Testing

Let’s take a look at how 🤗 Transformer models are tested and how you can write new tests and improve the existing ones.

There are 2 test suites in the repository:

  1. tests – tests for the general API

  2. examples – tests primarily for various applications that aren’t part of the API

How transformers are tested

  1. Once a PR is submitted it gets tested with 9 CircleCi jobs. Every new commit to that PR gets retested. These jobs are defined in this config file, so that if needed you can reproduce the same environment on your machine.

    These CI jobs don’t run @slow tests.

  2. There are 3 jobs run by github actions:

    • torch hub integration: checks whether torch hub integration works.

    • self-hosted (push): runs fast tests on GPU only on commits on master. It only runs if a commit on master has updated the code in one of the following folders: src, tests, .github (to prevent running on added model cards, notebooks, etc.)

    • self-hosted runner: runs normal and slow tests on GPU in tests and examples:

    RUN_SLOW=1 pytest tests/
    RUN_SLOW=1 pytest examples/
    

    The results can be observed here.

Running tests

Choosing which tests to run

This document goes into many details of how tests can be run. If after reading everything, you need even more details you will find them here.

Here are some most useful ways of running tests.

Run all:

pytest

or:

make test

Note that the latter is defined as:

python -m pytest -n auto --dist=loadfile -s -v ./tests/

which tells pytest to:

  • run as many test processes as they are CPU cores (which could be too many if you don’t have a ton of RAM!)

  • ensure that all tests from the same file will be run by the same test process

  • do not capture output

  • run in verbose mode

Getting the list of all tests

All tests of the test suite:

pytest --collect-only -q

All tests of a given test file:

pytest tests/test_optimization.py --collect-only -q

Run a specific test module

To run an individual test module:

pytest tests/test_logging.py

Run specific tests

Since unittest is used inside most of the tests, to run specific subtests you need to know the name of the unittest class containing those tests. For example, it could be:

pytest tests/test_optimization.py::OptimizationTest::test_adam_w

Here:

  • tests/test_optimization.py - the file with tests

  • OptimizationTest - the name of the class

  • test_adam_w - the name of the specific test function

If the file contains multiple classes, you can choose to run only tests of a given class. For example:

pytest tests/test_optimization.py::OptimizationTest

will run all the tests inside that class.

As mentioned earlier you can see what tests are contained inside the OptimizationTest class by running:

pytest tests/test_optimization.py::OptimizationTest --collect-only -q

You can run tests by keyword expressions.

To run only tests whose name contains adam:

pytest -k adam tests/test_optimization.py

To run all tests except those whose name contains adam:

pytest -k "not adam" tests/test_optimization.py

And you can combine the two patterns in one:

pytest -k "ada and not adam" tests/test_optimization.py

Run only modified tests

You can run the tests related to the unstaged files or the current branch (according to Git) by using pytest-picked. This is a great way of quickly testing your changes didn’t break anything, since it won’t run the tests related to files you didn’t touch.

pip install pytest-picked
pytest --picked

All tests will be run from files and folders which are modified, but not yet committed.

Automatically rerun failed tests on source modification

pytest-xdist provides a very useful feature of detecting all failed tests, and then waiting for you to modify files and continuously re-rerun those failing tests until they pass while you fix them. So that you don’t need to re start pytest after you made the fix. This is repeated until all tests pass after which again a full run is performed.

pip install pytest-xdist

To enter the mode: pytest -f or pytest --looponfail

File changes are detected by looking at looponfailroots root directories and all of their contents (recursively). If the default for this value does not work for you, you can change it in your project by setting a configuration option in setup.cfg:

[tool:pytest]
looponfailroots = transformers tests

or pytest.ini/tox.ini files:

[pytest]
looponfailroots = transformers tests

This would lead to only looking for file changes in the respective directories, specified relatively to the ini-file’s directory.

pytest-watch is an alternative implementation of this functionality.

Skip a test module

If you want to run all test modules, except a few you can exclude them by giving an explicit list of tests to run. For example, to run all except test_modeling_*.py tests:

pytest `ls -1 tests/*py | grep -v test_modeling`

Clearing state

CI builds and when isolation is important (against speed), cache should be cleared:

pytest --cache-clear tests

Running tests in parallel

As mentioned earlier make test runs tests in parallel via pytest-xdist plugin (-n X argument, e.g. -n 2 to run 2 parallel jobs).

pytest-xdist’s --dist= option allows one to control how the tests are grouped. --dist=loadfile puts the tests located in one file onto the same process.

Since the order of executed tests is different and unpredictable, if running the test suite with pytest-xdist produces failures (meaning we have some undetected coupled tests), use pytest-replay to replay the tests in the same order, which should help with then somehow reducing that failing sequence to a minimum.

Test order and repetition

It’s good to repeat the tests several times, in sequence, randomly, or in sets, to detect any potential inter-dependency and state-related bugs (tear down). And the straightforward multiple repetition is just good to detect some problems that get uncovered by randomness of DL.

Repeat tests

pip install pytest-flakefinder

And then run every test multiple times (50 by default):

pytest --flake-finder --flake-runs=5 tests/test_failing_test.py

Note

This plugin doesn’t work with -n flag from pytest-xdist.

Note

There is another plugin pytest-repeat, but it doesn’t work with unittest.

Run tests in a random order

pip install pytest-random-order

Important: the presence of pytest-random-order will automatically randomize tests, no configuration change or command line options is required.

As explained earlier this allows detection of coupled tests - where one test’s state affects the state of another. When pytest-random-order is installed it will print the random seed it used for that session, e.g:

pytest tests
[...]
Using --random-order-bucket=module
Using --random-order-seed=573663

So that if the given particular sequence fails, you can reproduce it by adding that exact seed, e.g.:

pytest --random-order-seed=573663
[...]
Using --random-order-bucket=module
Using --random-order-seed=573663

It will only reproduce the exact order if you use the exact same list of tests (or no list at all). Once you start to manually narrowing down the list you can no longer rely on the seed, but have to list them manually in the exact order they failed and tell pytest to not randomize them instead using --random-order-bucket=none, e.g.:

pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py

To disable the shuffling for all tests:

pytest --random-order-bucket=none

By default --random-order-bucket=module is implied, which will shuffle the files on the module levels. It can also shuffle on class, package, global and none levels. For the complete details please see its documentation.

Another randomization alternative is: pytest-randomly <https://github.com/pytest-dev/pytest-randomly>`__. This module has a very similar functionality/interface, but it doesn’t have the bucket modes available in pytest-random-order. It has the same problem of imposing itself once installed.

Look and feel variations

pytest-sugar

pytest-sugar is a plugin that improves the look-n-feel, adds a progressbar, and show tests that fail and the assert instantly. It gets activated automatically upon installation.

pip install pytest-sugar

To run tests without it, run:

pytest -p no:sugar

or uninstall it.

Report each sub-test name and its progress

For a single or a group of tests via pytest (after pip install pytest-pspec):

pytest --pspec tests/test_optimization.py

Instantly shows failed tests

pytest-instafail shows failures and errors instantly instead of waiting until the end of test session.

pip install pytest-instafail
pytest --instafail

To GPU or not to GPU

On a GPU-enabled setup, to test in CPU-only mode add CUDA_VISIBLE_DEVICES="":

CUDA_VISIBLE_DEVICES="" pytest tests/test_logging.py

or if you have multiple gpus, you can specify which one is to be used by pytest. For example, to use only the second gpu if you have gpus 0 and 1, you can run:

CUDA_VISIBLE_DEVICES="1" pytest tests/test_logging.py

This is handy when you want to run different tasks on different GPUs.

Some tests must be run on CPU-only, others on either CPU or GPU or TPU, yet others on multiple-GPUs. The following skip decorators are used to set the requirements of tests CPU/GPU/TPU-wise:

  • require_torch - this test will run only under torch

  • require_torch_gpu - as require_torch plus requires at least 1 GPU

  • require_torch_multigpu - as require_torch plus requires at least 2 GPUs

  • require_torch_non_multigpu - as require_torch plus requires 0 or 1 GPUs

  • require_torch_tpu - as require_torch plus requires at least 1 TPU

For example, here is a test that must be run only when there are 2 or more GPUs available and pytorch is installed:

@require_torch_multigpu
def test_example_with_multigpu():

If a test requires tensorflow use the require_tf decorator. For example:

@require_tf
def test_tf_thing_with_tensorflow():

These decorators can be stacked. For example, if a test is slow and requires at least one GPU under pytorch, here is how to set it up:

@require_torch_gpu
@slow
def test_example_slow_on_gpu():

Some decorators like @parametrized rewrite test names, therefore @require_* skip decorators have to be listed last for them to work correctly. Here is an example of the correct usage:

@parameterized.expand(...)
@require_torch_multigpu
def test_integration_foo():

This order problem doesn’t exist with @pytest.mark.parametrize, you can put it first or last and it will still work. But it only works with non-unittests.

Inside tests:

  • How many GPUs are available:

torch.cuda.device_count()

Output capture

During test execution any output sent to stdout and stderr is captured. If a test or a setup method fails, its according captured output will usually be shown along with the failure traceback.

To disable output capturing and to get the stdout and stderr normally, use -s or --capture=no:

pytest -s tests/test_logging.py

To send test results to JUnit format output:

py.test tests --junitxml=result.xml

Color control

To have no color (e.g., yellow on white background is not readable):

pytest --color=no tests/test_logging.py

Sending test report to online pastebin service

Creating a URL for each test failure:

pytest --pastebin=failed tests/test_logging.py

This will submit test run information to a remote Paste service and provide a URL for each failure. You may select tests as usual or add for example -x if you only want to send one particular failure.

Creating a URL for a whole test session log:

pytest --pastebin=all tests/test_logging.py

Writing tests

🤗 transformers tests are based on unittest, but run by pytest, so most of the time features from both systems can be used.

You can read here which features are supported, but the important thing to remember is that most pytest fixtures don’t work. Neither parametrization, but we use the module parameterized that works in a similar way.

Parametrization

Often, there is a need to run the same test multiple times, but with different arguments. It could be done from within the test, but then there is no way of running that test for just one set of arguments.

# test_this1.py
import unittest
from parameterized import parameterized
class TestMathUnitTest(unittest.TestCase):
    @parameterized.expand([
        ("negative", -1.5, -2.0),
        ("integer", 1, 1.0),
        ("large fraction", 1.6, 1),
    ])
    def test_floor(self, name, input, expected):
        assert_equal(math.floor(input), expected)

Now, by default this test will be run 3 times, each time with the last 3 arguments of test_floor being assigned the corresponding arguments in the parameter list.

and you could run just the negative and integer sets of params with:

pytest -k "negative and integer" tests/test_mytest.py

or all but negative sub-tests, with:

pytest -k "not negative" tests/test_mytest.py

Besides using the -k filter that was just mentioned, you can find out the exact name of each sub-test and run any or all of them using their exact names.

pytest test_this1.py --collect-only -q

and it will list:

test_this1.py::TestMathUnitTest::test_floor_0_negative
test_this1.py::TestMathUnitTest::test_floor_1_integer
test_this1.py::TestMathUnitTest::test_floor_2_large_fraction

So now you can run just 2 specific sub-tests:

pytest test_this1.py::TestMathUnitTest::test_floor_0_negative  test_this1.py::TestMathUnitTest::test_floor_1_integer

The module parameterized which is already in the developer dependencies of transformers works for both: unittests and pytest tests.

If, however, the test is not a unittest, you may use pytest.mark.parametrize (or you may see it being used in some existing tests, mostly under examples).

Here is the same example, this time using pytest’s parametrize marker:

# test_this2.py
import pytest
@pytest.mark.parametrize(
    "name, input, expected",
    [
        ("negative", -1.5, -2.0),
        ("integer", 1, 1.0),
        ("large fraction", 1.6, 1),
    ],
)
def test_floor(name, input, expected):
    assert_equal(math.floor(input), expected)

Same as with parameterized, with pytest.mark.parametrize you can have a fine control over which sub-tests are run, if the -k filter doesn’t do the job. Except, this parametrization function creates a slightly different set of names for the sub-tests. Here is what they look like:

pytest test_this2.py --collect-only -q

and it will list:

test_this2.py::test_floor[integer-1-1.0]
test_this2.py::test_floor[negative--1.5--2.0]
test_this2.py::test_floor[large fraction-1.6-1]

So now you can run just the specific test:

pytest test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[integer-1-1.0]

as in the previous example.

Temporary files and directories

Using unique temporary files and directories are essential for parallel test running, so that the tests won’t overwrite each other’s data. Also we want to get the temp files and directories removed at the end of each test that created them. Therefore, using packages like tempfile, which address these needs is essential.

However, when debugging tests, you need to be able to see what goes into the temp file or directory and you want to know it’s exact path and not having it randomized on every test re-run.

A helper class transformers.test_utils.TestCasePlus is best used for such purposes. It’s a sub-class of unittest.TestCase, so we can easily inherit from it in the test modules.

Here is an example of its usage:

from transformers.testing_utils import TestCasePlus
class ExamplesTests(TestCasePlus):
def test_whatever(self):
    tmp_dir = self.get_auto_remove_tmp_dir()

This code creates a unique temporary directory, and sets tmp_dir to its location.

In this and all the following scenarios the temporary directory will be auto-removed at the end of test, unless after=False is passed to the helper function.

  • Create a temporary directory of my choice and delete it at the end - useful for debugging when you want to monitor a specific directory:

def test_whatever(self):
    tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test")
  • Create a temporary directory of my choice and do not delete it at the end—useful for when you want to look at the temp results:

def test_whatever(self):
    tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test", after=False)
  • Create a temporary directory of my choice and ensure to delete it right away—useful for when you disabled deletion in the previous test run and want to make sure the that temporary directory is empty before the new test is run:

def test_whatever(self):
     tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test", before=True)

Note

In order to run the equivalent of rm -r safely, only subdirs of the project repository checkout are allowed if an explicit obj:tmp_dir is used, so that by mistake no /tmp or similar important part of the filesystem will get nuked. i.e. please always pass paths that start with ./.

Note

Each test can register multiple temporary directories and they all will get auto-removed, unless requested otherwise.

Skipping tests

This is useful when a bug is found and a new test is written, yet the bug is not fixed yet. In order to be able to commit it to the main repository we need make sure it’s skipped during make test.

Methods:

  • A skip means that you expect your test to pass only if some conditions are met, otherwise pytest should skip running the test altogether. Common examples are skipping windows-only tests on non-windows platforms, or skipping tests that depend on an external resource which is not available at the moment (for example a database).

  • A xfail means that you expect a test to fail for some reason. A common example is a test for a feature not yet implemented, or a bug not yet fixed. When a test passes despite being expected to fail (marked with pytest.mark.xfail), it’s an xpass and will be reported in the test summary.

One of the important differences between the two is that skip doesn’t run the test, and xfail does. So if the code that’s buggy causes some bad state that will affect other tests, do not use xfail.

Implementation

  • Here is how to skip whole test unconditionally:

@unittest.skip("this bug needs to be fixed")
def test_feature_x():

or via pytest:

@pytest.mark.skip(reason="this bug needs to be fixed")

or the xfail way:

@pytest.mark.xfail
def test_feature_x():

Here is how to skip a test based on some internal check inside the test:

def test_feature_x():
    if not has_something():
        pytest.skip("unsupported configuration")

or the whole module:

import pytest
if not pytest.config.getoption("--custom-flag"):
    pytest.skip("--custom-flag is missing, skipping tests", allow_module_level=True)

or the xfail way:

def test_feature_x():
    pytest.xfail("expected to fail until bug XYZ is fixed")

Here is how to skip all tests in a module if some import is missing:

docutils = pytest.importorskip("docutils", minversion="0.3")
  • Skip a test based on a condition:

@pytest.mark.skipif(sys.version_info < (3,6), reason="requires python3.6 or higher")
def test_feature_x():

or:

@unittest.skipIf(torch_device == "cpu", "Can't do half precision")
def test_feature_x():

or skip the whole module:

@pytest.mark.skipif(sys.platform == 'win32', reason="does not run on windows")
class TestClass():
    def test_feature_x(self):

More details, example and ways are here.

Custom markers

  • Slow tests

Tests that are too slow (e.g. once downloading huge model files) are marked with:

from transformers.testing_utils import slow
@slow
def test_integration_foo():

To run such tests set RUN_SLOW=1 env var, e.g.:

RUN_SLOW=1 pytest tests

Some decorators like @parametrized rewrite test names, therefore @slow and the rest of the skip decorators @require_* have to be listed last for them to work correctly. Here is an example of the correct usage:

@parameterized.expand(...)
@slow
def test_integration_foo():

Testing the stdout/stderr output

In order to test functions that write to stdout and/or stderr, the test can access those streams using the pytest’s capsys system. Here is how this is accomplished:

import sys
def print_to_stdout(s): print(s)
def print_to_stderr(s): sys.stderr.write(s)
def test_result_and_stdout(capsys):
    msg = "Hello"
    print_to_stdout(msg)
    print_to_stderr(msg)
    out, err = capsys.readouterr() # consume the captured output streams
    # optional: if you want to replay the consumed streams:
    sys.stdout.write(out)
    sys.stderr.write(err)
    # test:
    assert msg in out
    assert msg in err

And, of course, most of the time, stderr will come as a part of an exception, so try/except has to be used in such a case:

def raise_exception(msg): raise ValueError(msg)
def test_something_exception():
    msg = "Not a good value"
    error = ''
    try:
        raise_exception(msg)
    except Exception as e:
        error = str(e)
        assert msg in error, f"{msg} is in the exception:\n{error}"

Another approach to capturing stdout is via contextlib.redirect_stdout:

from io import StringIO
from contextlib import redirect_stdout
def print_to_stdout(s): print(s)
def test_result_and_stdout():
    msg = "Hello"
    buffer = StringIO()
    with redirect_stdout(buffer):
        print_to_stdout(msg)
    out = buffer.getvalue()
    # optional: if you want to replay the consumed streams:
    sys.stdout.write(out)
    # test:
    assert msg in out

An important potential issue with capturing stdout is that it may contain \r characters that in normal print reset everything that has been printed so far. There is no problem with pytest, but with pytest -s these characters get included in the buffer, so to be able to have the test run with and without -s, you have to make an extra cleanup to the captured output, using re.sub(r'~.*\r', '', buf, 0, re.M).

But, then we have a helper context manager wrapper to automatically take care of it all, regardless of whether it has some \r’s in it or not, so it’s a simple:

from transformers.testing_utils import CaptureStdout
with CaptureStdout() as cs:
    function_that_writes_to_stdout()
print(cs.out)

Here is a full test example:

from transformers.testing_utils import CaptureStdout
msg = "Secret message\r"
final = "Hello World"
with CaptureStdout() as cs:
    print(msg + final)
assert cs.out == final+"\n", f"captured: {cs.out}, expecting {final}"

If you’d like to capture stderr use the CaptureStderr class instead:

from transformers.testing_utils import CaptureStderr
with CaptureStderr() as cs:
    function_that_writes_to_stderr()
print(cs.err)

If you need to capture both streams at once, use the parent CaptureStd class:

from transformers.testing_utils import CaptureStd
with CaptureStd() as cs:
    function_that_writes_to_stdout_and_stderr()
print(cs.err, cs.out)

Capturing logger stream

If you need to validate the output of a logger, you can use CaptureLogger:

from transformers import logging
from transformers.testing_utils import CaptureLogger

msg = "Testing 1, 2, 3"
logging.set_verbosity_info()
logger = logging.get_logger("transformers.tokenization_bart")
with CaptureLogger(logger) as cl:
    logger.info(msg)
assert cl.out, msg+"\n"

Testing with environment variables

If you want to test the impact of environment variables for a specific test you can use a helper decorator transformers.testing_utils.mockenv

from transformers.testing_utils import mockenv
class HfArgumentParserTest(unittest.TestCase):
    @mockenv(TRANSFORMERS_VERBOSITY="error")
    def test_env_override(self):
        env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None)

Getting reproducible results

In some situations you may want to remove randomness for your tests. To get identical reproducable results set, you will need to fix the seed:

seed = 42

# python RNG
import random
random.seed(seed)

# pytorch RNGs
import torch
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)

# numpy RNG
import numpy as np
np.random.seed(seed)

# tf RNG
tf.random.set_seed(seed)

Debugging tests

To start a debugger at the point of the warning, do this:

pytest tests/test_logging.py -W error::UserWarning --pdb