[CUDA] Optimize QMoE SoftmaxTopK router for small-batch decode - #29026
Merged
Merged
Conversation
### Description
Optimizes the CUDA QMoE router top-k (`LaunchSoftmaxTopK`) for
small-batch / autoregressive decode by replacing the old
one-thread-per-row hot path with parallel CUB and warp-level top-k
kernels. The dispatch now uses the fastest specialized path for common
MoE expert counts while preserving the existing softmax normalization
and deterministic lower-index tie-breaking semantics.
This PR also factors the warp-level top-k sorting code into a reusable
CUDA helper header and adds direct CUDA-internal tests so the new
routing paths are covered independently of higher-level QMoE tests.
### Motivation and Context
The previous router path launched a 256-thread block per row but did all
top-k work in a single thread. In decode scenarios such as `num_rows ==
1`, that made the router latency-bound on a serial scan of all expert
logits and turned `SoftmaxTopKKernel` into a major MoE decode
bottleneck.
For a Qwen3-style MoE workload with 256 experts, top-8 routing, and 40
MoE layers, the original router accounted for roughly 50% of decode GPU
time. Moving the work to block/warp-parallel kernels removes that
bottleneck while keeping the same output ordering and scaling behavior.
### Key Changes
| Area | Change |
|---|---|
| QMoE router dispatch | Adds `DispatchSoftmaxTopK` routing for `k <=
64` and `num_experts <= 1024`, with a fallback to the original scalar
kernel for larger or uncommon shapes. |
| Tiny expert counts | Adds `SoftmaxTopKWarpBitonicKernel` for
`num_experts <= 32`, using one warp per row and in-register bitonic
sorting via warp shuffles. |
| Small expert counts | Adds `SoftmaxTopKWarpMergeKernel` for `32 <
num_experts <= 64`, using a single warp and CUB warp merge sort. |
| Larger common MoE counts | Uses `SoftmaxTopKMergeKernel` with CUB
block merge sort for `num_experts <= 128`, `256`, `512`, and `1024`. |
| Reusable top-k helpers | Adds
`onnxruntime/core/providers/cuda/cu_inc/topk_warp_sort.cuh` with
reusable warp bitonic and warp merge sort helpers. |
| Stable tie-breaking | Packs `(score, index)` into a `uint64_t` stable
sort key for the CUB merge paths, matching onnxruntime-genai's
lower-index tie-breaking and avoiding compound comparators. |
| Softmax cleanup | Factors shared softmax scale, safe reciprocal, top-k
normalization, warp reduction, and CUB block reduction helpers to keep
the optimized kernels consistent. |
| Tests | Adds CUDA-internal `SoftmaxTopK_*` tests covering warp
bitonic, warp merge, block merge, stable ties, normalization, `float`,
`half`, and `bfloat16`. |
### Performance
H200 measurements for the target QMoE decode scenario showed the router
cost dropping from roughly `5.56 ms/token` to `0.17 ms/token`, improving
end-to-end Qwen3.6-35B-A3B INT4 decode throughput from about `80 tok/s`
to `113 tok/s`.
Additional profiling of the `32 < num_experts <= 64` warp merge path
showed the packed `uint64_t` stable sort key is consistently faster than
a `{float, int}` struct comparator on H200:
| Experts | Sort-only packed/struct | Full softmax+top-k packed/struct |
|---:|---:|---:|
| 33 | 0.680x | 0.704x |
| 48 | 0.672x | 0.695x |
| 64 | 0.673x | 0.696x |
### Testing
- `lintrunner -a`
- `ninja onnxruntime_providers_cuda_ut`
- `ninja onnxruntime_provider_test`
- `GTEST_FILTER='CUDA_EP_Unittest.SoftmaxTopK_*'
./onnxruntime_provider_test --gtest_filter='CUDA_EP_Unittest.All'`
- `onnxruntime/test/python/transformers/test_qmoe_cuda.py -k parity`
(`44 passed`)
Tianlei Wu (tianleiwu)
requested review from
Akshay Sonawane (apsonawane) and
Sanaa Hamel (sanaa-hamel-microsoft)
June 12, 2026 07:28
Sanaa Hamel (sanaa-hamel-microsoft)
approved these changes
Jun 12, 2026
eserscor
approved these changes
Jun 12, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This cherry-picks #28980 to 1.27 release