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

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// 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 kValidInputShape = [batchSize, inputSize];
const kValidWeightShape = [3 * hiddenSize, inputSize];
const kValidRecurrentWeightShape = [3 * hiddenSize, hiddenSize];
const kValidHiddenStateShape = [batchSize, hiddenSize];
// Dimensions required of optional inputs.
const kValidBiasShape = [3 * hiddenSize];
const kValidRecurrentBiasShape = [3 * hiddenSize];
// Dimensions required of required output.
const kValidOutputShape = [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 kExampleHiddenStateDescriptor = {
dataType: 'float32',
shape: kValidHiddenStateShape
};
const kExampleBiasDescriptor = {
dataType: 'float32',
shape: kValidBiasShape
};
const kExampleRecurrentBiasDescriptor = {
dataType: 'float32',
shape: kValidRecurrentBiasShape
};
const kExampleOutputDescriptor = {
dataType: 'float32',
shape: kValidOutputShape
};
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', shape: kValidInputShape},
weight: {dataType: 'uint32', shape: kValidWeightShape},
recurrentWeight: {dataType: 'uint32', shape: kValidRecurrentWeightShape},
hiddenState: {dataType: 'uint32', shape: kValidHiddenStateShape},
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if the rank of input is not 2',
input: {dataType: 'float32', shape: [batchSize]},
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if the input.shape[1] is incorrect',
input: {dataType: 'float32', shape: [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', shape: [3 * hiddenSize, inputSize]},
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if rank of weight is not 2',
input: kExampleInputDescriptor,
weight: {dataType: 'float32', shape: [3 * hiddenSize]},
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if weight.shape[0] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: {dataType: 'float32', shape: [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', shape: [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', shape: [3 * hiddenSize]},
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if the recurrentWeight.shape is invalid',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: {dataType: 'float32', shape: [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', shape: [batchSize, hiddenSize]},
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if the rank of hiddenState is not 2',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: {dataType: 'float32', shape: [hiddenSize]},
hiddenSize: hiddenSize
},
{
name: '[gruCell] Throw if the hiddenState.shape is invalid',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: {dataType: 'float32', shape: [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', shape: [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', shape: [batchSize, 3 * hiddenSize]}}
},
{
name: '[gruCell] Throw if options.bias.shape[0] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: {bias: {dataType: 'float32', shape: [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', shape: [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', shape: [batchSize, 3 * hiddenSize]}
}
},
{
name:
'[gruCell] Throw if options.recurrentBias.shape[0] is not 3 * hiddenSize',
input: kExampleInputDescriptor,
weight: kExampleWeightDescriptor,
recurrentWeight: kExampleRecurrentWeightDescriptor,
hiddenState: kExampleHiddenStateDescriptor,
hiddenSize: hiddenSize,
options: {recurrentBias: {dataType: 'float16', shape: [4 * 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 hiddenState = builder.input('hiddenState', test.hiddenState);
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.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 &&
context.opSupportLimits().gruCell.input.dataTypes.includes(
test.input.dataType)) {
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.shape);
} 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');