1// RUN: mlir-opt %s --lower-sparse-ops-to-foreach --lower-sparse-foreach-to-scf --sparse-tensor-conversion --canonicalize --cse | FileCheck %s 2 3#SparseVector = #sparse_tensor.encoding<{ 4 map = (d0) -> (d0 : compressed) 5}> 6 7#SparseVector64 = #sparse_tensor.encoding<{ 8 map = (d0) -> (d0 : compressed), 9 posWidth = 64, 10 crdWidth = 64 11}> 12 13#SparseVector32 = #sparse_tensor.encoding<{ 14 map = (d0) -> (d0 : compressed), 15 posWidth = 32, 16 crdWidth = 32 17}> 18 19#CSR = #sparse_tensor.encoding<{ 20 map = (d0, d1) -> (d0 : dense, d1 : compressed) 21}> 22 23#CSC = #sparse_tensor.encoding<{ 24 map = (d0, d1) -> (d1 : dense, d0 : compressed) 25}> 26 27#SparseTensor = #sparse_tensor.encoding<{ 28 map = (d0, d1, d2) -> (d2 : dense, d0 : compressed, d1 : compressed) 29}> 30 31// CHECK-LABEL: func @sparse_nop( 32// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> !llvm.ptr 33// CHECK: return %[[A]] : !llvm.ptr 34func.func @sparse_nop(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> { 35 return %arg0 : tensor<?xf64, #SparseVector> 36} 37 38// CHECK-LABEL: func @sparse_dim1d( 39// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 40// CHECK: %[[C:.*]] = arith.constant 0 : index 41// CHECK: %[[D:.*]] = call @sparseLvlSize(%[[A]], %[[C]]) 42// CHECK: return %[[D]] : index 43func.func @sparse_dim1d(%arg0: tensor<?xf64, #SparseVector>) -> index { 44 %c = arith.constant 0 : index 45 %0 = tensor.dim %arg0, %c : tensor<?xf64, #SparseVector> 46 return %0 : index 47} 48 49// Querying the size of dimension 1 should do so; i.e., it should 50// not be permuted into a query for the size of level 2 (even though 51// dimension 1 is stored as level 2). 52// CHECK-LABEL: func @sparse_dim3d( 53// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 54// CHECK: %[[C:.*]] = arith.constant 2 : index 55// CHECK: %[[D:.*]] = call @sparseLvlSize(%[[A]], %[[C]]) 56// CHECK: return %[[D]] : index 57func.func @sparse_dim3d(%arg0: tensor<?x?x?xf64, #SparseTensor>) -> index { 58 %c = arith.constant 1 : index 59 %0 = tensor.dim %arg0, %c : tensor<?x?x?xf64, #SparseTensor> 60 return %0 : index 61} 62 63// Querying the size of a static dimension should be folded into a 64// constant (and we should be sure to get the size of dimension 1, 65// not dimension 2 nor level 1). 66// CHECK-LABEL: func @sparse_dim3d_const( 67// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 68// CHECK: %[[C:.*]] = arith.constant 20 : index 69// CHECK: return %[[C]] : index 70func.func @sparse_dim3d_const(%arg0: tensor<10x20x30xf64, #SparseTensor>) -> index { 71 %c = arith.constant 1 : index 72 %0 = tensor.dim %arg0, %c : tensor<10x20x30xf64, #SparseTensor> 73 return %0 : index 74} 75 76// CHECK-LABEL: func @sparse_new1d( 77// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> !llvm.ptr 78// CHECK-DAG: %[[DimShape0:.*]] = memref.alloca() : memref<1xindex> 79// CHECK-DAG: %[[DimShape:.*]] = memref.cast %[[DimShape0]] : memref<1xindex> to memref<?xindex> 80// CHECK: %[[Reader:.*]] = call @createCheckedSparseTensorReader(%[[A]], %[[DimShape]], %{{.*}}) 81// CHECK-DAG: %[[LvlTypes0:.*]] = memref.alloca() : memref<1xi64> 82// CHECK-DAG: %[[LvlTypes:.*]] = memref.cast %[[LvlTypes0]] : memref<1xi64> to memref<?xi64> 83// CHECK-DAG: %[[Iota0:.*]] = memref.alloca() : memref<1xindex> 84// CHECK-DAG: %[[Iota:.*]] = memref.cast %[[Iota0]] : memref<1xindex> to memref<?xindex> 85// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimShape]], %[[DimShape]], %[[LvlTypes]], %[[Iota]], %[[Iota]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[Reader]]) 86// CHECK: call @delSparseTensorReader(%[[Reader]]) 87// CHECK: return %[[T]] : !llvm.ptr 88func.func @sparse_new1d(%arg0: !llvm.ptr) -> tensor<128xf64, #SparseVector> { 89 %0 = sparse_tensor.new %arg0 : !llvm.ptr to tensor<128xf64, #SparseVector> 90 return %0 : tensor<128xf64, #SparseVector> 91} 92 93// CHECK-LABEL: func @sparse_new2d( 94// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> !llvm.ptr 95// CHECK-DAG: %[[DimShape0:.*]] = memref.alloca() : memref<2xindex> 96// CHECK-DAG: %[[DimShape:.*]] = memref.cast %[[DimShape0]] : memref<2xindex> to memref<?xindex> 97// CHECK: %[[Reader:.*]] = call @createCheckedSparseTensorReader(%[[A]], %[[DimShape]], %{{.*}}) 98// CHECK: %[[DimSizes:.*]] = call @getSparseTensorReaderDimSizes(%[[Reader]]) 99// CHECK-DAG: %[[LvlTypes0:.*]] = memref.alloca() : memref<2xi64> 100// CHECK-DAG: %[[LvlTypes:.*]] = memref.cast %[[LvlTypes0]] : memref<2xi64> to memref<?xi64> 101// CHECK-DAG: %[[Iota0:.*]] = memref.alloca() : memref<2xindex> 102// CHECK-DAG: %[[Iota:.*]] = memref.cast %[[Iota0]] : memref<2xindex> to memref<?xindex> 103// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizes]], %[[DimSizes]], %[[LvlTypes]], %[[Iota]], %[[Iota]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[Reader]]) 104// CHECK: call @delSparseTensorReader(%[[Reader]]) 105// CHECK: return %[[T]] : !llvm.ptr 106func.func @sparse_new2d(%arg0: !llvm.ptr) -> tensor<?x?xf32, #CSR> { 107 %0 = sparse_tensor.new %arg0 : !llvm.ptr to tensor<?x?xf32, #CSR> 108 return %0 : tensor<?x?xf32, #CSR> 109} 110 111// CHECK-LABEL: func @sparse_new3d( 112// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> !llvm.ptr 113// CHECK-DAG: %[[DimShape0:.*]] = memref.alloca() : memref<3xindex> 114// CHECK-DAG: %[[DimShape:.*]] = memref.cast %[[DimShape0]] : memref<3xindex> to memref<?xindex> 115// CHECK: %[[Reader:.*]] = call @createCheckedSparseTensorReader(%[[A]], %[[DimShape]], %{{.*}}) 116// CHECK: %[[DimSizes:.*]] = call @getSparseTensorReaderDimSizes(%[[Reader]]) 117// CHECK-DAG: %[[LvlTypes0:.*]] = memref.alloca() : memref<3xi64> 118// CHECK-DAG: %[[LvlTypes:.*]] = memref.cast %[[LvlTypes0]] : memref<3xi64> to memref<?xi64> 119// CHECK-DAG: %[[Dim2Lvl0:.*]] = memref.alloca() : memref<3xindex> 120// CHECK-DAG: %[[Dim2Lvl:.*]] = memref.cast %[[Dim2Lvl0]] : memref<3xindex> to memref<?xindex> 121// CHECK-DAG: %[[Lvl2Dim0:.*]] = memref.alloca() : memref<3xindex> 122// CHECK-DAG: %[[Lvl2Dim:.*]] = memref.cast %[[Lvl2Dim0]] : memref<3xindex> to memref<?xindex> 123// CHECK-DAG: %[[LvlSizes0:.*]] = memref.alloca() : memref<3xindex> 124// CHECK-DAG: %[[LvlSizes:.*]] = memref.cast %[[LvlSizes0]] : memref<3xindex> to memref<?xindex> 125// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizes]], %[[LvlSizes]], %[[LvlTypes]], %[[Dim2Lvl]], %[[Lvl2Dim]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %[[Reader]]) 126// CHECK: call @delSparseTensorReader(%[[Reader]]) 127// CHECK: return %[[T]] : !llvm.ptr 128func.func @sparse_new3d(%arg0: !llvm.ptr) -> tensor<?x?x?xf32, #SparseTensor> { 129 %0 = sparse_tensor.new %arg0 : !llvm.ptr to tensor<?x?x?xf32, #SparseTensor> 130 return %0 : tensor<?x?x?xf32, #SparseTensor> 131} 132 133// CHECK-LABEL: func @sparse_init( 134// CHECK-SAME: %[[I:.*]]: index, 135// CHECK-SAME: %[[J:.*]]: index) -> !llvm.ptr 136// CHECK-DAG: %[[Empty:.*]] = arith.constant 0 : i32 137// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index 138// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index 139// CHECK-DAG: %[[LvlTypes0:.*]] = memref.alloca() : memref<2xi64> 140// CHECK-DAG: %[[Sizes0:.*]] = memref.alloca() : memref<2xindex> 141// CHECK-DAG: %[[Iota0:.*]] = memref.alloca() : memref<2xindex> 142// CHECK-DAG: %[[LvlTypes:.*]] = memref.cast %[[LvlTypes0]] : memref<2xi64> to memref<?xi64> 143// CHECK-DAG: %[[Sizes:.*]] = memref.cast %[[Sizes0]] : memref<2xindex> to memref<?xindex> 144// CHECK-DAG: %[[Iota:.*]] = memref.cast %[[Iota0]] : memref<2xindex> to memref<?xindex> 145// CHECK-DAG: memref.store %[[I]], %[[Sizes0]][%[[C0]]] : memref<2xindex> 146// CHECK-DAG: memref.store %[[J]], %[[Sizes0]][%[[C1]]] : memref<2xindex> 147// CHECK-DAG: %[[NP:.*]] = llvm.mlir.zero : !llvm.ptr 148// CHECK: %[[T:.*]] = call @newSparseTensor(%[[Sizes]], %[[Sizes]], %[[LvlTypes]], %[[Iota]], %[[Iota]], %{{.*}}, %{{.*}}, %{{.*}}, %[[Empty]], %[[NP]]) 149// CHECK: return %[[T]] : !llvm.ptr 150func.func @sparse_init(%arg0: index, %arg1: index) -> tensor<?x?xf64, #CSR> { 151 %0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf64, #CSR> 152 %1 = sparse_tensor.load %0 : tensor<?x?xf64, #CSR> 153 return %1 : tensor<?x?xf64, #CSR> 154} 155 156// CHECK-LABEL: func @sparse_release( 157// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 158// CHECK: call @delSparseTensor(%[[A]]) : (!llvm.ptr) -> () 159// CHECK: return 160func.func @sparse_release(%arg0: tensor<128xf64, #SparseVector>) { 161 bufferization.dealloc_tensor %arg0 : tensor<128xf64, #SparseVector> 162 return 163} 164 165// CHECK-LABEL: func @sparse_nop_cast( 166// CHECK-SAME: %[[A:.*]]: !llvm.ptr) -> !llvm.ptr 167// CHECK: return %[[A]] : !llvm.ptr 168func.func @sparse_nop_cast(%arg0: tensor<64xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> { 169 %0 = tensor.cast %arg0 : tensor<64xf32, #SparseVector> to tensor<?xf32, #SparseVector> 170 return %0 : tensor<?xf32, #SparseVector> 171} 172 173// CHECK-LABEL: func @sparse_positions( 174// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 175// CHECK: %[[C:.*]] = arith.constant 0 : index 176// CHECK: %[[T:.*]] = call @sparsePositions0(%[[A]], %[[C]]) : (!llvm.ptr, index) -> memref<?xindex> 177// CHECK: return %[[T]] : memref<?xindex> 178func.func @sparse_positions(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> { 179 %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex> 180 return %0 : memref<?xindex> 181} 182 183// CHECK-LABEL: func @sparse_positions64( 184// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 185// CHECK: %[[C:.*]] = arith.constant 0 : index 186// CHECK: %[[T:.*]] = call @sparsePositions64(%[[A]], %[[C]]) : (!llvm.ptr, index) -> memref<?xi64> 187// CHECK: return %[[T]] : memref<?xi64> 188func.func @sparse_positions64(%arg0: tensor<128xf64, #SparseVector64>) -> memref<?xi64> { 189 %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector64> to memref<?xi64> 190 return %0 : memref<?xi64> 191} 192 193// CHECK-LABEL: func @sparse_positions32( 194// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 195// CHECK: %[[C:.*]] = arith.constant 0 : index 196// CHECK: %[[T:.*]] = call @sparsePositions32(%[[A]], %[[C]]) : (!llvm.ptr, index) -> memref<?xi32> 197// CHECK: return %[[T]] : memref<?xi32> 198func.func @sparse_positions32(%arg0: tensor<128xf64, #SparseVector32>) -> memref<?xi32> { 199 %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector32> to memref<?xi32> 200 return %0 : memref<?xi32> 201} 202 203// CHECK-LABEL: func @sparse_indices( 204// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 205// CHECK: %[[C:.*]] = arith.constant 0 : index 206// CHECK: %[[T:.*]] = call @sparseCoordinates0(%[[A]], %[[C]]) : (!llvm.ptr, index) -> memref<?xindex> 207// CHECK: return %[[T]] : memref<?xindex> 208func.func @sparse_indices(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> { 209 %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex> 210 return %0 : memref<?xindex> 211} 212 213// CHECK-LABEL: func @sparse_indices64( 214// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 215// CHECK: %[[C:.*]] = arith.constant 0 : index 216// CHECK: %[[T:.*]] = call @sparseCoordinates64(%[[A]], %[[C]]) : (!llvm.ptr, index) -> memref<?xi64> 217// CHECK: return %[[T]] : memref<?xi64> 218func.func @sparse_indices64(%arg0: tensor<128xf64, #SparseVector64>) -> memref<?xi64> { 219 %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector64> to memref<?xi64> 220 return %0 : memref<?xi64> 221} 222 223// CHECK-LABEL: func @sparse_indices32( 224// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 225// CHECK: %[[C:.*]] = arith.constant 0 : index 226// CHECK: %[[T:.*]] = call @sparseCoordinates32(%[[A]], %[[C]]) : (!llvm.ptr, index) -> memref<?xi32> 227// CHECK: return %[[T]] : memref<?xi32> 228func.func @sparse_indices32(%arg0: tensor<128xf64, #SparseVector32>) -> memref<?xi32> { 229 %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector32> to memref<?xi32> 230 return %0 : memref<?xi32> 231} 232 233// CHECK-LABEL: func @sparse_valuesf64( 234// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 235// CHECK: %[[T:.*]] = call @sparseValuesF64(%[[A]]) : (!llvm.ptr) -> memref<?xf64> 236// CHECK: return %[[T]] : memref<?xf64> 237func.func @sparse_valuesf64(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xf64> { 238 %0 = sparse_tensor.values %arg0 : tensor<128xf64, #SparseVector> to memref<?xf64> 239 return %0 : memref<?xf64> 240} 241 242// CHECK-LABEL: func @sparse_valuesf32( 243// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 244// CHECK: %[[T:.*]] = call @sparseValuesF32(%[[A]]) : (!llvm.ptr) -> memref<?xf32> 245// CHECK: return %[[T]] : memref<?xf32> 246func.func @sparse_valuesf32(%arg0: tensor<128xf32, #SparseVector>) -> memref<?xf32> { 247 %0 = sparse_tensor.values %arg0: tensor<128xf32, #SparseVector> to memref<?xf32> 248 return %0 : memref<?xf32> 249} 250 251// CHECK-LABEL: func @sparse_valuesi32( 252// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 253// CHECK: %[[T:.*]] = call @sparseValuesI32(%[[A]]) : (!llvm.ptr) -> memref<?xi32> 254// CHECK: return %[[T]] : memref<?xi32> 255func.func @sparse_valuesi32(%arg0: tensor<128xi32, #SparseVector>) -> memref<?xi32> { 256 %0 = sparse_tensor.values %arg0: tensor<128xi32, #SparseVector> to memref<?xi32> 257 return %0 : memref<?xi32> 258} 259 260// CHECK-LABEL: func @sparse_valuesi16( 261// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 262// CHECK: %[[T:.*]] = call @sparseValuesI16(%[[A]]) : (!llvm.ptr) -> memref<?xi16> 263// CHECK: return %[[T]] : memref<?xi16> 264func.func @sparse_valuesi16(%arg0: tensor<128xi16, #SparseVector>) -> memref<?xi16> { 265 %0 = sparse_tensor.values %arg0: tensor<128xi16, #SparseVector> to memref<?xi16> 266 return %0 : memref<?xi16> 267} 268 269// CHECK-LABEL: func @sparse_valuesi8( 270// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 271// CHECK: %[[T:.*]] = call @sparseValuesI8(%[[A]]) : (!llvm.ptr) -> memref<?xi8> 272// CHECK: return %[[T]] : memref<?xi8> 273func.func @sparse_valuesi8(%arg0: tensor<128xi8, #SparseVector>) -> memref<?xi8> { 274 %0 = sparse_tensor.values %arg0: tensor<128xi8, #SparseVector> to memref<?xi8> 275 return %0 : memref<?xi8> 276} 277 278// CHECK-LABEL: func @sparse_noe( 279// CHECK-SAME: %[[A:.*]]: !llvm.ptr) 280// CHECK-DAG: %[[C:.*]] = arith.constant 0 : index 281// CHECK-DAG: %[[T:.*]] = call @sparseValuesF64(%[[A]]) : (!llvm.ptr) -> memref<?xf64> 282// CHECK: %[[NOE:.*]] = memref.dim %[[T]], %[[C]] : memref<?xf64> 283// CHECK: return %[[NOE]] : index 284func.func @sparse_noe(%arg0: tensor<128xf64, #SparseVector>) -> index { 285 %0 = sparse_tensor.number_of_entries %arg0 : tensor<128xf64, #SparseVector> 286 return %0 : index 287} 288 289// CHECK-LABEL: func @sparse_reconstruct( 290// CHECK-SAME: %[[A:.*]]: !llvm.ptr 291// CHECK: return %[[A]] : !llvm.ptr 292func.func @sparse_reconstruct(%arg0: tensor<128xf32, #SparseVector>) -> tensor<128xf32, #SparseVector> { 293 %0 = sparse_tensor.load %arg0 : tensor<128xf32, #SparseVector> 294 return %0 : tensor<128xf32, #SparseVector> 295} 296 297// CHECK-LABEL: func @sparse_reconstruct_ins( 298// CHECK-SAME: %[[A:.*]]: !llvm.ptr 299// CHECK: call @endLexInsert(%[[A]]) : (!llvm.ptr) -> () 300// CHECK: return %[[A]] : !llvm.ptr 301func.func @sparse_reconstruct_ins(%arg0: tensor<128xf32, #SparseVector>) -> tensor<128xf32, #SparseVector> { 302 %0 = sparse_tensor.load %arg0 hasInserts : tensor<128xf32, #SparseVector> 303 return %0 : tensor<128xf32, #SparseVector> 304} 305 306// CHECK-LABEL: func @sparse_insert( 307// CHECK-SAME: %[[A:.*]]: !llvm.ptr, 308// CHECK-SAME: %[[B:.*]]: index, 309// CHECK-SAME: %[[C:.*]]: f32) -> !llvm.ptr { 310// CHECK-DAG: %[[M:.*]] = memref.alloca() : memref<1xindex> 311// CHECK-DAG: %[[V:.*]] = memref.alloca() : memref<f32> 312// CHECK-DAG: %[[MC:.*]] = memref.cast %[[M]] : memref<1xindex> to memref<?xindex> 313// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index 314// CHECK-DAG: memref.store %[[B]], %[[M]][%[[C0]]] : memref<1xindex> 315// CHECK-DAG: memref.store %[[C]], %[[V]][] : memref<f32> 316// CHECK: call @lexInsertF32(%[[A]], %[[MC]], %[[V]]) : (!llvm.ptr, memref<?xindex>, memref<f32>) -> () 317// CHECK: return %[[A]] : !llvm.ptr 318func.func @sparse_insert(%arg0: tensor<128xf32, #SparseVector>, 319 %arg1: index, 320 %arg2: f32) -> tensor<128xf32, #SparseVector> { 321 %0 = tensor.insert %arg2 into %arg0[%arg1] : tensor<128xf32, #SparseVector> 322 return %0 : tensor<128xf32, #SparseVector> 323} 324 325// CHECK-LABEL: func @sparse_expansion1() 326// CHECK: %[[N:.*]] = call @newSparseTensor 327// CHECK: %[[A:.*]] = memref.alloc() : memref<8xf64> 328// CHECK: %[[B:.*]] = memref.alloc() : memref<8xi1> 329// CHECK: %[[C:.*]] = memref.alloc() : memref<8xindex> 330// CHECK: %[[D:.*]] = memref.cast %[[C]] : memref<8xindex> to memref<?xindex> 331// CHECK-DAG: linalg.fill ins(%{{.*}} : f64) outs(%[[A]] : memref<8xf64>) 332// CHECK-DAG: linalg.fill ins(%{{.*}} : i1) outs(%[[B]] : memref<8xi1>) 333// CHECK: return %[[D]] : memref<?xindex> 334func.func @sparse_expansion1() -> memref<?xindex> { 335 %0 = tensor.empty() : tensor<4x8xf64, #CSR> 336 %values, %filled, %added, %count = sparse_tensor.expand %0 337 : tensor<4x8xf64, #CSR> to memref<?xf64>, memref<?xi1>, memref<?xindex> 338 return %added : memref<?xindex> 339} 340 341// CHECK-LABEL: func @sparse_expansion2() 342// CHECK: %[[N:.*]] = call @newSparseTensor 343// CHECK: %[[A:.*]] = memref.alloc() : memref<4xf64> 344// CHECK: %[[B:.*]] = memref.alloc() : memref<4xi1> 345// CHECK: %[[C:.*]] = memref.alloc() : memref<4xindex> 346// CHECK: %[[D:.*]] = memref.cast %[[C]] : memref<4xindex> to memref<?xindex> 347// CHECK-DAG: linalg.fill ins(%{{.*}} : f64) outs(%[[A]] : memref<4xf64>) 348// CHECK-DAG: linalg.fill ins(%{{.*}} : i1) outs(%[[B]] : memref<4xi1>) 349// CHECK: return %[[D]] : memref<?xindex> 350func.func @sparse_expansion2() -> memref<?xindex> { 351 %0 = tensor.empty() : tensor<4x8xf64, #CSC> 352 %values, %filled, %added, %count = sparse_tensor.expand %0 353 : tensor<4x8xf64, #CSC> to memref<?xf64>, memref<?xi1>, memref<?xindex> 354 return %added : memref<?xindex> 355} 356 357// CHECK-LABEL: func @sparse_expansion3( 358// CHECK: %[[C1:.*]] = arith.constant 1 : index 359// CHECK: %[[N:.*]] = call @newSparseTensor 360// CHECK: %[[S:.*]] = call @sparseLvlSize(%[[N]], %[[C1]]) 361// CHECK: %[[A:.*]] = memref.alloc(%[[S]]) : memref<?xf64> 362// CHECK: %[[B:.*]] = memref.alloc(%[[S]]) : memref<?xi1> 363// CHECK: %[[C:.*]] = memref.alloc(%[[S]]) : memref<?xindex> 364// CHECK-DAG: linalg.fill ins(%{{.*}} : f64) outs(%[[A]] : memref<?xf64>) 365// CHECK-DAG: linalg.fill ins(%{{.*}} : i1) outs(%[[B]] : memref<?xi1>) 366// CHECK: return %[[C]] : memref<?xindex> 367func.func @sparse_expansion3(%arg0: index, %arg1: index) -> memref<?xindex> { 368 %0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf64, #CSC> 369 %values, %filled, %added, %count = sparse_tensor.expand %0 370 : tensor<?x?xf64, #CSC> to memref<?xf64>, memref<?xi1>, memref<?xindex> 371 return %added : memref<?xindex> 372} 373 374// CHECK-LABEL: func @sparse_compression( 375// CHECK-SAME: %[[A:.*0]]: !llvm.ptr, 376// CHECK-SAME: %[[B:.*1]]: memref<?xf64>, 377// CHECK-SAME: %[[C:.*2]]: memref<?xi1>, 378// CHECK-SAME: %[[D:.*3]]: memref<?xindex>, 379// CHECK-SAME: %[[E:.*4]]: index, 380// CHECK-SAME: %[[F:.*5]]: index) -> !llvm.ptr { 381// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index 382// CHECK-DAG: %[[X:.*]] = memref.alloca() : memref<2xindex> 383// CHECK-DAG: %[[Y:.*]] = memref.cast %[[X]] : memref<2xindex> to memref<?xindex> 384// CHECK: memref.store %[[F]], %[[X]][%[[C0]]] : memref<2xindex> 385// CHECK: call @expInsertF64(%[[A]], %[[Y]], %[[B]], %[[C]], %[[D]], %[[E]]) 386// CHECK-DAG: memref.dealloc %[[B]] : memref<?xf64> 387// CHECK-DAG: memref.dealloc %[[C]] : memref<?xi1> 388// CHECK-DAG: memref.dealloc %[[D]] : memref<?xindex> 389// CHECK: return %[[A]] : !llvm.ptr 390func.func @sparse_compression(%tensor: tensor<8x8xf64, #CSR>, 391 %values: memref<?xf64>, 392 %filled: memref<?xi1>, 393 %added: memref<?xindex>, 394 %count: index, 395 %i: index) -> tensor<8x8xf64, #CSR> { 396 %0 = sparse_tensor.compress %values, %filled, %added, %count into %tensor[%i] 397 : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #CSR> 398 return %0 : tensor<8x8xf64, #CSR> 399} 400 401// CHECK-LABEL: func @sparse_and_dense_init( 402// CHECK: %[[S:.*]] = call @newSparseTensor 403// CHECK: %[[D:.*]] = tensor.empty 404// CHECK: return %[[S]], %[[D]] : !llvm.ptr, tensor<?x?xf64> 405func.func @sparse_and_dense_init(%arg0: index, %arg1: index) 406 -> (tensor<?x?xf64, #CSR>, tensor<?x?xf64>) { 407 %0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf64, #CSR> 408 %1 = sparse_tensor.load %0 : tensor<?x?xf64, #CSR> 409 %2 = tensor.empty(%arg0, %arg1) : tensor<?x?xf64> 410 return %1, %2 : tensor<?x?xf64, #CSR>, tensor<?x?xf64> 411} 412