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 enable-buffer-initialization=true 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#DCSR = #sparse_tensor.encoding<{ 35 map = (d0, d1) -> (d0 : compressed, d1 : compressed), 36 posWidth = 8, 37 crdWidth = 8 38}> 39 40#DCSC = #sparse_tensor.encoding<{ 41 map = (d0, d1) -> (d1 : compressed, d0 : compressed), 42 posWidth = 64, 43 crdWidth = 64 44}> 45 46#CSC = #sparse_tensor.encoding<{ 47 map = (d0, d1) -> (d1 : dense, d0 : compressed), 48 posWidth = 16, 49 crdWidth = 32 50}> 51 52// 53// Integration test that tests conversions between sparse tensors, 54// where the position and index sizes in the overhead storage change 55// in addition to layout. 56// 57module { 58 func.func @main() { 59 %c1 = arith.constant 1 : index 60 %t1 = arith.constant sparse< 61 [ [0,0], [0,1], [0,63], [1,0], [1,1], [31,0], [31,63] ], 62 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]> : tensor<32x64xf64> 63 %t2 = tensor.cast %t1 : tensor<32x64xf64> to tensor<?x?xf64> 64 65 // Dense to sparse. 66 %1 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<32x64xf64, #DCSR> 67 %2 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<32x64xf64, #DCSC> 68 %3 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<32x64xf64, #CSC> 69 70 // Sparse to sparse. 71 %4 = sparse_tensor.convert %1 : tensor<32x64xf64, #DCSR> to tensor<32x64xf64, #DCSC> 72 %5 = sparse_tensor.convert %2 : tensor<32x64xf64, #DCSC> to tensor<32x64xf64, #DCSR> 73 %6 = sparse_tensor.convert %3 : tensor<32x64xf64, #CSC> to tensor<32x64xf64, #DCSR> 74 75 // 76 // Verify the outputs. 77 // 78 // CHECK: ---- Sparse Tensor ---- 79 // CHECK-NEXT: nse = 7 80 // CHECK-NEXT: dim = ( 32, 64 ) 81 // CHECK-NEXT: lvl = ( 32, 64 ) 82 // CHECK-NEXT: pos[0] : ( 0, 3 ) 83 // CHECK-NEXT: crd[0] : ( 0, 1, 31 ) 84 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 ) 85 // CHECK-NEXT: crd[1] : ( 0, 1, 63, 0, 1, 0, 63 ) 86 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7 ) 87 // CHECK-NEXT: ---- 88 // 89 // CHECK: ---- Sparse Tensor ---- 90 // CHECK-NEXT: nse = 7 91 // CHECK-NEXT: dim = ( 32, 64 ) 92 // CHECK-NEXT: lvl = ( 64, 32 ) 93 // CHECK-NEXT: pos[0] : ( 0, 3 ) 94 // CHECK-NEXT: crd[0] : ( 0, 1, 63 ) 95 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 ) 96 // CHECK-NEXT: crd[1] : ( 0, 1, 31, 0, 1, 0, 31 ) 97 // CHECK-NEXT: values : ( 1, 4, 6, 2, 5, 3, 7 ) 98 // CHECK-NEXT: ---- 99 // 100 // CHECK: ---- Sparse Tensor ---- 101 // CHECK-NEXT: nse = 7 102 // CHECK-NEXT: dim = ( 32, 64 ) 103 // CHECK-NEXT: lvl = ( 64, 32 ) 104 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 7 ) 105 // CHECK-NEXT: crd[1] : ( 0, 1, 31, 0, 1, 0, 31 ) 106 // CHECK-NEXT: values : ( 1, 4, 6, 2, 5, 3, 7 ) 107 // CHECK-NEXT: ---- 108 // 109 // CHECK: ---- Sparse Tensor ---- 110 // CHECK-NEXT: nse = 7 111 // CHECK-NEXT: dim = ( 32, 64 ) 112 // CHECK-NEXT: lvl = ( 64, 32 ) 113 // CHECK-NEXT: pos[0] : ( 0, 3 ) 114 // CHECK-NEXT: crd[0] : ( 0, 1, 63 ) 115 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 ) 116 // CHECK-NEXT: crd[1] : ( 0, 1, 31, 0, 1, 0, 31 ) 117 // CHECK-NEXT: values : ( 1, 4, 6, 2, 5, 3, 7 ) 118 // CHECK-NEXT: ---- 119 // 120 // CHECK: ---- Sparse Tensor ---- 121 // CHECK-NEXT: nse = 7 122 // CHECK-NEXT: dim = ( 32, 64 ) 123 // CHECK-NEXT: lvl = ( 32, 64 ) 124 // CHECK-NEXT: pos[0] : ( 0, 3 ) 125 // CHECK-NEXT: crd[0] : ( 0, 1, 31 ) 126 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 ) 127 // CHECK-NEXT: crd[1] : ( 0, 1, 63, 0, 1, 0, 63 ) 128 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7 ) 129 // CHECK-NEXT: ---- 130 // 131 // CHECK: ---- Sparse Tensor ---- 132 // CHECK-NEXT: nse = 7 133 // CHECK-NEXT: dim = ( 32, 64 ) 134 // CHECK-NEXT: lvl = ( 32, 64 ) 135 // CHECK-NEXT: pos[0] : ( 0, 3 ) 136 // CHECK-NEXT: crd[0] : ( 0, 1, 31 ) 137 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 ) 138 // CHECK-NEXT: crd[1] : ( 0, 1, 63, 0, 1, 0, 63 ) 139 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7 ) 140 // CHECK-NEXT: ---- 141 // 142 sparse_tensor.print %1 : tensor<32x64xf64, #DCSR> 143 sparse_tensor.print %2 : tensor<32x64xf64, #DCSC> 144 sparse_tensor.print %3 : tensor<32x64xf64, #CSC> 145 sparse_tensor.print %4 : tensor<32x64xf64, #DCSC> 146 sparse_tensor.print %5 : tensor<32x64xf64, #DCSR> 147 sparse_tensor.print %6 : tensor<32x64xf64, #DCSR> 148 149 // Release the resources. 150 bufferization.dealloc_tensor %1 : tensor<32x64xf64, #DCSR> 151 bufferization.dealloc_tensor %2 : tensor<32x64xf64, #DCSC> 152 bufferization.dealloc_tensor %3 : tensor<32x64xf64, #CSC> 153 bufferization.dealloc_tensor %4 : tensor<32x64xf64, #DCSC> 154 bufferization.dealloc_tensor %5 : tensor<32x64xf64, #DCSR> 155 bufferization.dealloc_tensor %6 : tensor<32x64xf64, #DCSR> 156 157 return 158 } 159} 160