| commit | 96c1611163d3420c78e30b13c1f7211e3572e58b | [log] [tgz] |
|---|---|---|
| author | Christopher McGirr <7071833+chrsmcgrr@users.noreply.github.com> | Fri Jun 27 09:24:33 2025 +0200 |
| committer | GitHub <noreply@github.com> | Fri Jun 27 09:24:33 2025 +0200 |
| tree | 4d0773241b3d5f6048b4ab381626bc76d8f67e71 | |
| parent | 23384cd5817122ea05d66955559e4cc04795b307 [diff] |
[mlir][linalg] fix OuterUnitDims linalg.pack decomposition pattern (#141613)
Given the following example:
```
module {
func.func @main(%arg0: tensor<1x1x1x4x1xf32>, %arg1: tensor<1x1x4xf32>) -> tensor<1x1x1x4x1xf32> {
%pack = linalg.pack %arg1 outer_dims_perm = [1, 2, 0] inner_dims_pos = [2, 0] inner_tiles = [4, 1] into %arg0 : tensor<1x1x4xf32> -> tensor<1x1x1x4x1xf32>
return %pack : tensor<1x1x1x4x1xf32>
}
}
```
We would generate an invalid transpose operation because the calculated
permutation would be `[0, 2, 0]` which is semantically incorrect. As the
permutation must contain unique integers corresponding to the source
tensor dimensions.
The following change modifies how we calculate the permutation array and
ensures that the dimension indices given in the permutation array is
unique.
The above example would then translate to a transpose having a
permutation of `[1, 2, 0]`. Following the rule, that the `inner_dim_pos`
is appended to the permutation array and the preceding indices are
filled with the remaining dimensions.
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