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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=false enable-buffer-initialization=true25// 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 enable-buffer-initialization=true 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 35#map = affine_map<(d0) -> (d0)>36 37#SV = #sparse_tensor.encoding<{38 map = (d0) -> (d0 : compressed)39}>40 41module {42 43 // This directly yields an empty sparse vector.44 func.func @empty() -> tensor<10xf32, #SV> {45 %0 = tensor.empty() : tensor<10xf32, #SV>46 return %0 : tensor<10xf32, #SV>47 }48 49 // This also directly yields an empty sparse vector.50 func.func @empty_alloc() -> tensor<10xf32, #SV> {51 %0 = bufferization.alloc_tensor() : tensor<10xf32, #SV>52 return %0 : tensor<10xf32, #SV>53 }54 55 // This yields a hidden empty sparse vector (all zeros).56 func.func @zeros() -> tensor<10xf32, #SV> {57 %cst = arith.constant 0.0 : f3258 %0 = bufferization.alloc_tensor() : tensor<10xf32, #SV>59 %1 = linalg.generic {60 indexing_maps = [#map],61 iterator_types = ["parallel"]}62 outs(%0 : tensor<10xf32, #SV>) {63 ^bb0(%out: f32):64 linalg.yield %cst : f3265 } -> tensor<10xf32, #SV>66 return %1 : tensor<10xf32, #SV>67 }68 69 // This yields a filled sparse vector (all ones).70 func.func @ones() -> tensor<10xf32, #SV> {71 %cst = arith.constant 1.0 : f3272 %0 = bufferization.alloc_tensor() : tensor<10xf32, #SV>73 %1 = linalg.generic {74 indexing_maps = [#map],75 iterator_types = ["parallel"]}76 outs(%0 : tensor<10xf32, #SV>) {77 ^bb0(%out: f32):78 linalg.yield %cst : f3279 } -> tensor<10xf32, #SV>80 return %1 : tensor<10xf32, #SV>81 }82 83 //84 // Main driver.85 //86 func.func @main() {87 88 %0 = call @empty() : () -> tensor<10xf32, #SV>89 %1 = call @empty_alloc() : () -> tensor<10xf32, #SV>90 %2 = call @zeros() : () -> tensor<10xf32, #SV>91 %3 = call @ones() : () -> tensor<10xf32, #SV>92 93 //94 // Verify the output. In particular, make sure that95 // all empty sparse vector data structures are properly96 // finalized with a pair (0,0) for positions.97 //98 // CHECK: ---- Sparse Tensor ----99 // CHECK-NEXT: nse = 0100 // CHECK-NEXT: dim = ( 10 )101 // CHECK-NEXT: lvl = ( 10 )102 // CHECK-NEXT: pos[0] : ( 0, 0 )103 // CHECK-NEXT: crd[0] : ( )104 // CHECK-NEXT: values : ( )105 // CHECK-NEXT: ----106 //107 // CHECK-NEXT: ---- Sparse Tensor ----108 // CHECK-NEXT: nse = 0109 // CHECK-NEXT: dim = ( 10 )110 // CHECK-NEXT: lvl = ( 10 )111 // CHECK-NEXT: pos[0] : ( 0, 0 )112 // CHECK-NEXT: crd[0] : ( )113 // CHECK-NEXT: values : ( )114 // CHECK-NEXT: ----115 //116 // CHECK-NEXT: ---- Sparse Tensor ----117 // CHECK-NEXT: nse = 0118 // CHECK-NEXT: dim = ( 10 )119 // CHECK-NEXT: lvl = ( 10 )120 // CHECK-NEXT: pos[0] : ( 0, 0 )121 // CHECK-NEXT: crd[0] : ( )122 // CHECK-NEXT: values : ( )123 // CHECK-NEXT: ----124 //125 // CHECK-NEXT: ---- Sparse Tensor ----126 // CHECK-NEXT: nse = 10127 // CHECK-NEXT: dim = ( 10 )128 // CHECK-NEXT: lvl = ( 10 )129 // CHECK-NEXT: pos[0] : ( 0, 10 )130 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 )131 // CHECK-NEXT: values : ( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 )132 // CHECK-NEXT: ----133 //134 sparse_tensor.print %0 : tensor<10xf32, #SV>135 sparse_tensor.print %1 : tensor<10xf32, #SV>136 sparse_tensor.print %2 : tensor<10xf32, #SV>137 sparse_tensor.print %3 : tensor<10xf32, #SV>138 139 bufferization.dealloc_tensor %0 : tensor<10xf32, #SV>140 bufferization.dealloc_tensor %1 : tensor<10xf32, #SV>141 bufferization.dealloc_tensor %2 : tensor<10xf32, #SV>142 bufferization.dealloc_tensor %3 : tensor<10xf32, #SV>143 return144 }145}146