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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 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 VLA vectorization.32// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}33 34!Filename = !llvm.ptr35 36#SparseMatrix = #sparse_tensor.encoding<{37 map = (d0, d1) -> (d0 : compressed, d1 : compressed)38}>39 40#trait_sum_reduce = {41 indexing_maps = [42 affine_map<(i,j) -> (i,j)>, // A43 affine_map<(i,j) -> ()> // x (out)44 ],45 iterator_types = ["reduction", "reduction"],46 doc = "x += A(i,j)"47}48 49module {50 //51 // A kernel that sum-reduces a matrix to a single scalar.52 //53 func.func @kernel_sum_reduce(%arga: tensor<?x?xf16, #SparseMatrix>,54 %argx: tensor<f16>) -> tensor<f16> {55 %0 = linalg.generic #trait_sum_reduce56 ins(%arga: tensor<?x?xf16, #SparseMatrix>)57 outs(%argx: tensor<f16>) {58 ^bb(%a: f16, %x: f16):59 %0 = arith.addf %x, %a : f1660 linalg.yield %0 : f1661 } -> tensor<f16>62 return %0 : tensor<f16>63 }64 65 func.func private @getTensorFilename(index) -> (!Filename)66 67 //68 // Main driver that reads matrix from file and calls the sparse kernel.69 //70 func.func @main() {71 // Setup input sparse matrix from compressed constant.72 %d = arith.constant dense <[73 [ 1.1, 1.2, 0.0, 1.4 ],74 [ 0.0, 0.0, 0.0, 0.0 ],75 [ 3.1, 0.0, 3.3, 3.4 ]76 ]> : tensor<3x4xf16>77 %a = sparse_tensor.convert %d : tensor<3x4xf16> to tensor<?x?xf16, #SparseMatrix>78 79 %d0 = arith.constant 0.0 : f1680 // Setup memory for a single reduction scalar,81 // initialized to zero.82 %x = tensor.from_elements %d0 : tensor<f16>83 84 // Call the kernel.85 %0 = call @kernel_sum_reduce(%a, %x)86 : (tensor<?x?xf16, #SparseMatrix>, tensor<f16>) -> tensor<f16>87 88 // Print the result for verification.89 //90 // CHECK: 13.591 //92 %v = tensor.extract %0[] : tensor<f16>93 %vf = arith.extf %v: f16 to f3294 vector.print %vf : f3295 96 // Release the resources.97 bufferization.dealloc_tensor %0 : tensor<f16>98 bufferization.dealloc_tensor %a : tensor<?x?xf16, #SparseMatrix>99 100 return101 }102}103