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// META: title=test WebNN API prelu operation
// META: global=window
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Calculate the parametric version of rectified linear function (Parametric
// ReLU) on the input tensor element-wise. The calculation follows the
// expression max(0, x) + slope * min(0, x).
//
// MLOperand prelu(MLOperand input, MLOperand slope);
const preluTests = [
{
'name': 'prelu float32 0D scalar',
'graph': {
'inputs': {
'preluInput': {
'data': [-4.794857501983643],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [1.1202747821807861],
'descriptor': {shape: [], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [-5.371557712554932],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 1D constant tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [
9.343092918395996, 0.2800687253475189, -4.617084980010986,
1.1202747821807861, -1.4334710836410522, -3.157594919204712,
-6.28995418548584, -5.0107879638671875, -6.899077415466309,
3.5725347995758057, 6.861966609954834, -1.961531400680542,
4.5832037925720215, 2.6643502712249756, 9.192955017089844,
-9.554699897766113, -5.505102157592773, -2.3927369117736816,
3.58212947845459, -2.3224003314971924, -1.9816573858261108,
4.155889987945557, -1.799522042274475, 9.295849800109863
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.817113876342773, -1.342889666557312, 8.413617134094238,
6.108623504638672, 12.173455238342285, 3.3143365383148193,
1.1687211990356445, 0.7103435397148132, 46.32490539550781,
5.787421703338623, -25.7709903717041, 9.608142852783203,
7.3295159339904785, -10.535453796386719, 7.067296981811523,
9.439736366271973, 14.083043098449707, 20.718313217163086,
8.47507381439209, 4.551425457000732, 18.365745544433594,
-1.0895805358886719, 1.3258955478668213, -68.95950317382812
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 1D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [
9.343092918395996, 0.2800687253475189, -4.617084980010986,
1.1202747821807861, -1.4334710836410522, -3.157594919204712,
-6.28995418548584, -5.0107879638671875, -6.899077415466309,
3.5725347995758057, 6.861966609954834, -1.961531400680542,
4.5832037925720215, 2.6643502712249756, 9.192955017089844,
-9.554699897766113, -5.505102157592773, -2.3927369117736816,
3.58212947845459, -2.3224003314971924, -1.9816573858261108,
4.155889987945557, -1.799522042274475, 9.295849800109863
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.817113876342773, -1.342889666557312, 8.413617134094238,
6.108623504638672, 12.173455238342285, 3.3143365383148193,
1.1687211990356445, 0.7103435397148132, 46.32490539550781,
5.787421703338623, -25.7709903717041, 9.608142852783203,
7.3295159339904785, -10.535453796386719, 7.067296981811523,
9.439736366271973, 14.083043098449707, 20.718313217163086,
8.47507381439209, 4.551425457000732, 18.365745544433594,
-1.0895805358886719, 1.3258955478668213, -68.95950317382812
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 2D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [4, 6], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [
9.343092918395996, 0.2800687253475189, -4.617084980010986,
1.1202747821807861, -1.4334710836410522, -3.157594919204712,
-6.28995418548584, -5.0107879638671875, -6.899077415466309,
3.5725347995758057, 6.861966609954834, -1.961531400680542,
4.5832037925720215, 2.6643502712249756, 9.192955017089844,
-9.554699897766113, -5.505102157592773, -2.3927369117736816,
3.58212947845459, -2.3224003314971924, -1.9816573858261108,
4.155889987945557, -1.799522042274475, 9.295849800109863
],
'descriptor': {shape: [4, 6], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.817113876342773, -1.342889666557312, 8.413617134094238,
6.108623504638672, 12.173455238342285, 3.3143365383148193,
1.1687211990356445, 0.7103435397148132, 46.32490539550781,
5.787421703338623, -25.7709903717041, 9.608142852783203,
7.3295159339904785, -10.535453796386719, 7.067296981811523,
9.439736366271973, 14.083043098449707, 20.718313217163086,
8.47507381439209, 4.551425457000732, 18.365745544433594,
-1.0895805358886719, 1.3258955478668213, -68.95950317382812
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 3D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [
9.343092918395996, 0.2800687253475189, -4.617084980010986,
1.1202747821807861, -1.4334710836410522, -3.157594919204712,
-6.28995418548584, -5.0107879638671875, -6.899077415466309,
3.5725347995758057, 6.861966609954834, -1.961531400680542,
4.5832037925720215, 2.6643502712249756, 9.192955017089844,
-9.554699897766113, -5.505102157592773, -2.3927369117736816,
3.58212947845459, -2.3224003314971924, -1.9816573858261108,
4.155889987945557, -1.799522042274475, 9.295849800109863
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.817113876342773, -1.342889666557312, 8.413617134094238,
6.108623504638672, 12.173455238342285, 3.3143365383148193,
1.1687211990356445, 0.7103435397148132, 46.32490539550781,
5.787421703338623, -25.7709903717041, 9.608142852783203,
7.3295159339904785, -10.535453796386719, 7.067296981811523,
9.439736366271973, 14.083043098449707, 20.718313217163086,
8.47507381439209, 4.551425457000732, 18.365745544433594,
-1.0895805358886719, 1.3258955478668213, -68.95950317382812
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 4D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [
9.343092918395996, 0.2800687253475189, -4.617084980010986,
1.1202747821807861, -1.4334710836410522, -3.157594919204712,
-6.28995418548584, -5.0107879638671875, -6.899077415466309,
3.5725347995758057, 6.861966609954834, -1.961531400680542,
4.5832037925720215, 2.6643502712249756, 9.192955017089844,
-9.554699897766113, -5.505102157592773, -2.3927369117736816,
3.58212947845459, -2.3224003314971924, -1.9816573858261108,
4.155889987945557, -1.799522042274475, 9.295849800109863
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.817113876342773, -1.342889666557312, 8.413617134094238,
6.108623504638672, 12.173455238342285, 3.3143365383148193,
1.1687211990356445, 0.7103435397148132, 46.32490539550781,
5.787421703338623, -25.7709903717041, 9.608142852783203,
7.3295159339904785, -10.535453796386719, 7.067296981811523,
9.439736366271973, 14.083043098449707, 20.718313217163086,
8.47507381439209, 4.551425457000732, 18.365745544433594,
-1.0895805358886719, 1.3258955478668213, -68.95950317382812
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 5D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [
9.343092918395996, 0.2800687253475189, -4.617084980010986,
1.1202747821807861, -1.4334710836410522, -3.157594919204712,
-6.28995418548584, -5.0107879638671875, -6.899077415466309,
3.5725347995758057, 6.861966609954834, -1.961531400680542,
4.5832037925720215, 2.6643502712249756, 9.192955017089844,
-9.554699897766113, -5.505102157592773, -2.3927369117736816,
3.58212947845459, -2.3224003314971924, -1.9816573858261108,
4.155889987945557, -1.799522042274475, 9.295849800109863
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.817113876342773, -1.342889666557312, 8.413617134094238,
6.108623504638672, 12.173455238342285, 3.3143365383148193,
1.1687211990356445, 0.7103435397148132, 46.32490539550781,
5.787421703338623, -25.7709903717041, 9.608142852783203,
7.3295159339904785, -10.535453796386719, 7.067296981811523,
9.439736366271973, 14.083043098449707, 20.718313217163086,
8.47507381439209, 4.551425457000732, 18.365745544433594,
-1.0895805358886719, 1.3258955478668213, -68.95950317382812
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 broadcast 4D x 1D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [5.073923110961914, 0.480774462223053, -7.091750144958496],
'descriptor': {shape: [3], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.934283256530762, -2.3052449226379395, 8.413617134094238,
6.108623504638672, -4.082877159118652, 3.3143365383148193,
1.1687211990356445, -0.06815595179796219, 47.61863327026367,
5.787421703338623, -1.8056097030639648, 34.737422943115234,
7.3295159339904785, -1.901092767715454, 7.067296981811523,
9.439736366271973, -1.2299076318740845, 61.40629196166992,
8.47507381439209, 4.551425457000732, 65.72542572021484,
-1.330268144607544, 1.3258955478668213, 52.60881042480469
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 broadcast 4D x 1D slope of shape [1]',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [5.0114545822143555],
'descriptor': {shape: [1], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.775040626525879, -24.029211044311523, 8.413617134094238,
6.108623504638672, -42.558738708496094, 3.3143365383148193,
1.1687211990356445, -0.7104380130767822, -33.65017318725586,
5.787421703338623, -18.821155548095703, -24.54753875732422,
7.3295159339904785, -19.816442489624023, 7.067296981811523,
9.439736366271973, -12.82020378112793, -43.39335632324219,
8.47507381439209, 4.551425457000732, -46.44551467895508,
-1.3138903379440308, 1.3258955478668213, -37.17652893066406
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 broadcast 4D x 2D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [
4.874276161193848, -8.501633644104004, 1.1819270849227905,
-9.985190391540527, -4.424202919006348, -6.654683589935303
],
'descriptor': {shape: [2, 3], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.425349235534668, 40.764122009277344, 8.413617134094238,
6.108623504638672, 37.571624755859375, 3.3143365383148193,
1.1687211990356445, 1.2052156925201416, -7.936229228973389,
5.787421703338623, 16.615657806396484, 32.5965461730957,
7.3295159339904785, 33.61741256713867, 7.067296981811523,
9.439736366271973, 11.31790828704834, 57.621803283691406,
8.47507381439209, 4.551425457000732, -10.953948020935059,
2.617891550064087, 1.3258955478668213, 49.366512298583984
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 broadcast 4D x 3D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [5.073923110961914, 0.480774462223053, -7.091750144958496],
'descriptor': {shape: [1, 1, 3], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.934283256530762, -2.3052449226379395, 8.413617134094238,
6.108623504638672, -4.082877159118652, 3.3143365383148193,
1.1687211990356445, -0.06815595179796219, 47.61863327026367,
5.787421703338623, -1.8056097030639648, 34.737422943115234,
7.3295159339904785, -1.901092767715454, 7.067296981811523,
9.439736366271973, -1.2299076318740845, 61.40629196166992,
8.47507381439209, 4.551425457000732, 65.72542572021484,
-1.330268144607544, 1.3258955478668213, 52.60881042480469
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
{
'name': 'prelu float32 broadcast 4D x 4D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.549168109893799, -4.794857501983643, 8.413617134094238,
6.108623504638672, -8.492292404174805, 3.3143365383148193,
1.1687211990356445, -0.141762837767601, -6.714652061462402,
5.787421703338623, -3.755627393722534, -4.89828634262085,
7.3295159339904785, -3.9542298316955566, 7.067296981811523,
9.439736366271973, -2.558180093765259, -8.658834457397461,
8.47507381439209, 4.551425457000732, -9.267870903015137,
-0.262177437543869, 1.3258955478668213, -7.41831111907959
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'},
'constant': true
},
'preluSlope': {
'data': [5.0114545822143555, 5.0114545822143555],
'descriptor': {shape: [1, 2, 1, 1], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.775040626525879, -24.029211044311523, 8.413617134094238,
6.108623504638672, -42.558738708496094, 3.3143365383148193,
1.1687211990356445, -0.7104380130767822, -33.65017318725586,
5.787421703338623, -18.821155548095703, -24.54753875732422,
7.3295159339904785, -19.816442489624023, 7.067296981811523,
9.439736366271973, -12.82020378112793, -43.39335632324219,
8.47507381439209, 4.551425457000732, -46.44551467895508,
-1.3138903379440308, 1.3258955478668213, -37.17652893066406
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float32'}
}
}
}
},
// float16 tests
{
'name': 'prelu float16 0D scalar',
'graph': {
'inputs': {
'preluInput': {
'data': [-4.79296875],
'descriptor': {shape: [], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [1.1201171875],
'descriptor': {shape: [], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [-5.3671875],
'descriptor': {shape: [], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 1D constant tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [24], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [
9.34375, 0.280029296875, -4.6171875, 1.1201171875,
-1.43359375, -3.158203125, -6.2890625, -5.01171875,
-6.8984375, 3.572265625, 6.86328125, -1.9619140625,
4.58203125, 2.6640625, 9.1953125, -9.5546875,
-5.50390625, -2.392578125, 3.58203125, -2.322265625,
-1.9814453125, 4.15625, -1.7998046875, 9.296875
],
'descriptor': {shape: [24], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.8125, -1.341796875, 8.4140625, 6.109375, 12.171875,
3.314453125, 1.1689453125, 0.71044921875, 46.3125, 5.7890625,
-25.78125, 9.609375, 7.328125, -10.5390625, 7.06640625,
9.4375, 14.0859375, 20.703125, 8.4765625, 4.55078125,
18.359375, -1.08984375, 1.326171875, -68.9375
],
'descriptor': {shape: [24], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 1D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [24], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [
9.34375, 0.280029296875, -4.6171875, 1.1201171875,
-1.43359375, -3.158203125, -6.2890625, -5.01171875,
-6.8984375, 3.572265625, 6.86328125, -1.9619140625,
4.58203125, 2.6640625, 9.1953125, -9.5546875,
-5.50390625, -2.392578125, 3.58203125, -2.322265625,
-1.9814453125, 4.15625, -1.7998046875, 9.296875
],
'descriptor': {shape: [24], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.8125, -1.341796875, 8.4140625, 6.109375, 12.171875,
3.314453125, 1.1689453125, 0.71044921875, 46.3125, 5.7890625,
-25.78125, 9.609375, 7.328125, -10.5390625, 7.06640625,
9.4375, 14.0859375, 20.703125, 8.4765625, 4.55078125,
18.359375, -1.08984375, 1.326171875, -68.9375
],
'descriptor': {shape: [24], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 2D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [4, 6], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [
9.34375, 0.280029296875, -4.6171875, 1.1201171875,
-1.43359375, -3.158203125, -6.2890625, -5.01171875,
-6.8984375, 3.572265625, 6.86328125, -1.9619140625,
4.58203125, 2.6640625, 9.1953125, -9.5546875,
-5.50390625, -2.392578125, 3.58203125, -2.322265625,
-1.9814453125, 4.15625, -1.7998046875, 9.296875
],
'descriptor': {shape: [4, 6], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.8125, -1.341796875, 8.4140625, 6.109375, 12.171875,
3.314453125, 1.1689453125, 0.71044921875, 46.3125, 5.7890625,
-25.78125, 9.609375, 7.328125, -10.5390625, 7.06640625,
9.4375, 14.0859375, 20.703125, 8.4765625, 4.55078125,
18.359375, -1.08984375, 1.326171875, -68.9375
],
'descriptor': {shape: [4, 6], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 3D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 3, 4], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [
9.34375, 0.280029296875, -4.6171875, 1.1201171875,
-1.43359375, -3.158203125, -6.2890625, -5.01171875,
-6.8984375, 3.572265625, 6.86328125, -1.9619140625,
4.58203125, 2.6640625, 9.1953125, -9.5546875,
-5.50390625, -2.392578125, 3.58203125, -2.322265625,
-1.9814453125, 4.15625, -1.7998046875, 9.296875
],
'descriptor': {shape: [2, 3, 4], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.8125, -1.341796875, 8.4140625, 6.109375, 12.171875,
3.314453125, 1.1689453125, 0.71044921875, 46.3125, 5.7890625,
-25.78125, 9.609375, 7.328125, -10.5390625, 7.06640625,
9.4375, 14.0859375, 20.703125, 8.4765625, 4.55078125,
18.359375, -1.08984375, 1.326171875, -68.9375
],
'descriptor': {shape: [2, 3, 4], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 4D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [
9.34375, 0.280029296875, -4.6171875, 1.1201171875,
-1.43359375, -3.158203125, -6.2890625, -5.01171875,
-6.8984375, 3.572265625, 6.86328125, -1.9619140625,
4.58203125, 2.6640625, 9.1953125, -9.5546875,
-5.50390625, -2.392578125, 3.58203125, -2.322265625,
-1.9814453125, 4.15625, -1.7998046875, 9.296875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.8125, -1.341796875, 8.4140625, 6.109375, 12.171875,
3.314453125, 1.1689453125, 0.71044921875, 46.3125, 5.7890625,
-25.78125, 9.609375, 7.328125, -10.5390625, 7.06640625,
9.4375, 14.0859375, 20.703125, 8.4765625, 4.55078125,
18.359375, -1.08984375, 1.326171875, -68.9375
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 5D tensors',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [
9.34375, 0.280029296875, -4.6171875, 1.1201171875,
-1.43359375, -3.158203125, -6.2890625, -5.01171875,
-6.8984375, 3.572265625, 6.86328125, -1.9619140625,
4.58203125, 2.6640625, 9.1953125, -9.5546875,
-5.50390625, -2.392578125, 3.58203125, -2.322265625,
-1.9814453125, 4.15625, -1.7998046875, 9.296875
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-23.8125, -1.341796875, 8.4140625, 6.109375, 12.171875,
3.314453125, 1.1689453125, 0.71044921875, 46.3125, 5.7890625,
-25.78125, 9.609375, 7.328125, -10.5390625, 7.06640625,
9.4375, 14.0859375, 20.703125, 8.4765625, 4.55078125,
18.359375, -1.08984375, 1.326171875, -68.9375
],
'descriptor': {shape: [2, 2, 1, 2, 3], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 broadcast 4D x 1D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [5.07421875, 0.480712890625, -7.08984375],
'descriptor': {shape: [3], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.9296875, -2.3046875, 8.4140625, 6.109375,
-4.08203125, 3.314453125, 1.1689453125, -0.068115234375,
47.59375, 5.7890625, -1.8056640625, 34.71875,
7.328125, -1.9013671875, 7.06640625, 9.4375,
-1.2294921875, 61.375, 8.4765625, 4.55078125,
65.6875, -1.330078125, 1.326171875, 52.59375
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 broadcast 4D x 1D slope of shape [1]',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [5.01171875],
'descriptor': {shape: [1], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.7734375, -24.015625, 8.4140625, 6.109375, -42.5625,
3.314453125, 1.1689453125, -0.71044921875, -33.65625, 5.7890625,
-18.828125, -24.546875, 7.328125, -19.828125, 7.06640625,
9.4375, -12.8203125, -43.375, 8.4765625, 4.55078125,
-46.4375, -1.314453125, 1.326171875, -37.1875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 broadcast 4D x 2D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [4.875, -8.5, 1.181640625, -9.984375, -4.42578125, -6.65625],
'descriptor': {shape: [2, 3], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.421875, 40.75, 8.4140625, 6.109375, 37.59375,
3.314453125, 1.1689453125, 1.205078125, -7.93359375, 5.7890625,
16.625, 32.59375, 7.328125, 33.625, 7.06640625,
9.4375, 11.3203125, 57.625, 8.4765625, 4.55078125,
-10.9453125, 2.6171875, 1.326171875, 49.375
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 broadcast 4D x 3D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [5.07421875, 0.480712890625, -7.08984375],
'descriptor': {shape: [1, 1, 3], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.9296875, -2.3046875, 8.4140625, 6.109375,
-4.08203125, 3.314453125, 1.1689453125, -0.068115234375,
47.59375, 5.7890625, -1.8056640625, 34.71875,
7.328125, -1.9013671875, 7.06640625, 9.4375,
-1.2294921875, 61.375, 8.4765625, 4.55078125,
65.6875, -1.330078125, 1.326171875, 52.59375
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'}
}
}
}
},
{
'name': 'prelu float16 broadcast 4D x 4D slope',
'graph': {
'inputs': {
'preluInput': {
'data': [
-2.548828125, -4.79296875, 8.4140625, 6.109375,
-8.4921875, 3.314453125, 1.1689453125, -0.1417236328125,
-6.71484375, 5.7890625, -3.755859375, -4.8984375,
7.328125, -3.955078125, 7.06640625, 9.4375,
-2.55859375, -8.65625, 8.4765625, 4.55078125,
-9.265625, -0.26220703125, 1.326171875, -7.41796875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'},
'constant': true
},
'preluSlope': {
'data': [5.01171875],
'descriptor': {shape: [1, 1, 1, 1], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'prelu',
'arguments': [{'input': 'preluInput'}, {'slope': 'preluSlope'}],
'outputs': 'preluOutput'
}],
'expectedOutputs': {
'preluOutput': {
'data': [
-12.7734375, -24.015625, 8.4140625, 6.109375, -42.5625,
3.314453125, 1.1689453125, -0.71044921875, -33.65625, 5.7890625,
-18.828125, -24.546875, 7.328125, -19.828125, 7.06640625,
9.4375, -12.8203125, -43.375, 8.4765625, 4.55078125,
-46.4375, -1.314453125, 1.326171875, -37.1875
],
'descriptor': {shape: [2, 2, 2, 3], dataType: 'float16'}
}
}
}
}
];
if (navigator.ml) {
preluTests.forEach((test) => {
webnn_conformance_test(buildAndExecuteGraph, getPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}