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- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/reduce_log_sum_exp.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum_exp.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum_exp.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum_exp.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum_exp.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/reduce_log_sum_exp.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 reduceLogSumExp(MLOperand input, optional MLReduceOptions options
// = {});
const getReductionOperatorsPrecisionTolerance = (graphResources) => {
return {
metricType: 'ULP',
value: getReducedElementCount(graphResources) * 2 + 18,
};
};
const reduceLogSumExpTests = [
{
'name': 'reduceLogSumExp float32 0D constant tensor default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [0.7974132895469666],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 0.7974132895469666,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 0D constant tensor empty axes',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [0.7974132895469666],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments':
[{'input': 'reduceLogSumExpInput'}, {'options': {'axes': []}}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 0.7974132895469666,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSumExp float32 1D constant tensor all positive default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 1D tensor all positive default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 1D tensor all negative default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
-4.025670051574707, -9.444348335266113, -3.1193981170654297,
-5.943697929382324, -0.3701804578304291, -4.397126197814941,
-6.605968475341797, -5.534277439117432, -7.361471176147461,
-1.9987547397613525, -9.093968391418457, -8.693618774414062,
-8.416788101196289, -1.010741114616394, -9.814584732055664,
-9.725259780883789, -9.157071113586426, -0.001698818989098072,
-9.963415145874023, -5.991659641265869, -6.180599689483643,
-1.2336505651474, -0.44234341382980347, -6.990072250366211
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 1.1666961908340454,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSumExp float32 1D tensor all positive integers default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
1, 5, 7, 5, 7, 5, 4, 2, 1, 5, 8, 2,
4, 1, 4, 5, 4, 8, 6, 2, 7, 7, 8, 5
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 9.607237815856934,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSumExp float32 1D tensor all negative integers default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
-6, -3, -5, -1, -9, -5, -1, -2, -10, -1, -5, -7,
-7, -3, -10, -10, -8, -6, -2, -6, -1, -9, -5, -2
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 0.7001367211341858,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 2D tensor default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 3D tensor default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 4D tensor default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 5D tensor default options',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 1, 4, 1, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [{'input': 'reduceLogSumExpInput'}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 3D tensor options.axes',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments':
[{'input': 'reduceLogSumExpInput'}, {'options': {'axes': [2]}}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': [
8.55212688446045, 3.985233783721924, 5.52872896194458,
9.081488609313965, 6.996237754821777, 9.759706497192383
],
'descriptor': {shape: [2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 4D tensor options.axes',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments':
[{'input': 'reduceLogSumExpInput'}, {'options': {'axes': [0, 2]}}],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': [
4.66951847076416, 9.08117961883545, 8.533217430114746,
9.270560264587402, 6.450263977050781, 8.917200088500977
],
'descriptor': {shape: [2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 3D tensor options.keepDimensions=false',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [
{'input': 'reduceLogSumExpInput'},
{'options': {'keepDimensions': false}}
],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 3D tensor options.keepDimensions=true',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [
{'input': 'reduceLogSumExpInput'},
{'options': {'keepDimensions': true}}
],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': [10.39477825164795],
'descriptor': {shape: [1, 1, 1], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 4D tensor options.keepDimensions=false',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [
{'input': 'reduceLogSumExpInput'},
{'options': {'keepDimensions': false}}
],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': 10.39477825164795,
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'reduceLogSumExp float32 4D tensor options.keepDimensions=true',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [
{'input': 'reduceLogSumExpInput'},
{'options': {'keepDimensions': true}}
],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': [10.39477825164795],
'descriptor': {shape: [1, 1, 1, 1], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSumExp float32 4D tensor options.axes with options.keepDimensions=false',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [
{'input': 'reduceLogSumExpInput'},
{'options': {'axes': [1, 3], 'keepDimensions': false}}
],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': [
8.563796997070312, 5.500619411468506, 9.753945350646973,
9.20864486694336
],
'descriptor': {shape: [2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
'reduceLogSumExp float32 4D tensor options.axes with options.keepDimensions=true',
'graph': {
'inputs': {
'reduceLogSumExpInput': {
'data': [
0.7974132895469666, 5.046889781951904, 8.520371437072754,
1.4063042402267456, 0.11882661283016205, 0.2858544886112213,
1.9325640201568604, 3.7939958572387695, 2.6040232181549072,
4.937509536743164, 4.571482181549072, 0.786512017250061,
0.21018670499324799, 9.063042640686035, 4.099809646606445,
4.596248626708984, 0.2549232244491577, 1.159480094909668,
6.802876949310303, 5.234325408935547, 8.914905548095703,
9.166799545288086, 5.717507362365723, 0.3255050778388977
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
},
'operators': [{
'name': 'reduceLogSumExp',
'arguments': [
{'input': 'reduceLogSumExpInput'},
{'options': {'axes': [1, 3], 'keepDimensions': true}}
],
'outputs': 'reduceLogSumExpOutput'
}],
'expectedOutputs': {
'reduceLogSumExpOutput': {
'data': [
8.563796997070312, 5.500619411468506, 9.753945350646973,
9.20864486694336
],
'descriptor': {shape: [2, 1, 2, 1], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
reduceLogSumExpTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getReductionOperatorsPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}