Remove `eval` from at-memoize macro

[kmsquire]

Right now, the @memoize macro creates the cache dictionary using eval:

Memoize.jl/src/Memoize.jl

Lines 49 to 52 in 1709785

	fcachename = Symbol("##", f, "_memoized_cache")
	fcache = isdefined(Main, fcachename) ?
	getfield(Main, fcachename) :
	Core.eval(Main, :(const $fcachename = ($dicttype)()))

This is generally considered a bad idea in macros, and until recently, also caused an error here because the code was evaling into the Memoize package (see #32).

Right now, using eval gives the following behavior:

1. all memoized methods of a given function share the memoization cache
2. if a method is overwritten, the cache is cleared, and any other global resources created/held by the previous version of that method are released:

   Memoize.jl/test/runtests.jl

   Lines 257 to 266 in 1709785

   	finalized = false
   	@memoize function method_rewrite()
   	x = []
   	finalizer(x->(global finalized; finalized = true),x)
   	x
   	end
   	method_rewrite()
   	@memoize function method_rewrite() end
   	GC.gc()
   	@test finalized

I made one attempt to simply use a different cache dictionary for each method of a given function (JuliaCollections:1709785...JuliaCollections:a9170e5). This mostly works, but as-is causes the test above to fail, because the old cache is not released when the method is overwritten.

There may be an easy way around this, but it's not obvious to me right now.

Thoughts and/or pull requests welcome!

Cc: @cstjean

[o314]

Hello,

if a method is overwritten

It does not seem to me that multidispatch is handled. i will not address the
world age issue there and only consider, the multidispatch case.

Thru the example of factorial, i will try to make this code run

CASE A - ONE CACHE PER FUNC

const _FACT_LUT = Dict{Type{<:Tuple},Dict}()

# memoizing only(methods(factorial, Tuple{Int64}))
_FACT_LUT[Tuple{Int64}] = Dict{Int64,Int64}()
let _inst_lut = _FACT_LUT[Tuple{Int64}]
    _inst_lut[1] = 1
    for n in 2:20
        _inst_lut[n] = _inst_lut[n-1] * n
    end
end

# memoizing only(methods(factorial, Tuple{Int128}))
_FACT_LUT[Tuple{Int128}] = Dict{Int128,Int128}()
let _inst_lut = _FACT_LUT[Tuple{Int128}]
    _inst_lut[1] = 1
    for n in 2:34
        _inst_lut[n] = _inst_lut[n-1] * n
    end
end

CASE B - ONE CACHE FOR ALL FUNC

const _LUT = Dict{Type{<:Tuple},Dict}() 
# ...


# memoizing only(methods(factorial, Tuple{Int64}))
sig = Tuple{typeof(factorial),Union{Int64, UInt64}}
sigarg = Union{Int64, UInt64}
_LUT[sig] = Dict{sigarg,sigarg}()
let _inst_lut = _LUT[sig]
    # init (3rd arg ?)
    _inst_lut[1] = 1
    for n in 2:20
        _inst_lut[n] = _inst_lut[n-1] * n
    end
end

# memoizing only(methods(factorial, Tuple{Int128}))
sig = Tuple{typeof(factorial),Int128}
sigarg = Int128
_LUT[sig] = Dict{sigarg,sigarg}()
let _inst_lut = _LUT[sig]
    # init (3rd arg ?)
    _inst_lut[1] = 1
    for n in 2:34
        _inst_lut[n] = _inst_lut[n-1] * n
    end
end

---

to get that at macro

• force typed arg @ function sig expr
• collect them
• rebuild tupletype
  • case a, done with 1 lut per func computed elsewhere
  • case b, prepend the typeof(named_function_name) to the tupletype
• later, dispatch @ dict according to it

The main point is to have one subcache per method instance eg. dispatchtuple/tupletype; and a supercache to collect subcaches

Does that help you ?

[o314]

update

after

@memoized factorial(n::Int) =
    n == 1 ?
        1 :
        factorial(n-1) * n

factorial(10)

Try to generate something like

_CACHE[Tuple{typeof(factorial), Int}] =
    Dict{Tuple{Int64},Any}(
        (7,) => 5040,
        (4,) => 24,
        (8,) => 40320,
        (3,) => 6,
        (6,) => 720,
        (9,) => 362880,
        (2,) => 2,
        (5,) => 120,
        (10,) => 3628800,
        (1,) => 1)

ps Any should be Int @ Dict. has to be fixed
pps: i am storing tuple of one, one may unwrap them.

[cstjean]

It also causes all instances of a closure to share the same cache, which is wrong:

julia> g = []
       for x in 1:10
       push!(g, 
             @memoize f(y) = x+y)
       end

julia> g[1](1)
2

julia> g[2](1)
2

[cstjean]

Ah, this solution by @yuyichao might work out for us:

julia> begin
       local foo=Ref(1)
       g() = foo[]
       end
g (generic function with 1 method)

julia> @btime g()
  1.538 ns (0 allocations: 0 bytes)
1