| // RUN: mlir-opt %s -sparsifier="vl=8" | FileCheck %s |
| |
| #Dense = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : dense, d1 : dense) |
| }> |
| |
| #matvec = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (j)>, // b |
| affine_map<(i,j) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel", "reduction"], |
| doc = "X(i) += A(i,j) * B(j)" |
| } |
| |
| // CHECK-LABEL: llvm.func @kernel_matvec |
| // CHECK: llvm.intr.vector.reduce.fadd |
| func.func @kernel_matvec(%arga: tensor<?x?xf32, #Dense>, |
| %argb: tensor<?xf32>, |
| %argx: tensor<?xf32>) -> tensor<?xf32> { |
| %x = linalg.generic #matvec |
| ins(%arga, %argb: tensor<?x?xf32, #Dense>, tensor<?xf32>) |
| outs(%argx: tensor<?xf32>) { |
| ^bb(%a: f32, %b: f32, %x: f32): |
| %0 = arith.mulf %a, %b : f32 |
| %1 = arith.addf %x, %0 : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<?xf32> |
| return %x : tensor<?xf32> |
| } |
