softmax.https.any.js (14165B)
1 // META: title=test WebNN API softmax operation 2 // META: global=window 3 // META: variant=?cpu 4 // META: variant=?gpu 5 // META: variant=?npu 6 // META: script=../resources/utils.js 7 // META: timeout=long 8 9 'use strict'; 10 11 // https://www.w3.org/TR/webnn/#api-mlgraphbuilder-softmax-method 12 // Compute the softmax values of the N-D input tensor along the given axis. 13 // 14 // MLOperand softmax(MLOperand input, unsigned long axis); 15 16 const softmaxTests = [ 17 { 18 'name': 'softmax float32 2D constant tensor all positive', 19 'graph': { 20 'inputs': { 21 'softmaxInput': { 22 'data': [ 23 7.9037346839904785, 6.358251571655273, 4.833756923675537, 24 9.5791654586792, 0.21071857213974, 4.554958820343018, 25 7.150174140930176, 8.330297470092773, 1.5359858274459839, 26 6.63361930847168, 1.4539369344711304, 0.213418647646904, 27 5.257819652557373, 8.192137718200684, 8.16172981262207, 28 2.874434232711792, 8.950733184814453, 6.111632823944092, 29 1.6371468305587769, 0.27626121044158936, 5.02822732925415, 30 3.8983259201049805, 2.8967113494873047, 6.88947057723999 31 ], 32 'descriptor': {shape: [4, 6], dataType: 'float32'}, 33 'constant': true 34 } 35 }, 36 'operators': [{ 37 'name': 'softmax', 38 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 39 'outputs': 'softmaxOutput' 40 }], 41 'expectedOutputs': { 42 'softmaxOutput': { 43 'data': [ 44 0.15068615972995758, 0.03212761878967285, 45 0.006995180621743202, 0.8048291206359863, 46 0.00006871300138300285, 0.005293202120810747, 47 0.2057899534702301, 0.6698001027107239, 48 0.0007502624066546559, 0.1227685883641243, 49 0.0006911618984304368, 0.00019990770670119673, 50 0.012398251332342625, 0.23319464921951294, 51 0.22621041536331177, 0.0011435872875154018, 52 0.4979347288608551, 0.029118351638317108, 53 0.004253828432410955, 0.001090824487619102, 54 0.12633030116558075, 0.040812913328409195, 55 0.014990009367465973, 0.8125221133232117 56 ], 57 'descriptor': {shape: [4, 6], dataType: 'float32'} 58 } 59 } 60 } 61 }, 62 { 63 'name': 'softmax float32 2D tensor all positive', 64 'graph': { 65 'inputs': { 66 'softmaxInput': { 67 'data': [ 68 7.9037346839904785, 6.358251571655273, 4.833756923675537, 69 9.5791654586792, 0.21071857213974, 4.554958820343018, 70 7.150174140930176, 8.330297470092773, 1.5359858274459839, 71 6.63361930847168, 1.4539369344711304, 0.213418647646904, 72 5.257819652557373, 8.192137718200684, 8.16172981262207, 73 2.874434232711792, 8.950733184814453, 6.111632823944092, 74 1.6371468305587769, 0.27626121044158936, 5.02822732925415, 75 3.8983259201049805, 2.8967113494873047, 6.88947057723999 76 ], 77 'descriptor': {shape: [4, 6], dataType: 'float32'} 78 } 79 }, 80 'operators': [{ 81 'name': 'softmax', 82 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 83 'outputs': 'softmaxOutput' 84 }], 85 'expectedOutputs': { 86 'softmaxOutput': { 87 'data': [ 88 0.15068615972995758, 0.03212761878967285, 89 0.006995180621743202, 0.8048291206359863, 90 0.00006871300138300285, 0.005293202120810747, 91 0.2057899534702301, 0.6698001027107239, 92 0.0007502624066546559, 0.1227685883641243, 93 0.0006911618984304368, 0.00019990770670119673, 94 0.012398251332342625, 0.23319464921951294, 95 0.22621041536331177, 0.0011435872875154018, 96 0.4979347288608551, 0.029118351638317108, 97 0.004253828432410955, 0.001090824487619102, 98 0.12633030116558075, 0.040812913328409195, 99 0.014990009367465973, 0.8125221133232117 100 ], 101 'descriptor': {shape: [4, 6], dataType: 'float32'} 102 } 103 } 104 } 105 }, 106 { 107 'name': 'softmax float32 2D tensor all negative', 108 'graph': { 109 'inputs': { 110 'softmaxInput': { 111 'data': [ 112 -3.3118433952331543, -3.3389549255371094, -3.4102790355682373, 113 -6.697193145751953, -7.896223545074463, -3.308168888092041, 114 -3.2309720516204834, -4.315771579742432, -9.311088562011719, 115 -3.9236626625061035, -3.780721426010132, -6.034926891326904, 116 -3.9196677207946777, -2.2234842777252197, -9.326531410217285, 117 -1.4882491827011108, -6.302842617034912, -5.53147554397583, 118 -1.8421411514282227, -4.994808197021484, -9.527292251586914, 119 -4.985682964324951, -8.421041488647461, -6.235629558563232 120 ], 121 'descriptor': {shape: [4, 6], dataType: 'float32'} 122 } 123 }, 124 'operators': [{ 125 'name': 'softmax', 126 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 127 'outputs': 'softmaxOutput' 128 }], 129 'expectedOutputs': { 130 'softmaxOutput': { 131 'data': [ 132 0.2546302080154419, 0.24781952798366547, 0.2307596504688263, 133 0.008623254485428333, 0.002599793951958418, 0.2555675804615021, 134 0.40352678298950195, 0.13637976348400116, 0.0009232329903170466, 135 0.20185552537441254, 0.23287305235862732, 0.024441635236144066, 136 0.0551743283867836, 0.3008708655834198, 0.0002474947541486472, 137 0.6276082992553711, 0.0050902292132377625, 0.011008745059370995, 138 0.9090295433998108, 0.0388500951230526, 0.00041779119055718184, 139 0.039206232875585556, 0.0012629841221496463, 0.011233373545110226 140 ], 141 'descriptor': {shape: [4, 6], dataType: 'float32'} 142 } 143 } 144 } 145 }, 146 { 147 'name': 'softmax float32 3D constant tensor', 148 'graph': { 149 'inputs': { 150 'softmaxInput': { 151 'data': [ 152 0.4301910996437073, 0.5471914410591125, -1.1637765169143677, 153 0.18390046060085297, 0.583903968334198, 0.17356790602207184, 154 0.5397239923477173, -0.9535139799118042, -0.5920282602310181, 155 -0.17344485223293304, 0.14395014941692352, -0.37920907139778137 156 ], 157 'descriptor': {shape: [1, 3, 4], dataType: 'float32'}, 158 'constant': true 159 } 160 }, 161 'operators': [{ 162 'name': 'softmax', 163 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 164 'outputs': 'softmaxOutput' 165 }], 166 'expectedOutputs': { 167 'softmaxOutput': { 168 'data': [ 169 0.39589041471481323, 0.45983806252479553, 0.09812675416469574, 170 0.529077410697937, 0.4616699814796448, 0.31647709012031555, 171 0.5390242338180542, 0.16964708268642426, 0.142439603805542, 172 0.22368484735488892, 0.36284899711608887, 0.3012755215167999 173 ], 174 'descriptor': {shape: [1, 3, 4], dataType: 'float32'} 175 } 176 } 177 } 178 }, 179 { 180 'name': 'softmax float32 4D tensor', 181 'graph': { 182 'inputs': { 183 'softmaxInput': { 184 'data': [ 185 0.4301910996437073, 0.5471914410591125, -1.1637765169143677, 186 0.18390046060085297, 0.583903968334198, 0.17356790602207184, 187 0.5397239923477173, -0.9535139799118042, -0.5920282602310181, 188 -0.17344485223293304, 0.14395014941692352, -0.37920907139778137 189 ], 190 'descriptor': {shape: [3, 4, 1, 1], dataType: 'float32'} 191 } 192 }, 193 'operators': [{ 194 'name': 'softmax', 195 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 196 'outputs': 'softmaxOutput' 197 }], 198 'expectedOutputs': { 199 'softmaxOutput': { 200 'data': [ 201 0.3216537833213806, 0.3615773916244507, 0.06533370912075043, 202 0.25143513083457947, 0.35271573066711426, 0.23400123417377472, 203 0.33747196197509766, 0.07581108063459396, 0.17110128700733185, 204 0.26004093885421753, 0.3571779429912567, 0.2116798311471939 205 ], 206 'descriptor': {shape: [3, 4, 1, 1], dataType: 'float32'} 207 } 208 } 209 } 210 }, 211 212 // float16 tests 213 { 214 'name': 'softmax float16 2D constant tensor all positive', 215 'graph': { 216 'inputs': { 217 'softmaxInput': { 218 'data': [ 219 7.90234375, 6.359375, 4.83203125, 9.578125, 220 0.210693359375, 4.5546875, 7.1484375, 8.328125, 221 1.5361328125, 6.6328125, 1.4541015625, 0.21337890625, 222 5.2578125, 8.1953125, 8.1640625, 2.875, 223 8.953125, 6.11328125, 1.63671875, 0.2763671875, 224 5.02734375, 3.8984375, 2.896484375, 6.890625 225 ], 226 'descriptor': {shape: [4, 6], dataType: 'float16'}, 227 'constant': true 228 } 229 }, 230 'operators': [{ 231 'name': 'softmax', 232 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 233 'outputs': 'softmaxOutput' 234 }], 235 'expectedOutputs': { 236 'softmaxOutput': { 237 'data': [ 238 0.150634765625, 0.032196044921875, 239 0.006988525390625, 0.8046875, 240 0.00006878376007080078, 0.005298614501953125, 241 0.205810546875, 0.66943359375, 242 0.0007519721984863281, 0.1229248046875, 243 0.0006923675537109375, 0.0002002716064453125, 244 0.01236724853515625, 0.2333984375, 245 0.2261962890625, 0.0011415481567382812, 246 0.497802734375, 0.0290985107421875, 247 0.00424957275390625, 0.0010900497436523438, 248 0.1260986328125, 0.040771484375, 249 0.01497650146484375, 0.81298828125 250 ], 251 'descriptor': {shape: [4, 6], dataType: 'float16'} 252 } 253 } 254 } 255 }, 256 { 257 'name': 'softmax float16 2D tensor all positive', 258 'graph': { 259 'inputs': { 260 'softmaxInput': { 261 'data': [ 262 7.90234375, 6.359375, 4.83203125, 9.578125, 263 0.210693359375, 4.5546875, 7.1484375, 8.328125, 264 1.5361328125, 6.6328125, 1.4541015625, 0.21337890625, 265 5.2578125, 8.1953125, 8.1640625, 2.875, 266 8.953125, 6.11328125, 1.63671875, 0.2763671875, 267 5.02734375, 3.8984375, 2.896484375, 6.890625 268 ], 269 'descriptor': {shape: [4, 6], dataType: 'float16'} 270 } 271 }, 272 'operators': [{ 273 'name': 'softmax', 274 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 275 'outputs': 'softmaxOutput' 276 }], 277 'expectedOutputs': { 278 'softmaxOutput': { 279 'data': [ 280 0.150634765625, 0.032196044921875, 281 0.006988525390625, 0.8046875, 282 0.00006878376007080078, 0.005298614501953125, 283 0.205810546875, 0.66943359375, 284 0.0007519721984863281, 0.1229248046875, 285 0.0006923675537109375, 0.0002002716064453125, 286 0.01236724853515625, 0.2333984375, 287 0.2261962890625, 0.0011415481567382812, 288 0.497802734375, 0.0290985107421875, 289 0.00424957275390625, 0.0010900497436523438, 290 0.1260986328125, 0.040771484375, 291 0.01497650146484375, 0.81298828125 292 ], 293 'descriptor': {shape: [4, 6], dataType: 'float16'} 294 } 295 } 296 } 297 }, 298 { 299 'name': 'softmax float16 2D tensor all negative', 300 'graph': { 301 'inputs': { 302 'softmaxInput': { 303 'data': [ 304 -3.3125, -3.33984375, -3.41015625, -6.6953125, -7.89453125, 305 -3.30859375, -3.23046875, -4.31640625, -9.3125, -3.923828125, 306 -3.78125, -6.03515625, -3.919921875, -2.22265625, -9.328125, 307 -1.48828125, -6.3046875, -5.53125, -1.841796875, -4.99609375, 308 -9.5234375, -4.984375, -8.421875, -6.234375 309 ], 310 'descriptor': {shape: [4, 6], dataType: 'float16'} 311 } 312 }, 313 'operators': [{ 314 'name': 'softmax', 315 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 316 'outputs': 'softmaxOutput' 317 }], 318 'expectedOutputs': { 319 'softmaxOutput': { 320 'data': [ 321 0.254638671875, 0.2476806640625, 0.2308349609375, 322 0.00864410400390625, 0.002605438232421875, 0.255615234375, 323 0.40380859375, 0.1363525390625, 0.0009222030639648438, 324 0.2017822265625, 0.2327880859375, 0.024444580078125, 325 0.055145263671875, 0.301025390625, 0.00024700164794921875, 326 0.62744140625, 0.0050811767578125, 0.01100921630859375, 327 0.9091796875, 0.038787841796875, 0.00041937828063964844, 328 0.03924560546875, 0.0012617111206054688, 0.0112457275390625 329 ], 330 'descriptor': {shape: [4, 6], dataType: 'float16'} 331 } 332 } 333 } 334 }, 335 { 336 'name': 'softmax float16 4D tensor', 337 'graph': { 338 'inputs': { 339 'softmaxInput': { 340 'data': [ 341 0.43017578125, 0.54736328125, -1.1640625, 0.1839599609375, 342 0.583984375, 0.173583984375, 0.53955078125, -0.95361328125, 343 -0.591796875, -0.1734619140625, 0.1439208984375, -0.379150390625 344 ], 345 'descriptor': {shape: [3, 4, 1, 1], dataType: 'float16'} 346 } 347 }, 348 'operators': [{ 349 'name': 'softmax', 350 'arguments': [{'input': 'softmaxInput'}, {'axis': 1}], 351 'outputs': 'softmaxOutput' 352 }], 353 'expectedOutputs': { 354 'softmaxOutput': { 355 'data': [ 356 0.321533203125, 0.361572265625, 0.0653076171875, 0.25146484375, 357 0.352783203125, 0.2340087890625, 0.33740234375, 0.0758056640625, 358 0.171142578125, 0.260009765625, 0.357177734375, 0.211669921875 359 ], 360 'descriptor': {shape: [3, 4, 1, 1], dataType: 'float16'} 361 } 362 } 363 } 364 } 365 ]; 366 367 webnn_conformance_test( 368 softmaxTests, buildAndExecuteGraph, getPrecisionTolerance);