This package contains a collection of code reflection and investigation utilities that can be (potentially) useful for understanding, analyzing, and investigating julia code and the julia compiler.
Most of the reflection functions are named hierarchically by what they reflect over. For example:
func_specializations(f)lists all specializations for a given function.module_functions(m::Module)lists all the functions in a given module.module_globals_names(m::Module)lists the names of all global variables in a given module.
I've used func_specializations() and func_backedges() quite a bit when trying to
understand decisions the compiler makes.
For example, we can see that julia doesn't specialize functions for Type arguments by default:
julia> isintegertype(t) = t <: Integer
isintegertype (generic function with 1 method)
julia> isintegertype(Int)
true
julia> isintegertype(Float32)
false
julia> keys(LookingGlass.func_specializations(isintegertype))
Base.KeySet for a Dict{Tuple{Method,DataType},Core.TypeMapEntry} with 1 entry. Keys:
(isintegertype(t) in Main at none:1, Tuple{typeof(isintegertype),Type})But it does specialize on Type arguments if you access them via a where clause:
julia> isintegertype(::Type{T}) where T = T <: Integer
isintegertype (generic function with 2 methods)
julia> isintegertype(Int)
true
julia> isintegertype(Float32)
false
julia> keys(LookingGlass.func_specializations(isintegertype))
Base.KeySet for a Dict{Tuple{Method,DataType},Core.TypeMapEntry} with 3 entries. Keys:
(isintegertype(t) in Main at none:1, Tuple{typeof(isintegertype),Type})
(isintegertype(::Type{T}) where T in Main at none:1, Tuple{typeof(isintegertype),Type{Float32}})And you can use func_backedges(f) to observe inlining, among other things.
julia> foo(x) = 2x
foo (generic function with 1 method)
julia> bar(x) = foo(x) + 1
bar (generic function with 1 method)
julia> foo(2)
4
julia> LookingGlass.func_backedges(foo)
Dict{Any,Array{Any,1}} with 2 entries:
(foo(x) in Main at none:1, Tuple{typeof(foo),Int64}) => Any[]
:MethodTable => Any[]These functions provide reflection over Modules. This can be useful for example, when working on multithreading a package, where you may want to check for potential places where data races can occur -- all global mutable state. This can be covered via:
julia> # Non-const global variables
julia> LookingGlass.module_recursive_globals_names(FixedPointDecimals, constness=:nonconst, mutability=:all)
Dict{Module,Array{Symbol,1}} with 1 entry:
FixedPointDecimals => Symbol[]
julia> # And const-mutable global variables
julia> LookingGlass.module_recursive_globals_names(FixedPointDecimals, constness=:const, mutability=:mutable)
Dict{Module,Array{Symbol,1}} with 1 entry:
FixedPointDecimals => Symbol[]So we can see that FixedPointDecimals looks good. :) (Note that of course this doesn't cover all potential data races in a package, just some obvious good places to start looking.)