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, this 5// config could be moved to lit.local.cfg. However, there are downstream users that 6// do not use these LIT config files. Hence why this is kept inline. 7// 8// DEFINE: %{sparsifier_opts} = enable-runtime-library=true 9// 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_utils 13// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils 14// DEFINE: %{run_opts} = -e main -entry-point-result=void 15// 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// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false 22// RUN: %{compile} | %{run} | FileCheck %s 23// 24// Do the same run, but now with direct IR generation and vectorization. 25// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true 26// RUN: %{compile} | %{run} | FileCheck %s 27// 28// Do the same run, but now with direct IR generation and VLA vectorization. 29// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %} 30 31#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> 32 33#trait_op = { 34 indexing_maps = [ 35 affine_map<(i) -> (i)>, // a (in) 36 affine_map<(i) -> (i)>, // b (in) 37 affine_map<(i) -> (i)> // x (out) 38 ], 39 iterator_types = ["parallel"], 40 doc = "x(i) = a(i) OP b(i)" 41} 42 43module { 44 func.func @cadd(%arga: tensor<?xcomplex<f64>, #SparseVector>, 45 %argb: tensor<?xcomplex<f64>, #SparseVector>) 46 -> tensor<?xcomplex<f64>, #SparseVector> { 47 %c = arith.constant 0 : index 48 %d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector> 49 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 50 %0 = linalg.generic #trait_op 51 ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>, 52 tensor<?xcomplex<f64>, #SparseVector>) 53 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 54 ^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>): 55 %1 = complex.add %a, %b : complex<f64> 56 linalg.yield %1 : complex<f64> 57 } -> tensor<?xcomplex<f64>, #SparseVector> 58 return %0 : tensor<?xcomplex<f64>, #SparseVector> 59 } 60 61 func.func @cmul(%arga: tensor<?xcomplex<f64>, #SparseVector>, 62 %argb: tensor<?xcomplex<f64>, #SparseVector>) 63 -> tensor<?xcomplex<f64>, #SparseVector> { 64 %c = arith.constant 0 : index 65 %d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector> 66 %xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector> 67 %0 = linalg.generic #trait_op 68 ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>, 69 tensor<?xcomplex<f64>, #SparseVector>) 70 outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) { 71 ^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>): 72 %1 = complex.mul %a, %b : complex<f64> 73 linalg.yield %1 : complex<f64> 74 } -> tensor<?xcomplex<f64>, #SparseVector> 75 return %0 : tensor<?xcomplex<f64>, #SparseVector> 76 } 77 78 // Driver method to call and verify complex kernels. 79 func.func @main() { 80 // Setup sparse vectors. 81 %v1 = arith.constant sparse< 82 [ [0], [28], [31] ], 83 [ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f64>> 84 %v2 = arith.constant sparse< 85 [ [1], [28], [31] ], 86 [ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f64>> 87 %sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector> 88 %sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector> 89 90 // Call sparse vector kernels. 91 %0 = call @cadd(%sv1, %sv2) 92 : (tensor<?xcomplex<f64>, #SparseVector>, 93 tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 94 %1 = call @cmul(%sv1, %sv2) 95 : (tensor<?xcomplex<f64>, #SparseVector>, 96 tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector> 97 98 // 99 // Verify the results. 100 // 101 // CHECK: ---- Sparse Tensor ---- 102 // CHECK-NEXT: nse = 4 103 // CHECK-NEXT: dim = ( 32 ) 104 // CHECK-NEXT: lvl = ( 32 ) 105 // CHECK-NEXT: pos[0] : ( 0, 4 ) 106 // CHECK-NEXT: crd[0] : ( 0, 1, 28, 31 ) 107 // CHECK-NEXT: values : ( ( 511.13, 2 ), ( 1, 0 ), ( 5, 4 ), ( 8, 6 ) ) 108 // CHECK-NEXT: ---- 109 // 110 // CHECK-NEXT: ---- Sparse Tensor ---- 111 // CHECK-NEXT: nse = 2 112 // CHECK-NEXT: dim = ( 32 ) 113 // CHECK-NEXT: lvl = ( 32 ) 114 // CHECK-NEXT: pos[0] : ( 0, 2 ) 115 // CHECK-NEXT: crd[0] : ( 28, 31 ) 116 // CHECK-NEXT: values : ( ( 6, 8 ), ( 15, 18 ) ) 117 // CHECK-NEXT: ---- 118 // 119 sparse_tensor.print %0 : tensor<?xcomplex<f64>, #SparseVector> 120 sparse_tensor.print %1 : tensor<?xcomplex<f64>, #SparseVector> 121 122 // Release the resources. 123 bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f64>, #SparseVector> 124 bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f64>, #SparseVector> 125 bufferization.dealloc_tensor %0 : tensor<?xcomplex<f64>, #SparseVector> 126 bufferization.dealloc_tensor %1 : tensor<?xcomplex<f64>, #SparseVector> 127 return 128 } 129} 130