| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s |
| |
| #SV = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // A (in) |
| affine_map<(i) -> (i)> // X (out) |
| ], |
| iterator_types = ["parallel"] |
| } |
| |
| // CHECK-LABEL: func.func @allout_inplace( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<10xi32, #{{.*}}>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<10xf32>) -> tensor<10xf32> { |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex> |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex> |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xi32, #{{.*}}> to memref<?xi32> |
| // CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<10xf32> |
| // CHECK: linalg.fill ins(%[[VAL_3]] : f32) outs(%[[VAL_8]] : memref<10xf32>) |
| // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] { |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex> |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi32> |
| // CHECK: %[[VAL_14:.*]] = arith.sitofp %[[VAL_13]] : i32 to f32 |
| // CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<10xf32> |
| // CHECK: } |
| // CHECK: %[[VAL_15:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<10xf32> |
| // CHECK: return %[[VAL_15]] : tensor<10xf32> |
| // CHECK: } |
| func.func @allout_inplace(%arga: tensor<10xi32, #SV>, |
| %argb: tensor<10xf32>) -> tensor<10xf32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<10xi32, #SV>) |
| outs(%argb: tensor<10xf32>) { |
| ^bb(%a: i32, %x : f32): |
| %cst = arith.sitofp %a : i32 to f32 |
| linalg.yield %cst : f32 |
| } -> tensor<10xf32> |
| return %0 : tensor<10xf32> |
| } |
| |
| // CHECK-LABEL: func.func @allout_materialize( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<10xi32, #{{.*}}>) -> tensor<10xf32> { |
| // CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_4:.*]] = tensor.empty() : tensor<10xf32> |
| // CHECK: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex> |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex> |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xi32, #{{.*}}> to memref<?xi32> |
| // CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_4]] : memref<10xf32> |
| // CHECK: linalg.fill ins(%[[VAL_2]] : f32) outs(%[[VAL_8]] : memref<10xf32>) |
| // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex> |
| // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] { |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex> |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi32> |
| // CHECK: %[[VAL_14:.*]] = arith.sitofp %[[VAL_13]] : i32 to f32 |
| // CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<10xf32> |
| // CHECK: } |
| // CHECK: %[[VAL_15:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<10xf32> |
| // CHECK: return %[[VAL_15]] : tensor<10xf32> |
| // CHECK: } |
| func.func @allout_materialize(%arga: tensor<10xi32, #SV>) -> tensor<10xf32> { |
| %m = tensor.empty() : tensor<10xf32> |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<10xi32, #SV>) |
| outs(%m: tensor<10xf32>) { |
| ^bb(%a: i32, %x : f32): |
| %cst = arith.sitofp %a : i32 to f32 |
| linalg.yield %cst : f32 |
| } -> tensor<10xf32> |
| return %0 : tensor<10xf32> |
| } |
| |
| // CHECK-LABEL: func.func @update_inplace( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<10xf32, #{{.*}}>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<10xf32>) -> tensor<10xf32> { |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10xf32, #{{.*}}> to memref<?xindex> |
| // CHECK: %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10xf32, #{{.*}}> to memref<?xindex> |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #{{.*}}> to memref<?xf32> |
| // CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<10xf32> |
| // CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] { |
| // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex> |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf32> |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<10xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_12]], %[[VAL_13]] : f32 |
| // CHECK: memref.store %[[VAL_14]], %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<10xf32> |
| // CHECK: } |
| // CHECK: %[[VAL_15:.*]] = bufferization.to_tensor %[[VAL_7]] : memref<10xf32> |
| // CHECK: return %[[VAL_15]] : tensor<10xf32> |
| // CHECK: } |
| func.func @update_inplace(%arga: tensor<10xf32, #SV>, |
| %argb: tensor<10xf32>) -> tensor<10xf32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<10xf32, #SV>) |
| outs(%argb: tensor<10xf32>) { |
| ^bb(%a: f32, %x : f32): |
| %up = arith.addf %a, %x : f32 |
| linalg.yield %up : f32 |
| } -> tensor<10xf32> |
| return %0 : tensor<10xf32> |
| } |
