| // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py |
| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s |
| |
| #X = #sparse_tensor.encoding<{ |
| map = (d0, d1, d2) -> (d2 : dense, d0 : dense, d1 : dense) |
| }> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i,j,k) -> (k,i,j)>, // A (in) |
| affine_map<(i,j,k) -> (i,j,k)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel", "parallel"] |
| } |
| |
| // CHECK-LABEL: func @sparse_static_dims( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse{{[0-9]*}}>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> { |
| // CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 20 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index |
| // CHECK: %[[DEMAP:.*]] = sparse_tensor.reinterpret_map %[[VAL_0]] |
| // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[DEMAP]] : tensor<30x10x20xf32, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30x10xf32> |
| // CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_9]] : memref<20x30x10xf32>) |
| // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { |
| // CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { |
| // CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_12]] : index |
| // CHECK: %[[VAL_15:.*]] = arith.muli %[[VAL_13]], %[[VAL_2]] : index |
| // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] { |
| // CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_15]] : index |
| // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xf32> |
| // CHECK: memref.store %[[VAL_17]], %[[VAL_9]]{{\[}}%[[VAL_14]], %[[VAL_10]], %[[VAL_11]]] : memref<20x30x10xf32> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_18:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<20x30x10xf32> |
| // CHECK: return %[[VAL_18]] : tensor<20x30x10xf32> |
| // CHECK: } |
| func.func @sparse_static_dims(%arga: tensor<10x20x30xf32, #X>, |
| %argx: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<10x20x30xf32, #X>) |
| outs(%argx: tensor<20x30x10xf32>) { |
| ^bb(%a : f32, %x: f32): |
| linalg.yield %a : f32 |
| } -> tensor<20x30x10xf32> |
| return %0 : tensor<20x30x10xf32> |
| } |
| |
| // CHECK-LABEL: func @sparse_dynamic_dims( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse{{[0-9]*}}>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> { |
| // CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK: %[[DEMAP:.*]] = sparse_tensor.reinterpret_map %[[VAL_0]] |
| // CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[DEMAP]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.lvl %[[DEMAP]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse{{[0-9]*}}> |
| // CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?x?xf32> |
| // CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_10]] : memref<?x?x?xf32>) |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] { |
| // CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_11]], %[[VAL_8]] : index |
| // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] { |
| // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : index |
| // CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_14]], %[[VAL_6]] : index |
| // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_4]] { |
| // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_16]] : index |
| // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_17]]] : memref<?xf32> |
| // CHECK: memref.store %[[VAL_18]], %[[VAL_10]]{{\[}}%[[VAL_15]], %[[VAL_11]], %[[VAL_12]]] : memref<?x?x?xf32> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_19:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<?x?x?xf32> |
| // CHECK: return %[[VAL_19]] : tensor<?x?x?xf32> |
| // CHECK: } |
| func.func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>, |
| %argx: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?x?x?xf32, #X>) |
| outs(%argx: tensor<?x?x?xf32>) { |
| ^bb(%a : f32, %x: f32): |
| linalg.yield %a : f32 |
| } -> tensor<?x?x?xf32> |
| return %0 : tensor<?x?x?xf32> |
| } |
