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[Perf] Optimize moe permute by pre-allocate buffer, 9~14% kernel performance improvement - #43014

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May 28, 2026
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[Perf] Optimize moe permute by pre-allocate buffer, 9~14% kernel performance improvement#43014
vllm-bot merged 9 commits into
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wentao-optimize-moe-permute

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@yewentao256

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Purpose

Optimize moe permute by pre-allocate buffer, very good for small batch size, no effect for large batch size

Test

Covered in unit tests

Perf

python benchmarks/kernels/benchmark_moe_permute_unpermute.py for two times

Batch Size Now (us) Main (us) Improvement
1 6.28 7.34 14.44%🚀
2 6.53 7.34 11.04%🚀
4 6.69 7.40 9.59%🚀
8 6.83 7.60 10.13%🚀
16 7.77 7.89 1.52%🚀
24 7.67 7.90 2.91%🚀
32 7.88 7.72 -2.07%
48 8.04 7.90 -1.77%
64 8.28 8.48 2.36%
96 8.68 8.66 -0.23%
128 9.09 9.06 -0.33%
256 10.68 10.75 0.65%
512 12.72 13.01 2.23%
1024 17.72 17.44 -1.61%
1536 22.24 21.76 -2.21%
2048 27.22 26.63 -2.22%
3072 49.45 51.05 3.13%
4096 57.39 58.76 2.33%

Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>
@yewentao256 yewentao256 added the ready ONLY add when PR is ready to merge/full CI is needed label May 18, 2026
@mergify mergify Bot added performance Performance-related issues nvidia labels May 18, 2026

@gemini-code-assist gemini-code-assist Bot left a comment

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Code Review

This pull request introduces the MoEPermuteScratch class to enable the reuse of pre-allocated metadata and hidden state buffers during MoE permute operations, aiming to reduce memory allocation overhead. The implementation includes C++ kernel updates to support optional scratch tensors, integration into CUTLASS and Humming MoE expert layers, and new tests for buffer reuse. Feedback from the review identified a critical issue where model loading would crash on unsupported platforms (CUDA < 12.0) and a potential bug in the C++ tensor allocation logic where reshape might return a copy instead of a view, potentially breaking the scratch buffer functionality.

Comment thread vllm/model_executor/layers/fused_moe/moe_permute_unpermute.py
Comment thread csrc/moe/moe_permute_unpermute_op.cu Outdated
Signed-off-by: yewentao256 <zhyanwentao@126.com>
@yewentao256 yewentao256 changed the title [Perf] Optimize moe permute by pre-allocate buffer [Perf] Optimize moe permute by pre-allocate buffer, 9~14% kernel performance improvement May 19, 2026

@yewentao256 yewentao256 left a comment

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Hi @bnellnm could you give a review for this PR? Thanks!

Comment thread csrc/moe/moe_permute_unpermute_op.cu Outdated
auto sorted_row_idx = torch::empty_like(inv_permuted_idx);

size_t sorter_size =
CubKeyValueSorter::getWorkspaceSize(expanded_rows, n_expert);

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Is this the same as moe_permute_sort_workspace_size?

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Fixed

// operation is to make local expert high priority in following sort topk_ids
// and scan local expert_first_token_offset for each ep rank for next group
// gemm.
torch::Tensor topk_ids_for_sort = topk_ids;

@bnellnm bnellnm May 21, 2026

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This initialization seems redundant since maybe_allocate_tensor is called below?

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That is for expert_map case, let's keep as it is before #43108

per_out_ch: bool,
use_batched_format: bool,
topk_weights: torch.Tensor | None,
permute_scratch: MoEPermuteScratch,

@bnellnm bnellnm May 21, 2026

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Should this be an optional parameter if some configurations don't support it? I see there's an #ifdef in the .cu file where the scratch space isn't always supported.

Ditto for the other use sites.

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Fixed

Comment on lines +95 to +98
numel = topk_ids.numel()
topk_ids_int32 = self.topk_ids_int32[:numel].view_as(topk_ids)
topk_ids_int32.copy_(topk_ids)
return topk_ids_int32

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Is there a test case for when the type doesn't match?

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topk_ids is expected to be int32 in normal routing flows.

This conversion path is just defensive to preserve parity with the non-scratch path, I personally don't think it worth a dedicated test

Comment on lines +151 to +154
assert (
scratch.hidden_size is not None
and scratch.permuted_hidden_states is not None
)

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Should this assert be part of scratch.validate?

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Fixed

@bnellnm

bnellnm commented May 21, 2026

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Nice work! Just had a few comments.

yewentao256 and others added 4 commits May 21, 2026 15:48
Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>
@vllm-project vllm-project deleted a comment from mergify Bot May 21, 2026
@github-project-automation github-project-automation Bot moved this to Ready in NVIDIA May 21, 2026
@vllm-bot
vllm-bot merged commit 64e1218 into main May 28, 2026
177 of 180 checks passed
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vllm-bot deleted the wentao-optimize-moe-permute branch May 28, 2026 13:18
@github-project-automation github-project-automation Bot moved this from Ready to Done in NVIDIA May 28, 2026
sphinx07 pushed a commit to sphinx07/vllm-mgenai-gptoss that referenced this pull request May 29, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: Xiaoran Chen <xiaoran@fb.com>
Liuweixiong0118 pushed a commit to Liuweixiong0118/vllm that referenced this pull request Jun 1, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: Liuweixiong0118 <lwx34158427@gmail.com>
hynky1999 added a commit to macrodata-labs/vllm that referenced this pull request Jun 2, 2026
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* [BUGFIX] Multimodal benchmark with MistralTokenizer (#42965)

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mvanhorn pushed a commit to mvanhorn/vllm that referenced this pull request Jun 4, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
andakai pushed a commit to andakai/vllm that referenced this pull request Jun 4, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
knight0528 pushed a commit to knight0528/vllm that referenced this pull request Jun 8, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
waqahmed-amd-fi pushed a commit to waqahmed-amd-fi/vllm that referenced this pull request Jun 10, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
Signed-off-by: Waqar Ahmed <waqar.ahmed@amd.com>
choiceoh added a commit to choiceoh/vllm-dsv4 that referenced this pull request Jun 20, 2026
…rp-shuffle) + vllm-project#43014 (MoE permute pre-alloc)

Two csrc CUDA kernel perf optimizations applied as source commits so the
from-source build (docker/Dockerfile, real nvcc compile) actually recompiles
them. These are the ones that the prior overlay-on-prebuilt-binary lineage
(dsv4-tiera2/tiera3) could NOT pick up, because that lineage was a Python-source
overlay on the Aiden b12x prebuilt binaries and never recompiled csrc.

Applied (both verified: git apply --check clean on this eb99b8b/flat tree, on
the active hot-path, real perf wins):

  * vllm#44173 (commit 66c1760) — Warp-shuffle + vectorized
    silu_and_mul_per_block_quant. Pure kernel-internal change to
    csrc/quantization/fused_kernels/fused_silu_mul_block_quant.cu (no binding /
    signature change → drop-in). Bit-identical output (verified M=16..65536 in
    PR), ~2.4-2.6x kernel speedup at prefill scale. On the act_quant fusion pass
    hot-path (act_quant_fusion.py).
    NOTE: the originally-cited commit c09ad76 is the kernel's ORIGINAL
    creation (already present in this tree); 66c1760 is the actual warp-shuffle
    perf commit.

  * vllm#43014 (net of 4107002..f90eda5, 6 target files) — MoE permute
    pre-allocated buffer path. Adds moe_permute_with_scratch +
    moe_permute_sort_workspace_size C++ ops and the MoEPermuteScratch reused-
    buffer wrapper, wired into cutlass_moe.py + fused_humming_moe.py. 9-14%
    moe_permute kernel win. Verified the new ops are actually CALLED (not dead
    code): moe_permute_unpermute.py:214/:71.

Intentionally SKIPPED (adversarially analyzed; would be forced builds):

  * vllm#43162 (fused qnorm/rope/kv head-pad) — the csrc patch targets a
    templated switch-dispatch refactor (launchFusedDeepseekV4Templated /
    kNumHeadsQPadded) ABSENT from this older eb99b8b kernel, and its Python half
    targets the missing nested vllm/models/deepseek_v4/nvidia/ layout. Both
    halves fail to apply; landing it needs a full manual re-expression against a
    structurally different kernel + a non-existent file. (The kernel IS used here
    via deepseek_v4_attention.py:617, so the intent is relevant — but the patch
    is anchored to a tree generation we don't have.)

  * vllm#43554 ("router GEMM PDL") — MISLABELED. The PR is actually
    [Kernel] Remove NormGateLinear (a -836-line DELETION of the
    dsv4_norm_router_gemm kernel). Its only PDL content is incidental removal of
    an env-gate around pre-existing PDL in dsv3_router_gemm; it adds no GB10 PDL
    optimization. This tree ACTIVELY USES NormGateLinear (deepseek_v4.py
    self.norm_gate, norm_gate_linear.py:30) — applying it would regress, not
    optimize.

flashinfer vllm-project#3461/vllm-project#3624 remain the JIT overlay (tiera3); this commit adds the
vLLM csrc deltas that only a from-source build can realize.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
choiceoh added a commit to choiceoh/vllm-dsv4 that referenced this pull request Jun 20, 2026
…rize_with_alignment output-alignment crash guard)

Adds one more adversarially-verified pick on top of 2763c4d's vllm-project#44173 +
vllm-project#43014. The from-source build (real nvcc compile) recompiles this csrc header,
so the fix is runtime-effective (unlike the tiera2/tiera3 prebuilt-binary
overlay lineage that could not pick up csrc changes).

Applied (cherry-pick 4583630, --no-commit clean, exit 0; 2 files):

  * vllm#45466 ([Bugfix][Kernel], merged 2026-06-18) — Check output alignment
    in vectorize_with_alignment. The vector load/store path goes through
    vec_n_t<T,VEC_SIZE> (declared __align__(VEC_SIZE*sizeof(T))), so BOTH in and
    out must be aligned to their own vector width. Previously only `in` was
    checked ("output guaranteed same as input" assumption). reshape_and_cache_
    flash writes KV-cache rows at byte offsets that are a multiple of head_size;
    for head sizes not a multiple of VEC_SIZE this puts some `out` rows off the
    vector-width boundary -> vectorized store -> CUDA misaligned-address crash
    (issue vllm-project#41257). The fix adds an OUT_WIDTH alignment check to the fast-path
    predicate + a post-prefix co-alignment check that falls back to a fully
    scalar copy when in/out cannot be co-aligned. Bit-identical output (only
    chooses scalar vs vector path), strictly a hardening — never wrong, only
    slower on the rare unaligned row.
    HOT PATH confirmed in this tree: csrc/cache_kernels.cu (reshape_and_cache_
    flash, the KV-cache decode write path) includes vectorization_utils.cuh and
    calls vectorize_with_alignment; also used by w8a8/fp8 common.cu, int8
    scaled_quant.cu, layernorm_kernels.cu, layernorm_quant_kernels.cu,
    libtorch_stable per_token_group_quant.cu. Arch-portable header (compiles on
    sm_121a like every other arch). Zero downside even if DSV4's current head
    dims don't trip it today.

Intentionally SKIPPED this round (each adversarially analyzed; all are
DEAD-PATH on this GB10/sm_121a + b12x deployment, not forced builds):

  * b12x individual commits cb98da162 (SM120 dense FP8 GEMM) / c7089a418 /
    0ff2847b0 — b12x is a PREBUILT BINARY package here (import b12x.integration,
    flashinfer.b12x_fused_moe), not a source tree. These SHAs exist in no
    fetched ref (they target the newer b12x v0.23 generation, not the eb99b8b
    DSV4 base). Not cherry-pickable; full v0.23 ABI absorption remains a
    separate effort.

  * flashinfer vllm-project#3640 (SM120 NVFP4 attention) — DEAD PATH. DSV4 decode routes
    MLA through b12x_compressed_mla_decode (prebuilt) with a sparse_mla
    fallback; vLLM has ZERO call sites into flashinfer's nvfp4_attention_sm120.
    Also in no release tag yet (main-only, post-rc2-cut).

  * flashinfer vllm-project#3309 (MLA decode num_heads<128 fold) — DEAD PATH. Patches
    flashinfer cute_dsl.attention.mla_decode, but vLLM imports flashinfer
    cute_dsl ONLY for MoE/GEMM (blockscaled_gemm, fused_moe). DSV4 MLA-decode
    is b12x/sparse_mla. No call site.

  * DeepGEMM vllm-project#324 (nv_dev, sm121 MQA-logits / HC-prenorm) — DEAD PATH. OPEN (not
    merged), against deepseek-ai/DeepGEMM nv_dev. vLLM's
    is_device_capability_family(120) shunt in vllm/utils/deep_gemm.py returns
    BEFORE native DeepGEMM _lazy_init, sending MQA-logits + HC-prenorm to
    hand-written Triton sm12x kernels (sm12x_mqa.py, sm12x_deep_gemm_
    fallbacks.py). b12x covers the dense-GEMM/MoE surface. vllm-project#324's kernels would
    compile but never be called on GB10.

  * vllm#44217 ([Perf] dsv3_router_gemm heuristic) — DEAD PATH + out of csrc
    scope (Python-only). Gates the specialized kernel to is_hopper((9,0)) ||
    is_blackwell(family 100); GB10 is sm_121a (CC 12.1) = NEITHER, so
    allow_dsv3_router_gemm is already False here.

  * vllm#43557 (E8M0 scale MXFP4 W4A4 CUTLASS) — cherry-picks clean but DEAD
    code on sm_121a: mxfp4_experts_quant.cu is gated to FP4_ARCHS=10.0a/10.1a/
    10.3a (ENABLE_NVFP4_SM100). GB10 MXFP4 experts use Marlin, not this kernel.

  * vllm-project#42996/vllm-project#46006 (PDL for DeepGEMM), vllm-project#46070 (revert vllm-project#42379), vllm-project#44109 (weightless
    RMSNorm), vllm-project#45277 (build-infra), torch-stable-ABI migration series
    [6/n]-[12/n], vllm-project#43827 (DSv4 TRTLLM attn — the vllm-project#43162 nested-layout trap) —
    conflict / ABI-refactor / deletion / nested b12x-v0.23 layout absent here.

Methodology: clean cherry-pick != effective. The decisive gate for nearly every
SKIP was CODE ROUTING, not the diff applying: this DSV4-on-GB10 build sends its
hot kernels through b12x (prebuilt) + Triton sm12x fallbacks + Marlin MXFP4,
while upstream CUTLASS/DeepGEMM/specialized-kernel paths are arch-gated to
SM90/SM100 and do not execute on sm_121a. Picks that target those paths are
no-ops here regardless of how cleanly they apply.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
nkzhenhua pushed a commit to nkzhenhua/vllm that referenced this pull request Jun 24, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
philippesic pushed a commit to philippesic/vllm-semantic-cache that referenced this pull request Jul 19, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
plasticchris pushed a commit to plasticchris/vllm that referenced this pull request Jul 20, 2026
…ormance improvement (vllm-project#43014)

Signed-off-by: yewentao256 <zhyanwentao@126.com>
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