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backport of changes in #17057 that don't require new worlds #18679

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Merged
vtjnash merged 9 commits into from
Oct 4, 2016
Merged

backport of changes in #17057 that don't require new worlds #18679

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469031e
fix a slottypes bug exposed by making singleton objects more inferable
vtjnash Oct 3, 2016
d5baf6a
make inference more inferrable (aka faster)
vtjnash Sep 22, 2016
8e39436
make singleton objects more inferable and remove invalid @generated f…
vtjnash Sep 26, 2016
d8ea82e
avoid runtime @eval in @parallel test
vtjnash Sep 26, 2016
785adca
fix many generated functions definition ordering
vtjnash Sep 26, 2016
39d3699
fixup rebase error in "split SourceInfo out of LambdaInfo"
vtjnash Sep 26, 2016
f64fa84
throw actual error message for broken generated functions
vtjnash Sep 23, 2016
f299848
correct and update docs for defining generated functions
vtjnash Sep 26, 2016
d790275
lower prologue attributes to the kwarg function definition sans keywords
vtjnash Sep 28, 2016
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25 changes: 0 additions & 25 deletions base/abstractarray.jl
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Original file line number Diff line number Diff line change
@@ -1,32 +1,7 @@
# This file is a part of Julia. License is MIT: http://julialang.org/license

## Type aliases for convenience ##

typealias AbstractVector{T} AbstractArray{T,1}
typealias AbstractMatrix{T} AbstractArray{T,2}
typealias AbstractVecOrMat{T} Union{AbstractVector{T}, AbstractMatrix{T}}
typealias RangeIndex Union{Int, Range{Int}, AbstractUnitRange{Int}, Colon}
typealias DimOrInd Union{Integer, AbstractUnitRange}
typealias IntOrInd Union{Int, AbstractUnitRange}
typealias DimsOrInds{N} NTuple{N,DimOrInd}
typealias NeedsShaping Union{Tuple{Integer,Vararg{Integer}}, Tuple{OneTo,Vararg{OneTo}}}

macro _inline_pure_meta()
Expr(:meta, :inline, :pure)
end

## Basic functions ##

vect() = Array{Any,1}(0)
vect{T}(X::T...) = T[ X[i] for i=1:length(X) ]

function vect(X...)
T = promote_typeof(X...)
#T[ X[i] for i=1:length(X) ]
# TODO: this is currently much faster. should figure out why. not clear.
copy!(Array{T,1}(length(X)), X)
end

"""
size(A::AbstractArray, [dim...])
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21 changes: 21 additions & 0 deletions base/array.jl
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Expand Up @@ -2,6 +2,17 @@

## array.jl: Dense arrays

## Type aliases for convenience ##

typealias AbstractVector{T} AbstractArray{T,1}
typealias AbstractMatrix{T} AbstractArray{T,2}
typealias AbstractVecOrMat{T} Union{AbstractVector{T}, AbstractMatrix{T}}
typealias RangeIndex Union{Int, Range{Int}, AbstractUnitRange{Int}, Colon}
typealias DimOrInd Union{Integer, AbstractUnitRange}
typealias IntOrInd Union{Int, AbstractUnitRange}
typealias DimsOrInds{N} NTuple{N,DimOrInd}
typealias NeedsShaping Union{Tuple{Integer,Vararg{Integer}}, Tuple{OneTo,Vararg{OneTo}}}

typealias Vector{T} Array{T,1}
typealias Matrix{T} Array{T,2}
typealias VecOrMat{T} Union{Vector{T}, Matrix{T}}
Expand All @@ -14,6 +25,16 @@ typealias DenseVecOrMat{T} Union{DenseVector{T}, DenseMatrix{T}}

import Core: arraysize, arrayset, arrayref

vect() = Array{Any,1}(0)
vect{T}(X::T...) = T[ X[i] for i=1:length(X) ]

function vect(X...)
T = promote_typeof(X...)
#T[ X[i] for i=1:length(X) ]
# TODO: this is currently much faster. should figure out why. not clear.
return copy!(Array{T,1}(length(X)), X)
end

size(a::Array, d) = arraysize(a, d)
size(a::Vector) = (arraysize(a,1),)
size(a::Matrix) = (arraysize(a,1), arraysize(a,2))
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2 changes: 1 addition & 1 deletion base/coreimg.jl
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Expand Up @@ -45,8 +45,8 @@ Symbol(a::Array{UInt8,1}) =
ccall(:jl_symbol_n, Ref{Symbol}, (Ptr{UInt8}, Int32), a, length(a))

# core array operations
include("abstractarray.jl")
include("array.jl")
include("abstractarray.jl")

#TODO: eliminate Dict from inference
include("hashing.jl")
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