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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// RUN: %{compile} | %{run} | FileCheck %s22//23// Do the same run, but now with direct IR generation.24// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true25// RUN: %{compile} | %{run} | FileCheck %s26//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=true29// RUN: %{compile} | %{run} | FileCheck %s30//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#SparseMatrix = #sparse_tensor.encoding<{35 map = (d0, d1) -> (d0 : compressed, d1 : compressed)36}>37 38#SparseTensor = #sparse_tensor.encoding<{39 map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed)40}>41 42#redsum = {43 indexing_maps = [44 affine_map<(i,j,k) -> (i,j,k)>, // A45 affine_map<(i,j,k) -> (i,j,k)>, // B46 affine_map<(i,j,k) -> (i,j)> // X (out)47 ],48 iterator_types = ["parallel", "parallel", "reduction"],49 doc = "X(i,j) = SUM_k A(i,j,k) * B(i,j,k)"50}51 52module {53 func.func @redsum(%arga: tensor<?x?x?xi32, #SparseTensor>,54 %argb: tensor<?x?x?xi32, #SparseTensor>)55 -> tensor<?x?xi32, #SparseMatrix> {56 %c0 = arith.constant 0 : index57 %c1 = arith.constant 1 : index58 %d0 = tensor.dim %arga, %c0 : tensor<?x?x?xi32, #SparseTensor>59 %d1 = tensor.dim %arga, %c1 : tensor<?x?x?xi32, #SparseTensor>60 %xinit = tensor.empty(%d0, %d1): tensor<?x?xi32, #SparseMatrix>61 %0 = linalg.generic #redsum62 ins(%arga, %argb: tensor<?x?x?xi32, #SparseTensor>,63 tensor<?x?x?xi32, #SparseTensor>)64 outs(%xinit: tensor<?x?xi32, #SparseMatrix>) {65 ^bb(%a: i32, %b: i32, %x: i32):66 %0 = arith.muli %a, %b : i3267 %1 = arith.addi %x, %0 : i3268 linalg.yield %1 : i3269 } -> tensor<?x?xi32, #SparseMatrix>70 return %0 : tensor<?x?xi32, #SparseMatrix>71 }72 73 // Driver method to call and verify tensor kernel.74 func.func @main() {75 // Setup very sparse 3-d tensors.76 %t1 = arith.constant sparse<77 [ [1,1,3], [2,0,0], [2,2,1], [2,2,2], [2,2,3] ], [ 1, 2, 3, 4, 5 ]78 > : tensor<3x3x4xi32>79 %t2 = arith.constant sparse<80 [ [1,0,0], [1,1,3], [2,2,1], [2,2,3] ], [ 6, 7, 8, 9 ]81 > : tensor<3x3x4xi32>82 %st1 = sparse_tensor.convert %t183 : tensor<3x3x4xi32> to tensor<?x?x?xi32, #SparseTensor>84 %st2 = sparse_tensor.convert %t285 : tensor<3x3x4xi32> to tensor<?x?x?xi32, #SparseTensor>86 87 // Call kernel.88 %0 = call @redsum(%st1, %st2)89 : (tensor<?x?x?xi32, #SparseTensor>,90 tensor<?x?x?xi32, #SparseTensor>) -> tensor<?x?xi32, #SparseMatrix>91 92 //93 // Verify results. Only two entries stored in result. Correct structure.94 //95 // CHECK: ---- Sparse Tensor ----96 // CHECK-NEXT: nse = 297 // CHECK-NEXT: dim = ( 3, 3 )98 // CHECK-NEXT: lvl = ( 3, 3 )99 // CHECK-NEXT: pos[0] : ( 0, 2 )100 // CHECK-NEXT: crd[0] : ( 1, 2 )101 // CHECK-NEXT: pos[1] : ( 0, 1, 2 )102 // CHECK-NEXT: crd[1] : ( 1, 2 )103 // CHECK-NEXT: values : ( 7, 69 )104 // CHECK-NEXT: ----105 //106 sparse_tensor.print %0 : tensor<?x?xi32, #SparseMatrix>107 108 // Release the resources.109 bufferization.dealloc_tensor %st1 : tensor<?x?x?xi32, #SparseTensor>110 bufferization.dealloc_tensor %st2 : tensor<?x?x?xi32, #SparseTensor>111 bufferization.dealloc_tensor %0 : tensor<?x?xi32, #SparseMatrix>112 113 return114 }115}116