Download The PyPy Translation Tool Chain

10. The PyPy translation tool chain
Toon Verwaest
Thanks to Carl Friedrich Bolz for his kind permission to reuse and
adapt his notes.
The PyPy tool chain
Roadmap
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What is PyPy?
The PyPy interpreter
The PyPy translation tool chain
© Toon Verwaest
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The PyPy tool chain
Roadmap
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What is PyPy?
The PyPy Interpreter
The PyPy translation tool chain
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The PyPy tool chain
What is PyPy?
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Reimplementation of Python in Python
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Framework for building interpreters and VMs
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L * O * P configurations
— L dynamic languages
— O optimizations
— P platforms
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The PyPy tool chain
PyPy
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The PyPy tool chain
Roadmap
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What is PyPy?
The PyPy interpreter
The PyPy translation tool chain
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The PyPy tool chain
The PyPy Interpreter
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Python: imperative, object-oriented dynamic language
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Stack-based bytecode interpreter (like JVM, Smalltalk)
def f(x):
return x + 1
© Toon Verwaest
>>> dis.dis(f)
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0 LOAD_FAST
0 (x)
3 LOAD_CONST 1 (1)
6 BINARY_ADD
7 RETURN_VALUE
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The PyPy tool chain
The PyPy Bytecode Compiler
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Written in Python
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.py to .pyc
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Standard, flexible compiler
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Lexer
Parser
AST builder
Bytecode generator
You only have to build this once
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The PyPy tool chain
Bytecode interpreter
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Focuses on language semantics. No low-level details!
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Written in RPython
— This makes it very slow! About 2000x slower than CPython
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PyPy's Python bytecode compiler and interpreter are
not the hot topic of the PyPy project!
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The PyPy tool chain
Roadmap
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What is PyPy?
The PyPy interpreter
The PyPy translation tool chain
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The PyPy tool chain
The PyPy Translation Tool Chain
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Model-driven interpreter (VM) development
— Focus on language model rather than implementation details
— Executable models (meta-circular Python)
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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 tool chain
The PyPy Translation Tool Chain
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The PyPy tool chain
Inside the Translation Tool Chain
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The PyPy tool chain
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)
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def a_decorator(an_f):
def g(b):
an_f(b+10)
return g
@a_decorator
def f(a):
print a
f(4) -> 14
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The PyPy tool chain
PyPy Control-Flow Graph
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The PyPy tool chain
PyPy Control-Flow Graph
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Consists of Blocks and Links
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Starting from entry_point
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“Single Static Information” form
def f(n):
return 3*n+2
© Toon Verwaest
Block(v1): # input argument
v2 = mul(Constant(3), v1)
v3 = add(v2, Constant(2))
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The PyPy tool chain
PyPy CFG: “Static Single Information”
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Remember SSA: PHIs at dominance frontiers
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The PyPy tool chain
PyPy CFG: “Static Single Information”
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SSI: “PHIs” for all used variables
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Blocks as “functions without branches”
def test(a):
if a > 0:
if a > 5:
return 10
return 4
if a < - 10:
return 3
return 10
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The PyPy tool chain
Type Inference
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The PyPy tool chain
Why type inference?
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Python is dynamically typed
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We want to translate to statically typed code
— For efficiency reasons
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The PyPy tool chain
What do we need to infer?
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Type for every variable
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Messages sent to an object must be defined in the
compile-time type or a supertype
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The PyPy tool chain
How to infer types?
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Starting from entry_point
— Can reach the whole program
— We know type of arguments and
return-value
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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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The PyPy tool chain
Implications of applying type inference
Applying type inference restricts
type of input programs
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The PyPy tool chain
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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The PyPy tool chain
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”)
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The PyPy tool chain
RPython is Zen
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Subset of Python
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Informally: The subset of Python which is type inferable
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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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The PyPy tool chain
RTyper
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The PyPy tool chain
RTyper
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Bridge between annotator and low-level code generators
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Different low-level models for different target groups
— LLTypeSystem
— OOTypeSystem
C-style (structures, pointers and arrays)
JVM, CLI, Squeak (trace-off: single inheritance, )
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Does not need to iterate until a fixpoint is reached
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Replaces all operations by low-level ones
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The PyPy tool chain
Back-end Optimizations
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The PyPy tool chain
Back-end Optimizations
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Some general optimizations
— Inlining
— Constant folding
— Escape analysis (allocating objects on the stack)
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Partly assume code generation for optimizing back-end
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The PyPy tool chain
Back-end Optimizations: “Object Explosion”
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OO: lots of helper objects
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Allocating objects is expensive
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Replace unneeded objects with direct calls
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The PyPy tool chain
Preparation for Source Generation
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The PyPy tool chain
Exception Handling and Memory Management
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C has no support for:
— automatic memory management
— exception handling
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Translate explicit exception handling to flags and if/else
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Memory management in PyPy spirit:
— not language specific
— weave garbage collector in during translation
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The PyPy tool chain
JIT Compiler
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Makes VMs fast
— Dynamic information is key
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Is an implementation detail
Weave in while translating to low-level!
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Still under development
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“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 :-)”
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The PyPy tool chain
Code Generation
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The PyPy tool chain
Code Generation
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One C-function per Control-Flow Graph
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All low-level statements can be translated directly
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Gets compiled to binary format with C compiler
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The PyPy tool chain
Translation Demo
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The PyPy tool chain
PyPy Performance
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Translator
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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
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The PyPy tool chain
More PyPy & Getting Involved
http://codespeak.net/pypy
> http://morepypy.blogspot.com
> irc://irc.freenode.org/pypy
> PyPy sprints
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The PyPy tool chain
Summary
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PyPy project has two main parts
— Language interpreter models
— PyPy translation tool chain
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PyPy translation tool chain
— Has no typical parser
— Uses SSI
— Applies type inference
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Limits input from Python to RPython
— Compiles to low-level and object-oriented back-ends
— Weaves in implementation details
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The PyPy tool chain
Summary
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The PyPy tool chain
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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The PyPy tool chain
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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The PyPy tool chain
xxx
License
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http://creativecommons.org/licenses/by-sa/2.5/
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