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/block.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#BSR = #sparse_tensor.encoding<{ 35 map = (i, j) -> 36 ( i floordiv 2 : dense 37 , j floordiv 2 : compressed 38 , i mod 2 : dense 39 , j mod 2 : dense 40 ) 41}> 42 43#DSDD = #sparse_tensor.encoding<{ 44 map = (i, j, k, l) -> ( i : dense, j : compressed, k : dense, l : dense) 45}> 46 47#trait_scale_inplace = { 48 indexing_maps = [ 49 affine_map<(i,j) -> (i,j)> // X (out) 50 ], 51 iterator_types = ["parallel", "parallel"] 52} 53 54// 55// Example 2x2 block storage: 56// 57// +-----+-----+-----+ +-----+-----+-----+ 58// | 1 2 | . . | 4 . | | 1 2 | | 4 0 | 59// | . 3 | . . | . 5 | | 0 3 | | 0 5 | 60// +-----+-----+-----+ => +-----+-----+-----+ 61// | . . | 6 7 | . . | | | 6 7 | | 62// | . . | 8 . | . . | | | 8 0 | | 63// +-----+-----+-----+ +-----+-----+-----+ 64// 65// Stored as: 66// 67// positions[1] : 0 2 3 68// coordinates[1] : 0 2 1 69// values : 1.000000 2.000000 0.000000 3.000000 4.000000 0.000000 0.000000 5.000000 6.000000 7.000000 8.000000 0.000000 70// 71module { 72 73 func.func private @getTensorFilename(index) -> (!Filename) 74 75 func.func @scale(%arg0: tensor<?x?xf64, #BSR>) -> tensor<?x?xf64, #BSR> { 76 %c = arith.constant 3.0 : f64 77 %0 = linalg.generic #trait_scale_inplace 78 outs(%arg0: tensor<?x?xf64, #BSR>) { 79 ^bb(%x: f64): 80 %1 = arith.mulf %x, %c : f64 81 linalg.yield %1 : f64 82 } -> tensor<?x?xf64, #BSR> 83 return %0 : tensor<?x?xf64, #BSR> 84 } 85 86 func.func @main() { 87 %c0 = arith.constant 0 : index 88 %f0 = arith.constant 0.0 : f64 89 90 %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename) 91 %A = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #BSR> 92 93 // CHECK: ---- Sparse Tensor ---- 94 // CHECK-NEXT: nse = 12 95 // CHECK-NEXT: dim = ( 4, 6 ) 96 // CHECK-NEXT: lvl = ( 2, 3, 2, 2 ) 97 // CHECK-NEXT: pos[1] : ( 0, 2, 3 ) 98 // CHECK-NEXT: crd[1] : ( 0, 2, 1 ) 99 // CHECK-NEXT: values : ( 1, 2, 0, 3, 4, 0, 0, 5, 6, 7, 8, 0 ) 100 // CHECK-NEXT: ---- 101 sparse_tensor.print %A : tensor<?x?xf64, #BSR> 102 103 // CHECK-NEXT: ---- Sparse Tensor ---- 104 // CHECK-NEXT: nse = 12 105 // CHECK-NEXT: dim = ( 2, 3, 2, 2 ) 106 // CHECK-NEXT: lvl = ( 2, 3, 2, 2 ) 107 // CHECK-NEXT: pos[1] : ( 0, 2, 3 ) 108 // CHECK-NEXT: crd[1] : ( 0, 2, 1 ) 109 // CHECK-NEXT: values : ( 1, 2, 0, 3, 4, 0, 0, 5, 6, 7, 8, 0 ) 110 // CHECK-NEXT: ---- 111 %t1 = sparse_tensor.reinterpret_map %A : tensor<?x?xf64, #BSR> 112 to tensor<?x?x2x2xf64, #DSDD> 113 sparse_tensor.print %t1 : tensor<?x?x2x2xf64, #DSDD> 114 115 // CHECK-NEXT: ---- Sparse Tensor ---- 116 // CHECK-NEXT: nse = 12 117 // CHECK-NEXT: dim = ( 4, 6 ) 118 // CHECK-NEXT: lvl = ( 2, 3, 2, 2 ) 119 // CHECK-NEXT: pos[1] : ( 0, 2, 3 ) 120 // CHECK-NEXT: crd[1] : ( 0, 2, 1 ) 121 // CHECK-NEXT: values : ( 3, 6, 0, 9, 12, 0, 0, 15, 18, 21, 24, 0 ) 122 // CHECK-NEXT: ---- 123 %As = call @scale(%A) : (tensor<?x?xf64, #BSR>) -> (tensor<?x?xf64, #BSR>) 124 sparse_tensor.print %As : tensor<?x?xf64, #BSR> 125 126 // Release the resources. 127 bufferization.dealloc_tensor %A: tensor<?x?xf64, #BSR> 128 129 return 130 } 131} 132