1//-------------------------------------------------------------------------------------------------- 2// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS. 3// 4// Set-up that's shared across all tests in this directory. In principle, this 5// config could be moved to lit.local.cfg. However, there are downstream users that 6// do not use these LIT config files. Hence why this is kept inline. 7// 8// DEFINE: %{sparsifier_opts} = enable-runtime-library=true 9// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts} 10// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}" 11// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}" 12// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils 13// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils 14// DEFINE: %{run_opts} = -e main -entry-point-result=void 15// DEFINE: %{run} = mlir-runner %{run_opts} %{run_libs} 16// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs_sve} 17// 18// DEFINE: %{env} = 19//-------------------------------------------------------------------------------------------------- 20 21// RUN: %{compile} | %{run} | FileCheck %s 22// 23// Do the same run, but now with direct IR generation. 24// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false 25// RUN: %{compile} | %{run} | FileCheck %s 26// 27// Do the same run, but now with direct IR generation and vectorization. 28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true 29// RUN: %{compile} | %{run} | FileCheck %s 30// 31// Do the same run, but now with direct IR generation and VLA vectorization. 32// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %} 33 34#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> 35 36#trait_op1 = { 37 indexing_maps = [ 38 affine_map<(i) -> (i)>, // a (in) 39 affine_map<(i) -> (i)> // x (out) 40 ], 41 iterator_types = ["parallel"], 42 doc = "x(i) = OP a(i)" 43} 44 45#trait_op2 = { 46 indexing_maps = [ 47 affine_map<(i) -> (i)>, // a (in) 48 affine_map<(i) -> (i)>, // b (in) 49 affine_map<(i) -> (i)> // x (out) 50 ], 51 iterator_types = ["parallel"], 52 doc = "x(i) = a(i) OP b(i)" 53} 54 55module { 56 func.func @cops(%arga: tensor<?xcomplex<f64>, #SparseVector>, 57 %argb: tensor<?xcomplex<f64>, #SparseVector>) 58 -> tensor<?xcomplex<f64>, #SparseVector> { 59 %c0 = arith.constant 0 : index 60 %d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector> 61 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 62 %0 = linalg.generic #trait_op2 63 ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>, 64 tensor<?xcomplex<f64>, #SparseVector>) 65 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 66 ^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>): 67 %1 = complex.neg %b : complex<f64> 68 %2 = complex.sub %a, %1 : complex<f64> 69 linalg.yield %2 : complex<f64> 70 } -> tensor<?xcomplex<f64>, #SparseVector> 71 return %0 : tensor<?xcomplex<f64>, #SparseVector> 72 } 73 74 func.func @csin(%arga: tensor<?xcomplex<f64>, #SparseVector>) 75 -> tensor<?xcomplex<f64>, #SparseVector> { 76 %c0 = arith.constant 0 : index 77 %d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector> 78 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 79 %0 = linalg.generic #trait_op1 80 ins(%arga: tensor<?xcomplex<f64>, #SparseVector>) 81 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 82 ^bb(%a: complex<f64>, %x: complex<f64>): 83 %1 = complex.sin %a : complex<f64> 84 linalg.yield %1 : complex<f64> 85 } -> tensor<?xcomplex<f64>, #SparseVector> 86 return %0 : tensor<?xcomplex<f64>, #SparseVector> 87 } 88 89 func.func @complex_sqrt(%arga: tensor<?xcomplex<f64>, #SparseVector>) 90 -> tensor<?xcomplex<f64>, #SparseVector> { 91 %c0 = arith.constant 0 : index 92 %d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector> 93 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 94 %0 = linalg.generic #trait_op1 95 ins(%arga: tensor<?xcomplex<f64>, #SparseVector>) 96 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 97 ^bb(%a: complex<f64>, %x: complex<f64>): 98 %1 = complex.sqrt %a : complex<f64> 99 linalg.yield %1 : complex<f64> 100 } -> tensor<?xcomplex<f64>, #SparseVector> 101 return %0 : tensor<?xcomplex<f64>, #SparseVector> 102 } 103 104 func.func @complex_tanh(%arga: tensor<?xcomplex<f64>, #SparseVector>) 105 -> tensor<?xcomplex<f64>, #SparseVector> { 106 %c0 = arith.constant 0 : index 107 %d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector> 108 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 109 %0 = linalg.generic #trait_op1 110 ins(%arga: tensor<?xcomplex<f64>, #SparseVector>) 111 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 112 ^bb(%a: complex<f64>, %x: complex<f64>): 113 %1 = complex.tanh %a : complex<f64> 114 linalg.yield %1 : complex<f64> 115 } -> tensor<?xcomplex<f64>, #SparseVector> 116 return %0 : tensor<?xcomplex<f64>, #SparseVector> 117 } 118 119 func.func @clog1p_expm1(%arga: tensor<?xcomplex<f64>, #SparseVector>) 120 -> tensor<?xcomplex<f64>, #SparseVector> { 121 %c0 = arith.constant 0 : index 122 %d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector> 123 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 124 %0 = linalg.generic #trait_op1 125 ins(%arga: tensor<?xcomplex<f64>, #SparseVector>) 126 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 127 ^bb(%a: complex<f64>, %x: complex<f64>): 128 %1 = complex.log1p %a : complex<f64> 129 %2 = complex.expm1 %1 : complex<f64> 130 linalg.yield %2 : complex<f64> 131 } -> tensor<?xcomplex<f64>, #SparseVector> 132 return %0 : tensor<?xcomplex<f64>, #SparseVector> 133 } 134 135 func.func @cdiv(%arga: tensor<?xcomplex<f64>, #SparseVector>) 136 -> tensor<?xcomplex<f64>, #SparseVector> { 137 %c0 = arith.constant 0 : index 138 %d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector> 139 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 140 %c = complex.constant [2.0 : f64, 0.0 : f64] : complex<f64> 141 %0 = linalg.generic #trait_op1 142 ins(%arga: tensor<?xcomplex<f64>, #SparseVector>) 143 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 144 ^bb(%a: complex<f64>, %x: complex<f64>): 145 %1 = complex.div %a, %c : complex<f64> 146 linalg.yield %1 : complex<f64> 147 } -> tensor<?xcomplex<f64>, #SparseVector> 148 return %0 : tensor<?xcomplex<f64>, #SparseVector> 149 } 150 151 func.func @cabs(%arga: tensor<?xcomplex<f64>, #SparseVector>) 152 -> tensor<?xf64, #SparseVector> { 153 %c0 = arith.constant 0 : index 154 %d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector> 155 %xv = tensor.empty(%d) : tensor<?xf64, #SparseVector> 156 %0 = linalg.generic #trait_op1 157 ins(%arga: tensor<?xcomplex<f64>, #SparseVector>) 158 outs(%xv: tensor<?xf64, #SparseVector>) { 159 ^bb(%a: complex<f64>, %x: f64): 160 %1 = complex.abs %a : complex<f64> 161 linalg.yield %1 : f64 162 } -> tensor<?xf64, #SparseVector> 163 return %0 : tensor<?xf64, #SparseVector> 164 } 165 166 // Driver method to call and verify complex kernels. 167 func.func @main() { 168 // Setup sparse vectors. 169 %v1 = arith.constant sparse< 170 [ [0], [28], [31] ], 171 [ (-5.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f64>> 172 %v2 = arith.constant sparse< 173 [ [1], [28], [31] ], 174 [ (1.0, 0.0), (-2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f64>> 175 %sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector> 176 %sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector> 177 178 // Call sparse vector kernels. 179 %0 = call @cops(%sv1, %sv2) 180 : (tensor<?xcomplex<f64>, #SparseVector>, 181 tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 182 %1 = call @csin(%sv1) 183 : (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 184 %2 = call @complex_sqrt(%sv1) 185 : (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 186 %3 = call @complex_tanh(%sv2) 187 : (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 188 %4 = call @clog1p_expm1(%sv1) 189 : (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 190 %5 = call @cdiv(%sv1) 191 : (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 192 %6 = call @cabs(%sv1) 193 : (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xf64, #SparseVector> 194 195 // 196 // Verify the results. 197 // 198 // CHECK: ---- Sparse Tensor ---- 199 // CHECK-NEXT: nse = 4 200 // CHECK-NEXT: dim = ( 32 ) 201 // CHECK-NEXT: lvl = ( 32 ) 202 // CHECK-NEXT: pos[0] : ( 0, 4 ) 203 // CHECK-NEXT: crd[0] : ( 0, 1, 28, 31 ) 204 // CHECK-NEXT: values : ( ( -5.13, 2 ), ( 1, 0 ), ( 1, 4 ), ( 8, 6 ) ) 205 // CHECK-NEXT: ---- 206 // 207 // CHECK-NEXT: ---- Sparse Tensor ---- 208 // CHECK-NEXT: nse = 3 209 // CHECK-NEXT: dim = ( 32 ) 210 // CHECK-NEXT: lvl = ( 32 ) 211 // CHECK-NEXT: pos[0] : ( 0, 3 ) 212 // CHECK-NEXT: crd[0] : ( 0, 28, 31 ) 213 // CHECK-NEXT: values : ( ( 3.43887, 1.47097 ), ( 3.85374, -27.0168 ), ( -193.43, 57.2184 ) ) 214 // CHECK-NEXT: ---- 215 // 216 // CHECK-NEXT: ---- Sparse Tensor ---- 217 // CHECK-NEXT: nse = 3 218 // CHECK-NEXT: dim = ( 32 ) 219 // CHECK-NEXT: lvl = ( 32 ) 220 // CHECK-NEXT: pos[0] : ( 0, 3 ) 221 // CHECK-NEXT: crd[0] : ( 0, 28, 31 ) 222 // CHECK-NEXT: values : ( ( 0.433635, 2.30609 ), ( 2, 1 ), ( 2.53083, 1.18538 ) ) 223 // CHECK-NEXT: ---- 224 // 225 // CHECK-NEXT: ---- Sparse Tensor ---- 226 // CHECK-NEXT: nse = 3 227 // CHECK-NEXT: dim = ( 32 ) 228 // CHECK-NEXT: lvl = ( 32 ) 229 // CHECK-NEXT: pos[0] : ( 0, 3 ) 230 // CHECK-NEXT: crd[0] : ( 1, 28, 31 ) 231 // CHECK-NEXT: values : ( ( 0.761594, 0 ), ( -0.964028, 0 ), ( 0.995055, 0 ) ) 232 // CHECK-NEXT: ---- 233 // 234 // CHECK-NEXT: ---- Sparse Tensor ---- 235 // CHECK-NEXT: nse = 3 236 // CHECK-NEXT: dim = ( 32 ) 237 // CHECK-NEXT: lvl = ( 32 ) 238 // CHECK-NEXT: pos[0] : ( 0, 3 ) 239 // CHECK-NEXT: crd[0] : ( 0, 28, 31 ) 240 // CHECK-NEXT: values : ( ( -5.13, 2 ), ( 3, 4 ), ( 5, 6 ) ) 241 // CHECK-NEXT: ---- 242 // 243 // CHECK-NEXT: ---- Sparse Tensor ---- 244 // CHECK-NEXT: nse = 3 245 // CHECK-NEXT: dim = ( 32 ) 246 // CHECK-NEXT: lvl = ( 32 ) 247 // CHECK-NEXT: pos[0] : ( 0, 3 ) 248 // CHECK-NEXT: crd[0] : ( 0, 28, 31 ) 249 // CHECK-NEXT: values : ( ( -2.565, 1 ), ( 1.5, 2 ), ( 2.5, 3 ) ) 250 // CHECK-NEXT: ---- 251 // 252 // CHECK-NEXT: ---- Sparse Tensor ---- 253 // CHECK-NEXT: nse = 3 254 // CHECK-NEXT: dim = ( 32 ) 255 // CHECK-NEXT: lvl = ( 32 ) 256 // CHECK-NEXT: pos[0] : ( 0, 3 ) 257 // CHECK-NEXT: crd[0] : ( 0, 28, 31 ) 258 // CHECK-NEXT: values : ( 5.50608, 5, 7.81025 ) 259 // CHECK-NEXT: ---- 260 // 261 sparse_tensor.print %0 : tensor<?xcomplex<f64>, #SparseVector> 262 sparse_tensor.print %1 : tensor<?xcomplex<f64>, #SparseVector> 263 sparse_tensor.print %2 : tensor<?xcomplex<f64>, #SparseVector> 264 sparse_tensor.print %3 : tensor<?xcomplex<f64>, #SparseVector> 265 sparse_tensor.print %4 : tensor<?xcomplex<f64>, #SparseVector> 266 sparse_tensor.print %5 : tensor<?xcomplex<f64>, #SparseVector> 267 sparse_tensor.print %6 : tensor<?xf64, #SparseVector> 268 269 // Release the resources. 270 bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f64>, #SparseVector> 271 bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f64>, #SparseVector> 272 bufferization.dealloc_tensor %0 : tensor<?xcomplex<f64>, #SparseVector> 273 bufferization.dealloc_tensor %1 : tensor<?xcomplex<f64>, #SparseVector> 274 bufferization.dealloc_tensor %2 : tensor<?xcomplex<f64>, #SparseVector> 275 bufferization.dealloc_tensor %3 : tensor<?xcomplex<f64>, #SparseVector> 276 bufferization.dealloc_tensor %4 : tensor<?xcomplex<f64>, #SparseVector> 277 bufferization.dealloc_tensor %5 : tensor<?xcomplex<f64>, #SparseVector> 278 bufferization.dealloc_tensor %6 : tensor<?xf64, #SparseVector> 279 return 280 } 281} 282