// META: title=validation tests for WebNN API lstmCell 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 = [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');
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 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 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.recurrentBias = 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;
}
}
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 {
const label = 'lstm_cell_xxx';
options.label = label;
const regrexp = new RegExp('\\[' + label + '\\]');
assert_throws_with_label(
() => builder.lstmCell(
input, weight, recurrentWeight, hiddenState, cellState,
test.hiddenSize, options),
regrexp);
}
}, test.name));
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