What about creating a parametric type, with one parameter/closed-over variable?
On Friday, January 22, 2016 at 3:20:48 PM UTC-5, Tim Holy wrote: > > On Friday, January 22, 2016 12:03:02 PM Cedric St-Jean wrote: > > It looks like my understanding of FastAnonymous was flawed. Why doesn't > it > > create the type at macro time, and just instantiate it at runtime, > yielding > > 1 type / @anon ? Is there any complication that prevents that? > > Yes: > > for z in (1, 1.0) > f = @anon c->c+z > @show fieldtype(typeof(f), :z) > end > > yields this output: > > fieldtype(typeof(f),:z) = Int64 > fieldtype(typeof(f),:z) = Float64 > > The fields of the constructed "function" need concrete type, if you're > going to > get good performance. > > Best, > --Tim > > > > > > Cédric > > > > On Friday, January 22, 2016 at 2:15:20 PM UTC-5, Tim Holy wrote: > > > On Friday, January 22, 2016 10:21:31 AM Bryan Rivera wrote: > > > > For 1000 elements: > > > > > > > > 0.00019s vs 0.035s respectively > > > > > > > > Thanks! > > > > > > Glad it helped. > > > > > > > Is the reason the faster code has more allocations bc it is > > > > inserting vars into the single function? (Opposed to the slower > > > > code already having its vars filled in.) > > > > > > Every time you call @anon, it creates a brand-new type (and an > instance of > > > that type) that julia has never seen before. That requires JITting any > > > code > > > that gets invoked on this instance. So the usual advice, "run once to > JIT, > > > then do your timing" doesn't work in this case: it JITs every time. > > > > > > --Tim > > > > > > > On Friday, January 22, 2016 at 12:23:59 PM UTC-5, Tim Holy wrote: > > > > > Just use > > > > > > > > > > z = 1 > > > > > function2 = @anon c -> c + z > > > > > for z = 1:100 > > > > > > > > > > function2.z = z > > > > > # do whatever with function2, including making a copy > > > > > > > > > > end > > > > > > > > > > --Tim > > > > > > > > > > On Friday, January 22, 2016 08:55:25 AM Cedric St-Jean wrote: > > > > > > (non-mutating) Closures and FastAnonymous work essentially the > same > > > > > > way. > > > > > > > > > They store the data that is closed over (more or less) and a > > > > > > function > > > > > > > > > pointer. The thing is that there's only one data structure in > Julia > > > > > > for > > > > > > > > all > > > > > > > > > > > regular anonymous functions, whereas FastAnonymous creates one > per > > > > > > @anon > > > > > > > > > site. Because the FastAnonymous-created datatype is specific to > that > > > > > > function definition, the standard Julia machinery takes over and > > > > > > > > > > produces > > > > > > > > > > > efficient code. It's just as good as if the function had been > > > > > > defined > > > > > > > > > normally with `function foo(...) ... end` > > > > > > > > > > > > > > > > > > for z = 1:100 > > > > > > > > > > > > function2 = @anon c -> (c + z) > > > > > > > > > > > > dict[z] = function2 > > > > > > > > > > > > end > > > > > > > > > > > > > > > > > > So we end up creating multiple functions for each z value. > > > > > > > > > > > > > > > > > > In this code, whether you use @anon or not, Julia will create > 100 > > > > > > object > > > > > > > > > instances to store the z values. > > > > > > > > > > > > The speed difference between the two will soon be gone. > > > > > > <https://github.com/JuliaLang/julia/pull/13412> > > > > > > > > > > > > Cédric > > > > > > > > > > > > On Friday, January 22, 2016 at 11:31:36 AM UTC-5, Bryan Rivera > > > > > > wrote: > > > > > > > I have to do some investigating here. I thought we could do > > > > > > something > > > > > > > > > > like that but wasn't quite sure how it would look. > > > > > > > > > > > > > > Check this out: > > > > > > > > > > > > > > This code using FastAnonymous optimizes to the very same code > > > > > > below it > > > > > > > > > > where functions have been manually injected: > > > > > > > > > > > > > > using FastAnonymous > > > > > > > > > > > > > > > > > > > > > function function1(a, b, function2) > > > > > > > > > > > > > > if(a > b) > > > > > > > > > > > > > > c = a + b > > > > > > > return function2(c) > > > > > > > > > > > > > > else > > > > > > > > > > > > > > # do anything > > > > > > > # but return nothing > > > > > > > > > > > > > > end > > > > > > > > > > > > > > end > > > > > > > > > > > > > > > > > > > > > z = 10 > > > > > > > function2 = @anon c -> (c + z) > > > > > > > > > > > > > > > > > > > > > a = 1 > > > > > > > b = 2 > > > > > > > @code_llvm function1(a, b, function2) > > > > > > > @code_native function1(a, b, function2) > > > > > > > > > > > > > > Manually unrolled equivalent: > > > > > > > > > > > > > > function function1(a, b, z) > > > > > > > > > > > > > > if(a > b) > > > > > > > > > > > > > > c = a + b > > > > > > > return function2(c, z) > > > > > > > > > > > > > > else > > > > > > > > > > > > > > # do anything > > > > > > > # but return nothing > > > > > > > > > > > > > > end > > > > > > > > > > > > > > end > > > > > > > > > > > > > > > > > > > > > function function2(c, z) > > > > > > > > > > > > > > return c + z > > > > > > > > > > > > > > end > > > > > > > > > > > > > > > > > > > > > a = 1 > > > > > > > b = 2 > > > > > > > z = 10 > > > > > > > > > > > > > > > > > > > > > @code_llvm function1(a, b, z) > > > > > > > > > > > > > > @code_native function1(a, b, z) > > > > > > > > > > > > > > However, this is a bit too simplistic. My program actually > does > > > > > > this: > > > > > > > # Test to see if multiple functions are created. They are. > > > > > > > # We would only need to create a single function if we used > julia > > > > > > > > > > anon, > > > > > > > > > > > > but its time inefficient. > > > > > > > > > > > > > > dict = Dict{Int, Any}() > > > > > > > for z = 1:100 > > > > > > > > > > > > > > function2 = @anon c -> (c + z) > > > > > > > > > > > > > > dict[z] = function2 > > > > > > > > > > > > > > end > > > > > > > > > > > > > > > > > > > > > a = 1 > > > > > > > b = 2 > > > > > > > > > > > > > > function test() > > > > > > > > > > > > > > function1(a,b, dict[100]) > > > > > > > function1(a,b, dict[50]) > > > > > > > > > > > > > > end > > > > > > > > > > > > > > @code_llvm test() > > > > > > > @code_native test() > > > > > > > > > > > > > > > > > > > > > > > > > > > > So we end up creating multiple functions for each z value. We > > > > > > could > > > > > > > > use > > > > > > > > > > > > Julia's anon funs, which would only create a single function, > > > > > > however > > > > > > > > > > these > > > > > > > lamdas are less performant than FastAnon. > > > > > > > > > > > > > > So its a space vs time tradeoff, I want the speed of FastAnon, > > > > > > without > > > > > > > > the > > > > > > > > > > > > spacial overhead of storing multiple functions. > > > > > > > > > > > > > > Can we be greedy? :) > > > > > > > > > > > > > > On Thursday, January 21, 2016 at 9:56:51 PM UTC-5, Cedric > St-Jean > > > > > > > > > > wrote: > > > > > > >> Something like this? > > > > > > >> > > > > > > >> function function1(a, b, f) # Variable needed in callback fun > > > > > > > > > > injected. > > > > > > > > > > > >> if(a > b) > > > > > > >> > > > > > > >> c = a + b > > > > > > >> res = f(c) # Callback function has been injected. > > > > > > >> return res + 1 > > > > > > >> > > > > > > >> else > > > > > > >> > > > > > > >> # do anything > > > > > > >> # but return nothing > > > > > > >> > > > > > > >> end > > > > > > >> > > > > > > >> end > > > > > > >> > > > > > > >> type SomeCallBack > > > > > > >> > > > > > > >> z::Int > > > > > > >> > > > > > > >> end > > > > > > >> Base.call(callback::SomeCallBack, c) = c + callback.z > > > > > > >> > > > > > > >> function1(2, 1, SomeCallBack(10)) > > > > > > >> > > > > > > >> Because of JIT, this is 100% equivalent to your "callback > > > > > > function > > > > > > > > has > > > > > > > > > > > >> been injected" example, performance-wise. My feeling is that > > > > > > .call > > > > > > > > > >> overloading is not to be abused in Julia, so I would favor > using > > > > > > a > > > > > > > > > >> regular > > > > > > >> function call with a descriptive name instead of call > > > > > > overloading, > > > > > > > > but > > > > > > > > > > > >> the > > > > > > >> same performance guarantees apply. Does that answer your > > > > > > question? > > > > > > > > > >> On Thursday, January 21, 2016 at 9:02:50 PM UTC-5, Bryan > Rivera > > > > > > > > > > wrote: > > > > > > >>> I think what I wrote above might be too complicated, as it > is an > > > > > > > > > > attempt > > > > > > > > > > > >>> to solve this problem. > > > > > > >>> > > > > > > >>> In essence this is what I want: > > > > > > >>> > > > > > > >>> > > > > > > >>> # wasmerged, _, _, _ = elide_pairwise!(ttree1, ttree2, > canmerge; > > > > > > > > > > nbrs=idbgv) > > > > > > > > > > > >>> function function1(a, b, onGreaterThanCallback) > > > > > > >>> > > > > > > >>> if(a > b) > > > > > > >>> > > > > > > >>> c = a + b > > > > > > >>> res = onGreaterThanCallback(c, z) > > > > > > >>> return res + 1 > > > > > > >>> > > > > > > >>> else > > > > > > >>> > > > > > > >>> # do anything > > > > > > >>> # but return nothing > > > > > > >>> > > > > > > >>> end > > > > > > >>> > > > > > > >>> end > > > > > > >>> > > > > > > >>> > > > > > > >>> global onGreaterThanCallback = (c) -> c + z > > > > > > >>> > > > > > > >>> function1(a, b, onGreaterThanCallback) > > > > > > >>> > > > > > > >>> > > > > > > >>> Problems: > > > > > > >>> > > > > > > >>> The global variable. > > > > > > >>> > > > > > > >>> The anonymous function which has performance impact (vs > other > > > > > > >>> approaches). We could use Tim Holy's @anon, but then the > value > > > > > > of > > > > > > > > `z` > > > > > > > > > > > >>> is > > > > > > >>> fixed at function definition, which we don't always want. > > > > > > >>> > > > > > > >>> I think that the ideal optimization would look like this: > > > > > > >>> function function1(a, b, z) # Variable needed in > callback > > > > > > fun > > > > > > > > > >>> injected. > > > > > > >>> > > > > > > >>> if(a > b) > > > > > > >>> > > > > > > >>> c = a + b > > > > > > >>> res = c + z # Callback function has been injected. > > > > > > >>> return res + 1 > > > > > > >>> > > > > > > >>> else > > > > > > >>> > > > > > > >>> # do anything > > > > > > >>> # but return nothing > > > > > > >>> > > > > > > >>> end > > > > > > >>> > > > > > > >>> end > > > > > > >>> > > > > > > >>> > > > > > > >>> function1(a, b, z) > > > > > > >>> > > > > > > >>> In OO languages we would be using an abstract class or its > > > > > > > > > > equivalent. > > > > > > > > > > > >>> But I've thought about it, and read the discussions on > > > > > > interfaces, > > > > > > > > and > > > > > > > > > > > >>> don't see those solutions optimizing the code out like I did > > > > > > above. > > > > > > > > > >>> Any ideas? > >
