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// META: title=test WebNN API lstm operation
// META: global=window
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Long Short-Term Memory [LSTM] recurrent network uses an input, output,
// forget, and cell gate to compute the output state that rolls into the output
// across the temporal sequence of the network.
// enum MLRecurrentNetworkDirection {
// "forward",
// "backward",
// "both"
// };
//
// enum MLRecurrentNetworkActivation {
// "relu",
// "sigmoid",
// "tanh"
// };
//
// enum MLLstmWeightLayout {
// "iofg", // input-output-forget-cell gate ordering
// "ifgo" // input-forget-cell-output gate ordering
// };
//
// dictionary MLLstmOptions {
// MLOperand bias;
// MLOperand recurrentBias;
// MLOperand peepholeWeight;
// MLOperand initialHiddenState;
// MLOperand initialCellState;
// boolean returnSequence = false;
// MLRecurrentNetworkDirection direction = "forward";
// MLLstmWeightLayout layout = "iofg";
// sequence<MLRecurrentNetworkActivation> activations;
// };
//
// sequence<MLOperand> lstm(MLOperand input,
// MLOperand weight,
// MLOperand recurrentWeight,
// [EnforceRange] unsigned long steps,
// [EnforceRange] unsigned long hiddenSize,
// optional MLLstmOptions options = {});
const getLstmPrecisionTolerance = (graphResources) => {
const toleranceValueDict = {float32: 3, float16: 10};
const expectedDataType =
graphResources
.expectedOutputs[Object.keys(graphResources.expectedOutputs)[0]]
.descriptor.dataType;
return {metricType: 'ULP', value: toleranceValueDict[expectedDataType]};
};
const lstmTests = [
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias and options.activations=['relu', 'relu', 'relu']",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.peepholeWeight",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 6], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'peepholeWeight': 'lstmPeepholeWeight',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.initialHiddenState",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'},
'constant': true
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmInitialHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'initialHiddenState': 'lstmInitialHiddenState',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.initialCellState",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmInitialCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'initialCellState': 'lstmInitialCellState',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and explicit options.returnSequence=false",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'returnSequence': false,
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.returnSequence=true",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'returnSequence': true,
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput3': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and explicit options.direction='forward'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'direction': 'forward',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and explicit options.layout='iofg'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.layout='ifgo'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'layout': 'ifgo',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name': 'lstm float32 tensors steps=1 with all options',
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 6], dataType: 'float32'},
'constant': true
},
'lstmInitialHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmInitialCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'peepholeWeight': 'lstmPeepholeWeight',
'initialHiddenState': 'lstmInitialHiddenState',
'initialCellState': 'lstmInitialCellState',
'returnSequence': true,
'direction': 'forward',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput3': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name':
"lstm float32 tensors steps=2 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.direction='backward'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1, 3, 4, 1, 2],
'descriptor': {shape: [2, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'direction': 'backward',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [
10.469000816345215, 58.02900695800781, 74.52900695800781,
518.948974609375
],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [
5.510000228881836, 20.01000213623047, 19.110000610351564,
75.20999908447266
],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name': 'lstm float32 tensors steps=2 with all options',
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1, 3, 4, 1, 2],
'descriptor': {shape: [2, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float32'},
'constant': true
},
'lstmPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 6], dataType: 'float32'},
'constant': true
},
'lstmInitialHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmInitialCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'peepholeWeight': 'lstmPeepholeWeight',
'initialHiddenState': 'lstmInitialHiddenState',
'initialCellState': 'lstmInitialCellState',
'returnSequence': true,
'direction': 'backward',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [
10.469000816345215, 58.02900695800781, 74.52900695800781,
518.948974609375
],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [
5.510000228881836, 20.01000213623047, 19.110000610351564,
75.20999908447266
],
'descriptor': {shape: [1, 2, 2], dataType: 'float32'}
},
'lstmOutput3': {
'data': [
10.469000816345215, 58.02900695800781, 74.52900695800781,
518.948974609375, 1, 8, 1, 8
],
'descriptor': {shape: [2, 1, 2, 2], dataType: 'float32'}
}
}
}
},
{
'name': 'lstm float32 tensors steps=2 with bidirections',
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1, 3, 4, 1, 2],
'descriptor': {shape: [2, 2, 2], dataType: 'float32'}
},
'lstmWeight': {
'data': [
1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2,
1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2
],
'descriptor': {shape: [2, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmRecurrentWeight': {
'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
],
'descriptor': {shape: [2, 8, 2], dataType: 'float32'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [2, 8], dataType: 'float32'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [2, 8], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'returnSequence': true,
'direction': 'both',
'layout': 'iofg'
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [
0.5764073133468628, 0.8236227035522461, 0.6612355709075928,
0.8442635536193848, 0.5764073133468628, 0.8236227035522461,
0.8635294437408447, 0.9491351246833801
],
'descriptor': {shape: [2, 2, 2], dataType: 'float32'}
},
'lstmOutput2': {
'data': [
1.0171456336975098, 1.6205494403839111, 1.3388464450836182,
1.7642604112625122, 1.0171456336975098, 1.6205494403839111,
1.4856269359588623, 1.8449554443359375
],
'descriptor': {shape: [2, 2, 2], dataType: 'float32'}
},
'lstmOutput3': {
'data': [
0.3696063756942749, 0.6082833409309387, 0.7037754058837891,
0.7586681246757507, 0.5764073133468628, 0.8236227035522461,
0.8635294437408447, 0.9491351246833801, 0.5764073133468628,
0.8236227035522461, 0.6612355709075928, 0.8442635536193848,
0.3696063756942749, 0.6082833409309387, 0.3696063756942749,
0.6082833409309387
],
'descriptor': {shape: [2, 2, 2, 2], dataType: 'float32'}
}
}
}
},
// float16 tests
{
'name':
'lstm float16 tensors steps=1 with options.bias, options.recurrentBias',
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'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: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'returnSequence': false,
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias and options.activations=['relu', 'relu', 'relu']",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.peepholeWeight",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 6], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'peepholeWeight': 'lstmPeepholeWeight',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.initialHiddenState",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmInitialHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'initialHiddenState': 'lstmInitialHiddenState',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.initialCellState",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmInitialCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'initialCellState': 'lstmInitialCellState',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and explicit options.returnSequence=false",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'returnSequence': false,
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.returnSequence=true",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'returnSequence': true,
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput3': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and explicit options.direction='forward'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'direction': 'forward',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and explicit options.layout='iofg'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=1 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.layout='ifgo'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'layout': 'ifgo',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name': 'lstm float16 tensors steps=1 with all options',
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 6], dataType: 'float16'},
'constant': true
},
'lstmInitialHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmInitialCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 1},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'peepholeWeight': 'lstmPeepholeWeight',
'initialHiddenState': 'lstmInitialHiddenState',
'initialCellState': 'lstmInitialCellState',
'returnSequence': true,
'direction': 'forward',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [1, 4, 9, 36],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput3': {
'data': [1, 8, 27, 216],
'descriptor': {shape: [1, 1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name':
"lstm float16 tensors steps=2 with options.bias, options.recurrentBias, options.activations=['relu', 'relu', 'relu'] and options.direction='backward'",
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1, 3, 4, 1, 2],
'descriptor': {shape: [2, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'direction': 'backward',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [10.46875, 58.03125, 74.5, 519],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [5.5078125, 20.015625, 19.109375, 75.1875],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name': 'lstm float16 tensors steps=2 with all options',
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1, 3, 4, 1, 2],
'descriptor': {shape: [2, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [1, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [1, 8], dataType: 'float16'},
'constant': true
},
'lstmPeepholeWeight': {
'data': [0, 0, 0, 0, 0, 0],
'descriptor': {shape: [1, 6], dataType: 'float16'},
'constant': true
},
'lstmInitialHiddenState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmInitialCellState': {
'data': [0, 0, 0, 0],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'peepholeWeight': 'lstmPeepholeWeight',
'initialHiddenState': 'lstmInitialHiddenState',
'initialCellState': 'lstmInitialCellState',
'returnSequence': true,
'direction': 'backward',
'layout': 'iofg',
'activations': ['relu', 'relu', 'relu']
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [10.46875, 58.03125, 74.5, 519],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [5.5078125, 20.015625, 19.109375, 75.1875],
'descriptor': {shape: [1, 2, 2], dataType: 'float16'}
},
'lstmOutput3': {
'data': [10.46875, 58.03125, 74.5, 519, 1, 8, 1, 8],
'descriptor': {shape: [2, 1, 2, 2], dataType: 'float16'}
}
}
}
},
{
'name': 'lstm float16 tensors steps=2 with bidirections',
'graph': {
'inputs': {
'lstmInput': {
'data': [1, 2, 2, 1, 3, 4, 1, 2],
'descriptor': {shape: [2, 2, 2], dataType: 'float16'}
},
'lstmWeight': {
'data': [
1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2,
1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2, 1, -1, 2, -2
],
'descriptor': {shape: [2, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmRecurrentWeight': {
'data': [
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375,
0.0999755859375, 0.0999755859375, 0.0999755859375, 0.0999755859375
],
'descriptor': {shape: [2, 8, 2], dataType: 'float16'},
'constant': true
},
'lstmBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [2, 8], dataType: 'float16'},
'constant': true
},
'lstmRecurrentBias': {
'data': [1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2],
'descriptor': {shape: [2, 8], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'lstm',
'arguments': [
{'input': 'lstmInput'}, {'weight': 'lstmWeight'},
{'recurrentWeight': 'lstmRecurrentWeight'}, {'steps': 2},
{'hiddenSize': 2}, {
'options': {
'bias': 'lstmBias',
'recurrentBias': 'lstmRecurrentBias',
'returnSequence': true,
'direction': 'both',
'layout': 'iofg'
}
}
],
'outputs': ['lstmOutput1', 'lstmOutput2', 'lstmOutput3']
}],
'expectedOutputs': {
'lstmOutput1': {
'data': [
0.576171875, 0.82373046875, 0.6611328125, 0.84423828125,
0.576171875, 0.82373046875, 0.86376953125, 0.94921875
],
'descriptor': {shape: [2, 2, 2], dataType: 'float16'}
},
'lstmOutput2': {
'data': [
1.017578125, 1.6201171875, 1.3388671875, 1.7646484375, 1.017578125,
1.6201171875, 1.4853515625, 1.8447265625
],
'descriptor': {shape: [2, 2, 2], dataType: 'float16'}
},
'lstmOutput3': {
'data': [
0.36962890625, 0.6083984375, 0.70361328125, 0.7587890625,
0.576171875, 0.82373046875, 0.86376953125, 0.94921875, 0.576171875,
0.82373046875, 0.6611328125, 0.84423828125, 0.36962890625,
0.6083984375, 0.36962890625, 0.6083984375
],
'descriptor': {shape: [2, 2, 2, 2], dataType: 'float16'}
}
}
}
}
];
if (navigator.ml) {
lstmTests.filter(isTargetTest).forEach((test) => {
webnn_conformance_test(
buildAndExecuteGraph, getLstmPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}