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Test Info: Warnings
- This test has a WPT meta file that expects 1 subtest issues.
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/softmax.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softmax.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softmax.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softmax.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softmax.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softmax.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API softmax operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Compute the softmax values of the N-D input tensor along the given axis.
//
// MLOperand softmax(MLOperand input, unsigned long axis);
const softmaxTests = [
{
'name': 'softmax float32 2D constant tensor all positive',
'graph': {
'inputs': {
'softmaxInput': {
'data': [
7.9037346839904785, 6.358251571655273, 4.833756923675537,
9.5791654586792, 0.21071857213974, 4.554958820343018,
7.150174140930176, 8.330297470092773, 1.5359858274459839,
6.63361930847168, 1.4539369344711304, 0.213418647646904,
5.257819652557373, 8.192137718200684, 8.16172981262207,
2.874434232711792, 8.950733184814453, 6.111632823944092,
1.6371468305587769, 0.27626121044158936, 5.02822732925415,
3.8983259201049805, 2.8967113494873047, 6.88947057723999
],
'descriptor': {shape: [4, 6], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'softmax',
'arguments': [{'input': 'softmaxInput'}],
'outputs': 'softmaxOutput'
}],
'expectedOutputs': {
'softmaxOutput': {
'data': [
0.15068615972995758, 0.03212761878967285,
0.006995180621743202, 0.8048291206359863,
0.00006871300138300285, 0.005293202120810747,
0.2057899534702301, 0.6698001027107239,
0.0007502624066546559, 0.1227685883641243,
0.0006911618984304368, 0.00019990770670119673,
0.012398251332342625, 0.23319464921951294,
0.22621041536331177, 0.0011435872875154018,
0.4979347288608551, 0.029118351638317108,
0.004253828432410955, 0.001090824487619102,
0.12633030116558075, 0.040812913328409195,
0.014990009367465973, 0.8125221133232117
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'softmax float32 2D tensor all positive',
'graph': {
'inputs': {
'softmaxInput': {
'data': [
7.9037346839904785, 6.358251571655273, 4.833756923675537,
9.5791654586792, 0.21071857213974, 4.554958820343018,
7.150174140930176, 8.330297470092773, 1.5359858274459839,
6.63361930847168, 1.4539369344711304, 0.213418647646904,
5.257819652557373, 8.192137718200684, 8.16172981262207,
2.874434232711792, 8.950733184814453, 6.111632823944092,
1.6371468305587769, 0.27626121044158936, 5.02822732925415,
3.8983259201049805, 2.8967113494873047, 6.88947057723999
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'softmax',
'arguments': [{'input': 'softmaxInput'}],
'outputs': 'softmaxOutput'
}],
'expectedOutputs': {
'softmaxOutput': {
'data': [
0.15068615972995758, 0.03212761878967285,
0.006995180621743202, 0.8048291206359863,
0.00006871300138300285, 0.005293202120810747,
0.2057899534702301, 0.6698001027107239,
0.0007502624066546559, 0.1227685883641243,
0.0006911618984304368, 0.00019990770670119673,
0.012398251332342625, 0.23319464921951294,
0.22621041536331177, 0.0011435872875154018,
0.4979347288608551, 0.029118351638317108,
0.004253828432410955, 0.001090824487619102,
0.12633030116558075, 0.040812913328409195,
0.014990009367465973, 0.8125221133232117
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'softmax float32 2D tensor all negative',
'graph': {
'inputs': {
'softmaxInput': {
'data': [
-3.3118433952331543, -3.3389549255371094, -3.4102790355682373,
-6.697193145751953, -7.896223545074463, -3.308168888092041,
-3.2309720516204834, -4.315771579742432, -9.311088562011719,
-3.9236626625061035, -3.780721426010132, -6.034926891326904,
-3.9196677207946777, -2.2234842777252197, -9.326531410217285,
-1.4882491827011108, -6.302842617034912, -5.53147554397583,
-1.8421411514282227, -4.994808197021484, -9.527292251586914,
-4.985682964324951, -8.421041488647461, -6.235629558563232
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'softmax',
'arguments': [{'input': 'softmaxInput'}],
'outputs': 'softmaxOutput'
}],
'expectedOutputs': {
'softmaxOutput': {
'data': [
0.2546302080154419, 0.24781952798366547, 0.2307596504688263,
0.008623254485428333, 0.002599793951958418, 0.2555675804615021,
0.40352678298950195, 0.13637976348400116, 0.0009232329903170466,
0.20185552537441254, 0.23287305235862732, 0.024441635236144066,
0.0551743283867836, 0.3008708655834198, 0.0002474947541486472,
0.6276082992553711, 0.0050902292132377625, 0.011008745059370995,
0.9090295433998108, 0.0388500951230526, 0.00041779119055718184,
0.039206232875585556, 0.0012629841221496463, 0.011233373545110226
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'softmax float32 3D constant tensor',
'graph': {
'inputs': {
'softmaxInput': {
'data': [
0.4301910996437073, 0.5471914410591125, -1.1637765169143677,
0.18390046060085297, 0.583903968334198, 0.17356790602207184,
0.5397239923477173, -0.9535139799118042, -0.5920282602310181,
-0.17344485223293304, 0.14395014941692352, -0.37920907139778137
],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'softmax',
'arguments': [{'input': 'softmaxInput'}, {'axis': 1}],
'outputs': 'softmaxOutput'
}],
'expectedOutputs': {
'softmaxOutput': {
'data': [
0.39589041471481323, 0.45983806252479553, 0.09812675416469574,
0.529077410697937, 0.4616699814796448, 0.31647709012031555,
0.5390242338180542, 0.16964708268642426, 0.142439603805542,
0.22368484735488892, 0.36284899711608887, 0.3012755215167999
],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'softmax float32 4D tensor',
'graph': {
'inputs': {
'softmaxInput': {
'data': [
0.4301910996437073, 0.5471914410591125, -1.1637765169143677,
0.18390046060085297, 0.583903968334198, 0.17356790602207184,
0.5397239923477173, -0.9535139799118042, -0.5920282602310181,
-0.17344485223293304, 0.14395014941692352, -0.37920907139778137
],
'descriptor': {shape: [3, 4, 1, 1], dataType: 'float32'}
}
},
'operators': [{
'name': 'softmax',
'arguments': [{'input': 'softmaxInput'}, {'axis': 1}],
'outputs': 'softmaxOutput'
}],
'expectedOutputs': {
'softmaxOutput': {
'data': [
0.3216537833213806, 0.3615773916244507, 0.06533370912075043,
0.25143513083457947, 0.35271573066711426, 0.23400123417377472,
0.33747196197509766, 0.07581108063459396, 0.17110128700733185,
0.26004093885421753, 0.3571779429912567, 0.2116798311471939
],
'descriptor': {shape: [3, 4, 1, 1], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
softmaxTests.forEach((test) => {
webnn_conformance_test(buildAndExecuteGraph, getPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}