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Test Info:
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/tan.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/tan.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/tan.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/tan.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/tan.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/tan.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API element-wise tan 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 tangent of the input tensor, element-wise.
//
// MLOperand tan(MLOperand input);
const getTanPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 1 / 1024, float16: 1 / 512};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ATOL', value: toleranceValueDict[expectedDataType]};
};
const tanTests = [
{
'name': 'tan float32 0D scalar',
'graph': {
'inputs': {
'tanInput': {
'data': [52.69781494140625],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'tan',
'arguments': [{'input': 'tanInput'}],
'outputs': 'tanOutput'
}],
'expectedOutputs': {
'tanOutput': {
'data': [-0.8582430481910706],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'tan float32 1D constant tensor',
'graph': {
'inputs': {
'tanInput': {
'data': [
52.69781494140625, 70.06912994384766, 90.49689483642578,
24.65666961669922, 11.66512680053711, -50.95264434814453,
40.320064544677734, -9.641122817993164, -31.567750930786133,
45.59520721435547, -55.93085861206055, -44.602970123291016,
80.4539794921875, -2.314880847930908, -25.474767684936523,
62.589454650878906, -70.94403076171875, 62.84861755371094,
84.79766845703125, -95.58502960205078, 15.552484512329102,
-55.25654220581055, -26.884889602661133, 0.159261092543602
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'tan',
'arguments': [{'input': 'tanInput'}],
'outputs': 'tanOutput'
}],
'expectedOutputs': {
'tanOutput': {
'data': [
-0.8582430481910706, 1.410544753074646, -0.6978657245635986,
-0.5156278610229492, -1.2633823156356812, -0.8205758929252625,
-0.5734118819236755, -0.21978461742401123, -0.1530018001794815,
-23.731182098388672, 0.7106066942214966, -0.7149254679679871,
-2.7969717979431152, 1.086239218711853, -0.3560185432434082,
-0.24726025760173798, 3.7865755558013916, 0.016766052693128586,
-0.025338610634207726, -4.203672409057617, -0.1567438244819641,
3.495089292526245, 5.453553199768066, 0.16062140464782715
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'tan float32 1D tensor',
'graph': {
'inputs': {
'tanInput': {
'data': [
52.69781494140625, 70.06912994384766, 90.49689483642578,
24.65666961669922, 11.66512680053711, -50.95264434814453,
40.320064544677734, -9.641122817993164, -31.567750930786133,
45.59520721435547, -55.93085861206055, -44.602970123291016,
80.4539794921875, -2.314880847930908, -25.474767684936523,
62.589454650878906, -70.94403076171875, 62.84861755371094,
84.79766845703125, -95.58502960205078, 15.552484512329102,
-55.25654220581055, -26.884889602661133, 0.159261092543602
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'tan',
'arguments': [{'input': 'tanInput'}],
'outputs': 'tanOutput'
}],
'expectedOutputs': {
'tanOutput': {
'data': [
-0.8582430481910706, 1.410544753074646, -0.6978657245635986,
-0.5156278610229492, -1.2633823156356812, -0.8205758929252625,
-0.5734118819236755, -0.21978461742401123, -0.1530018001794815,
-23.731182098388672, 0.7106066942214966, -0.7149254679679871,
-2.7969717979431152, 1.086239218711853, -0.3560185432434082,
-0.24726025760173798, 3.7865755558013916, 0.016766052693128586,
-0.025338610634207726, -4.203672409057617, -0.1567438244819641,
3.495089292526245, 5.453553199768066, 0.16062140464782715
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'tan float32 2D tensor',
'graph': {
'inputs': {
'tanInput': {
'data': [
52.69781494140625, 70.06912994384766, 90.49689483642578,
24.65666961669922, 11.66512680053711, -50.95264434814453,
40.320064544677734, -9.641122817993164, -31.567750930786133,
45.59520721435547, -55.93085861206055, -44.602970123291016,
80.4539794921875, -2.314880847930908, -25.474767684936523,
62.589454650878906, -70.94403076171875, 62.84861755371094,
84.79766845703125, -95.58502960205078, 15.552484512329102,
-55.25654220581055, -26.884889602661133, 0.159261092543602
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'tan',
'arguments': [{'input': 'tanInput'}],
'outputs': 'tanOutput'
}],
'expectedOutputs': {
'tanOutput': {
'data': [
-0.8582430481910706, 1.410544753074646, -0.6978657245635986,
-0.5156278610229492, -1.2633823156356812, -0.8205758929252625,
-0.5734118819236755, -0.21978461742401123, -0.1530018001794815,
-23.731182098388672, 0.7106066942214966, -0.7149254679679871,
-2.7969717979431152, 1.086239218711853, -0.3560185432434082,
-0.24726025760173798, 3.7865755558013916, 0.016766052693128586,
-0.025338610634207726, -4.203672409057617, -0.1567438244819641,
3.495089292526245, 5.453553199768066, 0.16062140464782715
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'tan float32 3D tensor',
'graph': {
'inputs': {
'tanInput': {
'data': [
52.69781494140625, 70.06912994384766, 90.49689483642578,
24.65666961669922, 11.66512680053711, -50.95264434814453,
40.320064544677734, -9.641122817993164, -31.567750930786133,
45.59520721435547, -55.93085861206055, -44.602970123291016,
80.4539794921875, -2.314880847930908, -25.474767684936523,
62.589454650878906, -70.94403076171875, 62.84861755371094,
84.79766845703125, -95.58502960205078, 15.552484512329102,
-55.25654220581055, -26.884889602661133, 0.159261092543602
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'tan',
'arguments': [{'input': 'tanInput'}],
'outputs': 'tanOutput'
}],
'expectedOutputs': {
'tanOutput': {
'data': [
-0.8582430481910706, 1.410544753074646, -0.6978657245635986,
-0.5156278610229492, -1.2633823156356812, -0.8205758929252625,
-0.5734118819236755, -0.21978461742401123, -0.1530018001794815,
-23.731182098388672, 0.7106066942214966, -0.7149254679679871,
-2.7969717979431152, 1.086239218711853, -0.3560185432434082,
-0.24726025760173798, 3.7865755558013916, 0.016766052693128586,
-0.025338610634207726, -4.203672409057617, -0.1567438244819641,
3.495089292526245, 5.453553199768066, 0.16062140464782715
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'tan float32 4D tensor',
'graph': {
'inputs': {
'tanInput': {
'data': [
52.69781494140625, 70.06912994384766, 90.49689483642578,
24.65666961669922, 11.66512680053711, -50.95264434814453,
40.320064544677734, -9.641122817993164, -31.567750930786133,
45.59520721435547, -55.93085861206055, -44.602970123291016,
80.4539794921875, -2.314880847930908, -25.474767684936523,
62.589454650878906, -70.94403076171875, 62.84861755371094,
84.79766845703125, -95.58502960205078, 15.552484512329102,
-55.25654220581055, -26.884889602661133, 0.159261092543602
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'tan',
'arguments': [{'input': 'tanInput'}],
'outputs': 'tanOutput'
}],
'expectedOutputs': {
'tanOutput': {
'data': [
-0.8582430481910706, 1.410544753074646, -0.6978657245635986,
-0.5156278610229492, -1.2633823156356812, -0.8205758929252625,
-0.5734118819236755, -0.21978461742401123, -0.1530018001794815,
-23.731182098388672, 0.7106066942214966, -0.7149254679679871,
-2.7969717979431152, 1.086239218711853, -0.3560185432434082,
-0.24726025760173798, 3.7865755558013916, 0.016766052693128586,
-0.025338610634207726, -4.203672409057617, -0.1567438244819641,
3.495089292526245, 5.453553199768066, 0.16062140464782715
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'tan float32 5D tensor',
'graph': {
'inputs': {
'tanInput': {
'data': [
52.69781494140625, 70.06912994384766, 90.49689483642578,
24.65666961669922, 11.66512680053711, -50.95264434814453,
40.320064544677734, -9.641122817993164, -31.567750930786133,
45.59520721435547, -55.93085861206055, -44.602970123291016,
80.4539794921875, -2.314880847930908, -25.474767684936523,
62.589454650878906, -70.94403076171875, 62.84861755371094,
84.79766845703125, -95.58502960205078, 15.552484512329102,
-55.25654220581055, -26.884889602661133, 0.159261092543602
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'tan',
'arguments': [{'input': 'tanInput'}],
'outputs': 'tanOutput'
}],
'expectedOutputs': {
'tanOutput': {
'data': [
-0.8582430481910706, 1.410544753074646, -0.6978657245635986,
-0.5156278610229492, -1.2633823156356812, -0.8205758929252625,
-0.5734118819236755, -0.21978461742401123, -0.1530018001794815,
-23.731182098388672, 0.7106066942214966, -0.7149254679679871,
-2.7969717979431152, 1.086239218711853, -0.3560185432434082,
-0.24726025760173798, 3.7865755558013916, 0.016766052693128586,
-0.025338610634207726, -4.203672409057617, -0.1567438244819641,
3.495089292526245, 5.453553199768066, 0.16062140464782715
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
tanTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getTanPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}