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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 enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true29// RUN: %{compile} | %{run} | FileCheck %s30 31#BSR_row_rowmajor = #sparse_tensor.encoding<{32 map = (i, j) ->33 ( i floordiv 3 : dense34 , j floordiv 4 : compressed35 , i mod 3 : dense36 , j mod 4 : dense37 )38}>39 40#BSR_row_colmajor = #sparse_tensor.encoding<{41 map = (i, j) ->42 ( i floordiv 3 : dense43 , j floordiv 4 : compressed44 , j mod 4 : dense45 , i mod 3 : dense46 )47}>48 49#BSR_col_rowmajor = #sparse_tensor.encoding<{50 map = (i, j) ->51 ( j floordiv 4 : dense52 , i floordiv 3 : compressed53 , i mod 3 : dense54 , j mod 4 : dense55 )56}>57 58#BSR_col_colmajor = #sparse_tensor.encoding<{59 map = (i, j) ->60 ( j floordiv 4 : dense61 , i floordiv 3 : compressed62 , j mod 4 : dense63 , i mod 3 : dense64 )65}>66 67//68// Example 3x4 block storage of a 6x16 matrix:69//70// +---------+---------+---------+---------+71// | 1 2 . . | . . . . | . . . . | . . . . |72// | . . . . | . . . . | . . . . | . . . . |73// | . . . 3 | . . . . | . . . . | . . . . |74// +---------+---------+---------+---------+75// | . . . . | . . . . | 4 5 . . | . . . . |76// | . . . . | . . . . | . . . . | . . . . |77// | . . . . | . . . . | . . 6 7 | . . . . |78// +---------+---------+---------+---------+79//80// Storage for CSR block storage. Note that this essentially81// provides CSR storage of 2x4 blocks with either row-major82// or column-major storage within each 3x4 block of elements.83//84// positions[1] : 0 1 285// coordinates[1] : 0 286// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,87// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major]88//89// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3,90// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major]91//92// Storage for CSC block storage. Note that this essentially93// provides CSC storage of 4x2 blocks with either row-major94// or column-major storage within each 3x4 block of elements.95//96// positions[1] : 0 1 1 2 297// coordinates[1] : 0 198// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,99// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major]100//101// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3,102// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major]103//104module {105 106 107 //108 // CHECK: ---- Sparse Tensor ----109 // CHECK-NEXT: nse = 24110 // CHECK-NEXT: dim = ( 6, 16 )111 // CHECK-NEXT: lvl = ( 2, 4, 3, 4 )112 // CHECK-NEXT: pos[1] : ( 0, 1, 2 )113 // CHECK-NEXT: crd[1] : ( 0, 2 )114 // CHECK-NEXT: values : ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 )115 // CHECK-NEXT: ----116 //117 func.func @foo1() {118 // Build.119 %c0 = arith.constant 0 : index120 %f0 = arith.constant 0.0 : f64121 %m = arith.constant sparse<122 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ],123 [ 1., 2., 3., 4., 5., 6., 7.]124 > : tensor<6x16xf64>125 %s1 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_rowmajor>126 // Test.127 sparse_tensor.print %s1 : tensor<?x?xf64, #BSR_row_rowmajor>128 // Release.129 bufferization.dealloc_tensor %s1: tensor<?x?xf64, #BSR_row_rowmajor>130 return131 }132 133 //134 // CHECK-NEXT: ---- Sparse Tensor ----135 // CHECK-NEXT: nse = 24136 // CHECK-NEXT: dim = ( 6, 16 )137 // CHECK-NEXT: lvl = ( 2, 4, 4, 3 )138 // CHECK-NEXT: pos[1] : ( 0, 1, 2 )139 // CHECK-NEXT: crd[1] : ( 0, 2 )140 // CHECK-NEXT: values : ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 )141 // CHECK-NEXT: ----142 //143 func.func @foo2() {144 // Build.145 %c0 = arith.constant 0 : index146 %f0 = arith.constant 0.0 : f64147 %m = arith.constant sparse<148 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ],149 [ 1., 2., 3., 4., 5., 6., 7.]150 > : tensor<6x16xf64>151 %s2 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_colmajor>152 // Test.153 sparse_tensor.print %s2 : tensor<?x?xf64, #BSR_row_colmajor>154 // Release.155 bufferization.dealloc_tensor %s2: tensor<?x?xf64, #BSR_row_colmajor>156 return157 }158 159 //160 // CHECK-NEXT: ---- Sparse Tensor ----161 // CHECK-NEXT: nse = 24162 // CHECK-NEXT: dim = ( 6, 16 )163 // CHECK-NEXT: lvl = ( 4, 2, 3, 4 )164 // CHECK-NEXT: pos[1] : ( 0, 1, 1, 2, 2 )165 // CHECK-NEXT: crd[1] : ( 0, 1 )166 // CHECK-NEXT: values : ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 )167 // CHECK-NEXT: ----168 //169 func.func @foo3() {170 // Build.171 %c0 = arith.constant 0 : index172 %f0 = arith.constant 0.0 : f64173 %m = arith.constant sparse<174 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ],175 [ 1., 2., 3., 4., 5., 6., 7.]176 > : tensor<6x16xf64>177 %s3 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_rowmajor>178 // Test.179 sparse_tensor.print %s3 : tensor<?x?xf64, #BSR_col_rowmajor>180 // Release.181 bufferization.dealloc_tensor %s3: tensor<?x?xf64, #BSR_col_rowmajor>182 return183 }184 185 //186 // CHECK-NEXT: ---- Sparse Tensor ----187 // CHECK-NEXT: nse = 24188 // CHECK-NEXT: dim = ( 6, 16 )189 // CHECK-NEXT: lvl = ( 4, 2, 4, 3 )190 // CHECK-NEXT: pos[1] : ( 0, 1, 1, 2, 2 )191 // CHECK-NEXT: crd[1] : ( 0, 1 )192 // CHECK-NEXT: values : ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 )193 // CHECK-NEXT: ----194 //195 func.func @foo4() {196 // Build.197 %c0 = arith.constant 0 : index198 %f0 = arith.constant 0.0 : f64199 %m = arith.constant sparse<200 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ],201 [ 1., 2., 3., 4., 5., 6., 7.]202 > : tensor<6x16xf64>203 %s4 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_colmajor>204 // Test.205 sparse_tensor.print %s4 : tensor<?x?xf64, #BSR_col_colmajor>206 // Release.207 bufferization.dealloc_tensor %s4: tensor<?x?xf64, #BSR_col_colmajor>208 return209 }210 211 func.func @main() {212 call @foo1() : () -> ()213 call @foo2() : () -> ()214 call @foo3() : () -> ()215 call @foo4() : () -> ()216 return217 }218}219