Recursive call signature keeps changing - recursion

I am going to implement a program that uses recursion quite a bit. So, before I started to get stack overflows exceptions, I figured it would be nice to have a trampoline implemented and use thunks in case it was needed.
A first try I did was with factorial. Here the code:
callable(f) = !isempty(methods(f))
function trampoline(f, arg1, arg2)
v = f(arg1, arg2)
while callable(v)
v = v()
end
return v
end
function factorial(n, continuation)
if n == 1
continuation(1)
else
(() -> factorial(n-1, (z -> (() -> continuation(n*z)))))
end
end
function cont(x)
x
end
Also, I implemented a naive factorial to check if, as a matter of fact, I would be preventing stack overflows:
function factorial_overflow(n)
if n == 1
1
else
n*factorial_overflow(n-1)
end
end
The results are:
julia> factorial_overflow(140000)
ERROR: StackOverflowError:
#JITing with a small input
julia> trampoline(factorial, 10, cont)
3628800
#Testing
julia> trampoline(factorial, 140000, cont)
0
So, yes, I am avoiding StacksOverflows. And yes, I know the result is nonsense as I am getting integers overflows, but here I just cared about the stack. A production version of course would have that fixed.
(Also, I know for the factorial case there is a built-in, I wouldn't use either of these, I made them for testing my trampoline).
The trampoline version takes a lot of time when running for the first time, and then it gets quick... when computing the same or lower values.
If I did trampoline(factorial, 150000, cont) I will have some compiling time again.
It seems to me (educated guess) that I am JITing many different signatures for factorial: one for every thunk generated.
My question is: can I avoid this?

I think the problem is that every closure is its own type, which is specialized on the captured variables. To avoid this specialization, one can instead use functors, that are not fully specialized:
struct L1
f
n::Int
z::Int
end
(o::L1)() = o.f(o.n*o.z)
struct L2
f
n::Int
end
(o::L2)(z) = L1(o.f, o.n, z)
struct Factorial
f
c
n::Int
end
(o::Factorial)() = o.f(o.n-1, L2(o.c, o.n))
callable(f) = false
callable(f::Union{Factorial, L1, L2}) = true
function myfactorial(n, continuation)
if n == 1
continuation(1)
else
Factorial(myfactorial, continuation, n)
end
end
function cont(x)
x
end
function trampoline(f, arg1, arg2)
v = f(arg1, arg2)
while callable(v)
v = v()
end
return v
end
Note that the function fields are untyped. Now the function run much faster on the first run:
julia> #time trampoline(myfactorial, 10, cont)
0.020673 seconds (4.24 k allocations: 264.427 KiB)
3628800
julia> #time trampoline(myfactorial, 10, cont)
0.000009 seconds (37 allocations: 1.094 KiB)
3628800
julia> #time trampoline(myfactorial, 14000, cont)
0.001277 seconds (55.55 k allocations: 1.489 MiB)
0
julia> #time trampoline(myfactorial, 14000, cont)
0.001197 seconds (55.55 k allocations: 1.489 MiB)
0
I just translated every closure in your code into a corresponding functor. This might not be needed and probably there are be better solutions, but it works and hopefully demonstrates the approach.
Edit:
To make the reason for the slowdown more clear, one can use:
function factorial(n, continuation)
if n == 1
continuation(1)
else
tmp = (z -> (() -> continuation(n*z)))
#show typeof(tmp)
(() -> factorial(n-1, tmp))
end
end
This outputs:
julia> trampoline(factorial, 10, cont)
typeof(tmp) = ##31#34{Int64,#cont}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,#cont}}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,##31#34{Int64,#cont}}}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,#cont}}}}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,#cont}}}}}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,#cont}}}}}}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,#cont}}}}}}}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,#cont}}}}}}}}
typeof(tmp) = ##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,##31#34{Int64,#cont}}}}}}}}}
3628800
tmp is a closure. Its automatically created type ##31#34 looks similar to
struct Tmp{T,F}
n::T
continuation::F
end
The specialization on the type F of the continuation field is the reason for the long compilation times.
By using L2 instead, which is not specialized on the corresponding field f, the continuation argument to factorial has always the type L2 and the problem is avoided.

Related

Type-stability in Julia's product iterator

I am trying to make A in the following code type-stable.
using Primes: factor
function f(n::T, p::T, k::T) where {T<:Integer}
return rand(T, n * p^k)
end
function g(m::T, n::T) where {T<:Integer}
i = 0
for A in Iterators.product((f(n, p, T(k)) for (p, k) in factor(m))...)
i = sum(A)
end
return i
end
Note that f is type-stable. The variable A is not type-stable because the product iterator will return different sized tuples depending on the values of n and m. If there was an iterator like the product iterator that returned a Vector instead of a Tuple, I believe that the type-instability would go away.
Does anyone have any suggestions to make A type-stable in the above code?
Edit: I should add that f returns a variable-sized Vector of type T.
One way I have solved the type-stability is by doing this.
function g(m::T, n::T) where {T<:Integer}
B = Vector{T}[T[]]
for (p, k) in factor(m)
C = Vector{T}[]
for (b, r) in Iterators.product(B, f(n, p, T(k)))
c = copy(b)
push!(c, r)
push!(C, c)
end
B = C
end
for A in B
i = sum(A)
end
return i
end
This (and in particular, A) is now type-stable, but at the cost lots of memory. I'm not sure of a better way to do this.
It's not easy to get this completely type stable, but you can isolate the type instability with a function barrier. Convert the factorization to a tuple in an outer function, which you pass to an inner function which is type stable. This gives just one dynamic dispatch, instead of many:
# inner, type stable
function _g(n, tup)
i = 0
for A in Iterators.product((f(n, p, k) for (p, k) in tup)...)
i += sum(A) # or i = sum(A), whatever
end
return i
end
# outer function
g(m::T, n::T) where {T<:Integer} = _g(n, Tuple(factor(m)))
Some benchmarks:
julia> #btime g(7, 210); # OP version
149.600 μs (7356 allocations: 172.62 KiB)
julia> #btime g(7, 210); # my version
1.140 μs (6 allocations: 11.91 KiB)
You should expect to hit compilation occasionally, whenever you get a number that contains a new number of factors.

Is Julia not evaluating the Function?

I'm playing around with the Collatz conjecture (see below), and I have this function:
function txpo(max_n)
for n ∈ 1:max_n
x = n
while x ≥ n && x != 1
x = iseven(x) ? x÷2 : 3x+1
end
end
end
On purpose I don't return anything or check anything, to make it as fast as possible. And boy, is it fast... - actually too fast, I think.
using BenchmarkTools
#btime txpo(100_000_000_000_000_000_000_000_000_000_000_000_000)
2.337 ns (0 allocations: 0 bytes)
Running through 10^38 while loops takes only 2ns? Julia is fast, but I would be surprised if it was that fast. It seems like the code is not evaluated. Or what am I missing here?
Collatz Conjecture
Take a whole number and build a series by using the following rule: if the number is even divide by 2; if the number is odd multiply by 3 and add 1. Conjecture: every series will end up in the loop 4-2-1-4.
Indeed, the whole function body is optimized out:
julia> function txpo(max_n)
for n ∈ 1:max_n
x = n
while x ≥ n && x != 1
x = iseven(x) ? x÷2 : 3x+1
end
end
end
txpo (generic function with 1 method)
julia> #code_llvm txpo(100000000000)
; # REPL[1]:1 within `txpo`
define void #julia_txpo_179(i64 signext %0) #0 {
top:
; # REPL[1]:5 within `txpo`
ret void
}
If you use 10^38, this only changes the function signature to accept 128-bit integers.
The LLVM IR code is basically the same as this Julia code:
julia_txpo_179(_0::Int64) = nothing
This makes perfect sense because why execute a function that doesn't return anything that depends on its computations and has no side-effects?
Finally, the native code that your CPU executes is literally retq, a.k.a. "return to caller":
julia> #code_native txpo(100000000000)
.section __TEXT,__text,regular,pure_instructions
; ┌ # REPL[1]:5 within `txpo`
retq
nopw %cs:(%rax,%rax)
; └
julia>

Access struct fields within a function by string argument

Say I have a struct,
struct MyStruct
a
b
end
Is there some way to write a function like the following
function doSomething(x::MyStruct,fieldName::String)
y = x.fieldName
return f(y)
end
I could not find anything about this in the documentation / forums.
You can access fields with Symbols, so you can convert the string to a symbol and then use getproperty:
julia> struct MyStruct
a
b
end
julia> function doSomething(x::MyStruct, name::String)
s = Symbol(name)
return getproperty(x, s)
end
doSomething (generic function with 1 method)
julia> doSomething(MyStruct(1, 2), "a")
1
Note, however, that this will probably be very inefficient, since the compiler most likely can't see through this and thus your code might be type-unstable, see https://docs.julialang.org/en/v1/manual/performance-tips/.
If you plan to get the value only few times redrikekre's solution is OK. However, using metaprogramming you can write code without efficiency penalty, see the getDoSomething2() function below.
Consider those three functions:
function doSomethingNative(x)
return x.a
end
function doSomething(x, name::String)
return getproperty(x, Symbol(name))
end
function getDoSomething2(name::String)
field = Symbol(name)
code = quote
(obj) -> obj.$field
end
return eval(code)
end
Now the setup:
using BenchmarkTools
struct MyStruct
a
b
end
x = MyStruct(5,6)
Now the benchmarks:
julia> #btime doSomethingNative($x)
0.001 ns (0 allocations: 0 bytes)
5
julia> #btime doSomething($x,"a")
36.266 ns (0 allocations: 0 bytes)
5
julia> const doSomething2 = getDoSomething2("a");
julia> #btime doSomething2($x)
0.001 ns (0 allocations: 0 bytes)
5
If you run #code_native doSomethingNative(x) and #code_native doSomething2(x) you will see that the assembly output is identical.

Speed up deepcopy for new type

Question: I have a new type type MyFloat; x::Float64 ; end. I want to perform a deepcopy on a Vector{MyFloat}. Using Julia v0.5.0 on Ubuntu 16.04, the operation runs roughly 150 times slower than a deepcopy call on an equivalent length Vector{Float64}. Is it possible to speed up a deepcopy on my Vector{MyFloat}?
Code snippet: The 150 times slowdown can be seen with the following code snippet which can be pasted to the REPL:
#Just my own floating point type
type MyFloat
x::Float64
end
#This function performs N deepcopy operations on a Vector{MyFloat} of length J
function f1(J::Int, N::Int)
v = MyFloat.(rand(J))
x = [ deepcopy(v) for n = 1:N ]
end
#The same as f1, but on Vector{Float64} instead of Vector{MyFloat}
function f2(J::Int, N::Int)
v = rand(J)
x = [ deepcopy(v) for n = 1:N ]
end
#Pre-compilation step
f1(2, 2);
f2(2, 2);
#Timings
#time f1(100, 15000);
#time f2(100, 15000);
On my machine this produces:
julia> #time f1(100, 15000);
1.944410 seconds (4.61 M allocations: 167.888 MB, 7.72% gc time)
julia> #time f2(100, 15000);
0.013513 seconds (45.01 k allocations: 19.113 MB, 78.80% gc time)
Looking at the answer here it sounds like I can speed things up by defining my own copy method for MyFloat. I've tried things like:
Base.deepcopy(x::MyFloat)::MyFloat = MyFloat(x.x);
Base.deepcopy(v::Vector{MyFloat})::Vector{MyFloat} = [ MyFloat(y.x) for y in v ]
Base.copy(x::MyFloat)::MyFloat = MyFloat(x.x)
Base.copy(v::Vector{MyFloat})::Vector{MyFloat} = [ MyFloat(y.x) for y in v ]
but this doesn't make any difference.
Final note: Letting a = MyFloat.([1.0, 2.0]), I could just use b = copy(a) and there is no speed penalty. This is fine, as long as I am careful to only ever do operations like b[1] = MyFloat(3.0) (which will modify b but not a). But if I get sloppy and accidentally write b[1].x = 3.0, then this will modify both a and b.
By the way, it is entirely possible that I do not have a deep understanding of the differences between copy and deepcopy... I have read this great blog post (thanks #ChrisRackauckas), but I'm certainly a bit fuzzy about what is happening at a deeper level.
Try changing type MyFloat in the definition to immutable MyFloat or struct MyFloat (the keyword changed in 0.6). This makes the times almost equal.
As #Gnimuc mentioned, a mutable, which is not a bitstype, makes Julia keep track of a lot of other stuff. See here and in the comments.

Julia pi approximation slow

I have pi approximation code very similar to that on official page:
function piaprox()
sum = 1.0
for i = 2:m-1
sum = sum + (1.0/(i*i))
end
end
m = parse(Int,ARGS[1])
opak = parse(Int,ARGS[2])
#time for i = 0:opak
piaprox()
end
When I try to compare time of C and Julia, then Julia is significantly slower, almost 38 sec for m = 100000000 (time of C is 0.1608328933 sec). Why this is happening?
julia> m=100000000
julia> function piaprox()
sum = 1.0
for i = 2:m-1
sum = sum + (1.0/(i*i))
end
end
piaprox (generic function with 1 method)
julia> #time piaprox()
28.482094 seconds (600.00 M allocations: 10.431 GB, 3.28% gc time)
I would like to mention two very important paragraphs from Performance Tips section of julia documentation:
Avoid global variables A global variable might have its value, and
therefore its type, change at any point. This makes it difficult for
the compiler to optimize code using global variables. Variables should
be local, or passed as arguments to functions, whenever possible.....
The macro #code_warntype (or its function variant code_warntype()) can
sometimes be helpful in diagnosing type-related problems.
julia> #code_warntype piaprox();
Variables:
sum::Any
#s1::Any
i::Any
It's clear from #code_warntype output that compiler could not recognize types of local variables in piaprox(). So we try to declare types and remove global variables:
function piaprox(m::Int)
sum::Float64 = 1.0
i::Int = 0
for i = 2:m-1
sum = sum + (1.0/(i*i))
end
end
julia> #time piaprox(100000000 )
0.009023 seconds (11.10 k allocations: 399.769 KB)
julia> #code_warntype piaprox(100000000);
Variables:
m::Int64
sum::Float64
i::Int64
#s1::Int64
EDIT
as #user3662120 commented, the super fast behavior of the answer is result of a mistake, without a return value LLVM might ignore the for loop, by adding a return line the #time result would be:
julia> #time piaprox(100000000)
0.746795 seconds (11.11 k allocations: 400.294 KB, 0.45% gc time)
1.644934057834575

Resources