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Test Info: Warnings
- This test has a WPT meta file that expects 4 subtest issues.
- This WPT test may be referenced by the following Test IDs:
- /webnn/validation_tests/layerNormalization.https.any.html - WPT Dashboard Interop Dashboard
- /webnn/validation_tests/layerNormalization.https.any.worker.html - WPT Dashboard Interop Dashboard
// META: title=validation tests for WebNN API layerNormalization operation
// META: global=window,dedicatedworker
// META: script=../resources/utils_validation.js
'use strict';
const kExampleInputDescriptor = {
dataType: 'float32',
dimensions: [2, 2]
};
validateInputFromAnotherBuilder('layerNormalization');
multi_builder_test(async (t, builder, otherBuilder) => {
const scaleFromOtherBuilder =
otherBuilder.input('scale', kExampleInputDescriptor);
const options = {scale: scaleFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
assert_throws_js(TypeError, () => builder.layerNormalization(input, options));
}, '[layerNormalization] throw if scale option is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const biasFromOtherBuilder =
otherBuilder.input('bias', kExampleInputDescriptor);
const options = {bias: biasFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
assert_throws_js(TypeError, () => builder.layerNormalization(input, options));
}, '[layerNormalization] throw if bias option is from another builder');
const tests = [
{
name: '[layerNormalization] Test with default options for scalar input.',
input: {dataType: 'float32', dimensions: []},
output: {dataType: 'float32', dimensions: []},
},
{
name: '[layerNormalization] Test when the input data type is float16.',
input: {dataType: 'float16', dimensions: []},
output: {dataType: 'float16', dimensions: []},
},
{
name: '[layerNormalization] Test with given axes.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
axes: [3],
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
},
{
name: '[layerNormalization] Test with given scale.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
scale: {dataType: 'float32', dimensions: [2, 3, 4]},
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
},
{
name: '[layerNormalization] Test with a non-default epsilon value.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
epsilon: 1e-4, // default epsilon=1e-5
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
},
{
name: '[layerNormalization] Test with given axes, scale and bias.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
scale: {dataType: 'float32', dimensions: [3, 4]},
bias: {dataType: 'float32', dimensions: [3, 4]},
axes: [2, 3],
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
},
{
name: '[layerNormalization] Test with nonconsecutive axes.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4, 5, 6]},
options: {
scale: {dataType: 'float32', dimensions: [2, 4, 6]},
bias: {dataType: 'float32', dimensions: [2, 4, 6]},
axes: [1, 3, 5],
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4, 5, 6]},
},
{
name: '[layerNormalization] Test with axes in descending order.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4, 5, 6]},
options: {
scale: {dataType: 'float32', dimensions: [6, 5, 4, 3, 2]},
bias: {dataType: 'float32', dimensions: [6, 5, 4, 3, 2]},
axes: [5, 4, 3, 2, 1]
},
output: {dataType: 'float32', dimensions: [1, 2, 3, 4, 5, 6]},
},
{
name:
'[layerNormalization] Throw if the input data type is not one of the floating point types.',
input: {dataType: 'uint32', dimensions: [1, 2, 3, 4]},
},
{
name:
'[layerNormalization] Throw if the axis is greater than the input rank.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
axes: [1, 2, 4],
},
},
{
name: '[layerNormalization] Throw if the axes have duplications.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {axes: [3, 3]},
},
{
name:
'[layerNormalization] Throw if the bias data type doesn\'t match input data type',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
scale: {dataType: 'float32', dimensions: [3, 4]},
bias: {dataType: 'float16', dimensions: [3, 4]},
axes: [2, 3],
},
},
{
name:
'[layerNormalization] Throw if the scale data type doesn\'t match input data type',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
scale: {dataType: 'float16', dimensions: [3, 4]},
bias: {dataType: 'float32', dimensions: [3, 4]},
axes: [2, 3],
},
},
{
name:
'[layerNormalization] Throw if the bias dimensions doesn\'t match axis dimensions.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
bias: {
dataType: 'float32',
dimensions: [3, 3, 4]
}, // for 4D input, default axes = [1,2,3]
},
},
{
name:
'[layerNormalization] Throw if the scale dimensions doesn\'t match axis dimensions.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
scale: {
dataType: 'float32',
dimensions: [3, 3, 4]
}, // for 4D input, default axes = [1,2,3]
},
},
{
name:
'[layerNormalization] Throw if the bias rank doesn\'t match axis rank.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
bias: {
dataType: 'float32',
dimensions: [1, 2, 3, 4]
}, // for 4D input, default axes = [1,2,3]
},
},
{
name:
'[layerNormalization] Throw if the scale rank doesn\'t match axis rank.',
input: {dataType: 'float32', dimensions: [1, 2, 3, 4]},
options: {
scale: {
dataType: 'float32',
dimensions: [1, 2, 3, 4]
}, // for 4D input, default axes = [1,2,3]
},
},
];
tests.forEach(
test => promise_test(async t => {
const input = builder.input(
'input',
{dataType: test.input.dataType, dimensions: test.input.dimensions});
if (test.options && test.options.bias) {
test.options.bias = builder.input('bias', {
dataType: test.options.bias.dataType,
dimensions: test.options.bias.dimensions
});
}
if (test.options && test.options.scale) {
test.options.scale = builder.input('scale', {
dataType: test.options.scale.dataType,
dimensions: test.options.scale.dimensions
});
}
if (test.output) {
const output = builder.layerNormalization(input, test.options);
assert_equals(output.dataType(), test.output.dataType);
assert_array_equals(output.shape(), test.output.dimensions);
} else {
assert_throws_js(
TypeError, () => builder.layerNormalization(input, test.options));
}
}, test.name));