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 25// RUN: %{compile} | %{run} | FileCheck %s 26// 27// Do the same run, but now with vectorization. 28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=4 29// RUN: %{compile} | %{run} | FileCheck %s 30// 31// Do the same run, but now with VLA vectorization. 32// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %} 33 34#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)> 35#SparseMatrix = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed) }> 36 37module @func_sparse.2 { 38 // Do elementwise x+1 when true, x-1 when false 39 func.func public @condition(%cond: i1, %arg0: tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix> { 40 %1 = scf.if %cond -> (tensor<2x3x4xf64, #SparseMatrix>) { 41 %cst_2 = arith.constant dense<1.000000e+00> : tensor<f64> 42 %cst_3 = arith.constant dense<1.000000e+00> : tensor<2x3x4xf64> 43 %2 = tensor.empty() : tensor<2x3x4xf64, #SparseMatrix> 44 %3 = linalg.generic { 45 indexing_maps = [#map, #map, #map], 46 iterator_types = ["parallel", "parallel", "parallel"]} 47 ins(%arg0, %cst_3 : tensor<2x3x4xf64, #SparseMatrix>, tensor<2x3x4xf64>) 48 outs(%2 : tensor<2x3x4xf64, #SparseMatrix>) { 49 ^bb0(%arg1: f64, %arg2: f64, %arg3: f64): 50 %4 = arith.subf %arg1, %arg2 : f64 51 linalg.yield %4 : f64 52 } -> tensor<2x3x4xf64, #SparseMatrix> 53 scf.yield %3 : tensor<2x3x4xf64, #SparseMatrix> 54 } else { 55 %cst_2 = arith.constant dense<1.000000e+00> : tensor<f64> 56 %cst_3 = arith.constant dense<1.000000e+00> : tensor<2x3x4xf64> 57 %2 = tensor.empty() : tensor<2x3x4xf64, #SparseMatrix> 58 %3 = linalg.generic { 59 indexing_maps = [#map, #map, #map], 60 iterator_types = ["parallel", "parallel", "parallel"]} 61 ins(%arg0, %cst_3 : tensor<2x3x4xf64, #SparseMatrix>, tensor<2x3x4xf64>) 62 outs(%2 : tensor<2x3x4xf64, #SparseMatrix>) { 63 ^bb0(%arg1: f64, %arg2: f64, %arg3: f64): 64 %4 = arith.addf %arg1, %arg2 : f64 65 linalg.yield %4 : f64 66 } -> tensor<2x3x4xf64, #SparseMatrix> 67 scf.yield %3 : tensor<2x3x4xf64, #SparseMatrix> 68 } 69 return %1 : tensor<2x3x4xf64, #SparseMatrix> 70 } 71 72 func.func public @main() { 73 %src = arith.constant dense<[ 74 [ [ 1.0, 2.0, 3.0, 4.0 ], 75 [ 5.0, 6.0, 7.0, 8.0 ], 76 [ 9.0, 10.0, 11.0, 12.0 ] ], 77 [ [ 13.0, 14.0, 15.0, 16.0 ], 78 [ 17.0, 18.0, 19.0, 20.0 ], 79 [ 21.0, 22.0, 23.0, 24.0 ] ] 80 ]> : tensor<2x3x4xf64> 81 82 %t = arith.constant 1 : i1 83 %f = arith.constant 0 : i1 84 85 %sm = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #SparseMatrix> 86 87 %sm_t = call @condition(%t, %sm) : (i1, tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix> 88 %sm_f = call @condition(%f, %sm) : (i1, tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix> 89 90 // 91 // CHECK: ---- Sparse Tensor ---- 92 // CHECK-NEXT: nse = 24 93 // CHECK-NEXT: dim = ( 2, 3, 4 ) 94 // CHECK-NEXT: lvl = ( 2, 3, 4 ) 95 // CHECK-NEXT: pos[0] : ( 0, 2 ) 96 // CHECK-NEXT: crd[0] : ( 0, 1 ) 97 // CHECK-NEXT: pos[1] : ( 0, 3, 6 ) 98 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 1, 2 ) 99 // CHECK-NEXT: pos[2] : ( 0, 4, 8, 12, 16, 20, 24 ) 100 // CHECK-NEXT: crd[2] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 ) 101 // CHECK-NEXT: values : ( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ) 102 // CHECK-NEXT: ---- 103 // CHECK: ---- Sparse Tensor ---- 104 // CHECK-NEXT: nse = 24 105 // CHECK-NEXT: dim = ( 2, 3, 4 ) 106 // CHECK-NEXT: lvl = ( 2, 3, 4 ) 107 // CHECK-NEXT: pos[0] : ( 0, 2 ) 108 // CHECK-NEXT: crd[0] : ( 0, 1 ) 109 // CHECK-NEXT: pos[1] : ( 0, 3, 6 ) 110 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 1, 2 ) 111 // CHECK-NEXT: pos[2] : ( 0, 4, 8, 12, 16, 20, 24 ) 112 // CHECK-NEXT: crd[2] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 ) 113 // CHECK-NEXT: values : ( 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 ) 114 // CHECK-NEXT: ---- 115 // 116 sparse_tensor.print %sm_t : tensor<2x3x4xf64, #SparseMatrix> 117 sparse_tensor.print %sm_f : tensor<2x3x4xf64, #SparseMatrix> 118 119 bufferization.dealloc_tensor %sm : tensor<2x3x4xf64, #SparseMatrix> 120 bufferization.dealloc_tensor %sm_t : tensor<2x3x4xf64, #SparseMatrix> 121 bufferization.dealloc_tensor %sm_f : tensor<2x3x4xf64, #SparseMatrix> 122 return 123 } 124} 125