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- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/gru.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API gru operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Gated Recurrent Unit recurrent network uses an update, reset, and new gate
// to compute the output state that rolls into the output across the temporal
// sequence of the network.
//
// enum MLGruWeightLayout {
// "zrn", // update-reset-new gate ordering
// "rzn" // reset-update-new gate ordering
// };
//
// enum MLRecurrentNetworkActivation {
// "relu",
// "sigmoid",
// "tanh"
// };
//
// enum MLRecurrentNetworkDirection {
// "forward",
// "backward",
// "both"
// };
//
// dictionary MLGruOptions {
// MLOperand bias;
// MLOperand recurrentBias;
// MLOperand initialHiddenState;
// boolean resetAfter = true;
// boolean returnSequence = false;
// MLRecurrentNetworkDirection direction = "forward";
// MLGruWeightLayout layout = "zrn";
// sequence<MLRecurrentNetworkActivation> activations;
// };
//
// sequence<MLOperand> gru(MLOperand input,
// MLOperand weight,
// MLOperand recurrentWeight,
// [EnforceRange] unsigned long steps,
// [EnforceRange] unsigned long hiddenSize,
// optional MLGruOptions options = {});
const getGruPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 3};
const expectedDataType =
graphResources
.expectedOutputs[Object.keys(graphResources.expectedOutputs)[0]]
.descriptor.dataType;
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const gruTests = [
{
'name':
'gru float32 tensors steps=1 with options.bias, options.recurrentBias and options.activations=[\'relu\', \'relu\']',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [1, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'resetAfter': false,
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput']
}],
'expectedOutputs': {
'gruOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gru float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\'] and explicit options.direction=\'forward\'',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [1, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'resetAfter': false,
'direction': 'forward',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput']
}],
'expectedOutputs': {
'gruOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gru float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\'] and explicit options.layout=\'zrn\'',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [1, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'resetAfter': false,
'layout': 'zrn',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput']
}],
'expectedOutputs': {
'gruOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gru float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\'] and options.layout=\'rzn\'',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [1, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 1, 1, 1, 1, 2, 1, 2, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 1, 1, 1, 1, 2, 1, 2, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'resetAfter': false,
'layout': 'rzn',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput']
}],
'expectedOutputs': {
'gruOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gru float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\'] and options.initialHiddenState',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [1, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruInitialHiddenState': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'initialHiddenState': 'gruInitialHiddenState',
'resetAfter': false,
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput']
}],
'expectedOutputs': {
'gruOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'gru float32 tensors steps=1 all options',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [1, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruInitialHiddenState': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'initialHiddenState': 'gruInitialHiddenState',
'resetAfter': false,
'returnSequence': true,
'direction': 'forward',
'layout': 'zrn',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput1', 'gruOutput2']
}],
'expectedOutputs': {
'gruOutput1': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
},
'gruOutput2': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [1, 1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gru float32 tensors steps=2 with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\'] and options.direction=\'backward\'',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1, 3, 4, 1, 2, 1, 1],
'descriptor': {shape: [2, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'resetAfter': false,
'direction': 'backward',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput']
}],
'expectedOutputs': {
'gruOutput': {
'data': [
0, 0, -0.24974998831748963, -18.59588623046875, -2.0657243728637697,
-10.551867485046387, -1.3937838077545167, -15.2454833984375,
-1.1589999198913575, -9.476999282836914, -1.1589999198913575,
-11.319169044494629
],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gru float32 tensors steps=2 with options.bias, options.recurrentBias, options.direction=\'backward\', options.activations=[\'relu\', \'relu\'] and explicit options.returnSequence=false',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1, 3, 4, 1, 2, 1, 1],
'descriptor': {shape: [2, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'resetAfter': false,
'returnSequence': false,
'direction': 'backward',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput']
}],
'expectedOutputs': {
'gruOutput': {
'data': [
0, 0, -0.24974998831748963, -18.59588623046875, -2.0657243728637697,
-10.551867485046387, -1.3937838077545167, -15.2454833984375,
-1.1589999198913575, -9.476999282836914, -1.1589999198913575,
-11.319169044494629
],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gru float32 tensors steps=2 with options.bias, options.recurrentBias, options.direction=\'backward\', options.activations=[\'relu\', \'relu\'] and options.returnSequence=true',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1, 3, 4, 1, 2, 1, 1],
'descriptor': {shape: [2, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'resetAfter': false,
'returnSequence': true,
'direction': 'backward',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput1', 'gruOutput2']
}],
'expectedOutputs': {
'gruOutput1': {
'data': [
0, 0, -0.24974998831748963, -18.59588623046875, -2.0657243728637697,
-10.551867485046387, -1.3937838077545167, -15.2454833984375,
-1.1589999198913575, -9.476999282836914, -1.1589999198913575,
-11.319169044494629
],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
},
'gruOutput2': {
'data': [
0,
0,
-0.24974998831748963,
-18.59588623046875,
-2.0657243728637697,
-10.551867485046387,
-1.3937838077545167,
-15.2454833984375,
-1.1589999198913575,
-9.476999282836914,
-1.1589999198913575,
-11.319169044494629,
0,
0,
-0.25,
-4.760000228881836,
0,
0,
-0.25,
-3.8399999141693117,
-1,
-3,
-1,
-3.4100000858306886
],
'descriptor': {shape: [2, 1, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'gru float32 tensors steps=2 with all options',
'graph': {
'inputs': {
'gruInput': {
'data': [1, 2, 2, 1, 1, 1, 3, 4, 1, 2, 1, 1],
'descriptor': {shape: [2, 3, 2], dataType: 'float32'}
},
'gruWeight': {
'data': [
1, -1, 2, -2, 0.5, -0.5, 0, 0.1, 1, -1, 2, -2,
0.5, -0.5, 0, 0.1, 1, -1, 2, -2, 0.5, -0.5, 0, 0.1
],
'descriptor': {shape: [1, 12, 2], dataType: 'float32'}
},
'gruRecurrentWeight': {
'data': [
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
],
'descriptor': {shape: [1, 12, 4], dataType: 'float32'}
},
'gruBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [1, 12], dataType: 'float32'}
},
'gruInitialHiddenState': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'gru',
'arguments': [
{'input': 'gruInput'}, {'weight': 'gruWeight'},
{'recurrentWeight': 'gruRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 4}, {
'options': {
'bias': 'gruBias',
'recurrentBias': 'gruRecurrentBias',
'initialHiddenState': 'gruInitialHiddenState',
'resetAfter': false,
'returnSequence': true,
'direction': 'backward',
'layout': 'zrn',
'activations': ['relu', 'relu']
}
}
],
'outputs': ['gruOutput1', 'gruOutput2']
}],
'expectedOutputs': {
'gruOutput1': {
'data': [
0, 0, -0.24974998831748963, -18.59588623046875, -2.0657243728637697,
-10.551867485046387, -1.3937838077545167, -15.2454833984375,
-1.1589999198913575, -9.476999282836914, -1.1589999198913575,
-11.319169044494629
],
'descriptor': {shape: [1, 3, 4], dataType: 'float32'}
},
'gruOutput2': {
'data': [
0,
0,
-0.24974998831748963,
-18.59588623046875,
-2.0657243728637697,
-10.551867485046387,
-1.3937838077545167,
-15.2454833984375,
-1.1589999198913575,
-9.476999282836914,
-1.1589999198913575,
-11.319169044494629,
0,
0,
-0.25,
-4.760000228881836,
0,
0,
-0.25,
-3.8399999141693117,
-1,
-3,
-1,
-3.4100000858306886
],
'descriptor': {shape: [2, 1, 3, 4], dataType: 'float32'}
}
}
}
},
];
if (navigator.ml) {
gruTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getGruPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}