# Frequently Asked Questions¶

## What is RPython?¶

RPython is a framework for implementing interpreters and virtual machines for programming languages, especially dynamic languages.

## Can RPython compile normal Python programs to C?¶

No, RPython is not a Python compiler.

In Python, it is mostly impossible to prove anything about the types that a program will manipulate by doing a static analysis. It should be clear if you are familiar with Python, but if in doubt see [BRETT].

If you want a fast Python program, please use the PyPy JIT instead.

 [BRETT] Brett Cannon, Localized Type Inference of Atomic Types in Python, http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.90.3231

## What is this RPython language?¶

RPython is a restricted subset of the Python language. It is used for implementing dynamic language interpreters within the PyPy toolchain. The restrictions ensure that type inference (and so, ultimately, translation to other languages) of RPython programs is possible.

The property of “being RPython” always applies to a full program, not to single functions or modules (the translation toolchain does a full program analysis). The translation toolchain follows all calls recursively and discovers what belongs to the program and what does not.

RPython program restrictions mostly limit the ability to mix types in arbitrary ways. RPython does not allow the binding of two different types in the same variable. In this respect (and in some others) it feels a bit like Java. Other features not allowed in RPython are the use of special methods (__xxx__) except __init__ and __del__, and the use of reflection capabilities (e.g. __dict__).

You cannot use most existing standard library modules from RPython. The exceptions are some functions in os, math and time that have native support.

To read more about the RPython limitations read the RPython description.

## Does RPython have anything to do with Zope’s Restricted Python?¶

No. Zope’s RestrictedPython aims to provide a sandboxed execution environment for CPython. PyPy’s RPython is the implementation language for dynamic language interpreters. However, PyPy also provides a robust sandboxed Python Interpreter.

## What’s the "NOT_RPYTHON" I see in some docstrings?¶

If you put “NOT_RPYTHON” into the docstring of a function and that function is found while trying to translate an RPython program, the translation process stops and reports this as an error. You can therefore mark functions as “NOT_RPYTHON” to make sure that they are never analyzed.

This method of marking a function as not RPython is outdated. For new code, please use the @objectmodel.not_rpython decorator instead.

## Couldn’t we simply take a Python syntax tree and turn it into Lisp?¶

It’s not necessarily nonsense, but it’s not really The PyPy Way. It’s pretty hard, without some kind of type inference, to translate this Python:

a + b


into anything significantly more efficient than this Common Lisp:

(py:add a b)


And making type inference possible is what RPython is all about.

You could make #'py:add a generic function and see if a given CLOS implementation is fast enough to give a useful speed (but I think the coercion rules would probably drive you insane first). – mwh

## Do I have to rewrite my programs in RPython?¶

No, and you shouldn’t try. First and foremost, RPython is a language designed for writing interpreters. It is a restricted subset of Python. If your program is not an interpreter but tries to do “real things”, like use any part of the standard Python library or any 3rd-party library, then it is not RPython to start with. You should only look at RPython if you try to write your own interpreter.

If your goal is to speed up Python code, then look at the regular PyPy, which is a full and compliant Python 2.7 interpreter (which happens to be written in RPython). Not only is it not necessary for you to rewrite your code in RPython, it might not give you any speed improvements even if you manage to.

Yes, it is possible with enough effort to compile small self-contained pieces of RPython code doing a few performance-sensitive things. But this case is not interesting for us. If you needed to rewrite the code in RPython, you could as well have rewritten it in C or C++ or Java for example. These are much more supported, much more documented languages :-)

The above paragraphs are not the whole truth. It is true that there are cases where writing a program as RPython gives you substantially better speed than running it on top of PyPy. However, the attitude of the core group of people behind PyPy is to answer: “then report it as a performance bug against PyPy!”.

Here is a more diluted way to put it. The “No, don’t!” above is a general warning we give to new people. They are likely to need a lot of help from some source, because RPython is not so simple nor extensively documented; but at the same time, we, the pypy core group of people, are not willing to invest time in supporting 3rd-party projects that do very different things than interpreters for dynamic languages — just because we have other interests and there are only so many hours a day. So as a summary I believe it is only fair to attempt to point newcomers at existing alternatives, which are more mainstream and where they will get help from many people.

If anybody seriously wants to promote RPython anyway, they are welcome to: we won’t actively resist such a plan. There are a lot of things that could be done to make RPython a better Java-ish language for example, starting with supporting non-GIL-based multithreading, but we don’t implement them because they have little relevance to us. This is open source, which means that anybody is free to promote and develop anything; but it also means that you must let us choose not to go into that direction ourselves.

## Which backends are there for the RPython toolchain?¶

Currently, the only backend is C. It can translate the entire PyPy interpreter. To learn more about backends take a look at the translation document.

## Could we use LLVM?¶

In theory yes. But we tried to use it 5 or 6 times already, as a translation backend or as a JIT backend — and failed each time.

In more details: using LLVM as a (static) translation backend is pointless nowadays because you can generate C code and compile it with clang. (Note that compiling PyPy with clang gives a result that is not faster than compiling it with gcc.) We might in theory get extra benefits from LLVM’s GC integration, but this requires more work on the LLVM side before it would be remotely useful. Anyway, it could be interfaced via a custom primitive in the C code. (The latest such experimental backend is in the branch llvm-translation-backend, which can translate PyPy with or without the JIT on Linux.)

On the other hand, using LLVM as our JIT backend looks interesting as well — but again we made an attempt, and it failed: LLVM has no way to patch the generated machine code, and is not suited at all to tracing JITs. Even one big method JIT trying to use LLVM has given up for similar reasons; read that blog post for more details.

So the position of the core PyPy developers is that if anyone wants to make an N+1’th attempt with LLVM, they are welcome, and they will receive a bit of help on the IRC channel, but they are left with the burden of proof that it works.

## Compiling PyPy swaps or runs out of memory¶

This is documented (here and here). It needs 4 GB of RAM to run “rpython targetpypystandalone” on top of PyPy, a bit more when running on top of CPython. If you have less than 4 GB free, it will just swap forever (or fail if you don’t have enough swap). And we mean free: if the machine has 4 GB in total, then it will swap.

On 32-bit, divide the numbers by two. (We didn’t try recently, but in the past it was possible to compile a 32-bit version on a 2 GB Linux machine with nothing else running: no Gnome/KDE, for example.)

## How do I compile my own interpreters?¶

Begin by reading Andrew Brown’s tutorial .

## Can RPython modules for PyPy be translated independently?¶

No, you have to rebuild the entire interpreter. This means two things:

• It is imperative to use test-driven development. You have to exhaustively test your module in pure Python, before even attempting to translate it. Once you translate it, you should have only a few typing issues left to fix, but otherwise the result should work out of the box.
• Second, and perhaps most important: do you have a really good reason for writing the module in RPython in the first place? Nowadays you should really look at alternatives, like writing it in pure Python, using cffi if it needs to call C code.

In this context it is not that important to be able to translate RPython modules independently of translating the complete interpreter. (It could be done given enough efforts, but it’s a really serious undertaking. Consider it as quite unlikely for now.)

## Why does the translator draw a Mandelbrot fractal while translating?¶

Because it’s fun.