Boost the almighty blue-silver dragon with some magical elixir! 🧙🧙‍♀️🧙‍♂️
In the de-facto way of using MLIR, we need to work with C/C++, TableGen, CMake and Python (in most of cases). Each language or tool here has some functionalities and convenience we want to leverage. There is nothing wrong choosing the most popular and upstream-supported solution, but having alternative ways to build MLIR-based projects is still valuable or at least worth trying.
Elixir could actually be a good fit as a MLIR front end. Elixir has SSA, pattern-matching, pipe-operator. We can use these language features to define MLIR patterns and pass pipeline in a natural and uniformed way. Elixir is strong-typed but not static-typed which makes it a great choice for quickly building prototypes to validate and explore new ideas.
To build a piece of IR in Beaver:
Func.func some_func(function_type: Type.function([], [Type.i(32)])) do
region do
block _() do
v0 = Arith.constant(value: Attribute.integer(Type.i(32), 0)) >>> Type.i(32)
cond0 = Arith.constant(true) >>> Type.i(1)
CF.cond_br(cond0, Beaver.Env.block(bb1), {Beaver.Env.block(bb2), [v0]}) >>> []
end
block bb1() do
v1 = Arith.constant(value: Attribute.integer(Type.i(32), 0)) >>> Type.i(32)
_add = Arith.addi(v0, v0) >>> Type.i(32)
CF.br({Beaver.Env.block(bb2), [v1]}) >>> []
end
block bb2(arg >>> Type.i(32)) do
v2 = Arith.constant(value: Attribute.integer(Type.i(32), 0)) >>> Type.i(32)
add = Arith.addi(arg, v2) >>> Type.i(32)
Func.return(add) >>> []
end
end
endAnd a small example to showcase what it is like to define and run a pass in Beaver (with some monad magic):
defmodule ToyPass do
@moduledoc false
use Beaver
alias MLIR.Dialect.{Func, TOSA}
require Func
import Beaver.Pattern
use MLIR.Pass, on: "builtin.module"
defpat replace_add_op() do
a = value()
b = value()
res = type()
{op, _t} = TOSA.add(a, b) >>> {:op, [res]}
rewrite op do
{r, _} = TOSA.sub(a, b) >>> {:op, [res]}
replace(op, with: r)
end
end
def run(%MLIR.Operation{} = operation) do
with 1 <- Beaver.Walker.regions(operation) |> Enum.count(),
{:ok, _} <-
MLIR.apply_(MLIR.Module.from_operation(operation), [replace_add_op(benefit: 2)]) do
:ok
else
_ -> raise "unreachable"
end
end
end
use Beaver
import MLIR.Transform
ctx = MLIR.Context.create()
~m"""
module {
func.func @tosa_add(%arg0: tensor<1x3xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x3xf32> {
%0 = "tosa.add"(%arg0, %arg1) : (tensor<1x3xf32>, tensor<2x1xf32>) -> tensor<2x3xf32>
return %0 : tensor<2x3xf32>
}
}
""".(ctx)
|> Beaver.Composer.append(ToyPass)
|> canonicalize
|> Beaver.Composer.run!()- Powered by Elixir's composable modularity and meta-programming features, provide a simple, intuitive, and extensible interface for MLIR.
- Edit-Build-Test-Debug Loop at seconds. Everything in Elixir and Zig are compiled in parallel.
- Compile Elixir to native/WASM/GPU with the help from MLIR.
- Revisit and reincarnate symbolic AI in the HW-accelerated world. Erlang/Elixir has a Prolog root!
- Introduce a new stack to machine learning.
- Higher-level: Elixir
- Representation: MLIR
- Lower-level: Zig
Beaver is an umbrella species increase biodiversity. We hope this project could enable other compilers and applications in the way a beaver pond becomes the habitat of many other creatures. Many Elixir projects also use animal names as their package names and it is often about raising awareness of endangered species. To read more about why beavers are important to our planet, check out this National Geographic article.
Beaver is essentially LLVM/MLIR on Erlang/Elixir. It is kind of interesting to see a crossover of two well established communities and four sub-communities. Here are some brief information about each of them.
-
Explain this MLIR thing to me in one sentence
MLIR could be regarded as the XML for compilers and an MLIR dialect acts like HTTP standard which gives the generic format real-world semantics and functionalities.
-
Check out the home page of MLIR.
-
What's so good about this programming language Elixir?
- It gets compiled to Erlang and runs on BEAM (Erlang's VM). So it has all the fault-tolerance and concurrency features of Erlang.
- As a Lisp, Elixir has all the good stuff of a Lisp-y language including hygienic macro, protocol-based polymorphism.
- Elixir has a powerful module system to persist compile-time data and this allows library users to easily adjust runtime behavior.
- Minimum, very few keywords. Most of the language is built with itself.
- Check out the official guide of Elixir.
- Tutorial: Your first compiler with Beaver!
The package can be installed
by adding beaver to your list of dependencies in mix.exs:
def deps do
[
{:beaver, "~> 0.3.9"}
]
endAdd this to your .formatter.exs will have the formatter properly transform the macros introduced by beaver
import_deps: [:beaver],- Charm: Compile a subset of Elixir to native targets.
- MLIR Accelerated Nx: A backend for Nx.
To implement a MLIR toolkit, we at least need these group of APIs:
- IR API, to create and update Ops and blocks in the IR
- Pass API, to create and run passes
- Pattern API, in which you declare the transformation of a specific structure of Ops
We implement the IR API and Pass API with the help of the MLIR C API. There are both lower level APIs generated from the C headers and higher level APIs that are more idiomatic in Elixir. The Pattern API is implemented with the help from the PDL dialect. We are using the lower level IR APIs to compile your Elixir code to PDL. Another way to look at this is that Elixir/Erlang pattern matching is serving as a frontend alternative to PDLL.
It is very common to use builder pattern to construct IR, especially in an OO programming language like C++/Python. One problem this approach has is that the compiler code looks very different from the code it is generating. Because Erlang/Elixir is SSA by its nature, in Beaver a MLIR Op's creation is very declarative and its container will transform it with the correct contextual information. By doing this, we could:
- Keep compiler code's structure as close as possible to the generated code, with less noise and more readability.
- Allow dialects of different targets and semantic to introduce different DSL. For instance, CPU, SIMD, GPU could all have their specialized transformation tailored for their own unique concepts.
One example:
module do
v2 = Arith.constant(1) >>> ~t<i32>
end
# module/1 is a macro, it will transformed the SSA `v2= Arith.constant..` to:
v2 =
%Beaver.SSA{}
|> Beaver.SSA.put_arguments(value: ~a{1})
|> Beaver.SSA.put_block(Beaver.Env.block())
|> Beaver.SSA.put_ctx(Beaver.Env.context())
|> Beaver.SSA.put_results(~t<i32>)
|> Arith.constant()Also, using the declarative way to construct IR, proper dominance and operand reference is formed naturally.
SomeDialect.some_op do
region do
block entry() do
x = Arith.constant(1) >>> ~t<i32>
y = Arith.constant(1) >>> ~t<i32>
end
end
region do
block entry() do
z = Arith.addi(x, y) >>> ~t<i32>
end
end
end
# will be transformed to:
SomeDialect.some_op(
regions: fn -> do
region = Beaver.Env.region() # first region created
block = Beaver.Env.block()
x = Arith.constant(...)
y = Arith.constant(...)
region = Beaver.Env.region() # second region created
block = Beaver.Env.block()
z = Arith.addi([x, y, ...]) # x and y dominate z
end
)There should be a 1:1 mapping between Beaver SSA DSL to MLIR SSA. It is possible to do a roundtrip parsing MLIR text format and dump it to Beaver DSL which is Elixir AST essentially. This makes it possible to easily debug a piece of IR in a more programmable and readable way.
In Beaver, working with MLIR should be in one format, no matter it is generating, transforming, debugging.
When possible, lower level C APIs should be wrapped as Elixir struct with support to common Elixir protocols. For instance the iteration over one MLIR operation's operands, results, successors, attributes, regions should be implemented in Elixir's Enumerable protocol. This enable the possibility to use the rich collection of functions in Elixir standard libraries and Hex packages.
Elixir is a programming language built for all purposes. There are multiple sub-ecosystems in the general Erlang/Elixir ecosystem. Each sub-ecosystem appears distinct/unrelated to each other, but they actually complement each other in the real world production. To name a few:
- Phoenix Framework for web application and realtime message
- Nerves Project for embedded device and IoT
- Nx for tensor and numerical
Each of these sub-ecosystems starts with a seed project/library. Beaver should evolve to become a sub-ecosystem for compilers built with Elixir and MLIR.
When calling higher-level APIs, it is ideal not to have MLIR context passing around everywhere. If no MLIR context provided, an attribute and type getter should return an anonymous function with MLIR context as argument. In Erlang, all values are copied, so it is very safe to pass around these anonymous functions. When creating an operation, these functions will be called with the MLIR context in an operation state. With this approach we achieve both succinctness and modularity, not having a global MLIR context. Usually a function accepting a MLIR context to create an operation or type is called a "creator" in Beaver.
Please refer to Beaver's contributing guide