1// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s 2 3#CSR = #sparse_tensor.encoding<{ 4 map = (d0, d1) -> (d0 : dense, d1 : compressed), 5}> 6 7#DCSR = #sparse_tensor.encoding<{ 8 map = (d0, d1) -> (d0 : compressed, d1 : compressed) 9}> 10 11#SparseTensor = #sparse_tensor.encoding<{ 12 map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed) 13}> 14 15#trait_scale_inpl = { 16 indexing_maps = [ 17 affine_map<(i,j) -> (i,j)> // X (out) 18 ], 19 iterator_types = ["parallel", "parallel"], 20 doc = "X(i,j) *= 2 or X(i,j) += X(i,j)" 21} 22 23// CHECK-LABEL: func.func @sparse_simply_dynamic1( 24// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse{{[0-9]*}}>) -> tensor<32x16xf32, #sparse{{[0-9]*}}> { 25// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index 26// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index 27// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2.000000e+00 : f32 28// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex> 29// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex> 30// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xf32> 31// CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref<?xindex> 32// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> 33// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] { 34// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_9]]] : memref<?xindex> 35// CHECK: %[[VAL_11:.*]] = arith.addi %[[VAL_9]], %[[VAL_2]] : index 36// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex> 37// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_2]] { 38// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xf32> 39// CHECK: %[[VAL_15:.*]] = arith.mulf %[[VAL_14]], %[[VAL_3]] : f32 40// CHECK: memref.store %[[VAL_15]], %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xf32> 41// CHECK: } 42// CHECK: } 43// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse{{[0-9]*}}> 44// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse{{[0-9]*}}> 45// CHECK: } 46func.func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> { 47 %c = arith.constant 2.0 : f32 48 %0 = linalg.generic #trait_scale_inpl 49 outs(%argx: tensor<32x16xf32, #DCSR>) { 50 ^bb(%x: f32): 51 %1 = arith.mulf %x, %c : f32 52 linalg.yield %1 : f32 53 } -> tensor<32x16xf32, #DCSR> 54 return %0 : tensor<32x16xf32, #DCSR> 55} 56 57// CHECK-LABEL: func.func @sparse_simply_dynamic2( 58// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse{{[0-9]*}}>) -> tensor<32x16xf32, #sparse{{[0-9]*}}> { 59// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index 60// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index 61// CHECK-DAG: %[[VAL_3:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex> 62// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xindex> 63// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse{{[0-9]*}}> to memref<?xf32> 64// CHECK: %[[VAL_6:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xindex> 65// CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_2]]] : memref<?xindex> 66// CHECK: scf.for %[[VAL_8:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_2]] { 67// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_8]]] : memref<?xindex> 68// CHECK: %[[VAL_10:.*]] = arith.addi %[[VAL_8]], %[[VAL_2]] : index 69// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_10]]] : memref<?xindex> 70// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_9]] to %[[VAL_11]] step %[[VAL_2]] { 71// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32> 72// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32> 73// CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32 74// CHECK: memref.store %[[VAL_15]], %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32> 75// CHECK: } 76// CHECK: } 77// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse{{[0-9]*}}> 78// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse{{[0-9]*}}> 79// CHECK: } 80func.func @sparse_simply_dynamic2(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> { 81 %0 = linalg.generic #trait_scale_inpl 82 outs(%argx: tensor<32x16xf32, #DCSR>) { 83 ^bb(%x: f32): 84 %1 = arith.addf %x, %x : f32 85 linalg.yield %1 : f32 86 } -> tensor<32x16xf32, #DCSR> 87 return %0 : tensor<32x16xf32, #DCSR> 88} 89 90#trait_scale = { 91 indexing_maps = [ 92 affine_map<(i,j) -> (i,j)>, // A 93 affine_map<(i,j) -> (i,j)> // X (out) 94 ], 95 iterator_types = ["parallel", "parallel"], 96 doc = "X(i,j) = A(i,j) * 2.0" 97} 98 99// CHECK-LABEL: func.func @sparse_truly_dynamic( 100// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse{{[0-9]*}}>) -> tensor<10x20xf32, #sparse{{[0-9]*}}> { 101// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 10 : index 102// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index 103// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index 104// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2.000000e+00 : f32 105// CHECK-DAG: %[[VAL_5:.*]] = tensor.empty() : tensor<10x20xf32, #sparse{{[0-9]*}}> 106// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xindex> 107// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xindex> 108// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xf32> 109// CHECK: %[[VAL_9:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] iter_args(%[[VAL_11:.*]] = %[[VAL_5]]) -> (tensor<10x20xf32, #sparse{{[0-9]*}}>) { 110// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xindex> 111// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_10]], %[[VAL_3]] : index 112// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex> 113// CHECK: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_11]]) -> (tensor<10x20xf32, #sparse{{[0-9]*}}>) { 114// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex> 115// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xf32> 116// CHECK: %[[VAL_20:.*]] = arith.mulf %[[VAL_19]], %[[VAL_4]] : f32 117// CHECK: %[[VAL_21:.*]] = tensor.insert %[[VAL_20]] into %[[VAL_17]]{{\[}}%[[VAL_10]], %[[VAL_18]]] : tensor<10x20xf32, #sparse{{[0-9]*}}> 118// CHECK: scf.yield %[[VAL_21]] : tensor<10x20xf32, #sparse{{[0-9]*}}> 119// CHECK: } 120// CHECK: scf.yield %[[VAL_22:.*]] : tensor<10x20xf32, #sparse{{[0-9]*}}> 121// CHECK: } 122// CHECK: %[[VAL_23:.*]] = sparse_tensor.load %[[VAL_24:.*]] hasInserts : tensor<10x20xf32, #sparse{{[0-9]*}}> 123// CHECK: return %[[VAL_23]] : tensor<10x20xf32, #sparse{{[0-9]*}}> 124// CHECK: } 125func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20xf32, #DCSR> { 126 %s = arith.constant 2.0 : f32 127 %xm = tensor.empty() : tensor<10x20xf32, #DCSR> 128 %0 = linalg.generic #trait_scale 129 ins(%arga: tensor<10x20xf32, #CSR>) 130 outs(%xm: tensor<10x20xf32, #DCSR>) { 131 ^bb(%a: f32, %x: f32): 132 %1 = arith.mulf %a, %s : f32 133 linalg.yield %1 : f32 134 } -> tensor<10x20xf32, #DCSR> 135 return %0 : tensor<10x20xf32, #DCSR> 136} 137 138#trait_sumred = { 139 indexing_maps = [ 140 affine_map<(i,j,k) -> (i,j,k)>, // A 141 affine_map<(i,j,k) -> (i,j,k)>, // B 142 affine_map<(i,j,k) -> (i,j)> // X (out) 143 ], 144 iterator_types = ["parallel", "parallel", "reduction"], 145 doc = "X(i,j) = SUM_k A(i,j,k) * B(i,j,k)" 146} 147 148// CHECK-LABEL: func.func @sumred( 149// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xi32, #sparse{{[0-9]*}}>, 150// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xi32, #sparse{{[0-9]*}}>) -> tensor<?x?xi32, #sparse{{[0-9]*}}> { 151// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index 152// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index 153// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32 154// CHECK-DAG: %[[VAL_FALSE:.*]] = arith.constant false 155// CHECK-DAG: %[[VAL_TRUE:.*]] = arith.constant true 156// CHECK-DAG: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xi32, #sparse{{[0-9]*}}> 157// CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xi32, #sparse{{[0-9]*}}> 158// CHECK-DAG: %[[VAL_7:.*]] = tensor.empty(%[[VAL_5]], %[[VAL_6]]) : tensor<?x?xi32, #sparse{{[0-9]*}}> 159// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 160// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 161// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 162// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 163// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 164// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 165// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xi32> 166// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 167// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 168// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 169// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 170// CHECK-DAG: %[[VAL_19:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 171// CHECK-DAG: %[[VAL_20:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xindex> 172// CHECK-DAG: %[[VAL_21:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xi32, #sparse{{[0-9]*}}> to memref<?xi32> 173// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_2]]] : memref<?xindex> 174// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex> 175// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_2]]] : memref<?xindex> 176// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_3]]] : memref<?xindex> 177// CHECK: %[[VAL_26:.*]]:3 = scf.while (%[[VAL_27:.*]] = %[[VAL_22]], %[[VAL_28:.*]] = %[[VAL_24]], %[[VAL_29:.*]] = %[[VAL_7]]) : (index, index, tensor<?x?xi32, #sparse{{[0-9]*}}>) -> (index, index, tensor<?x?xi32, #sparse{{[0-9]*}}>) { 178// CHECK: %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_27]], %[[VAL_23]] : index 179// CHECK: %[[VAL_31:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_25]] : index 180// CHECK: %[[VAL_32:.*]] = arith.andi %[[VAL_30]], %[[VAL_31]] : i1 181// CHECK: scf.condition(%[[VAL_32]]) %[[VAL_27]], %[[VAL_28]], %[[VAL_29]] : index, index, tensor<?x?xi32, #sparse{{[0-9]*}}> 182// CHECK: } do { 183// CHECK: ^bb0(%[[VAL_33:.*]]: index, %[[VAL_34:.*]]: index, %[[VAL_35:.*]]: tensor<?x?xi32, #sparse{{[0-9]*}}>): 184// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_33]]] : memref<?xindex> 185// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_34]]] : memref<?xindex> 186// CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_36]] : index 187// CHECK: %[[VAL_39:.*]] = arith.select %[[VAL_38]], %[[VAL_37]], %[[VAL_36]] : index 188// CHECK: %[[VAL_40:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index 189// CHECK: %[[VAL_41:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index 190// CHECK: %[[VAL_42:.*]] = arith.andi %[[VAL_40]], %[[VAL_41]] : i1 191// CHECK: %[[VAL_43:.*]] = scf.if %[[VAL_42]] -> (tensor<?x?xi32, #sparse{{[0-9]*}}>) { 192// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_33]]] : memref<?xindex> 193// CHECK: %[[VAL_45:.*]] = arith.addi %[[VAL_33]], %[[VAL_3]] : index 194// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_45]]] : memref<?xindex> 195// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_34]]] : memref<?xindex> 196// CHECK: %[[VAL_48:.*]] = arith.addi %[[VAL_34]], %[[VAL_3]] : index 197// CHECK: %[[VAL_49:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_48]]] : memref<?xindex> 198// CHECK: %[[VAL_50:.*]]:3 = scf.while (%[[VAL_51:.*]] = %[[VAL_44]], %[[VAL_52:.*]] = %[[VAL_47]], %[[VAL_53:.*]] = %[[VAL_35]]) : (index, index, tensor<?x?xi32, #sparse{{[0-9]*}}>) -> (index, index, tensor<?x?xi32, #sparse{{[0-9]*}}>) { 199// CHECK: %[[VAL_54:.*]] = arith.cmpi ult, %[[VAL_51]], %[[VAL_46]] : index 200// CHECK: %[[VAL_55:.*]] = arith.cmpi ult, %[[VAL_52]], %[[VAL_49]] : index 201// CHECK: %[[VAL_56:.*]] = arith.andi %[[VAL_54]], %[[VAL_55]] : i1 202// CHECK: scf.condition(%[[VAL_56]]) %[[VAL_51]], %[[VAL_52]], %[[VAL_53]] : index, index, tensor<?x?xi32, #sparse{{[0-9]*}}> 203// CHECK: } do { 204// CHECK: ^bb0(%[[VAL_57:.*]]: index, %[[VAL_58:.*]]: index, %[[VAL_59:.*]]: tensor<?x?xi32, #sparse{{[0-9]*}}>): 205// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_57]]] : memref<?xindex> 206// CHECK: %[[VAL_61:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_58]]] : memref<?xindex> 207// CHECK: %[[VAL_62:.*]] = arith.cmpi ult, %[[VAL_61]], %[[VAL_60]] : index 208// CHECK: %[[VAL_63:.*]] = arith.select %[[VAL_62]], %[[VAL_61]], %[[VAL_60]] : index 209// CHECK: %[[VAL_64:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_63]] : index 210// CHECK: %[[VAL_65:.*]] = arith.cmpi eq, %[[VAL_61]], %[[VAL_63]] : index 211// CHECK: %[[VAL_66:.*]] = arith.andi %[[VAL_64]], %[[VAL_65]] : i1 212// CHECK: %[[VAL_67:.*]] = scf.if %[[VAL_66]] -> (tensor<?x?xi32, #sparse{{[0-9]*}}>) { 213// CHECK: %[[VAL_68:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_57]]] : memref<?xindex> 214// CHECK: %[[VAL_69:.*]] = arith.addi %[[VAL_57]], %[[VAL_3]] : index 215// CHECK: %[[VAL_70:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_69]]] : memref<?xindex> 216// CHECK: %[[VAL_71:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_58]]] : memref<?xindex> 217// CHECK: %[[VAL_72:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index 218// CHECK: %[[VAL_73:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_72]]] : memref<?xindex> 219// CHECK: %[[VAL_74:.*]]:5 = scf.while (%[[VAL_75:.*]] = %[[VAL_68]], %[[VAL_76:.*]] = %[[VAL_71]], %[[VAL_77:.*]] = %[[VAL_4]], %[[VAL_200:.*]] = %[[VAL_FALSE]], %[[VAL_78:.*]] = %[[VAL_59]]) : (index, index, i32, i1, tensor<?x?xi32, #sparse{{[0-9]*}}>) -> (index, index, i32, i1, tensor<?x?xi32, #sparse{{[0-9]*}}>) { 220// CHECK: %[[VAL_79:.*]] = arith.cmpi ult, %[[VAL_75]], %[[VAL_70]] : index 221// CHECK: %[[VAL_80:.*]] = arith.cmpi ult, %[[VAL_76]], %[[VAL_73]] : index 222// CHECK: %[[VAL_81:.*]] = arith.andi %[[VAL_79]], %[[VAL_80]] : i1 223// CHECK: scf.condition(%[[VAL_81]]) %[[VAL_75]], %[[VAL_76]], %[[VAL_77]], %[[VAL_200]], %[[VAL_78]] : index, index, i32, i1, tensor<?x?xi32, #sparse{{[0-9]*}}> 224// CHECK: } do { 225// CHECK: ^bb0(%[[VAL_82:.*]]: index, %[[VAL_83:.*]]: index, %[[VAL_84:.*]]: i32, %[[VAL_201:.*]]: i1, %[[VAL_85:.*]]: tensor<?x?xi32, #sparse{{[0-9]*}}>): 226// CHECK: %[[VAL_86:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_82]]] : memref<?xindex> 227// CHECK: %[[VAL_87:.*]] = memref.load %[[VAL_20]]{{\[}}%[[VAL_83]]] : memref<?xindex> 228// CHECK: %[[VAL_88:.*]] = arith.cmpi ult, %[[VAL_87]], %[[VAL_86]] : index 229// CHECK: %[[VAL_89:.*]] = arith.select %[[VAL_88]], %[[VAL_87]], %[[VAL_86]] : index 230// CHECK: %[[VAL_90:.*]] = arith.cmpi eq, %[[VAL_86]], %[[VAL_89]] : index 231// CHECK: %[[VAL_91:.*]] = arith.cmpi eq, %[[VAL_87]], %[[VAL_89]] : index 232// CHECK: %[[VAL_92:.*]] = arith.andi %[[VAL_90]], %[[VAL_91]] : i1 233// CHECK: %[[VAL_93:.*]]:3 = scf.if %[[VAL_92]] -> (i32, i1, tensor<?x?xi32, #sparse{{[0-9]*}}>) { 234// CHECK: %[[VAL_94:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_82]]] : memref<?xi32> 235// CHECK: %[[VAL_95:.*]] = memref.load %[[VAL_21]]{{\[}}%[[VAL_83]]] : memref<?xi32> 236// CHECK: %[[VAL_96:.*]] = arith.muli %[[VAL_94]], %[[VAL_95]] : i32 237// CHECK: %[[VAL_97:.*]] = arith.addi %[[VAL_84]], %[[VAL_96]] : i32 238// CHECK: scf.yield %[[VAL_97]], %[[VAL_TRUE]], %[[VAL_85]] : i32, i1, tensor<?x?xi32, #sparse{{[0-9]*}}> 239// CHECK: } else { 240// CHECK: scf.yield %[[VAL_84]], %[[VAL_201]], %[[VAL_85]] : i32, i1, tensor<?x?xi32, #sparse{{[0-9]*}}> 241// CHECK: } 242// CHECK: %[[VAL_98:.*]] = arith.cmpi eq, %[[VAL_86]], %[[VAL_89]] : index 243// CHECK: %[[VAL_99:.*]] = arith.addi %[[VAL_82]], %[[VAL_3]] : index 244// CHECK: %[[VAL_100:.*]] = arith.select %[[VAL_98]], %[[VAL_99]], %[[VAL_82]] : index 245// CHECK: %[[VAL_101:.*]] = arith.cmpi eq, %[[VAL_87]], %[[VAL_89]] : index 246// CHECK: %[[VAL_102:.*]] = arith.addi %[[VAL_83]], %[[VAL_3]] : index 247// CHECK: %[[VAL_103:.*]] = arith.select %[[VAL_101]], %[[VAL_102]], %[[VAL_83]] : index 248// CHECK: scf.yield %[[VAL_100]], %[[VAL_103]], %[[VAL_104:.*]]#0, %[[VAL_104]]#1, %[[VAL_104]]#2 : index, index, i32, i1, tensor<?x?xi32, #sparse{{[0-9]*}}> 249// CHECK: } 250// CHECK: %[[VAL_202:.*]] = scf.if %[[VAL_74]]#3 -> (tensor<?x?xi32, #sparse{{[0-9]*}}>) { 251// CHECK: %[[VAL_105:.*]] = tensor.insert %[[VAL_74]]#2 into %[[VAL_74]]#4{{\[}}%[[VAL_39]], %[[VAL_63]]] : tensor<?x?xi32, #sparse{{[0-9]*}}> 252// CHECK: scf.yield %[[VAL_105]] : tensor<?x?xi32, #sparse{{[0-9]*}}> 253// CHECK: } else { 254// CHECK: scf.yield %[[VAL_74]]#4 : tensor<?x?xi32, #sparse{{[0-9]*}}> 255// CHECK: } 256// CHECK: scf.yield %[[VAL_202]] : tensor<?x?xi32, #sparse{{[0-9]*}}> 257// CHECK: } else { 258// CHECK: scf.yield %[[VAL_59]] : tensor<?x?xi32, #sparse{{[0-9]*}}> 259// CHECK: } 260// CHECK: %[[VAL_107:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_63]] : index 261// CHECK: %[[VAL_108:.*]] = arith.addi %[[VAL_57]], %[[VAL_3]] : index 262// CHECK: %[[VAL_109:.*]] = arith.select %[[VAL_107]], %[[VAL_108]], %[[VAL_57]] : index 263// CHECK: %[[VAL_110:.*]] = arith.cmpi eq, %[[VAL_61]], %[[VAL_63]] : index 264// CHECK: %[[VAL_111:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index 265// CHECK: %[[VAL_112:.*]] = arith.select %[[VAL_110]], %[[VAL_111]], %[[VAL_58]] : index 266// CHECK: scf.yield %[[VAL_109]], %[[VAL_112]], %[[VAL_113:.*]] : index, index, tensor<?x?xi32, #sparse{{[0-9]*}}> 267// CHECK: } 268// CHECK: scf.yield %[[VAL_114:.*]]#2 : tensor<?x?xi32, #sparse{{[0-9]*}}> 269// CHECK: } else { 270// CHECK: scf.yield %[[VAL_35]] : tensor<?x?xi32, #sparse{{[0-9]*}}> 271// CHECK: } 272// CHECK: %[[VAL_115:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index 273// CHECK: %[[VAL_116:.*]] = arith.addi %[[VAL_33]], %[[VAL_3]] : index 274// CHECK: %[[VAL_117:.*]] = arith.select %[[VAL_115]], %[[VAL_116]], %[[VAL_33]] : index 275// CHECK: %[[VAL_118:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index 276// CHECK: %[[VAL_119:.*]] = arith.addi %[[VAL_34]], %[[VAL_3]] : index 277// CHECK: %[[VAL_120:.*]] = arith.select %[[VAL_118]], %[[VAL_119]], %[[VAL_34]] : index 278// CHECK: scf.yield %[[VAL_117]], %[[VAL_120]], %[[VAL_121:.*]] : index, index, tensor<?x?xi32, #sparse{{[0-9]*}}> 279// CHECK: } 280// CHECK: %[[VAL_122:.*]] = sparse_tensor.load %[[VAL_123:.*]]#2 hasInserts : tensor<?x?xi32, #sparse{{[0-9]*}}> 281// CHECK: return %[[VAL_122]] : tensor<?x?xi32, #sparse{{[0-9]*}}> 282// CHECK: } 283func.func @sumred(%arga: tensor<?x?x?xi32, #SparseTensor>, 284 %argb: tensor<?x?x?xi32, #SparseTensor>) -> tensor<?x?xi32, #DCSR> { 285 %c0 = arith.constant 0 : index 286 %c1 = arith.constant 1 : index 287 %d0 = tensor.dim %arga, %c0 : tensor<?x?x?xi32, #SparseTensor> 288 %d1 = tensor.dim %arga, %c1 : tensor<?x?x?xi32, #SparseTensor> 289 %xinit = tensor.empty(%d0, %d1) : tensor<?x?xi32, #DCSR> 290 %0 = linalg.generic #trait_sumred 291 ins(%arga, %argb: tensor<?x?x?xi32, #SparseTensor>, 292 tensor<?x?x?xi32, #SparseTensor>) 293 outs(%xinit: tensor<?x?xi32, #DCSR>) { 294 ^bb(%a: i32, %b: i32, %x: i32): 295 %0 = arith.muli %a, %b : i32 296 %1 = arith.addi %x, %0 : i32 297 linalg.yield %1 : i32 298 } -> tensor<?x?xi32, #DCSR> 299 return %0 : tensor<?x?xi32, #DCSR> 300} 301 302#trait_matmat = { 303 indexing_maps = [ 304 affine_map<(i,j,k) -> (i,k)>, // A 305 affine_map<(i,j,k) -> (k,j)>, // B 306 affine_map<(i,j,k) -> (i,j)> // C (out) 307 ], 308 iterator_types = ["parallel", "parallel", "reduction"], 309 doc = "C(i,j) = SUM_k A(i,k) * B(k,j)" 310} 311 312// CHECK-LABEL: func.func @matmat( 313// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32, #sparse{{[0-9]*}}>, 314// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32, #sparse{{[0-9]*}}>) -> tensor<?x?xf32, #sparse{{[0-9]*}}> { 315// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index 316// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index 317// CHECK-DAG: %[[VAL_4:.*]] = arith.constant false 318// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true 319// CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32, #sparse{{[0-9]*}}> 320// CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf32, #sparse{{[0-9]*}}> 321// CHECK-DAG: %[[VAL_8:.*]] = tensor.empty(%[[VAL_6]], %[[VAL_7]]) : tensor<?x?xf32, #sparse{{[0-9]*}}> 322// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 323// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 324// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 325// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 326// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xf32> 327// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 328// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 329// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 330// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xindex> 331// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xf32> 332// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref<?xindex> 333// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex> 334// CHECK: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_8]]) -> (tensor<?x?xf32, #sparse{{[0-9]*}}>) { 335// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref<?xindex> 336// CHECK: %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]], %[[VAL_28:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<?x?xf32, #sparse{{[0-9]*}}> to memref<?xf32>, memref<?xi1>, memref<?xindex> 337// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xindex> 338// CHECK: %[[VAL_30:.*]] = arith.addi %[[VAL_22]], %[[VAL_3]] : index 339// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_30]]] : memref<?xindex> 340// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_2]]] : memref<?xindex> 341// CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_3]]] : memref<?xindex> 342// CHECK: %[[VAL_34:.*]]:4 = scf.while (%[[VAL_35:.*]] = %[[VAL_29]], %[[VAL_36:.*]] = %[[VAL_32]], %[[VAL_37:.*]] = %[[VAL_28]], %[[VAL_38:.*]] = %[[VAL_23]]) : (index, index, index, tensor<?x?xf32, #sparse{{[0-9]*}}>) -> (index, index, index, tensor<?x?xf32, #sparse{{[0-9]*}}>) { 343// CHECK: %[[VAL_39:.*]] = arith.cmpi ult, %[[VAL_35]], %[[VAL_31]] : index 344// CHECK: %[[VAL_40:.*]] = arith.cmpi ult, %[[VAL_36]], %[[VAL_33]] : index 345// CHECK: %[[VAL_41:.*]] = arith.andi %[[VAL_39]], %[[VAL_40]] : i1 346// CHECK: scf.condition(%[[VAL_41]]) %[[VAL_35]], %[[VAL_36]], %[[VAL_37]], %[[VAL_38]] : index, index, index, tensor<?x?xf32, #sparse{{[0-9]*}}> 347// CHECK: } do { 348// CHECK: ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index, %[[VAL_44:.*]]: index, %[[VAL_45:.*]]: tensor<?x?xf32, #sparse{{[0-9]*}}>): 349// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_42]]] : memref<?xindex> 350// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_43]]] : memref<?xindex> 351// CHECK: %[[VAL_48:.*]] = arith.cmpi ult, %[[VAL_47]], %[[VAL_46]] : index 352// CHECK: %[[VAL_49:.*]] = arith.select %[[VAL_48]], %[[VAL_47]], %[[VAL_46]] : index 353// CHECK: %[[VAL_50:.*]] = arith.cmpi eq, %[[VAL_46]], %[[VAL_49]] : index 354// CHECK: %[[VAL_51:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_49]] : index 355// CHECK: %[[VAL_52:.*]] = arith.andi %[[VAL_50]], %[[VAL_51]] : i1 356// CHECK: %[[VAL_53:.*]]:2 = scf.if %[[VAL_52]] -> (index, tensor<?x?xf32, #sparse{{[0-9]*}}>) { 357// CHECK: %[[VAL_54:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_42]]] : memref<?xf32> 358// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_43]]] : memref<?xindex> 359// CHECK: %[[VAL_56:.*]] = arith.addi %[[VAL_43]], %[[VAL_3]] : index 360// CHECK: %[[VAL_57:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_56]]] : memref<?xindex> 361// CHECK: %[[VAL_58:.*]] = scf.for %[[VAL_59:.*]] = %[[VAL_55]] to %[[VAL_57]] step %[[VAL_3]] iter_args(%[[VAL_60:.*]] = %[[VAL_44]]) -> (index) { 362// CHECK: %[[VAL_61:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_59]]] : memref<?xindex> 363// CHECK: %[[VAL_62:.*]] = memref.load %[[VAL_25]]{{\[}}%[[VAL_61]]] : memref<?xf32> 364// CHECK: %[[VAL_63:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_59]]] : memref<?xf32> 365// CHECK: %[[VAL_64:.*]] = arith.mulf %[[VAL_54]], %[[VAL_63]] : f32 366// CHECK: %[[VAL_65:.*]] = arith.addf %[[VAL_62]], %[[VAL_64]] : f32 367// CHECK: %[[VAL_66:.*]] = memref.load %[[VAL_26]]{{\[}}%[[VAL_61]]] : memref<?xi1> 368// CHECK: %[[VAL_67:.*]] = arith.cmpi eq, %[[VAL_66]], %[[VAL_4]] : i1 369// CHECK: %[[VAL_68:.*]] = scf.if %[[VAL_67]] -> (index) { 370// CHECK: memref.store %[[VAL_5]], %[[VAL_26]]{{\[}}%[[VAL_61]]] : memref<?xi1> 371// CHECK: memref.store %[[VAL_61]], %[[VAL_27]]{{\[}}%[[VAL_60]]] : memref<?xindex> 372// CHECK: %[[VAL_69:.*]] = arith.addi %[[VAL_60]], %[[VAL_3]] : index 373// CHECK: scf.yield %[[VAL_69]] : index 374// CHECK: } else { 375// CHECK: scf.yield %[[VAL_60]] : index 376// CHECK: } 377// CHECK: memref.store %[[VAL_65]], %[[VAL_25]]{{\[}}%[[VAL_61]]] : memref<?xf32> 378// CHECK: scf.yield %[[VAL_70:.*]] : index 379// CHECK: } 380// CHECK: scf.yield %[[VAL_71:.*]], %[[VAL_45]] : index, tensor<?x?xf32, #sparse{{[0-9]*}}> 381// CHECK: } else { 382// CHECK: scf.yield %[[VAL_44]], %[[VAL_45]] : index, tensor<?x?xf32, #sparse{{[0-9]*}}> 383// CHECK: } 384// CHECK: %[[VAL_72:.*]] = arith.cmpi eq, %[[VAL_46]], %[[VAL_49]] : index 385// CHECK: %[[VAL_73:.*]] = arith.addi %[[VAL_42]], %[[VAL_3]] : index 386// CHECK: %[[VAL_74:.*]] = arith.select %[[VAL_72]], %[[VAL_73]], %[[VAL_42]] : index 387// CHECK: %[[VAL_75:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_49]] : index 388// CHECK: %[[VAL_76:.*]] = arith.addi %[[VAL_43]], %[[VAL_3]] : index 389// CHECK: %[[VAL_77:.*]] = arith.select %[[VAL_75]], %[[VAL_76]], %[[VAL_43]] : index 390// CHECK: scf.yield %[[VAL_74]], %[[VAL_77]], %[[VAL_78:.*]]#0, %[[VAL_78]]#1 : index, index, index, tensor<?x?xf32, #sparse{{[0-9]*}}> 391// CHECK: } 392// CHECK: %[[VAL_79:.*]] = sparse_tensor.compress %[[VAL_25]], %[[VAL_26]], %[[VAL_27]], %[[VAL_80:.*]]#2 into %[[VAL_80]]#3{{\[}}%[[VAL_24]]] : memref<?xf32>, memref<?xi1>, memref<?xindex>, tensor<?x?xf32, #sparse{{[0-9]*}}> 393// CHECK: scf.yield %[[VAL_79]] : tensor<?x?xf32, #sparse{{[0-9]*}}> 394// CHECK: } 395// CHECK: %[[VAL_81:.*]] = sparse_tensor.load %[[VAL_82:.*]] hasInserts : tensor<?x?xf32, #sparse{{[0-9]*}}> 396// CHECK: return %[[VAL_81]] : tensor<?x?xf32, #sparse{{[0-9]*}}> 397// CHECK: } 398func.func @matmat(%arga: tensor<?x?xf32, #DCSR>, 399 %argb: tensor<?x?xf32, #DCSR>) -> tensor<?x?xf32, #DCSR> { 400 %c0 = arith.constant 0 : index 401 %c1 = arith.constant 1 : index 402 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf32, #DCSR> 403 %d1 = tensor.dim %argb, %c1 : tensor<?x?xf32, #DCSR> 404 %cinit = tensor.empty(%d0, %d1) : tensor<?x?xf32, #DCSR> 405 %0 = linalg.generic #trait_matmat 406 ins(%arga, %argb: tensor<?x?xf32, #DCSR>, 407 tensor<?x?xf32, #DCSR>) 408 outs(%cinit: tensor<?x?xf32, #DCSR>) { 409 ^bb(%a: f32, %b: f32, %c: f32): 410 %1 = arith.mulf %a, %b : f32 411 %2 = arith.addf %c, %1 : f32 412 linalg.yield %2 : f32 413 } -> tensor<?x?xf32, #DCSR> 414 return %0 : tensor<?x?xf32, #DCSR> 415} 416