| // RUN: mlir-opt %s -affine-super-vectorize="virtual-vector-size=32,256 test-fastest-varying=1,0 vectorize-reductions=true" -verify-diagnostics |
| |
| // TODO: Vectorization of reduction loops along the reduction dimension is not |
| // supported for higher-rank vectors yet, so we are just checking that an |
| // error message is produced. |
| |
| // expected-error@+1 {{Vectorizing reductions is supported only for 1-D vectors}} |
| func @vecdim_reduction_2d(%in: memref<256x512x1024xf32>, %out: memref<256xf32>) { |
| %cst = arith.constant 0.000000e+00 : f32 |
| affine.for %i = 0 to 256 { |
| %sum_j = affine.for %j = 0 to 512 iter_args(%red_iter_j = %cst) -> (f32) { |
| %sum_k = affine.for %k = 0 to 1024 iter_args(%red_iter_k = %cst) -> (f32) { |
| %ld = affine.load %in[%i, %j, %k] : memref<256x512x1024xf32> |
| %add = arith.addf %red_iter_k, %ld : f32 |
| affine.yield %add : f32 |
| } |
| %add = arith.addf %red_iter_j, %sum_k : f32 |
| affine.yield %add : f32 |
| } |
| affine.store %sum_j, %out[%i] : memref<256xf32> |
| } |
| return |
| } |
| |
