Additional features

This section discusses various features that did not fit in naturally in one of the previous sections.

Function overloading

Sometimes the types in a function depend on each other in ways that can’t be captured with a Union. For example, the __getitem__ ([] bracket indexing) method can take an integer and return a single item, or take a slice and return a Sequence of items. You might be tempted to annotate it like so:

from typing import Sequence, TypeVar, Union
T = TypeVar('T')

class MyList(Sequence[T]):
    def __getitem__(self, index: Union[int, slice]) -> Union[T, Sequence[T]]:
        if isinstance(index, int):
            ...  # Return a T here
        elif isinstance(index, slice):
            ...  # Return a sequence of Ts here
        else:
            raise TypeError(...)

But this is too loose, as it implies that when you pass in an int you might sometimes get out a single item and sometimes a sequence. The return type depends on the parameter type in a way that can’t be expressed using a type variable. Instead, we can use overloading to give the same function multiple type annotations (signatures) and accurately describe the function’s behavior.

from typing import overload, Sequence, TypeVar, Union
T = TypeVar('T')

class MyList(Sequence[T]):

    # The @overload definitions are just for the type checker,
    # and overwritten by the real implementation below.
    @overload
    def __getitem__(self, index: int) -> T:
        pass  # Don't put code here

    # All overloads and the implementation must be adjacent
    # in the source file, and overload order may matter:
    # when two overloads may overlap, the more specific one
    # should come first.
    @overload
    def __getitem__(self, index: slice) -> Sequence[T]:
        pass  # Don't put code here

    # The implementation goes last, without @overload.
    # It may or may not have type hints; if it does,
    # these are checked against the overload definitions
    # as well as against the implementation body.
    def __getitem__(self, index: Union[int, slice]) -> Union[T, Sequence[T]]:
        # This is exactly the same as before.
        if isinstance(index, int):
            ...  # Return a T here
        elif isinstance(index, slice):
            ...  # Return a sequence of Ts here
        else:
            raise TypeError(...)

Calls to overloaded functions are type checked against the variants, not against the implementation. A call like my_list[5] would have type T, not Union[T, Sequence[T]] because it matches the first overloaded definition, and ignores the type annotations on the implementation of __getitem__. The code in the body of the definition of __getitem__ is checked against the annotations on the corresponding declaration. In this case the body is checked with index: Union[int, slice] and a return type Union[T, Sequence[T]]. If there are no annotations on the corresponding definition, then code in the function body is not type checked.

The annotations on the function body must be compatible with the types given for the overloaded variants listed above it. The type checker will verify that all the types for the overloaded variants are compatible with the types given for the implementation. In this case it checks that the parameter type int and the return type T are compatible with Union[int, slice] and Union[T, Sequence[T]] for the first variant. For the second variant it verifies that the parameter type slice and the return type Sequence[T] are compatible with Union[int, slice] and Union[T, Sequence[T]].

Overloaded function variants are still ordinary Python functions and they still define a single runtime object. There is no automatic dispatch happening, and you must manually handle the different types in the implementation (usually with isinstance() checks, as shown in the example).

The overload variants must be adjacent in the code. This makes code clearer, as you don’t have to hunt for overload variants across the file.

Overloads in stub files are exactly the same, except there is no implementation.

Note

As generic type variables are erased at runtime when constructing instances of generic types, an overloaded function cannot have variants that only differ in a generic type argument, e.g. List[int] and List[str].

Note

If you just need to constrain a type variable to certain types or subtypes, you can use a value restriction.

The attrs package

attrs is a package that lets you define classes without writing boilerplate code. Mypy can detect uses of the package and will generate the necessary method definitions for decorated classes using the type annotations it finds. Type annotations can be added as follows:

import attr

@attr.s
class A:
    one: int = attr.ib()          # Variable annotation (Python 3.6+)
    two = attr.ib()  # type: int  # Type comment
    three = attr.ib(type=int)     # type= argument

If you’re using auto_attribs=True you must use variable annotations.

import attr

@attr.s(auto_attribs=True)
class A:
    one: int
    two: int = 7
    three: int = attr.ib(8)

Typeshed has a couple of “white lie” annotations to make type checking easier. attr.ib and attr.Factory actually return objects, but the annotation says these return the types that they expect to be assigned to. That enables this to work:

import attr
from typing import Dict

@attr.s(auto_attribs=True)
class A:
    one: int = attr.ib(8)
    two: Dict[str, str] = attr.Factory(dict)
    bad: str = attr.ib(16)   # Error: can't assign int to str

Caveats/Known Issues

  • The detection of attr classes and attributes works by function name only. This means that if you have your own helper functions that, for example, return attr.ib() mypy will not see them.

  • All boolean arguments that mypy cares about must be literal True or False. e.g the following will not work:

    import attr
YES = True
@attr.s(init=YES)
class A:
    ...

  • Currently, converter only supports named functions. If mypy finds something else it will complain about not understanding the argument and the type annotation in __init__ will be replaced by Any.

  • Validator decorators and default decorators are not type-checked against the attribute they are setting/validating.

  • Method definitions added by mypy currently overwrite any existing method definitions.

Using a remote cache to speed up mypy runs

Mypy performs type checking incrementally, reusing results from previous runs to speed up successive runs. If you are type checking a large codebase, mypy can still be sometimes slower than desirable. For example, if you create a new branch based on a much more recent commit than the target of the previous mypy run, mypy may have to process almost every file, as a large fraction of source files may have changed. This can also happen after you’ve rebased a local branch.

Mypy supports using a remote cache to improve performance in cases such as the above. In a large codebase, remote caching can sometimes speed up mypy runs by a factor of 10, or more.

Mypy doesn’t include all components needed to set this up – generally you will have to perform some simple integration with your Continuous Integration (CI) or build system to configure mypy to use a remote cache. This discussion assumes you have a CI system set up for the mypy build you want to speed up, and that you are using a central git repository. Generalizing to different environments should not be difficult.

Here are the main components needed:

  • A shared repository for storing mypy cache files for all landed commits.
  • CI build that uploads mypy incremental cache files to the shared repository for each commit for which the CI build runs.
  • A wrapper script around mypy that developers use to run mypy with remote caching enabled.

Below we discuss each of these components in some detail.

Shared repository for cache files

You need a repository that allows you to upload mypy cache files from your CI build and make the cache files available for download based on a commit id. A simple approach would be to produce an archive of the .mypy_cache directory (which contains the mypy cache data) as a downloadable build artifact from your CI build (depending on the capabilities of your CI system). Alternatively, you could upload the data to a web server or to S3, for example.

Continuous Integration build

The CI build would run a regular mypy build and create an archive containing the .mypy_cache directory produced by the build. Finally, it will produce the cache as a build artifact or upload it to a repository where it is accessible by the mypy wrapper script.

Your CI script might work like this:

  • Run mypy normally. This will generate cache data under the .mypy_cache directory.
  • Create a tarball from the .mypy_cache directory.
  • Determine the current git master branch commit id (say, using git rev-parse HEAD).
  • Upload the tarball to the shared repository with a name derived from the commit id.

Mypy wrapper script

The wrapper script is used by developers to run mypy locally during development instead of invoking mypy directly. The wrapper first populates the local .mypy_cache directory from the shared repository and then runs a normal incremental build.

The wrapper script needs some logic to determine the most recent central repository commit (by convention, the origin/master branch for git) the local development branch is based on. In a typical git setup you can do it like this:

git merge-base HEAD origin/master

The next step is to download the cache data (contents of the .mypy_cache directory) from the shared repository based on the commit id of the merge base produced by the git command above. The script will decompress the data so that mypy will start with a fresh .mypy_cache. Finally, the script runs mypy normally. And that’s all!

Caching with mypy daemon

You can also use remote caching with the mypy daemon. The remote cache will significantly speed up the first dmypy check run after starting or restarting the daemon.

The mypy daemon requires extra fine-grained dependency data in the cache files which aren’t included by default. To use caching with the mypy daemon, use the --cache-fine-grained option in your CI build:

$ mypy --cache-fine-grained <args...>

This flag adds extra information for the daemon to the cache. In order to use this extra information, you will also need to use the --use-fine-grained-cache option with dmypy start or dmypy restart. Example:

$ dmypy start -- --use-fine-grained-cache <options...>

Now your first dmypy check run should be much faster, as it can use cache information to avoid processing the whole program.

Refinements

There are several optional refinements that may improve things further, at least if your codebase is hundreds of thousands of lines or more:

  • If the wrapper script determines that the merge base hasn’t changed from a previous run, there’s no need to download the cache data and it’s better to instead reuse the existing local cache data.
  • If you use the mypy daemon, you may want to restart the daemon each time after the merge base or local branch has changed to avoid processing a potentially large number of changes in an incremental build, as this can be much slower than downloading cache data and restarting the daemon.
  • If the current local branch is based on a very recent master commit, the remote cache data may not yet be available for that commit, as there will necessarily be some latency to build the cache files. It may be a good idea to look for cache data for, say, the 5 latest master commits and use the most recent data that is available.
  • If the remote cache is not accessible for some reason (say, from a public network), the script can still fall back to a normal incremental build.
  • You can have multiple local cache directories for different local branches using the --cache-dir option. If the user switches to an existing branch where downloaded cache data is already available, you can continue to use the existing cache data instead of redownloading the data.
  • You can set up your CI build to use a remote cache to speed up the CI build. This would be particularly useful if each CI build starts from a fresh state without access to cache files from previous builds. It’s still recommended to run a full, non-incremental mypy build to create the cache data, as repeatedly updating cache data incrementally could result in drift over a long time period (due to a mypy caching issue, perhaps).

Extended Callable types

As an experimental mypy extension, you can specify Callable types that support keyword arguments, optional arguments, and more. When you specify the arguments of a Callable, you can choose to supply just the type of a nameless positional argument, or an “argument specifier” representing a more complicated form of argument. This allows one to more closely emulate the full range of possibilities given by the def statement in Python.

As an example, here’s a complicated function definition and the corresponding Callable:

from typing import Callable
from mypy_extensions import (Arg, DefaultArg, NamedArg,
                             DefaultNamedArg, VarArg, KwArg)

def func(__a: int,  # This convention is for nameless arguments
         b: int,
         c: int = 0,
         *args: int,
         d: int,
         e: int = 0,
         **kwargs: int) -> int:
    ...

F = Callable[[int,  # Or Arg(int)
              Arg(int, 'b'),
              DefaultArg(int, 'c'),
              VarArg(int),
              NamedArg(int, 'd'),
              DefaultNamedArg(int, 'e'),
              KwArg(int)],
             int]

f: F = func

Argument specifiers are special function calls that can specify the following aspects of an argument:

  • its type (the only thing that the basic format supports)
  • its name (if it has one)
  • whether it may be omitted
  • whether it may or must be passed using a keyword
  • whether it is a *args argument (representing the remaining positional arguments)
  • whether it is a **kwargs argument (representing the remaining keyword arguments)

The following functions are available in mypy_extensions for this purpose:

def Arg(type=Any, name=None):
    # A normal, mandatory, positional argument.
    # If the name is specified it may be passed as a keyword.

def DefaultArg(type=Any, name=None):
    # An optional positional argument (i.e. with a default value).
    # If the name is specified it may be passed as a keyword.

def NamedArg(type=Any, name=None):
    # A mandatory keyword-only argument.

def DefaultNamedArg(type=Any, name=None):
    # An optional keyword-only argument (i.e. with a default value).

def VarArg(type=Any):
    # A *args-style variadic positional argument.
    # A single VarArg() specifier represents all remaining
    # positional arguments.

def KwArg(type=Any):
    # A **kwargs-style variadic keyword argument.
    # A single KwArg() specifier represents all remaining
    # keyword arguments.

In all cases, the type argument defaults to Any, and if the name argument is omitted the argument has no name (the name is required for NamedArg and DefaultNamedArg). A basic Callable such as

MyFunc = Callable[[int, str, int], float]

is equivalent to the following:

MyFunc = Callable[[Arg(int), Arg(str), Arg(int)], float]

A Callable with unspecified argument types, such as

MyOtherFunc = Callable[..., int]

is (roughly) equivalent to

MyOtherFunc = Callable[[VarArg(), KwArg()], int]

Note

This feature is experimental. Details of the implementation may change and there may be unknown limitations. IMPORTANT: Each of the functions above currently just returns its type argument, so the information contained in the argument specifiers is not available at runtime. This limitation is necessary for backwards compatibility with the existing typing.py module as present in the Python 3.5+ standard library and distributed via PyPI.