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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// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/ds.mtx"22// RUN: %{compile} | env %{env} %{run} | FileCheck %s23//24// Do the same run, but now with direct IR generation.25// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false26// RUN: %{compile} | env %{env} %{run} | FileCheck %s27//28// Do the same run, but now with direct IR generation and vectorization.29// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true30// RUN: %{compile} | env %{env} %{run} | FileCheck %s31 32!Filename = !llvm.ptr33 34#CSR = #sparse_tensor.encoding<{35 map = (i, j) -> ( i : dense, j : compressed)36}>37 38#CSR_hi = #sparse_tensor.encoding<{39 map = (i, j) -> ( i : dense, j : loose_compressed)40}>41 42#NV_24 = #sparse_tensor.encoding<{43 map = ( i, j ) -> ( i : dense,44 j floordiv 4 : dense,45 j mod 4 : structured[2, 4]),46 crdWidth = 847}>48 49#NV_58 = #sparse_tensor.encoding<{50 map = ( i, j ) -> ( i : dense,51 j floordiv 8 : dense,52 j mod 8 : structured[5, 8]),53 crdWidth = 854}>55 56module {57 58 func.func private @getTensorFilename(index) -> (!Filename)59 60 //61 // Input matrix:62 //63 // [[0.0, 0.0, 1.0, 2.0, 0.0, 3.0, 0.0, 4.0],64 // [0.0, 5.0, 6.0, 0.0, 7.0, 0.0, 0.0, 8.0],65 // [9.0, 0.0, 10.0, 0.0, 11.0, 12.0, 0.0, 0.0]]66 //67 func.func @main() {68 %c0 = arith.constant 0 : index69 %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)70 71 %A1 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #CSR>72 %A2 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #CSR_hi>73 %A3 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #NV_24>74 %A4 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #NV_58>75 76 //77 // CSR:78 //79 // CHECK: ---- Sparse Tensor ----80 // CHECK-NEXT: nse = 1281 // CHECK-NEXT: dim = ( 3, 8 )82 // CHECK-NEXT: lvl = ( 3, 8 )83 // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12 )84 // CHECK-NEXT: crd[1] : ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5 )85 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )86 // CHECK-NEXT: ----87 //88 sparse_tensor.print %A1 : tensor<?x?xf64, #CSR>89 90 //91 // CSR_hi:92 //93 // CHECK-NEXT: ---- Sparse Tensor ----94 // CHECK-NEXT: nse = 1295 // CHECK-NEXT: dim = ( 3, 8 )96 // CHECK-NEXT: lvl = ( 3, 8 )97 // CHECK-NEXT: pos[1] : ( 0, 4, 4, 8, 8, 12, {{.*}} )98 // CHECK-NEXT: crd[1] : ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5 )99 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )100 // CHECK-NEXT: ----101 //102 sparse_tensor.print %A2 : tensor<?x?xf64, #CSR_hi>103 104 //105 // NV_24:106 //107 // CHECK-NEXT: ---- Sparse Tensor ----108 // CHECK-NEXT: nse = 12109 // CHECK-NEXT: dim = ( 3, 8 )110 // CHECK-NEXT: lvl = ( 3, 2, 4 )111 // CHECK-NEXT: crd[2] : ( 2, 3, 1, 3, 1, 2, 0, 3, 0, 2, 0, 1 )112 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )113 // CHECK-NEXT: ----114 // CHECK-NEXT: ---- Sparse Tensor ----115 //116 sparse_tensor.print %A3 : tensor<?x?xf64, #NV_24>117 118 //119 // NV_58:120 //121 // CHECK-NEXT: nse = 12122 // CHECK-NEXT: dim = ( 3, 8 )123 // CHECK-NEXT: lvl = ( 3, 1, 8 )124 // CHECK-NEXT: crd[2] : ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5 )125 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )126 // CHECK-NEXT: ----127 //128 sparse_tensor.print %A4 : tensor<?x?xf64, #NV_58>129 130 // Release the resources.131 bufferization.dealloc_tensor %A1: tensor<?x?xf64, #CSR>132 bufferization.dealloc_tensor %A2: tensor<?x?xf64, #CSR_hi>133 bufferization.dealloc_tensor %A3: tensor<?x?xf64, #NV_24>134 bufferization.dealloc_tensor %A4: tensor<?x?xf64, #NV_58>135 136 return137 }138}139