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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 direct IR generation and vectorization.28// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true29 30// RUN: %{compile} | %{run} | FileCheck %s31//32// Do the same run, but now with direct IR generation and VLA vectorization.33// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}34 35#trait_mul = {36 indexing_maps = [37 affine_map<(i,j,k) -> (i,k)>, // A (in)38 affine_map<(i,j,k) -> (j,k)>, // B (in, transposed)39 affine_map<(i,j,k) -> (i,j)> // X (out)40 ],41 iterator_types = ["parallel", "parallel", "reduction"],42 doc = "X(i,j) *= A(i,j) * B(j,i)"43}44 45#CSR = #sparse_tensor.encoding<{46 map = ( i, j ) -> (i : dense, j : compressed)47}>48 49#BSR = #sparse_tensor.encoding<{50 map = ( i, j ) ->51 ( i floordiv 2 : dense,52 j floordiv 2 : compressed,53 i mod 2 : dense,54 j mod 2 : dense55 )56}>57 58#NV_24 = #sparse_tensor.encoding<{59 map = ( i, j ) ->60 ( i : dense,61 j floordiv 4 : dense,62 j mod 4 : structured[2, 4]63 ),64}>65 66module {67 68 func.func @mul(%arg0: tensor<4x8xf64>,69 %arg1: tensor<4x8xf64, #BSR>) -> tensor<4x4xf64> {70 %out = arith.constant dense<0.0> : tensor<4x4xf64>71 %0 = linalg.generic #trait_mul72 ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64, #BSR>)73 outs(%out: tensor<4x4xf64>) {74 ^bb(%x: f64, %y : f64, %z : f64):75 %1 = arith.mulf %x, %y : f6476 %2 = arith.addf %1, %z : f6477 linalg.yield %2 : f6478 } -> tensor<4x4xf64>79 return %0 : tensor<4x4xf64>80 }81 82 func.func @mul_24(%arg0: tensor<4x8xf64>,83 %arg1: tensor<4x8xf64, #NV_24>) -> tensor<4x4xf64> {84 %out = arith.constant dense<0.0> : tensor<4x4xf64>85 %0 = linalg.generic #trait_mul86 ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64, #NV_24>)87 outs(%out: tensor<4x4xf64>) {88 ^bb(%x: f64, %y : f64, %z : f64):89 %1 = arith.mulf %x, %y : f6490 %2 = arith.addf %1, %z : f6491 linalg.yield %2 : f6492 } -> tensor<4x4xf64>93 return %0 : tensor<4x4xf64>94 }95 96 func.func @mul_csr_bsr(%arg0: tensor<4x8xf64, #CSR>,97 %arg1: tensor<4x8xf64, #BSR>) -> tensor<4x4xf64> {98 %out = arith.constant dense<0.0> : tensor<4x4xf64>99 %0 = linalg.generic #trait_mul100 ins(%arg0, %arg1: tensor<4x8xf64, #CSR>, tensor<4x8xf64, #BSR>)101 outs(%out: tensor<4x4xf64>) {102 ^bb(%x: f64, %y : f64, %z : f64):103 %1 = arith.mulf %x, %y : f64104 %2 = arith.addf %1, %z : f64105 linalg.yield %2 : f64106 } -> tensor<4x4xf64>107 return %0 : tensor<4x4xf64>108 }109 110 func.func @mul_dense(%arg0: tensor<4x8xf64>,111 %arg1: tensor<4x8xf64>) -> tensor<4x4xf64> {112 %out = arith.constant dense<0.0> : tensor<4x4xf64>113 %0 = linalg.generic #trait_mul114 ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64>)115 outs(%out: tensor<4x4xf64>) {116 ^bb(%x: f64, %y : f64, %z : f64):117 %1 = arith.mulf %x, %y : f64118 %2 = arith.addf %1, %z : f64119 linalg.yield %2 : f64120 } -> tensor<4x4xf64>121 return %0 : tensor<4x4xf64>122 }123 124 //125 // Output utility.126 //127 func.func @dump_dense_f64(%arg0: tensor<4x4xf64>) {128 %c0 = arith.constant 0 : index129 %d0 = arith.constant -1.0 : f64130 %0 = vector.transfer_read %arg0[%c0, %c0], %d0: tensor<4x4xf64>, vector<4x4xf64>131 vector.print %0 : vector<4x4xf64>132 return133 }134 135 //136 // Main driver.137 //138 func.func @main() {139 %c0 = arith.constant 0 : index140 141 %td = arith.constant dense<[[ 1.0, 2.0, 0.0, 0.0, 0.0, 0.0, 4.0, 5.0],142 [ 6.0, 7.0, 0.0, 0.0, 0.0, 0.0, 10.0, 11.0],143 [ 0.0, 0.0, 12.0, 13.0, 16.0, 17.0, 0.0, 0.0],144 [ 0.0, 0.0, 18.0, 19.0, 22.0, 23.0, 0.0, 0.0]]> : tensor<4x8xf64>145 146 %a = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #BSR>147 %b = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #NV_24>148 %c = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #CSR>149 150 %d = call @mul_dense(%td, %td)151 : (tensor<4x8xf64>, tensor<4x8xf64>) -> tensor<4x4xf64>152 %s = call @mul(%td, %a)153 : (tensor<4x8xf64>, tensor<4x8xf64, #BSR>) -> tensor<4x4xf64>154 %s24 = call @mul_24(%td, %b)155 : (tensor<4x8xf64>, tensor<4x8xf64, #NV_24>) -> tensor<4x4xf64>156 %scsr = call @mul_csr_bsr(%c, %a)157 : (tensor<4x8xf64, #CSR>, tensor<4x8xf64, #BSR>) -> tensor<4x4xf64>158 159 // CHECK-COUNT-4: ( ( 46, 115, 0, 0 ), ( 115, 306, 0, 0 ), ( 0, 0, 858, 1206 ), ( 0, 0, 1206, 1698 ) )160 call @dump_dense_f64(%d) : (tensor<4x4xf64>) -> ()161 call @dump_dense_f64(%s) : (tensor<4x4xf64>) -> ()162 call @dump_dense_f64(%s24) : (tensor<4x4xf64>) -> ()163 call @dump_dense_f64(%scsr) : (tensor<4x4xf64>) -> ()164 165 bufferization.dealloc_tensor %a : tensor<4x8xf64, #BSR>166 bufferization.dealloc_tensor %b : tensor<4x8xf64, #NV_24>167 bufferization.dealloc_tensor %c : tensor<4x8xf64, #CSR>168 bufferization.dealloc_tensor %d : tensor<4x4xf64>169 bufferization.dealloc_tensor %s : tensor<4x4xf64>170 bufferization.dealloc_tensor %s24 : tensor<4x4xf64>171 bufferization.dealloc_tensor %scsr : tensor<4x4xf64>172 173 return174 }175}176