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Test Info:
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/reduce_log_sum.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API reduction operations
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Reduce the input tensor along all dimensions, or along the axes specified in
// the axes array parameter.
//
// dictionary MLReduceOptions {
// sequence<[EnforceRange] unsigned long> axes;
// boolean keepDimensions = false;
// };
//
// MLOperand reduceLogSum(MLOperand input, optional MLReduceOptions options
// = {});
const getReductionOperatorsPrecisionTolerance = (graphResources) => {
return {
metricType: 'ULP',
value: getReducedElementCount(graphResources) + 18,
};
};
const reduceLogSumTests = [
{
'name': 'reduceLogSum float32 0D constant tensor default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [64.54827117919922],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 4.167413234710693,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 0D constant tensor empty axes',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [64.54827117919922],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments':
[{'input': 'reduceLogSumInput'}, {'options': {'axes': []}}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 4.167413234710693,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSum float32 1D constant tensor all non-negative default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 1D tensor all non-negative default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSum float32 1D tensor all non-negative integers default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
63, 82, 49, 23, 98, 67, 15, 9, 89, 7, 69, 61,
47, 50, 41, 39, 58, 52, 35, 83, 81, 7, 34, 9
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.063048362731934,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 2D tensor default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 3D tensor default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 4D tensor default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 5D tensor default options',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [{'input': 'reduceLogSumInput'}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 3D tensor options.axes',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments':
[{'input': 'reduceLogSumInput'}, {'options': {'axes': [2]}}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': [
5.593751907348633, 4.773046016693115, 5.3115739822387695,
5.2497639656066895, 4.973392486572266, 5.373587131500244
],
'descriptor': {shape: [2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 4D tensor options.axes',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments':
[{'input': 'reduceLogSumInput'}, {'options': {'axes': [0, 2]}}],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': [
5.410027980804443, 5.367736339569092, 5.399682998657227,
4.652334213256836, 4.744638442993164, 5.565346717834473
],
'descriptor': {shape: [2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 3D tensor options.keepDimensions=false',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [
{'input': 'reduceLogSumInput'}, {'options': {'keepDimensions': false}}
],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 3D tensor options.keepDimensions=true',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [
{'input': 'reduceLogSumInput'}, {'options': {'keepDimensions': true}}
],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': [7.039101600646973],
'descriptor': {shape: [1, 1, 1], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 4D tensor options.keepDimensions=false',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [
{'input': 'reduceLogSumInput'}, {'options': {'keepDimensions': false}}
],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': 7.039101600646973,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSum float32 4D tensor options.keepDimensions=true',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [
{'input': 'reduceLogSumInput'}, {'options': {'keepDimensions': true}}
],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': [7.039101600646973],
'descriptor': {shape: [1, 1, 1, 1], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSum float32 4D tensor options.axes with options.keepDimensions=false',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [
{'input': 'reduceLogSumInput'},
{'options': {'axes': [1, 3], 'keepDimensions': false}}
],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': [
5.7273993492126465, 5.64375114440918, 5.453810214996338,
5.758983135223389
],
'descriptor': {shape: [2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSum float32 4D tensor options.axes with options.keepDimensions=true',
'graph': {
'inputs': {
'reduceLogSumInput': {
'data': [
64.54827117919922, 97.87423706054688, 26.529027938842773,
79.79046630859375, 50.394989013671875, 14.578407287597656,
20.866817474365234, 32.43873596191406, 64.91233825683594,
71.54029846191406, 11.137068748474121, 55.079307556152344,
43.791351318359375, 13.831947326660156, 97.39019775390625,
35.507755279541016, 52.27586364746094, 82.83865356445312,
8.568099021911621, 0.8337112069129944, 69.23146057128906,
3.8541641235351562, 70.5567398071289, 71.99264526367188
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSum',
'arguments': [
{'input': 'reduceLogSumInput'},
{'options': {'axes': [1, 3], 'keepDimensions': true}}
],
'outputs': 'reduceLogSumOutput'
}],
'expectedOutputs': {
'reduceLogSumOutput': {
'data': [
5.7273993492126465, 5.64375114440918, 5.453810214996338,
5.758983135223389
],
'descriptor': {shape: [2, 1, 2, 1], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
reduceLogSumTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getReductionOperatorsPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}