| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification="parallelization-strategy=any-storage-any-loop" | \ |
| // RUN: FileCheck %s |
| |
| #CSR = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : dense, d1 : compressed) |
| }> |
| |
| #trait_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: func.func @matvec( |
| // CHECK-SAME: %[[TMP_arg0:.*]]: tensor<16x32xf32, #sparse{{[0-9]*}}>, |
| // CHECK-SAME: %[[TMP_arg1:.*]]: tensor<32xf32>, |
| // CHECK-SAME: %[[TMP_arg2:.*]]: tensor<16xf32>) -> tensor<16xf32> { |
| // CHECK-DAG: %[[TMP_c16:.*]] = arith.constant 16 : index |
| // CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index |
| // CHECK: %[[TMP_0:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} |
| // CHECK: %[[TMP_1:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} |
| // CHECK: %[[TMP_2:.*]] = sparse_tensor.values %[[TMP_arg0]] |
| // CHECK: %[[TMP_3:.*]] = bufferization.to_memref %[[TMP_arg1]] : memref<32xf32> |
| // CHECK: %[[TMP_4:.*]] = bufferization.to_memref %[[TMP_arg2]] : memref<16xf32> |
| // CHECK: scf.parallel (%[[TMP_arg3:.*]]) = (%[[TMP_c0]]) to (%[[TMP_c16]]) step (%[[TMP_c1]]) { |
| // CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_4]][%[[TMP_arg3]]] : memref<16xf32> |
| // CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_0]][%[[TMP_arg3]]] : memref<?xindex> |
| // CHECK: %[[TMP_8:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index |
| // CHECK: %[[TMP_9:.*]] = memref.load %[[TMP_0]][%[[TMP_8]]] : memref<?xindex> |
| // CHECK: %[[TMP_10:.*]] = scf.parallel (%[[TMP_arg4:.*]]) = (%[[TMP_7]]) to (%[[TMP_9]]) step (%[[TMP_c1]]) init (%[[TMP_6]]) -> f32 { |
| // CHECK: %[[TMP_11:.*]] = memref.load %[[TMP_1]][%[[TMP_arg4]]] : memref<?xindex> |
| // CHECK: %[[TMP_12:.*]] = memref.load %[[TMP_2]][%[[TMP_arg4]]] : memref<?xf32> |
| // CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_3]][%[[TMP_11]]] : memref<32xf32> |
| // CHECK: %[[TMP_14:.*]] = arith.mulf %[[TMP_12]], %[[TMP_13]] : f32 |
| // CHECK: scf.reduce(%[[TMP_14]] : f32) { |
| // CHECK: ^bb0(%[[TMP_arg5:.*]]: f32, %[[TMP_arg6:.*]]: f32): |
| // CHECK: %[[TMP_15:.*]] = arith.addf %[[TMP_arg5]], %[[TMP_arg6]] : f32 |
| // CHECK: scf.reduce.return %[[TMP_15]] : f32 |
| // CHECK: } |
| // CHECK: } |
| // CHECK: memref.store %[[TMP_10]], %[[TMP_4]][%[[TMP_arg3]]] : memref<16xf32> |
| // CHECK: scf.reduce |
| // CHECK: } |
| // CHECK: %[[TMP_5:.*]] = bufferization.to_tensor %[[TMP_4]] : memref<16xf32> |
| // CHECK: return %[[TMP_5]] : tensor<16xf32> |
| func.func @matvec(%arga: tensor<16x32xf32, #CSR>, |
| %argb: tensor<32xf32>, |
| %argx: tensor<16xf32>) -> tensor<16xf32> { |
| %0 = linalg.generic #trait_matvec |
| ins(%arga, %argb : tensor<16x32xf32, #CSR>, tensor<32xf32>) |
| outs(%argx: tensor<16xf32>) { |
| ^bb(%A: f32, %b: f32, %x: f32): |
| %0 = arith.mulf %A, %b : f32 |
| %1 = arith.addf %0, %x : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<16xf32> |
| return %0 : tensor<16xf32> |
| } |
