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

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