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This CL adds IDL and mojo definition of reverse operator according to the spec issue [1] and implements it on DirectML backend. [1] webmachinelearning/webnn#773 Bug: 376707210 Change-Id: I0d42b49b87ce243db9d44512e6000c7ee901077b Cq-Include-Trybots: luci.chromium.try:win11-blink-rel, mac14.arm64-blink-rel, mac14-blink-rel, mac15.arm64-blink-rel, mac15-blink-rel, linux-blink-rel
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// META: title=test WebNN API reverse operation | ||
// META: global=window,dedicatedworker | ||
// META: variant=?cpu | ||
// META: variant=?gpu | ||
// META: variant=?npu | ||
// META: script=../resources/utils.js | ||
// META: timeout=long | ||
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'use strict'; | ||
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// https://www.w3.org/TR/webnn/#api-mlgraphbuilder-reverse-method | ||
// Reverse the order of the input tensor along specified axes. | ||
// | ||
// dictionary MLReverseOptions : MLOperatorOptions { | ||
// sequence<[EnforceRange] unsigned long> axes; | ||
// }; | ||
// | ||
// MLOperand reverse(MLOperand input, optional MLReverseOptions options = {}); | ||
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const reverseTests = [ | ||
{ | ||
'name': 'reverse float32 2D input with default options', | ||
'graph': { | ||
'inputs': { | ||
'reverseInput': { | ||
'data': [ | ||
-30.0561466217041, 99.56941986083984, 88.04620361328125, | ||
-91.87507629394531, -23.7972354888916, -91.28665161132812, | ||
-63.15204620361328, 12.0669527053833, -96.1172866821289, | ||
-44.77365493774414, -80.08650970458984, -64.43756866455078 | ||
], | ||
'descriptor': {shape: [3, 4], dataType: 'float32'} | ||
} | ||
}, | ||
'operators': [{ | ||
'name': 'reverse', | ||
'arguments': [{'input': 'reverseInput'}], | ||
'outputs': 'reverseOutput' | ||
}], | ||
'expectedOutputs': { | ||
'reverseOutput': { | ||
'data': [ | ||
-64.43756866455078, -80.08650970458984, -44.77365493774414, | ||
-96.1172866821289, 12.0669527053833, -63.15204620361328, | ||
-91.28665161132812, -23.7972354888916, -91.87507629394531, | ||
88.04620361328125, 99.56941986083984, -30.0561466217041 | ||
], | ||
'descriptor': {shape: [3, 4], dataType: 'float32'} | ||
} | ||
} | ||
} | ||
}, | ||
{ | ||
'name': 'reverse float32 3D input options.axes=[1, 2]', | ||
'graph': { | ||
'inputs': { | ||
'reverseInput': { | ||
'data': [ | ||
-30.0561466217041, 99.56941986083984, 88.04620361328125, | ||
-91.87507629394531, -23.7972354888916, -91.28665161132812, | ||
-63.15204620361328, 12.0669527053833, -96.1172866821289, | ||
-44.77365493774414, -80.08650970458984, -64.43756866455078 | ||
], | ||
'descriptor': {shape: [3, 2, 2], dataType: 'float32'} | ||
} | ||
}, | ||
'operators': [{ | ||
'name': 'reverse', | ||
'arguments': [{'input': 'reverseInput'}, {'options': {'axes': [1, 2]}}], | ||
'outputs': 'reverseOutput' | ||
}], | ||
'expectedOutputs': { | ||
'reverseOutput': { | ||
'data': [ | ||
-91.87507629394531, 88.04620361328125, 99.56941986083984, | ||
-30.0561466217041, 12.0669527053833, -63.15204620361328, | ||
-91.28665161132812, -23.7972354888916, -64.43756866455078, | ||
-80.08650970458984, -44.77365493774414, -96.1172866821289 | ||
], | ||
'descriptor': {shape: [3, 2, 2], dataType: 'float32'} | ||
} | ||
} | ||
} | ||
}, | ||
{ | ||
'name': 'reverse float32 4D input options.axes=[3, 1]', | ||
'graph': { | ||
'inputs': { | ||
'reverseInput': { | ||
'data': [ | ||
-30.0561466217041, 99.56941986083984, 88.04620361328125, | ||
-91.87507629394531, -23.7972354888916, -91.28665161132812, | ||
-63.15204620361328, 12.0669527053833, -96.1172866821289, | ||
-44.77365493774414, -80.08650970458984, -64.43756866455078 | ||
], | ||
'descriptor': {shape: [3, 2, 1, 2], dataType: 'float32'} | ||
} | ||
}, | ||
'operators': [{ | ||
'name': 'reverse', | ||
'arguments': [{'input': 'reverseInput'}, {'options': {'axes': [3, 1]}}], | ||
'outputs': 'reverseOutput' | ||
}], | ||
'expectedOutputs': { | ||
'reverseOutput': { | ||
'data': [ | ||
-91.87507629394531, 88.04620361328125, 99.56941986083984, | ||
-30.0561466217041, 12.0669527053833, -63.15204620361328, | ||
-91.28665161132812, -23.7972354888916, -64.43756866455078, | ||
-80.08650970458984, -44.77365493774414, -96.1172866821289 | ||
], | ||
'descriptor': {shape: [3, 2, 1, 2], dataType: 'float32'} | ||
} | ||
} | ||
} | ||
}, | ||
{ | ||
'name': 'reverse float32 4D input options.axes=[]', | ||
'graph': { | ||
'inputs': { | ||
'reverseInput': { | ||
'data': [ | ||
-30.0561466217041, 99.56941986083984, 88.04620361328125, | ||
-91.87507629394531, -23.7972354888916, -91.28665161132812, | ||
-63.15204620361328, 12.0669527053833, -96.1172866821289, | ||
-44.77365493774414, -80.08650970458984, -64.43756866455078 | ||
], | ||
'descriptor': {shape: [2, 1, 2, 3], dataType: 'float32'} | ||
} | ||
}, | ||
'operators': [{ | ||
'name': 'reverse', | ||
'arguments': [{'input': 'reverseInput'}, {'options': {'axes': []}}], | ||
'outputs': 'reverseOutput' | ||
}], | ||
'expectedOutputs': { | ||
'reverseOutput': { | ||
'data': [ | ||
-30.0561466217041, 99.56941986083984, 88.04620361328125, | ||
-91.87507629394531, -23.7972354888916, -91.28665161132812, | ||
-63.15204620361328, 12.0669527053833, -96.1172866821289, | ||
-44.77365493774414, -80.08650970458984, -64.43756866455078 | ||
], | ||
'descriptor': {shape: [2, 1, 2, 3], dataType: 'float32'} | ||
} | ||
} | ||
} | ||
} | ||
]; | ||
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if (navigator.ml) { | ||
reverseTests.forEach((test) => { | ||
webnn_conformance_test(buildGraphAndCompute, getPrecisionTolerance, test); | ||
}); | ||
} else { | ||
test(() => assert_implements(navigator.ml, 'missing navigator.ml')); | ||
} |
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// META: title=validation tests for WebNN API reverse operation | ||
// META: global=window,dedicatedworker | ||
// META: variant=?cpu | ||
// META: variant=?gpu | ||
// META: variant=?npu | ||
// META: script=../resources/utils_validation.js | ||
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'use strict'; | ||
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const tests = [ | ||
{ | ||
name: '[reverse] Test reverse with default options', | ||
input: {dataType: 'float32', shape: [3, 3]}, | ||
output: {dataType: 'float32', shape: [3, 3]} | ||
}, | ||
{ | ||
name: '[reverse] Test reverse with axes = [0, 1]', | ||
input: {dataType: 'int32', shape: [1, 2, 3]}, | ||
axes: [0, 1], | ||
output: {dataType: 'int32', shape: [1, 2, 3]} | ||
}, | ||
{ | ||
name: '[reverse] Throw if axes is greater than input rank', | ||
input: {dataType: 'float32', shape: [3, 3]}, | ||
axes: [3] | ||
}, | ||
{ | ||
name: '[reverse] Throw if axes is duplicated', | ||
input: {dataType: 'float32', shape: [1, 2, 3, 4]}, | ||
axes: [2, 2, 3] | ||
} | ||
]; | ||
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tests.forEach(test => promise_test(async t => { | ||
const builder = new MLGraphBuilder(context); | ||
const input = builder.input('input', test.input); | ||
const options = {}; | ||
if (test.axes) { | ||
options.axes = test.axes; | ||
} | ||
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if (test.output) { | ||
const output = builder.reverse(input, options); | ||
assert_equals(output.dataType, test.output.dataType); | ||
assert_array_equals(output.shape, test.output.shape); | ||
} else { | ||
const label = 'reverse_1' | ||
options.label = label; | ||
const regexp = new RegExp('\\[' + label + '\\]'); | ||
assert_throws_with_label( | ||
() => builder.reverse(input, options), regexp); | ||
} | ||
}, test.name)); | ||
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multi_builder_test(async (t, builder, otherBuilder) => { | ||
const input = | ||
otherBuilder.input('input', {dataType: 'float32', shape: [3, 3]}); | ||
assert_throws_js(TypeError, () => builder.reverse(input)); | ||
}, '[reverse] Throw if input is from another builder'); |