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// META: title=test WebNN API element-wise neg 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 numerical negative value of the input tensor, element-wise.
//
// MLOperand neg(MLOperand input);
const getNegPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 0, float16: 0};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const negTests = [
{
'name': 'neg float32 positive 0D scalar',
'graph': {
'inputs': {
'negInput': {
'data': [94.23045349121094],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [-94.23045349121094],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'neg float32 negative 0D scalar',
'graph': {
'inputs': {
'negInput': {
'data': [-58.334503173828125],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [58.334503173828125],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'neg float32 1D constant tensor',
'graph': {
'inputs': {
'negInput': {
'data': [
-58.334503173828125, 94.23045349121094, -67.69306945800781,
-36.0666389465332, 17.115114212036133, 59.2606315612793,
-43.77507781982422, -14.875581741333008, 22.50856590270996,
98.67680358886719, 2.315542221069336, -89.86896514892578,
-14.28854751586914, 16.22245216369629, -4.688417911529541,
-44.46965026855469, -52.139259338378906, 24.165390014648438,
-66.4577865600586, -11.172324180603027, -25.024961471557617,
22.26478385925293, 35.29130172729492, -86.18817138671875
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [
58.334503173828125, -94.23045349121094, 67.69306945800781,
36.0666389465332, -17.115114212036133, -59.2606315612793,
43.77507781982422, 14.875581741333008, -22.50856590270996,
-98.67680358886719, -2.315542221069336, 89.86896514892578,
14.28854751586914, -16.22245216369629, 4.688417911529541,
44.46965026855469, 52.139259338378906, -24.165390014648438,
66.4577865600586, 11.172324180603027, 25.024961471557617,
-22.26478385925293, -35.29130172729492, 86.18817138671875
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'neg float32 1D tensor',
'graph': {
'inputs': {
'negInput': {
'data': [
-58.334503173828125, 94.23045349121094, -67.69306945800781,
-36.0666389465332, 17.115114212036133, 59.2606315612793,
-43.77507781982422, -14.875581741333008, 22.50856590270996,
98.67680358886719, 2.315542221069336, -89.86896514892578,
-14.28854751586914, 16.22245216369629, -4.688417911529541,
-44.46965026855469, -52.139259338378906, 24.165390014648438,
-66.4577865600586, -11.172324180603027, -25.024961471557617,
22.26478385925293, 35.29130172729492, -86.18817138671875
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [
58.334503173828125, -94.23045349121094, 67.69306945800781,
36.0666389465332, -17.115114212036133, -59.2606315612793,
43.77507781982422, 14.875581741333008, -22.50856590270996,
-98.67680358886719, -2.315542221069336, 89.86896514892578,
14.28854751586914, -16.22245216369629, 4.688417911529541,
44.46965026855469, 52.139259338378906, -24.165390014648438,
66.4577865600586, 11.172324180603027, 25.024961471557617,
-22.26478385925293, -35.29130172729492, 86.18817138671875
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'neg float32 2D tensor',
'graph': {
'inputs': {
'negInput': {
'data': [
-58.334503173828125, 94.23045349121094, -67.69306945800781,
-36.0666389465332, 17.115114212036133, 59.2606315612793,
-43.77507781982422, -14.875581741333008, 22.50856590270996,
98.67680358886719, 2.315542221069336, -89.86896514892578,
-14.28854751586914, 16.22245216369629, -4.688417911529541,
-44.46965026855469, -52.139259338378906, 24.165390014648438,
-66.4577865600586, -11.172324180603027, -25.024961471557617,
22.26478385925293, 35.29130172729492, -86.18817138671875
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [
58.334503173828125, -94.23045349121094, 67.69306945800781,
36.0666389465332, -17.115114212036133, -59.2606315612793,
43.77507781982422, 14.875581741333008, -22.50856590270996,
-98.67680358886719, -2.315542221069336, 89.86896514892578,
14.28854751586914, -16.22245216369629, 4.688417911529541,
44.46965026855469, 52.139259338378906, -24.165390014648438,
66.4577865600586, 11.172324180603027, 25.024961471557617,
-22.26478385925293, -35.29130172729492, 86.18817138671875
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'neg float32 3D tensor',
'graph': {
'inputs': {
'negInput': {
'data': [
-58.334503173828125, 94.23045349121094, -67.69306945800781,
-36.0666389465332, 17.115114212036133, 59.2606315612793,
-43.77507781982422, -14.875581741333008, 22.50856590270996,
98.67680358886719, 2.315542221069336, -89.86896514892578,
-14.28854751586914, 16.22245216369629, -4.688417911529541,
-44.46965026855469, -52.139259338378906, 24.165390014648438,
-66.4577865600586, -11.172324180603027, -25.024961471557617,
22.26478385925293, 35.29130172729492, -86.18817138671875
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [
58.334503173828125, -94.23045349121094, 67.69306945800781,
36.0666389465332, -17.115114212036133, -59.2606315612793,
43.77507781982422, 14.875581741333008, -22.50856590270996,
-98.67680358886719, -2.315542221069336, 89.86896514892578,
14.28854751586914, -16.22245216369629, 4.688417911529541,
44.46965026855469, 52.139259338378906, -24.165390014648438,
66.4577865600586, 11.172324180603027, 25.024961471557617,
-22.26478385925293, -35.29130172729492, 86.18817138671875
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'neg float32 4D tensor',
'graph': {
'inputs': {
'negInput': {
'data': [
-58.334503173828125, 94.23045349121094, -67.69306945800781,
-36.0666389465332, 17.115114212036133, 59.2606315612793,
-43.77507781982422, -14.875581741333008, 22.50856590270996,
98.67680358886719, 2.315542221069336, -89.86896514892578,
-14.28854751586914, 16.22245216369629, -4.688417911529541,
-44.46965026855469, -52.139259338378906, 24.165390014648438,
-66.4577865600586, -11.172324180603027, -25.024961471557617,
22.26478385925293, 35.29130172729492, -86.18817138671875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [
58.334503173828125, -94.23045349121094, 67.69306945800781,
36.0666389465332, -17.115114212036133, -59.2606315612793,
43.77507781982422, 14.875581741333008, -22.50856590270996,
-98.67680358886719, -2.315542221069336, 89.86896514892578,
14.28854751586914, -16.22245216369629, 4.688417911529541,
44.46965026855469, 52.139259338378906, -24.165390014648438,
66.4577865600586, 11.172324180603027, 25.024961471557617,
-22.26478385925293, -35.29130172729492, 86.18817138671875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'neg float32 5D tensor',
'graph': {
'inputs': {
'negInput': {
'data': [
-58.334503173828125, 94.23045349121094, -67.69306945800781,
-36.0666389465332, 17.115114212036133, 59.2606315612793,
-43.77507781982422, -14.875581741333008, 22.50856590270996,
98.67680358886719, 2.315542221069336, -89.86896514892578,
-14.28854751586914, 16.22245216369629, -4.688417911529541,
-44.46965026855469, -52.139259338378906, 24.165390014648438,
-66.4577865600586, -11.172324180603027, -25.024961471557617,
22.26478385925293, 35.29130172729492, -86.18817138671875
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'neg',
'arguments': [{'input': 'negInput'}],
'outputs': 'negOutput'
}],
'expectedOutputs': {
'negOutput': {
'data': [
58.334503173828125, -94.23045349121094, 67.69306945800781,
36.0666389465332, -17.115114212036133, -59.2606315612793,
43.77507781982422, 14.875581741333008, -22.50856590270996,
-98.67680358886719, -2.315542221069336, 89.86896514892578,
14.28854751586914, -16.22245216369629, 4.688417911529541,
44.46965026855469, 52.139259338378906, -24.165390014648438,
66.4577865600586, 11.172324180603027, 25.024961471557617,
-22.26478385925293, -35.29130172729492, 86.18817138671875
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
negTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getNegPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}