operations-with-special-names.https.any.js (3281B)
1 // META: title=test input with special character names 2 // META: global=window 3 // META: variant=?cpu 4 // META: variant=?gpu 5 // META: variant=?npu 6 // META: script=../resources/utils.js 7 // META: timeout=long 8 9 'use strict'; 10 11 // https://www.w3.org/TR/webnn/#dom-mloperatoroptions-label 12 13 let mlContext; 14 15 // Skip tests if WebNN is unimplemented. 16 promise_setup(async () => { 17 assert_implements(navigator.ml, 'missing navigator.ml'); 18 mlContext = await navigator.ml.createContext(contextOptions); 19 }); 20 21 const specialNameArray = [ 22 ['12-L#!.☺', '🤦🏼♂️124DS#!F'], 23 24 // Escape Sequence 25 ['\0node_a', '\0node_b'], 26 ['node\0a', 'node\0b'], 27 28 // Hexadecimal Escape Sequences 29 // '\x41'→ 'A' 30 ['\x41\x41\x41', '\x42\x42\x42'], 31 32 // Unicode & Hexadecimal Characters 33 // "\u00A9" → "©" 34 // "\xA9" → "©" 35 // "\u2665" → "♥" 36 // "\u2026" → "…" 37 // "\U0001F600" → 😀 (Grinning Face Emoji) 38 ['\u00A9\xA9\u2665\u2026', '\U0001F600'] 39 ]; 40 41 specialNameArray.forEach((name) => { 42 promise_test(async () => { 43 // The following code builds a graph as: 44 // constant1 ---+ 45 // +--- Add (label_0) ---> intermediateOutput1 ---+ 46 // input1 ---+ | 47 // +--- Mul---> output 48 // constant2 ---+ | 49 // +--- Add (label_1) ---> intermediateOutput2 ---+ 50 // input2 ---+ 51 52 const TENSOR_DIMS = [1, 2, 2, 2]; 53 const TENSOR_SIZE = 8; 54 55 const builder = new MLGraphBuilder(mlContext); 56 const desc = { dataType: 'float32', shape: TENSOR_DIMS }; 57 const constantBuffer1 = new Float32Array(TENSOR_SIZE).fill(0.5); 58 const constant1 = builder.constant(desc, constantBuffer1); 59 60 const input1 = builder.input('input1', desc); 61 const constantBuffer2 = new Float32Array(TENSOR_SIZE).fill(0.5); 62 const constant2 = builder.constant(desc, constantBuffer2); 63 64 const input2 = builder.input('input2', desc); 65 66 const intermediateOutput1 = builder.add(constant1, input1, {label: name[0]}); 67 const intermediateOutput2 = builder.add(constant2, input2, {label: name[1]}); 68 69 const output = builder.mul(intermediateOutput1, intermediateOutput2); 70 const graph = await builder.build({'output': output}); 71 72 const inputBuffer1 = new Float32Array(TENSOR_SIZE).fill(1); 73 const inputBuffer2 = new Float32Array(TENSOR_SIZE).fill(1); 74 75 desc.writable = true; 76 const inputTensor1 = await mlContext.createTensor(desc); 77 const inputTensor2 = await mlContext.createTensor(desc); 78 mlContext.writeTensor(inputTensor1, inputBuffer1); 79 mlContext.writeTensor(inputTensor2, inputBuffer2); 80 81 const outputTensor = await mlContext.createTensor({ 82 ...desc, 83 readable: true, 84 writable: false, 85 }); 86 87 const inputs = { 88 'input1': inputTensor1, 89 'input2': inputTensor2, 90 }; 91 const outputs = {'output': outputTensor}; 92 mlContext.dispatch(graph, inputs, outputs); 93 94 assert_array_equals( 95 new Float32Array(await mlContext.readTensor(outputTensor)), 96 Float32Array.from([2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25])); 97 }, `'add' nodes with special character name '${name[0]}' and '${name[1]}'`); 98 });