# Contributing Guidelines

Contents

- Contributing Guidelines

  - Getting involved

  - Your first contribution

    - Source Control

    - Get Access

    - Clone

    - Edit

    - Merge Request

  - Architecture

    - Where to start?

  - Building

    - Coding Guide

  - Testing

    - Test driven development

    - Running PyPy's unit tests

  - App level testing

  - Testing After Translation

  - Tooling & Utilities

    - Interpreter-level console

    - pyinteractive.py options

    - Tracing bytecode and operations on objects

  - Demos

  - graphviz & pygame for flow graph viewing (highly recommended)

PyPy is a very large project that has a reputation of being hard to dive into. Some of this fame is warranted, some of it is purely accidental. There are three important lessons that everyone willing to contribute should learn:

- PyPy has layers. There are many pieces of architecture that are very well separated from each other. More about this below, but often the manifestation of this is that things are at a different layer than you would expect them to be. For example if you are looking for the JIT implementation, you will not find it in the implementation of the Python programming language.

- Because of the above, we are very serious about Test Driven Development. It's not only what we believe in, but also that PyPy's architecture is working very well with TDD in mind and not so well without it. Often development means progressing in an unrelated corner, one unittest at a time; and then flipping a giant switch, bringing it all together. (It generally works out of the box. If it doesn't, then we didn't write enough unit tests.) It's worth repeating - PyPy's approach is great if you do TDD, and not so great otherwise.

- PyPy uses an entirely different set of tools - most of them included in the PyPy repository. There is no Makefile, nor autoconf. More below.

The first thing to remember is that PyPy project is very different than most projects out there. It's also different from a classic compiler project, so academic courses about compilers often don't apply or lead in the wrong direction. However, if you want to understand how designing & building a runtime works in the real world then this is a great project!

## Getting involved

PyPy employs a relatively standard open-source development process. You are encouraged as a first step to join our pypy-dev mailing list and IRC channel, details of which can be found in our contact section. The folks there are very friendly, and can point you in the right direction.

We give out commit rights usually fairly liberally, so if you want to do something with PyPy, you can become a "developer" by logging into https://foss.heptapod.net and clicking the "Request Access" link on the PyPy group page. We also run coding sprints which are separately announced and are usually announced on the blog.

Like any Open Source project, issues should be filed on the issue tracker, and merge requests to fix issues are welcome.

Further Reading: Contact

## Your first contribution

The first and most important rule how not to contribute to PyPy is "just hacking a feature". This won't work, and you'll find your PR will typically require a lot of re-work. There are a few reasons why not:

- build times are large

- PyPy has very thick layer separation

- context of the cPython runtime is often required

Instead, reach out on the dev mailing list or the IRC channel, and we're more than happy to help! :)

Some ideas for first contributions are:

- Documentation - this will give you an understanding of the pypy architecture

- Test failures - find a failing test in the nightly builds, and fix it

- Missing language features - these are listed in our issue tracker

### Source Control

PyPy's main repositories are hosted here: https://foss.heptapod.net/pypy.

Heptapod is a friendly fork of GitLab Community Edition supporting Mercurial. https://foss.heptapod.net is a public instance for Free and Open-Source Software (more information here).

Thanks to Octobus and Clever Cloud for providing this service!

#

### Get Access

As stated above, you need to request access to the repo. Since the free hosting on foss.heptapod.net does not allow personal forks, you need permissions to push your changes directly to our repo. Once you sign in to https://foss.heptapod.net using either a new login or your GitHub or Atlassian logins, you can get developer status for pushing directly to the project (just ask by clicking the link at foss.heptapod.net/pypy just under the logo, and you'll get it, basically). Once you have it you can rewrite your file `.hg/hgrc` to contain `default = ssh://hg@foss.heptapod.net/pypy/pypy`. Your changes will then be pushed directly to a branch on the official repo, and we will review the branches you want to merge.

### Clone

- Clone the PyPy repo to your local machine with the command `hg clone https://foss.heptapod.net/pypy/pypy`. It takes a minute or two operation but only ever needs to be done once. See also https://pypy.org/download.html#building-from-source . If you already cloned the repo before, even if some time ago, then you can reuse the same clone by editing the file `.hg/hgrc` in your clone to contain the line `default = https://foss.heptapod.net/pypy/pypy`, and then do `hg pull && hg up`. If you already have such a clone but don't want to change it, you can clone that copy with `hg clone /path/to/other/copy`, and then edit `.hg/hgrc` as above and do `hg pull && hg up`.

- Now you have a complete copy of the PyPy repo. Make a long-lived branch with a command like `hg branch name_of_your_branch`.

### Edit

- Edit things. Use `hg diff` to see what you changed. Use `hg add` to make Mercurial aware of new files you added, e.g. new test files. Use `hg status` to see if there are such files. Write and run tests! (See the rest of this page.)

- Commit regularly with `hg commit`. A one-line commit message is fine. We love to have tons of commits; make one as soon as you have some progress, even if it is only some new test that doesn't pass yet, or fixing things even if not all tests pass. Step by step, you are building the history of your changes, which is the point of a version control system. (There are commands like `hg log` and `hg up` that you should read about later, to learn how to navigate this history.)

- The commits stay on your machine until you do `hg push` to "push" them back to the repo named in the file `.hg/hgrc`. Repos are basically just collections of commits (a commit is also called a changeset): there is one repo per url, plus one for each local copy on each local machine. The commands `hg push` and `hg pull` copy commits around, with the goal that all repos in question end up with the exact same set of commits. By opposition, `hg up` only updates the "working copy" by reading the local repository, i.e. it makes the files that you see correspond to the latest (or any other) commit locally present.

- You should push often; there is no real reason not to. Remember that even if they are pushed, with the setup above, the commits are only in the branch you named. Yes, they are publicly visible, but don't worry about someone walking around the many branches of PyPy saying "hah, look at the bad coding style of that person". Try to get into the mindset that your work is not secret and it's fine that way. We might not accept it as is for PyPy, asking you instead to improve some things, but we are not going to judge you unless you don't write tests.

### Merge Request

- The final step is to open a merge request, so that we know that you'd like to merge that branch back to the original `pypy/pypy` repo. This can also be done several times if you have interesting intermediate states, but if you get there, then we're likely to proceed to the next stage, which is...

- If you get closer to the regular day-to-day development, you'll notice that we generally push small changes as one or a few commits directly to the branch `default` or `py3.6`. Also, we often collaborate even if we are on other branches, which do not really "belong" to anyone. At this point you'll need `hg merge` and learn how to resolve conflicts that sometimes occur when two people try to push different commits in parallel on the same branch. But it is likely an issue for later `:-)`

## Architecture

PyPy has layers. Just like ogres or onions. Those layers help us keep the respective parts separated enough to be worked on independently and make the complexity manageable. This is, again, just a sanity requirement for such a complex project. For example writing a new optimization for the JIT usually does not involve touching a Python interpreter at all or the JIT assembler backend or the garbage collector. Instead it requires writing small tests in `rpython/jit/metainterp/optimizeopt/test/test_*` and fixing files there. After that, you can just compile PyPy and things should just work.

Further Reading: architecture

### Where to start?

PyPy is made from parts that are relatively independent of each other. You should start looking at the part that attracts you most (all paths are relative to the PyPy top level directory). You may look at our directory reference or start off at one of the following points:

- pypy/interpreter contains the bytecode interpreter: bytecode dispatcher in pypy/interpreter/pyopcode.py, frame and code objects in pypy/interpreter/eval.py and pypy/interpreter/pyframe.py, function objects and argument passing in pypy/interpreter/function.py and pypy/interpreter/argument.py, the object space interface definition in pypy/interpreter/baseobjspace.py, modules in pypy/interpreter/module.py and pypy/interpreter/mixedmodule.py. Core types supporting the bytecode interpreter are defined in pypy/interpreter/typedef.py.

- pypy/interpreter/pyparser contains a recursive descent parser, and grammar files that allow it to parse the syntax of various Python versions. Once the grammar has been processed, the parser can be translated by the above machinery into efficient code.

- pypy/interpreter/astcompiler contains the compiler. This contains a modified version of the compiler package from CPython that fixes some bugs and is translatable.

- pypy/objspace/std contains the Standard object space. The main file is pypy/objspace/std/objspace.py. For each type, the file `xxxobject.py` contains the implementation for objects of type `xxx`, as a first approximation. (Some types have multiple implementations.)

## Building

For building PyPy, we recommend installing a pre-built PyPy first (see Downloading and Installing PyPy). It is possible to build PyPy with CPython, but it will take a lot longer to run -- depending on your architecture, between two and three times as long.

Further Reading: Build

### Coding Guide

As well as the usual pep8 and formatting standards, there are a number of naming conventions and coding styles that are important to understand before browsing the source.

Further Reading: Coding Guide

## Testing

### Test driven development

Instead, we practice a lot of test driven development. This is partly because of very high quality requirements for compilers and partly because there is simply no other way to get around such complex project, that will keep you sane. There are probably people out there who are smart enough not to need it, we're not one of those. You may consider familiarizing yourself with pytest, since this is a tool we use for tests. We ship our own tweaked version of pytest in the top of the tree, so `python -m pytest` will pick up our version, which means our tests need to run with that version of pytest.

We also have post-translation tests in the `extra_tests` directory that are run in a virtual environment from a separate directory, so they use a more up-to-date version of pytest. As much as possible, these are meant to be pass with CPython as well.

### Running PyPy's unit tests

PyPy development always was and is still thoroughly test-driven. There are two modes of tests: those that run on top of RPython before translation (untranslated tests) and those that run on top of a translated `pypy` (app tests). Since RPython is a dialect of Python2, the untranslated tests run with a python2 host.

The PyPy source tree comes with an inlined version of `py.test` which you can invoke by typing:

    python2 pytest.py -h

You will need the build requirements to run tests successfully, since many of them compile little pieces of PyPy and then run the tests inside that minimal interpreter. The cpyext tests also require pycparser, and many tests build cases with hypothesis.

Now on to running some tests. PyPy has many different test directories and you can use shell completion to point at directories or files:

    python2 pytest.py pypy/interpreter/test/test_pyframe.py

    # or for running tests of a whole subdirectory
    python2 pytest.py pypy/interpreter/

Beware trying to run "all" pypy tests by pointing to the root directory or even the top level subdirectory `pypy`. It takes hours and uses huge amounts of RAM and is not recommended.

To run CPython regression tests, you should start with a translated PyPy and run the tests as you would with CPython (see below). You can, however, also attempt to run the tests before translation, but be aware that it is done with a hack that doesn't work in all cases and it is usually extremely slow: `py.test lib-python/2.7/test/test_datetime.py`. Usually, a better idea is to extract a minimal failing test of at most a few lines, and put it into one of our own tests in `pypy/*/test/`.

## App level testing

While the usual invocation of python2 pytest.py runs app-level tests on an untranslated PyPy that runs on top of CPython, we have a test extension to run tests directly on the host python. This is very convenient for modules such as cpyext, to compare and contrast test results between CPython and PyPy.

App-level tests (ones whose file name start with `apptest_` not `test_`) run directly on the host interpreter when passing -D or --direct-apptest to pytest:

    pypy3 -m pytest -D pypy/interpreter/test/apptest_pyframe.py

Mixed-level tests (the usual ones that start with `test_`) are invoked by using the -A or --runappdirect option to pytest:

    python2 pytest.py -A pypy/module/cpyext/test

where python2 can be either python2 or pypy2. On the py3 branch, the collection phase must be run with python2 so untranslated tests are run with:

    python2 pytest.py -A pypy/module/cpyext/test --python=path/to/pypy3

## Testing After Translation

If you run translation, you will end up with a binary named `pypy-c` (or `pypy3-c` for the Python3 branches) in the directory where you ran the translation.

To run a test from the standard CPython regression test suite, use the regular Python way, i.e. (use the exact binary name):

    ./pypy3-c -m test.test_datetime
    # or
    ./pypy3-c lib-python/3/test/test_audit.py

## Tooling & Utilities

If you are interested in the inner workings of the PyPy Python interpreter, there are some features of the untranslated Python interpreter that allow you to introspect its internals.

### Interpreter-level console

To start interpreting Python with PyPy, install a C compiler that is supported by distutils and use Python 2.7 or greater to run PyPy:

    cd pypy
    python bin/pyinteractive.py

After a few seconds (remember: this is running on top of CPython), you should be at the PyPy prompt, which is the same as the Python prompt, but with an extra ">".

If you press <Ctrl-C> on the console you enter the interpreter-level console, a usual CPython console. You can then access internal objects of PyPy (e.g. the object space) and any variables you have created on the PyPy prompt with the prefix `w_`:

    >>>> a = 123
    >>>> <Ctrl-C>
    *** Entering interpreter-level console ***
    >>> w_a
    W_IntObject(123)

The mechanism works in both directions. If you define a variable with the `w_` prefix on the interpreter-level, you will see it on the app-level:

    >>> w_l = space.newlist([space.wrap(1), space.wrap("abc")])
    >>> <Ctrl-D>
    *** Leaving interpreter-level console ***

    KeyboardInterrupt
    >>>> l
    [1, 'abc']

Note that the prompt of the interpreter-level console is only '>>>' since it runs on CPython level. If you want to return to PyPy, press <Ctrl-D> (under Linux) or <Ctrl-Z>, <Enter> (under Windows).

Also note that not all modules are available by default in this mode (for example: `_continuation` needed by `greenlet`) , you may need to use one of `--withmod-...` command line options.

You may be interested in reading more about the distinction between interpreter-level and app-level.

### pyinteractive.py options

To list the PyPy interpreter command line options, type:

    cd pypy
    python bin/pyinteractive.py --help

pyinteractive.py supports most of the options that CPython supports too (in addition to a large amount of options that can be used to customize pyinteractive.py). As an example of using PyPy from the command line, you could type:

    python pyinteractive.py --withmod-time -c "from test import pystone; pystone.main(10)"

Alternatively, as with regular Python, you can simply give a script name on the command line:

    python pyinteractive.py --withmod-time ../../lib-python/2.7/test/pystone.py 10

The `--withmod-xxx` option enables the built-in module `xxx`. By default almost none of them are, because initializing them takes time. If you want anyway to enable all built-in modules, you can use `--allworkingmodules`.

See our configuration sections for details about what all the commandline options do.

### Tracing bytecode and operations on objects

You can use a simple tracing mode to monitor the interpretation of bytecodes. To enable it, set `__pytrace__ = 1` on the interactive PyPy console:

    >>>> __pytrace__ = 1
    Tracing enabled
    >>>> x = 5
            <module>:           LOAD_CONST    0 (5)
            <module>:           STORE_NAME    0 (x)
            <module>:           LOAD_CONST    1 (None)
            <module>:           RETURN_VALUE    0
    >>>> x
            <module>:           LOAD_NAME    0 (x)
            <module>:           PRINT_EXPR    0
    5
            <module>:           LOAD_CONST    0 (None)
            <module>:           RETURN_VALUE    0
    >>>>

## Demos

The example-interpreter repository contains an example interpreter written using the RPython translation toolchain.

## graphviz & pygame for flow graph viewing (highly recommended)

graphviz and pygame are both necessary if you want to look at generated flow graphs:

> graphviz: https://www.graphviz.org/Download.php

> pygame: https://www.pygame.org/download.shtml