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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// Reduction in this file _are_ supported by the AArch64 SVE backend35 36#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>37#CSR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : dense, d1 : compressed)}>38#CSC = #sparse_tensor.encoding<{39 map = (d0, d1) -> (d1 : dense, d0 : compressed)40}>41 42//43// Traits for tensor operations.44//45#trait_matmul = {46 indexing_maps = [47 affine_map<(i,j,k) -> (i,k)>, // A48 affine_map<(i,j,k) -> (k,j)>, // B49 affine_map<(i,j,k) -> (i,j)> // C (out)50 ],51 iterator_types = ["parallel", "parallel", "reduction"],52 doc = "C(i,j) = SUM_k A(i,k) * B(k,j)"53}54 55module {56 func.func @min_plus_csrcsr(%arga: tensor<?x?xf64, #CSR>,57 %argb: tensor<?x?xf64, #CSR>) -> tensor<?x?xf64, #CSR> {58 %c0 = arith.constant 0 : index59 %c1 = arith.constant 1 : index60 %maxf = arith.constant 1.0e999 : f6461 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #CSR>62 %d1 = tensor.dim %argb, %c1 : tensor<?x?xf64, #CSR>63 %xm = tensor.empty(%d0, %d1) : tensor<?x?xf64, #CSR>64 %0 = linalg.generic #trait_matmul65 ins(%arga, %argb: tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSR>)66 outs(%xm: tensor<?x?xf64, #CSR>) {67 ^bb(%a: f64, %b: f64, %output: f64):68 %1 = sparse_tensor.binary %a, %b : f64, f64 to f6469 overlap = {70 ^bb0(%x: f64, %y: f64):71 %3 = arith.addf %x, %y : f6472 sparse_tensor.yield %3 : f6473 }74 left={}75 right={}76 %2 = sparse_tensor.reduce %1, %output, %maxf : f64 {77 ^bb0(%x: f64, %y: f64):78 %cmp = arith.cmpf "olt", %x, %y : f6479 %3 = arith.select %cmp, %x, %y : f6480 sparse_tensor.yield %3 : f6481 }82 linalg.yield %2 : f6483 } -> tensor<?x?xf64, #CSR>84 return %0 : tensor<?x?xf64, #CSR>85 }86 87 func.func @min_plus_csrcsc(%arga: tensor<?x?xf64, #CSR>,88 %argb: tensor<?x?xf64, #CSC>) -> tensor<?x?xf64, #CSR> {89 %c0 = arith.constant 0 : index90 %c1 = arith.constant 1 : index91 %maxf = arith.constant 1.0e999 : f6492 %d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #CSR>93 %d1 = tensor.dim %argb, %c1 : tensor<?x?xf64, #CSC>94 %xm = tensor.empty(%d0, %d1) : tensor<?x?xf64, #CSR>95 %0 = linalg.generic #trait_matmul96 ins(%arga, %argb: tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSC>)97 outs(%xm: tensor<?x?xf64, #CSR>) {98 ^bb(%a: f64, %b: f64, %output: f64):99 %1 = sparse_tensor.binary %a, %b : f64, f64 to f64100 overlap = {101 ^bb0(%x: f64, %y: f64):102 %3 = arith.addf %x, %y : f64103 sparse_tensor.yield %3 : f64104 }105 left={}106 right={}107 %2 = sparse_tensor.reduce %1, %output, %maxf : f64 {108 ^bb0(%x: f64, %y: f64):109 %cmp = arith.cmpf "olt", %x, %y : f64110 %3 = arith.select %cmp, %x, %y : f64111 sparse_tensor.yield %3 : f64112 }113 linalg.yield %2 : f64114 } -> tensor<?x?xf64, #CSR>115 return %0 : tensor<?x?xf64, #CSR>116 }117 118 // Driver method to call and verify vector kernels.119 func.func @main() {120 %c0 = arith.constant 0 : index121 122 // Setup sparse matrices.123 %m1 = arith.constant sparse<124 [ [0,0], [0,1], [1,0], [2,2], [2,3], [2,4], [3,0], [3,2], [3,3] ],125 [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]126 > : tensor<4x5xf64>127 %m2 = arith.constant sparse<128 [ [0,0], [1,3], [2,0], [2,3], [3,1], [4,1] ],129 [6.0, 5.0, 4.0, 3.0, 2.0, 11.0 ]130 > : tensor<5x4xf64>131 %sm1 = sparse_tensor.convert %m1 : tensor<4x5xf64> to tensor<?x?xf64, #CSR>132 %sm2r = sparse_tensor.convert %m2 : tensor<5x4xf64> to tensor<?x?xf64, #CSR>133 %sm2c = sparse_tensor.convert %m2 : tensor<5x4xf64> to tensor<?x?xf64, #CSC>134 135 // Call sparse matrix kernels.136 %5 = call @min_plus_csrcsr(%sm1, %sm2r)137 : (tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSR>) -> tensor<?x?xf64, #CSR>138 %6 = call @min_plus_csrcsc(%sm1, %sm2c)139 : (tensor<?x?xf64, #CSR>, tensor<?x?xf64, #CSC>) -> tensor<?x?xf64, #CSR>140 141 //142 // Verify the results.143 //144 // CHECK: ---- Sparse Tensor ----145 // CHECK-NEXT: nse = 9146 // CHECK-NEXT: dim = ( 4, 5 )147 // CHECK-NEXT: lvl = ( 4, 5 )148 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )149 // CHECK-NEXT: crd[1] : ( 0, 1, 0, 2, 3, 4, 0, 2, 3 )150 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9 )151 // CHECK-NEXT: ----152 // CHECK: ---- Sparse Tensor ----153 // CHECK-NEXT: nse = 6154 // CHECK-NEXT: dim = ( 5, 4 )155 // CHECK-NEXT: lvl = ( 5, 4 )156 // CHECK-NEXT: pos[1] : ( 0, 1, 2, 4, 5, 6 )157 // CHECK-NEXT: crd[1] : ( 0, 3, 0, 3, 1, 1 )158 // CHECK-NEXT: values : ( 6, 5, 4, 3, 2, 11 )159 // CHECK-NEXT: ----160 // CHECK: ---- Sparse Tensor ----161 // CHECK-NEXT: nse = 9162 // CHECK-NEXT: dim = ( 4, 4 )163 // CHECK-NEXT: lvl = ( 4, 4 )164 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )165 // CHECK-NEXT: crd[1] : ( 0, 3, 0, 0, 1, 3, 0, 1, 3 )166 // CHECK-NEXT: values : ( 7, 7, 9, 8, 7, 7, 12, 11, 11 )167 // CHECK-NEXT: ----168 // CHECK: ---- Sparse Tensor ----169 // CHECK-NEXT: nse = 9170 // CHECK-NEXT: dim = ( 4, 4 )171 // CHECK-NEXT: lvl = ( 4, 4 )172 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 9 )173 // CHECK-NEXT: crd[1] : ( 0, 3, 0, 0, 1, 3, 0, 1, 3 )174 // CHECK-NEXT: values : ( 7, 7, 9, 8, 7, 7, 12, 11, 11 )175 // CHECK-NEXT: ----176 //177 sparse_tensor.print %sm1 : tensor<?x?xf64, #CSR>178 sparse_tensor.print %sm2r : tensor<?x?xf64, #CSR>179 sparse_tensor.print %5 : tensor<?x?xf64, #CSR>180 sparse_tensor.print %6 : tensor<?x?xf64, #CSR>181 182 // Release the resources.183 bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #CSR>184 bufferization.dealloc_tensor %sm2r : tensor<?x?xf64, #CSR>185 bufferization.dealloc_tensor %sm2c : tensor<?x?xf64, #CSC>186 bufferization.dealloc_tensor %5 : tensor<?x?xf64, #CSR>187 bufferization.dealloc_tensor %6 : tensor<?x?xf64, #CSR>188 return189 }190}191