diff options
Diffstat (limited to 'testing/web-platform/tests/webnn/validation_tests/gru.https.any.js')
| -rw-r--r-- | testing/web-platform/tests/webnn/validation_tests/gru.https.any.js | 795 |
1 files changed, 421 insertions, 374 deletions
diff --git a/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js b/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js index 295baab9c2..50d39d297a 100644 --- a/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js +++ b/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js @@ -1,398 +1,445 @@ // META: title=validation tests for WebNN API gru operation // META: global=window,dedicatedworker // META: script=../resources/utils_validation.js -// META: timeout=long 'use strict'; -const steps = 2, batchSize = 3, inputSize = 4, hiddenSize = 5, - numDirections = 1; +const steps = 2, batchSize = 3, inputSize = 4, hiddenSize = 5, oneDirection = 1, + bothDirections = 2; + +// Dimensions required of required inputs. +const kValidInputDimensions = [steps, batchSize, inputSize]; +const kValidWeightDimensions = [oneDirection, 3 * hiddenSize, inputSize]; +const kValidRecurrentWeightDimensions = + [oneDirection, 3 * hiddenSize, hiddenSize]; +// Dimensions required of optional inputs. +const kValidBiasDimensions = [oneDirection, 3 * hiddenSize]; +const kValidRecurrentBiasDimensions = [oneDirection, 3 * hiddenSize]; +const kValidInitialHiddenStateDimensions = + [oneDirection, batchSize, hiddenSize]; + +// Example descriptors which are valid according to the above dimensions. +const kExampleInputDescriptor = { + dataType: 'float32', + dimensions: kValidInputDimensions +}; +const kExampleWeightDescriptor = { + dataType: 'float32', + dimensions: kValidWeightDimensions +}; +const kExampleRecurrentWeightDescriptor = { + dataType: 'float32', + dimensions: kValidRecurrentWeightDimensions +}; +const kExampleBiasDescriptor = { + dataType: 'float32', + dimensions: kValidBiasDimensions +}; +const kExampleRecurrentBiasDescriptor = { + dataType: 'float32', + dimensions: kValidRecurrentBiasDimensions +}; +const kExampleInitialHiddenStateDescriptor = { + dataType: 'float32', + dimensions: kValidInitialHiddenStateDimensions +}; const tests = [ - { - name: '[gru] Test with default options', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - outputs: [ - { dataType: 'float32', dimensions: [numDirections, batchSize, hiddenSize] } - ] - }, - { - name: '[gru] Test with given options', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [/*numDirections=*/ 2, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [/*numDirections=*/ 2, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { - bias: { - dataType: 'float32', - dimensions: [/*numDirections=*/ 2, 3 * hiddenSize] - }, - recurrentBias: { - dataType: 'float32', - dimensions: [/*numDirections=*/ 2, 3 * hiddenSize] - }, - initialHiddenState: { - dataType: 'float32', - dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize] - }, - restAfter: true, - returnSequence: true, - direction: 'both', - layout: 'rzn', - activations: ['sigmoid', 'relu'] - }, - outputs: [ - { - dataType: 'float32', - dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize] - }, - { - dataType: 'float32', - dimensions: [steps, /*numDirections=*/ 2, batchSize, hiddenSize] - } - ] - }, - { - name: '[gru] TypeError is expected if steps equals to zero', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 4 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 4 * hiddenSize, hiddenSize] - }, - steps: 0, - hiddenSize: hiddenSize, - }, - { - name: '[gru] TypeError is expected if hiddenSize equals to zero', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: 0 - }, - { - name: '[gru] TypeError is expected if hiddenSize is too large', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: 4294967295, + { + name: '[gru] Test with default options', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: hiddenSize, + outputs: [ + {dataType: 'float32', dimensions: [oneDirection, batchSize, hiddenSize]} + ] + }, + { + name: '[gru] Test with given options', + input: kExampleInputDescriptor, + weight: { + dataType: 'float32', + dimensions: [bothDirections, 3 * hiddenSize, inputSize] }, - { - name: - '[gru] TypeError is expected if the data type of the inputs is not one of the floating point types', - input: { dataType: 'uint32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'uint32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'uint32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize + recurrentWeight: { + dataType: 'float32', + dimensions: [bothDirections, 3 * hiddenSize, hiddenSize] }, - { - name: - '[gru] TypeError is expected if the rank of input is not 3', - input: { dataType: 'float32', dimensions: [steps, batchSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize + steps: steps, + hiddenSize: hiddenSize, + options: { + bias: {dataType: 'float32', dimensions: [bothDirections, 3 * hiddenSize]}, + recurrentBias: + {dataType: 'float32', dimensions: [bothDirections, 3 * hiddenSize]}, + initialHiddenState: { + dataType: 'float32', + dimensions: [bothDirections, batchSize, hiddenSize] + }, + restAfter: true, + returnSequence: true, + direction: 'both', + layout: 'rzn', + activations: ['sigmoid', 'relu'] }, - { - name: - '[gru] TypeError is expected if input.dimensions[0] is not equal to steps', - input: { dataType: 'float32', dimensions: [1000, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize + outputs: [ + { + dataType: 'float32', + dimensions: [bothDirections, batchSize, hiddenSize] + }, + { + dataType: 'float32', + dimensions: [steps, bothDirections, batchSize, hiddenSize] + } + ] + }, + { + name: '[gru] TypeError is expected if steps equals to zero', + input: kExampleInputDescriptor, + weight: { + dataType: 'float32', + dimensions: [oneDirection, 4 * hiddenSize, inputSize] }, - { - name: '[gru] TypeError is expected if weight.dimensions[1] is not 3 * hiddenSize', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 4 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize + recurrentWeight: { + dataType: 'float32', + dimensions: [oneDirection, 4 * hiddenSize, hiddenSize] }, - { - name: - '[gru] TypeError is expected if the rank of recurrentWeight is not 3', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: - { dataType: 'float32', dimensions: [numDirections, 3 * hiddenSize] }, - steps: steps, - hiddenSize: hiddenSize + steps: 0, + hiddenSize: hiddenSize, + }, + { + name: '[gru] TypeError is expected if hiddenSize equals to zero', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: 0 + }, + { + name: '[gru] TypeError is expected if hiddenSize is too large', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: 4294967295, + }, + { + name: + '[gru] TypeError is expected if the data type of the inputs is not one of the floating point types', + input: {dataType: 'uint32', dimensions: kValidInputDimensions}, + weight: {dataType: 'uint32', dimensions: kValidWeightDimensions}, + recurrentWeight: + {dataType: 'uint32', dimensions: kValidRecurrentWeightDimensions}, + steps: steps, + hiddenSize: hiddenSize + }, + { + name: '[gru] TypeError is expected if the rank of input is not 3', + input: {dataType: 'float32', dimensions: [steps, batchSize]}, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: hiddenSize + }, + { + name: + '[gru] TypeError is expected if input.dimensions[0] is not equal to steps', + input: {dataType: 'float32', dimensions: [1000, batchSize, inputSize]}, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: hiddenSize + }, + { + name: + '[gru] TypeError is expected if weight.dimensions[1] is not 3 * hiddenSize', + input: kExampleInputDescriptor, + weight: { + dataType: 'float32', + dimensions: [oneDirection, 4 * hiddenSize, inputSize] }, - { - name: - '[gru] TypeError is expected if the recurrentWeight.dimensions is invalid', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: - { dataType: 'float32', dimensions: [numDirections, 4 * hiddenSize, inputSize] }, - steps: steps, - hiddenSize: hiddenSize - }, - { - name: - '[gru] TypeError is expected if the size of options.activations is not 2', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { activations: ['sigmoid', 'tanh', 'relu'] } - }, - { - name: - '[gru] TypeError is expected if the rank of options.bias is not 2', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { bias: { dataType: 'float32', dimensions: [numDirections] } } - }, - { - name: - '[gru] TypeError is expected if options.bias.dimensions[1] is not 3 * hiddenSize', - input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float32', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { bias: { dataType: 'float32', dimensions: [numDirections, hiddenSize] } } - }, - { - name: - '[gru] TypeError is expected if options.recurrentBias.dimensions[1] is not 3 * hiddenSize', - input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { - recurrentBias: { dataType: 'float16', dimensions: [numDirections, 4 * hiddenSize] } - } + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: hiddenSize + }, + { + name: '[gru] TypeError is expected if the rank of recurrentWeight is not 3', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: + {dataType: 'float32', dimensions: [oneDirection, 3 * hiddenSize]}, + steps: steps, + hiddenSize: hiddenSize + }, + { + name: + '[gru] TypeError is expected if the recurrentWeight.dimensions is invalid', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: { + dataType: 'float32', + dimensions: [oneDirection, 4 * hiddenSize, inputSize] }, - { - name: - '[gru] TypeError is expected if the rank of options.initialHiddenState is not 3', - input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { - initialHiddenState: { - dataType: 'float16', - dimensions: [numDirections, batchSize] - } - } - }, - { - name: - '[gru] TypeError is expected if options.initialHiddenState.dimensions[2] is not inputSize', - input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { - initialHiddenState: { - dataType: 'float16', - dimensions: [numDirections, batchSize, 3 * hiddenSize] - } - } - }, - { - name: - '[gru] TypeError is expected if the dataType of options.initialHiddenState is incorrect', - input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] }, - weight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, inputSize] - }, - recurrentWeight: { - dataType: 'float16', - dimensions: [numDirections, 3 * hiddenSize, hiddenSize] - }, - steps: steps, - hiddenSize: hiddenSize, - options: { - initialHiddenState: { - dataType: 'uint64', - dimensions: [numDirections, batchSize, hiddenSize] - } - } + steps: steps, + hiddenSize: hiddenSize + }, + { + name: + '[gru] TypeError is expected if the size of options.activations is not 2', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: hiddenSize, + options: {activations: ['sigmoid', 'tanh', 'relu']} + }, + { + name: '[gru] TypeError is expected if the rank of options.bias is not 2', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: hiddenSize, + options: {bias: {dataType: 'float32', dimensions: [oneDirection]}} + }, + { + name: + '[gru] TypeError is expected if options.bias.dimensions[1] is not 3 * hiddenSize', + input: kExampleInputDescriptor, + weight: kExampleWeightDescriptor, + recurrentWeight: kExampleRecurrentWeightDescriptor, + steps: steps, + hiddenSize: hiddenSize, + options: + {bias: {dataType: 'float32', dimensions: [oneDirection, hiddenSize]}} + }, + { + name: + '[gru] TypeError is expected if options.recurrentBias.dimensions[1] is not 3 * hiddenSize', + input: {dataType: 'float16', dimensions: kValidInputDimensions}, + weight: {dataType: 'float16', dimensions: kValidWeightDimensions}, + recurrentWeight: + {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions}, + steps: steps, + hiddenSize: hiddenSize, + options: { + recurrentBias: + {dataType: 'float16', dimensions: [oneDirection, 4 * hiddenSize]} } + }, + { + name: + '[gru] TypeError is expected if the rank of options.initialHiddenState is not 3', + input: {dataType: 'float16', dimensions: kValidInputDimensions}, + weight: {dataType: 'float16', dimensions: kValidWeightDimensions}, + recurrentWeight: + {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions}, + steps: steps, + hiddenSize: hiddenSize, + options: { + initialHiddenState: + {dataType: 'float16', dimensions: [oneDirection, batchSize]} + } + }, + { + name: + '[gru] TypeError is expected if options.initialHiddenState.dimensions[2] is not inputSize', + input: {dataType: 'float16', dimensions: kValidInputDimensions}, + weight: {dataType: 'float16', dimensions: kValidWeightDimensions}, + recurrentWeight: + {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions}, + steps: steps, + hiddenSize: hiddenSize, + options: { + initialHiddenState: { + dataType: 'float16', + dimensions: [oneDirection, batchSize, 3 * hiddenSize] + } + } + }, + { + name: + '[gru] TypeError is expected if the dataType of options.initialHiddenState is incorrect', + input: {dataType: 'float16', dimensions: kValidInputDimensions}, + weight: {dataType: 'float16', dimensions: kValidWeightDimensions}, + recurrentWeight: + {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions}, + steps: steps, + hiddenSize: hiddenSize, + options: { + initialHiddenState: { + dataType: 'uint64', + dimensions: [oneDirection, batchSize, hiddenSize] + } + } + } ]; 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 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 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.initialHiddenState) { - options.initialHiddenState = builder.input('initialHiddenState', { - dataType: test.options.initialHiddenState.dataType, - dimensions: test.options.initialHiddenState.dimensions - }); - } - if (test.options.resetAfter) { - options.resetAfter = test.options.resetAfter; - } - if (test.options.returnSequence) { - options.returnSequence = test.options.returnSequence; - } - if (test.options.direction) { - options.direction = test.options.direction; - } - 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]())); - } + 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.initialHiddenState) { + options.initialHiddenState = builder.input('initialHiddenState', { + dataType: test.options.initialHiddenState.dataType, + dimensions: test.options.initialHiddenState.dimensions + }); } + if (test.options.resetAfter) { + options.resetAfter = test.options.resetAfter; + } + if (test.options.returnSequence) { + options.returnSequence = test.options.returnSequence; + } + if (test.options.direction) { + options.direction = test.options.direction; + } + 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.gru( - input, weight, recurrentWeight, test.steps, 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.gru( - input, weight, recurrentWeight, test.steps, test.hiddenSize, - options)); + if (test.outputs) { + const outputs = builder.gru( + input, weight, recurrentWeight, test.steps, 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); } - }, test.name));
\ No newline at end of file + } else { + assert_throws_js( + TypeError, + () => builder.gru( + input, weight, recurrentWeight, test.steps, test.hiddenSize, + options)); + } + }, test.name)); + +multi_builder_test(async (t, builder, otherBuilder) => { + const inputFromOtherBuilder = + otherBuilder.input('input', kExampleInputDescriptor); + + const weight = builder.input('weight', kExampleWeightDescriptor); + const recurrentWeight = + builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor); + assert_throws_js( + TypeError, + () => builder.gru( + inputFromOtherBuilder, weight, recurrentWeight, steps, hiddenSize)); +}, '[gru] 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); + assert_throws_js( + TypeError, + () => builder.gru( + input, weightFromOtherBuilder, recurrentWeight, steps, hiddenSize)); +}, '[gru] 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); + assert_throws_js( + TypeError, + () => builder.gru( + input, weight, recurrentWeightFromOtherBuilder, steps, hiddenSize)); +}, '[gru] throw if recurrentWeight 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); + assert_throws_js( + TypeError, + () => builder.gru( + input, weight, recurrentWeight, steps, hiddenSize, options)); +}, '[gru] throw if bias option is from another builder'); + +multi_builder_test(async (t, builder, otherBuilder) => { + const recurrentBiasFromOtherBuilder = + otherBuilder.input('recurrentBias', kExampleRecurrentBiasDescriptor); + const options = {recurrentBias: recurrentBiasFromOtherBuilder}; + + const input = builder.input('input', kExampleInputDescriptor); + const weight = builder.input('weight', kExampleWeightDescriptor); + const recurrentWeight = + builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor); + assert_throws_js( + TypeError, + () => builder.gru( + input, weight, recurrentWeight, steps, hiddenSize, options)); +}, '[gru] throw if recurrentBias option is from another builder'); + +multi_builder_test(async (t, builder, otherBuilder) => { + const initialHiddenStateFromOtherBuilder = otherBuilder.input( + 'initialHiddenState', kExampleInitialHiddenStateDescriptor); + const options = {initialHiddenState: initialHiddenStateFromOtherBuilder}; + + const input = builder.input('input', kExampleInputDescriptor); + const weight = builder.input('weight', kExampleWeightDescriptor); + const recurrentWeight = + builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor); + assert_throws_js( + TypeError, + () => builder.gru( + input, weight, recurrentWeight, steps, hiddenSize, options)); +}, '[gru] throw if initialHiddenState 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); + assert_throws_js( + TypeError, + () => builder.gru( + input, weight, recurrentWeight, steps, hiddenSize, options)); +}, '[gru] throw if any activation option is from another builder'); |