Reuse mocha tests for web platform tests

[huningxin]

Raised by @anssiko in 3 September 2020 WebML CG meeting,

anssik: interested in figuring out if we can reuse mocha tests for w-p-t tests

https://‌web-platform-tests.org/

[BruceDai]

@huningxin @anssiko I converted WebNN add and mul tests using Web Platform Tests testharness.js framework from such add tests and mul tests of @huningxin 's PR #1. My converted tests are on this link, and the report of these tests goes here, PTAL.

Following Writing Tests guidelines of Web Platform Tests, I did three small modifications to convert from @huningxin 's mocha tests.

1. Created test html page, then imported both testharness.js and testharnessreport.js scripts.
2. Used testharness.js promise_test interface instead of mocha it interface.
3. Used testharness.js assertions instead of chai assertions in mocha tests.

What're your opinions? Thanks.

[anssiko]

Thanks @BruceDai! Given the conversion from mocha to w-p-t test harness seemed to be a reasonable effort, I'd recommend we focus on w-p-t tests that are reusable in the future standardization phases (in particular, latest at CR transition).

@wchao1115 might be interested in taking a look at your converted tests, since he is familiar with conformance testing of ML operations.

[BruceDai]

I will implement convert tool for WPT tests, thanks.

[BruceDai]

Update.

We've drafted some tests for later WPT tests contribution, including:

1. IDL tests
   Implemented idlharness.https.any.js which could generate tests for Web IDL fragments using the JavaScript Tests (testharness.js) infrastructure. Now there're each 351 tests with 216 pass tests on latest Chrome stable (93.0.4577.82).

2. JavaScript tests (testharness.js)
   Implemented these tests by converting from 328 op tests and 60 model tests of webnn-polyfill tests. Now there're 388 pass tests on latest Chrome stable (93.0.4577.82).

Above tests are with webnn.idl of w3c/webref which automatically extracted from https://www.w3.org/TR/webnn/ and latest unminimized webnn-poyfill.js which was generated by building this webnn-polyfill project.

I'm not sure that Web ML community (Working Group) would be interested in such tests or whether I'm doing the right thing?

Here's a preview on https://brucedai.github.io/wpt/webnn/index.html, PTAL, thanks.

@anssiko @wchao1115 @huningxin Would you please give me some advice and some pieces of guidance? Thanks.

Notes: It would take time to load tests since unminimized webnn-poyfill.js script and weights files of model tests are large size files.

[huningxin]

Thanks much @BruceDai . The preview looks nice. As @anssiko mentioned, the WPT is a request for standardization phases (where we are now). So your development is quite helpful. I am interested in the status and the path forward.

I think this is a good topic for WebML WG meeting and probably for TPAC.

@anssiko @wchao1115 and @dontcallmedom , WDYT?

[anssiko]

Thanks @BruceDai for your efforts in converting the tests into w-p-t!

@dontcallmedom has expertise in w-p-t and can probably address your questions.

We've added the conformance testing of WebNN API to the TPAC agenda and will discuss this area more broadly at that meeting.

[dontcallmedom]

@BruceDai the ultimate goal should be to submit the converted tests as a pull request to https://github.com/web-platform-tests/wpt in a newly created /webnn directory - presumably at that point, the polyfill itself would import them from there rather than have the tests duplicated here.

Let me know what specific guidance you might want to move forward in that direction

[BruceDai]

Thanks @dontcallmedom @anssiko and @huningxin. And I'm sorry for late replay because of the holiday.

presumably at that point, the polyfill itself would import them from there rather than have the tests duplicated here.

@dontcallmedom Yes, I quite agree with you.

I will submit a PR of IDL tests for WebNN API using webnn-poyfill.js to WPT firstly.

[wchao1115]

Thanks @BruceDai

Aside from the choice of the tool to use, the test methodology is also important. The fundamental thing about floating-point math is that there is an inherent computational error built into the process and so the result could vary from operation to operation, or even from machine to machine. Typically for ML, we test the compute results by comparing them to a certain baseline values, and if the result is "close enough" to the baseline, the comparison is considered passed. This is how the epsilon value (or the so-called tolerance value) of a function like assert_approx_equal could be used.

An important topic then becomes: so what tolerance values to use in the comparisons? Given that the computational error is accumulative, there is no single answer. For example, you could normally use a smaller tolerance value for element-wise mul but would need a bigger value for element-wise sqrt just because of the greater accumulation of errors of the additional complexity, and even bigger value for a highly complex one such as convolution and gemm. So, the answer is that it depends. An empirical estimation of a tolerance value to use can also be based on a real experimentation on a real hardware.

For DirectML, we chose the double-precision results from a standard CPU as our baseline values or the so-called ideal result. We then define tolerance values from the ideal results for each operation we test. The comparison itself, however, is not done in absolute values, but rather in term of ULPs.

The ULP or the unit of least precision is an important concept in floating point math. It is the distance between two consecutive floating-point values. When you define the tolerance as in the units of ULP, you no longer compare a floating-point value to another, rather you measure the distance between the two representations and see if it's greater than the minimal allowable distance.

Here is a sample implementation of such a comparison method. The same algorithm can also be extended for FP16 and even FP64.

template<typename T>
int64_t GetBitwise(T value) {
    int64_t bitwiseValue = (value < T(0)) ? ~int64_t(0) : 0; // Extend sign.
    *std::launder(reinterpret_cast<T*>(&bitwiseValue)) = value;
    return bitwiseValue;
}

bool CompareUlp(float a, float b, uint64_t ulp) {
    return static_cast<uint64_t>(abs(GetBitwise(a) - GetBitwise(b))) > ulp;
}

A key benefit of ULP-based tolerances is that it's hardware agnostic. Some compute hardware emulates their floating-point result from fixed-point instructions; the ULP comparison would still work because the comparison will still be relative to its native representation. There is also an opportunity to standardize a set of ULP tolerances across different operations.