PyO3
Rust bindings for Python. This includes running and interacting with Python code from a Rust binary, as well as writing native Python modules.
A comparison with rust-cpython can be found in the guide.
Usage
PyO3 supports Python 3.5 and up. The minimum required Rust version is 1.37.0-nightly 2019-07-19.
PyPy is also supported (via cpyext) for Python 3.5 only, targeted PyPy version is 7.0.0. Please refer to the pypy section.
You can either write a native Python module in Rust, or use Python from a Rust binary.
However, on some OSs, you need some additional packages. E.g. if you are on Ubuntu 18.04, please run
sudo apt install python3-dev python-dev
Using Rust from Python
PyO3 can be used to generate a native Python module.
Cargo.toml
[package]
name = "string-sum"
version = "0.1.0"
edition = "2018"
[lib]
name = "string_sum"
crate-type = ["cdylib"]
[dependencies.pyo3]
version = "0.8.2"
features = ["extension-module"]
src/lib.rs
# #![allow(unused_variables)] #fn main() { use pyo3::prelude::*; use pyo3::wrap_pyfunction; #[pyfunction] /// Formats the sum of two numbers as string fn sum_as_string(a: usize, b: usize) -> PyResult<String> { Ok((a + b).to_string()) } /// This module is a python module implemented in Rust. #[pymodule] fn string_sum(py: Python, m: &PyModule) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(sum_as_string))?; Ok(()) } #}
On Windows and Linux, you can build normally with cargo build --release
. On macOS, you need to set additional linker arguments. One option is to compile with cargo rustc --release -- -C link-arg=-undefined -C link-arg=dynamic_lookup
, the other is to create a .cargo/config
with the following content:
[target.x86_64-apple-darwin]
rustflags = [
"-C", "link-arg=-undefined",
"-C", "link-arg=dynamic_lookup",
]
For developing, you can copy and rename the shared library from the target folder: On MacOS, rename libstring_sum.dylib
to string_sum.so
, on Windows libstring_sum.dll
to string_sum.pyd
and on Linux libstring_sum.so
to string_sum.so
. Then open a Python shell in the same folder and you'll be able to import string_sum
.
To build, test and publish your crate as a Python module, you can use maturin or setuptools-rust. You can find an example for setuptools-rust in examples/word-count, while maturin should work on your crate without any configuration.
Using Python from Rust
Add pyo3
to your Cargo.toml
like this:
[dependencies]
pyo3 = "0.8.2"
Example program displaying the value of sys.version
and the current user name:
use pyo3::prelude::*; use pyo3::types::IntoPyDict; fn main() -> Result<(), ()> { let gil = Python::acquire_gil(); let py = gil.python(); main_(py).map_err(|e| { // We can't display python error type via ::std::fmt::Display, // so print error here manually. e.print_and_set_sys_last_vars(py); }) } fn main_(py: Python) -> PyResult<()> { let sys = py.import("sys")?; let version: String = sys.get("version")?.extract()?; let locals = [("os", py.import("os")?)].into_py_dict(py); let code = "os.getenv('USER') or os.getenv('USERNAME') or 'Unknown'"; let user: String = py.eval(code, None, Some(&locals))?.extract()?; println!("Hello {}, I'm Python {}", user, version); Ok(()) }
Our guide has a section with lots of examples about this topic.
Examples and tooling
- examples/word-count Counting the occurrences of a word in a text file
- hyperjson A hyper-fast Python module for reading/writing JSON data using Rust's serde-json
- rust-numpy Rust binding of NumPy C-API
- html-py-ever Using html5ever through kuchiki to speed up html parsing and css-selecting.
- pyo3-built Simple macro to expose metadata obtained with the
built
crate as aPyDict
- point-process High level API for pointprocesses as a Python library
- autopy A simple, cross-platform GUI automation library for Python and Rust.
- orjson Fast Python JSON library
Type Conversions
PyO3 provides some handy traits to convert between Python types and Rust types.
.extract()
The easiest way to convert a Python object to a Rust value is using .extract()?
.
ToPyObject
trait
ToPyObject
trait is a conversion trait that allows various objects to be
converted into PyObject
. IntoPy<PyObject>
serves the
same purpose, except that it consumes self
.
FromPyObject
and RefFromPyObject
trait
*args
and **kwargs
for python object call
There are several ways how to pass positional and keyword arguments to a Python object call.
The ObjectProtocol
trait provides two methods:
call
- call any callable Python object.call_method
- call a specific method on the object, shorthand forget_attr
thencall
.
Both methods accept args
and kwargs
arguments.
use pyo3::prelude::*; use pyo3::types::{PyDict, PyTuple}; struct SomeObject; impl SomeObject { fn new(py: Python) -> PyObject { PyDict::new(py).to_object(py) } } fn main() { let arg1 = "arg1"; let arg2 = "arg2"; let arg3 = "arg3"; let gil = Python::acquire_gil(); let py = gil.python(); let obj = SomeObject::new(py); // call object without empty arguments obj.call0(py); // call object with PyTuple let args = PyTuple::new(py, &[arg1, arg2, arg3]); obj.call1(py, args); // pass arguments as rust tuple let args = (arg1, arg2, arg3); obj.call1(py, args); }
kwargs
can be None
or Some(&PyDict)
. You can use the
IntoPyDict
trait to convert other dict-like containers, e.g. HashMap
, BTreeMap
as well as tuples with up to 10 elements and Vec
s where each element is a two-element tuple.
use pyo3::prelude::*; use pyo3::types::{IntoPyDict, PyDict}; use std::collections::HashMap; struct SomeObject; impl SomeObject { fn new(py: Python) -> PyObject { PyDict::new(py).to_object(py) } } fn main() { let key1 = "key1"; let val1 = 1; let key2 = "key2"; let val2 = 2; let gil = Python::acquire_gil(); let py = gil.python(); let obj = SomeObject::new(py); // call object with PyDict let kwargs = [(key1, val1)].into_py_dict(py); obj.call(py, (), Some(kwargs)); // pass arguments as Vec let kwargs = vec![(key1, val1), (key2, val2)]; obj.call(py, (), Some(kwargs.into_py_dict(py))); // pass arguments as HashMap let mut kwargs = HashMap::<&str, i32>::new(); kwargs.insert(key1, 1); obj.call(py, (), Some(kwargs.into_py_dict(py))); }
IntoPy<T>
Many conversions in PyO3 can't use std::convert::Into
because they need a GIL token. That's why the IntoPy<T>
trait offers an into_py
method that works just like into
, except for taking a Python<'_>
argument.
Eventually, traits such as ToPyObject
will be replaced by this trait and a FromPy
trait will be added that will implement IntoPy
, just like with From
and Into
.
Python Exceptions
Define a new exception
You can use the create_exception!
macro to define a new exception type:
# #![allow(unused_variables)] #fn main() { use pyo3::create_exception; create_exception!(module, MyError, pyo3::exceptions::Exception); #}
module
is the name of the containing module.MyError
is the name of the new exception type.
For example:
use pyo3::prelude::*; use pyo3::create_exception; use pyo3::types::IntoPyDict; use pyo3::exceptions::Exception; create_exception!(mymodule, CustomError, Exception); fn main() { let gil = Python::acquire_gil(); let py = gil.python(); let ctx = [("CustomError", py.get_type::<CustomError>())].into_py_dict(py); py.run("assert str(CustomError) == \"<class 'mymodule.CustomError'>\"", None, Some(&ctx)).unwrap(); py.run("assert CustomError('oops').args == ('oops',)", None, Some(&ctx)).unwrap(); }
Raise an exception
To raise an exception, first you need to obtain an exception type and construct a new PyErr
, then call the PyErr::restore()
method to write the exception back to the Python interpreter's global state.
use pyo3::{Python, PyErr}; use pyo3::exceptions; fn main() { let gil = Python::acquire_gil(); let py = gil.python(); PyErr::new::<exceptions::TypeError, _>("Error").restore(py); assert!(PyErr::occurred(py)); drop(PyErr::fetch(py)); }
If you already have a Python exception instance, you can simply call PyErr::from_instance()
.
PyErr::from_instance(py, err).restore(py);
If a Rust type exists for the exception, then it is possible to use the new
method.
For example, each standard exception defined in the pyo3::exceptions
module
has a corresponding Rust type, exceptions defined by create_exception!
and import_exception!
macro
have Rust types as well.
# #![allow(unused_variables)] #fn main() { # use pyo3::exceptions; # use pyo3::prelude::*; # fn check_for_error() -> bool {false} fn my_func(arg: PyObject) -> PyResult<()> { if check_for_error() { Err(exceptions::ValueError::py_err("argument is wrong")) } else { Ok(()) } } #}
Check exception type
Python has an isinstance
method to check an object's type,
in PyO3 there is a Python::is_instance()
method which does the same thing.
use pyo3::Python; use pyo3::types::{PyBool, PyList}; fn main() { let gil = Python::acquire_gil(); let py = gil.python(); assert!(py.is_instance::<PyBool, _>(PyBool::new(py, true)).unwrap()); let list = PyList::new(py, &[1, 2, 3, 4]); assert!(!py.is_instance::<PyBool, _>(list.as_ref()).unwrap()); assert!(py.is_instance::<PyList, _>(list.as_ref()).unwrap()); }
Python::is_instance()
calls the underlying PyType::is_instance
method to do the actual work.
To check the type of an exception, you can simply do:
# use pyo3::exceptions; # use pyo3::prelude::*; # fn main() { # let gil = Python::acquire_gil(); # let py = gil.python(); # let err = exceptions::TypeError::py_err(()); err.is_instance::<exceptions::TypeError>(py); # }
Handle Rust Errors
The vast majority of operations in this library will return PyResult<T>
.
This is an alias for the type Result<T, PyErr>
.
A PyErr
represents a Python exception.
Errors within the PyO3 library are also exposed as Python exceptions.
The PyO3 library handles Python exceptions in two stages. During the first stage, a PyErr
instance is
created. At this stage, holding Python's GIL is not required. During the second stage, an actual Python
exception instance is created and set active in the Python interpreter.
In simple cases, for custom errors adding an implementation of std::convert::From<T>
trait
for this custom error is enough. PyErr::new
accepts an argument in the form
of ToPyObject + 'static
. If the 'static
constraint can not be satisfied or
more complex arguments are required, the
PyErrArguments
trait can be implemented. In that case, actual exception argument creation is delayed
until a Python
object is available.
# #![allow(unused_variables)] #fn main() { # use pyo3::{exceptions, PyErr, PyResult}; # use std::error::Error; # use std::fmt; # # #[derive(Debug)] # struct CustomIOError; # # impl Error for CustomIOError {} # # impl fmt::Display for CustomIOError { # fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { # write!(f, "Oh no!") # } # } # # fn bind(_addr: &str) -> Result<(), CustomIOError> { # Err(CustomIOError) # } impl std::convert::From<CustomIOError> for PyErr { fn from(err: CustomIOError) -> PyErr { exceptions::OSError::py_err(err.to_string()) } } fn connect(s: String) -> PyResult<bool> { bind("127.0.0.1:80")?; Ok(true) } #}
The code snippet above will raise an OSError
in Python if bind()
returns a CustomIOError
.
The std::convert::From<T>
trait is implemented for most of the Rust standard library's error
types so the try!
macro or the ?
operator can be used.
# #![allow(unused_variables)] #fn main() { use pyo3::prelude::*; fn parse_int(s: String) -> PyResult<usize> { Ok(s.parse::<usize>()?) } #}
The code snippet above will raise a ValueError
in Python if String::parse()
returns an error.
Using exceptions defined in python code
It is possible to use an exception defined in Python code as a native Rust type.
The import_exception!
macro allows importing a specific exception class and defines a zero-sized Rust type
for that exception.
# #![allow(unused_variables)] #fn main() { use pyo3::prelude::*; use pyo3::import_exception; import_exception!(io, UnsupportedOperation); fn tell(file: PyObject) -> PyResult<u64> { use pyo3::exceptions::*; let gil = Python::acquire_gil(); let py = gil.python(); match file.call_method0(py, "tell") { Err(_) => Err(UnsupportedOperation::py_err("not supported: tell")), Ok(x) => x.extract::<u64>(py), } } #}
pyo3::exceptions
defines exceptions for several standard library modules.
Python Modules
As shown in the Getting Started chapter, you can create a module as follows:
use pyo3::prelude::*; // add bindings to the generated python module // N.B: names: "librust2py" must be the name of the `.so` or `.pyd` file /// This module is implemented in Rust. #[pymodule] fn rust2py(py: Python, m: &PyModule) -> PyResult<()> { // PyO3 aware function. All of our python interfaces could be declared in a separate module. // Note that the `#[pyfn()]` annotation automatically converts the arguments from // Python objects to Rust values; and the Rust return value back into a Python object. #[pyfn(m, "sum_as_string")] fn sum_as_string_py(_py: Python, a:i64, b:i64) -> PyResult<String> { let out = sum_as_string(a, b); Ok(out) } Ok(()) } // logic implemented as a normal rust function fn sum_as_string(a:i64, b:i64) -> String { format!("{}", a + b).to_string() } # fn main() {}
The #[pymodule]
procedural macro attribute takes care of exporting the initialization function of your module to Python. It can take as an argument the name of your module, which must be the name of the .so
or .pyd
file; the default is the Rust function's name.
To import the module, either copy the shared library as described in Get Started or use a tool, e.g. maturin develop
with maturin or python setup.py develop
with setuptools-rust.
Documentation
The Rust doc comments of the module initialization function will be applied automatically as the Python doc string of your module.
import rust2py
print(rust2py.__doc__)
Which means that the above Python code will print This module is implemented in Rust.
.
Modules as objects
In Python, modules are first class objects. This means that you can store them as values or add them to dicts or other modules:
# #![allow(unused_variables)] #fn main() { use pyo3::prelude::*; use pyo3::{wrap_pyfunction, wrap_pymodule}; use pyo3::types::IntoPyDict; #[pyfunction] fn subfunction() -> String { "Subfunction".to_string() } #[pymodule] fn submodule(_py: Python, module: &PyModule) -> PyResult<()> { module.add_wrapped(wrap_pyfunction!(subfunction))?; Ok(()) } #[pymodule] fn supermodule(_py: Python, module: &PyModule) -> PyResult<()> { module.add_wrapped(wrap_pymodule!(submodule))?; Ok(()) } fn nested_call() { let gil = GILGuard::acquire(); let py = gil.python(); let supermodule = wrap_pymodule!(supermodule)(py); let ctx = [("supermodule", supermodule)].into_py_dict(py); py.run("assert supermodule.submodule.subfunction() == 'Subfunction'", None, Some(&ctx)).unwrap(); } #}
This way, you can create a module hierarchy within a single extension module.
Python Functions
PyO3 supports two ways to define a free function in Python. Both require registering the function to a module.
One way is defining the function in the module definition.
use pyo3::prelude::*; #[pymodule] fn rust2py(py: Python, m: &PyModule) -> PyResult<()> { // Note that the `#[pyfn()]` annotation automatically converts the arguments from // Python objects to Rust values; and the Rust return value back into a Python object. #[pyfn(m, "sum_as_string")] fn sum_as_string_py(_py: Python, a:i64, b:i64) -> PyResult<String> { Ok(format!("{}", a + b)) } Ok(()) } # fn main() {}
The other is annotating a function with #[pyfunction]
and then adding it
to the module using the wrap_pyfunction!
macro.
use pyo3::prelude::*; use pyo3::wrap_pyfunction; #[pyfunction] fn double(x: usize) -> usize { x * 2 } #[pymodule] fn module_with_functions(py: Python, m: &PyModule) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(double)).unwrap(); Ok(()) } # fn main() {}
Argument parsing
Both the #[pyfunction]
and #[pyfn]
attributes support specifying details of
argument parsing. The details are given in the section "Method arguments" in
the Classes chapter. Here is an example for a function that accepts
arbitrary keyword arguments (**kwargs
in Python syntax) and returns the number
that was passed:
# extern crate pyo3; use pyo3::prelude::*; use pyo3::wrap_pyfunction; use pyo3::types::PyDict; #[pyfunction(kwds="**")] fn num_kwds(kwds: Option<&PyDict>) -> usize { kwds.map_or(0, |dict| dict.len()) } #[pymodule] fn module_with_functions(py: Python, m: &PyModule) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(num_kwds)).unwrap(); Ok(()) } # fn main() {}
Making the function signature available to Python
In order to make the function signature available to Python to be retrieved via
inspect.signature
, simply make sure the first line of your docstring is
formatted like in the example below. Please note that the newline after the
--
is mandatory. The /
signifies the end of positional-only arguments. This
is not a feature of this library in particular, but the general format used by
CPython for annotating signatures of built-in functions.
# #![allow(unused_variables)] #fn main() { use pyo3::prelude::*; /// add(a, b, /) /// -- /// /// This function adds two unsigned 64-bit integers. #[pyfunction] fn add(a: u64, b: u64) -> u64 { a + b } #}
When annotated like this, signatures are also correctly displayed in IPython.
>>> pyo3_test.add?
Signature: pyo3_test.add(a, b, /)
Docstring: This function adds two unsigned 64-bit integers.
Type: builtin_function_or_method
Closures
Currently, there are no conversions between Fn
s in Rust and callables in Python. This would definitely be possible and very useful, so contributions are welcome. In the meantime, you can do the following:
Calling a Python function in Rust
You can use ObjectProtocol::is_callable
to check if you got a callable, which is true for functions (including lambdas), methods and objects with a __call__
method. You can call the object with ObjectProtocol::call
with the args as first parameter and the kwargs (or None
) as second parameter. There are also ObjectProtocol::call0
with no args and ObjectProtocol::call1
with only the positional args.
Calling Rust Fn
s in Python
If you have a static function, you can expose it with #[pyfunction]
and use wrap_pyfunction!
to get the corresponding PyObject
. For dynamic functions, e.g. lambda and functions that were passed as arguments, you must put them in some kind of owned container, e.g. a box. (A long-term solution will be a special container similar to wasm-bindgen's Closure
). You can then use a #[pyclass]
struct with that container as a field as a way to pass the function over the FFI barrier. You can even make that class callable with __call__
so it looks like a function in Python code.
Python Classes
Define new class
To define a custom Python class, a Rust struct needs to be annotated with the
#[pyclass]
attribute.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; #[pyclass] struct MyClass { num: i32, debug: bool, } #}
The above example generates implementations for PyTypeInfo
and PyTypeObject
for MyClass
.
Get Python objects from pyclass
You can use pyclass
es like normal rust structs.
However, if instantiated normally, you can't treat pyclass
es as Python objects.
To get a Python object which includes pyclass
, we have to use some special methods.
PyRef
PyRef
is a special reference, which ensures that the referred struct is a part of
a Python object, and you are also holding the GIL.
You can get an instance of PyRef
by PyRef::new
, which does 3 things:
- Allocates a Python object in the Python heap
- Copies the Rust struct into the Python object
- Returns a reference to it
You can use PyRef
just like &T
, because it implements Deref<Target=T>
.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # use pyo3::types::PyDict; #[pyclass] struct MyClass { num: i32, debug: bool, } let gil = Python::acquire_gil(); let py = gil.python(); let obj = PyRef::new(py, MyClass { num: 3, debug: true }).unwrap(); assert_eq!(obj.num, 3); let dict = PyDict::new(py); // You can treat a `PyRef` as a Python object dict.set_item("obj", obj).unwrap(); #}
PyRefMut
PyRefMut
is a mutable version of PyRef
.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; #[pyclass] struct MyClass { num: i32, debug: bool, } let gil = Python::acquire_gil(); let py = gil.python(); let mut obj = PyRefMut::new(py, MyClass { num: 3, debug: true }).unwrap(); obj.num = 5; #}
Py
Py
is an object wrapper which stores an object longer than the GIL lifetime.
You can use it to avoid lifetime problems.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; #[pyclass] struct MyClass { num: i32, } fn return_myclass() -> Py<MyClass> { let gil = Python::acquire_gil(); let py = gil.python(); Py::new(py, MyClass { num: 1 }).unwrap() } let gil = Python::acquire_gil(); let obj = return_myclass(); assert_eq!(obj.as_ref(gil.python()).num, 1); #}
Customizing the class
The #[pyclass]
macro accepts the following parameters:
name=XXX
- Set the class name shown in Python code. By default, the struct name is used as the class name.freelist=XXX
- Thefreelist
parameter adds support of free allocation list to custom class. The performance improvement applies to types that are often created and deleted in a row, so that they can benefit from a freelist.XXX
is a number of items for the free list.gc
- Classes with thegc
parameter participate in Python garbage collection. If a custom class contains references to other Python objects that can be collected, thePyGCProtocol
trait has to be implemented.weakref
- Adds support for Python weak references.extends=BaseType
- Use a custom base class. The baseBaseType
must implementPyTypeInfo
.dict
- Adds__dict__
support, so that the instances of this type have a dictionary containing arbitrary instance variables.module="XXX"
- Set the name of the module the class will be shown as defined in. If not given, the class will be a virtual member of thebuiltins
module.subclass
- Allows Python classes to inherit from this class. This feature is hidden behind aunsound-subclass
feature because it is currently causing segmentation faults
Constructor
By default it is not possible to create an instance of a custom class from Python code.
To declare a constructor, you need to define a method and annotate it with the #[new]
attribute. Only Python's __new__
method can be specified, __init__
is not available.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # use pyo3::PyRawObject; #[pyclass] struct MyClass { num: i32, } #[pymethods] impl MyClass { #[new] fn new(obj: &PyRawObject, num: i32) { obj.init({ MyClass { num, } }); } } #}
Rules for the new
method:
- If no method marked with
#[new]
is declared, object instances can only be created from Rust, but not from Python. - The first parameter is the raw object and the custom
new
method must initialize the object with an instance of the struct using theinit
method. The type of the object may be the type object of a derived class declared in Python. - The first parameter must have type
&PyRawObject
. - For details on the parameter list, see the
Method arguments
section below. - The return value must be
T
orPyResult<T>
whereT
is ignored, so it can be just()
as in the example above.
Inheritance
By default, PyObject
is used as the base class. To override this default,
use the extends
parameter for pyclass
with the full path to the base class.
The new
method of subclasses must call their parent's new
method.
# use pyo3::prelude::*;
# use pyo3::PyRawObject;
#[pyclass]
struct BaseClass {
val1: usize,
}
#[pymethods]
impl BaseClass {
#[new]
fn new(obj: &PyRawObject) {
obj.init(BaseClass { val1: 10 });
}
pub fn method(&self) -> PyResult<()> {
Ok(())
}
}
#[pyclass(extends=BaseClass)]
struct SubClass {
val2: usize,
}
#[pymethods]
impl SubClass {
#[new]
fn new(obj: &PyRawObject) {
obj.init(SubClass { val2: 10 });
BaseClass::new(obj);
}
fn method2(&self) -> PyResult<()> {
self.get_base().method()
}
}
The ObjectProtocol
trait provides a get_base()
method, which returns a reference
to the instance of the base struct.
Object properties
Property descriptor methods can be defined in a #[pymethods]
impl
block only and have to be
annotated with #[getter]
and #[setter]
attributes. For example:
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # #[pyclass] # struct MyClass { # num: i32, # } # #[pymethods] impl MyClass { #[getter] fn num(&self) -> PyResult<i32> { Ok(self.num) } } #}
A getter or setter's function name is used as the property name by default. There are several ways how to override the name.
If a function name starts with get_
or set_
for getter or setter respectively,
the descriptor name becomes the function name with this prefix removed. This is also useful in case of
Rust keywords like type
(raw identifiers
can be used since Rust 2018).
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # #[pyclass] # struct MyClass { # num: i32, # } # #[pymethods] impl MyClass { #[getter] fn get_num(&self) -> PyResult<i32> { Ok(self.num) } #[setter] fn set_num(&mut self, value: i32) -> PyResult<()> { self.num = value; Ok(()) } } #}
In this case, a property num
is defined and available from Python code as self.num
.
Both the #[getter]
and #[setter]
attributes accept one parameter.
If this parameter is specified, it is used as the property name, i.e.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # #[pyclass] # struct MyClass { # num: i32, # } # #[pymethods] impl MyClass { #[getter(number)] fn num(&self) -> PyResult<i32> { Ok(self.num) } #[setter(number)] fn set_num(&mut self, value: i32) -> PyResult<()> { self.num = value; Ok(()) } } #}
In this case, the property number
is defined and available from Python code as self.number
.
For simple cases where a member variable is just read and written with no side effects, you can also declare getters and setters in your Rust struct field definition, for example:
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; #[pyclass] struct MyClass { #[pyo3(get, set)] num: i32 } #}
Then it is available from Python code as self.num
.
Instance methods
To define a Python compatible method, an impl
block for your struct has to be annotated with the
#[pymethods]
attribute. PyO3 generates Python compatible wrappers for all functions in this
block with some variations, like descriptors, class method static methods, etc.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # #[pyclass] # struct MyClass { # num: i32, # } # #[pymethods] impl MyClass { fn method1(&self) -> PyResult<i32> { Ok(10) } fn set_method(&mut self, value: i32) -> PyResult<()> { self.num = value; Ok(()) } } #}
Calls to these methods are protected by the GIL, so both &self
and &mut self
can be used.
The return type must be PyResult<T>
or T
for some T
that implements IntoPy<PyObject>
;
the latter is allowed if the method cannot raise Python exceptions.
A Python
parameter can be specified as part of method signature, in this case the py
argument
gets injected by the method wrapper, e.g.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # #[pyclass] # struct MyClass { # num: i32, # debug: bool, # } #[pymethods] impl MyClass { fn method2(&self, py: Python) -> PyResult<i32> { Ok(10) } } #}
From the Python perspective, the method2
in this example does not accept any arguments.
Class methods
To create a class method for a custom class, the method needs to be annotated
with the #[classmethod]
attribute.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # use pyo3::types::PyType; # #[pyclass] # struct MyClass { # num: i32, # debug: bool, # } #[pymethods] impl MyClass { #[classmethod] fn cls_method(cls: &PyType) -> PyResult<i32> { Ok(10) } } #}
Declares a class method callable from Python.
- The first parameter is the type object of the class on which the method is called. This may be the type object of a derived class.
- The first parameter implicitly has type
&PyType
. - For details on
parameter-list
, see the documentation ofMethod arguments
section. - The return type must be
PyResult<T>
orT
for someT
that implementsIntoPy<PyObject>
.
Static methods
To create a static method for a custom class, the method needs to be annotated with the
#[staticmethod]
attribute. The return type must be T
or PyResult<T>
for some T
that implements
IntoPy<PyObject>
.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; # #[pyclass] # struct MyClass { # num: i32, # debug: bool, # } #[pymethods] impl MyClass { #[staticmethod] fn static_method(param1: i32, param2: &str) -> PyResult<i32> { Ok(10) } } #}
Callable objects
To specify a custom __call__
method for a custom class, the method needs to be annotated with
the #[call]
attribute. Arguments of the method are specified as for instance methods.
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; use pyo3::types::PyTuple; # #[pyclass] # struct MyClass { # num: i32, # debug: bool, # } #[pymethods] impl MyClass { #[call] #[args(args="*")] fn __call__(&self, args: &PyTuple) -> PyResult<i32> { println!("MyClass has been called"); Ok(self.num) } } #}
Method arguments
By default, PyO3 uses function signatures to determine which arguments are required. Then it scans
the incoming args
and kwargs
parameters. If it can not find all required
parameters, it raises a TypeError
exception. It is possible to override the default behavior
with the #[args(...)]
attribute. This attribute accepts a comma separated list of parameters in
the form of attr_name="default value"
. Each parameter has to match the method parameter by name.
Each parameter can be one of the following types:
"*"
: var arguments separator, each parameter defined after"*"
is a keyword-only parameter. Corresponds to python'sdef meth(*, arg1.., arg2=..)
.args="*"
: "args" is var args, corresponds to Python'sdef meth(*args)
. Type of theargs
parameter has to be&PyTuple
.kwargs="**"
: "kwargs" receives keyword arguments, corresponds to Python'sdef meth(**kwargs)
. The type of thekwargs
parameter has to beOption<&PyDict>
.arg="Value"
: arguments with default value. Corresponds to Python'sdef meth(arg=Value)
. If thearg
argument is defined after var arguments, it is treated as a keyword-only argument. Note thatValue
has to be valid rust code, PyO3 just inserts it into the generated code unmodified.
Example:
# #![allow(unused_variables)] #fn main() { # use pyo3::prelude::*; use pyo3::types::{PyDict, PyTuple}; # # #[pyclass] # struct MyClass { # num: i32, # debug: bool, # } # #[pymethods] impl MyClass { #[args(arg1=true, args="*", arg2=10, args3="\"Hello\"", kwargs="**")] fn method(&self, arg1: bool, args: &PyTuple, arg2: i32, arg3: &str, kwargs: Option<&PyDict>) -> PyResult<i32> { Ok(1) } } #}
Class customizations
Python's object model defines several protocols for different object behavior, like sequence,
mapping or number protocols. PyO3 defines separate traits for each of them. To provide specific
Python object behavior, you need to implement the specific trait for your struct. Important note,
each protocol implementation block has to be annotated with the #[pyproto]
attribute.
Basic object customization
The PyObjectProtocol
trait provides several basic customizations.
Attribute access
To customize object attribute access, define the following methods:
fn __getattr__(&self, name: FromPyObject) -> PyResult<impl IntoPy<PyObject>>
fn __setattr__(&mut self, name: FromPyObject, value: FromPyObject) -> PyResult<()>
fn __delattr__(&mut self, name: FromPyObject) -> PyResult<()>
Each method corresponds to Python's self.attr
, self.attr = value
and del self.attr
code.
String Conversions
-
fn __repr__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>
-
fn __str__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>
Possible return types for
__str__
and__repr__
arePyResult<String>
orPyResult<PyString>
. -
fn __bytes__(&self) -> PyResult<PyBytes>
Provides the conversion to
bytes
. -
fn __format__(&self, format_spec: &str) -> PyResult<impl ToPyObject<ObjectType=PyString>>
Special method that is used by the
format()
builtin and thestr.format()
method. Possible return types arePyResult<String>
orPyResult<PyString>
.
Comparison operators
-
fn __richcmp__(&self, other: impl FromPyObject, op: CompareOp) -> PyResult<impl ToPyObject>
Overloads Python comparison operations (
==
,!=
,<
,<=
,>
, and>=
). Theop
argument indicates the comparison operation being performed. The return type will normally bePyResult<bool>
, but any Python object can be returned. Ifother
is not of the type specified in the signature, the generated code will automaticallyreturn NotImplemented
. -
fn __hash__(&self) -> PyResult<impl PrimInt>
Objects that compare equal must have the same hash value. The return type must be
PyResult<T>
whereT
is one of Rust's primitive integer types.
Other methods
-
fn __bool__(&self) -> PyResult<bool>
Determines the "truthyness" of the object.
Garbage Collector Integration
If your type owns references to other Python objects, you will need to
integrate with Python's garbage collector so that the GC is aware of
those references.
To do this, implement the PyGCProtocol
trait for your struct.
It includes two methods __traverse__
and __clear__
.
These correspond to the slots tp_traverse
and tp_clear
in the Python C API.
__traverse__
must call visit.call()
for each reference to another Python object.
__clear__
must clear out any mutable references to other Python objects
(thus breaking reference cycles). Immutable references do not have to be cleared,
as every cycle must contain at least one mutable reference.
Example:
# #![allow(unused_variables)] #fn main() { extern crate pyo3; use pyo3::prelude::*; use pyo3::PyTraverseError; use pyo3::gc::{PyGCProtocol, PyVisit}; #[pyclass] struct ClassWithGCSupport { obj: Option<PyObject>, } #[pyproto] impl PyGCProtocol for ClassWithGCSupport { fn __traverse__(&self, visit: PyVisit) -> Result<(), PyTraverseError> { if let Some(ref obj) = self.obj { visit.call(obj)? } Ok(()) } fn __clear__(&mut self) { if let Some(obj) = self.obj.take() { // Release reference, this decrements ref counter. let gil = GILGuard::acquire(); let py = gil.python(); py.release(obj); } } } #}
Special protocol trait implementations have to be annotated with the #[pyproto]
attribute.
It is also possible to enable GC for custom classes using the gc
parameter of the pyclass
attribute.
i.e. #[pyclass(gc)]
. In that case instances of custom class participate in Python garbage
collection, and it is possible to track them with gc
module methods. When using the gc
parameter,
it is required to implement the PyGCProtocol
trait, failure to do so will result in an error
at compile time:
#[pyclass(gc)]
struct GCTracked {} // Fails because it does not implement PyGCProtocol
Iterator Types
Iterators can be defined using the
PyIterProtocol
trait.
It includes two methods __iter__
and __next__
:
fn __iter__(slf: PyRefMut<Self>) -> PyResult<impl IntoPy<PyObject>>
fn __next__(slf: PyRefMut<Self>) -> PyResult<Option<impl IntoPy<PyObject>>>
Returning Ok(None)
from __next__
indicates that that there are no further items.
Example:
# #![allow(unused_variables)] #fn main() { extern crate pyo3; use pyo3::prelude::*; use pyo3::PyIterProtocol; #[pyclass] struct MyIterator { iter: Box<Iterator<Item = PyObject> + Send>, } #[pyproto] impl PyIterProtocol for MyIterator { fn __iter__(slf: PyRefMut<Self>) -> PyResult<Py<MyIterator>> { Ok(slf.into()) } fn __next__(mut slf: PyRefMut<Self>) -> PyResult<Option<PyObject>> { Ok(slf.iter.next()) } } #}
Manually implementing pyclass
TODO: Which traits to implement (basically PyTypeCreate: PyObjectAlloc + PyTypeInfo + PyMethodsProtocol + Sized
) and what they mean.
How methods are implemented
Users should be able to define a #[pyclass]
with or without #[pymethods]
, while PyO3 needs a
trait with a function that returns all methods. Since it's impossible to make the code generation in
pyclass dependent on whether there is an impl block, we'd need to implement the trait on
#[pyclass]
and override the implementation in #[pymethods]
, which is to the best of my knowledge
only possible with the specialization feature, which can't be used on stable.
To escape this we use inventory, which allows us to collect impl
s from arbitrary source code by exploiting some binary trick. See inventory: how it works and pyo3_derive_backend::py_class::impl_inventory
for more details.
Call Python functions from Rust
Want to run just an expression? Then use eval.
Python::eval
is
a method to execute a Python expression
and returns the evaluated value as PyAny
.
use pyo3::prelude::*; use pyo3::types::IntoPyDict; fn main() -> Result<(), ()> { let gil = Python::acquire_gil(); let py = gil.python(); let result = py.eval("[i * 10 for i in range(5)]", None, None).map_err(|e| { e.print_and_set_sys_last_vars(py); })?; let res: Vec<i64> = result.extract().unwrap(); assert_eq!(res, vec![0, 10, 20, 30, 40]); Ok(()) }
Want to run statements? Then use run.
Python::run
is a method to execute one or more
Python statements.
This method returns nothing, but you can get objects via locals
dict.
You can also use the py_run!
macro, which is a shorthand for Python::run
.
Since py_run!
can cause panic, we recommend you to use this macro only for testing
your Python extensions quickly.
use pyo3::{PyObjectProtocol, prelude::*, py_run}; # fn main() { #[pyclass] struct UserData { id: u32, name: String, } #[pymethods] impl UserData { fn as_tuple(&self) -> (u32, String) { (self.id, self.name.clone()) } } #[pyproto] impl PyObjectProtocol for UserData { fn __repr__(&self) -> PyResult<String> { Ok(format!("User {}(id: {})", self.name, self.id)) } } let gil = Python::acquire_gil(); let py = gil.python(); let userdata = UserData { id: 34, name: "Yu".to_string(), }; let userdata = PyRef::new(py, userdata).unwrap(); let userdata_as_tuple = (34, "Yu"); py_run!(py, userdata userdata_as_tuple, r#" assert repr(userdata) == "User Yu(id: 34)" assert userdata.as_tuple() == userdata_as_tuple "#); # }
You have a python file or Python function? Then use PyModule.
PyModule also can execute Python codes by calling a function.
use pyo3::{prelude::*, types::{IntoPyDict, PyModule}}; # fn main() -> PyResult<()> { let gil = Python::acquire_gil(); let py = gil.python(); let activators = PyModule::from_code(py, " def relu(x): return max(0.0, x) def leaky_relu(x, slope=0.01): return x if x >= 0 else x * slope ", "activators.py", "activators")?; let relu_result: f64 = activators.call1("relu", (-1.0,))?.extract()?; assert_eq!(relu_result, 0.0); let kwargs = [("slope", 0.2)].into_py_dict(py); let lrelu_result: f64 = activators .call("leaky_relu", (-1.0,), Some(kwargs))? .extract()?; assert_eq!(lrelu_result, -0.2); # Ok(()) }
Parallelism
CPython has the infamous GIL (Global Interpreter Lock), which prevents developers from getting true parallelism when running pure Python code. With PyO3, you can release the GIL when executing Rust code to achieve true parallelism.
The Python::allow_threads
method temporarily releases the GIL, thus allowing other Python threads to run.
impl Python {
pub fn allow_threads<T, F>(self, f: F) -> T where F: Send + FnOnce() -> T {}
}
Let's take a look at our word-count example,
where we have a wc_parallel
function that utilizes the rayon crate to count words in parallel.
fn wc_parallel(lines: &str, search: &str) -> i32 {
lines.par_lines()
.map(|line| wc_line(line, search))
.sum()
}
Then in the Python bridge, we have a function search
exposed to the Python runtime which calls wc_parallel
inside a closure passed to Python::allow_threads
to enable true parallelism:
#[pymodule]
fn word_count(py: Python, m: &PyModule) -> PyResult<()> {
#[pyfn(m, "search")]
fn search(py: Python, path: String, search: String) -> PyResult<i32> {
let mut file = File::open(path)?;
let mut contents = String::new();
file.read_to_string(&mut contents)?;
let count = py.allow_threads(move || wc_parallel(&contents, &search));
Ok(count)
}
Ok(())
}
Benchmark
Let's benchmark the word-count
example to verify that we did unlock true parallelism with PyO3.
We are using pytest-benchmark
to benchmark three word count functions:
The benchmark script can be found here,
then we can run pytest tests
to benchmark them.
On MacBook Pro (Retina, 15-inch, Mid 2015) the benchmark gives:
Debugging
Macros
PyO3's attributes (#[pyclass]
, #[pymodule]
, etc.) are procedural macros, which means that they rewrite the source of the annotated item. You can view the generated source with the following command, which also expands a few other things:
cargo rustc --profile=check -- -Z unstable-options --pretty=expanded > expanded.rs; rustfmt expanded.rs
(You might need to install rustfmt if you don't already have it.)
You can also debug classic !
-macros by adding -Z trace-macros
:
cargo rustc --profile=check -- -Z unstable-options --pretty=expanded -Z trace-macros > expanded.rs; rustfmt expanded.rs
See cargo expand for a more elaborate version of those commands.
Running with Valgrind
Valgrind is a tool to detect memory management bugs such as memory leaks.
You first need to install a debug build of Python, otherwise Valgrind won't produce usable results. In Ubuntu there's e.g. a python3-dbg
package.
Activate an environment with the debug interpreter and recompile. If you're on Linux, use ldd
with the name of your binary and check that you're linking e.g. libpython3.6dm.so.1.0
instead of libpython3.6m.so.1.0
.
Download the suppressions file for cpython.
Run Valgrind with valgrind --suppressions=valgrind-python.supp ./my-command --with-options
Getting a stacktrace
The best start to investigate a crash such as an segmentation fault is a backtrace.
- Link against a debug build of python as described in the previous chapter
- Run
gdb <my-binary>
- Enter
r
to run - After the crash occurred, enter
bt
orbt full
to print the stacktrace
Advanced topics
ffi
PyO3 exposes much of Python's C API through the ffi
module.
The C API is naturally unsafe and requires you to manage reference counts, errors and specific invariants yourself. Please refer to the C API Reference Manual and The Rustonomicon before using any function from that API.
Testing
Currently, #341 causes cargo test
to fail with weird linking errors when the extension-module
feature is activated. For now you can work around this by making the extension-module
feature optional and running the tests with cargo test --no-default-features
:
[dependencies.pyo3]
version = "0.8.1"
[features]
extension-module = ["pyo3/extension-module"]
default = ["extension-module"]
Building and Distribution
Python version
PyO3 uses a build script to determine the Python version and set the correct linker arguments. By default it uses the python3
executable. You can override the Python interpreter by setting PYTHON_SYS_EXECUTABLE
, e.g., PYTHON_SYS_EXECUTABLE=python3.6
.
Linking
Different linker arguments must be set for libraries/extension modules and binaries, which includes both standalone binaries and tests. (More specifically, binaries must be told where to find libpython and libraries must not link to libpython for manylinux compliance).
Since PyO3's build script can't know whether you're building a binary or a library, you have to activate the extension-module
feature to get the build options for a library, or it'll default to binary.
If you have e.g. a library crate and a profiling crate alongside, you need to use optional features. E.g. you put the following in the library crate:
[dependencies]
pyo3 = "0.6"
[lib]
name = "hyperjson"
crate-type = ["rlib", "cdylib"]
[features]
default = ["pyo3/extension-module"]
And this in the profiling crate:
[dependencies]
my_main_crate = { path = "..", default-features = false }
pyo3 = "0.6"
On Linux/macOS you might have to change LD_LIBRARY_PATH
to include libpython, while on windows you might need to set LIB
to include pythonxy.lib
(where x and y are major and minor version), which is normally either in the libs
or Lib
folder of a Python installation.
Distribution
There are two ways to distribute your module as a Python package: the old, setuptools-rust, and the new, maturin. setuptools-rust needs some configuration files (setup.py
, MANIFEST.in
, build-wheels.sh
, etc.) and external tools (docker, twine). maturin doesn't need any configuration files. It can not yet build sdist though (pyo3/maturin#2).
Cross Compiling
Cross compiling PyO3 modules is relatively straightforward and requires a few pieces of software:
- A toolchain for your target.
- The appropriate options in your Cargo
.config
for the platform you're targeting and the toolchain you are using. - A Python interpreter that's already been compiled for your target.
- The headers that match the above interpreter.
See https://github.com/japaric/rust-cross for a primer on cross compiling Rust in general.
After you've obtained the above, you can build a cross compiled PyO3 module by setting a few extra environment variables:
PYO3_CROSS_INCLUDE_DIR
: This variable must be set to the directory containing the headers for the target's Python interpreter.PYO3_CROSS_LIB_DIR
: This variable must be set to the directory containing the target's libpython DSO.
An example might look like the following (assuming your target's sysroot is at /home/pyo3/cross/sysroot
and that your target is armv7
):
export PYO3_CROSS_INCLUDE_DIR="/home/pyo3/cross/sysroot/usr/include"
export PYO3_CROSS_LIB_DIR="/home/pyo3/cross/sysroot/usr/lib"
cargo build --target armv7-unknown-linux-gnueabihf
PyPy Support
Using PyPy is supported via cpyext.
Support is only provided for building rust extension for code running under PyPy. This means PyPy cannot be called from rust via cpyext. Note that there some differences in the ffi module between pypy and cpython.
This is a limitation of cpyext and supported for embedding cpyext is not planned.
Compilation against PyPy is done by exporting the PYTHON_SYS_EXECUTABLE
to a pypy binary or by compiling in a PyPy virtualenv.
For example, PYTHON_SYS_EXECUTABLE="/path/to/pypy3" /path/to/pypy3 setup.py install
Unsupported Features
These are features currently supported by PyO3, but not yet implemented in cpyext.
- Complex number functions (
_Py_c_sum
,_Py_c_sum
..) - Conversion to rust's i128, u128 types.
PySequence_Count
(which is used to count number of element in array)PyDict_MergeFromSeq2
(used inPyDict::from_sequence
)
Appendix: PyO3 and rust-cpython
PyO3 began as fork of rust-cpython when rust-cpython wasn't maintained. Over the time PyO3 has become fundamentally different from rust-cpython.
This chapter is based on the discussion in PyO3/pyo3#55.
Macros
While rust-cpython has a macro based dsl for declaring modules and classes, PyO3 uses proc macros and specialization. PyO3 also doesn't change your struct and functions so you can still use them as normal Rust functions. The disadvantage is that specialization currently only works on nightly.
rust-cpython
py_class!(class MyClass |py| {
data number: i32;
def __new__(_cls, arg: i32) -> PyResult<MyClass> {
MyClass::create_instance(py, arg)
}
def half(&self) -> PyResult<i32> {
Ok(self.number(py) / 2)
}
});
pyo3
# #![allow(unused_variables)] #fn main() { use pyo3::prelude::*; use pyo3::PyRawObject; #[pyclass] struct MyClass { num: u32, } #[pymethods] impl MyClass { #[new] fn new(obj: &PyRawObject, num: u32) { obj.init({ MyClass { num, } }); } fn half(&self) -> PyResult<u32> { Ok(self.num / 2) } } #}
Ownership and lifetimes
All objects are owned by the PyO3 library and all APIs available with references, while in rust-cpython, you own python objects.
Here is an example of the PyList API:
rust-cpython
impl PyList {
fn new(py: Python) -> PyList {...}
fn get_item(&self, py: Python, index: isize) -> PyObject {...}
}
pyo3
impl PyList {
fn new(py: Python) -> &PyList {...}
fn get_item(&self, index: isize) -> &PyObject {...}
}
Because PyO3 allows only references to Python objects, all references have the GIL lifetime. So the owned Python object is not required, and it is safe to have functions like fn py<'p>(&'p self) -> Python<'p> {}
.
Error handling
rust-cpython requires a Python
parameter for constructing a PyErr
, so error handling ergonomics is pretty bad. It is not possible to use ?
with Rust errors.
PyO3 on other hand does not require Python
for constructing a PyErr
, it is only required if you want to raise an exception in Python with the PyErr::restore()
method. Due to various std::convert::From<E> for PyErr
implementations for Rust standard error types E
, propagating ?
is supported automatically.