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Create an internal API which deploys off of JACCPreferences.backend #86

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Create an internal API which deploys off of JACCPreferences.backend #86

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785d0af
Modify all CPU code
kmp5VT May 3, 2024
ca0d9de
More updates, GPU and array portion
kmp5VT May 3, 2024
50bd061
convert Array to array
kmp5VT May 3, 2024
694c2c8
Add comment
kmp5VT May 3, 2024
a8f9158
Allow JACC to precompile convert preference to symbol and use that to…
kmp5VT May 8, 2024
c1ffbbc
explicitly overwrite JACC.arraytype
kmp5VT May 8, 2024
a31b1d9
Add error about backend package not loaded.
kmp5VT May 8, 2024
17522a8
remove comment
kmp5VT May 9, 2024
70410a9
update AMDGPU tests
kmp5VT May 9, 2024
75a2040
update oneapi tests
kmp5VT May 9, 2024
5180159
Merge remote-tracking branch 'origin/main' into kmp5/refactor/internals
kmp5VT May 9, 2024
89b2301
Don't precompile
kmp5VT May 9, 2024
599aa7d
Move JACC_BACKEND_TYPE to JACCPreferences
kmp5VT May 9, 2024
fea569a
Merge branch 'main' into kmp5/refactor/internals
kmp5VT May 13, 2024
bb4fb35
Move JACCArrayType to its own file and bring into preferences.jl
kmp5VT May 13, 2024
5632023
Fix JACC call in JACC.BLAS
kmp5VT May 13, 2024
be2cec1
Small updates to threads test
kmp5VT May 13, 2024
04c9b65
Merge branch 'kmp5/refactor/internals' of https://github.com/kmp5VT/J…
kmp5VT May 13, 2024
d497ea8
Make sure eltypes are consistent in cuda tests
kmp5VT May 13, 2024
2f95204
Add back JACC BLAS, works fine on AMDGPU
kmp5VT May 13, 2024
28380a0
Merge branch 'main' into kmp5/refactor/internals
kmp5VT May 16, 2024
60e6387
Small updates to experimental `shared` function
kmp5VT May 16, 2024
09f6a33
`test_threads.jl` now can be run with any JACC backend
kmp5VT May 16, 2024
00f2ca8
Updates to tests since test_threads supports different backends
kmp5VT May 17, 2024
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21 changes: 10 additions & 11 deletions ext/JACCAMDGPU/JACCAMDGPU.jl
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Original file line number Diff line number Diff line change
@@ -1,19 +1,17 @@
module JACCAMDGPU

using JACC, AMDGPU

include("array.jl")

function JACC.parallel_for(N::I, f::F, x...) where {I <: Integer, F <: Function}
using JACC: JACCArrayType
function JACC.parallel_for(::JACCArrayType{<:ROCArray}, N::Integer, f::Function, x...)
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We are trying to keep the API as simple as possible for domain scientists. Please see #62 for a similar discussion.

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I think I am confused. The outward facing API is exactly the same as the current implementation its just these two functions

function parallel_for(N, f::Function, x...)

function parallel_reduce(N, f::Function, x...)

Internally JACC will create a struct based on the JACC.JACCPreference.backend variable and will only allow users to utilize a single Array backend type just like the current implementation. The only real outward facing change for the current users is JACC.Array -> JACC.array as to not export and overwrite Base.Array which could cause issues in other libraries.

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@williamfgc williamfgc May 9, 2024 •
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This PR is breaking the current API. What I am trying to understand is the value added for this approach, we are adding internal boilerplate, but the end result in similar to the current implementation unless I am missing something. As for pre-compilation, we intentionally want to prevent compiling on non-supported platforms as we had a number of reported issues in the past on our systems. Multiple dispatch is a JIT "runtime concept" we are trying to avoid at all with weakdependencies.

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@williamfgc williamfgc May 9, 2024 •
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See for example my comment here v0.0.3 wouldn't even compile when JACC.jl is consumed from an application and would just hang. Adding more applications help reveal a lot of these type of issues.

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to not export and overwrite Base.Array which could cause issues in other libraries.

We are not overwriting Base.Array , JACC.Array is just an alias to existing Array types. Also, it's not clear what issues this could cause unless JACC.Array is misused outside its current scope. We can always implement a JACC.ToHost(JACC.Array)::Base.Array function to guarantee a host array.

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@kmp5VT kmp5VT May 9, 2024 •
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Can you please provide an example where this implementation breaks the current API outside of migrating Array to an array function?

I have found some issues with the current main branch

julia> using JACC
julia> JACC.JACCPreferences.backend
"cuda"
julia> JACC.Array
Core.Array

If you don't have the correct backend loaded then JACC will still run with "threads" because JACC.Array = Base.Array. This would be unexpected behavior. The system I have written here forces the component matching JACC.JACCPreferences.backend to be loaded for the user facing API to work.

julia> using JACC
julia> begin
@show JACC.JACCPreferences.backend
    function seq_axpy(N, alpha, x, y)
        Threads.@threads for i in 1:N
            @inbounds x[i] += alpha * y[i]
        end
    end

    function axpy(i, alpha, x, y)
        if i <= length(x)
            @inbounds x[i] += alpha * y[i]
        end
    end

    N = 10
    # Generate random vectors x and y of length N for the interval [0, 100]
    x = round.(rand(Float32, N) * 100)
    y = round.(rand(Float32, N) * 100)
    alpha = 2.5

    x_host_JACC = Array(x)
    y_host_JACC = Array(y)
    JACC.parallel_for(N, axpy, alpha, x_host_JACC, y_host_JACC)
end
JACC.JACCPreferences.backend = "cuda"

ERROR: The backend cuda is either not recognized or the associated package is not loaded.
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] arraytype(::Val{:cuda})
   @ JACC ~/.julia/dev/JACC.jl/src/helper.jl:15
 [3] JACC_BACKEND_TYPE()
   @ JACC ~/.julia/dev/JACC.jl/src/JACC.jl:14
 [4] parallel_for(::Int64, ::Function, ::Float64, ::Vector{Float32}, ::Vararg{Vector{Float32}})
   @ JACC ~/.julia/dev/JACC.jl/src/JACC.jl:17
 [5] top-level scope
   @ ~/.julia/dev/JACC.jl/test/tests_threads.jl:44

Currently the alias to array makes a global variable called Array and exports it which effectively overwrites the Base.Array . This can easily cause an issue outside of JACC

julia> using JACC
julia> begin 
       a = Array{Float32}(undef, 2,3)
       fill!(a, 10)
       a .+= 1
       end
WARNING: both JACC and Base export "Array"; uses of it in module Main must be qualified
ERROR: UndefVarError: `Array` not defined
Stacktrace:
 [1] top-level scope
   @ REPL[2]:2

The system here proposed does not need to alias Array because the alias is effectively stored as a compiled parameter inside the module.

Another issue is that if a different backend introduced after loading JACC, the definition of JACC.Array changes

julia> using JACC
using[ Info: Precompiling JACC [0979c8fe-16a4-4796-9b82-89a9f10403ea]
[ Info: Skipping precompilation since __precompile__(false). Importing JACC [0979c8fe-16a4-4796-9b82-89a9f10403ea].
julia> using CUDA
JACC.Precompiling JACCCUDA
  ? JACC → JACCCUDA
[ Info: Precompiling JACCCUDA [2fb45ac4-0993-536e-a71a-0b5526d52098]
┌ Warning: Module JACC with build ID ffffffff-ffff-ffff-000a-3724e6101b14 is missing from the cache.
│ This may mean JACC [0979c8fe-16a4-4796-9b82-89a9f10403ea] does not support precompilation but is imported by a module that does.
└ @ Base loading.jl:1948
[ Info: Skipping precompilation since __precompile__(false). Importing JACCCUDA [2fb45ac4-0993-536e-a71a-0b5526d52098].
julia> JACC.JACCPreferences.backend
"threads"
julia> JACC.Array
CuArray
julia> using AMDGPU
┌ Warning: Device libraries are unavailable, device intrinsics will be disabled.
└ @ AMDGPU ~/.julia/packages/AMDGPU/gtxsf/src/AMDGPU.jl:186
julia> JACC.Array
ROCArray

This is not possible with the implementation proposed because the forward facing API effectively chooses the backend based solely on the JACCPreferences.backend variable.

I understand that you don't want to mix backends and that is specifically why I designed the code to internally construct the JACCArrayType based on the Symbol(JACCPreferences.backend) variable. To enforce this we should only document the two functions in JACC.jl and suggest using the JACC.JACCPreferences.set_backend variable to change the backend arraytype.

In regards to the JIT and multiple dispatch, I am still learning Julia so I could be wrong but I am not sure that is a concern based on the design here. The function JACC_BACKEND_TYPE() is compiler infer- able and defined at compile time

julia> @code_warntype JACC.JACC_BACKEND_TYPE()
MethodInstance for JACC.JACC_BACKEND_TYPE()
  from JACC_BACKEND_TYPE() @ JACC ~/.julia/dev/JACC.jl/src/JACC.jl:13
Arguments
  #self#::Core.Const(JACC.JACC_BACKEND_TYPE)
Body::JACC.JACCArrayType{Array}
1 ─ %1 = JACC.JACCArrayType::Core.Const(JACC.JACCArrayType)
│   %2 = JACC.JACCPreferences.backend::Core.Const("threads")
│   %3 = JACC.Symbol(%2)::Core.Const(:threads)
│   %4 = JACC.Val(%3)::Core.Const(Val{:threads}())
│   %5 = JACC.arraytype(%4)::Core.Const(Array)
│   %6 = Core.apply_type(%1, %5)::Core.Const(JACC.JACCArrayType{Array})
│   %7 = (%6)()::Core.Const(JACC.JACCArrayType{Array}())
└──      return %7
julia> @code_typed JACC.JACC_BACKEND_TYPE()
CodeInfo(
1 ─     return $(QuoteNode(JACC.JACCArrayType{Array}()))
) => JACC.JACCArrayType{Array}

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Can you please provide an example where this implementation breaks the current API outside of migrating Array to an array function?

I think this is self-explanatory.

Currently the alias to array makes a global variable called Array and exports it which effectively overwrites the Base.Array . This can easily cause an issue outside of JACC

using X is certainly not recommended, but import X due to name clashing, I think this is different from overriding Base.Array.

This is not possible with the implementation proposed because the forward facing API effectively chooses the backend based solely on the JACCPreferences.backend variable.

In regards to the JIT and multiple dispatch, I am still learning Julia so I could be wrong but I am not sure that is a concern based on the design here. The function JACC_BACKEND_TYPE() is compiler infer- able and defined at compile time

Packages still need to be downloaded and precompiled. That's where we saw the reported issues on our systems. Hence why we are being very conservative as explained in our last call. Even more with relatively new Julia version, features and rapidly evolving stack for AMD and Intel GPUs.

I just think the motivation and problems tackled for our systems by the current API and implementation are very different from those in this PR.

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@williamfgc I do not understand your systems as the code patterns here work on my clusters without issue and all of the unittests in the module pass. I would love to understand how this PR breaks on your systems if you could run the code in this PR and give me examples that would be appreciated. I specifically tailored the design this PR from our conversations on Zoom. Further, I connected with @pedrovalerolara to collaborate with you on code design with the potential of integrating JACC into the ITensors. I do not have a specific use cases for your code and have not used any of your software previously.

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That would be a good starting point for collaboration. Feel free to integrate JACC with ITensors and understand the trade-offs and the motivation of the current design before changing all internals. As discussed in the call, we target DOE HPC systems, which are not simple to deploy and use, especially with new stuff like the Julia ecosystem.

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Alternative idea: can we do struct Array{T,N} end and then have Array{T,N}(...) = JACC.arraytype(){T,N}(...)? This would preserve the idea that JACC.Array can become another array type, without messing with modifying module globals at runtime.

numThreads = 512
threads = min(N, numThreads)
blocks = ceil(Int, N / threads)
@roc groupsize=threads gridsize=blocks _parallel_for_amdgpu(f, x...)
AMDGPU.synchronize()
end

function JACC.parallel_for(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_for(::JACCArrayType{<:ROCArray},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
numThreads = 16
Mthreads = min(M, numThreads)
Nthreads = min(N, numThreads)
Expand All @@ -24,8 +22,8 @@ function JACC.parallel_for(
AMDGPU.synchronize()
end

function JACC.parallel_reduce(
N::I, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_reduce(::JACCArrayType{<:ROCArray},
N::Integer, f::Function, x...)
numThreads = 512
threads = min(N, numThreads)
blocks = ceil(Int, N / threads)
Expand All @@ -40,8 +38,8 @@ function JACC.parallel_reduce(
return rret
end

function JACC.parallel_reduce(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_reduce(::JACCArrayType{<:ROCArray},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
numThreads = 16
Mthreads = min(M, numThreads)
Nthreads = min(N, numThreads)
Expand Down Expand Up @@ -309,7 +307,8 @@ function reduce_kernel_amdgpu_MN((M, N), red, ret)
end

function __init__()
const JACC.Array = AMDGPU.ROCArray{T, N} where {T, N}
# const JACC.Array = AMDGPU.ROCArray{T, N} where {T, N}
const JACC.JAT = JACCArrayType{ROCArray}()
end

end # module JACCAMDGPU
8 changes: 0 additions & 8 deletions ext/JACCAMDGPU/array.jl
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22 changes: 10 additions & 12 deletions ext/JACCCUDA/JACCCUDA.jl
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Original file line number Diff line number Diff line change
@@ -1,11 +1,8 @@
module JACCCUDA

using JACC, CUDA

# overloaded array functions
include("array.jl")

function JACC.parallel_for(N::I, f::F, x...) where {I <: Integer, F <: Function}
using JACC: JACCArrayType
function JACC.parallel_for(::JACCArrayType{<:CuArray}, N::Integer, f::Function, x...)
parallel_args = (N, f, x...)
parallel_kargs = cudaconvert.(parallel_args)
parallel_tt = Tuple{Core.Typeof.(parallel_kargs)...}
Expand All @@ -16,8 +13,8 @@ function JACC.parallel_for(N::I, f::F, x...) where {I <: Integer, F <: Function}
parallel_kernel(parallel_kargs...; threads = threads, blocks = blocks)
end

function JACC.parallel_for(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_for(::JACCArrayType{<:CuArray},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
numThreads = 16
Mthreads = min(M, numThreads)
Nthreads = min(N, numThreads)
Expand All @@ -27,8 +24,8 @@ function JACC.parallel_for(
f, x...)
end

function JACC.parallel_reduce(
N::I, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_reduce(::JACCArrayType{<:CuArray},
N::Integer, f::Function, x...)
numThreads = 512
threads = min(N, numThreads)
blocks = ceil(Int, N / threads)
Expand All @@ -41,8 +38,8 @@ function JACC.parallel_reduce(
return rret
end

function JACC.parallel_reduce(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_reduce(::JACCArrayType{<:CuArray},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
numThreads = 16
Mthreads = min(M, numThreads)
Nthreads = min(N, numThreads)
Expand Down Expand Up @@ -313,7 +310,8 @@ function reduce_kernel_cuda_MN((M, N), red, ret)
end

function __init__()
const JACC.Array = CUDA.CuArray{T, N} where {T, N}
# const JACC.Array = CUDA.CuArray{T, N} where {T, N}
const JACC.JAT = JACCArrayType{CuArray}()
end

end # module JACCCUDA
8 changes: 0 additions & 8 deletions ext/JACCCUDA/array.jl
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19 changes: 10 additions & 9 deletions ext/JACCONEAPI/JACCONEAPI.jl
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Expand Up @@ -2,17 +2,17 @@
module JACCONEAPI

using JACC, oneAPI

function JACC.parallel_for(N::I, f::F, x...) where {I <: Integer, F <: Function}
using JACC: JACCArrayType
function JACC.parallel_for(::JACCArrayType{<:oneArray}, N::Integer, f::Function, x...)
#maxPossibleItems = oneAPI.oneL0.compute_properties(device().maxTotalGroupSize)
maxPossibleItems = 256
items = min(N, maxPossibleItems)
groups = ceil(Int, N / items)
oneAPI.@sync @oneapi items=items groups=groups _parallel_for_oneapi(f, x...)
end

function JACC.parallel_for(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_for(::JACCArrayType{<:oneArray},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
maxPossibleItems = 16
Mitems = min(M, maxPossibleItems)
Nitems = min(N, maxPossibleItems)
Expand All @@ -22,8 +22,8 @@ function JACC.parallel_for(
f, x...)
end

function JACC.parallel_reduce(
N::I, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_reduce(::JACCArrayType{<:oneArray},
N::Integer, f::Function, x...)
numItems = 256
items = min(N, numItems)
groups = ceil(Int, N / items)
Expand All @@ -35,8 +35,8 @@ function JACC.parallel_reduce(
return rret
end

function JACC.parallel_reduce(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
function JACC.parallel_reduce(::JACCArrayType{<:oneArray},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
numItems = 16
Mitems = min(M, numItems)
Nitems = min(N, numItems)
Expand Down Expand Up @@ -301,7 +301,8 @@ function reduce_kernel_oneapi_MN((M, N), red, ret)
end

function __init__()
const JACC.Array = oneAPI.oneArray{T, N} where {T, N}
# const JACC.Array = oneAPI.oneArray{T, N} where {T, N}
const JACC.JAT = JACCArrayType{oneArray}()
end

end # module JACCONEAPI
49 changes: 7 additions & 42 deletions src/JACC.jl
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Expand Up @@ -8,55 +8,20 @@ include("helper.jl")
# overloaded array functions
include("array.jl")

export Array, @atomic
export @atomic
export parallel_for

global Array

function parallel_for(N::I, f::F, x...) where {I <: Integer, F <: Function}
@maybe_threaded for i in 1:N
f(i, x...)
end
end

function parallel_for(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
@maybe_threaded for j in 1:N
for i in 1:M
f(i, j, x...)
end
end
end

function parallel_reduce(N::I, f::F, x...) where {I <: Integer, F <: Function}
tmp = zeros(Threads.nthreads())
ret = zeros(1)
@maybe_threaded for i in 1:N
tmp[Threads.threadid()] = tmp[Threads.threadid()] .+ f(i, x...)
end
for i in 1:Threads.nthreads()
ret = ret .+ tmp[i]
end
return ret
function parallel_for(N, f::Function, x...)
return parallel_for(JACC.JAT, N, f, x...)
end

function parallel_reduce(
(M, N)::Tuple{I, I}, f::F, x...) where {I <: Integer, F <: Function}
tmp = zeros(Threads.nthreads())
ret = zeros(1)
@maybe_threaded for j in 1:N
for i in 1:M
tmp[Threads.threadid()] = tmp[Threads.threadid()] .+ f(i, j, x...)
end
end
for i in 1:Threads.nthreads()
ret = ret .+ tmp[i]
end
return ret
function parallel_reduce(N, f::Function, x...)
return parallel_reduce(JACC.JAT, N, f, x...)
end

function __init__()
const JACC.Array = Base.Array{T, N} where {T, N}
# const JACC.Array = Base.Array{T, N} where {T, N}
const JACC.JAT = JACCArrayType{Array}()
end

end # module JACC
4 changes: 4 additions & 0 deletions src/JACCPreferences.jl
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Expand Up @@ -12,6 +12,10 @@ function set_backend(new_backend::String)

# Set it in our runtime values, as well as saving it to disk
@set_preferences!("backend"=>new_backend_lc)
## Technically you don't need to restart Julia to make the backend take effect.
## One just needs to rerun the `__init()__` function. Also if you don't load the
## backend that matches the `backend` variable Array = Base.Array which could cause
## issues in ones code.
@info("New backend set; restart your Julia session for this change to take effect!")
end

Expand Down
48 changes: 46 additions & 2 deletions src/array.jl
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@@ -1,8 +1,52 @@

function zeros(T, dims...)
return Base.zeros(T, dims...)
return fill!(similar(arraytype(){T}, dims...), zero(T))
end

function ones(T, dims...)
return Base.ones(T, dims...)
return fill!(similar(arraytype(){T}, dims...), one(T))
end

array(T::AbstractArray) = arraytype()(T)

function parallel_for(::JACCArrayType{<:Array}, N::Integer, f::Function, x...)
@maybe_threaded for i in 1:N
f(i, x...)
end
end

function parallel_for(::JACCArrayType{<:Array},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
@maybe_threaded for j in 1:N
for i in 1:M
f(i, j, x...)
end
end
end

function parallel_reduce(::JACCArrayType{<:Array}, N::Integer, f::Function, x...)
tmp = Base.zeros(Threads.nthreads())
ret = Base.zeros(1)
@maybe_threaded for i in 1:N
tmp[Threads.threadid()] = tmp[Threads.threadid()] .+ f(i, x...)
end
for i in 1:Threads.nthreads()
ret = ret .+ tmp[i]
end
return ret
end

function parallel_reduce(::JACCArrayType{<:Array},
(M, N)::Tuple{Integer, Integer}, f::Function, x...)
tmp = Base.zeros(Threads.nthreads())
ret = Base.zeros(1)
@maybe_threaded for j in 1:N
for i in 1:M
tmp[Threads.threadid()] = tmp[Threads.threadid()] .+ f(i, j, x...)
end
end
for i in 1:Threads.nthreads()
ret = ret .+ tmp[i]
end
return ret
end
6 changes: 6 additions & 0 deletions src/helper.jl
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Expand Up @@ -6,3 +6,9 @@ macro maybe_threaded(ex)
return esc(:(Threads.@threads :static $ex))
end
end

struct JACCArrayType{T}
end

arraytype() = arraytype(JACC.JAT)
arraytype(::JACCArrayType{T}) where {T} = T
12 changes: 6 additions & 6 deletions test/tests_cuda.jl
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Expand Up @@ -15,7 +15,7 @@ end
dims = (N)
a = round.(rand(Float32, dims) * 100)

a_device = JACC.Array(a)
a_device = JACC.array(a)
JACC.parallel_for(N, f, a_device)

a_expected = a .+ 5.0
Expand All @@ -39,8 +39,8 @@ end
y = round.(rand(Float32, N) * 100)
alpha = 2.5

x_device = JACC.Array(x)
y_device = JACC.Array(y)
x_device = JACC.array(x)
y_device = JACC.array(y)
JACC.parallel_for(N, axpy, alpha, x_device, y_device)

x_expected = x
Expand All @@ -59,9 +59,9 @@ end
# Generate random vectors x and y of length N for the interval [0, 100]
alpha = 2.5

x = JACC.Array(round.(rand(Float32, N) * 100))
y = JACC.Array(round.(rand(Float32, N) * 100))
counter = JACC.Array{Int32}([0])
x = JACC.array(round.(rand(Float32, N) * 100))
y = JACC.array(round.(rand(Float32, N) * 100))
counter = JACC.array(Int32[0])
JACC.parallel_for(N, axpy_counter!, alpha, x, y, counter)

@test Array(counter)[1] == N
Expand Down