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Purpose
Small-batch Triton sparse decode exposes insufficient parallel work inside pages and can schedule splits beyond the page-table capacity. This patch adds page splitting, bounds split count using CPU-visible table capacity, skips empty slices on the GPU, and fuses single-page calls with an actually one-column table into direct output.
The specialization targets GQA16, head dimension 128 and page size 128 with contiguous BF16 queries and BF16/E4M3 KV. General page splitting is enabled on SM120; the bounded single-page path supports SM103/SM120. Other geometries use the existing implementation. No sequence-length device-to-host synchronization is added. Empty slices overwrite scratch output and LSE; stable LSE merging handles empty rows and CUDA Graph replay after lengths shrink or grow. Scalar and physical-token/head FP8 scales and PDL are supported.
Test Result
Baseline: b7e8dd8. RTX 5090, driver 595.71.05, Torch 2.13.0+cu132, Triton 3.7.1; installed native vLLM, no platform shims.
Each independent invocation measures 72 cases (12 shapes × three KV modes × seeds 2027/4093), with a 256-column preallocated page table, three alternating baseline/candidate CUDA Graph timing rounds of 60 ms. Both implementations are checked against a dense reference. Timings include the complete attention call and merge.
Worst speedup: 0.9907× / 0.9963×; no case below 0.98×. Maximum relative RMS error against reference: 0.3269%.
Production metadata slices page-table rows, not columns: short context alone does not select the direct single-page path. The primary results above deliberately use padded tables. These are operator measurements, not indexer performance or model/serving throughput. Integrated native validation is on SM120; the SM103 single-page specialization has historical B300 prototype evidence but has not been rerun in this integrated revision.
Test Plan
.venv/bin/python -m pytest tests/kernels/test_minimax_m3_sparse_attn_fp8_scale.py -q..venv/bin/python -m pytest tests/kernels/attention/test_minimax_m3.py -k 'decode_sparse_attention or decode_wrong_layout' -q(NHD/HND, speculative query lengths, padded requests).compute-sanitizer --tool memcheckon the 64-case suite: 0 errors.Reproduce performance after extracting the pinned baseline:
git show b7e8dd8f372c1324c376ba5dc03d92264eae8f24:vllm/models/minimax_m3/common/ops/sparse_attn.py > /tmp/msa_baseline.py .venv/bin/python benchmarks/kernels/benchmark_minimax_m3_sparse_decode.py --baseline /tmp/msa_baseline.py --matrix heldout --seeds 2027 4093 --pad-capacity 256 .venv/bin/python benchmarks/kernels/check_minimax_m3_decode_model.py --baseline-source /tmp/msa_baseline.py --output /tmp/msa-model-baseline.json .venv/bin/python benchmarks/kernels/check_minimax_m3_decode_model.py --reference-output /tmp/msa-model-baseline.json --output /tmp/msa-model-candidate.jsonRelated work
#55372 / #47763 build unions across speculative query rows to reuse KV. This patch keeps independent query rows and changes page-level work decomposition. #48994 and #56483 introduce FlashInfer backends; this patch optimizes the existing Triton implementation. It is not a standalone sentinel/empty-row fix duplicating #54597 or #49054. Duplicate search results are retained in the review package.
AI assistance
OpenAI Codex assisted implementation, tests and measurements. The human submitter has confirmed reviewing the code and test results and reproducing the tests on their own server.