| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification -cse | FileCheck %s |
| |
| #Dense = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : dense, d1 : dense) |
| }> |
| |
| #trait_scale = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = X(i,j) * 2.0" |
| } |
| |
| // CHECK-LABEL: func.func @sparse_scale( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1xf32, #sparse{{[0-9]*}}>) |
| // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f32 |
| // CHECK: %[[VAL_3:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1x1xf32, #sparse{{[0-9]*}}> to memref<?xf32> |
| // CHECK: %[[VAL_4:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32> |
| // CHECK: %[[VAL_5:.*]] = arith.mulf %[[VAL_4]], %[[VAL_2]] : f32 |
| // CHECK: memref.store %[[VAL_5]], %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32> |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<1x1xf32, #sparse{{[0-9]*}}> |
| // CHECK: return %[[VAL_6]] : tensor<1x1xf32, #sparse{{[0-9]*}}> |
| func.func @sparse_scale(%argx: tensor<1x1xf32, #Dense>) -> tensor<1x1xf32, #Dense> { |
| %c = arith.constant 2.0 : f32 |
| %0 = linalg.generic #trait_scale |
| outs(%argx: tensor<1x1xf32, #Dense>) { |
| ^bb(%x: f32): |
| %1 = arith.mulf %x, %c : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<1x1xf32, #Dense> |
| return %0 : tensor<1x1xf32, #Dense> |
| } |
