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1// NOTE: this test requires gpu-sm802//3// DEFINE: %{compile} = mlir-opt %s \4// DEFINE:   --sparsifier="enable-gpu-libgen gpu-triple=nvptx64-nvidia-cuda gpu-chip=sm_80 gpu-features=+ptx71 gpu-format=%gpu_compilation_format5// DEFINE: %{run} = mlir-runner \6// DEFINE:   --shared-libs=%mlir_cuda_runtime \7// DEFINE:   --shared-libs=%mlir_c_runner_utils \8// DEFINE:   --e main --entry-point-result=void \9// DEFINE: | FileCheck %s10//11// with RT lib (SoA COO):12//13// RUN: %{compile} enable-runtime-library=true"  | %{run}14//15// without RT lib (AoS COO): note, may fall back to CPU16//17// RUN: %{compile} enable-runtime-library=false" | %{run}18 19#SortedCOO = #sparse_tensor.encoding<{20  map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)21}>22 23#CSR = #sparse_tensor.encoding<{24  map = (d0, d1) -> (d0 : dense, d1 : compressed),25  posWidth = 32,26  crdWidth = 3227}>28 29#CSC = #sparse_tensor.encoding<{30  map = (d0, d1) -> (d1 : dense, d0 : compressed),31  posWidth = 64,32  crdWidth = 6433}>34 35module {36  llvm.func @mgpuCreateSparseEnv()37  llvm.func @mgpuDestroySparseEnv()38 39  // Compute matrix vector y = Ax on COO with default index coordinates.40  func.func @matvecCOO(%A: tensor<?x?xf64, #SortedCOO>, %x: tensor<?xf64>, %y_in: tensor<?xf64>) -> tensor<?xf64> {41    %y_out = linalg.matvec42      ins(%A, %x: tensor<?x?xf64, #SortedCOO>, tensor<?xf64>)43      outs(%y_in: tensor<?xf64>) -> tensor<?xf64>44    return %y_out : tensor<?xf64>45  }46 47  // Compute matrix vector y = Ax on CSR with 32-bit positions and coordinates.48  func.func @matvecCSR(%A: tensor<?x?xf64, #CSR>, %x: tensor<?xf64>, %y_in: tensor<?xf64>) -> tensor<?xf64> {49    %y_out = linalg.matvec50      ins(%A, %x: tensor<?x?xf64, #CSR>, tensor<?xf64>)51      outs(%y_in: tensor<?xf64>) -> tensor<?xf64>52    return %y_out : tensor<?xf64>53  }54 55  // Compute matrix vector y = Ax on CSC with 64-bit positions and coordinates.56  func.func @matvecCSC(%A: tensor<?x?xf64, #CSC>, %x: tensor<?xf64>, %y_in: tensor<?xf64>) -> tensor<?xf64> {57    %y_out = linalg.matvec58      ins(%A, %x: tensor<?x?xf64, #CSC>, tensor<?xf64>)59      outs(%y_in: tensor<?xf64>) -> tensor<?xf64>60    return %y_out : tensor<?xf64>61  }62 63  func.func @main() {64    llvm.call @mgpuCreateSparseEnv() : () -> ()65    %f0 = arith.constant 0.0 : f6466    %f1 = arith.constant 1.0 : f6467    %c0 = arith.constant 0 : index68    %c1 = arith.constant 1 : index69 70    // Stress test with a dense matrix DA.71    %DA = tensor.generate {72    ^bb0(%i: index, %j: index):73      %k = arith.addi %i, %j : index74      %l = arith.index_cast %k : index to i6475      %f = arith.uitofp %l : i64 to f6476      tensor.yield %f : f6477    } : tensor<64x64xf64>78 79    // Convert to a "sparse" m x n matrix A.80    %Acoo = sparse_tensor.convert %DA : tensor<64x64xf64> to tensor<?x?xf64, #SortedCOO>81    %Acsr = sparse_tensor.convert %DA : tensor<64x64xf64> to tensor<?x?xf64, #CSR>82    %Acsc = sparse_tensor.convert %DA : tensor<64x64xf64> to tensor<?x?xf64, #CSC>83 84    // Initialize dense vector with n elements:85    //   (1, 2, 3, 4, ..., n)86    %d1 = tensor.dim %Acoo, %c1 : tensor<?x?xf64, #SortedCOO>87    %x = tensor.generate %d1 {88    ^bb0(%i : index):89      %k = arith.addi %i, %c1 : index90      %j = arith.index_cast %k : index to i6491      %f = arith.uitofp %j : i64 to f6492      tensor.yield %f : f6493    } : tensor<?xf64>94 95    // Initialize dense vectors to m zeros and m ones.96    %d0 = tensor.dim %Acoo, %c0 : tensor<?x?xf64, #SortedCOO>97    %y0 = tensor.generate %d0 {98    ^bb0(%i : index):99      tensor.yield %f0 : f64100    } : tensor<?xf64>101    %y1 = tensor.generate %d0 {102    ^bb0(%i : index):103      tensor.yield %f1 : f64104    } : tensor<?xf64>105 106    // Call the kernels.107    %0 = call @matvecCOO(%Acoo, %x, %y0) : (tensor<?x?xf64, #SortedCOO>,108                                            tensor<?xf64>,109					    tensor<?xf64>) -> tensor<?xf64>110    %1 = call @matvecCSR(%Acsr, %x, %y0) : (tensor<?x?xf64, #CSR>,111                                            tensor<?xf64>,112					    tensor<?xf64>) -> tensor<?xf64>113    %2 = call @matvecCSC(%Acsc, %x, %y0) : (tensor<?x?xf64, #CSC>,114                                            tensor<?xf64>,115					    tensor<?xf64>) -> tensor<?xf64>116    %3 = call @matvecCOO(%Acoo, %x, %y1) : (tensor<?x?xf64, #SortedCOO>,117                                            tensor<?xf64>,118					    tensor<?xf64>) -> tensor<?xf64>119    %4 = call @matvecCSR(%Acsr, %x, %y1) : (tensor<?x?xf64, #CSR>,120                                            tensor<?xf64>,121					    tensor<?xf64>) -> tensor<?xf64>122    %5 = call @matvecCSC(%Acsc, %x, %y1) : (tensor<?x?xf64, #CSC>,123                                            tensor<?xf64>,124					    tensor<?xf64>) -> tensor<?xf64>125 126    //127    // Sanity check on the results.128    //129    // CHECK-COUNT-3: ( 87360, 89440, 91520, 93600, 95680, 97760, 99840, 101920, 104000, 106080, 108160, 110240, 112320, 114400, 116480, 118560, 120640, 122720, 124800, 126880, 128960, 131040, 133120, 135200, 137280, 139360, 141440, 143520, 145600, 147680, 149760, 151840, 153920, 156000, 158080, 160160, 162240, 164320, 166400, 168480, 170560, 172640, 174720, 176800, 178880, 180960, 183040, 185120, 187200, 189280, 191360, 193440, 195520, 197600, 199680, 201760, 203840, 205920, 208000, 210080, 212160, 214240, 216320, 218400 )130    //131    // CHECK-COUNT-3: ( 87361, 89441, 91521, 93601, 95681, 97761, 99841, 101921, 104001, 106081, 108161, 110241, 112321, 114401, 116481, 118561, 120641, 122721, 124801, 126881, 128961, 131041, 133121, 135201, 137281, 139361, 141441, 143521, 145601, 147681, 149761, 151841, 153921, 156001, 158081, 160161, 162241, 164321, 166401, 168481, 170561, 172641, 174721, 176801, 178881, 180961, 183041, 185121, 187201, 189281, 191361, 193441, 195521, 197601, 199681, 201761, 203841, 205921, 208001, 210081, 212161, 214241, 216321, 218401 )132    //133    %pb0 = vector.transfer_read %0[%c0], %f0 : tensor<?xf64>, vector<64xf64>134    vector.print %pb0 : vector<64xf64>135    %pb1 = vector.transfer_read %1[%c0], %f0 : tensor<?xf64>, vector<64xf64>136    vector.print %pb1 : vector<64xf64>137    %pb2 = vector.transfer_read %2[%c0], %f0 : tensor<?xf64>, vector<64xf64>138    vector.print %pb2 : vector<64xf64>139    %pb3 = vector.transfer_read %3[%c0], %f0 : tensor<?xf64>, vector<64xf64>140    vector.print %pb3 : vector<64xf64>141    %pb4 = vector.transfer_read %4[%c0], %f0 : tensor<?xf64>, vector<64xf64>142    vector.print %pb4 : vector<64xf64>143    %pb5 = vector.transfer_read %5[%c0], %f0 : tensor<?xf64>, vector<64xf64>144    vector.print %pb5 : vector<64xf64>145 146    // Release the resources.147    bufferization.dealloc_tensor %Acoo : tensor<?x?xf64, #SortedCOO>148    bufferization.dealloc_tensor %Acsr : tensor<?x?xf64, #CSR>149    bufferization.dealloc_tensor %Acsc : tensor<?x?xf64, #CSC>150 151    llvm.call @mgpuDestroySparseEnv() : () -> ()152    return153  }154}155