Doc and wiki reorg #15
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I like how in commits I'm the only one who capitalizes the subject line and here I'm the only one who doesn't ;-) |
ghost
assigned
|
I'm currently working on this. Have done the very beginning of porting the manual to the wiki. |
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burrowsa
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Mar 24, 2014
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StefanKarpinski
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Feb 8, 2018
Typealias String after String -> AbstractString rename
Keno
added a commit
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Feb 10, 2019
Consider the following function:
```
julia> function foo(a, b)
ntuple(i->(a+b; i), Val(4))
end
foo (generic function with 1 method)
```
(In particular note that the return type of the closure does not depend on the types
of `a` and b`). Unfortunately, prior to this change, inference was unable to determine
the return type in this situation:
```
julia> code_typed(foo, Tuple{Any, Any}, trace=true)
Refused to call generated function with non-concrete argument types ntuple(::getfield(Main, Symbol("##15#16")){_A,_B} where _B where _A, ::Val{4}) [GeneratedNotConcrete]
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##15#16)::Const(##15#16, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##15#16{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##15#16{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::Any
└── return %6
) => Any
```
Looking at the definition of ntuple
https://github.com/JuliaLang/julia/blob/abb09f88804c4e74c752a66157e767c9b0f8945d/base/ntuple.jl#L45-L56
we see that it is a generated function an inference thus refuses to invoke it,
unless it can prove the concrete type of *all* arguments to the function. As
the above example illustrates, this restriction is more stringent than necessary.
It is true that we cannot invoke generated functions on arbitrary abstract
signatures (because we neither want to the user to have to be able to nor
do we trust that users are able to preverse monotonicity - i.e. that the return
type of the generated code will always be a subtype of the return type of a more
abstract signature).
However, if some piece of information is not used (the type of the passed function
in this case), there is no problem with calling the generated function (since
information that is unnused cannot possibly affect monotnicity).
This PR allows us to recognize pieces of information that are *syntactically* unused,
and call the generated functions, even if we do not have those pieces of information.
As a result, we are now able to infer the return type of the above function:
```
julia> code_typed(foo, Tuple{Any, Any})
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##3#4)::Const(##3#4, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##3#4{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##3#4{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::NTuple{4,Int64}
└── return %6
) => NTuple{4,Int64}
```
In particular, we use the new frontent `used` flags from the previous commit.
One additional complication is that we want to accesss these flags without
uncompressing the generator source, so we change the compression scheme to
place the flags at a known location.
Keno
added a commit
that referenced
this issue
Feb 10, 2019
Consider the following function:
```
julia> function foo(a, b)
ntuple(i->(a+b; i), Val(4))
end
foo (generic function with 1 method)
```
(In particular note that the return type of the closure does not depend on the types
of `a` and b`). Unfortunately, prior to this change, inference was unable to determine
the return type in this situation:
```
julia> code_typed(foo, Tuple{Any, Any}, trace=true)
Refused to call generated function with non-concrete argument types ntuple(::getfield(Main, Symbol("##15#16")){_A,_B} where _B where _A, ::Val{4}) [GeneratedNotConcrete]
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##15#16)::Const(##15#16, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##15#16{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##15#16{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::Any
└── return %6
) => Any
```
Looking at the definition of ntuple
https://github.com/JuliaLang/julia/blob/abb09f88804c4e74c752a66157e767c9b0f8945d/base/ntuple.jl#L45-L56
we see that it is a generated function an inference thus refuses to invoke it,
unless it can prove the concrete type of *all* arguments to the function. As
the above example illustrates, this restriction is more stringent than necessary.
It is true that we cannot invoke generated functions on arbitrary abstract
signatures (because we neither want to the user to have to be able to nor
do we trust that users are able to preverse monotonicity - i.e. that the return
type of the generated code will always be a subtype of the return type of a more
abstract signature).
However, if some piece of information is not used (the type of the passed function
in this case), there is no problem with calling the generated function (since
information that is unnused cannot possibly affect monotnicity).
This PR allows us to recognize pieces of information that are *syntactically* unused,
and call the generated functions, even if we do not have those pieces of information.
As a result, we are now able to infer the return type of the above function:
```
julia> code_typed(foo, Tuple{Any, Any})
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##3#4)::Const(##3#4, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##3#4{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##3#4{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::NTuple{4,Int64}
└── return %6
) => NTuple{4,Int64}
```
In particular, we use the new frontent `used` flags from the previous commit.
One additional complication is that we want to accesss these flags without
uncompressing the generator source, so we change the compression scheme to
place the flags at a known location.
Fixes #31004
Keno
added a commit
that referenced
this issue
Feb 10, 2019
Consider the following function:
```
julia> function foo(a, b)
ntuple(i->(a+b; i), Val(4))
end
foo (generic function with 1 method)
```
(In particular note that the return type of the closure does not depend on the types
of `a` and b`). Unfortunately, prior to this change, inference was unable to determine
the return type in this situation:
```
julia> code_typed(foo, Tuple{Any, Any}, trace=true)
Refused to call generated function with non-concrete argument types ntuple(::getfield(Main, Symbol("##15#16")){_A,_B} where _B where _A, ::Val{4}) [GeneratedNotConcrete]
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##15#16)::Const(##15#16, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##15#16{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##15#16{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::Any
└── return %6
) => Any
```
Looking at the definition of ntuple
https://github.com/JuliaLang/julia/blob/abb09f88804c4e74c752a66157e767c9b0f8945d/base/ntuple.jl#L45-L56
we see that it is a generated function an inference thus refuses to invoke it,
unless it can prove the concrete type of *all* arguments to the function. As
the above example illustrates, this restriction is more stringent than necessary.
It is true that we cannot invoke generated functions on arbitrary abstract
signatures (because we neither want to the user to have to be able to nor
do we trust that users are able to preverse monotonicity - i.e. that the return
type of the generated code will always be a subtype of the return type of a more
abstract signature).
However, if some piece of information is not used (the type of the passed function
in this case), there is no problem with calling the generated function (since
information that is unnused cannot possibly affect monotnicity).
This PR allows us to recognize pieces of information that are *syntactically* unused,
and call the generated functions, even if we do not have those pieces of information.
As a result, we are now able to infer the return type of the above function:
```
julia> code_typed(foo, Tuple{Any, Any})
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##3#4)::Const(##3#4, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##3#4{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##3#4{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::NTuple{4,Int64}
└── return %6
) => NTuple{4,Int64}
```
In particular, we use the new frontent `used` flags from the previous commit.
One additional complication is that we want to accesss these flags without
uncompressing the generator source, so we change the compression scheme to
place the flags at a known location.
Fixes #31004
Keno
added a commit
that referenced
this issue
Feb 10, 2019
Consider the following function:
```
julia> function foo(a, b)
ntuple(i->(a+b; i), Val(4))
end
foo (generic function with 1 method)
```
(In particular note that the return type of the closure does not depend on the types
of `a` and b`). Unfortunately, prior to this change, inference was unable to determine
the return type in this situation:
```
julia> code_typed(foo, Tuple{Any, Any}, trace=true)
Refused to call generated function with non-concrete argument types ntuple(::getfield(Main, Symbol("##15#16")){_A,_B} where _B where _A, ::Val{4}) [GeneratedNotConcrete]
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##15#16)::Const(##15#16, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##15#16{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##15#16{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::Any
└── return %6
) => Any
```
Looking at the definition of ntuple
https://github.com/JuliaLang/julia/blob/abb09f88804c4e74c752a66157e767c9b0f8945d/base/ntuple.jl#L45-L56
we see that it is a generated function an inference thus refuses to invoke it,
unless it can prove the concrete type of *all* arguments to the function. As
the above example illustrates, this restriction is more stringent than necessary.
It is true that we cannot invoke generated functions on arbitrary abstract
signatures (because we neither want to the user to have to be able to nor
do we trust that users are able to preverse monotonicity - i.e. that the return
type of the generated code will always be a subtype of the return type of a more
abstract signature).
However, if some piece of information is not used (the type of the passed function
in this case), there is no problem with calling the generated function (since
information that is unnused cannot possibly affect monotnicity).
This PR allows us to recognize pieces of information that are *syntactically* unused,
and call the generated functions, even if we do not have those pieces of information.
As a result, we are now able to infer the return type of the above function:
```
julia> code_typed(foo, Tuple{Any, Any})
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##3#4)::Const(##3#4, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##3#4{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##3#4{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::NTuple{4,Int64}
└── return %6
) => NTuple{4,Int64}
```
In particular, we use the new frontent `used` flags from the previous commit.
One additional complication is that we want to accesss these flags without
uncompressing the generator source, so we change the compression scheme to
place the flags at a known location.
Fixes #31004
Keno
added a commit
that referenced
this issue
Feb 11, 2019
Consider the following function:
```
julia> function foo(a, b)
ntuple(i->(a+b; i), Val(4))
end
foo (generic function with 1 method)
```
(In particular note that the return type of the closure does not depend on the types
of `a` and b`). Unfortunately, prior to this change, inference was unable to determine
the return type in this situation:
```
julia> code_typed(foo, Tuple{Any, Any}, trace=true)
Refused to call generated function with non-concrete argument types ntuple(::getfield(Main, Symbol("##15#16")){_A,_B} where _B where _A, ::Val{4}) [GeneratedNotConcrete]
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##15#16)::Const(##15#16, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##15#16{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##15#16{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::Any
└── return %6
) => Any
```
Looking at the definition of ntuple
https://github.com/JuliaLang/julia/blob/abb09f88804c4e74c752a66157e767c9b0f8945d/base/ntuple.jl#L45-L56
we see that it is a generated function an inference thus refuses to invoke it,
unless it can prove the concrete type of *all* arguments to the function. As
the above example illustrates, this restriction is more stringent than necessary.
It is true that we cannot invoke generated functions on arbitrary abstract
signatures (because we neither want to the user to have to be able to nor
do we trust that users are able to preverse monotonicity - i.e. that the return
type of the generated code will always be a subtype of the return type of a more
abstract signature).
However, if some piece of information is not used (the type of the passed function
in this case), there is no problem with calling the generated function (since
information that is unnused cannot possibly affect monotnicity).
This PR allows us to recognize pieces of information that are *syntactically* unused,
and call the generated functions, even if we do not have those pieces of information.
As a result, we are now able to infer the return type of the above function:
```
julia> code_typed(foo, Tuple{Any, Any})
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##3#4)::Const(##3#4, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##3#4{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##3#4{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::NTuple{4,Int64}
└── return %6
) => NTuple{4,Int64}
```
In particular, we use the new frontent `used` flags from the previous commit.
One additional complication is that we want to accesss these flags without
uncompressing the generator source, so we change the compression scheme to
place the flags at a known location.
Fixes #31004
Keno
added a commit
that referenced
this issue
Feb 13, 2019
Consider the following function:
```
julia> function foo(a, b)
ntuple(i->(a+b; i), Val(4))
end
foo (generic function with 1 method)
```
(In particular note that the return type of the closure does not depend on the types
of `a` and b`). Unfortunately, prior to this change, inference was unable to determine
the return type in this situation:
```
julia> code_typed(foo, Tuple{Any, Any}, trace=true)
Refused to call generated function with non-concrete argument types ntuple(::getfield(Main, Symbol("##15#16")){_A,_B} where _B where _A, ::Val{4}) [GeneratedNotConcrete]
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##15#16)::Const(##15#16, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##15#16{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##15#16{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::Any
└── return %6
) => Any
```
Looking at the definition of ntuple
https://github.com/JuliaLang/julia/blob/abb09f88804c4e74c752a66157e767c9b0f8945d/base/ntuple.jl#L45-L56
we see that it is a generated function an inference thus refuses to invoke it,
unless it can prove the concrete type of *all* arguments to the function. As
the above example illustrates, this restriction is more stringent than necessary.
It is true that we cannot invoke generated functions on arbitrary abstract
signatures (because we neither want to the user to have to be able to nor
do we trust that users are able to preverse monotonicity - i.e. that the return
type of the generated code will always be a subtype of the return type of a more
abstract signature).
However, if some piece of information is not used (the type of the passed function
in this case), there is no problem with calling the generated function (since
information that is unnused cannot possibly affect monotnicity).
This PR allows us to recognize pieces of information that are *syntactically* unused,
and call the generated functions, even if we do not have those pieces of information.
As a result, we are now able to infer the return type of the above function:
```
julia> code_typed(foo, Tuple{Any, Any})
1-element Array{Any,1}:
CodeInfo(
1 ─ %1 = Main.:(##3#4)::Const(##3#4, false)
│ %2 = Core.typeof(a)::DataType
│ %3 = Core.typeof(b)::DataType
│ %4 = Core.apply_type(%1, %2, %3)::Type{##3#4{_A,_B}} where _B where _A
│ %5 = %new(%4, a, b)::##3#4{_A,_B} where _B where _A
│ %6 = Main.ntuple(%5, $(QuoteNode(Val{4}())))::NTuple{4,Int64}
└── return %6
) => NTuple{4,Int64}
```
In particular, we use the new frontent `used` flags from the previous commit.
One additional complication is that we want to accesss these flags without
uncompressing the generator source, so we change the compression scheme to
place the flags at a known location.
Fixes #31004
yakir12
pushed a commit
to yakir12/julia
that referenced
this issue
Jun 17, 2019
yakir12
pushed a commit
to yakir12/julia
that referenced
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Jun 17, 2019
Long options are trimmed to the width of the terminal (JuliaLang#15)
cmcaine
pushed a commit
to cmcaine/julia
that referenced
this issue
Sep 24, 2020
Additional info for adding exercises in README
jmert
added a commit
to jmert/julia
that referenced
this issue
Nov 4, 2020
If the sparse array does not have a concrete index type, then union
splitting occurs over the possible `<:Integer` types permitted by
`SparseMatrixCSC`:
```julia
julia> code_warntype(nnz, (SparseMatrixCSC{Float64,<:Integer},), optimize=true, debuginfo=:none)
Variables
#self#::Core.Const(SparseArrays.nnz)
S::SparseMatrixCSC{Float64, var"#s96"} where var"#s96"<:Integer
Body::Any
1 ── %1 = SparseArrays.getfield(S, :colptr)::Vector{var"#s96"} where var"#s96"<:Integer
│ %2 = SparseArrays.getfield(S, :n)::Int64
│ %3 = Base.add_int(%2, 1)::Int64
│ %4 = Base.getindex(%1, %3)::Integer
│ %5 = (isa)(%4, Int64)::Bool
└─── goto JuliaLang#3 if not %5
2 ── %7 = π (%4, Int64)
│ %8 = Base.sub_int(%7, 1)::Int64
└─── goto JuliaLang#15
3 ── %10 = (isa)(%4, BigInt)::Bool
└─── goto JuliaLang#14 if not %10
4 ── %12 = π (%4, BigInt)
│ %13 = Base.slt_int(1, 0)::Bool
└─── goto JuliaLang#6 if not %13
5 ── %15 = Base.bitcast(UInt64, 1)::UInt64
│ %16 = Base.neg_int(%15)::UInt64
│ %17 = Base.GMP.MPZ.add_ui::typeof(Base.GMP.MPZ.add_ui)
│ %18 = invoke %17(%12::BigInt, %16::UInt64)::BigInt
└─── goto JuliaLang#13
6 ── %20 = Core.lshr_int(1, 63)::Int64
│ %21 = Core.trunc_int(Core.UInt8, %20)::UInt8
│ %22 = Core.eq_int(%21, 0x01)::Bool
└─── goto JuliaLang#8 if not %22
7 ── invoke Core.throw_inexacterror(:check_top_bit::Symbol, UInt64::Type{UInt64}, 1::Int64)
└─── unreachable
8 ── goto JuliaLang#9
9 ── %27 = Core.bitcast(Core.UInt64, 1)::UInt64
└─── goto JuliaLang#10
10 ─ goto JuliaLang#11
11 ─ goto JuliaLang#12
12 ─ %31 = Base.GMP.MPZ.sub_ui::typeof(Base.GMP.MPZ.sub_ui)
│ %32 = invoke %31(%12::BigInt, %27::UInt64)::BigInt
└─── goto JuliaLang#13
13 ┄ %34 = φ (JuliaLang#5 => %18, JuliaLang#12 => %32)::Any
└─── goto JuliaLang#15
14 ─ %36 = (%4 - 1)::Any
└─── goto JuliaLang#15
15 ┄ %38 = φ (JuliaLang#2 => %8, JuliaLang#13 => %34, JuliaLang#14 => %36)::Any
│ %39 = SparseArrays.Int(%38)::Any
└─── return %39
```
It appears that union splitting over the subtraction by one includes
an `Any` branch that widens the return type of `nnz`. By instead
converting the index type to `Int` before subtracting, type inference
is able to infer that all paths give an `Int` result:
```julia
julia> code_warntype(nnz, (SparseMatrixCSC{Float64,<:Integer},), optimize=true, debuginfo=:none)
Variables
#self#::Core.Const(SparseArrays.nnz)
S::SparseMatrixCSC{Float64, var"#s96"} where var"#s96"<:Integer
Body::Int64
1 ── %1 = SparseArrays.getfield(S, :colptr)::Vector{var"#s96"} where var"#s96"<:Integer
│ %2 = SparseArrays.getfield(S, :n)::Int64
│ %3 = Base.add_int(%2, 1)::Int64
│ %4 = Base.getindex(%1, %3)::Integer
│ %5 = (isa)(%4, BigInt)::Bool
└─── goto JuliaLang#14 if not %5
2 ── %7 = π (%4, BigInt)
│ %8 = Base.getfield(%7, :size)::Int32
│ %9 = Base.flipsign_int(%8, %8)::Int32
│ %10 = Core.sext_int(Core.Int64, %9)::Int64
│ %11 = Base.sle_int(0, %10)::Bool
└─── goto JuliaLang#4 if not %11
3 ── %13 = Core.sext_int(Core.Int64, %9)::Int64
│ %14 = Base.sle_int(%13, 1)::Bool
└─── goto JuliaLang#5
4 ── nothing
5 ┄─ %17 = φ (JuliaLang#3 => %14, JuliaLang#4 => false)::Bool
└─── goto JuliaLang#12 if not %17
6 ── %19 = Base.getfield(%7, :size)::Int32
│ %20 = Core.sext_int(Core.Int64, %19)::Int64
│ %21 = (%20 === 0)::Bool
└─── goto JuliaLang#8 if not %21
7 ── goto JuliaLang#9
8 ── %24 = Base.getfield(%7, :d)::Ptr{UInt64}
│ %25 = Base.pointerref(%24, 1, 1)::UInt64
│ %26 = Base.bitcast(Int64, %25)::Int64
│ %27 = Base.getfield(%7, :size)::Int32
│ %28 = Core.sext_int(Core.Int64, %27)::Int64
│ %29 = Base.flipsign_int(%26, %28)::Int64
└─── goto JuliaLang#9
9 ┄─ %31 = φ (JuliaLang#7 => 0, JuliaLang#8 => %29)::Int64
│ %32 = Base.getfield(%7, :size)::Int32
│ %33 = Core.sext_int(Core.Int64, %32)::Int64
│ %34 = Base.slt_int(0, %33)::Bool
│ %35 = Base.slt_int(0, %31)::Bool
│ %36 = (%34 === %35)::Bool
│ %37 = Base.not_int(%36)::Bool
└─── goto JuliaLang#11 if not %37
10 ─ %39 = Base.GMP.nameof(Int64)::Any
│ %40 = Base.GMP.InexactError(%39, Int64, %7)::Any
│ Base.GMP.throw(%40)
└─── unreachable
11 ─ goto JuliaLang#13
12 ─ %44 = Base.GMP.nameof(Int64)::Any
│ %45 = Base.GMP.InexactError(%44, Int64, %7)::Any
│ Base.GMP.throw(%45)
└─── unreachable
13 ─ goto JuliaLang#15
14 ─ %49 = SparseArrays.Int(%4)::Int64
└─── goto JuliaLang#15
15 ┄ %51 = φ (JuliaLang#13 => %31, JuliaLang#14 => %49)::Int64
│ %52 = Base.sub_int(%51, 1)::Int64
└─── return %52
```
LilithHafner
referenced
this issue
in LilithHafner/julia
Oct 11, 2021
add sample_by_weights function
staticfloat
added a commit
that referenced
this issue
Jun 25, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
This solution adds a check against `jl_error_sym` as a data structure
that gets initialized relatively late in the bringup process.
staticfloat
added a commit
that referenced
this issue
Jun 25, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
This solution adds a check against `jl_error_sym` as a data structure
that gets initialized relatively late in the bringup process.
staticfloat
added a commit
that referenced
this issue
Jun 27, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
This solution adds a check against `jl_error_sym` as a data structure
that gets initialized relatively late in the bringup process.
staticfloat
added a commit
that referenced
this issue
Jun 29, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
To prevent this, we simply avoid calling `jl_errorf` this early in the
process, punting the problem to a later PR that can update guard
conditions within `jl_error*`.
staticfloat
added a commit
that referenced
this issue
Jun 29, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
To prevent this, we simply avoid calling `jl_errorf` this early in the
process, punting the problem to a later PR that can update guard
conditions within `jl_error*`.
staticfloat
added a commit
that referenced
this issue
Jun 29, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
To prevent this, we simply avoid calling `jl_errorf` this early in the
process, punting the problem to a later PR that can update guard
conditions within `jl_error*`.
staticfloat
added a commit
that referenced
this issue
Jun 29, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
To prevent this, we simply avoid calling `jl_errorf` this early in the
process, punting the problem to a later PR that can update guard
conditions within `jl_error*`.
vchuravy
pushed a commit
that referenced
this issue
Jul 19, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
To prevent this, we simply avoid calling `jl_errorf` this early in the
process, punting the problem to a later PR that can update guard
conditions within `jl_error*`.
(cherry picked from commit 21ab24e)
pcjentsch
pushed a commit
to pcjentsch/julia
that referenced
this issue
Aug 18, 2022
When calling `jl_error()` or `jl_errorf()`, we must check to see if we
are so early in the bringup process that it is dangerous to attempt to
construct a backtrace because the data structures used to provide line
information are not properly setup.
This can be easily triggered by running:
```
julia -C invalid
```
On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name"
branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn
calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part
of the backtrace printing process, which fails as the object maps are
not fully initialized. See the below `gdb` stacktrace for details:
```
$ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid
...
fatal: error thrown and no exception handler available.
ErrorException("Invalid CPU name "invalid".")
Thread 1 "julia" received signal SIGSEGV, Segmentation fault.
0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory.
#0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277
JuliaLang#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258
JuliaLang#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181
JuliaLang#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210
JuliaLang#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636
JuliaLang#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657
JuliaLang#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090
JuliaLang#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605
JuliaLang#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638
JuliaLang#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654
JuliaLang#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77
JuliaLang#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823
JuliaLang#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044
JuliaLang#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585
JuliaLang#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648
JuliaLang#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131
JuliaLang#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184
JuliaLang#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424
JuliaLang#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157
JuliaLang#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741
JuliaLang#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728
JuliaLang#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705
JuliaLang#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59
```
To prevent this, we simply avoid calling `jl_errorf` this early in the
process, punting the problem to a later PR that can update guard
conditions within `jl_error*`.
github-merge-queue bot
pushed a commit
that referenced
this issue
Jul 15, 2023
…d and inlined) (#43322) A follow up attemp to fix #27988. (close #47493 close #50554) Examples: ```julia julia> using LazyArrays julia> bc = @~ @. 1*(1 + 1) + 1*1; julia> bc2 = @~ 1 .* 1 .- 1 .* 1 .^2 .+ 1 .* 1 .+ 1 .^ 3; ``` On master: <details><summary> click for details </summary> <p> ```julia julia> @code_typed Broadcast.flatten(bc).f(1,1,1,1,1) CodeInfo( 1 ─ %1 = Core.getfield(args, 1)::Int64 │ %2 = Core.getfield(args, 2)::Int64 │ %3 = Core.getfield(args, 3)::Int64 │ %4 = Core.getfield(args, 4)::Int64 │ %5 = Core.getfield(args, 5)::Int64 │ %6 = invoke Base.Broadcast.var"#13#14"{Base.Broadcast.var"#16#18"{Base.Broadcast.var"#15#17", Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}, typeof(+)}}(Base.Broadcast.var"#16#18"{Base.Broadcast.var"#15#17", Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}, typeof(+)}(Base.Broadcast.var"#15#17"(), Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}(Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}(Base.Broadcast.var"#15#17"())), Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"())), Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"())), +))(%1::Int64, %2::Int64, %3::Vararg{Int64}, %4, %5)::Tuple{Int64, Int64, Vararg{Int64}} │ %7 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"())), %6)::Tuple{Int64, Int64} │ %8 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"())), %6)::Tuple{Vararg{Int64}} │ %9 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#16#18"{Base.Broadcast.var"#9#11", Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}, typeof(*)}(Base.Broadcast.var"#9#11"(), Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}(Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}(Base.Broadcast.var"#15#17"())), Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"())), Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"())), *), %8)::Tuple{Int64} │ %10 = Core.getfield(%7, 1)::Int64 │ %11 = Core.getfield(%7, 2)::Int64 │ %12 = Base.mul_int(%10, %11)::Int64 │ %13 = Core.getfield(%9, 1)::Int64 │ %14 = Base.add_int(%12, %13)::Int64 └── return %14 ) => Int64 julia> @code_typed Broadcast.flatten(bc2).f(1,1,1,^,1,Val(2),1,1,^,1,Val(3)) CodeInfo( 1 ─ %1 = Core.getfield(args, 1)::Int64 │ %2 = Core.getfield(args, 2)::Int64 │ %3 = Core.getfield(args, 3)::Int64 │ %4 = Core.getfield(args, 5)::Int64 │ %5 = Core.getfield(args, 7)::Int64 │ %6 = Core.getfield(args, 8)::Int64 │ %7 = Core.getfield(args, 10)::Int64 │ %8 = invoke Base.Broadcast.var"#13#14"{Base.Broadcast.var"#16#18"{Base.Broadcast.var"#15#17", Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}}, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}}, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}}, typeof(Base.literal_pow)}}(Base.Broadcast.var"#16#18"{Base.Broadcast.var"#15#17", Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}}, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}}, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}}, typeof(Base.literal_pow)}(Base.Broadcast.var"#15#17"(), Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}}(Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}(Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}(Base.Broadcast.var"#15#17"()))), Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"()))), Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"()))), Base.literal_pow))(%3::Int64, ^::Function, %4::Vararg{Any}, $(QuoteNode(Val{2}())), %5, %6, ^, %7, $(QuoteNode(Val{3}())))::Tuple{Int64, Any, Vararg{Any}} │ %9 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"())), %8)::Tuple{Int64, Any} │ %10 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"())), %8)::Tuple │ %11 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#15#17"(), %10)::Tuple │ %12 = Core.getfield(%9, 1)::Int64 │ %13 = Core.getfield(%9, 2)::Any │ %14 = (*)(%12, %13)::Any │ %15 = Core.tuple(%14)::Tuple{Any} │ %16 = Core._apply_iterate(Base.iterate, Core.tuple, %15, %11)::Tuple{Any, Vararg{Any}} │ %17 = Base.mul_int(%1, %2)::Int64 │ %18 = Core.tuple(%17)::Tuple{Int64} │ %19 = Core._apply_iterate(Base.iterate, Core.tuple, %18, %16)::Tuple{Int64, Any, Vararg{Any}} │ %20 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"())), %19)::Tuple{Int64, Any} │ %21 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"())), %19)::Tuple │ %22 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#16#18"{Base.Broadcast.var"#15#17", Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}, typeof(*)}(Base.Broadcast.var"#15#17"(), Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}(Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}(Base.Broadcast.var"#15#17"())), Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"())), Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"())), *), %21)::Tuple{Any, Vararg{Any}} │ %23 = Core.getfield(%20, 1)::Int64 │ %24 = Core.getfield(%20, 2)::Any │ %25 = (-)(%23, %24)::Any │ %26 = Core.tuple(%25)::Tuple{Any} │ %27 = Core._apply_iterate(Base.iterate, Core.tuple, %26, %22)::Tuple{Any, Any, Vararg{Any}} │ %28 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"())), %27)::Tuple{Any, Any} │ %29 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"())), %27)::Tuple │ %30 = Core._apply_iterate(Base.iterate, Base.Broadcast.var"#16#18"{Base.Broadcast.var"#9#11", Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}}, Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}}, Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}}, typeof(Base.literal_pow)}(Base.Broadcast.var"#9#11"(), Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}}(Base.Broadcast.var"#13#14"{Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}}(Base.Broadcast.var"#13#14"{Base.Broadcast.var"#15#17"}(Base.Broadcast.var"#15#17"()))), Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}}(Base.Broadcast.var"#23#24"{Base.Broadcast.var"#25#26"}(Base.Broadcast.var"#25#26"()))), Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}}(Base.Broadcast.var"#19#20"{Base.Broadcast.var"#21#22"}(Base.Broadcast.var"#21#22"()))), Base.literal_pow), %29)::Tuple{Any} │ %31 = Core.getfield(%28, 1)::Any │ %32 = Core.getfield(%28, 2)::Any │ %33 = (+)(%31, %32)::Any │ %34 = Core.getfield(%30, 1)::Any │ %35 = (+)(%33, %34)::Any └── return %35 ) => Any ``` </p> </details> On this PR ```julia julia> @code_typed Broadcast.flatten(bc).f(1,1,1,1,1) CodeInfo( 1 ─ %1 = Core.getfield(args, 1)::Int64 │ %2 = Core.getfield(args, 2)::Int64 │ %3 = Core.getfield(args, 3)::Int64 │ %4 = Core.getfield(args, 4)::Int64 │ %5 = Core.getfield(args, 5)::Int64 │ %6 = Base.add_int(%2, %3)::Int64 │ %7 = Base.mul_int(%1, %6)::Int64 │ %8 = Base.mul_int(%4, %5)::Int64 │ %9 = Base.add_int(%7, %8)::Int64 └── return %9 ) => Int64 julia> @code_typed Broadcast.flatten(bc2).f(1,1,1,^,1,Val(2),1,1,^,1,Val(3)) CodeInfo( 1 ─ %1 = Core.getfield(args, 1)::Int64 │ %2 = Core.getfield(args, 2)::Int64 │ %3 = Core.getfield(args, 3)::Int64 │ %4 = Core.getfield(args, 5)::Int64 │ %5 = Core.getfield(args, 7)::Int64 │ %6 = Core.getfield(args, 8)::Int64 │ %7 = Core.getfield(args, 10)::Int64 │ %8 = Base.mul_int(%1, %2)::Int64 │ %9 = Base.mul_int(%4, %4)::Int64 │ %10 = Base.mul_int(%3, %9)::Int64 │ %11 = Base.sub_int(%8, %10)::Int64 │ %12 = Base.mul_int(%5, %6)::Int64 │ %13 = Base.add_int(%11, %12)::Int64 │ %14 = Base.mul_int(%7, %7)::Int64 │ %15 = Base.mul_int(%14, %7)::Int64 │ %16 = Base.add_int(%13, %15)::Int64 └── return %16 ) => Int64 ```
Keno
pushed a commit
that referenced
this issue
Oct 9, 2023
Handle world-age errors in incremental lowering
vchuravy
pushed a commit
that referenced
this issue
Jan 23, 2024
) Stdlib: StyledStrings URL: https://github.com/JuliaLang/StyledStrings.jl.git Stdlib branch: main Julia branch: master Old commit: 61e7b10 New commit: 302a0d0 Julia version: 1.11.0-DEV StyledStrings version: 1.11.0 Bump invoked by: @vchuravy Powered by: [BumpStdlibs.jl](https://github.com/JuliaLang/BumpStdlibs.jl) Diff: JuliaLang/StyledStrings.jl@61e7b10...302a0d0 ``` $ git log --oneline 61e7b10..302a0d0 302a0d0 Directly import ScopedValue 3fab35e Fix showing AnnotatedChar with colour 44f5fd7 Restrict the Base docstrings included in the docs 4dee5d9 Add readme c49ae82 Add documentation task to CI 38ae1b3 Setting the terminal colour to :default is special 2709150 Adjust face merge tests after inheritance change 036631f Swap inheritance processing in face merge 9b35f08 Merge branch 'jn/Statefulempty' [#21] 508ab57 Refactor zip of eachindex to just use pairs 02b3f81 Remove use of length of Stateful 41c8218 Merge branch 'lh/ci-codecov' [#15] d581fda Disable codecov commenting in every PR a8a25ba Merge branch 'lh/ci-old-julia' [#13] b7fca5b Merge branch 'lh/newline' [#16] 984485e Adjust newline parsing to work with CLRF encoding 27b02d1 Test styled"" parsing of \-continued newlines a7981fe Merge pull request #17 from caleb-allen/add-beep-face c1ab675 Add repl_prompt_beep face 0d8f5df Merge pull request #18 from JuliaLang/whitespace-fixup 1ef0f90 Nicer whitespace alignment 91a24f8 Disable CI for older versions of Julia 63ff132 add tagbot 792fda7 add CI 506afe3 add .gitignore 74e7135 Load the JULIA_*_COLOR env vars for compat 87d1fb5 Replace within-module eval with hygienic eval 4777e60 Touchups to documented examples ``` Co-authored-by: Dilum Aluthge <[email protected]>
quinnj
pushed a commit
that referenced
this issue
Jan 26, 2024
`@something` eagerly unwraps any `Some` given to it, while keeping the
variable between its arguments the same. This can be an issue if a
previously unpacked value is used as input to `@something`, leading to a
type instability on more than two arguments (e.g. because of a fallback
to `Some(nothing)`). By using different variables for each argument,
type inference has an easier time handling these cases that are isolated
to single branches anyway.
This also adds some comments to the macro, since it's non-obvious what
it does.
Benchmarking the specific case I encountered this in led to a ~2x
performance improvement on multiple machines.
1.10-beta3/master:
```
[sukera@tower 01]$ jl1100 -q --project=. -L 01.jl -e 'bench()'
v"1.10.0-beta3"
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 38.670 μs … 70.350 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 43.340 μs ┊ GC (median): 0.00%
Time (mean ± σ): 43.395 μs ± 1.518 μs ┊ GC (mean ± σ): 0.00% ± 0.00%
▆█▂ ▁▁
▂▂▂▂▂▂▂▂▂▁▂▂▂▃▃▃▂▂▃▃▃▂▂▂▂▂▄▇███▆██▄▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃
38.7 μs Histogram: frequency by time 48 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
```
This PR:
```
[sukera@tower 01]$ julia -q --project=. -L 01.jl -e 'bench()'
v"1.11.0-DEV.970"
BenchmarkTools.Trial: 10000 samples with 1 evaluation.
Range (min … max): 22.820 μs … 44.980 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 24.300 μs ┊ GC (median): 0.00%
Time (mean ± σ): 24.370 μs ± 832.239 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
▂▅▇██▇▆▅▁
▂▂▂▂▂▂▂▂▃▃▄▅▇███████████▅▄▃▃▂▂▂▂▂▂▂▂▂▂▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▂▂ ▃
22.8 μs Histogram: frequency by time 27.7 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
```
<details>
<summary>Benchmarking code (spoilers for Advent Of Code 2023 Day 01,
Part 01). Running this requires the input of that Advent Of Code
day.</summary>
```julia
using BenchmarkTools
using InteractiveUtils
isdigit(d::UInt8) = UInt8('0') <= d <= UInt8('9')
someDigit(c::UInt8) = isdigit(c) ? Some(c - UInt8('0')) : nothing
function part1(data)
total = 0
may_a = nothing
may_b = nothing
for c in data
digitRes = someDigit(c)
may_a = @something may_a digitRes Some(nothing)
may_b = @something digitRes may_b Some(nothing)
if c == UInt8('\n')
digit_a = may_a::UInt8
digit_b = may_b::UInt8
total += digit_a*0xa + digit_b
may_a = nothing
may_b = nothing
end
end
return total
end
function bench()
data = read("input.txt")
display(VERSION)
println()
display(@benchmark part1($data))
nothing
end
```
</details>
<details>
<summary>`@code_warntype` before</summary>
```julia
julia> @code_warntype part1(data)
MethodInstance for part1(::Vector{UInt8})
from part1(data) @ Main ~/Documents/projects/AOC/2023/01/01.jl:7
Arguments
#self#::Core.Const(part1)
data::Vector{UInt8}
Locals
@_3::Union{Nothing, Tuple{UInt8, Int64}}
may_b::Union{Nothing, UInt8}
may_a::Union{Nothing, UInt8}
total::Int64
c::UInt8
digit_b::UInt8
digit_a::UInt8
val@_10::Any
val@_11::Any
digitRes::Union{Nothing, Some{UInt8}}
@_13::Union{Some{Nothing}, Some{UInt8}, UInt8}
@_14::Union{Some{Nothing}, Some{UInt8}}
@_15::Some{Nothing}
@_16::Union{Some{Nothing}, Some{UInt8}, UInt8}
@_17::Union{Some{Nothing}, UInt8}
@_18::Some{Nothing}
Body::Int64
1 ── (total = 0)
│ (may_a = Main.nothing)
│ (may_b = Main.nothing)
│ %4 = data::Vector{UInt8}
│ (@_3 = Base.iterate(%4))
│ %6 = (@_3 === nothing)::Bool
│ %7 = Base.not_int(%6)::Bool
└─── goto #24 if not %7
2 ┄─ Core.NewvarNode(:(digit_b))
│ Core.NewvarNode(:(digit_a))
│ Core.NewvarNode(:(val@_10))
│ %12 = @_3::Tuple{UInt8, Int64}
│ (c = Core.getfield(%12, 1))
│ %14 = Core.getfield(%12, 2)::Int64
│ (digitRes = Main.someDigit(c))
│ (val@_11 = may_a)
│ %17 = (val@_11::Union{Nothing, UInt8} !== Base.nothing)::Bool
└─── goto #4 if not %17
3 ── (@_13 = val@_11::UInt8)
└─── goto #11
4 ── (val@_11 = digitRes)
│ %22 = (val@_11::Union{Nothing, Some{UInt8}} !== Base.nothing)::Bool
└─── goto #6 if not %22
5 ── (@_14 = val@_11::Some{UInt8})
└─── goto #10
6 ── (val@_11 = Main.Some(Main.nothing))
│ %27 = (val@_11::Core.Const(Some(nothing)) !== Base.nothing)::Core.Const(true)
└─── goto #8 if not %27
7 ── (@_15 = val@_11::Core.Const(Some(nothing)))
└─── goto #9
8 ── Core.Const(:(@_15 = Base.nothing))
9 ┄─ (@_14 = @_15)
10 ┄ (@_13 = @_14)
11 ┄ %34 = @_13::Union{Some{Nothing}, Some{UInt8}, UInt8}
│ (may_a = Base.something(%34))
│ (val@_10 = digitRes)
│ %37 = (val@_10::Union{Nothing, Some{UInt8}} !== Base.nothing)::Bool
└─── goto #13 if not %37
12 ─ (@_16 = val@_10::Some{UInt8})
└─── goto #20
13 ─ (val@_10 = may_b)
│ %42 = (val@_10::Union{Nothing, UInt8} !== Base.nothing)::Bool
└─── goto #15 if not %42
14 ─ (@_17 = val@_10::UInt8)
└─── goto #19
15 ─ (val@_10 = Main.Some(Main.nothing))
│ %47 = (val@_10::Core.Const(Some(nothing)) !== Base.nothing)::Core.Const(true)
└─── goto #17 if not %47
16 ─ (@_18 = val@_10::Core.Const(Some(nothing)))
└─── goto #18
17 ─ Core.Const(:(@_18 = Base.nothing))
18 ┄ (@_17 = @_18)
19 ┄ (@_16 = @_17)
20 ┄ %54 = @_16::Union{Some{Nothing}, Some{UInt8}, UInt8}
│ (may_b = Base.something(%54))
│ %56 = c::UInt8
│ %57 = Main.UInt8('\n')::Core.Const(0x0a)
│ %58 = (%56 == %57)::Bool
└─── goto #22 if not %58
21 ─ (digit_a = Core.typeassert(may_a, Main.UInt8))
│ (digit_b = Core.typeassert(may_b, Main.UInt8))
│ %62 = total::Int64
│ %63 = (digit_a * 0x0a)::UInt8
│ %64 = (%63 + digit_b)::UInt8
│ (total = %62 + %64)
│ (may_a = Main.nothing)
└─── (may_b = Main.nothing)
22 ┄ (@_3 = Base.iterate(%4, %14))
│ %69 = (@_3 === nothing)::Bool
│ %70 = Base.not_int(%69)::Bool
└─── goto #24 if not %70
23 ─ goto #2
24 ┄ return total
```
</details>
<details>
<summary>`@code_native debuginfo=:none` Before </summary>
```julia
julia> @code_native debuginfo=:none part1(data)
.text
.file "part1"
.globl julia_part1_418 # -- Begin function julia_part1_418
.p2align 4, 0x90
.type julia_part1_418,@function
julia_part1_418: # @julia_part1_418
# %bb.0: # %top
push rbp
mov rbp, rsp
push r15
push r14
push r13
push r12
push rbx
sub rsp, 40
mov rax, qword ptr [rdi + 8]
test rax, rax
je .LBB0_1
# %bb.2: # %L17
mov rcx, qword ptr [rdi]
dec rax
mov r10b, 1
xor r14d, r14d
# implicit-def: $r12b
# implicit-def: $r13b
# implicit-def: $r9b
# implicit-def: $sil
mov qword ptr [rbp - 64], rax # 8-byte Spill
mov al, 1
mov dword ptr [rbp - 48], eax # 4-byte Spill
# implicit-def: $al
# kill: killed $al
xor eax, eax
mov qword ptr [rbp - 56], rax # 8-byte Spill
mov qword ptr [rbp - 72], rcx # 8-byte Spill
# implicit-def: $cl
jmp .LBB0_3
.p2align 4, 0x90
.LBB0_8: # in Loop: Header=BB0_3 Depth=1
mov dword ptr [rbp - 48], 0 # 4-byte Folded Spill
.LBB0_24: # %post_union_move
# in Loop: Header=BB0_3 Depth=1
movzx r13d, byte ptr [rbp - 41] # 1-byte Folded Reload
mov r12d, r8d
cmp qword ptr [rbp - 64], r14 # 8-byte Folded Reload
je .LBB0_13
.LBB0_25: # %guard_exit113
# in Loop: Header=BB0_3 Depth=1
inc r14
mov r10d, ebx
.LBB0_3: # %L19
# =>This Inner Loop Header: Depth=1
mov rax, qword ptr [rbp - 72] # 8-byte Reload
xor ebx, ebx
xor edi, edi
movzx r15d, r9b
movzx ecx, cl
movzx esi, sil
mov r11b, 1
# implicit-def: $r9b
movzx edx, byte ptr [rax + r14]
lea eax, [rdx - 58]
lea r8d, [rdx - 48]
cmp al, -10
setae bl
setb dil
test r10b, 1
cmovne r15d, edi
mov edi, 0
cmovne ecx, ebx
mov bl, 1
cmovne esi, edi
test r15b, 1
jne .LBB0_7
# %bb.4: # %L76
# in Loop: Header=BB0_3 Depth=1
mov r11b, 2
test cl, 1
jne .LBB0_5
# %bb.6: # %L78
# in Loop: Header=BB0_3 Depth=1
mov ebx, r10d
mov r9d, r15d
mov byte ptr [rbp - 41], r13b # 1-byte Spill
test sil, 1
je .LBB0_26
.LBB0_7: # %L82
# in Loop: Header=BB0_3 Depth=1
cmp al, -11
jbe .LBB0_9
jmp .LBB0_8
.p2align 4, 0x90
.LBB0_5: # in Loop: Header=BB0_3 Depth=1
mov ecx, r8d
mov sil, 1
xor ebx, ebx
mov byte ptr [rbp - 41], r8b # 1-byte Spill
xor r9d, r9d
xor ecx, ecx
cmp al, -11
ja .LBB0_8
.LBB0_9: # %L90
# in Loop: Header=BB0_3 Depth=1
test byte ptr [rbp - 48], 1 # 1-byte Folded Reload
jne .LBB0_23
# %bb.10: # %L115
# in Loop: Header=BB0_3 Depth=1
cmp dl, 10
jne .LBB0_11
# %bb.14: # %L122
# in Loop: Header=BB0_3 Depth=1
test r15b, 1
jne .LBB0_15
# %bb.12: # %L130.thread
# in Loop: Header=BB0_3 Depth=1
movzx eax, byte ptr [rbp - 41] # 1-byte Folded Reload
mov bl, 1
add eax, eax
lea eax, [rax + 4*rax]
add al, r12b
movzx eax, al
add qword ptr [rbp - 56], rax # 8-byte Folded Spill
mov al, 1
mov dword ptr [rbp - 48], eax # 4-byte Spill
cmp qword ptr [rbp - 64], r14 # 8-byte Folded Reload
jne .LBB0_25
jmp .LBB0_13
.p2align 4, 0x90
.LBB0_23: # %L115.thread
# in Loop: Header=BB0_3 Depth=1
mov al, 1
# implicit-def: $r8b
mov dword ptr [rbp - 48], eax # 4-byte Spill
cmp dl, 10
jne .LBB0_24
jmp .LBB0_21
.LBB0_11: # in Loop: Header=BB0_3 Depth=1
mov r8d, r12d
jmp .LBB0_24
.LBB0_1:
xor eax, eax
mov qword ptr [rbp - 56], rax # 8-byte Spill
.LBB0_13: # %L159
mov rax, qword ptr [rbp - 56] # 8-byte Reload
add rsp, 40
pop rbx
pop r12
pop r13
pop r14
pop r15
pop rbp
ret
.LBB0_21: # %L122.thread
test r15b, 1
jne .LBB0_15
# %bb.22: # %post_box_union58
movabs rdi, offset .L_j_str1
movabs rax, offset ijl_type_error
movabs rsi, 140008511215408
movabs rdx, 140008667209736
call rax
.LBB0_15: # %fail
cmp r11b, 1
je .LBB0_19
# %bb.16: # %fail
movzx eax, r11b
cmp eax, 2
jne .LBB0_17
# %bb.20: # %box_union54
movzx eax, byte ptr [rbp - 41] # 1-byte Folded Reload
movabs rcx, offset jl_boxed_uint8_cache
mov rdx, qword ptr [rcx + 8*rax]
jmp .LBB0_18
.LBB0_26: # %L80
movabs rax, offset ijl_throw
movabs rdi, 140008495049392
call rax
.LBB0_19: # %box_union
movabs rdx, 140008667209736
jmp .LBB0_18
.LBB0_17:
xor edx, edx
.LBB0_18: # %post_box_union
movabs rdi, offset .L_j_str1
movabs rax, offset ijl_type_error
movabs rsi, 140008511215408
call rax
.Lfunc_end0:
.size julia_part1_418, .Lfunc_end0-julia_part1_418
# -- End function
.type .L_j_str1,@object # @_j_str1
.section .rodata.str1.1,"aMS",@progbits,1
.L_j_str1:
.asciz "typeassert"
.size .L_j_str1, 11
.section ".note.GNU-stack","",@progbits
```
</details>
<details>
<summary>`@code_warntype` After</summary>
```julia
[sukera@tower 01]$ julia -q --project=. -L 01.jl
julia> data = read("input.txt");
julia> @code_warntype part1(data)
MethodInstance for part1(::Vector{UInt8})
from part1(data) @ Main ~/Documents/projects/AOC/2023/01/01.jl:7
Arguments
#self#::Core.Const(part1)
data::Vector{UInt8}
Locals
@_3::Union{Nothing, Tuple{UInt8, Int64}}
may_b::Union{Nothing, UInt8}
may_a::Union{Nothing, UInt8}
total::Int64
val@_7::Union{}
val@_8::Union{}
c::UInt8
digit_b::UInt8
digit_a::UInt8
##215::Some{Nothing}
##216::Union{Nothing, UInt8}
##217::Union{Nothing, Some{UInt8}}
##212::Some{Nothing}
##213::Union{Nothing, Some{UInt8}}
##214::Union{Nothing, UInt8}
digitRes::Union{Nothing, Some{UInt8}}
@_19::Union{Nothing, UInt8}
@_20::Union{Nothing, UInt8}
@_21::Nothing
@_22::Union{Nothing, UInt8}
@_23::Union{Nothing, UInt8}
@_24::Nothing
Body::Int64
1 ── (total = 0)
│ (may_a = Main.nothing)
│ (may_b = Main.nothing)
│ %4 = data::Vector{UInt8}
│ (@_3 = Base.iterate(%4))
│ %6 = @_3::Union{Nothing, Tuple{UInt8, Int64}}
│ %7 = (%6 === nothing)::Bool
│ %8 = Base.not_int(%7)::Bool
└─── goto #24 if not %8
2 ┄─ Core.NewvarNode(:(val@_7))
│ Core.NewvarNode(:(val@_8))
│ Core.NewvarNode(:(digit_b))
│ Core.NewvarNode(:(digit_a))
│ Core.NewvarNode(:(##215))
│ Core.NewvarNode(:(##216))
│ Core.NewvarNode(:(##217))
│ Core.NewvarNode(:(##212))
│ Core.NewvarNode(:(##213))
│ %19 = @_3::Tuple{UInt8, Int64}
│ (c = Core.getfield(%19, 1))
│ %21 = Core.getfield(%19, 2)::Int64
│ %22 = c::UInt8
│ (digitRes = Main.someDigit(%22))
│ %24 = may_a::Union{Nothing, UInt8}
│ (##214 = %24)
│ %26 = Base.:!::Core.Const(!)
│ %27 = ##214::Union{Nothing, UInt8}
│ %28 = Base.isnothing(%27)::Bool
│ %29 = (%26)(%28)::Bool
└─── goto #4 if not %29
3 ── %31 = ##214::UInt8
│ (@_19 = Base.something(%31))
└─── goto #11
4 ── %34 = digitRes::Union{Nothing, Some{UInt8}}
│ (##213 = %34)
│ %36 = Base.:!::Core.Const(!)
│ %37 = ##213::Union{Nothing, Some{UInt8}}
│ %38 = Base.isnothing(%37)::Bool
│ %39 = (%36)(%38)::Bool
└─── goto #6 if not %39
5 ── %41 = ##213::Some{UInt8}
│ (@_20 = Base.something(%41))
└─── goto #10
6 ── %44 = Main.Some::Core.Const(Some)
│ %45 = Main.nothing::Core.Const(nothing)
│ (##212 = (%44)(%45))
│ %47 = Base.:!::Core.Const(!)
│ %48 = ##212::Core.Const(Some(nothing))
│ %49 = Base.isnothing(%48)::Core.Const(false)
│ %50 = (%47)(%49)::Core.Const(true)
└─── goto #8 if not %50
7 ── %52 = ##212::Core.Const(Some(nothing))
│ (@_21 = Base.something(%52))
└─── goto #9
8 ── Core.Const(nothing)
│ Core.Const(:(val@_8 = Base.something(Base.nothing)))
│ Core.Const(nothing)
│ Core.Const(:(val@_8))
└─── Core.Const(:(@_21 = %58))
9 ┄─ %60 = @_21::Core.Const(nothing)
└─── (@_20 = %60)
10 ┄ %62 = @_20::Union{Nothing, UInt8}
└─── (@_19 = %62)
11 ┄ %64 = @_19::Union{Nothing, UInt8}
│ (may_a = %64)
│ %66 = digitRes::Union{Nothing, Some{UInt8}}
│ (##217 = %66)
│ %68 = Base.:!::Core.Const(!)
│ %69 = ##217::Union{Nothing, Some{UInt8}}
│ %70 = Base.isnothing(%69)::Bool
│ %71 = (%68)(%70)::Bool
└─── goto #13 if not %71
12 ─ %73 = ##217::Some{UInt8}
│ (@_22 = Base.something(%73))
└─── goto #20
13 ─ %76 = may_b::Union{Nothing, UInt8}
│ (##216 = %76)
│ %78 = Base.:!::Core.Const(!)
│ %79 = ##216::Union{Nothing, UInt8}
│ %80 = Base.isnothing(%79)::Bool
│ %81 = (%78)(%80)::Bool
└─── goto #15 if not %81
14 ─ %83 = ##216::UInt8
│ (@_23 = Base.something(%83))
└─── goto #19
15 ─ %86 = Main.Some::Core.Const(Some)
│ %87 = Main.nothing::Core.Const(nothing)
│ (##215 = (%86)(%87))
│ %89 = Base.:!::Core.Const(!)
│ %90 = ##215::Core.Const(Some(nothing))
│ %91 = Base.isnothing(%90)::Core.Const(false)
│ %92 = (%89)(%91)::Core.Const(true)
└─── goto #17 if not %92
16 ─ %94 = ##215::Core.Const(Some(nothing))
│ (@_24 = Base.something(%94))
└─── goto #18
17 ─ Core.Const(nothing)
│ Core.Const(:(val@_7 = Base.something(Base.nothing)))
│ Core.Const(nothing)
│ Core.Const(:(val@_7))
└─── Core.Const(:(@_24 = %100))
18 ┄ %102 = @_24::Core.Const(nothing)
└─── (@_23 = %102)
19 ┄ %104 = @_23::Union{Nothing, UInt8}
└─── (@_22 = %104)
20 ┄ %106 = @_22::Union{Nothing, UInt8}
│ (may_b = %106)
│ %108 = Main.:(==)::Core.Const(==)
│ %109 = c::UInt8
│ %110 = Main.UInt8('\n')::Core.Const(0x0a)
│ %111 = (%108)(%109, %110)::Bool
└─── goto #22 if not %111
21 ─ %113 = may_a::Union{Nothing, UInt8}
│ (digit_a = Core.typeassert(%113, Main.UInt8))
│ %115 = may_b::Union{Nothing, UInt8}
│ (digit_b = Core.typeassert(%115, Main.UInt8))
│ %117 = Main.:+::Core.Const(+)
│ %118 = total::Int64
│ %119 = Main.:+::Core.Const(+)
│ %120 = Main.:*::Core.Const(*)
│ %121 = digit_a::UInt8
│ %122 = (%120)(%121, 0x0a)::UInt8
│ %123 = digit_b::UInt8
│ %124 = (%119)(%122, %123)::UInt8
│ (total = (%117)(%118, %124))
│ (may_a = Main.nothing)
└─── (may_b = Main.nothing)
22 ┄ (@_3 = Base.iterate(%4, %21))
│ %129 = @_3::Union{Nothing, Tuple{UInt8, Int64}}
│ %130 = (%129 === nothing)::Bool
│ %131 = Base.not_int(%130)::Bool
└─── goto #24 if not %131
23 ─ goto #2
24 ┄ %134 = total::Int64
└─── return %134
```
</details>
<details>
<summary>`@code_native debuginfo=:none` After </summary>
```julia
julia> @code_native debuginfo=:none part1(data)
.text
.file "part1"
.globl julia_part1_1203 # -- Begin function julia_part1_1203
.p2align 4, 0x90
.type julia_part1_1203,@function
julia_part1_1203: # @julia_part1_1203
; Function Signature: part1(Array{UInt8, 1})
# %bb.0: # %top
#DEBUG_VALUE: part1:data <- [DW_OP_deref] $rdi
push rbp
mov rbp, rsp
push r15
push r14
push r13
push r12
push rbx
sub rsp, 40
vxorps xmm0, xmm0, xmm0
#APP
mov rax, qword ptr fs:[0]
#NO_APP
lea rdx, [rbp - 64]
vmovaps xmmword ptr [rbp - 64], xmm0
mov qword ptr [rbp - 48], 0
mov rcx, qword ptr [rax - 8]
mov qword ptr [rbp - 64], 4
mov rax, qword ptr [rcx]
mov qword ptr [rbp - 72], rcx # 8-byte Spill
mov qword ptr [rbp - 56], rax
mov qword ptr [rcx], rdx
#DEBUG_VALUE: part1:data <- [DW_OP_deref] 0
mov r15, qword ptr [rdi + 16]
test r15, r15
je .LBB0_1
# %bb.2: # %L34
mov r14, qword ptr [rdi]
dec r15
mov r11b, 1
mov r13b, 1
# implicit-def: $r12b
# implicit-def: $r10b
xor eax, eax
jmp .LBB0_3
.p2align 4, 0x90
.LBB0_4: # in Loop: Header=BB0_3 Depth=1
xor r11d, r11d
mov ebx, edi
mov r10d, r8d
.LBB0_9: # %L114
# in Loop: Header=BB0_3 Depth=1
mov r12d, esi
test r15, r15
je .LBB0_12
.LBB0_10: # %guard_exit126
# in Loop: Header=BB0_3 Depth=1
inc r14
dec r15
mov r13d, ebx
.LBB0_3: # %L36
# =>This Inner Loop Header: Depth=1
movzx edx, byte ptr [r14]
test r13b, 1
movzx edi, r13b
mov ebx, 1
mov ecx, 0
cmove ebx, edi
cmovne edi, ecx
movzx ecx, r10b
lea esi, [rdx - 48]
lea r9d, [rdx - 58]
movzx r8d, sil
cmove r8d, ecx
cmp r9b, -11
ja .LBB0_4
# %bb.5: # %L89
# in Loop: Header=BB0_3 Depth=1
test r11b, 1
jne .LBB0_8
# %bb.6: # %L102
# in Loop: Header=BB0_3 Depth=1
cmp dl, 10
jne .LBB0_7
# %bb.13: # %L106
# in Loop: Header=BB0_3 Depth=1
test r13b, 1
jne .LBB0_14
# %bb.11: # %L114.thread
# in Loop: Header=BB0_3 Depth=1
add ecx, ecx
mov bl, 1
mov r11b, 1
lea ecx, [rcx + 4*rcx]
add cl, r12b
movzx ecx, cl
add rax, rcx
test r15, r15
jne .LBB0_10
jmp .LBB0_12
.p2align 4, 0x90
.LBB0_8: # %L102.thread
# in Loop: Header=BB0_3 Depth=1
mov r11b, 1
# implicit-def: $sil
cmp dl, 10
jne .LBB0_9
jmp .LBB0_15
.LBB0_7: # in Loop: Header=BB0_3 Depth=1
mov esi, r12d
jmp .LBB0_9
.LBB0_1:
xor eax, eax
.LBB0_12: # %L154
mov rcx, qword ptr [rbp - 56]
mov rdx, qword ptr [rbp - 72] # 8-byte Reload
mov qword ptr [rdx], rcx
add rsp, 40
pop rbx
pop r12
pop r13
pop r14
pop r15
pop rbp
ret
.LBB0_15: # %L106.thread
test r13b, 1
jne .LBB0_14
# %bb.16: # %post_box_union47
movabs rax, offset jl_nothing
movabs rcx, offset jl_small_typeof
movabs rdi, offset ".L_j_str_typeassert#1"
mov rdx, qword ptr [rax]
mov rsi, qword ptr [rcx + 336]
movabs rax, offset ijl_type_error
mov qword ptr [rbp - 48], rsi
call rax
.LBB0_14: # %post_box_union
movabs rax, offset jl_nothing
movabs rcx, offset jl_small_typeof
movabs rdi, offset ".L_j_str_typeassert#1"
mov rdx, qword ptr [rax]
mov rsi, qword ptr [rcx + 336]
movabs rax, offset ijl_type_error
mov qword ptr [rbp - 48], rsi
call rax
.Lfunc_end0:
.size julia_part1_1203, .Lfunc_end0-julia_part1_1203
# -- End function
.type ".L_j_str_typeassert#1",@object # @"_j_str_typeassert#1"
.section .rodata.str1.1,"aMS",@progbits,1
".L_j_str_typeassert#1":
.asciz "typeassert"
.size ".L_j_str_typeassert#1", 11
.section ".note.GNU-stack","",@progbits
```
</details>
Co-authored-by: Sukera <[email protected]>
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Doc stuff should perhaps move to the wiki, wherever it makes sense. Some of the stuff is notes, and then there is the manual as well.
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