That bug was fixed already, and the fix didn’t involve changing anything with @pure as it was a bug with the type system in a conversion method.
I’m only guessing, but I think that the reason why "return value allocation is definitely not pure" is that it depends on where heap memory is available, and this is is a global state.
Example:
f() = []
f() === f() # returns false
The function f is not pure, since calling it with the same (empty) set of immutable arguments does not return the same result.
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This doesn’t actually matter for my use case though. I don’t need the output to be ===. It only generates some symbols which get pasted into a function.
The @pure annotation tells the compiler that you guarantee the output to be ===. If you don’t have or need that guarantee, then you should let compiler know by removing the annotation.
Alright then, I will remove that annotation in this particular case, but there many valid use cases of it in other places of my package.
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Julia has gotten significantly better at constant propagation since you first wrote this, so I would be surprised if many of them actually give you a performance benefit anymore in any case.
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At the risk of sounding like a breaking record, this is probably not true, mostly just because there isn’t really a way to use @pure correctly, ever. It doesn’t have well defined semantics. At some point we need a version of it that’s well-defined. The point people are trying to make here is that it’s best to avoid @pure because the compiler makes zero guarantees about what it means, so if you don’t 100% need it, you’re better off without it. Even if nothing breaks right now, future versions of Julia can and will break and crash your code. You might not care, but that’s the reason people try to get you to stop using it.
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Yes, I’m well aware of all that. Having @pure does actually improve my code in a lot of places, it’s not something I can automatically remove from everywhere. I agree it is worth looking into how much the constant propagation has improved and to check if some of the pure annotations can be removed, but it’s not currently a high priority.
That may be a relevant factor but I think a much bigger and harder to avoid problem is that the method table itself is a global mutable state. Hence any function which relies on the state of the method table is itself impure.
That means no generic, multiple dispatch functions should be called inside a @pure function body, which is almost impossible in Julia.
This mutability in the method table may not effect you if you’re not hot-modifying it, however, it seems like it’s liable to cause weird, hard to reproduce bugs if other people were to try and build tools on top of Grassmann.jl, adding their own methods and overloading your functions (or if someone pirated the methods you use, or if you pirate someone else’s methods).
This seems especially worrisome since you do have a tenancy to advertise Grassmann.jl as something people should adapt their project to make use of, so if someone did act on that it could result in an unfortunate situation.
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It is clearly stated in my repository that there is no warranty. Those who are serious about using Grassmann.jl should consider consulting with me.
Hum, too bad. I was really excited by your post, because I have to solve systems like A\b in two occasions: interpolating orbit and inside the Kalman filter (which applies to the attitude and orbit estimation inside the satellite). This performance gain would be very handy. However, given all the comments, maybe I will need to wait until those concerns are solved.
Anyway, great work! It is very interesting.
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@Ronis_BR, I don’t think you will encounter a major issue with the A\b method, would be interesting to see what you try out! Another interesting aspect would be using bivectors and quaternions for that also. As @Mason mentioned, what you probably don’t want to do is overload internal Grassmann methods.
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Could somebody please help me figure out what’s going on with this performance variance?
So what’s exactly going on here? Why is it faster when I paste it into the REPL?
This doesn’t happen with the Base.in or ∈ method, which is fast without pasting into the REPL
julia> @btime $(barycenter(A)) ∈ $A
61.499 ns (0 allocations: 0 bytes)
So, for the linear solve method there is a performance gain from pasting into REPL but not for ∈.
Can anyone help me figure out why the linear solve method changes in performance like this?
That sure seems like the sort of strangeness you might see from Base.@pure abuse; order of definition suddenly matters.
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Whether or not @pure is used doesn’t affect this, you need to actually back up your claim with proof.
I’m not saying it with certainty, but I do have a mental model for how it might happen. Of course it might be something else, but personally I don’t have much motivation to look into strange performance of a package I know is intentionally in unsafe and quirky territory.
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It’s certainly not intentionally unsafe, although it may be “quirky territory” and experimental.
Regarding the performance difference upon function redefinition, this issue is possibly related: https://github.com/JuliaLang/julia/issues/32552.
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It seems easy enough to do a global find and replace to remove the @pure and see what happens to the benchmarks.
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Indeed, it’s easy to test and prove that @mbauman is wrong. If you remove all usage of @pure:
julia> @btime $A\$(v1+2v2+3v3+4v4+5v5)
13.384 μs (150 allocations: 11.94 KiB)
And after pasting the Base.:\ method into the REPL for the second time
julia> @btime $A\$(v1+2v2+3v3+4v4+5v5)
9.251 μs (146 allocations: 11.78 KiB)
As you can see, without the usage of @pure the same performance difference exists after pasting the method into the REPL, and also it is much slower without the @pure annotations.
It’s most likely that this is the same issue @schmrlng referenced above.