Background: Python Integers in the PyPy JIT

The optimizer of PyPy's JITs operates on traces, which are linear sequences of instructions with guards. The instructions in the traces operate on different machine-level data types, machine integers, doubles, pointers, bools, etc. In this post we'll be mostly concerned with machine integers.

To given some wider context I'll explain a bit how Python ints in the user code relate to the types that are used in traces when the PyPy Python implementation is used. When PyPy turns a regular Python 3 function into a trace, there is a lot of work happening in the JIT frontend to try to observe and infer the types that the Python function concretely uses at runtime. The traces are generated under these typing assumptions. Therefore, code that uses ints in the Python code can typically be translated into traces that operate on machine integers. In order to make sure that the Python integer semantics are upheld, many of the operations in the traces need to check that the integer results of some operations still fit into a machine integer. If that is not the case (a rare situation for most programs), the trace is left via a guard, execution falls back to the interpreter, and there a big integer representation is chosen for the too big value (the big integer representation is done via a pointer and some storage on the heap).

All of this machinery is not going to be too relevant for the rest of the post. For the post it's important to know that trace instructions operate on machine integers and other low-level types, and some of the operations can optionally check whether the results still fit into a machine integer. These trace operations are improved by the optimizer, which tries to transform the trace into one that behaves the same, but is less costly to execute.

Background: Bounds Analysis in PyPy's JIT

The optimizer of PyPy's JIT has an analysis based on abstract interpretation that tries to find out whether the integer values stored in a variable are actually not using the full 64 bit (or 32 bit) range, but instead fit into some smaller range. This means that for every integer variable x in a trace, the JIT compiler tracks upper and lower bounds of the runtime value of that variable: a range [a, b] such that for every concrete runtime value v that gets stored in variable x, a <= v <= b must be true. a and b start out as the most general MININT and MAXINT, but sometimes there is extra information that makes it possible to improve these known bounds, and that is often useful to optimize the code.

A typical example is that the JIT knows that the length of a string is non-negative, so for this kind of code: x = len(s) where s is a string, x gets a range [0, MAXINT] assigned. With this information we could for example remove a check x + 10 < 0 completely, because it can never be true.

The bounds information is useful for optimization, but the analysis of the bounds is also a source of bugs in the JIT, because the reasoning is often subtle and easy to get wrong in corner cases. We already use a number of testing techniques to try to make sure that it is correct. A simple one is property-based testing using Hypothesis on the operations on bounds. Even though Hypothesis is fantastic, it unfortunately does not catch absolutely all the bugs even if we'd like it too, as we'll see in the next section.

Motivation: A JIT Miscompilation

I am currently supervising a Bachelor thesis by Nico Rittinghaus, who is extending the integer analysis in the JIT. He'll probably write a separate blog post about that soon. In the process of his work, the current bounds analysis code got a lot of scrutiny, and we found out that one of the unit tests of the bounds analysis was actually incorrect, and the example code in that unit test was optimized incorrectly. This case of incorrect optimization is not a big deal for regular Python code, because it involved a "wrapping integer addition operation", i.e. one where overflowing results just wrap around to negative values. All the additions and other arithmetic operations that the PyPy Python frontend generates actually have overflow checks (to be able to switch to a big integer representation if needed). However, it's still possible to trigger the problem with the __pypy__.intop.int_add API which is a function that exposes wraparound arithmetic on Python ints.

Here's the miscompilation. The JIT optimizes the following function:

import __pypy__

def wrong(x):
    a = __pypy__.intop.int_add(x, 10)
    if a < 15:
        if x < 6:
            return 0
        return 1
    return 2

Into the following code:

import __pypy__

def wrong(x):
    a = __pypy__.intop.int_add(x, 10)
    if a < 15:
        return 0
    return 2

Basically the faulty reasoning of the JIT looks like this: if int_add(x, 10) < 15 then it must follow that x < 5, which is stronger than x < 6, so the second if is always true. This sounds good, but is actually wrong if the addition + 10 wrapped around. So if x == MAXINT, then int_add(x, 10) == MININT + 9 < 15. But MAXINT < 5 is not correct.

Note how the same reasoning with overflow-checking addition is correct! If x + 10 < 15 and the + didn't overflow, then indeed x < 6. And if your mind bends starting to think about all this, you understand some of the difficulty of getting the JIT correct in this area.

How could we have avoided this bug?

One exercise I try to do after finding bugs is to reflect on ways that the bug could have been avoided. I think this is particularly important in the JIT, where bugs are potentially really annoying to find and can cause very strange behaviour in basically arbitrary Python code.

It's easy to always answer this question with "try to think more carefully when working", but that approach cannot be relied on in complicated situations, because humans don't concentrate perfectly for long stretches of time.

A situation-specific problem I identified was the bad design of the range analysis API. A range is not just represented by two numbers, instead it's two numbers and two bools that are supposed to represent that some operation did or did not underflow/overflow. The meaning of these bools was quite hard to grasp and easy to get wrong, so probably they should never have been introduced in the first place (and my bugfix indeed removed them).

But in the rest of this blog post I want to talk about another, systematic approach that can be applied to the problem of mis-optimizations of integer operations, and that is done by applying an SMT solver to the problem.

An SMT solver (Satisfyability Modulo Theories) is a tool that can be used to find out whether mathematical formulas are "satisfiable", i.e. whether some chosen set of inputs exists that will make the formulas evaluate to true. SMT solvers are commonly used in a wide range of CS applications including program correctness proofs, program synthesis, etc. The most widely known one is probably Z3 by Microsoft Research which has the nice advantage of coming with an easy-to-use Python binding.

Going into this I basically knew next to nothing about SMT solvers (despite having been embedded in a formal methods research group for years!) so it was an interesting new world to learn about.

As briefly mentioned in the introduction, the approach I took followed a similar (but much more properly executed) one applied to LLVM operations, called Alive2. Krister Waldfridsson has done similar work for GCC recently, described on his blog.

Z3 Proof of Concept

The first thing I did was to try to get Z3 find the above bug, by encoding the input program into an SMT formula by hand and trying to get Z3 to prove the condition that the JIT thinks is always true. The Z3 code for this looks as follows:

from z3 import BitVec, Implies, prove
x = BitVec('x', 64)
a = x + 10
cond1 = a < 15
cond2 = x < 6
prove(Implies(cond1, cond2))

Here, x is defined to be a bit vector variable of width 64, which is a datatype that can be used to represent bounded machine integers. Addition on bit vectors performs wraparound arithmetic, like the __pypy__.intop.int_add call in the original code. The JIT optimized the second condition away, so essentially it was convinced that the first condition implies the second one. The above snippet tries to get Z3 to confirm this.

When run, the above program prints:

counterexample
[x = 9223372036854775803]

Which shows the bug. As a small side-note, I thought it was cool that the process of "proving" something in Z3 basically means trying to find an example for the negation of the formula. If no counterexample can be found for the negation, the original formula is true. If the original formula turns out to be false (like here) we get a nice example that shows the problem to go with it.

It's not realistic to hand-translate all the hundreds of unit-tests into Z3 formulas and then ask Z3 to prove the optimizations. Instead, we want to have a program that does this for us.

SMT Checking of the JIT Optimizer

What we want from this program is the following: given an unoptimized trace and its optimized version, we want to use Z3 to check whether the optimized trace behaves identically to the unoptimized one. One question is what "behaves identically" means. What we care about is the outputs of the trace being the same values, no matter how they are computed. Also, for every guard we want to make sure that it fails in identical ways in the optimized and unoptimized versions. A guard is only allowed to be optimized away if it can never fail. The code that comes after a guard can assume that the guard has not failed, because otherwise execution would have left the trace. All of this should be true regardless for the values of the input variables of the trace.

So in order to check that the two traces are behaving identically, we do the following:

• We create Z3 variables for every input variable. We use the same input variables both for the unoptimized as well as the optimized trace.

• We align the two traces at the corresponding guards. Thankfully the optimizer keeps track of which optimized guard corresponds to which unoptimized input guard.

• All the operations before a guard are translated into Z3 formulas, for both versions of the trace.

• For two corresponding guards, we ask Z3 to prove that the guard conditions are identical.

• For a guard that was optimized away we ask Z3 to prove that the condition is always true.

• After a guard, we tell Z3 that from now on it can assume that the guard condition is true.

• We repeat this, guard for guard, until we reach the end of the trace. There, we ask Z3 to prove that the output variables in the unoptimized trace and the optimized trace are identical (every trace can return one or many values).

I implemented this, it's not a lot of code, basically a couple of hundred lines of (somewhat hacky) Python code. So far I only support integer operations. Here are some parts of the code to give you a flavor of what this looks like.

This is the code that translates operations into Z3 formulas:

def add_to_solver(self, ops, state):
    for op in ops:
        if op.type != 'v': # is it an operation with a result
            res = self.newvar(op)
        else: # or does it return void
            res = None

       # ...

        # convert arguments
        if op.numargs() == 1:
            arg0 = self.convertarg(op, 0)
        elif op.numargs() == 2:
            arg0 = self.convertarg(op, 0)
            arg1 = self.convertarg(op, 1)

        # compute results
        if opname == "int_add":
            expr = arg0 + arg1
        elif opname == "int_sub":
            expr = arg0 - arg1
        elif opname == "int_mul":
            expr = arg0 * arg1
        elif opname == "int_and":
            expr = arg0 & arg1
        elif opname == "int_or":
            expr = arg0 | arg1
        elif opname == "int_xor":
            expr = arg0 ^ arg1

        # ...  more operations, some shown below

        self.solver.add(res == expr)

New Z3 variables are defined by the helper function newvar, which adds the operation to a dictionary box_to_z3 mapping boxes (=variables) to Z3 variables. Due to the SSA property that traces have, a variable must be defined before its first use.

Here's what newvar looks like (LONG_BIT is a constant that is either 64 or 32, depending on the target architecture):

def newvar(self, box, repr=None):
    # ... some logic around making the string representation
    # somewhat nicer omitted
    result = z3.BitVec(repr, LONG_BIT)
    self.box_to_z3[box] = result
    return result

The convert method turns an operation argument (either a constant or a variable) into a Z3 formula (either a constant bit vector or an already defined Z3 variable). convertarg is a helper function that takes an operation, reads its nth argument and converts it.

def convert(self, box):
    if isinstance(box, ConstInt):
        return z3.BitVecVal(box.getint(), LONG_BIT)
    return self.box_to_z3[box]

def convertarg(self, box, arg):
    return self.convert(box.getarg(arg))

The lookup of variables in box_to_z3 that convert does cannot fail, because the variable must have been defined before use.

Comparisons return the bit vector 0 or bit vector 1, we use a helper function cond to turn the Z3 truth value of the comparison into a bit vector:

def cond(self, z3expr):
    return z3.If(z3expr, TRUEBV, FALSEBV)


def add_to_solver(self, ops, state):
        # ... start as above

        # more cases
        elif opname == "int_eq":
            expr = self.cond(arg0 == arg1)
        elif opname == "int_ne":
            expr = self.cond(arg0 != arg1)
        elif opname == "int_lt":
            expr = self.cond(arg0 < arg1)
        elif opname == "int_le":
            expr = self.cond(arg0 <= arg1)
        elif opname == "int_gt":
            expr = self.cond(arg0 > arg1)
        elif opname == "int_ge":
            expr = self.cond(arg0 >= arg1)
        elif opname == "int_is_true":
            expr = self.cond(arg0 != FALSEBV)
        elif opname == "uint_lt":
            expr = self.cond(z3.ULT(arg0, arg1))
        elif opname == "uint_le":
            expr = self.cond(z3.ULE(arg0, arg1))
        elif opname == "uint_gt":
            expr = self.cond(z3.UGT(arg0, arg1))
        elif opname == "uint_ge":
            expr = self.cond(z3.UGE(arg0, arg1))
        elif opname == "int_is_zero":
            expr = self.cond(arg0 == FALSEBV)

        # ... rest as above

So basically for every trace operation that operates on integers I had to give a translation into Z3 formulas, which is mostly straightforward.

Guard operations get converted into a Z3 boolean by their own helper function, which looks like this:

def guard_to_condition(self, guard, state):
    opname = guard.getopname()
    if opname == "guard_true":
        return self.convertarg(guard, 0) == TRUEBV
    elif opname == "guard_false":
        return self.convertarg(guard, 0) == FALSEBV
    elif opname == "guard_value":
        return self.convertarg(guard, 0) == self.convertarg(guard, 1)

    # ... some more exist, shown below

Some operations are a bit trickier. An important example in the context of this blog post are integer operations that check for overflow. The overflow operations return a result, but also a boolean whether the operation overflowed or not.

def add_to_solver(self, ops, state):

        # ... more cases

        elif opname == "int_add_ovf":
            expr = arg0 + arg1
            m = z3.SignExt(LONG_BIT, arg0) + z3.SignExt(LONG_BIT, arg1)
            state.no_ovf = m == z3.SignExt(LONG_BIT, expr)
        elif opname == "int_sub_ovf":
            expr = arg0 - arg1
            m = z3.SignExt(LONG_BIT, arg0) - z3.SignExt(LONG_BIT, arg1)
            state.no_ovf = m == z3.SignExt(LONG_BIT, expr)
        elif opname == "int_mul_ovf":
            expr = arg0 * arg1
            m = z3.SignExt(LONG_BIT, arg0) * z3.SignExt(LONG_BIT, arg1)
            state.no_ovf = m == z3.SignExt(LONG_BIT, expr)

        # ...

The boolean is computed by comparing the result of the bit vector operation with the result of converting the input bit vectors into an abstract (arbitrary precision) integer and the result back to bit vectors. Let's go through the addition case step by step, the other cases work analogously.

The addition in the first elif that computes expr is an addition on bit vectors, therefore it is performing wraparound arithmetic. z3.SignExt(LONG_BIT, arg0) sign-extends arg0 from a bit vector of LONG_BIT bits to an abstract, arbitrary precision integer. The addition in the second line is therefore an addition between abstract integers, so it will never overflow and just compute the correct result as an integer.

The condition to check for overflow is now: if the results of the two different ways to do the addition are the same, then overflow did not occur. So in order to compute state.no_ovf in the addition case the code converts the result of the bit vector wraparound addition to an abstract integer (using SignExt again), and then compares that to the integer result.

This boolean can then be checked by the guard operations guard_no_overflow and guard_overflow.

def guard_to_condition(self, guard, state):

    # ... more cases

    elif opname == "guard_no_overflow":
        assert state.no_ovf is not None
        return state.no_ovf
    elif opname == "guard_overflow":
        assert state.no_ovf is not None
        return z3.Not(state.no_ovf)

    # ... more cases

Finding the Bug, Again

Let's actually make all of this more concrete by applying it to the trace of our original bug. The input trace and the incorrectly optimized trace for that look like this (differences highlighted):

# input                       # optimized
[i0]                          [i0]
i1 = int_add(i0, 10)          i1 = int_add(i0, 10)
i2 = int_lt(i1, 15)           i2 = int_lt(i1, 15)
guard_true(i2)                guard_true(i2)
i3 = int_lt(i0, 6)            jump(0)
guard_true(i3)
jump(0)

Note that the trace represents just one of the paths through the control flow graph of the original function, which is typical for tracing JITs (the other paths could incrementally get added later).

The first guards in both these traces correspond to each other, so the first chunks to check are the first three operations (lines 1-4). Those operations don't get changed by the optimizer at all.

These two identical traces get translated to the following Z3 formulas:

i1unoptimized == input_i0 + 10
i2unoptimized == If(i1unoptimized < 15, 1, 0)
i1optimized == input_i0 + 10
i2optimized == If(i1optimized < 15, 1, 0)

To check that the two corresponding guards are the same, the solver is asked to prove that (i2unoptimized == 1) == (i2optimized == 1). This is correct, because the formulas for i2unoptimized and i2optimized are completely identical.

After checking that the guards behave the same, we add the knowledge to the solver that the guards passed. So the Z3 formulas become:

i1unoptimized == input_i0 + 10
i2unoptimized == If(i1unoptimized < 15, 1, 0)
i1optimized == input_i0 + 10
i2optimized == If(i1optimized < 15, 1, 0)
i1optimized == 1
i2optimized == 1

Now we continue with the remaining operations of the two traces (lines 6-8).

We start by adding the int_lt operation in the unoptimized trace to the Z3 formulas:

...
i3unoptimized == If(input_i0 < 6, 1, 0)

Because the second guard was optimized away, we need to ask Z3 to prove that i3unoptimized == 1 is always true, which fails and gives the following counterexample:

input_i0 = 9223372036854775800
i1unoptimized = 9223372036854775810
i2unoptimized = 0
i1optimized = 9223372036854775810
i2optimized = 1
i3unoptimized = 0

Thus demonstrating the bug. The fact that the Z3-based equivalence check also managed to find the original motivating bug without manually translating it to a formula is a good confirmation that the approach works.

Second bug

So with this code I applied the Z3-based equivalence check to all our optimizer unit tests. In addition to the bug we've been discussing the whole post, it also found another buggy test! I had found it too by hand by staring at all the tests in the process of writing all the Z3 infrastructure, but it was still a good confirmation that the process worked. This bug was in the range analysis for int_neg, integer negation. It failed to account that -MININT == MININT and therefore did a mis-optimization along the following lines:

import __pypy__

def wrong(x):
    a = __pypy__.intop.int_sub(0, x)
    if a < 0:
        if x > 0:
            return 0
        return 1
    return 2

Which was wrongly optimized into:

import __pypy__

def wrong(x):
    a = __pypy__.intop.int_sub(0, x)
    if a < 0:
        return 0
    return 2

This is wrong precisely for x == MININT.

Generating Random Traces

These two bugs were the only two that the Z3 checker found for existing unit tests. To try to find some more bugs I combined PyPy's existing random trace generator with the Z3 optimization checker. The random trace generator has so far been mostly used to find bugs in the machine code backends, particularly also in the register allocator. So far we haven't used it with our optimizer, but my experiments show that we should have!

I'm going to describe a little bit how the random trace generator works. It's actually not that complicated, but there's one neat trick to it.

The basic idea is straightforward, it starts out with an empty trace with a random number of input variables. Then it adds some number of operations to the trace, either regular operations or guards. Every operation takes already existing variables as input.

The neat trick is that our random trace generator keeps a concrete random example value for every one of the input variables, and an example result for every operation. In this way, it is possible to generate guards that are consistent with the example values to ensure that running the trace to its end is possible with at least one set of values.

Here's an example random trace that is generated, together with the random example inputs and the results of every operation at the end of every line:

[i0, i1, i2, i3, i4, i5] # example values: 9, 11, -8, -95, 46, 57
i6 = int_add_ovf(i3, i0) # -86
guard_no_overflow()
i7 = int_sub(i2, -35/ci) # 27
i8 = uint_ge(i3, i5) # 1
guard_true(i8)
i9 = int_lt(i7, i8) # 0
i10 = int_mul_ovf(34/ci, i7) # 918
guard_no_overflow()
i11 = int_and(i10, 63/ci) # 22
i12 = int_rshift(i3, i11) # -1
i13 = int_is_zero(i7) # 0
i14 = int_is_true(i13) # 0
guard_false(i13)
i15 = int_lt(i8, i4) # 1
i16 = int_and(i6, i0) # 8
i17 = uint_ge(i6, -6/ci) # 0
finish()

Note how every guard generated is true for the example values.

I have been running this combination of random trace generation and Z3 checking for many nights and it has found some bugs, which I'll describe in the next section. It should probably be run for a lot longer, but still a useful exercise already.

In this mode, I'm giving every Z3 call a time limit to make sure that the random tests don't just take arbitrarily long. This means that asking Z3 to prove something can have three outcomes, either it's proved, or Z3 finds a counterexample, or Z3 times out.

Bugs Found

In addition to the two bugs I've already described, I'll briefly list the additional bugs that were found by optimizing random traces and then trying to prove the equivalence with Z3.

Most of the bugs were actually identified by optimizing random traces alone, not by the Z3 component. They manifested as assert failures in the JIT compiler.

• The JIT concluded after 12 == int_mul(x, 12) that x == 1, which is incorrect if overflow occurred (a counterexample is 0x8000000000000001).

• An amusing bug, where from 0 == int_lshift(0x1000000000000000, x) with x <= 0 <= 15, the JIT concluded that 0x1000000000000000 == 0, triggering an assert. This wrong conclusion was again caused by not taking the possibility of overflow into account.

• A corner case in an optimization for chained integer additions with a constant, where in complex enough expressions, the wrong IR API was used (which works correctly in simple cases). Again, this triggered an assert.

This shows that we should have been fuzzing our JIT optimizer already (not a surprising observation in hindsight, fuzz all the things!).

Thankfully, there was also one further bug that really failed in the Z3 verifier. It's a bug in common subexpression elimination / arithmetic simplification, which again does not take overflow correctly into account.

The buggy trace looks like this (unfortunately it's not easily possible to show this bug in Python code).

[a, b]
c = int_add(a, b)
r = int_sub_ovf(c, b)
guard_no_ovf()
finish(r)

This was optimized to:

[a, b]
finish(a)

Which is incorrect, because the guard can fail given the right inputs. But the optimizer concluded that the subtraction is safe, because its the inverse of an earlier addition, not taking into account that this earlier addition can have overflowed.

Note that a related optimization is actually correct. Given this code:

[a, b]
c = int_add_ovf(a, b)
guard_no_ovf()
r = int_sub(c, b)
finish(r)

It can be optimized to:

[a, b]
c = int_add_ovf(a, b)
guard_no_ovf()
finish(a)

Future Work and Conclusion

In the current form the Z3 checker is only a start, even though it has already been concretely useful. There are various directions into which we could extend it. In addition to generate random tests completely from scratch, we could also start from the existing manually written unit-tests and randomly mutate those.

I also want to extend the Z3 checker with support more operations, heap operations in particular (but it's not quite clear to me how to model garbage collection).

I also want to try to switch the code away from the Z3 API and use the more general smtlib interface directly, in order to be able to use other SMT checkers than Z3, eg CVC4.

But all in all this was a fun and not too hard way to find a bunch of bugs in our optimizer! And the infrastructure is now in place, which means that we run some random test cases every time we execute our tests. This is going to be particularly useful when we do further work on the integer reasoning of the JIT (like Nico is doing, for example). As of time of writing of this post, all the bugs mentioned have been fixed and the Z3 code has landed on the default branch and runs as part of PyPy's CI infrastructure.

Acknowledgements

Thanks to Saam Barati, Max Bernstein, Joshua Schmidt and Martin Berger, for great feedback on drafts of this post!

Greatest Hits

In terms of visitors, the top five posts on the old blog were – on the new blog we simply don't have stats (yet?):

1. Let's remove the global interpreter lock

2. Tutorial: Writing an Interpreter with PyPy, Part 1

3. PyPy's new JSON parser

4. PyPy gets funding from Mozilla for Python 3.5 support

5. How to make your code 80 times faster

The number of posts per year developed like this:

The most prolific authors are:

1. Maciej Fijałkowski

2. Carl Friedrich Bolz-Tereick

3. Armin Rigo

4. Antonio Cuni

5. Matti Picus

Several blog posts have made it to the Hacker News front page, three of them to number 1:

• PyPy-STM: first “interesting” release (discussion)

• Let's Remove the Global Interpreter Lock (discussion)

• Inside cpyext: Why emulating CPython C API is so Hard (discussion)

Personal Favourites

While looking through the posts, there were a few that stood out to me in some way, so here's a subjective list of ones that I had fun looking at again:

• 2008: Sprint Discussions: JIT Generator Planning

• 2009: PyPy gets a new compiler

• 2010: Oh, and btw: PyPy gets funding through "Eurostars"

• 2011: Realtime image processing in Python

• 2012: Architecture of Cppyy

• 2013: 10 years of PyPy

• 2014: PyPy IO Improvements

• 2015: Automatic SIMD vectorization support in PyPy

• 2016: PyPy Enterprise Edition

• 2017: Async HTTP benchmarks on PyPy3

• 2018: Improving SyntaxError in PyPy

• 2018: The First 15 Years of PyPy — a Personal Retrospective

• 2019: PyPy for low-latency systems

• 2020: PyPy and CFFI have moved to Heptapod

• 2021: Some Ways that PyPy uses Graphviz

We'd like to thank our authors, guest authors, commenters, users and readers who have stuck with us through one and a half decades! If there's any particular topics you would like to read something about, or any guest posts you'd like to write, let us know!

Interpreter

In this post we will mainly concern ourselves with optimizing programs that allocate memory. We assume that our language is garbage collected and memory safe. The new operations that we will optimize are alloc (allocates some new object), store (stores a value into a fixed field of an object), load (loads the value from a field in the object).

We are leaving out a lot of details of a "real" system here, usually an alloc operation would get some extra information, for example the type of the freshly allocated object or at least its size. load and store would typically have some kind of field offset and maybe some information about the field's type

Here's a simple program that uses these operations:

var0 = getarg(0)
obj0 = alloc()
store(obj0, 0, var0)
var1 = load(obj0, 0)
print(var1)

The code allocates a new object obj0, stores var0 into field 0 of the object, the loads the same field and prints the result of the load.

Before we get started in writing the optimizer for these operations, let's try to understand the semantics of the new operations a bit better. To do this, we can sketch a small interpreter for basic blocks, supporting only getarg, alloc, store, load, print:

def test_interpret():
    bb = Block()
    var0 = bb.getarg(0)
    obj = bb.alloc()
    sto = bb.store(obj, 0, var0)
    var1 = bb.load(obj, 0)
    bb.print(var1)
    assert interpret(bb, 17) == 17

class Object:
    def __init__(self):
        self.contents: dict[int, Any] = {}

    def store(self, idx : int, value : Any):
        self.contents[idx] = value

    def load(self, idx : int):
        return self.contents[idx]

def get_num(op, index=1):
    assert isinstance(op.arg(index), Constant)
    return op.arg(index).value

def interpret(bb : Block, *args : tuple[Any]):
    def argval(op, i):
        arg = op.arg(i)
        if isinstance(arg, Constant):
            return arg.value
        else:
            assert isinstance(arg, Operation)
            return arg.info

    for index, op in enumerate(bb):
        if op.name == "getarg":
            res = args[get_num(op, 0)]
        elif op.name == "alloc":
            res = Object()
        elif op.name == "load":
            fieldnum = get_num(op)
            res = argval(op, 0).load(fieldnum)
        elif op.name == "store":
            obj = argval(op, 0)
            fieldnum = get_num(op)
            fieldvalue = argval(op, 2)
            obj.store(fieldnum, fieldvalue)
            # no result, only side effect
            continue
        elif op.name == "print":
            res = argval(op, 0)
            print(res)
            return res
        else:
            raise NotImplementedError(
                f"{op.name} not supported")
        op.info = res

The interpreter walks the operations of a block, executing each one in turn. It uses the info field to store the result of each already executed Operation. In this interpreter sketch we stop at the first print that we execute and return its argument for the simple but bad reason that it makes test_interpret easier to write.

Objects in the interpreter are represented using a class Object, which stores the object's field into a Python dictionary. As written above, this is a simplification, in a real system the alloc operation might for example take some kind of type as an argument, that describes which kinds of fields an object has and how they are laid out in memory, which would allow more efficient storage of the content. But we don't want to care about this level of detail in the post, so using a dict in the interpreter is good enough.

Version 1: Naive Attempt

In many programs, some allocated objects don't live for very long and have a completely predictable lifetime. They get allocated, used for a while, and then there is no way to reference them any more, so the garbage collector will reclaim them. The very first example block had such an allocation:

var0 = getarg(0)
obj0 = alloc()
store(obj0, 0, var0)
var1 = load(obj0, 0)
print(var1)

Here obj0 is written to, then read from, and then it's no longer used. We want to optimize such programs to remove this alloc operation. The optimized version of this program would look like this:

var0 = getarg(0)
print(var0)

The alloc, store and load operations have been completely removed. This is a pretty important optimizations for PyPy's JIT: Allocations, memory reads and writes are quite costly and occur a lot in Python, so getting rid of as many of them as possible is instrumental for performance.

Implementing the optimization is not a lot of code! However, understanding all the corner cases of the optimization and making sure that the resulting program behave correctly is not completely trivial. Therefore we will develop the optimization step by step, in a test driven fashion: I will start each section with a new test that shows a bug in the version of the optimization that we have so far.

Let's start in a really naive way. Here's the first test we would like to pass, using the example program above:

def test_remove_unused_allocation():
    bb = Block()
    var0 = bb.getarg(0)
    obj = bb.alloc()
    sto = bb.store(obj, 0, var0)
    var1 = bb.load(obj, 0)
    bb.print(var1)
    opt_bb = optimize_alloc_removal(bb)
    # the virtual object looks like this:
    #  obj
    # ┌──────────┐
    # │ 0: var0  │
    # └──────────┘
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = print(optvar0)"""

We will define a class VirtualObject that is basically identical to Object above. But it will not be used by the interpreter, instead we will use it during optimization.

class VirtualObject:
    def __init__(self):
        self.contents: dict[int, Value] = {}

    def store(self, idx, value):
        self.contents[idx] = value

    def load(self, idx):
        return self.contents[idx]

The structure of the optimizer is going to be like those in the first blog post. The optimizer makes a single pass over all operations. It removes some and emits others.

This first version of the allocation removal optimizer is going to be extremely optimistic. It simply assumes that all the allocations in the program can be optimized away. That is not realistic in practice. We will have to refine this approach later, but it's a good way to start. That means whenever the optimizer sees an alloc operation, it removes it and creates a VirtualObject object which stores the information that is known during optimization about the result of the alloc. Like in the interpreter, the VirtualObject is stored in the .info field of the Operation instance that represents the alloc.

When the optimizer sees a store operation, it will also remove it and instead execute the store by calling the VirtualObject.store method. Here is one important difference between the interpreter and the optimizer: In the interpreter, the values that were stored into an Object (and thus put into the object's .contents dictionary) were runtime values, for example integers or other objects. In the optimizer however, the fields of the VirtualObject store Value instances, either Constant instances or Operation instances.

When the optimizer sees a load operation, it also removes it, and replaces the load with the Operation (or Constant) that is stored in the VirtualObject at that point:

def optimize_alloc_removal(bb):
    opt_bb = Block()
    for op in bb:
        if op.name == "alloc":
            op.info = VirtualObject()
            continue
        if op.name == "load":
            info = op.arg(0).info
            field = get_num(op)
            op.make_equal_to(info.load(field))
            continue
        if op.name == "store":
            info = op.arg(0).info
            field = get_num(op)
            info.store(field, op.arg(2))
            continue
        opt_bb.append(op)
    return opt_bb

This is the first version of the optimization. It doesn't handle all kinds of difficult cases, and we'll have to do something about its optimism. But, already in this minimalistic form, we can write a slightly more complicated test with two allocations, one object pointing to the other. It works correctly too, both allocations are removed:

def test_remove_two_allocations():
    bb = Block()
    var0 = bb.getarg(0)
    obj0 = bb.alloc()
    sto1 = bb.store(obj0, 0, var0)
    obj1 = bb.alloc()
    sto2 = bb.store(obj1, 0, obj0)
    var1 = bb.load(obj1, 0)
    var2 = bb.load(var1, 0)
    bb.print(var2)
    # the virtual objects look like this:
    #  obj0
    # ┌──────┐
    # │ 0: ╷ │
    # └────┼─┘
    #      │
    #      ▼
    #     obj1
    #   ┌─────────┐
    #   │ 0: var0 │
    #   └─────────┘
    # therefore
    # var1 is the same as obj0
    # var2 is the same as var0
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = print(optvar0)"""

Version 2: Re-Materializing Allocations

To make it easier to talk about how the optimizer operates, let's introduce some terminology. As already seen by the choice of the class name VirtualObject, we will call an object virtual if the optimizer has optimized away the alloc operation that creates the object. Other objects are equivalently not virtual, for example those that have existed before we enter the current code block.

The first problem that we need to fix is the assumption that every allocation can be removed. So far we only looked at small programs where every allocation could be removed, or equivalently, where every object is virtual. A program that creates virtual objects, stores into and loads from them, and then forgets the objects. In this simple case removing the allocations is fine. As we saw in the previous section, it's also fine to have a virtual object reference another virtual, both allocations can be removed.

What are the cases were we can't remove an allocation? The first version of the optimizer simply assumed that every allocation can be removed. This can't work. We will replace this assumption with the following simple heuristic:

If a reference to a virtual object a is stored into an object b that is not virtual, then a will also stop being virtual. If an object a that was virtual stops being virtual, we say that it escapes. ¹

The simplest test case for this happening looks like this:

def test_materialize():
    bb = Block()
    var0 = bb.getarg(0)
    obj = bb.alloc()
    sto = bb.store(var0, 0, obj)
    opt_bb = optimize_alloc_removal(bb)
    #  obj is virtual, without any fields
    # ┌───────┐
    # │ empty │
    # └───────┘
    # then we store a reference to obj into
    # field 0 of var0. Since var0 is not virtual,
    # obj escapes, so we have to put it back
    # into the optimized basic block
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = alloc()
optvar2 = store(optvar0, 0, optvar1)"""
    # so far, fails like this:
    # the line:
    # info.store(field, op.arg(2))
    # produces an AttributeError because info
    # is None

If the optimizer reaches a point where a virtual object escapes (like the store operation in the test), the optimizer has already removed the alloc operation that created the virtual object. If the object escapes, we don't want to go back in the operations list and re-insert the alloc operation, that sounds potentially very complicated. Instead, we re-insert the alloc operation that will recreate the virtual object at the point of escape using a helper function materialize.

def materialize(opt_bb, value: Operation) -> None:
    assert not isinstance(value, Constant)
    assert isinstance(value, Operation)
    info = value.info
    assert isinstance(info, VirtualObject)
    assert value.name == "alloc"
    # put the alloc operation back into the trace
    opt_bb.append(value)

I've added a number of fairly strong assertions to materialize to encode our current assumptions about the situations in which it expects to be called. We will remove some of them later as we generalize the code.

Now that we have materialize we need to change optimize_alloc_removal to recognize the case of storing a virtual object into a non-virtual one. We can recognize Operation instances that produced a virtual object by looking at their .info field. If it is None, the object is not virtual, otherwise it is. If we store something into a virtual object, we leave the code as above. If we store a virtual object into an object that is not virtual, we will first materialize the virtual object, and then emit the store.

def optimize_alloc_removal(bb):
    opt_bb = Block()
    for op in bb:
        if op.name == "alloc":
            op.info = VirtualObject()
            continue
        if op.name == "load":
            info = op.arg(0).info
            field = get_num(op)
            op.make_equal_to(info.load(field))
            continue
        if op.name == "store":
            info = op.arg(0).info
            if info: # virtual
                field = get_num(op)
                info.store(field, op.arg(2))
                continue
            else: # not virtual
                # first materialize the
                # right hand side
                materialize(opt_bb, op.arg(2))
                # then emit the store via
                # the general path below
        opt_bb.append(op)
    return opt_bb

This is the general idea, and it is enough to pass test_materialize. But of course there are still a number of further problems that we now need to solve.

Version 3: Don't Materialize Twice

The first problem is the fact that after we materialize a virtual object, it is no longer virtual. So if it escapes a second time, it should not be materialized a second time. A test for that case could simply repeat the store operation:

def test_dont_materialize_twice():
    # obj is again an empty virtual object,
    # and we store it into var0 *twice*.
    # this should only materialize it once
    bb = Block()
    var0 = bb.getarg(0)
    obj = bb.alloc()
    sto0 = bb.store(var0, 0, obj)
    sto1 = bb.store(var0, 0, obj)
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = alloc()
optvar2 = store(optvar0, 0, optvar1)
optvar3 = store(optvar0, 0, optvar1)"""
    # fails so far: the operations that we get
    # at the moment are:
    # optvar0 = getarg(0)
    # optvar1 = alloc()
    # optvar2 = store(optvar0, 0, optvar1)
    # optvar3 = alloc()
    # optvar4 = store(optvar0, 0, optvar3)
    # ie the object is materialized twice,
    # which is incorrect

We solve the problem by setting the .info field of an object that we materialize to None to mark it as no longer being virtual.

def materialize(opt_bb, value: Operation) -> None:
    assert not isinstance(value, Constant)
    assert isinstance(value, Operation)
    info = value.info
    if info is None:
        return # already materialized
    assert value.name == "alloc"
    # put the alloc operation back into the trace
    opt_bb.append(value)
    # but only once
    value.info = None

# optimize_alloc_removal unchanged

This fixes the problem, only one alloc is created. This fix also allows another test case to pass, one where we store a non-virtual into another non-virtual, code which we cannot optimize at all:

def test_materialize_non_virtuals():
    # in this example we store a non-virtual var1
    # into another non-virtual var0
    # this should just lead to no optimization at
    # all
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.getarg(1)
    sto = bb.store(var0, 0, var1)
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = getarg(1)
optvar2 = store(optvar0, 0, optvar1)"""

Version 4: Materialization of Constants

Another straightforward extension is to support materializing constants. A constant is never virtual, so materializing it should do nothing.

def test_materialization_constants():
    # in this example we store the constant 17
    # into the non-virtual var0
    # again, this will not be optimized
    bb = Block()
    var0 = bb.getarg(0)
    sto = bb.store(var0, 0, 17)
    opt_bb = optimize_alloc_removal(bb)
    # the previous line fails so far, triggering
    # the assert:
    # assert not isinstance(value, Constant)
    # in materialize
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = store(optvar0, 0, 17)"""

To implement that case, we check for value being a constant and return early:

def materialize(opt_bb, value: Operation) -> None:
    if isinstance(value, Constant):
        return
    assert isinstance(value, Operation)
    info = value.info
    if info is None:
        return # already materialized
    assert value.name == "alloc"
    # put the alloc operation back into the trace
    opt_bb.append(value)
    # but only once
    value.info = None

# optimize_alloc_removal unchanged

Version 5: Materializing Fields

Now we need to solve a more difficult problem. So far, the virtual objects that we have materialized have all been empty, meaning they didn't have any fields written to at the point of materialization. Let's write a test for this:

def test_materialize_fields():
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.getarg(1)
    obj = bb.alloc()
    contents0 = bb.store(obj, 0, 8)
    contents1 = bb.store(obj, 1, var1)
    sto = bb.store(var0, 0, obj)

    # the virtual obj looks like this
    #  obj
    # ┌──────┬──────────┐
    # │ 0: 8 │ 1: var1  │
    # └──────┴──────────┘
    # then it needs to be materialized
    # this is the first example where a virtual
    # object that we want to materialize has any
    # content and is not just an empty object
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = getarg(1)
optvar2 = alloc()
optvar3 = store(optvar2, 0, 8)
optvar4 = store(optvar2, 1, optvar1)
optvar5 = store(optvar0, 0, optvar2)"""
    # fails so far! the operations we get
    # at the moment are:
    # optvar0 = getarg(0)
    # optvar1 = getarg(1)
    # optvar2 = alloc()
    # optvar3 = store(optvar0, 0, optvar2)
    # which is wrong, because the store operations
    # into optvar1 got lost

To fix this problem, we need to re-create a store operation for every element of the .contents dictionary of the virtual object we are materializing. ²

def materialize(opt_bb, value: Operation) -> None:
    if isinstance(value, Constant):
        return
    assert isinstance(value, Operation)
    info = value.info
    if info is None:
        return # already materialized
    assert value.name == "alloc"
    # put the alloc operation back into the trace
    opt_bb.append(value)
    # put the content back
    for idx, val in info.contents.items():
        # re-create store operation
        opt_bb.store(value, idx, val)
    # only materialize once
    value.info = None

# optimize_alloc_removal unchanged

This is enough to pass the test.

Version 6: Recursive Materialization

In the above example, the fields of the virtual objects contained only constants or non-virtual objects. However, we could have a situation where a whole tree of virtual objects is built, and then the root of the tree escapes. This makes it necessary to escape the whole tree. Let's write a test for a small tree of two virtual objects:

def test_materialize_chained_objects():
    bb = Block()
    var0 = bb.getarg(0)
    obj0 = bb.alloc()
    obj1 = bb.alloc()
    contents = bb.store(obj0, 0, obj1)
    const = bb.store(obj1, 0, 1337)
    sto = bb.store(var0, 0, obj0)
    #  obj0
    # ┌──────┐
    # │ 0: ╷ │
    # └────┼─┘
    #      │
    #      ▼
    #     obj1
    #   ┌─────────┐
    #   │ 0: 1337 │
    #   └─────────┘
    # now obj0 escapes
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = alloc()
optvar2 = alloc()
optvar3 = store(optvar2, 0, 1337)
optvar4 = store(optvar1, 0, optvar2)
optvar5 = store(optvar0, 0, optvar1)"""
    # fails in an annoying way! the resulting
    # basic block is not in proper SSA form
    # so printing it fails. The optimized
    # block would look like this:
    # optvar0 = getarg(0)
    # optvar1 = alloc()
    # optvar3 = store(optvar1, 0, optvar2)
    # optvar4 = store(optvar0, 0, optvar1)
    # where optvar2 is an ``alloc`` Operation
    # that is not itself in the output block

To fix it, materialize needs to call itself recursively for all the field values of the virtual object:

def materialize(opt_bb, value: Operation) -> None:
    if isinstance(value, Constant):
        return
    assert isinstance(value, Operation)
    info = value.info
    if info is None:
        return # already materialized
    assert value.name == "alloc"
    # put the alloc operation back into the trace
    opt_bb.append(value)
    # put the content back
    for idx, val in sorted(info.contents.items()):
        # materialize recursively
        materialize(opt_bb, val)
        opt_bb.store(value, idx, val)
    # only materialize once
    value.info = None

# optimize_alloc_removal unchanged

Getting there, the materialization logic is almost done. We need to fix a subtle remaining problem though.

Version 7: Dealing with Object Cycles

The bug we need to fix in this section is a bit tricky, and does not immediately occur in a lot of programs. In fact, in PyPy a variant of it was hiding out in our optimizer until we found it much later (despite us being aware of the general problem and correctly dealing with it in other cases).

The problem is this: a virtual object can (directly or indirectly) point to itself, and we must carefully deal with that case to avoid infinite recursion in materialize. Here's the simplest test:

def test_object_graph_cycles():
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.alloc()
    var2 = bb.store(var1, 0, var1)
    var3 = bb.store(var0, 1, var1)
    #   ┌────────┐
    #   ▼        │
    #  obj0      │
    # ┌──────┐   │
    # │ 0: ╷ │   │
    # └────┼─┘   │
    #      │     │
    #      └─────┘
    # obj0 points to itself, and then it is
    # escaped
    opt_bb = optimize_alloc_removal(bb)
    # the previous line fails with an
    # InfiniteRecursionError
    # materialize calls itself, infinitely

    # what we want is instead this output:
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = alloc()
optvar2 = store(optvar1, 0, optvar1)
optvar3 = store(optvar0, 1, optvar1)"""

The fix is not a big change, but a little bit subtle nevertheless. We have to change the order in which things are done in materialize. Right after emitting the alloc, we set the .info to None, to mark the object as not virtual. Only afterwards do we re-create the stores and call materialize recursively. If a recursive call reaches the same object, it's already marked as non-virtual, so materialize won't recurse further:

def materialize(opt_bb, value: Operation) -> None:
    if isinstance(value, Constant):
        return
    assert isinstance(value, Operation)
    info = value.info
    if info is None:
        return # already materialized
    assert value.name == "alloc"
    # put the alloc operation back into the trace
    opt_bb.append(value)
    # only materialize once
    value.info = None
    # put the content back
    for idx, val in sorted(info.contents.items()):
        # materialize recursively
        materialize(opt_bb, val)
        opt_bb.store(value, idx, val)

Version 8: Loading from non-virtual objects

Now materialize is done. We need to go back to optimize_alloc_removal and improve it further. The last time we changed it, we added a case analysis to the code dealing with store, distinguishing between storing to a virtual and to a non-virtual object. We need to add an equivalent distinction to the load case, because right now loading from a non-virtual crashes.

def test_load_non_virtual():
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.load(var0, 0)
    bb.print(var1)
    # the next line fails in the line
    # op.make_equal_to(info.load(field))
    # because info is None
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = load(optvar0, 0)
optvar2 = print(optvar1)"""

To fix it, we split the load code into two cases, leaving the virtual path as before, and letting the load from a non-virtual fall through to the general code at the end of the function.

def optimize_alloc_removal(bb):
    opt_bb = Block()
    for op in bb:
        if op.name == "alloc":
            op.info = VirtualObject()
            continue
        if op.name == "load":
            info = op.arg(0).info
            if info: # virtual
                field = get_num(op)
                op.make_equal_to(info.load(field))
                continue
            # otherwise not virtual, use the
            # general path below
        if op.name == "store":
            info = op.arg(0).info
            if info: # virtual
                field = get_num(op)
                info.store(field, op.arg(2))
                continue
            else: # not virtual
                # first materialize the
                # right hand side
                materialize(opt_bb, op.arg(2))
                # then emit the store via
                # the general path below
        opt_bb.append(op)
    return opt_bb

Version 9 (Final): Materialize on Other Operations

We're almost at the end now. There's one final generalization left to do. We started with the heuristic that storing a virtual into a non-virtual would escape it. This should be generalized. Every time we pass a virtual into any operation where it is not the first argument of a load and a store should also escape it (imagine passing the virtual to some function call). Let's test this as usual with our print operation:

def test_materialize_on_other_ops():
    # materialize not just on store
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.alloc()
    var2 = bb.print(var1)
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = alloc()
optvar2 = print(optvar1)"""
    # again, the resulting basic block is not in
    # valid SSA form

To fix this, we will take the call to materialize out of the store code path and instead put it into the generic code path the end of the while loop:

# materialize is unchanged
def materialize(opt_bb, value: Value) -> None:
    if isinstance(value, Constant):
        return
    assert isinstance(value, Operation)
    info = value.info
    if not info:
        # Already materialized
        return
    assert value.name == "alloc"
    opt_bb.append(value)
    value.info = None
    for idx, val in sorted(info.contents.items()):
        materialize(opt_bb, val)
        opt_bb.store(value, idx, val)

def optimize_alloc_removal(bb):
    opt_bb = Block()
    for op in bb:
        if op.name == "alloc":
            op.info = VirtualObject()
            continue
        if op.name == "load":
            info = op.arg(0).info
            if info: # virtual
                field = get_num(op)
                op.make_equal_to(info.load(field))
                continue
        if op.name == "store":
            info = op.arg(0).info
            if info: # virtual
                field = get_num(op)
                info.store(field, op.arg(2))
                continue
        # materialize all the arguments of
        # operations that are put into the
        # output basic block
        for arg in op.args:
            materialize(opt_bb, arg.find())
        opt_bb.append(op)
    return opt_bb

That's it, we're done. It's not a lot of code, but actually quite a powerful optimization. In addition to removing allocations for objects that are only used briefly and in predictable ways, it also has another effect. If an object is allocated, used in a number of operations and then escapes further down in the block, the operations in between can often be optimized away. This is demonstrated by the next test (which already passes):

def test_sink_allocations():
    bb = Block()
    var0 = bb.getarg(0)
    var1 = bb.alloc()
    var2 = bb.store(var1, 0, 123)
    var3 = bb.store(var1, 1, 456)
    var4 = bb.load(var1, 0)
    var5 = bb.load(var1, 1)
    var6 = bb.add(var4, var5)
    var7 = bb.store(var1, 0, var6)
    var8 = bb.store(var0, 1, var1)
    opt_bb = optimize_alloc_removal(bb)
    assert bb_to_str(opt_bb, "optvar") == """\
optvar0 = getarg(0)
optvar1 = add(123, 456)
optvar2 = alloc()
optvar3 = store(optvar2, 0, optvar1)
optvar4 = store(optvar2, 1, 456)
optvar5 = store(optvar0, 1, optvar2)"""

Note that the addition is not optimized away, because the code from this blog post does not contain constant folding and the other optimizations from the last one. Combining them would not be too hard though.

Conclusion

That's it! The core idea of PyPy's allocation removal optimization in one or two screens of code. The real implementation has a number of refinements, but the core ideas are all here.

I'm not going to show any benchmark numbers or anything like that here, if you are interested in numbers you could look at the evaluation Section 6. "Implementation and Evaluation" of the paper that describes the work.

There's a complementary optimization that improves load and store operations for objects that are not virtual. I'll probably not write that down as another post, but Max Bernstein and I developed that together on a PyPy Twitch channel channel a few weeks ago, here's the recording:

Footnotes

¹ This is how PyPy uses the terminology, not really used consistently by other projects. The term "escape" is fairly standard throughout the escape analysis literature. The term "virtual" was used originally in Armin Rigo's Psyco but is e.g. also used by the paper Partial Escape Analysis and Scalar Replacement for Java.

² The order in which we put the store operations back is relying on dictionary iteration order, which is insertion order. That's not a bad ordering, we could also be explicit and sort the fields in some order (ideally the order in which the object lays them out in memory).

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