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- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/quantizeLinear.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/quantizeLinear.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/quantizeLinear.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/quantizeLinear.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/quantizeLinear.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/quantizeLinear.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API quantizeLinear 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 floating-point input down to a low-precision integer
// (typically uint8 with a zero-point bias) following the expression:
// output = clamp(roundToNearestEvens(input / scale) + zeroPoint, 0, 255).
//
// MLOperand quantizeLinear(
// MLOperand input, MLOperand scale, MLOperand zeroPoint,
// optional MLOperatorOptions options = {});
const getQuantizeLinearPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {int8: 1, uint8: 1, int4: 1, uint4: 1};
const expectedDataType =
getExpectedDataTypeOfSingleOutput(graphResources.expectedOutputs);
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const quantizeLinearTests = [
{
'name':
'quantizeLinear float32 0D scalar tensor with int8 scalar zeroPoint',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [10.794857501983643],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [1],
'descriptor': {shape: [], dataType: 'int8'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput':
{'data': [11], 'descriptor': {shape: [], dataType: 'int8'}}
}
}
},
{
'name': 'quantizeLinear float32 1D constant tensor broadcasting zeroPoint',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672
],
'descriptor': {shape: [4], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [
9.343092918395996,
0.2800687253475189,
-4.617084980010986,
1.1202747821807861,
],
'descriptor': {shape: [4], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [128, 128, 128, 128],
'descriptor': {shape: [4], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [128, 111, 126, 133],
'descriptor': {shape: [4], dataType: 'uint8'}
}
}
}
},
{
'name':
'quantizeLinear float32 2D constant tensor broadcasting zeroPoint and scale',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672
],
'descriptor': {shape: [2, 2], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [9.343092918395996],
'descriptor': {shape: [1], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [128],
'descriptor': {shape: [1], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [128, 127, 129, 129],
'descriptor': {shape: [2, 2], dataType: 'uint8'}
}
}
}
},
{
'name':
'quantizeLinear float32 4D constant tensor broadcasting scale and zeroPoint',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672
],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [0.2800687253475189, -4.617084980010986],
'descriptor': {shape: [2, 1], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [128, 128],
'descriptor': {shape: [2, 1], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [119, 111, 126, 127],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'uint8'}
}
}
}
},
{
'name':
'per-tensor quantizeLinear for float32 4D constant',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672
],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [0.2800687253475189, -4.617084980010986, 0.2800687253475189,
-4.617084980010986],
'descriptor': {shape: [2, 2], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [128, 128, 128, 128],
'descriptor': {shape: [2, 2], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [119, 129, 158, 127],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'uint8'}
}
}
}
},
{
'name':
'quantizeLinear float32 3D input with implicit block_size = [1, 2, 1].',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672
],
'descriptor': {shape: [1, 4, 1], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [0.2800687253475189, -4.617084980010986],
'descriptor': {shape: [2, 1], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [128, 189],
'descriptor': {shape: [2, 1], dataType: 'uint8'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [119, 111, 187, 188],
'descriptor': {shape: [1, 4, 1], dataType: 'uint8'}
}
}
}
},
{
'name':
'quantizeLinear float32 tensor with int4 zeroPoint which has odd size',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [4.794857501983643],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [-4],
'descriptor': {shape: [], dataType: 'int4'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput':
{'data': [-1], 'descriptor': {shape: [], dataType: 'int4'}}
}
}
},
{
'name':
'quantizeLinear float32 tensor with int4 zeroPoint which has even size',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [4.794857501983643, 3.23434354545],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861, 1.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [-6, -5],
'descriptor': {shape: [2], dataType: 'int4'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput':
{'data': [-2, -2], 'descriptor': {shape: [2], dataType: 'int4'}}
}
}
},
{
'name':
'quantizeLinear float32 2D tensor with int4 zeroPoint which has even size',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
4.794857501983643, 3.23434354545, 2.794857501983643,
5.794857501983643, 0, 7.23434354545
],
'descriptor': {shape: [3, 2], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861, 2.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [-6, -5],
'descriptor': {shape: [2], dataType: 'int4'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [-2, -3, -4, -3, -5, -2],
'descriptor': {shape: [3, 2], dataType: 'int4'}
}
}
}
},
{
'name': 'quantizeLinear int4 zeroPoint with block_size = [3, 2]',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
4.794857501983643, 3.23434354545, 2.794857501983643,
5.794857501983643, 0, 7.23434354545, 4.794857501983643,
3.23434354545, 2.794857501983643, 5.794857501983643, 0,
7.23434354545
],
'descriptor': {shape: [3, 4], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861, 2.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [-6, -5],
'descriptor': {shape: [2], dataType: 'int4'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [-2, -3, -4, -3, -5, 0, -2, -3, -4, -1, -5, -2],
'descriptor': {shape: [3, 4], dataType: 'int4'}
}
}
}
},
{
'name':
'quantizeLinear float32 tensor with uint4 zeroPoint which has odd size',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
4.794857501983643, 2.794857501983643, 1.794857501983643, 0,
3.794857501983643
],
'descriptor': {shape: [5], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [12],
'descriptor': {shape: [], dataType: 'uint4'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [16, 14, 13, 12, 15],
'descriptor': {shape: [5], dataType: 'uint4'}
}
}
}
},
{
'name':
'quantizeLinear float32 tensor with uint4 zeroPoint which has even size',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [4.794857501983643, 3.23434354545],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861, 1.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [1, 5],
'descriptor': {shape: [2], dataType: 'uint4'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput':
{'data': [5, 8], 'descriptor': {shape: [2], dataType: 'uint4'}}
}
}
},
{
'name': 'quantizeLinear uint4 zeroPoint with block_size = 3',
'graph': {
'inputs': {
'quantizeLinearInput': {
'data': [
4.794857501983643, 3.23434354545, 1.794857501983643, 2.23434354545,
4.794857501983643, 3.23434354545
],
'descriptor': {shape: [6], dataType: 'float32'},
'constant': true
},
'quantizeLinearScale': {
'data': [1.1202747821807861, 1.1202747821807861],
'descriptor': {shape: [2], dataType: 'float32'},
'constant': true
},
'quantizeLinearZeroPoint': {
'data': [1, 5],
'descriptor': {shape: [2], dataType: 'uint4'},
'constant': true
}
},
'operators': [{
'name': 'quantizeLinear',
'arguments': [
{'input': 'quantizeLinearInput'}, {'scale': 'quantizeLinearScale'},
{'zeroPoint': 'quantizeLinearZeroPoint'}
],
'outputs': 'quantizeLinearOutput'
}],
'expectedOutputs': {
'quantizeLinearOutput': {
'data': [5, 4, 3, 7, 9, 8],
'descriptor': {shape: [6], dataType: 'uint4'}
}
}
}
}
];
if (navigator.ml) {
quantizeLinearTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getQuantizeLinearPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}