allow @nospecialize-d push! to take arbitrary items

[aviatesk]

Currently the @nospecialize-d push!(::Vector{Any}, ...) can only
take a single item and we will end up with runtime dispatch when we try
to call it with multiple items:

julia> code_typed(push!, (Vector{Any}, Any))
1-element Vector{Any}:
 CodeInfo(
1 ─      $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000001, 0x0000000000000001))::Nothing
│   %2 = Base.arraylen(a)::Int64
│        Base.arrayset(true, a, item, %2)::Vector{Any}
└──      return a
) => Vector{Any}

julia> code_typed(push!, (Vector{Any}, Any, Any))
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = Base.append!(a, iter)::Vector{Any}
└──      return %1
) => Vector{Any}

This commit extends it so that it can take arbitrarily long items.
Our compiler should still be able to optimize the single-input case as
before by unrolling using the constant item length information:

julia> code_typed(push!, (Vector{Any}, Any))
1-element Vector{Any}:
 CodeInfo(
1 ─      $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000001, 0x0000000000000001))::Nothing
│   %2 = Base.arraylen(a)::Int64
│        Base.arrayset(true, a, item, %2)::Vector{Any}
└──      return a
) => Vector{Any}

julia> code_typed(push!, (Vector{Any}, Any, Any))
1-element Vector{Any}:
 CodeInfo(
1 ─ %1  = Base.arraylen(a)::Int64
│         $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000002, 0x0000000000000002))::Nothing
└──       goto #7 if not true
2 ┄ %4  = φ (#1 => 1, #6 => %14)::Int64
│   %5  = φ (#1 => 1, #6 => %15)::Int64
│   %6  = Base.getfield(x, %4, true)::Any
│   %7  = Base.add_int(%1, %4)::Int64
│         Base.arrayset(true, a, %6, %7)::Vector{Any}
│   %9  = (%5 === 2)::Bool
└──       goto #4 if not %9
3 ─       goto #5
4 ─ %12 = Base.add_int(%5, 1)::Int64
└──       goto #5
5 ┄ %14 = φ (#4 => %12)::Int64
│   %15 = φ (#4 => %12)::Int64
│   %16 = φ (#3 => true, #4 => false)::Bool
│   %17 = Base.not_int(%16)::Bool
└──       goto #7 if not %17
6 ─       goto #2
7 ┄       return a
) => Vector{Any}

---

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.

[vtjnash]

this seems to be failing on many CI bots

[aviatesk]

It seems like push!(::Vector{Any}, ::Any) produces the following IR:

julia> code_typed((Vector{Any},Any)) do xs, x
           push!(xs, x)
       end
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = Base.arraylen(xs)::Int64
│        $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000001, 0x0000000000000001))::Nothing
└──      goto #3 if not true
2 ─ %4 = Base.add_int(%1, 1)::Int64
└──      Base.arrayset(true, xs, x, %4)::Vector{Any}
3 ┄      goto #4
4 ─      return xs
) => Vector{Any}

So the loop can't be fully optimized on Julia level actually, but I confirmed LLVM can further optimize it down the road and the performance doesn't regress.

EDIT: A nanosolidier result suggested that loop can be left unoptimized. For now, I'd like to specialize the single-arg case and optimize it manually.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - no performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.