PEP 590 – Vectorcall: a fast calling protocol for CPython
- Author:
- Mark Shannon <mark at hotpy.org>, Jeroen Demeyer <J.Demeyer at UGent.be>
- BDFL-Delegate:
- Petr Viktorin <encukou at gmail.com>
- Status:
- Accepted
- Type:
- Standards Track
- Created:
- 29-Mar-2019
- Python-Version:
- 3.8
- Post-History:
Table of Contents
Abstract
This PEP introduces a new C API to optimize calls of objects. It introduces a new “vectorcall” protocol and calling convention. This is based on the “fastcall” convention, which is already used internally by CPython. The new features can be used by any user-defined extension class.
Most of the new API is private in CPython 3.8. The plan is to finalize semantics and make it public in Python 3.9.
NOTE: This PEP deals only with the Python/C API, it does not affect the Python language or standard library.
Motivation
The choice of a calling convention impacts the performance and flexibility of code on either side of the call. Often there is tension between performance and flexibility.
The current tp_call
[2] calling convention is sufficiently flexible to cover all cases, but its performance is poor.
The poor performance is largely a result of having to create intermediate tuples, and possibly intermediate dicts, during the call.
This is mitigated in CPython by including special-case code to speed up calls to Python and builtin functions.
Unfortunately, this means that other callables such as classes and third party extension objects are called using the
slower, more general tp_call
calling convention.
This PEP proposes that the calling convention used internally for Python and builtin functions is generalized and published so that all calls can benefit from better performance. The new proposed calling convention is not fully general, but covers the large majority of calls. It is designed to remove the overhead of temporary object creation and multiple indirections.
Another source of inefficiency in the tp_call
convention is that it has one function pointer per class,
rather than per object.
This is inefficient for calls to classes as several intermediate objects need to be created.
For a class cls
, at least one intermediate object is created for each call in the sequence
type.__call__
, cls.__new__
, cls.__init__
.
This PEP proposes an interface for use by extension modules. Such interfaces cannot effectively be tested, or designed, without having the consumers in the loop. For that reason, we provide private (underscore-prefixed) names. The API may change (based on consumer feedback) in Python 3.9, where we expect it to be finalized, and the underscores removed.
Specification
The function pointer type
Calls are made through a function pointer taking the following parameters:
PyObject *callable
: The called objectPyObject *const *args
: A vector of argumentssize_t nargs
: The number of arguments plus the optional flagPY_VECTORCALL_ARGUMENTS_OFFSET
(see below)PyObject *kwnames
: EitherNULL
or a tuple with the names of the keyword arguments
This is implemented by the function pointer type:
typedef PyObject *(*vectorcallfunc)(PyObject *callable, PyObject *const *args, size_t nargs, PyObject *kwnames);
Changes to the PyTypeObject
struct
The unused slot printfunc tp_print
is replaced with tp_vectorcall_offset
. It has the type Py_ssize_t
.
A new tp_flags
flag is added, _Py_TPFLAGS_HAVE_VECTORCALL
,
which must be set for any class that uses the vectorcall protocol.
If _Py_TPFLAGS_HAVE_VECTORCALL
is set, then tp_vectorcall_offset
must be a positive integer.
It is the offset into the object of the vectorcall function pointer of type vectorcallfunc
.
This pointer may be NULL
, in which case the behavior is the same as if _Py_TPFLAGS_HAVE_VECTORCALL
was not set.
The tp_print
slot is reused as the tp_vectorcall_offset
slot to make it easier for external projects to backport the vectorcall protocol to earlier Python versions.
In particular, the Cython project has shown interest in doing that (see https://mail.python.org/pipermail/python-dev/2018-June/153927.html).
Descriptor behavior
One additional type flag is specified: Py_TPFLAGS_METHOD_DESCRIPTOR
.
Py_TPFLAGS_METHOD_DESCRIPTOR
should be set if the callable uses the descriptor protocol to create a bound method-like object.
This is used by the interpreter to avoid creating temporary objects when calling methods
(see _PyObject_GetMethod
and the LOAD_METHOD
/CALL_METHOD
opcodes).
Concretely, if Py_TPFLAGS_METHOD_DESCRIPTOR
is set for type(func)
, then:
func.__get__(obj, cls)(*args, **kwds)
(withobj
not None) must be equivalent tofunc(obj, *args, **kwds)
.func.__get__(None, cls)(*args, **kwds)
must be equivalent tofunc(*args, **kwds)
.
There are no restrictions on the object func.__get__(obj, cls)
.
The latter is not required to implement the vectorcall protocol.
The call
The call takes the form ((vectorcallfunc)(((char *)o)+offset))(o, args, n, kwnames)
where
offset
is Py_TYPE(o)->tp_vectorcall_offset
.
The caller is responsible for creating the kwnames
tuple and ensuring that there are no duplicates in it.
n
is the number of positional arguments plus possibly the PY_VECTORCALL_ARGUMENTS_OFFSET
flag.
PY_VECTORCALL_ARGUMENTS_OFFSET
The flag PY_VECTORCALL_ARGUMENTS_OFFSET
should be added to n
if the callee is allowed to temporarily change args[-1]
.
In other words, this can be used if args
points to argument 1 in the allocated vector.
The callee must restore the value of args[-1]
before returning.
Whenever they can do so cheaply (without allocation), callers are encouraged to use PY_VECTORCALL_ARGUMENTS_OFFSET
.
Doing so will allow callables such as bound methods to make their onward calls cheaply.
The bytecode interpreter already allocates space on the stack for the callable,
so it can use this trick at no additional cost.
See [3] for an example of how PY_VECTORCALL_ARGUMENTS_OFFSET
is used by a callee to avoid allocation.
For getting the actual number of arguments from the parameter n
,
the macro PyVectorcall_NARGS(n)
must be used.
This allows for future changes or extensions.
New C API and changes to CPython
The following functions or macros are added to the C API:
PyObject *_PyObject_Vectorcall(PyObject *obj, PyObject *const *args, size_t nargs, PyObject *keywords)
: Callsobj
with the given arguments. Note thatnargs
may include the flagPY_VECTORCALL_ARGUMENTS_OFFSET
. The actual number of positional arguments is given byPyVectorcall_NARGS(nargs)
. The argumentkeywords
is a tuple of keyword names orNULL
. An empty tuple has the same effect as passingNULL
. This uses either the vectorcall protocol ortp_call
internally; if neither is supported, an exception is raised.PyObject *PyVectorcall_Call(PyObject *obj, PyObject *tuple, PyObject *dict)
: Call the object (which must support vectorcall) with the old*args
and**kwargs
calling convention. This is mostly meant to put in thetp_call
slot.Py_ssize_t PyVectorcall_NARGS(size_t nargs)
: Given a vectorcallnargs
argument, return the actual number of arguments. Currently equivalent tonargs & ~PY_VECTORCALL_ARGUMENTS_OFFSET
.
Subclassing
Extension types inherit the type flag _Py_TPFLAGS_HAVE_VECTORCALL
and the value tp_vectorcall_offset
from the base class,
provided that they implement tp_call
the same way as the base class.
Additionally, the flag Py_TPFLAGS_METHOD_DESCRIPTOR
is inherited if tp_descr_get
is implemented the same way as the base class.
Heap types never inherit the vectorcall protocol because
that would not be safe (heap types can be changed dynamically).
This restriction may be lifted in the future, but that would require
special-casing __call__
in type.__setattribute__
.
Finalizing the API
The underscore in the names _PyObject_Vectorcall
and
_Py_TPFLAGS_HAVE_VECTORCALL
indicates that this API may change in minor
Python versions.
When finalized (which is planned for Python 3.9), they will be renamed to
PyObject_Vectorcall
and Py_TPFLAGS_HAVE_VECTORCALL
.
The old underscore-prefixed names will remain available as aliases.
The new API will be documented as normal, but will warn of the above.
Semantics for the other names introduced in this PEP (PyVectorcall_NARGS
,
PyVectorcall_Call
, Py_TPFLAGS_METHOD_DESCRIPTOR
,
PY_VECTORCALL_ARGUMENTS_OFFSET
) are final.
Internal CPython changes
Changes to existing classes
The function
, builtin_function_or_method
, method_descriptor
, method
, wrapper_descriptor
, method-wrapper
classes will use the vectorcall protocol
(not all of these will be changed in the initial implementation).
For builtin_function_or_method
and method_descriptor
(which use the PyMethodDef
data structure),
one could implement a specific vectorcall wrapper for every existing calling convention.
Whether or not it is worth doing that remains to be seen.
Using the vectorcall protocol for classes
For a class cls
, creating a new instance using cls(xxx)
requires multiple calls.
At least one intermediate object is created for each call in the sequence
type.__call__
, cls.__new__
, cls.__init__
.
So it makes a lot of sense to use vectorcall for calling classes.
This really means implementing the vectorcall protocol for type
.
Some of the most commonly used classes will use this protocol,
probably range
, list
, str
, and type
.
The PyMethodDef
protocol and Argument Clinic
Argument Clinic [4] automatically generates wrapper functions around lower-level callables, providing safe unboxing of primitive types and
other safety checks.
Argument Clinic could be extended to generate wrapper objects conforming to the new vectorcall
protocol.
This will allow execution to flow from the caller to the Argument Clinic generated wrapper and
thence to the hand-written code with only a single indirection.
Third-party extension classes using vectorcall
To enable call performance on a par with Python functions and built-in functions,
third-party callables should include a vectorcallfunc
function pointer,
set tp_vectorcall_offset
to the correct value and add the _Py_TPFLAGS_HAVE_VECTORCALL
flag.
Any class that does this must implement the tp_call
function and make sure its behaviour is consistent with the vectorcallfunc
function.
Setting tp_call
to PyVectorcall_Call
is sufficient.
Performance implications of these changes
This PEP should not have much impact on the performance of existing code (neither in the positive nor the negative sense). It is mainly meant to allow efficient new code to be written, not to make existing code faster.
Nevertheless, this PEP optimizes for METH_FASTCALL
functions.
Performance of functions using METH_VARARGS
will become slightly worse.
Stable ABI
Nothing from this PEP is added to the stable ABI (PEP 384).
Alternative Suggestions
bpo-29259
PEP 590 is close to what was proposed in bpo-29259 [1].
The main difference is that this PEP stores the function pointer
in the instance rather than in the class.
This makes more sense for implementing functions in C,
where every instance corresponds to a different C function.
It also allows optimizing type.__call__
, which is not possible with bpo-29259.
PEP 576 and PEP 580
Both PEP 576 and PEP 580 are designed to enable 3rd party objects to be both expressive and performant (on a par with CPython objects). The purpose of this PEP is provide a uniform way to call objects in the CPython ecosystem that is both expressive and as performant as possible.
This PEP is broader in scope than PEP 576 and uses variable rather than fixed offset function-pointers. The underlying calling convention is similar. Because PEP 576 only allows a fixed offset for the function pointer, it would not allow the improvements to any objects with constraints on their layout.
PEP 580 proposes a major change to the PyMethodDef
protocol used to define builtin functions.
This PEP provides a more general and simpler mechanism in the form of a new calling convention.
This PEP also extends the PyMethodDef
protocol, but merely to formalise existing conventions.
Other rejected approaches
A longer, 6 argument, form combining both the vector and optional tuple and dictionary arguments was considered.
However, it was found that the code to convert between it and the old tp_call
form was overly cumbersome and inefficient.
Also, since only 4 arguments are passed in registers on x64 Windows, the two extra arguments would have non-negligible costs.
Removing any special cases and making all calls use the tp_call
form was also considered.
However, unless a much more efficient way was found to create and destroy tuples, and to a lesser extent dictionaries,
then it would be too slow.
Acknowledgements
Victor Stinner for developing the original “fastcall” calling convention internally to CPython. This PEP codifies and extends his work.
References
Reference implementation
A minimal implementation can be found at https://github.com/markshannon/cpython/tree/vectorcall-minimal
Copyright
This document has been placed in the public domain.
Source: https://github.com/python/peps/blob/main/pep-0590.rst
Last modified: 2022-02-01 02:49:58 GMT