shared_arraybuffer_constant.https.any.js

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// META: title=test WebNN API constant with shared array buffer

// META: global=window,dedicatedworker

// META: variant=?cpu

// META: variant=?gpu

// META: variant=?npu

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

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

// META: timeout=long

'use strict';

// Skip tests if WebNN is unimplemented.

promise_setup(async () => {

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

});

// https://www.w3.org/TR/webnn/#api-mlgraphbuilder-constant-buffer

const testContents = Int32Array.from([0, 1, 2, 3, 4, 5, 6, 7]);

const sharedArrayBuffer = new SharedArrayBuffer(testContents.byteLength);

const typedArray = new Int32Array(sharedArrayBuffer);

typedArray.set(testContents);

let mlContext;

let mlGraph;

let outputTensor;

promise_setup(async () => {

  try {

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

  } catch (e) {

    throw new AssertionError(

        `Unable to create mlContext for ${variant} variant. ${e}`);

  try {

    outputTensor = await mlContext.createTensor({

      dataType: 'int32',

      shape: [8],

      readable: true,

});

  } catch (e) {

    throw new AssertionError(

        `Unable to create tensor for ${variant} variant. ${e}`);

});

promise_test(async () => {

  const builder = new MLGraphBuilder(mlContext);

  const constant =

      builder.constant({dataType: 'int32', shape: [8]}, sharedArrayBuffer);

  const output = builder.identity(constant);

  const mlGraph = await builder.build({output});

  mlContext.dispatch(mlGraph, {}, {output: outputTensor});

  const results = new Int32Array(await mlContext.readTensor(outputTensor));

  assert_array_equals(results, testContents);

}, `constant() with a SharedArrayBuffer`);

promise_test(async () => {

  const builder = new MLGraphBuilder(mlContext);

  const constant =

      builder.constant({dataType: 'int32', shape: [8]}, typedArray);

  const output = builder.identity(constant);

  const mlGraph = await builder.build({output});

  mlContext.dispatch(mlGraph, {}, {output: outputTensor});

  const results = new Int32Array(await mlContext.readTensor(outputTensor));

  assert_array_equals(results, testContents);

}, `constant() with a typeArray from a SharedArrayBuffer`);