operations-with-special-names.https.any.js

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This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/operations-with-special-names.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/operations-with-special-names.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/operations-with-special-names.https.any.html?npu - WPT Dashboard Interop Dashboard

// META: title=test input with special character names

// META: global=window

// META: variant=?cpu

// META: variant=?gpu

// META: variant=?npu

// META: script=../resources/utils.js

// META: timeout=long

'use strict';

// https://www.w3.org/TR/webnn/#dom-mloperatoroptions-label

let mlContext;

// Skip tests if WebNN is unimplemented.

promise_setup(async () => {

  assert_implements(navigator.ml, 'missing navigator.ml');

  mlContext = await navigator.ml.createContext(contextOptions);

});

const specialNameArray = [

  ['12-L#!.☺', '🤦🏼‍♂️124DS#!F'],

  // Escape Sequence

  ['\0node_a', '\0node_b'],

  ['node\0a', 'node\0b'],

  // Hexadecimal Escape Sequences

  // '\x41'→ 'A'

  ['\x41\x41\x41', '\x42\x42\x42'],

  // Unicode & Hexadecimal Characters

  //   "\u00A9" → "©"

  //   "\xA9" → "©"

  //   "\u2665" → "♥"

  //   "\u2026" → "…"

  //   "\U0001F600" → 😀 (Grinning Face Emoji)

  ['\u00A9\xA9\u2665\u2026', '\U0001F600']

];

specialNameArray.forEach((name) => {

  promise_test(async () => {

    // The following code builds a graph as:

    // constant1 ---+

    //              +--- Add (label_0) ---> intermediateOutput1 ---+

    // input1    ---+                                              |

    //                                                             +--- Mul---> output

    // constant2 ---+                                              |

    //              +--- Add (label_1) ---> intermediateOutput2 ---+

    // input2    ---+

    const TENSOR_DIMS = [1, 2, 2, 2];

    const TENSOR_SIZE = 8;

    const builder = new MLGraphBuilder(mlContext);

    const desc = { dataType: 'float32', shape: TENSOR_DIMS };

    const constantBuffer1 = new Float32Array(TENSOR_SIZE).fill(0.5);

    const constant1 = builder.constant(desc, constantBuffer1);

    const input1 = builder.input('input1', desc);

    const constantBuffer2 = new Float32Array(TENSOR_SIZE).fill(0.5);

    const constant2 = builder.constant(desc, constantBuffer2);

    const input2 = builder.input('input2', desc);

    const intermediateOutput1 = builder.add(constant1, input1, {label: name[0]});

    const intermediateOutput2 = builder.add(constant2, input2, {label: name[1]});

    const output = builder.mul(intermediateOutput1, intermediateOutput2);

    const graph = await builder.build({'output': output});

    const inputBuffer1 = new Float32Array(TENSOR_SIZE).fill(1);

    const inputBuffer2 = new Float32Array(TENSOR_SIZE).fill(1);

    desc.writable = true;

    const inputTensor1 = await mlContext.createTensor(desc);

    const inputTensor2 = await mlContext.createTensor(desc);

    mlContext.writeTensor(inputTensor1, inputBuffer1);

    mlContext.writeTensor(inputTensor2, inputBuffer2);

    const outputTensor = await mlContext.createTensor({

      ...desc,

      readable: true,

      writable: false,

});

    const inputs = {

      'input1': inputTensor1,

      'input2': inputTensor2,

};

    const outputs = {'output': outputTensor};

    mlContext.dispatch(graph, inputs, outputs);

    assert_array_equals(

      new Float32Array(await mlContext.readTensor(outputTensor)),

      Float32Array.from([2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25, 2.25]));

  }, `'add' nodes with special character name '${name[0]}' and '${name[1]}'`);

});