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Test Info:
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/min.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/min.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/min.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/min.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/min.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/min.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API element-wise min 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 element-wise binary minimum of the two input tensors.
// MLOperand min(MLOperand a, MLOperand b);
const getMinPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 0, float16: 0};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const minTests = [
{
'name': 'min float32 1D constant tensors',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
},
'inputB': {
'data': [
-40.10139083862305, 86.25190734863281, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
80.49571228027344, 46.6654052734375, 62.80685806274414,
49.81534194946289, -76.52043151855469, 84.5990982055664,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 46.450077056884766, -71.25122833251953,
-69.85066223144531, 40.676490783691406, -18.700122833251953,
20.14988136291504, 41.95068359375, 23.482912063598633
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-40.10139083862305, -38.2254524230957, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, -76.52043151855469, -40.39130401611328,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 1D tensors',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [24], dataType: 'float32'}
},
'inputB': {
'data': [
-40.10139083862305, 86.25190734863281, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
80.49571228027344, 46.6654052734375, 62.80685806274414,
49.81534194946289, -76.52043151855469, 84.5990982055664,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 46.450077056884766, -71.25122833251953,
-69.85066223144531, 40.676490783691406, -18.700122833251953,
20.14988136291504, 41.95068359375, 23.482912063598633
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-40.10139083862305, -38.2254524230957, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, -76.52043151855469, -40.39130401611328,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 2D tensors',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
},
'inputB': {
'data': [
-40.10139083862305, 86.25190734863281, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
80.49571228027344, 46.6654052734375, 62.80685806274414,
49.81534194946289, -76.52043151855469, 84.5990982055664,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 46.450077056884766, -71.25122833251953,
-69.85066223144531, 40.676490783691406, -18.700122833251953,
20.14988136291504, 41.95068359375, 23.482912063598633
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-40.10139083862305, -38.2254524230957, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, -76.52043151855469, -40.39130401611328,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 3D tensors',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
},
'inputB': {
'data': [
-40.10139083862305, 86.25190734863281, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
80.49571228027344, 46.6654052734375, 62.80685806274414,
49.81534194946289, -76.52043151855469, 84.5990982055664,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 46.450077056884766, -71.25122833251953,
-69.85066223144531, 40.676490783691406, -18.700122833251953,
20.14988136291504, 41.95068359375, 23.482912063598633
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-40.10139083862305, -38.2254524230957, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, -76.52043151855469, -40.39130401611328,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 4D tensors',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
},
'inputB': {
'data': [
-40.10139083862305, 86.25190734863281, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
80.49571228027344, 46.6654052734375, 62.80685806274414,
49.81534194946289, -76.52043151855469, 84.5990982055664,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 46.450077056884766, -71.25122833251953,
-69.85066223144531, 40.676490783691406, -18.700122833251953,
20.14988136291504, 41.95068359375, 23.482912063598633
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-40.10139083862305, -38.2254524230957, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, -76.52043151855469, -40.39130401611328,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 5D tensors',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float32'}
},
'inputB': {
'data': [
-40.10139083862305, 86.25190734863281, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
80.49571228027344, 46.6654052734375, 62.80685806274414,
49.81534194946289, -76.52043151855469, 84.5990982055664,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 46.450077056884766, -71.25122833251953,
-69.85066223144531, 40.676490783691406, -18.700122833251953,
20.14988136291504, 41.95068359375, 23.482912063598633
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-40.10139083862305, -38.2254524230957, 51.280174255371094,
-57.64906311035156, -97.56107330322266, -28.881731033325195,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, -76.52043151855469, -40.39130401611328,
50.47281265258789, -18.01728630065918, 5.198459148406982,
-47.82608413696289, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 broadcast 1D to 4D',
'graph': {
'inputs': {
'inputA': {
'data': [34.42634582519531],
'descriptor': {shape: [1], dataType: 'float32'}
},
'inputB': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-36.06953048706055, -38.2254524230957, 34.42634582519531,
-16.610267639160156, 34.42634582519531, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 34.42634582519531,
-39.096099853515625, 34.42634582519531, -40.39130401611328,
34.42634582519531, 0.03283197432756424, 34.42634582519531,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 broadcast 2D to 4D',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
},
'inputB': {
'data': [
-19.072668075561523, -78.27516174316406, -13.436244010925293,
-93.01346588134766, -72.27899169921875, 63.14110565185547
],
'descriptor': {shape: [2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-36.06953048706055, -78.27516174316406, -13.436244010925293,
-93.01346588134766, -72.27899169921875, -17.77212905883789,
-76.01380920410156, -78.27516174316406, -13.436244010925293,
-93.01346588134766, -72.27899169921875, -40.39130401611328,
-19.072668075561523, -78.27516174316406, -13.436244010925293,
-93.01346588134766, -72.27899169921875, -82.3099365234375,
-80.47379302978516, -78.27516174316406, -73.2723617553711,
-93.01346588134766, -72.27899169921875, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 broadcast 3D to 4D',
'graph': {
'inputs': {
'inputA': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
},
'inputB': {
'data': [
23.231731414794922, 84.62673950195312, -83.33529663085938,
-22.82455825805664
],
'descriptor': {shape: [2, 2, 1], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-36.06953048706055, -38.2254524230957, 23.231731414794922,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-83.33529663085938, -83.33529663085938, -83.33529663085938,
-39.096099853515625, -22.82455825805664, -40.39130401611328,
23.231731414794922, 0.03283197432756424, 23.231731414794922,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-83.33529663085938, -83.33529663085938, -83.33529663085938,
-33.74562072753906, -22.82455825805664, -22.82455825805664
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'min float32 broadcast 4D to 4D',
'graph': {
'inputs': {
'inputA': {
'data': [34.42634582519531],
'descriptor': {shape: [1, 1, 1, 1], dataType: 'float32'}
},
'inputB': {
'data': [
-36.06953048706055, -38.2254524230957, 62.07444381713867,
-16.610267639160156, 65.99324798583984, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 53.60376739501953,
-39.096099853515625, 96.94400787353516, -40.39130401611328,
74.14437103271484, 0.03283197432756424, 38.79835510253906,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'min',
'arguments': [{'a': 'inputA'}, {'b': 'inputB'}],
'outputs': 'output'
}],
'expectedOutputs': {
'output': {
'data': [
-36.06953048706055, -38.2254524230957, 34.42634582519531,
-16.610267639160156, 34.42634582519531, -17.77212905883789,
-76.01380920410156, -69.59134674072266, 34.42634582519531,
-39.096099853515625, 34.42634582519531, -40.39130401611328,
34.42634582519531, 0.03283197432756424, 34.42634582519531,
-17.720787048339844, 17.383201599121094, -82.3099365234375,
-80.47379302978516, -31.389848709106445, -73.2723617553711,
-33.74562072753906, -21.70152473449707, 4.945605278015137
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
minTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getMinPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}