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// RUN: %{compile} | %{run} | FileCheck %s 22// 23// Do the same run, but now with direct IR generation. 24// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true 25// RUN: %{compile} | %{run} | FileCheck %s 26// 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=true 29// RUN: %{compile} | %{run} | FileCheck %s 30// 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 39 affine_map<(i) -> (i)> // x (out) 40 ], 41 iterator_types = ["parallel"], 42 doc = "x(i) = OP a(i)" 43} 44 45module { 46 func.func @sparse_absf(%arg0: tensor<?xf64, #SparseVector>) 47 -> tensor<?xf64, #SparseVector> { 48 %c0 = arith.constant 0 : index 49 %d = tensor.dim %arg0, %c0 : tensor<?xf64, #SparseVector> 50 %xin = tensor.empty(%d) : tensor<?xf64, #SparseVector> 51 %0 = linalg.generic #trait_op 52 ins(%arg0: tensor<?xf64, #SparseVector>) 53 outs(%xin: tensor<?xf64, #SparseVector>) { 54 ^bb0(%a: f64, %x: f64) : 55 %result = math.absf %a : f64 56 linalg.yield %result : f64 57 } -> tensor<?xf64, #SparseVector> 58 return %0 : tensor<?xf64, #SparseVector> 59 } 60 61 func.func @sparse_absi(%arg0: tensor<?xi32, #SparseVector>) 62 -> tensor<?xi32, #SparseVector> { 63 %c0 = arith.constant 0 : index 64 %d = tensor.dim %arg0, %c0 : tensor<?xi32, #SparseVector> 65 %xin = tensor.empty(%d) : tensor<?xi32, #SparseVector> 66 %0 = linalg.generic #trait_op 67 ins(%arg0: tensor<?xi32, #SparseVector>) 68 outs(%xin: tensor<?xi32, #SparseVector>) { 69 ^bb0(%a: i32, %x: i32) : 70 %result = math.absi %a : i32 71 linalg.yield %result : i32 72 } -> tensor<?xi32, #SparseVector> 73 return %0 : tensor<?xi32, #SparseVector> 74 } 75 76 // Driver method to call and verify sign kernel. 77 func.func @main() { 78 %c0 = arith.constant 0 : index 79 %df = arith.constant 99.99 : f64 80 %di = arith.constant 9999 : i32 81 82 %pnan = arith.constant 0x7FF0000001000000 : f64 83 %nnan = arith.constant 0xFFF0000001000000 : f64 84 %pinf = arith.constant 0x7FF0000000000000 : f64 85 %ninf = arith.constant 0xFFF0000000000000 : f64 86 87 // Setup sparse vectors. 88 %v1 = arith.constant sparse< 89 [ [0], [3], [5], [11], [13], [17], [18], [20], [21], [28], [29], [31] ], 90 [ -1.5, 1.5, -10.2, 11.3, 1.0, -1.0, 91 0x7FF0000001000000, // +NaN 92 0xFFF0000001000000, // -NaN 93 0x7FF0000000000000, // +Inf 94 0xFFF0000000000000, // -Inf 95 -0.0, // -Zero 96 0.0 // +Zero 97 ] 98 > : tensor<32xf64> 99 %v2 = arith.constant sparse< 100 [ [0], [3], [5], [11], [13], [17], [18], [21], [31] ], 101 [ -2147483648, -2147483647, -1000, -1, 0, 102 1, 1000, 2147483646, 2147483647 103 ] 104 > : tensor<32xi32> 105 %sv1 = sparse_tensor.convert %v1 106 : tensor<32xf64> to tensor<?xf64, #SparseVector> 107 %sv2 = sparse_tensor.convert %v2 108 : tensor<32xi32> to tensor<?xi32, #SparseVector> 109 110 // Call abs kernels. 111 %0 = call @sparse_absf(%sv1) : (tensor<?xf64, #SparseVector>) 112 -> tensor<?xf64, #SparseVector> 113 114 %1 = call @sparse_absi(%sv2) : (tensor<?xi32, #SparseVector>) 115 -> tensor<?xi32, #SparseVector> 116 117 // 118 // Verify the results. 119 // 120 // CHECK: ---- Sparse Tensor ---- 121 // CHECK-NEXT: nse = 12 122 // CHECK-NEXT: dim = ( 32 ) 123 // CHECK-NEXT: lvl = ( 32 ) 124 // CHECK-NEXT: pos[0] : ( 0, 12 ) 125 // CHECK-NEXT: crd[0] : ( 0, 3, 5, 11, 13, 17, 18, 20, 21, 28, 29, 31 ) 126 // CHECK-NEXT: values : ( 1.5, 1.5, 10.2, 11.3, 1, 1, nan, nan, inf, inf, 0, 0 ) 127 // CHECK-NEXT: ---- 128 // 129 // CHECK-NEXT: ---- Sparse Tensor ---- 130 // CHECK-NEXT: nse = 9 131 // CHECK-NEXT: dim = ( 32 ) 132 // CHECK-NEXT: lvl = ( 32 ) 133 // CHECK-NEXT: pos[0] : ( 0, 9 ) 134 // CHECK-NEXT: crd[0] : ( 0, 3, 5, 11, 13, 17, 18, 21, 31 ) 135 // CHECK-NEXT: values : ( -2147483648, 2147483647, 1000, 1, 0, 1, 1000, 2147483646, 2147483647 ) 136 // CHECK-NEXT: ---- 137 // 138 sparse_tensor.print %0 : tensor<?xf64, #SparseVector> 139 sparse_tensor.print %1 : tensor<?xi32, #SparseVector> 140 141 // Release the resources. 142 bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector> 143 bufferization.dealloc_tensor %sv2 : tensor<?xi32, #SparseVector> 144 bufferization.dealloc_tensor %0 : tensor<?xf64, #SparseVector> 145 bufferization.dealloc_tensor %1 : tensor<?xi32, #SparseVector> 146 return 147 } 148} 149