1// RUN: mlir-opt %s --canonicalize --cse | FileCheck %s 2 3#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> 4 5// CHECK-LABEL: func @sparse_nop_dense2dense_convert( 6// CHECK-SAME: %[[A:.*]]: tensor<64xf32>) 7// CHECK-NOT: sparse_tensor.convert 8// CHECK: return %[[A]] : tensor<64xf32> 9func.func @sparse_nop_dense2dense_convert(%arg0: tensor<64xf32>) -> tensor<64xf32> { 10 %0 = sparse_tensor.convert %arg0 : tensor<64xf32> to tensor<64xf32> 11 return %0 : tensor<64xf32> 12} 13 14// CHECK-LABEL: func @sparse_dce_convert( 15// CHECK-SAME: %[[A:.*]]: tensor<64xf32>) 16// CHECK-NOT: sparse_tensor.convert 17// CHECK: return 18func.func @sparse_dce_convert(%arg0: tensor<64xf32>) { 19 %0 = sparse_tensor.convert %arg0 : tensor<64xf32> to tensor<64xf32, #SparseVector> 20 return 21} 22 23// CHECK-LABEL: func @sparse_dce_getters( 24// CHECK-SAME: %[[A:.*]]: tensor<64xf32, #sparse{{[0-9]*}}>) 25// CHECK-NOT: sparse_tensor.positions 26// CHECK-NOT: sparse_tensor.coordinates 27// CHECK-NOT: sparse_tensor.values 28// CHECK: return 29func.func @sparse_dce_getters(%arg0: tensor<64xf32, #SparseVector>) { 30 %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex> 31 %1 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex> 32 %2 = sparse_tensor.values %arg0 : tensor<64xf32, #SparseVector> to memref<?xf32> 33 return 34} 35 36// CHECK-LABEL: func @sparse_concat_dce( 37// CHECK-NOT: sparse_tensor.concatenate 38// CHECK: return 39func.func @sparse_concat_dce(%arg0: tensor<2xf64, #SparseVector>, 40 %arg1: tensor<3xf64, #SparseVector>, 41 %arg2: tensor<4xf64, #SparseVector>) { 42 %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index} 43 : tensor<2xf64, #SparseVector>, 44 tensor<3xf64, #SparseVector>, 45 tensor<4xf64, #SparseVector> to tensor<9xf64, #SparseVector> 46 return 47} 48 49// CHECK-LABEL: func @sparse_get_specifier_dce_fold( 50// CHECK-SAME: %[[A0:.*]]: !sparse_tensor.storage_specifier 51// CHECK-SAME: %[[A1:.*]]: index, 52// CHECK-SAME: %[[A2:.*]]: index) 53// CHECK-NOT: sparse_tensor.storage_specifier.set 54// CHECK-NOT: sparse_tensor.storage_specifier.get 55// CHECK: return %[[A1]] 56func.func @sparse_get_specifier_dce_fold(%arg0: !sparse_tensor.storage_specifier<#SparseVector>, %arg1: index, %arg2: index) -> index { 57 %0 = sparse_tensor.storage_specifier.set %arg0 lvl_sz at 0 with %arg1 58 : !sparse_tensor.storage_specifier<#SparseVector> 59 %1 = sparse_tensor.storage_specifier.set %0 pos_mem_sz at 0 with %arg2 60 : !sparse_tensor.storage_specifier<#SparseVector> 61 %2 = sparse_tensor.storage_specifier.get %1 lvl_sz at 0 62 : !sparse_tensor.storage_specifier<#SparseVector> 63 return %2 : index 64} 65 66 67 68#COO = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)}> 69 70// CHECK-LABEL: func @sparse_reorder_coo( 71// CHECK-SAME: %[[A:.*]]: tensor<?x?xf32, #sparse{{[0-9]*}}> 72// CHECK-NOT: %[[R:.*]] = sparse_tensor.reorder_coo 73// CHECK: return %[[A]] 74func.func @sparse_reorder_coo(%arg0 : tensor<?x?xf32, #COO>) -> tensor<?x?xf32, #COO> { 75 %ret = sparse_tensor.reorder_coo quick_sort %arg0 : tensor<?x?xf32, #COO> to tensor<?x?xf32, #COO> 76 return %ret : tensor<?x?xf32, #COO> 77} 78 79 80#BSR = #sparse_tensor.encoding<{ 81 map = ( i, j ) -> 82 ( i floordiv 2 : dense, 83 j floordiv 3 : compressed, 84 i mod 2 : dense, 85 j mod 3 : dense 86 ) 87}> 88 89// CHECK-LABEL: func @sparse_crd_translate( 90// CHECK-NOT: sparse_tensor.crd_translate 91func.func @sparse_crd_translate(%arg0: index, %arg1: index) -> (index, index) { 92 %l0, %l1, %l2, %l3 = sparse_tensor.crd_translate dim_to_lvl [%arg0, %arg1] as #BSR : index, index, index, index 93 %d0, %d1 = sparse_tensor.crd_translate lvl_to_dim [%l0, %l1, %l2, %l3] as #BSR : index, index 94 return %d0, %d1 : index, index 95} 96 97// CHECK-LABEL: func.func @sparse_lvl_0( 98// CHECK: %[[C5:.*]] = arith.constant 5 : index 99// CHECK: return %[[C5]] : index 100func.func @sparse_lvl_0(%t : tensor<10x?xi32, #BSR>) -> index { 101 %lvl = arith.constant 0 : index 102 %l0 = sparse_tensor.lvl %t, %lvl : tensor<10x?xi32, #BSR> 103 return %l0 : index 104} 105 106// CHECK-LABEL: func.func @sparse_lvl_3( 107// CHECK: %[[C3:.*]] = arith.constant 3 : index 108// CHECK: return %[[C3]] : index 109func.func @sparse_lvl_3(%t : tensor<?x?xi32, #BSR>) -> index { 110 %lvl = arith.constant 3 : index 111 %l0 = sparse_tensor.lvl %t, %lvl : tensor<?x?xi32, #BSR> 112 return %l0 : index 113} 114 115#DSDD = #sparse_tensor.encoding<{ 116 map = (i, j, k, l) -> (i: dense, j: compressed, k: dense, l: dense) 117}> 118 119 120// CHECK-LABEL: func.func @sparse_reinterpret_map( 121// CHECK-NOT: sparse_tensor.reinterpret_map 122func.func @sparse_reinterpret_map(%t0 : tensor<6x12xi32, #BSR>) -> tensor<6x12xi32, #BSR> { 123 %t1 = sparse_tensor.reinterpret_map %t0 : tensor<6x12xi32, #BSR> 124 to tensor<3x4x2x3xi32, #DSDD> 125 %t2 = sparse_tensor.reinterpret_map %t1 : tensor<3x4x2x3xi32, #DSDD> 126 to tensor<6x12xi32, #BSR> 127 return %t2 : tensor<6x12xi32, #BSR> 128} 129