[nehaprakriya]

Purpose

Improve decode throughput for MXFP8 MoE models on AMD CDNA4 (gfx950 / MI355X) by replacing the fixed Triton tile sizes in rocm_native.py and mxfp8_native_moe.py with per-shape tiles tuned at real MTP decode shapes.

Linear: At decode (M≤256, K=6144 qkv/gate_up projections), BLOCK_N=32 with per-M buckets (M≤48/80/112/256) outperforms the previous BLOCK_N=64/128 tile by 1.1–1.6× in kernel time. Prefill (M≥1024) keeps the wide 128×256 tile.

MoE: At decode (block_m=16), GEMM1 (K=6144→N=1536) uses BLOCK_N=32/BLOCK_K=512/waves_per_eu=4 and GEMM2 (K=768→N=6144) uses BLOCK_N=64/BLOCK_K=256. Prefill keeps the wide baseline tile.

Tile size does not affect tl.dot_scaled numerics — output is bit-identical to the previous dispatch.

Test Plan

python -m sglang.bench_serving \
  --model MiniMaxAI/MiniMax-M3-MXFP8 \
  --num-prompts 200 --request-rate inf \
  --input-len 8192 --output-len 1024 \
  --max-concurrency 32

Test Result

MiniMax-M3 MXFP8, ISL=8192 OSL=1024 concurrency=32, 4× MI355X (gfx950):

Metric	Before	After	Δ
Output throughput (tok/s)	1284	1330	+3.6%
TPOT (ms)	24.9	23.7	−4.8%
TTFT (ms)	312	308	−1.3%

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[tjtanaa]

what is the impact on the non-mtp use case?

[nehaprakriya]

@tjtanaa, thanks for the review. There isn’t an MTP vs non‑MTP switch here: tile choice is purely shape‑based from the runtime GEMM (M, K, …).

For non‑MTP workloads that still isn’t special‑cased — it’s the same table:

When M is large (the prefill / long‑context regime, e.g. the M ≥ 1024 bucket in the comment), we intentionally select the wide baseline tile (same high‑throughput strategy as before for large M). So this isn’t “large M gets a new experimental tile”; it’s “large M stays on the known‑good wide path.”
For smaller M (typical decode / skinny projections), we pick per‑bucket tuned tiles instead of a single fixed tile — that’s where the measured win came from in the MTP decode benchmark, but any workload (MTP or not) that hits those M ranges gets the same selection.
The None sentinel row is just the catch‑all for shapes that don’t match an explicit bucket so we don’t leave gaps (not “non‑MTP fallback” specifically).
Net: non‑MTP doesn’t bypass the table; for large M it should land in the wide‑tile row (baseline‑aligned), and for small M it can pick a decode‑tuned row when applicable. I’ll add a short non‑MTP bench line in the PR if you want hard numbers for that path too.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @nehaprakriya.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

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[Fangzhou-Ai]

I am preparing a MiniMax-M3.1 MI355X follow-up stacked on #63232 at 8d0f05a0c33c91cc4d3f0a6f189474420af47174. There is overlap with this PR's MXFP8 dispatch area, so I compared the approaches before proposing the follow-up.

The follow-up preserves the current #63232 selector and adds gfx950 choices only for33–64 rows (QKV only49–64), for M3.1 TP4 QKV N2560/K6144, output N6144/K2048, shared gate/up N1536/K6144 and shared down N6144/K768. It does not modify MXFP8 MoE kernels, quantization or the Triton kernel body. This PR replaces an older M3 N2304/N1536 dispatch throughM256 and also changes MoE kernels; the scope and selected tiles therefore differ.

On the current #63232 kernel body, using prequantized MXFP8 operands, rotating weights larger than400MiB, HIP graph/event timing and correctness against FP32 matmul of exactly dequantized operands, the retained M64 choices measured17.54→13.28µs,13.97→7.58µs,9.96→8.74µs and5.17→4.47µs respectively. A coarse sweep confirmed those choices for the tested shapes. I also measured this PR's applicable tile choices on that same current body. The extended existing numerical suite passed169 cases.

The follow-up also contains separate MiniMax CP/DBO and existing batch-sharded sampling enablement fixes. Its cached serving gains measure the combined change; I am not attributing them solely to tile selection. The full-duration AgentX replay is still running, and the draft will keep that limitation explicit. AI assistance was used for the investigation and implementation; the submitting human has confirmed reviewing the changes and running the relevant tests.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @nehaprakriya.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork