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- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/gru_cell.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru_cell.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru_cell.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru_cell.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru_cell.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/gru_cell.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API gruCell operation
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// A single time step of the Gated Recurrent Unit recurrent network using an
// update gate and a reset gate to compute the hidden state that rolls into the
// output across the temporal sequence of a recurrent network.
//
// enum MLGruWeightLayout {
// "zrn", // update-reset-new gate ordering
// "rzn" // reset-update-new gate ordering
// };
//
// enum MLRecurrentNetworkActivation {
// "relu",
// "sigmoid",
// "tanh"
// };
//
// dictionary MLGruCellOptions {
// MLOperand bias;
// MLOperand recurrentBias;
// boolean resetAfter = true;
// MLGruWeightLayout layout = "zrn";
// sequence<MLRecurrentNetworkActivation> activations;
// };
//
// MLOperand gruCell(MLOperand input,
// MLOperand weight,
// MLOperand recurrentWeight,
// MLOperand hiddenState,
// [EnforceRange] unsigned long hiddenSize,
// optional MLGruCellOptions options = {});
const getGruCellPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 3};
const expectedDataType =
graphResources
.expectedOutputs[Object.keys(graphResources.expectedOutputs)[0]]
.descriptor.dataType;
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const gruCellTests = [
{
'name':
'gruCell float32 tensors with options.bias, options.recurrentBias and options.activations=[\'relu\', \'relu\']',
'graph': {
'inputs': {
'gruCellInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [3, 2], dataType: 'float32'}
},
'gruCellWeight': {
'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: [12, 2], dataType: 'float32'}
},
'gruCellRecurrentWeight': {
'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: [12, 4], dataType: 'float32'}
},
'gruCellHiddenState': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'descriptor': {shape: [3, 4], dataType: 'float32'}
},
'gruCellBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
},
'gruCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
}
},
'operators': [{
'name': 'gruCell',
'arguments': [
{'input': 'gruCellInput'}, {'weight': 'gruCellWeight'},
{'recurrentWeight': 'gruCellRecurrentWeight'},
{'hiddenState': 'gruCellHiddenState'}, {'hiddenSize': 4}, {
'options': {
'bias': 'gruCellBias',
'recurrentBias': 'gruCellRecurrentBias',
'resetAfter': false,
'activations': ['relu', 'relu']
}
}
],
'outputs': 'gruCellOutput'
}],
'expectedOutputs': {
'gruCellOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gruCell float32 tensors with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\'] and and explicit options.layout=\'zrn\'',
'graph': {
'inputs': {
'gruCellInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [3, 2], dataType: 'float32'}
},
'gruCellWeight': {
'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: [12, 2], dataType: 'float32'}
},
'gruCellRecurrentWeight': {
'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: [12, 4], dataType: 'float32'}
},
'gruCellHiddenState': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'descriptor': {shape: [3, 4], dataType: 'float32'}
},
'gruCellBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
},
'gruCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
}
},
'operators': [{
'name': 'gruCell',
'arguments': [
{'input': 'gruCellInput'}, {'weight': 'gruCellWeight'},
{'recurrentWeight': 'gruCellRecurrentWeight'},
{'hiddenState': 'gruCellHiddenState'}, {'hiddenSize': 4}, {
'options': {
'bias': 'gruCellBias',
'recurrentBias': 'gruCellRecurrentBias',
'resetAfter': false,
'layout': 'zrn',
'activations': ['relu', 'relu']
}
}
],
'outputs': 'gruCellOutput'
}],
'expectedOutputs': {
'gruCellOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [3, 4], dataType: 'float32'}
}
}
}
},
{
'name':
'gruCell float32 tensors with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\'] and and options.layout=\'rzn\'',
'graph': {
'inputs': {
'gruCellInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [3, 2], dataType: 'float32'}
},
'gruCellWeight': {
'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: [12, 2], dataType: 'float32'}
},
'gruCellRecurrentWeight': {
'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: [12, 4], dataType: 'float32'}
},
'gruCellHiddenState': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'descriptor': {shape: [3, 4], dataType: 'float32'}
},
'gruCellBias': {
'data': [1, 1, 1, 1, 1, 2, 1, 2, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
},
'gruCellRecurrentBias': {
'data': [1, 1, 1, 1, 1, 2, 1, 2, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
}
},
'operators': [{
'name': 'gruCell',
'arguments': [
{'input': 'gruCellInput'}, {'weight': 'gruCellWeight'},
{'recurrentWeight': 'gruCellRecurrentWeight'},
{'hiddenState': 'gruCellHiddenState'}, {'hiddenSize': 4}, {
'options': {
'bias': 'gruCellBias',
'recurrentBias': 'gruCellRecurrentBias',
'resetAfter': false,
'layout': 'rzn',
'activations': ['relu', 'relu']
}
}
],
'outputs': 'gruCellOutput'
}],
'expectedOutputs': {
'gruCellOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'gruCell float32 tensors with all options',
'graph': {
'inputs': {
'gruCellInput': {
'data': [1, 2, 2, 1, 1, 1],
'descriptor': {shape: [3, 2], dataType: 'float32'}
},
'gruCellWeight': {
'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: [12, 2], dataType: 'float32'}
},
'gruCellRecurrentWeight': {
'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: [12, 4], dataType: 'float32'}
},
'gruCellHiddenState': {
'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'descriptor': {shape: [3, 4], dataType: 'float32'}
},
'gruCellBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
},
'gruCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5],
'descriptor': {shape: [12], dataType: 'float32'}
}
},
'operators': [{
'name': 'gruCell',
'arguments': [
{'input': 'gruCellInput'}, {'weight': 'gruCellWeight'},
{'recurrentWeight': 'gruCellRecurrentWeight'},
{'hiddenState': 'gruCellHiddenState'}, {'hiddenSize': 4}, {
'options': {
'bias': 'gruCellBias',
'recurrentBias': 'gruCellRecurrentBias',
'resetAfter': false,
'layout': 'zrn',
'activations': ['relu', 'relu']
}
}
],
'outputs': 'gruCellOutput'
}],
'expectedOutputs': {
'gruCellOutput': {
'data':
[0, 0, -0.25, -3.84, -4, -15, -2.25, -3.41, -1, -3, -1, -3.41],
'descriptor': {shape: [3, 4], dataType: 'float32'}
}
}
}
},
];
if (navigator.ml) {
gruCellTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getGruCellPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}