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Test Info: Warnings
- This test has a WPT meta file that expects 26 subtest issues.
- This WPT test may be referenced by the following Test IDs:
- /webnn/validation_tests/gru.https.any.html - WPT Dashboard Interop Dashboard
- /webnn/validation_tests/gru.https.any.worker.html - WPT Dashboard Interop Dashboard
// META: title=validation tests for WebNN API gru operation
// META: global=window,dedicatedworker
// 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 kValidInputDimensions = [steps, batchSize, inputSize];
const kValidWeightDimensions = [oneDirection, 3 * hiddenSize, inputSize];
const kValidRecurrentWeightDimensions =
[oneDirection, 3 * hiddenSize, hiddenSize];
// Dimensions required of optional inputs.
const kValidBiasDimensions = [oneDirection, 3 * hiddenSize];
const kValidRecurrentBiasDimensions = [oneDirection, 3 * hiddenSize];
const kValidInitialHiddenStateDimensions =
[oneDirection, batchSize, hiddenSize];
// Example descriptors which are valid according to the above dimensions.
const kExampleInputDescriptor = {
dataType: 'float32',
dimensions: kValidInputDimensions
};
const kExampleWeightDescriptor = {
dataType: 'float32',
dimensions: kValidWeightDimensions
};
const kExampleRecurrentWeightDescriptor = {
dataType: 'float32',
dimensions: kValidRecurrentWeightDimensions
};
const kExampleBiasDescriptor = {
dataType: 'float32',
dimensions: kValidBiasDimensions
};
const kExampleRecurrentBiasDescriptor = {
dataType: 'float32',
dimensions: kValidRecurrentBiasDimensions
};
const kExampleInitialHiddenStateDescriptor = {
dataType: 'float32',
dimensions: kValidInitialHiddenStateDimensions
};
const tests = [
{
name: '[gru] Test with default options',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize,
outputs: [
{dataType: 'float32', dimensions: [oneDirection, batchSize, hiddenSize]}
]
},
{
name: '[gru] Test with given options',
input: kExampleInputDescriptor,
weight: {
dataType: 'float32',
dimensions: [bothDirections, 3 * hiddenSize, inputSize]
},
recurrentWeight: {
dataType: 'float32',
dimensions: [bothDirections, 3 * hiddenSize, hiddenSize]
},
steps: steps,
hiddenSize: hiddenSize,
options: {
bias: {dataType: 'float32', dimensions: [bothDirections, 3 * hiddenSize]},
recurrentBias:
{dataType: 'float32', dimensions: [bothDirections, 3 * hiddenSize]},
initialHiddenState: {
dataType: 'float32',
dimensions: [bothDirections, batchSize, hiddenSize]
},
restAfter: true,
returnSequence: true,
direction: 'both',
layout: 'rzn',
activations: ['sigmoid', 'relu']
},
outputs: [
{
dataType: 'float32',
dimensions: [bothDirections, batchSize, hiddenSize]
},
{
dataType: 'float32',
dimensions: [steps, bothDirections, batchSize, hiddenSize]
}
]
},
{
name: '[gru] TypeError is expected if steps equals to zero',
input: kExampleInputDescriptor,
weight: {
dataType: 'float32',
dimensions: [oneDirection, 4 * hiddenSize, inputSize]
},
recurrentWeight: {
dataType: 'float32',
dimensions: [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', dimensions: kValidInputDimensions},
weight: {dataType: 'uint32', dimensions: kValidWeightDimensions},
recurrentWeight:
{dataType: 'uint32', dimensions: kValidRecurrentWeightDimensions},
steps: steps,
hiddenSize: hiddenSize
},
{
name: '[gru] TypeError is expected if the rank of input is not 3',
input: {dataType: 'float32', dimensions: [steps, batchSize]},
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize
},
{
name:
'[gru] TypeError is expected if input.dimensions[0] is not equal to steps',
input: {dataType: 'float32', dimensions: [1000, batchSize, inputSize]},
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize
},
{
name:
'[gru] TypeError is expected if weight.dimensions[1] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: {
dataType: 'float32',
dimensions: [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', dimensions: [oneDirection, 3 * hiddenSize]},
steps: steps,
hiddenSize: hiddenSize
},
{
name:
'[gru] TypeError is expected if the recurrentWeight.dimensions is invalid',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: {
dataType: 'float32',
dimensions: [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', dimensions: [oneDirection]}}
},
{
name:
'[gru] TypeError is expected if options.bias.dimensions[1] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
steps: steps,
hiddenSize: hiddenSize,
options:
{bias: {dataType: 'float32', dimensions: [oneDirection, hiddenSize]}}
},
{
name:
'[gru] TypeError is expected if options.recurrentBias.dimensions[1] is not 3 * hiddenSize',
input: {dataType: 'float16', dimensions: kValidInputDimensions},
weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
recurrentWeight:
{dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
steps: steps,
hiddenSize: hiddenSize,
options: {
recurrentBias:
{dataType: 'float16', dimensions: [oneDirection, 4 * hiddenSize]}
}
},
{
name:
'[gru] TypeError is expected if the rank of options.initialHiddenState is not 3',
input: {dataType: 'float16', dimensions: kValidInputDimensions},
weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
recurrentWeight:
{dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
steps: steps,
hiddenSize: hiddenSize,
options: {
initialHiddenState:
{dataType: 'float16', dimensions: [oneDirection, batchSize]}
}
},
{
name:
'[gru] TypeError is expected if options.initialHiddenState.dimensions[2] is not inputSize',
input: {dataType: 'float16', dimensions: kValidInputDimensions},
weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
recurrentWeight:
{dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
steps: steps,
hiddenSize: hiddenSize,
options: {
initialHiddenState: {
dataType: 'float16',
dimensions: [oneDirection, batchSize, 3 * hiddenSize]
}
}
},
{
name:
'[gru] TypeError is expected if the dataType of options.initialHiddenState is incorrect',
input: {dataType: 'float16', dimensions: kValidInputDimensions},
weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
recurrentWeight:
{dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
steps: steps,
hiddenSize: hiddenSize,
options: {
initialHiddenState: {
dataType: 'uint64',
dimensions: [oneDirection, batchSize, hiddenSize]
}
}
}
];
tests.forEach(
test => promise_test(async t => {
const input = builder.input(
'input',
{dataType: test.input.dataType, dimensions: test.input.dimensions});
const weight = builder.input(
'weight',
{dataType: test.weight.dataType, dimensions: test.weight.dimensions});
const recurrentWeight = builder.input('recurrentWeight', {
dataType: test.recurrentWeight.dataType,
dimensions: test.recurrentWeight.dimensions
});
const options = {};
if (test.options) {
if (test.options.bias) {
options.bias = builder.input('bias', {
dataType: test.options.bias.dataType,
dimensions: test.options.bias.dimensions
});
}
if (test.options.recurrentBias) {
options.bias = builder.input('recurrentBias', {
dataType: test.options.recurrentBias.dataType,
dimensions: test.options.recurrentBias.dimensions
});
}
if (test.options.initialHiddenState) {
options.initialHiddenState = builder.input('initialHiddenState', {
dataType: test.options.initialHiddenState.dataType,
dimensions: test.options.initialHiddenState.dimensions
});
}
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.forEach(
activation => options.activations.push(builder[activation]()));
}
}
if (test.outputs) {
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].dimensions);
}
} else {
assert_throws_js(
TypeError,
() => builder.gru(
input, weight, recurrentWeight, test.steps, test.hiddenSize,
options));
}
}, 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');
multi_builder_test(async (t, builder, otherBuilder) => {
const activation = builder.clamp();
const activationFromOtherBuilder = otherBuilder.clamp();
const options = {activations: [activation, activationFromOtherBuilder]};
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 any activation option is from another builder');