# User Manual¶

So you want to write algorithms that are easy to maintain as in Python and you want performance as in FORTRAN or C++? Lets give a try to Pythran! Pythran is a Python-to-c++ translator that turns Python modules into native c++11 modules. From a user point of view, you still import your module, but under the hood… There is much more happening!

## Disclaimer¶

Pythran is not a full Python-to-c++ converter, as is shedskin. Instead it takes a subset of the Python language and turns it into heavily templatized c++ code instantiated for your particular types.

Say hello to:

• polymorphic functions (!)
• lambdas
• list comprehension
• map, reduce and the like
• dictionary, set, list
• exceptions
• file handling
• (partial) numpy support

Say bye bye to:

• classes
• polymorphic variables [ but not all of them :-) ]

In a nutshell, Pythran makes it possible to write numerical algorithms in Python and to have them run faster. Nuff said.

## Prerequisite¶

Pythran depends on the following packages:

ply>=3.4
gast~=0.5.0
numpy
beniget~=0.4.0


You also need a modern C++11 enabled compiler (e.g. g++>=5, clang>=3.5), that supports atomic operations (N3290) and variadic template (N2555).

## Installation from Sources¶

The prefered way to install Pythran is using pip install pythran or conda install pythran. Yet if you want to install from sources, here is the procedure.

First get the sources:

$> git clone https://github.com/serge-sans-paille/pythran  From the source directory, run: $> pip install .


pythran should now be on your PATH. If not, it’s possible pip installed to .local (this happens if the default site-packages location requires elevated permissions) - fix this by setting your path to:

$> export PATH=$PATH:$HOME/.local/bin  It makes the pythran command available to you. ## Making Sure Everything is working¶ The setup.py scripts automates this. The test target, as in: $> python setup.py test


runs a whole (and long) validation suite (you will need to install the pytest module first to use it).

If these tests fail, you are likely missing some of the requirements. You can set site specific flags in your ~/.pythranrc, read the doc a bit further!

## First Steps¶

To begin with, you need… a Python function in a module. Something like:

<<dprod.py>>
def dprod(arr0, arr1):
return sum([x*y for x,y in zip(arr0, arr1)])


is perfect. But due to \_o< typing, arr0 and arr1 can be of any type, so Pythran needs a small hint there. Add the following line somewhere in your file, say at the top head, or right before the function definition:

#pythran export dprod(int list, int list)


This basically tells Pythran the type of the forthcoming arguments.

Afterwards, frenetically type:

$> pythran dprod.py  \o/ a dprod.so native module has been created and you can play with it right now, as if it where a normal module: >>> import dprod # this imports the native version if available >>> dprod.dprod([1,2], [3,4]) 11  The speedup will not be terrific because of the conversion cost from Python to C++. So let’s try again with a well-known example. Let me introduce the almighty matrix multiply!: <<mm.py>> def zero(n,m): return [[0]*n for col in range(m)] def matrix_multiply(m0, m1): new_matrix = zero(len(m0),len(m1[0])) for i in range(len(m0)): for j in range(len(m1[0])): for k in range(len(m1)): new_matrix[i][j] += m0[i][k]*m1[k][j] return new_matrix  This is a slightly more complex example, as a few intrinsics such as range and len are used, with a function call and even nested list comprehension. But Pythran can make its way through this. As you only want to export the matrix_multiply function, you can safely ignore the zero function and just add: #pythran export matrix_multiply(float list list, float list list)  to the source file. Note how Pythran can combine different types and infer the resulting type. It also respects the nested list structure of Python, so you are not limited to matrices… Enough talk, run: $> pythran mm.py


One touch of magic wand and you have your native binary. Be amazed by the generation of a mm.so native module that runs around 20x faster than the original one. timeit approved!

But scientific computing in Python usually means Numpy. Here is a well-known Numpy snippet:

<<arc_distance.py>>
import numpy as np
def arc_distance(theta_1, phi_1, theta_2, phi_2):
"""
Calculates the pairwise arc distance
between all points in vector a and b.
"""
temp = (np.sin((theta_2-theta_1)/2)**2
+ np.cos(theta_1)*np.cos(theta_2) * np.sin((phi_2-phi_1)/2)**2)
distance_matrix = 2 * np.arctan2(np.sqrt(temp), np.sqrt(1-temp))
return distance_matrix


This example uses a lot of Numpy ufunc. Pythran is reasonably good at handling such expressions. As you already know, you need to export it, giving its argument types by adding:

#pythran export arc_distance(float[], float[], float[], float[])


To the input file. You can compile it as the previous code:

$> pythran arc_distance.py  and you’ll get a decent binary. But what you really want to do is: $> pythran -DUSE_XSIMD -fopenmp -march=native arc_distance.py


which basically tells the compiler to parallelize and vectorize loops using whatever hardware available on your machine. Then you’ll get really fast code!

## Concerning Pythran specifications¶

The #pythran export commands are critical to Pythran. In fact if they are missing, Pythran will complain loudly (and fail miserably). So let us dive into this complex language!

There is currently only one Pythran command, the export command. Its syntax is:

#pythran export function_name(argument_type* [, argument_type ? *])


where function_name is the name of a function defined in the module, and argument_type* is a comma separated list of argument types, composed of any combination of basic types and constructed types. argument_type ? * is a comma separated list of optional argument types, similar to argument_type but followed by a ?.

What is an argument_type? Anything that looks like a Python basic type! Constructed types are either tuples, introduced by parenthesis, like (int, (float, str)) or lists (resp. set), introduced by the list (resp. set) keyword:

argument_type = basic_type
| (argument_type+)    # this is a tuple
| argument_type list    # this is a list
| argument_type set    # this is a set
| argument_type []+    # this is a ndarray, C-style
| argument_type [::]+    # this is a strided ndarray
| argument_type [:,...,:]+ # this is a ndarray, Cython style
| argument_type [:,...,3]+ # this is a ndarray, some dimension fixed
| argument_type:argument_type dict    # this is a dictionary

basic_type = bool | byte | int | float | str | None | slice
| uint8 | uint16 | uint32 | uint64 | uintp
| int8 | int16 | int32 | int64 | intp
| float32 | float64 | float128
| complex64 | complex128 | complex256


Note

When using a 2D array, overloads of the function involved are created to accept both C-style and Fortran-style arrays. To avoid generating too many functions, one can force the memory layout using order(C) or order(F) after the array decalaration, as in int[:,:] order(C).

The same syntax can be used to export global variable (in read only mode):

#pythran export var_name


In a similar manner to the Python import statement, it’s possible to chain the export, as in:

#pythran export var_name0, var_name1, function_name(argument_type0)


Multiple overloads can be specified for the same Python function:

#pythran export function_name(argument_type0)
#pythran export function_name(argument_type1)


In the case of function with default parameters, you can either omit the parameter, and in that case it uses the default one, or explicitly state it’s argument type:

#pythran export function_name()
#pythran export function_name(argument_type0)
#pythran export function_name(argument_type0, argument_type1)
def function_name(a0=1, a1=True):
pass


When specifying multiple overloads, instead of listing them, you can use the or operator to list the alternatives, as in:

#pythran export function_name(type0 or type1, type2, type3 or type4)


which is exactly equivalent to:

#pythran export function_name(type0, type2, type3)
#pythran export function_name(type0, type2, type4)
#pythran export function_name(type1, type2, type3)
#pythran export function_name(type1, type2, type4)


Easy enough, isn’t it?

Note

Multiline exports are supported, just use comments to protect them, as in:

#pythran export river_boa(int,
#                         float,
#                         bool)


Note

It is in fact possible to analyse a code without specifications, but you cannot go further that generic (a.k.a. heavily templated) c++ code. Use the -e switch!

### .pythran files¶

Instead of writing the export lines in the .py file, it is possible to write them, without the #pythran prefix, inside a file that has the same path has the .py file, but with the .pythran extension. For instance, file I_love.py can have its export lines in the I_love.pythran file, using the syntax:

export function_name(argument_type*)


### Limitations¶

Pythran tries hard to produce code that has the same observable behavior as the original Python code. Unfortunately it’s not always possible:

• Pythran does not support heterogeneous containers (except tuples).
• There is no BigInt support. All integer operations are performed on np.int_, which maps to C long type. Beware that as a consequence, the size of this type is system-dependent.
• In most cases (with the notable exception of numpy.ndarray), Pythran is working on a deep copy of the original Python arguments. This copy shares no memory relationship with the original object, which means that modifying the argument content in Pythran won’t modify the original argument content. Likewise, objects generated by Pythran cannot share reference (in the sense of is) with one of the input argument. Of curse, this limitation doesn’t apply to non exported functions.

## GIL Interaction¶

As Pythran translates the Python code in native code that only depends on libpython for data translation, it can release the GIL during the actual function run. And that’s what it does :-) Put an another way, you can rip some speedup at the Python level just by spawning multiple threading.Thread.

## IPython Integration¶

The magic function %%pythran is made available to ipython users through an extension. The extension is located in the extensions/ directory and can be loaded using IPython’s magic function:

%load_ext pythran.magic


Once done, you can pythranize your code from the IPython shell:

%%pythran
#pythran export foo()
def foo(): print 'hello'


You can pass arguments to this magic, as in:

%%pythran(-O2 -fopenmp)
#pythran export foo()
def foo(): print 'hello'


## Distutils Integration¶

When distributing a Python application with Pythran modules, you can either:

• declare the module as a regular Python module. After all, they are 100% Python compatible.

• declare them as a PythranExtension and Pythran will compile them:

from distutils.core import setup

# These two lines are required to be able to use pythran in the setup.py
import setuptools
setuptools.dist.Distribution(dict(setup_requires='pythran'))

from pythran.dist import PythranExtension, PythranBuildExt
setup(...,
ext_modules=[PythranExtension("mymodule", ["mymodule.py"])],
cmdclass={"build_ext": PythranBuildExt})


PythranBuildExt is optional, but necessary to build extensions with different C++ compilers. It derives from distuil’s build_ext by default, but you can change its base class by using PythranBuildExt[base_cls] instead.

• all configuration options supported in .pythranrc can also be passed through the optional config argument, in the form of a list, e.g. config=['compiler.blas=openblas']

Note

There’s no strong compatibility guarantee between Pythran version at C++ level. As a consequence, a code distrubuted under pythran version 0.x should depend on that exact version, as version 0.y may introduce some changes.

This behavior is likely to change with revisions >= 1.

## Capsule Corp¶

Instead of creating functions that can be used from Python code, Pythran can produce native functions to be used by other Python extension, using the Capsule mechanism. To do so, just add the capsule keyword to the export line:

#pythran export capsule foo(double*, double)


Note that pointer types are only supported within the context of a capsule, as they don’t match any real Python type. Any Pythran type is valid as capsule parameter, but beware that non scalar or pointer types only make sense withing the Pythran context.

## Debug Mode¶

Pythran honors the NDEBUG macro. If set through -DNDEBUG (which should be the default, check python-config --cflags), it disables all assert statement and doesn’t perform any runtime check for indexing bounds etc. However, if unset through -UNDEBUG, all assert are executed and eventually raise an AssertionError. Additionnaly, many internal checks are done and may fail with a C-ish assertion.

By default Pythran does not generate thread-safe code for non-OpenMP code: reference counting for automatic deletion of objects is not done atomically by default. It’s still possible to force pythran to generate thread-safe reference counting by defining the flag THREAD_SAFE_REF_COUNT via -DTHREAD_SAFE_REF_COUNT. There is a small performance penalty associated with this.

One can use -o <filename> or --output=<filename> to control the name of the generated file. If <filename> contains the %{ext} pattern, it is replaced by the extension that matches your current platform.

A failing compilation? A lust for c++ tangled code? Give a try to the -E switch that stops the compilation process right after c++ code generation, so that you can inspect it.

Want more performance? Big fan of -Ofast -march=native? Pythran _automagically_ forwards these switches to the underlying compiler!

Tired of typing the same compiler switches again and again? Store them in $XDG_CONFIG_HOME/.pythranrc! Wants to try your own compiler? Update the CC and CXX fields from your pythranrc, or set the same environment variables to the right compilers. Environment variables have greater precedence than configuration file. Pythran also honors the CXXFLAGS and LDFLAGS environment variables. The careful reader might have noticed the -p flag from the command line. It makes it possible to define your own optimization sequence: pythran -pConstantFolding -pmy_package.MyOptimization  runs the ConstantFolding optimization from pythran.optimizations followed by a custom optimization found in the my_package package, loaded from PYTHONPATH. When importing a Python module, one can check for the presence of the __pythran__ variable at the module scope to see if the module has been pythranized: import foo if hasattr(foo, '__pythran__'): print(r'\_o<')  This variable is a tuple that holds three fields: 1. pythran’s version 2. compilation date 3. sha256 value of the input code ## Adding OpenMP directives¶ OpenMP is a standard set of directives for C, C++ and FORTRAN that makes it easier to turn a sequential program into a multi-threaded one. Pythran translates OpenMP-like code annotation into OpenMP directives: r=0 #omp parallel for reduction(+:r) for x,y in zip(l1,l2): r+=x*y  OpenMP directive parsing is enabled by -fopenmp when using g++ as the back-end compiler. Be careful with the indentation. It has to be correct! Alternatively, one can run the great: $> pythran -ppythran.analyses.ParallelMaps -e as.py


which runs a code analyzer that displays extra information concerning parallel map found in the code.

## Getting Pure C++¶

Pythran can be used to generate raw templated C++ code, without any Python glue. To do so use the -e switch. It will turn the Python code into C++ code you can call from a C++ program. In that case there is no need for a particular Pythran specification.

## Read the optimized Python code¶

Curious Python developers might want to study how Pythran transforms their codes. With the -P switch, Pythran optimizes the Python code, prints the result and stops there. Pythran does not care about PEP 8, so a Python formatter is often useful:

\$> pythran -P arc_distance.py | yapf


## Customizing Your .pythranrc¶

Pythran checks for a file named .pythranrc and use it to replace the site configuration. Here are a few tricks!

You can change the default location of the pythran configuration file using the environment variable PYTHRANRC:

PYTHRANRC=/opt/company/pythran/config.pythranrc pythran arc_distance.py


All the options in the .pythranrc file can be specified when running pythran by using the command line argument –config= . For example:

pythran --config compiler.blas=pythran-openblas this_file.py


would specify that pythran-openblas is the blas library to use.

Options specified using command-line arguments override the options found in the .pythranrc file

### [compiler]¶

This section contains compiler flags configuration. For education purpose, the default linux configuration is

[compiler]
defines=
undefs=
include_dirs=
libs=
library_dirs=
cflags=-std=c++11 -fno-math-errno -fvisibility=hidden -fno-wrapv -Wno-unused-function -Wno-int-in-bool-context -Wno-unknown-warning-option
ldflags=-fvisibility=hidden -Wl,-strip-all
blas=blas
CC=
CXX=
ignoreflags=-Wstrict-prototypes

CC: Path to the C compiler to use Path to the C++ compiler to use Preprocessor definitions. Pythran is sensible to USE_XSIMD and PYTHRAN_OPENMP_MIN_ITERATION_COUNT. The former turns on xsimd vectorization and the latter controls the minimal loop trip count to turn a sequential loop into a parallel loop. Some preprocessor definitions to remove. Additional include directories to search for headers. Additional random compiler flags (-f, -O). Optimization flags generally go there. The default is to set -std=c++11 for C++11 support. Libraries to use during the link process. A typical extension is to add tcmalloc_minimal to use the allocator from https://code.google.com/p/gperftools/. Extra directories to search for required libraries. Additional random linker flags. BLAS library to use. none, pythran-openblas, blas, openblas, atlas or mkl are viable choices. none prevents from linking with blas. pythran-openblas requires the pythran-openblas package, which provides a statically linked version of OpenBLAS. Other options are system dependant. Depending on your setup, you may need to update include_dirs to point to the location of the BLAS headers, e.g. /usr/include/openblas. Space-separated list of compiler flags that should not be forwarded to the pythran backend compiler when inherited, for instance, from python-config. For instance -Wstrict-prototypes is a C-only option that should be pruned.

### [pythran]¶

This one contains internal configuration settings. Play with it at your own risk!

optimizations: A list of import paths pointing to transformation classes. This contains the optimization pipeline of Pythran! If you design your own optimizations, register them here! Set this to True for faster and still Numpy-compliant complex multiplications. Not very portable, but generally works on Linux.

### [typing]¶

Another internal setting stuff. This controls the accuracy of the typing phase. An extract from the default setting file should convince you not to touch it:

[typing]

# maximum number of container access taken into account during type inference
# increasing this value inreases typing accuracy
# but slows down compilation time, to the point of making g++ crash
max_container_type = 2

# maximum number of combiner per user function
# increasing this value inreases typing accuracy
# but slows down compilation time, to the point of making g++ crash
max_combiner = 2


### [backend]¶

This controls some behavior of the C++ backend, so the default should be safe:

# set to true if you want intermediate C++ code to be annotated with a reference
# to the original python code
annotate = false


## F.A.Q.¶

1. Supported compiler versions:
• g++ version 4.9 and above
• clang++ version 3.5 and above

## Troubleshooting¶

Plenty of them! Seriously, Pythran is software, so it will crash. You may make it abort in unusual ways! And more importantly, please provide feedback to serge_sans_paille using its email serge.guelton@telecom-bretagne.eu, the IRC channel #pythran on OFTC, or the mailing list pythran@freelists.org

glhf!