| commit | c26db586a06204a9e6f9912f41fce62a559a0196 | [log] [tgz] |
|---|---|---|
| author | Prakhar Dixit <dixitprakhar11@gmail.com> | Wed Apr 30 20:51:54 2025 +0530 |
| committer | GitHub <noreply@github.com> | Wed Apr 30 17:21:54 2025 +0200 |
| tree | 6915bbeb7f4ebbd4e695a92c0e26cb8400fab301 | |
| parent | 75f040ab3e119e0ee87963ec9d2a01b1e6d4039e [diff] |
[mlir][affine] Modify assertion into a user visible diagnostic (#136474)
Fixes #122227
The loop’s induction variable (%i) is used to compute two different
indices via affine.apply.
And the Vectorization Assumption is Violated i.e, Each vectorized loop
should contribute at most one non-invariant index.
**Minimal example crashing :**
```
#map = affine_map<(d0)[s0] -> (d0 mod s0)>
#map1 = affine_map<(d0)[s0] -> (d0 floordiv s0)>
func.func @single_loop_unrolling_2D_access_pattern(%arg0: index) -> memref<2x2xf32> {
%c2 = arith.constant 2 : index
%cst = arith.constant 1.0 : f32
%alloc = memref.alloc() : memref<2x2xf32>
affine.for %i = 0 to 4 {
%row = affine.apply #map1(%i)[%c2]
%col = affine.apply #map(%i)[%c2]
affine.store %cst, %alloc[%row, %col] : memref<2x2xf32>
}
return %alloc : memref<2x2xf32>
}
```
The single loop %i contributes two indices (%row and %col) to the 2D
memref access.
The permutation map expects one index per vectorized loop dimension, but
here one loop (%i) maps to two indices (dim=0 and dim=1).
The code detects this when trying to assign the second index (dim=1) to
the same vector dimension (perm[0]).
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