concisely identifies the new packed-varlen HD256 GQA training attention feature and supported architectures.
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Actionable comments posted: 2
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @flashinfer/experimental/cake_gqa_train/cake_backend.py:
- Around line 203-205: Update run_record to bind torch’s current CUDA device to
device_index before entering tvm_ffi.use_torch_stream(), so the FFI launch uses
the operand device’s stream.
Review comments at @flashinfer/gqa_train.py:
- Line 55: Update the _FEATURE string used by ExperimentalWarning to include
SM107 alongside SM100 and SM103, so warnings and entry-point docstring banners
name all supported architectures.
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…7 in the experimental warning
run_record now makes the operands' device current around tvm_ffi.use_torch_stream(), so the FFI binding reads
that device's current torch stream even when another device is current. A new test runs the forward and the
backward with another device current and a side stream current on the operands' device, and checks the results
against the same call made on that device. The experimental feature name lists SM107 next to SM100 / SM103.
Co-Authored-By: Claude <noreply@anthropic.com>
#7958)
Cake's HD256 dense GQA training attention (`flashinfer.gqa_train`, first
shipped in #7774) runs the forward and first-order backward passes. Its
inputs are packed by `cu_seqlens`, and it applies a bottom-right-aligned
causal mask to each segment. The kernel has one data-type variant: BF16
inputs and outputs with FP32 accumulation. Its backward writes dK/dV in
one of three forms: FP32 window gradients, an FP32 add into caller-owned
destination rows through an int32 row map, or BF16. This PR speeds up
the backward of that variant. In the backward main kernel of all three
dK/dV forms, each thread that reads accumulators from tensor memory now
releases them itself instead of handing them to a forwarder warp. The
other kernels and the API are unchanged. The baseline is the previous
Cake version of this kernel, from #7774 on FlashInfer main. On the same
BF16 inputs, O, LSE, dK and dV are bitwise equal to the baseline on
every shape, and dQ differs only by atomic-add order. On GB200, the
backward geometric-mean speedup over the 9 shapes below is 1.023x (range
1.015x-1.036x), and the training step speeds up by 1.012x (range
1.006x-1.019x). Every shape is faster in both columns. With the same
protocol at 10 rounds each, the backward geometric-mean speedup is
1.029x on NVIDIA B300 (sm_103a) and 1.055x on Rubin (sm_107a), and the
training step speeds up by 1.018x and 1.033x. `issue1`-`issue3` are the
three packed layouts of #7073 (context-parallel zigzag, CP 8 rank 0).
`zz_cp{N}_r{R}_{T}` is rank R of an N-way context-parallel zigzag split
of T tokens packed from several documents of irregular lengths (`zz_cp1`
is a plain packed batch).
**NVIDIA GB200 (sm_100a), CUDA 13.4 (prebuilt kernels): one training
step of one attention layer under non-reentrant activation checkpointing
(2 x forward + backward, including initialization of the FP32 dK/dV
destinations), FP32 window gradients through the row map; "Backward" =
preprocess + main + dQ postprocess; each value is the median over 24
rounds (4 runs, arm order alternated every round), each round the median
of 10 back-to-back steps; this PR vs the previous Cake version on the
same BF16 inputs; H_q = 24, H_kv = 4, head dim 256; speedup = previous
ms / this PR ms.**
| Shape | T_q | T_k | Segments | Backward, previous ms | Backward, this
PR ms | Backward speedup | Step, previous ms | Step, this PR ms | Step
speedup |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| issue1 | 30,193 | 256,933 | 12 | 40.762 | 39.767 | 1.025x | 58.781 |
58.238 | 1.009x |
| issue2 | 32,737 | 278,362 | 6 | 91.994 | 89.385 | 1.029x | 129.208 |
127.332 | 1.015x |
| issue3 | 32,388 | 275,373 | 4 | 134.703 | 132.360 | 1.018x | 190.766 |
188.186 | 1.014x |
| zz_cp8_r3_120000 | 15,000 | 127,512 | 10 | 15.892 | 15.541 | 1.023x |
23.354 | 23.072 | 1.012x |
| zz_cp4_r1_96000 | 24,000 | 108,003 | 6 | 24.724 | 24.133 | 1.024x |
35.808 | 35.348 | 1.013x |
| zz_cp2_r0_40000 | 20,000 | 50,001 | 4 | 22.889 | 22.554 | 1.015x |
32.972 | 32.784 | 1.006x |
| zz_cp8_r7_300000 | 37,499 | 318,788 | 16 | 103.535 | 102.055 | 1.015x
| 145.588 | 144.036 | 1.011x |
| zz_cp16_r0_420000 | 26,249 | 433,126 | 6 | 163.043 | 159.467 | 1.022x
| 234.695 | 231.901 | 1.012x |
| zz_cp1_r0_16461 | 16,461 | 24,693 | 8 | 2.774 | 2.678 | 1.036x | 4.353
| 4.270 | 1.019x |
Cake has no other data-type or layout variant of this kernel, and the
kernel is prebuilt for `sm_100a`, `sm_103a` and `sm_107a`.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **Refactor**
* Updated processing coordination for experimental grouped-query
attention training. No changes to public interfaces or user-facing
behavior are indicated.
* **Chores**
* Refreshed generated build metadata for supported library variants.
Supported architectures, CUDA minimum versions, and available variants
remain unchanged.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Co-authored-by: Yingyi Huang <averyh@nvidia.com>
Co-authored-by: Claude <noreply@anthropic.com>
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Cake's HD256 dense GQA training attention covers the forward and first-order backward passes. It runs on inputs packed by
cu_seqlens, with a bottom-right-aligned causal mask per segment so query and key lengths can differ. The kernel has one data-type variant: BF16 inputs and outputs with FP32 accumulation. Its backward writes dK/dV in one of three forms: FP32 window gradients, an FP32 add into caller-owned destination rows through an int32 row map, or BF16. This PR delivers that variant, which is the first Cake version of the kernel, as the experimentalflashinfer.gqa_trainAPI (varlen_gqa_attention,varlen_gqa_attention_fwd,varlen_gqa_attention_bwd). The baseline is FlashAttention-4 (CuTe DSL) HD256 varlen forward and backward (flash-attn-4 4.0.0b38.dev9 built from FlashAttention PR 2968, head f4015896), plus the FP32index_add_workaround from #7073 that adds its BF16 window gradients into the destination rows. Against that baseline, the geometric-mean speedup over the 9 shapes below is 1.029x. Total time improves by 1.020x, and every shape is at least 1.003x faster.issue1-issue3are the three packed layouts of #7073 (context-parallel zigzag, CP 8 rank 0).zz_cp{N}_r{R}_{T}is rank R of an N-way context-parallel zigzag split of T tokens packed from several documents of irregular lengths (zz_cp1is a plain packed batch).NVIDIA GB200 (sm_100a), CUDA 13.4: one training step of one attention layer under non-reentrant activation checkpointing (2 x forward + backward, including initialization), median of 10 steps; Cake vs FlashAttention-4 + FP32
index_add_on the same BF16 inputs; H_q = 24, H_kv = 4, head dim 256.Cake ships no other data-type or layout variant of this kernel, and it is prebuilt for
sm_100a,sm_103aandsm_107a.Summary by CodeRabbit