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// RUN: %{compile} | %{run} | FileCheck %s 22// 23// Do the same run, but now with direct IR generation. 24// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false 25// RUN: %{compile} | %{run} | FileCheck %s 26// 27// Do the same run, but now with direct IR generation and vectorization. 28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true 29// RUN: %{compile} | %{run} | FileCheck %s 30// 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#SparseVector = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }> 35 36#trait_mul_s = { 37 indexing_maps = [ 38 affine_map<(i) -> (i)> // x (out) 39 ], 40 iterator_types = ["parallel"], 41 doc = "x(i) = x(i) * 2.0" 42} 43 44module { 45 func.func @main() { 46 %f1 = arith.constant 1.0 : f32 47 %f2 = arith.constant 2.0 : f32 48 %f3 = arith.constant 3.0 : f32 49 %f4 = arith.constant 4.0 : f32 50 %c0 = arith.constant 0 : index 51 %c1 = arith.constant 1 : index 52 %c3 = arith.constant 3 : index 53 %c8 = arith.constant 8 : index 54 %c1023 = arith.constant 1023 : index 55 56 // Build the sparse vector from straightline code. 57 %0 = tensor.empty() : tensor<1024xf32, #SparseVector> 58 %1 = tensor.insert %f1 into %0[%c0] : tensor<1024xf32, #SparseVector> 59 %2 = tensor.insert %f2 into %1[%c1] : tensor<1024xf32, #SparseVector> 60 %3 = tensor.insert %f3 into %2[%c3] : tensor<1024xf32, #SparseVector> 61 %4 = tensor.insert %f4 into %3[%c1023] : tensor<1024xf32, #SparseVector> 62 %5 = sparse_tensor.load %4 hasInserts : tensor<1024xf32, #SparseVector> 63 64 // 65 // CHECK: ---- Sparse Tensor ---- 66 // CHECK-NEXT: nse = 4 67 // CHECK-NEXT: dim = ( 1024 ) 68 // CHECK-NEXT: lvl = ( 1024 ) 69 // CHECK-NEXT: pos[0] : ( 0, 4 ) 70 // CHECK-NEXT: crd[0] : ( 0, 1, 3, 1023 ) 71 // CHECK-NEXT: values : ( 1, 2, 3, 4 ) 72 // CHECK-NEXT: ---- 73 // 74 sparse_tensor.print %5 : tensor<1024xf32, #SparseVector> 75 76 // Build another sparse vector in a loop. 77 %6 = tensor.empty() : tensor<1024xf32, #SparseVector> 78 %7 = scf.for %i = %c0 to %c8 step %c1 iter_args(%vin = %6) -> tensor<1024xf32, #SparseVector> { 79 %ii = arith.muli %i, %c3 : index 80 %vout = tensor.insert %f1 into %vin[%ii] : tensor<1024xf32, #SparseVector> 81 scf.yield %vout : tensor<1024xf32, #SparseVector> 82 } 83 %8 = sparse_tensor.load %7 hasInserts : tensor<1024xf32, #SparseVector> 84 85 // 86 // CHECK-NEXT: ---- Sparse Tensor ---- 87 // CHECK-NEXT: nse = 8 88 // CHECK-NEXT: dim = ( 1024 ) 89 // CHECK-NEXT: lvl = ( 1024 ) 90 // CHECK-NEXT: pos[0] : ( 0, 8 ) 91 // CHECK-NEXT: crd[0] : ( 0, 3, 6, 9, 12, 15, 18, 21 ) 92 // CHECK-NEXT: values : ( 1, 1, 1, 1, 1, 1, 1, 1 ) 93 // CHECK-NEXT: ---- 94 // 95 sparse_tensor.print %8 : tensor<1024xf32, #SparseVector> 96 97 // Free resources. 98 bufferization.dealloc_tensor %5 : tensor<1024xf32, #SparseVector> 99 bufferization.dealloc_tensor %8 : tensor<1024xf32, #SparseVector> 100 return 101 } 102} 103