softsign.https.any.js (24895B)
1 // META: title=test WebNN API softsign 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-softsign-method 12 // Compute the softsign function of the input tensor. The calculation follows 13 // the expression x / (1 + |x|). 14 // 15 // MLOperand softsign(MLOperand input); 16 17 const softsignTests = [ 18 { 19 'name': 'softsign positive float32 1D constant tensor', 20 'graph': { 21 'inputs': { 22 'softsignInput': { 23 'data': [ 24 1.5834133625030518, 4.078719139099121, 8.883357048034668, 25 8.070859909057617, 8.211773872375488, 2.4554004669189453, 26 0.653374195098877, 7.866281032562256, 3.123955249786377, 27 8.013792037963867, 3.940986156463623, 1.813172698020935, 28 2.3906760215759277, 1.335968017578125, 9.416410446166992, 29 0.4432569146156311, 5.236661911010742, 9.42424201965332, 30 7.816190242767334, 5.849185943603516, 8.780370712280273, 31 5.120515823364258, 7.117222309112549, 4.599106788635254 32 ], 33 'descriptor': {shape: [24], dataType: 'float32'}, 34 'constant': true 35 } 36 }, 37 'operators': [{ 38 'name': 'softsign', 39 'arguments': [{'input': 'softsignInput'}], 40 'outputs': 'softsignOutput' 41 }], 42 'expectedOutputs': { 43 'softsignOutput': { 44 'data': [ 45 0.6129152178764343, 0.8030999898910522, 0.8988198041915894, 46 0.8897568583488464, 0.8914432525634766, 0.7105979323387146, 47 0.3951762318611145, 0.8872131109237671, 0.7575143575668335, 48 0.8890588879585266, 0.7976112365722656, 0.6445294618606567, 49 0.7050735354423523, 0.5719119310379028, 0.9039976596832275, 50 0.30712267756462097, 0.8396578431129456, 0.9040697813034058, 51 0.8865723013877869, 0.8539972305297852, 0.8977543711662292, 52 0.8366150856018066, 0.8768051266670227, 0.8214001059532166 53 ], 54 'descriptor': {shape: [24], dataType: 'float32'} 55 } 56 } 57 } 58 }, 59 { 60 'name': 'softsign positive float32 0D tensor', 61 'graph': { 62 'inputs': { 63 'softsignInput': { 64 'data': [1.5834133625030518], 65 'descriptor': {shape: [], dataType: 'float32'} 66 } 67 }, 68 'operators': [{ 69 'name': 'softsign', 70 'arguments': [{'input': 'softsignInput'}], 71 'outputs': 'softsignOutput' 72 }], 73 'expectedOutputs': { 74 'softsignOutput': { 75 'data': [0.6129152178764343], 76 'descriptor': {shape: [], dataType: 'float32'} 77 } 78 } 79 } 80 }, 81 { 82 'name': 'softsign negative float32 0D tensor', 83 'graph': { 84 'inputs': { 85 'softsignInput': { 86 'data': [-2.597844123840332], 87 'descriptor': {shape: [], dataType: 'float32'} 88 } 89 }, 90 'operators': [{ 91 'name': 'softsign', 92 'arguments': [{'input': 'softsignInput'}], 93 'outputs': 'softsignOutput' 94 }], 95 'expectedOutputs': { 96 'softsignOutput': { 97 'data': [-0.7220557928085327], 98 'descriptor': {shape: [], dataType: 'float32'} 99 } 100 } 101 } 102 }, 103 { 104 'name': 'softsign positive float32 1D tensor', 105 'graph': { 106 'inputs': { 107 'softsignInput': { 108 'data': [ 109 1.5834133625030518, 4.078719139099121, 8.883357048034668, 110 8.070859909057617, 8.211773872375488, 2.4554004669189453, 111 0.653374195098877, 7.866281032562256, 3.123955249786377, 112 8.013792037963867, 3.940986156463623, 1.813172698020935, 113 2.3906760215759277, 1.335968017578125, 9.416410446166992, 114 0.4432569146156311, 5.236661911010742, 9.42424201965332, 115 7.816190242767334, 5.849185943603516, 8.780370712280273, 116 5.120515823364258, 7.117222309112549, 4.599106788635254 117 ], 118 'descriptor': {shape: [24], dataType: 'float32'} 119 } 120 }, 121 'operators': [{ 122 'name': 'softsign', 123 'arguments': [{'input': 'softsignInput'}], 124 'outputs': 'softsignOutput' 125 }], 126 'expectedOutputs': { 127 'softsignOutput': { 128 'data': [ 129 0.6129152178764343, 0.8030999898910522, 0.8988198041915894, 130 0.8897568583488464, 0.8914432525634766, 0.7105979323387146, 131 0.3951762318611145, 0.8872131109237671, 0.7575143575668335, 132 0.8890588879585266, 0.7976112365722656, 0.6445294618606567, 133 0.7050735354423523, 0.5719119310379028, 0.9039976596832275, 134 0.30712267756462097, 0.8396578431129456, 0.9040697813034058, 135 0.8865723013877869, 0.8539972305297852, 0.8977543711662292, 136 0.8366150856018066, 0.8768051266670227, 0.8214001059532166 137 ], 138 'descriptor': {shape: [24], dataType: 'float32'} 139 } 140 } 141 } 142 }, 143 { 144 'name': 'softsign negative float32 1D tensor', 145 'graph': { 146 'inputs': { 147 'softsignInput': { 148 'data': [ 149 -2.597844123840332, -0.4449555575847626, -9.095475196838379, 150 -3.7480077743530273, -1.3867290019989014, -8.220329284667969, 151 -3.538342237472534, -9.364588737487793, -6.283252239227295, 152 -5.002012252807617, -8.245729446411133, -3.775470495223999, 153 -4.087255001068115, -7.381676197052002, -5.8829216957092285, 154 -8.338910102844238, -6.60154914855957, -4.491941928863525, 155 -3.5247786045074463, -4.43991231918335, -5.234262466430664, 156 -1.5911732912063599, -9.106277465820312, -8.523774147033691 157 ], 158 'descriptor': {shape: [24], dataType: 'float32'} 159 } 160 }, 161 'operators': [{ 162 'name': 'softsign', 163 'arguments': [{'input': 'softsignInput'}], 164 'outputs': 'softsignOutput' 165 }], 166 'expectedOutputs': { 167 'softsignOutput': { 168 'data': [ 169 -0.7220557928085327, -0.3079372048377991, -0.9009457230567932, 170 -0.7893853783607483, -0.5810165405273438, -0.891543984413147, 171 -0.7796552181243896, -0.9035176634788513, -0.8626986742019653, 172 -0.8333892226219177, -0.8918419480323792, -0.7905965447425842, 173 -0.8034303188323975, -0.8806921243667603, -0.8547128438949585, 174 -0.8929211497306824, -0.8684478402137756, -0.8179150223731995, 175 -0.7789947390556335, -0.8161734938621521, -0.8395960927009583, 176 -0.6140744686126709, -0.9010515809059143, -0.894999623298645 177 ], 178 'descriptor': {shape: [24], dataType: 'float32'} 179 } 180 } 181 } 182 }, 183 { 184 'name': 'softsign float32 2D tensor', 185 'graph': { 186 'inputs': { 187 'softsignInput': { 188 'data': [ 189 -8.343496322631836, -6.920152187347412, 2.699638843536377, 190 -8.663105010986328, -3.1905343532562256, 7.657886981964111, 191 6.650215148925781, 6.058011054992676, 0.6634320616722107, 192 5.8058037757873535, -0.32821124792099, 1.2704304456710815, 193 -9.946120262145996, 6.905375003814697, -0.031071536242961884, 194 -3.9696409702301025, 6.270823001861572, -2.639260768890381, 195 3.0513505935668945, 7.426476955413818, -8.454667091369629, 196 7.135868072509766, -4.986093997955322, -7.859614849090576 197 ], 198 'descriptor': {shape: [4, 6], dataType: 'float32'} 199 } 200 }, 201 'operators': [{ 202 'name': 'softsign', 203 'arguments': [{'input': 'softsignInput'}], 204 'outputs': 'softsignOutput' 205 }], 206 'expectedOutputs': { 207 'softsignOutput': { 208 'data': [ 209 -0.8929736614227295, -0.8737397789955139, 0.7297033667564392, 210 -0.8965135812759399, -0.7613669633865356, 0.8844983577728271, 211 0.8692847490310669, 0.8583170175552368, 0.3988332748413086, 212 0.8530665636062622, -0.24710771441459656, 0.5595548748970032, 213 -0.9086434245109558, 0.8735038042068481, -0.03013519011437893, 214 -0.798778235912323, 0.8624640107154846, -0.7252188920974731, 215 0.7531687617301941, 0.88132643699646, -0.8942321538925171, 216 0.8770874738693237, -0.8329461812973022, -0.8871282935142517 217 ], 218 'descriptor': {shape: [4, 6], dataType: 'float32'} 219 } 220 } 221 } 222 }, 223 { 224 'name': 'softsign float32 3D tensor', 225 'graph': { 226 'inputs': { 227 'softsignInput': { 228 'data': [ 229 -8.343496322631836, -6.920152187347412, 2.699638843536377, 230 -8.663105010986328, -3.1905343532562256, 7.657886981964111, 231 6.650215148925781, 6.058011054992676, 0.6634320616722107, 232 5.8058037757873535, -0.32821124792099, 1.2704304456710815, 233 -9.946120262145996, 6.905375003814697, -0.031071536242961884, 234 -3.9696409702301025, 6.270823001861572, -2.639260768890381, 235 3.0513505935668945, 7.426476955413818, -8.454667091369629, 236 7.135868072509766, -4.986093997955322, -7.859614849090576 237 ], 238 'descriptor': {shape: [2, 3, 4], dataType: 'float32'} 239 } 240 }, 241 'operators': [{ 242 'name': 'softsign', 243 'arguments': [{'input': 'softsignInput'}], 244 'outputs': 'softsignOutput' 245 }], 246 'expectedOutputs': { 247 'softsignOutput': { 248 'data': [ 249 -0.8929736614227295, -0.8737397789955139, 0.7297033667564392, 250 -0.8965135812759399, -0.7613669633865356, 0.8844983577728271, 251 0.8692847490310669, 0.8583170175552368, 0.3988332748413086, 252 0.8530665636062622, -0.24710771441459656, 0.5595548748970032, 253 -0.9086434245109558, 0.8735038042068481, -0.03013519011437893, 254 -0.798778235912323, 0.8624640107154846, -0.7252188920974731, 255 0.7531687617301941, 0.88132643699646, -0.8942321538925171, 256 0.8770874738693237, -0.8329461812973022, -0.8871282935142517 257 ], 258 'descriptor': {shape: [2, 3, 4], dataType: 'float32'} 259 } 260 } 261 } 262 }, 263 { 264 'name': 'softsign float32 4D tensor', 265 'graph': { 266 'inputs': { 267 'softsignInput': { 268 'data': [ 269 -8.343496322631836, -6.920152187347412, 2.699638843536377, 270 -8.663105010986328, -3.1905343532562256, 7.657886981964111, 271 6.650215148925781, 6.058011054992676, 0.6634320616722107, 272 5.8058037757873535, -0.32821124792099, 1.2704304456710815, 273 -9.946120262145996, 6.905375003814697, -0.031071536242961884, 274 -3.9696409702301025, 6.270823001861572, -2.639260768890381, 275 3.0513505935668945, 7.426476955413818, -8.454667091369629, 276 7.135868072509766, -4.986093997955322, -7.859614849090576 277 ], 278 'descriptor': {shape: [1, 2, 3, 4], dataType: 'float32'} 279 } 280 }, 281 'operators': [{ 282 'name': 'softsign', 283 'arguments': [{'input': 'softsignInput'}], 284 'outputs': 'softsignOutput' 285 }], 286 'expectedOutputs': { 287 'softsignOutput': { 288 'data': [ 289 -0.8929736614227295, -0.8737397789955139, 0.7297033667564392, 290 -0.8965135812759399, -0.7613669633865356, 0.8844983577728271, 291 0.8692847490310669, 0.8583170175552368, 0.3988332748413086, 292 0.8530665636062622, -0.24710771441459656, 0.5595548748970032, 293 -0.9086434245109558, 0.8735038042068481, -0.03013519011437893, 294 -0.798778235912323, 0.8624640107154846, -0.7252188920974731, 295 0.7531687617301941, 0.88132643699646, -0.8942321538925171, 296 0.8770874738693237, -0.8329461812973022, -0.8871282935142517 297 ], 298 'descriptor': {shape: [1, 2, 3, 4], dataType: 'float32'} 299 } 300 } 301 } 302 }, 303 { 304 'name': 'softsign float32 5D tensor', 305 'graph': { 306 'inputs': { 307 'softsignInput': { 308 'data': [ 309 -8.343496322631836, -6.920152187347412, 2.699638843536377, 310 -8.663105010986328, -3.1905343532562256, 7.657886981964111, 311 6.650215148925781, 6.058011054992676, 0.6634320616722107, 312 5.8058037757873535, -0.32821124792099, 1.2704304456710815, 313 -9.946120262145996, 6.905375003814697, -0.031071536242961884, 314 -3.9696409702301025, 6.270823001861572, -2.639260768890381, 315 3.0513505935668945, 7.426476955413818, -8.454667091369629, 316 7.135868072509766, -4.986093997955322, -7.859614849090576 317 ], 318 'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float32'} 319 } 320 }, 321 'operators': [{ 322 'name': 'softsign', 323 'arguments': [{'input': 'softsignInput'}], 324 'outputs': 'softsignOutput' 325 }], 326 'expectedOutputs': { 327 'softsignOutput': { 328 'data': [ 329 -0.8929736614227295, -0.8737397789955139, 0.7297033667564392, 330 -0.8965135812759399, -0.7613669633865356, 0.8844983577728271, 331 0.8692847490310669, 0.8583170175552368, 0.3988332748413086, 332 0.8530665636062622, -0.24710771441459656, 0.5595548748970032, 333 -0.9086434245109558, 0.8735038042068481, -0.03013519011437893, 334 -0.798778235912323, 0.8624640107154846, -0.7252188920974731, 335 0.7531687617301941, 0.88132643699646, -0.8942321538925171, 336 0.8770874738693237, -0.8329461812973022, -0.8871282935142517 337 ], 338 'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float32'} 339 } 340 } 341 } 342 }, 343 344 // float16 tests 345 { 346 'name': 'softsign positive float16 1D constant tensor', 347 'graph': { 348 'inputs': { 349 'softsignInput': { 350 'data': [ 351 1.5830078125, 4.078125, 8.8828125, 8.0703125, 8.2109375, 352 2.455078125, 0.6533203125, 7.8671875, 3.123046875, 8.015625, 353 3.94140625, 1.8134765625, 2.390625, 1.3359375, 9.4140625, 354 0.443359375, 5.23828125, 9.421875, 7.81640625, 5.84765625, 355 8.78125, 5.12109375, 7.1171875, 4.59765625 356 ], 357 'descriptor': {shape: [24], dataType: 'float16'}, 358 'constant': true 359 } 360 }, 361 'operators': [{ 362 'name': 'softsign', 363 'arguments': [{'input': 'softsignInput'}], 364 'outputs': 'softsignOutput' 365 }], 366 'expectedOutputs': { 367 'softsignOutput': { 368 'data': [ 369 0.61279296875, 0.80322265625, 0.89892578125, 0.8896484375, 370 0.8916015625, 0.71044921875, 0.395263671875, 0.88720703125, 371 0.75732421875, 0.88916015625, 0.7978515625, 0.64453125, 372 0.705078125, 0.57177734375, 0.90380859375, 0.30712890625, 373 0.83984375, 0.90380859375, 0.88671875, 0.85400390625, 374 0.89794921875, 0.83642578125, 0.876953125, 0.8212890625 375 ], 376 'descriptor': {shape: [24], dataType: 'float16'} 377 } 378 } 379 } 380 }, 381 { 382 'name': 'softsign positive float16 0D tensor', 383 'graph': { 384 'inputs': { 385 'softsignInput': { 386 'data': [1.5830078125], 387 'descriptor': {shape: [], dataType: 'float16'} 388 } 389 }, 390 'operators': [{ 391 'name': 'softsign', 392 'arguments': [{'input': 'softsignInput'}], 393 'outputs': 'softsignOutput' 394 }], 395 'expectedOutputs': { 396 'softsignOutput': { 397 'data': [0.61279296875], 398 'descriptor': {shape: [], dataType: 'float16'} 399 } 400 } 401 } 402 }, 403 { 404 'name': 'softsign negative float16 0D tensor', 405 'graph': { 406 'inputs': { 407 'softsignInput': { 408 'data': [-2.59765625], 409 'descriptor': {shape: [], dataType: 'float16'} 410 } 411 }, 412 'operators': [{ 413 'name': 'softsign', 414 'arguments': [{'input': 'softsignInput'}], 415 'outputs': 'softsignOutput' 416 }], 417 'expectedOutputs': { 418 'softsignOutput': { 419 'data': [-0.72216796875], 420 'descriptor': {shape: [], dataType: 'float16'} 421 } 422 } 423 } 424 }, 425 { 426 'name': 'softsign positive float16 1D tensor', 427 'graph': { 428 'inputs': { 429 'softsignInput': { 430 'data': [ 431 1.5830078125, 4.078125, 8.8828125, 8.0703125, 8.2109375, 432 2.455078125, 0.6533203125, 7.8671875, 3.123046875, 8.015625, 433 3.94140625, 1.8134765625, 2.390625, 1.3359375, 9.4140625, 434 0.443359375, 5.23828125, 9.421875, 7.81640625, 5.84765625, 435 8.78125, 5.12109375, 7.1171875, 4.59765625 436 ], 437 'descriptor': {shape: [24], dataType: 'float16'} 438 } 439 }, 440 'operators': [{ 441 'name': 'softsign', 442 'arguments': [{'input': 'softsignInput'}], 443 'outputs': 'softsignOutput' 444 }], 445 'expectedOutputs': { 446 'softsignOutput': { 447 'data': [ 448 0.61279296875, 0.80322265625, 0.89892578125, 0.8896484375, 449 0.8916015625, 0.71044921875, 0.395263671875, 0.88720703125, 450 0.75732421875, 0.88916015625, 0.7978515625, 0.64453125, 451 0.705078125, 0.57177734375, 0.90380859375, 0.30712890625, 452 0.83984375, 0.90380859375, 0.88671875, 0.85400390625, 453 0.89794921875, 0.83642578125, 0.876953125, 0.8212890625 454 ], 455 'descriptor': {shape: [24], dataType: 'float16'} 456 } 457 } 458 } 459 }, 460 { 461 'name': 'softsign negative float16 1D tensor', 462 'graph': { 463 'inputs': { 464 'softsignInput': { 465 'data': [ 466 -2.59765625, -0.445068359375, -9.09375, -3.748046875, -1.38671875, 467 -8.21875, -3.5390625, -9.3671875, -6.28515625, -5.00390625, 468 -8.2421875, -3.775390625, -4.0859375, -7.3828125, -5.8828125, 469 -8.3359375, -6.6015625, -4.4921875, -3.525390625, -4.44140625, 470 -5.234375, -1.5908203125, -9.109375, -8.5234375 471 ], 472 'descriptor': {shape: [24], dataType: 'float16'} 473 } 474 }, 475 'operators': [{ 476 'name': 'softsign', 477 'arguments': [{'input': 'softsignInput'}], 478 'outputs': 'softsignOutput' 479 }], 480 'expectedOutputs': { 481 'softsignOutput': { 482 'data': [ 483 -0.72216796875, -0.30810546875, -0.90087890625, -0.78955078125, 484 -0.5810546875, -0.8916015625, -0.77978515625, -0.9033203125, 485 -0.86279296875, -0.83349609375, -0.8916015625, -0.79052734375, 486 -0.80322265625, -0.880859375, -0.8544921875, -0.89306640625, 487 -0.86865234375, -0.81787109375, -0.77880859375, -0.81640625, 488 -0.83935546875, -0.6142578125, -0.90087890625, -0.89501953125 489 ], 490 'descriptor': {shape: [24], dataType: 'float16'} 491 } 492 } 493 } 494 }, 495 { 496 'name': 'softsign float16 2D tensor', 497 'graph': { 498 'inputs': { 499 'softsignInput': { 500 'data': [ 501 -8.34375, -6.921875, 2.69921875, -8.6640625, 502 -3.19140625, 7.65625, 6.6484375, 6.05859375, 503 0.66357421875, 5.8046875, -0.328125, 1.2705078125, 504 -9.9453125, 6.90625, -0.03106689453125, -3.96875, 505 6.26953125, -2.638671875, 3.05078125, 7.42578125, 506 -8.453125, 7.13671875, -4.984375, -7.859375 507 ], 508 'descriptor': {shape: [4, 6], dataType: 'float16'} 509 } 510 }, 511 'operators': [{ 512 'name': 'softsign', 513 'arguments': [{'input': 'softsignInput'}], 514 'outputs': 'softsignOutput' 515 }], 516 'expectedOutputs': { 517 'softsignOutput': { 518 'data': [ 519 -0.89306640625, -0.87353515625, 0.7294921875, 520 -0.896484375, -0.76123046875, 0.88427734375, 521 0.869140625, 0.8583984375, 0.39892578125, 522 0.85302734375, -0.2470703125, 0.5595703125, 523 -0.90869140625, 0.87353515625, -0.0301361083984375, 524 -0.798828125, 0.8623046875, -0.72509765625, 525 0.7529296875, 0.88134765625, -0.89404296875, 526 0.876953125, -0.8330078125, -0.88720703125 527 ], 528 'descriptor': {shape: [4, 6], dataType: 'float16'} 529 } 530 } 531 } 532 }, 533 { 534 'name': 'softsign float16 3D tensor', 535 'graph': { 536 'inputs': { 537 'softsignInput': { 538 'data': [ 539 -8.34375, -6.921875, 2.69921875, -8.6640625, 540 -3.19140625, 7.65625, 6.6484375, 6.05859375, 541 0.66357421875, 5.8046875, -0.328125, 1.2705078125, 542 -9.9453125, 6.90625, -0.03106689453125, -3.96875, 543 6.26953125, -2.638671875, 3.05078125, 7.42578125, 544 -8.453125, 7.13671875, -4.984375, -7.859375 545 ], 546 'descriptor': {shape: [2, 3, 4], dataType: 'float16'} 547 } 548 }, 549 'operators': [{ 550 'name': 'softsign', 551 'arguments': [{'input': 'softsignInput'}], 552 'outputs': 'softsignOutput' 553 }], 554 'expectedOutputs': { 555 'softsignOutput': { 556 'data': [ 557 -0.89306640625, -0.87353515625, 0.7294921875, 558 -0.896484375, -0.76123046875, 0.88427734375, 559 0.869140625, 0.8583984375, 0.39892578125, 560 0.85302734375, -0.2470703125, 0.5595703125, 561 -0.90869140625, 0.87353515625, -0.0301361083984375, 562 -0.798828125, 0.8623046875, -0.72509765625, 563 0.7529296875, 0.88134765625, -0.89404296875, 564 0.876953125, -0.8330078125, -0.88720703125 565 ], 566 'descriptor': {shape: [2, 3, 4], dataType: 'float16'} 567 } 568 } 569 } 570 }, 571 { 572 'name': 'softsign float16 4D tensor', 573 'graph': { 574 'inputs': { 575 'softsignInput': { 576 'data': [ 577 -8.34375, -6.921875, 2.69921875, -8.6640625, 578 -3.19140625, 7.65625, 6.6484375, 6.05859375, 579 0.66357421875, 5.8046875, -0.328125, 1.2705078125, 580 -9.9453125, 6.90625, -0.03106689453125, -3.96875, 581 6.26953125, -2.638671875, 3.05078125, 7.42578125, 582 -8.453125, 7.13671875, -4.984375, -7.859375 583 ], 584 'descriptor': {shape: [1, 2, 3, 4], dataType: 'float16'} 585 } 586 }, 587 'operators': [{ 588 'name': 'softsign', 589 'arguments': [{'input': 'softsignInput'}], 590 'outputs': 'softsignOutput' 591 }], 592 'expectedOutputs': { 593 'softsignOutput': { 594 'data': [ 595 -0.89306640625, -0.87353515625, 0.7294921875, 596 -0.896484375, -0.76123046875, 0.88427734375, 597 0.869140625, 0.8583984375, 0.39892578125, 598 0.85302734375, -0.2470703125, 0.5595703125, 599 -0.90869140625, 0.87353515625, -0.0301361083984375, 600 -0.798828125, 0.8623046875, -0.72509765625, 601 0.7529296875, 0.88134765625, -0.89404296875, 602 0.876953125, -0.8330078125, -0.88720703125 603 ], 604 'descriptor': {shape: [1, 2, 3, 4], dataType: 'float16'} 605 } 606 } 607 } 608 }, 609 { 610 'name': 'softsign float16 5D tensor', 611 'graph': { 612 'inputs': { 613 'softsignInput': { 614 'data': [ 615 -8.34375, -6.921875, 2.69921875, -8.6640625, 616 -3.19140625, 7.65625, 6.6484375, 6.05859375, 617 0.66357421875, 5.8046875, -0.328125, 1.2705078125, 618 -9.9453125, 6.90625, -0.03106689453125, -3.96875, 619 6.26953125, -2.638671875, 3.05078125, 7.42578125, 620 -8.453125, 7.13671875, -4.984375, -7.859375 621 ], 622 'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float16'} 623 } 624 }, 625 'operators': [{ 626 'name': 'softsign', 627 'arguments': [{'input': 'softsignInput'}], 628 'outputs': 'softsignOutput' 629 }], 630 'expectedOutputs': { 631 'softsignOutput': { 632 'data': [ 633 -0.89306640625, -0.87353515625, 0.7294921875, 634 -0.896484375, -0.76123046875, 0.88427734375, 635 0.869140625, 0.8583984375, 0.39892578125, 636 0.85302734375, -0.2470703125, 0.5595703125, 637 -0.90869140625, 0.87353515625, -0.0301361083984375, 638 -0.798828125, 0.8623046875, -0.72509765625, 639 0.7529296875, 0.88134765625, -0.89404296875, 640 0.876953125, -0.8330078125, -0.88720703125 641 ], 642 'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float16'} 643 } 644 } 645 } 646 } 647 ]; 648 649 webnn_conformance_test( 650 softsignTests, buildAndExecuteGraph, getPrecisionTolerance);