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Test Info:
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/exp.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/exp.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/exp.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/exp.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/exp.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/exp.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API element-wise exp 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 exponential of the input tensor, element-wise.
//
// MLOperand exp(MLOperand input);
const getExpPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 32, float16: 1};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const expTests = [
{
'name': 'exp float32 0D scalar',
'graph': {
'inputs': {
'expInput': {
'data': [0.3421436548233032],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'exp',
'arguments': [{'input': 'expInput'}],
'outputs': 'expOutput'
}],
'expectedOutputs': {
'expOutput': {
'data': [1.4079625606536865],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'exp float32 1D constant tensor',
'graph': {
'inputs': {
'expInput': {
'data': [
0.3421436548233032, -3.310965061187744, -3.6967575550079346,
-5.105378150939941, 5.47104024887085, -0.06790750473737717,
2.7373435497283936, -3.5470757484436035, 5.339224815368652,
-1.2636781930923462, -0.9162953495979309, -9.088432312011719,
-4.016050815582275, 4.670352935791016, 7.326992034912109,
8.294342994689941, -7.345414161682129, -0.9275799989700317,
-1.7085379362106323, -9.73737907409668, -1.9747875928878784,
8.203149795532227, -7.267597675323486, -3.5890684127807617
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'exp',
'arguments': [{'input': 'expInput'}],
'outputs': 'expOutput'
}],
'expectedOutputs': {
'expOutput': {
'data': [
1.4079625606536865, 0.03648095205426216, 0.024803820997476578,
0.006064045242965221, 237.70733642578125, 0.9343469142913818,
15.44589900970459, 0.02880876138806343, 208.35113525390625,
0.2826126217842102, 0.39999815821647644, 0.00011296502634650096,
0.018024004995822906, 106.73540496826172, 1520.8004150390625,
4001.173583984375, 0.0006455459515564144, 0.3955096900463104,
0.18113042414188385, 0.0000590350573475007, 0.1387907862663269,
3652.4365234375, 0.0006977862794883549, 0.02762405201792717
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'exp float32 1D tensor',
'graph': {
'inputs': {
'expInput': {
'data': [
0.3421436548233032, -3.310965061187744, -3.6967575550079346,
-5.105378150939941, 5.47104024887085, -0.06790750473737717,
2.7373435497283936, -3.5470757484436035, 5.339224815368652,
-1.2636781930923462, -0.9162953495979309, -9.088432312011719,
-4.016050815582275, 4.670352935791016, 7.326992034912109,
8.294342994689941, -7.345414161682129, -0.9275799989700317,
-1.7085379362106323, -9.73737907409668, -1.9747875928878784,
8.203149795532227, -7.267597675323486, -3.5890684127807617
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'exp',
'arguments': [{'input': 'expInput'}],
'outputs': 'expOutput'
}],
'expectedOutputs': {
'expOutput': {
'data': [
1.4079625606536865, 0.03648095205426216, 0.024803820997476578,
0.006064045242965221, 237.70733642578125, 0.9343469142913818,
15.44589900970459, 0.02880876138806343, 208.35113525390625,
0.2826126217842102, 0.39999815821647644, 0.00011296502634650096,
0.018024004995822906, 106.73540496826172, 1520.8004150390625,
4001.173583984375, 0.0006455459515564144, 0.3955096900463104,
0.18113042414188385, 0.0000590350573475007, 0.1387907862663269,
3652.4365234375, 0.0006977862794883549, 0.02762405201792717
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'exp float32 2D tensor',
'graph': {
'inputs': {
'expInput': {
'data': [
0.3421436548233032, -3.310965061187744, -3.6967575550079346,
-5.105378150939941, 5.47104024887085, -0.06790750473737717,
2.7373435497283936, -3.5470757484436035, 5.339224815368652,
-1.2636781930923462, -0.9162953495979309, -9.088432312011719,
-4.016050815582275, 4.670352935791016, 7.326992034912109,
8.294342994689941, -7.345414161682129, -0.9275799989700317,
-1.7085379362106323, -9.73737907409668, -1.9747875928878784,
8.203149795532227, -7.267597675323486, -3.5890684127807617
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'exp',
'arguments': [{'input': 'expInput'}],
'outputs': 'expOutput'
}],
'expectedOutputs': {
'expOutput': {
'data': [
1.4079625606536865, 0.03648095205426216, 0.024803820997476578,
0.006064045242965221, 237.70733642578125, 0.9343469142913818,
15.44589900970459, 0.02880876138806343, 208.35113525390625,
0.2826126217842102, 0.39999815821647644, 0.00011296502634650096,
0.018024004995822906, 106.73540496826172, 1520.8004150390625,
4001.173583984375, 0.0006455459515564144, 0.3955096900463104,
0.18113042414188385, 0.0000590350573475007, 0.1387907862663269,
3652.4365234375, 0.0006977862794883549, 0.02762405201792717
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'exp float32 3D tensor',
'graph': {
'inputs': {
'expInput': {
'data': [
0.3421436548233032, -3.310965061187744, -3.6967575550079346,
-5.105378150939941, 5.47104024887085, -0.06790750473737717,
2.7373435497283936, -3.5470757484436035, 5.339224815368652,
-1.2636781930923462, -0.9162953495979309, -9.088432312011719,
-4.016050815582275, 4.670352935791016, 7.326992034912109,
8.294342994689941, -7.345414161682129, -0.9275799989700317,
-1.7085379362106323, -9.73737907409668, -1.9747875928878784,
8.203149795532227, -7.267597675323486, -3.5890684127807617
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'exp',
'arguments': [{'input': 'expInput'}],
'outputs': 'expOutput'
}],
'expectedOutputs': {
'expOutput': {
'data': [
1.4079625606536865, 0.03648095205426216, 0.024803820997476578,
0.006064045242965221, 237.70733642578125, 0.9343469142913818,
15.44589900970459, 0.02880876138806343, 208.35113525390625,
0.2826126217842102, 0.39999815821647644, 0.00011296502634650096,
0.018024004995822906, 106.73540496826172, 1520.8004150390625,
4001.173583984375, 0.0006455459515564144, 0.3955096900463104,
0.18113042414188385, 0.0000590350573475007, 0.1387907862663269,
3652.4365234375, 0.0006977862794883549, 0.02762405201792717
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'exp float32 4D tensor',
'graph': {
'inputs': {
'expInput': {
'data': [
0.3421436548233032, -3.310965061187744, -3.6967575550079346,
-5.105378150939941, 5.47104024887085, -0.06790750473737717,
2.7373435497283936, -3.5470757484436035, 5.339224815368652,
-1.2636781930923462, -0.9162953495979309, -9.088432312011719,
-4.016050815582275, 4.670352935791016, 7.326992034912109,
8.294342994689941, -7.345414161682129, -0.9275799989700317,
-1.7085379362106323, -9.73737907409668, -1.9747875928878784,
8.203149795532227, -7.267597675323486, -3.5890684127807617
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'exp',
'arguments': [{'input': 'expInput'}],
'outputs': 'expOutput'
}],
'expectedOutputs': {
'expOutput': {
'data': [
1.4079625606536865, 0.03648095205426216, 0.024803820997476578,
0.006064045242965221, 237.70733642578125, 0.9343469142913818,
15.44589900970459, 0.02880876138806343, 208.35113525390625,
0.2826126217842102, 0.39999815821647644, 0.00011296502634650096,
0.018024004995822906, 106.73540496826172, 1520.8004150390625,
4001.173583984375, 0.0006455459515564144, 0.3955096900463104,
0.18113042414188385, 0.0000590350573475007, 0.1387907862663269,
3652.4365234375, 0.0006977862794883549, 0.02762405201792717
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'exp float32 5D tensor',
'graph': {
'inputs': {
'expInput': {
'data': [
0.3421436548233032, -3.310965061187744, -3.6967575550079346,
-5.105378150939941, 5.47104024887085, -0.06790750473737717,
2.7373435497283936, -3.5470757484436035, 5.339224815368652,
-1.2636781930923462, -0.9162953495979309, -9.088432312011719,
-4.016050815582275, 4.670352935791016, 7.326992034912109,
8.294342994689941, -7.345414161682129, -0.9275799989700317,
-1.7085379362106323, -9.73737907409668, -1.9747875928878784,
8.203149795532227, -7.267597675323486, -3.5890684127807617
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'exp',
'arguments': [{'input': 'expInput'}],
'outputs': 'expOutput'
}],
'expectedOutputs': {
'expOutput': {
'data': [
1.4079625606536865, 0.03648095205426216, 0.024803820997476578,
0.006064045242965221, 237.70733642578125, 0.9343469142913818,
15.44589900970459, 0.02880876138806343, 208.35113525390625,
0.2826126217842102, 0.39999815821647644, 0.00011296502634650096,
0.018024004995822906, 106.73540496826172, 1520.8004150390625,
4001.173583984375, 0.0006455459515564144, 0.3955096900463104,
0.18113042414188385, 0.0000590350573475007, 0.1387907862663269,
3652.4365234375, 0.0006977862794883549, 0.02762405201792717
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
expTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getExpPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}