mozilla-central/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js (file symbol)

Source code

Revision control

Copy as Markdown

Other Tools

Test Info: Warnings

// META: title=validation tests for WebNN API gru operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils_validation.js
'use strict';
const steps = 2, batchSize = 3, inputSize = 4, hiddenSize = 5, oneDirection = 1,
bothDirections = 2;
// Dimensions required of required inputs.
const kValidInputShape = [steps, batchSize, inputSize];
const kValidWeightShape = [oneDirection, 3 * hiddenSize, inputSize];
const kValidRecurrentWeightShape = [oneDirection, 3 * hiddenSize, hiddenSize];
// Dimensions required of optional inputs.
const kValidBiasShape = [oneDirection, 3 * hiddenSize];
const kValidRecurrentBiasShape = [oneDirection, 3 * hiddenSize];
const kValidInitialHiddenStateShape = [oneDirection, batchSize, hiddenSize];
// Example descriptors which are valid according to the above dimensions.
const kExampleInputDescriptor = {
dataType: 'float32',
shape: kValidInputShape
};
const kExampleWeightDescriptor = {
dataType: 'float32',
shape: kValidWeightShape
};
const kExampleRecurrentWeightDescriptor = {
dataType: 'float32',
shape: kValidRecurrentWeightShape
};
const kExampleBiasDescriptor = {
dataType: 'float32',
shape: kValidBiasShape
};
const kExampleRecurrentBiasDescriptor = {
dataType: 'float32',
shape: kValidRecurrentBiasShape
};
const kExampleInitialHiddenStateDescriptor = {
dataType: 'float32',
shape: kValidInitialHiddenStateShape
};
const tests = [
{
name: '[gru] Test with default options',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize,
outputs:
[{dataType: 'float32', shape: [oneDirection, batchSize, hiddenSize]}]
},
{
name: '[gru] Test with given options',
input: kExampleInputDescriptor,
weight: {
dataType: 'float32',
shape: [bothDirections, 3 * hiddenSize, inputSize]
},
recurrentWeight: {
dataType: 'float32',
shape: [bothDirections, 3 * hiddenSize, hiddenSize]
},
steps: steps,
hiddenSize: hiddenSize,
options: {
bias: {dataType: 'float32', shape: [bothDirections, 3 * hiddenSize]},
recurrentBias:
{dataType: 'float32', shape: [bothDirections, 3 * hiddenSize]},
initialHiddenState:
{dataType: 'float32', shape: [bothDirections, batchSize, hiddenSize]},
restAfter: true,
returnSequence: true,
direction: 'both',
layout: 'rzn',
activations: ['sigmoid', 'relu']
},
outputs: [
{dataType: 'float32', shape: [bothDirections, batchSize, hiddenSize]}, {
dataType: 'float32',
shape: [steps, bothDirections, batchSize, hiddenSize]
}
]
},
{
name: '[gru] TypeError is expected if steps equals to zero',
input: kExampleInputDescriptor,
weight:
{dataType: 'float32', shape: [oneDirection, 4 * hiddenSize, inputSize]},
recurrentWeight: {
dataType: 'float32',
shape: [oneDirection, 4 * hiddenSize, hiddenSize]
},
steps: 0,
hiddenSize: hiddenSize,
},
{
name: '[gru] TypeError is expected if hiddenSize equals to zero',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: 0
},
{
name: '[gru] TypeError is expected if hiddenSize is too large',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: 4294967295,
},
{
name:
'[gru] TypeError is expected if the data type of the inputs is not one of the floating point types',
input: {dataType: 'uint32', shape: kValidInputShape},
weight: {dataType: 'uint32', shape: kValidWeightShape},
recurrentWeight: {dataType: 'uint32', shape: kValidRecurrentWeightShape},
steps: steps,
hiddenSize: hiddenSize
},
{
name: '[gru] TypeError is expected if the rank of input is not 3',
input: {dataType: 'float32', shape: [steps, batchSize]},
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize
},
{
name: '[gru] TypeError is expected if input.shape[0] is not equal to steps',
input: {dataType: 'float32', shape: [1000, batchSize, inputSize]},
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize
},
{
name:
'[gru] TypeError is expected if weight.shape[1] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight:
{dataType: 'float32', shape: [oneDirection, 4 * hiddenSize, inputSize]},
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize
},
{
name: '[gru] TypeError is expected if the rank of recurrentWeight is not 3',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight:
{dataType: 'float32', shape: [oneDirection, 3 * hiddenSize]},
steps: steps,
hiddenSize: hiddenSize
},
{
name: '[gru] TypeError is expected if the recurrentWeight.shape is invalid',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight:
{dataType: 'float32', shape: [oneDirection, 4 * hiddenSize, inputSize]},
steps: steps,
hiddenSize: hiddenSize
},
{
name:
'[gru] TypeError is expected if the size of options.activations is not 2',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize,
options: {activations: ['sigmoid', 'tanh', 'relu']}
},
{
name: '[gru] TypeError is expected if the rank of options.bias is not 2',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize,
options: {bias: {dataType: 'float32', shape: [oneDirection]}}
},
{
name:
'[gru] TypeError is expected if options.bias.shape[1] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize,
options: {bias: {dataType: 'float32', shape: [oneDirection, hiddenSize]}}
},
{
name:
'[gru] TypeError is expected if options.recurrentBias.shape[1] is not 3 * hiddenSize',
input: {dataType: 'float16', shape: kValidInputShape},
weight: {dataType: 'float16', shape: kValidWeightShape},
recurrentWeight: {dataType: 'float16', shape: kValidRecurrentWeightShape},
steps: steps,
hiddenSize: hiddenSize,
options: {
recurrentBias:
{dataType: 'float16', shape: [oneDirection, 4 * hiddenSize]}
}
},
{
name:
'[gru] TypeError is expected if the rank of options.initialHiddenState is not 3',
input: {dataType: 'float16', shape: kValidInputShape},
weight: {dataType: 'float16', shape: kValidWeightShape},
recurrentWeight: {dataType: 'float16', shape: kValidRecurrentWeightShape},
steps: steps,
hiddenSize: hiddenSize,
options: {
initialHiddenState:
{dataType: 'float16', shape: [oneDirection, batchSize]}
}
},
{
name:
'[gru] TypeError is expected if options.initialHiddenState.shape[2] is not inputSize',
input: {dataType: 'float16', shape: kValidInputShape},
weight: {dataType: 'float16', shape: kValidWeightShape},
recurrentWeight: {dataType: 'float16', shape: kValidRecurrentWeightShape},
steps: steps,
hiddenSize: hiddenSize,
options: {
initialHiddenState: {
dataType: 'float16',
shape: [oneDirection, batchSize, 3 * hiddenSize]
}
}
},
{
name:
'[gru] TypeError is expected if the dataType of options.initialHiddenState is incorrect',
input: {dataType: 'float16', shape: kValidInputShape},
weight: {dataType: 'float16', shape: kValidWeightShape},
recurrentWeight: {dataType: 'float16', shape: kValidRecurrentWeightShape},
steps: steps,
hiddenSize: hiddenSize,
options: {
initialHiddenState:
{dataType: 'uint64', shape: [oneDirection, batchSize, hiddenSize]}
}
}
];
tests.forEach(
test => promise_test(async t => {
const builder = new MLGraphBuilder(context);
const input = builder.input('input', test.input);
const weight = builder.input('weight', test.weight);
const recurrentWeight =
builder.input('recurrentWeight', test.recurrentWeight);
const options = {};
if (test.options) {
if (test.options.bias) {
options.bias = builder.input('bias', test.options.bias);
}
if (test.options.recurrentBias) {
options.recurrentBias =
builder.input('recurrentBias', test.options.recurrentBias);
}
if (test.options.initialHiddenState) {
options.initialHiddenState = builder.input(
'initialHiddenState', test.options.initialHiddenState);
}
if (test.options.resetAfter) {
options.resetAfter = test.options.resetAfter;
}
if (test.options.returnSequence) {
options.returnSequence = test.options.returnSequence;
}
if (test.options.direction) {
options.direction = test.options.direction;
}
if (test.options.layout) {
options.layout = test.options.layout;
}
if (test.options.activations) {
options.activations = test.options.activations;
}
}
if (test.outputs &&
context.opSupportLimits().gru.input.dataTypes.includes(
test.input.dataType)) {
const outputs = builder.gru(
input, weight, recurrentWeight, test.steps, test.hiddenSize,
options);
assert_equals(outputs.length, test.outputs.length);
for (let i = 0; i < outputs.length; ++i) {
assert_equals(outputs[i].dataType, test.outputs[i].dataType);
assert_array_equals(outputs[i].shape, test.outputs[i].shape);
}
} else {
const label = 'gru_xxx';
options.label = label;
const regrexp = new RegExp('\\[' + label + '\\]');
assert_throws_with_label(
() => builder.gru(
input, weight, recurrentWeight, test.steps, test.hiddenSize,
options),
regrexp);
}
}, test.name));
multi_builder_test(async (t, builder, otherBuilder) => {
const inputFromOtherBuilder =
otherBuilder.input('input', kExampleInputDescriptor);
const weight = builder.input('weight', kExampleWeightDescriptor);
const recurrentWeight =
builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
assert_throws_js(
TypeError,
() => builder.gru(
inputFromOtherBuilder, weight, recurrentWeight, steps, hiddenSize));
}, '[gru] throw if input is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const weightFromOtherBuilder =
otherBuilder.input('weight', kExampleWeightDescriptor);
const input = builder.input('input', kExampleInputDescriptor);
const recurrentWeight =
builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
assert_throws_js(
TypeError,
() => builder.gru(
input, weightFromOtherBuilder, recurrentWeight, steps, hiddenSize));
}, '[gru] throw if weight is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const recurrentWeightFromOtherBuilder =
otherBuilder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
const input = builder.input('input', kExampleInputDescriptor);
const weight = builder.input('weight', kExampleWeightDescriptor);
assert_throws_js(
TypeError,
() => builder.gru(
input, weight, recurrentWeightFromOtherBuilder, steps, hiddenSize));
}, '[gru] throw if recurrentWeight is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const biasFromOtherBuilder =
otherBuilder.input('bias', kExampleBiasDescriptor);
const options = {bias: biasFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
const weight = builder.input('weight', kExampleWeightDescriptor);
const recurrentWeight =
builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
assert_throws_js(
TypeError,
() => builder.gru(
input, weight, recurrentWeight, steps, hiddenSize, options));
}, '[gru] throw if bias option is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const recurrentBiasFromOtherBuilder =
otherBuilder.input('recurrentBias', kExampleRecurrentBiasDescriptor);
const options = {recurrentBias: recurrentBiasFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
const weight = builder.input('weight', kExampleWeightDescriptor);
const recurrentWeight =
builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
assert_throws_js(
TypeError,
() => builder.gru(
input, weight, recurrentWeight, steps, hiddenSize, options));
}, '[gru] throw if recurrentBias option is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const initialHiddenStateFromOtherBuilder = otherBuilder.input(
'initialHiddenState', kExampleInitialHiddenStateDescriptor);
const options = {initialHiddenState: initialHiddenStateFromOtherBuilder};
const input = builder.input('input', kExampleInputDescriptor);
const weight = builder.input('weight', kExampleWeightDescriptor);
const recurrentWeight =
builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
assert_throws_js(
TypeError,
() => builder.gru(
input, weight, recurrentWeight, steps, hiddenSize, options));
}, '[gru] throw if initialHiddenState option is from another builder');