)]}'
{
  "commit": "67a05a45aec34d779bc7ff8968bd1c93133a037c",
  "tree": "09e875a6a33b68a3ea889ad0a7c52f0d5f727b63",
  "parents": [
    "dc29ee7d1bcfcec5a58d42e29125bbda937bbbbc"
  ],
  "author": {
    "name": "Han-Chung Wang",
    "email": "hanhan0912@gmail.com",
    "time": "Sun Dec 15 19:57:04 2024 -0800"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Mon Dec 16 03:57:04 2024 +0000"
  },
  "message": "[DT][NFC] Internalize transposeNarrowN logic to LayoutAttrInterface Impl  (#19453)\n\nWhether applying transposition from narrow-N to narrow-M is backend\r\nimplementation details, and we do not need to expose it to the type\r\nconverter. The encoding itself has enough information, like indexing\r\nmaps, narrow dimensions, etc., to infer the shapes and encoding info.\r\nInstead of updating the RankedTensorType and the attached encoding in\r\ntype converter, we can just cook the logic in `getEncodingInfo` methods.\r\nFrom the encoding, we know that whether it is narrow-N case, and we can\r\nupdate the MaterializeEncodingInfo correspondingly. The type converter\r\ncan infer the transposed tensor type from it. Thus, we can simplify the\r\nlogic in the type conversion.\r\n\r\nThe documentation of `transposeNarrowN` is moved to\r\n`[CPU|GPU]EncodingExternalModels.cpp` because all the implementation\r\nlocates at the files.\r\n\r\nSigned-off-by: hanhanW \u003chanhan0912@gmail.com\u003e",
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