remove many unneeded pure annotations

Removing actually may enable inference to get a sharper result,
since it is no longer being directed to ignore backedges and correctness assumptions

Replaces pure annotations in promotion with inline

base/inference.jl

@@ -1865,8 +1865,8 @@ function abstract_call(f::ANY, fargs::Union{Tuple{},Vector{Any}}, argtypes::Vect
     t = pure_eval_call(f, argtypes, atype, sv)
     t !== false && return t
 
-    if istopfunction(tm, f, :promote_type) || istopfunction(tm, f, :typejoin)
-        return Type
+    if istopfunction(tm, f, :typejoin) || f === return_type
+        return Type # don't try to infer these function edges directly -- it won't actually come up with anything useful
     elseif length(argtypes) == 2 && istopfunction(tm, f, :typename)
         return typename_static(argtypes[2])
     end

base/linalg/uniformscaling.jl

@@ -1,7 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 import Base: copy, ctranspose, getindex, show, transpose, one, zero, inv,
-             @_pure_meta, hcat, vcat, hvcat
+             hcat, vcat, hvcat
 import Base.LinAlg: SingularException
 
 struct UniformScaling{T<:Number}
@@ -201,7 +201,7 @@ promote_to_arrays(n,k, ::Type{T}, A, B, C) where {T} =
     (promote_to_arrays_(n[k], T, A), promote_to_arrays_(n[k+1], T, B), promote_to_arrays_(n[k+2], T, C))
 promote_to_arrays(n,k, ::Type{T}, A, B, Cs...) where {T} =
     (promote_to_arrays_(n[k], T, A), promote_to_arrays_(n[k+1], T, B), promote_to_arrays(n,k+2, T, Cs...)...)
-promote_to_array_type(A::Tuple{Vararg{Union{AbstractVecOrMat,UniformScaling}}}) = (@_pure_meta; Matrix)
+promote_to_array_type(A::Tuple{Vararg{Union{AbstractVecOrMat,UniformScaling}}}) = Matrix
 
 for (f,dim,name) in ((:hcat,1,"rows"), (:vcat,2,"cols"))
     @eval begin

base/promotion.jl

@@ -122,14 +122,14 @@ end
 
 ## promotion mechanism ##
 
-promote_type()  = (@_pure_meta; Bottom)
-promote_type(T) = (@_pure_meta; T)
-promote_type(T, S, U, V...) = (@_pure_meta; promote_type(T, promote_type(S, U, V...)))
+promote_type()  = Bottom
+promote_type(T) = T
+promote_type(T, S, U, V...) = (@_inline_meta; promote_type(T, promote_type(S, U, V...)))
 
-promote_type(::Type{Bottom}, ::Type{Bottom}) = (@_pure_meta; Bottom)
-promote_type(::Type{T}, ::Type{T}) where {T} = (@_pure_meta; T)
-promote_type(::Type{T}, ::Type{Bottom}) where {T} = (@_pure_meta; T)
-promote_type(::Type{Bottom}, ::Type{T}) where {T} = (@_pure_meta; T)
+promote_type(::Type{Bottom}, ::Type{Bottom}) = Bottom
+promote_type(::Type{T}, ::Type{T}) where {T} = T
+promote_type(::Type{T}, ::Type{Bottom}) where {T} = T
+promote_type(::Type{Bottom}, ::Type{T}) where {T} = T
 
 """
     promote_type(type1, type2)
@@ -152,7 +152,7 @@ BigFloat
 ```
 """
 function promote_type(::Type{T}, ::Type{S}) where {T,S}
-    @_pure_meta
+    @_inline_meta
     # Try promote_rule in both orders. Typically only one is defined,
     # and there is a fallback returning Bottom below, so the common case is
     #   promote_type(T, S) =>
@@ -161,26 +161,29 @@ function promote_type(::Type{T}, ::Type{S}) where {T,S}
     promote_result(T, S, promote_rule(T,S), promote_rule(S,T))
 end
 
-promote_rule(T, S) = (@_pure_meta; Bottom)
+promote_rule(::Type{<:Any}, ::Type{<:Any}) = Bottom
 
-promote_result(t,s,T,S) = (@_pure_meta; promote_type(T,S))
+promote_result(::Type{<:Any},::Type{<:Any},::Type{T},::Type{S}) where {T,S} = (@_inline_meta; promote_type(T,S))
 # If no promote_rule is defined, both directions give Bottom. In that
 # case use typejoin on the original types instead.
-promote_result(::Type{T},::Type{S},::Type{Bottom},::Type{Bottom}) where {T,S} = (@_pure_meta; typejoin(T, S))
+promote_result(::Type{T},::Type{S},::Type{Bottom},::Type{Bottom}) where {T,S} = (@_inline_meta; typejoin(T, S))
 
 promote() = ()
 promote(x) = (x,)
 function promote(x::T, y::S) where {T,S}
+    @_inline_meta
     (convert(promote_type(T,S),x), convert(promote_type(T,S),y))
 end
-promote_typeof(x) = (@_pure_meta; typeof(x))
-promote_typeof(x, xs...) = (@_pure_meta; promote_type(typeof(x), promote_typeof(xs...)))
+promote_typeof(x) = typeof(x)
+promote_typeof(x, xs...) = (@_inline_meta; promote_type(typeof(x), promote_typeof(xs...)))
 function promote(x, y, z)
+    @_inline_meta
     (convert(promote_typeof(x,y,z), x),
      convert(promote_typeof(x,y,z), y),
      convert(promote_typeof(x,y,z), z))
 end
 function promote(x, y, zs...)
+    @_inline_meta
     (convert(promote_typeof(x,y,zs...), x),
      convert(promote_typeof(x,y,zs...), y),
      convert(Tuple{Vararg{promote_typeof(x,y,zs...)}}, zs)...)
@@ -195,16 +198,16 @@ end
 # happens, and +(promote(x,y)...) is called again, causing a stack
 # overflow.
 function promote_result(::Type{T},::Type{S},::Type{Bottom},::Type{Bottom}) where {T<:Number,S<:Number}
-    @_pure_meta
+    @_inline_meta
     promote_to_supertype(T, S, typejoin(T,S))
 end
 
 # promote numeric types T and S to typejoin(T,S) if T<:S or S<:T
 # for example this makes promote_type(Integer,Real) == Real without
 # promoting arbitrary pairs of numeric types to Number.
-promote_to_supertype(::Type{T}, ::Type{T}, ::Type{T}) where {T<:Number}           = (@_pure_meta; T)
-promote_to_supertype(::Type{T}, ::Type{S}, ::Type{T}) where {T<:Number,S<:Number} = (@_pure_meta; T)
-promote_to_supertype(::Type{T}, ::Type{S}, ::Type{S}) where {T<:Number,S<:Number} = (@_pure_meta; S)
+promote_to_supertype(::Type{T}, ::Type{T}, ::Type{T}) where {T<:Number}           = (@_inline_meta; T)
+promote_to_supertype(::Type{T}, ::Type{S}, ::Type{T}) where {T<:Number,S<:Number} = (@_inline_meta; T)
+promote_to_supertype(::Type{T}, ::Type{S}, ::Type{S}) where {T<:Number,S<:Number} = (@_inline_meta; S)
 promote_to_supertype(::Type{T}, ::Type{S}, ::Type) where {T<:Number,S<:Number} =
     error("no promotion exists for ", T, " and ", S)
 
@@ -304,15 +307,15 @@ minmax(x::Real, y::Real) = minmax(promote(x, y)...)
 # "Promotion" that takes a function into account and tries to preserve
 # non-concrete types. These are meant to be used mainly by elementwise
 # operations, so it is advised against overriding them
-_default_type(T::Type) = (@_pure_meta; T)
+_default_type(T::Type) = (@_inline_meta; T)
 
 if isdefined(Core, :Inference)
     const _return_type = Core.Inference.return_type
 else
     _return_type(f::ANY, t::ANY) = Any
 end
 
-promote_op(::Any...) = (@_pure_meta; Any)
+promote_op(::Any...) = (@_inline_meta; Any)
 function promote_op{S}(f, ::Type{S})
     @_inline_meta
     T = _return_type(f, Tuple{_default_type(S)})

base/sparse/abstractsparse.jl

@@ -20,4 +20,4 @@ issparse(S::LinAlg.UnitLowerTriangular{<:Any,<:AbstractSparseMatrix}) = true
 issparse(S::UpperTriangular{<:Any,<:AbstractSparseMatrix}) = true
 issparse(S::LinAlg.UnitUpperTriangular{<:Any,<:AbstractSparseMatrix}) = true
 
-indtype(S::AbstractSparseArray{<:Any,Ti}) where {Ti} = (Base.@_pure_meta; Ti)
+indtype(S::AbstractSparseArray{<:Any,Ti}) where {Ti} = Ti

base/sparse/sparsevector.jl

@@ -2,7 +2,7 @@
 
 ### Common definitions
 
-import Base: scalarmax, scalarmin, sort, find, findnz, @_pure_meta
+import Base: scalarmax, scalarmin, sort, find, findnz
 import Base.LinAlg: promote_to_array_type, promote_to_arrays_
 
 ### The SparseVector
@@ -962,8 +962,8 @@ function hvcat(rows::Tuple{Vararg{Int}}, X::_SparseConcatGroup...)
 end
 
 # make sure UniformScaling objects are converted to sparse matrices for concatenation
-promote_to_array_type(A::Tuple{Vararg{Union{_SparseConcatGroup,UniformScaling}}}) = (@_pure_meta; SparseMatrixCSC)
-promote_to_array_type(A::Tuple{Vararg{Union{_DenseConcatGroup,UniformScaling}}}) = (@_pure_meta; Matrix)
+promote_to_array_type(A::Tuple{Vararg{Union{_SparseConcatGroup,UniformScaling}}}) = SparseMatrixCSC
+promote_to_array_type(A::Tuple{Vararg{Union{_DenseConcatGroup,UniformScaling}}}) = Matrix
 promote_to_arrays_(n::Int, ::Type{SparseMatrixCSC}, J::UniformScaling) = sparse(J, n, n)
 
 # Concatenations strictly involving un/annotated dense matrices/vectors should yield dense arrays