diff --git a/base/abstractarray.jl b/base/abstractarray.jl
index 9b0883b5a9e9e..2def7116b0a2d 100644
--- a/base/abstractarray.jl
+++ b/base/abstractarray.jl
@@ -1768,10 +1768,10 @@ function mapslices(f, A::AbstractArray, dims::AbstractVector)
 end
 
 # These are needed because map(eltype, As) is not inferrable
-promote_eltype_op(::Any) = (@_pure_meta; Any)
-promote_eltype_op(op, A) = (@_pure_meta; promote_op(op, eltype(A)))
-promote_eltype_op(op, A, B) = (@_pure_meta; promote_op(op, eltype(A), eltype(B)))
-promote_eltype_op(op, A, B, C, D...) = (@_pure_meta; promote_eltype_op(op, eltype(A), promote_eltype_op(op, B, C, D...)))
+promote_eltype_op(::Any) = Any
+promote_eltype_op(op, A) = (@_inline_meta; promote_op(op, eltype(A)))
+promote_eltype_op(op, A, B) = (@_inline_meta; promote_op(op, eltype(A), eltype(B)))
+promote_eltype_op(op, A, B, C, D...) = (@_inline_meta; promote_eltype_op(op, eltype(A), promote_eltype_op(op, B, C, D...)))
 
 ## 1 argument
 
diff --git a/base/broadcast.jl b/base/broadcast.jl
index fa09ac08c46e8..fbeb15b1a3542 100644
--- a/base/broadcast.jl
+++ b/base/broadcast.jl
@@ -3,7 +3,7 @@
 module Broadcast
 
 using Base.Cartesian
-using Base: promote_eltype_op, _default_eltype, linearindices, tail, OneTo, to_shape,
+using Base: @pure, promote_eltype_op, _promote_op, linearindices, tail, OneTo, to_shape,
             _msk_end, unsafe_bitgetindex, bitcache_chunks, bitcache_size, dumpbitcache
 import Base: .+, .-, .*, ./, .\, .//, .==, .<, .!=, .<=, .÷, .%, .<<, .>>, .^
 import Base: broadcast
@@ -257,26 +257,22 @@ end
 @inline broadcast_elwise_op(f, As...) =
     broadcast!(f, similar(Array{promote_eltype_op(f, As...)}, broadcast_indices(As...)), As...)
 
-ftype(f, A) = typeof(f)
-ftype(f, A...) = typeof(a -> f(a...))
-ftype(T::DataType, A) = Type{T}
-ftype(T::DataType, A...) = Type{T}
-ziptype(A) = Tuple{eltype(A)}
-ziptype(A, B) = Iterators.Zip2{Tuple{eltype(A)}, Tuple{eltype(B)}}
-@inline ziptype(A, B, C, D...) = Iterators.Zip{Tuple{eltype(A)}, ziptype(B, C, D...)}
+@pure typestuple(a) = Tuple{eltype(a)}
+@pure typestuple(T::Type) = Tuple{Type{T}}
+@pure typestuple(a, b...) = Tuple{typestuple(a).types..., typestuple(b...).types...}
 
 # broadcast methods that dispatch on the type of the final container
-@inline function broadcast_c(f, ::Type{Array}, As...)
-    T = _default_eltype(Base.Generator{ziptype(As...), ftype(f, As...)})
-    shape = broadcast_indices(As...)
+@inline function broadcast_c(f, ::Type{Array}, A, Bs...)
+    T = _promote_op(f, typestuple(A, Bs...))
+    shape = broadcast_indices(A, Bs...)
     iter = CartesianRange(shape)
     if isleaftype(T)
-        return broadcast_t(f, T, shape, iter, As...)
+        return broadcast_t(f, T, shape, iter, A, Bs...)
     end
     if isempty(iter)
         return similar(Array{T}, shape)
     end
-    return broadcast_t(f, Any, shape, iter, As...)
+    return broadcast_t(f, Any, shape, iter, A, Bs...)
 end
 function broadcast_c(f, ::Type{Tuple}, As...)
     shape = broadcast_indices(As...)
@@ -351,7 +347,7 @@ julia> string.(("one","two","three","four"), ": ", 1:4)
  "four: 4"
 ```
 """
-@inline broadcast(f, As...) = broadcast_c(f, containertype(As...), As...)
+@inline broadcast(f, A, Bs...) = broadcast_c(f, containertype(A, Bs...), A, Bs...)
 
 """
     bitbroadcast(f, As...)
diff --git a/base/promotion.jl b/base/promotion.jl
index 2273a3882b50c..9b639d84c16d3 100644
--- a/base/promotion.jl
+++ b/base/promotion.jl
@@ -222,18 +222,22 @@ minmax(x::Real, y::Real) = minmax(promote(x, y)...)
 # operations, so it is advised against overriding them
 _default_type(T::Type) = (@_pure_meta; T)
 
+if isdefined(Core, :Inference)
+    _promote_op(f::ANY, t::ANY) = Core.Inference.return_type(f, t)
+else
+    _promote_op(f::ANY, t::ANY) = Any
+end
+
 promote_op(::Any...) = (@_pure_meta; Any)
 function promote_op{S}(f, ::Type{S})
     @_inline_meta
-    Z = Tuple{_default_type(S)}
-    T = _default_eltype(Generator{Z, typeof(f)})
+    T = _promote_op(f, Tuple{_default_type(S)})
     isleaftype(S) && return isleaftype(T) ? T : Any
     return typejoin(S, T)
 end
 function promote_op{R,S}(f, ::Type{R}, ::Type{S})
     @_inline_meta
-    Z = Iterators.Zip2{Tuple{_default_type(R)}, Tuple{_default_type(S)}}
-    T = _default_eltype(Generator{Z, typeof(a -> f(a...))})
+    T = _promote_op(f, Tuple{_default_type(R), _default_type(S)})
     isleaftype(R) && isleaftype(S) && return isleaftype(T) ? T : Any
     return typejoin(R, S, T)
 end
diff --git a/test/broadcast.jl b/test/broadcast.jl
index 21cee3fed274a..bd20d69c5a20c 100644
--- a/test/broadcast.jl
+++ b/test/broadcast.jl
@@ -341,14 +341,17 @@ end
 immutable StrangeType18623 end
 StrangeType18623(x) = x
 StrangeType18623(x,y) = (x,y)
-@test @inferred broadcast(StrangeType18623, 1:3) == [1,2,3]
-@test @inferred broadcast(StrangeType18623, 1:3, 4:6) == [(1,4),(2,5),(3,6)]
+@test @inferred(broadcast(StrangeType18623, 1:3)) == [1,2,3]
+@test @inferred(broadcast(StrangeType18623, 1:3, 4:6)) == [(1,4),(2,5),(3,6)]
 
 @test typeof(Int.(Number[1, 2, 3])) === typeof((x->Int(x)).(Number[1, 2, 3]))
 
-@test @inferred broadcast(CartesianIndex, 1:2) == [CartesianIndex(1), CartesianIndex(2)]
-@test @inferred broadcast(CartesianIndex, 1:2, 3:4) == [CartesianIndex(1,3), CartesianIndex(2,4)]
+@test @inferred(broadcast(CartesianIndex, 1:2)) == [CartesianIndex(1), CartesianIndex(2)]
+@test @inferred(broadcast(CartesianIndex, 1:2, 3:4)) == [CartesianIndex(1,3), CartesianIndex(2,4)]
 
 # Issue 18622
-@test @inferred muladd.([1.0], [2.0], [3.0])::Vector{Float64} == [5.0]
-@test @inferred tuple.(1:3, 4:6, 7:9)::Vector{Tuple{Int,Int,Int}} == [(1,4,7), (2,5,8), (3,6,9)]
+@test @inferred(broadcast(muladd, [1.0], [2.0], [3.0])) == [5.0]
+@test @inferred(broadcast(tuple, 1:3, 4:6, 7:9)) == [(1,4,7), (2,5,8), (3,6,9)]
+
+# 19419
+@test @inferred(broadcast(round, Int, [1])) == [1]