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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 direct IR generation and 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 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#DCSR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed, d1 : compressed)}>36 37//38// Traits for tensor operations.39//40#trait_vec_scale = {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_vec_op = {48 indexing_maps = [49 affine_map<(i) -> (i)>, // a (in)50 affine_map<(i) -> (i)>, // b (in)51 affine_map<(i) -> (i)> // x (out)52 ],53 iterator_types = ["parallel"]54}55#trait_mat_op = {56 indexing_maps = [57 affine_map<(i,j) -> (i,j)>, // A (in)58 affine_map<(i,j) -> (i,j)>, // B (in)59 affine_map<(i,j) -> (i,j)> // X (out)60 ],61 iterator_types = ["parallel", "parallel"],62 doc = "X(i,j) = A(i,j) OP B(i,j)"63}64 65//66// Contains test cases for the sparse_tensor.binary operator (different cases when left/right/overlap67// is empty/identity, etc).68//69 70module {71 // Creates a new sparse vector using the minimum values from two input sparse vectors.72 // When there is no overlap, include the present value in the output.73 func.func @vector_min(%arga: tensor<?xi32, #SparseVector>,74 %argb: tensor<?xi32, #SparseVector>) -> tensor<?xi32, #SparseVector> {75 %c = arith.constant 0 : index76 %d = tensor.dim %arga, %c : tensor<?xi32, #SparseVector>77 %xv = tensor.empty(%d) : tensor<?xi32, #SparseVector>78 %0 = linalg.generic #trait_vec_op79 ins(%arga, %argb: tensor<?xi32, #SparseVector>, tensor<?xi32, #SparseVector>)80 outs(%xv: tensor<?xi32, #SparseVector>) {81 ^bb(%a: i32, %b: i32, %x: i32):82 %1 = sparse_tensor.binary %a, %b : i32, i32 to i3283 overlap={84 ^bb0(%a0: i32, %b0: i32):85 %2 = arith.minsi %a0, %b0: i3286 sparse_tensor.yield %2 : i3287 }88 left=identity89 right=identity90 linalg.yield %1 : i3291 } -> tensor<?xi32, #SparseVector>92 return %0 : tensor<?xi32, #SparseVector>93 }94 95 // Creates a new sparse vector by multiplying a sparse vector with a dense vector.96 // When there is no overlap, leave the result empty.97 func.func @vector_mul(%arga: tensor<?xf64, #SparseVector>,98 %argb: tensor<?xf64>) -> tensor<?xf64, #SparseVector> {99 %c = arith.constant 0 : index100 %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>101 %xv = tensor.empty(%d) : tensor<?xf64, #SparseVector>102 %0 = linalg.generic #trait_vec_op103 ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64>)104 outs(%xv: tensor<?xf64, #SparseVector>) {105 ^bb(%a: f64, %b: f64, %x: f64):106 %1 = sparse_tensor.binary %a, %b : f64, f64 to f64107 overlap={108 ^bb0(%a0: f64, %b0: f64):109 %ret = arith.mulf %a0, %b0 : f64110 sparse_tensor.yield %ret : f64111 }112 left={}113 right={}114 linalg.yield %1 : f64115 } -> tensor<?xf64, #SparseVector>116 return %0 : tensor<?xf64, #SparseVector>117 }118 119 // Take a set difference of two sparse vectors. The result will include only those120 // sparse elements present in the first, but not the second vector.121 func.func @vector_setdiff(%arga: tensor<?xf64, #SparseVector>,122 %argb: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {123 %c = arith.constant 0 : index124 %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>125 %xv = tensor.empty(%d) : tensor<?xf64, #SparseVector>126 %0 = linalg.generic #trait_vec_op127 ins(%arga, %argb: tensor<?xf64, #SparseVector>, tensor<?xf64, #SparseVector>)128 outs(%xv: tensor<?xf64, #SparseVector>) {129 ^bb(%a: f64, %b: f64, %x: f64):130 %1 = sparse_tensor.binary %a, %b : f64, f64 to f64131 overlap={}132 left=identity133 right={}134 linalg.yield %1 : f64135 } -> tensor<?xf64, #SparseVector>136 return %0 : tensor<?xf64, #SparseVector>137 }138 139 // Return the index of each entry140 func.func @vector_index(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> {141 %c = arith.constant 0 : index142 %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>143 %xv = tensor.empty(%d) : tensor<?xi32, #SparseVector>144 %0 = linalg.generic #trait_vec_scale145 ins(%arga: tensor<?xf64, #SparseVector>)146 outs(%xv: tensor<?xi32, #SparseVector>) {147 ^bb(%a: f64, %x: i32):148 %idx = linalg.index 0 : index149 %1 = sparse_tensor.binary %a, %idx : f64, index to i32150 overlap={151 ^bb0(%x0: f64, %i: index):152 %ret = arith.index_cast %i : index to i32153 sparse_tensor.yield %ret : i32154 }155 left={}156 right={}157 linalg.yield %1 : i32158 } -> tensor<?xi32, #SparseVector>159 return %0 : tensor<?xi32, #SparseVector>160 }161 162 // Adds two sparse matrices when they intersect. Where they don't intersect,163 // negate the 2nd argument's values; ignore 1st argument-only values.164 func.func @matrix_intersect(%arga: tensor<?x?xf64, #DCSR>,165 %argb: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {166 %c0 = arith.constant 0 : index167 %c1 = arith.constant 1 : index168 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>169 %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>170 %xv = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR>171 %0 = linalg.generic #trait_mat_op172 ins(%arga, %argb: tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>)173 outs(%xv: tensor<?x?xf64, #DCSR>) {174 ^bb(%a: f64, %b: f64, %x: f64):175 %1 = sparse_tensor.binary %a, %b: f64, f64 to f64176 overlap={177 ^bb0(%x0: f64, %y0: f64):178 %ret = arith.addf %x0, %y0 : f64179 sparse_tensor.yield %ret : f64180 }181 left={}182 right={183 ^bb0(%x1: f64):184 %lret = arith.negf %x1 : f64185 sparse_tensor.yield %lret : f64186 }187 linalg.yield %1 : f64188 } -> tensor<?x?xf64, #DCSR>189 return %0 : tensor<?x?xf64, #DCSR>190 }191 192 // Tensor addition (use semi-ring binary operation).193 func.func @add_tensor_1(%A: tensor<4x4xf64, #DCSR>,194 %B: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {195 %C = tensor.empty() : tensor<4x4xf64, #DCSR>196 %0 = linalg.generic #trait_mat_op197 ins(%A, %B: tensor<4x4xf64, #DCSR>,198 tensor<4x4xf64, #DCSR>)199 outs(%C: tensor<4x4xf64, #DCSR>) {200 ^bb0(%a: f64, %b: f64, %c: f64) :201 %result = sparse_tensor.binary %a, %b : f64, f64 to f64202 overlap={203 ^bb0(%x: f64, %y: f64):204 %ret = arith.addf %x, %y : f64205 sparse_tensor.yield %ret : f64206 }207 left=identity208 right=identity209 linalg.yield %result : f64210 } -> tensor<4x4xf64, #DCSR>211 return %0 : tensor<4x4xf64, #DCSR>212 }213 214 // Same as @add_tensor_1, but use sparse_tensor.yield instead of identity to yield value.215 func.func @add_tensor_2(%A: tensor<4x4xf64, #DCSR>,216 %B: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {217 %C = tensor.empty() : tensor<4x4xf64, #DCSR>218 %0 = linalg.generic #trait_mat_op219 ins(%A, %B: tensor<4x4xf64, #DCSR>,220 tensor<4x4xf64, #DCSR>)221 outs(%C: tensor<4x4xf64, #DCSR>) {222 ^bb0(%a: f64, %b: f64, %c: f64) :223 %result = sparse_tensor.binary %a, %b : f64, f64 to f64224 overlap={225 ^bb0(%x: f64, %y: f64):226 %ret = arith.addf %x, %y : f64227 sparse_tensor.yield %ret : f64228 }229 left={230 ^bb0(%x: f64):231 sparse_tensor.yield %x : f64232 }233 right={234 ^bb0(%y: f64):235 sparse_tensor.yield %y : f64236 }237 linalg.yield %result : f64238 } -> tensor<4x4xf64, #DCSR>239 return %0 : tensor<4x4xf64, #DCSR>240 }241 242 // Performs triangular add/sub operation (using semi-ring binary op).243 func.func @triangular(%A: tensor<4x4xf64, #DCSR>,244 %B: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {245 %C = tensor.empty() : tensor<4x4xf64, #DCSR>246 %0 = linalg.generic #trait_mat_op247 ins(%A, %B: tensor<4x4xf64, #DCSR>,248 tensor<4x4xf64, #DCSR>)249 outs(%C: tensor<4x4xf64, #DCSR>) {250 ^bb0(%a: f64, %b: f64, %c: f64) :251 %row = linalg.index 0 : index252 %col = linalg.index 1 : index253 %result = sparse_tensor.binary %a, %b : f64, f64 to f64254 overlap={255 ^bb0(%x: f64, %y: f64):256 %cmp = arith.cmpi "uge", %col, %row : index257 %upperTriangleResult = arith.addf %x, %y : f64258 %lowerTriangleResult = arith.subf %x, %y : f64259 %ret = arith.select %cmp, %upperTriangleResult, %lowerTriangleResult : f64260 sparse_tensor.yield %ret : f64261 }262 left=identity263 right={264 ^bb0(%y: f64):265 %cmp = arith.cmpi "uge", %col, %row : index266 %lowerTriangleResult = arith.negf %y : f64267 %ret = arith.select %cmp, %y, %lowerTriangleResult : f64268 sparse_tensor.yield %ret : f64269 }270 linalg.yield %result : f64271 } -> tensor<4x4xf64, #DCSR>272 return %0 : tensor<4x4xf64, #DCSR>273 }274 275 // Perform sub operation (using semi-ring binary op) with a constant threshold.276 func.func @sub_with_thres(%A: tensor<4x4xf64, #DCSR>,277 %B: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {278 %C = tensor.empty() : tensor<4x4xf64, #DCSR>279 // Defines out-block constant bounds.280 %thres_out_up = arith.constant 2.0 : f64281 %thres_out_lo = arith.constant -2.0 : f64282 283 %0 = linalg.generic #trait_mat_op284 ins(%A, %B: tensor<4x4xf64, #DCSR>,285 tensor<4x4xf64, #DCSR>)286 outs(%C: tensor<4x4xf64, #DCSR>) {287 ^bb0(%a: f64, %b: f64, %c: f64) :288 %result = sparse_tensor.binary %a, %b : f64, f64 to f64289 overlap={290 ^bb0(%x: f64, %y: f64):291 // Defines in-block constant bounds.292 %thres_up = arith.constant 1.0 : f64293 %thres_lo = arith.constant -1.0 : f64294 %result = arith.subf %x, %y : f64295 %cmp = arith.cmpf "oge", %result, %thres_up : f64296 %tmp = arith.select %cmp, %thres_up, %result : f64297 %cmp1 = arith.cmpf "ole", %tmp, %thres_lo : f64298 %ret = arith.select %cmp1, %thres_lo, %tmp : f64299 sparse_tensor.yield %ret : f64300 }301 left={302 ^bb0(%x: f64):303 // Uses out-block constant bounds.304 %cmp = arith.cmpf "oge", %x, %thres_out_up : f64305 %tmp = arith.select %cmp, %thres_out_up, %x : f64306 %cmp1 = arith.cmpf "ole", %tmp, %thres_out_lo : f64307 %ret = arith.select %cmp1, %thres_out_lo, %tmp : f64308 sparse_tensor.yield %ret : f64309 }310 right={311 ^bb0(%y: f64):312 %ny = arith.negf %y : f64313 %cmp = arith.cmpf "oge", %ny, %thres_out_up : f64314 %tmp = arith.select %cmp, %thres_out_up, %ny : f64315 %cmp1 = arith.cmpf "ole", %tmp, %thres_out_lo : f64316 %ret = arith.select %cmp1, %thres_out_lo, %tmp : f64317 sparse_tensor.yield %ret : f64318 }319 linalg.yield %result : f64320 } -> tensor<4x4xf64, #DCSR>321 return %0 : tensor<4x4xf64, #DCSR>322 }323 324 // Performs isEqual only on intersecting elements.325 func.func @intersect_equal(%A: tensor<4x4xf64, #DCSR>,326 %B: tensor<4x4xf64, #DCSR>) -> tensor<4x4xi8, #DCSR> {327 %C = tensor.empty() : tensor<4x4xi8, #DCSR>328 %0 = linalg.generic #trait_mat_op329 ins(%A, %B: tensor<4x4xf64, #DCSR>,330 tensor<4x4xf64, #DCSR>)331 outs(%C: tensor<4x4xi8, #DCSR>) {332 ^bb0(%a: f64, %b: f64, %c: i8) :333 %result = sparse_tensor.binary %a, %b : f64, f64 to i8334 overlap={335 ^bb0(%x: f64, %y: f64):336 %cmp = arith.cmpf "oeq", %x, %y : f64337 %ret = arith.extui %cmp : i1 to i8338 sparse_tensor.yield %ret : i8339 }340 left={}341 right={}342 linalg.yield %result : i8343 } -> tensor<4x4xi8, #DCSR>344 return %0 : tensor<4x4xi8, #DCSR>345 }346 347 // Keeps values on left, negate value on right, ignore value when overlapping.348 func.func @only_left_right(%A: tensor<4x4xf64, #DCSR>,349 %B: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {350 %C = tensor.empty() : tensor<4x4xf64, #DCSR>351 %0 = linalg.generic #trait_mat_op352 ins(%A, %B: tensor<4x4xf64, #DCSR>,353 tensor<4x4xf64, #DCSR>)354 outs(%C: tensor<4x4xf64, #DCSR>) {355 ^bb0(%a: f64, %b: f64, %c: f64) :356 %result = sparse_tensor.binary %a, %b : f64, f64 to f64357 overlap={}358 left=identity359 right={360 ^bb0(%y: f64):361 %ret = arith.negf %y : f64362 sparse_tensor.yield %ret : f64363 }364 linalg.yield %result : f64365 } -> tensor<4x4xf64, #DCSR>366 return %0 : tensor<4x4xf64, #DCSR>367 }368 369 // Driver method to call and verify kernels.370 func.func @main() {371 %c0 = arith.constant 0 : index372 373 // Setup sparse vectors.374 %v1 = arith.constant sparse<375 [ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],376 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]377 > : tensor<32xf64>378 %v2 = arith.constant sparse<379 [ [1], [3], [4], [10], [16], [18], [21], [28], [29], [31] ],380 [11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0 ]381 > : tensor<32xf64>382 %v3 = arith.constant dense<383 [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,384 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 0., 1.]385 > : tensor<32xf64>386 %v1_si = arith.fptosi %v1 : tensor<32xf64> to tensor<32xi32>387 %v2_si = arith.fptosi %v2 : tensor<32xf64> to tensor<32xi32>388 389 %sv1 = sparse_tensor.convert %v1 : tensor<32xf64> to tensor<?xf64, #SparseVector>390 %sv2 = sparse_tensor.convert %v2 : tensor<32xf64> to tensor<?xf64, #SparseVector>391 %sv1_si = sparse_tensor.convert %v1_si : tensor<32xi32> to tensor<?xi32, #SparseVector>392 %sv2_si = sparse_tensor.convert %v2_si : tensor<32xi32> to tensor<?xi32, #SparseVector>393 %dv3 = tensor.cast %v3 : tensor<32xf64> to tensor<?xf64>394 395 // Setup sparse matrices.396 %m1 = arith.constant sparse<397 [ [0,0], [0,1], [1,7], [2,2], [2,4], [2,7], [3,0], [3,2], [3,3] ],398 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]399 > : tensor<4x8xf64>400 %m2 = arith.constant sparse<401 [ [0,0], [0,7], [1,0], [1,6], [2,1], [2,7] ],402 [6.0, 5.0, 4.0, 3.0, 2.0, 1.0 ]403 > : tensor<4x8xf64>404 %sm1 = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>405 %sm2 = sparse_tensor.convert %m2 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>406 407 %m3 = arith.constant dense<408 [ [ 1.0, 0.0, 3.0, 0.0],409 [ 0.0, 2.0, 0.0, 0.0],410 [ 0.0, 0.0, 0.0, 4.0],411 [ 3.0, 4.0, 0.0, 0.0] ]> : tensor<4x4xf64>412 %m4 = arith.constant dense<413 [ [ 1.0, 0.0, 1.0, 1.0],414 [ 0.0, 0.5, 0.0, 0.0],415 [ 1.0, 5.0, 2.0, 0.0],416 [ 2.0, 0.0, 0.0, 0.0] ]> : tensor<4x4xf64>417 418 %sm3 = sparse_tensor.convert %m3 : tensor<4x4xf64> to tensor<4x4xf64, #DCSR>419 %sm4 = sparse_tensor.convert %m4 : tensor<4x4xf64> to tensor<4x4xf64, #DCSR>420 421 // Call sparse vector kernels.422 %0 = call @vector_min(%sv1_si, %sv2_si)423 : (tensor<?xi32, #SparseVector>,424 tensor<?xi32, #SparseVector>) -> tensor<?xi32, #SparseVector>425 %1 = call @vector_mul(%sv1, %dv3)426 : (tensor<?xf64, #SparseVector>,427 tensor<?xf64>) -> tensor<?xf64, #SparseVector>428 %2 = call @vector_setdiff(%sv1, %sv2)429 : (tensor<?xf64, #SparseVector>,430 tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>431 %3 = call @vector_index(%sv1)432 : (tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector>433 434 // Call sparse matrix kernels.435 %5 = call @matrix_intersect(%sm1, %sm2)436 : (tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>437 %6 = call @add_tensor_1(%sm3, %sm4)438 : (tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>439 %7 = call @add_tensor_2(%sm3, %sm4)440 : (tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>441 %8 = call @triangular(%sm3, %sm4)442 : (tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>443 %9 = call @sub_with_thres(%sm3, %sm4)444 : (tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>445 %10 = call @intersect_equal(%sm3, %sm4)446 : (tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>) -> tensor<4x4xi8, #DCSR>447 %11 = call @only_left_right(%sm3, %sm4)448 : (tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>449 450 //451 // Verify the results.452 //453 // CHECK: ---- Sparse Tensor ----454 // CHECK-NEXT: nse = 9455 // CHECK-NEXT: dim = ( 32 )456 // CHECK-NEXT: lvl = ( 32 )457 // CHECK-NEXT: pos[0] : ( 0, 9 )458 // CHECK-NEXT: crd[0] : ( 0, 3, 11, 17, 20, 21, 28, 29, 31 )459 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9 )460 // CHECK-NEXT: ----461 //462 // CHECK-NEXT: ---- Sparse Tensor ----463 // CHECK-NEXT: nse = 10464 // CHECK-NEXT: dim = ( 32 )465 // CHECK-NEXT: lvl = ( 32 )466 // CHECK-NEXT: pos[0] : ( 0, 10 )467 // CHECK-NEXT: crd[0] : ( 1, 3, 4, 10, 16, 18, 21, 28, 29, 31 )468 // CHECK-NEXT: values : ( 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 )469 // CHECK-NEXT: ----470 //471 // CHECK-NEXT: ---- Sparse Tensor ----472 // CHECK-NEXT: nse = 14473 // CHECK-NEXT: dim = ( 32 )474 // CHECK-NEXT: lvl = ( 32 )475 // CHECK-NEXT: pos[0] : ( 0, 14 )476 // CHECK-NEXT: crd[0] : ( 0, 1, 3, 4, 10, 11, 16, 17, 18, 20, 21, 28, 29, 31 )477 // CHECK-NEXT: values : ( 1, 11, 2, 13, 14, 3, 15, 4, 16, 5, 6, 7, 8, 9 )478 // CHECK-NEXT: ----479 //480 // CHECK-NEXT: ---- Sparse Tensor ----481 // CHECK-NEXT: nse = 9482 // CHECK-NEXT: dim = ( 32 )483 // CHECK-NEXT: lvl = ( 32 )484 // CHECK-NEXT: pos[0] : ( 0, 9 )485 // CHECK-NEXT: crd[0] : ( 0, 3, 11, 17, 20, 21, 28, 29, 31 )486 // CHECK-NEXT: values : ( 0, 6, 3, 28, 0, 6, 56, 72, 9 )487 // CHECK-NEXT: ----488 //489 // CHECK-NEXT: ---- Sparse Tensor ----490 // CHECK-NEXT: nse = 4491 // CHECK-NEXT: dim = ( 32 )492 // CHECK-NEXT: lvl = ( 32 )493 // CHECK-NEXT: pos[0] : ( 0, 4 )494 // CHECK-NEXT: crd[0] : ( 0, 11, 17, 20 )495 // CHECK-NEXT: values : ( 1, 3, 4, 5 )496 // CHECK-NEXT: ----497 //498 // CHECK-NEXT: ---- Sparse Tensor ----499 // CHECK-NEXT: nse = 9500 // CHECK-NEXT: dim = ( 32 )501 // CHECK-NEXT: lvl = ( 32 )502 // CHECK-NEXT: pos[0] : ( 0, 9 )503 // CHECK-NEXT: crd[0] : ( 0, 3, 11, 17, 20, 21, 28, 29, 31 )504 // CHECK-NEXT: values : ( 0, 3, 11, 17, 20, 21, 28, 29, 31 )505 // CHECK-NEXT: ----506 //507 // CHECK-NEXT: ---- Sparse Tensor ----508 // CHECK-NEXT: nse = 6509 // CHECK-NEXT: dim = ( 4, 8 )510 // CHECK-NEXT: lvl = ( 4, 8 )511 // CHECK-NEXT: pos[0] : ( 0, 3 )512 // CHECK-NEXT: crd[0] : ( 0, 1, 2 )513 // CHECK-NEXT: pos[1] : ( 0, 2, 4, 6 )514 // CHECK-NEXT: crd[1] : ( 0, 7, 0, 6, 1, 7 )515 // CHECK-NEXT: values : ( 7, -5, -4, -3, -2, 7 )516 // CHECK-NEXT: ----517 //518 // CHECK-NEXT: ---- Sparse Tensor ----519 // CHECK-NEXT: nse = 10520 // CHECK-NEXT: dim = ( 4, 4 )521 // CHECK-NEXT: lvl = ( 4, 4 )522 // CHECK-NEXT: pos[0] : ( 0, 4 )523 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )524 // CHECK-NEXT: pos[1] : ( 0, 3, 4, 8, 10 )525 // CHECK-NEXT: crd[1] : ( 0, 2, 3, 1, 0, 1, 2, 3, 0, 1 )526 // CHECK-NEXT: values : ( 2, 4, 1, 2.5, 1, 5, 2, 4, 5, 4 )527 // CHECK-NEXT: ----528 //529 // CHECK-NEXT: ---- Sparse Tensor ----530 // CHECK-NEXT: nse = 10531 // CHECK-NEXT: dim = ( 4, 4 )532 // CHECK-NEXT: lvl = ( 4, 4 )533 // CHECK-NEXT: pos[0] : ( 0, 4 )534 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )535 // CHECK-NEXT: pos[1] : ( 0, 3, 4, 8, 10 )536 // CHECK-NEXT: crd[1] : ( 0, 2, 3, 1, 0, 1, 2, 3, 0, 1 )537 // CHECK-NEXT: values : ( 2, 4, 1, 2.5, 1, 5, 2, 4, 5, 4 )538 // CHECK-NEXT: ----539 //540 // CHECK-NEXT: ---- Sparse Tensor ----541 // CHECK-NEXT: nse = 10542 // CHECK-NEXT: dim = ( 4, 4 )543 // CHECK-NEXT: lvl = ( 4, 4 )544 // CHECK-NEXT: pos[0] : ( 0, 4 )545 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )546 // CHECK-NEXT: pos[1] : ( 0, 3, 4, 8, 10 )547 // CHECK-NEXT: crd[1] : ( 0, 2, 3, 1, 0, 1, 2, 3, 0, 1 )548 // CHECK-NEXT: values : ( 2, 4, 1, 2.5, -1, -5, 2, 4, 1, 4 )549 // CHECK-NEXT: ----550 //551 // CHECK-NEXT: ---- Sparse Tensor ----552 // CHECK-NEXT: nse = 10553 // CHECK-NEXT: dim = ( 4, 4 )554 // CHECK-NEXT: lvl = ( 4, 4 )555 // CHECK-NEXT: pos[0] : ( 0, 4 )556 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )557 // CHECK-NEXT: pos[1] : ( 0, 3, 4, 8, 10 )558 // CHECK-NEXT: crd[1] : ( 0, 2, 3, 1, 0, 1, 2, 3, 0, 1 )559 // CHECK-NEXT: values : ( 0, 1, -1, 1, -1, -2, -2, 2, 1, 2 )560 // CHECK-NEXT: ----561 //562 // CHECK-NEXT: ---- Sparse Tensor ----563 // CHECK-NEXT: nse = 4564 // CHECK-NEXT: dim = ( 4, 4 )565 // CHECK-NEXT: lvl = ( 4, 4 )566 // CHECK-NEXT: pos[0] : ( 0, 3 )567 // CHECK-NEXT: crd[0] : ( 0, 1, 3 )568 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 4 )569 // CHECK-NEXT: crd[1] : ( 0, 2, 1, 0 )570 // CHECK-NEXT: values : ( 1, 0, 0, 0 )571 // CHECK-NEXT: ----572 //573 // CHECK-NEXT: ---- Sparse Tensor ----574 // CHECK-NEXT: nse = 6575 // CHECK-NEXT: dim = ( 4, 4 )576 // CHECK-NEXT: lvl = ( 4, 4 )577 // CHECK-NEXT: pos[0] : ( 0, 3 )578 // CHECK-NEXT: crd[0] : ( 0, 2, 3 )579 // CHECK-NEXT: pos[1] : ( 0, 1, 5, 6 )580 // CHECK-NEXT: crd[1] : ( 3, 0, 1, 2, 3, 1 )581 // CHECK-NEXT: values : ( -1, -1, -5, -2, 4, 4 )582 //583 sparse_tensor.print %sv1 : tensor<?xf64, #SparseVector>584 sparse_tensor.print %sv2 : tensor<?xf64, #SparseVector>585 sparse_tensor.print %0 : tensor<?xi32, #SparseVector>586 sparse_tensor.print %1 : tensor<?xf64, #SparseVector>587 sparse_tensor.print %2 : tensor<?xf64, #SparseVector>588 sparse_tensor.print %3 : tensor<?xi32, #SparseVector>589 sparse_tensor.print %5 : tensor<?x?xf64, #DCSR>590 sparse_tensor.print %6 : tensor<4x4xf64, #DCSR>591 sparse_tensor.print %7 : tensor<4x4xf64, #DCSR>592 sparse_tensor.print %8 : tensor<4x4xf64, #DCSR>593 sparse_tensor.print %9 : tensor<4x4xf64, #DCSR>594 sparse_tensor.print %10 : tensor<4x4xi8, #DCSR>595 sparse_tensor.print %11 : tensor<4x4xf64, #DCSR>596 597 // Release the resources.598 bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>599 bufferization.dealloc_tensor %sv2 : tensor<?xf64, #SparseVector>600 bufferization.dealloc_tensor %sv1_si : tensor<?xi32, #SparseVector>601 bufferization.dealloc_tensor %sv2_si : tensor<?xi32, #SparseVector>602 bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #DCSR>603 bufferization.dealloc_tensor %sm2 : tensor<?x?xf64, #DCSR>604 bufferization.dealloc_tensor %sm3 : tensor<4x4xf64, #DCSR>605 bufferization.dealloc_tensor %sm4 : tensor<4x4xf64, #DCSR>606 bufferization.dealloc_tensor %0 : tensor<?xi32, #SparseVector>607 bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector>608 bufferization.dealloc_tensor %2 : tensor<?xf64, #SparseVector>609 bufferization.dealloc_tensor %3 : tensor<?xi32, #SparseVector>610 bufferization.dealloc_tensor %5 : tensor<?x?xf64, #DCSR>611 bufferization.dealloc_tensor %6 : tensor<4x4xf64, #DCSR>612 bufferization.dealloc_tensor %7 : tensor<4x4xf64, #DCSR>613 bufferization.dealloc_tensor %8 : tensor<4x4xf64, #DCSR>614 bufferization.dealloc_tensor %9 : tensor<4x4xf64, #DCSR>615 bufferization.dealloc_tensor %10 : tensor<4x4xi8, #DCSR>616 bufferization.dealloc_tensor %11 : tensor<4x4xf64, #DCSR>617 return618 }619}620