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[ROCm] MXFP8 linear: one tile row for 17-64 token decode batches - #64195
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A BLOCK_M=16 tile re-streams the whole weight once per 16 rows. For 16 < M <= 64, cover M with a single tile row (BLOCK_M 32 or 64). Timed with weights read from HBM (rotating copies larger than the MALL), this is 1.2-1.9x faster on MiniMax-M3 TP2/TP4 dense shapes at M=32/64. M <= 16 is unchanged. Adds M3 TP4 shapes to the correctness test. Signed-off-by: Limpus, David <dlimpus@amd.com>
Signed-off-by: Limpus, David <dlimpus@amd.com>
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…ecode-tiles-a294 Signed-off-by: Limpus, David <dlimpus@amd.com>
Covers wq_b's K=1280 at M=48 (BLOCK_K=256) and the largest new tile, 32x32x1024 with 2 stages, used by the TP=2 qkv (N=4992) at M=32. Signed-off-by: Limpus, David <dlimpus@amd.com>
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This pull request has merge conflicts that must be resolved before it can be |
Select gfx950 tiles by M, local N and K while preserving the existing fallback outside the tuned range. Fold the M48 QKV exception into the table and use the narrow tile from vllm-project#64195 for V4.1 wqa/wkv M48 and M64. Extend the existing linear tests across the tuned buckets and TP2 shapes. Co-authored-by: OpenAI Codex Co-authored-by: Claude Code Signed-off-by: jiacao-amd <jiahui.cao@amd.com>
Overview
On ROCm (gfx950),
RocmDotScaledMxfp8LinearKerneluses aBLOCK_M=16tile for every decode batch up to M=64, so from 17 rows on the grid re-reads the whole weight from HBM once per 16 rows. This PR gives 17 ≤ M ≤ 64 a single tile row (BLOCK_M32 or 64). It changes_select_cfgonly.On MiniMax-M3 at M=64 it is 1.2–2.0x per GEMM, an estimated ~1.8 ms/step saved at TP2 and ~0.8 ms at TP4 (of 39.7 ms at C64).
Validation
Correctness.
tests/kernels/quantization/test_rocm_mxfp8_linear.py: 113 passed on MI355X. It adds the M3 TP4 shapes at M ∈ {16, 17, 32, 33, 48, 64, 65},wq_b(K=1280) at M=48, and M3 TP2 qkv at M=32.Microbenchmark. Single MI355X, 200 CUDA-graph-replayed launches over more than 512 MB of rotating weight copies, so every launch reads its weight from HBM. Without the rotation the 256 MB MALL hides the re-reads. Activation quant is excluded. Times in µs, main → this PR, M=32 / M=64:
DeepSeek-V4.1 attention linears (
V41_ATTN_SHAPES), same method, one run per arm:Known regressions.
wo_aat M=64 regresses from 8.7 to 9.3 µs; the 64-row tile is picked because N ≥ 2048. A 32-row tile would fix it but slows M3 TP4 qkv and TP2 gate_up (K=6144) from about 13.4 to 17 µs, so it is left as is.wo_aand TP8wq_bat M=32 (+0.2–0.3 µs) and M3 TP4 sh down at M=32 (4.2 → 4.3) are within noise.Pending: end-to-end decode A/B on main (MiniMax-M3, TP4), waiting on 4 GPUs.
Details
BLOCK_M=64leaves too few N-tiles, so those shapes use 32 and keep the old tile at M ≤ 32. Otherwise TP4 sh gate_up regresses from 10.1 to 13.1 µs.Pull Request Checklist
I used vLLM's
/pr-checklistskill. (Mandatory for agents, optional for humans).AI assistance was used during the creation of this PR.
Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.
Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.
Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.
Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.