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No type inference on struct fields #319
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Note that julia> struct struct_offset2{A<:OffsetMatrix{Int64}}
x::A
end which should always lead to a concretely typed instance, and would help with subsequent type inference. julia> tst3 = struct_offset2(x);
julia> @code_typed fn(tst3)
CodeInfo(
1 ─ %1 = Base.getfield(x, :x)::OffsetMatrix{Int64, Matrix{Int64}}
└── goto #6 if not true
2 ─ %3 = Core.tuple(1, 1)::Tuple{Int64, Int64}
│ %4 = Base.getfield(%1, :parent)::Matrix{Int64}
│ %5 = Base.arraysize(%4, 1)::Int64
│ %6 = Base.arraysize(%4, 2)::Int64
│ %7 = Base.slt_int(%5, 0)::Bool
│ %8 = Core.ifelse::typeof(Core.ifelse)
│ %9 = (%8)(%7, 0, %5)::Int64
│ %10 = Base.slt_int(%6, 0)::Bool
│ %11 = Core.ifelse::typeof(Core.ifelse)
│ %12 = (%11)(%10, 0, %6)::Int64
│ %13 = Base.getfield(%1, :offsets)::Tuple{Int64, Int64}
│ %14 = Base.getfield(%13, 1, true)::Int64
│ %15 = Base.getfield(%13, 2, true)::Int64
│ %16 = Base.sub_int(1, %14)::Int64
│ %17 = Base.sle_int(1, %16)::Bool
│ %18 = Base.sle_int(%16, %9)::Bool
│ %19 = Base.and_int(%17, %18)::Bool
│ %20 = Base.sub_int(1, %15)::Int64
│ %21 = Base.sle_int(1, %20)::Bool
│ %22 = Base.sle_int(%20, %12)::Bool
│ %23 = Base.and_int(%21, %22)::Bool
│ %24 = Base.and_int(%23, true)::Bool
│ %25 = Base.and_int(%19, %24)::Bool
└── goto #4 if not %25
3 ─ Base.nothing::Nothing
└── goto #5
4 ─ invoke Base.throw_boundserror(%1::OffsetMatrix{Int64, Matrix{Int64}}, %3::Tuple{Int64, Int64})::Union{}
└── unreachable
5 ─ nothing::Nothing
6 ┄ %32 = Base.getfield(%1, :offsets)::Tuple{Int64, Int64}
│ %33 = Base.getfield(%32, 1, true)::Int64
│ %34 = Base.getfield(%32, 2, true)::Int64
│ %35 = Base.sub_int(1, %33)::Int64
│ %36 = Base.sub_int(1, %34)::Int64
│ %37 = Base.getfield(%1, :parent)::Matrix{Int64}
│ %38 = Base.arrayref(false, %37, %35, %36)::Int64
└── goto #7
7 ─ return %38
) => Int64 |
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When functions are given as an input a struct whose field is an
OffsetArray
type inference on that function fails if the code tries to access an element of the array and defaults to::Any
.If my understanding of the profiler is correct, this causes run-time dispatching for subsequent code that depends on this and significantly deteriorates the performance of the code.
Produces the following output:
Now, the usual approach to fixing this would be to let julia optimize on the function boudaries, by separating the function so that all types are known at compile time.
However, this still results in untyped code, despite the fact that the intermediate function is correctly typed.
Is this expected behaviour?
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