lstmCell.https.any.js

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

Enable keyboard shortcuts

Source code

File a bug in Testing :: web-platform-tests

Revision control

Copy as Markdown

Other Tools

Test Info: Warnings

This test has a WPT meta file that expects 39 subtest issues.
This WPT test may be referenced by the following Test IDs:
- /webnn/validation_tests/lstmCell.https.any.html - WPT Dashboard Interop Dashboard
- /webnn/validation_tests/lstmCell.https.any.worker.html - WPT Dashboard Interop Dashboard

// META: title=validation tests for WebNN API lstmCell operation

// META: global=window,dedicatedworker

// 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 = [4 * hiddenSize, inputSize];

const kValidRecurrentWeightDimensions = [4 * hiddenSize, hiddenSize];

const kValidHiddenStateDimensions = [batchSize, hiddenSize];

const kValidCellStateDimensions = [batchSize, hiddenSize];

// Dimensions required of optional inputs.

const kValidBiasDimensions = [4 * hiddenSize];

const kValidPeepholeWeightDimensions = [3 * 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 kExampleCellStateDescriptor = {

  dataType: 'float32',

  dimensions: kValidCellStateDimensions

};

const kExampleBiasDescriptor = {

  dataType: 'float32',

  dimensions: kValidBiasDimensions

};

const kExamplePeepholeWeightDescriptor = {

  dataType: 'float32',

  dimensions: kValidPeepholeWeightDimensions

};

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);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          inputFromOtherBuilder, weight, recurrentWeight, hiddenState,

          cellState, hiddenSize));

}, '[lstmCell] 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);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          input, weightFromOtherBuilder, recurrentWeight, hiddenState,

          cellState, hiddenSize));

}, '[lstmCell] 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);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          input, weight, recurrentWeightFromOtherBuilder, hiddenState,

          cellState, hiddenSize));

}, '[lstmCell] 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);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          input, weight, recurrentWeight, hiddenStateFromOtherBuilder,

          cellState, hiddenSize));

}, '[lstmCell] throw if hiddenState is from another builder');

multi_builder_test(async (t, builder, otherBuilder) => {

  const cellStateFromOtherBuilder =

      otherBuilder.input('cellState', kExampleCellStateDescriptor);

  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.lstmCell(

          input, weight, recurrentWeight, hiddenState,

          cellStateFromOtherBuilder, hiddenSize));

}, '[lstmCell] throw if cellState 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);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,

          options));

}, '[lstmCell] throw if bias option is from another builder');

multi_builder_test(async (t, builder, otherBuilder) => {

  const recurrentBiasFromOtherBuilder =

      otherBuilder.input('bias', kExampleBiasDescriptor);

  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);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,

          options));

}, '[lstmCell] throw if recurrentBias option is from another builder');

multi_builder_test(async (t, builder, otherBuilder) => {

  const peepholeWeightFromOtherBuilder =

      otherBuilder.input('peepholeWeight', kExamplePeepholeWeightDescriptor);

  const options = {peepholeWeight: peepholeWeightFromOtherBuilder};

  const input = builder.input('input', kExampleInputDescriptor);

  const weight = builder.input('weight', kExampleWeightDescriptor);

  const recurrentWeight =

      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);

  const hiddenState =

      builder.input('hiddenState', kExampleHiddenStateDescriptor);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,

          options));

}, '[lstmCell] throw if peepholeWeight 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);

  const hiddenState =

      builder.input('hiddenState', kExampleHiddenStateDescriptor);

  const cellState = builder.input('cellState', kExampleCellStateDescriptor);

  assert_throws_js(

      TypeError,

      () => builder.lstmCell(

          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,

          options));

}, '[lstmCell] throw if activation option is from another builder');

const tests = [

    name: '[lstmCell] Test with default options',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    outputs: [

      {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

      {dataType: 'float16', dimensions: [batchSize, hiddenSize]}

},

    name: '[lstmCell] Test with given options',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {

      bias: {dataType: 'float32', dimensions: [4 * hiddenSize]},

      recurrentBias: {dataType: 'float32', dimensions: [4 * hiddenSize]},

      peepholeWeight: {dataType: 'float32', dimensions: [3 * hiddenSize]},

      layout: 'ifgo',

      activations: ['sigmoid', 'relu', 'tanh']

},

    outputs: [

      {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

      {dataType: 'float32', dimensions: [batchSize, hiddenSize]}

},

    name: '[lstmCell] Throw if hiddenSize is equal to zero',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: 0

},

    name: '[lstmCell] Throw if hiddenSize is too large',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: 4294967295

},

    name:

        '[lstmCell] Throw if the input data type is not one of the floating point types',

    input: {dataType: 'uint32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the rank of input is not 2',

    input: {dataType: 'float32', dimensions: [batchSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the shape of input is incorrect',

    input: {dataType: 'float32', dimensions: [batchSize, 1000]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the data type of weight is incorrect',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the rank of weight is not 2',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize, 1000]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the shape of weight is incorrect',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [1000, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the data type of recurrentWeight is incorrect',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the rank of recurrentWeight is not 2',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [1000, 4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the shape of recurrentWeight is incorrect',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight: {dataType: 'float32', dimensions: [1000, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the data type of hiddenState is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'int64', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the rank of hiddenState is not 2',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the shape of hiddenState is incorrect',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, 1000]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the data type of cellState is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the rank of cellState is not 2',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the shape of cellState is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, 1000]},

    hiddenSize: hiddenSize

},

    name: '[lstmCell] Throw if the data type of options.bias is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {bias: {dataType: 'int8', dimensions: [4 * hiddenSize]}}

},

    name: '[lstmCell] Throw if the rank of options.bias is not 1',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {bias: {dataType: 'float16', dimensions: [4 * hiddenSize, 1000]}}

},

    name: '[lstmCell] Throw if the shape of options.bias is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {bias: {dataType: 'float16', dimensions: [1000]}}

},

    name:

        '[lstmCell] Throw if the data type of options.recurrentBias is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {recurrentBias: {dataType: 'uint8', dimensions: [4 * hiddenSize]}}

},

    name: '[lstmCell] Throw if the rank of options.recurrentBias is not 1',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {

      recurrentBias: {dataType: 'float16', dimensions: [4 * hiddenSize, 1000]}

},

    name: '[lstmCell] Throw if the shape of options.recurrentBias is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {recurrentBias: {dataType: 'float16', dimensions: [1000]}}

},

    name:

        '[lstmCell] Throw if the data type of options.peepholeWeight is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options:

        {peepholeWeight: {dataType: 'float32', dimensions: [3 * hiddenSize]}}

},

    name: '[lstmCell] Throw if the rank of options.peepholeWeight is not 1',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {peepholeWeight: {dataType: 'float16', dimensions: []}}

},

    name:

        '[lstmCell] Throw if the shape of options.peepholeWeight is incorrect',

    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {peepholeWeight: {dataType: 'float16', dimensions: [1000]}}

},

    name: '[lstmCell] Throw if the size of options.activations is not 3',

    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},

    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},

    recurrentWeight:

        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},

    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},

    hiddenSize: hiddenSize,

    options: {activations: ['sigmoid', 'tanh', 'sigmoid', 'tanh']}

];

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 hiddenState = builder.input('hiddenState', {

        dataType: test.hiddenState.dataType,

        dimensions: test.hiddenState.dimensions

});

      const cellState = builder.input('cellState', {

        dataType: test.cellState.dataType,

        dimensions: test.cellState.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.peepholeWeight) {

          options.peepholeWeight = builder.input('peepholeWeight', {

            dataType: test.options.peepholeWeight.dataType,

            dimensions: test.options.peepholeWeight.dimensions

});

        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.lstmCell(

            input, weight, recurrentWeight, hiddenState, cellState,

            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.lstmCell(

                input, weight, recurrentWeight, hiddenState, cellState,

                test.hiddenSize, options));

    }, test.name));