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[Docs] Added documentation on pytest target parametrization.
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Follow-up from apache#8542, to document existing features.
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Lunderberg committed Aug 3, 2021
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Expand Up @@ -29,3 +29,4 @@ various areas of the TVM stack.
relay_add_pass
relay_bring_your_own_codegen
codebase_walkthrough
pytest_target_parametrization
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.. Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
.. http://www.apache.org/licenses/LICENSE-2.0
.. Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
Python Target Parametrization
=============================

Summary
-------

A single python function in the test suite can expand to several
parametrized unit tests, each of which tests a single target device.
In order for a test to be run, all of the following must be true.

- The test exists in a file or directory that has been passed to
`pytest`.

- The pytest marks applied to the function, either explicitly or
through target parametrization, must be compatible with the
expression passed to pytest's `-m` argument.

- For parametrized tests using the `target` fixture, the target must
appear in the environment variable `TVM_TEST_TARGETS`.

- For parametrized tests using the `target` fixture, the build
configuration in `config.cmake` must enable the corresponding
runtime.

Unit-Test File Contents
-----------------------

.. _pytest-marks: https://docs.pytest.org/en/6.2.x/mark.html

Each test file has one or more test functions. The test functions can
be decorated with `pytest marks <pytest-marks>`_ to include metadata.
The most frequently applied marks are as follows.

- ``@pytest.mark.gpu`` - Tags a function as using GPU
capabilities. This has no effect on its own, but can be paired with
command-line arguments ``-m gpu`` or ``-m 'not gpu'`` to restrict
which tests pytest will executed. Typically not called on its own.

- ``@tvm.testing.uses_gpu`` - Applies ``@pytest.mark.gpu``. Needed
only for tests that explicitly loop over
``tvm.testing.enabled_targets()``.

- ``@tvm.testing.requires_gpu`` - Applies ``@tvm.testing.uses_gpu``,
and additionally marks that the test should be skipped
(``@pytest.mark.skipif``) entirely if no GPU is present.

- ``@tvfm.testing.requires_RUNTIME`` - Several decorators
(e.g. ``@tvm.testing.requires_cuda``), each of which skips a test if
the specified runtime cannot be used. A runtime cannot be used if it
is disabled in the ``config.cmake``, or if a compatible device is
not present. For runtimes that use the GPU, this includes
``@tvm.testing.requires_gpu``.

A single test can loop over ``tvm.testing.enabled_targets()`` to
validate all enabled targets, based on the environment variable
``TVM_TEST_TARGETS``, the build configuration, and the physical
hardware present. Most current tests use this style, but it is not
recommended for new tests. The pytest output for this style silently
skips runtimes that are disabled in ``config.cmake``, or do not have a
device on which they can run. In addition, the test halts on the
first target to fail, which is ambiguous as to whether the error
occurs on a particular target, or on every target.

.. code-block:: python
# Old style, not recommend anymore
def test_function():
for target,dev in tvm.testing.enabled_targets():
# Test code goes here
The recommended method to run a test on multiple targets is by
parametrizing the test. This can be done explicitly for a fixed list
of targets by decorating with
``@tvm.testing.parametrize_targets('target_1', 'target_2', ...)``, and
accepting ``target`` or ``dev`` as function arguments. The function will
be run once for each target listed, and the success/failure of each
target is reported separately. Each test run is decorated with the
``@tvm.testing.requires_RUNTIME`` that corresponds to the target being
used. As a result, if a target is disabled in ``config.cmake`` or does
not have appropriate hardware to run, it will be explicitly listed as
skipped.

.. code-block:: python
# New style, explicit listing of targets to use.
@tvm.testing.parametrize_target('llvm', 'cuda')
def test_function(target, dev):
# Test code goes here
For tests that should run correctly on all targets, the decorator can
be omitted. Any test that accepts a ``target`` or ``dev`` argument
will automatically be parametrized over all targets specified in
``TVM_TEST_TARGETS``. The parametrization provides the same
pass/fail/skipped report for each target, while allowing the test
suite to be easily extended to cover additional targets.

.. code-block:: python
# New style, implicitly parametrized to run on all
# targets in environment variable TVM_TEST_TARGETS
def test_function(target, dev):
# Test code goes here
The ``@tvm.testing.parametrize_targets`` can also be used as a bare
decorator to explicitly draw attention to the parametrization, but has
no additional effect.

.. code-block:: python
# New style, explicitly parametrized to run on all
# targets in environment variable TVM_TEST_TARGETS
@tvm.testing.parametrize_targets
def test_function(target, dev):
# Test code goes here
Specific targets can be excluded or marked as expected to fail using
the `@tvm.testing.exclude_targets` or
`@tvm.testing.known_failing_targets` decorators. For more information
on their intended use cases, please see their docstrings.

Lastly, in some cases it may be necessary to parametrize across
multiple parameters. For instance, there may be target-specific
implementations that should be tested, where some targets have more
than one implementation. These can be done by explicitly
parametrizing over tuples of arguments, such as shown below. In these
cases, only the explicitly listed targets will run, but they will
still have the appropriate `@tvm.testing.requires_RUNTIME` mark
applied to them.

.. code-block:: python
@pytest.mark.parametrize('target,impl', [
('llvm', cpu_implementation),
('cuda', gpu_implementation_small_batch),
('cuda', gpu_implementation_large_batch),
])
def test_function(target, dev, impl):
# Test code goes here
Running locally
---------------

To run the python unit-tests locally, use the command ``pytest`` in
the ``${TVM_HOME}`` directory.

- Environment variables
- ``TVM_TEST_TARGETS`` should be a semicolon-separated list of
targets to run. If unset, will default to the targets defined in
``tvm.testing.DEFAULT_TEST_TARGETS``.

Note: If ``TVM_TEST_TARGETS`` does not contain any targets that
are both enabled, and have an accessible device of that type,
then the tests will fall back to running on the ``llvm`` target
only.

- ``TVM_LIBRARY_PATH`` should be a path to the ``libtvm.so``
library. This can be used, for example, to run tests using a
debug build. If unset, will search for ``libtvm.so`` relative to
the TVM source directory.

- Command-line arguments

- Passing a path to a folder or file will run only the unit tests
in that folder or file. This can be useful, for example, to
avoid running tests located in ``tests/python/frontend`` on a
system without a specific frontend installed.

- The ``-m`` argument only runs unit tests that are tagged with a
specific pytest marker. The most frequent usage is to use ``m
gpu`` to run only tests that are marked with
``@pytest.mark.gpu`` and use a GPU to run. It can also be used
to run only tests that do not use a GPU, by passing ``m 'not
gpu'``.

Note: This filtering takes place after the selection of targets
based on the ``TVM_TEST_TARGETS`` environment variable. Even if
``-m gpu`` is specified, if ``TVM_TEST_TARGETS`` does not
contain GPU targets, no GPU tests will be run.

Running in local docker container
---------------------------------

.. _tlcpack: https://hub.docker.com/u/tlcpack

The ``docker/bash.sh`` script can be used to run unit tests inside the
same docker image as is used by the CI. The first argument should
specify which docker image to run (e.g. ``docker/bash.sh ci_gpu``).
Allowed image names are defined at the top of the Jenkinsfile located
in the TVM source directory, and map to images at `tlcpack`_.

If no additional arguments are given, the docker image will be loaded
with an interactive bash session. If a script is passed as an
optional argument (e.g. ``docker/bash.sh ci_gpu tests/scripts/task_python_unittest.sh``), then that script will be
executed inside the docker image.

Note: The docker images contain all system dependencies, but do not
include the ``build/config.cmake`` configuration file for those
systems. The TVM source directory is used as the home directory of
the docker image, and so this will default to using the same
config/build directories as the local config. One solution is to
maintain separate ``build_local`` and ``build_docker`` directories,
and make a symlink from ``build`` to the appropriate folder when
entering/exiting docker.

Running in CI
-------------

Everything in the CI starts from the task definitions present in the
Jenkinsfile. This includes defining which docker image gets used,
what the compile-time configuration is, and which tests are included
in which stages.

- Docker images

Each task of the Jenkinsfile (e.g. 'BUILD: CPU') makes calls to
``docker/bash.sh``. The argument following the call to
docker/bash.sh defines the docker image in CI, just as it does
locally.

- Compile-time configuration

The docker image does not have the ``config.cmake`` file built into
it, so this is the first step in each of the ``BUILD`` tasks. This
is done using the ``tests/scripts/task_config_build_*.sh`` scripts.
Which script is used depends on the build being tested, and is
specified in the Jenkinsfile.

Each ``BUILD`` task concludes by packing a library for use in later
tests.

- Which tests run

The ``Unit Test`` and ``Integration Test`` stages of the Jenkinsfile
determine how ``pytest`` is called. Each task starts by unpacking a
compiled library that was previous compiled in the ``BUILD`` stage,
then runs a test script
(e.g. ``tests/script/task_python_unittest.sh``). These scripts set
the files/folders and command-line options that are passed to
``pytest``.

Several of these scripts include the ``-m gpu`` option, which
restricts the tests to only run tests that include the
``@pytest.mark.gpu`` mark.

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