This post follows from several conversations with CF Bolz-Tereick, Philip Zucker, Chris Fallin, and Max Willsey.
Compilers are all about program representations. They take in a program in one language, transform some number of ways through some different internal languages, and output the program in another language^{1}.
Part of the value in the inter-language transformations is optimizing the program. This can mean making it faster, smaller, or something else. Optimizing requires making changes to the program. For example, consider the following piece of code in a made-up IR:
v0 = ...
v1 = Const 8
v2 = v0 * v1
If the compiler wants to optimize the instruction for v2
, it has to go
through and logically replace all uses of v2
with the replacement
instruction. This is a rewrite.
Many compilers will go through and iterate through every instruction and check
if that instruction op
uses the original instruction v2
. If it does, it
will swap all of its uses of v2
with the replacement instruction.
void very_specific_optimization(Instr* instr) {
if (instr->IsMul() && instr->Operand(1)->IsConst() &&
instr->Operand(1)->AsConst()->Value() == 8) {
Instr* replacement = new LeftShift(instr->Operand(0), new Const(3));
for (auto op : block.ops) {
if (op->uses(instr)) {
op->replace_use(instr, replacement);
}
}
}
}
This is fine. It’s very traditional. Depending on the size and complexity of your programs, this can work. It’s how the Cinder JIT works for its two IRs. It’s very far from causing any performance problems in the compiler. But there are other compilers with other constraints and therefore other approaches to doing these rewrites.
In this post, I’m going to take you on a bit of a meandering walk. We’ll start from an alternative to find-and-replace called union-find and then incrementally add features until we accidentally have built another data strucutre called an e-graph. Hopefully it removes some of the mystery.
I love union-find. It enables fast, easy, in-place IR rewrites for compiler
authors. Its API has two main functions: union
and find
. The minimal
implementation is about 15 lines of code and is embeddable directly in your IR.
Instead of iterating through every operation in the basic block and swapping pointers, we instead mark our IR node as “pointing to” another node. The below snippet replaces the entire loop in the previous example:
instr->make_equal_to(new LeftShift(instr->Operand(0), new Const(3)));
This notion of a forwarding pointer can be either embedded in the IR node itself or in an auxiliary table. Each node maintains its source of truth, and each rewrite takes only one pointer swap (yes, there’s some pointer chasing, but it’s very little pointer chasing^{2}). It’s a classic time-space trade-off, though. You have to store ~1 additional pointer of space for each IR node.
See below an adaptation of CF’s implementation from the toy optimizer series^{3}:
from __future__ import annotations
from dataclasses import dataclass
from typing import Optional
@dataclass
class Expr:
forwarded: Optional[Expr] = dataclasses.field(
default=None,
init=False,
compare=False,
hash=False,
)
def find(self) -> Expr:
"""Return the representative of the set containing `self`."""
expr = self
while expr is not None:
next = expr.forwarded
if next is None:
return expr
expr = next
return expr
def make_equal_to(self, other) -> None:
"""Union the set containing `self` with the set containing `other`."""
found = self.find()
if found is not other:
found.forwarded = other
Union-find can be so fast because it is limited in its expressiveness:
(If you want to read more about it, check out the first half of my other post, Vectorizing ML models for fun, the toy optimizer, allocation removal in the toy optimizer, and abstract interpretation in the toy optimizer.)
This is really great for some compiler optimizations, like the strength reduction we did above. Here is the IR snippet again:
v0 = ...
v1 = Const 8
v2 = v0 * v1
A strength reduction pass might rewrite v2
as a left shift instead of a
multiplication (v2.make_equal_to(LeftShift(v0, Const(3)))
) because left
shifts are often faster than multiplications. That’s great; we got a small
speedup.
But seemingly obvious local rewrites can have less-local consequences. Consider
the expression (a * 2) / 2
, which is the example from the e-graphs
good website and paper. If our strength
reduction pass eagerly rewrites a * 2
to a << 1
, we’ve lost some
information.
This rewrite stops another hypothetical pass from recognizing that expressions
of the form (a * b) / b
are equivalent to a * (b / b)
and therefore
equivalent to a
. This is because rewrites that use union-find are
destructive; we’ve gotten rid of the multiplication. How might we find it
again?
Let’s make this more concrete and conjure a little math IR. We’ll base it on
the Expr
base class because it’s rewritable using union-find.
@dataclass
class Const(Expr):
value: int
@dataclass
class Var(Expr):
name: str
@dataclass
class BinaryOp(Expr):
left: Expr
right: Expr
@dataclass
class Add(BinaryOp):
pass
@dataclass
class Mul(BinaryOp):
pass
@dataclass
class Div(BinaryOp):
pass
@dataclass
class LeftShift(BinaryOp):
pass
It’s just constants and variables and binary operations but it’ll do for our demo.
Let’s also write a little optimization pass that can do limited constant
folding, simplification, and strength reduction. We have a function
optimize_one
that looks at an individual operation and tries to simplify it
and a function optimize
that applies optimize_one
to a list of
operations—a basic block, if you will.
def is_const(op: Expr, value: int) -> bool:
return isinstance(op, Const) and op.value == value
def optimize_one(op: Expr) -> None:
if isinstance(op, BinaryOp):
left = op.left.find()
right = op.right.find()
if isinstance(op, Add):
if isinstance(left, Const) and isinstance(right, Const):
op.make_equal_to(Const(left.value + right.value))
elif is_const(left, 0):
op.make_equal_to(right)
elif is_const(right, 0):
op.make_equal_to(left)
elif isinstance(op, Mul):
if is_const(left, 1):
op.make_equal_to(right)
elif is_const(right, 1):
op.make_equal_to(left)
elif is_const(right, 2):
op.make_equal_to(Add(left, left))
op.make_equal_to(LeftShift(left, Const(1)))
def optimize(ops: list[Expr]):
for op in ops:
optimize_one(op.find())
Let’s give it a go and see what it does to our initial smaller IR snippet that added two constants^{4}:
ops = [
a := Const(1),
b := Const(2),
c := Add(a, b),
]
print("BEFORE:")
for op in ops:
print(f"v{op.id} =", op.find())
optimize(ops)
print("AFTER:")
for op in ops:
print(f"v{op.id} =", op.find())
# BEFORE:
# v0 = Const<1>
# v1 = Const<2>
# v2 = Add v0 v1
# AFTER:
# v0 = Const<1>
# v1 = Const<2>
# v2 = Const<3>
Alright, it works. We can fold 1+2
to 3
. Hurrah. But the point of this
section of the post is to discover the equivalence classes implicitly
constructed by the union-find structure. Let’s write a function to do that.
To build such a function, we’ll need to iterate over all operations created. I
chose to explicitly keep track of every operation in a list, but you could also
write a function to walk the forwarded
chains of all reachable operations.
every_op = []
@dataclass
class Expr:
# ...
def __post_init__(self) -> None:
every_op.append(self)
# ...
def discover_eclasses(ops: list[Expr]) -> dict[Expr, set[Expr]]:
eclasses: dict[Expr, set[Expr]] = {}
for op in ops:
found = op.find()
if found not in eclasses:
# Key by the representative
eclasses[found] = set()
eclasses[found].add(op)
if op is not found:
# Alias the entries so that looking up non-representatives also
# finds equivalent operations
eclasses[op] = eclasses[found]
return eclasses
# ...
print("ECLASSES:")
eclasses = discover_eclasses(every_op.copy())
for op in ops:
print(f"v{op.id} =", eclasses[op])
# BEFORE:
# v0 = Const<1>
# v1 = Const<2>
# v2 = Add v0 v1
# AFTER:
# v0 = Const<1>
# v1 = Const<2>
# v2 = Const<3>
# ECLASSES:
# v0 = {Const<1>}
# v1 = {Const<2>}
# v2 = {Const<3>, Add v0 v1}
Let’s go back to our more complicated IR example from the egg website, this time expressed in our little IR:
ops = [
a := Var("a"),
b := Const(2),
c := Mul(a, b),
d := Div(c, b),
]
If we run our optimizer on it right now, we’ll eagerly rewrite the
multiplication into a left-shift, but then rediscover the multiply in the
equivalence classes (now I’ve added little *
to indicate the union-find
representatives of each equivalence class):
BEFORE:
v0 = Var<a>
v1 = Const<2>
v2 = Mul v0 v1
v3 = Div v2 v1
AFTER:
v0 = Var<a>
v1 = Const<2>
v2 = LeftShift v0 v5
v3 = Div v6 v1
ECLASSES:
v0 = * {Var<a>}
v1 = * {Const<2>}
v2 = {LeftShift v0 v5, Add v0 v0, Mul v0 v1}
v3 = * {Div v6 v1}
v4 = {LeftShift v0 v5, Add v0 v0, Mul v0 v1}
v5 = * {Const<1>}
v6 = * {LeftShift v0 v5, Add v0 v0, Mul v0 v1}
That solves one problem: at any point, we can enumerate the equivalence classes stored in the union-find structure. But, like all data structures, the union-find representation we’ve chosen has a trade-off: fast to rewrite, slow to enumerate. We’ll accept that for now.
This enumeration feature on its own does not comprise one of the APIs of an
e-graph. To graft on e-matching to union-find, we’ll need to do one more step:
a search. Some would call it match
.
So we can rediscover the multiplication even after reducing it to a left shift. That’s nice. But how can we do pattern matching on this data representation?
Let’s return to (a * b) / b
. This corresponds to the IR-land Python
expression of Div(Mul(a, b), b)
for any expressions a
and b
(and keeping
the b
s equal, which is not the default in a Python match
pattern).
For a given operation, we can see if there is a Div
in its equivalence class
by looping over the entire equivalence class:
def optimize_match(op: Expr, eclasses: dict[Expr, set[Expr]]):
# Find cases of the form a / b
for e0 in eclasses[op]:
if isinstance(e0, Div):
# ...
That’s all well and good, but how do we find if it’s a Div
of a Mul
? We
loop again!
def optimize_match(op: Expr, eclasses: dict[Expr, set[Expr]]):
# Find cases of the form (a * b) / c
for e0 in eclasses[op]:
if isinstance(e0, Div):
div_left = e0.left
div_right = e0.right
for e1 in eclasses[div_left]:
if isinstance(e1, Mul):
# ...
Note how we don’t need to call .find()
on anything because we’ve already
aliased the set in the equivalence classes dictionary for convenience.
And how do we hold the b
s equal? Well, we can check if they match:
def optimize_match(op: Expr, eclasses: dict[Expr, set[Expr]]):
# Find cases of the form (a * b) / b
for e0 in eclasses[op]:
if isinstance(e0, Div):
div_left = e0.left
div_right = e0.right
for e1 in eclasses[div_left]:
if isinstance(e1, Mul):
mul_left = e1.left
mul_right = e1.right
if mul_right == div_right:
# ...
And then we can rewrite the Div
to the Mul
’s left child:
def optimize_match(op: Expr, eclasses: dict[Expr, set[Expr]]):
# Find cases of the form (a * b) / b and rewrite to a
for e0 in eclasses[op]:
if isinstance(e0, Div):
div_left = e0.left
div_right = e0.right
for e1 in eclasses[div_left]:
if isinstance(e1, Mul):
mul_left = e1.left
mul_right = e1.right
if mul_right == div_right:
op.make_equal_to(mul_left)
return
If we run this optimization function for every node in our basic block, we end up with:
AFTER:
v0 = Var<a>
v1 = Const<2>
v2 = LeftShift v0 v5
v3 = Var<a>
where v3
corresponds to our original big expression. Congratulations, you’ve
successfully implemented a time-traveling compiler pass!
Unfortunately, it’s very specific: our match conditions are hard-coded into the loop structure and the loop structure (how many levels of nesting) is hard-coded into the function. This is the sort of thing that our programming giants invented SQL to solve^{5}.
We don’t have time or brainpower to implement a full query language^{6} and I ran out of ideas for making a small embedded matching DSL, so you will have to take my word for it that it’s tractable.
One thing to note: after every write with make_equal_to
, we need to
rediscover the eclasses if we want to read from them again. I think this is
what the egg people call a “rebuild” and part of what made their paper
interesting was finding a way to do this less often or faster.
Another thing we need to do, I think, is iterate until convergence. It’s not guaranteed that we will always reach the so-called “congruence closure” with one pass over all of the operations, matching and rewriting. In some cases (which?), the graph may not even converge at all!
Now what we have is a bunch of parallel worlds for our basic block where each operation is actually a set of equivalent operations. But which element of the set should we pick? One approach, the one we were taking before, is to just pick the representative as the desired final form of each operation. This is a very union-find style approach. It’s straightforward, it’s fast, and it works well in a situation where we only ever do strength reduction type rewrites.
But e-graphs popped into the world because people wanted to explore a bigger state space. It’s possible that the representative of an equivalence class is locally optimal but not globally optimal. If our optimality function—our cost function—considers the entire program, we have to find a way to broaden our search.
The final piece of the e-graph API is an extract
function. This function
finds the “lowest cost” or “most optimal” version of the program in the
e-graph.
The simplest extraction function is to iterate through the cartesian product of all of the equivalence classes for each IR node and find the one that minimizes the whole-program cost.
import itertools
def extract(program: list[Expr], eclasses: dict[Expr, set[Expr]]) -> list[Expr]:
best_cost = float("inf")
best_program = program
for trial_program in itertools.product(*[eclasses[op] for op in program]):
cost = whole_program_cost(trial_program)
if cost < best_cost:
best_cost = cost
best_program = trial_program.copy()
return best_program
Unfortunately, this is slow. As the number of nodes grows, your base grows and as the number of rewrites grows, your exponent grows. It’s bad news bears. There are a bunch of different approaches that don’t involve exhaustive search, but they do not always produce the globally optimal program. It’s an active area of research.
Another thing to note is that the cost function isn’t normally built-in to e-graph implementations; usually they allow library users to provide at least their own cost functions, if not the entirety of extract.
This brings us to a complete e-graph implementation. We started with a simple
union-find, and by incrementally adding match
and extract
, we ended with a
full e-graph.
A bit of a subtle note here is that unlike our more procedural/functional
pattern matching situation with a manual make_equal_to
, many (most?) e-graph
implementations tend to suggest that the library user provides a set of
declarative syntactic rewrite rules.
egglog uses a custom Datalog-like
DSL;
Cranelift
uses a DSL called ISLE; Ego
uses an embedded DSL.
;; This is a Datalog-like pair of rules from an egglog example
(rewrite (If T t f) t)
(rewrite (If F t f) f)
This requires embedding your compiler’s notion of the program into the library’s domain, then extracting back out from the library’s domain into your own IR.
Part of the motivation for this blog post was to provide a kind of e-graph that can be embedded directly into your compiler project without mapping back and forth.
Cranelift uses a modified form of e-graph called an aegraph. It’s different in that the entire e-graph can be topo sorted and equality arrows can only point to earlier nodes. There are probably some very interesting trade-offs here but I am not an expert and you should probably read Chris Fallin’s excellent post.
In a Zulip thread, Chris writes:
aegraphs are really about three key things:
- a persistent immutable data structure that encodes eclasses directly – via the use of “union nodes” that refer to two other nodes. This lets us refer to a “snapshot in time” of an eclass, before we union more things into it, which turns out to be important for acyclicity
- a rewrite strategy that is eager (“bottom-up”): as soon as we create a node, we apply rewrite rules just to that node. That was actually kind of my entrypoint to doing something “different” than egg: I was wondering how to apply rules in a more efficient way than “iterate over all nodes and apply all rules” and, well, doing rewrites just once is about as good as one can do
- acyclicity: in a classical egraph one can get cycles when merging nodes even when no cycles exist in the input. Consider
x + 0
, and a rule that rewrites that tox
. Then we have one eclass that refers to itself – in essence it’s encoding the equivalence in both directions, so it could also be(((x + 0) + 0) + 0)
or longer to infinity; that’s what the cycle denotes. To avoid that we have to refer to a snapshot of the eclass as we know it in the args, and never re-intern a value node with new (union’d) arg eclasses. That enables the persistent immutable data structure; and requires the eager rewrite to work.(in my talk about this I have this wonky three-sided figure where each of these concepts mutually reinforces the other…)
One of the things that surprised me is that the single-pass eager rewrite does actually work – it works if one’s rules are structured in a certain way. The case one wants to avoid is where A rewrites to B, C rewrites to B, and then a better version of A is actually C (but no direct rewrite exists) – that’s where later unification in a full egraph would have grouped A and C together and that equivalence would be visible, but eager rewrites with snapshotted eclasses does not. It turns out the way we write rules in Cranelift at least is “directional” enough that we don’t have this in practice (it would require C to be better than B, even though we have a C->B rewrite).
There’s a whole other side to Cranelift’s use of aegraphs having to do with control flow, “elaboration”, the way we do GVN (without partial redundancy) and LICM, keep side effects in the right place, and getting the reconstructed/reserialized sequence of computations correct with respect to dominance (extraction needs to worry about the domtree!).
PyPy has “union find” in its optimizer but it’s smarter than normal union-find. It also has smart constructors and some other features that make its optimizer more e-graph like than union-find like. Perhaps CF will write a blog post about this some day.
And of course check out egg and egglog, the main e-graph libraries around. And Metatheory.jl, too.
Please let me know what thoughts you have! This is a very new subject for me.
Thanks to Kartik Agaram, Max Willsey, and Chris Fallin for reviewing drafts of this post.
This is not to say that the languages have to be distinct; the compiler can, say, take in a program in C and emit a program in C. And sometimes the term “language” gets fuzzy, since (for example) C23 is technically a different language than C99 but they are both recognizable as C. But there’s value in a C23 to C99 compiler because not all compilers can take in C23 input in the front-end yet. And also sometimes the term compiler comes with an implication that the input language is in some way “higher level” than the output language, and this vibe ended up producing the term “transpiler”, but eh. Compiler take program in and compiler emit program out. ↩
The naive implementations shown in this post are not the optimal ones that everyone oohs and ahhs about. Those have things like path compression. The nice thing is that the path compression is an add-on feature that doesn’t change the API at all. Then if you get hamstrung by the inverse Ackermann function, you have other problems with the size of your IR graph. ↩
See also this tidy little union-find implementation by Phil from his blog post:
uf = {}
def find(x):
while x in uf:
x = uf[x]
return x
def union(x,y):
x = find(x)
y = find(y)
if x != y:
uf[x] = y
return y
I really enjoy that it reads like a margin note. The only downside, IMO,
is that it requires the IR operations to be both hashable and comparable
(__hash__
and __eq__
). ↩
I sneakily added some printing niceties to the Expr
class that I didn’t show here. They’re not important for the point I’m
making and appear in the full code listing. ↩
This is a bit of a head-nod to the good folks working on egg and egglog, a team comprised of compilers people and database people. They have realized that the e-graph and the relational database are very similar and are building tools that do a neat domain crossover. Read the paper (open access) if you are interested in learning more!
Yihong Zhang has implemented egraph-sqlite, which is delightfully small, in Racket. I would love to see it ported to other langauges for fun and learning! ↩
As soon as I wrote this I thought “how hard could it be?” and went off to learn more and find the smallest SQL-like implementation. I eventually found SQLToy (~500LOC JS) and ported it to Python (~200 LOC). I don’t know that having this embedded in the post or a minimal e-graph library would help, exactly, but it was a fun learning experience. ↩