Style and Conventions

2.1 Style Guide

Composer generally follows Google's Python Style Guide for how to format and structure code.

2.2. Code Formatting

Composer uses the yapf formatter for general formatting, isort to sort imports, and docformatter to format docstrings.

To (auto)format code, run

make style

To verify that styling and typing (see below) is correct, run:

make lint

The configuration is stored in pyproject.toml.

3. Type Annotations and Typechecking

Composer aims to annotate all functions with type annotations (introduced in PEP 526. Type annotations help statically catch TypeError and AttributeError bugs, in addition to other benefits, as outlined in the PEP.

Composer uses pyright to validate type annotations. To check typing, run:

make lint

The pyright configuration is stored in pyproject.toml.

Debugging

Here are some suggestions to deal with pyright errors:

Suppose a variable could be one of multiple types, like the following:

def foo(x: Union[int, None]):
    return x + 5  # type error -- None + 5 is not allowed!

PyRight will complain since None + 5 is not a valid operation. Instead, add a check to ensure that x is not None:

def foo(x: Union[int, None]):
    if x is None:
        raise TypeError("x must be an integer, not None!")
    return x + 5  # valid

Assert statements also work. However, assert statements should not be used for data validation (see the assert statement section below).

def foo(x: Union[int, None]):
    assert x is not None, "x should never be None"
    return x + 5  # valid

For variables where it is impossible for pyright to infer the correct type, use cast.
As a last resort, add a # type: ignore comment to the line where pyright emits an error. Immediately following this statement, paste in the error emitted by pyright, so other contributors will know why this error was silenced.

4. Public APIs

A public API, generally speaking, can be invoked by a user without a leading underscore in any portion of the path. The following are examples of public APIs:

Standalone functions in public modules (e.g. composer.utils.dist.get_world_size)
Classes in public modules (e.g. composer.trainer.trainer.Trainer)
Public methods in public classes (e.g. composer.trainer.trainer.Trainer.fit)
Public modules (e.g. composer.trainer.trainer)

The following rules apply to public APIs:

All public APIs must have a docstring (see the Documentation section below)
All parameters must have type annotations.
To minimize user imports, parameters should should use native PyTorch or Python types whenever possible.

It is acceptable to use a union of types, so long as one of the options is a primitive. For example, in the constructor for composer.trainer.trainer.Trainer, the device parameter is annotated like the following:
```
from typing import Optional, Union

from composer.trainer.devices import Device

class Trainer:
    def __init__(
        self,
        ...,
        device: Union[str, Device],

    ):
        if isinstance(device, str):
            device = Device(device)
        ...
```
This signature allows a user to pass a string for a device, rather than having to import our custom device class.

Parameters that are for power users (such as load_object_store) in the Trainer are exempt from this rule. These parameters can require custom imports.
Parameters that could take a sequence of elements should also allow None or a singleton. This simplifies the user API by not having to construct a list (or tuple) to hold a single element (or no element). For example, use Optional[Union[torch.Tensor, Sequence[torch.Tensor]].

The composer.utils.ensure_tuple helper method can convert a singleton, list, or tuple into a tuple. For example
```
def foo(x: Optional[Union[Tensor, Sequence[Tensor]]) -> Tuple[Tensor, ...]:
    return ensure_tuple(x)  # ensures that the result is always a (potentially empty) tuple of tensors
```

5. Use of `assert`

assert should be used only in test cases and for verifying invariants (likely required for type checking), not for data validation. As asserts can be disabled in python by using the -O flag (e.g. python -O path/to/script.py), they are not guaranteed to run. For data validation, instead use a style like the following:

if parameter is None:
    raise ValueError("parameter must be specified and cannot be None")

6. Imports and `init.py`

All imports in composer should be absolute -- that is, they do not begin with a period.

6.1 External Dependencies

All external dependencies must be specified in both setup.py for pip and meta.yaml for Anaconda.
If a dependency is not core to Composer (e.g. it is for a model, dataset, algorithm, or some callbacks):
1. It must be specified in a entry of the extra_deps dictionary of setup.py. This dictionary groups dependencies that can be conditionally installed. An entry named foo can be installed with pip install mosaicml[foo]. For example, running pip install mosaicml[unet] will install everything in install_requires, along with monai and scikit-learn.
2. It must also be specified in the run_constrained and the test.requires section.
3. The import must be conditionally imported in the code. For example:
```
def unet():
    try:
        import monai
    except ImportError as e:
        raise ImportError(textwrap.dedent("""\
            Composer was installed without unet support. To use unet with Composer, run: `pip install mosaicml
            [unet]` if using pip or `conda install -c conda-forge monai` if using Anaconda""") from e
```
  This style allows users to perform minimal install of Composer without triggering ImportErrors if an optional dependency is missing.
4. If the dependency is core to Composer, add the dependency to the install_requires section of setup.py and the requirements.run section of meta.yaml.

6.2 Use of `all`

All public modules must define __all__ to be the list of members that should be re-exported. The variable is necessary to 1) limit what from XXX import * imports, and 2) ensure that the documentation only includes exported members, not unrelated re-imports.

For example, from composer/callbacks/memory_monitor.py

"""Log memory usage during training."""
import logging
from typing import Dict, Union

import torch.cuda

from composer.core import Logger, State
from composer.core.callback import Callback

log = logging.getLogger(__name__)

__all__ = ["MemoryMonitor"]  # export only the MemoryMonitor, not other imports like `Logger`, `State`, or `Callback`


class MemoryMonitor(Callback):
    ...

6.3 `init.py`

All public classes and functions should be added to the module's __init__.py.

from composer.path.to.module.file import MyClass as MyClass
from composer.path.to.module.file import my_func as my_func

If a file only contains public functions, then the following is also acceptable:

from composer.path.to.module import my_file as my_file

7. Documentation

Composer uses Google Style Docstrings.

The following guidelines apply to documentation.

Each function that needs a docstring must have its input arguments and return statement (if not None) annotated.

Each argument annotation should include the type. If the argument has a default value, the type annotation should specify "optional", and the docstring should say the default value. Some examples:

def foo(bar: int):
    """Foo.

    Args:
        bar (int): Required bar.
    """
    ...

def foo2(bar: int = 42):
    """Foo2.

    Args:
        bar (int, optional): The first Argument. Default: ``42``.
    """
    ...

def foo3(bar: Optional[int] = None):
    """Foo3.

    Args:
        bar (int, optional): The first Argument. Default: ``None``.
    """
    ...

def foo4(bar: Union[int, str] = 42):
    """Foo4.

    Args:
        bar (int | str, optional): The first Argument. Default: ``42``.
    """
    ...

def foo5(bar: int) -> int:
    """Foo5.

    Args:
        bar (int): Required bar.

    Returns:
        int: Description of return statement.
    """
    ...

def foo6(bar: int) -> Tuple[int, str]:
    """Foo6.

    Args:
        bar (int): Required bar.

    Returns:
        a (int): Returned value.
        b (str): Returned value.
    """
    ...

For examples in docstrings, use .. doctest:: or .. testcode:: . See the Sphinx Doctest Extension for all of the available directives. Do not use .. code-block:: for Python examples, as they are untested.

Any test fixtures for doctests should go in docs/source/doctest_fixtures.py or in a .. testsetup:: block.

For example:

def my_function(x: Optional[torch.Tensor]) -> torch.Tensor:
    """blah function

    Args:
        input (torch.Tensor): Your guess.

    Returns:
        torch.Tensor: How good your input is.

    Raises:
        ValueError: If your input is negative.

    Example:
        .. testsetup::

            # optional setup section, not shown in docs
            import torch
            x = torch.randn(42)


        .. testcode::

            # shown in docs; runs after testsetup
            my_function(x)
    """
    ...

To check doctests, run:

cd docs && make clean && make html && make doctest

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STYLE_GUIDE.md

STYLE_GUIDE.md

Style and Conventions

2.1 Style Guide

2.2. Code Formatting

3. Type Annotations and Typechecking

Debugging

4. Public APIs

5. Use of `assert`

6. Imports and `init.py`

6.1 External Dependencies

6.2 Use of `all`

6.3 `init.py`

7. Documentation

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STYLE_GUIDE.md

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STYLE_GUIDE.md

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Style and Conventions

2.1 Style Guide

2.2. Code Formatting

3. Type Annotations and Typechecking

Debugging

4. Public APIs

5. Use of assert

6. Imports and __init__.py

6.1 External Dependencies

6.2 Use of __all__

6.3 __init__.py

7. Documentation

5. Use of `assert`

6. Imports and `init.py`

6.2 Use of `all`

6.3 `init.py`