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1//2// NOTE: this test requires gpu-sm803//4// RUN: mlir-opt %s \5// RUN: --sparsifier="enable-runtime-library=false parallelization-strategy=dense-outer-loop gpu-triple=nvptx64-nvidia-cuda gpu-chip=sm_80 gpu-features=+ptx71 gpu-format=%gpu_compilation_format" \6// RUN: | mlir-runner \7// RUN: --shared-libs=%mlir_cuda_runtime \8// RUN: --shared-libs=%mlir_c_runner_utils \9// RUN: --e main --entry-point-result=void \10// RUN: | FileCheck %s11 12#CSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>13 14module {15 // Compute matrix vector y = Ax16 func.func @matvec(%A: tensor<?x?xf64, #CSR>, %x: tensor<?xf64>, %y_in: tensor<?xf64>) -> tensor<?xf64> {17 %y_out = linalg.matvec18 ins(%A, %x: tensor<?x?xf64, #CSR>, tensor<?xf64>)19 outs(%y_in: tensor<?xf64>) -> tensor<?xf64>20 return %y_out : tensor<?xf64>21 }22 23 func.func @main() {24 %f0 = arith.constant 0.0 : f6425 %c0 = arith.constant 0 : index26 %c1 = arith.constant 1 : index27 28 // Stress test with a dense matrix DA.29 %DA = tensor.generate {30 ^bb0(%i: index, %j: index):31 %k = arith.addi %i, %j : index32 %l = arith.index_cast %k : index to i6433 %f = arith.uitofp %l : i64 to f6434 tensor.yield %f : f6435 } : tensor<1024x64xf64>36 37 // Convert to a "sparse" m x n matrix A.38 %A = sparse_tensor.convert %DA : tensor<1024x64xf64> to tensor<?x?xf64, #CSR>39 40 // Initialize dense vector with n elements:41 // (1, 2, 3, 4, ..., n)42 %d1 = tensor.dim %A, %c1 : tensor<?x?xf64, #CSR>43 %x = tensor.generate %d1 {44 ^bb0(%i : index):45 %k = arith.addi %i, %c1 : index46 %j = arith.index_cast %k : index to i6447 %f = arith.uitofp %j : i64 to f6448 tensor.yield %f : f6449 } : tensor<?xf64>50 51 // Initialize dense vector to m zeros.52 %d0 = tensor.dim %A, %c0 : tensor<?x?xf64, #CSR>53 %y = tensor.generate %d0 {54 ^bb0(%i : index):55 tensor.yield %f0 : f6456 } : tensor<?xf64>57 58 // Call the kernel.59 %0 = call @matvec(%A, %x, %y) : (tensor<?x?xf64, #CSR>, tensor<?xf64>, tensor<?xf64>) -> tensor<?xf64>60 61 //62 // Sanity check on results.63 //64 // CHECK: ( 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 )65 //66 %pb0 = vector.transfer_read %0[%c0], %f0 : tensor<?xf64>, vector<64xf64>67 vector.print %pb0 : vector<64xf64>68 69 // Release the resources.70 bufferization.dealloc_tensor %A : tensor<?x?xf64, #CSR>71 return72 }73}74