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// RUN: %{compile} | %{run} | FileCheck %s 21// 22// Do the same run, but now with direct IR generation. 23// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false 24// RUN: %{compile} | %{run} | FileCheck %s 25// 26// Do the same run, but now with vectorization. 27// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true 28// RUN: %{compile} | %{run} | FileCheck %s 29// 30// Do the same run, but now with VLA vectorization. 31// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %} 32 33// UNSUPPORTED: target=aarch64{{.*}}, mlir_arm_emulator 34 35!Filename = !llvm.ptr 36 37#SparseMatrix = #sparse_tensor.encoding<{ 38 map = (d0, d1) -> (d0 : compressed, d1 : compressed) 39}> 40 41#trait_sum_reduce = { 42 indexing_maps = [ 43 affine_map<(i,j) -> (i,j)>, // A 44 affine_map<(i,j) -> ()> // x (out) 45 ], 46 iterator_types = ["reduction", "reduction"], 47 doc = "x += A(i,j)" 48} 49 50module { 51 // 52 // A kernel that sum-reduces a matrix to a single scalar. 53 // 54 func.func @kernel_sum_reduce(%arga: tensor<?x?xbf16, #SparseMatrix>, 55 %argx: tensor<bf16>) -> tensor<bf16> { 56 %0 = linalg.generic #trait_sum_reduce 57 ins(%arga: tensor<?x?xbf16, #SparseMatrix>) 58 outs(%argx: tensor<bf16>) { 59 ^bb(%a: bf16, %x: bf16): 60 %0 = arith.addf %x, %a : bf16 61 linalg.yield %0 : bf16 62 } -> tensor<bf16> 63 return %0 : tensor<bf16> 64 } 65 66 func.func private @getTensorFilename(index) -> (!Filename) 67 68 // 69 // Main driver that reads matrix from file and calls the sparse kernel. 70 // 71 func.func @main() { 72 // Setup input sparse matrix from compressed constant. 73 %d = arith.constant dense <[ 74 [ 1.1, 1.2, 0.0, 1.4 ], 75 [ 0.0, 0.0, 0.0, 0.0 ], 76 [ 3.1, 0.0, 3.3, 3.4 ] 77 ]> : tensor<3x4xbf16> 78 %a = sparse_tensor.convert %d : tensor<3x4xbf16> to tensor<?x?xbf16, #SparseMatrix> 79 80 %d0 = arith.constant 0.0 : bf16 81 // Setup memory for a single reduction scalar, 82 // initialized to zero. 83 %x = tensor.from_elements %d0 : tensor<bf16> 84 85 // Call the kernel. 86 %0 = call @kernel_sum_reduce(%a, %x) 87 : (tensor<?x?xbf16, #SparseMatrix>, tensor<bf16>) -> tensor<bf16> 88 89 // Print the result for verification. 90 // 91 // CHECK: 13.5 92 // 93 %v = tensor.extract %0[] : tensor<bf16> 94 %vf = arith.extf %v: bf16 to f32 95 vector.print %vf : f32 96 97 // Release the resources. 98 bufferization.dealloc_tensor %a : tensor<?x?xbf16, #SparseMatrix> 99 bufferization.dealloc_tensor %0 : tensor<bf16> 100 101 return 102 } 103} 104