mozilla-central/testing/web-platform/tests/webnn/conformance_tests/byob_readtensor.https.any.js (file symbol)

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// META: title=test WebNN API tensor operations
// 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');
});
const testContents = Uint32Array.from([0, 1, 2, 3, 4, 5, 6, 7]);
let mlContext;
let mlTensor;
promise_setup(async () => {
try {
mlContext = await navigator.ml.createContext(contextOptions);
} catch (e) {
throw new AssertionError(
`Unable to create context for ${variant} variant. ${e}`);
}
try {
mlTensor = await mlContext.createTensor({
dataType: 'int32',
shape: [2, 4],
readable: true,
writable: true,
});
} catch (e) {
throw new AssertionError(
`Unable to create tensor for ${variant} variant. ${e}`);
}
mlContext.writeTensor(mlTensor, testContents);
});
promise_test(async (t) => {
const arrayBuffer = new ArrayBuffer(testContents.byteLength - 4);
await promise_rejects_js(
t, TypeError, mlContext.readTensor(mlTensor, arrayBuffer));
}, `readTensor() with an ArrayBuffer that is too small should reject`);
promise_test(async (t) => {
const typedArray = new Uint32Array(testContents.length - 1);
await promise_rejects_js(
t, TypeError, mlContext.readTensor(mlTensor, typedArray));
}, `readTensor() with a TypedArray that is too small should reject`);
promise_test(async (t) => {
const arrayBuffer = new ArrayBuffer(testContents.byteLength);
const typedArray = new Uint32Array(arrayBuffer);
arrayBuffer.transfer();
await promise_rejects_js(
t, TypeError, mlContext.readTensor(mlTensor, arrayBuffer));
await promise_rejects_js(
t, TypeError, mlContext.readTensor(mlTensor, typedArray));
}, `readTensor() with a detached ArrayBuffer should reject`);
promise_test(async (t) => {
const arrayBuffer = new ArrayBuffer(testContents.byteLength);
const typedArray = new Uint32Array(arrayBuffer);
const checks = Promise.all([
promise_rejects_js(
t, TypeError, mlContext.readTensor(mlTensor, arrayBuffer)),
promise_rejects_js(
t, TypeError, mlContext.readTensor(mlTensor, typedArray)),
]);
arrayBuffer.transfer();
await checks;
}, `Detaching an ArrayBuffer while readTensor() is in progress should reject`);
promise_test(async () => {
const arrayBuffer = new ArrayBuffer(testContents.byteLength);
await mlContext.readTensor(mlTensor, arrayBuffer);
assert_array_equals(new Uint32Array(arrayBuffer), testContents);
}, `readTensor() with an ArrayBuffer`);
promise_test(async () => {
const sharedArrayBuffer = new SharedArrayBuffer(testContents.byteLength);
await mlContext.readTensor(mlTensor, sharedArrayBuffer);
assert_array_equals(new Uint32Array(sharedArrayBuffer), testContents);
}, `readTensor() with a SharedArrayBuffer`);
promise_test(async () => {
const sharedArrayBuffer = new SharedArrayBuffer(testContents.byteLength);
const typedArray = new Uint32Array(sharedArrayBuffer);
await mlContext.readTensor(mlTensor, typedArray);
assert_array_equals(typedArray, testContents);
}, `readTensor() with a typeArray from a SharedArrayBuffer`);
promise_test(async () => {
// Create a slightly larger ArrayBuffer and set up the TypedArray at an
// offset to make sure the MLTensor contents are written to the correct
// offset.
const arrayBuffer = new ArrayBuffer(testContents.byteLength + 4);
const typedArray = new Uint32Array(arrayBuffer, 4);
await mlContext.readTensor(mlTensor, typedArray);
assert_array_equals(typedArray, testContents);
}, `readTensor() with a TypedArray`);
promise_test(async () => {
const arrayBuffer = new ArrayBuffer(testContents.byteLength * 2);
await mlContext.readTensor(mlTensor, arrayBuffer);
assert_array_equals(
new Uint32Array(arrayBuffer).subarray(0, testContents.length),
testContents);
// The rest of the array should remain uninitialized.
assert_array_equals(
new Uint32Array(arrayBuffer)
.subarray(testContents.length, testContents.length * 2),
new Uint32Array(testContents.length));
}, `readTensor() with a larger ArrayBuffer`);
promise_test(async () => {
// Create a slightly larger ArrayBuffer and set up the TypedArray at an
// offset to make sure the MLTensor contents are written to the correct
// offset.
const arrayBuffer = new ArrayBuffer(testContents.byteLength * 2 + 4);
const typedArray = new Uint32Array(arrayBuffer, 4);
await mlContext.readTensor(mlTensor, typedArray);
assert_array_equals(
typedArray.subarray(0, testContents.length), testContents);
// The rest of the array should remain uninitialized.
assert_array_equals(
typedArray.subarray(testContents.length, testContents.length * 2),
new Uint32Array(testContents.length));
}, `readTensor() with a larger TypedArray`);
promise_test(async (t) => {
const tensor = await mlContext.createTensor({
dataType: 'int32',
shape: [2, 2],
readable: true,
});
const arrayBufferView = new Int32Array(2 * 2);
const arrayBuffer = arrayBufferView.buffer;
// Reading a destroyed MLTensor should reject.
tensor.destroy();
await promise_rejects_dom(
t, 'InvalidStateError', mlContext.readTensor(tensor, arrayBuffer));
await promise_rejects_dom(
t, 'InvalidStateError', mlContext.readTensor(tensor, arrayBufferView));
}, `readTensor() rejects on a destroyed MLTensor`);
promise_test(async (t) => {
const tensor = await mlContext.createTensor({
dataType: 'int32',
shape: [2, 2],
readable: true,
});
const arrayBufferView = new Int32Array(2 * 2);
const arrayBuffer = arrayBufferView.buffer;
const checks = Promise.all([
promise_rejects_dom(
t, 'InvalidStateError', mlContext.readTensor(tensor, arrayBuffer)),
promise_rejects_dom(
t, 'InvalidStateError', mlContext.readTensor(tensor, arrayBufferView)),
]);
tensor.destroy();
await checks;
}, `readTensor() rejects when the MLTensor is destroyed`);