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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} | %{env} %{run} | FileCheck %s22//23// Do the same run, but now with direct IR generation.24// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false25// RUN: %{compile} | %{env} %{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// RUN: %{compile} | %{env} %{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} | %{env} %{run_sve} | FileCheck %s %}33 34 35#COO_2D = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton), posWidth = 32, crdWidth = 32 }>36#COO_3D = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton(nonunique), d2 : singleton), posWidth = 32, crdWidth = 32 }>37 38module {39 func.func private @printMemref3dF32(%ptr : tensor<?x?x?xf32> {bufferization.access = "read"}) attributes { llvm.emit_c_interface }40 func.func private @printMemref2dF32(%ptr : tensor<?x?xf32> {bufferization.access = "read"}) attributes { llvm.emit_c_interface }41 42 func.func @test_sparse_rhs(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> {43 %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>44 %0 = tensor.empty() : tensor<5x6xf32>45 %cst = arith.constant 0.000000e+00 : f3246 %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>47 %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>48 %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>49 %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>50 51 // Note: tensor.collapse_shape is a metadata-only operation on dense tensors52 // but requires reallocation on sparse tensors.53 bufferization.dealloc_tensor %collapsed : tensor<6x6xf32, #COO_2D>54 55 return %ret1 : tensor<?x?x?xf32>56 }57 58 func.func @test_sparse_all(%arg0: tensor<5x6xf32, #COO_2D>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> {59 %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>60 %0 = tensor.empty() : tensor<5x6xf32>61 %cst = arith.constant 0.000000e+00 : f3262 %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>63 %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>64 %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>65 %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>66 67 // Note: tensor.collapse_shape is a metadata-only operation on dense tensors68 // but requires reallocation on sparse tensors.69 bufferization.dealloc_tensor %collapsed : tensor<6x6xf32, #COO_2D>70 71 return %ret1 : tensor<?x?x?xf32>72 }73 74 func.func @test_dense(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32>) -> tensor<?x?x?xf32> {75 %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32> into tensor<6x6xf32>76 %0 = tensor.empty() : tensor<5x6xf32>77 %cst = arith.constant 0.000000e+00 : f3278 %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>79 %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>80 %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>81 %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>82 return %ret1 : tensor<?x?x?xf32>83 }84 85 func.func @test_sparse_all_2(%arg0: tensor<5x6xf32, #COO_2D>, %arg1: tensor<2x3x6xf32, #COO_3D>) -> tensor<?x?x?xf32> {86 // collapse the first two level this time, as this is the level requires coiterations.87 %collapsed = tensor.collapse_shape %arg1 [[0, 1], [2]] : tensor<2x3x6xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>88 %0 = tensor.empty() : tensor<5x6xf32>89 %cst = arith.constant 0.000000e+00 : f3290 %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>91 %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>92 %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>93 %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>94 95 // Note: tensor.collapse_shape is a metadata-only operation on dense tensors96 // but requires reallocation on sparse tensors.97 bufferization.dealloc_tensor %collapsed : tensor<6x6xf32, #COO_2D>98 99 return %ret1 : tensor<?x?x?xf32>100 }101 102 103 func.func @main() {104 // Setup two sparse vectors.105 %d1 = arith.constant sparse<106 [ [0, 0], [1, 1], [2, 2], [2, 3], [4, 5] ],107 [1.0, 2.0, 3.0, 4.0, 5.0]108 > : tensor<5x6xf32>109 110 %d2 = arith.constant sparse<111 [ [0, 0, 0], [1, 1, 1], [2, 1, 1] ],112 [ 6.0, 7.0, 8.0]113 > : tensor<6x2x3xf32>114 %shape = arith.constant dense<[2, 3, 6]> : tensor<3xi32>115 116 %d3 = tensor.reshape %d2(%shape): (tensor<6x2x3xf32>, tensor<3xi32>) -> tensor<2x3x6xf32>117 %s1 = sparse_tensor.convert %d1 : tensor<5x6xf32> to tensor<5x6xf32, #COO_2D>118 %s2 = sparse_tensor.convert %d2 : tensor<6x2x3xf32> to tensor<6x2x3xf32, #COO_3D>119 %s3 = sparse_tensor.convert %d3 : tensor<2x3x6xf32> to tensor<2x3x6xf32, #COO_3D>120 121 // CHECK: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =122 // CHECK-NEXT:[123 // CHECK-SAME: [124 // CHECK-SAME: [6, 0, 0],125 // CHECK-NEXT: [0, 0, 0]],126 // CHECK-NEXT: [127 // CHECK-SAME: [0, 0, 0],128 // CHECK-NEXT: [0, 14, 0]],129 // CHECK-NEXT: [130 // CHECK-SAME: [0, 0, 0],131 // CHECK-NEXT: [0, 24, 0]],132 // CHECK-NEXT: [133 // CHECK-SAME: [0, 0, 0],134 // CHECK-NEXT: [0, 0, 0]],135 // CHECK-NEXT: [136 // CHECK-SAME: [0, 0, 0],137 // CHECK-NEXT: [0, 0, 0]]]138 %do1 = call @test_dense(%d1, %d2) : (tensor<5x6xf32>, tensor<6x2x3xf32>) -> tensor<?x?x?xf32>139 call @printMemref3dF32(%do1) : (tensor<?x?x?xf32>) -> ()140 141 // Same results.142 // CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =143 // CHECK-NEXT:[144 // CHECK-SAME: [145 // CHECK-SAME: [6, 0, 0],146 // CHECK-NEXT: [0, 0, 0]],147 // CHECK-NEXT: [148 // CHECK-SAME: [0, 0, 0],149 // CHECK-NEXT: [0, 14, 0]],150 // CHECK-NEXT: [151 // CHECK-SAME: [0, 0, 0],152 // CHECK-NEXT: [0, 24, 0]],153 // CHECK-NEXT: [154 // CHECK-SAME: [0, 0, 0],155 // CHECK-NEXT: [0, 0, 0]],156 // CHECK-NEXT: [157 // CHECK-SAME: [0, 0, 0],158 // CHECK-NEXT: [0, 0, 0]]]159 %so1 = call @test_sparse_rhs(%d1, %s2): (tensor<5x6xf32>, tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32>160 call @printMemref3dF32(%so1) : (tensor<?x?x?xf32>) -> ()161 162 // Same results.163 // CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =164 // CHECK-NEXT:[165 // CHECK-SAME: [166 // CHECK-SAME: [6, 0, 0],167 // CHECK-NEXT: [0, 0, 0]],168 // CHECK-NEXT: [169 // CHECK-SAME: [0, 0, 0],170 // CHECK-NEXT: [0, 14, 0]],171 // CHECK-NEXT: [172 // CHECK-SAME: [0, 0, 0],173 // CHECK-NEXT: [0, 24, 0]],174 // CHECK-NEXT: [175 // CHECK-SAME: [0, 0, 0],176 // CHECK-NEXT: [0, 0, 0]],177 // CHECK-NEXT: [178 // CHECK-SAME: [0, 0, 0],179 // CHECK-NEXT: [0, 0, 0]]]180 %so2 = call @test_sparse_all(%s1, %s2): (tensor<5x6xf32, #COO_2D>, tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32>181 call @printMemref3dF32(%so2) : (tensor<?x?x?xf32>) -> ()182 183 // Same results.184 // CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =185 // CHECK-NEXT:[186 // CHECK-SAME: [187 // CHECK-SAME: [6, 0, 0],188 // CHECK-NEXT: [0, 0, 0]],189 // CHECK-NEXT: [190 // CHECK-SAME: [0, 0, 0],191 // CHECK-NEXT: [0, 14, 0]],192 // CHECK-NEXT: [193 // CHECK-SAME: [0, 0, 0],194 // CHECK-NEXT: [0, 24, 0]],195 // CHECK-NEXT: [196 // CHECK-SAME: [0, 0, 0],197 // CHECK-NEXT: [0, 0, 0]],198 // CHECK-NEXT: [199 // CHECK-SAME: [0, 0, 0],200 // CHECK-NEXT: [0, 0, 0]]]201 %so3 = call @test_sparse_all_2(%s1, %s3): (tensor<5x6xf32, #COO_2D>, tensor<2x3x6xf32, #COO_3D>) -> tensor<?x?x?xf32>202 call @printMemref3dF32(%so2) : (tensor<?x?x?xf32>) -> ()203 204 bufferization.dealloc_tensor %s1 : tensor<5x6xf32, #COO_2D>205 bufferization.dealloc_tensor %s2 : tensor<6x2x3xf32, #COO_3D>206 bufferization.dealloc_tensor %s3 : tensor<2x3x6xf32, #COO_3D>207 bufferization.dealloc_tensor %do1 : tensor<?x?x?xf32>208 bufferization.dealloc_tensor %so1 : tensor<?x?x?xf32>209 bufferization.dealloc_tensor %so2 : tensor<?x?x?xf32>210 bufferization.dealloc_tensor %so3 : tensor<?x?x?xf32>211 212 return213 }214}215