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#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)>, // A 45 affine_map<(i,j,k) -> (i,j,k)>, // B 46 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 : index 57 %c1 = arith.constant 1 : index 58 %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 #redsum 62 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 : i32 67 %1 = arith.addi %x, %0 : i32 68 linalg.yield %1 : i32 69 } -> 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 %t1 83 : tensor<3x3x4xi32> to tensor<?x?x?xi32, #SparseTensor> 84 %st2 = sparse_tensor.convert %t2 85 : 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 = 2 97 // 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 return 114 } 115} 116