mozilla-central/testing/web-platform/tests/webnn/conformance_tests/sign.https.any.js (file symbol)

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// META: title=test WebNN API sign operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Represents the sign operation that return elementwise -1/0/1 depending on
// element sign.
//
// MLOperand sign(MLOperand input, optional MLOperatorOptions options = {});
const getSignPrecisionTolerance = (graphResources) => {
return {metricType: 'ULP', value: 0};
};
const signTests = [
{
'name': 'sign float32 1D constant tensor',
'graph': {
'inputs': {
'signInput': {
'data': [
-0.946033775806427, 0.9996118545532227, 0.21998752653598785,
-0.22639396786689758
],
'descriptor': {shape: [4], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'sign',
'arguments': [{'input': 'signInput'}],
'outputs': 'signOutput'
}],
'expectedOutputs': {
'signOutput': {
'data': [-1, 1, 1, -1],
'descriptor': {shape: [4], dataType: 'float32'}
}
}
}
},
{
'name': 'sign float16 1D tensor',
'graph': {
'inputs': {
'signInput': {
'data': [
-0.946033775806427, 0.9996118545532227, 0.21998752653598785,
-0.22639396786689758
],
'descriptor': {shape: [4], dataType: 'float16'}
}
},
'operators': [{
'name': 'sign',
'arguments': [{'input': 'signInput'}],
'outputs': 'signOutput'
}],
'expectedOutputs': {
'signOutput': {
'data': [-1, 1, 1, -1],
'descriptor': {shape: [4], dataType: 'float16'}
}
}
}
},
{
'name': 'sign float32 1D tensor',
'graph': {
'inputs': {
'signInput': {
'data': [
-0.946033775806427, 0.9996118545532227, 0.21998752653598785, 0.0
],
'descriptor': {shape: [4], dataType: 'float32'}
}
},
'operators': [{
'name': 'sign',
'arguments': [{'input': 'signInput'}],
'outputs': 'signOutput'
}],
'expectedOutputs': {
'signOutput': {
'data': [-1, 1, 1, 0],
'descriptor': {shape: [4], dataType: 'float32'}
}
}
}
},
{
'name': 'sign float32 1D tensor with -infinity and +infinity',
'graph': {
'inputs': {
'signInput': {
'data': [-0.946033775806427, 0.9996118545532227, -Infinity, Infinity],
'descriptor': {shape: [4], dataType: 'float32'}
}
},
'operators': [{
'name': 'sign',
'arguments': [{'input': 'signInput'}],
'outputs': 'signOutput'
}],
'expectedOutputs': {
'signOutput': {
'data': [-1, 1, -1, 1],
'descriptor': {shape: [4], dataType: 'float32'}
}
}
}
},
{
'name': 'sign int32 2D tensor',
'graph': {
'inputs': {
'signInput': {
'data': [-1, 0, 1, 2],
'descriptor': {shape: [2, 2], dataType: 'int32'}
}
},
'operators': [{
'name': 'sign',
'arguments': [{'input': 'signInput'}],
'outputs': 'signOutput'
}],
'expectedOutputs': {
'signOutput': {
'data': [-1, 0, 1, 1],
'descriptor': {shape: [2, 2], dataType: 'int32'}
}
}
}
},
{
'name': 'sign int64 3D tensor',
'graph': {
'inputs': {
'signInput': {
'data': [-1, 0, 1, 2, -2, -1, 0, 1],
'descriptor': {shape: [2, 2, 2], dataType: 'int64'}
}
},
'operators': [{
'name': 'sign',
'arguments': [{'input': 'signInput'}],
'outputs': 'signOutput'
}],
'expectedOutputs': {
'signOutput': {
'data': [-1, 0, 1, 1, -1, -1, 0, 1],
'descriptor': {shape: [2, 2, 2], dataType: 'int64'}
}
}
}
},
{
'name': 'sign int8 4D tensor',
'graph': {
'inputs': {
'signInput': {
'data': [-1, 0, 1, 2, -2, -1, 0, 1],
'descriptor': {shape: [1, 2, 2, 2], dataType: 'int8'}
}
},
'operators': [{
'name': 'sign',
'arguments': [{'input': 'signInput'}],
'outputs': 'signOutput'
}],
'expectedOutputs': {
'signOutput': {
'data': [-1, 0, 1, 1, -1, -1, 0, 1],
'descriptor': {shape: [1, 2, 2, 2], dataType: 'int8'}
}
}
}
},
];
if (navigator.ml) {
signTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getSignPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}