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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/softplus.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softplus.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softplus.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softplus.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softplus.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softplus.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API softplus 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 softplus function of the input tensor. The calculation follows
// the expression ln(1 + exp(x)).
//
// MLOperand softplus(MLOperand input);
const softplusTests = [
{
'name': 'softplus float32 1D constant tensor',
'graph': {
'inputs': {
'softplusInput': {
'data': [
5.626614570617676, 5.167487144470215, 4.0146355628967285,
9.480032920837402, 9.989937782287598, 7.065441131591797,
2.132680892944336, 8.187150955200195, 5.169976234436035,
2.1044998168945312, 3.523329496383667, 4.136340618133545,
1.741871953010559, 5.145224094390869, 5.015515327453613,
0.04590393602848053, 2.957089900970459, 3.959244728088379,
5.517927169799805, 7.192322254180908, 8.764925003051758,
1.373470425605774, 8.930668830871582, 8.660283088684082
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'softplus',
'arguments': [{'input': 'softplusInput'}],
'outputs': 'softplusOutput'
}],
'expectedOutputs': {
'softplusOutput': {
'data': [
5.630208969116211, 5.1731696128845215, 4.032524108886719,
9.480109214782715, 9.989983558654785, 7.0662946701049805,
2.2446866035461426, 8.187429428100586, 5.175644874572754,
2.219529390335083, 3.552403688430786, 4.152195453643799,
1.903303623199463, 5.151034355163574, 5.022127628326416,
0.7163625359535217, 3.007754325866699, 3.978142499923706,
5.521933078765869, 7.1930742263793945, 8.765081405639648,
1.5991919040679932, 8.930801391601562, 8.660456657409668
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'softplus float32 0D tensor',
'graph': {
'inputs': {
'softplusInput': {
'data': [5.626614570617676],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'softplus',
'arguments': [{'input': 'softplusInput'}],
'outputs': 'softplusOutput'
}],
'expectedOutputs': {
'softplusOutput': {
'data': [5.630208969116211],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'softplus float32 1D tensor',
'graph': {
'inputs': {
'softplusInput': {
'data': [
5.626614570617676, 5.167487144470215, 4.0146355628967285,
9.480032920837402, 9.989937782287598, 7.065441131591797,
2.132680892944336, 8.187150955200195, 5.169976234436035,
2.1044998168945312, 3.523329496383667, 4.136340618133545,
1.741871953010559, 5.145224094390869, 5.015515327453613,
0.04590393602848053, 2.957089900970459, 3.959244728088379,
5.517927169799805, 7.192322254180908, 8.764925003051758,
1.373470425605774, 8.930668830871582, 8.660283088684082
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'softplus',
'arguments': [{'input': 'softplusInput'}],
'outputs': 'softplusOutput'
}],
'expectedOutputs': {
'softplusOutput': {
'data': [
5.630208969116211, 5.1731696128845215, 4.032524108886719,
9.480109214782715, 9.989983558654785, 7.0662946701049805,
2.2446866035461426, 8.187429428100586, 5.175644874572754,
2.219529390335083, 3.552403688430786, 4.152195453643799,
1.903303623199463, 5.151034355163574, 5.022127628326416,
0.7163625359535217, 3.007754325866699, 3.978142499923706,
5.521933078765869, 7.1930742263793945, 8.765081405639648,
1.5991919040679932, 8.930801391601562, 8.660456657409668
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'softplus float32 2D tensor',
'graph': {
'inputs': {
'softplusInput': {
'data': [
5.626614570617676, 5.167487144470215, 4.0146355628967285,
9.480032920837402, 9.989937782287598, 7.065441131591797,
2.132680892944336, 8.187150955200195, 5.169976234436035,
2.1044998168945312, 3.523329496383667, 4.136340618133545,
1.741871953010559, 5.145224094390869, 5.015515327453613,
0.04590393602848053, 2.957089900970459, 3.959244728088379,
5.517927169799805, 7.192322254180908, 8.764925003051758,
1.373470425605774, 8.930668830871582, 8.660283088684082
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'softplus',
'arguments': [{'input': 'softplusInput'}],
'outputs': 'softplusOutput'
}],
'expectedOutputs': {
'softplusOutput': {
'data': [
5.630208969116211, 5.1731696128845215, 4.032524108886719,
9.480109214782715, 9.989983558654785, 7.0662946701049805,
2.2446866035461426, 8.187429428100586, 5.175644874572754,
2.219529390335083, 3.552403688430786, 4.152195453643799,
1.903303623199463, 5.151034355163574, 5.022127628326416,
0.7163625359535217, 3.007754325866699, 3.978142499923706,
5.521933078765869, 7.1930742263793945, 8.765081405639648,
1.5991919040679932, 8.930801391601562, 8.660456657409668
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'softplus float32 3D tensor',
'graph': {
'inputs': {
'softplusInput': {
'data': [
5.626614570617676, 5.167487144470215, 4.0146355628967285,
9.480032920837402, 9.989937782287598, 7.065441131591797,
2.132680892944336, 8.187150955200195, 5.169976234436035,
2.1044998168945312, 3.523329496383667, 4.136340618133545,
1.741871953010559, 5.145224094390869, 5.015515327453613,
0.04590393602848053, 2.957089900970459, 3.959244728088379,
5.517927169799805, 7.192322254180908, 8.764925003051758,
1.373470425605774, 8.930668830871582, 8.660283088684082
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'softplus',
'arguments': [{'input': 'softplusInput'}],
'outputs': 'softplusOutput'
}],
'expectedOutputs': {
'softplusOutput': {
'data': [
5.630208969116211, 5.1731696128845215, 4.032524108886719,
9.480109214782715, 9.989983558654785, 7.0662946701049805,
2.2446866035461426, 8.187429428100586, 5.175644874572754,
2.219529390335083, 3.552403688430786, 4.152195453643799,
1.903303623199463, 5.151034355163574, 5.022127628326416,
0.7163625359535217, 3.007754325866699, 3.978142499923706,
5.521933078765869, 7.1930742263793945, 8.765081405639648,
1.5991919040679932, 8.930801391601562, 8.660456657409668
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'softplus float32 4D tensor',
'graph': {
'inputs': {
'softplusInput': {
'data': [
5.626614570617676, 5.167487144470215, 4.0146355628967285,
9.480032920837402, 9.989937782287598, 7.065441131591797,
2.132680892944336, 8.187150955200195, 5.169976234436035,
2.1044998168945312, 3.523329496383667, 4.136340618133545,
1.741871953010559, 5.145224094390869, 5.015515327453613,
0.04590393602848053, 2.957089900970459, 3.959244728088379,
5.517927169799805, 7.192322254180908, 8.764925003051758,
1.373470425605774, 8.930668830871582, 8.660283088684082
],
'descriptor': {shape: [1, 2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'softplus',
'arguments': [{'input': 'softplusInput'}],
'outputs': 'softplusOutput'
}],
'expectedOutputs': {
'softplusOutput': {
'data': [
5.630208969116211, 5.1731696128845215, 4.032524108886719,
9.480109214782715, 9.989983558654785, 7.0662946701049805,
2.2446866035461426, 8.187429428100586, 5.175644874572754,
2.219529390335083, 3.552403688430786, 4.152195453643799,
1.903303623199463, 5.151034355163574, 5.022127628326416,
0.7163625359535217, 3.007754325866699, 3.978142499923706,
5.521933078765869, 7.1930742263793945, 8.765081405639648,
1.5991919040679932, 8.930801391601562, 8.660456657409668
],
'descriptor': {shape: [1, 2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'softplus float32 5D tensor',
'graph': {
'inputs': {
'softplusInput': {
'data': [
5.626614570617676, 5.167487144470215, 4.0146355628967285,
9.480032920837402, 9.989937782287598, 7.065441131591797,
2.132680892944336, 8.187150955200195, 5.169976234436035,
2.1044998168945312, 3.523329496383667, 4.136340618133545,
1.741871953010559, 5.145224094390869, 5.015515327453613,
0.04590393602848053, 2.957089900970459, 3.959244728088379,
5.517927169799805, 7.192322254180908, 8.764925003051758,
1.373470425605774, 8.930668830871582, 8.660283088684082
],
'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'softplus',
'arguments': [{'input': 'softplusInput'}],
'outputs': 'softplusOutput'
}],
'expectedOutputs': {
'softplusOutput': {
'data': [
5.630208969116211, 5.1731696128845215, 4.032524108886719,
9.480109214782715, 9.989983558654785, 7.0662946701049805,
2.2446866035461426, 8.187429428100586, 5.175644874572754,
2.219529390335083, 3.552403688430786, 4.152195453643799,
1.903303623199463, 5.151034355163574, 5.022127628326416,
0.7163625359535217, 3.007754325866699, 3.978142499923706,
5.521933078765869, 7.1930742263793945, 8.765081405639648,
1.5991919040679932, 8.930801391601562, 8.660456657409668
],
'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
softplusTests.forEach((test) => {
webnn_conformance_test(buildAndExecuteGraph, getPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}