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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=false25// RUN: %{compile} | %{run} | FileCheck %s26//27// Do the same run, but now with vectorization.28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=4 29// RUN: %{compile} | %{run} | FileCheck %s30//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 false39 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 : f6451 linalg.yield %4 : f6452 } -> 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 : f6465 linalg.yield %4 : f6466 } -> 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 : i183 %f = arith.constant 0 : i184 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 = 2493 // 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 = 24105 // 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 return123 }124}125