Topics and goals

• work on HPy APIs, discussions around next steps for the project

• continuing new and ongoing ports to HPy, including Cython, NumPy, Pillow, Matplotlib

• 3.10 support on PyPy and GraalPy

• preparing the next PyPy release

• discussions around ways to improve collaboration between the different Python implementations

What is a sprint?

The experience of the PyPy project has shown the benefits of regular sprints. They are focussed one week physical meetings where people pair-program on new features and discuss future plans. Coming to one is a great way to get started with a project!

Location

The sprint will take place in a seminar room of the computer science department. It is in the building 25.12, room 02.50 (second floor) of the university campus. For travel instructions see

https://www.cs.hhu.de/lehrstuehle-und-arbeitsgruppen/softwaretechnik-und-programmiersprachen/kontakt/service/lage-und-anreise

We ask participants to wear masks during the indoor working hours.

Exact times

Work days: starting September 19th (~morning), ending September 23rd (~afternoon). We will do a to-be-planned social activity on Wednesday afternoon.

Registration

Please register by editing this file or by opening a pull request:

https://foss.heptapod.net/pypy/extradoc/-/blob/branch/extradoc/sprintinfo/ddorf2022/people.txt

or by sending a quick mail to the pypy-dev mailing list:

http://mail.python.org/mailman/listinfo/pypy-dev

Implementing the Intermediate Representation

Let's start modelling the intermediate representation with Python classes. First we define a base class of all values that can be used as arguments in operations, and let's also add a class that represents constants:

import pytest
from typing import Optional, Any

class Value:
    pass

class Constant(Value):
    def __init__(self, value: Any):
        self.value = value

    def __repr__(self):
        return f"Constant({self.value})"

One consequence of the fact that every variable is assigned to only once is that variables are in a one-to-one correspondence with the right-hand-side of their unique assignments. That means that we don't need a class that represents variables at all. Instead, it's sufficient to have a class that represents an operation (the right-hand side), and that by definition is the same as the variable (left-hand side) that it defines:

class Operation(Value):
    def __init__(self, name: str, args: list[Value]):
        self.name = name
        self.args = args

    def __repr__(self):
        return f"Operation({self.name}, {self.args})"

    def arg(self, index: int):
        return self.args[index]

Now we can instantiate these two classes to represent the example sequence of operations above:

def test_construct_example():
    # first we need something to represent
    # "a" and "b". In our limited view, we don't
    # know where they come from, so we will define
    # them with a pseudo-operation called "getarg"
    # which takes a number n as an argument and
    # returns the n-th input argument. The proper
    # SSA way to do this would be phi-nodes.

    a = Operation("getarg", [Constant(0)])
    b = Operation("getarg", [Constant(1)])
    # var1 = add(b, 17)
    var1 = Operation("add", [b, Constant(17)])
    # var2 = mul(a, var1)
    var2 = Operation("mul", [a, var1])
    # var3 = add(b, 17)
    var3 = Operation("add", [b, Constant(17)])
    # var4 = add(var2, var3)
    var4 = Operation("add", [var2, var3])

    sequence = [a, b, var1, var2, var3, var4]
    # nothing to test really, it shouldn't crash

Usually, complicated programs are represented as a control flow graph in a compiler, which represents all the possible paths that control can take while executing the program. Every node in the control flow graph is a basic block. A basic block is a linear sequence of operations with no control flow inside of it.

When optimizing a program, a compiler usually looks at the whole control flow graph of a function. However, that is still too complicated! So let's simplify further and look at only at optimizations we can do when looking at a single basic block and its sequence of instructions (they are called local optimizations).

Let's define a class representing basic blocks and let's also add some convenience functions for constructing sequences of operations, because the code in test_construct_example is a bit annoying.

class Block(list):
    def opbuilder(opname):
        def wraparg(arg):
            if not isinstance(arg, Value):
                arg = Constant(arg)
            return arg
        def build(self, *args):
            # construct an Operation, wrap the
            # arguments in Constants if necessary
            op = Operation(opname,
                [wraparg(arg) for arg in args])
            # add it to self, the basic block
            self.append(op)
            return op
        return build

    # a bunch of operations we support
    add = opbuilder("add")
    mul = opbuilder("mul")
    getarg = opbuilder("getarg")
    dummy = opbuilder("dummy")
    lshift = opbuilder("lshift")

def test_convencience_block_construction():
    bb = Block()
    # a again with getarg, the following line
    # defines the Operation instance and
    # immediately adds it to the basic block bb
    a = bb.getarg(0)
    assert len(bb) == 1
    assert bb[0].name == "getarg"

    # it's a Constant
    assert bb[0].args[0].value == 0

    # b with getarg
    b = bb.getarg(1)
    # var1 = add(b, 17)
    var1 = bb.add(b, 17)
    # var2 = mul(a, var1)
    var2 = bb.mul(a, var1)
    # var3 = add(b, 17)
    var3 = bb.add(b, 17)
    # var4 = add(var2, var3)
    var4 = bb.add(var2, var3)
    assert len(bb) == 6

That's a good bit of infrastructure to make the tests easy to write. One thing we are lacking though is a way to print the basic blocks into a nicely readable textual representation. Because in the current form, the repr of a Block is very annoying, the output of pretty-printing bb in the test above looks like this:

[Operation('getarg', [Constant(0)]),
 Operation('getarg', [Constant(1)]),
 Operation('add',
           [Operation('getarg',
                      [Constant(1)]),
                 Constant(17)]),
 Operation('mul',
           [Operation('getarg',
                      [Constant(0)]),
                 Operation('add',
                           [Operation('getarg',
                                      [Constant(1)]),
                            Constant(17)])]),
 Operation('add',
           [Operation('getarg',
                      [Constant(1)]),
            Constant(17)]),
 Operation('add',
           [Operation('mul',
                       [Operation('getarg',
                                  [Constant(0)]),
                             Operation('add',
                                       [Operation('getarg',
                                                  [Constant(1)]),
                                        Constant(17)])]),
                 Operation('add',
                           [Operation('getarg',
                                           [Constant(1)]),
                                 Constant(17)])])]

It's impossible to see what is going on here, because the Operations in the basic block appear several times, once as elements of the list but then also as arguments to operations further down in the list. So we need some code that turns things back into a readable textual representation, so we have a chance to debug.

def bb_to_str(bb: Block, varprefix: str = "var"):
    # the implementation is not too important,
    # look at the test below to see what the
    # result looks like

    def arg_to_str(arg: Value):
        if isinstance(arg, Constant):
            return str(arg.value)
        else:
            # the key must exist, otherwise it's
            # not a valid SSA basic block:
            # the variable must be defined before
            # its first use
            return varnames[arg]

    varnames = {}
    res = []
    for index, op in enumerate(bb):
        # give the operation a name used while
        # printing:
        var = f"{varprefix}{index}"
        varnames[op] = var
        arguments = ", ".join(
            arg_to_str(op.arg(i))
                for i in range(len(op.args))
        )
        strop = f"{var} = {op.name}({arguments})"
        res.append(strop)
    return "\n".join(res)

def test_basicblock_to_str():
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.add(5, 4)
    var2 = bb.add(var1, var0)

    assert bb_to_str(bb) == """\
var0 = getarg(0)
var1 = add(5, 4)
var2 = add(var1, var0)"""

    # with a different prefix for the invented
    # variable names:
    assert bb_to_str(bb, "x") == """\
x0 = getarg(0)
x1 = add(5, 4)
x2 = add(x1, x0)"""

    # and our running example:
    bb = Block()
    a = bb.getarg(0)
    b = bb.getarg(1)
    var1 = bb.add(b, 17)
    var2 = bb.mul(a, var1)
    var3 = bb.add(b, 17)
    var4 = bb.add(var2, var3)

    assert bb_to_str(bb, "v") == """\
v0 = getarg(0)
v1 = getarg(1)
v2 = add(v1, 17)
v3 = mul(v0, v2)
v4 = add(v1, 17)
v5 = add(v3, v4)"""
    # Note the re-numbering of the variables! We
    # don't attach names to Operations at all, so
    # the printing will just number them in
    # sequence, can sometimes be a source of
    # confusion.

This is much better. Now we're done with the basic infrastructure, we can define sequences of operations and print them in a readable way. Next we need a central data structure that is used when actually optimizing basic blocks.

Storing Equivalences between Operations Using a Union-Find Data Structure

When optimizing a sequence of operations, we want to make it less costly to execute. For that we typically want to remove operations (and sometimes replace operations with less expensive ones). We can remove operations if they do redundant computation, like case of the duplicate add(v1, 17) in the example. So what we want to do is to turn the running input sequence:

v0 = getarg(0)
v1 = getarg(1)
v2 = add(v1, 17)
v3 = mul(v0, v2)
v4 = add(v1, 17)
v5 = add(v3, v4)

Into the following optimized output sequence:

optvar0 = getarg(0)
optvar1 = getarg(1)
optvar2 = add(optvar1, 17)
optvar3 = mul(optvar0, optvar2)
optvar4 = add(optvar3, optvar2)

We left out the second add (which defines v4), and then replaced the usage of v4 with v2 in the final operation that defines v5.

What we effectively did was discover that v2 and v4 are equivalent and then replaced v4 with v2. In general, we might discover more such equivalences, and we need a data structure to store them. A good data structure to store these equivalences is Union Find (also called Disjoint-set data structure), which stores a collection of disjoint sets. Disjoint means, that no operation can appear in more than one set. The sets in our concrete case are the sets of operations that compute the same result.

When we start out, every operation is in its own singleton set, with no other member. As we discover more equivalences, we will unify sets into larger sets of operations that all compute the same result. So one operation the data structure supports is union, to unify two sets, we'll call that make_equal_to in the code below.

The other operation the data structure supports is find, which takes an operation and returns a "representative" of the set of all equivalent operations. Two operations are in the same set, if the representative that find returns for them is the same.

The exact details of how the data structure works are only sort of important (even though it's very cool, I promise!). It's OK to skip over the implementation. We will add the data structure right into our Value, Constant and Operation classes:

class Value:
    def find(self):
        raise NotImplementedError("abstract")
    def _set_forwarded(self, value: Value):
        raise NotImplementedError("abstract")


class Operation(Value):
    def __init__(self, name: str, args: list[Value]):
        self.name = name
        self.args = args
        self.forwarded = None

    def __repr__(self):
        return (
            f"Operation({self.name},"
            f"{self.args}, {self.forwarded})"
        )

    def find(self) -> Value:
        # returns the "representative" value of
        # self, in the union-find sense
        op = self
        while isinstance(op, Operation):
            # could do path compression here too
            # but not essential
            next = op.forwarded
            if next is None:
                return op
            op = next
        return op

    def arg(self, index):
        # change to above: return the
        # representative of argument 'index'
        return self.args[index].find()

    def make_equal_to(self, value: Value):
        # this is "union" in the union-find sense,
        # but the direction is important! The
        # representative of the union of Operations
        # must be either a Constant or an operation
        # that we know for sure is not optimized
        # away.

        self.find()._set_forwarded(value)

    def _set_forwarded(self, value: Value):
        self.forwarded = value


class Constant(Value):
    def __init__(self, value: Any):
        self.value = value

    def __repr__(self):
        return f"Constant({self.value})"

    def find(self):
        return self

    def _set_forwarded(self, value: Value):
        # if we found out that an Operation is
        # equal to a constant, it's a compiler bug
        # to find out that it's equal to another
        # constant
        assert isinstance(value, Constant) and \
            value.value == self.value

def test_union_find():
    # construct three operation, and unify them
    # step by step
    bb = Block()
    a1 = bb.dummy(1)
    a2 = bb.dummy(2)
    a3 = bb.dummy(3)

    # at the beginning, every op is its own
    # representative, that means every
    # operation is in a singleton set
    # {a1} {a2} {a3}
    assert a1.find() is a1
    assert a2.find() is a2
    assert a3.find() is a3

    # now we unify a2 and a1, then the sets are
    # {a1, a2} {a3}
    a2.make_equal_to(a1)
    # they both return a1 as the representative
    assert a1.find() is a1
    assert a2.find() is a1
    # a3 is still different
    assert a3.find() is a3

    # now they are all in the same set {a1, a2, a3}
    a3.make_equal_to(a2)
    assert a1.find() is a1
    assert a2.find() is a1
    assert a3.find() is a1

    # now they are still all the same, and we
    # also learned that they are the same as the
    # constant 6
    # the single remaining set then is
    # {6, a1, a2, a3}
    c = Constant(6)
    a2.make_equal_to(c)
    assert a1.find() is c
    assert a2.find() is c
    assert a3.find() is c

    # union with the same constant again is fine
    a2.make_equal_to(c)

Constant Folding

Now comes the first actual optimization, a simple constant folding pass. It will remove operations where all the arguments are constants and replace them with the constant result.

Every pass has the same structure: we go over all operations in the basic block in order and decide for each operation whether it can be removed. For the constant folding pass, we can remove all the operations with constant arguments (but we'll implement only the add case here).

I will show a buggy version of the constant folding pass first. It has a problem that is related to why we need the union-find data structure. We will fix it a bit further down.

def constfold_buggy(bb: Block) -> Block:
    opt_bb = Block()

    for op in bb:
        # basic idea: go over the list and do
        # constant folding of add where possible
        if op.name == "add":
            arg0 = op.args[0]
            arg1 = op.args[1]
            if isinstance(arg0, Constant) and \
                    isinstance(arg1, Constant):
                # can constant-fold! that means we
                # learned a new equality, namely
                # that op is equal to a specific
                # constant
                value = arg0.value + arg1.value
                op.make_equal_to(Constant(value))
                # don't need to have the operation
                # in the optimized basic block
                continue
        # otherwise the operation is not
        # constant-foldable and we put into the
        # output list
        opt_bb.append(op)
    return opt_bb


def test_constfold_simple():
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.add(5, 4)
    var2 = bb.add(var1, var0)

    opt_bb = constfold_buggy(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = add(9, optvar0)"""

@pytest.mark.xfail
def test_constfold_buggy_limitation():
    # this test fails! it shows the problem with
    # the above simple constfold_buggy pass

    bb = Block()
    var0 = bb.getarg(0)
    # this is folded
    var1 = bb.add(5, 4)
    # we want this folded too, but it doesn't work
    var2 = bb.add(var1, 10)
    var3 = bb.add(var2, var0)

    opt_bb = constfold_buggy(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = add(19, optvar0)"""

Why does the test fail? The opt_bb printed output looks like this:

optvar0 = getarg(0)
optvar1 = add(9, 10)
optvar2 = add(optvar1, optvar0)

The problem is that when we optimize the second addition in constfold_buggy, the argument of that operation is an Operation not a Constant, so constant-folding is not applied to the second add. However, we have already learned that the argument var1 to the operation var2 is equal to Constant(9). This information is stored in the union-find data structure. So what we are missing are suitable find calls in the constant folding pass, to make use of the previously learned equalities.

Here's the fixed version:

def constfold(bb: Block) -> Block:
    opt_bb = Block()

    for op in bb:
        # basic idea: go over the list and do
        # constant folding of add where possible
        if op.name == "add":
            # >>> changed
            arg0 = op.arg(0) # uses .find()
            arg1 = op.arg(1) # uses .find()
            # <<< end changes
            if isinstance(arg0, Constant) and \
                    isinstance(arg1, Constant):
                # can constant-fold! that means we
                # learned a new equality, namely
                # that op is equal to a specific
                # constant
                value = arg0.value + arg1.value
                op.make_equal_to(Constant(value))
                # don't need to have the operation
                # in the optimized basic block
                continue
        # otherwise the operation is not
        # constant-foldable and we put into the
        # output list
        opt_bb.append(op)
    return opt_bb


def test_constfold_two_ops():
    # now it works!
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.add(5, 4)
    var2 = bb.add(var1, 10)
    var3 = bb.add(var2, var0)
    opt_bb = constfold(bb)

    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = add(19, optvar0)"""

Common Subexpression Elimination

The constfold pass only discovers equalities between Operations and Constants. Let's do a second pass that also discovers equalities between Operations and other Operations.

A simple optimization that does that has this property common subexpression elimination (CSE), which will finally optimize away the problem in the introductory example code that we had above.

def cse(bb: Block) -> Block:
    # structure is the same, loop over the input,
    # add some but not all operations to the
    # output

    opt_bb = Block()

    for op in bb:
        # only do CSE for add here, but it
        # generalizes
        if op.name == "add":
            arg0 = op.arg(0)
            arg1 = op.arg(1)
            # Check whether we have emitted the
            # same operation already
            prev_op = find_prev_add_op(
                arg0, arg1, opt_bb)
            if prev_op is not None:
                # if yes, we can optimize op away
                # and replace it with the earlier
                # result, which is an Operation
                # that was already emitted to
                # opt_bb
                op.make_equal_to(prev_op)
                continue
        opt_bb.append(op)
    return opt_bb


def eq_value(val0, val1):
    if isinstance(val0, Constant) and \
            isinstance(val1, Constant):
        # constants compare by their value
        return val0.value == val1.value
    # everything else by identity
    return val0 is val1


def find_prev_add_op(arg0: Value, arg1: Value,
        opt_bb: Block) -> Optional[Operation]:
    # Really naive and quadratic implementation.
    # What we do is walk over the already emitted
    # operations and see whether we emitted an add
    # with the current arguments already. A real
    # implementation might use a hashmap of some
    # kind, or at least only look at a limited
    # window of instructions.
    for opt_op in opt_bb:
        if opt_op.name != "add":
            continue
        # It's important to call arg here,
        # for the same reason why we
        # needed it in constfold: we need to
        # make sure .find() is called
        if eq_value(arg0, opt_op.arg(0)) and \
                eq_value(arg1, opt_op.arg(1)):
            return opt_op
    return None


def test_cse():
    bb = Block()
    a = bb.getarg(0)
    b = bb.getarg(1)
    var1 = bb.add(b, 17)
    var2 = bb.mul(a, var1)
    var3 = bb.add(b, 17)
    var4 = bb.add(var2, var3)

    opt_bb = cse(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = getarg(1)
optvar2 = add(optvar1, 17)
optvar3 = mul(optvar0, optvar2)
optvar4 = add(optvar3, optvar2)"""

Strength Reduction

Now we have one pass that replaces Operations with Constants and one that replaces Operations with previously existing Operations. Let's now do one final pass that replaces Operations by newly invented Operations, a simple strength reduction. This one will be simple.

def strength_reduce(bb: Block) -> Block:
    opt_bb = Block()
    for op in bb:
        if op.name == "add":
            arg0 = op.arg(0)
            arg1 = op.arg(1)
            if arg0 is arg1:
                # x + x turns into x << 1
                newop = opt_bb.lshift(arg0, 1)
                op.make_equal_to(newop)
                continue
        opt_bb.append(op)
    return opt_bb

def test_strength_reduce():
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.add(var0, var0)

    opt_bb = strength_reduce(bb)

    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = lshift(optvar0, 1)"""

Putting Things Together

Let's combine the passes into one single pass, so that we are going over all the operations only exactly once, instead of having to look at every operation once for all the different passes.

def optimize(bb: Block) -> Block:
    opt_bb = Block()

    for op in bb:
        if op.name == "add":
            arg0 = op.arg(0)
            arg1 = op.arg(1)

            # constant folding
            if isinstance(arg0, Constant) and \
                    isinstance(arg1, Constant):
                value = arg0.value + arg1.value
                op.make_equal_to(Constant(value))
                continue

            # cse
            prev_op = find_prev_add_op(
                arg0, arg1, opt_bb)
            if prev_op is not None:
                op.make_equal_to(prev_op)
                continue

            # strength reduce:
            # x + x turns into x << 1
            if arg0 is arg1:
                newop = opt_bb.lshift(arg0, 1)
                op.make_equal_to(newop)
                continue

            # and while we are at it, let's do some
            # arithmetic simplification:
            # a + 0 => a
            if eq_value(arg0, Constant(0)):
                op.make_equal_to(arg1)
                continue
            if eq_value(arg1, Constant(0)):
                op.make_equal_to(arg0)
                continue
        opt_bb.append(op)
    return opt_bb


def test_single_pass():
    bb = Block()
    # constant folding
    var0 = bb.getarg(0)
    var1 = bb.add(5, 4)
    var2 = bb.add(var1, 10)
    var3 = bb.add(var2, var0)

    opt_bb = optimize(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = add(19, optvar0)"""

    # cse + strength reduction
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.getarg(1)
    var2 = bb.add(var0, var1)
    var3 = bb.add(var0, var1) # the same as var3
    var4 = bb.add(var2, 2)
    var5 = bb.add(var3, 2) # the same as var4
    var6 = bb.add(var4, var5)

    opt_bb = optimize(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = getarg(1)
optvar2 = add(optvar0, optvar1)
optvar3 = add(optvar2, 2)
optvar4 = lshift(optvar3, 1)"""

    # removing + 0
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.add(16, -16)
    var2 = bb.add(var0, var1)
    var3 = bb.add(0, var2)
    var4 = bb.add(var2, var3)

    opt_bb = optimize(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = lshift(optvar0, 1)"""

Conclusion

That's it for now. Why is this architecture cool? From a software engineering point of view, sticking everything into a single function like in optimize above is obviously not great, and if you wanted to do this for real you would try to split the cases into different functions that are individually digestible, or even use a DSL that makes the pattern matching much more readable. But the advantage of the architecture is that it's quite efficient, it makes it possible to pack a lot of good optimizations into a single pass over a basic block.

Of course this works even better if you are in a tracing context, where everything is put into a trace, which is basically one incredibly long basic block. In a JIT context it's also quite important that the optimizer itself runs quickly.

Various other optimizations are possible in this model. I plan to write a follow-up post that show how to implement what is arguably PyPy's most important optimization.

Some Further Pointers

This post is only a short introduction and is taking some shortcuts, I wanted to also give some (non-exhaustive) pointers to more general literature about the touched topics.

The approach to CSE described here is usually can be seen as value numbering, it's normally really implemented with a hashmap though. Here's a paper that describes various styles of implementing that, even beyond a single basic block. The paper also partly takes the perspective of discovering equivalence classes of operations that compute the same result.

A technique that leans even more fully into finding equivalences between operations is using e-graphs and then applying equality saturation (this is significantly more advanced that what I described here though). A cool modern project that applies this technique is egg.

If you squint a bit, you can generally view a constant folding pass as a very simple form of Partial Evaluation: every operation that has constant arguments is constant-folded away, and the remaining ones are "residualized", i.e. put into the output program. This point of view is not super important for the current post, but will become important in the next one.

Acknowledgements: Thanks to Thorsten Ball for getting me to write this and for his enthusiastic feedback. I also got great feedback from Max Bernstein, Matti Picus and Per Vogensen. A conversation with Peng Wu that we had many many years ago and that stuck with me made me keep thinking about various ways to view compiler optimizations.

How is PyPy Tested?

In this post I want to give an overview of how the PyPy project does and thinks about testing. PyPy takes testing quite seriously and has done some from the start of the project. Here I want to present the different styles of tests that PyPy has, when we use them and how I think about them.

class TestW_IntObject:
    ...

    def test_hash(self):
        w_x = W_IntObject(42)
        w_result = w_x.descr_hash(self.space)
        assert isinstance(w_result, W_IntObject)
        assert w_result.intval == 42

def test_hash():
    assert hash(42) == 42

def test_load_attr(self):
    src = '''
        class A(object):
            pass
        a = A()
        a.x = 1
        def main(n):
            i = 0
            while i < n:
                i = i + a.x
            return i
    '''
    log = self.run(src, [1000])
    assert log.result == 1000
    loop, = log.loops_by_filename(self.filepath)
    assert loop.match("""
        i9 = int_lt(i5, i6)
        guard_true(i9, descr=...)
        guard_not_invalidated(descr=...)
        i10 = int_add(i5, 1)
        --TICK--
        jump(..., descr=...)
    """)

PyPy v7.3.9 security release

The PyPy team is proud to release version 7.3.9 of PyPy. This is a security release to match the recent CPython release and updates the portable pypy tarballs with bzip2 1.0.8, openssl1.1.1n, and libexpat 2.4.7. Along the way this release fixes some issues discovered after the 7.3.8 release and updates sqlite3 to 3.38.2. It includes:

• PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.18+ (the + is for backported security updates)

• PyPy3.7, which is an interpreter supporting the syntax and the features of Python 3.7, including the stdlib for CPython 3.7.13. This will be the last release of PyPy3.7.

• PyPy3.8, which is an interpreter supporting the syntax and the features of Python 3.8, including the stdlib for CPython 3.8.13.

• PyPy3.9, which is an interpreter supporting the syntax and the features of Python 3.9, including the stdlib for CPython 3.9.12. We relate to this as "beta" quality. We welcome testing of this version, if you discover incompatibilities, please report them so we can gain confidence in the version.

The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, all APIs are compatible with the other 7.3 releases. Highlights of the release, since the release of 7.3.8 in February 2022, include:

• Fixed some failing stdlib tests on PyPy3.9

• Update the bundled libexpat to 2.4.6 and sqlite3 to 3.38.2

We recommend updating. You can find links to download the v7.3.9 releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work. If PyPy is helping you out, we would love to hear about it and encourage submissions to our blog via a pull request to https://github.com/pypy/pypy.org

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on PyPy, or general help with making RPython's JIT even better. Since the 7.3.7 release, we have accepted contributions from 6 new contributors, thanks for pitching in, and welcome to the project!

If you are a python library maintainer and use C-extensions, please consider making a HPy / CFFI / cppyy version of your library that would be performant on PyPy. In any case both cibuildwheel and the multibuild system support building wheels for PyPy.

PyPy v7.3.8: release of python 2.7, 3.7, 3.8, and 3.9-beta

The PyPy team is proud to release version 7.3.8 of PyPy. It has been only a few months since our last release, but we have some nice speedups and bugfixes we wish to share. The release includes four different interpreters:

• PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.18+ (the + is for backported security updates)

• PyPy3.7, which is an interpreter supporting the syntax and the features of Python 3.7, including the stdlib for CPython 3.7.12. This will be the last release of PyPy3.7.

• PyPy3.8, which is an interpreter supporting the syntax and the features of Python 3.8, including the stdlib for CPython 3.8.12. This is our third release of this interpreter, and we are removing the "beta" tag.

• PyPy3.9, which is an interpreter supporting the syntax and the features of Python 3.9, including the stdlib for CPython 3.9.10. As this is our first release of this interpreter, we relate to this as "beta" quality. We welcome testing of this version, if you discover incompatibilities, please report them so we can gain confidence in the version.

The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, all APIs are compatible with the other 7.3 releases. Highlights of the release, since the release of 7.3.7 in late October 2021, include:

• PyPy3.9 uses an RPython version of the PEG parser which brought with it a cleanup of the lexer and parser in general

• Fixed a regression in PyPy3.8 when JITting empty list comprehensions

• Tweaked some issues around changing the file layout after packaging to make the on-disk layout of PyPy3.8 more compatible with CPython. This requires setuptools>=58.1.0

• RPython now allows the target executable to have a . in its name, so PyPy3.9 will produce a pypy3.9-c and libpypy3.9-c.so. Changing the name of the shared object to be version-specific (it used to be libpypy3-c.so) will allow it to live alongside other versions.

• Building PyPy3.9+ accepts a --platlibdir argument like CPython.

• Improvement in ssl's use of CFFI buffers to speed up recv and recvinto

• Update the packaged OpenSSL to 1.1.1m

We recommend updating. You can find links to download the v7.3.8 releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work. If PyPy is helping you out, we would love to hear about it and encourage submissions to our blog via a pull request to https://github.com/pypy/pypy.org

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on PyPy, or general help with making RPython's JIT even better. Since the previous release, we have accepted contributions from 6 new contributors, thanks for pitching in, and welcome to the project!

If you are a python library maintainer and use C-extensions, please consider making a HPy / CFFI / cppyy version of your library that would be performant on PyPy. In any case both cibuildwheel and the multibuild system support building wheels for PyPy.

Natural Language Processing for Icelandic with PyPy: A Case Study

Icelandic is one of the smallest languages of the world, with about 370.000 speakers. It is a language in the Germanic family, most similar to Norwegian, Danish and Swedish, but closer to the original Old Norse spoken throughout Scandinavia until about the 14th century CE.

As with other small languages, there are worries that the language may not survive in a digital world, where all kinds of fancy applications are developed first - and perhaps only - for the major languages. Voice assistants, chatbots, spelling and grammar checking utilities, machine translation, etc., are increasingly becoming staples of our personal and professional lives, but if they don’t exist for Icelandic, Icelanders will gravitate towards English or other languages where such tools are readily available.

Iceland is a technology-savvy country, with world-leading adoption rates of the Internet, PCs and smart devices, and a thriving software industry. So the government figured that it would be worthwhile to fund a 5-year plan to build natural language processing (NLP) resources and other infrastructure for the Icelandic language. The project focuses on collecting data and developing open source software for a range of core applications, such as tokenization, vocabulary lookup, n-gram statistics, part-of-speech tagging, named entity recognition, spelling and grammar checking, neural language models and speech processing.

My name is Vilhjálmur Þorsteinsson, and I’m the founder and CEO of a software startup Miðeind in Reykjavík, Iceland, that employs 10 software engineers and linguists and focuses on NLP and AI for the Icelandic language. The company participates in the government’s language technology program, and has contributed significantly to the program’s core tools (e.g., a tokenizer and a parser), spelling and grammar checking modules, and a neural machine translation stack.

When it came to a choice of programming languages and development tools for the government program, the requirements were for a major, well supported, vendor-and-OS-agnostic FOSS platform with a large and diverse community, including in the NLP space. The decision to select Python as a foundational language for the project was a relatively easy one. That said, there was a bit of trepidation around the well known fact that CPython can be slow for inner-core tasks, such as tokenization and parsing, that can see heavy workloads in production.

I first became aware of PyPy in early 2016 when I was developing a crossword game Netskrafl in Python 2.7 for Google App Engine. I had a utility program that compressed a dictionary into a Directed Acyclic Word Graph and was taking 160 seconds  to run on CPython 2.7, so I tried PyPy and to my amazement saw a 4x speedup (down to 38 seconds), with literally no effort besides downloading the PyPy runtime.

This led me to select PyPy as the default Python interpreter for my company’s Python development efforts as well as for our production websites and API servers, a role in which it remains to this day. We have followed PyPy’s upgrades along the way, being just about to migrate our minimally required language version from 3.6 to 3.7.

In NLP, speed and memory requirements can be quite important for software usability. On the other hand, NLP logic and algorithms are often complex and challenging to program, so programmer productivity and code clarity are also critical success factors. A pragmatic approach balances these factors, avoids premature optimization and seeks a careful compromise between maximal run-time efficiency and minimal programming and maintenance effort.

Turning to our use cases, our Icelandic text tokenizer "Tokenizer" is fairly light, runs tight loops and performs a large number of small, repetitive operations. It runs very well on PyPy’s JIT and has not required further optimization.

Our Icelandic parser Greynir (known on PyPI as reynir) is, if I may say so myself, a piece of work. It parses natural language text according to a hand-written context-free grammar, using an Earley-type algorithm as enhanced by Scott and Johnstone. The CFG contains almost 7,000 nonterminals and 6,000 terminals, and the parser handles ambiguity as well as left, right and middle recursion. It returns a packed parse forest for each input sentence, which is then pruned by a scoring heuristic down to a single best result tree.

This parser was originally coded in pure Python and turned out to be unusably slow when run on CPython - but usable on PyPy, where it was 3-4x faster. However, when we started applying it to heavier production workloads, it  became apparent that it needed to be faster still. We then proceeded to convert the innermost Earley parsing loop from Python to tight C++ and to call it from PyPy via CFFI, with callbacks for token-terminal matching functions (“business logic”) that remained on the Python side. This made the parser much faster (on the order of 100x faster than the original on CPython) and quick enough for our production use cases. Even after moving much of the heavy processing to C++ and using CFFI, PyPy still gives a significant speed boost over CPython.

Connecting C++ code with PyPy proved to be quite painless using CFFI, although we had to figure out a few magic incantations in our build module to make it compile smoothly during setup from source on Windows and MacOS in addition to Linux. Of course, we build binary PyPy and CPython wheels for the most common targets so most users don’t have to worry about setup requirements.

With the positive experience from the parser project, we proceeded to take a similar approach for two other core NLP packages: our compressed vocabulary package BinPackage (known on PyPI as islenska) and our trigrams database package Icegrams. These packages both take large text input (3.1 million word forms with inflection data in the vocabulary case; 100 million tokens in the trigrams case) and compress it into packed binary structures. These structures are then memory-mapped at run-time using mmap and queried via Python functions with a lookup time in the microseconds range. The low-level data structure navigation is done in C++, called from Python via CFFI. The ex-ante preparation, packing, bit-fiddling and data structure generation is fast enough with PyPy, so we haven’t seen a need to optimize that part further.

To showcase our tools, we host public (and open source) websites such as greynir.is for our parsing, named entity recognition and query stack and yfirlestur.is for our spell and grammar checking stack. The server code on these sites is all Python running on PyPy using Flask, wrapped in gunicorn and hosted on nginx. The underlying database is PostgreSQL accessed via SQLAlchemy and psycopg2cffi. This setup has served us well for 6 years and counting, being fast, reliable and having helpful and supporting communities.

As can be inferred from the above, we are avid fans of PyPy and commensurately thankful for the great work by the PyPy team over the years. PyPy has enabled us to use Python for a larger part of our toolset than CPython alone would have supported, and its smooth integration with C/C++ through CFFI has helped us attain a better tradeoff between performance and programmer productivity in our projects. We wish for PyPy a great and bright future and also look forward to exciting related developments on the horizon, such as HPy.

Error Message Style Guides of Various Languages

PyPy has been trying to produce good SyntaxErrors and other errors for a long time. CPython has also made an enormous push to improve its SyntaxErrors in the last few releases. These improvements are great, but the process feels somewhat arbitrary sometimes. To see what other languages are doing, I asked people on Twitter whether they know of error message style guides for other programming languages.

Wonderfully, people answered me with lots of helpful links (full list at the end of the post), thank you everybody! All those sources are very interesting and contain many great points, I recommend reading them directly! In this post, I'll try to summarize some common themes or topics that I thought were particularly interesting.

PyPy v7.3.7: bug-fix release of 3.7, 3.8

We are releasing a PyPy 7.3.7 to fix the recent 7.3.6 release's binary incompatibility with the previous 7.3.x releases. We mistakenly added fields to PyFrameObject and PyDateTime_CAPI that broke the promise of binary compatibility, which means that c-extension wheels compiled for 7.3.5 will not work with 7.3.6 and via-versa. Please do not use 7.3.6.

We have added a cursory test for binary API breakage to the https://github.com/pypy/binary-testing repo which hopefully will prevent such mistakes in the future.

Additionally, a few smaller bugs were fixed:

• Use uint for the request argument of fcntl.ioctl (issue 3568)

• Fix incorrect tracing of while True` body in 3.8 (issue 3577)

• Properly close resources when using a conncurrent.futures.ProcessPool (issue 3317)

• Fix the value of LIBDIR in _sysconfigdata in 3.8 (issue 3582)

You can find links to download the v7.3.7 releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work. If PyPy is helping you out, we would love to hear about it and encourage submissions to our blog site via a pull request to https://github.com/pypy/pypy.org

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on PyPy, or general help with making RPython's JIT even better.

If you are a python library maintainer and use C-extensions, please consider making a CFFI / cppyy version of your library that would be performant on PyPy. In any case both cibuildwheel and the multibuild system support building wheels for PyPy.

PyPy v7.3.6: release of python 2.7, 3.7, and 3.8-beta

The PyPy team is proud to release version 7.3.6 of PyPy, which includes three different interpreters:

• PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.18+ (the + is for backported security updates)

• PyPy3.7, which is an interpreter supporting the syntax and the features of Python 3.7, including the stdlib for CPython 3.7.12.

• PyPy3.8, which is an interpreter supporting the syntax and the features of Python 3.8, including the stdlib for CPython 3.8.12. Since this is our first release of the interpreter, we relate to this as "beta" quality. We welcome testing of this version, if you discover incompatibilites, please report them so we can gain confidence in the version.

The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, all APIs are compatible with the other 7.3 releases. Highlights of the release, since the release of 7.3.5 in May 2021, include:

• We have merged a backend for HPy, the better C-API interface. The backend implements HPy version 0.0.3.

• Translation of PyPy into a binary, known to be slow, is now about 40% faster. On a modern machine, PyPy3.8 can translate in about 20 minutes.

• PyPy Windows 64 is now available on conda-forge, along with nearly 700 commonly used binary packages. This new offering joins the more than 1000 conda packages for PyPy on Linux and macOS. Many thanks to the conda-forge maintainers for pushing this forward over the past 18 months.

• Speed improvements were made to io, sum, _ssl and more. These were done in response to user feedback.

• The 3.8 version of the release contains a beta-quality improvement to the JIT to better support compiling huge Python functions by breaking them up into smaller pieces.

• The release of Python3.8 required a concerted effort. We were greatly helped by @isidentical (Batuhan Taskaya) and other new contributors.

• The 3.8 package now uses the same layout as CPython, and many of the PyPy-specific changes to sysconfig, distutils.sysconfig, and distutils.commands.install.py have been removed. The stdlib now is located in <base>/lib/pypy3.8 on posix systems, and in <base>/Lib on Windows. The include files on windows remain the same. On posix they are in <base>/include/pypy3.8. Note we still use the pypy prefix to prevent mixing the files with CPython (which uses python.

We recommend updating. You can find links to download the v7.3.6 releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work. If PyPy is helping you out, we would love to hear about it and encourage submissions to our blog via a pull request to https://github.com/pypy/pypy.org

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on PyPy, or general help with making RPython's JIT even better. Since the previous release, we have accepted contributions from 7 new contributors, thanks for pitching in, and welcome to the project!

If you are a python library maintainer and use C-extensions, please consider making a CFFI / cppyy version of your library that would be performant on PyPy. In any case both cibuildwheel and the multibuild system support building wheels for PyPy.