RPython is a restricted subset of Python that is amenable to static analysis. Although there are additions to the language and some things might surprisingly work, this is a rough list of restrictions that should be considered. Note that there are tons of special cased restrictions that you’ll encounter as you go. The exact definition is “RPython is everything that our translation toolchain can accept” :)
variables should contain values of at most one type as described in Object restrictions at each control flow point, that means for example that joining control paths using the same variable to contain both a string and a int must be avoided. It is allowed to mix None (basically with the role of a null pointer) with many other types: wrapped objects, class instances, lists, dicts, strings, etc. but not with int, floats or tuples.
all module globals are considered constants. Their binding must not be changed at run-time. Moreover, global (i.e. prebuilt) lists and dictionaries are supposed to be immutable: modifying e.g. a global list will give inconsistent results. However, global instances don’t have this restriction, so if you need mutable global state, store it in the attributes of some prebuilt singleton instance.
all allowed, for loops restricted to builtin types, generators very restricted.
range and xrange are identical. range does not necessarily create an array, only if the result is modified. It is allowed everywhere and completely implemented. The only visible difference to CPython is the inaccessibility of the xrange fields start, stop and step.
run-time definition of classes or functions is not allowed.
generators are supported, but their exact scope is very limited. you can’t merge two different generator in one control point.
fully supported see below Exception rules for restrictions on exceptions raised by built-in operations
We are using
integer, float, boolean
a lot of, but not all string methods are supported and those that are supported, not necesarilly accept all arguments. Indexes can be negative. In case they are not, then you get slightly more efficient code if the translator can prove that they are non-negative. When slicing a string it is necessary to prove that the slice start and stop indexes are non-negative. There is no implicit str-to-unicode cast anywhere. Simple string formatting using the % operator works, as long as the format string is known at translation time; the only supported formatting specifiers are %s, %d, %x, %o, %f, plus %r but only for user-defined instances. Modifiers such as conversion flags, precision, length etc. are not supported. Moreover, it is forbidden to mix unicode and strings when formatting.
no variable-length tuples; use them to store or return pairs or n-tuples of values. Each combination of types for elements and length constitute a separate and not mixable type.
lists are used as an allocated array. Lists are over-allocated, so list.append() is reasonably fast. However, if you use a fixed-size list, the code is more efficient. Annotator can figure out most of the time that your list is fixed-size, even when you use list comprehension. Negative or out-of-bound indexes are only allowed for the most common operations, as follows:
- indexing: positive and negative indexes are allowed. Indexes are checked when requested by an IndexError exception clause.
- slicing: the slice start must be within bounds. The stop doesn’t need to, but it must not be smaller than the start. All negative indexes are disallowed, except for the [:-1] special case. No step. Slice deletion follows the same rules.
- slice assignment: only supports lst[x:y] = sublist, if len(sublist) == y - x. In other words, slice assignment cannot change the total length of the list, but just replace items.
- other operators: +, +=, in, *, *=, ==, != work as expected.
- methods: append, index, insert, extend, reverse, pop. The index used in pop() follows the same rules as for indexing above. The index used in insert() must be within bounds and not negative.
dicts with a unique key type only, provided it is hashable. Custom hash functions and custom equality will not be honored. Use rpython.rlib.objectmodel.r_dict for custom hash functions.
May be used to create allocated, initialized arrays.
- statically called functions may use defaults and a variable number of arguments (which may be passed as a list instead of a tuple, so write code that does not depend on it being a tuple).
- dynamic dispatch enforces the use of signatures that are equal for all possible called function, or at least “compatible enough”. This concerns mainly method calls, when the method is overridden or in any way given different definitions in different classes. It also concerns the less common case of explicitly manipulated function objects. Describing the exact compatibility rules is rather involved (but if you break them, you should get explicit errors from the rtyper and not obscure crashes.)
A number of builtin functions can be used. The precise set can be found in rpython/annotator/builtin.py (see def builtin_xxx()). Some builtin functions may be limited in what they support, though.
int, float, str, ord, chr... are available as simple conversion functions. Note that int, float, str... have a special meaning as a type inside of isinstance only.
- methods and other class attributes do not change after startup
- single inheritance is fully supported
- use rpython.rlib.objectmodel.import_from_mixin(M) in a class body to copy the whole content of a class M. This can be used to implement mixins: functions and staticmethods are duplicated (the other class attributes are just copied unmodified).
- classes are first-class objects too
Normal rules apply. The only special methods that are honoured are __init__, __del__, __len__, __getitem__, __setitem__, __getslice__, __setslice__, and __iter__. To handle slicing, __getslice__ and __setslice__ must be used; using __getitem__ and__setitem__ for slicing isn’t supported. Additionally, using negative indices for slicing is still not support, even when using __getslice__.
This layout makes the number of types to take care about quite limited.
While implementing the integer type, we stumbled over the problem that integers are quite in flux in CPython right now. Starting with Python 2.4, integers mutate into longs on overflow. In contrast, we need a way to perform wrap-around machine-sized arithmetic by default, while still being able to check for overflow when we need it explicitly. Moreover, we need a consistent behavior before and after translation.
We use normal integers for signed arithmetic. It means that before translation we get longs in case of overflow, and after translation we get a silent wrap-around. Whenever we need more control, we use the following helpers (which live in rpython/rlib/rarithmetic.py):
This special function should only be used with a single arithmetic operation as its argument, e.g. z = ovfcheck(x+y). Its intended meaning is to perform the given operation in overflow-checking mode.
At run-time, in Python, the ovfcheck() function itself checks the result and raises OverflowError if it is a long. But the code generators use ovfcheck() as a hint: they replace the whole ovfcheck(x+y) expression with a single overflow-checking addition in C.
This function is used for wrap-around arithmetic. It returns the lower bits of its argument, masking away anything that doesn’t fit in a C “signed long int”. Its purpose is, in Python, to convert from a Python long that resulted from a previous operation back to a Python int. The code generators ignore intmask() entirely, as they are doing wrap-around signed arithmetic all the time by default anyway. (We have no equivalent of the “int” versus “long int” distinction of C at the moment and assume “long ints” everywhere.)
In a few cases (e.g. hash table manipulation), we need machine-sized unsigned arithmetic. For these cases there is the r_uint class, which is a pure Python implementation of word-sized unsigned integers that silently wrap around. (“word-sized” and “machine-sized” are used equivalently and mean the native size, which you get using “unsigned long” in C.) The purpose of this class (as opposed to helper functions as above) is consistent typing: both Python and the annotator will propagate r_uint instances in the program and interpret all the operations between them as unsigned. Instances of r_uint are special-cased by the code generators to use the appropriate low-level type and operations. Mixing of (signed) integers and r_uint in operations produces r_uint that means unsigned results. To convert back from r_uint to signed integers, use intmask().
Exceptions are by default not generated for simple cases.:
#!/usr/bin/python lst = [1,2,3,4,5] item = lst[i] # this code is not checked for out-of-bound access try: item = lst[i] except IndexError: # complain
Code with no exception handlers does not raise exceptions (after it has been translated, that is. When you run it on top of CPython, it may raise exceptions, of course). By supplying an exception handler, you ask for error checking. Without, you assure the system that the operation cannot fail. This rule does not apply to function calls: any called function is assumed to be allowed to raise any exception.
x = 5.1 x = x + 1.2 # not checked for float overflow try: x = x + 1.2 except OverflowError: # float result too big
z = some_function(x, y) # can raise any exception try: z = some_other_function(x, y) except IndexError: # only catches explicitly-raised IndexErrors in some_other_function() # other exceptions can be raised, too, and will not be caught here.
The ovfcheck() function described above follows the same rule: in case of overflow, it explicitly raise OverflowError, which can be caught anywhere.
Exceptions explicitly raised or re-raised will always be generated.
PyPy is debuggable on top of CPython¶
PyPy has the advantage that it is runnable on standard CPython. That means, we can run all of PyPy with all exception handling enabled, so we might catch cases where we failed to adhere to our implicit assertions.