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📝 WalkthroughWalkthroughThe PR adds optional sorted-I/O execution to the cuTile BF16 MoE pipeline. It sizes buffers for padded expert-sorted rows, updates grouped GEMM input and output handling, enables the mode through runner thresholds, and tests equivalence with unsorted execution. ChangesSorted-I/O execution
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Refactor Suggested reviewers: Sequence Diagram(s)sequenceDiagram
participant CuTileBf16Runner
participant Workspace
participant run_moe
participant GroupedGEMM
CuTileBf16Runner->>Workspace: allocate sorted-I/O buffers
CuTileBf16Runner->>run_moe: pass sorted_io
run_moe->>GroupedGEMM: run GEMM1 with sorted output
GroupedGEMM->>Workspace: store padded sorted rows
run_moe->>GroupedGEMM: run GEMM2 with sorted input
Merge Risk: 🟡 Moderate · up to Large BF16 MoE requests on supported SM89 devices can select the new sorted path and fail to compile or launch. Gate sorted I/O to TMA-capable architectures before merging. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
🧪 Generate unit tests (beta)
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Inline comments:
In `@flashinfer/fused_moe/runners.py`:
- Around line 3013-3018: Update CuTileBf16Runner._use_sorted_io to require a
TMA-capable architecture, allowing sorted I/O only for SM90, SM120, and SM121
while preserving the existing assignment-count and intermediate-size thresholds.
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📒 Files selected for processing (3)
flashinfer/fused_moe/cutile/moe.pyflashinfer/fused_moe/runners.pytests/moe/test_unified_moe_cutile.py
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Thanks @elwhyjay, I did not forget this PR; I'll try to get to it soon |
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@bkryu No worries at all. I know reviewers are busy with their own work and other reviews. Thanks for the update! |
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📌 Description
Adds an expert-sorted intermediate layout to the cuTile BF16 fused MoE (
flashinfer/fused_moe/cutile/moe.py) and enables it from the runner for large routing batches.While profiling the #4646 testlist on an RTX PRO 6000 (SM120) for #4857, GEMM2 was the slow kernel at 8192 tokens: it reached 176-204 TFLOPS against 282-288 for GEMM1 (cuBLAS bf16 on that card is about 409). GEMM2's A operand is one activation row per assignment, read through
ct.gatherwith no reuse, and the tactics the tuner picks at large M use tile_k=32, so each gathered row segment is only 64 bytes. GEMM1's token rows are shared across top_k assignments and hit L2, which is why it did not show the same problem. The W4A4 path already avoids this with its sorted-IO buffers; the BF16 path did not have an equivalent.With
sorted_io=True, GEMM1 scatters its output tiles into the padded expert-sorted row space (the same scatter as before, only with sorted row indices), the activation runs on that buffer, and GEMM2 loads its A tiles withct.load(..., allow_tma=True). The GEMM2 epilogue still scatters to assignment order, socombineis untouched and the two paths produce bitwise-identical outputs (covered by a new test). Padded rows only feed GEMM2 rows that the epilogue drops. The workspace grows the GEMM1/activation buffers to the padded row space only when the runner asks for the sorted path.CuTileBf16Runnerturns it on fornum_assignments >= 32768andintermediate_size >= 1024; both constants live inrunners.py. Below that the gather is fine and the padded buffers would only add traffic. In my measurements a TMAct.storefor the sorted GEMM1 tile was 13-18% slower than the scatter it would replace, so the sorted output keeps the scatter; I left a comment in the kernel about that since it is not obvious.Measured on an RTX PRO 6000 Blackwell (SM120), cuTile BF16, Nemotron-3.5-Lightning shape (H=2688, I=1856, E=128, top_k=6, ReLU2), CUDA graph timing, both autotuned:
Qwen3.6-35B-A3B (I=512) stays on the gather path; with the sorted path forced on it lost about 3% there, which is what the intermediate-size condition is for. I might be missing a shape family where the threshold should differ, so happy to adjust either constant.
🔍 Related Issues
#4857 (cuTile MoE performance on SM120). Baseline numbers and the per-kernel breakdown are from the #4646 testlist.
🚀 Pull Request Checklist
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✅ Pre-commit Checks
pre-commitby runningpip install pre-commit(or used your preferred method).pre-commit install.pre-commit run --all-filesand fixed any reported issues.🧪 Tests
unittest, etc.).New
test_cutile_bf16_sorted_io_matches_unsortedruns SwiGLU and ReLU2 on a shape with partial tiles on every GEMM edge (H=192, I=96, 64 tokens x top_k 2 over 4 experts) with the sorted path forced on and off and asserts bitwise equality.pytest tests/moe/test_unified_moe_cutile.pypasses on the RTX PRO 6000 (81 passed). The benchmark reference check passes for the Nemotron shape at 2048/4096/8192 tokens with the path on.Summary by CodeRabbit
New Features
Bug Fixes