| // RUN: mlir-opt %s --sparse-reinterpret-map --sparsification --canonicalize --cse | FileCheck %s |
| |
| #DCSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }> |
| #SparseTensor = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed) }> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(d0, d1, d2) -> (d0, d2)>, |
| affine_map<(d0, d1, d2) -> (d0, d1, d2)> |
| ], |
| iterator_types = ["parallel", "parallel", "parallel"] |
| } |
| |
| // CHECK-LABEL: @main( |
| // CHECK-SAME: %[[TMP_arg0:.*]]: tensor<4x5xi32, |
| // CHECK-DAG: %[[TMP_c3:.*]] = arith.constant 3 : index |
| // CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index |
| // CHECK: %[[TMP_0:.*]] = tensor.empty() |
| // CHECK: %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} |
| // CHECK: %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} |
| // CHECK: %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} |
| // CHECK: %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} |
| // CHECK: %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] |
| // CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex> |
| // CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex> |
| // CHECK: %[[T:.*]] = scf.for %[[TMP_arg1:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] {{.*}} { |
| // CHECK: %[[TMP_9:.*]] = memref.load %[[TMP_2]][%[[TMP_arg1]]] : memref<?xindex> |
| // CHECK: %[[L1:.*]] = scf.for %[[TMP_arg2:.*]] = %[[TMP_c0]] to %[[TMP_c3]] step %[[TMP_c1]] {{.*}} { |
| // CHECK: %[[TMP_10:.*]] = memref.load %[[TMP_3]][%[[TMP_arg1]]] : memref<?xindex> |
| // CHECK: %[[TMP_11:.*]] = arith.addi %[[TMP_arg1]], %[[TMP_c1]] : index |
| // CHECK: %[[TMP_12:.*]] = memref.load %[[TMP_3]][%[[TMP_11]]] : memref<?xindex> |
| // CHECK: %[[L2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_10]] to %[[TMP_12]] step %[[TMP_c1]] {{.*}} { |
| // CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_4]][%[[TMP_arg3]]] : memref<?xindex> |
| // CHECK: %[[TMP_14:.*]] = memref.load %[[TMP_5]][%[[TMP_arg3]]] : memref<?xi32> |
| // CHECK: %[[Y:.*]] = tensor.insert %[[TMP_14]] into %{{.*}}[%[[TMP_9]], %[[TMP_arg2]], %[[TMP_13]]] |
| // CHECK: scf.yield %[[Y]] |
| // CHECK: } |
| // CHECK: scf.yield %[[L2]] |
| // CHECK: } |
| // CHECK: scf.yield %[[L1]] |
| // CHECK: } |
| // CHECK: %[[TMP_8:.*]] = sparse_tensor.load %[[T]] hasInserts |
| // CHECK: return %[[TMP_8]] |
| module @func_sparse { |
| func.func public @main(%arg0: tensor<4x5xi32, #DCSR>) -> tensor<4x3x5xi32, #SparseTensor> { |
| %0 = tensor.empty() : tensor<4x3x5xi32, #SparseTensor> |
| %1 = linalg.generic #trait |
| ins(%arg0 : tensor<4x5xi32, #DCSR>) outs(%0 : tensor<4x3x5xi32, #SparseTensor>) { |
| ^bb0(%in: i32, %out: i32): |
| linalg.yield %in : i32 |
| } -> tensor<4x3x5xi32, #SparseTensor> |
| return %1 : tensor<4x3x5xi32, #SparseTensor> |
| } |
| } |
