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Test Info:
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/ceil.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/ceil.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/ceil.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/ceil.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/ceil.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/ceil.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API element-wise ceil 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 ceiling of the input tensor, element-wise.
//
// MLOperand ceil(MLOperand input);
const getCeilPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 0, float16: 0};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const ceilTests = [
{
'name': 'ceil float32 0D scalar',
'graph': {
'inputs': {
'ceilInput': {
'data': [67.38941955566406],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'ceil',
'arguments': [{'input': 'ceilInput'}],
'outputs': 'ceilOutput'
}],
'expectedOutputs': {
'ceilOutput':
{'data': [68], 'descriptor': {shape: [], dataType: 'float32'}}
}
}
},
{
'name': 'ceil float32 1D constant tensor',
'graph': {
'inputs': {
'ceilInput': {
'data': [
67.38941955566406, 36.78218460083008, 99.10649108886719,
-22.58710479736328, 32.70173645019531, 17.68880844116211,
5.631034851074219, 12.965238571166992, 83.1319351196289,
-29.292461395263672, 19.84463119506836, 65.2790298461914,
26.31110954284668, 24.285673141479492, -48.39767074584961,
-5.617412567138672, 61.53380584716797, -87.81197357177734,
69.71428680419922, 5.0031023025512695, 84.36833953857422,
-9.390542030334473, -27.856616973876953, -34.895931243896484
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'ceil',
'arguments': [{'input': 'ceilInput'}],
'outputs': 'ceilOutput'
}],
'expectedOutputs': {
'ceilOutput': {
'data': [
68, 37, 100, -22, 33, 18, 6, 13, 84, -29, 20, 66,
27, 25, -48, -5, 62, -87, 70, 6, 85, -9, -27, -34
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'ceil float32 1D tensor',
'graph': {
'inputs': {
'ceilInput': {
'data': [
67.38941955566406, 36.78218460083008, 99.10649108886719,
-22.58710479736328, 32.70173645019531, 17.68880844116211,
5.631034851074219, 12.965238571166992, 83.1319351196289,
-29.292461395263672, 19.84463119506836, 65.2790298461914,
26.31110954284668, 24.285673141479492, -48.39767074584961,
-5.617412567138672, 61.53380584716797, -87.81197357177734,
69.71428680419922, 5.0031023025512695, 84.36833953857422,
-9.390542030334473, -27.856616973876953, -34.895931243896484
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'ceil',
'arguments': [{'input': 'ceilInput'}],
'outputs': 'ceilOutput'
}],
'expectedOutputs': {
'ceilOutput': {
'data': [
68, 37, 100, -22, 33, 18, 6, 13, 84, -29, 20, 66,
27, 25, -48, -5, 62, -87, 70, 6, 85, -9, -27, -34
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'ceil float32 2D tensor',
'graph': {
'inputs': {
'ceilInput': {
'data': [
67.38941955566406, 36.78218460083008, 99.10649108886719,
-22.58710479736328, 32.70173645019531, 17.68880844116211,
5.631034851074219, 12.965238571166992, 83.1319351196289,
-29.292461395263672, 19.84463119506836, 65.2790298461914,
26.31110954284668, 24.285673141479492, -48.39767074584961,
-5.617412567138672, 61.53380584716797, -87.81197357177734,
69.71428680419922, 5.0031023025512695, 84.36833953857422,
-9.390542030334473, -27.856616973876953, -34.895931243896484
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'ceil',
'arguments': [{'input': 'ceilInput'}],
'outputs': 'ceilOutput'
}],
'expectedOutputs': {
'ceilOutput': {
'data': [
68, 37, 100, -22, 33, 18, 6, 13, 84, -29, 20, 66,
27, 25, -48, -5, 62, -87, 70, 6, 85, -9, -27, -34
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'ceil float32 3D tensor',
'graph': {
'inputs': {
'ceilInput': {
'data': [
67.38941955566406, 36.78218460083008, 99.10649108886719,
-22.58710479736328, 32.70173645019531, 17.68880844116211,
5.631034851074219, 12.965238571166992, 83.1319351196289,
-29.292461395263672, 19.84463119506836, 65.2790298461914,
26.31110954284668, 24.285673141479492, -48.39767074584961,
-5.617412567138672, 61.53380584716797, -87.81197357177734,
69.71428680419922, 5.0031023025512695, 84.36833953857422,
-9.390542030334473, -27.856616973876953, -34.895931243896484
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'ceil',
'arguments': [{'input': 'ceilInput'}],
'outputs': 'ceilOutput'
}],
'expectedOutputs': {
'ceilOutput': {
'data': [
68, 37, 100, -22, 33, 18, 6, 13, 84, -29, 20, 66,
27, 25, -48, -5, 62, -87, 70, 6, 85, -9, -27, -34
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'ceil float32 4D tensor',
'graph': {
'inputs': {
'ceilInput': {
'data': [
67.38941955566406, 36.78218460083008, 99.10649108886719,
-22.58710479736328, 32.70173645019531, 17.68880844116211,
5.631034851074219, 12.965238571166992, 83.1319351196289,
-29.292461395263672, 19.84463119506836, 65.2790298461914,
26.31110954284668, 24.285673141479492, -48.39767074584961,
-5.617412567138672, 61.53380584716797, -87.81197357177734,
69.71428680419922, 5.0031023025512695, 84.36833953857422,
-9.390542030334473, -27.856616973876953, -34.895931243896484
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'ceil',
'arguments': [{'input': 'ceilInput'}],
'outputs': 'ceilOutput'
}],
'expectedOutputs': {
'ceilOutput': {
'data': [
68, 37, 100, -22, 33, 18, 6, 13, 84, -29, 20, 66,
27, 25, -48, -5, 62, -87, 70, 6, 85, -9, -27, -34
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'ceil float32 5D tensor',
'graph': {
'inputs': {
'ceilInput': {
'data': [
67.38941955566406, 36.78218460083008, 99.10649108886719,
-22.58710479736328, 32.70173645019531, 17.68880844116211,
5.631034851074219, 12.965238571166992, 83.1319351196289,
-29.292461395263672, 19.84463119506836, 65.2790298461914,
26.31110954284668, 24.285673141479492, -48.39767074584961,
-5.617412567138672, 61.53380584716797, -87.81197357177734,
69.71428680419922, 5.0031023025512695, 84.36833953857422,
-9.390542030334473, -27.856616973876953, -34.895931243896484
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'ceil',
'arguments': [{'input': 'ceilInput'}],
'outputs': 'ceilOutput'
}],
'expectedOutputs': {
'ceilOutput': {
'data': [
68, 37, 100, -22, 33, 18, 6, 13, 84, -29, 20, 66,
27, 25, -48, -5, 62, -87, 70, 6, 85, -9, -27, -34
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
ceilTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getCeilPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}