#
Testing

Let’s take a look at how 🤗 Transformers 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 `main`. It only runs if a commit on `main` 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/utils/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

Logical `and` and `or` can be used to indicate whether all keywords should match or either. `not` can be used to negate.

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

For example to run both `test_adafactor` and `test_adam_w` you can use:

    pytest -k "test_adafactor or test_adam_w" tests/test_optimization.py

Note that we use `or` here, since we want either of the keywords to match to include both.

If you want to include only tests that include both patterns, `and` is to be used:

    pytest -k "test and ada" tests/test_optimization.py

###
Run accelerate tests

Sometimes you need to run `accelerate` tests on your models. For that you can just add `-m accelerate_tests` to your command, if let’s say you want to run these tests on `OPT` run:

    RUN_SLOW=1 pytest -m accelerate_tests tests/models/opt/test_modeling_opt.py

###
Run documentation tests

In order to test whether the documentation examples are correct, you should check that the `doctests` are passing. As an example, let’s use `WhisperModel.forward`’s docstring:

    r"""
    Returns:

    Example:
        ```python
        >>> import torch
        >>> from transformers import WhisperModel, WhisperFeatureExtractor
        >>> from datasets import load_dataset

        >>> model = WhisperModel.from_pretrained("openai/whisper-base")
        >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base")
        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
        >>> input_features = inputs.input_features
        >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
        >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
        >>> list(last_hidden_state.shape)
        [1, 2, 512]
        ```"""

Just run the following line to automatically test every docstring example in the desired file:

    pytest --doctest-modules <path_to_file_or_dir>

If the file has a markdown extention, you should add the `--doctest-glob="*.md"` argument.

###
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

- pytest-flakefinder:

    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

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

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`. 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/utils/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/utils/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_multi_gpu` - as `require_torch` plus requires at least 2 GPUs
- `require_torch_non_multi_gpu` - as `require_torch` plus requires 0 or 1 GPUs
- `require_torch_up_to_2_gpus` - as `require_torch` plus requires 0 or 1 or 2 GPUs
- `require_torch_xla` - as `require_torch` plus requires at least 1 TPU

Let’s depict the GPU requirements in the following table:

n gpus | decorator
--- | ---
`>= 0` | `@require_torch`
`>= 1` | `@require_torch_gpu`
`>= 2` | `@require_torch_multi_gpu`
`< 2` | `@require_torch_non_multi_gpu`
`< 3` | `@require_torch_up_to_2_gpus`

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_multi_gpu
    def test_example_with_multi_gpu():

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_multi_gpu
    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:

    from transformers.testing_utils import get_gpu_count

    n_gpu = get_gpu_count()  # works with torch and tf

###
Testing with a specific PyTorch backend or device

To run the test suite on a specific torch device add `TRANSFORMERS_TEST_DEVICE="$device"` where `$device` is the target backend. For example, to test on CPU only:

    TRANSFORMERS_TEST_DEVICE="cpu" pytest tests/utils/test_logging.py

This variable is useful for testing custom or less common PyTorch backends such as `mps`, `xpu` or `npu`. It can also be used to achieve the same effect as `CUDA_VISIBLE_DEVICES` by targeting specific GPUs or testing in CPU-only mode.

Certain devices will require an additional import after importing `torch` for the first time. This can be specified using the environment variable `TRANSFORMERS_TEST_BACKEND`:

    TRANSFORMERS_TEST_BACKEND="torch_npu" pytest tests/utils/test_logging.py

Alternative backends may also require the replacement of device-specific functions. For example `torch.cuda.manual_seed` may need to be replaced with a device-specific seed setter like `torch.npu.manual_seed` or `torch.xpu.manual_seed` to correctly set a random seed on the device. To specify a new backend with backend-specific device functions when running the test suite, create a Python device specification file `spec.py` in the format:

    import torch
    import torch_npu # for xpu, replace it with `import intel_extension_for_pytorch`
    # !! Further additional imports can be added here !!

    # Specify the device name (eg. 'cuda', 'cpu', 'npu', 'xpu', 'mps')
    DEVICE_NAME = 'npu'

    # Specify device-specific backends to dispatch to.
    # If not specified, will fallback to 'default' in 'testing_utils.py`
    MANUAL_SEED_FN = torch.npu.manual_seed
    EMPTY_CACHE_FN = torch.npu.empty_cache
    DEVICE_COUNT_FN = torch.npu.device_count

This format also allows for specification of any additional imports required. To use this file to replace equivalent methods in the test suite, set the environment variable `TRANSFORMERS_TEST_DEVICE_SPEC` to the path of the spec file, e.g. `TRANSFORMERS_TEST_DEVICE_SPEC=spec.py`.

Currently, only `MANUAL_SEED_FN`, `EMPTY_CACHE_FN` and `DEVICE_COUNT_FN` are supported for device-specific dispatch.

### Distributed training

`pytest` can’t deal with distributed training directly. If this is attempted - the sub-processes don’t do the right thing and end up thinking they are `pytest` and start running the test suite in loops. It works, however, if one spawns a normal process that then spawns off multiple workers and manages the IO pipes.

Here are some tests that use it:

- test_trainer_distributed.py
- test_deepspeed.py

To jump right into the execution point, search for the `execute_subprocess_async` call in those tests.

You will need at least 2 GPUs to see these tests in action:

    CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/test_trainer_distributed.py

### 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/utils/test_logging.py

To send test results to JUnit format output:

    pytest tests --junitxml=result.xml

### Color control

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

    pytest --color=no tests/utils/test_logging.py

### Sending test report to online pastebin service

Creating a URL for each test failure:

    pytest --pastebin=failed tests/utils/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/utils/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.

### Files and directories

In tests often we need to know where things are relative to the current test file, and it’s not trivial since the test could be invoked from more than one directory or could reside in sub-directories with different depths. A helper class `transformers.test_utils.TestCasePlus` solves this problem by sorting out all the basic paths and provides easy accessors to them:

- `pathlib` objects (all fully resolved):

  - `test_file_path` - the current test file path, i.e. `__file__`
  - `test_file_dir` - the directory containing the current test file
  - `tests_dir` - the directory of the `tests` test suite
  - `examples_dir` - the directory of the `examples` test suite
  - `repo_root_dir` - the directory of the repository
  - `src_dir` - the directory of `src` (i.e. where the `transformers` sub-dir resides)

- stringified paths---same as above but these return paths as strings, rather than `pathlib` objects:

  - `test_file_path_str`
  - `test_file_dir_str`
  - `tests_dir_str`
  - `examples_dir_str`
  - `repo_root_dir_str`
  - `src_dir_str`

To start using those all you need is to make sure that the test resides in a subclass of `transformers.test_utils.TestCasePlus`. For example:

    from transformers.testing_utils import TestCasePlus

    class PathExampleTest(TestCasePlus):
        def test_something_involving_local_locations(self):
            data_dir = self.tests_dir / "fixtures/tests_samples/wmt_en_ro"

If you don’t need to manipulate paths via `pathlib` or you just need a path as a string, you can always invoked `str()` on the `pathlib` object or use the accessors ending with `_str`. For example:

    from transformers.testing_utils import TestCasePlus

    class PathExampleTest(TestCasePlus):
        def test_something_involving_stringified_locations(self):
            examples_dir = self.examples_dir_str

### 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 temporary 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 temporary 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.

- Create a unique temporary dir:

    def test_whatever(self):
        tmp_dir = self.get_auto_remove_tmp_dir()

`tmp_dir` will contain the path to the created temporary dir. It will be automatically removed at the end of the test.

- Create a temporary dir of my choice, ensure it’s empty before the test starts and don’t empty it after the test.

    def test_whatever(self):
        tmp_dir = self.get_auto_remove_tmp_dir("./xxx")

This is useful for debug when you want to monitor a specific directory and want to make sure the previous tests didn’t leave any data in there.

- You can override the default behavior by directly overriding the `before` and `after` args, leading to one of the following behaviors:

  - `before=True`: the temporary dir will always be cleared at the beginning of the test.
  - `before=False`: if the temporary dir already existed, any existing files will remain there.
  - `after=True`: the temporary dir will always be deleted at the end of the test.
  - `after=False`: the temporary dir will always be left intact at the end of the test.

In order to run the equivalent of `rm -r` safely, only subdirs of the project repository checkout are allowed if an explicit `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 `./`.

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

### Temporary sys.path override

If you need to temporary override `sys.path` to import from another test for example, you can use the `ExtendSysPath` context manager. Example:

    import os
    from transformers.testing_utils import ExtendSysPath

    bindir = os.path.abspath(os.path.dirname(__file__))
    with ExtendSysPath(f"{bindir}/.."):
        from test_trainer import TrainerIntegrationCommon  # noqa

### 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’s how to skip a test based on internal checks within 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.

### Slow tests

The library of tests is ever-growing, and some of the tests take minutes to run, therefore we can’t afford waiting for an hour for the test suite to complete on CI. Therefore, with some exceptions for essential tests, slow tests should be marked as in the example below:

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

Once a test is marked as `@slow`, to run such tests set `RUN_SLOW=1` env var, e.g.:

    RUN_SLOW=1 pytest tests

Some decorators like `@parameterized` 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():

As explained at the beginning of this document, slow tests get to run on a scheduled basis, rather than in PRs CI checks. So it’s possible that some problems will be missed during a PR submission and get merged. Such problems will get caught during the next scheduled CI job. But it also means that it’s important to run the slow tests on your machine before submitting the PR.

Here is a rough decision making mechanism for choosing which tests should be marked as slow:

If the test is focused on one of the library’s internal components (e.g., modeling files, tokenization files, pipelines), then we should run that test in the non-slow test suite. If it’s focused on an other aspect of the library, such as the documentation or the examples, then we should run these tests in the slow test suite. And then, to refine this approach we should have exceptions:

- All tests that need to download a heavy set of weights or a dataset that is larger than ~50MB (e.g., model or tokenizer integration tests, pipeline integration tests) should be set to slow. If you’re adding a new model, you should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is discussed in the following paragraphs.
- All tests that need to do a training not specifically optimized to be fast should be set to slow.
- We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to `@slow`. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked as `@slow`.
- If a test completes under 1 second on CI (including downloads if any) then it should be a normal test regardless.

Collectively, all the non-slow tests need to cover entirely the different internals, while remaining fast. For example, a significant coverage can be achieved by testing with specially created tiny models with random weights. Such models have the very minimal number of layers (e.g., 2), vocab size (e.g., 1000), etc. Then the `@slow` tests can use large slow models to do qualitative testing. To see the use of these simply look for tiny models with:

    grep tiny tests examples

Here is a an example of a script that created the tiny model stas/tiny-wmt19-en-de. You can easily adjust it to your specific model’s architecture.

It’s easy to measure the run-time incorrectly if for example there is an overheard of downloading a huge model, but if you test it locally the downloaded files would be cached and thus the download time not measured. Hence check the execution speed report in CI logs instead (the output of `pytest --durations=0 tests`).

That report is also useful to find slow outliers that aren’t marked as such, or which need to be re-written to be fast. If you notice that the test suite starts getting slow on CI, the top listing of this report will show the slowest tests.

### 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)

Also, to aid debugging test issues, by default these context managers automatically replay the captured streams on exit from the context.

### 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.models.bart.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)

At times an external program needs to be called, which requires setting `PYTHONPATH` in `os.environ` to include multiple local paths. A helper class `transformers.test_utils.TestCasePlus` comes to help:

    from transformers.testing_utils import TestCasePlus

    class EnvExampleTest(TestCasePlus):
        def test_external_prog(self):
            env = self.get_env()
            # now call the external program, passing `env` to it

Depending on whether the test file was under the `tests` test suite or `examples` it’ll correctly set up `env[PYTHONPATH]` to include one of these two directories, and also the `src` directory to ensure the testing is done against the current repo, and finally with whatever `env[PYTHONPATH]` was already set to before the test was called if anything.

This helper method creates a copy of the `os.environ` object, so the original remains intact.

### Getting reproducible results

In some situations you may want to remove randomness for your tests. To get identical reproducible 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/utils/test_logging.py -W error::UserWarning --pdb

## Working with github actions workflows

To trigger a self-push workflow CI job, you must:

1. Create a new branch on `transformers` origin (not a fork!).
2. The branch name has to start with either `ci_` or `ci-` (`main` triggers it too, but we can’t do PRs on `main`). It also gets triggered only for specific paths - you can find the up-to-date definition in case it changed since this document has been written here under push:
3. Create a PR from this branch.
4. Then you can see the job appear here. It may not run right away if there is a backlog.

## Testing Experimental CI Features

Testing CI features can be potentially problematic as it can interfere with the normal CI functioning. Therefore if a new CI feature is to be added, it should be done as following.

1. Create a new dedicated job that tests what needs to be tested
2. The new job must always succeed so that it gives us a green ✓ (details below).
3. Let it run for some days to see that a variety of different PR types get to run on it (user fork branches, non-forked branches, branches originating from github.com UI direct file edit, various forced pushes, etc. - there are so many) while monitoring the experimental job’s logs (not the overall job green as it’s purposefully always green)
4. When it’s clear that everything is solid, then merge the new changes into existing jobs.

That way experiments on CI functionality itself won’t interfere with the normal workflow.

Now how can we make the job always succeed while the new CI feature is being developed?

Some CIs, like TravisCI support ignore-step-failure and will report the overall job as successful, but CircleCI and Github Actions as of this writing don’t support that.

So the following workaround can be used:

1. `set +euo pipefail` at the beginning of the run command to suppress most potential failures in the bash script.
2. the last command must be a success: `echo "done"` or just `true` will do

Here is an example:

    - run:
        name: run CI experiment
        command: |
            set +euo pipefail
            echo "setting run-all-despite-any-errors-mode"
            this_command_will_fail
            echo "but bash continues to run"
            # emulate another failure
            false
            # but the last command must be a success
            echo "during experiment do not remove: reporting success to CI, even if there were failures"

For simple commands you could also do:

    cmd_that_may_fail || true

Of course, once satisfied with the results, integrate the experimental step or job with the rest of the normal jobs, while removing `set +euo pipefail` or any other things you may have added to ensure that the experimental job doesn’t interfere with the normal CI functioning.

This whole process would have been much easier if we only could set something like `allow-failure` for the experimental step, and let it fail without impacting the overall status of PRs. But as mentioned earlier CircleCI and Github Actions don’t support it at the moment.

You can vote for this feature and see where it is at these CI-specific threads:

- Github Actions:
- CircleCI:

## DeepSpeed integration

For a PR that involves the DeepSpeed integration, keep in mind our CircleCI PR CI setup doesn’t have GPUs. Tests requiring GPUs are run on a different CI nightly. This means if you get a passing CI report in your PR, it doesn’t mean the DeepSpeed tests pass.

To run DeepSpeed tests:

    RUN_SLOW=1 pytest tests/deepspeed/test_deepspeed.py

Any changes to the modeling or PyTorch examples code requires running the model zoo tests as well.

    RUN_SLOW=1 pytest tests/deepspeed

< > Update on GitHub