PEP: 510 Title: Specialize functions with guards Version: $Revision$
Last-Modified: $Date$ Author: Victor Stinner <vstinner@python.org>
Status: Rejected Type: Standards Track Content-Type: text/x-rst Created:
04-Jan-2016 Python-Version: 3.6

Rejection Notice

This PEP was rejected by its author since the design didn't show any
significant speedup, but also because of the lack of time to implement
the most advanced and complex optimizations.

Abstract

Add functions to the Python C API to specialize pure Python functions:
add specialized codes with guards. It allows to implement static
optimizers respecting the Python semantics.

Rationale

Python semantics

Python is hard to optimize because almost everything is mutable: builtin
functions, function code, global variables, local variables, ... can be
modified at runtime. Implement optimizations respecting the Python
semantics requires to detect when "something changes", we will call
these checks "guards".

This PEP proposes to add a public API to the Python C API to add
specialized codes with guards to a function. When the function is
called, a specialized code is used if nothing changed, otherwise use the
original bytecode.

Even if guards help to respect most parts of the Python semantics, it's
hard to optimize Python without making subtle changes on the exact
behaviour. CPython has a long history and many applications rely on
implementation details. A compromise must be found between "everything
is mutable" and performance.

Writing an optimizer is out of the scope of this PEP.

Why not a JIT compiler?

There are multiple JIT compilers for Python actively developed:

-   PyPy
-   Pyston
-   Numba
-   Pyjion

Numba is specific to numerical computation. Pyston and Pyjion are still
young. PyPy is the most complete Python interpreter, it is generally
faster than CPython in micro- and many macro-benchmarks and has a very
good compatibility with CPython (it respects the Python semantics).
There are still issues with Python JIT compilers which avoid them to be
widely used instead of CPython.

Many popular libraries like numpy, PyGTK, PyQt, PySide and wxPython are
implemented in C or C++ and use the Python C API. To have a small memory
footprint and better performances, Python JIT compilers do not use
reference counting to use a faster garbage collector, do not use C
structures of CPython objects and manage memory allocations differently.
PyPy has a cpyext module which emulates the Python C API but it has
worse performances than CPython and does not support the full Python C
API.

New features are first developed in CPython. In January 2016, the latest
CPython stable version is 3.5, whereas PyPy only supports Python 2.7 and
3.2, and Pyston only supports Python 2.7.

Even if PyPy has a very good compatibility with Python, some modules are
still not compatible with PyPy: see PyPy Compatibility Wiki. The
incomplete support of the Python C API is part of this problem. There
are also subtle differences between PyPy and CPython like reference
counting: object destructors are always called in PyPy, but can be
called "later" than in CPython. Using context managers helps to control
when resources are released.

Even if PyPy is much faster than CPython in a wide range of benchmarks,
some users still report worse performances than CPython on some specific
use cases or unstable performances.

When Python is used as a scripting program for programs running less
than 1 minute, JIT compilers can be slower because their startup time is
higher and the JIT compiler takes time to optimize the code. For
example, most Mercurial commands take a few seconds.

Numba now supports ahead of time compilation, but it requires decorator
to specify arguments types and it only supports numerical types.

CPython 3.5 has almost no optimization: the peephole optimizer only
implements basic optimizations. A static compiler is a compromise
between CPython 3.5 and PyPy.

Note

There was also the Unladen Swallow project, but it was abandoned in
2011.

Examples

Following examples are not written to show powerful optimizations
promising important speedup, but to be short and easy to understand,
just to explain the principle.

Hypothetical myoptimizer module

Examples in this PEP uses a hypothetical myoptimizer module which
provides the following functions and types:

-   specialize(func, code, guards): add the specialized code code with
    guards guards to the function func
-   get_specialized(func): get the list of specialized codes as a list
    of (code, guards) tuples where code is a callable or code object and
    guards is a list of a guards
-   GuardBuiltins(name): guard watching for builtins.__dict__[name] and
    globals()[name]. The guard fails if builtins.__dict__[name] is
    replaced, or if globals()[name] is set.

Using bytecode

Add specialized bytecode where the call to the pure builtin function
chr(65) is replaced with its result "A":

    import myoptimizer

    def func():
        return chr(65)

    def fast_func():
        return "A"

    myoptimizer.specialize(func, fast_func.__code__,
                           [myoptimizer.GuardBuiltins("chr")])
    del fast_func

Example showing the behaviour of the guard:

    print("func(): %s" % func())
    print("#specialized: %s" % len(myoptimizer.get_specialized(func)))
    print()

    import builtins
    builtins.chr = lambda obj: "mock"

    print("func(): %s" % func())
    print("#specialized: %s" % len(myoptimizer.get_specialized(func)))

Output:

    func(): A
    #specialized: 1

    func(): mock
    #specialized: 0

The first call uses the specialized bytecode which returns the string
"A". The second call removes the specialized code because the builtin
chr() function was replaced, and executes the original bytecode calling
chr(65).

On a microbenchmark, calling the specialized bytecode takes 88 ns,
whereas the original function takes 145 ns (+57 ns): 1.6 times as fast.

Using builtin function

Add the C builtin chr() function as the specialized code instead of a
bytecode calling chr(obj):

    import myoptimizer

    def func(arg):
        return chr(arg)

    myoptimizer.specialize(func, chr,
                           [myoptimizer.GuardBuiltins("chr")])

Example showing the behaviour of the guard:

    print("func(65): %s" % func(65))
    print("#specialized: %s" % len(myoptimizer.get_specialized(func)))
    print()

    import builtins
    builtins.chr = lambda obj: "mock"

    print("func(65): %s" % func(65))
    print("#specialized: %s" % len(myoptimizer.get_specialized(func)))

Output:

    func(): A
    #specialized: 1

    func(): mock
    #specialized: 0

The first call calls the C builtin chr() function (without creating a
Python frame). The second call removes the specialized code because the
builtin chr() function was replaced, and executes the original bytecode.

On a microbenchmark, calling the C builtin takes 95 ns, whereas the
original bytecode takes 155 ns (+60 ns): 1.6 times as fast. Calling
directly chr(65) takes 76 ns.

Choose the specialized code

Pseudo-code to choose the specialized code to call a pure Python
function:

    def call_func(func, args, kwargs):
        specialized = myoptimizer.get_specialized(func)
        nspecialized = len(specialized)
        index = 0
        while index < nspecialized:
            specialized_code, guards = specialized[index]

            for guard in guards:
                check = guard(args, kwargs)
                if check:
                    break

            if not check:
                # all guards succeeded:
                # use the specialized code
                return specialized_code
            elif check == 1:
                # a guard failed temporarily:
                # try the next specialized code
                index += 1
            else:
                assert check == 2
                # a guard will always fail:
                # remove the specialized code
                del specialized[index]

        # if a guard of each specialized code failed, or if the function
        # has no specialized code, use original bytecode
        code = func.__code__

Changes

Changes to the Python C API:

-   Add a PyFuncGuardObject object and a PyFuncGuard_Type type

-   Add a PySpecializedCode structure

-   Add the following fields to the PyFunctionObject structure:

        Py_ssize_t nb_specialized;
        PySpecializedCode *specialized;

-   Add function methods:

    -   PyFunction_Specialize()
    -   PyFunction_GetSpecializedCodes()
    -   PyFunction_GetSpecializedCode()
    -   PyFunction_RemoveSpecialized()
    -   PyFunction_RemoveAllSpecialized()

None of these function and types are exposed at the Python level.

All these additions are explicitly excluded of the stable ABI.

When a function code is replaced (func.__code__ = new_code), all
specialized codes and guards are removed.

Function guard

Add a function guard object:

    typedef struct {
        PyObject ob_base;
        int (*init) (PyObject *guard, PyObject *func);
        int (*check) (PyObject *guard, PyObject **stack, int na, int nk);
    } PyFuncGuardObject;

The init() function initializes a guard:

-   Return 0 on success
-   Return 1 if the guard will always fail: PyFunction_Specialize() must
    ignore the specialized code
-   Raise an exception and return -1 on error

The check() function checks a guard:

-   Return 0 on success
-   Return 1 if the guard failed temporarily
-   Return 2 if the guard will always fail: the specialized code must be
    removed
-   Raise an exception and return -1 on error

stack is an array of arguments: indexed arguments followed by (key,
value) pairs of keyword arguments. na is the number of indexed
arguments. nk is the number of keyword arguments: the number of (key,
value) pairs. stack contains na + nk * 2 objects.

Specialized code

Add a specialized code structure:

    typedef struct {
        PyObject *code;        /* callable or code object */
        Py_ssize_t nb_guard;
        PyObject **guards;     /* PyFuncGuardObject objects */
    } PySpecializedCode;

Function methods

PyFunction_Specialize

Add a function method to specialize the function, add a specialized code
with guards:

    int PyFunction_Specialize(PyObject *func,
                              PyObject *code, PyObject *guards)

If code is a Python function, the code object of the code function is
used as the specialized code. The specialized Python function must have
the same parameter defaults, the same keyword parameter defaults, and
must not have specialized code.

If code is a Python function or a code object, a new code object is
created and the code name and first line number of the code object of
func are copied. The specialized code must have the same cell variables
and the same free variables.

Result:

-   Return 0 on success
-   Return 1 if the specialization has been ignored
-   Raise an exception and return -1 on error

PyFunction_GetSpecializedCodes

Add a function method to get the list of specialized codes:

    PyObject* PyFunction_GetSpecializedCodes(PyObject *func)

Return a list of (code, guards) tuples where code is a callable or code
object and guards is a list of PyFuncGuard objects. Raise an exception
and return NULL on error.

PyFunction_GetSpecializedCode

Add a function method checking guards to choose a specialized code:

    PyObject* PyFunction_GetSpecializedCode(PyObject *func,
                                            PyObject **stack,
                                            int na, int nk)

See check() function of guards for stack, na and nk arguments. Return a
callable or a code object on success. Raise an exception and return NULL
on error.

PyFunction_RemoveSpecialized

Add a function method to remove a specialized code with its guards by
its index:

    int PyFunction_RemoveSpecialized(PyObject *func, Py_ssize_t index)

Return 0 on success or if the index does not exist. Raise an exception
and return -1 on error.

PyFunction_RemoveAllSpecialized

Add a function method to remove all specialized codes and guards of a
function:

    int PyFunction_RemoveAllSpecialized(PyObject *func)

Return 0 on success. Raise an exception and return -1 if func is not a
function.

Benchmark

Microbenchmark on python3.6 -m timeit -s 'def f(): pass' 'f()' (best of
3 runs):

-   Original Python: 79 ns
-   Patched Python: 79 ns

According to this microbenchmark, the changes has no overhead on calling
a Python function without specialization.

Implementation

The issue #26098: PEP 510: Specialize functions with guards contains a
patch which implements this PEP.

Other implementations of Python

This PEP only contains changes to the Python C API, the Python API is
unchanged. Other implementations of Python are free to not implement new
additions, or implement added functions as no-op:

-   PyFunction_Specialize(): always return 1 (the specialization has
    been ignored)
-   PyFunction_GetSpecializedCodes(): always return an empty list
-   PyFunction_GetSpecializedCode(): return the function code object, as
    the existing PyFunction_GET_CODE() macro

Discussion

Thread on the python-ideas mailing list: RFC: PEP: Specialized functions
with guards.

Copyright

This document has been placed in the public domain.