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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// 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#DCSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }>35#CSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>36#CDR = #sparse_tensor.encoding<{map = (d0, d1) -> (d0 : compressed, d1 : dense)}>37#CSC = #sparse_tensor.encoding<{38 map = (d0, d1) -> (d1 : dense, d0 : compressed)39}>40 41#map = affine_map<(d0, d1, d2, d3) -> (d0 + d1, d3 + d2)>42#map1 = affine_map<(d0, d1, d2, d3) -> (d1, d2)>43#map2 = affine_map<(d0, d1, d2, d3) -> (d0, d3)>44 45// An example of a 2D convolution with a sparse filter.46module {47 48 func.func @conv2d(%input: tensor<8x8xi32>,49 %filter: tensor<3x3xi32>,50 %output: tensor<6x6xi32>) -> tensor<6x6xi32> {51 %0 = linalg.conv_2d52 ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32>)53 outs (%output: tensor<6x6xi32>) -> tensor<6x6xi32>54 return %0 : tensor<6x6xi32>55 }56 57 func.func @conv2d_CSR_dense_rotated(%arg0: tensor<8x8xi32, #CSR>,58 %arg1: tensor<3x3xi32>) -> tensor<6x6xi32> {59 %s = arith.constant dense<0> : tensor<6x6xi32>60 %0 = linalg.generic {indexing_maps = [#map, #map1, #map2],61 iterator_types = ["parallel", "reduction", "reduction", "parallel"]}62 ins(%arg0, %arg1 : tensor<8x8xi32, #CSR>, tensor<3x3xi32>)63 outs(%s : tensor<6x6xi32>) attrs = {sorted = true} {64 ^bb0(%in: i32, %in_0: i32, %out: i32):65 %1 = arith.muli %in, %in_0 : i3266 %2 = arith.addi %out, %1 : i3267 linalg.yield %2 : i3268 } -> tensor<6x6xi32>69 return %0 : tensor<6x6xi32>70 }71 72 func.func @conv2d_sparse_out(%input: tensor<8x8xi32>,73 %filter: tensor<3x3xi32>) -> tensor<6x6xi32, #DCSR> {74 %s = tensor.empty() : tensor<6x6xi32, #DCSR>75 %0 = linalg.conv_2d76 ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32>)77 outs (%s: tensor<6x6xi32, #DCSR>) -> tensor<6x6xi32, #DCSR>78 return %0 : tensor<6x6xi32, #DCSR>79 }80 81 func.func @conv2d_all_sparse_DCSR(%input: tensor<8x8xi32, #DCSR>,82 %filter: tensor<3x3xi32>) -> tensor<6x6xi32, #DCSR> {83 %s = tensor.empty() : tensor<6x6xi32, #DCSR>84 %0 = linalg.conv_2d85 ins (%input, %filter: tensor<8x8xi32, #DCSR>, tensor<3x3xi32>)86 outs (%s: tensor<6x6xi32, #DCSR>) -> tensor<6x6xi32, #DCSR>87 return %0 : tensor<6x6xi32, #DCSR>88 }89 90 func.func @conv2d_all_sparse_CSR(%input: tensor<8x8xi32, #CSR>,91 %filter: tensor<3x3xi32>) -> tensor<6x6xi32, #CSR> {92 %s = tensor.empty() : tensor<6x6xi32, #CSR>93 %0 = linalg.conv_2d94 ins (%input, %filter: tensor<8x8xi32, #CSR>, tensor<3x3xi32>)95 outs (%s: tensor<6x6xi32, #CSR>) -> tensor<6x6xi32, #CSR>96 return %0 : tensor<6x6xi32, #CSR>97 }98 99 func.func @conv2d_all_sparse_CD(%input: tensor<8x8xi32, #CDR>,100 %filter: tensor<3x3xi32>) -> tensor<6x6xi32, #CDR> {101 %s = tensor.empty() : tensor<6x6xi32, #CDR>102 %0 = linalg.conv_2d103 ins (%input, %filter: tensor<8x8xi32, #CDR>, tensor<3x3xi32>)104 outs (%s: tensor<6x6xi32, #CDR>) -> tensor<6x6xi32, #CDR>105 return %0 : tensor<6x6xi32, #CDR>106 }107 108 func.func @conv2d_all_sparse_CSC(%input: tensor<8x8xi32, #CSC>,109 %filter: tensor<3x3xi32>) -> tensor<6x6xi32, #CSC> {110 %s = tensor.empty() : tensor<6x6xi32, #CSC>111 %0 = linalg.conv_2d112 ins (%input, %filter: tensor<8x8xi32, #CSC>, tensor<3x3xi32>)113 outs (%s: tensor<6x6xi32, #CSC>) -> tensor<6x6xi32, #CSC>114 return %0 : tensor<6x6xi32, #CSC>115 }116 117 func.func @main() {118 %c0 = arith.constant 0 : index119 %i0 = arith.constant 0 : i32120 121 // A typical edge detection filter.122 %filter = arith.constant dense<[123 [ 1, 0, -1 ],124 [ 0, 0, 0 ],125 [ -1, 0, 1 ]126 ]> : tensor<3x3xi32>127 128 %input = arith.constant dense<[129 [ 1, 2, 3, 4, 0, 6, 7, 8 ],130 [ 2, 2, 4, 4, 0, 0, 6, 8 ],131 [ 2, 2, 4, 4, 0, 0, 6, 8 ],132 [ 2, 2, 3, 4, 0, 0, 7, 8 ],133 [ 1, 3, 3, 4, 0, 0, 6, 8 ],134 [ 3, 2, 3, 4, 0, 0, 7, 8 ],135 [ 1, 3, 3, 4, 3, 6, 6, 8 ],136 [ 1, 3, 3, 4, 3, 0, 7, 8 ]137 ]> : tensor<8x8xi32>138 %sparse_input_DCSR = sparse_tensor.convert %input139 : tensor<8x8xi32> to tensor<8x8xi32, #DCSR>140 %sparse_input_CSR = sparse_tensor.convert %input141 : tensor<8x8xi32> to tensor<8x8xi32, #CSR>142 %sparse_input_CD = sparse_tensor.convert %input143 : tensor<8x8xi32> to tensor<8x8xi32, #CDR>144 %sparse_input_CSC = sparse_tensor.convert %input145 : tensor<8x8xi32> to tensor<8x8xi32, #CSC>146 147 // Call the kernel.148 %output = arith.constant dense<0> : tensor<6x6xi32>149 %0 = call @conv2d(%input, %filter, %output)150 : (tensor<8x8xi32>,151 tensor<3x3xi32>, tensor<6x6xi32>) -> tensor<6x6xi32>152 %1 = call @conv2d_sparse_out(%input, %filter)153 : (tensor<8x8xi32>,154 tensor<3x3xi32>) -> tensor<6x6xi32, #DCSR>155 %2 = call @conv2d_all_sparse_DCSR(%sparse_input_DCSR, %filter)156 : (tensor<8x8xi32, #DCSR>,157 tensor<3x3xi32>) -> tensor<6x6xi32, #DCSR>158 %3 = call @conv2d_all_sparse_CSR(%sparse_input_CSR, %filter)159 : (tensor<8x8xi32, #CSR>,160 tensor<3x3xi32>) -> tensor<6x6xi32, #CSR>161 %4 = call @conv2d_all_sparse_CD(%sparse_input_CD, %filter)162 : (tensor<8x8xi32, #CDR>,163 tensor<3x3xi32>) -> tensor<6x6xi32, #CDR>164 %5 = call @conv2d_all_sparse_CSC(%sparse_input_CSC, %filter)165 : (tensor<8x8xi32, #CSC>,166 tensor<3x3xi32>) -> tensor<6x6xi32, #CSC>167 %6 = call @conv2d_CSR_dense_rotated(%sparse_input_CSR, %filter)168 : (tensor<8x8xi32, #CSR>,169 tensor<3x3xi32>) -> tensor<6x6xi32>170 171 // Verify the output.172 //173 // CHECK: ( ( 0, 0, -1, -6, -1, 6 ),174 // CHECK-SAME: ( -1, 0, 1, 0, 1, 0 ),175 // CHECK-SAME: ( 0, -1, 1, 0, 0, 0 ),176 // CHECK-SAME: ( -1, 0, 0, 0, 0, 0 ),177 // CHECK-SAME: ( 0, 0, 3, 6, -3, -6 ),178 // CHECK-SAME: ( 2, -1, 3, 0, -3, 0 ) )179 //180 %v = vector.transfer_read %0[%c0, %c0], %i0181 : tensor<6x6xi32>, vector<6x6xi32>182 vector.print %v : vector<6x6xi32>183 184 //185 // Should be the same as dense output.186 //187 // CHECK: ---- Sparse Tensor ----188 // CHECK-NEXT: nse = 36189 // CHECK-NEXT: dim = ( 6, 6 )190 // CHECK-NEXT: lvl = ( 6, 6 )191 // CHECK-NEXT: pos[0] : ( 0, 6 )192 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5 )193 // CHECK-NEXT: pos[1] : ( 0, 6, 12, 18, 24, 30, 36 )194 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5 )195 // CHECK-NEXT: values : ( 0, 0, -1, -6, -1, 6, -1, 0, 1, 0, 1, 0, 0, -1, 1, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 3, 6, -3, -6, 2, -1, 3, 0, -3, 0 )196 // CHECK-NEXT: ----197 //198 sparse_tensor.print %1 : tensor<6x6xi32, #DCSR>199 200 //201 // Should be the same as dense output.202 //203 // CHECK: ---- Sparse Tensor ----204 // CHECK-NEXT: nse = 36205 // CHECK-NEXT: dim = ( 6, 6 )206 // CHECK-NEXT: lvl = ( 6, 6 )207 // CHECK-NEXT: pos[0] : ( 0, 6 )208 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5 )209 // CHECK-NEXT: pos[1] : ( 0, 6, 12, 18, 24, 30, 36 )210 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5 )211 // CHECK-NEXT: values : ( 0, 0, -1, -6, -1, 6, -1, 0, 1, 0, 1, 0, 0, -1, 1, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 3, 6, -3, -6, 2, -1, 3, 0, -3, 0 )212 // CHECK-NEXT: ----213 //214 sparse_tensor.print %2 : tensor<6x6xi32, #DCSR>215 216 //217 // Should be the same as dense output.218 //219 // CHECK: ---- Sparse Tensor ----220 // CHECK-NEXT: nse = 36221 // CHECK-NEXT: dim = ( 6, 6 )222 // CHECK-NEXT: lvl = ( 6, 6 )223 // CHECK-NEXT: pos[1] : ( 0, 6, 12, 18, 24, 30, 36 )224 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5 )225 // CHECK-NEXT: values : ( 0, 0, -1, -6, -1, 6, -1, 0, 1, 0, 1, 0, 0, -1, 1, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 3, 6, -3, -6, 2, -1, 3, 0, -3, 0 )226 // CHECK-NEXT: ----227 //228 sparse_tensor.print %3 : tensor<6x6xi32, #CSR>229 230 //231 // Should be the same as dense output.232 //233 // CHECK: ---- Sparse Tensor ----234 // CHECK-NEXT: nse = 36235 // CHECK-NEXT: dim = ( 6, 6 )236 // CHECK-NEXT: lvl = ( 6, 6 )237 // CHECK-NEXT: pos[0] : ( 0, 6 )238 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5 )239 // CHECK-NEXT: values : ( 0, 0, -1, -6, -1, 6, -1, 0, 1, 0, 1, 0, 0, -1, 1, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 3, 6, -3, -6, 2, -1, 3, 0, -3, 0 )240 // CHECK-NEXT: ----241 //242 sparse_tensor.print %4 : tensor<6x6xi32, #CDR>243 244 //245 // Should be the same as dense output.246 //247 // CHECK: ---- Sparse Tensor ----248 // CHECK-NEXT: nse = 36249 // CHECK-NEXT: dim = ( 6, 6 )250 // CHECK-NEXT: lvl = ( 6, 6 )251 // CHECK-NEXT: pos[1] : ( 0, 6, 12, 18, 24, 30, 36 )252 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5 )253 // CHECK-NEXT: values : ( 0, -1, 0, -1, 0, 2, 0, 0, -1, 0, 0, -1, -1, 1, 1, 0, 3, 3, -6, 0, 0, 0, 6, 0, -1, 1, 0, 0, -3, -3, 6, 0, 0, 0, -6, 0 )254 // CHECK-NEXT: ----255 //256 sparse_tensor.print %5 : tensor<6x6xi32, #CSC>257 258 //259 // Should be the same as dense output.260 // CHECK: ( ( 0, 0, -1, -6, -1, 6 ),261 // CHECK-SAME: ( -1, 0, 1, 0, 1, 0 ),262 // CHECK-SAME: ( 0, -1, 1, 0, 0, 0 ),263 // CHECK-SAME: ( -1, 0, 0, 0, 0, 0 ),264 // CHECK-SAME: ( 0, 0, 3, 6, -3, -6 ),265 // CHECK-SAME: ( 2, -1, 3, 0, -3, 0 ) )266 //267 %v6 = vector.transfer_read %6[%c0, %c0], %i0268 : tensor<6x6xi32>, vector<6x6xi32>269 vector.print %v : vector<6x6xi32>270 271 // Release the resources.272 bufferization.dealloc_tensor %sparse_input_DCSR : tensor<8x8xi32, #DCSR>273 bufferization.dealloc_tensor %sparse_input_CSR : tensor<8x8xi32, #CSR>274 bufferization.dealloc_tensor %sparse_input_CSC : tensor<8x8xi32, #CSC>275 bufferization.dealloc_tensor %sparse_input_CD : tensor<8x8xi32, #CDR>276 277 bufferization.dealloc_tensor %0 : tensor<6x6xi32>278 bufferization.dealloc_tensor %1 : tensor<6x6xi32, #DCSR>279 bufferization.dealloc_tensor %2 : tensor<6x6xi32, #DCSR>280 bufferization.dealloc_tensor %3 : tensor<6x6xi32, #CSR>281 bufferization.dealloc_tensor %4 : tensor<6x6xi32, #CDR>282 bufferization.dealloc_tensor %5 : tensor<6x6xi32, #CSC>283 bufferization.dealloc_tensor %6 : tensor<6x6xi32>284 285 return286 }287}288