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The RPython Typer

The RPython Typer lives in the directory `rpython/rtyper/`_.

Overview

The RPython Typer is the bridge between the Annotator and the low-level code generators. The annotations of the Annotator are high-level, in the sense that they describe RPython types like lists or instances of user-defined classes. In general, though, to emit code we need to represent these high-level annotations in the low-level model of the target language; for C, this means structures and pointers and arrays. The Typer both determines the appropriate low-level type for each annotation, and tries to replace all operations in the control flow graphs with one or a few low-level operations. Just like low-level types, there is only a fairly restricted set of low-level operations, along the lines of reading or writing from or to a field of a structure.

In theory, this step is optional; some code generators might be able to read the high-level types directly. However, we expect that case to be the exception. "Compiling" high-level types into low-level ones is rather more messy than one would expect. This was the motivation for making this step explicit and isolated in a single place. After Typing, the graphs can only contain very few operations, which makes the job of the code generators much simpler.

Example: Integer operations

Integer operations are the easiest. Assume a graph containing the following operation:

v3 = add(v1, v2)

annotated:

v1 -> SomeInteger()
v2 -> SomeInteger()
v3 -> SomeInteger()

then obviously we want to type it and replace it with:

v3 = int_add(v1, v2)

where -- in C notation -- all three variables v1, v2 and v3 are typed int. This is done by attaching an attribute concretetype to v1, v2 and v3 (which might be instances of Variable or possibly Constant). In our model, this concretetype is rpython.rtyper.lltypesystem.lltype.Signed. Of course, the purpose of replacing the operation called add with int_add is that code generators no longer have to worry about what kind of addition (or concatenation maybe?) it means.

The process in more details

The RPython Typer has a structure similar to that of the Annotator: both consider each block of the flow graphs in turn, and perform some analysis on each operation. In both cases the analysis of an operation depends on the annotations of its input arguments. This is reflected in the usage of the same __extend__ syntax in the source files (compare e.g. `rpython/annotator/binaryop.py`_ and `rpython/rtyper/rint.py`_).

The analogy stops here, though: while it runs, the Annotator is in the middle of computing the annotations, so it might need to reflow and generalize until a fixpoint is reached. The Typer, by contrast, works on the final annotations that the Annotator computed, without changing them, assuming that they are globally consistent. There is no need to reflow: the Typer considers each block only once. And unlike the Annotator, the Typer completely modifies the flow graph, by replacing each operation with some low-level operations.

In addition to replacing operations, the RTyper creates a concretetype attribute on all Variables and Constants in the flow graphs, which tells code generators which type to use for each of them. This attribute is a low-level type, as described below.

Representations

Representations -- the Repr classes -- are the most important internal classes used by the RTyper. (They are internal in the sense that they are an "implementation detail" and their instances just go away after the RTyper is finished; the code generators should only use the concretetype attributes, which are not Repr instances but low-level types.)

A representation contains all the logic about mapping a specific SomeXxx() annotation to a specific low-level type. For the time being, the RTyper assumes that each SomeXxx() instance needs only one "canonical" representation. For example, all variables annotated with SomeInteger() will correspond to the Signed low-level type via the IntegerRepr representation. More subtly, variables annotated SomeList() can correspond either to a structure holding an array of items of the correct type, or -- if the list in question is just a range() with a constant step -- a structure with just start and stop fields.

This example shows that two representations may need very different low-level implementations for the same high-level operations. This is the reason for turning representations into explicit objects.

The base Repr class is defined in `rpython/rtyper/rmodel.py`_. Most of the rpython/r*.py files define one or a few subclasses of Repr. The method getrepr() of the RTyper will build and cache a single Repr instance per SomeXxx() instance; moreover, two SomeXxx() instances that are equal get the same Repr instance.

The key attribute of a Repr instance is called lowleveltype, which is what gets copied into the attribute concretetype of the Variables that have been given this representation. The RTyper also computes a concretetype for Constants, to match the way they are used in the low-level operations (for example, int_add(x, 1) requires a Constant(1) with concretetype=Signed, but an untyped add(x, 1) works with a Constant(1) that must actually be a PyObject at run-time).

In addition to lowleveltype, each Repr subclass provides a set of methods called rtype_op_xxx() which define how each high-level operation op_xxx is turned into low-level operations.

Low-Level Types

The RPython Typer uses a standard low-level model which we believe can correspond rather directly to various target languages such as C. This model is implemented in the first part of `rpython/rtyper/lltypesystem/lltype.py`_.

The second part of `rpython/rtyper/lltypesystem/lltype.py`_ is a runnable implementation of these types, for testing purposes. It allows us to write and test plain Python code using a malloc() function to obtain and manipulate structures and arrays. This is useful for example to implement and test RPython types like 'list' with its operations and methods.

The basic assumption is that Variables (i.e. local variables and function arguments and return value) all contain "simple" values: basically, just integers or pointers. All the "container" data structures (struct and array) are allocated in the heap, and they are always manipulated via pointers. (There is no equivalent to the C notion of local variable of a struct type.)

Here is a quick tour:

>>> from rpython.rtyper.lltypesystem.lltype import *

Here are a few primitive low-level types, and the typeOf() function to figure them out:

>>> Signed
<Signed>
>>> typeOf(5)
<Signed>
>>> typeOf(r_uint(12))
<Unsigned>
>>> typeOf('x')
<Char>

Let's say that we want to build a type "point", which is a structure with two integer fields "x" and "y":

>>> POINT = GcStruct('point', ('x', Signed), ('y', Signed))
>>> POINT
<GcStruct point { x: Signed, y: Signed }>

The structure is a GcStruct, which means a structure that can be allocated in the heap and eventually freed by some garbage collector. (For platforms where we use reference counting, think about GcStruct as a struct with an additional reference counter field.)

Giving a name ('point') to the GcStruct is only for clarity: it is used in the representation.

>>> p = malloc(POINT)
>>> p
<* struct point { x=0, y=0 }>
>>> p.x = 5
>>> p.x
5
>>> p
<* struct point { x=5, y=0 }>

malloc() allocates a structure from the heap, initializes it to 0 (currently), and returns a pointer to it. The point of all this is to work with a very limited, easily controllable set of types, and define implementations of types like list in this elementary world. The malloc() function is a kind of placeholder, which must eventually be provided by the code generator for the target platform; but as we have just seen its Python implementation in `rpython/rtyper/lltypesystem/lltype.py`_ works too, which is primarily useful for testing, interactive exploring, etc.

The argument to malloc() is the structure type directly, but it returns a pointer to the structure, as typeOf() tells you:

>>> typeOf(p)
<* GcStruct point { x: Signed, y: Signed }>

For the purpose of creating structures with pointers to other structures, we can declare pointer types explicitly:

>>> typeOf(p) == Ptr(POINT)
True
>>> BIZARRE = GcStruct('bizarre', ('p1', Ptr(POINT)), ('p2', Ptr(POINT)))
>>> b = malloc(BIZARRE)
>>> b.p1
<* None>
>>> b.p1 = b.p2 = p
>>> b.p1.y = 42
>>> b.p2.y
42

The world of low-level types is more complicated than integers and GcStructs, though. The next pages are a reference guide.

Primitive Types

Signed
a signed integer in one machine word (a long, in C)
Unsigned
a non-signed integer in one machine word (unsigned long)
Float
a 64-bit float (double)
Char
a single character (char)
Bool
a boolean value
Void
a constant. Meant for variables, function arguments, structure fields, etc. which should disappear from the generated code.

Structure Types

Structure types are built as instances of rpython.rtyper.lltypesystem.lltype.Struct:

MyStructType = Struct('somename',  ('field1', Type1), ('field2', Type2)...)
MyStructType = GcStruct('somename',  ('field1', Type1), ('field2', Type2)...)

This declares a structure (or a Pascal record) containing the specified named fields with the given types. The field names cannot start with an underscore. As noted above, you cannot directly manipulate structure objects, but only pointer to structures living in the heap.

By contrast, the fields themselves can be of primitive, pointer or container type. When a structure contains another structure as a field we say that the latter is "inlined" in the former: the bigger structure contains the smaller one as part of its memory layout.

A structure can also contain an inlined array (see below), but only as its last field: in this case it is a "variable-sized" structure, whose memory layout starts with the non-variable fields and ends with a variable number of array items. This number is determined when a structure is allocated in the heap. Variable-sized structures cannot be inlined in other structures.

GcStructs have a platform-specific GC header (e.g. a reference counter); only these can be dynamically malloc()ed. The non-GC version of Struct does not have any header, and is suitable for being embedded ("inlined") inside other structures. As an exception, a GcStruct can be embedded as the first field of a GcStruct: the parent structure uses the same GC header as the substructure.

Array Types

An array type is built as an instance of rpython.rtyper.lltypesystem.lltype.Array:

MyIntArray = Array(Signed)
MyOtherArray = Array(MyItemType)
MyOtherArray = GcArray(MyItemType)

Or, for arrays whose items are structures, as a shortcut:

MyArrayType = Array(('field1', Type1), ('field2', Type2)...)

You can build arrays whose items are either primitive or pointer types, or (non-GC non-varsize) structures.

GcArrays can be malloc()ed. The length must be specified when malloc() is called, and arrays cannot be resized; this length is stored explicitly in a header.

The non-GC version of Array can be used as the last field of a structure, to make a variable-sized structure. The whole structure can then be malloc()ed, and the length of the array is specified at this time.

Pointer Types

As in C, pointers provide the indirection needed to make a reference modifiable or sharable. Pointers can only point to a structure, an array, a function (see below) or a PyObject (see below). Pointers to primitive types, if needed, must be done by pointing to a structure with a single field of the required type. Pointer types are declared by:

Ptr(TYPE)

At run-time, pointers to GC structures (GcStruct, GcArray and PyObject) hold a reference to what they are pointing to. Pointers to non-GC structures that can go away when their container is deallocated (Struct, Array) must be handled with care: the bigger structure of which they are part of could be freed while the Ptr to the substructure is still in use. In general, it is a good idea to avoid passing around pointers to inlined substructures of malloc()ed structures. (The testing implementation of `rpython/rtyper/lltypesystem/lltype.py`_ checks to some extent that you are not trying to use a pointer to a structure after its container has been freed, using weak references. But pointers to non-GC structures are not officially meant to be weak references: using them after what they point to has been freed just crashes.)

The malloc() operation allocates and returns a Ptr to a new GC structure or array. In a refcounting implementation, malloc() would allocate enough space for a reference counter before the actual structure, and initialize it to 1. Note that the testing implementation also allows malloc() to allocate a non-GC structure or array with a keyword argument immortal=True. Its purpose is to declare and initialize prebuilt data structures which the code generators will turn into static immortal non-GC'ed data.

Function Types

The declaration:

MyFuncType = FuncType([Type1, Type2, ...], ResultType)

declares a function type taking arguments of the given types and returning a result of the given type. All these types must be primitives or pointers. The function type itself is considered to be a "container" type: if you wish, a function contains the bytes that make up its executable code. As with structures and arrays, they can only be manipulated through pointers.

The testing implementation allows you to "create" functions by calling functionptr(TYPE, name, **attrs). The extra attributes describe the function in a way that isn't fully specified now, but the following attributes might be present:

_callable:a Python callable, typically a function object.
graph:the flow graph of the function.

The PyObject Type

This is a special type, for compatibility with CPython: it stands for a structure compatible with PyObject. This is also a "container" type (thinking about C, this is PyObject, not PyObject*), so it is usually manipulated via a Ptr. A typed graph can still contain generic space operations (add, getitem, etc.) provided they are applied on objects whose low-level type is Ptr(PyObject). In fact, code generators that support this should consider that the default type of a variable, if none is specified, is Ptr(PyObject). In this way, they can generate the correct code for fully-untyped flow graphs.

The testing implementation allows you to "create" PyObjects by calling pyobjectptr(obj).

Opaque Types

Opaque types represent data implemented in a back-end specific way. This data cannot be inspected or manipulated.

There is a predefined opaque type RuntimeTypeInfo; at run-time, a value of type RuntimeTypeInfo represents a low-level type. In practice it is probably enough to be able to represent GcStruct and GcArray types. This is useful if we have a pointer of type Ptr(S) which can at run-time point either to a malloc'ed S alone, or to the S first field of a larger malloc'ed structure. The information about the exact larger type that it points to can be computed or passed around as a Ptr(RuntimeTypeInfo). Pointer equality on Ptr(RuntimeTypeInfo) can be used to check the type at run-time.

At the moment, for memory management purposes, some back-ends actually require such information to be available at run-time in the following situation: when a GcStruct has another GcStruct as its first field. A reference-counting back-end needs to be able to know when a pointer to the smaller structure actually points to the larger one, so that it can also decref the extra fields. Depending on the situation, it is possible to reconstruct this information without having to store a flag in each and every instance of the smaller GcStruct. For example, the instances of a class hierarchy can be implemented by nested GcStructs, with instances of subclasses extending instances of parent classes by embedding the parent part of the instance as the first field. In this case, there is probably already a way to know the run-time class of the instance (e.g. a vtable pointer), but the back-end cannot guess this. This is the reason for which RuntimeTypeInfo was originally introduced: just after the GcStruct is created, the function attachRuntimeTypeInfo() should be called to attach to the GcStruct a low-level function of signature Ptr(GcStruct) -> Ptr(RuntimeTypeInfo). This function will be compiled by the back-end and automatically called at run-time. In the above example, it would follow the vtable pointer and fetch the opaque Ptr(RuntimeTypeInfo) from the vtable itself. (The reference-counting GenC back-end uses a pointer to the deallocation function as the opaque RuntimeTypeInfo.)

Implementing RPython types

As hinted above, the RPython types (e.g. 'list') are implemented in some "restricted-restricted Python" format by manipulating only low-level types, as provided by the testing implementation of malloc() and friends. What occurs then is that the same (tested!) very-low-level Python code -- which looks really just like C -- is then transformed into a flow graph and integrated with the rest of the user program. In other words, we replace an operation like add between two variables annotated as SomeList, with a direct_call operation invoking this very-low-level list concatenation.

This list concatenation flow graph is then annotated as usual, with one difference: the annotator has to be taught about malloc() and the way the pointer thus obtained can be manipulated. This generates a flow graph which is hopefully completely annotated with SomePtr() annotation. Introduced just for this case, SomePtr maps directly to a low-level pointer type. This is the only change needed to the Annotator to allow it to perform type inference of our very-low-level snippets of code.

See for example `rpython/rtyper/rlist.py`_.

HighLevelOp interface

In the absence of more extensive documentation about how RPython types are implemented, here is the interface and intended usage of the 'hop' argument that appears everywhere. A 'hop' is a HighLevelOp instance, which represents a single high-level operation that must be turned into one or several low-level operations.

hop.llops
A list-like object that records the low-level operations that correspond to the current block's high-level operations.
hop.genop(opname, list_of_variables, resulttype=resulttype)
Append a low-level operation to hop.llops. The operation has the given opname and arguments, and returns the given low-level resulttype. The arguments should come from the hop.input*() functions described below.
hop.gendirectcall(ll_function, var1, var2...)
Like hop.genop(), but produces a direct_call operation that invokes the given low-level function, which is automatically annotated with low-level types based on the input arguments.
hop.inputargs(r1, r2...)
Reads the high-level Variables and Constants that are the arguments of the operation, and convert them if needed so that they have the specified representations. You must provide as many representations as the operation has arguments. Returns a list of (possibly newly converted) Variables and Constants.
hop.inputarg(r, arg=i)
Same as inputargs(), but only converts and returns the ith argument.
hop.inputconst(lltype, value)
Returns a Constant with a low-level type and value.

Manipulation of HighLevelOp instances (this is used e.g. to insert a 'self' implicit argument to translate method calls):

hop.copy()
Returns a fresh copy that can be manipulated with the functions below.
hop.r_s_popfirstarg()
Removes the first argument of the high-level operation. This doesn't really changes the source SpaceOperation, but modifies 'hop' in such a way that methods like inputargs() no longer see the removed argument.
hop.v_s_insertfirstarg(v_newfirstarg, s_newfirstarg)
Insert an argument in front of the hop. It must be specified by a Variable (as in calls to hop.genop()) and a corresponding annotation.
hop.swap_fst_snd_args()
Self-descriptive.

Exception handling:

hop.has_implicit_exception(cls)
Checks if hop is in the scope of a branch catching the exception 'cls'. This is useful for high-level operations like 'getitem' that have several low-level equivalents depending on whether they should check for an IndexError or not. Calling has_implicit_exception() also has a side-effect: the rtyper records that this exception is being taken care of explicitly.
hop.exception_is_here()
To be called with no argument just before a llop is generated. It means that the llop in question will be the one that should be protected by the exception catching. If has_implicit_exception() was called before, then exception_is_here() verifies that all except links in the graph have indeed been checked for with an has_implicit_exception(). This is not verified if has_implicit_exception() has never been called -- useful for 'direct_call' and other operations that can just raise any exception.
hop.exception_cannot_occur()
The RTyper normally verifies that exception_is_here() was really called once for each high-level operation that is in the scope of exception-catching links. By saying exception_cannot_occur(), you say that after all this particular operation cannot raise anything. (It can be the case that unexpected exception links are attached to flow graphs; e.g. any method call within a try:finally: block will have an Exception branch to the finally part, which only the RTyper can remove if exception_cannot_occur() is called.)

The LLInterpreter

The LLInterpreter is a simple piece of code that is able to interpret flow graphs. This is very useful for testing purposes, especially if you work on the RPython Typer. The most useful interface for it is the interpret function in the file `rpython/rtyper/test/test_llinterp.py`_. It takes as arguments a function and a list of arguments with which the function is supposed to be called. Then it generates the flow graph, annotates it according to the types of the arguments you passed to it and runs the LLInterpreter on the result. Example:

def test_invert():
    def f(x):
        return ~x
    res = interpret(f, [3])
    assert res == ~3

Furthermore there is a function interpret_raises which behaves much like py.test.raises. It takes an exception as a first argument, the function to be called as a second and the list of function arguments as a third. Example:

def test_raise():
    def raise_exception(i):
        if i == 42:
            raise IndexError
        elif i == 43:
            raise ValueError
        return i
    res = interpret(raise_exception, [41])
    assert res == 41
    interpret_raises(IndexError, raise_exception, [42])
    interpret_raises(ValueError, raise_exception, [43])
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