Pythran annotations in Python files
Project description
FluidPythran is a pure Python package (requiring Python >= 3.6 or Pypy3) to help to write Python code that can use Pythran if it is available.
Let’s recall that “Pythran is an ahead-of-time (AOT) compiler for a subset of the Python language, with a focus on scientific computing. It takes a Python module annotated with a few interface description and turns it into a native Python module with the same interface, but (hopefully) faster.”
Pythran is able to produce very efficient C++ code and binaries from high level Numpy code. If the algorithm is easier to express without loops, don’t write loops!
Pythran always releases the GIL and can use SIMD instructions and OpenMP!
Pythran is not a hard dependency of FluidPythran: Python code using FluidPythran run fine without Pythran and without compilation (and of course without speedup)!
Overview
Python + Numpy + Pythran is a great combo to easily write highly efficient scientific programs and libraries.
To use Pythran, one needs to isolate the numerical kernels functions in modules that are compiled by Pythran. The C++ code produced by Pythran never uses the Python interpreter. It means that only a subset of what is doable in Python can be done in Pythran files. Some language features are not supported by Pythran (for example no classes) and most of the extension packages cannot be used in Pythran files (basically only Numpy and some Scipy functions).
Another cause of frustration for Python developers when using Pythran is related to manual writting of Pythran function signatures in comments, which can not be automated. Pythran uses C++ templates but Pythran users can not think with this concept. We would like to be able to express the templated nature of Pythran with modern Python syntax (in particular type annotations). Finally, another limitation is that it is not possible to use Pythran for just-in-time (JIT) compilation so one needs to manually write all argument types.
With FluidPythran, we try to overcome these limitations. FluidPythran provides few supplementary Pythran commands and a small Python API to define Pythran functions without writing the Pythran modules. The code of the numerical kernels can stay in the modules and in the classes where they were written. The Pythran files (i.e. the files compiled by Pythran), which are usually written by the user, are produced automatically by FluidPythran.
Bonus: There are FluidPythran syntaxes for both ahead-of-time and just-in-time compilations!
At run time, FluidPythran uses when possible the pythranized functions, but let’s stress again that codes using FluidPythran work fine without Pythran (of course without speedup)!
To summarize, a strategy to quickly develop a very efficient scientific application/library with Python could be:
Use modern Python coding, standard Numpy/Scipy for the computations and all the cool libraries you want.
Profile your applications on real cases, detect the bottlenecks and apply standard optimizations with Numpy.
Add few lines of FluidPythran to compile the hot spots.
Implementation details: Under the hood, FluidPythran creates Pythran files (one per module for AOT compilation and one per function for JIT compilation) that can be compiled at build, import or run times depending of the cases. Note that the developers can still read the Pythran files if needed.
Installation
pip install fluidpythran
A short tour of FluidPythran syntaxes
Command # pythran def
import h5py
import mpi4py
from fluidpythran import pythran_def
# pythran def myfunc(int, float)
@pythran_def
def myfunc(a, b):
return a * b
...
Most of this code looks familiar to Pythran users. The differences:
One can use (for example) h5py and mpi4py (of course not in the Pythran functions).
# pythran def
instead of# pythran export
(to stress that it is not the same command).A tiny bit of Python… The decorator
@pythran_def
replaces the Python function by the pythranized function if FluidPythran has been used to produced the associated Pythran file.
Pythran using type annotations
The previous example can be rewritten without Pythran commands:
import h5py
import mpi4py
from fluidpythran import pythran_def
@pythran_def
def myfunc(a: int, b: float):
return a * b
...
Nice but very limited… So it is possible to mix type hints and #
pythran def
commands. Moreover, one can also elegantly define many Pythran
signatures with type variables (see these examples in the documentation).
Command # pythran block
FluidPythran blocks can be used with classes and more generally in functions with lines that cannot be compiled by Pythran.
from fluidpythran import FluidPythran
fp = FluidPythran()
class MyClass:
...
def func(self, n):
a, b = self.something_that_cannot_be_pythranized()
if fp.is_pythranized:
result = fp.use_pythranized_block("name_block")
else:
# pythran block (
# float a, b;
# int n
# ) -> result
# pythran block (
# complex a, b;
# int n
# ) -> result
result = a**n + b**n
return self.another_func_that_cannot_be_pythranized(result)
For blocks, we need a little bit more of Python.
At import time, we have
fp = FluidPythran()
, which detects which Pythran module should be used and imports it. This is done at import time since we want to be very fast at run time.In the function, we define a block with three lines of Python and special Pythran annotations (
# pythran block
). The 3 lines of Python are used (i) at run time to choose between the two branches (is_pythranized
or not) and (ii) at compile time to detect the blocks.
Note that the annotations in the command # pythran block
are different
(and somehow easier to write) than in the standard command # pythran
export
.
Cached Just-In-Time compilation
With FluidPythran, one can use the Ahead-Of-Time compiler Pythran in a Just-In-Time mode. It is really the easiest way to speedup a function with Pythran, just by adding a decorator! And it works also in notebooks!
It is a “work in progress” so (i) it can be buggy and (ii) the API is not great, but it is a good start!
import numpy as np
# pythran import numpy as numpy
from fluidpythran import cachedjit, used_by_cachedjit
@used_by_cachedjit("func1")
def func0(a, b):
return a + b
@cachedjit
def func1(a, b):
return np.exp(a) * b * func0(a, b)
Note that the @cachedjit
decorator takes into account type hints (see
the example in the documentation).
Implementation details for just-in-time compilation: A Pythran file is
produced for each “cachedjited” function (function decorated with
@cachedjit
). The file is compiled at the first call of the function and
the compiled version is used as soon as it is ready. The warmup can be quite
long but the compiled version is saved and can be reused (without warmup!) by
another process.
Python classes: @pythran_def
for methods
Just a NotImplemented idea! See https://bitbucket.org/fluiddyn/fluidpythran/issues/3
For simple methods only using simple attributes, if could be simple and very useful to support this:
from fluidpythran import Type, NDim, Array, pythran_def
import numpy as np
T = Type(int, np.float64)
N = NDim(1)
A1 = Array[T, N]
A2 = Array[float, N+1]
class MyClass:
arr0: A1
arr1: A1
arr2: A2
def __init__(self, n, dtype=int):
self.arr0 = np.zeros(n, dtype=dtype)
self.arr1 = np.zeros(n, dtype=dtype)
self.arr2 = np.zeros(n)
@pythran_def
def compute(self, alpha: int):
tmp = (self.arr0 + self.arr1).mean()
return tmp ** alpha * self.arr2
Make the Pythran files
There is a command-line tool fluidpythran
which makes the associated
Pythran files from Python files with annotations and fluidpythran code.
There is also a function make_pythran_files
that can be used in a
setup.py like this:
from pathlib import Path
from fluidpythran.dist import make_pythran_files
here = Path(__file__).parent.absolute()
paths = ["fluidsim/base/time_stepping/pseudo_spect.py"]
make_pythran_files([here / path for path in paths])
Note that FluidPythran never uses Pythran. Compiling the associated Pythran file can be done if wanted (see for example how it is done in the example package example_package_fluidpythran or in fluidsim’s setup.py).
License
FluidDyn is distributed under the CeCILL-B License, a BSD compatible french license.
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