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#CCC = #sparse_tensor.encoding<{ 28 map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed), 29 posWidth = 64, 30 crdWidth = 32 31}> 32 33#DenseCSR = #sparse_tensor.encoding<{ 34 map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 : compressed), 35 posWidth = 64, 36 crdWidth = 32 37}> 38 39#CSRDense = #sparse_tensor.encoding<{ 40 map = (d0, d1, d2) -> (d0 : dense, d1 : compressed, d2 : dense), 41 posWidth = 64, 42 crdWidth = 32 43}> 44 45// 46// Test assembly operation with CCC, dense-CSR and CSR-dense. 47// 48module { 49 // 50 // Main driver. 51 // 52 func.func @main() { 53 %c0 = arith.constant 0 : index 54 %f0 = arith.constant 0.0 : f32 55 56 // 57 // Setup CCC. 58 // 59 60 %data0 = arith.constant dense< 61 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ]> : tensor<8xf32> 62 %pos00 = arith.constant dense< 63 [ 0, 3 ]> : tensor<2xi64> 64 %crd00 = arith.constant dense< 65 [ 0, 2, 3 ]> : tensor<3xi32> 66 %pos01 = arith.constant dense< 67 [ 0, 2, 4, 5 ]> : tensor<4xi64> 68 %crd01 = arith.constant dense< 69 [ 0, 1, 1, 2, 1 ]> : tensor<5xi32> 70 %pos02 = arith.constant dense< 71 [ 0, 2, 4, 5, 7, 8 ]> : tensor<6xi64> 72 %crd02 = arith.constant dense< 73 [ 0, 1, 0, 1, 0, 0, 1, 0 ]> : tensor<8xi32> 74 75 %s0 = sparse_tensor.assemble (%pos00, %crd00, %pos01, %crd01, %pos02, %crd02), %data0 : 76 (tensor<2xi64>, tensor<3xi32>, 77 tensor<4xi64>, tensor<5xi32>, 78 tensor<6xi64>, tensor<8xi32>), tensor<8xf32> to tensor<4x3x2xf32, #CCC> 79 80 // 81 // Setup DenseCSR. 82 // 83 84 %data1 = arith.constant dense< 85 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 86 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0 ]> : tensor<16xf32> 87 %pos1 = arith.constant dense< 88 [ 0, 2, 3, 4, 6, 6, 7, 9, 11, 13, 14, 15, 16 ]> : tensor<13xi64> 89 %crd1 = arith.constant dense< 90 [ 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]> : tensor<16xi32> 91 92 %s1 = sparse_tensor.assemble (%pos1, %crd1), %data1 : (tensor<13xi64>, tensor<16xi32>), tensor<16xf32> to tensor<4x3x2xf32, #DenseCSR> 93 94 // 95 // Setup CSRDense. 96 // 97 98 %data2 = arith.constant dense< 99 [ 1.0, 2.0, 0.0, 3.0, 4.0, 0.0, 5.0, 6.0, 0.0, 7.0, 8.0, 100 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 0.0, 0.0, 15.0, 0.0, 16.0 ]> : tensor<22xf32> 101 %pos2 = arith.constant dense< 102 [ 0, 3, 5, 8, 11 ]> : tensor<5xi64> 103 %crd2 = arith.constant dense< 104 [ 0, 1, 2, 0, 2, 0, 1, 2, 0, 1, 2 ]> : tensor<11xi32> 105 106 %s2 = sparse_tensor.assemble (%pos2, %crd2), %data2 : (tensor<5xi64>, tensor<11xi32>), tensor<22xf32> to tensor<4x3x2xf32, #CSRDense> 107 108 // 109 // Verify. 110 // 111 // CHECK: ---- Sparse Tensor ---- 112 // CHECK-NEXT: nse = 8 113 // CHECK-NEXT: dim = ( 4, 3, 2 ) 114 // CHECK-NEXT: lvl = ( 4, 3, 2 ) 115 // CHECK-NEXT: pos[0] : ( 0, 3 ) 116 // CHECK-NEXT: crd[0] : ( 0, 2, 3 ) 117 // CHECK-NEXT: pos[1] : ( 0, 2, 4, 5 ) 118 // CHECK-NEXT: crd[1] : ( 0, 1, 1, 2, 1 ) 119 // CHECK-NEXT: pos[2] : ( 0, 2, 4, 5, 7, 8 ) 120 // CHECK-NEXT: crd[2] : ( 0, 1, 0, 1, 0, 0, 1, 0 ) 121 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8 ) 122 // CHECK-NEXT: ---- 123 // CHECK: ---- Sparse Tensor ---- 124 // CHECK-NEXT: nse = 16 125 // CHECK-NEXT: dim = ( 4, 3, 2 ) 126 // CHECK-NEXT: lvl = ( 4, 3, 2 ) 127 // CHECK-NEXT: pos[2] : ( 0, 2, 3, 4, 6, 6, 7, 9, 11, 13, 14, 15, 16 ) 128 // CHECK-NEXT: crd[2] : ( 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1 ) 129 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ) 130 // CHECK-NEXT: ---- 131 // CHECK: ---- Sparse Tensor ---- 132 // CHECK-NEXT: nse = 22 133 // CHECK-NEXT: dim = ( 4, 3, 2 ) 134 // CHECK-NEXT: lvl = ( 4, 3, 2 ) 135 // CHECK-NEXT: pos[1] : ( 0, 3, 5, 8, 11 ) 136 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 2, 0, 1, 2, 0, 1, 2 ) 137 // CHECK-NEXT: values : ( 1, 2, 0, 3, 4, 0, 5, 6, 0, 7, 8, 9, 10, 11, 12, 13, 14, 0, 0, 15, 0, 16 ) 138 // CHECK-NEXT: ---- 139 // 140 sparse_tensor.print %s0 : tensor<4x3x2xf32, #CCC> 141 sparse_tensor.print %s1 : tensor<4x3x2xf32, #DenseCSR> 142 sparse_tensor.print %s2 : tensor<4x3x2xf32, #CSRDense> 143 144 // TODO: This check is no longer needed once the codegen path uses the 145 // buffer deallocation pass. "dealloc_tensor" turn into a no-op in the 146 // codegen path. 147 %has_runtime = sparse_tensor.has_runtime_library 148 scf.if %has_runtime { 149 // sparse_tensor.assemble copies buffers when running with the runtime 150 // library. Deallocations are not needed when running in codegen mode. 151 bufferization.dealloc_tensor %s0 : tensor<4x3x2xf32, #CCC> 152 bufferization.dealloc_tensor %s1 : tensor<4x3x2xf32, #DenseCSR> 153 bufferization.dealloc_tensor %s2 : tensor<4x3x2xf32, #CSRDense> 154 } 155 156 return 157 } 158} 159