| // RUN: mlir-opt %s --sparsifier="enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true" |
| |
| #MAT_D_C = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : dense, d1 : compressed) |
| }> |
| |
| #MAT_C_C_P = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d1 : compressed, d0 : compressed) |
| }> |
| |
| #MAT_C_D_P = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d1 : compressed, d0 : dense) |
| }> |
| |
| // |
| // Ensures only last loop is vectorized |
| // (vectorizing the others would crash). |
| // |
| // CHECK-LABEL: llvm.func @foo |
| // CHECK: llvm.intr.masked.load |
| // CHECK: llvm.intr.masked.scatter |
| // |
| func.func @foo(%arg0: tensor<2x4xf64, #MAT_C_C_P>, |
| %arg1: tensor<3x4xf64, #MAT_C_D_P>, |
| %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> { |
| %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index} |
| : tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64> |
| return %0 : tensor<9x4xf64> |
| } |
