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// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/ds.mtx" 22// RUN: %{compile} | env %{env} %{run} | FileCheck %s 23// 24// Do the same run, but now with direct IR generation. 25// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false 26// RUN: %{compile} | env %{env} %{run} | FileCheck %s 27// 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=true 30// RUN: %{compile} | env %{env} %{run} | FileCheck %s 31 32!Filename = !llvm.ptr 33 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 = 8 47}> 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 = 8 54}> 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 : index 69 %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 = 12 81 // 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 = 12 95 // 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 = 12 109 // 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 = 12 122 // 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 return 137 } 138} 139