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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=false25// 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 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#DCSR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed, d1 : compressed)}>35 36//37// Traits for 2-d tensor (aka matrix) operations.38//39#trait_scale = {40 indexing_maps = [41 affine_map<(i,j) -> (i,j)>, // A (in)42 affine_map<(i,j) -> (i,j)> // X (out)43 ],44 iterator_types = ["parallel", "parallel"],45 doc = "X(i,j) = A(i,j) * 2.0"46}47#trait_scale_inpl = {48 indexing_maps = [49 affine_map<(i,j) -> (i,j)> // X (out)50 ],51 iterator_types = ["parallel", "parallel"],52 doc = "X(i,j) *= 2.0"53}54#trait_op = {55 indexing_maps = [56 affine_map<(i,j) -> (i,j)>, // A (in)57 affine_map<(i,j) -> (i,j)>, // B (in)58 affine_map<(i,j) -> (i,j)> // X (out)59 ],60 iterator_types = ["parallel", "parallel"],61 doc = "X(i,j) = A(i,j) OP B(i,j)"62}63 64module {65 // Scales a sparse matrix into a new sparse matrix.66 func.func @matrix_scale(%arga: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {67 %s = arith.constant 2.0 : f6468 %c0 = arith.constant 0 : index69 %c1 = arith.constant 1 : index70 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>71 %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>72 %xm = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR>73 %0 = linalg.generic #trait_scale74 ins(%arga: tensor<?x?xf64, #DCSR>)75 outs(%xm: tensor<?x?xf64, #DCSR>) {76 ^bb(%a: f64, %x: f64):77 %1 = arith.mulf %a, %s : f6478 linalg.yield %1 : f6479 } -> tensor<?x?xf64, #DCSR>80 return %0 : tensor<?x?xf64, #DCSR>81 }82 83 // Scales a sparse matrix in place.84 func.func @matrix_scale_inplace(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {85 %s = arith.constant 2.0 : f6486 %0 = linalg.generic #trait_scale_inpl87 outs(%argx: tensor<?x?xf64, #DCSR>) {88 ^bb(%x: f64):89 %1 = arith.mulf %x, %s : f6490 linalg.yield %1 : f6491 } -> tensor<?x?xf64, #DCSR>92 return %0 : tensor<?x?xf64, #DCSR>93 }94 95 // Adds two sparse matrices element-wise into a new sparse matrix.96 func.func @matrix_add(%arga: tensor<?x?xf64, #DCSR>,97 %argb: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {98 %c0 = arith.constant 0 : index99 %c1 = arith.constant 1 : index100 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>101 %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>102 %xv = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR>103 %0 = linalg.generic #trait_op104 ins(%arga, %argb: tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>)105 outs(%xv: tensor<?x?xf64, #DCSR>) {106 ^bb(%a: f64, %b: f64, %x: f64):107 %1 = arith.addf %a, %b : f64108 linalg.yield %1 : f64109 } -> tensor<?x?xf64, #DCSR>110 return %0 : tensor<?x?xf64, #DCSR>111 }112 113 // Multiplies two sparse matrices element-wise into a new sparse matrix.114 func.func @matrix_mul(%arga: tensor<?x?xf64, #DCSR>,115 %argb: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {116 %c0 = arith.constant 0 : index117 %c1 = arith.constant 1 : index118 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #DCSR>119 %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #DCSR>120 %xv = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DCSR>121 %0 = linalg.generic #trait_op122 ins(%arga, %argb: tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>)123 outs(%xv: tensor<?x?xf64, #DCSR>) {124 ^bb(%a: f64, %b: f64, %x: f64):125 %1 = arith.mulf %a, %b : f64126 linalg.yield %1 : f64127 } -> tensor<?x?xf64, #DCSR>128 return %0 : tensor<?x?xf64, #DCSR>129 }130 131 // Driver method to call and verify matrix kernels.132 func.func @main() {133 %c0 = arith.constant 0 : index134 %d1 = arith.constant 1.1 : f64135 136 // Setup sparse matrices.137 %m1 = arith.constant sparse<138 [ [0,0], [0,1], [1,7], [2,2], [2,4], [2,7], [3,0], [3,2], [3,3] ],139 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]140 > : tensor<4x8xf64>141 %m2 = arith.constant sparse<142 [ [0,0], [0,7], [1,0], [1,6], [2,1], [2,7] ],143 [6.0, 5.0, 4.0, 3.0, 2.0, 1.0 ]144 > : tensor<4x8xf64>145 %sm1 = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>146 // TODO: Use %sm1 when we support sparse tensor copies.147 %sm1_dup = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>148 %sm2 = sparse_tensor.convert %m2 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>149 150 // Call sparse matrix kernels.151 %0 = call @matrix_scale(%sm1)152 : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>153 %1 = call @matrix_scale_inplace(%sm1_dup)154 : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>155 %2 = call @matrix_add(%1, %sm2)156 : (tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>157 %3 = call @matrix_mul(%1, %sm2)158 : (tensor<?x?xf64, #DCSR>, tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>159 160 //161 // Verify the results.162 //163 // CHECK: ---- Sparse Tensor ----164 // CHECK-NEXT: nse = 9165 // CHECK-NEXT: dim = ( 4, 8 )166 // CHECK-NEXT: lvl = ( 4, 8 )167 // CHECK-NEXT: pos[0] : ( 0, 4 )168 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )169 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )170 // CHECK-NEXT: crd[1] : ( 0, 1, 7, 2, 4, 7, 0, 2, 3 )171 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9 )172 // CHECK-NEXT: ----173 //174 sparse_tensor.print %sm1 : tensor<?x?xf64, #DCSR>175 176 //177 // CHECK: ---- Sparse Tensor ----178 // CHECK-NEXT: nse = 6179 // CHECK-NEXT: dim = ( 4, 8 )180 // CHECK-NEXT: lvl = ( 4, 8 )181 // CHECK-NEXT: pos[0] : ( 0, 3 )182 // CHECK-NEXT: crd[0] : ( 0, 1, 2 )183 // CHECK-NEXT: pos[1] : ( 0, 2, 4, 6 )184 // CHECK-NEXT: crd[1] : ( 0, 7, 0, 6, 1, 7 )185 // CHECK-NEXT: values : ( 6, 5, 4, 3, 2, 1 )186 // CHECK-NEXT: ----187 //188 sparse_tensor.print %sm2 : tensor<?x?xf64, #DCSR>189 190 //191 // CHECK: ---- Sparse Tensor ----192 // CHECK-NEXT: nse = 9193 // CHECK-NEXT: dim = ( 4, 8 )194 // CHECK-NEXT: lvl = ( 4, 8 )195 // CHECK-NEXT: pos[0] : ( 0, 4 )196 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )197 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )198 // CHECK-NEXT: crd[1] : ( 0, 1, 7, 2, 4, 7, 0, 2, 3 )199 // CHECK-NEXT: values : ( 2, 4, 6, 8, 10, 12, 14, 16, 18 )200 // CHECK-NEXT: ----201 //202 sparse_tensor.print %0 : tensor<?x?xf64, #DCSR>203 204 //205 // CHECK: ---- Sparse Tensor ----206 // CHECK-NEXT: nse = 9207 // CHECK-NEXT: dim = ( 4, 8 )208 // CHECK-NEXT: lvl = ( 4, 8 )209 // CHECK-NEXT: pos[0] : ( 0, 4 )210 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )211 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )212 // CHECK-NEXT: crd[1] : ( 0, 1, 7, 2, 4, 7, 0, 2, 3 )213 // CHECK-NEXT: values : ( 2, 4, 6, 8, 10, 12, 14, 16, 18 )214 // CHECK-NEXT: ----215 //216 sparse_tensor.print %1 : tensor<?x?xf64, #DCSR>217 218 //219 // CHECK: ---- Sparse Tensor ----220 // CHECK-NEXT: nse = 13221 // CHECK-NEXT: dim = ( 4, 8 )222 // CHECK-NEXT: lvl = ( 4, 8 )223 // CHECK-NEXT: pos[0] : ( 0, 4 )224 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )225 // CHECK-NEXT: pos[1] : ( 0, 3, 6, 10, 13 )226 // CHECK-NEXT: crd[1] : ( 0, 1, 7, 0, 6, 7, 1, 2, 4, 7, 0, 2, 3 )227 // CHECK-NEXT: values : ( 8, 4, 5, 4, 3, 6, 2, 8, 10, 13, 14, 16, 18 )228 // CHECK-NEXT: ----229 //230 sparse_tensor.print %2 : tensor<?x?xf64, #DCSR>231 232 //233 // CHECK: ---- Sparse Tensor ----234 // CHECK-NEXT: nse = 2235 // CHECK-NEXT: dim = ( 4, 8 )236 // CHECK-NEXT: lvl = ( 4, 8 )237 // CHECK-NEXT: pos[0] : ( 0, 2 )238 // CHECK-NEXT: crd[0] : ( 0, 2 )239 // CHECK-NEXT: pos[1] : ( 0, 1, 2 )240 // CHECK-NEXT: crd[1] : ( 0, 7 )241 // CHECK-NEXT: values : ( 12, 12 )242 // CHECK-NEXT: ----243 //244 sparse_tensor.print %3 : tensor<?x?xf64, #DCSR>245 246 // Release the resources.247 bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #DCSR>248 bufferization.dealloc_tensor %sm1_dup : tensor<?x?xf64, #DCSR>249 bufferization.dealloc_tensor %sm2 : tensor<?x?xf64, #DCSR>250 bufferization.dealloc_tensor %0 : tensor<?x?xf64, #DCSR>251 bufferization.dealloc_tensor %2 : tensor<?x?xf64, #DCSR>252 bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR>253 return254 }255}256