Please read this FAQ entry first!
RPython is a subset of Python that can be statically compiled. The PyPy
interpreter is written mostly in RPython (with pieces in Python), while
the RPython compiler is written in Python. The hard to understand part
is that Python is a meta-programming language for RPython, that is,
“being valid RPython” is a question that only makes sense on the
live objects after the imports are done.
This might require more explanation. You start writing RPython from
entry_point, a good starting point is
rpython/translator/goal/targetnopstandalone.py. This does not do all that
much, but is a start. Now if code analyzed (in this case
calls some functions, those calls will be followed. Those followed calls
have to be RPython themselves (and everything they call etc.), however not
entire module files. To show how you can use metaprogramming, we can do
a silly example (note that closures are not RPython):
def generator(operation): if operation == 'add': def f(a, b): return a + b else: def f(a, b): return a - b return f add = generator('add') sub = generator('sub') def entry_point(argv): print add(sub(int(argv), 3) 4) return 0
In this example
entry_point is RPython,
sub are RPython,
generator is not.
The following introductory level articles are available:
- Laurence Tratt – Fast Enough VMs in Fast Enough Time.
- How to write interpreters in RPython and part 2 by Andrew Brown.
The translator is a tool based on the PyPy interpreter which can translate sufficiently static RPython programs into low-level code (in particular it can be used to translate the full Python interpreter). To be able to experiment with it you need to download and install the usual (CPython) version of:
To start the interactive translator shell do:
cd rpython python bin/translatorshell.py
Test snippets of translatable code are provided in the file
rpython/translator/test/snippet.py, which is imported under the name
snippet. For example:
>>> t = Translation(snippet.is_perfect_number, [int]) >>> t.view()
After that, the graph viewer pops up, that lets you interactively inspect the flow graph. To move around, click on something that you want to inspect. To get help about how to use it, press ‘H’. To close it again, press ‘Q’.
We have a type annotator that can completely infer types for functions like
is_perfect_number (as well as for much larger examples):
>>> t.annotate() >>> t.view()
Move the mouse over variable names (in red) to see their inferred types.
The graph can be turned into C code:
>>> t.rtype() >>> lib = t.compile_c()
The first command replaces the operations with other low level versions that
only use low level types that are available in C (e.g. int). The compiled
version is now in a
.so library. You can run it say using ctypes:
>>> f = get_c_function(lib, snippet.is_perfect_number) >>> f(5) 0 >>> f(6) 1
There is a small-to-medium demo showing the translator and the annotator:
python bin/rpython --view --annotate translator/goal/bpnn.py
bpnn.py to display itself as a call graph and class
hierarchy. Clicking on functions shows the flow graph of the particular
function. Clicking on a class shows the attributes of its instances. All
this information (call graph, local variables’ types, attributes of
instances) is computed by the annotator.
To turn this example to C code (compiled to the executable
python bin/rpython translator/goal/bpnn.py
To translate full RPython programs, there is the script
rpython/bin/. Examples for this are a slightly changed version of
python bin/rpython translator/goal/targetrpystonedalone
This will produce the executable “targetrpystonedalone-c”.
There are several environment variables you can find useful while playing with the RPython:
- RPython uses temporary session directories to store files that are generated during the
translation process(e.g., translated C files).
PYPY_USESSION_DIRserves as a base directory for these session dirs. The default value for this variable is the system’s temporary dir.
- By default RPython keeps only the last
PYPY_USESSION_KEEP(defaults to 3) session dirs inside
PYPY_USESSION_DIR. Increase this value if you want to preserve C files longer (useful when producing lots of lldebug builds).
- rpython/translator contains the code analysis and generation stuff. Start reading from translator.py, from which it should be easy to follow the pieces of code involved in the various translation phases.
- rpython/annotator contains the data model for the type annotation that can be inferred about a graph. The graph “walker” that uses this is in rpython/annotator/annrpython.py.
- rpython/rtyper contains the code of the RPython typer. The typer transforms annotated flow graphs in a way that makes them very similar to C code so that they can be easy translated. The graph transformations are controlled by the code in rpython/rtyper/rtyper.py. The object model that is used can be found in rpython/rtyper/lltypesystem/lltype.py. For each RPython type there is a file rxxxx.py that contains the low level functions needed for this type.
- rpython/rlib contains the RPython standard library, things that you can use from rpython.