539 lines
24 KiB
JavaScript
539 lines
24 KiB
JavaScript
// META: title=validation tests for WebNN API lstmCell operation
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// META: global=window
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// META: variant=?cpu
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// META: variant=?gpu
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// META: variant=?npu
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// META: script=../resources/utils_validation.js
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'use strict';
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const batchSize = 3, inputSize = 4, hiddenSize = 5;
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// Dimensions required of required inputs.
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const kValidInputShape = [batchSize, inputSize];
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const kValidWeightShape = [4 * hiddenSize, inputSize];
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const kValidRecurrentWeightShape = [4 * hiddenSize, hiddenSize];
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const kValidHiddenStateShape = [batchSize, hiddenSize];
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const kValidCellStateShape = [batchSize, hiddenSize];
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// Dimensions required of optional inputs.
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const kValidBiasShape = [4 * hiddenSize];
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const kValidPeepholeWeightShape = [3 * hiddenSize];
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// Example descriptors which are valid according to the above dimensions.
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const kExampleInputDescriptor = {
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dataType: 'float32',
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shape: kValidInputShape
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};
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const kExampleWeightDescriptor = {
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dataType: 'float32',
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shape: kValidWeightShape
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};
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const kExampleRecurrentWeightDescriptor = {
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dataType: 'float32',
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shape: kValidRecurrentWeightShape
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};
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const kExampleHiddenStateDescriptor = {
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dataType: 'float32',
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shape: kValidHiddenStateShape
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};
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const kExampleCellStateDescriptor = {
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dataType: 'float32',
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shape: kValidCellStateShape
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};
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const kExampleBiasDescriptor = {
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dataType: 'float32',
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shape: kValidBiasShape
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};
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const kExamplePeepholeWeightDescriptor = {
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dataType: 'float32',
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shape: kValidPeepholeWeightShape
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};
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multi_builder_test(async (t, builder, otherBuilder) => {
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const inputFromOtherBuilder =
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otherBuilder.input('input', kExampleInputDescriptor);
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const weight = builder.input('weight', kExampleWeightDescriptor);
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const recurrentWeight =
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builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const hiddenState =
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builder.input('hiddenState', kExampleHiddenStateDescriptor);
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const cellState = builder.input('cellState', kExampleCellStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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inputFromOtherBuilder, weight, recurrentWeight, hiddenState,
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cellState, hiddenSize));
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}, '[lstmCell] throw if input is from another builder');
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multi_builder_test(async (t, builder, otherBuilder) => {
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const weightFromOtherBuilder =
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otherBuilder.input('weight', kExampleWeightDescriptor);
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const input = builder.input('input', kExampleInputDescriptor);
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const recurrentWeight =
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builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const hiddenState =
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builder.input('hiddenState', kExampleHiddenStateDescriptor);
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const cellState = builder.input('cellState', kExampleCellStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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input, weightFromOtherBuilder, recurrentWeight, hiddenState,
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cellState, hiddenSize));
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}, '[lstmCell] throw if weight is from another builder');
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multi_builder_test(async (t, builder, otherBuilder) => {
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const recurrentWeightFromOtherBuilder =
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otherBuilder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const input = builder.input('input', kExampleInputDescriptor);
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const weight = builder.input('weight', kExampleWeightDescriptor);
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const hiddenState =
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builder.input('hiddenState', kExampleHiddenStateDescriptor);
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const cellState = builder.input('cellState', kExampleCellStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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input, weight, recurrentWeightFromOtherBuilder, hiddenState,
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cellState, hiddenSize));
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}, '[lstmCell] throw if recurrentWeight is from another builder');
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multi_builder_test(async (t, builder, otherBuilder) => {
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const hiddenStateFromOtherBuilder =
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otherBuilder.input('hiddenState', kExampleHiddenStateDescriptor);
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const input = builder.input('input', kExampleInputDescriptor);
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const weight = builder.input('weight', kExampleWeightDescriptor);
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const recurrentWeight =
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builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const cellState = builder.input('cellState', kExampleCellStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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input, weight, recurrentWeight, hiddenStateFromOtherBuilder,
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cellState, hiddenSize));
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}, '[lstmCell] throw if hiddenState is from another builder');
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multi_builder_test(async (t, builder, otherBuilder) => {
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const cellStateFromOtherBuilder =
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otherBuilder.input('cellState', kExampleCellStateDescriptor);
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const input = builder.input('input', kExampleInputDescriptor);
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const weight = builder.input('weight', kExampleWeightDescriptor);
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const recurrentWeight =
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builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const hiddenState =
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builder.input('hiddenState', kExampleHiddenStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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input, weight, recurrentWeight, hiddenState,
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cellStateFromOtherBuilder, hiddenSize));
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}, '[lstmCell] throw if cellState is from another builder');
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multi_builder_test(async (t, builder, otherBuilder) => {
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const biasFromOtherBuilder =
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otherBuilder.input('bias', kExampleBiasDescriptor);
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const options = {bias: biasFromOtherBuilder};
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const input = builder.input('input', kExampleInputDescriptor);
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const weight = builder.input('weight', kExampleWeightDescriptor);
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const recurrentWeight =
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builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const hiddenState =
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builder.input('hiddenState', kExampleHiddenStateDescriptor);
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const cellState = builder.input('cellState', kExampleCellStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,
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options));
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}, '[lstmCell] throw if bias option is from another builder');
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multi_builder_test(async (t, builder, otherBuilder) => {
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const recurrentBiasFromOtherBuilder =
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otherBuilder.input('bias', kExampleBiasDescriptor);
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const options = {recurrentBias: recurrentBiasFromOtherBuilder};
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const input = builder.input('input', kExampleInputDescriptor);
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const weight = builder.input('weight', kExampleWeightDescriptor);
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const recurrentWeight =
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builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const hiddenState =
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builder.input('hiddenState', kExampleHiddenStateDescriptor);
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const cellState = builder.input('cellState', kExampleCellStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,
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options));
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}, '[lstmCell] throw if recurrentBias option is from another builder');
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multi_builder_test(async (t, builder, otherBuilder) => {
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const peepholeWeightFromOtherBuilder =
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otherBuilder.input('peepholeWeight', kExamplePeepholeWeightDescriptor);
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const options = {peepholeWeight: peepholeWeightFromOtherBuilder};
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const input = builder.input('input', kExampleInputDescriptor);
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const weight = builder.input('weight', kExampleWeightDescriptor);
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const recurrentWeight =
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builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
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const hiddenState =
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builder.input('hiddenState', kExampleHiddenStateDescriptor);
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const cellState = builder.input('cellState', kExampleCellStateDescriptor);
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assert_throws_js(
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TypeError,
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() => builder.lstmCell(
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input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,
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options));
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}, '[lstmCell] throw if peepholeWeight option is from another builder');
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const tests = [
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{
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name: '[lstmCell] Test with default options',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize,
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outputs: [
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{dataType: 'float16', shape: [batchSize, hiddenSize]},
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{dataType: 'float16', shape: [batchSize, hiddenSize]}
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]
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},
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{
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name: '[lstmCell] Test with given options',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize,
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options: {
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bias: {dataType: 'float32', shape: [4 * hiddenSize]},
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recurrentBias: {dataType: 'float32', shape: [4 * hiddenSize]},
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peepholeWeight: {dataType: 'float32', shape: [3 * hiddenSize]},
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layout: 'ifgo',
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activations: ['sigmoid', 'relu', 'tanh']
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},
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outputs: [
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{dataType: 'float32', shape: [batchSize, hiddenSize]},
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{dataType: 'float32', shape: [batchSize, hiddenSize]}
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]
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},
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{
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name: '[lstmCell] Throw if hiddenSize is equal to zero',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: 0
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},
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{
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name: '[lstmCell] Throw if hiddenSize is too large',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: 4294967295
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},
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{
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name:
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'[lstmCell] Throw if the input data type is not one of the floating point types',
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input: {dataType: 'uint32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the rank of input is not 2',
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input: {dataType: 'float32', shape: [batchSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the shape of input is incorrect',
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input: {dataType: 'float32', shape: [batchSize, 1000]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the data type of weight is incorrect',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the rank of weight is not 2',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize, 1000]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the shape of weight is incorrect',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [1000, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the data type of recurrentWeight is incorrect',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the rank of recurrentWeight is not 2',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight:
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{dataType: 'float32', shape: [1000, 4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the shape of recurrentWeight is incorrect',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [1000, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the data type of hiddenState is incorrect',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'int64', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the rank of hiddenState is not 2',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the shape of hiddenState is incorrect',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, 1000]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the data type of cellState is incorrect',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the rank of cellState is not 2',
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input: {dataType: 'float32', shape: [batchSize, inputSize]},
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weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float32', shape: [batchSize]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the shape of cellState is incorrect',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float16', shape: [batchSize, 1000]},
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hiddenSize: hiddenSize
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},
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{
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name: '[lstmCell] Throw if the data type of options.bias is incorrect',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize,
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options: {bias: {dataType: 'int8', shape: [4 * hiddenSize]}}
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},
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{
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name: '[lstmCell] Throw if the rank of options.bias is not 1',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize,
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options: {bias: {dataType: 'float16', shape: [4 * hiddenSize, 1000]}}
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},
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{
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name: '[lstmCell] Throw if the shape of options.bias is incorrect',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
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recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
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hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
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hiddenSize: hiddenSize,
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options: {bias: {dataType: 'float16', shape: [1000]}}
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},
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{
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name:
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'[lstmCell] Throw if the data type of options.recurrentBias is incorrect',
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input: {dataType: 'float16', shape: [batchSize, inputSize]},
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weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
|
|
recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
|
|
hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
hiddenSize: hiddenSize,
|
|
options: {recurrentBias: {dataType: 'uint8', shape: [4 * hiddenSize]}}
|
|
},
|
|
{
|
|
name: '[lstmCell] Throw if the rank of options.recurrentBias is not 1',
|
|
input: {dataType: 'float16', shape: [batchSize, inputSize]},
|
|
weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
|
|
recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
|
|
hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
hiddenSize: hiddenSize,
|
|
options:
|
|
{recurrentBias: {dataType: 'float16', shape: [4 * hiddenSize, 1000]}}
|
|
},
|
|
{
|
|
name: '[lstmCell] Throw if the shape of options.recurrentBias is incorrect',
|
|
input: {dataType: 'float16', shape: [batchSize, inputSize]},
|
|
weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
|
|
recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
|
|
hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
hiddenSize: hiddenSize,
|
|
options: {recurrentBias: {dataType: 'float16', shape: [1000]}}
|
|
},
|
|
{
|
|
name:
|
|
'[lstmCell] Throw if the data type of options.peepholeWeight is incorrect',
|
|
input: {dataType: 'float16', shape: [batchSize, inputSize]},
|
|
weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
|
|
recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
|
|
hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
hiddenSize: hiddenSize,
|
|
options: {peepholeWeight: {dataType: 'float32', shape: [3 * hiddenSize]}}
|
|
},
|
|
{
|
|
name: '[lstmCell] Throw if the rank of options.peepholeWeight is not 1',
|
|
input: {dataType: 'float16', shape: [batchSize, inputSize]},
|
|
weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
|
|
recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
|
|
hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
hiddenSize: hiddenSize,
|
|
options: {peepholeWeight: {dataType: 'float16', shape: []}}
|
|
},
|
|
{
|
|
name:
|
|
'[lstmCell] Throw if the shape of options.peepholeWeight is incorrect',
|
|
input: {dataType: 'float16', shape: [batchSize, inputSize]},
|
|
weight: {dataType: 'float16', shape: [4 * hiddenSize, inputSize]},
|
|
recurrentWeight: {dataType: 'float16', shape: [4 * hiddenSize, hiddenSize]},
|
|
hiddenState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
cellState: {dataType: 'float16', shape: [batchSize, hiddenSize]},
|
|
hiddenSize: hiddenSize,
|
|
options: {peepholeWeight: {dataType: 'float16', shape: [1000]}}
|
|
},
|
|
{
|
|
name: '[lstmCell] Throw if the size of options.activations is not 3',
|
|
input: {dataType: 'float32', shape: [batchSize, inputSize]},
|
|
weight: {dataType: 'float32', shape: [4 * hiddenSize, inputSize]},
|
|
recurrentWeight: {dataType: 'float32', shape: [4 * hiddenSize, hiddenSize]},
|
|
hiddenState: {dataType: 'float32', shape: [batchSize, hiddenSize]},
|
|
cellState: {dataType: 'float32', shape: [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', test.input);
|
|
const weight = builder.input('weight', test.weight);
|
|
const recurrentWeight =
|
|
builder.input('recurrentWeight', test.recurrentWeight);
|
|
const hiddenState = builder.input('hiddenState', test.hiddenState);
|
|
const cellState = builder.input('cellState', test.cellState);
|
|
|
|
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.peepholeWeight) {
|
|
options.peepholeWeight =
|
|
builder.input('peepholeWeight', test.options.peepholeWeight);
|
|
}
|
|
if (test.options.layout) {
|
|
options.layout = test.options.layout;
|
|
}
|
|
if (test.options.activations) {
|
|
options.activations = test.options.activations;
|
|
}
|
|
}
|
|
|
|
if (test.outputs &&
|
|
context.opSupportLimits().lstmCell.input.dataTypes.includes(
|
|
test.input.dataType)) {
|
|
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].shape);
|
|
}
|
|
} 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));
|