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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=false enable-buffer-initialization=true25// 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// 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#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>35 36#trait_op = {37 indexing_maps = [38 affine_map<(i) -> (i)>, // a (in)39 affine_map<(i) -> (i)>, // b (in)40 affine_map<(i) -> (i)> // x (out)41 ],42 iterator_types = ["parallel"],43 doc = "x(i) = a(i) OP b(i)"44}45 46module {47 func.func @cadd(%arga: tensor<?xcomplex<f32>, #SparseVector>,48 %argb: tensor<?xcomplex<f32>, #SparseVector>)49 -> tensor<?xcomplex<f32>, #SparseVector> {50 %c = arith.constant 0 : index51 %d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>52 %xv = tensor.empty(%d) : tensor<?xcomplex<f32>, #SparseVector>53 %0 = linalg.generic #trait_op54 ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,55 tensor<?xcomplex<f32>, #SparseVector>)56 outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {57 ^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):58 %1 = complex.add %a, %b : complex<f32>59 linalg.yield %1 : complex<f32>60 } -> tensor<?xcomplex<f32>, #SparseVector>61 return %0 : tensor<?xcomplex<f32>, #SparseVector>62 }63 64 func.func @cmul(%arga: tensor<?xcomplex<f32>, #SparseVector>,65 %argb: tensor<?xcomplex<f32>, #SparseVector>)66 -> tensor<?xcomplex<f32>, #SparseVector> {67 %c = arith.constant 0 : index68 %d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>69 %xv = tensor.empty(%d) : tensor<?xcomplex<f32>, #SparseVector>70 %0 = linalg.generic #trait_op71 ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,72 tensor<?xcomplex<f32>, #SparseVector>)73 outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {74 ^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):75 %1 = complex.mul %a, %b : complex<f32>76 linalg.yield %1 : complex<f32>77 } -> tensor<?xcomplex<f32>, #SparseVector>78 return %0 : tensor<?xcomplex<f32>, #SparseVector>79 }80 81 // Driver method to call and verify complex kernels.82 func.func @main() {83 // Setup sparse vectors.84 %v1 = arith.constant sparse<85 [ [0], [28], [31] ],86 [ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f32>>87 %v2 = arith.constant sparse<88 [ [1], [28], [31] ],89 [ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f32>>90 %sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>91 %sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>92 93 // Call sparse vector kernels.94 %0 = call @cadd(%sv1, %sv2)95 : (tensor<?xcomplex<f32>, #SparseVector>,96 tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>97 %1 = call @cmul(%sv1, %sv2)98 : (tensor<?xcomplex<f32>, #SparseVector>,99 tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>100 101 //102 // Verify the results.103 //104 // CHECK: ---- Sparse Tensor ----105 // CHECK-NEXT: nse = 4106 // CHECK-NEXT: dim = ( 32 )107 // CHECK-NEXT: lvl = ( 32 )108 // CHECK-NEXT: pos[0] : ( 0, 4 )109 // CHECK-NEXT: crd[0] : ( 0, 1, 28, 31 )110 // CHECK-NEXT: values : ( ( 511.13, 2 ), ( 1, 0 ), ( 5, 4 ), ( 8, 6 ) )111 // CHECK-NEXT: ----112 //113 // CHECK-NEXT: ---- Sparse Tensor ----114 // CHECK-NEXT: nse = 2115 // CHECK-NEXT: dim = ( 32 )116 // CHECK-NEXT: lvl = ( 32 )117 // CHECK-NEXT: pos[0] : ( 0, 2 )118 // CHECK-NEXT: crd[0] : ( 28, 31 )119 // CHECK-NEXT: values : ( ( 6, 8 ), ( 15, 18 ) )120 // CHECK-NEXT: ----121 //122 sparse_tensor.print %0 : tensor<?xcomplex<f32>, #SparseVector>123 sparse_tensor.print %1 : tensor<?xcomplex<f32>, #SparseVector>124 125 // Release the resources.126 bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f32>, #SparseVector>127 bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f32>, #SparseVector>128 bufferization.dealloc_tensor %0 : tensor<?xcomplex<f32>, #SparseVector>129 bufferization.dealloc_tensor %1 : tensor<?xcomplex<f32>, #SparseVector>130 return131 }132}133