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- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/lstm_cell.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/lstm_cell.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/lstm_cell.https.any.html?npu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/lstm_cell.https.any.worker.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/lstm_cell.https.any.worker.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/lstm_cell.https.any.worker.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API lstmCell 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 Long Short-Term Memory [LSTM] recurrent network
// using a cell state, an input, output, and forget gate to compute the cell
// state and the hidden state of the next time step that rolls into the output
// across the temporal sequence of the network.
//
// enum MLRecurrentNetworkActivation {
// "relu",
// "sigmoid",
// "tanh"
// };
//
// enum MLLstmWeightLayout {
// "iofg", // input-output-forget-cell gate ordering
// "ifgo" // input-forget-cell-output gate ordering
// };
//
// dictionary MLLstmCellOptions : MLOperatorOptions {
// MLOperand bias;
// MLOperand recurrentBias;
// MLOperand peepholeWeight;
// MLLstmWeightLayout layout = "iofg";
// sequence<MLRecurrentNetworkActivation> activations;
// };
//
// sequence<MLOperand> lstmCell(MLOperand input,
// MLOperand weight,
// MLOperand recurrentWeight,
// MLOperand hiddenState,
// MLOperand cellState,
// [EnforceRange] unsigned long hiddenSize,
// optional MLLstmCellOptions options = {});
const getLstmCellPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 1};
const expectedDataType =
graphResources
.expectedOutputs[Object.keys(graphResources.expectedOutputs)[0]]
.descriptor.dataType;
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const lstmCellTests = [
{
'name':
'lstmCell float32 tensors with options.bias, options.recurrentBias and options.activations=[\'relu\', \'relu\', \'relu\']',
'graph': {
'inputs': {
'lstmCellInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellRecurrentWeight': {
'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
],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
},
'lstmCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstmCell',
'arguments': [
{'input': 'lstmCellInput'}, {'weight': 'lstmCellWeight'},
{'recurrentWeight': 'lstmCellRecurrentWeight'},
{'hiddenState': 'lstmCellHiddenState'},
{'cellState': 'lstmCellCellState'}, {'hiddenSize': 2}, {
'options': {
'bias': 'lstmCellBias',
'recurrentBias': 'lstmCellRecurrentBias',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmCellOutput1', 'lstmCellOutput2']
}],
'expectedOutputs': {
'lstmCellOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
'lstmCell float32 tensors with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\', \'relu\'] and options.peepholeWeight',
'graph': {
'inputs': {
'lstmCellInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellRecurrentWeight': {
'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
],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
},
'lstmCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
},
'lstmCellPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [6], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstmCell',
'arguments': [
{'input': 'lstmCellInput'}, {'weight': 'lstmCellWeight'},
{'recurrentWeight': 'lstmCellRecurrentWeight'},
{'hiddenState': 'lstmCellHiddenState'},
{'cellState': 'lstmCellCellState'}, {'hiddenSize': 2}, {
'options': {
'bias': 'lstmCellBias',
'recurrentBias': 'lstmCellRecurrentBias',
'peepholeWeight': 'lstmCellPeepholeWeight',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmCellOutput1', 'lstmCellOutput2']
}],
'expectedOutputs': {
'lstmCellOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
'lstmCell float32 tensors with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\', \'relu\'] and explicit options.layout=\'iofg\'',
'graph': {
'inputs': {
'lstmCellInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellRecurrentWeight': {
'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
],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
},
'lstmCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstmCell',
'arguments': [
{'input': 'lstmCellInput'}, {'weight': 'lstmCellWeight'},
{'recurrentWeight': 'lstmCellRecurrentWeight'},
{'hiddenState': 'lstmCellHiddenState'},
{'cellState': 'lstmCellCellState'}, {'hiddenSize': 2}, {
'options': {
'bias': 'lstmCellBias',
'recurrentBias': 'lstmCellRecurrentBias',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmCellOutput1', 'lstmCellOutput2']
}],
'expectedOutputs': {
'lstmCellOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
'lstmCell float32 tensors with options.bias, options.recurrentBias, options.activations=[\'relu\', \'relu\', \'relu\'] and options.layout=\'ifgo\'',
'graph': {
'inputs': {
'lstmCellInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellRecurrentWeight': {
'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
],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
},
'lstmCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstmCell',
'arguments': [
{'input': 'lstmCellInput'}, {'weight': 'lstmCellWeight'},
{'recurrentWeight': 'lstmCellRecurrentWeight'},
{'hiddenState': 'lstmCellHiddenState'},
{'cellState': 'lstmCellCellState'}, {'hiddenSize': 2}, {
'options': {
'bias': 'lstmCellBias',
'recurrentBias': 'lstmCellRecurrentBias',
'layout': 'ifgo',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmCellOutput1', 'lstmCellOutput2']
}],
'expectedOutputs': {
'lstmCellOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [2, 2], dataType: 'float32'}
}
}
}
},
{
'name': 'lstmCell float32 tensors with all options',
'graph': {
'inputs': {
'lstmCellInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellRecurrentWeight': {
'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
],
'descriptor': {shape: [8, 2], dataType: 'float32'}
},
'lstmCellHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
},
'lstmCellRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [8], dataType: 'float32'}
},
'lstmCellPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [6], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstmCell',
'arguments': [
{'input': 'lstmCellInput'}, {'weight': 'lstmCellWeight'},
{'recurrentWeight': 'lstmCellRecurrentWeight'},
{'hiddenState': 'lstmCellHiddenState'},
{'cellState': 'lstmCellCellState'}, {'hiddenSize': 2}, {
'options': {
'bias': 'lstmCellBias',
'recurrentBias': 'lstmCellRecurrentBias',
'peepholeWeight': 'lstmCellPeepholeWeight',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmCellOutput1', 'lstmCellOutput2']
}],
'expectedOutputs': {
'lstmCellOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [2, 2], dataType: 'float32'}
},
'lstmCellOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [2, 2], dataType: 'float32'}
}
}
}
}
];
if (navigator.ml) {
lstmCellTests.forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getLstmCellPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}