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Add gfx950 (MI355X) preload tuning table for preshuffle GEMM#411

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andyluo7 wants to merge 1 commit intoROCm:mainfrom
andyluo7:gfx950-preload-tuning
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Add gfx950 (MI355X) preload tuning table for preshuffle GEMM#411
andyluo7 wants to merge 1 commit intoROCm:mainfrom
andyluo7:gfx950-preload-tuning

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Summary

gfx950 (MI355X) has different LDS/VMEM latencies compared to gfx942 (MI300X). Without tuned preload values, preshuffle GEMM regresses -14.6% E2E on DeepSeek-R1 MoE workloads. With gfx950-specific preload table, the regression is eliminated (-0.7%, within noise).

E2E Results (DeepSeek-R1, TP=8)

Config MI355X tok/s Δ vs baseline
Baseline (no FlyDSL) 109.05
FlyDSL main (gfx942 configs) 93.11 -14.6%
FlyDSL + gfx950 preload table 108.31 -0.7%

Changes

  • Add _TILE_PRELOAD_TABLE_GFX950 with tuned (dsrd_preload, dvmem_preload) values for common tile sizes on MI355X
  • Update _get_preload() to accept gpu_arch parameter and select the appropriate table
  • Use gfx950 table for both FP8/INT8 and BF16 paths on MI355X

Testing

Tuned on Tensorwave MI355X cluster (mia1-p02-g32) with DeepSeek-R1 MoE expert shapes (N=7168, K=18432).

gfx950 has different LDS/VMEM latencies compared to gfx942 (MI300X).
Without tuned preload values, preshuffle GEMM regresses -14.6% E2E on
DeepSeek-R1 MoE workloads. With gfx950-specific preload table, the
regression is eliminated (-0.7%, within noise).

Changes:
- Add _TILE_PRELOAD_TABLE_GFX950 with tuned (dsrd_preload, dvmem_preload)
  values for common tile sizes on MI355X
- Update _get_preload() to accept gpu_arch parameter and select the
  appropriate table
- Use gfx950 table for both FP8/INT8 and BF16 paths on MI355X

Tuned on Tensorwave MI355X cluster with DeepSeek-R1 MoE expert shapes
(N=7168, K=18432).
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