fix definition of ntuple with a Val argument to not expect inference to be non-terminating

this same issue applied to several other similar functions

similarly, need to avoid nesting the use of functional code (like map)
too deeply inside the array code to avoid the appearance of indeterminate recursion in inference

Author: vtjnash

base/abstractarray.jl

@@ -1160,30 +1160,32 @@ cat_similar(A::AbstractArray, T, shape) = similar(A, T, shape)
 
 cat_shape(dims, shape::Tuple) = shape
 @inline cat_shape(dims, shape::Tuple, nshape::Tuple, shapes::Tuple...) =
-    cat_shape(dims, _cshp(dims, (), shape, nshape), shapes...)
-
-_cshp(::Tuple{}, out, ::Tuple{}, ::Tuple{}) = out
-_cshp(::Tuple{}, out, ::Tuple{}, nshape) = (out..., nshape...)
-_cshp(dims, out, ::Tuple{}, ::Tuple{}) = (out..., map(b -> 1, dims)...)
-@inline _cshp(dims, out, shape, ::Tuple{}) =
-    _cshp(tail(dims), (out..., shape[1] + dims[1]), tail(shape), ())
-@inline _cshp(dims, out, ::Tuple{}, nshape) =
-    _cshp(tail(dims), (out..., nshape[1]), (), tail(nshape))
-@inline function _cshp(::Tuple{}, out, shape, ::Tuple{})
-    _cs(length(out) + 1, false, shape[1], 1)
-    _cshp((), (out..., 1), tail(shape), ())
-end
-@inline function _cshp(::Tuple{}, out, shape, nshape)
-    next = _cs(length(out) + 1, false, shape[1], nshape[1])
-    _cshp((), (out..., next), tail(shape), tail(nshape))
-end
-@inline function _cshp(dims, out, shape, nshape)
-    next = _cs(length(out) + 1, dims[1], shape[1], nshape[1])
-    _cshp(tail(dims), (out..., next), tail(shape), tail(nshape))
-end
-
-_cs(d, concat, a, b) = concat ? (a + b) : (a == b ? a : throw(DimensionMismatch(string(
-    "mismatch in dimension ", d, " (expected ", a, " got ", b, ")"))))
+    cat_shape(dims, _cshp(1, dims, shape, nshape), shapes...)
+
+_cshp(ndim::Int, ::Tuple{}, ::Tuple{}, ::Tuple{}) = ()
+_cshp(ndim::Int, ::Tuple{}, ::Tuple{}, nshape) = nshape
+_cshp(ndim::Int, dims, ::Tuple{}, ::Tuple{}) = ntuple(b -> 1, Val{length(dims)})
+@inline _cshp(ndim::Int, dims, shape, ::Tuple{}) =
+    (shape[1] + dims[1], _cshp(ndim + 1, tail(dims), tail(shape), ())...)
+@inline _cshp(ndim::Int, dims, ::Tuple{}, nshape) =
+    (nshape[1], _cshp(ndim + 1, tail(dims), (), tail(nshape))...)
+@inline function _cshp(ndim::Int, ::Tuple{}, shape, ::Tuple{})
+    _cs(ndim, shape[1], 1)
+    (1, _cshp(ndim + 1, (), tail(shape), ())...)
+end
+@inline function _cshp(ndim::Int, ::Tuple{}, shape, nshape)
+    next = _cs(ndim, shape[1], nshape[1])
+    (next, _cshp(ndim + 1, (), tail(shape), tail(nshape))...)
+end
+@inline function _cshp(ndim::Int, dims, shape, nshape)
+	a = shape[1]
+	b = nshape[1]
+    next = dims[1] ? a + b : _cs(ndim, a, b)
+    (next, _cshp(ndim + 1, tail(dims), tail(shape), tail(nshape))...)
+end
+
+_cs(d, a, b) = (a == b ? a : throw(DimensionMismatch(
+    "mismatch in dimension $d (expected $a got $b)")))
 
 dims2cat{n}(::Type{Val{n}}) = ntuple(i -> (i == n), Val{n})
 dims2cat(dims) = ntuple(i -> (i in dims), maximum(dims))

base/array.jl

@@ -73,12 +73,7 @@ end
 size(a::Array, d) = arraysize(a, d)
 size(a::Vector) = (arraysize(a,1),)
 size(a::Matrix) = (arraysize(a,1), arraysize(a,2))
-size(a::Array) = (@_inline_meta; _size((), a))
-_size(out::NTuple{N}, A::Array{_,N}) where {_,N} = out
-function _size(out::NTuple{M}, A::Array{_,N}) where _ where M where N
-    @_inline_meta
-    _size((out..., size(A,M+1)), A)
-end
+size(a::Array{<:Any,N}) where {N} = (@_inline_meta; ntuple(M -> size(a, M), Val{N}))
 
 asize_from(a::Array, n) = n > ndims(a) ? () : (arraysize(a,n), asize_from(a, n+1)...)
 

base/broadcast.jl

@@ -45,23 +45,22 @@ promote_containertype(::Type{T}, ::Type{T}) where {T} = T
 ## Calculate the broadcast indices of the arguments, or error if incompatible
 # array inputs
 broadcast_indices() = ()
-broadcast_indices(A) = broadcast_indices(containertype(A), A)
-broadcast_indices(::ScalarType, A) = ()
-broadcast_indices(::Type{Tuple}, A) = (OneTo(length(A)),)
-broadcast_indices(::Type{Array}, A::Ref) = ()
-broadcast_indices(::Type{Array}, A) = indices(A)
-@inline broadcast_indices(A, B...) = broadcast_shape((), broadcast_indices(A), map(broadcast_indices, B)...)
+broadcast_indices(A) = _broadcast_indices(containertype(A), A)
+@inline broadcast_indices(A, B...) = broadcast_shape(broadcast_indices(A), broadcast_indices(B...))
+_broadcast_indices(::Type, A) = ()
+_broadcast_indices(::Type{Tuple}, A) = (OneTo(length(A)),)
+_broadcast_indices(::Type{Array}, A::Ref) = ()
+_broadcast_indices(::Type{Array}, A) = indices(A)
 
 # shape (i.e., tuple-of-indices) inputs
 broadcast_shape(shape::Tuple) = shape
-@inline broadcast_shape(shape::Tuple, shape1::Tuple, shapes::Tuple...) = broadcast_shape(_bcs((), shape, shape1), shapes...)
+@inline broadcast_shape(shape::Tuple, shape1::Tuple, shapes::Tuple...) = broadcast_shape(_bcs(shape, shape1), shapes...)
 # _bcs consolidates two shapes into a single output shape
-_bcs(out, ::Tuple{}, ::Tuple{}) = out
-@inline _bcs(out, ::Tuple{}, newshape) = _bcs((out..., newshape[1]), (), tail(newshape))
-@inline _bcs(out, shape, ::Tuple{}) = _bcs((out..., shape[1]), tail(shape), ())
-@inline function _bcs(out, shape, newshape)
-    newout = _bcs1(shape[1], newshape[1])
-    _bcs((out..., newout), tail(shape), tail(newshape))
+_bcs(::Tuple{}, ::Tuple{}) = ()
+@inline _bcs(::Tuple{}, newshape::Tuple) = (newshape[1], _bcs((), tail(newshape))...)
+@inline _bcs(shape::Tuple, ::Tuple{}) = (shape[1], _bcs(tail(shape), ())...)
+@inline function _bcs(shape::Tuple, newshape::Tuple)
+    return (_bcs1(shape[1], newshape[1]), _bcs(tail(shape), tail(newshape))...)
 end
 # _bcs1 handles the logic for a single dimension
 _bcs1(a::Integer, b::Integer) = a == 1 ? b : (b == 1 ? a : (a == b ? a : throw(DimensionMismatch("arrays could not be broadcast to a common size"))))

base/multidimensional.jl

@@ -138,9 +138,10 @@ module IteratorsMD
     eachindex(::IndexCartesian, A::AbstractArray) = CartesianRange(indices(A))
 
     @inline eachindex(::IndexCartesian, A::AbstractArray, B::AbstractArray...) =
-        CartesianRange(maxsize((), A, B...))
-    maxsize(sz) = sz
-    @inline maxsize(sz, A, B...) = maxsize(maxt(sz, size(A)), B...)
+        CartesianRange(maxsize(A, B...))
+    maxsize() = ()
+    @inline maxsize(A) = size(A)
+    @inline maxsize(A, B...) = maxt(size(A), maxsize(B...))
     @inline maxt(a::Tuple{}, b::Tuple{}) = ()
     @inline maxt(a::Tuple{}, b::Tuple)   = b
     @inline maxt(a::Tuple,   b::Tuple{}) = a