llvm-project

c26db586 - [mlir][affine] Modify assertion into a user visible diagnostic (#136474)

[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]).