)]}'
{
  "commit": "5e31a849f6ebfd92760764537cc75ac4584f35d0",
  "tree": "cb46a68e55a28e438e8cda63f829d4d5964976d5",
  "parents": [
    "b60b87ca48290afe1cbc3c51b35a4e81fa652f80"
  ],
  "author": {
    "name": "Pooja Hemashekar",
    "email": "hemashekar@roofline.ai",
    "time": "Thu Jul 23 15:43:10 2026 +0200"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Thu Jul 23 15:43:10 2026 +0200"
  },
  "message": "[Codegen] KernelDispatch recognition and tiling for data-tiled conv (#24716)\n\nTeaches LLVMCPU KernelDispatch to recognize data-tiled convolution\ndispatches produced by encoding materialization and assigns them a\ndedicated root lowering config that vectorizes the output width (ow) and\nthe inner data-tiling channel dims (oc_inner, ic_inner, sizes k0/c0),\nand distributes batch (unit tiles), output channel blocks (oc_outer),\nand output height, with peeling enabled.\n\nCo-authored-by: Jelle Schuhmacher \u003cschuehmacher@roofline.ai\u003e\nAssisted-by: Claude code\nSigned-off-by: Pooja Hemashekar \u003chemashekar@roofline.ai\u003e\n\n---------\n\nSigned-off-by: Pooja Hemashekar \u003chemashekar@roofline.ai\u003e\nCo-authored-by: Jelle Schuhmacher \u003cschuehmacher@roofline.ai\u003e",
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