- News
- Date: 7/28/2024 - Higher Ranked Types have been added to the language
- Date: 7/5/2024 - GADTs have been added to the language
- Date: 6/17/2024 - The Python + Cuda backend now supports all of Spiral's featues
- Date: 3/30/2024 - Added existentials to the language
- Date: 8/27/2023 - Check out the Spiral playlist on Youtube
- Date: 1/7/2023 - UPMEM demo & backend
- Date: 12/21/2022 - Python backend
- Date: 7/13/2022 - C backend
- Date: 8/21/2021 - Updated the docs
- The Spiral Language
We wanted to write a map function over higher kindeds, so we added it in. Check out the Higher Ranked Types section in the docs for more info. Unlike with GADTs, we didn't really need these, but it is better not to restrict ourselves when it comes to language design. Maybe now that we've meet a minor need, it will lead to other more important uses being discovered for it.
We need them for the ML library, so they've been added to the language. Check out the GADT section in the docs for more info.
In support of single threaded GPU programming, the Python + Cuda backend has been extended with C++ style reference counting so it supports heap allocated closures, recursive unions and layout types. The Cuda backend in particular has novel function types that were added to the language specifically for it: fptr and closure. The vanilla functions can be converted to function pointers and closures specifically using to_fptr and to_closure. Because the these types use native C++ features based on shared pointers for ref counting, they are completely interoperable with existing C++ libraries.
Whereas what we had before could be considered a prototype backend, this one has support for the full range of Spiral's language features. The tiny Python + Cuda backend has grown up. It is now a fully fledged one.
Even if you don't want to use function pointers or closures, it's also possible to interop with C++ using just regular Spiral functions as long as they aren't returned or passed through join points, and in general, as long as their types aren't printed anywhere other than their origin by the codegen. They'll get compiled to stack allocated C++ functors that can capture free variables and be passed into macro expressions. Furthermore, the core library now provides two new dynamic array classes that can be used directly in device side Cuda kernels. The serialization code for those new classes has been added to the core library as well.
The video will be added on the 28th on the Spiral playlist for anybody interested in how PL development looks like. Unfortunately, during the course of the video I couldn't get the compilation times for the NL Holdem to go down significantly using the new dynamic arrays. It will be up to the Cuda team to bring those down in the end.
Next in the series, we will be continuing work on the ML library for Spiral, and using it to train the RL agents on various poker games. The games themselves are already implemented and architected so they'll be easily usable in ML pipelines, so there is only a little bit more to go until that happens.
Existentials have been added to the language in v2.8.0. This introduces a new exists keyword to the language, so it is breaking change. I suggest renaming variables called exists to exists'.
In other news, I am revisiting my work from 2018 with the new Spiral and it is going well. Check out the Youtube channel if you haven't already. There you can see me program in Spiral directly. We're working on a ML library and poker game in a single fully fused kernel. FPGAs didn't work out, and AI chips are a disappointment, but I regret overlooking the gains in general purpose computing capabilities in GPUs.
Who knows, maybe they'll save us after all?
At this point in time, I am convinced that AI chips are vaporware. But I discovered FPGAs have quite an interesting programming model, so I am going to give them a try in the Staged Functional Programming In Spiral playlist. Check it out!
I finally have an example of how Spiral can be used to program a novel piece of hardware. This article and the examples within should demonstrate what Spiral can do on the UPMEM device. Spiral is currently at v2.3.7. I've replaced the Cython backend with a Python one, did a host of improvement on the language, and now it should be rock solid. If anyone in the AI/PIM business wants to program in a super efficient, high level functional language instead of C, don't hesitate to get in touch. These backends are quick and easy to make for me.
Created the Python backend. Updated the language to v2.3.0. The next step will be to fuse the Python and the C backends. Currently I am working on an UPMEM backend Python + C for Spiral, and having a prototype Python and C backends is the first step towards making it.
Created the C backend for Spiral and updated the language to v2.2.0. I regret not doing this last year, so I decided to finally do it. Compared to the F#, or the Cython backend, the C one is not too useful because C does not have any worthwhile libraries to take advantage of. But it is going to serve a prototype for AI chip C backends, and I needed to do it in order to get a grasp on reference counting. I did pretty well at that. I have gone through the testing suite, but it isn't too thorough or designed to catch memory errors, so for the time being it might be worth putting the programs produced by it through Cee Studio. Consider it in beta right now. From here on out, the best kind of test would be to use it in the real world.
When I get some novel hardware it is going to be easy to adapt it to them. Until then, I won't bother using it for anything in particular.
Updated the documentation for v2.1. Spiral is quite stable in its current iteration and is no longer the alpha that it was at the end of January. The bugs should be dealt with and no features will get removed or added.
As the world inexorably hurls towards the black maw of tomorrow, the power to face it is needed.
Throughout the history of programming languages, the choice was between fast or expressive; the two traditions are crystallized by the C and the Lisp family of languages. There has been a lot of effort into this, but always as languages developed and moved forward they stepped away from the bare metal and in turn lost some of that core vitality that is needed for performance.
The culprit for this is the heap allocation by default dogma introduced by Lisp decades ago. It is a crutch for languages with weak type systems.
Abstraction by heap allocation is a dead end. It works moderately well on the current generation of computers where CPU is still the dominant driver of computation.
It cannot work for devices like GPUs and the rest coming down the line. Many of the most important computational devices of the future won't support heap allocation, so an alternative is needed to draw out their full power. It is of absolute importance that a language for that task has excellent control over inlining. Inlining, therefore, must come as a guarantee in the language and be a part of the type system.
Inlining is a trade-off that expresses the exchange of memory for computation. It should be the default instead of heap allocating.
A language good enough at propagating information so as to be capable of expressing inlining guarantees is also powerful enough for expressing a lot of other things well - without any abstraction overhead.
- Structural introspection through pattern matching not just for unions, but for all core types.
- Seamless interoperability between different language backends.
- First class functions, pairs, records and unions.
- Composable layouts of data structures.
- Symbols as singleton types.
- Top-down type inference via unification
- Extensibility via prototypes.
Spiral is such a language.
Statically typed and with a lightweight, very powerful type system giving it expressiveness of dynamic languages and the speed of C, Spiral is the crystallization of staged functional programming. It boasts of having intensional polymorphism and first-class staging. Its primary purpose is the creation of ML libraries for novel kinds of AI hardware.
The language is published on the VS Code marketplace. Getting it is just a matter of installing the The Spiral Language plugin. This will install both the VS Code editor plugin and the compiler itself. The compiler itself requires the .NET 8 SDK and is portable across platforms. The language server uses a websocket connection to communicate with the editor so allow it in the firewall.
Stable - I haven't run into a bug in a long while, so the language is fairly solid. And rather than adding features, I've taken out the experimental syntax of v2.0 and do not have plans to remove or add anything else. The editor support is missing autocomplete and highlighting of unused vars. Hovers could benefit from having links in them and being more informative. F# has Spiral beat as far as editor support is concerned. Now that I've removed the paired symbol patterns, renaming should be a lot easier to implement. Monadic syntax, pattern exhaustiveness checking during type inference, allowing top level constants, interpolated strings, dedicated VS Code theme are also on my TODO list.
I have no need for this right now, and I probably won't bother working on it unless Spiral gets significant sponsorship. What I hope to add at some point are backends for AI chips. Though those won't be too hard as my targets will be some variant of C, no doubt about it.
The hierarchy of Spiral programs is a graph of packages, who internally have a sequence of modules, who internally have a sequence of top level statements such as type definitions and functions, who internally have a sequence of their own local statements.
Spiral files (either .spi or .spir) can be parsed without a dependency on any other file, but in order for type inference to work, they have to have an owner package.spiproj (both the name and suffix has to match) file and be a part of the sequence. The package file that is their owner is the first one that is found when searching outwards from the folder the .spi/.spir file is in. And on the flip side, package.spiproj files can only refer to files that they own - if any of the subdirectories have another package file, then that will be an error.
The way to start a Spiral project is to create an empty package.spiproj file in some folder.
This is its content if you want a project with the file a.spi.
modules:
a
Now, if the folder does not have a.spi you should see an error in the editor indicating as much. The project files are interactive - instead of creating the file in the editor's tree explorer, you can place the cursor on a and select the code action Create file. in order to actually create it. The files and packages that exist will also have links to them in the package file.
Here are all the forms allowed for the modules field.
modules:
a
b-
c*
d*-
some_folder/
z
x
y
a and b- when created are a.spi and b.spi respectively. b- however has special behavior in that it inlines the module into the enclosing scope. While everything in a will have its file name as its module name, b's statements will get included directly into the enclosing one.
c* and d*- when created are c.spir and d.spir respectively. Similarly as for b-, the - in d*- acts as the include postfix. .spi and .spir files have important differences in their processing that will be covered in later sections - for now the documentation will be covering regular top-down .spi modules.
some_folder/ is in fact a folder, and z and x are its submodules.
The parsing for the modules field in the package file is indentation sensitive so y won't be considered as part of the some_folder folder.
You can delete and rename files and folders from the package file using a code action. Renaming will change the file or folder name on the disk, but it won't actually rename the references to it.
Besides modules, it is also possible to provide packages.
Suppose you have a folder with subfolders a, b and c, each of which have their own package.spiproj file. If you want c to refer to a and b here is how it should be done.
packages:
a
b
Packages also support the include the postfix -. This is useful for including the core library for example (assuming it is there in the directory.)
packages:
core-
a
b
By default, the module directory is current, and the package directory is the parent, but it is possible to set them explicitly.
packageDir: e:/spiral_packages
moduleDir: src
packageDir and moduleDir fields both support relative and absolute paths.
Package names also support the | prefix. Most tests import the core like this...
packages: |core-
Instead of looking for the package in the parent folder of the package file, the | unary operator instructs the compiler to look in the parent folder of its own executable - in other words, the plugin folder itself. This is a convenience for bundling the core library with the plugin.
Besides those 4, the package file schema also supports name and version fields, but those do not affect compilation in any way at the moment.
The great thing about packages is that their processing is done concurrently. While modules are processed strictly sequentially as in F#, packages are more flexible. Circular links between packages though are not allowed and will report an error.
As long as any of the loaded packages has an error, type inference won't work - the changes will be cached until the package errors are resolved and only show the previous results.
The Spiral language is split into the top-down (.spi) and the bottom-up (.spir) segments. The difference between the two is that the top-down actually has an ML-styled type system based on unification while the bottom-up does type propagation via partial evaluation.
This is a major innovation in v2 of Spiral. In its previous version, Spiral did not have a top-down segment.
I spent a year programming like that, but after a while I got tired of it. After a year of it, I started to realize that expressiveness and power, while worthy and necessary goals in themselves are not all there is. I was in love with it for a while as it was such a new perspective on both programming and static typing, but in the end I came to know the truth - just because the partial evaluator can do anything it does not mean it should do everything.
The bottom-up has high expressiveness and power, but the user has to pay a cost for that even when such a power is not needed so there is great benefit to putting a weaker, but easier to use type system on top of it. I expect that at least 99.9% of the time the programming in Spiral will be done with the help of the top-down type system. The top-down segment is the easy part of Spiral.
For the following part I will be using a package.spiproj file with the core library and an arbitrary module to write things in.
packages: core-
modules: a
The way to compile a module is to have it open in the editor and then use Spiral: Build File from the command palette. In VS Code, the easiest way to bring it up is to press F1. It is also keyed to Ctrl + Shift + P by default. I recommend keying the Spiral build file command to something like Ctrl + F1 to make it more convenient to use.
Here is an example module a.spi:
inl main () = 1i32
Using the build command will partially evaluate the main function. This will create a.fsx with the following residual program as output. The Python backend is also an option, but the F# is the default one and produces nicer looking code so it will be used for the examples in this document.
1In its top level segment, Spiral is fairly similar to F# and various ML-family languages like OCaml. It is an eager and impure statically typed functional language without a doubt. So whatever skills you pick up in those languages will be directly transferable to Spiral. F# in particular has many learning resources whose breadth would be too much for me to cover in this document, so I'd recommend it, especially for beginners. This document is intended for those already proficient in functional languages and people who know how to program, and want to take their skills to the next level.
My goals from here on out will be as follows:
- Cover all the language features with examples.
- Talk about the design considerations of Spiral and why it is designed the way it is.
- Present the latest iteration (as of 8/17/2021) of the RL project I've been working on as an example of a serious Spiral project.
In particular when I get to join points and functions, the material will get hard to follow for a beginner. This next segment though should be straightforward.
Here are some rudimentary examples of programs and their output.
inl main () =
inl x = 1
inl y = 2
x + y
If you try to compile the above you will actually get an error that the main function should not be a forall. If you hover the mouse cursor over the main you will see that its type is forall 'a {number}. () -> 'a. Here 'a is the type variable with the number constraint. () is the unit type and () -> 'a indicates it is a function from unit to a.
Spiral most closely resembles F# its design, and here is the first difference. Like Haskell it supports polymorphic number literals. In fact if you hover over 1 or 2 you will see that they are of type 'a.
In order to compile this segment what needs to be done is have the literals be concrete.
inl main () =
inl x = 1i32
inl y = 2
x + y
3Now, the compiled output program is just a single int 3.
inl main () =
inl x = 1f64
inl y = 2
x + y
3.000000It is easy enough to change the constant to a float. An alternative is to just provide a type annotation somewhere.
inl main () : u16 =
inl x = 1
inl y = 2
x + y
3usNow the literal is inferred to be a 16-bit unsigned int. Here is one more...
inl main () =
inl x = 1 : i64
inl y = 2
x + y
3LHere the literal is now a 64-bit signed int. Primitive number types in Spiral consist of signed ints (i8,i16,i32,i64), unsigned ints (u8,u16,u32,u64) and floats (f32,f64). Other primitive types are bool, string and char.
Unlike mutable heap layout types (think references) and arrays, the primitive types are tracked exactly at compile time. The reason why the output program comes up to 3, is because the partial evaluator will keep 1 and 2 in memory and add them together.
In regular languages, whether something happens at compile time or runtime is vague and indistinct, but Spiral is a lot more careful about such considerations. Performance is one of the reasons, but language interop is just as important as well.
Using ~ it is easy to instruct the partial evaluator to push the variable tracking to runtime.
inl main () =
inl ~x = 1 : i64
inl y = 2
x + y
let v0 : int64 = 1L
v0 + 2L~ is called the dyn pattern. Whenever a variable is passed through the dyn pattern during a bind, it gets pushed to runtime. In the above example, only x has been dyned so only the let statement for it has been generated in the compiled code.
inl main () =
inl ~x = 1 : i64
inl ~y = 2
x + y
let v0 : int64 = 1L
let v1 : int64 = 2L
v0 + v1The above capability to push compile time data to runtime is hugely important for functions. In languages without exposure to partial evaluation such as F#, you might have ordinary let statements and let inline equivalents and that is it. You can define a function and then give a suggestion to the compiler to inline it, and that is the whole story when it comes to inlining in most languages.
Spiral is more flexible.
The following example is equivalent to the first one.
inl add a b = a + b
inl main () =
inl x = 1i32
inl y = 2
add x y
3It is possible to use the dyn pattern on function arguments.
inl add ~a ~b = a + b
inl main () =
inl x = 1i32
inl y = 2
add x y
let v0 : int32 = 1
let v1 : int32 = 2
v0 + v1Here is an example that performs several additions.
inl add ~a ~b : i32 = a + b
inl main () = add 1 2, add 3 4, add 5 6
let v0 : int32 = 1
let v1 : int32 = 2
let v2 : int32 = v0 + v1
let v3 : int32 = 3
let v4 : int32 = 4
let v5 : int32 = v3 + v4
let v6 : int32 = 5
let v7 : int32 = 6
let v8 : int32 = v6 + v7
struct (v2, v5, v8)It convenient to have the function itself be the one to decide whether it wants runtime or compile time variables.
The above example is trivial, but for larger functions it would be better if they could be compiled to actual methods. The way to accomplish that is to wrap the body of the expression in a join point.
inl add ~a ~b : i32 = join a + b
inl main () = add 1 2, add 3 4, add 5 6
let rec method0 (v0 : int32, v1 : int32) : int32 =
v0 + v1
let v0 : int32 = 1
let v1 : int32 = 2
let v2 : int32 = method0(v0, v1)
let v3 : int32 = 3
let v4 : int32 = 4
let v5 : int32 = method0(v3, v4)
let v6 : int32 = 5
let v7 : int32 = 6
let v8 : int32 = method0(v6, v7)
struct (v2, v5, v8)The join point converts all the runtime variables passed into its scope into method arguments in the resulting compiled code. During partial evaluation after making the environment as a part of its key, it then partially evaluates the method body which in the above case is just a + b.
In order to understand join points better, it would be instructive to show what happens when arguments aren't being dyned.
inl add ~a b : i32 = join a + b
inl main () = add 1 2, add 3 4, add 5 6
let rec method0 (v0 : int32) : int32 =
v0 + 2
and method1 (v0 : int32) : int32 =
v0 + 4
and method2 (v0 : int32) : int32 =
v0 + 6
let v0 : int32 = 1
let v1 : int32 = method0(v0)
let v2 : int32 = 3
let v3 : int32 = method1(v2)
let v4 : int32 = 5
let v5 : int32 = method2(v4)
struct (v1, v3, v5)The constants are different so the join point gets compiled to different methods.
inl add ~a b : i32 = join a + b
inl main () = add 1 10, add 3 10, add 5 10
let rec method0 (v0 : int32) : int32 =
v0 + 10
let v0 : int32 = 1
let v1 : int32 = method0(v0)
let v2 : int32 = 3
let v3 : int32 = method0(v2)
let v4 : int32 = 5
let v5 : int32 = method0(v4)
struct (v1, v3, v5)If the constant b happened to be the same, then the join point would end up needing only a single method in the compiled code. Without any dyning at all, here is what would happen.
inl add a b : i32 = join a + b
inl main () = add 1 2, add 3 4, add 5 6
let rec method0 () : int32 =
3
and method1 () : int32 =
7
and method2 () : int32 =
11
let v0 : int32 = method0()
let v1 : int32 = method1()
let v2 : int32 = method2()
struct (v0, v1, v2)This is just for illustration of what join points are doing. In actual programming practice, you'd generally want to either inline everything or dyn all the arguments and wrap them in a join point. To do the latter, let is a convenient shorthand.
let add a b : i32 = a + b
inl main () = add 1 2, add 3 4, add 5 6
let rec method0 (v0 : int32, v1 : int32) : int32 =
v0 + v1
let v0 : int32 = 1
let v1 : int32 = 2
let v2 : int32 = method0(v0, v1)
let v3 : int32 = 3
let v4 : int32 = 4
let v5 : int32 = method0(v3, v4)
let v6 : int32 = 5
let v7 : int32 = 6
let v8 : int32 = method0(v6, v7)
struct (v2, v5, v8)inl add ~a ~b : i32 = join a + b is equivalent to let add a b : i32 = a + b.
These examples cover the essence of join points. All they do is specialize the body of their expressions with respect to their environment.
While the essence of their functionality is easy to understand, these examples are just scratching the surface of their usefulness. If it was just performance, they would not be useless, but they would not be enough to motivate me to make a language with them as one of the most essential features.
New in v2.3.7: In Jan 2023, I finally have a demo that shows what I wanted to do here. The following of the section here was written in early 2021.
It is a pity I cannot demonstrate this benefit at the moment as Spiral as I haven't gotten access to an AI chip in order to make a backend for it yet, but it is worth describing how it used to work in the previous version.
The previous version of Spiral, had a Cuda backend with a join point variant specific to it.
The way the whole thing worked is that the compiler would produce an F# file, similar to how it is done now, and in addition to that, there would also be a C file with the methods specialized by Cuda join points. This C file would further be processed by the Cuda compiler into a .ptx one which is the high level assembly language used by LLVM.
The Cuda join point itself would not necessarily return a type like regular ones do, because the F# codegen would not be able to actually run it, and generating the code to run it would be too complex for it. Instead what the Cuda join point would do is return a runtime string with the name of the specialized method along with the array of runtime vars passed into it.
This is exactly the data you need in order to call Cuda kernels through the Cuda library functions that expose that functionality for other platforms. Calling the Cuda kernel from .NET was not as simple as simply wrapping the expression in a join point like for regular methods, instead it was necessary to pass a lambda to some run function, which is almost as easy. The Cuda join point was an integral part of connecting the F# to the Cuda backend.
Having this machinery in place made it trivial to make all sorts of complex Cuda kernels and call them from the .NET land. And as an example, because of Spiral's inlining capabilities it was possible to have map functions that are autodifferentiated on the GPU. Before working on Spiral, in 2016 I tried making an ML library in raw F# and completely got stuck on how to move beyond the Cuda kernels being raw text strings. Compared to that the ML library I wrote in previous Spiral was a major leap in quality and extensibility compared to the one in F#, afforded completely thanks to the novel abstraction capabilities of the language.
In Spiral, inlining is composable. It is not necessarily the case that one is restricted to putting join points at function body starts.
inl add a b : i32 = a + b
inl main () =
inl ~x = true
if x then join add 1 2
else add 3 4
let rec method0 () : int32 =
3
let v0 : bool = true
if v0 then
method0()
else
7Join points can be put everywhere an expression can. Here is some more of their magic.
inl add a b : i32 = a + b
inl main () =
inl f g = join g()
inl x = 1
inl y = 2
f (fun () => add x y)
let rec method0 () : int32 =
3
method0()Functions are also partially evaluated in Spiral.
Suppose 1 and 2 were runtime variables.
inl add a b : i32 = a + b
inl main () =
inl f g = join g()
inl ~x = 1
inl ~y = 2
f (fun () => add x y)
let rec method0 (v0 : int32, v1 : int32) : int32 =
v0 + v1
let v0 : int32 = 1
let v1 : int32 = 2
method0(v0, v1)This example demonstrates how functions interact with join point specialization. As long as functions aren't dyned, the partial evaluator tracks them by their body and environment.
Here is what happens when functions are dyned.
inl add a b : i32 = a + b
inl main () =
inl ~x = 1
inl ~y = 2
inl f z = add x y + z
inl ~g = f
g 3, g 4, g 5
let rec closure0 (v0 : int32, v1 : int32) (v2 : int32) : int32 =
let v3 : int32 = v0 + v1
v3 + v2
let v0 : int32 = 1
let v1 : int32 = 2
let v2 : (int32 -> int32) = closure0(v0, v1)
let v3 : int32 = v2 3
let v4 : int32 = v2 4
let v5 : int32 = v2 5
struct (v3, v4, v5)Dyning does closure conversion of relevant functions. At runtime it creates a heap allocated object with the necessary runtime variables and specializes the function body. After that the object can be passed around and applied at runtime in multiple places.
- The dyn pattern.
- Join point returns.
- If branch and match case returns when their conditional is not statically known.
- Stores to runtime data structures such as arrays.
As a language, Spiral is designed to be sensible about when various abstractions such as functions should be heap allocated and not. F# and other functional languages provide no guarantees when a function will get inlined or not. Guarantees are important for performance as much as they are for interop.
inl main () =
inl f g = join g()
This was a part of one of the previous examples. Suppose g was dyned, like in the following example.
inl add a b : i32 = a + b
inl main () =
inl f ~g = join g()
inl ~x = 1
inl ~y = 2
f (fun () => add x y)
let rec closure0 (v0 : int32, v1 : int32) () : int32 =
v0 + v1
and method0 (v0 : (unit -> int32)) : int32 =
v0 ()
let v0 : int32 = 1
let v1 : int32 = 2
let v2 : (unit -> int32) = closure0(v0, v1)
method0(v2)Then the join point would get passed a closure.
Closures are troublesome for interop because they cannot be sent past language boundaries.
In the Cuda backend for example, very little could go past it, namely:
- Primitive number types.
- Arrays of them.
Runtime strings cannot. Chars would give memory corruption errors. .NET booleans aren't blittable either.
Arrays of compound structures like tuples or records aren't blittable either. The fact that I could use tensors which arranged arrays of tuples into tuples of arrays was a major benefit when it came to interop.
Spiral tracks runtime variables in data structures individually so passing them through backend boundaries is not a problem, but in .NET languages this is something that takes special care. .NET structs for example have their order and padding automated by the compiler, while C structs are sequential, but the C compiler also has leeway to insert padding into them for some reason.
Non-recursive union types could be serialized in theory, but it has not been implemented as I did not require that functionality at the time. The Cuda backend itself supported non-recursive union types though.
The lesson from all of this is - the simpler the runtime representations the language uses the better it is at interop.
F# or Scala or Haskell, or any similar language with advanced type systems do not have this focus on making inlining composable. I do not think it would be possible to take any of those languages and make them a serious C competitor.
But with Spiral, suppose you picked a backend that did not have garbage collection and you disallowed its ability to do closure conversion. It would still retain the vast majority of its expressive power.
My years of experience tell me that the situations where you actually want to heap allocate a function and store it in an array or a reference are actually fairly rare. And the situations where you are abstracting some repeated functionality through higher order functions are quite distinct from those.
Compared to other functional languages, Spiral has added complexity because of all of its partial evaluation magic. That is true. But if you forget the dyn pattern and the inl statements, and only use let the language can essentially be treated as any other ML variant.
Given its syntactical resemblance to F# and given that it compiles to it, Spiral might seem like a .NET language, but that is not necessarily the case. The reason I picked F# as the compilation target is partly familiarity - up to that point F# was my primary language, and party platform specific reasons. JVM for example does not support tail call optimization (TCO). Neither do the various Javascript engines. Neither does Python.
Back in 2017 during the work on the first Spiral, I actually tried making Spiral a proper .NET language, but in the end realized that .NET is huge. It was really quite difficult to make progress in this direction, and to make matters worse, on the Cuda side I also needed a system to interface with its C++ libraries. I asked around and tried looking for ways of getting the types from C++ header files, but that quickly turned into a dead end as C++ was too complex as a language. Parsing C++ actually requires a full C++ compiler.
So for language interop, I ended up settling on macros. This is actually fairly similar to how ReasonML and Fable interface with Javascript. The new Spiral also uses macros for interop, but they got a refresh. The old Spiral had fairly flexible and powerful macros that were ugly to write and look at. The new Spiral has less powerful, but nicer and more elegant string-interpolated ones.
This is the general theme of the move from v0.09 to v2 - the language has been redesigned from the ground up in order to improve its ergonomics and compilation times.
Here is an example of them.
inl main () =
inl ~x = "asd"
$"// This is a comment"
inl ~y = "qwe"
$"printfn \"x=%s, y=%s\" !x !y"
()
let v0 : string = "asd"
// This is a comment
let v1 : string = "qwe"
printfn "x=%s, y=%s" v0 v1Using ! it is possible to splice in the term variables, and ` can be used to do the same for type variables.
Here is how it is possible to create .NET objects. For anyone browsing this doc, note that these examples are easier to read in the editor as it provides semantic highlighting.
type resize_array a = $"ResizeArray<`a>"
inl resize_array_create forall a. : resize_array a = $"ResizeArray<`a>()"
inl resize_array_add forall a. (x : resize_array a) (v : a) : () = $"!x.Add(!v)"
inl main () =
inl ~x = resize_array_create
resize_array_add x 1i32
resize_array_add x 2
resize_array_add x 3
let v0 : ResizeArray<int32> = ResizeArray<int32>()
v0.Add(1)
v0.Add(2)
v0.Add(3)This is not as nice as having direct interop with .NET that F# gives you. If one wanted to program purely on the .NET platform, and the task did not require composable inlining or heavy serialization I would not recommend Spiral over F#. But the system shown here does have an advantage in that it makes the creation of a language backend easy.
The F# one is a bit less than 400 lines of code, and macros give us access to all the .NET libraries right away. The situation would be the same if Spiral were compiling to Cuda, Java, Python or anything else.
New in v2.3.1: Macros can now be started with an apostrophe. For example:
F# would say that let id x = x is just a function, and according to the previous section, Spiral would contribute to the conversation by saying: 'No, no, this is a function with a dyn pattern on its argument and a join point wrapped around its body.' It shows a more nuanced way of thinking by pointing out a reasonable decomposition for a function into several orthogonal concepts.
When it comes to data structures, many languages mix various orthogonal concepts to arrive at their foundation.
For example, consider an F# record.
type R = { a : int; b : string }In F# this is a record, but in Spiral the equivalent would be a nominal type + heap layout type + a record type.
In Spiral, compound types such as pairs and records do not have a runtime footprint.
inl main () = join 1i32, 2i32, 3i32
let rec method0 () : struct (int32 * int32 * int32) =
struct (1, 2, 3)
method0()This is convenient as heap allocation is not necessary for them. If the target was a C backend, the obvious benefit is that the user would never need to hesitate when using records or pairs. F# tuples and records are heap allocated by default and it is up to the compiler to optimize the heap allocations away if it can.
Spiral on the other hand gives a hard guarantee on what their runtime representation will be. Much like for inlining, what you see is what you get. inl statements aren't a suggestion to the compiler - it will always inline.
Here is the same example, except with a record.
inl main () = join {a=1i32; b=2i32; c=3i32}
let rec method0 () : struct (int32 * int32 * int32) =
struct (1, 2, 3)
method0()Spiral records are lexically ordered, and they get compiled down to ordinary stack allocated structs. Their keys get erased. A benefit of this is that it becomes possible to nest records without any penalty.
inl main () = join {q = {a=1i32; n={b=2i32; c=3i32}}}
let rec method0 () : struct (int32 * int32 * int32) =
struct (1, 2, 3)
method0()The same applies to pairs. In fact 1,2,3 is equivalent to 1,(2,3) in Spiral. In F# though these would be distinct.
A motivation for having pairs as opposed to tuples in Spiral is that later on when I implement tensors, this will make it possible to easily partially apply their outermost dimension. If you have a 3d tensor for example, typically you need to apply it all at once in various languages, but in Spiral I want to make it easy to write something like t ! 0 which would then apply the outermost dimension and return a 2d tensor.
In F#, function arrows are right associative a -> b -> c is equivalent to a -> (b -> c). The pairs being that way as well gives them a symmetry that can be exploited to make data structures behave as applicables.
At any rate, heap allocation is desirable in some scenarios and Spiral makes it easy to change the layout of its types.
inl main () = join heap {q = {a=1i32; n={b=2i32; c=3i32}}}
type Heap0 = {l0 : int32; l1 : int32; l2 : int32}
let rec method0 () : Heap0 =
{l0 = 1; l1 = 2; l2 = 3} : Heap0
method0()heap is a core library function of type forall a. a -> heap a. It will dyn the input and construct a runtime heap allocated object consisting of them.
Heap allocated objects can be nested.
inl main () = join heap {q = {a=1i32; n = heap {b=2i32; c=3i32}}}
type Heap1 = {l0 : int32; l1 : int32}
and Heap0 = {l0 : int32; l1 : Heap1}
let rec method0 () : Heap0 =
let v0 : Heap1 = {l0 = 2; l1 = 3} : Heap1
{l0 = 1; l1 = v0} : Heap0
method0()They can be used on primitive types.
inl main () = join heap {q = {a=1i32; n={b=heap 2i32; c=3i32}}}
type Heap1 = {l0 : int32}
and Heap0 = {l0 : int32; l1 : Heap1; l2 : int32}
let rec method0 () : Heap0 =
let v0 : Heap1 = {l0 = 2} : Heap1
{l0 = 1; l1 = v0; l2 = 3} : Heap0
method0()This is useful as Spiral does not have references, but it does have heap mutable types.
inl main () =
inl a = mut {q=1i32; w=2i32}
a <- {q=3; w=4}
type Mut0 = {mutable l0 : int32; mutable l1 : int32}
let v0 : Mut0 = {l0 = 1; l1 = 2} : Mut0
v0.l0 <- 3
v0.l1 <- 4The same flattening semantics work for heap mutable types as for regular heap types.
inl main () =
inl a = mut {q = {a=1i32; n = {b=2i32; c=3i32}}}
a <- {q={a=4; n={b=5; c=6}}}
type Mut0 = {mutable l0 : int32; mutable l1 : int32; mutable l2 : int32}
let v0 : Mut0 = {l0 = 1; l1 = 2; l2 = 3} : Mut0
v0.l0 <- 4
v0.l1 <- 5
v0.l2 <- 6Heap mutables when combined with records do have some extra benefits.
inl main () =
inl a = mut {q = {a=1i32; n = {b=2i32; c=3i32}}}
a.q.n <- {b=5; c=6}
type Mut0 = {mutable l0 : int32; mutable l1 : int32; mutable l2 : int32}
let v0 : Mut0 = {l0 = 1; l1 = 2; l2 = 3} : Mut0
v0.l1 <- 5
v0.l2 <- 6This nested indexing on the left side is specific to records, there is no way to get the same effect for pairs for example.
With this, the functionality of layout types has been covered. Here is how to turn layout types into regular ones.
inl main () =
inl a = heap 1i32
inl b = mut 2i32
!a, *b
type Heap0 = {l0 : int32}
and Mut0 = {mutable l0 : int32}
let v0 : Heap0 = {l0 = 1} : Heap0
let v1 : Mut0 = {l0 = 2} : Mut0
let v2 : int32 = v0.l0
let v3 : int32 = v1.l0
struct (v2, v3)! and * are not keywords, they are plain unary operators defined in the core library. Similarly to in F#, unary operators can be defined with ~ prefixed to their name. For example, inl (~!) x = ....
In Spiral, nominals are just compile time wrappers around some underlying type. They are similar to type aliases in that they are type level functions, but they also retain the arguments applied to them. They have a specific pattern for their destructuring. As mentioned in the previous section, here is what an F# record would be equivalent to in Spiral.
nominal t = heap {a : i32; b : i32}
inl main () = t (heap {a=1; b=2})
type Heap0 = {l0 : int32; l1 : int32}
let v0 : Heap0 = {l0 = 1; l1 = 2} : Heap0
v0As can be seen, the wrapper never appears in the compiled code. It bears mentioning that the generated code samples are in fact perfectly predictable. Spiral does not do any black box or speculative optimization, so you can count on this behavior to happen every time.
Here is how nominals can be destructured in patterns.
nominal t = heap {a : i32; b : i32}
inl main () =
inl x = t (heap {a=1; b=2})
match x with
| t p => p.a + p.b
type Heap0 = {l0 : int32; l1 : int32}
let v0 : Heap0 = {l0 = 1; l1 = 2} : Heap0
let v1 : int32 = v0.l0
let v2 : int32 = v0.l1
v1 + v2In F# and most statically typed languages it is not possible to bind member accessors. In Spiral that is not a problem.
inl main () =
inl x = .asd
x
()It is possible to use x to access a record or a module field like so.
inl main () =
inl x = .asd
{asd = "qwe"} x
"qwe"If you hover the cursor over x, you will see that the type is .asd. Symbols are a bit like strings, except their type happens to be whatever their value is.
In the top-down segment this is of limited use. Suppose you wrote a program like this...
inl main () =
inl x = {a="asd"; b=true}
inl f k = x k
f .a, f .b
Expected symbol as a record key.
Got: 'a
You get an error that it is expecting a symbol key known at compile time, but it is instead getting a type variable. The top-down system is not powerful enough to deal with this. The real bottom-up one is.
inl main () : () = real
inl x = {a="asd"; b=true}
inl f k = x k
f .a, f .b
struct ("asd", true)The disadvantage of using the bottom-up system is that you lose the top-down benefits of type inference. Not only will the type errors get deferred to the partial evaluation stage, type application and type annotations for closures and recursive join points have to be set manually. This is tedious or difficult depending on the approach and more details will be provided in a later segment. For now, let us stick to the top-down part of Spiral
An amazing number of functional languages have crappy records, but in Spiral they are no more and no less than they should be.
For example, they support nested (lensic) updates.
inl main () =
inl x = {q = 1i32; w={a="asd"; b=true}}
{x.w with b=false}
struct (1, "asd", false)The above code fragment is not actually the behavior you'd get in F#. In F#, what would happen is that x.w would grab the nested record and update only that one instead.
let x = {|q=1; w={|a="asd"; b=true|}|}
{|x with w={|x.w with b=false|}|}This is what the record update code fragment would be equivalent to in F#. That F# does not do this is actually fairly annoying at times. Furthermore, one thing I've found that eases record handling significantly that F# does not support is record punning.
inl main () =
inl x = {q = 1i32; w={a="asd"; b=true}}
inl b = false
{x.w with b}
struct (1, "asd", false)Spiral can do more, here is how key removal can be done.
inl main () =
inl x = {q = 1i32; w={a="asd"; b=true}}
{x.w without b}
struct (1, "asd")It supports key injection for removals.
inl main () =
inl k = .b
inl x = {q = 1i32; w={a="asd"; b=true}}
{x.w without $k}
struct (1, "asd")Key injection also works for updates.
inl main () =
inl k = .b
inl x = {q = 1i32; w={a="asd"; b=true}}
{x.w with $k=false}
struct (1, "asd", false)This is one of those things which is more useful in the bottom-up segment than the top-down one. The above only demonstrates set-updates. There are also the modify-updates. Instead of writing a = b, use the #= operator instead.
inl main () =
inl k = .b
inl flip x = not x
inl x = {q = 1i32; w={a="asd"; b=true}}
{x.w with $k#=flip}
struct (1, "asd", false)The above is the injecting version of the modify update. b #= flip would work just as fine.
Just like in updates, injection and punning works in patterns. Here are a few examples.
inl main () : i32 =
inl f {a=a b=b} = a + b
f {a=1; b=2}
3The above is equivalent to...
inl main () : i32 =
inl f {a b} = a + b
f {a=1; b=2}
3Here are two record injection examples. First is the regular one that does explicit rebinding.
inl main () : i32 =
inl k, k' = .q, .w
inl f {$k=a $k'=b} = a + b
f {q=1; w=2}
3Here is the punny record injection pattern.
inl main () : i32 =
inl k, k' = .q, .w
inl f {$k $k'} = k + k'
f {q=1; w=2}
3The record injection patterns aren't too useful in the top-down segment, but in the bottom-up segment which was the entirety of Spiral in the previous version, I often used them.
inl main () =
inl k, k' = .b, .w
inl flip x = x = false
inl x = {q = 1i32; w={a="asd"; b=true}}
{x$k' with $k#=flip}
Also as shown above, it is possible to use the injection pattern on the left side of with.
Here is how union types are defined and constructed.
union t =
| A : i32 // .A * i32
| B : f64 // .B * f64
| C : string // .C * string
inl main () = C "asd"
type [<Struct>] US0 =
| US0_0 of f0_0 : int32
| US0_1 of f1_0 : float
| US0_2 of f2_0 : string
US0_2("asd")Under the hood, the union is a nominal wrapper + a symbol/value pair that matches one of its type cases. The union keyword exports the constructors into the term scope and they can be used as regular functions.
union t =
| A : i32 // .A * i32
| B // .B * ()
inl main () = B
type [<Struct>] US0 =
| US0_0 of f0_0 : int32
| US0_1
US0_1Regardless of whether the B case is written as the above or B : (), its constructor will not require an argument to be passed into it. This is just a bit of syntax sugar to make it easier to use. At the time of writing (8/17/2021) it is not possible to define top level term level statements that aren't functions, but in the future I might loosen the restriction to allow constants. Until then the unit union constructors are special.
For completeness, here is an example of how union destructuring works in Spiral. It is quite similar to how it is in other functional languages.
union t =
| A : i32
| C : string
inl main () =
inl ~x = C "Hello"
match x with
| A x => A (x * 2)
| C => C "zxc"
type [<Struct>] US0 =
| US0_0 of f0_0 : int32
| US0_1 of f1_0 : string
let v0 : string = "Hello"
let v1 : US0 = US0_1(v0)
match v1 with
| US0_0(v2) -> (* A *)
let v3 : int32 = v2 * 2
US0_0(v3)
| US0_1(v5) -> (* C *)
US0_1("zxc")The union patterns start with a capital letter, and it is not possible to define uppercase variables in Spiral due to that. Note that unlike in F#, Spiral does not require an argument to be given. If that is the case, it will be assumed to be a wildcard. In the above example, C => will be desugared to C _ =>.
One final thing, Spiral makes a distinction between recursive and non-recursive unions. Lists for example need to be defined with union rec.
union rec list a =
| Nil
| Cons : a * list a
inl main() = Cons (1i32,Nil)
type UH0 =
| UH0_0 of int32 * UH0
| UH0_1
let v0 : UH0 = UH0_1
UH0_0(1, v0)Only union rec can be a mutually recursive type definition in Spiral.
union rec a =
| A : b
| StopA
and union b =
| B : a
| StopB
inl main () =
A (B StopA)
type UH1 =
| UH1_0 of UH0
| UH1_1
and UH0 =
| UH0_0 of UH1
| UH0_1
let v0 : UH0 = UH0_1
let v1 : UH1 = UH1_0(v0)
UH0_0(v1)The distinction between recursive and non recursive unions also determines their allocation pattern. It is intended that non-recursive unions be allocated on the stack, and the recursive unions on the heap.
However there are some backend specific caveats to this. F#'s struct unions are inefficient and are allocated as tuples on the stack. Meaning, rather than being compiled as true unions under the hood, the space each instance of an union type takes up at runtime is the sum of sizes of all the fields in every constructor instead of just one.
As of Spiral 2.4.11. In order to support arrays with static dimension sizes in the HLS C++ backend, type level literals have been added to the language.
nominal static_array dim el = $"array<@dim,`el>"
A new macro splicing prefix operator @ has been added to the language as well. What it will do is splice type level literals, as well as symbols into the macro and throw an error otherwise. It was necessary to add because otherwise the macro would not have passed in the variables properly.
For turning literals to type literals, a new prefix operator has been added at the term level as well !@.
real
inl f forall t. = ()
f `@"qwe"
Here is an example of it in use. You can also use !@ to grab a variable from the term level. Symbols can also be passed into it that way as well.
real
inl x = .qwe
inl f forall t. = ()
f `@x
String, integers, floats and chars can all be type level literals. You can also write them out directly at the type level.
typecase 16 * (f32 * i32) with
| ~dim * ~el => $"array<@dim,`el> \v" : static_array dim el
You can't do meaninful computation with them in the top down segment and they are intended to be used in the real segment instead. The top down segment's intent is to help you propagate them, and not much else.
In the C/C++ backends, \v can be used to declare arrays without needing to return them.
nominal static_array dim el = $"array<@dim,`el>"
inl main () : () = real
inl _ =
typecase 16 * (f32 * i32) with
| ~dim * ~el => $"array<@dim,`el> \v" : static_array dim el
()
#include <cstdint>
template <int dim, typename el> struct array { el v[dim]; };
typedef struct {
float v0;
int32_t v1;
} Tuple0;
static inline Tuple0 TupleCreate0(float v0, int32_t v1){
Tuple0 x;
x.v0 = v0; x.v1 = v1;
return x;
}
void main(){
array<16l,Tuple0> v0;
return ;
}The issue is that the array syntax is so awkward in C that in order to declare arrays, we need to fold he tags into them. Only in the C/C++ backends does the \v have special meaning. You can use multiple of them in the same macro. It requires the same number of free vars in its bindings as there are \vs in the macro.
Spiral supports a form of ad-hoc polymorphism even in the top-down segment. Functionally, they are equivalent to Haskell's single parameter typeclasses. It is possible to use them to implement the monad typeclass for example. Here is a simpler example to start things off.
nominal a = i32
nominal b = f64
prototype is_bigger_than_five t : t -> bool
instance is_bigger_than_five a = fun (a x) => 5 <= x
instance is_bigger_than_five b = fun (b x) => 5 <= x
inl main () = is_bigger_than_five (a 1), is_bigger_than_five (b 6)
struct (false, true)The above is similar to writing the following using the bottom-up segment.
nominal a = i32
nominal b = f64
inl is_bigger_than_five x : bool = real
open real_core
match x with
| a x => 5i32 <= x
| b x => 5f64 <= x
inl main () = is_bigger_than_five (a 1), is_bigger_than_five (b 6)
struct (false, true)Going deeper, the prototype is_bigger_than_five t : t -> bool provides the type signature for the is_bigger_than_five function.
instance is_bigger_than_five a = fun (a x) => 5 <= x
instance is_bigger_than_five b = fun (b x) => 5 <= x
After that, the prototype instances act as match cases. This is a convenient way of providing ad-hoc overloading during the top-down segment.
nominal a = i32
nominal b = f64
nominal c = i64
prototype is_bigger_than_five a : a -> bool
instance is_bigger_than_five a = fun (a x) => 5 <= x
instance is_bigger_than_five b = fun (b x) => 5 <= x
inl main () = is_bigger_than_five (a 1), is_bigger_than_five (b 6), is_bigger_than_five (c 7)
Since the instance for nominal c does not exist, the above example is erroneous and the error message says as much.
Besides the benefit of being inferable during the top-down segment, the prototype instances can be defined in different modules than the prototype as long as the nominal is defined there.
prototype (>>=) m a : forall b. m a -> (a -> m b) -> m b
prototype on_succ m a : a -> m a
Here is how the monadic bind >>= is defined in Spiral. The first element following the prototype is the name, followed by at least one type variable which acts as a type selector. During partial evaluation, the matching is done on the type itself rather than one of the values, which allows for return polymorphism as on_succ demonstrates.
The type variables after the first one are the ones related to it, the kind of the selector is derived from them.
In Spiral, the kinds of the foralls are not automatically derived, instead when an annotation is not provided they are assumed to be *.
prototype is_bigger_than_five a : a -> bool
In this simple prototype for example, the kind of a is *. In the more complex monadic bind the kind of m is * -> *.
In...
prototype (>>=) m a : forall b. m a -> (a -> m b) -> m b
prototype on_succ m a : a -> m a
...the m a is purposely made to resemble destructuring. When writing instances, you'd imagine substituting m a with list a or some other nominal that matches the signature.
It should be mentioned - all union types are always wrapped in a nominal which is why prototype instances can be defined for them. It has been mentioned that language's sometimes mix multiple concepts in order to establish their foundational data structures. In Spiral's case, its unions are nominals + raw unions under the hood. The language does not allow taking off the nominal wrapper from unions during destructuring.
They are a compile-time only feature that allow the hidding of type variables.
union rec layers a c =
| Layer : layer a c
| Compose : exists b. layers a b * layers b c
You can now store type variables directly into the exists data type without needing to bind them in a forall. Their intended use case is the composition of neural network layers in the ML library, and are intended to cover up for the lack of GADTs in the language.
inl x : exists t{number}. t * f32 = exists 2i32, 234
inl (exists e. y,_) = x
They can be constructerd and subsequently destructured as shown above. Destructuring an existential like in the example above would introduce a new type variable into scope. The limitation for this feature is that none of the backends can generate the runtime code for them and will instead raise errors when encountering them, and destructuring runtime existentials would also result in an error during the partial evaluation stage.
inl a = exists [i32; f32] 2
In the bottom-up segment, the type variables need to be passed into the existential up front and allow passing of arbitrary types at the term level.
Spiral (as of v2.12.0) has the generalized algebraic datatypes. Existentials combined with specialized union constructors are what makes them. The regular union types have been extended so now you can write something like the following using the :: operator to denote the GADT union cases.
union rec graph t =
| Map :: forall dim t. (exists a. (layer_state -> a -> t) * graph (tensor dim a)) -> graph (tensor dim t)
| RowMap :: forall dim t.
(exists a. (layer_state -> primitives.row_config -> tensor (int * int) a -> int -> tensor (int * int) int -> tensor (int * int) t) * graph (tensor dim a))
-> graph (tensor dim t)
| RowReduce :: forall dim t.
(exists a. (layer_state -> primitives.row_config -> tensor (int * int) a -> int -> tensor (int * int) int -> t) * graph (tensor dim a))
-> graph (tensor dim t)
| Zip :: forall a b. graph a * graph b -> graph (a * b)
| Apply :: forall b el. graph (tensor (int * b) el) * graph int -> graph (tensor b el)
| Matmul :: forall dim t. graph (tensor dim t) * graph (tensor dim t) -> graph (tensor dim t)
| Weight :: forall dim t. graph (tensor dim t)
| Input :: forall dim t. (exists key{symbol}. key) * dim -> graph (tensor dim t)
| InputScalar :: (model_sizes -> graph int) -> graph int
This is a prototype of the graph datatype for the ML library currently being worked on (as of 7/5/2024), and many of the cases wouldn't have been possible without this feature. Regular union types are simply not expressive for what we want to do, so GADTs were added to the language.
Unlike in other languages with the similar feature, in Spiral the GADT union cases have to use the type variables from the foralls in their constructor. For those not in the constructor that are intended to be hidden away in the body, the existentials are necessary. The benefit of this arrangement is that GADTs that do not use existentials can be efficiently compiled and don't have to be recursive either.
Let's check out how they work on a simpler example.
union t y =
| A :: int -> t i32
| B :: t f32
let f forall y. (x : t y) =
match x with
| A =>
inl x = x
()
| B =>
inl x = x
()
inl main() : () =
inl x = f (A 2)
inl x = f B
()
If you hover with the cursor over the x variable in the first inl x = x statement, you'll see that it's type is t i32. Similarly, the x in the other match branch is of type t f32. What GADTs allow you is similar to a typecase statement in the top down segment. They allow the user to specialize type variables by the destructuring of an union type.
Besides for allowing the data types to be as expressive as regular functions in terms of typing, GADTs can be efficiently compiled as long as they don't use existentials.
When we compile the above, here is what we get.
type [<Struct>] US0 =
| US0_0 of f0_0 : int32
and [<Struct>] US1 =
| US1_0
let rec method0 (v0 : US0) : unit =
match v0 with
| US0_0(v1) -> (* A *)
()
and method1 (v0 : US1) : unit =
match v0 with
| US1_0 -> (* B *)
()
let v0 : int32 = 2
let v1 : US0 = US0_0(v0)
method0(v1)
let v2 : US1 = US1_0
method1(v2)When the unions are specialized to their principal types in the partial evaluator, the non-constructible cases are eliminated. You cannot have an t i32 that also has the B clause, so that one is automatically eliminated. If you try writing a function that takes in an t i32, you won't be able to destructure the B case at all.
The syntax for them is simple, consider the following union type.
union option t =
| Some : t
| None
Translating these to the GADT format would involve giving the union case constructors their full type.
union option t =
| Some :: forall x. x -> option x
| None :: forall x. option x
These two types are otherwise equivalent, but while vanilla unions are fully generic, GADTs can be specialized making them a lot more powerful and expressive.
It's possible to mix and match regular and GADT union cases without issue.
Spiral (as of v.2.13.0) has support for higher ranked types in the top down segment. The foralls can be written as types anywhere, and types containing them can be stored in nominals and union types. Here is an example.
inl main() =
inl id forall t. (x : t) : t = x
inl f forall x. (id : forall t. t -> t) (x : x) : int * bool * float = id 1, id true, id 123
f `$id false
struct (1, true, 123.0f)Previuosly, it would have been impossible to put a forall as a part of a variable's type. Now it can be done anywhere.
In particular what we wanted to do is write the following function:
union rec compound (f : * -> *) t =
| Singleton :: forall (f : * -> *) t. f t -> compound f (singleton t)
| Pair :: forall (f : * -> *) a b. compound f a * compound f b -> compound f (a * b)
inl rec compound_map forall (f : * -> *) (g : * -> *) t. (f : forall t. f t -> g t) : compound f t -> compound g t = function
| Singleton x => Singleton(f x)
| Pair(a,b) => Pair(compound_map `$f a, compound_map `$f b)
What having foralls in variables allows us to do is write a function that can map over higher kindeds like the compound_map above. We make good use of it in the ML library. The special `$ operator has been added to the language to help support its use. It blocks automatic type variable application.
f `$id false
If you hover over the id in the editor you'll see that is says forall t. t -> t, just as it's type signature. If it weren't for the `$ operator, the variable would have had its foralls automatically applied and would not be passable as an argument into the given function due to it expecting a forall, but the identity having a concrete type.
Much like existentials, foralls are a compile time feature only. The code using them cannot be generated by any of the backends, and they'll give an error upon dyning. They have to be inlined and specialized away.
In today's statically typed languages, type systems span a wide range from weak and monomorphic like C's, to polymorphic like F#'s, to dependently typed ones like Agda's. It is really remarkable that increasing type system sophistication seems to result in decreasing performance of languages.
It is a widespread meme that compared to, say Python, Java is faster because it is statically typed. But C is considered faster than Java, but there is no doubt that Java has a superior type system.
To illustrate the trouble that abstractions cause, consider the identity function. Here is how it would be written in F#.
let id x = xIn the editor the type shows up as 'a -> 'a, but with the foralls made explicit its type is forall 'a. 'a -> 'a. The forall is troublesome, it is not something that can be compiled to machine code straightforwardly. There are two different compilation strategies that compilers can use to deal with the forall.
- The first is to use uniform representations for every data type. Dynamic languages use this by default, and in .NET languages that corresponds to upcasting a variable to heap allocated base type.
let id (x : obj) = xAs a way of illustration in F#, this would in essence compile the identity function to something that is equivalent to obj -> obj.
Dynamic languages follow this strategy. Also JVM languages, and Haskell and Ocaml.
The advantage of this approach is that one single identity function can be used for any kind of data type. The disadvantage of it is that primitives like ints, and stack allocated structs would need to be heap allocated and passed via reference into that function. This heap allocation happens under the hood and is invisible to the user, but is a real source of memory traffic.
- Monomorphization. Spiral uses this, as does C++ for its templates, and Rust for example.
inl main () =
let id x = x
inl _ = id 1i32
inl _ = id 2f64
inl _ = id 3i8
inl _ = id true
()
let rec method0 (v0 : int32) : int32 =
v0
and method1 (v0 : float) : float =
v0
and method2 (v0 : int8) : int8 =
v0
and method3 (v0 : bool) : bool =
v0
let v0 : int32 = 1
let v1 : int32 = method0(v0)
let v2 : float = 2.000000
let v3 : float = method1(v2)
let v4 : int8 = 3y
let v5 : int8 = method2(v4)
let v6 : bool = true
let v7 : bool = method3(v6)
()
Now the primitives no longer require boxing in order to be passed into the identity function - the compiler specializes it for each datatype, but the disadvantage is that this requires more work at compile time, and it produces more code at runtime.
This is the memory tradeoff between heap allocation and code size.
This tradeoff is a very real phenomenon that users often unwittingly make, and it explains most of the performance difference between different languages.
Dynamic languages are all the way on the heap allocation side of the axis. Their primitives and data structures are all heap allocated by default. By default should imply unless the optimizer gets to them. But dynamic languages tend to have very flexible semantics that frequently inhibits that.
For dynamic languages with good optimizers like Javascript for example, it is well known that to get optimized code in it the way to do it is to write it as if it had a static type system. That is why semi-dynamic languages like the .NET ones, which have a dynamic runtime, GC and heap allocate by default, but also have static type systems generally have better performance than dynamic ones.
Spiral does stack allocation by default for all its primitives (except strings) and errs on the side of too much inlining, but this tradeoff does for it as well.
inl init nearTo f : a i32 _ =
inl ar = create nearTo
loop.for' {from=0; nearTo} (fun i => set ar i (f i))
ar
inl map f ar = init (length ar) (fun i => f (index ar i))
inl main () =
init 10 id
|> map ((+) 2)
|> map ((*) 10)
|> map ((/) 2)
|> map ((-) 5)
|> map ((%) 4)
let rec method0 (v0 : (int32 []), v1 : int32) : unit =
let v2 : bool = v1 < 10
if v2 then
let v3 : int32 = v1 + 1
v0.[int v1] <- v1
method0(v0, v3)
and method1 (v0 : int32, v1 : (int32 []), v2 : (int32 []), v3 : int32) : unit =
let v4 : bool = v3 < v0
if v4 then
let v5 : int32 = v3 + 1
let v6 : int32 = v1.[int v3]
let v7 : int32 = 2 + v6
v2.[int v3] <- v7
method1(v0, v1, v2, v5)
and method2 (v0 : int32, v1 : (int32 []), v2 : (int32 []), v3 : int32) : unit =
let v4 : bool = v3 < v0
if v4 then
let v5 : int32 = v3 + 1
let v6 : int32 = v1.[int v3]
let v7 : int32 = 10 * v6
v2.[int v3] <- v7
method2(v0, v1, v2, v5)
and method3 (v0 : int32, v1 : (int32 []), v2 : (int32 []), v3 : int32) : unit =
let v4 : bool = v3 < v0
if v4 then
let v5 : int32 = v3 + 1
let v6 : int32 = v1.[int v3]
let v7 : int32 = 2 / v6
v2.[int v3] <- v7
method3(v0, v1, v2, v5)
and method4 (v0 : int32, v1 : (int32 []), v2 : (int32 []), v3 : int32) : unit =
let v4 : bool = v3 < v0
if v4 then
let v5 : int32 = v3 + 1
let v6 : int32 = v1.[int v3]
let v7 : int32 = 5 - v6
v2.[int v3] <- v7
method4(v0, v1, v2, v5)
and method5 (v0 : int32, v1 : (int32 []), v2 : (int32 []), v3 : int32) : unit =
let v4 : bool = v3 < v0
if v4 then
let v5 : int32 = v3 + 1
let v6 : int32 = v1.[int v3]
let v7 : int32 = 4 % v6
v2.[int v3] <- v7
method5(v0, v1, v2, v5)
let v0 : (int32 []) = Array.zeroCreate<int32> (10)
let v1 : int32 = 0
method0(v0, v1)
let v2 : int32 = v0.Length
let v3 : (int32 []) = Array.zeroCreate<int32> (v2)
let v4 : int32 = 0
method1(v2, v0, v3, v4)
let v5 : int32 = v3.Length
let v6 : (int32 []) = Array.zeroCreate<int32> (v5)
let v7 : int32 = 0
method2(v5, v3, v6, v7)
let v8 : int32 = v6.Length
let v9 : (int32 []) = Array.zeroCreate<int32> (v8)
let v10 : int32 = 0
method3(v8, v6, v9, v10)
let v11 : int32 = v9.Length
let v12 : (int32 []) = Array.zeroCreate<int32> (v11)
let v13 : int32 = 0
method4(v11, v9, v12, v13)
let v14 : int32 = v12.Length
let v15 : (int32 []) = Array.zeroCreate<int32> (v14)
let v16 : int32 = 0
method5(v14, v12, v15, v16)
v15The above program demonstrates how a bunch of maps get compiled to separate loops. In total, the output comes to 70 lines of code. But there is a lot of duplicate code in the loops.
It is easy to lower the resulting output size by doing more heap allocation at runtime. All it takes is a single character change. Instead of inlining the function, the following example passes them as closures at runtime.
inl init nearTo f : a i32 _ =
inl ar = create nearTo
loop.for' {from=0; nearTo} (fun i => set ar i (f i))
ar
inl map ~f ar = init (length ar) (fun i => f (index ar i))
inl main () =
init 10 id
|> map ((+) 2)
|> map ((*) 10)
|> map ((/) 2)
|> map ((-) 5)
|> map ((%) 4)
let rec method0 (v0 : (int32 []), v1 : int32) : unit =
let v2 : bool = v1 < 10
if v2 then
let v3 : int32 = v1 + 1
v0.[int v1] <- v1
method0(v0, v3)
and closure0 () (v0 : int32) : int32 =
2 + v0
and method1 (v0 : int32, v1 : (int32 -> int32), v2 : (int32 []), v3 : (int32 []), v4 : int32) : unit =
let v5 : bool = v4 < v0
if v5 then
let v6 : int32 = v4 + 1
let v7 : int32 = v2.[int v4]
let v8 : int32 = v1 v7
v3.[int v4] <- v8
method1(v0, v1, v2, v3, v6)
and closure1 () (v0 : int32) : int32 =
10 * v0
and closure2 () (v0 : int32) : int32 =
2 / v0
and closure3 () (v0 : int32) : int32 =
5 - v0
and closure4 () (v0 : int32) : int32 =
4 % v0
let v0 : (int32 []) = Array.zeroCreate<int32> (10)
let v1 : int32 = 0
method0(v0, v1)
let v2 : (int32 -> int32) = closure0()
let v3 : int32 = v0.Length
let v4 : (int32 []) = Array.zeroCreate<int32> (v3)
let v5 : int32 = 0
method1(v3, v2, v0, v4, v5)
let v6 : (int32 -> int32) = closure1()
let v7 : int32 = v4.Length
let v8 : (int32 []) = Array.zeroCreate<int32> (v7)
let v9 : int32 = 0
method1(v7, v6, v4, v8, v9)
let v10 : (int32 -> int32) = closure2()
let v11 : int32 = v8.Length
let v12 : (int32 []) = Array.zeroCreate<int32> (v11)
let v13 : int32 = 0
method1(v11, v10, v8, v12, v13)
let v14 : (int32 -> int32) = closure3()
let v15 : int32 = v12.Length
let v16 : (int32 []) = Array.zeroCreate<int32> (v15)
let v17 : int32 = 0
method1(v15, v14, v12, v16, v17)
let v18 : (int32 -> int32) = closure4()
let v19 : int32 = v16.Length
let v20 : (int32 []) = Array.zeroCreate<int32> (v19)
let v21 : int32 = 0
method1(v19, v18, v16, v20, v21)
v20With the change, the output is 17 lines shorter than the previous one. It comes down to 53 lines.
The expense paid in lines of code is a quarter less, but now the resulting program would allocate closures on the heap. This along with the loop now requiring virtual calls to apply the closure would make the resulting program slower to execute than the first one. The first one's loops since they have the operations inlined directly might get vectorized and made even faster because of that.
It is not the case that performance is maximized by being all the way on the right side of the axis - too much inlining and specialization can hurt performance, but in general the place to look for the optimized spot would be best done by starting from there.
There is a saying that there are no fast or slow languages, only fast or slow implementations, but speaking as a language designer I do not agree with this. It is fairly obvious looking at the real world that some languages are very hard to optimize, and I do not think it is the case that the reason Python or Ruby are slow is because not enough money was thrown at them by Google or Microsoft.
The heap allocation vs code size presents a framework for thinking about the performance of languages. Some languages might claim that they are fast, and might have flashy benchmarks comparing themselves to C. But regardless, you can look at what its defaults are - does it heap allocate basic data structures? Does it heap allocate primitives? Does it specialize like C++ or Spiral, or does it use dynamic language tricks to generate code like JVM and .NET ones do? Does it do the slow thing and then rely on the optimizer to make itself fast, or does it do the right thing from the start?
I feel confident about stating that Spiral is a performant language even without providing benchmarks, and the examples in the previous section were all demonstrations of what its compilation defaults are. They are the lead into this framework. Spiral has sensible defaults and gives the user the partial evaluation tools to make the heap allocation/code size tradeoff in a sensible manner.
Languages with dependent types are an interesting case study, they are further on the heap allocation side. They are slow because they are hard to compile, meaning they require dynamic runtimes. Idris for example compiles to Scheme. This is not exceptional among high level languages, but they require them much more severely. Since I like to push into extremities and am familiar with dependently typed languages, I did try to come up with a top-down type system with dependent types for Spiral, but I failed. I could not figure out something that meshes well with the partial evaluator.
Here is an example that demonstrates what I got hung up on in pseudo-code. Imagine if F# or Spiral had dependent types and as a thought experiment try to imagine how this would be compiled.
let (y : (if x < 10 then int else string)) = if x < 10 then 0 else "asd"
if x < 10 then y + 10 else y + "qwe"In Spiral, F# and other statically typed languages with strong, but not dependent type systems, the types actually do have a 1:1 correspondence with their underlying representation.
How exactly can the type if x < 10 then int else string be compiled? Is it some kind of union type? That seems to be a reasonable avenue to go down on at first, but the difficulties of that become apparent very quickly.
In the branches of if x < 10 then y + 10 else y + "qwe" how should y be destructured if it is a union type under the hood? Thinking about it logically, we know that y is an int in the then branch, and a string in the else branch, but where is the hook to actually unbox the union? This kind of thinking does not really make sense to me.
As if it were a force of nature, there is an inexorable pull towards admitting that despite being statically typed, the types in dependently typed languages are unmoored from their underlying representation. Much like in dynamically typed languages. And the most natural way of compiling the above fragment would be to forget the type signatures and just execute it in a computational context that has uniform representation for all its data types.
The way to performantly compile dependently typed languages is a mystery to me. Whereas the simpler type system of Spiral has great synergy with the partial evaluator and is easy to compile.
Arrays in Spiral are more complicated than I wanted them to be. The reason for that is that different backends have different ways of indexing into them. The .NET arrays which F# uses are built in at the IL level and use i32 dimensions. The old Cython backend used Numpy tensors as arrays under the hood and those could be indexed in using ints of arbitrary type. It was hell dealing with discrepancies of different backends, and to resolve the issue, I defined a nominal over a generic array type in the core library.
// Array with a dimension.
nominal a dim a = array a
Here are the generic create, index, set and length prototypes that can be used on it.
// Creates an array.
prototype create ar dim el : dim -> ar dim el
// Indexes into an array.
prototype index ar dim el : ar dim el -> dim -> el
// Sets the value of an array at the specified index.
prototype set ar dim el : ar dim el -> dim -> el -> ()
// Returns the length of an array.
prototype length ar dim el : ar dim el -> dim
This gives an uniform interface over them and the Cython lists. Here is an example of their use.
inl main () =
inl x : a u64 (i32 * i32 * i32) = create 10
set x 5 (1,2,3)
index x 5
let v0 : (struct (int32 * int32 * int32) []) = Array.zeroCreate<struct (int32 * int32 * int32)> (System.Convert.ToInt32(10UL))
v0.[int 5UL] <- struct (1, 2, 3)
v0.[int 5UL]In the F# backend if a value larger than 2 ** 31 is passed to create, an exception gets raised even if they are nominally u64 in their dimension type.
The am module of the core library has various array combinator functions which also work on Cython lists. They are all implemented in am.generic. Here is how map is implemented for example.
// Maps an array.
inl map f ar = init (length ar) (fun i => f (index ar i))
This has the extremely generic signature of forall 'a 'b ('c : * -> * -> *) {index; length} 'd {number} ('e : * -> * -> *) {create; set}. ('a -> 'b) -> 'c 'd 'a -> 'e 'd 'b. Having such a generic signature actually gives trouble to the type inferencer and necessitates the use of type annotations everywhere. Though the am.generic functions can be useful when you want to map from an Numpy array to a Cython list for example, the am function itself has more restricted and commonly used definitions.
// Maps an array.
inl map forall (ar : * -> * -> *) {index; length; create; set} dim {number} el el'. : _ -> ar dim el -> ar dim el' = map
Here is how map is defined in the am module.
inl main () : a u64 (i32 * i32 * i32) =
am.init 10 fun x => 5,4,3
|> am.map fun a,b,c => c,b,a
type Mut0 = {mutable l0 : uint64}
let rec method0 (v0 : Mut0) : bool =
let v1 : uint64 = v0.l0
v1 < 10UL
and method1 (v0 : uint64, v1 : Mut0) : bool =
let v2 : uint64 = v1.l0
v2 < v0
let v0 : (struct (int32 * int32 * int32) []) = Array.zeroCreate<struct (int32 * int32 * int32)> (System.Convert.ToInt32(10UL))
let v1 : Mut0 = {l0 = 0UL} : Mut0
while method0(v1) do
let v3 : uint64 = v1.l0
v0.[int v3] <- struct (5, 4, 3)
let v4 : uint64 = v3 + 1UL
v1.l0 <- v4
let v5 : uint64 = System.Convert.ToUInt64 v0.Length
let v6 : (struct (int32 * int32 * int32) []) = Array.zeroCreate<struct (int32 * int32 * int32)> (System.Convert.ToInt32(v5))
let v7 : Mut0 = {l0 = 0UL} : Mut0
while method1(v5, v7) do
let v9 : uint64 = v7.l0
let struct (v10 : int32, v11 : int32, v12 : int32) = v0.[int v9]
v6.[int v9] <- struct (v12, v11, v10)
let v13 : uint64 = v9 + 1UL
v7.l0 <- v13
v6
Here it is in practice. To avoid the heap allocation associated with imperative loops, I'd have prefered to have used tail recursive loops instead, but those play badly with Cython's reference counting and kept overflowing.
The partial evaluator for Spiral v0.09 originally started off as a type system. I only realized that I was working on a partial evaluator after a few months. In 2016 while working on an ML library I got stuck on how exactly to propagate information to Cuda kernels. If the deep learning wave of the 2010s did not need GPUs, F# would have been entirely sufficient as a language, but it was just not powerful enough and I blamed the type system.
So in 2017, when I started work on Spiral I made F# the base and the first thing I got rid of was the type system. I read academic papers and books on type systems, but they were useless so I realized I had to do it on my own. Of course, I wanted the language to be statically typed - it had to be. Because how could GPUs possibly handle uniform representation that dynamic languages use? And rather than just suggestions, I need inlining to be guaranteed, otherwise how could first class function be compiled on the GPU? The same goes for other abstractions like tuples and records.
My earliest memory of working on it was trying to memoize a function's evaluation. This was the first big challenge. I realized that it was possible to split running the function from actually evaluating it and that was how the concept of join points came to be. Along those lines most of what is in the bottom-up segment I actually discovered myself, though I am sure the concepts are strewn throughout language implementations and academic papers.
In this section what I would like to impress is that the bottom-up Spiral is really a great language in its own right. It is the real Spiral, the top-down type inferencer is just a wrapper, albeit a very useful one. It is the bottom-up Spiral that gets to the heart of what computation is really about.
If you picked C as the starting point, and tried to evolve it so that it gets all the advantages of functional languages without any of the disadvantages, the bottom-up Spiral is what you would get.
These next examples will be in a .spir file. Here is how the package.spiproj file should look like.
packages: |core-
modules: a*
From the perspective of the written code, the bottom-up segment generally means the code in .spir files and in the real bodies. From the perspective of compilation phases, the bottom-up segment happens during partial evaluation. Parsing and the type inference would all be a part of the top-down segment.
The most important feature of the bottom-up Spiral are its first class functions and join points. From the perspective of a C level language, they are an incredible leap in expressiveness and power and they require zero runtime footprint, essentially allowing the use of regular functions for what would be metaprogramming in any other language.
They are what makes Spiral very suitable for systems programming. First class functions aren't a novelty themselves - they were discovered in the early 20th century as a part of lambda calculus. The way Spiral presents them though is novel.
In the top-down segment I demonstrated how functions can be dyned and converted into a runtime closure, but this capability is not fundamental to them. In Spiral that is an addon; closure conversion is just another kind of join point which has fake arguments passed through it. Whereas in heap allocation by default kind of languages, closure conversion is fundamental and inlining is something that happens optionally.
In the bottom-up segment annotations have to be provided manually.
inl main () =
inl id = (fun x => x) : i32 -> i32
inl ~x = id
()
let rec closure0 () (v0 : int32) : int32 =
v0
let v0 : (int32 -> int32) = closure0()
()It is possible to give the wrong annotation in which case a trace would show up.
inl main () =
inl id = (fun x => x) : i32 -> i32
inl ~x = id
()
Error trace on line: 1, column: 10 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl main () =
^
Error trace on line: 2, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl id = (fun x => x) : i32 -> f32
^
Error trace on line: 3, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = id
^
Error trace on line: 3, column: 9 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = id
^
The annotation of the function does not match its body's type.
Got: i32
Expected: f32
The way the annotations have to be provided is fairly dumb - anybody seriously using closures will most likely do it from the top-down segment which will fill in the annotations automatically. Here is a more complex example with two nested functions.
inl main () =
inl const = (fun x => (fun _ => x) : () -> i32) : i32 -> () -> i32
inl ~x = const
()
let rec closure1 (v0 : int32) () : int32 =
v0
and closure0 () (v0 : int32) : (unit -> int32) =
closure1(v0)
let v0 : (int32 -> (unit -> int32)) = closure0()
()It is tempting to omit the inner annotation, but that won't work.
inl main () =
inl const = (fun x _ => x) : i32 -> () -> i32
inl ~x = const
()
Error trace on line: 1, column: 10 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl main () =
^
Error trace on line: 2, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl const = (fun x _ => x) : i32 -> () -> i32
^
Error trace on line: 3, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = const
^
Error trace on line: 3, column: 9 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = const
^
Cannot convert a function that is not annotated into a type.
This is how v2 works, there isn't some iron rule that the language has to be designed like this. A language that uses partial evaluation for everything could do more processing in order to distribute the annotation if it is possible. Or it could be possible to omit the return type like it was the case in Spiral v0.09.
In general though, this fanciness is not necessary. Speaking from experience, whenever I've used closures in F#, they tended to be a lot simpler than functions I would use to structure the program. Having closures that return other closures is not the kind of situation I've had to come across. Functions, yes, I use that technique all the time. It is a large part of what makes functional programming so expressive. Closures, no. It is easier to bundle their arguments into a tuple, and it is a practice I'd recommend doing in Spiral.
So this kind of design is perfectly fine from my point of view.
It was not the case in v0.09 where closure conversion worked differently, but in v2 the annotation has to be direct and cannot be changed once set.
inl main () =
inl id = (fun x => x)
inl ~x = id : i32 -> i32
()
Error trace on line: 1, column: 10 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl main () =
^
Error trace on line: 2, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl id = (fun x => x)
^
Error trace on line: 3, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = id : i32 -> i32
^
Error trace on line: 3, column: 14 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = id : i32 -> i32
^
Cannot convert a function that is not annotated into a type.
The compiler tries converting the left side of the annotation into a type, but it encounters an unannotated function and raises a type error.
The following also does not work. Annotations in arguments aren't actually annotations, but type patterns.
inl main () =
inl id (x : i32) : i32 = x
inl ~x = id
()
Error trace on line: 1, column: 10 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl main () =
^
Error trace on line: 2, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl id (x : i32) : i32 = x
^
Error trace on line: 3, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = id
^
Error trace on line: 3, column: 9 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl ~x = id
^
Cannot convert a function that is not annotated into a type.
The top-down type inferencer will treat type patterns as annotations, but in the bottom-up segment they have an entirely different role to play.
inl main () =
inl f = function
| (x : i32) => "int"
| (x : f64) => "float"
| _ => "something else"
f 1, f 2f64, f true
struct ("int", "float", "something else")Top-down this would be an invalid program. Originally, I wanted top-down to be more flexible and tried designing a top-down type system that could cover the full range of bottom-up Spiral's features, but in the end decided it was too difficult and settled on the current top-down system that can only compile a restricted subset of the bottom-up Spiral programs.
The bottom-up gives you so much power and expressiveness up front for such a light implementation load that it is astonishing. You could wrap the above in a join point and it would be specialized.
Something as simple as overloading could be done with prototypes which are a straight rip of Haskell's typeclasses. Haskell's typeclasses have accumulated a bevy of extensions over the years making them a lot more capable than Spiral's. Spiral's prototypes are the simplest possible implementation of them. Spiral does give the guarantee that it will always inline them though.
The way Spiral's prototypes are implemented is by direct matching on the type during partial evaluation, not by passing a dictionary of closures at compile time.
Haskell's typeclasses are capable indeed, but they pale in comparison to what you can easily do in bottom-up Spiral. Suppose you wanted to count the number elements in a tuple, here is how that could be done in Spiral.
open real_core
inl main () =
inl rec f = function
| (a,b) => f a + f b
| _ => 1
f (1,2,3)I am thinking whether this could be done with typeclasses in Haskell, and I think that if the scenario was just restricted to pairs you could iterate over them using the typeclass machinery, but the real problem is the | _ => 1 branch. Typeclasses have no good way of expressing that. But even if that was somehow possible these are just the pairs, things would only get more troublesome from there. Here is how to count the number of elements in a record. There is no good way of expressing this within the language of typeclasses.
open real_core
inl main () =
inl rec f = function
| a,b => f a + f b
| {} as x => record_foldl (fun {state key value} => state + f value) 0 x
| _ => 1
f (1,2,3,{q=4,5; w=6,7})
7This is one of the program examples that I tried wrapping my head around from a top-down perspective and in the end gave up. How could this be typed top-down? Bottom-up it is quite clear, but even the most advanced top-down type systems to date cannot cover this particular example.
Spiral's top-down type inferencer is 1.5k LOC, while Haskell's is over 70k making it 10x the size of the entire Spiral compiler, so it clearly cannot compare in terms of sophistication. And a language that does bottom-up partial evaluation cannot compete with the top-down type inferring languages in terms of ease of use and ergonomics. But together the two sides can achieve amazing synergy and get the best of both worlds.
It is not just the shape of the data that can be matched on. As long as the values are known at compile time, it is possible to use some tricks from dependently typed languages. In the top-down segment the following would give a type error because the branches are different. During the bottom-up segment the type checking is deferred until the last moment and the compilation succeeds because there is enough information to completely ignore one of the branches.
open real_core
inl main () =
inl x = 1
if x < 10 then x + 10 else "asd"
11It is possible to raise type errors during partial evaluation manually.
open real_core
inl main () =
inl f = function
| (x : i32) => "int"
| (x : f64) => "float"
| _ => error_type "Expected an i32 or f64."
f "qwe"
Error trace on line: 3, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl f = function
^
Error trace on line: 7, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
f "qwe"
^
Error trace on line: 4, column: 12 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
| (x : i32) => "int"
^
Error trace on line: 5, column: 12 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
| (x : f64) => "float"
^
Error trace on line: 6, column: 16 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
| _ => error_type "Expected an i32 or f64."
^
Expected an i32 or f64.
The real_core module has a large number of primitives for testing types. Pattern matching can test for specific symbols or nominals, but cannot test whether a variable is a symbol or a nominal so there are various inbuilt ops for that. It is possible to test whether something is a compile time literal using lit_is. Together with error_type that makes it possible to implement an assert that raises a type error if both the conditional and the message are strings, otherwise it compiles to an runtime exception.
This is how it is implemented in real_core.
// Asserts an expression. If the conditional and the message are literals it raises a type error instead.
inl assert c msg =
if c = false then
if lit_is c && lit_is msg then error_type msg
else failwith `(()) msg
Here is an example of it in use.
open real_core
inl main () =
inl assert_less_than_ten x = assert (x < 10) "The argument must be less than 10."
assert_less_than_ten 11
Error trace on line: 4, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
assert_less_than_ten 11
^
Error trace on line: 3, column: 34 in module: c:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1\a.spir.
inl assert_less_than_ten x = assert (x < 10) "The argument must be less than 10."
^
Error trace on line: 128, column: 5 in module: c:\Users\Marko\Source\Repos\The Spiral Language\VS Code Plugin\core\real_core.spir.
if c = false then
^
Error trace on line: 129, column: 9 in module: c:\Users\Marko\Source\Repos\The Spiral Language\VS Code Plugin\core\real_core.spir.
if lit_is c && lit_is msg then error_type msg
^
Error trace on line: 129, column: 40 in module: c:\Users\Marko\Source\Repos\The Spiral Language\VS Code Plugin\core\real_core.spir.
if lit_is c && lit_is msg then error_type msg
^
The argument must be less than 10.
And when the argument is dyned, it compiles to a panic.
open real_core
inl main () =
inl assert_less_than_ten x = assert (x < 10) "The argument must be less than 10."
assert_less_than_ten (dyn 11)
let v0 : uint32 = 11u
let v1 : bool = v0 < 10u
let v2 : bool = v1 = false
if v2 then
failwith<unit> "The argument must be less than 10."It could be done using macros if absolutely necessary, but otherwise right now Spiral does not have any exception catching mechanisms. It might have in the future, but right now catching exceptions goes beyond the immediate scope of what the language is intended for.
Some languages need pragmas or macros to do loop unrolling, but it is fairly easy to do it naturally in Spiral. In Spiral the . operator is similar to ; in F#. It can be used to separate statements on the same line.
open real_core
inl main () =
inl rec loop f i =
if lit_is i = false then error_type "Expected i to be a literal."
if 0 < i then f i . loop f (i-1) else ()
loop (fun i => $"// line !i" : ()) 5
// line 5
// line 4
// line 3
// line 2
// line 1Languages generally take care to prevent user code from crashing them, but based on all my experience of programming in the old Spiral, I can state that these kinds of errors are very rare and are easy to isolate when they do happen by selectively cutting out pieces until the program compiles. The vast majority of my bug fixing time during the v0.09 was taken up by regular ones. So in my view this kind of language design is not a problem.
Since ensuring termination would require solving the halting problem, the Spiral compiler does not do any checks beforehand to make sure some piece of code at compile time does not cause it to stack overflow. Consider the following.
open real_core
inl main () =
inl rec f () = 1 + f()
f()
PS C:\Users\Marko\Source\Repos\The Spiral Language\Spiral Compilation Tests\compilation_tests\tutorial1> dotnet "c:\Users\Marko\.vscode\extensions\mrakgr.spiral-lang-vscode-2.0.29\compiler\Spiral.dll" port=13805
Server bound to: tcp://*:13805 & tcp://*:13806
Stack overflow.
If you press Ctrl + `, it will bring up the terminal view. Amongst them there will be Spiral Server with similar output to the above. During plugin startup, the terminal is used to start the language server. If the first line runs successfully, it should print out...
Server bound to: tcp://*:13805 & tcp://*:13806
If the dotnet command fails, that means that the .NET SDK has not been installed properly. The first TCP port is by default 13805 and the second is first plus one. It is possible to modify this in the Spiral settings. It is also possible to hide the server terminal on startup using a config option as well.
Stack overflow.
This happens during partial evaluation once the build has been started on the offending file.
When the server crashes the editor support will cease to work, and the best option is to restart it using Spiral: Start Server command which will dispose of the old terminal and start a fresh one. Only one server instance can occupy the same port at a time - in theory, this could allow multiple editor instances to reuse the same server.
If the editor starts getting wonky, check whether there are any exceptions being thrown in the server terminal. Any unexpected .NET exceptions there are compiler bugs and I'd appreciate their report.
Being able to see the console information can be useful in some cases.
open real_core
inl main () =
inl x = 1f64
print_static x
inl ~y = x
print_static y
Server bound to: tcp://*:13805 & tcp://*:13806
1.000000f64
f64
The above output shows in the shell window, not the compiled file. The last two lines are from the print_static. You can see how x is known to the partial evaluator as a compile time literal, but after it has been dyned, it gets tracked as a runtime variable.
open real_core
inl main () =
inl rec loop f i =
if lit_is i = false then error_type "Expected i to be a literal."
if 0 < i then f i . loop f (i-1) else ()
loop (fun i => print_static {got = i}) 5
Server bound to: tcp://*:13805 & tcp://*:13806
{got : 5i32}
{got : 4i32}
{got : 3i32}
{got : 2i32}
{got : 1i32}
The print_static statements get executed as a part of partial evaluation. When the codebase gets bigger they are good for ensuring whether the partial evaluator is tracing a particular segment. They might be good for experimentation to get a better sense for how the system works. Sprinkling print_static statements can be useful for narrowing down the locations of stack overflow errors.
The top-down segment does a great service to the user by doing type inference and filling in the annotations for functions and join points where necessary. It might be surprising to discover that it is actually possible to do inference in a bottom-up fashion programmatically.
Here is how the identity function could be written in the bottom-up segment. Since the type inferencer is not filling in the foralls or type application, it is necessary to do that by hand. This shows how the type inferencer would compile let id x = x. The type inferencer in addition to filling in the types, also removes the non-essential ones such as in function arguments.
inl main () =
inl id forall t. ~x = (join x) : t
id `i32 1
let rec method0 (v0 : int32) : int32 =
v0
let v0 : int32 = 1
method0(v0)The ` unary operator can be used to access the type scope. Otherwise the term and the type scopes are segregated.
Since the above is in the bottom-up segment, instead of passing in the type, it is possible to omit the need to pass the forall entirely.
inl main () =
inl id x = join x
id 1
let rec method0 () : int32 =
1
method0()Alternatively, here is how to infer the type bottom-up style. It is necessary to instruct the compiler on how to derive it, and since we have the element itself, using it to get its type is a natural choice.
inl main () =
inl id' forall t. ~x = (join x) : t
inl id x = id' `(`x) x
id 1
let rec method0 (v0 : int32) : int32 =
v0
let v0 : int32 = 1
method0(v0)While in the type scope, using the ` operator opens the term scope again. The partial evaluator processes the term and converts the resulting expression into a type. This illustrates the essence of bottom-up type inference using the simplest possible example.
When doing bottom-up programming implementing identity as inl id x = x is a natural choice. But sometimes having the type ahead of time is necessary.
A good example are the various array functions like map.
type array el = a i32 el
inl init nearTo f : array _ =
inl ar = create nearTo
loop.for' {from=0; nearTo} (fun i => set ar i (f i))
ar
inl map f (ar : array _) = init (length ar) (fun i => f (index ar i))
For the purpose of the next example, this is how I will implement it in the arraym module.
The type of map is forall 'a 'b. ('a -> 'b) -> array 'a -> array 'b. How would such a function be callable from the bottom-up segment assuming we only had the unannotated f and the array?
First, it is necessary to extract the element from the array itself. The typecase construct allows matching on a type, but its body can be opened into the term scope. The ? is meant to be seen as a hole to be filled by the user in the following pseudo-code examples.
inl map f ar =
typecase `ar with
| arraym.array ~a => arraym.map `a ? f ar
Here we are matching on the type of ar, and ~a is the metavar to which the array's element gets bound to. Unlike in regular pattern matching it is possible to use repeat metavars to do equality checking. ~t * ~t for example would test whether the both sides of a pair unify to the same type.
The ? is troublesome here.
We need a type, so we must open the type scope.
inl map f ar =
typecase `ar with
| arraym.array ~a => arraym.map `a `(?) f ar
We don't actually have any convenient data structure to extract b from, but we can get it by running f. For that we need to use f, so we should open the term scope again.
inl map f ar =
typecase `ar with
| arraym.array ~a => arraym.map `a `(`(f ?)) f ar
We can't apply f with a here because that is a type and f wants a term. What we can do is turn it into a term however using the double grave unary operator.
inl map f ar =
typecase `ar with
| arraym.array ~a => arraym.map `a `(`(f ``a)) f ar
It creates a term out of a type. This is fine, since the code in the type scope will not get generated. If you try using that operator on the term level and the code with the TypeToVar op gets passed into the code generator you will get a trace + type error during code generation.
The compiler cannot possibly know how to create a value from an arbitrary type so this move can only ever be used for type inference.
Here is the full example.
inl map f ar =
typecase `ar with
| arraym.array ~el => arraym.map `el `(`(f ``el)) f ar
inl main () =
inl x = arraym.init `i32 10 (fun x => x)
map (fun x => x,x) x
let rec method0 (v0 : (int32 []), v1 : int32) : unit =
let v2 : bool = v1 < 10
if v2 then
let v3 : int32 = v1 + 1
v0.[int v1] <- v1
method0(v0, v3)
and method1 (v0 : int32, v1 : (int32 []), v2 : (struct (int32 * int32) []), v3 : int32) : unit =
let v4 : bool = v3 < v0
if v4 then
let v5 : int32 = v3 + 1
let v6 : int32 = v1.[int v3]
v2.[int v3] <- struct (v6, v6)
method1(v0, v1, v2, v5)
let v0 : (int32 []) = Array.zeroCreate<int32> (10)
let v1 : int32 = 0
method0(v0, v1)
let v3 : int32 = v0.Length
let v4 : (struct (int32 * int32) []) = Array.zeroCreate<struct (int32 * int32)> (v3)
let v5 : int32 = 0
method1(v3, v0, v4, v5)
v4I had to implement map similarly to this in the old Spiral. The reason why inferring the type is needed is because the output array needs its type to be known ahead of time. It is not possible to do something like set it to some metavariable and unify it with its first use because of join points. In order to do their specialization, they need to have concrete terms ahead of time.
This general approach to type inference is what I followed in the old Spiral. I went into it far further than this; I experimented with all sorts of tricks there. For one Cuda kernel for example, I was raising an exception with the thrown type and catching it. I had special ops just to do that.
Consider the following function. How would it be possible to infer the type of the following?
inl main () =
inl f = function
| (a,b,c) => (a,b,c)
| (a,b) => (a,b,b)
| a => (a,a)
f (f (f (f 1)))
struct (1, 1, 1)If I ran it only once, I'd get struct (1, 1). So the type of its input argument should be a union of i32, i32 * i32 and i32 * i32 * i32. In order to infer the type of this what is necessary is to keep running the function until its type stabilizes. For this purpose, the old Spiral had particularly flexible union types that could be built up during partial evaluation instead of just specified at the top level like in v2.
This is not an academic exercise either, I had to use this technique to infer the types of the internal state of an RNN used as the model for a poker agent otherwise I would not be able to store it. It is fairly remarkable how far it is possible to get without any type annotations in a static language, Spiral v0.09 is definitely a record setter in that regard.
Today, I consider these techniques better off sealed. They are an anti-pattern. If bottom-up type inference sounds difficult, don't worry because it in fact is. The top-down is here for a reason in v2.
Besides requiring a lot of programming effort to be used, I suspect it was one of the reasons why towards the end the compile times were so poor on that agent.
To demonstrate why, consider the previous example again.
inl map f ar =
typecase `ar with
| arraym.array ~el => arraym.map `el `(`(f ``el)) f ar
The part here where f is applied in order to extract its type is not free. Having to evaluate it twice, once to infer its return type, and once to actually generate its code, will in fact take twice as much time during compilation than just having to do it once.
It is possible to reduce this cost by wrapping f in a join point.
inl map f ar =
inl f x = join f x
typecase `ar with
| array ~a => array.map `a `(`(f ``a)) f ar
This would reduce the compile time to about the cost of a single run, but now there will be a function call in generated code. You can only hope that whatever compiler is consuming it after that decides to inline it.
I do not like this. Spiral is all about giving the user guarantees - for when functions are inlined, for how the values are propagated, and when data structures are heap allocated. The language being predictable is what will make hitting performance targets tractable for the user.
Bottom-up programming just does not scale well to large codebases. It is very useful when you need all the power a statically typed programming language can give you, but most of the time you don't and would rather have instant editor feedback instead. When I first discovered all these bottom-up type inference techniques I felt very clever for breaking new ground, but using them where top-down type inference would suffice is just a waste of time.
I previously mentioned that very few things can go past language boundaries. One of the structures that can was an array of primitives. The trouble is that the regular arrays gets compiled to tuples. Consider the following top-down program.
inl main () =
inl x : a u64 (i32 * i32 * i32) = create 10
()
let v0 : (struct (int32 * int32 * int32) []) = Array.zeroCreate<struct (int32 * int32 * int32)> 10
()For the sake of language interop, it would be better if this got compiled to three separate arrays and got tracked that way. It could be built into the language, but some things are more easily done as a library. Spiral's bottom-up introspection makes it possible.
Here is how it is done in the core library. I'll go through the contents of the iam_real* module one by one. The 4 functions here all match on the input element and then branch if it is a pair or a record, otherwise they do their operation.
open real_core
inl create' forall ar el. size =
inl rec f forall el. =
typecase el with
| ~a * ~b => f `a, f `b
| {} => record_type_map (fun k => f) `el
| _ => create `ar `(`size) `el size
f `el
Here record_type_map takes in the record type as an argument and maps over all its elements. It takes in a function as the first argument and applies to it the term key and the type value of each of the record fields.
In the real segment the foralls can be in arbitrary places while in the top down segments they are more restricted.
| {} => record_type_map (fun k => forall v. => f `v) `el
This is an equivalent way of expressing the record matching case.
inl index' ar i =
inl rec f = function
| a, b => f a, f b
| {} as ar => record_map (fun {value} => f value) ar
| ar => typecase `ar with ~ar ~dim ~el => index `ar `dim `el ar i
f ar
inl set' ar i v =
inl rec f = function
| (a, b), (va,vb) => f (a, va) . f (b, vb)
| ({} & ar, {} & v) => record_iter (fun {key value} => f (ar key, value)) v
| ar,v => typecase `ar with ~ar ~dim ~el => set `ar `dim `el ar i v
f (ar,v)
Hopefully what these two do should be straightforward by now. Given an ar argument, they branch on each of its pair and record elements and index or set them individually. If you imagine that Spiral records are immutable maps then record_map and record_iter are the equivalent of F#'s map and iter over them. If you look at the source code of Spiral, you'd find that Spiral records are implemented using F#'s immutable maps and the partial evaluator ops use these operations.
inl length' forall dim. ar =
inl g a b = match a with None => b() | _ => a
inl rec f = function
| a, b => g (f a) (fun _ => f b)
| {} as ar => record_fold (fun {state value} => g state (fun _ => f value)) (None `dim) ar
| ar => typecase `ar with ~ar ~dim ~el => Some `dim (length `ar `dim `el ar)
match f ar with
| None => error_type "Cannot get the length of an inverse array with no fields."
| Some x => x
The length was actually the hardest of the bunch for me to implement because I needed to consider the case where there are no elements to fetch from. This could for example be an inverse array with an empty record as the element type. I needed to use an option type in order to shortcut when a match is found, otherwise a call to length would get generated for every field of the inverse array. Note that since the option type is never dyned this is only a compile time expense and the type never gets used in the generated code.
If that weren't the case...
| None => error_type "Cannot get the length of an inverse array with no fields."
...this would lead to a type error during partial evaluation time.
inl g a b = match a with None => b() | _ => a
This function here is responsible for the shortcutting behavior. Unlike in imperative languages, it is not possible to abort a record fold in the middle, so this is the next best solution.
With these 4 functions in tow, it is possible to implement the inverse array type.
// The inverse array module.
open iam_real
nominal inv (ar : * -> * -> *) dim el = `(create' `ar `el ``dim)
instance create inv ar = fun size => inv (real create' `ar `el size)
instance index inv ar = fun (inv ar) i => real index' ar i
instance set inv ar = fun (inv ar) i v => real set' ar i v
instance length inv ar = fun (inv ar) => real length' `dim ar
The prototype instances merely call the functions in the iam_real* module. Here is an example of inverse arrays in action. Since the required prototype constraints are met, the inverse arrays can be freely used with library functions in the am module.
inl main () =
inl x : inv a i32 {a : i32; b : i32} = am.init 10 (fun x => {a=x; b=x})
x |> am.map (fun {a b} => a*2,b+3,a-2*b)
type Mut0 = {mutable l0 : int32}
let rec method0 (v0 : Mut0) : bool =
let v1 : int32 = v0.l0
v1 < 10
and method1 (v0 : int32, v1 : Mut0) : bool =
let v2 : int32 = v1.l0
v2 < v0
let v0 : (int32 []) = Array.zeroCreate<int32> (10)
let v1 : (int32 []) = Array.zeroCreate<int32> (10)
let v2 : Mut0 = {l0 = 0} : Mut0
while method0(v2) do
let v4 : int32 = v2.l0
v0.[int v4] <- v4
v1.[int v4] <- v4
let v5 : int32 = v4 + 1
v2.l0 <- v5
let v6 : int32 = v0.Length
let v7 : (int32 []) = Array.zeroCreate<int32> (v6)
let v8 : (int32 []) = Array.zeroCreate<int32> (v6)
let v9 : (int32 []) = Array.zeroCreate<int32> (v6)
let v10 : Mut0 = {l0 = 0} : Mut0
while method1(v6, v10) do
let v12 : int32 = v10.l0
let v13 : int32 = v0.[int v12]
let v14 : int32 = v1.[int v12]
let v15 : int32 = v13 * 2
let v16 : int32 = v14 + 3
let v17 : int32 = 2 * v14
let v18 : int32 = v13 - v17
v7.[int v12] <- v15
v8.[int v12] <- v16
v9.[int v12] <- v18
let v19 : int32 = v12 + 1
v10.l0 <- v19
struct (v7, v8, v9)For comparison here is what happens when the above operations are attempted with regular arrays. I'll just remove inv from the type of x.
inl main () =
inl x : a i32 {a : i32; b : i32} = am.init 10 (fun x => {a=x; b=x})
x |> am.map (fun {a b} => a*2,b+3,a-2*b)
type Mut0 = {mutable l0 : int32}
let rec method0 (v0 : Mut0) : bool =
let v1 : int32 = v0.l0
v1 < 10
and method1 (v0 : int32, v1 : Mut0) : bool =
let v2 : int32 = v1.l0
v2 < v0
let v0 : (struct (int32 * int32) []) = Array.zeroCreate<struct (int32 * int32)> (10)
let v1 : Mut0 = {l0 = 0} : Mut0
while method0(v1) do
let v3 : int32 = v1.l0
v0.[int v3] <- struct (v3, v3)
let v4 : int32 = v3 + 1
v1.l0 <- v4
let v5 : int32 = v0.Length
let v6 : (struct (int32 * int32 * int32) []) = Array.zeroCreate<struct (int32 * int32 * int32)> (v5)
let v7 : Mut0 = {l0 = 0} : Mut0
while method1(v5, v7) do
let v9 : int32 = v7.l0
let struct (v10 : int32, v11 : int32) = v0.[int v9]
let v12 : int32 = v10 * 2
let v13 : int32 = v11 + 3
let v14 : int32 = 2 * v11
let v15 : int32 = v10 - v14
v6.[int v9] <- struct (v12, v13, v15)
let v16 : int32 = v9 + 1
v7.l0 <- v16
v6F# has value structs so maybe this functionality on its own is not a big deal. It certainly has been advantageous in the old Cython backend which allocated composite types on the heap, but in the old Spiral what I've particularly found this functionality useful for is getting arrays past language boundaries. Different languages, F# and C for example, have structs of different sizes and it is hard to keep them straight. Primitive types on the other hand are much easier to pass around.
Note: the following examples need the Spiral ML library. The project has a new core library intended for Cuda programming.
open corebase
open corecuda
open coreext
inl main() =
run fun () =>
let qwe() : () = $'printf("hello\\n")'
qwe()
__device__ void qwe_0();
__device__ void qwe_0(){
printf("hello\n");
return ;
}
extern "C" __global__ void entry0() {
return qwe_0();
}This is how it normally compiles in the Python + Cuda backend, but suppose we changed the name a little?
open corebase
open corecuda
open coreext
inl main() =
run fun () =>
let noinline_qwe() : () = $'printf("hello\\n")'
noinline_qwe()
__device__ void noinline_qwe_0();
__device__ __noinline__ void noinline_qwe_0(){
printf("hello\n");
return ;
}
extern "C" __global__ void entry0() {
return noinline_qwe_0();
}As you can see, named join points that are prefixed with noinline get the __noinline__ annotation in generated code.
Why is this useful?
open corebase
open corecuda
open coreext
inl main() =
run fun () =>
let noinline_qwe() : () =
__syncthreads()
$'printf("hello\\n")'
if thread_index() < 15 then
$'printf("true\\n")'
noinline_qwe()
else
$'printf("false\\n")'
noinline_qwe()
__device__ void noinline_qwe_0();
__device__ __noinline__ void noinline_qwe_0(){
__syncthreads();
printf("hello\n");
return ;
}
extern "C" __global__ void entry0() {
int v0;
v0 = threadIdx.x;
bool v1;
v1 = v0 < 15l;
if (v1){
printf("true\n");
return noinline_qwe_0();
} else {
printf("false\n");
return noinline_qwe_0();
}
}If the function was not marked with __noinline__, once we run this the kernel would have ended up handing. Thread synchronization using __syncthreads requires all the threads in the block to execute the same instruction. Without the __noinline__ the compiler would have inlined that small function and the kernel would have gotten stuck.
On other words, __noinline__ allows the divergent threads in the kernel to reconverge on a function call.
inl main() =
run fun () =>
inl x : array int = $"`int \v[5];"
()
extern "C" __global__ void entry0() {
int v0[5];;
return ;
}As documented previously, it is often useful to be able to declare local arrays in Cuda code, and the \v string can be used in the macro for that purpose. Also, as newer versions of Spiral support macro expressions, you can pass complex types into the macro directly without needing to bind them in a typecase first.
inl main() =
run fun () =>
inl x : array (int * float) = $"`(int * float) \v[5];"
()
struct Tuple0;
struct Tuple0 {
int v0;
float v1;
__device__ Tuple0() = default;
__device__ Tuple0(int t0, float t1) : v0(t0), v1(t1) {}
};
extern "C" __global__ void entry0() {
Tuple0 v0[5];;
return ;
}This behavior is safe. If you tried doing something like...
inl main() =
run fun () =>
inl x : (int * float) = $"`(int * float) \v"
()
Error trace on line: 8, column: 13 in module: c:\Spiral_s_ML_Library\tests\test14.spi.
run fun () =>
^
Error trace on line: 9, column: 9 in module: c:\Spiral_s_ML_Library\tests\test14.spi.
inl x : (int * float) = $"`(int * float) \v"
^
Error trace on line: 9, column: 33 in module: c:\Spiral_s_ML_Library\tests\test14.spi.
inl x : (int * float) = $"`(int * float) \v"
^
The special \v macro requires the same number of free vars in its binding as there are \v in the code.
The Cuda codegenerator makes sure that the right amount of free vars are in the macro as they are in the generated code.
inl main() =
run fun () =>
inl x : heap (int * int * int) = heap (1,2,3)
inl a,b,c = !x
()
struct Heap0;
struct Heap0 {
int refc{0};
int v0;
int v1;
int v2;
__device__ Heap0() = default;
__device__ Heap0(int t0, int t1, int t2) : v0(t0), v1(t1), v2(t2) {}
};
extern "C" __global__ void entry0() {
sptr<Heap0> v0;
v0 = sptr<Heap0>{new Heap0{1l, 2l, 3l}};
int & v1 = v0.base->v0; int & v2 = v0.base->v1; int & v3 = v0.base->v2;
return ;
}As layout types in the Cuda backend would be pretty useless without this capability, it has been implemented in Spiral v2.15.0. It's necessary keep in mind that when indexing into layout types that references, and not value types will be generated in the resulting output. And trying to mutate the values on the stack will end up mutating them in the layout type itself even if they are intended to be immutable heap types.
As of v2.15.0, a new layout type has been added to the language.
inl main() : () =
inl _ = join_backend Cuda
inl x = stack_mut {x = true; y = 1i32}
inl _ = x.x, x.y
x.x <- false
()
extern "C" __global__ void entry0() {
StackMut0 v0{true, 1};
bool & v1 = v0.v0;
int & v2 = v0.v1;
v0.v0 = false;
return ;
}On the stack they are constructed as value types, but in functions they are passed by reference. Since they are C++ reference types, they cannot be returned from join points and conditionals.
The purpose of them is to match the forward passing semantics of heap mutable types, and easily swap between the two to see if allocating on the heap or the stack is better. Much like for the other layout types, indexing into them returns references.
- There is a name collision between Spiral and a DSL by the same name. This is purely coincidental and there is no association between the two.