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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#CCC = #sparse_tensor.encoding<{28 map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed),29 posWidth = 64,30 crdWidth = 3231}>32 33#DenseCSR = #sparse_tensor.encoding<{34 map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 : compressed),35 posWidth = 64,36 crdWidth = 3237}>38 39#CSRDense = #sparse_tensor.encoding<{40 map = (d0, d1, d2) -> (d0 : dense, d1 : compressed, d2 : dense),41 posWidth = 64,42 crdWidth = 3243}>44 45//46// Test assembly operation with CCC, dense-CSR and CSR-dense.47//48module {49 //50 // Main driver.51 //52 func.func @main() {53 %c0 = arith.constant 0 : index54 %f0 = arith.constant 0.0 : f3255 56 //57 // Setup CCC.58 //59 60 %data0 = arith.constant dense<61 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ]> : tensor<8xf32>62 %pos00 = arith.constant dense<63 [ 0, 3 ]> : tensor<2xi64>64 %crd00 = arith.constant dense<65 [ 0, 2, 3 ]> : tensor<3xi32>66 %pos01 = arith.constant dense<67 [ 0, 2, 4, 5 ]> : tensor<4xi64>68 %crd01 = arith.constant dense<69 [ 0, 1, 1, 2, 1 ]> : tensor<5xi32>70 %pos02 = arith.constant dense<71 [ 0, 2, 4, 5, 7, 8 ]> : tensor<6xi64>72 %crd02 = arith.constant dense<73 [ 0, 1, 0, 1, 0, 0, 1, 0 ]> : tensor<8xi32>74 75 %s0 = sparse_tensor.assemble (%pos00, %crd00, %pos01, %crd01, %pos02, %crd02), %data0 :76 (tensor<2xi64>, tensor<3xi32>,77 tensor<4xi64>, tensor<5xi32>,78 tensor<6xi64>, tensor<8xi32>), tensor<8xf32> to tensor<4x3x2xf32, #CCC>79 80 //81 // Setup DenseCSR.82 //83 84 %data1 = arith.constant dense<85 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,86 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ]> : tensor<16xf32>87 %pos1 = arith.constant dense<88 [ 0, 2, 3, 4, 6, 6, 7, 9, 11, 13, 14, 15, 16 ]> : tensor<13xi64>89 %crd1 = arith.constant dense<90 [ 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]> : tensor<16xi32>91 92 %s1 = sparse_tensor.assemble (%pos1, %crd1), %data1 : (tensor<13xi64>, tensor<16xi32>), tensor<16xf32> to tensor<4x3x2xf32, #DenseCSR>93 94 //95 // Setup CSRDense.96 //97 98 %data2 = arith.constant dense<99 [ 1.0, 2.0, 0.0, 3.0, 4.0, 0.0, 5.0, 6.0, 0.0, 7.0, 8.0,100 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 0.0, 0.0, 15.0, 0.0, 16.0 ]> : tensor<22xf32>101 %pos2 = arith.constant dense<102 [ 0, 3, 5, 8, 11 ]> : tensor<5xi64>103 %crd2 = arith.constant dense<104 [ 0, 1, 2, 0, 2, 0, 1, 2, 0, 1, 2 ]> : tensor<11xi32>105 106 %s2 = sparse_tensor.assemble (%pos2, %crd2), %data2 : (tensor<5xi64>, tensor<11xi32>), tensor<22xf32> to tensor<4x3x2xf32, #CSRDense>107 108 //109 // Verify.110 //111 // CHECK: ---- Sparse Tensor ----112 // CHECK-NEXT: nse = 8113 // CHECK-NEXT: dim = ( 4, 3, 2 )114 // CHECK-NEXT: lvl = ( 4, 3, 2 )115 // CHECK-NEXT: pos[0] : ( 0, 3 )116 // CHECK-NEXT: crd[0] : ( 0, 2, 3 )117 // CHECK-NEXT: pos[1] : ( 0, 2, 4, 5 )118 // CHECK-NEXT: crd[1] : ( 0, 1, 1, 2, 1 )119 // CHECK-NEXT: pos[2] : ( 0, 2, 4, 5, 7, 8 )120 // CHECK-NEXT: crd[2] : ( 0, 1, 0, 1, 0, 0, 1, 0 )121 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8 )122 // CHECK-NEXT: ----123 // CHECK: ---- Sparse Tensor ----124 // CHECK-NEXT: nse = 16125 // CHECK-NEXT: dim = ( 4, 3, 2 )126 // CHECK-NEXT: lvl = ( 4, 3, 2 )127 // CHECK-NEXT: pos[2] : ( 0, 2, 3, 4, 6, 6, 7, 9, 11, 13, 14, 15, 16 )128 // CHECK-NEXT: crd[2] : ( 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1 )129 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 )130 // CHECK-NEXT: ----131 // CHECK: ---- Sparse Tensor ----132 // CHECK-NEXT: nse = 22133 // CHECK-NEXT: dim = ( 4, 3, 2 )134 // CHECK-NEXT: lvl = ( 4, 3, 2 )135 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 8, 11 )136 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 2, 0, 1, 2, 0, 1, 2 )137 // CHECK-NEXT: values : ( 1, 2, 0, 3, 4, 0, 5, 6, 0, 7, 8, 9, 10, 11, 12, 13, 14, 0, 0, 15, 0, 16 )138 // CHECK-NEXT: ----139 //140 sparse_tensor.print %s0 : tensor<4x3x2xf32, #CCC>141 sparse_tensor.print %s1 : tensor<4x3x2xf32, #DenseCSR>142 sparse_tensor.print %s2 : tensor<4x3x2xf32, #CSRDense>143 144 // TODO: This check is no longer needed once the codegen path uses the145 // buffer deallocation pass. "dealloc_tensor" turn into a no-op in the146 // codegen path.147 %has_runtime = sparse_tensor.has_runtime_library148 scf.if %has_runtime {149 // sparse_tensor.assemble copies buffers when running with the runtime150 // library. Deallocations are not needed when running in codegen mode.151 bufferization.dealloc_tensor %s0 : tensor<4x3x2xf32, #CCC>152 bufferization.dealloc_tensor %s1 : tensor<4x3x2xf32, #DenseCSR>153 bufferization.dealloc_tensor %s2 : tensor<4x3x2xf32, #CSRDense>154 }155 156 return157 }158}159