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#MAT_C_C = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed, d1 : compressed)}> 35#MAT_D_C = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : dense, d1 : compressed)}> 36#MAT_C_D = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed, d1 : dense)}> 37#MAT_D_D = #sparse_tensor.encoding<{ 38 map = (d0, d1) -> (d1 : dense, d0 : dense) 39}> 40 41#MAT_C_C_P = #sparse_tensor.encoding<{ 42 map = (d0, d1) -> (d1 : compressed, d0 : compressed) 43}> 44 45#MAT_C_D_P = #sparse_tensor.encoding<{ 46 map = (d0, d1) -> (d1 : compressed, d0 : dense) 47}> 48 49#MAT_D_C_P = #sparse_tensor.encoding<{ 50 map = (d0, d1) -> (d1 : dense, d0 : compressed) 51}> 52 53module { 54 func.func private @printMemrefF64(%ptr : tensor<*xf64>) 55 func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface } 56 57 // 58 // Tests with permutation. 59 // 60 61 // Concats all sparse matrices (with different encodings) to a sparse matrix. 62 func.func @concat_sparse_sparse_perm(%arg0: tensor<2x4xf64, #MAT_C_C_P>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C_P> { 63 %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index} 64 : tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C_P> 65 return %0 : tensor<9x4xf64, #MAT_C_C_P> 66 } 67 68 // Concats all sparse matrices (with different encodings) to a dense matrix. 69 func.func @concat_sparse_dense_perm(%arg0: tensor<2x4xf64, #MAT_C_C_P>, %arg1: tensor<3x4xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> { 70 %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index} 71 : tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64> 72 return %0 : tensor<9x4xf64> 73 } 74 75 // Concats mix sparse and dense matrices to a sparse matrix. 76 func.func @concat_mix_sparse_perm(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> { 77 %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index} 78 : tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C> 79 return %0 : tensor<9x4xf64, #MAT_C_C> 80 } 81 82 // Concats mix sparse and dense matrices to a dense matrix. 83 func.func @concat_mix_dense_perm(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C_P>) -> tensor<9x4xf64> { 84 %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index} 85 : tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P> to tensor<9x4xf64> 86 return %0 : tensor<9x4xf64> 87 } 88 89 func.func @dump_mat_9x4(%A: tensor<9x4xf64, #MAT_C_C>) { 90 %c = sparse_tensor.convert %A : tensor<9x4xf64, #MAT_C_C> to tensor<9x4xf64> 91 %cu = tensor.cast %c : tensor<9x4xf64> to tensor<*xf64> 92 call @printMemrefF64(%cu) : (tensor<*xf64>) -> () 93 94 %n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_C_C> 95 vector.print %n : index 96 97 %1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_C_C> to memref<?xf64> 98 call @printMemref1dF64(%1) : (memref<?xf64>) -> () 99 100 return 101 } 102 103 func.func @dump_mat_dense_9x4(%A: tensor<9x4xf64>) { 104 %u = tensor.cast %A : tensor<9x4xf64> to tensor<*xf64> 105 call @printMemrefF64(%u) : (tensor<*xf64>) -> () 106 107 return 108 } 109 110 // Driver method to call and verify kernels. 111 func.func @main() { 112 %m42 = arith.constant dense< 113 [ [ 1.0, 0.0 ], 114 [ 3.1, 0.0 ], 115 [ 0.0, 2.0 ], 116 [ 0.0, 0.0 ] ]> : tensor<4x2xf64> 117 %m43 = arith.constant dense< 118 [ [ 1.0, 0.0, 1.0 ], 119 [ 1.0, 0.0, 0.5 ], 120 [ 0.0, 0.0, 1.0 ], 121 [ 5.0, 2.0, 0.0 ] ]> : tensor<4x3xf64> 122 %m24 = arith.constant dense< 123 [ [ 1.0, 0.0, 3.0, 0.0], 124 [ 0.0, 2.0, 0.0, 0.0] ]> : tensor<2x4xf64> 125 %m34 = arith.constant dense< 126 [ [ 1.0, 0.0, 1.0, 1.0], 127 [ 0.0, 0.5, 0.0, 0.0], 128 [ 1.0, 5.0, 2.0, 0.0] ]> : tensor<3x4xf64> 129 %m44 = arith.constant dense< 130 [ [ 0.0, 0.0, 1.5, 1.0], 131 [ 0.0, 3.5, 0.0, 0.0], 132 [ 1.0, 5.0, 2.0, 0.0], 133 [ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64> 134 135 %sm24cc = sparse_tensor.convert %m24 : tensor<2x4xf64> to tensor<2x4xf64, #MAT_C_C> 136 %sm34cd = sparse_tensor.convert %m34 : tensor<3x4xf64> to tensor<3x4xf64, #MAT_C_D> 137 %sm44dc = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C> 138 139 %sm24ccp = sparse_tensor.convert %m24 : tensor<2x4xf64> to tensor<2x4xf64, #MAT_C_C_P> 140 %sm34cdp = sparse_tensor.convert %m34 : tensor<3x4xf64> to tensor<3x4xf64, #MAT_C_D_P> 141 %sm44dcp = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C_P> 142 143 // 144 // CHECK: ---- Sparse Tensor ---- 145 // CHECK-NEXT: nse = 18 146 // CHECK-NEXT: dim = ( 9, 4 ) 147 // CHECK-NEXT: lvl = ( 4, 9 ) 148 // CHECK-NEXT: pos[0] : ( 0, 4 ) 149 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 ) 150 // CHECK-NEXT: pos[1] : ( 0, 5, 11, 16, 18 ) 151 // CHECK-NEXT: crd[1] : ( 0, 2, 4, 7, 8, 1, 3, 4, 6, 7, 8, 0, 2, 4, 5, 7, 2, 5 ) 152 // CHECK-NEXT: values : ( 1, 1, 1, 1, 1, 2, 0.5, 5, 3.5, 5, 0.5, 3, 1, 2, 1.5, 2, 1, 1 ) 153 // CHECK-NEXT: ---- 154 // 155 %4 = call @concat_sparse_sparse_perm(%sm24ccp, %sm34cd, %sm44dc) 156 : (tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C_P> 157 sparse_tensor.print %4 : tensor<9x4xf64, #MAT_C_C_P> 158 159 // CHECK: {{\[}}[1, 0, 3, 0], 160 // CHECK-NEXT: [0, 2, 0, 0], 161 // CHECK-NEXT: [1, 0, 1, 1], 162 // CHECK-NEXT: [0, 0.5, 0, 0], 163 // CHECK-NEXT: [1, 5, 2, 0], 164 // CHECK-NEXT: [0, 0, 1.5, 1], 165 // CHECK-NEXT: [0, 3.5, 0, 0], 166 // CHECK-NEXT: [1, 5, 2, 0], 167 // CHECK-NEXT: [1, 0.5, 0, 0]] 168 %5 = call @concat_sparse_dense_perm(%sm24ccp, %sm34cdp, %sm44dc) 169 : (tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> 170 call @dump_mat_dense_9x4(%5) : (tensor<9x4xf64>) -> () 171 172 // 173 // CHECK: ---- Sparse Tensor ---- 174 // CHECK-NEXT: nse = 18 175 // CHECK-NEXT: dim = ( 9, 4 ) 176 // CHECK-NEXT: lvl = ( 9, 4 ) 177 // CHECK-NEXT: pos[0] : ( 0, 9 ) 178 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8 ) 179 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 7, 10, 12, 13, 16, 18 ) 180 // CHECK-NEXT: crd[1] : ( 0, 2, 1, 0, 2, 3, 1, 0, 1, 2, 2, 3, 1, 0, 1, 2, 0, 1 ) 181 // CHECK-NEXT: values : ( 1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5 ) 182 // CHECK-NEXT: ---- 183 // 184 %6 = call @concat_mix_sparse_perm(%m24, %sm34cdp, %sm44dc) 185 : (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> 186 sparse_tensor.print %6 : tensor<9x4xf64, #MAT_C_C> 187 188 // CHECK: {{\[}}[1, 0, 3, 0], 189 // CHECK-NEXT: [0, 2, 0, 0], 190 // CHECK-NEXT: [1, 0, 1, 1], 191 // CHECK-NEXT: [0, 0.5, 0, 0], 192 // CHECK-NEXT: [1, 5, 2, 0], 193 // CHECK-NEXT: [0, 0, 1.5, 1], 194 // CHECK-NEXT: [0, 3.5, 0, 0], 195 // CHECK-NEXT: [1, 5, 2, 0], 196 // CHECK-NEXT: [1, 0.5, 0, 0]] 197 %7 = call @concat_mix_dense_perm(%m24, %sm34cd, %sm44dcp) 198 : (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P>) -> tensor<9x4xf64> 199 call @dump_mat_dense_9x4(%7) : (tensor<9x4xf64>) -> () 200 201 // Release resources. 202 bufferization.dealloc_tensor %sm24cc : tensor<2x4xf64, #MAT_C_C> 203 bufferization.dealloc_tensor %sm34cd : tensor<3x4xf64, #MAT_C_D> 204 bufferization.dealloc_tensor %sm44dc : tensor<4x4xf64, #MAT_D_C> 205 bufferization.dealloc_tensor %sm24ccp : tensor<2x4xf64, #MAT_C_C_P> 206 bufferization.dealloc_tensor %sm34cdp : tensor<3x4xf64, #MAT_C_D_P> 207 bufferization.dealloc_tensor %sm44dcp : tensor<4x4xf64, #MAT_D_C_P> 208 bufferization.dealloc_tensor %4 : tensor<9x4xf64, #MAT_C_C_P> 209 bufferization.dealloc_tensor %5 : tensor<9x4xf64> 210 bufferization.dealloc_tensor %6 : tensor<9x4xf64, #MAT_C_C> 211 bufferization.dealloc_tensor %7 : tensor<9x4xf64> 212 return 213 } 214} 215