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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, this5// config could be moved to lit.local.cfg. However, there are downstream users that6// do not use these LIT config files. Hence why this is kept inline.7//8// DEFINE: %{sparsifier_opts} = enable-runtime-library=true9// 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_utils13// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils14// DEFINE: %{run_opts} = -e main -entry-point-result=void15// 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 %s23//24// Do the same run, but now with direct IR generation.25// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false26// RUN: %{compile} | env %{env} %{run} | FileCheck %s27//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=true30// RUN: %{compile} | env %{env} %{run} | FileCheck %s31//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.ptr36 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)>, // A48 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 tensor56// to an all-dense annotated "sparse" tensor, which effectively57// result in inserting the nonzero elements into a linearized array.58//59// Note that there is a subtle difference between a non-annotated60// tensor and an all-dense annotated tensor. Both tensors are assumed61// dense, but the former remains an n-dimensional memref whereas the62// latter is linearized into a one-dimensional memref that is further63// lowered into a storage scheme that is backed by the runtime support64// 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 : index71 %c1 = arith.constant 1 : index72 %c2 = arith.constant 2.0 : f6473 %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_assign77 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 : f6481 linalg.yield %0 : f6482 } -> 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 : f6493 %c0 = arith.constant 0 : index94 %c1 = arith.constant 1 : index95 96 // Read the sparse matrix from file, construct sparse storage.97 %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)98 %a = sparse_tensor.new %fileName99 : !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 = 25110 // 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 return122 }123}124