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 2 2 Roadmap > What is PyPy? > The PyPy interpreter > The PyPy translation tool chain

© Toon Verwaest The PyPy tool chain 3 3 Roadmap > What is PyPy? > The PyPy Interpreter > The PyPy translation tool chain

© Toon Verwaest The PyPy tool chain 4 4 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

© Toon Verwaest The PyPy tool chain 5 PyPy

© Toon Verwaest The PyPy tool chain 6 Roadmap > What is PyPy? > The PyPy interpreter > The PyPy translation tool chain

© Toon Verwaest The PyPy tool chain 7 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

© Toon Verwaest The PyPy tool chain 8 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

© Toon Verwaest The PyPy tool chain 9 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!

© Toon Verwaest The PyPy tool chain 10 Roadmap > What is PyPy? > The PyPy interpreter > The PyPy translation tool chain

© Toon Verwaest The PyPy tool chain 11 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)‏

© Toon Verwaest The PyPy tool chain 12 The PyPy Translation Tool Chain

© Toon Verwaest The PyPy tool chain 13 Inside the Translation Tool Chain

© Toon Verwaest The PyPy tool chain 14 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 def f(a): print a f(4) -> 14

© Toon Verwaest The PyPy tool chain 15 PyPy Control-Flow Graph

© Toon Verwaest The PyPy tool chain 16 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))‏

© Toon Verwaest The PyPy tool chain 17 PyPy CFG: “Static Single Information” > Remember SSA: PHIs at dominance frontiers

© Toon Verwaest The PyPy tool chain 18 PyPy CFG: “Static Single Information” def test(a): if a > 0: if a > 5: return 10 return 4 if a < - 10: return 3 return 10 > SSI: “PHIs” for all used variables – Blocks as “functions without branches”

© Toon Verwaest The PyPy tool chain 19 Type Inference

© Toon Verwaest The PyPy tool chain 20 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 © Toon Verwaest The PyPy tool chain

© Toon Verwaest The PyPy tool chain 22 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

© Toon Verwaest The PyPy tool chain 23 Implications of applying type inference Applying type inference restricts type of input programs

© Toon Verwaest The PyPy tool chain 24 RPython: Demo def plus(a, b): return a + b def entry_point(arv=None): print plus(20, 22)‏ print plus(“4”, “2”)‏

© Toon Verwaest The PyPy tool chain 25 RPython: 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 © Toon Verwaest The PyPy tool chain 26

© Toon Verwaest The PyPy tool chain 27 RTyper

© Toon Verwaest The PyPy tool chain 28 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

© Toon Verwaest The PyPy tool chain 29 Back-end Optimizations

© Toon Verwaest The PyPy tool chain 30 Back-end Optimizations > Some general optimizations —Inlining —Constant folding —Escape analysis (allocating objects on the stack)‏ > Partly assume code generation for optimizing back-end

© Toon Verwaest The PyPy tool chain 31 Back-end Optimizations: “Object Explosion” > OO: lots of helper objects > Allocating objects is expensive > Replace unneeded objects with direct calls

© Toon Verwaest The PyPy tool chain 32 Preparation for Source Generation

© Toon Verwaest The PyPy tool chain 33 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

© Toon Verwaest The PyPy tool chain 34 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!

© Toon Verwaest The PyPy tool chain 35 Code Generation

© Toon Verwaest The PyPy tool chain 36 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

© Toon Verwaest The PyPy tool chain 37 Translation Demo

© Toon Verwaest The PyPy tool chain 38 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!

© Toon Verwaest The PyPy tool chain 39 More PyPy & Getting Involved > > > irc://irc.freenode.org/pypy > PyPy sprints

© Toon Verwaest The PyPy tool chain 40 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

© Toon Verwaest The PyPy tool chain 41 Summary

© Toon Verwaest The PyPy tool chain 42 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?

© Toon Verwaest The PyPy tool chain 43 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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