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 direct IR generation and vectorization. 28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true 29// RUN: %{compile} | %{run} | FileCheck %s 30// 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 : index 78 %df = arith.constant -1.0 : f64 79 80 // 81 // CHECK: ---- Sparse Tensor ---- 82 // CHECK-NEXT: nse = 6 83 // 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 = 6 93 // 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 = 6 101 // 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 return 122 } 123 124} 125