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.

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.

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

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

PyObject* PyFunction_GetSpecializedCodes(PyObject *func)

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

int PyFunction_RemoveSpecialized(PyObject *func, Py_ssize_t index)

int PyFunction_RemoveAllSpecialized(PyObject *func)

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.