// META: title=validation tests for WebNN API gruCell operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils_validation.js
'use strict';
const batchSize = 3, inputSize = 4, hiddenSize = 5;
// Dimensions required of required inputs.
const kValidInputDimensions = [batchSize, inputSize];
const kValidWeightDimensions = [3 * hiddenSize, inputSize];
const kValidRecurrentWeightDimensions = [3 * hiddenSize, hiddenSize];
const kValidHiddenStateDimensions = [batchSize, hiddenSize];
// Dimensions required of optional inputs.
const kValidBiasDimensions = [3 * hiddenSize];
const kValidRecurrentBiasDimensions = [3 * hiddenSize];
// Dimensions required of required output.
const kValidOutputDimensions = [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 kExampleHiddenStateDescriptor = {
dataType: 'float32',
dimensions: kValidHiddenStateDimensions
};
const kExampleBiasDescriptor = {
dataType: 'float32',
dimensions: kValidBiasDimensions
};
const kExampleRecurrentBiasDescriptor = {
dataType: 'float32',
dimensions: kValidRecurrentBiasDimensions
};
const kExampleOutputDescriptor = {
dataType: 'float32',
dimensions: kValidOutputDimensions
};
const tests = [
{
name: '[gruCell] Test with default options',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
output: kExampleOutputDescriptor
},
{
name: '[gruCell] Test with given options',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: {
bias: kExampleBiasDescriptor,
recurrentBias: kExampleRecurrentBiasDescriptor,
restAfter: true,
layout: 'rzn',
activations: ['sigmoid', 'relu']
},
output: kExampleOutputDescriptor
},
{
name: '[gruCell] Throw if hiddenSize equals to zero',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: 0
},
{
name: '[gruCell] Throw if hiddenSize is too large',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: 4294967295,
},
{
name:
'[gruCell] Throw 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
},
hiddenState: {
dataType: 'uint32',
dimensions: kValidHiddenStateDimensions
},
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if the rank of input is not 2',
input: { dataType: 'float32', dimensions: [batchSize] },
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if the input.dimensions[1] is incorrect',
input: { dataType: 'float32', dimensions: [inputSize, inputSize] },
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if data type of weight is not one of the floating point types',
input: kExampleInputDescriptor,
weight: {
dataType: 'int8',
dimensions: [3 * hiddenSize, inputSize]
},
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if rank of weight is not 2',
input: kExampleInputDescriptor,
weight: {
dataType: 'float32',
dimensions: [3 * hiddenSize]
},
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if weight.dimensions[0] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: {
dataType: 'float32',
dimensions: [4 * hiddenSize, inputSize]
},
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if data type of recurrentWeight is not one of the floating point types',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: {
dataType: 'int32',
dimensions: [3 * hiddenSize, hiddenSize]
},
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if the rank of recurrentWeight is not 2',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight:
{ dataType: 'float32', dimensions: [3 * hiddenSize] },
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if the recurrentWeight.dimensions is invalid',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight:
{ dataType: 'float32', dimensions: [4 * hiddenSize, inputSize] },
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if data type of hiddenState is not one of the floating point types',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight:
kExampleRecurrentWeightDescriptor,
hiddenState: {
dataType: 'uint32',
dimensions: [batchSize, hiddenSize]
},
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if the rank of hiddenState is not 2',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight:
kExampleRecurrentWeightDescriptor,
hiddenState: {
dataType: 'float32',
dimensions: [hiddenSize]
},
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if the hiddenState.dimensions is invalid',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: {
dataType: 'float32',
dimensions: [batchSize, 3 * hiddenSize]
},
hiddenSize: hiddenSize
},
{
name:
'[gruCell] Throw if the size of options.activations is not 2',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: { activations: ['sigmoid', 'tanh', 'relu'] }
},
{
name:
'[gruCell] Throw if data type of options.bias is not one of the floating point types',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: { bias: { dataType: 'uint8', dimensions: [3 * hiddenSize] } }
},
{
name:
'[gruCell] Throw if the rank of options.bias is not 1',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: { bias: { dataType: 'float32', dimensions: [batchSize, 3 * hiddenSize] } }
},
{
name:
'[gruCell] Throw if options.bias.dimensions[0] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: { bias: { dataType: 'float32', dimensions: [2 * hiddenSize] } }
},
{
name:
'[gruCell] Throw if data type of options.recurrentBias is not one of the floating point types',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: { recurrentBias: { dataType: 'int8', dimensions: [3 * hiddenSize] } }
},
{
name:
'[gruCell] Throw if the rank of options.recurrentBias is not 1',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: { recurrentBias: { dataType: 'float32', dimensions: [batchSize, 3 * hiddenSize] } }
},
{
name:
'[gruCell] Throw if options.recurrentBias.dimensions[0] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: {
recurrentBias: { dataType: 'float16', dimensions: [4 * hiddenSize] }
}
}
];
tests.forEach(
test =>
promise_test(async t => {
const builder = new MLGraphBuilder(context);
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 hiddenState = builder.input('hiddenState', {
dataType: test.hiddenState.dataType,
dimensions: test.hiddenState.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.recurrentBias = builder.input('recurrentBias', {
dataType: test.options.recurrentBias.dataType,
dimensions: test.options.recurrentBias.dimensions
});
}
if (test.options.resetAfter) {
options.resetAfter = test.options.resetAfter;
}
if (test.options.layout) {
options.layout = test.options.layout;
}
if (test.options.activations) {
options.activations = test.options.activations;
}
}
if (test.output) {
const output = builder.gruCell(
input, weight, recurrentWeight, hiddenState, test.hiddenSize,
options);
assert_equals(output.dataType(), test.output.dataType);
assert_array_equals(output.shape(), test.output.dimensions);
} else {
const label = 'gru_cell_xxx';
options.label = label;
const regrexp = new RegExp('\\[' + label + '\\]');
assert_throws_with_label(
() => builder.gruCell(
input, weight, recurrentWeight, hiddenState,
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);
const hiddenState =
builder.input('hiddenState', kExampleHiddenStateDescriptor);
assert_throws_js(
TypeError,
() => builder.gruCell(
inputFromOtherBuilder, weight, recurrentWeight, hiddenState,
hiddenSize));
}, '[gruCell] 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);
const hiddenState =
builder.input('hiddenState', kExampleHiddenStateDescriptor);
assert_throws_js(
TypeError,
() => builder.gruCell(
input, weightFromOtherBuilder, recurrentWeight, hiddenState,
hiddenSize));
}, '[gruCell] 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);
const hiddenState =
builder.input('hiddenState', kExampleHiddenStateDescriptor);
assert_throws_js(
TypeError,
() => builder.gruCell(
input, weight, recurrentWeightFromOtherBuilder, hiddenState,
hiddenSize));
}, '[gruCell] throw if recurrentWeight is from another builder');
multi_builder_test(async (t, builder, otherBuilder) => {
const hiddenStateFromOtherBuilder =
otherBuilder.input('hiddenState', kExampleHiddenStateDescriptor);
const input = builder.input('input', kExampleInputDescriptor);
const weight = builder.input('weight', kExampleWeightDescriptor);
const recurrentWeight =
builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
assert_throws_js(
TypeError,
() => builder.gruCell(
input, weight, recurrentWeight, hiddenStateFromOtherBuilder,
hiddenSize));
}, '[gruCell] throw if hiddenState 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);
const hiddenState =
builder.input('hiddenState', kExampleHiddenStateDescriptor);
assert_throws_js(
TypeError,
() => builder.gruCell(
input, weight, recurrentWeight, hiddenState, hiddenSize, options));
}, '[gruCell] 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);
const hiddenState =
builder.input('hiddenState', kExampleHiddenStateDescriptor);
assert_throws_js(
TypeError,
() => builder.gruCell(
input, weight, recurrentWeight, hiddenState, hiddenSize, options));
}, '[gruCell] throw if recurrentBias option is from another builder');
Codebase Browser
chromium