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.
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).
Easy to get started. Compiled code has the look and feel of regular Python code. Mypyc supports familiar Python syntax and idioms.
Expressive types. Mypyc fully supports standard Python type hints. Mypyc has local type inference, generics, optional types, tuple types, union types, and more. Type hints act as machine-checked documentation, making code not only faster but also easier to understand and modify.
Python ecosystem. Mypyc runs on top of CPython, the standard Python implementation. You can use any third-party libraries, including C extensions, installed with pip. Mypyc uses only valid Python syntax, so all Python editors and IDEs work perfectly.
Fast program startup. Mypyc uses ahead-of-time compilation, so compilation does not slow down program startup. Slow program startup is a common issue with JIT compilers.
Migration path for existing code. Existing Python code often requires only minor changes to compile using mypyc.
Waiting for compilation is optional. Compiled code also runs as normal Python code. You can use interpreted Python during development, with familiar and fast workflows.
Runtime type safety. Mypyc protects you from segfaults and memory corruption. Any unexpected runtime type safety violation is a bug in mypyc. Runtime values are checked against type annotations. (Without mypyc, type annotations are ignored at runtime.)
Find errors statically. Mypyc uses mypy for static type checking that helps catch many bugs.
Fix only performance bottlenecks. Often most time is spent in a few Python modules or functions. Add type annotations and compile these modules for easy performance gains.
Compile it all. During development you can use interpreted mode, for a quick edit-run cycle. In releases all non-test code is compiled. This is how mypy achieved a 4x performance improvement over interpreted Python.
Take advantage of existing type hints. If you already use type annotations in your code, adopting mypyc will be easier. You’ve already done most of the work needed to use mypyc.
Alternative to a lower-level language. Instead of writing performance-critical code in C, C++, Cython or Rust, you may get good performance while staying in the comfort of Python.
Migrate C extensions. Maintaining C extensions is not always fun for a Python developer. With mypyc you may get performance similar to the original C, with the convenience of Python.
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
typingmodule, 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.
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
TypeErrorif 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
Mypyc has memory-efficient, unboxed representations for integers and booleans.
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.