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 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#BSR_row_rowmajor = #sparse_tensor.encoding<{ 32 map = (i, j) -> 33 ( i floordiv 3 : dense 34 , j floordiv 4 : compressed 35 , i mod 3 : dense 36 , j mod 4 : dense 37 ) 38}> 39 40#BSR_row_colmajor = #sparse_tensor.encoding<{ 41 map = (i, j) -> 42 ( i floordiv 3 : dense 43 , j floordiv 4 : compressed 44 , j mod 4 : dense 45 , i mod 3 : dense 46 ) 47}> 48 49#BSR_col_rowmajor = #sparse_tensor.encoding<{ 50 map = (i, j) -> 51 ( j floordiv 4 : dense 52 , i floordiv 3 : compressed 53 , i mod 3 : dense 54 , j mod 4 : dense 55 ) 56}> 57 58#BSR_col_colmajor = #sparse_tensor.encoding<{ 59 map = (i, j) -> 60 ( j floordiv 4 : dense 61 , i floordiv 3 : compressed 62 , j mod 4 : dense 63 , i mod 3 : dense 64 ) 65}> 66 67// 68// Example 3x4 block storage of a 6x16 matrix: 69// 70// +---------+---------+---------+---------+ 71// | 1 2 . . | . . . . | . . . . | . . . . | 72// | . . . . | . . . . | . . . . | . . . . | 73// | . . . 3 | . . . . | . . . . | . . . . | 74// +---------+---------+---------+---------+ 75// | . . . . | . . . . | 4 5 . . | . . . . | 76// | . . . . | . . . . | . . . . | . . . . | 77// | . . . . | . . . . | . . 6 7 | . . . . | 78// +---------+---------+---------+---------+ 79// 80// Storage for CSR block storage. Note that this essentially 81// provides CSR storage of 2x4 blocks with either row-major 82// or column-major storage within each 3x4 block of elements. 83// 84// positions[1] : 0 1 2 85// coordinates[1] : 0 2 86// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 87// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major] 88// 89// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 90// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major] 91// 92// Storage for CSC block storage. Note that this essentially 93// provides CSC storage of 4x2 blocks with either row-major 94// or column-major storage within each 3x4 block of elements. 95// 96// positions[1] : 0 1 1 2 2 97// coordinates[1] : 0 1 98// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 99// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major] 100// 101// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 102// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major] 103// 104module { 105 106 107 // 108 // CHECK: ---- Sparse Tensor ---- 109 // CHECK-NEXT: nse = 24 110 // CHECK-NEXT: dim = ( 6, 16 ) 111 // CHECK-NEXT: lvl = ( 2, 4, 3, 4 ) 112 // CHECK-NEXT: pos[1] : ( 0, 1, 2 ) 113 // CHECK-NEXT: crd[1] : ( 0, 2 ) 114 // CHECK-NEXT: values : ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 ) 115 // CHECK-NEXT: ---- 116 // 117 func.func @foo1() { 118 // Build. 119 %c0 = arith.constant 0 : index 120 %f0 = arith.constant 0.0 : f64 121 %m = arith.constant sparse< 122 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ], 123 [ 1., 2., 3., 4., 5., 6., 7.] 124 > : tensor<6x16xf64> 125 %s1 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_rowmajor> 126 // Test. 127 sparse_tensor.print %s1 : tensor<?x?xf64, #BSR_row_rowmajor> 128 // Release. 129 bufferization.dealloc_tensor %s1: tensor<?x?xf64, #BSR_row_rowmajor> 130 return 131 } 132 133 // 134 // CHECK-NEXT: ---- Sparse Tensor ---- 135 // CHECK-NEXT: nse = 24 136 // CHECK-NEXT: dim = ( 6, 16 ) 137 // CHECK-NEXT: lvl = ( 2, 4, 4, 3 ) 138 // CHECK-NEXT: pos[1] : ( 0, 1, 2 ) 139 // CHECK-NEXT: crd[1] : ( 0, 2 ) 140 // CHECK-NEXT: values : ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 ) 141 // CHECK-NEXT: ---- 142 // 143 func.func @foo2() { 144 // Build. 145 %c0 = arith.constant 0 : index 146 %f0 = arith.constant 0.0 : f64 147 %m = arith.constant sparse< 148 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ], 149 [ 1., 2., 3., 4., 5., 6., 7.] 150 > : tensor<6x16xf64> 151 %s2 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_colmajor> 152 // Test. 153 sparse_tensor.print %s2 : tensor<?x?xf64, #BSR_row_colmajor> 154 // Release. 155 bufferization.dealloc_tensor %s2: tensor<?x?xf64, #BSR_row_colmajor> 156 return 157 } 158 159 // 160 // CHECK-NEXT: ---- Sparse Tensor ---- 161 // CHECK-NEXT: nse = 24 162 // CHECK-NEXT: dim = ( 6, 16 ) 163 // CHECK-NEXT: lvl = ( 4, 2, 3, 4 ) 164 // CHECK-NEXT: pos[1] : ( 0, 1, 1, 2, 2 ) 165 // CHECK-NEXT: crd[1] : ( 0, 1 ) 166 // CHECK-NEXT: values : ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 ) 167 // CHECK-NEXT: ---- 168 // 169 func.func @foo3() { 170 // Build. 171 %c0 = arith.constant 0 : index 172 %f0 = arith.constant 0.0 : f64 173 %m = arith.constant sparse< 174 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ], 175 [ 1., 2., 3., 4., 5., 6., 7.] 176 > : tensor<6x16xf64> 177 %s3 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_rowmajor> 178 // Test. 179 sparse_tensor.print %s3 : tensor<?x?xf64, #BSR_col_rowmajor> 180 // Release. 181 bufferization.dealloc_tensor %s3: tensor<?x?xf64, #BSR_col_rowmajor> 182 return 183 } 184 185 // 186 // CHECK-NEXT: ---- Sparse Tensor ---- 187 // CHECK-NEXT: nse = 24 188 // CHECK-NEXT: dim = ( 6, 16 ) 189 // CHECK-NEXT: lvl = ( 4, 2, 4, 3 ) 190 // CHECK-NEXT: pos[1] : ( 0, 1, 1, 2, 2 ) 191 // CHECK-NEXT: crd[1] : ( 0, 1 ) 192 // CHECK-NEXT: values : ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 ) 193 // CHECK-NEXT: ---- 194 // 195 func.func @foo4() { 196 // Build. 197 %c0 = arith.constant 0 : index 198 %f0 = arith.constant 0.0 : f64 199 %m = arith.constant sparse< 200 [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ], 201 [ 1., 2., 3., 4., 5., 6., 7.] 202 > : tensor<6x16xf64> 203 %s4 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_colmajor> 204 // Test. 205 sparse_tensor.print %s4 : tensor<?x?xf64, #BSR_col_colmajor> 206 // Release. 207 bufferization.dealloc_tensor %s4: tensor<?x?xf64, #BSR_col_colmajor> 208 return 209 } 210 211 func.func @main() { 212 call @foo1() : () -> () 213 call @foo2() : () -> () 214 call @foo3() : () -> () 215 call @foo4() : () -> () 216 return 217 } 218} 219