Goals and Architecture Overview ¶

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Goals and Architecture Overview

This document gives an overview of the goals and architecture of PyPy. See getting started for a practical introduction and starting points.

Mission statement ¶

We aim to provide:

a common translation and support framework for producing implementations of dynamic languages, emphasizing a clean separation between language specification and implementation aspects. We call this the RPython toolchain.

a compliant, flexible and fast implementation of the Python Language which uses the above toolchain to enable new advanced high-level features without having to encode the low-level details. We call this PyPy.

By separating concerns in this way, our implementation of Python - and other dynamic languages - is able to automatically generate a Just-in-Time compiler for any dynamic language. It also allows a mix-and-match approach to implementation decisions, including many that have historically been outside of a user’s control, such as target platform, memory and threading models, garbage collection strategies, and optimizations applied, including whether or not to have a JIT in the first place.

High Level Goals ¶

RPython - the Translation Toolchain ¶

Traditionally, language interpreters are written in a target platform language such as C/Posix, Java or C#. Each implementation provides a fundamental mapping between application source code and the target environment. One of the goals of the “all-encompassing” environments, such as the .NET framework and to some extent the Java virtual machine, is to provide standardized and higher level functionalities in order to support language implementers for writing language implementations.

PyPy is experimenting with a more ambitious approach. We are using a subset of the high-level language Python, called RPython, in which we write languages as simple interpreters with few references to and dependencies on lower level details. The RPython toolchain produces a concrete virtual machine for the platform of our choice by inserting appropriate lower level aspects. The result can be customized by selecting other feature and platform configurations.

Our goal is to provide a possible solution to the problem of language implementers: having to write l * o * p interpreters for l dynamic languages and p platforms with o crucial design decisions. PyPy aims at making it possible to change each of these variables independently such that:

l: the language that we analyze can be evolved or entirely replaced;
o: we can tweak and optimize the translation process to produce platform specific code based on different models and trade-offs;
p: we can write new translator back-ends to target different physical and virtual platforms.

By contrast, a standardized target environment - say .NET - enforces p=1 as far as it’s concerned. This helps making o a bit smaller by providing a higher-level base to build upon. Still, we believe that enforcing the use of one common environment is not necessary. PyPy’s goal is to give weight to this claim - at least as far as language implementation is concerned - showing an approach to the l * o * p problem that does not rely on standardization.

The most ambitious part of this goal is to generate Just-In-Time Compilers in a language-independent way, instead of only translating the source interpreter into an interpreter for the target platform. This is an area of language implementation that is commonly considered very challenging because of the involved complexity.

PyPy - the Python Interpreter ¶

Our main motivation for developing the translation framework is to provide a full featured, customizable, fast and very compliant Python implementation, working on and interacting with a large variety of platforms and allowing the quick introduction of new advanced language features.

This Python implementation is written in RPython as a relatively simple interpreter, in some respects easier to understand than CPython, the C reference implementation of Python. We are using its high level and flexibility to quickly experiment with features or implementation techniques in ways that would, in a traditional approach, require pervasive changes to the source code. For example, PyPy’s Python interpreter can optionally provide lazily computed objects - a small extension that would require global changes in CPython. Another example is the garbage collection technique: changing CPython to use a garbage collector not based on reference counting would be a major undertaking, whereas in PyPy it is an issue localized in the translation framework, and fully orthogonal to the interpreter source code.

PyPy Architecture ¶

As you would expect from a project implemented using ideas from the world of Extreme Programming, the architecture of PyPy has evolved over time and continues to evolve. Nevertheless, the high level architecture is stable. As described above, there are two rather independent basic subsystems: the PyPy Python Interpreter and the RPython Translation Toolchain.

RPython Translation Toolchain ¶

The job of the RPython toolchain is to translate RPython programs into an efficient version of that program for one of the various target platforms, generally one that is considerably lower-level than Python.

The approach we have taken is to reduce the level of abstraction of the source RPython program in several steps, from the high level down to the level of the target platform, whatever that may be. Currently we support two broad flavours of target platforms: the ones that assume a C-like memory model with structures and pointers, and the ones that assume an object-oriented model with classes, instances and methods (as, for example, the Java and .NET virtual machines do).

The RPython toolchain never sees the RPython source code or syntax trees, but rather starts with the code objects that define the behaviour of the function objects one gives it as input. It can be considered as “freezing” a pre-imported RPython program into an executable form suitable for the target platform.

The steps of the translation process can be summarized as follows:

The code object of each source functions is converted to a control flow graph by the Flow Object Space.
The control flow graphs are processed by the Annotator, which performs whole-program type inference to annotate each variable of the control flow graph with the types it may take at run-time.
The information provided by the annotator is used by the RTyper to convert the high level operations of the control flow graphs into operations closer to the abstraction level of the target platform.
Optionally, various transformations can then be applied which, for example, perform optimizations such as inlining, add capabilities such as stackless-style concurrency, or insert code for the garbage collector.
Then, the graphs are converted to source code for the target platform and compiled into an executable.

This process is described in much more detail in the document about the RPython toolchain and in the paper Compiling dynamic language implementations.

PyPy Python Interpreter ¶

PyPy’s Python Interpreter is written in RPython and implements the full Python language. This interpreter very closely emulates the behavior of CPython. It contains the following key components:

a bytecode compiler responsible for producing Python code objects from the source code of a user application;
a bytecode evaluator responsible for interpreting Python code objects;
a standard object space, responsible for creating and manipulating the Python objects seen by the application.

The bytecode compiler is the preprocessing phase that produces a compact bytecode format via a chain of flexible passes (tokenizer, lexer, parser, abstract syntax tree builder, bytecode generator). The bytecode evaluator interprets this bytecode. It does most of its work by delegating all actual manipulations of user objects to the object space. The latter can be thought of as the library of built-in types. It defines the implementation of the user objects, like integers and lists, as well as the operations between them, like addition or truth-value-testing.

This division between bytecode evaluator and object space is very important, as it gives a lot of flexibility. One can plug in different object spaces to get different or enriched behaviours of the Python objects. Additionally, a special more abstract object space, the flow object space, allows us to reuse the bytecode evaluator for our translation framework.