10. the pypy translation tool chain toon verwaest thanks to carl friedrich bolz for his kind...

10. The PyPy translation tool chain

Toon Verwaest

Thanks to Carl Friedrich Bolz for his kind permission to reuse and adapt his notes.

© Toon Verwaest

The PyPy tool chain

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Roadmap

> What is PyPy?> The PyPy interpreter> The PyPy translation tool chain

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Roadmap

> What is PyPy?> The PyPy Interpreter> The PyPy translation tool chain

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What is PyPy?

> Reimplementation of Python in Python

> Framework for building interpreters and VMs

> L * O * P configurations— L dynamic languages— O optimizations— P platforms

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PyPy

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Roadmap


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The PyPy Interpreter

> Python: imperative, object-oriented dynamic language

> Stack-based bytecode interpreter (like JVM, Smalltalk)

def f(x):return x + 1

>>> dis.dis(f)

2 0 LOAD_FAST 0 (x)

3 LOAD_CONST 1 (1)

6 BINARY_ADD

7 RETURN_VALUE

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The PyPy Bytecode Compiler

> Written in Python

> .py to .pyc

> Standard, flexible compiler— Lexer— Parser— AST builder— Bytecode generator

> You only have to build this once

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Bytecode interpreter

> Focuses on language semantics. No low-level details!

> Written in RPython— This makes it very slow! About 2000x slower than CPython

> PyPy's Python bytecode compiler and interpreter are not the hot topic of the PyPy project!

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Roadmap


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The PyPy Translation Tool Chain

> Model-driven interpreter (VM) development— Focus on language model rather than implementation details— Executable models (meta-circular Python)

> Translate models to low-level (LL) back-ends— Considerably lower than Python— Weave in implementation details (GC, JIT)— Allow compilation to different back-ends (OO, procedural)

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The PyPy Translation Tool Chain

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Inside the Translation Tool Chain

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PyPy “Parser”

> Tool chain starts from loaded Python bytecode> Translator shares Python environment with the target> Relies on Python's reflective capabilities> Allows meta-programming (runtime initialization)

def a_decorator(an_f):def g(b):

an_f(b+10)return g

@a_decoratordef f(a):

print a

f(4) -> 14

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PyPy Control-Flow Graph

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PyPy Control-Flow Graph

> Consists of Blocks and Links

> Starting from entry_point

> “Single Static Information” form

def f(n):

return 3*n+2

Block(v1): # input argument

v2 = mul(Constant(3), v1)

v3 = add(v2, Constant(2))

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PyPy CFG: “Static Single Information”

> Remember SSA: PHIs at dominance frontiers

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PyPy CFG: “Static Single Information”

def test(a):if a > 0:

if a > 5:return 10

return 4if a < - 10:

return 3return 10

> SSI: “PHIs” for all used variables– Blocks as “functions without branches”

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Type Inference

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Why type inference?

> Python is dynamically typed

> We want to translate to statically typed code— For efficiency reasons

What do we need to infer?

> Type for every variable

> Messages sent to an object must be defined in the compile-time type or a supertype

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How to infer types?

> Starting from entry_point— Can reach the whole program— We know type of arguments and

return-value

> Forward propagation— Iteratively, until all links in

the CFG have been followed at least once

— Results in a large dictionary mapping variables to types

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Implications of applying type inference

Applying type inference restrictstype of input programs

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RPython: Demo

def plus(a, b):return a + b

def entry_point(arv=None):print plus(20, 22)print plus(“4”, “2”)

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RPython: Demo

@objectmodel.specialize.argtype(0)def plus(a, b):

return a + b

def entry_point(arv=None):print plus(20, 22)print plus(“4”, “2”)

RPython is Zen

> Subset of Python

> Informally: The subset of Python which is type inferable

> Actually: type inferable stabilized bytecode— Allows load-time meta-programming (see parser)— Messages sent to an object must be defined in the compile-time

type or supertype

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RTyper

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RTyper

> Bridge between annotator and low-level code generators

> Different low-level models for different target groups— LLTypeSystem C-style (structures, pointers and arrays)

— OOTypeSystem JVM, CLI, Squeak (trace-off: single inheritance, )

> Does not need to iterate until a fixpoint is reached

> Replaces all operations by low-level ones

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Back-end Optimizations

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Back-end Optimizations

> Some general optimizations — Inlining— Constant folding— Escape analysis (allocating objects on the stack)

> Partly assume code generation for optimizing back-end

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Back-end Optimizations: “Object Explosion”

> OO: lots of helper objects

> Allocating objects is expensive

> Replace unneeded objects with direct calls

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Exception Handling and Memory Management

> C has no support for:— automatic memory management— exception handling

> Translate explicit exception handling to flags and if/else

> Memory management in PyPy spirit:— not language specific— weave garbage collector in during translation

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JIT Compiler

> Makes VMs fast— Dynamic information is key

> Is an implementation detail

> Still under development

> “As you surely know, the key idea of PyPy is that we are too lazy to write a JIT of our own: so, instead of passing nights writing a JIT, we pass years coding a JIT generator that writes the JIT for us :-)”

Weave in while translating to low-level!

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Code Generation

> One C-function per Control-Flow Graph

> All low-level statements can be translated directly

> Gets compiled to binary format with C compiler

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PyPy Performance

> Translator— Slow— Uses quite some memory— Produces lots of source code (200 kloc for 5 kloc source)

— But: our models are executable (2000x slower than CPython)

> Resulting Interpreter— Currently: two times slower to two times faster than CPython— First experiments with JIT: up to 500x faster for special cases— But most importantly: very adaptable!

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More PyPy & Getting Involved

> http://codespeak.net/pypy> http://morepypy.blogspot.com> irc://irc.freenode.org/pypy> PyPy sprints

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Summary

> PyPy project has two main parts— Language interpreter models— PyPy translation tool chain

> PyPy translation tool chain— Has no typical parser— Uses SSI— Applies type inference

– Limits input from Python to RPython— Compiles to low-level and object-oriented back-ends— Weaves in implementation details

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What you should know!

What is the goal of the PyPy project?What are the main steps of the PyPy toolchain?

When is a program RPython?

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Can you answer these questions?

> Why do we want to keep the language model separated from implementation details?

> Why wouldn't we want to keep those details separated?> Why is it not really a problem that the tool chain can only

compile RPython code?

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xxx

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License

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10. the pypy translation tool chain toon verwaest thanks to carl friedrich bolz for his kind...

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