| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s |
| |
| #DCSR = #sparse_tensor.encoding<{ |
| map = (d0, d1) -> (d0 : compressed, d1 : compressed) |
| }> |
| |
| #transpose_trait = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (j,i)>, // A |
| affine_map<(i,j) -> (i,j)> // X |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = A(j,i)" |
| } |
| |
| // TODO: improve auto-conversion followed by yield |
| |
| // CHECK-LABEL: func.func @sparse_transpose_auto( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<3x4xf64, #sparse{{[0-9]*}}>) -> tensor<4x3xf64, #sparse{{[0-9]*}}> { |
| // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = tensor.empty() : tensor<4x3xf64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.convert %[[VAL_0]] : tensor<3x4xf64, #sparse{{[0-9]*}}> to tensor<3x4xf64, #sparse{{[0-9]*}}> |
| // CHECK: %[[DEMAP:.*]] = sparse_tensor.reinterpret_map %[[VAL_4]] : tensor<3x4xf64, #sparse{{[0-9]*}}> to tensor<4x3xf64, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[DEMAP]] {level = 0 : index} : tensor<4x3xf64, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[DEMAP]] {level = 0 : index} : tensor<4x3xf64, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[DEMAP]] {level = 1 : index} : tensor<4x3xf64, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[DEMAP]] {level = 1 : index} : tensor<4x3xf64, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[DEMAP]] : tensor<4x3xf64, #sparse{{[0-9]*}}> to memref<?xf64> |
| // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex> |
| // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_3]]) -> (tensor<4x3xf64, #sparse{{[0-9]*}}>) { |
| // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex> |
| // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex> |
| // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index |
| // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex> |
| // CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_16]] to %[[VAL_18]] step %[[VAL_2]] iter_args(%[[VAL_21:.*]] = %[[VAL_14]]) -> (tensor<4x3xf64, #sparse{{[0-9]*}}>) { |
| // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex> |
| // CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xf64> |
| // CHECK: %[[VAL_24:.*]] = tensor.insert %[[VAL_23]] into %[[VAL_21]]{{\[}}%[[VAL_15]], %[[VAL_22]]] : tensor<4x3xf64, #sparse{{[0-9]*}}> |
| // CHECK: scf.yield %[[VAL_24]] : tensor<4x3xf64, #sparse{{[0-9]*}}> |
| // CHECK: } |
| // CHECK: scf.yield %[[VAL_25:.*]] : tensor<4x3xf64, #sparse{{[0-9]*}}> |
| // CHECK: } |
| // CHECK: %[[VAL_26:.*]] = sparse_tensor.load %[[VAL_27:.*]] hasInserts : tensor<4x3xf64, #sparse{{[0-9]*}}> |
| // CHECK: return %[[VAL_26]] : tensor<4x3xf64, #sparse{{[0-9]*}}> |
| // CHECK: } |
| func.func @sparse_transpose_auto(%arga: tensor<3x4xf64, #DCSR>) |
| -> tensor<4x3xf64, #DCSR> { |
| %i = tensor.empty() : tensor<4x3xf64, #DCSR> |
| %0 = linalg.generic #transpose_trait |
| ins(%arga: tensor<3x4xf64, #DCSR>) |
| outs(%i: tensor<4x3xf64, #DCSR>) { |
| ^bb(%a: f64, %x: f64): |
| linalg.yield %a : f64 |
| } -> tensor<4x3xf64, #DCSR> |
| return %0 : tensor<4x3xf64, #DCSR> |
| } |
