Introduction

Since last summer, I've been looking on and off into a weird and hard to reproduce crash bug in PyPy. It was manifesting only on CI, and it seemed to always happen in the AST rewriting phase of pytest, the symptoms being that PyPy would crash with a segfault. All my attempts to reproduce it locally failed, and my attempts to try to understand the problem by dumping the involved ASTs lead nowhere.

A few weeks ago, we got two more bug reports, the last one by the authors of the nanobind binding generator, with the same symptoms: crash in AST rewriting, only on CI. I decided to make a more serious push to try to find the bug this time. Ultimately the problem turned out to be several bugs in PyPy's garbage collector (GC) that had been there since its inception in 2013. Understanding the situation turned out to be quite involved, additionally complicated by this being the first time that I was working on this particular aspect of PyPy's GC. Since the bug was so much work to find, I thought I'd write a blog post about it.

The blog post consists of three parts: first a chronological description of what I did to find the bug, a technical explanation of what goes wrong, some reflections on the bug (and then a bonus bug I also found in the process).

Finding the Bug

I started from the failing nanobind CI runs that ended with a segfault of the PyPy interpreter. This was only an intermittent problem, not every run was failing. When I tried to just run the test suite locally, I couldn't get it to fail. Therefore at first I tried to learn more about what was happening by looking on the CI runners.

Running on CI

I forked the nanobind repo and hacked the CI script in order to get it to use a PyPy build with full debug information and more assertions turned on. In order to increase the probability of seeing the crash I added an otherwise unused matrix variable to the CI script that just contained 32 parameters. This means every build is done 32 times (sorry Github for wasting your CPUs 😕). With that amount of repetition, I got at least one job of every build that was crashing.

Then I added the -Xfaulthandler option to the PyPy command which will use the faulthandler module try to print a Python stacktrace if the VM segfaults to confirm that PyPy was indeed crashing in the AST rewriting phase of pytest, which pytest uses for nicer assertions. I experimented with hacking our faulthandler implementation to also give me a C-level callstack, but that didn't work as well as I hoped.

Then I tried to run gdb on CI to try to get it to print a C callstack at the crash point. You can get gdb to execute commands as if typed at the prompt with the -ex commandline option, I used something like this:

gdb -ex "set confirm off" -ex "set pagination off" -ex \
    "set debuginfod enabled off" -ex run -ex where -ex quit \
    --args <command> <arguments>

But unfortunately the crash never occurred when running in gdb.

Afterwards I tried the next best thing, which was configuring the CI runner to dump a core file and upload it as a build artifact, which worked. Looking at the cores locally only sort of worked, because I am running a different version of Ubuntu than the CI runners. So I used tmate to be able to log into the CI runner after a crash and interactively used gdb there. Unfortunately what I learned from that was that the bug was some kind of memory corruption, which is always incredibly unpleasant to debug. Basically the header word of a Python object had been corrupted somehow at the point of the crash, which means that it's vtable wasn't usable any more.

(Sidenote: PyPy doesn't really use a vtable pointer, instead it uses half a word in the header for the vtable, and the other half for flags that the GC needs to keep track of the state of the object. Corrupting all this is still bad.)

Reproducing Locally

At that point it was clear that I had to push to reproduce the problem on my laptop, to allow me to work on the problem more directly and not to always have to go via the CI runner. Memory corruption bugs often have a lot of randomness (depending on which part of memory gets modified, things might crash or more likely just happily keep running). Therefore I decided to try to brute-force reproducing the crash by simply running the tests many many times. Since the crash happened in the AST rewriting phase of pytest, and that happens only if no pyc files of the bytecode-compiled rewritten ASTs exist, I made sure to delete them before every test run.

To repeat the test runs I used multitime, which is a simple program that runs a command repeatedly. It's meant for lightweight benchmarking purposes, but it also halts the execution of the command if that command exits with an error (and it sleeps a small random time between runs, which might help with randomizing the situation, maybe). Here's a demo:

(Max pointed out autoclave to me when reviewing this post, which is a more dedicated tool for this job.)

Thankfully, running the tests repeatedly eventually lead to a crash, solving my "only happens on CI" problem. I then tried various variants to exclude possible sources of errors. The first source of errors to exclude in PyPy bugs is the just-in-time compiler, so I reran the tests with --jit off to see whether I could still get it to crash, and thankfully I eventually could (JIT bugs are often very annoying).

Next source of bugs to exclude where C-extensions. Since those were the tests of nanobind, a framework for creating C-extension modules I was a bit worried that the bug might be in our emulation of CPython's C-API. But running PyPy with the -v option (which will print all the imports as they happen) confirmed that at the point of crash no C-extension had been imported yet.

Using rr

I still couldn't get the bug to happen in GDB, so the tool I tried next was rr, the "reverse debugger". rr can record the execution of a program and later replay it arbitrarily often. This gives you a time-traveling debugger that allows you to execute the program backwards in addition to forwards. Eventually I managed to get the crash to happen when running the tests with rr record --chaos (--chaos randomizes some decisions that rr takes, to try to increase the chance of reproducing bugs).

Using rr well is quite hard, and I'm not very good at it. The main approach I use with rr to debug memory corruption is to replay the crash, then set a watchpoint for the corrupted memory location, then use the command reverse-continue to find the place in the code that mutated the memory location. reverse-continue is like continue, except that it will execute the program backwards from the current point. Here's a little demo of this:

Doing this for my bug revealed that the object that was being corrupted was erroneously collected by the garbage collector. For some reason the GC had wrongly decided that the object was no longer reachable and therefore put the object into a freelist by writing a pointer to the next entry in the freelist into the first word of the object, overwriting the object's header. The next time the object was used things crashed.

Side-quest: wrong GC assertions

At this point in the process, I got massively side-tracked. PyPy's GC has a number of debug modes that you can optionally turn on. Those slow down the program execution a lot, but they should in theory help to understand why the GC goes wrong. When I turned them on, I was getting a failing assertion really early in the test execution, complaining about an invariant violation in the GC logic. At first this made me very happy. I thought that this would help me fix the bug more quickly.

Extremely frustratingly, after two days of work I concluded that the assertion logic itself was wrong. I have fixed that in the meantime too, the details of that are in the bonus section at the end of the post.

Using GDB scripting to find the real bug

After that disaster I went back to the earlier rr recording without GC assertions and tried to understand in more detail why the GC decided to free an object that was still being referenced. To be able to do that I used the GDB Python scripting API to write some helper commands to understand the state of the GC heap (rr is an extension of GDB, so the GDB scripting API works in rr too).

The first (small) helper command I wrote with the GDB scripting API was a way to pretty-print the currently active GC flags of a random PyPy object, starting just from the pointer. The more complex command I wrote was an object tracer, which follows pointers to GC objects starting from a root object to explore the object graph. The object tracer isn't complete, it doesn't deal with all the complexities of PyPy's GC. But it was good enough to help me with my problem, I found out that the corrupted object was stored in an array.

As an example, here's a function that uses the GDB API to walk one of the helper data structures of the GC, a stack of pointers:

def walk_addr_stack(obj):
    """ walk an instance of the AddressStack class (which is a linked list of
    arrays of 1019 pointers).

    the first of the arrays is only partially filled with used_in_last_chunk
    items, all the other chunks are full."""
    if obj.type.code == gdb.TYPE_CODE_PTR:
        obj = obj.dereference()
    used_in_last_chunk = lookup(obj, "used_in_last_chunk")
    chunk = lookup(obj, "inst_chunk").dereference()
    while 1:
        items = lookup(chunk, "items")
        for i in range(used_in_last_chunk):
            yield items[i]
        chunk = lookup(chunk, "next")
        if not chunk:
            break
        chunk = chunk.dereference()
        used_in_last_chunk = 1019

The full file of supporting code I wrote can be found in this gist. This is pretty rough throw-away code, however.

In the following recording I show a staged debugging session with some of the extra commands I wrote with the Python API. The details aren't important, I just wanted to give a bit of a flavor of what inspecting objects looks like:

The next step was to understand why the array content wasn't being correctly traced by the GC, which I eventually managed with some conditional breakpoints, more watchpoints, and using reverse-continue. It turned out to be a bug that occurs when the content of one array was memcopied into another array. The technical details of why the array wasn't traced correctly are described in detail in the next section.

Writing a unit test

To try to make sure I really understood the bug correctly I then wrote a GC unit test that shows the problem. Like most of PyPy, our GC is written in RPython, a (somewhat strange) subset/dialect of Python2, which can be compiled to C code. However, since it is also valid Python2 code, it can be unit-tested on top of a Python2 implementation (which is one of the reasons why we keep maintaining PyPy2).

In the GC unit tests you have a lot of control about what order things happen in, e.g. how objects are allocated, when garbage collection phases happen, etc. After some trying I managed to write a test that crashes with the same kind of memory corruption that my original crash exhibited: an object that is still reachable via an array is collected by the GC. To give you a flavor of what this kind of test looks like, here's an (edited for clarity) version of the test I eventually managed to write

def test_incrementality_bug_arraycopy(self):
    source = self.malloc(VAR, 8) # first array
    # the stackroots list emulates the C stack
    self.stackroots.append(source)
    target = self.malloc(VAR, 8) # second array
    self.stackroots.append(target)
    node = self.malloc(S) # unrelated object, will be collected
    node.x = 5
    # store reference into source array, calling the write barrier
    self.writearray(source, 0, node)
    val = self.gc.collect_step()
    source = self.stackroots[0] # reload arrays, they might have moved
    target = self.stackroots[1]
    # this GC step traces target
    val = self.gc.collect_step()

    # emulate what a memcopy of arrays does
    res = self.gc.writebarrier_before_copy(source, target, 0, 0, 2)
    assert res
    target[0] = source[0] # copy two elements of the arrays
    target[1] = source[1]
    # now overwrite the reference to node in source
    self.writearray(source, 0, lltype.nullptr(S))
    # this GC step traces source
    self.gc.collect_step()
    # some more collection steps, crucially target isn't traced again
    # but node is deleted
    for i in range(3):
        self.gc.collect_step()
    # used to crash, node got collected
    assert target[0].x == 5

One of the good properties of testing our GC that way is that all the memory is emulated. The crash in the last line of the test isn't a segfault at all, instead you get a nice exception saying that you tried to access a freed chunk of memory and you can then debug this with a python2 debugger.

Fixing the Bug

With the unit test in hand, fixing the test was relatively straightforward (the diff in its simplest form is anyway only a single line change). After this first version of my fix, I talked to Armin Rigo who helped me find different case that was still wrong, in the same area of the code.

I also got help by the developers at PortaOne who are using PyPy on their servers and had seen some mysterious PyPy crashes recently, that looked related to the GC. They did test deployments of my fixes in their various stages to their servers to try to see whether stability improved for them. Unfortunately in the end it turned out that their crashes are an unrelated GC bug related to object pinning, which we haven't resolved yet.

Writing a GC fuzzer/property based test

Finding bugs in the GC is always extremely disconcerting, particularly since this one manged to hide for so long (more than ten years!). Therefore I wanted to use these bugs as motivation to try to find more problems in PyPy's GC. Given the ridiculous effectiveness of fuzzing, I used hypothesis to write a property-based test. Every test performs a sequence of randomly chosen steps from the following list:

• allocate an object
• read a random field from a random object
• write a random reference into a random object
• drop a random stack reference
• perform one GC step
• allocate an array
• read a random index from a random array
• write to an array
• memcopy between two arrays

This approach of doing a sequence of steps is pretty close to the stateful testing approach of hypothesis, but I just implemented it manually with the data strategy.

Every one of those steps is always performed on both the tested GC, and on some regular Python objects. The Python objects provide the "ground truth" of what the heap should look like, so we can compare the state of the GC objects with the state of the Python objects to find out whether the GC made a mistake.

In order to check whether the test is actually useful, I reverted my bug fixes and made sure that the test re-finds both the spurious GC assertion error and the problems with memcopying an array.

In addition, the test also found corner cases in my fix. There was a situation that I hadn't accounted for, which the test found after eventually. I also plan on adding a bunch of other GC features as steps in the test to stress them too (for example weakrefs, identity hashes, pinning, maybe finalization).

At the point of publishing this post, the fixes got merged to the 2.7/3.9/3.10 branches of PyPy, and will be part of the next release (v7.3.16).

The technical details of the bug

In order to understand the technical details of the bug, I need to give some background explanations about PyPy's GC.

PyPy's incremental GC

PyPy uses an incremental generational mark-sweep GC. It's generational and therefore has minor collections (where only young objects get collected) and major collections (collecting long-lived objects eventually, using a mark-and-sweep algorithm). Young objects are allocated in a nursery using a bump-pointer allocator, which makes allocation quite efficient. They are moved out of the nursery by minor collections. In order to find references from old to young objects the GC uses a write barrier to detect writes into old objects.

The GC is also incremental, which means that its major collections aren't done all at once (which would lead to long pauses). Instead, major collections are sliced up into small steps, which are done directly after a minor collection (the GC isn't concurrent though, which would mean that the GC does work in a separate thread).

The incremental GC uses tri-color marking to reason about the reachable part of the heap during the marking phase, where every old object can be:

• black: already marked, reachable, definitely survives the collection
• grey: will survive, but still needs to be marked
• white: potentially dead

The color of every object is encoded by setting flags in the object header.

The GC maintains the invariant that black objects must never point to white objects. At the start of a major collection cycle the stack roots are turned gray. During the mark phase of a major collection cycle, the GC will trace gray objects, until none are left. To trace a gray object, all the objects it references have to be marked grey if they are white so far. After a grey object is traced, it can be marked black (because all the referenced objects are now either black or gray). Eventually, there are no gray objects left. At that point (because no white object can be reached from a black one) all the white objects are known to be unreachable and can therefore be freed.

The GC is incremental because every collection step will only trace a limited number of gray objects, before giving control back to the program. This leads to a problem: if an already traced (black) object is changed between two marking steps of the GC, the program can mutate that object and write a new reference into one of its fields. This could lead to an invariant violation, if the referenced object is white. Therefore, the GC uses the write barrier (which it needs anyway to find references from old to young objects) to mark all black objects that are modified gray, and then trace them again at one of the later collection steps.

The special write barrier of memcopy

Arrays use a different kind of write barrier than normal objects. Since they can be arbitrarily large, tracing them can take a long time. Therefore it's potentially wasteful to trace them fully at a minor collection. To fix this, the array write barrier keeps more granular information about which parts of the array have been modified since the last collection step. Then only the modified parts of the array need to be traced, not the whole array.

In addition, there is another optimization for arrays, which is that memcopy is treated specially by the GC. If memcopy is implemented by simply writing a loop that copies the content of one array to the other, that will invoke the write barrier every single loop iteration for the write of every array element, costing a lot of overhead. Here's some pseudo-code:

def arraycopy(source, dest, source_start, dest_start, length):
    for i in range(length):
        value = source[source_start + i]
        dest[dest_start + i] = value # <- write barrier inserted here

Therefore the GC has a special memcopy-specific write barrier that will perform the GC logic once before the memcopy loop, and then use a regular (typically SIMD-optimized) memcopy implementation from libc. Roughly like this:

def arraycopy(source, dest, source_start, dest_start, length):
    gc_writebarrier_before_array_copy(source, dest, source_start, dest_start, length)
    raw_memcopy(cast_to_voidp(source) + source_start,
                cast_to_voidp(dest) + dest_start,
                sizeof(itemtype(source)) * length)

(this is really a rough sketch. The real code is much more complicated.)

The bug

The bugs turned out to be precisely in this memcopy write barrier. When we implemented the current GC, we adapted our previous GC, which was a generational mark-sweep GC but not incremental. We started with most of the previous GC's code, including the write barriers. The regular write barriers were adapted to the new incremental assumptions, in particular the need for the write barrier to also turn black objects back to gray when they are modified during a marking phase. This was simply not done at all for the memcopy write barrier, at least in two of the code paths. Fixing this problem fixes the unit tests and stops the crashes.

Reflections

The way the bug was introduced is really typical. A piece of code (the memcopy write barrier) was written under a set of assumptions. Then those assumptions changed later. Not all the code pieces that relied on these assumptions to be correct were updated. It's pretty hard to prevent this in all situations.

I still think we could have done more to prevent the bug occurring. Writing a property-based test for the GC would have been a good idea given the complexity of the GC, and definitely something we did in other parts of our code at the time (just using the random module mostly, we started using hypothesis later).

It's a bit of a mystery to me why this bug managed to be undetected for so long. Memcopy happens in a lot of pretty core operations of e.g. lists in Python (list.extend, to name just one example). To speculate, I would suspect that all the other preconditions for the bug occurring made it pretty rare:

• the content of an old list that is not yet marked needs to be copied into another old list that is marked already
• the source of the copy needs to also store an object that has no other references
• the source of the copy then needs to be overwritten with other data
• then the next collection steps need to be happening at the right points
• ...

Given the complexity of the GC logic I also wonder whether some lightweight formal methods would have been a good idea. Formalizing some of the core invariants in B or TLA+ and then model checking them up to some number of objects would have found this problem pretty quickly. There are also correctness proofs for GC algorithms in some research papers, but I don't have a good overview of the literature to point to any that are particularly good or bad. Going such a more formal route might have fixed this and probably a whole bunch of other bugs, but of course it's a pretty expensive (and tedious) approach.

While it was super annoying to track this down, it was definitely good to learn a bit more about how to use rr and the GDB scripting interface.

Bonus Section: The Wrong Assertion

Some more technical information about the wrong assertion is in this section.

Background: pre-built objects

PyPy's VM-building bootstrapping process can "freeze" a bunch of heap objects into the final binary. This allows the VM to start up quickly, because those frozen objects are loaded by the OS as part of the binary.

Those frozen pre-built objects are parts of the 'roots' of the garbage collector and need to be traced. However, tracing all the pre-built objects at every collection would be very expensive, because there are a lot of them (about 150,000 in a PyPy 3.10 binary). Tracing them all is also not necessary, because most of them are never modified. Unmodified pre-built objects can only reference other pre-built objects, which can never be deallocated anyway. Therefore we have an optimization that uses the write barrier (which we need anyway to find old-to-young pointers) to notice when a pre-built object gets modified for the very first time. If that happens, it gets added to the set of pre-built objects that gets counted as a root, and is traced as a root at collections from then on.

The wrong assertion

The assertion that triggered when I turned on the GC debug mode was saying that the GC found a reference from a black to a white object, violating its invariant. Unmodified pre-built objects count as black, and they aren't roots, because they can only ever reference other pre-built objects. However, when a pre-built object gets modified for the first time, it becomes part of the root set and will be marked gray. This logic works fine.

The wrong assertion triggers if a pre-built object is mutated for the very first time in the middle of an incremental marking phase. While the pre-built object gets added to the root set just fine, and will get traced before the marking phase ends, this is encoded slightly differently for pre-built objects, compared to "regular" old objects. Therefore, the invariant checking code wrongly reported a black->white pointer in this situation.

To fix it I also wrote a unit test checking the problem, made sure that the GC hypothesis test also found the bug, and then fixed the wrong assertion to take the color encoding of pre-built objects into account.

The bug managed to be invisible because we don't tend to turn on the GC assertions very often. We only do that when we find a GC bug, which is of course also when we need it the most to be correct.

Acknowledgements

Thanks to Matti Picus, Max Bernstein, Wouter van Heyst for giving me feedback on drafts of the post. Thanks to Armin Rigo for reviewing the code and pointing out holes in my thinking. Thanks to the original reporters of the various forms of the bug, including Lily Foote, David Hewitt, Wenzel Jakob.

PyPy has moved its canonical repo and issue tracker from https://foss.heptapod.net/pypy/pypy to https://github.com/pypy/pypy. Obviously, this means development will now be tracked in Git rather than Mercurial.

Motivation

We still feel Mercurial is a better version control system. The named branch model and user interface are superior. But

• foss.heptapod.net is not well indexed in google/bing/duckduckgo search, so people find it harder to search for issues in the project.

• Since Heptapod has tightened its spam control, we get reports that users create issues only to have them flagged as spam.

• Open Source has become synonymous with GitHub, and we are too small to change that.

• Much of the current development comes as a reaction to fixing issues. Tracking interlocking issues is easier if all the code is on the same platform.

• The FAQ presents two arguments against the move. Github notes solves much of point (1): the difficulty of discovering provenance of commits, although not entirely. But the main problem is point (2), it turns out that not moving to GitHub is an impediment to contribution and issue reporting.

• People who wish to continue to use Mercurial can use the same method below to push to GitHub.

• GitHub is more resource rich than foss.heptapod.net. We could add CI jobs to replace some of our aging buildbot infrastructure.

Method

The migration required two parts: migrating the code and then migrating the issues and merge requests.

Code migration 1: code and notes

I used a fork of git-remote-hg to create a local Git repo with all the changesets. Then I wanted to add a Git note to each commit with the branch it came from. So I prepared a file with two columns: the Git commit hash, and the corresponding branch from Mercurial. Mercurial can describe each commit in two ways: either the commit hash or by a number index. I used hg log to convert an index i to the Mercurial hash, and then git-hg-helper from git-remote-hg to convert the Mercurial hash to a Git hash:

$(cd pypy-git; git-hg-helper git-rev $(cd ../pypy-hg; hg log -r $i -T"{node}\n"))

Then I used hg log again to print the Mercurial branch for the index i:

$(cd pypy-hg; hg log -r $i -T'{branch}\n')

Putting these two together, I could loop over all the commits by their numerical index to prepare the file. Then I iterated over each line in the file, and added the Git note. Since the git note add command works on the current HEAD, I needed to checkout each commit in turn and then add the note:

git checkout -q <hash> && git notes --ref refs/notes/branch add -m branch:<branch>

I could then use git push --all to push to GitHub.

Code migration 2: prepare the branches

PyPy has almost 500 open branches. The code migration created all the branch HEADs, but git push --all did not push them. I needed to check them out and push each one. So I created a file with all the branch names

cd pypy-hg; hg branches | cut -f1 -d" " > branches.txt

and then push each one to the GitHub repo

while read branch; do git checkout branches/$branch && git push origin branches/$branch; done < branches.txt

Note that the branches were named branches/XXX by the migration, not branch/XXX. This confuses the merge request migration, more about that later.

Issue and merge request migration

I used the solution from node-gitlab-2-github which worked almost perfectly. It is important to do the conversion on a private repo otherwise every mention of a sucessfully mapped user name notifies the user about the transfer. This can be quite annoying for a repo the size of PyPy with 600 merge requests and over 4000 issues. Issues transfered without a problem: the script properly retained the issue numbers. However the script does not convert the Mercurial hashes to Git hashes, so the bare hashes in comments show up without a link to the commit. Merge requests are more of a problem:

• The Mercurial named branch "disappears" once it is merged, so a merge request to a merged branch does not find the target branch name in Git. The conversion creates an issue instead with the label gitlab merge request.
• For some reason, the branches created by git-remote-hg are called branches/XXX and not branch/XXX as expected by GitLab. This messes up the merge request/PR conversion. For some of the branches (open PRs and main target branches) I manually created additional branches without the es. The net result is that open merge requests became open PRs, merged merge requests became issues, and closed-not-merged merge requests were not migrated.

Layered conversions

PyPy already migrated once from Bitbucket to Heptapod. Many of the issues reflect the multiple transitions: they have lines like "Created originally on Bitbucket by XXX" from the first transition, and an additional line "In Heptapod" from this transition.

Credits

We would like to express our gratitude to the Octobus team who support Heptapod. The transition from Bitbucket was quite an effort, and they have generously hosted our developement since then. We wish them all the best, and still believe that Mercurial should have "won".

Next steps

While the repo at GitHub is live, there are still a few more things we need to do:

• Documentation needs an update for the new repo and the build automation from readthedocs must be adjusted.
• The wiki should be copied from Heptapod.
• buildbot.pypy.org should also look at the new repo. I hope the code is up to the task of interacting with a Git repo.
• speed.pypy.org tracks changes, it too needs to reference the new location
• To keep tracking branches with Git notes on new commits, I activated a github action by Julian to add a Git branch note to each commit. Please see the README there for directions on using Git notes.
• Some of the merge requests were not migrated. If someone wants to, they could migrate those once they figure out the branch naming problems.

Additionally, now is the time for all of you to prove the move is worthwhile:

• Star the repo, let others know how to find it,
• Help fix some of the open issues or file new ones,
• Take advantage of the more familiar workflow to get involved in the project,
• Suggest ways to improve the migration: are there things I missed or could have done better?

How will development change?

Heptapod did not allow personal forks, so we were generous with a commit bit to the main repo. Additionally, we (well, me) have been using a commit-directly-to-main workflow. We will now be adopting a more structured workflow. Please fork the repo and submit a pull request for any changes. We can now add some pre-merge CI to check that the PR at least passes the first stage of translation. The live and active branches will be:

• main: what was "default" in Mercurial, it is the Python2.7 interpreter and the base of the RPython interpreter,
• py3.9: the Python3.9 interpreter, which also includes all RPython changes from main. This is exactly like on Mercurial, and
• py3.10: the Python3.10 interpreter, which also includes all RPython changes from main and all bugfixes from py3.9. This is exactly like on Mercurial.

Working between the repos

Finding commits

If you want to figure out how a Mercurial commit relates to a Git commit, you can use git-hg-helper. You run it in the Git repo. It takes the full long hash from one repo and gives you the corresponding hash of the other repo:

$ git-hg-helper git-rev d64027c4c2b903403ceeef2c301f5132454491df
4527e62ad94b0e940a5b0f9f20d29428672f93f7
$ git-hg-helper hg-rev 4527e62ad94b0e940a5b0f9f20d29428672f93f7
d64027c4c2b903403ceeef2c301f5132454491df

Finding branches

Branches migrated from Mercurial will have a branches prefix, not branch. While GitLab uses branch for its prefix, the git-remote-hg script uses branches. New work should be in a PR targeting main, py3.9 or py3.10.

Thanks for helping to make PyPy better.

Matti

In cooperation with RISC-V International, who funded a part of this project, we recently created a workflow to use RPython to take a Sail RISC-V model and automatically create a RISC-V ISA emulator from it, which we call Pydrofoil. The simulator sped up booting a linux emulator from 35 minutes (using the standard Sail-generated emulator in C) to 2 minutes, a speedup of 17.5x. More details about the process are in the RISC-V blog post.

A few take-aways from the project:

• While PyPy has shown it can speed up generic python code about 4x, the technology behind PyPy can really shine in other areas.

• RPython is malleable and can be molded to many tasks, the RPython meta-JIT is very flexible.

• A JIT is well-suited for the problem of emulation, because it can perform dynamic binary translation.

PyPy can solve real world performance problems, even somewhat unusual ones. Please get in touch and let us know how we can help you solve yours!