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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=4 29// 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#CSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>35 36#trait_scale = {37 indexing_maps = [38 affine_map<(i,j) -> (i,j)> // X (out)39 ],40 iterator_types = ["parallel", "parallel"],41 doc = "X(i,j) = X(i,j) * 2"42}43 44//45// Integration test that lowers a kernel annotated as sparse to actual sparse46// code, initializes a matching sparse storage scheme from a dense tensor,47// and runs the resulting code with the JIT compiler.48//49module {50 //51 // A kernel that scales a sparse matrix A by a factor of 2.0.52 //53 func.func @sparse_scale(%argx: tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR> {54 %c = arith.constant 2.0 : f3255 %0 = linalg.generic #trait_scale56 outs(%argx: tensor<8x8xf32, #CSR>) {57 ^bb(%x: f32):58 %1 = arith.mulf %x, %c : f3259 linalg.yield %1 : f3260 } -> tensor<8x8xf32, #CSR>61 return %0 : tensor<8x8xf32, #CSR>62 }63 64 //65 // Main driver that converts a dense tensor into a sparse tensor66 // and then calls the sparse scaling kernel with the sparse tensor67 // as input argument.68 //69 func.func @main() {70 %c0 = arith.constant 0 : index71 %f0 = arith.constant 0.0 : f3272 73 // Initialize a dense tensor.74 %0 = arith.constant dense<[75 [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0],76 [0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],77 [0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0],78 [0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0],79 [0.0, 1.0, 0.0, 0.0, 5.0, 0.0, 0.0, 0.0],80 [0.0, 1.0, 1.0, 0.0, 0.0, 6.0, 0.0, 0.0],81 [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 7.0, 1.0],82 [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 8.0]83 ]> : tensor<8x8xf32>84 85 // Convert dense tensor to sparse tensor and call sparse kernel.86 %1 = sparse_tensor.convert %0 : tensor<8x8xf32> to tensor<8x8xf32, #CSR>87 %2 = call @sparse_scale(%1)88 : (tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR>89 90 // Print the resulting compacted values for verification.91 //92 // CHECK: ---- Sparse Tensor ----93 // CHECK-NEXT: nse = 1694 // CHECK-NEXT: dim = ( 8, 8 )95 // CHECK-NEXT: lvl = ( 8, 8 )96 // CHECK-NEXT: pos[1] : ( 0, 3, 4, 5, 6, 8, 11, 14, 16 )97 // CHECK-NEXT: crd[1] : ( 0, 2, 7, 1, 2, 3, 1, 4, 1, 2, 5, 2, 6, 7, 2, 7 )98 // CHECK-NEXT: values : ( 2, 2, 2, 4, 6, 8, 2, 10, 2, 2, 12, 2, 14, 2, 2, 16 )99 // CHECK-NEXT: ----100 //101 sparse_tensor.print %2 : tensor<8x8xf32, #CSR>102 103 // Release the resources.104 bufferization.dealloc_tensor %1 : tensor<8x8xf32, #CSR>105 106 return107 }108}109