scalars.https.any.js

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// META: title=test that scalar values work as expected

// META: global=window,dedicatedworker

// META: variant=?cpu

// META: variant=?gpu

// META: variant=?npu

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

// META: timeout=long

'use strict';

let mlContext;

// Skip tests if WebNN is unimplemented.

promise_setup(async () => {

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

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

});

promise_test(async () => {

  const builder = new MLGraphBuilder(mlContext);

  const inputOperand = builder.input('input', {dataType: 'int32', shape: []});

  const constantOperand = builder.constant(

      {dataType: 'int32', shape: [4]}, Int32Array.from([3, 2, 1, 7]));

  const addOperand = builder.add(inputOperand, constantOperand);

  const [inputTensor, outputTensor, mlGraph] = await Promise.all([

    mlContext.createTensor({dataType: 'int32', shape: [], writable: true}),

    mlContext.createTensor({dataType: 'int32', shape: [4], readable: true}),

    builder.build({'output': addOperand})

]);

  mlContext.writeTensor(inputTensor, Int32Array.from([4]));

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

  assert_array_equals(

      new Int32Array(await mlContext.readTensor(outputTensor)),

      Int32Array.from([7, 6, 5, 11]));

}, 'scalar input');

promise_test(async () => {

  const builder = new MLGraphBuilder(mlContext);

  const inputOperand = builder.input('input', {dataType: 'float32', shape: []});

  const constantOperand = builder.constant(

      {dataType: 'float32', shape: []}, Float32Array.from([3]));

  const addOperand = builder.add(inputOperand, constantOperand);

  const [inputTensor, outputTensor, mlGraph] = await Promise.all([

    mlContext.createTensor({dataType: 'float32', shape: [], writable: true}),

    mlContext.createTensor({dataType: 'float32', shape: [], readable: true}),

    builder.build({'output': addOperand})

]);

  mlContext.writeTensor(inputTensor, Float32Array.from([4]));

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

  assert_array_equals(

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

      Float32Array.from([7]));

}, 'float32 scalar input, constant, and output');

promise_test(async () => {

  const builder = new MLGraphBuilder(mlContext);

  const inputOperand = builder.input('input', {dataType: 'int32', shape: []});

  const constantOperand =

      builder.constant({dataType: 'int32', shape: []}, Int32Array.from([3]));

  const addOperand = builder.add(inputOperand, constantOperand);

  const [inputTensor, outputTensor, mlGraph] = await Promise.all([

    mlContext.createTensor({dataType: 'int32', shape: [], writable: true}),

    mlContext.createTensor({dataType: 'int32', shape: [], readable: true}),

    builder.build({'output': addOperand})

]);

  mlContext.writeTensor(inputTensor, Int32Array.from([4]));

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

  assert_array_equals(

      new Int32Array(await mlContext.readTensor(outputTensor)),

      Int32Array.from([7]));

}, 'int32 scalar input, constant, and output');