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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 direct IR generation and vectorization.28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true29// RUN: %{compile} | %{run} | FileCheck %s30//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#SparseVector = #sparse_tensor.encoding<{35 map = (d0) -> (d0 : compressed)36}>37 38#SparseMatrix = #sparse_tensor.encoding<{39 map = (d0, d1) -> (d0 : compressed, d1 : compressed)40}>41 42#Sparse3dTensor = #sparse_tensor.encoding<{43 map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed)44}>45 46module {47 48 func.func @reshape0(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<2x6xf64, #SparseMatrix> {49 %shape = arith.constant dense <[ 2, 6 ]> : tensor<2xi32>50 %0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<2xi32>) -> tensor<2x6xf64, #SparseMatrix>51 return %0 : tensor<2x6xf64, #SparseMatrix>52 }53 54 func.func @reshape1(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector> {55 %shape = arith.constant dense <[ 12 ]> : tensor<1xi32>56 %0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<1xi32>) -> tensor<12xf64, #SparseVector>57 return %0 : tensor<12xf64, #SparseVector>58 }59 60 func.func @reshape2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<2x3x2xf64, #Sparse3dTensor> {61 %shape = arith.constant dense <[ 2, 3, 2 ]> : tensor<3xi32>62 %0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<3xi32>) -> tensor<2x3x2xf64, #Sparse3dTensor>63 return %0 : tensor<2x3x2xf64, #Sparse3dTensor>64 }65 66 67 func.func @main() {68 %m = arith.constant dense <[ [ 1.1, 0.0, 1.3, 0.0 ],69 [ 2.1, 0.0, 2.3, 0.0 ],70 [ 3.1, 0.0, 3.3, 0.0 ]]> : tensor<3x4xf64>71 %sm = sparse_tensor.convert %m : tensor<3x4xf64> to tensor<3x4xf64, #SparseMatrix>72 73 %reshaped0 = call @reshape0(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<2x6xf64, #SparseMatrix>74 %reshaped1 = call @reshape1(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector>75 %reshaped2 = call @reshape2(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<2x3x2xf64, #Sparse3dTensor>76 77 %c0 = arith.constant 0 : index78 %df = arith.constant -1.0 : f6479 80 //81 // CHECK: ---- Sparse Tensor ----82 // CHECK-NEXT: nse = 683 // CHECK-NEXT: dim = ( 2, 6 )84 // CHECK-NEXT: lvl = ( 2, 6 )85 // CHECK-NEXT: pos[0] : ( 0, 2 )86 // CHECK-NEXT: crd[0] : ( 0, 1 )87 // CHECK-NEXT: pos[1] : ( 0, 3, 6 )88 // CHECK-NEXT: crd[1] : ( 0, 2, 4, 0, 2, 4 )89 // CHECK-NEXT: values : ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3 )90 // CHECK-NEXT: ----91 // CHECK: ---- Sparse Tensor ----92 // CHECK-NEXT: nse = 693 // CHECK-NEXT: dim = ( 12 )94 // CHECK-NEXT: lvl = ( 12 )95 // CHECK-NEXT: pos[0] : ( 0, 6 )96 // CHECK-NEXT: crd[0] : ( 0, 2, 4, 6, 8, 10 )97 // CHECK-NEXT: values : ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3 )98 // CHECK-NEXT: ----99 // CHECK: ---- Sparse Tensor ----100 // CHECK-NEXT: nse = 6101 // CHECK-NEXT: dim = ( 2, 3, 2 )102 // CHECK-NEXT: lvl = ( 2, 3, 2 )103 // CHECK-NEXT: pos[0] : ( 0, 2 )104 // CHECK-NEXT: crd[0] : ( 0, 1 )105 // CHECK-NEXT: pos[1] : ( 0, 3, 6 )106 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 1, 2 )107 // CHECK-NEXT: pos[2] : ( 0, 1, 2, 3, 4, 5, 6 )108 // CHECK-NEXT: crd[2] : ( 0, 0, 0, 0, 0, 0 )109 // CHECK-NEXT: values : ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3 )110 // CHECK-NEXT: ----111 //112 sparse_tensor.print %reshaped0: tensor<2x6xf64, #SparseMatrix>113 sparse_tensor.print %reshaped1: tensor<12xf64, #SparseVector>114 sparse_tensor.print %reshaped2: tensor<2x3x2xf64, #Sparse3dTensor>115 116 bufferization.dealloc_tensor %sm : tensor<3x4xf64, #SparseMatrix>117 bufferization.dealloc_tensor %reshaped0 : tensor<2x6xf64, #SparseMatrix>118 bufferization.dealloc_tensor %reshaped1 : tensor<12xf64, #SparseVector>119 bufferization.dealloc_tensor %reshaped2 : tensor<2x3x2xf64, #Sparse3dTensor>120 121 return122 }123 124}125