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Contributing to GraalPy

Thanks for considering to contribute! To get you started, read about the structure of this implementation.

You will need to sign the Oracle Contributor Agreement for us to be able to merge your work.

Please also take some time to review our code of conduct for contributors.

Getting Started

The first thing you want to do is to set up mx. This is the build tool we use to develop GraalVM languages.

git clone https://github.com/graalvm/mx.git

Make sure to add the mx directory to your PATH.

You can always use the latest stable JDK for development. You can also download a suitable JDK using mx:

mx fetch-jdk

Make sure that the JAVA_HOME environment variable is set.

For building GraalPy, you will also need some native build tools and libraries. On a Debian based system, install:

sudo apt install build-essential libc++-12-dev zlib1g-dev cmake

Lastly, download maven, extract it and include it on your PATH.

Once you have all the necessary tools, you can run mx python-jvm in this repository. This will initially download the required dependencies next to the repository and build Python. If it succeeds without errors, you should already be able to run mx python and get a REPL.

For development, we recommend running mx ideinit next. This will generate configurations for Eclipse, IntelliJ, and NetBeans so that you can open the projects in these IDEs. If you use another editor with support for the Eclipse language server we have also had reports of useable development setups with that, but it's not something we support.

Development Layout

Besides the source code of the Python interpreter, we have some useful mx functions defined under the mx.graalpython directory. As you make changes, you can always test them with mx python-jvm && mx python. Additionally, there are various "gates" that we use on our CI systems to check any code that is committed. You can run all the gates with the command mx python-gate or just some by using mx python-gate --tags [TAG]. Interesting gates to run that cover a good chunk of the code things are:

  • python-unittest - Run the unit tests written in Python, including those for the C extension API
  • python-junit - Run the unit tests written in Java.
  • python-license - Check that all files have the correct copyright headers applied to them

Built-In Modules and Classes

For the most part, built-in modules and classes are implemented in the com.oracle.graal.python.builtins package. For each module or class, there's a Java class annotated with @CoreFunctions. Each function in a module or a class is implemented in a Node annotated with @Builtin. Take a look at the existing implementations to get a feel for how this is done. For now, when adding new classes or modules, they need to be added to the list in com.oracle.graal.python.builtins.Python3Core.

Some built-in functions, modules, and classes are implemented in pure Python. The files for these are in the graalpython/lib-graalpython directory. These files are listed in the Java com.oracle.graal.python.builtins.Python3Core class. Take a look at these files to see what they do. If a file is called exactly as a built-in module is, it is executed in the context of that module during startup, so some of our modules are implemented both in Java and Python. If the name does not match an existing module, the file is executed just for its side-effects.

When implementing a new (or fixing an existing) built-in, take a look at the CPython source. The layout and naming of modules and types is kept similar to the CPython source so it should be relatively easy to find the right piece of code. For some special dunder methods (__add__, __getitem__, __getattribute__, ...) you may have to figure out the C API slot names for them to find the right piece of code (nb_add, sq_item, tp_getattr, ...).

You will find that often there are specific C API methods that are called to convert or coerce arguments, to look up methods either starting on the object or only on the class, to call a callable object or invoke a method, and more. In general, most of these methods should have equivalents in our libs nodes in the com.oracle.graal.python.lib package. If something is missing that is commonly used, we probably have some Node for it somewhere, but it may be a good idea to create a lib node and migrate usages.

GraalPy has its own variant of the Argument Clinic preprocessor. It is activated by:

  • extending PythonXXXClinicBuiltinNode (e.g. PythonBinaryClinicBuiltinNode),
  • using @ArgumentClinic annotations on the built-in node class,
  • and overriding the getArgumentClinic method to return the class that will be generated from the annotations (it will be named the same as the node class plus ClinicProviderGen suffix).

Sometimes, you will not easily find what exactly happens for a given piece of code when that involves more than just a simple built-in call. The dis CPython module can often help get an angle on what a particular piece of code is doing. You can call dis.dis on any Python function and it will print details of the bytecode and associated data, which can be a good starting point to browse through the CPython source.

Python C API

The C implementation and headers for our C API are in the graalpython/com.oracle.graal.python.cext directory. The naming is analogous to CPython's source names. This directory also includes a modules directory for built-in modules that we have adapted from CPython.

Debug options

GraalPy provides multiple debug options. It is possible to either debug the Python code using a Chrome debugger, or the Java code using your preferred IDE.

To debug Python code using Chrome, run this:

mx python --inspect --inspect.Internal your_script.py

This will open a debug server, which can be accessed in a Chrome Browser via the URL chrome://inspect.

For debugging Java-implemented code run:

mx python --experimental-options -debug-java your_script.py

The command will also start a debug server, which can be used in an IDE. If the IDE was initialized properly by using the command mentioned above, the existing GraalDebug run configuration can be used to debug.

Both of these commands also work when you have a graalpy executable, e.g. inside a venv.

Advanced Commands to Develop and Debug

Here are some advanced commands to debug test failures and fix issues.

First, we have three sets of unit tests in the base repository:

  1. Our own Python-bases unit tests
  2. JUnit tests
  3. Python's standard library tests

To run the first, you can use this command:

mx python-gate --tags python-unittest

If some of the tests fail, you can re-run just a single test like this, substituting TEST-PATTERN (and possibly the file glob on the third line) with the test you want to run. Note that you can insert -d to debug on the Java level or use --inspect to debug in the Chrome debugger.

mx [-d] graalpytest [--inspect] test_*.py -k TEST-PATTERN

To run the JUnit tests, you can use this command:

mx python-gate --tags python-junit

To run a subset of the tests, you can use the following. Again, you can use -d to attach with a Java debugger.

mx [-d] punittest JAVA-TEST-CLASSNAME

To run the Python standard library tests, you can use the following:

mx python-gate --tags python-tagged-unittest

Note that we use "tag files", small *.txt files that select which tests to run, so we only run tests that we know should pass. To run a subset of those tests, use the following command:

mx [-d] python-run-cpython-unittest [--inspect] NAME-OF-CPYTHON-UNITTEST

A tag file can be regenerated with

mx python-retag-unittests NAME-OF-CPYTHON-UNITTEST

There are also several other gates that may fail with changes. One of these is our style gate, which checks formatting rules and copyrights. To auto-fix most issues, run the following commands. Anything that's reported as an error after this command you have to fix manually. Note that to really match what's in the gate, you have to set the JDT environment variable to the path of an Eclipse compiler Jar file, and the ECLIPSE_EXE environment variable to the path of an eclipse executable.

mx python-style --fix
mx python-gate --tags style

Another important gate is the one that checks if you broke Native Image building. To test if building a native executable still works, you can use the following command. This will create a native executable called graalpy and print its path as the last output, if successful.

mx python-svm

If you made changes to the parser, you may have to regenerate the golden files like so:

find graalpython -name '*.scope' -delete
find graalpython -name '*.tast' -delete
mx punittest com.oracle.graal.python.pegparser

If you made changes to the bytecode compiler, you may have to regenerate its golden files:

find graalpython -name '*.co' -delete
mx punittest com.oracle.graal.python.test.compiler

Benchmarking

We use the mx facilities for benchmarking. Use this to list the available Python suites and VM configurations:

mx benchmark --list

If you just want to run a single benchmark from, for example, the meso suite, you can use this:

mx benchmark meso --list

Then if you want to run something, use (for example):

mx benchmark meso:nbody3

To select which Python VM you want to use, you can pass the arguments separated by --:

mx benchmark meso:nbody3 \
    -- --python-vm=cpython

For additional arguments to the Python launcher, you can separate them by another double-dash:

mx benchmark meso:nbody3 \
    -- --python-vm=graalpython \
    -- --python.EmulateJython \
    -Dgraal.Dump= \
    -Dgraal.MethodFilter=*measure*

A note on terminology

Note that there may be a little confusion about the configuration names of benchmarks.

GraalVM Community Edition and Oracle GraalVM configurations

We have benchmarks for GraalVM Community Edition and Oracle GraalVM. For historical reasons, these are sometimes referred to in some config files as CE and EE; core and enterprise; graalvm_ce and graalvm_ee; or graalpython_core and graalpython_enterprise, respectively.

Different GraalVM Python configurations

There are also different options for how the Python interpreter is run, passed via the --python-vm-config parameter:

  • default - run using the standard options
  • default-multi - run using a shared engine, which is the mode that is recommended to embedders that want to spawn multiple isolated Python contexts
  • native - same as default, its name is due to the fact that it runs C extensions using a mixture of LLVM bitcode interpreted and compiled via GraalVM and real native libraries
  • sandboxed - this name is historical - this configuration requires Oracle GraalVM and runs all C extensions purely as LLVM bitcode on the GraalVM, without any access to the native OS libraries, i.e., using the --llvm.managed option for GraalVM.

Configuration of the underlying GraalVM runtime

Finally, there are the --jvm and --jvm-config configuration options for mx benchmark. By default, the commands presented above will run on the JVM in server mode, using the Graal compiler in what we call hosted mode. This is almost the same but not quite the --jvm mode you will get when running the graalpy executable from a full GraalVM, and usually good enough if you want to look at the compiler graphs or peak performance numbers. In our CI, however, we always build a full GraalVM and benchmark using that, since that is what we ship. There, we have two different configurations corresponding to the launcher options available for the GraalVM graalpy executable: jvm and native. The first runs on top of HotSpot using the Graal compiler, and the second runs the AOT compiled GraalVM native executable of Python.

Building a GraalVM Python configuration can be achieved for the CE version like so:

mx --env ../../graal/vm/mx.vm/ce \
    --exclude-components=slgm \
    --dynamicimports /vm graalvm-show
mx --env ../../graal/vm/mx.vm/ce \
    --exclude-components=slgm \
    --dynamicimports /vm build

The first command will print some information about the GraalVM configuration that is about to be built, and the second will build it.

IMPORTANT: The first command should tell you that the Config name is ce_python, otherwise the next commands will not work.

To run the JVM configuration:

mx --env ../../graal/vm/mx.vm/ce \
    --exclude-components=slgm \
    --dynamicimports /vm benchmark meso:nbody3 \
    -- --python-vm=graalpython \
    --jvm=graalvm-ce-python \
    --jvm-config=jvm \
    --python-vm-config=default --

To run the Native Image configuration:

mx --env ../../graal/vm/mx.vm/ce \
    --exclude-components=slgm \
    --dynamicimports /vm benchmark meso:nbody3 \
    -- --python-vm=graalpython \
    --jvm=graalvm-ce-python \
    --jvm-config=native \
    --python-vm-config=default --

Finding Memory Leaks

For best performance we keep references to long-lived user objects (mostly functions, classes, and modules) directly in the AST nodes when using the default configuration of a single Python context (as is used when running the launcher). For better sharing of warm-up and where absolutely best peak performance is not needed, contexts can be configured with a shared engine and the ASTs will be shared across contexts. However, that implies we must not store any user objects strongly in the ASTs. We test that we have no PythonObjects alive after a Context is closed that are run as part of our JUnit tests. These can be run by themselves, for example, like so:

mx python-leak-test --lang python \
    --shared-engine \
      --code 'import site, json' \
      --forbidden-class com.oracle.graal.python.builtins.objects.object.PythonObject \
      --keep-dump

The --keep-dump option will print the heapdump location and leave the file there rather than deleting it. It can then be opened for example with VisualVM to check for the paths of any leaked object, if there are any.