Xoshiro: allow any non-negative integer as a seed, via SHA2_256 (Juli…

…aLang#41558)

doc/src/devdocs/subarrays.md

@@ -19,14 +19,14 @@ julia> A = rand(2,3,4);
 
 julia> S1 = view(A, :, 1, 2:3)
 2×2 view(::Array{Float64, 3}, :, 1, 2:3) with eltype Float64:
- 0.342284 0.831961
- 0.237287 0.435938
+ 0.839622 0.711389
+ 0.967143 0.103929
 
 julia> S2 = view(A, 1, :, 2:3)
 3×2 view(::Array{Float64, 3}, 1, :, 2:3) with eltype Float64:
- 0.342284 0.831961
- 0.988944 0.927795
- 0.178426 0.404876
+ 0.839622 0.711389
+ 0.789764 0.806704
+ 0.566704 0.962715
 ```
 ```@meta
 DocTestSetup = nothing

doc/src/manual/performance-tips.md

@@ -77,12 +77,12 @@ julia> function sum_global()
  end;
 
 julia> @time sum_global()
- 0.010414 seconds (9.07 k allocations: 373.448 KiB, 98.40% compilation time)
-493.6199223951192
+ 0.011539 seconds (9.08 k allocations: 373.386 KiB, 98.69% compilation time)
+523.0007221951678
 
 julia> @time sum_global()
- 0.000108 seconds (3.49 k allocations: 70.156 KiB)
-493.6199223951192
+ 0.000091 seconds (3.49 k allocations: 70.156 KiB)
+523.0007221951678
 ```
 
 On the first call (`@time sum_global()`) the function gets compiled. (If you've not yet used [`@time`](@ref)
@@ -113,12 +113,12 @@ julia> function sum_arg(x)
  end;
 
 julia> @time sum_arg(x)
- 0.007971 seconds (3.96 k allocations: 200.171 KiB, 99.83% compilation time)
-493.6199223951192
+ 0.007551 seconds (3.98 k allocations: 200.548 KiB, 99.77% compilation time)
+523.0007221951678
 
 julia> @time sum_arg(x)
- 0.000003 seconds (1 allocation: 16 bytes)
-493.6199223951192
+ 0.000006 seconds (1 allocation: 16 bytes)
+523.0007221951678
 ```
 
 The 1 allocation seen is from running the `@time` macro itself in global scope. If we instead run
@@ -128,8 +128,8 @@ the timing in a function, we can see that indeed no allocations are performed:
 julia> time_sum(x) = @time sum_arg(x);
 
 julia> time_sum(x)
- 0.000001 seconds
-493.6199223951192
+ 0.000002 seconds
+523.0007221951678
 ```
 
 In some situations, your function may need to allocate memory as part of its operation, and this

stdlib/LinearAlgebra/test/dense.jl

@@ -22,10 +22,10 @@ Random.seed!(1234323)
  @testset "for $elty" for elty in (Float32, Float64, ComplexF32, ComplexF64)
  ainit = convert(Matrix{elty}, ainit)
  for a in (copy(ainit), view(ainit, 1:n, 1:n))
- @test cond(a,1) ≈ 122.15725126320953 atol=0.5
- @test cond(a,2) ≈ 78.44837047684149 atol=0.5
- @test cond(a,Inf) ≈ 174.10761543202744 atol=0.4
- @test cond(a[:,1:5]) ≈ 6.7492840150789135 atol=0.01
+ @test cond(a,1) ≈ 198.3324294531168 atol=0.5
+ @test cond(a,2) ≈ 85.93920079319506 atol=0.5
+ @test cond(a,Inf) ≈ 149.7523084803039 atol=0.4
+ @test cond(a[:,1:5]) ≈ 8.319279144493297 atol=0.01
  @test_throws ArgumentError cond(a,3)
  end
  end

stdlib/LinearAlgebra/test/eigen.jl

@@ -163,7 +163,7 @@ end
 end
 
 @testset "eigen of an Adjoint" begin
- Random.seed!(1)
+ Random.seed!(4)
  A = randn(3,3)
  @test eigvals(A') == eigvals(copy(A'))
  @test eigen(A') == eigen(copy(A'))

stdlib/Random/Project.toml

@@ -3,6 +3,7 @@ uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [deps]
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+SHA = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

stdlib/Random/docs/src/index.md

@@ -151,22 +151,22 @@ Scalar and array methods for `Die` now work as expected:
 
 ```jldoctest Die; setup = :(Random.seed!(1))
 julia> rand(Die)
-Die(7)
+Die(5)
 
 julia> rand(MersenneTwister(0), Die)
 Die(11)
 
 julia> rand(Die, 3)
 3-element Vector{Die}:
- Die(13)
- Die(8)
- Die(20)
+ Die(9)
+ Die(15)
+ Die(14)
 
 julia> a = Vector{Die}(undef, 3); rand!(a)
 3-element Vector{Die}:
- Die(4)
- Die(14)
- Die(10)
+ Die(19)
+ Die(7)
+ Die(17)
 ```
 
 #### A simple sampler without pre-computed data
@@ -183,9 +183,9 @@ julia> rand(Die(4))
 
 julia> rand(Die(4), 3)
 3-element Vector{Any}:
- 3
  2
- 4
+ 3
+ 3
 ```
 
 Given a collection type `S`, it's currently assumed that if `rand(::S)` is defined, an object of type `eltype(S)` will be produced. In the last example, a `Vector{Any}` is produced; the reason is that `eltype(Die) == Any`. The remedy is to define `Base.eltype(::Type{Die}) = Int`.

stdlib/Random/src/RNGs.jl

@@ -384,7 +384,7 @@ end
 
 seed!(rng::_GLOBAL_RNG, ::Nothing) = seed!(rng) # to resolve ambiguity
 
-seed!(seed::Union{Nothing,Integer,Vector{UInt32},Vector{UInt64},NTuple{4,UInt64}}=nothing) =
+seed!(seed::Union{Nothing,Integer,Vector{UInt32},Vector{UInt64}}=nothing) =
  seed!(GLOBAL_RNG, seed)
 
 rng_native_52(::_GLOBAL_RNG) = rng_native_52(default_rng())

stdlib/Random/src/Random.jl

@@ -13,6 +13,7 @@ include("DSFMT.jl")
 using .DSFMT
 using Base.GMP.MPZ
 using Base.GMP: Limb
+import SHA
 
 using Base: BitInteger, BitInteger_types, BitUnsigned, require_one_based_indexing
 

stdlib/Random/src/Xoshiro.jl

@@ -117,65 +117,21 @@ end
 
 # Shared implementation between Xoshiro and TaskLocalRNG -- seeding
 
-function seed!(x::Union{TaskLocalRNG,Xoshiro})
+function seed!(rng::Union{TaskLocalRNG,Xoshiro})
  # as we get good randomness from RandomDevice, we can skip hashing
- parent = RandomDevice()
- # Constants have nothing up their sleeve, see task.c
- # 0x02011ce34bce797f == hash(UInt(1))|0x01
- # 0x5a94851fb48a6e05 == hash(UInt(2))|0x01
- # 0x3688cf5d48899fa7 == hash(UInt(3))|0x01
- # 0x867b4bb4c42e5661 == hash(UInt(4))|0x01
- setstate!(x,
- 0x02011ce34bce797f * rand(parent, UInt64),
- 0x5a94851fb48a6e05 * rand(parent, UInt64),
- 0x3688cf5d48899fa7 * rand(parent, UInt64),
- 0x867b4bb4c42e5661 * rand(parent, UInt64))
+ rd = RandomDevice()
+ setstate!(rng, rand(rd, UInt64), rand(rd, UInt64), rand(rd, UInt64), rand(rd, UInt64))
 end
 
-function seed!(rng::Union{TaskLocalRNG,Xoshiro}, seed::NTuple{4,UInt64})
- # TODO: Consider a less ad-hoc construction
- # We can afford burning a handful of cycles here, and we don't want any
- # surprises with respect to bad seeds / bad interactions.
-
- s0 = s = Base.hash_64_64(seed[1])
- s1 = s += Base.hash_64_64(seed[2])
- s2 = s += Base.hash_64_64(seed[3])
- s3 = s += Base.hash_64_64(seed[4])
-
+function seed!(rng::Union{TaskLocalRNG,Xoshiro}, seed::Union{Vector{UInt32}, Vector{UInt64}})
+ c = SHA.SHA2_256_CTX()
+ SHA.update!(c, reinterpret(UInt8, seed))
+ s0, s1, s2, s3 = reinterpret(UInt64, SHA.digest!(c))
  setstate!(rng, s0, s1, s2, s3)
-
- rand(rng, UInt64)
- rand(rng, UInt64)
- rand(rng, UInt64)
- rand(rng, UInt64)
- rng
 end
 
-function seed!(rng::Union{TaskLocalRNG, Xoshiro}, seed::UInt128)
- seed0 = seed % UInt64
- seed1 = (seed>>>64) % UInt64
- seed!(rng, (seed0, seed1, zero(UInt64), zero(UInt64)))
-end
-seed!(rng::Union{TaskLocalRNG, Xoshiro}, seed::Integer) = seed!(rng, UInt128(seed))
-
-function seed!(rng::Union{TaskLocalRNG, Xoshiro}, seed::AbstractVector{UInt64})
- if length(seed) > 4
- throw(ArgumentError("seed should have no more than 256 bits"))
- end
- seed0 = length(seed)>0 ? seed[1] : UInt64(0)
- seed1 = length(seed)>1 ? seed[2] : UInt64(0)
- seed2 = length(seed)>2 ? seed[3] : UInt64(0)
- seed3 = length(seed)>3 ? seed[4] : UInt64(0)
- seed!(rng, (seed0, seed1, seed2, seed3))
-end
+seed!(rng::Union{TaskLocalRNG, Xoshiro}, seed::Integer) = seed!(rng, make_seed(seed))
 
-function seed!(rng::Union{TaskLocalRNG, Xoshiro}, seed::AbstractVector{UInt32})
- if iseven(length(seed))
- seed!(rng, reinterpret(UInt64, seed))
- else
- seed!(rng, UInt64[reinterpret(UInt64, @view(seed[begin:end-1])); seed[end] % UInt64])
- end
-end
 
 @inline function rand(rng::Union{TaskLocalRNG, Xoshiro}, ::SamplerType{UInt128})
  first = rand(rng, UInt64)

stdlib/Random/src/misc.jl

@@ -53,13 +53,13 @@ number generator, see [Random Numbers](@ref).
 # Examples
 ```jldoctest
 julia> Random.seed!(3); randstring()
-"h8BzxSoS"
+"Lxz5hUwn"
 
 julia> randstring(MersenneTwister(3), 'a':'z', 6)
 "ocucay"
 
 julia> randstring("ACGT")
-"CTTACTGC"
+"TGCTCCTC"
 ```
 
 !!! note

stdlib/Random/test/runtests.jl

@@ -688,7 +688,7 @@ end
 @testset "$RNG(seed) & Random.seed!(m::$RNG, seed) produce the same stream" for RNG=(MersenneTwister,Xoshiro)
  seeds = Any[0, 1, 2, 10000, 10001, rand(UInt32, 8), rand(UInt128, 3)...]
  if RNG == Xoshiro
- push!(seeds, rand(UInt64, rand(1:4)), Tuple(rand(UInt64, 4)))
+ push!(seeds, rand(UInt64, rand(1:4)))
  end
  for seed=seeds
  m = RNG(seed)
@@ -699,7 +699,7 @@ end
 end
 
 @testset "Random.seed!(seed) sets Random.GLOBAL_SEED" begin
- seeds = Any[0, rand(UInt128), rand(UInt64, 4), Tuple(rand(UInt64, 4))]
+ seeds = Any[0, rand(UInt128), rand(UInt64, 4)]
 
  for seed=seeds
  Random.seed!(seed)

stdlib/SparseArrays/src/sparsematrix.jl

@@ -1642,12 +1642,13 @@ argument specifies a random number generator, see [Random Numbers](@ref).
 ```jldoctest; setup = :(using Random; Random.seed!(1234))
 julia> sprand(Bool, 2, 2, 0.5)
 2×2 SparseMatrixCSC{Bool, Int64} with 2 stored entries:
- 1 ⋅
- ⋅ 1
+ 1 1
+ ⋅ ⋅
 
 julia> sprand(Float64, 3, 0.75)
-3-element SparseVector{Float64, Int64} with 1 stored entry:
- [3] = 0.787459
+3-element SparseVector{Float64, Int64} with 2 stored entries:
+ [1] = 0.795547
+ [2] = 0.49425
 ```
 """
 function sprand(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat, rfn::Function, ::Type{T}=eltype(rfn(r, 1))) where T
@@ -1688,9 +1689,9 @@ argument specifies a random number generator, see [Random Numbers](@ref).
 # Examples
 ```jldoctest; setup = :(using Random; Random.seed!(0))
 julia> sprandn(2, 2, 0.75)
-2×2 SparseMatrixCSC{Float64, Int64} with 1 stored entry:
-  ⋅ ⋅
- ⋅ 1.32078
+2×2 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
+ -1.20577  ⋅
+ 0.311817 -0.234641
 ```
 """
 sprandn(r::AbstractRNG, m::Integer, n::Integer, density::AbstractFloat) =

stdlib/SparseArrays/test/sparse.jl

@@ -1750,7 +1750,7 @@ end
  A = guardseed(1234321) do
  triu(sprand(10, 10, 0.2))
  end
- @test getcolptr(SparseArrays.droptol!(A, 0.01)) == [1, 1, 3, 4, 5, 6, 7, 11, 13, 15, 18]
+ @test getcolptr(SparseArrays.droptol!(A, 0.01)) == [1, 1, 1, 1, 2, 2, 2, 4, 4, 5, 5]
  @test isequal(SparseArrays.droptol!(sparse([1], [1], [1]), 1), SparseMatrixCSC(1, 1, Int[1, 1], Int[], Int[]))
 end