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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, this5// config could be moved to lit.local.cfg. However, there are downstream users that6// do not use these LIT config files. Hence why this is kept inline.7//8// DEFINE: %{sparsifier_opts} = enable-runtime-library=true9// 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_utils13// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils14// DEFINE: %{run_opts} = -e main -entry-point-result=void15// 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 %s22//23// Do the same run, but now with direct IR generation.24// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true25// RUN: %{compile} | %{run} | FileCheck %s26//27// Do the same run, but now with vectorization.28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true29// RUN: %{compile} | %{run} | FileCheck %s30//31// Do the same run, but now with 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#DCSR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed, d1 : compressed)}>36 37//38// Traits for tensor operations.39//40#trait_vec = {41 indexing_maps = [42 affine_map<(i) -> (i)>, // a (in)43 affine_map<(i) -> (i)> // x (out)44 ],45 iterator_types = ["parallel"]46}47#trait_mat = {48 indexing_maps = [49 affine_map<(i,j) -> (i,j)>, // A (in)50 affine_map<(i,j) -> (i,j)> // X (out)51 ],52 iterator_types = ["parallel", "parallel"]53}54 55module {56 // Invert the structure of a sparse vector. Present values become missing.57 // Missing values are filled with 1 (i32). Output is sparse.58 func.func @vector_complement_sparse(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> {59 %c = arith.constant 0 : index60 %ci1 = arith.constant 1 : i3261 %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>62 %xv = tensor.empty(%d) : tensor<?xi32, #SparseVector>63 %0 = linalg.generic #trait_vec64 ins(%arga: tensor<?xf64, #SparseVector>)65 outs(%xv: tensor<?xi32, #SparseVector>) {66 ^bb(%a: f64, %x: i32):67 %1 = sparse_tensor.unary %a : f64 to i3268 present={}69 absent={70 sparse_tensor.yield %ci1 : i3271 }72 linalg.yield %1 : i3273 } -> tensor<?xi32, #SparseVector>74 return %0 : tensor<?xi32, #SparseVector>75 }76 77 // Invert the structure of a sparse vector, where missing values are78 // filled with 1. For a dense output, the sparsifier initializes79 // the buffer to all zero at all other places.80 func.func @vector_complement_dense(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32> {81 %c = arith.constant 0 : index82 %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>83 %xv = tensor.empty(%d) : tensor<?xi32>84 %0 = linalg.generic #trait_vec85 ins(%arga: tensor<?xf64, #SparseVector>)86 outs(%xv: tensor<?xi32>) {87 ^bb(%a: f64, %x: i32):88 %1 = sparse_tensor.unary %a : f64 to i3289 present={}90 absent={91 %ci1 = arith.constant 1 : i3292 sparse_tensor.yield %ci1 : i3293 }94 linalg.yield %1 : i3295 } -> tensor<?xi32>96 return %0 : tensor<?xi32>97 }98 99 // Negate existing values. Fill missing ones with +1.100 func.func @vector_negation(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {101 %c = arith.constant 0 : index102 %cf1 = arith.constant 1.0 : f64103 %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>104 %xv = tensor.empty(%d) : tensor<?xf64, #SparseVector>105 %0 = linalg.generic #trait_vec106 ins(%arga: tensor<?xf64, #SparseVector>)107 outs(%xv: tensor<?xf64, #SparseVector>) {108 ^bb(%a: f64, %x: f64):109 %1 = sparse_tensor.unary %a : f64 to f64110 present={111 ^bb0(%x0: f64):112 %ret = arith.negf %x0 : f64113 sparse_tensor.yield %ret : f64114 }115 absent={116 sparse_tensor.yield %cf1 : f64117 }118 linalg.yield %1 : f64119 } -> tensor<?xf64, #SparseVector>120 return %0 : tensor<?xf64, #SparseVector>121 }122 123 // Performs B[i] = i * A[i].124 func.func @vector_magnify(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {125 %c = arith.constant 0 : index126 %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>127 %xv = tensor.empty(%d) : tensor<?xf64, #SparseVector>128 %0 = linalg.generic #trait_vec129 ins(%arga: tensor<?xf64, #SparseVector>)130 outs(%xv: tensor<?xf64, #SparseVector>) {131 ^bb(%a: f64, %x: f64):132 %idx = linalg.index 0 : index133 %1 = sparse_tensor.unary %a : f64 to f64134 present={135 ^bb0(%x0: f64):136 %tmp = arith.index_cast %idx : index to i64137 %idxf = arith.uitofp %tmp : i64 to f64138 %ret = arith.mulf %x0, %idxf : f64139 sparse_tensor.yield %ret : f64140 }141 absent={}142 linalg.yield %1 : f64143 } -> tensor<?xf64, #SparseVector>144 return %0 : tensor<?xf64, #SparseVector>145 }146 147 // Clips values to the range [3, 7].148 func.func @matrix_clip(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {149 %c0 = arith.constant 0 : index150 %c1 = arith.constant 1 : index151 %cfmin = arith.constant 3.0 : f64152 %cfmax = arith.constant 7.0 : f64153 %d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>154 %d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>155 %xv = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR>156 %0 = linalg.generic #trait_mat157 ins(%argx: tensor<?x?xf64, #DCSR>)158 outs(%xv: tensor<?x?xf64, #DCSR>) {159 ^bb(%a: f64, %x: f64):160 %1 = sparse_tensor.unary %a: f64 to f64161 present={162 ^bb0(%x0: f64):163 %mincmp = arith.cmpf "ogt", %x0, %cfmin : f64164 %x1 = arith.select %mincmp, %x0, %cfmin : f64165 %maxcmp = arith.cmpf "olt", %x1, %cfmax : f64166 %x2 = arith.select %maxcmp, %x1, %cfmax : f64167 sparse_tensor.yield %x2 : f64168 }169 absent={}170 linalg.yield %1 : f64171 } -> tensor<?x?xf64, #DCSR>172 return %0 : tensor<?x?xf64, #DCSR>173 }174 175 // Slices matrix and only keep the value of the lower-right corner of the original176 // matrix (i.e., A[2/d0 ..][2/d1 ..]), and set other values to 99.177 func.func @matrix_slice(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {178 %c0 = arith.constant 0 : index179 %c1 = arith.constant 1 : index180 %d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>181 %d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>182 %xv = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR>183 %0 = linalg.generic #trait_mat184 ins(%argx: tensor<?x?xf64, #DCSR>)185 outs(%xv: tensor<?x?xf64, #DCSR>) {186 ^bb(%a: f64, %x: f64):187 %row = linalg.index 0 : index188 %col = linalg.index 1 : index189 %1 = sparse_tensor.unary %a: f64 to f64190 present={191 ^bb0(%x0: f64):192 %v = arith.constant 99.0 : f64193 %two = arith.constant 2 : index194 %r = arith.muli %two, %row : index195 %c = arith.muli %two, %col : index196 %cmp1 = arith.cmpi "ult", %r, %d0 : index197 %tmp = arith.select %cmp1, %v, %x0 : f64198 %cmp2 = arith.cmpi "ult", %c, %d1 : index199 %result = arith.select %cmp2, %v, %tmp : f64200 sparse_tensor.yield %result : f64201 }202 absent={}203 linalg.yield %1 : f64204 } -> tensor<?x?xf64, #DCSR>205 return %0 : tensor<?x?xf64, #DCSR>206 }207 208 // Driver method to call and verify vector kernels.209 func.func @main() {210 %cmu = arith.constant -99 : i32211 %c0 = arith.constant 0 : index212 213 // Setup sparse vectors.214 %v1 = arith.constant sparse<215 [ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],216 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]217 > : tensor<32xf64>218 %sv1 = sparse_tensor.convert %v1 : tensor<32xf64> to tensor<?xf64, #SparseVector>219 220 // Setup sparse matrices.221 %m1 = arith.constant sparse<222 [ [0,0], [0,1], [1,7], [2,2], [2,4], [2,7], [3,0], [3,2], [3,3] ],223 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]224 > : tensor<4x8xf64>225 %sm1 = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>226 227 // Call sparse vector kernels.228 %0 = call @vector_complement_sparse(%sv1)229 : (tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector>230 %1 = call @vector_negation(%sv1)231 : (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>232 %2 = call @vector_magnify(%sv1)233 : (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>234 235 // Call sparse matrix kernels.236 %3 = call @matrix_clip(%sm1)237 : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>238 %4 = call @matrix_slice(%sm1)239 : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>240 241 // Call kernel with dense output.242 %5 = call @vector_complement_dense(%sv1) : (tensor<?xf64, #SparseVector>) -> tensor<?xi32>243 244 //245 // Verify the results.246 //247 // CHECK: ---- Sparse Tensor ----248 // CHECK-NEXT: nse = 9249 // CHECK-NEXT: dim = ( 32 )250 // CHECK-NEXT: lvl = ( 32 )251 // CHECK-NEXT: pos[0] : ( 0, 9 )252 // CHECK-NEXT: crd[0] : ( 0, 3, 11, 17, 20, 21, 28, 29, 31 )253 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9 )254 // CHECK-NEXT: ----255 // CHECK: ---- Sparse Tensor ----256 // CHECK-NEXT: nse = 23257 // CHECK-NEXT: dim = ( 32 )258 // CHECK-NEXT: lvl = ( 32 )259 // CHECK-NEXT: pos[0] : ( 0, 23 )260 // CHECK-NEXT: crd[0] : ( 1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 22, 23, 24, 25, 26, 27, 30 )261 // CHECK-NEXT: values : ( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 )262 // CHECK-NEXT: ----263 // CHECK: ---- Sparse Tensor ----264 // CHECK-NEXT: nse = 32265 // CHECK-NEXT: dim = ( 32 )266 // CHECK-NEXT: lvl = ( 32 )267 // CHECK-NEXT: pos[0] : ( 0, 32 )268 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 )269 // CHECK-NEXT: values : ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 )270 // CHECK-NEXT: ----271 // CHECK: ---- Sparse Tensor ----272 // CHECK-NEXT: nse = 9273 // CHECK-NEXT: dim = ( 32 )274 // CHECK-NEXT: lvl = ( 32 )275 // CHECK-NEXT: pos[0] : ( 0, 9 )276 // CHECK-NEXT: crd[0] : ( 0, 3, 11, 17, 20, 21, 28, 29, 31 )277 // CHECK-NEXT: values : ( 0, 6, 33, 68, 100, 126, 196, 232, 279 )278 // CHECK-NEXT: ----279 // CHECK: ---- Sparse Tensor ----280 // CHECK-NEXT: nse = 9281 // CHECK-NEXT: dim = ( 4, 8 )282 // CHECK-NEXT: lvl = ( 4, 8 )283 // CHECK-NEXT: pos[0] : ( 0, 4 )284 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )285 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )286 // CHECK-NEXT: crd[1] : ( 0, 1, 7, 2, 4, 7, 0, 2, 3 )287 // CHECK-NEXT: values : ( 3, 3, 3, 4, 5, 6, 7, 7, 7 )288 // CHECK-NEXT: ----289 // CHECK: ---- Sparse Tensor ----290 // CHECK-NEXT: nse = 9291 // CHECK-NEXT: dim = ( 4, 8 )292 // CHECK-NEXT: lvl = ( 4, 8 )293 // CHECK-NEXT: pos[0] : ( 0, 4 )294 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )295 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )296 // CHECK-NEXT: crd[1] : ( 0, 1, 7, 2, 4, 7, 0, 2, 3 )297 // CHECK-NEXT: values : ( 99, 99, 99, 99, 5, 6, 99, 99, 99 )298 // CHECK-NEXT: ----299 // CHECK-NEXT: ( 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0 )300 //301 sparse_tensor.print %sv1 : tensor<?xf64, #SparseVector>302 sparse_tensor.print %0 : tensor<?xi32, #SparseVector>303 sparse_tensor.print %1 : tensor<?xf64, #SparseVector>304 sparse_tensor.print %2 : tensor<?xf64, #SparseVector>305 sparse_tensor.print %3 : tensor<?x?xf64, #DCSR>306 sparse_tensor.print %4 : tensor<?x?xf64, #DCSR>307 %v = vector.transfer_read %5[%c0], %cmu: tensor<?xi32>, vector<32xi32>308 vector.print %v : vector<32xi32>309 310 // Release the resources.311 bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>312 bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #DCSR>313 bufferization.dealloc_tensor %0 : tensor<?xi32, #SparseVector>314 bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector>315 bufferization.dealloc_tensor %2 : tensor<?xf64, #SparseVector>316 bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR>317 bufferization.dealloc_tensor %4 : tensor<?x?xf64, #DCSR>318 bufferization.dealloc_tensor %5 : tensor<?xi32>319 return320 }321}322