3.11. Type Annotation Mypyc

3.11.1. About

Mypyc compiles Python modules to C extensions. It uses standard Python type hints to generate fast code.

The compiled language is a strict, gradually typed Python variant. It restricts the use of some dynamic Python features to gain performance, but it’s mostly compatible with standard Python.

Mypyc uses mypy to perform type checking and type inference. Most type system features in the stdlib typing module are supported.

Compiled modules can import arbitrary Python modules and third-party libraries. You can compile anything from a single performance-critical module to your entire codebase. You can run the modules you compile also as normal, interpreted Python modules.

Existing code with type annotations is often 1.5x to 5x faster when compiled. Code tuned for mypyc can be 5x to 10x faster.

Mypyc currently aims to speed up non-numeric code, such as server applications. Mypyc is also used to compile itself (and mypy).

3.11.2. Differences from Cython

Mypyc targets many similar use cases as Cython. Mypyc does many things differently, however:

  • No need to use non-standard syntax, such as cpdef, or extra decorators to get good performance. Clean, normal-looking type-annotated Python code can be fast without language extensions. This makes it practical to compile entire codebases without a developer productivity hit.

  • Mypyc has first-class support for features in the typing module, such as tuple types, union types and generics.

  • Mypyc has powerful type inference, provided by mypy. Variable type annotations are not needed for optimal performance.

  • Mypyc fully integrates with mypy for robust and seamless static type checking.

  • Mypyc performs strict enforcement of type annotations at runtime, resulting in better runtime type safety and easier debugging.

Unlike Cython, mypyc doesn’t directly support interfacing with C libraries or speeding up numeric code.

3.11.3. How does it work

Mypyc uses several techniques to produce fast code:

  • Mypyc uses ahead-of-time compilation to native code. This removes CPython interpreter overhead.

  • Mypyc enforces type annotations (and type comments) at runtime, raising TypeError if runtime values don’t match annotations. Value types only need to be checked in the boundaries between dynamic and static typing.

  • Compiled code uses optimized, type-specific primitives.

  • Mypyc uses early binding to resolve called functions and name references at compile time. Mypyc avoids many dynamic namespace lookups.

  • Classes are compiled to C extension classes. They use vtables for fast method calls and attribute access.

  • Mypyc treats compiled functions, classes, and attributes declared Final as immutable.

  • Mypyc has memory-efficient, unboxed representations for integers and booleans.

3.11.4. Development Status

Mypyc is currently alpha software. It’s only recommended for production use cases with careful testing, and if you are willing to contribute fixes or to work around issues you will encounter.

3.11.5. Example

>>> import time
>>> def fib(n: int) -> int:
...     if n <= 1:
...         return n
...     else:
...         return fib(n-2) + fib(n-1)
>>> start = time.time()
>>> result = fib(32)  
>>> stop = time.time()
>>> duration = stop - start
>>> print(duration)  
$ python3 fib.py
$ mypyc fib.py
$ python3 -c "import fib"

After compilation, the program is about 10x faster.

Mypy will generate a C extension for fib in the current working directory. For example, on a Linux system the generated file may be called: fib.cpython-310m-x86_64-linux-gnu.so

Since C extensions can't be run as programs, use python3 -c to run the compiled module as a program


__name__ in fib.py would now be "fib", not "__main__"

3.11.6. Automation

... from setuptools import setup
... from mypyc.build import mypycify
... setup(
...     name='mylib',
...     packages=['mylib'],
...     ext_modules=mypycify([
...         'mylib/__init__.py',
...         'mylib/mod.py',
...     ]),
... )
$ python3 setup.py bdist_wheel

The wheel is created under dist/.

You can include most mypy command line options in the list of arguments passed to mypycify(). For example, here we use the --disallow-untyped-defs flag to require that all functions have type annotations

... from setuptools import setup
... from mypyc.build import mypycify
... setup(
...     name='frobnicate',
...     packages=['frobnicate'],
...     ext_modules=mypycify([
...         '--disallow-untyped-defs',  # Pass a mypy flag
...         'frobnicate.py',
...     ]),
... )  

3.11.7. Configuration

Configuration in pyproject.toml file:

# Import discovery
files = ["src"]
namespace_packages = false
explicit_package_bases = false
ignore_missing_imports = false
follow_imports = "normal"
follow_imports_for_stubs = false
no_site_packages = false
no_silence_site_packages = false
# Platform configuration
python_version = "3.10"
platform = "linux-64"
# Disallow dynamic typing
disallow_any_unimported = false # TODO
disallow_any_expr = false # TODO
disallow_any_decorated = false # TODO
disallow_any_explicit = false # TODO
disallow_any_generics = true
disallow_subclassing_any = true
# Untyped definitions and calls
disallow_untyped_calls = true
disallow_untyped_defs = true
disallow_incomplete_defs = true
check_untyped_defs = true
disallow_untyped_decorators = true
# None and Optional handling
no_implicit_optional = true
strict_optional = true
# Configuring warnings
warn_redundant_casts = true
warn_unused_ignores = true
warn_no_return = true
warn_return_any = true
warn_unreachable = false # GH#27396
# Suppressing errors
show_none_errors = true
ignore_errors = false
enable_error_code = "ignore-without-code"
# Miscellaneous strictness flags
allow_untyped_globals = false
allow_redefinition = false
local_partial_types = false
implicit_reexport = true
strict_equality = true
# Configuring error messages
show_error_context = false
show_column_numbers = false
show_error_codes = true

3.11.8. Runtime type checking

Non-erased types in annotations will be type checked at runtime. For example, consider this function:

>>> def twice(x: int) -> int:
...     return x * 2

If you try to call this function with a float or str argument, you'll get a type error on the call site, even if the call site is not being type checked:

>>> result = twice(2)       # OK
>>> result = twice(2.0)     # TypeError
>>> result = twice('two')   # TypeError

3.11.9. Final values

Compiled code replaces a reference to an attribute declared Final with the value of the attribute computed at compile time. This is an example of early binding. Example:


>>> from typing import Final


>>> MAX: Final = 100
>>> def limit_to_max(x: int) -> int:
...      if x > MAX:
...          return MAX
...      return x

Change to:

>>> def limit_to_max(x: int) -> int:
...      if x > 100:
...          return 100
...      return x

The two references to MAX don't involve any module namespace lookups, and are equivalent to the second code listing.

3.11.11. Further Reading

3.11.12. References