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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#SortedCOO = #sparse_tensor.encoding<{35 map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)36}>37 38#SortedCOOSoA = #sparse_tensor.encoding<{39 map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa))40}>41 42#CSR = #sparse_tensor.encoding<{43 map = (d0, d1) -> (d0 : dense, d1 : compressed)44}>45 46#trait = {47 indexing_maps = [48 affine_map<(i,j) -> (i,j)>, // A49 affine_map<(i,j) -> (i,j)>, // B50 affine_map<(i,j) -> (i,j)> // X (out)51 ],52 iterator_types = ["parallel", "parallel"],53 doc = "X(i,j) = A(i,j) + B(i,j)"54}55 56module {57 func.func @add_coo_csr(%arga: tensor<8x8xf32, #CSR>,58 %argb: tensor<8x8xf32, #SortedCOOSoA>)59 -> tensor<8x8xf32> {60 %empty = tensor.empty() : tensor<8x8xf32>61 %zero = arith.constant 0.000000e+00 : f3262 %init = linalg.fill63 ins(%zero : f32)64 outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>65 %0 = linalg.generic #trait66 ins(%arga, %argb: tensor<8x8xf32, #CSR>,67 tensor<8x8xf32, #SortedCOOSoA>)68 outs(%init: tensor<8x8xf32>) {69 ^bb(%a: f32, %b: f32, %x: f32):70 %0 = arith.addf %a, %b : f3271 linalg.yield %0 : f3272 } -> tensor<8x8xf32>73 return %0 : tensor<8x8xf32>74 }75 76 func.func @add_coo_coo(%arga: tensor<8x8xf32, #SortedCOO>,77 %argb: tensor<8x8xf32, #SortedCOOSoA>)78 -> tensor<8x8xf32> {79 %empty = tensor.empty() : tensor<8x8xf32>80 %zero = arith.constant 0.000000e+00 : f3281 %init = linalg.fill82 ins(%zero : f32)83 outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>84 %0 = linalg.generic #trait85 ins(%arga, %argb: tensor<8x8xf32, #SortedCOO>,86 tensor<8x8xf32, #SortedCOOSoA>)87 outs(%init: tensor<8x8xf32>) {88 ^bb(%a: f32, %b: f32, %x: f32):89 %0 = arith.addf %a, %b : f3290 linalg.yield %0 : f3291 } -> tensor<8x8xf32>92 return %0 : tensor<8x8xf32>93 }94 95 func.func @add_coo_coo_out_coo(%arga: tensor<8x8xf32, #SortedCOO>,96 %argb: tensor<8x8xf32, #SortedCOOSoA>)97 -> tensor<8x8xf32, #SortedCOOSoA> {98 %init = tensor.empty() : tensor<8x8xf32, #SortedCOOSoA>99 %0 = linalg.generic #trait100 ins(%arga, %argb: tensor<8x8xf32, #SortedCOO>,101 tensor<8x8xf32, #SortedCOOSoA>)102 outs(%init: tensor<8x8xf32, #SortedCOOSoA>) {103 ^bb(%a: f32, %b: f32, %x: f32):104 %0 = arith.addf %a, %b : f32105 linalg.yield %0 : f32106 } -> tensor<8x8xf32, #SortedCOOSoA>107 return %0 : tensor<8x8xf32, #SortedCOOSoA>108 }109 110 111 func.func @add_coo_dense(%arga: tensor<8x8xf32>,112 %argb: tensor<8x8xf32, #SortedCOOSoA>)113 -> tensor<8x8xf32> {114 %empty = tensor.empty() : tensor<8x8xf32>115 %zero = arith.constant 0.000000e+00 : f32116 %init = linalg.fill117 ins(%zero : f32)118 outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>119 %0 = linalg.generic #trait120 ins(%arga, %argb: tensor<8x8xf32>,121 tensor<8x8xf32, #SortedCOOSoA>)122 outs(%init: tensor<8x8xf32>) {123 ^bb(%a: f32, %b: f32, %x: f32):124 %0 = arith.addf %a, %b : f32125 linalg.yield %0 : f32126 } -> tensor<8x8xf32>127 return %0 : tensor<8x8xf32>128 }129 130 func.func @main() {131 %c0 = arith.constant 0 : index132 %c1 = arith.constant 1 : index133 %c8 = arith.constant 8 : index134 135 %A = arith.constant dense<136 [ [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ],137 [ 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1 ],138 [ 2.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2 ],139 [ 3.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3 ],140 [ 4.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4 ],141 [ 5.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5 ],142 [ 6.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6 ],143 [ 7.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7 ] ]144 > : tensor<8x8xf32>145 %B = arith.constant dense<146 [ [ 7.8, 2.8, 3.8, 0.8, 3.8, 0.1, 7.8, 8.8 ],147 [ 3.3, 2.3, 1.3, 4.3, 3.3, 6.3, 9.3, 8.3 ],148 [ 6.6, 2.6, 3.6, 4.6, 3.6, 6.6, 7.6, 7.6 ],149 [ 1.0, 3.0, 3.0, 4.0, 3.0, 6.0, 7.0, 8.0 ],150 [ 0.1, 2.1, 3.1, 4.1, 3.1, 6.1, 7.1, 8.1 ],151 [ 4.4, 2.4, 3.4, 4.4, 3.4, 6.4, 8.4, 8.4 ],152 [ 5.5, 3.5, 1.5, 4.5, 3.5, 6.5, 7.5, 8.5 ],153 [ 7.7, 2.7, 3.7, 0.7, 5.7, 3.7, 3.7, 0.7 ] ]154 > : tensor<8x8xf32>155 156 // Stress test with a "sparse" version of A and B.157 %CSR_A = sparse_tensor.convert %A158 : tensor<8x8xf32> to tensor<8x8xf32, #CSR>159 %COO_A = sparse_tensor.convert %A160 : tensor<8x8xf32> to tensor<8x8xf32, #SortedCOO>161 %COO_B = sparse_tensor.convert %B162 : tensor<8x8xf32> to tensor<8x8xf32, #SortedCOOSoA>163 164 %C1 = call @add_coo_dense(%A, %COO_B) : (tensor<8x8xf32>,165 tensor<8x8xf32, #SortedCOOSoA>)166 -> tensor<8x8xf32>167 %C2 = call @add_coo_csr(%CSR_A, %COO_B) : (tensor<8x8xf32, #CSR>,168 tensor<8x8xf32, #SortedCOOSoA>)169 -> tensor<8x8xf32>170 %C3 = call @add_coo_coo(%COO_A, %COO_B) : (tensor<8x8xf32, #SortedCOO>,171 tensor<8x8xf32, #SortedCOOSoA>)172 -> tensor<8x8xf32>173 %COO_RET = call @add_coo_coo_out_coo(%COO_A, %COO_B) : (tensor<8x8xf32, #SortedCOO>,174 tensor<8x8xf32, #SortedCOOSoA>)175 -> tensor<8x8xf32, #SortedCOOSoA>176 %C4 = sparse_tensor.convert %COO_RET : tensor<8x8xf32, #SortedCOOSoA> to tensor<8x8xf32>177 178 //179 // Verify computed matrix C.180 //181 // CHECK-COUNT-4: ( 8.8, 4.8, 6.8, 4.8, 8.8, 6.1, 14.8, 16.8 )182 // CHECK-NEXT-COUNT-4: ( 4.4, 4.4, 4.4, 8.4, 8.4, 12.4, 16.4, 16.4 )183 // CHECK-NEXT-COUNT-4: ( 8.8, 4.8, 6.8, 8.8, 8.8, 12.8, 14.8, 15.8 )184 // CHECK-NEXT-COUNT-4: ( 4.3, 5.3, 6.3, 8.3, 8.3, 12.3, 14.3, 16.3 )185 // CHECK-NEXT-COUNT-4: ( 4.5, 4.5, 6.5, 8.5, 8.5, 12.5, 14.5, 16.5 )186 // CHECK-NEXT-COUNT-4: ( 9.9, 4.9, 6.9, 8.9, 8.9, 12.9, 15.9, 16.9 )187 // CHECK-NEXT-COUNT-4: ( 12.1, 6.1, 5.1, 9.1, 9.1, 13.1, 15.1, 17.1 )188 // CHECK-NEXT-COUNT-4: ( 15.4, 5.4, 7.4, 5.4, 11.4, 10.4, 11.4, 9.4 )189 //190 %f0 = arith.constant 0.0 : f32191 scf.for %i = %c0 to %c8 step %c1 {192 %v1 = vector.transfer_read %C1[%i, %c0], %f0193 : tensor<8x8xf32>, vector<8xf32>194 %v2 = vector.transfer_read %C2[%i, %c0], %f0195 : tensor<8x8xf32>, vector<8xf32>196 %v3 = vector.transfer_read %C3[%i, %c0], %f0197 : tensor<8x8xf32>, vector<8xf32>198 %v4 = vector.transfer_read %C4[%i, %c0], %f0199 : tensor<8x8xf32>, vector<8xf32>200 vector.print %v1 : vector<8xf32>201 vector.print %v2 : vector<8xf32>202 vector.print %v3 : vector<8xf32>203 vector.print %v4 : vector<8xf32>204 }205 206 //207 // Ensure that COO-SoA output has the same values.208 //209 // CHECK: ---- Sparse Tensor ----210 // CHECK-NEXT: nse = 64211 // CHECK-NEXT: dim = ( 8, 8 )212 // CHECK-NEXT: lvl = ( 8, 8 )213 // CHECK-NEXT: pos[0] : ( 0, 64 )214 // CHECK-NEXT: crd[0] : ( 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2,215 // CHECK-SAME: 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4,216 // CHECK-SAME: 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7,217 // CHECK-SAME: 7, 7, 7, 7 )218 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3,219 // CHECK-SAME: 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7,220 // CHECK-SAME: 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3,221 // CHECK-SAME: 4, 5, 6, 7 )222 // CHECK-NEXT: values : ( 8.8, 4.8, 6.8, 4.8, 8.8, 6.1, 14.8, 16.8, 4.4, 4.4, 4.4, 8.4,223 // CHECK-SAME: 8.4, 12.4, 16.4, 16.4, 8.8, 4.8, 6.8, 8.8, 8.8, 12.8, 14.8,224 // CHECK-SAME: 15.8, 4.3, 5.3, 6.3, 8.3, 8.3, 12.3, 14.3, 16.3, 4.5, 4.5,225 // CHECK-SAME: 6.5, 8.5, 8.5, 12.5, 14.5, 16.5, 9.9, 4.9, 6.9, 8.9, 8.9,226 // CHECK-SAME: 12.9, 15.9, 16.9, 12.1, 6.1, 5.1, 9.1, 9.1, 13.1, 15.1, 17.1,227 // CHECK-SAME: 15.4, 5.4, 7.4, 5.4, 11.4, 10.4, 11.4, 9.4 )228 // CHECK-NEXT: ----229 //230 sparse_tensor.print %COO_RET : tensor<8x8xf32, #SortedCOOSoA>231 232 // Release resources.233 bufferization.dealloc_tensor %C1 : tensor<8x8xf32>234 bufferization.dealloc_tensor %C2 : tensor<8x8xf32>235 bufferization.dealloc_tensor %C3 : tensor<8x8xf32>236 bufferization.dealloc_tensor %C4 : tensor<8x8xf32>237 bufferization.dealloc_tensor %CSR_A : tensor<8x8xf32, #CSR>238 bufferization.dealloc_tensor %COO_A : tensor<8x8xf32, #SortedCOO>239 bufferization.dealloc_tensor %COO_B : tensor<8x8xf32, #SortedCOOSoA>240 bufferization.dealloc_tensor %COO_RET : tensor<8x8xf32, #SortedCOOSoA>241 242 243 return244 }245}246