TensorFlow uses DocTest to
test code snippets in Python docstrings. The snippet must be executable Python
code. To enable testing, prepend the line with
>>> (three left-angle
brackets). For example, here's a excerpt from the
tf.concat function in the
def concat(values, axis, name="concat"): """Concatenates tensors along one dimension. ... >>> t1 = [[1, 2, 3], [4, 5, 6]] >>> t2 = [[7, 8, 9], [10, 11, 12]] >>> concat([t1, t2], 0) <tf.Tensor: shape=(4, 3), dtype=int32, numpy= array([[ 1, 2, 3], [ 4, 5, 6], [ 7, 8, 9], [10, 11, 12]], dtype=int32)> <... more description or code snippets ...> Args: values: A list of `tf.Tensor` objects or a single `tf.Tensor`. axis: 0-D `int32` `Tensor`. Dimension along which to concatenate. Must be in the range `[-rank(values), rank(values))`. As in Python, indexing for axis is 0-based. Positive axis in the rage of `[0, rank(values))` refers to `axis`-th dimension. And negative axis refers to `axis + rank(values)`-th dimension. name: A name for the operation (optional). Returns: A `tf.Tensor` resulting from concatenation of the input tensors. """ <code here>
Make the code testable with DocTest
Currently, many docstrings use backticks (```) to identify code. To make the code testable with DocTest:
- Remove the backticks (```) and use the left-brackets (>>>) in front of each line. Use (...) in front of continued lines.
- Add a newline to separate DocTest snippets from Markdown text to render properly on tensorflow.org.
TensorFlow uses a few customizations to the builtin doctest logic:
- It does not compare float values as text: Float values are extracted from
the text and compared using
rtoltolerences. This allows :
- Clearer docs - Authors don't need to include all decimal places.
- More robust tests - Numerical changes in the underlying implementation should never cause a doctest to fail.
- It only checks the output if the author includes output for a line. This allows for clearer docs because authors usually don't need to capture irrelevant intermediate values to prevent them from being printed.
- Overall: The goal of doctest is to provide documentation, and confirm that
the documentation works. This is different from unit-testing. So:
- Keep examples simple.
- Avoid long or complicated outputs.
- Use round numbers if possible.
- Output format: The output of the snippet needs to be directly beneath the code that’s generating the output. Also, the output in the docstring has to be exactly equal to what the output would be after the code is executed. See the above example. Also, check out this part in the DocTest documentation. If the output exceeds the 80 line limit, you can put the extra output on the new line and DocTest will recognize it. For example, see multi-line blocks below.
- Globals: The
osmodules are always available in TensorFlow's DocTest.
Use symbols: In DocTest you can directly access symbols defined in the same file. To use a symbol that’s not defined in the current file, please use TensorFlow’s public API
xxx. As you can see in the example below,
`random.normal`is accessed via
`tf.random.normal`. This is because
`random.normal`is not visible in
def NewLayer(): “””This layer does cool stuff. Example usage: >>> x = tf.random.normal((1, 28, 28, 3)) >>> new_layer = NewLayer(x) >>> new_layer <tf.Tensor: shape=(1, 14, 14, 3), dtype=int32, numpy=...> “””
Floating point values: The TensorFlow doctest extracts float values from the result strings, and compares using
np.allclosewith reasonable tolerances (
rtol=1e-6). This way authors do not need to worry about overly precise docstrings causing failures due to numerical issues. Simply paste in the expected value.
Non-deterministic output: Use ellipsis(
...) for the uncertain parts and DocTest will ignore that substring.
x = tf.random.normal((1,))
<tf.Tensor: shape=(1,), dtype=float32, numpy=..., dtype=float32)>
- Multi-line blocks: DocTest is strict about the difference between a single and a multi-line statement. Note the usage of (...) below:
if x > 0:
print("X is positive")
- Exceptions: Exception details are ignored except the Exception that’s raised. See this for more details.
np_var = np.array([1, 2])
Traceback (most recent call last):
ValueError: Unexpectedly found an instance of type `<class 'numpy.ndarray'>`.
Test on your local machine
There are two ways to test the code in the docstring locally:
If you are only changing the docstring of a class/function/method, then you can test it by passing that file's path to tf_doctest.py. For example:
python tf_doctest.py --file=<file_path>
This will run it using your installed version of TensorFlow. To be sure you're running the same code that you're testing:
If you are changing the code and the docstring of a class/function/method, then you will need to build TensorFlow from source. Once you are setup to build from source, you can run the tests:
bazel run //tensorflow/tools/docs:tf_doctest
bazel run //tensorflow/tools/docs:tf_doctest -- --module=ops.array_ops
--moduleis relative to