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- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/dequantizeLinear.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/dequantizeLinear.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/dequantizeLinear.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/dequantizeLinear.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/dequantizeLinear.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/dequantizeLinear.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API dequantizeLinear operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Calculate a low precision integer operand
// (typically uint8 with a zero-point bias) to floating point:
// output = (input - zeroPoint) * scale.
//
// MLOperand dequantizeLinear(
// MLOperand input, MLOperand scale, MLOperand zeroPoint,
// optional MLOperatorOptions options = {});
const getDequantizeLinearPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 1};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const dequantizeLinearTests = [
{
'name': 'dequantizeLinear int8 0D tensor with float32 scalar scale',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [123],
'descriptor': {shape: [], dataType: 'int8'},
'constant': false
},
'dequantizeLinearScale': {
'data': [1.1202747821807861],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [3],
'descriptor': {shape: [], dataType: 'int8'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [134.43296813964844],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear constant input',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [123],
'descriptor': {shape: [], dataType: 'int8'},
'constant': true
},
'dequantizeLinearScale': {
'data': [1.1202747821807861],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [3],
'descriptor': {shape: [], dataType: 'int8'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [134.43296813964844],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear uint8 1D constant tensor broadcasting zeroPoint',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [12, 24, 35, 123],
'descriptor': {shape: [4], dataType: 'uint8'},
'constant': true
},
'dequantizeLinearScale': {
'data': [
9.343092918395996,
0.2800687253475189,
-4.617084980010986,
1.1202747821807861,
],
'descriptor': {shape: [4], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [128, 128, 128, 128],
'descriptor': {shape: [4], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [
-1083.798828125, -29.127147674560547, 429.388916015625,
-5.601373672485352
],
'descriptor': {shape: [4], dataType: 'float32'}
}
}
}
},
{
'name':
'dequantizeLinear uint8 1D constant tensor with implicit block_size = 2.',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [12, 24, 35, 123],
'descriptor': {shape: [4], dataType: 'uint8'},
'constant': true
},
'dequantizeLinearScale': {
'data': [
9.343092918395996,
-4.617084980010986,
],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [128, 110],
'descriptor': {shape: [2], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [
-1083.798828125, -971.681640625, 346.2813720703125,
-60.0221061706543
],
'descriptor': {shape: [4], dataType: 'float32'}
}
}
}
},
{
'name':
'dequantizeLinear int8 4D constant tensor broadcasting scale and zeroPoint',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [-124, 0, 23, 122],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'int8'},
'constant': true
},
'dequantizeLinearScale': {
'data': [0.2800687253475189, -4.617084980010986],
'descriptor': {shape: [2, 1], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [12, 12],
'descriptor': {shape: [2, 1], dataType: 'int8'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [
-38.08934783935547, -3.3608245849609375, -50.787933349609375,
-507.87933349609375
],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear int8 4D constant tensor with block_size = [3, 2]',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [
-124, 0, 23, 122, 12, 23, 45, 36, 67, 78, -22, 0,
-34, -45, -56, -67, 89, 30, 12, 23, 56, 67, 56, -12
],
'descriptor': {shape: [6, 4], dataType: 'int8'},
'constant': true
},
'dequantizeLinearScale': {
'data': [
0.2800687253475189, -4.617084980010986, 1.2800687253475189,
-3.617084980010986
],
'descriptor': {shape: [2, 2], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [1, 3, 5, 12],
'descriptor': {shape: [2, 2], dataType: 'int8'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [
-35.00859069824219,
-0.2800687253475189,
-92.3416976928711,
-549.43310546875,
3.0807559490203857,
6.1615118980407715,
-193.91757202148438,
-152.36380004882812,
18.484535217285156,
21.565292358398438,
115.4271240234375,
13.851255416870117,
-49.92267990112305,
-64.0034408569336,
245.96177673339844,
285.7497253417969,
107.52577209472656,
32.0017204284668,
0,
-39.787933349609375,
65.28350830078125,
79.36426544189453,
-159.1517333984375,
86.81004333496094
],
'descriptor': {shape: [6, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear uint4 1D tensor with even input size',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [15, 0],
'descriptor': {shape: [2], dataType: 'uint4'},
'constant': true
},
'dequantizeLinearScale': {
'data': [1.1202747821807861, 1.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [0, 1],
'descriptor': {shape: [2], dataType: 'uint4'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [16.804121017456055, -1.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear uint4 1D tensor with odd input size',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [10, 12, 14],
'descriptor': {shape: [3], dataType: 'uint4'},
'constant': true
},
'dequantizeLinearScale': {
'data': [1.1202747821807861],
'descriptor': {shape: [1], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [2],
'descriptor': {shape: [1], dataType: 'uint4'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [8.962198257446289, 11.202747344970703, 13.443297386169434],
'descriptor': {shape: [3], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear uint4 4D constant tensor broadcasting zeroPoint',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [0, 1, 10, 15],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'uint4'},
'constant': true
},
'dequantizeLinearScale': {
'data': [
9.343092918395996,
-4.617084980010986,
],
'descriptor': {shape: [2, 1], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [2, 3],
'descriptor': {shape: [2, 1], dataType: 'uint4'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [
-18.686185836791992, -9.343092918395996, -32.31959533691406,
-55.40502166748047
],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear uint4 3D input with block_size = [1, 1, 2]',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [0, 1, 10, 15],
'descriptor': {shape: [1, 1, 4], dataType: 'uint4'},
'constant': true
},
'dequantizeLinearScale': {
'data': [
9.343092918395996,
-4.617084980010986,
],
'descriptor': {shape: [1, 2], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [2, 3],
'descriptor': {shape: [1, 2], dataType: 'uint4'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [
-18.686185836791992, -9.343092918395996, -32.31959533691406,
-55.40502166748047
],
'descriptor': {shape: [1, 1, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear int4 1D tensor with even size',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [-8, -3],
'descriptor': {shape: [2], dataType: 'int4'},
'constant': true
},
'dequantizeLinearScale': {
'data': [1.1202747821807861, 1.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [0, -2],
'descriptor': {shape: [2], dataType: 'int4'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [-8.962198257446289, -1.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'}
}
}
}
},
{
'name': 'dequantizeLinear int4 1D tensor with odd size',
'graph': {
'inputs': {
'dequantizeLinearInput': {
'data': [-1, 7, 0],
'descriptor': {shape: [3], dataType: 'int4'},
'constant': true
},
'dequantizeLinearScale': {
'data': [1.1202747821807861],
'descriptor': {shape: [1], dataType: 'float32'},
'constant': true
},
'dequantizeLinearZeroPoint': {
'data': [-3],
'descriptor': {shape: [1], dataType: 'int4'},
'constant': true
}
},
'operators': [{
'name': 'dequantizeLinear',
'arguments': [
{'input': 'dequantizeLinearInput'},
{'scale': 'dequantizeLinearScale'},
{'zeroPoint': 'dequantizeLinearZeroPoint'}
],
'outputs': 'dequantizeLinearOutput'
}],
'expectedOutputs': {
'dequantizeLinearOutput': {
'data': [2.2405495643615723, 11.202747344970703, 3.3608243465423584],
'descriptor': {shape: [3], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
dequantizeLinearTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getDequantizeLinearPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}