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// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" 22// RUN: %{compile} | env %{env} %{run} | FileCheck %s 23// 24// Do the same run, but now with direct IR generation. 25// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false 26// RUN: %{compile} | env %{env} %{run} | FileCheck %s 27// 28// Do the same run, but now with direct IR generation and vectorization. 29// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true 30// RUN: %{compile} | env %{env} %{run} | FileCheck %s 31// 32// Do the same run, but now with direct IR generation and VLA vectorization. 33// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | env %{env} %{run_sve} | FileCheck %s %} 34 35!Filename = !llvm.ptr 36 37#DenseMatrix = #sparse_tensor.encoding<{ 38 map = (d0, d1) -> (d0 : dense, d1 : dense) 39}> 40 41#SparseMatrix = #sparse_tensor.encoding<{ 42 map = (d0, d1) -> (d0 : dense, d1 : compressed), 43}> 44 45#trait_assign = { 46 indexing_maps = [ 47 affine_map<(i,j) -> (i,j)>, // A 48 affine_map<(i,j) -> (i,j)> // X (out) 49 ], 50 iterator_types = ["parallel", "parallel"], 51 doc = "X(i,j) = A(i,j) * 2" 52} 53 54// 55// Integration test that demonstrates assigning a sparse tensor 56// to an all-dense annotated "sparse" tensor, which effectively 57// result in inserting the nonzero elements into a linearized array. 58// 59// Note that there is a subtle difference between a non-annotated 60// tensor and an all-dense annotated tensor. Both tensors are assumed 61// dense, but the former remains an n-dimensional memref whereas the 62// latter is linearized into a one-dimensional memref that is further 63// lowered into a storage scheme that is backed by the runtime support 64// library. 65module { 66 // 67 // A kernel that assigns multiplied elements from A to X. 68 // 69 func.func @dense_output(%arga: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64, #DenseMatrix> { 70 %c0 = arith.constant 0 : index 71 %c1 = arith.constant 1 : index 72 %c2 = arith.constant 2.0 : f64 73 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #SparseMatrix> 74 %d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #SparseMatrix> 75 %init = tensor.empty(%d0, %d1) : tensor<?x?xf64, #DenseMatrix> 76 %0 = linalg.generic #trait_assign 77 ins(%arga: tensor<?x?xf64, #SparseMatrix>) 78 outs(%init: tensor<?x?xf64, #DenseMatrix>) { 79 ^bb(%a: f64, %x: f64): 80 %0 = arith.mulf %a, %c2 : f64 81 linalg.yield %0 : f64 82 } -> tensor<?x?xf64, #DenseMatrix> 83 return %0 : tensor<?x?xf64, #DenseMatrix> 84 } 85 86 func.func private @getTensorFilename(index) -> (!Filename) 87 88 // 89 // Main driver that reads matrix from file and calls the kernel. 90 // 91 func.func @main() { 92 %d0 = arith.constant 0.0 : f64 93 %c0 = arith.constant 0 : index 94 %c1 = arith.constant 1 : index 95 96 // Read the sparse matrix from file, construct sparse storage. 97 %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename) 98 %a = sparse_tensor.new %fileName 99 : !Filename to tensor<?x?xf64, #SparseMatrix> 100 101 // Call the kernel. 102 %0 = call @dense_output(%a) 103 : (tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64, #DenseMatrix> 104 105 // 106 // Print the linearized 5x5 result for verification. 107 // 108 // CHECK: ---- Sparse Tensor ---- 109 // CHECK-NEXT: nse = 25 110 // CHECK-NEXT: dim = ( 5, 5 ) 111 // CHECK-NEXT: lvl = ( 5, 5 ) 112 // CHECK-NEXT: values : ( 2, 0, 0, 2.8, 0, 0, 4, 0, 0, 5, 0, 0, 6, 0, 0, 8.2, 0, 0, 8, 0, 0, 10.4, 0, 0, 10 ) 113 // CHECK-NEXT: ---- 114 // 115 sparse_tensor.print %0 : tensor<?x?xf64, #DenseMatrix> 116 117 // Release the resources. 118 bufferization.dealloc_tensor %a : tensor<?x?xf64, #SparseMatrix> 119 bufferization.dealloc_tensor %0 : tensor<?x?xf64, #DenseMatrix> 120 121 return 122 } 123} 124