)]}'
{
  "commit": "07e76973cb30ed4b8320166a1ee28167c15a39a3",
  "tree": "a174d1deda7d02bb5b27f1533d609401e02899d7",
  "parents": [
    "8206e32c9ba18322c4092cb46dcd3b6b185362c0"
  ],
  "author": {
    "name": "Vivian Zhang",
    "email": "zhyuhang88@gmail.com",
    "time": "Wed Mar 11 13:44:30 2026 -0700"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Wed Mar 11 13:44:30 2026 -0700"
  },
  "message": "[GPUHeuristics] Refactor MMA heuristic seeds to be architecture-specific (#23717)\n\nEach GPU architecture now defines a constexpr ArchSeedSet containing\ngemm, scaled gemm, and convolution seed arrays indexed by GemmSize. The\narchitecture is determined from the target and the corresponding seed\nset is returned (e.g. RDNA4 uses tuned seeds from RX 9070 XT\nbenchmarking, others use the default). GemmSize enum values are changed\nto start at 0 for direct array indexing.\n\nAdd RDNA4 (gfx1201) config tests covering small, medium, and large\nmatmul and convolution shapes.\n\nRDNA4 benchmark results (RX 9070 XT, vs default seeds):\n- GEMM shapes: ~40% of shapes improved, 15.9% geometric mean speedup.\n- Prod Conv shapes: ~25% of shapes improved, 6% geometric mean speedup.\n- Proxy Conv shapes: ~30% of shapes improved, 14.5% geometric mean\nspeedup.\n\n---------\n\nSigned-off-by: yzhang93 \u003czhyuhang88@gmail.com\u003e\nCo-authored-by: Claude \u003cnoreply@anthropic.com\u003e",
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