Unleashes the potential for SROA of mutables using the novel Julia-level
escape analysis (on top of #43800):
1. alias-aware SROA, mutable ϕ-node elimination
2. `isdefined` check elimination
3. load-forwarding for non-eliminable but non-escaping mutables

---

1. alias-aware SROA, mutable ϕ-node elimination

EA's alias analysis allows this new SROA to handle nested mutables allocations
pretty well. Now we can eliminate the heap allocations completely from
this insanely nested examples by the single analysis/optimization pass:
```julia
julia> function refs(x)
           (Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref((x))))))))))))[][][][][][][][][][]
       end
refs (generic function with 1 method)

julia> refs("julia"); @allocated refs("julia")
0
```

EA can also analyze escape of ϕ-node as well as its aliasing.
Mutable ϕ-nodes would be eliminated even for a very tricky case as like:
```julia
julia> code_typed((Bool,String,)) do cond, x
           # these allocation form multiple ϕ-nodes
           if cond
               ϕ2 = ϕ1 = Ref{Any}("foo")
           else
               ϕ2 = ϕ1 = Ref{Any}("bar")
           end
           ϕ2[] = x
           y = ϕ1[] # => x
           return y
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     goto #3 if not cond
2 ─     goto #4
3 ─     nothing::Nothing
4 ┄     return x
) => Any
```

Combined with the powerful alias analysis and ϕ-node handling,
the following realistic examples will be fully optimized:
```julia
julia> # demonstrate the power of our field / alias analysis with realistic end to end examples
       # adapted from http://wiki.luajit.org/Allocation-Sinking-Optimization#implementation%5B
       abstract type AbstractPoint{T} end

julia> struct Point{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> mutable struct MPoint{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> add(a::P, b::P) where P<:AbstractPoint = P(a.x + b.x, a.y + b.y)
add (generic function with 1 method)

julia> function compute_point(T, n, ax, ay, bx, by)
           a = T(ax, ay)
           b = T(bx, by)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end
compute_point (generic function with 2 methods)

julia> function compute_point(n, a, b)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end
compute_point (generic function with 2 methods)

julia> function compute_point!(n, a, b)
           for i in 0:(n-1)
               a′ = add(add(a, b), b)
               a.x = a′.x
               a.y = a′.y
           end
       end
compute_point! (generic function with 1 method)

julia> compute_point(MPoint, 10, 1+.5, 2+.5, 2+.25, 4+.75);

julia> compute_point(MPoint, 10, 1+.5im, 2+.5im, 2+.25im, 4+.75im);

julia> @test @allocated(compute_point(MPoint, 10000, 1+.5, 2+.5, 2+.25, 4+.75)) == 0
Test Passed

julia> @test @allocated(compute_point(MPoint, 10000, 1+.5im, 2+.5im, 2+.25im, 4+.75im)) == 0
Test Passed

julia> compute_point(10, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75));

julia> compute_point(10, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im));

julia> @test @allocated(compute_point(10000, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75))) == 0
Test Passed

julia> @test @allocated(compute_point(10000, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im))) == 0
Test Passed

julia> af, bf = MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75);

julia> ac, bc = MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im);

julia> compute_point!(10, af, bf);

julia> compute_point!(10, ac, bc);

julia> @test @allocated(compute_point!(10000, af, bf)) == 0
Test Passed

julia> @test @allocated(compute_point!(10000, ac, bc)) == 0
Test Passed
```

2. `isdefined` check elimination

This commit also implements a simple optimization to eliminate
`isdefined` call by checking load-fowardability.
This optimization may be especially useful to eliminate extra allocation
involved with a capturing closure, e.g.:
```julia
julia> callit(f, args...) = f(args...);

julia> function isdefined_elim()
           local arr::Vector{Any}
           callit() do
               arr = Any[]
           end
           return arr
       end;

julia> code_typed(isdefined_elim)
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Vector{Any}, svec(Any, Int64), 0, :(:ccall), Vector{Any}, 0, 0))::Vector{Any}
└──      goto #3 if not true
2 ─      goto #4
3 ─      $(Expr(:throw_undef_if_not, :arr, false))::Any
4 ┄      return %1
) => Vector{Any}
```

3. load-forwarding for non-eliminable but non-escaping mutables

EA also allows us to forward loads even when the mutable allocation
can't be eliminated but still its fields are known precisely.
The load forwarding might be useful since it may derive new type information
that succeeding optimization passes can use (or just because it allows
simpler code transformations down the load):
```julia
julia> code_typed((Bool,String,)) do c, s
           r = Ref{Any}(s)
           if c
               return r[]::String # adce_pass! will further eliminate this type assert call also
           else
               return r
           end
       end
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = %new(Base.RefValue{Any}, s)::Base.RefValue{Any}
└──      goto #3 if not c
2 ─      return s
3 ─      return %1
) => Union{Base.RefValue{Any}, String}
```

---

Please refer to the newly added test cases for more examples.
Also, EA's alias analysis already succeeds to reason about arrays, and
so this EA-based SROA will hopefully be generalized for array SROA as well.

Unleashes the potential for SROA of mutables using the novel Julia-level
escape analysis (on top of #43800):
1. alias-aware SROA, mutable ϕ-node elimination
2. `isdefined` check elimination
3. load-forwarding for non-eliminable but non-escaping mutables

---

1. alias-aware SROA, mutable ϕ-node elimination

EA's alias analysis allows this new SROA to handle nested mutables allocations
pretty well. Now we can eliminate the heap allocations completely from
this insanely nested examples by the single analysis/optimization pass:
```julia
julia> function refs(x)
           (Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref((x))))))))))))[][][][][][][][][][]
       end
refs (generic function with 1 method)

julia> refs("julia"); @allocated refs("julia")
0
```

EA can also analyze escape of ϕ-node as well as its aliasing.
Mutable ϕ-nodes would be eliminated even for a very tricky case as like:
```julia
julia> code_typed((Bool,String,)) do cond, x
           # these allocation form multiple ϕ-nodes
           if cond
               ϕ2 = ϕ1 = Ref{Any}("foo")
           else
               ϕ2 = ϕ1 = Ref{Any}("bar")
           end
           ϕ2[] = x
           y = ϕ1[] # => x
           return y
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     goto #3 if not cond
2 ─     goto #4
3 ─     nothing::Nothing
4 ┄     return x
) => Any
```

Combined with the powerful alias analysis and ϕ-node handling,
the following realistic examples will be fully optimized:
```julia
julia> # demonstrate the power of our field / alias analysis with realistic end to end examples
       # adapted from http://wiki.luajit.org/Allocation-Sinking-Optimization#implementation%5B
       abstract type AbstractPoint{T} end

julia> struct Point{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> mutable struct MPoint{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> add(a::P, b::P) where P<:AbstractPoint = P(a.x + b.x, a.y + b.y)
add (generic function with 1 method)

julia> function compute_point(T, n, ax, ay, bx, by)
           a = T(ax, ay)
           b = T(bx, by)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end
compute_point (generic function with 2 methods)

julia> function compute_point(n, a, b)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end
compute_point (generic function with 2 methods)

julia> function compute_point!(n, a, b)
           for i in 0:(n-1)
               a′ = add(add(a, b), b)
               a.x = a′.x
               a.y = a′.y
           end
       end
compute_point! (generic function with 1 method)

julia> compute_point(MPoint, 10, 1+.5, 2+.5, 2+.25, 4+.75);

julia> compute_point(MPoint, 10, 1+.5im, 2+.5im, 2+.25im, 4+.75im);

julia> @test @allocated(compute_point(MPoint, 10000, 1+.5, 2+.5, 2+.25, 4+.75)) == 0
Test Passed

julia> @test @allocated(compute_point(MPoint, 10000, 1+.5im, 2+.5im, 2+.25im, 4+.75im)) == 0
Test Passed

julia> compute_point(10, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75));

julia> compute_point(10, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im));

julia> @test @allocated(compute_point(10000, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75))) == 0
Test Passed

julia> @test @allocated(compute_point(10000, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im))) == 0
Test Passed

julia> af, bf = MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75);

julia> ac, bc = MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im);

julia> compute_point!(10, af, bf);

julia> compute_point!(10, ac, bc);

julia> @test @allocated(compute_point!(10000, af, bf)) == 0
Test Passed

julia> @test @allocated(compute_point!(10000, ac, bc)) == 0
Test Passed
```

2. `isdefined` check elimination

This commit also implements a simple optimization to eliminate
`isdefined` call by checking load-fowardability.
This optimization may be especially useful to eliminate extra allocation
involved with a capturing closure, e.g.:
```julia
julia> callit(f, args...) = f(args...);

julia> function isdefined_elim()
           local arr::Vector{Any}
           callit() do
               arr = Any[]
           end
           return arr
       end;

julia> code_typed(isdefined_elim)
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Vector{Any}, svec(Any, Int64), 0, :(:ccall), Vector{Any}, 0, 0))::Vector{Any}
└──      goto #3 if not true
2 ─      goto #4
3 ─      $(Expr(:throw_undef_if_not, :arr, false))::Any
4 ┄      return %1
) => Vector{Any}
```

3. load-forwarding for non-eliminable but non-escaping mutables

EA also allows us to forward loads even when the mutable allocation
can't be eliminated but still its fields are known precisely.
The load forwarding might be useful since it may derive new type information
that succeeding optimization passes can use (or just because it allows
simpler code transformations down the load):
```julia
julia> code_typed((Bool,String,)) do c, s
           r = Ref{Any}(s)
           if c
               return r[]::String # adce_pass! will further eliminate this type assert call also
           else
               return r
           end
       end
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = %new(Base.RefValue{Any}, s)::Base.RefValue{Any}
└──      goto #3 if not c
2 ─      return s
3 ─      return %1
) => Union{Base.RefValue{Any}, String}
```

---

Please refer to the newly added test cases for more examples.
Also, EA's alias analysis already succeeds to reason about arrays, and
so this EA-based SROA will hopefully be generalized for array SROA as well.

Unleashes the potential for SROA of mutables using the novel Julia-level
escape analysis (on top of #43800):
1. alias-aware SROA, mutable ϕ-node elimination
2. `isdefined` check elimination
3. load-forwarding for non-eliminable but non-escaping mutables

---

1. alias-aware SROA, mutable ϕ-node elimination

EA's alias analysis allows this new SROA to handle nested mutables allocations
pretty well. Now we can eliminate the heap allocations completely from
this insanely nested examples by the single analysis/optimization pass:
```julia
julia> function refs(x)
           (Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref((x))))))))))))[][][][][][][][][][]
       end
refs (generic function with 1 method)

julia> refs("julia"); @allocated refs("julia")
0
```

EA can also analyze escape of ϕ-node as well as its aliasing.
Mutable ϕ-nodes would be eliminated even for a very tricky case as like:
```julia
julia> code_typed((Bool,String,)) do cond, x
           # these allocation form multiple ϕ-nodes
           if cond
               ϕ2 = ϕ1 = Ref{Any}("foo")
           else
               ϕ2 = ϕ1 = Ref{Any}("bar")
           end
           ϕ2[] = x
           y = ϕ1[] # => x
           return y
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     goto #3 if not cond
2 ─     goto #4
3 ─     nothing::Nothing
4 ┄     return x
) => Any
```

Combined with the powerful alias analysis and ϕ-node handling,
the following realistic examples will be fully optimized:
```julia
julia> # demonstrate the power of our field / alias analysis with realistic end to end examples
       # adapted from http://wiki.luajit.org/Allocation-Sinking-Optimization#implementation%5B
       abstract type AbstractPoint{T} end

julia> struct Point{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> mutable struct MPoint{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> add(a::P, b::P) where P<:AbstractPoint = P(a.x + b.x, a.y + b.y)
add (generic function with 1 method)

julia> function compute_point(T, n, ax, ay, bx, by)
           a = T(ax, ay)
           b = T(bx, by)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end
compute_point (generic function with 2 methods)

julia> function compute_point(n, a, b)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end
compute_point (generic function with 2 methods)

julia> function compute_point!(n, a, b)
           for i in 0:(n-1)
               a′ = add(add(a, b), b)
               a.x = a′.x
               a.y = a′.y
           end
       end
compute_point! (generic function with 1 method)

julia> compute_point(MPoint, 10, 1+.5, 2+.5, 2+.25, 4+.75);

julia> compute_point(MPoint, 10, 1+.5im, 2+.5im, 2+.25im, 4+.75im);

julia> @test @allocated(compute_point(MPoint, 10000, 1+.5, 2+.5, 2+.25, 4+.75)) == 0
Test Passed

julia> @test @allocated(compute_point(MPoint, 10000, 1+.5im, 2+.5im, 2+.25im, 4+.75im)) == 0
Test Passed

julia> compute_point(10, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75));

julia> compute_point(10, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im));

julia> @test @allocated(compute_point(10000, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75))) == 0
Test Passed

julia> @test @allocated(compute_point(10000, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im))) == 0
Test Passed

julia> af, bf = MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75);

julia> ac, bc = MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im);

julia> compute_point!(10, af, bf);

julia> compute_point!(10, ac, bc);

julia> @test @allocated(compute_point!(10000, af, bf)) == 0
Test Passed

julia> @test @allocated(compute_point!(10000, ac, bc)) == 0
Test Passed
```

2. `isdefined` check elimination

This commit also implements a simple optimization to eliminate
`isdefined` call by checking load-fowardability.
This optimization may be especially useful to eliminate extra allocation
involved with a capturing closure, e.g.:
```julia
julia> callit(f, args...) = f(args...);

julia> function isdefined_elim()
           local arr::Vector{Any}
           callit() do
               arr = Any[]
           end
           return arr
       end;

julia> code_typed(isdefined_elim)
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Vector{Any}, svec(Any, Int64), 0, :(:ccall), Vector{Any}, 0, 0))::Vector{Any}
└──      goto #3 if not true
2 ─      goto #4
3 ─      $(Expr(:throw_undef_if_not, :arr, false))::Any
4 ┄      return %1
) => Vector{Any}
```

3. load-forwarding for non-eliminable but non-escaping mutables

EA also allows us to forward loads even when the mutable allocation
can't be eliminated but still its fields are known precisely.
The load forwarding might be useful since it may derive new type information
that succeeding optimization passes can use (or just because it allows
simpler code transformations down the load):
```julia
julia> code_typed((Bool,String,)) do c, s
           r = Ref{Any}(s)
           if c
               return r[]::String # adce_pass! will further eliminate this type assert call also
           else
               return r
           end
       end
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = %new(Base.RefValue{Any}, s)::Base.RefValue{Any}
└──      goto #3 if not c
2 ─      return s
3 ─      return %1
) => Union{Base.RefValue{Any}, String}
```

---

Please refer to the newly added test cases for more examples.
Also, EA's alias analysis already succeeds to reason about arrays, and
so this EA-based SROA will hopefully be generalized for array SROA as well.

Unleashes the potential for SROA of mutables using the novel Julia-level
escape analysis (on top of #43800):
1. alias-aware SROA, mutable ϕ-node elimination
2. `isdefined` check elimination
3. load-forwarding for non-eliminable but analyzable mutables

---

1. alias-aware SROA, mutable ϕ-node elimination

EA's alias analysis allows this new SROA to handle nested mutables allocations
pretty well. Now we can eliminate the heap allocations completely from
this insanely nested examples by the single analysis/optimization pass:
```julia
julia> function refs(x)
           (Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref((x))))))))))))[][][][][][][][][][]
       end
refs (generic function with 1 method)

julia> refs("julia"); @allocated refs("julia")
0
```

EA can also analyze escape of ϕ-node as well as its aliasing.
Mutable ϕ-nodes would be eliminated even for a very tricky case as like:
```julia
julia> code_typed((Bool,String,)) do cond, x
           # these allocation form multiple ϕ-nodes
           if cond
               ϕ2 = ϕ1 = Ref{Any}("foo")
           else
               ϕ2 = ϕ1 = Ref{Any}("bar")
           end
           ϕ2[] = x
           y = ϕ1[] # => x
           return y
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     goto #3 if not cond
2 ─     goto #4
3 ─     nothing::Nothing
4 ┄     return x
) => Any
```

Combined with the alias analysis and ϕ-node handling above,
allocations in the following "realistic" examples will be optimized:
```julia
julia> # demonstrate the power of our field / alias analysis with realistic end to end examples
       # adapted from http://wiki.luajit.org/Allocation-Sinking-Optimization#implementation%5B
       abstract type AbstractPoint{T} end

julia> struct Point{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> mutable struct MPoint{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> add(a::P, b::P) where P<:AbstractPoint = P(a.x + b.x, a.y + b.y);

julia> function compute_point(T, n, ax, ay, bx, by)
           a = T(ax, ay)
           b = T(bx, by)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end;

julia> function compute_point(n, a, b)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end;

julia> function compute_point!(n, a, b)
           for i in 0:(n-1)
               a′ = add(add(a, b), b)
               a.x = a′.x
               a.y = a′.y
           end
       end;

julia> compute_point(MPoint, 10, 1+.5, 2+.5, 2+.25, 4+.75);

julia> compute_point(MPoint, 10, 1+.5im, 2+.5im, 2+.25im, 4+.75im);

julia> @allocated compute_point(MPoint, 10000, 1+.5, 2+.5, 2+.25, 4+.75)
0

julia> @allocated compute_point(MPoint, 10000, 1+.5im, 2+.5im, 2+.25im, 4+.75im)
0

julia> compute_point(10, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75));

julia> compute_point(10, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im));

julia> @allocated compute_point(10000, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75))
0

julia> @allocated compute_point(10000, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im))
0

julia> af, bf = MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75);

julia> ac, bc = MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im);

julia> compute_point!(10, af, bf);

julia> compute_point!(10, ac, bc);

julia> @allocated compute_point!(10000, af, bf)
0

julia> @allocated compute_point!(10000, ac, bc)
0
```

2. `isdefined` check elimination

This commit also implements a simple optimization to eliminate
`isdefined` call by checking load-fowardability.
This optimization may be especially useful to eliminate extra allocation
involved with a capturing closure, e.g.:
```julia
julia> callit(f, args...) = f(args...);

julia> function isdefined_elim()
           local arr::Vector{Any}
           callit() do
               arr = Any[]
           end
           return arr
       end;

julia> code_typed(isdefined_elim)
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Vector{Any}, svec(Any, Int64), 0, :(:ccall), Vector{Any}, 0, 0))::Vector{Any}
└──      goto #3 if not true
2 ─      goto #4
3 ─      $(Expr(:throw_undef_if_not, :arr, false))::Any
4 ┄      return %1
) => Vector{Any}
```

3. load-forwarding for non-eliminable but analyzable mutables

EA also allows us to forward loads even when the mutable allocation
can't be eliminated but still its fields are known precisely.
The load forwarding might be useful since it may derive new type information
that succeeding optimization passes can use (or just because it allows
simpler code transformations down the load):
```julia
julia> code_typed((Bool,String,)) do c, s
           r = Ref{Any}(s)
           if c
               return r[]::String # adce_pass! will further eliminate this type assert call also
           else
               return r
           end
       end
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = %new(Base.RefValue{Any}, s)::Base.RefValue{Any}
└──      goto #3 if not c
2 ─      return s
3 ─      return %1
) => Union{Base.RefValue{Any}, String}
```

---

Please refer to the newly added test cases for more examples.
Also, EA's alias analysis already succeeds to reason about arrays, and
so this EA-based SROA will hopefully be generalized for array SROA as well.

JuliaLang/julia#43800 will define new module `EscapeAnalysis`
inside `Core.Compiler`, and it requires this minor special handling.

This commit ports [EscapeAnalysis.jl](https://github.com/aviatesk/EscapeAnalysis.jl) into Julia base.
You can find the documentation of this escape analysis at [this GitHub page](https://aviatesk.github.io/EscapeAnalysis.jl/dev/)[^1].

[^1]: The same documentation will be included into Julia's developer
      documentation by this commit.

This escape analysis will hopefully be an enabling technology for various
memory-related optimizations at Julia's high level compilation pipeline.
Possible target optimization includes alias aware SROA (JuliaLang#43888),
array SROA (JuliaLang#43909), `mutating_arrayfreeze` optimization (JuliaLang#42465),
stack allocation of mutables, finalizer elision and so on[^2].

[^2]: It would be also interesting if LLVM-level optimizations can consume
      IPO information derived by this escape analysis to broaden
      optimization possibilities.

The primary motivation for porting EA by this PR is to check its impact
on latency as well as to get feedbacks from a broader range of developers.
The plan is that we first introduce EA to Julia Base by this commit, and
then merge the depending PRs built on top of this commit later.

This commit simply defines EA inside Julia base compiler and enables the
existing test suite with it. In this commit we don't run EA at all, and
so this commit shouldn't affect Julia-level compilation latency.

In the depending PRs, EA will run in two stages:
- `IPO EA`: run EA on pre-inlining state to generate IPO-valid cache
- `Local EA`: run EA on post-inlining state to generate local escape
              information used for various optimizations

In order to integrate `IPO EA` with our compilation cache system,
this commit also implements a new `CodeInstance.argescapes` field that
keeps the IPO-valid cache generated by `IPO EA`.

JuliaLang/julia#43800 will define new module `EscapeAnalysis`
inside `Core.Compiler`, and it requires this minor special handling.

This commit ports [EscapeAnalysis.jl](https://github.com/aviatesk/EscapeAnalysis.jl) into Julia base.
You can find the documentation of this escape analysis at [this GitHub page](https://aviatesk.github.io/EscapeAnalysis.jl/dev/)[^1].

[^1]: The same documentation will be included into Julia's developer
      documentation by this commit.

This escape analysis will hopefully be an enabling technology for various
memory-related optimizations at Julia's high level compilation pipeline.
Possible target optimization includes alias aware SROA (JuliaLang#43888),
array SROA (JuliaLang#43909), `mutating_arrayfreeze` optimization (JuliaLang#42465),
stack allocation of mutables, finalizer elision and so on[^2].

[^2]: It would be also interesting if LLVM-level optimizations can consume
      IPO information derived by this escape analysis to broaden
      optimization possibilities.

The primary motivation for porting EA by this PR is to check its impact
on latency as well as to get feedbacks from a broader range of developers.
The plan is that we first introduce EA to Julia Base by this commit, and
then merge the depending PRs built on top of this commit later.

This commit simply defines EA inside Julia base compiler and enables the
existing test suite with it. In this commit we don't run EA at all, and
so this commit shouldn't affect Julia-level compilation latency.

In the depending PRs, EA will run in two stages:
- `IPO EA`: run EA on pre-inlining state to generate IPO-valid cache
- `Local EA`: run EA on post-inlining state to generate local escape
              information used for various optimizations

In order to integrate `IPO EA` with our compilation cache system,
this commit also implements a new `CodeInstance.argescapes` field that
keeps the IPO-valid cache generated by `IPO EA`.

This commit ports [EscapeAnalysis.jl](https://github.com/aviatesk/EscapeAnalysis.jl) into Julia base.
You can find the documentation of this escape analysis at [this GitHub page](https://aviatesk.github.io/EscapeAnalysis.jl/dev/)[^1].

[^1]: The same documentation will be included into Julia's developer
      documentation by this commit.

This escape analysis will hopefully be an enabling technology for various
memory-related optimizations at Julia's high level compilation pipeline.
Possible target optimization includes alias aware SROA (JuliaLang#43888),
array SROA (JuliaLang#43909), `mutating_arrayfreeze` optimization (JuliaLang#42465),
stack allocation of mutables, finalizer elision and so on[^2].

[^2]: It would be also interesting if LLVM-level optimizations can consume
      IPO information derived by this escape analysis to broaden
      optimization possibilities.

The primary motivation for porting EA by this PR is to check its impact
on latency as well as to get feedbacks from a broader range of developers.
The plan is that we first introduce EA to Julia Base by this commit, and
then merge the depending PRs built on top of this commit later.

This commit simply defines EA inside Julia base compiler and enables the
existing test suite with it. In this commit we don't run EA at all, and
so this commit shouldn't affect Julia-level compilation latency.

In the depending PRs, EA will run in two stages:
- `IPO EA`: run EA on pre-inlining state to generate IPO-valid cache
- `Local EA`: run EA on post-inlining state to generate local escape
              information used for various optimizations

In order to integrate `IPO EA` with our compilation cache system,
this commit also implements a new `CodeInstance.argescapes` field that
keeps the IPO-valid cache generated by `IPO EA`.

Enhances SROA of mutables using the novel Julia-level escape analysis (on top of #43800):
1. alias-aware SROA, mutable ϕ-node elimination
2. `isdefined` check elimination
3. load-forwarding for non-eliminable but analyzable mutables

---

1. alias-aware SROA, mutable ϕ-node elimination

EA's alias analysis allows this new SROA to handle nested mutables allocations
pretty well. Now we can eliminate the heap allocations completely from
this insanely nested examples by the single analysis/optimization pass:
```julia
julia> function refs(x)
           (Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref((x))))))))))))[][][][][][][][][][]
       end
refs (generic function with 1 method)

julia> refs("julia"); @allocated refs("julia")
0
```

EA can also analyze escape of ϕ-node as well as its aliasing.
Mutable ϕ-nodes would be eliminated even for a very tricky case as like:
```julia
julia> code_typed((Bool,String,)) do cond, x
           # these allocation form multiple ϕ-nodes
           if cond
               ϕ2 = ϕ1 = Ref{Any}("foo")
           else
               ϕ2 = ϕ1 = Ref{Any}("bar")
           end
           ϕ2[] = x
           y = ϕ1[] # => x
           return y
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     goto #3 if not cond
2 ─     goto #4
3 ─     nothing::Nothing
4 ┄     return x
) => Any
```

Combined with the alias analysis and ϕ-node handling above,
allocations in the following "realistic" examples will be optimized:
```julia
julia> # demonstrate the power of our field / alias analysis with realistic end to end examples
       # adapted from http://wiki.luajit.org/Allocation-Sinking-Optimization#implementation%5B
       abstract type AbstractPoint{T} end

julia> struct Point{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> mutable struct MPoint{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> add(a::P, b::P) where P<:AbstractPoint = P(a.x + b.x, a.y + b.y);

julia> function compute_point(T, n, ax, ay, bx, by)
           a = T(ax, ay)
           b = T(bx, by)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end;

julia> function compute_point(n, a, b)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end;

julia> function compute_point!(n, a, b)
           for i in 0:(n-1)
               a′ = add(add(a, b), b)
               a.x = a′.x
               a.y = a′.y
           end
       end;

julia> compute_point(MPoint, 10, 1+.5, 2+.5, 2+.25, 4+.75);

julia> compute_point(MPoint, 10, 1+.5im, 2+.5im, 2+.25im, 4+.75im);

julia> @allocated compute_point(MPoint, 10000, 1+.5, 2+.5, 2+.25, 4+.75)
0

julia> @allocated compute_point(MPoint, 10000, 1+.5im, 2+.5im, 2+.25im, 4+.75im)
0

julia> compute_point(10, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75));

julia> compute_point(10, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im));

julia> @allocated compute_point(10000, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75))
0

julia> @allocated compute_point(10000, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im))
0

julia> af, bf = MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75);

julia> ac, bc = MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im);

julia> compute_point!(10, af, bf);

julia> compute_point!(10, ac, bc);

julia> @allocated compute_point!(10000, af, bf)
0

julia> @allocated compute_point!(10000, ac, bc)
0
```

2. `isdefined` check elimination

This commit also implements a simple optimization to eliminate
`isdefined` call by checking load-fowardability.
This optimization may be especially useful to eliminate extra allocation
involved with a capturing closure, e.g.:
```julia
julia> callit(f, args...) = f(args...);

julia> function isdefined_elim()
           local arr::Vector{Any}
           callit() do
               arr = Any[]
           end
           return arr
       end;

julia> code_typed(isdefined_elim)
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Vector{Any}, svec(Any, Int64), 0, :(:ccall), Vector{Any}, 0, 0))::Vector{Any}
└──      goto #3 if not true
2 ─      goto #4
3 ─      $(Expr(:throw_undef_if_not, :arr, false))::Any
4 ┄      return %1
) => Vector{Any}
```

3. load-forwarding for non-eliminable but analyzable mutables

EA also allows us to forward loads even when the mutable allocation
can't be eliminated but still its fields are known precisely.
The load forwarding might be useful since it may derive new type information
that succeeding optimization passes can use (or just because it allows
simpler code transformations down the load):
```julia
julia> code_typed((Bool,String,)) do c, s
           r = Ref{Any}(s)
           if c
               return r[]::String # adce_pass! will further eliminate this type assert call also
           else
               return r
           end
       end
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = %new(Base.RefValue{Any}, s)::Base.RefValue{Any}
└──      goto #3 if not c
2 ─      return s
3 ─      return %1
) => Union{Base.RefValue{Any}, String}
```

---

Please refer to the newly added test cases for more examples.
Also, EA's alias analysis already succeeds to reason about arrays, and
so this EA-based SROA will hopefully be generalized for array SROA as well.

Enhances SROA of mutables using the novel Julia-level escape analysis (on top of #43800):
1. alias-aware SROA, mutable ϕ-node elimination
2. `isdefined` check elimination
3. load-forwarding for non-eliminable but analyzable mutables

---

1. alias-aware SROA, mutable ϕ-node elimination

EA's alias analysis allows this new SROA to handle nested mutables allocations
pretty well. Now we can eliminate the heap allocations completely from
this insanely nested examples by the single analysis/optimization pass:
```julia
julia> function refs(x)
           (Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref(Ref((x))))))))))))[][][][][][][][][][]
       end
refs (generic function with 1 method)

julia> refs("julia"); @allocated refs("julia")
0
```

EA can also analyze escape of ϕ-node as well as its aliasing.
Mutable ϕ-nodes would be eliminated even for a very tricky case as like:
```julia
julia> code_typed((Bool,String,)) do cond, x
           # these allocation form multiple ϕ-nodes
           if cond
               ϕ2 = ϕ1 = Ref{Any}("foo")
           else
               ϕ2 = ϕ1 = Ref{Any}("bar")
           end
           ϕ2[] = x
           y = ϕ1[] # => x
           return y
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     goto #3 if not cond
2 ─     goto #4
3 ─     nothing::Nothing
4 ┄     return x
) => Any
```

Combined with the alias analysis and ϕ-node handling above,
allocations in the following "realistic" examples will be optimized:
```julia
julia> # demonstrate the power of our field / alias analysis with realistic end to end examples
       # adapted from http://wiki.luajit.org/Allocation-Sinking-Optimization#implementation%5B
       abstract type AbstractPoint{T} end

julia> struct Point{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> mutable struct MPoint{T} <: AbstractPoint{T}
           x::T
           y::T
       end

julia> add(a::P, b::P) where P<:AbstractPoint = P(a.x + b.x, a.y + b.y);

julia> function compute_point(T, n, ax, ay, bx, by)
           a = T(ax, ay)
           b = T(bx, by)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end;

julia> function compute_point(n, a, b)
           for i in 0:(n-1)
               a = add(add(a, b), b)
           end
           a.x, a.y
       end;

julia> function compute_point!(n, a, b)
           for i in 0:(n-1)
               a′ = add(add(a, b), b)
               a.x = a′.x
               a.y = a′.y
           end
       end;

julia> compute_point(MPoint, 10, 1+.5, 2+.5, 2+.25, 4+.75);

julia> compute_point(MPoint, 10, 1+.5im, 2+.5im, 2+.25im, 4+.75im);

julia> @allocated compute_point(MPoint, 10000, 1+.5, 2+.5, 2+.25, 4+.75)
0

julia> @allocated compute_point(MPoint, 10000, 1+.5im, 2+.5im, 2+.25im, 4+.75im)
0

julia> compute_point(10, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75));

julia> compute_point(10, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im));

julia> @allocated compute_point(10000, MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75))
0

julia> @allocated compute_point(10000, MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im))
0

julia> af, bf = MPoint(1+.5, 2+.5), MPoint(2+.25, 4+.75);

julia> ac, bc = MPoint(1+.5im, 2+.5im), MPoint(2+.25im, 4+.75im);

julia> compute_point!(10, af, bf);

julia> compute_point!(10, ac, bc);

julia> @allocated compute_point!(10000, af, bf)
0

julia> @allocated compute_point!(10000, ac, bc)
0
```

2. `isdefined` check elimination

This commit also implements a simple optimization to eliminate
`isdefined` call by checking load-fowardability.
This optimization may be especially useful to eliminate extra allocation
involved with a capturing closure, e.g.:
```julia
julia> callit(f, args...) = f(args...);

julia> function isdefined_elim()
           local arr::Vector{Any}
           callit() do
               arr = Any[]
           end
           return arr
       end;

julia> code_typed(isdefined_elim)
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = $(Expr(:foreigncall, :(:jl_alloc_array_1d), Vector{Any}, svec(Any, Int64), 0, :(:ccall), Vector{Any}, 0, 0))::Vector{Any}
└──      goto #3 if not true
2 ─      goto #4
3 ─      $(Expr(:throw_undef_if_not, :arr, false))::Any
4 ┄      return %1
) => Vector{Any}
```

3. load-forwarding for non-eliminable but analyzable mutables

EA also allows us to forward loads even when the mutable allocation
can't be eliminated but still its fields are known precisely.
The load forwarding might be useful since it may derive new type information
that succeeding optimization passes can use (or just because it allows
simpler code transformations down the load):
```julia
julia> code_typed((Bool,String,)) do c, s
           r = Ref{Any}(s)
           if c
               return r[]::String # adce_pass! will further eliminate this type assert call also
           else
               return r
           end
       end
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = %new(Base.RefValue{Any}, s)::Base.RefValue{Any}
└──      goto #3 if not c
2 ─      return s
3 ─      return %1
) => Union{Base.RefValue{Any}, String}
```

---

Please refer to the newly added test cases for more examples.
Also, EA's alias analysis already succeeds to reason about arrays, and
so this EA-based SROA will hopefully be generalized for array SROA as well.