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Remove precompilation
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@chriselrod
chriselrod committed Apr 18, 2023
1 parent 07121c7 commit 0195916
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Showing 3 changed files with 21 additions and 32 deletions.
2 changes: 1 addition & 1 deletion Project.toml
View file
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "VectorizedRNG"
uuid = "33b4df10-0173-11e9-2a0c-851a7edac40e"
authors = ["Chris Elrod <elrodc@gmail.com>"]
version = "0.2.23"
version = "0.2.24"

[deps]
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
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14 changes: 0 additions & 14 deletions src/VectorizedRNG.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -123,18 +123,4 @@ function __init__()
__init()
end

@static if VERSION >= v"1.8.0-beta1"
let
while false
end
__init()
x64 = Vector{Float64}(undef, 16)
rand!(local_rng(), x64)
randn!(local_rng(), x64)
x32 = Vector{Float32}(undef, 16)
rand!(local_rng(), x32)
randn!(local_rng(), x32)
end
end

end # module
37 changes: 20 additions & 17 deletions src/api.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -233,14 +233,14 @@ end
) where {K} = vload(ptr, args...)
@inline _vload(x::Number, args::Vararg{Any,K}) where {K} = x

function random_sample_u2!(
@inline function samplevector!(
f::F,
rng::AbstractVRNG{P},
x::AbstractArray{T},
α,
β,
γ,
g::G = identity
g::G
) where {F,P,T,G}
state = getstate(rng, Val{2}(), pick_vector_width(UInt64))
GC.@preserve x begin
Expand Down Expand Up @@ -290,14 +290,14 @@ function random_sample_u2!(
end # GC preserve
x
end
function random_sample_u2!(
@inline function samplevector!(
f::F,
rng::AbstractVRNG{P},
x::AbstractArray{T},
::StaticInt{0},
β,
γ,
g::G = identity
g::G
) where {F,P,T,G}
state = getstate(rng, Val{2}(), pick_vector_width(UInt64))
GC.@preserve x begin
Expand Down Expand Up @@ -353,7 +353,7 @@ function Random.rand!(
β = StaticInt{0}(),
γ = StaticInt{1}()
) where {T<:Union{Float32,Float64},F}
random_sample_u2!(random_uniform, rng, x, α, β, γ, f)
samplevector!(random_uniform, rng, x, α, β, γ, f)
end

function Random.rand!(
Expand All @@ -363,7 +363,7 @@ function Random.rand!(
β = StaticInt{0}(),
γ = StaticInt{1}()
) where {T<:Union{Float32,Float64}}
random_sample_u2!(random_uniform, rng, x, α, β, γ, identity)
samplevector!(random_uniform, rng, x, α, β, γ, identity)
end

function Random.randn!(
Expand All @@ -373,7 +373,7 @@ function Random.randn!(
β = StaticInt{0}(),
γ = StaticInt{1}()
) where {T<:Union{Float32,Float64}}
random_sample_u2!(random_normal, rng, x, α, β, γ)
samplevector!(random_normal, rng, x, α, β, γ, identity)
end
@inline function random_unsigned(
state::AbstractState,
Expand All @@ -383,13 +383,14 @@ end
nextstate(state, Val{N}())
end
function Random.rand!(rng::AbstractVRNG, x::AbstractArray{UInt64})
random_sample_u2!(
samplevector!(
random_unsigned,
rng,
x,
StaticInt{0}(),
StaticInt{0}(),
StaticInt{1}()
StaticInt{1}(),
identity
)
end

Expand All @@ -399,7 +400,7 @@ function Random.rand!(
x::StaticArraysCore.MArray{<:Tuple,T}
) where {T<:Union{Float32,Float64}}
GC.@preserve x begin
random_sample_u2!(random_uniform, rng, PtrArray(x), α, β, γ)
samplevector!(random_uniform, rng, PtrArray(x), α, β, γ, identity)
end
return x
end
Expand All @@ -413,7 +414,7 @@ function Random.rand!(
}
a = MArray{S,UInt64}(undef)
GC.@preserve a begin
random_sample_u2!(random_uniform, rng, PtrArray(a), α, β, γ)
samplevector!(random_uniform, rng, PtrArray(a), α, β, γ, identity)
end
x .= a
end
Expand All @@ -422,7 +423,7 @@ function Random.randn!(
x::StaticArraysCore.MArray{<:Tuple,T}
) where {T<:Union{Float32,Float64}}
GC.@preserve x begin
random_sample_u2!(random_normal, rng, PtrArray(x), α, β, γ)
samplevector!(random_normal, rng, PtrArray(x), α, β, γ, identity)
end
return x
end
Expand All @@ -436,7 +437,7 @@ function Random.randn!(
}
a = MArray{S,UInt64}(undef)
GC.@preserve a begin
random_sample_u2!(random_normal, rng, PtrArray(a), α, β, γ)
samplevector!(random_normal, rng, PtrArray(a), α, β, γ, identity)
end
x .= a
end
Expand All @@ -445,13 +446,14 @@ function Random.rand!(
rng::AbstractVRNG,
x::StaticArraysCore.MArray{<:Tuple,UInt64}
)
random_sample_u2!(
samplevector!(
random_unsigned,
rng,
x,
StaticInt{0}(),
StaticInt{0}(),
StaticInt{1}()
StaticInt{1}(),
identity
)
end
function Random.rand!(
Expand All @@ -460,13 +462,14 @@ function Random.rand!(
) where {S<:Tuple,SA<:StaticArraysCore.StaticArray{S,UInt64}}
a = MArray{S,UInt64}(undef)
GC.@preserve a begin
random_sample_u2!(
samplevector!(
random_unsigned,
rng,
PtrArray(a),
StaticInt{0}(),
StaticInt{0}(),
StaticInt{1}()
StaticInt{1}(),
identity
)
end
x .= a
Expand Down

5 comments on commit 0195916

@chriselrod
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Registration pull request created: JuliaRegistries/General/81842

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.2.24 -m "<description of version>" 0195916663a8f8dbfc9afa87a5155d6aa52380bf
git push origin v0.2.24

@timholy
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Here you reported that you removed this because it caused allocation. I tried restoring it and got

julia> @btime rand!($rng, $x64)
  8.054 ns (0 allocations: 0 bytes)

which doesn't reproduce the problem. I'm just guessing, though. If you have a reproducer I'd be very grateful.

@chriselrod
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@chriselrod chriselrod commented on 0195916 Jul 30, 2023 •
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@timholy, I can't reproduce the allocation at the moment, but I do get an >8x performance regression from precompilation.
Using:

julia> versioninfo()
Julia Version 1.11.0-DEV.193
Commit 4825a0cda8 (2023-07-28 23:57 UTC)
Platform Info:
  OS: Linux (x86_64-redhat-linux)
  CPU: 8 × 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, tigerlake)
  Threads: 11 on 8 virtual cores

with

$ git checkout 07121c744d13765516e56426b1a39c9e26eb1ef5
julia> using VectorizedRNG, Random

julia> drng = Random.default_rng(); lrng = local_rng();

julia> @benchmark rand!($drng, $x64)
BenchmarkTools.Trial: 7651 samples with 651 evaluations.
 Range (min … max):  189.528 ns …  1.022 μs  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     195.982 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   196.846 ns ± 10.616 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

          ▃  ▁ ▇  █▄  ▆                                         
  ▂▂▁▁▇▃▂▅█▆▄█▇█▅▆██▆▇█▆▅▄▆▄▄▃▆▄▃▃▄▃▂▂▂▂▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▃
  190 ns          Histogram: frequency by time          212 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark rand!($lrng, $x64)
BenchmarkTools.Trial: 10000 samples with 232 evaluations.
 Range (min … max):  321.810 ns … 690.384 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     350.211 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   351.910 ns ±  14.740 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

               █▇  ▆▃  ▇▂▁▁█▂▂ ▁▁ ▁▁  ▂▅▂  ▁                     
  ▁▁▁▁▁▁▃▅▂▂▆▅▃██▅▇██▇███████████████████▇▇█▆▄▅▆▄▃▅▅▃▃▂▁▂▂▁▂▂▁▁ ▄
  322 ns           Histogram: frequency by time          386 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.


julia> x64 = Vector{Float64}(undef, 16);

julia> @benchmark rand!($lrng, $x64)
BenchmarkTools.Trial: 10000 samples with 993 evaluations.
 Range (min … max):  36.073 ns … 179.427 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     43.142 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   42.534 ns ±   3.070 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

                                   ▄▅█    ▄                     
  ▂▂▁▂▂▂▃▂▄▃▃█▃▇▃▄▄▃▄▃▃▃▂▃▂▂▂▂▃▃▃▂▂███▅▃███▄▃▅▅▆▃▂▃▃▃▃▂▂▂▂▂▂▂▂ ▃
  36.1 ns         Histogram: frequency by time         47.9 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

Versus the master bracnh

julia> using VectorizedRNG, Random
Precompiling VectorizedRNG
  1 dependency successfully precompiled in 2 seconds. 30 already precompiled.

julia> drng = Random.default_rng(); lrng = local_rng();

julia> x64 = Vector{Float64}(undef, 255);

julia> @benchmark rand!($drng, $x64)
BenchmarkTools.Trial: 7735 samples with 641 evaluations.
 Range (min … max):  192.515 ns … 437.150 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     198.988 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   200.141 ns ±   7.497 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

              █                                                  
  ▂▄▂▆▅▃▄▃▃█▅▄█▇▅▇▄▄▅▅▄█▆▄▄▃▃▂▂▂▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▂
  193 ns           Histogram: frequency by time          221 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark rand!($lrng, $x64)
BenchmarkTools.Trial: 10000 samples with 991 evaluations.
 Range (min … max):  40.011 ns … 162.111 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     40.981 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   41.033 ns ±   1.627 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▇▆        ▆█▃    ▁▁ ▁▅▃                        ▁▁            ▂
  ██▄▃▁▁▁▄█▅████▆▅▇██▇████▆▆▅█▇▇▆▇▇▇▆▅▆█▇▆▆▄▅▃▆▅▅███▇▆▆▆▆▆▅▇▇▇ █
  40 ns         Histogram: log(frequency) by time      45.2 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> x64 = Vector{Float64}(undef, 16);

julia> @benchmark rand!($lrng, $x64)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  4.759 ns … 52.906 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     7.022 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   6.820 ns ±  1.551 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

   ▁▃▄▄▅▄▄▄▄▃▂▂▁▁▁▁▂  █▇▅▅▁▂▂▂▂▃▃▂▂▁                         ▂
  ████████████████████████████████████▆▇▅▆▇▆▄▄▆▄▃▃▄▂▄▂▃▂▄▄▄▄ █
  4.76 ns      Histogram: log(frequency) by time     11.2 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

No allocations, but 357 ns vs 41 ns mean time.

The fact that it does not allocate makes it harder to detect and realize that something is wrong.
Allocations are easier to test than runtime in unit tests. Perhaps looking at the code_llvm would work, assuming we don't get lied to. The current state of affairs is that it's safer to just not precompile, so few of my libraries do.

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I cannot reproduce on Julia 1.9.3:

julia> using VectorizedRNG, Random
[ Info: Precompiling VectorizedRNG [33b4df10-0173-11e9-2a0c-851a7edac40e]

julia> drng = Random.default_rng(); lrng = local_rng();

julia> x64 = Vector{Float64}(undef, 255);

julia> @benchmark rand!($drng, $x64)
BenchmarkTools.Trial: 9698 samples with 937 evaluations.
 Range (min … max):  104.732 ns … 229.638 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     109.067 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   109.176 ns ±   3.211 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

            ▇▅         █                                         
  ▂▂▁▁▂▂▂▂▂▂██▂▂▂▃▃▆▅▄▅█▃▃▂▃▃▄▄▄▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃
  105 ns           Histogram: frequency by time          119 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @benchmark rand!($lrng, $x64)
BenchmarkTools.Trial: 10000 samples with 990 evaluations.
 Range (min … max):  44.895 ns … 72.201 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     46.005 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   45.661 ns ±  0.753 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▁▆▇█▇▅▂                  ▃▆▇█▆▄▁                  ▁▁▂▂▁     ▃
  ███████▇▃▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▆███████▇▆▇▇▇▇▇▆▇█▇▇▆█▇▇▇█████████▇ █
  44.9 ns      Histogram: log(frequency) by time      47.4 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> x64 = Vector{Float64}(undef, 256);

julia> @benchmark rand!($lrng, $x64)
BenchmarkTools.Trial: 10000 samples with 991 evaluations.
 Range (min … max):  41.219 ns … 107.426 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     44.272 ns               ┊ GC (median):    0.00%
 Time  (mean ± σ):   44.761 ns ±   1.031 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

                       ▁          ██▄        ▁█▇▃         ▃▃▁  ▂
  ▆▄▁▁▁▁▁▁▁▁▄▁▃▁▁▃▁▁▁▃▅█▇▄▁▁▁▁▃▁▁▄███▁▃▁▁▃▁▁▁████▆▇███████████ █
  41.2 ns       Histogram: log(frequency) by time      46.5 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> x64 = Vector{Float64}(undef, 16);

julia> @benchmark rand!($lrng, $x64)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  5.288 ns … 61.774 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     5.884 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   5.913 ns ±  0.640 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

                     ▁█    ▆                                  
  ▂▂▃▂▁▂▂▂▁▁▁▁▂▂▃▅▃▂▂██▅▂▂██▅▂▁▂▁▂▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▁▂▂▁▂▁▁▁▁▂▂ ▃
  5.29 ns        Histogram: frequency by time        6.95 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> versioninfo()
Julia Version 1.9.3-DEV.0
Commit 6fc1be04ee* (2023-07-06 14:55 UTC)
Platform Info:
  OS: Linux (x86_64-redhat-linux)
  CPU: 8 × 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, tigerlake)
  Threads: 8 on 8 virtual cores

Note that Base is also nearly 2x faster here.
I've noticed that Julia master is slower than the release version in general, but perhaps that's to be expected for an unreleased version.

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