The Interpreter-Level backend
PyPy often makes use of application-level helper methods. The idea of the 'geninterplevel' backend is to automatically transform such application level implementations to their equivalent representation at interpreter level. Then, the RPython to C translation hopefully can produce more efficient code than always re-interpreting these methods.
One property of translation from application level Python to Python is, that the produced code does the same thing as the corresponding interpreted code, but no interpreter is needed any longer to execute this code.
One issue we had so far was of bootstrapping: some pieces of the interpreter (e.g. exceptions) were written in geninterped code. It is unclear how much of it is left, thought.
That bootstrap issue is (was?) solved by invoking a new bytecode interpreter which runs on FlowObjspace. FlowObjspace is complete without complicated initialization. It is able to do abstract interpretation of any Rpythonic code, without actually implementing anything. It just records all the operations the bytecode interpreter would have done by building flowgraphs for all the code. What the Python backend does is just to produce correct Python code from these flowgraphs and return it as source code. In the produced code Python operations recorded in the original flowgraphs are replaced by calls to the corresponding methods in the object space interface.
Let's try a little example. You might want to look at the flowgraph that it produces. Here, we directly run the Python translation and look at the generated source. See also the header section of the implementation for the interface:
>>> from pypy.translator.geninterplevel import translate_as_module >>> entrypoint, source = translate_as_module(""" ... ... def g(n): ... i = 0 ... while n: ... i = i + n ... n = n - 1 ... return i ... ... """)
This call has invoked a PyPy bytecode interpreter running on FlowObjspace, recorded every possible codepath into a flowgraph, and then rendered the following source code:
#!/bin/env python # -*- coding: LATIN-1 -*- def initapp2interpexec(space): """NOT_RPYTHON""" def g(space, w_n_1): goto = 3 # startblock while True: if goto == 1: v0 = space.is_true(w_n) if v0 == True: goto = 2 else: goto = 4 if goto == 2: w_1 = space.add(w_0, w_n) w_2 = space.sub(w_n, gi_1) w_n, w_0 = w_2, w_1 goto = 1 continue if goto == 3: w_n, w_0 = w_n_1, gi_0 goto = 1 continue if goto == 4: return w_0 fastf_g = g g3dict = space.newdict() gs___name__ = space.new_interned_str('__name__') gs_app2interpexec = space.new_interned_str('app2interpexec') space.setitem(g3dict, gs___name__, gs_app2interpexec) gs_g = space.new_interned_str('g') from pypy.interpreter import gateway gfunc_g = space.wrap(gateway.interp2app(fastf_g, unwrap_spec=[gateway.ObjSpace, gateway.W_Root])) space.setitem(g3dict, gs_g, gfunc_g) gi_1 = space.wrap(1) gi_0 = space.wrap(0) return g3dict
You see that actually a single function is produced: initapp2interpexec. This is the function that you will call with a space as argument. It defines a few functions and then does a number of initialization steps, builds the global objects the function need, and produces the PyPy function object gfunc_g.
The return value is g3dict, which contains a module name and the function we asked for.
Let's have a look at the body of this code: The definition of g is used as fast_g in the gateway.interp2app which constructs a PyPy function object which takes care of argument unboxing (based on the unwrap_spec), and of invoking the original g.
We look at the definition of g itself which does the actual computation. Comparing to the flowgraph, you see a code block for every block in the graph. Since Python has no goto statement, the jumps between the blocks are implemented by a loop that switches over a goto variable.
. if goto == 1: v0 = space.is_true(w_n) if v0 == True: goto = 2 else: goto = 4
This is the implementation of the "while n:". There is no implicit state, everything is passed over to the next block by initializing its input variables. This directly resembles the nature of flowgraphs. They are completely stateless.
. if goto == 2: w_1 = space.add(w_0, w_n) w_2 = space.sub(w_n, gi_1) w_n, w_0 = w_2, w_1 goto = 1 continue
The "i = i + n" and "n = n - 1" instructions. You see how every instruction produces a new variable. The state is again shuffled around by assigning to the input variables w_n and w_0 of the next target, block 1.
Note that it is possible to rewrite this by re-using variables, trying to produce nested blocks instead of the goto construction and much more. The source would look much more like what we used to write by hand. For the C backend, this doesn't make much sense since the compiler optimizes it for us. For the Python interpreter it could give a bit more speed. But this is a temporary format and will get optimized anyway when we produce the executable.
Interplevel Snippets in the Sources
Code written in application space can consist of complete files to be translated, or they can be tiny snippets scattered all over a source file, similar to our example from above.
Translation of these snippets is done automatically and cached in pypy/_cache with the modulename and the md5 checksum appended to it as file name. If you have run your copy of pypy already, this folder should exist and have some generated files in it. These files consist of the generated code plus a little code that auto-destructs the cached file (plus .pyc/.pyo versions) if it is executed as __main__. On windows this means you can wipe a cached code snippet clear by double-clicking it. Note also that the auto-generated __init__.py file wipes the whole directory when executed.