tor-browser

The Tor Browser
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inputs-are-not-modified.https.any.js (3060B)

      1 // META: title=test that input tensors are not modified during a call to dispatch()
      2 // META: global=window,worker
      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/#api-mlcontext-dispatch
     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 promise_test(async () => {
     22  const builder = new MLGraphBuilder(mlContext);
     23  const inputOperand =
     24      builder.input('input', {dataType: 'float32', shape: [4]});
     25  const hardSwishOperand = builder.hardSwish(inputOperand);
     26  // Add some other operator for the output tensor to bind to; otherwise there
     27  // is no reason to implement hardSwish "in-place".
     28  const outputOperand = builder.identity(hardSwishOperand);
     29 
     30  const [inputTensor, outputTensor, mlGraph] = await Promise.all([
     31    mlContext.createTensor({
     32      dataType: 'float32',
     33      shape: [4],
     34      readable: true,
     35      writable: true,
     36    }),
     37    mlContext.createTensor({dataType: 'float32', shape: [4], readable: true}),
     38    builder.build({'output': outputOperand})
     39  ]);
     40 
     41  const inputData = Float32Array.from([-4, -1, 1, 4]);
     42  mlContext.writeTensor(inputTensor, inputData);
     43 
     44  mlContext.dispatch(mlGraph, {'input': inputTensor}, {'output': outputTensor});
     45 
     46  // Wait for graph execution to complete.
     47  await mlContext.readTensor(outputTensor);
     48 
     49  // The input tensor should not be modified.
     50  assert_array_equals(
     51      new Float32Array(await mlContext.readTensor(inputTensor)), inputData);
     52 }, 'input tensor is not modified: hardSwish');
     53 
     54 promise_test(async () => {
     55  const builder = new MLGraphBuilder(mlContext);
     56  const inputOperand =
     57      builder.input('input', {dataType: 'float32', shape: [4]});
     58  const constantOperand = builder.constant(
     59      {dataType: 'float32', shape: [4]}, Float32Array.from([-2, 0, 3, 4]));
     60  const mulOperand = builder.mul(inputOperand, constantOperand);
     61  // Add some other operator for the output tensor to bind to; otherwise there
     62  // is no reason to implement mul "in-place".
     63  const outputOperand = builder.add(mulOperand, constantOperand);
     64 
     65  const [inputTensor, outputTensor, mlGraph] = await Promise.all([
     66    mlContext.createTensor({
     67      dataType: 'float32',
     68      shape: [4],
     69      readable: true,
     70      writable: true,
     71    }),
     72    mlContext.createTensor({dataType: 'float32', shape: [4], readable: true}),
     73    builder.build({'output': outputOperand})
     74  ]);
     75 
     76  const inputData = Float32Array.from([1, 2, 3, 4]);
     77  mlContext.writeTensor(inputTensor, inputData);
     78  mlContext.dispatch(mlGraph, {'input': inputTensor}, {'output': outputTensor});
     79 
     80  // Wait for graph execution to complete.
     81  await mlContext.readTensor(outputTensor);
     82 
     83  // The input tensor should not be modified.
     84  assert_array_equals(
     85      new Float32Array(await mlContext.readTensor(inputTensor)), inputData);
     86 }, 'input tensor is not modified: mul');