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make NNlibCUDA an extension #492

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merged 11 commits into from
Jun 14, 2023
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make NNlibCUDA an extension #492

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18726be
make NNlibCUDA an extension
CarloLucibello May 16, 2023
0c20230
cuDNN extension
CarloLucibello Jun 10, 2023
0a8628e
update workflows
CarloLucibello Jun 10, 2023
ff149c2
uncomment when 1.10 is out
CarloLucibello Jun 10, 2023
0c5d8f1
cleanup
CarloLucibello Jun 10, 2023
e665445
cleanup
CarloLucibello Jun 10, 2023
7274a65
test Project.toml
CarloLucibello Jun 11, 2023
60f25f4
cleanup
CarloLucibello Jun 11, 2023
e555e68
rethink extensions
CarloLucibello Jun 11, 2023
53d288f
batchnorm
CarloLucibello Jun 11, 2023
af6c8e1
cleanup
CarloLucibello Jun 11, 2023
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21 changes: 7 additions & 14 deletions .buildkite/pipeline.yml
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@@ -1,30 +1,22 @@
steps:
- label: "GPU julia v1.6"
- label: "CUDA - Julia v1.9"
plugins:
- JuliaCI/julia#v1:
version: "1.6"
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Without this, we have no way to tell if changes to NNlib break NNlibCUDA on Julia <1.9. So either we add something like I mentioned in #495 (comment) or we create a separate Reverse CI step/pipeline for NNlibCUDA.

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I don't understand the issue here. Changes to NNlib won't effect julia < 1.9 users since NNlib is julia >= 1.9 from now on

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For backports, if there is ever gonna be one, we can test locally.

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@ToucheSir ToucheSir Jun 14, 2023 •
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I missed that Project.toml also bumped Julia compat. To be clear then, merging this would mean we're stopping feature development for Julia <1.9 and now maintaining two backport branches (one for NNlib and one for NNlibCUDA)? I recall there being mixed success with backport branches in FluxML before, are there any lessons learned from that so that this doesn't run into the same issues? cc @FluxML/committers

Edit: I misspoke about the two backport branches. Depending on how we want to handle extensions in Flux, this may require three backport branches, right?

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To be clear then, merging this would mean we're stopping feature development for Julia <1.9 and now maintaining two backport branches (one for NNlib and one for NNlibCUDA)?

we are stopping development for julia < 1.9 and we will maintain backport branches in case we feel the need and care enough about backporting something, which I consider an unlikely event. In expectation, the benefits far outweigh the drawbacks.

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1 vote for moving to 1.9 soon!

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Ok, in that case I'll cast my vote for this too. The rest of the PR LGTM.

version: "1.9"
- JuliaCI/julia-test#v1: ~
- JuliaCI/julia-coverage#v1:
codecov: true
dirs:
- src
# commands:
# - julia --project=test -e """
# Pkg.develop(url = \"https://github.com/FluxML/NNlibCUDA.jl\")
# Pkg.instantiate()
# Pkg.build()
# Pkg.status()
# Pkg.test()
# Pkg.test(\"NNlibCUDA\")
# """
- ext
agents:
queue: "juliagpu"
cuda: "*"
env:
NNLIB_TEST_CUDA: true
timeout_in_minutes: 60

- label: "GPU julia v1"
- label: "CUDA - Julia v1"
plugins:
- JuliaCI/julia#v1:
version: "1"
Expand All @@ -33,6 +25,7 @@ steps:
codecov: true
dirs:
- src
- ext
agents:
queue: "juliagpu"
cuda: "*"
Expand All @@ -55,10 +48,10 @@ steps:
if: build.pull_request.labels includes "benchmark"
timeout_in_minutes: 30

- label: "AMDGPU - Julia 1.9"
- label: "AMDGPU - Julia v1.9"
plugins:
- JuliaCI/julia#v1:
version: 1.9-nightly
version: "1.9"
- JuliaCI/julia-test#v1:
- JuliaCI/julia-coverage#v1:
codecov: true
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1 change: 0 additions & 1 deletion .gitignore
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Expand Up @@ -12,7 +12,6 @@ deps.jl
*.log
.vscode/
/Manifest.toml
lib/NNlibCUDA/Manifest.toml
benchmark/Manifest.toml
benchmark/*.json
benchmark/report.md
11 changes: 9 additions & 2 deletions Project.toml
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name = "NNlib"
uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
version = "0.8.20"
version = "0.9.0"

[deps]
Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
Atomix = "a9b6321e-bd34-4604-b9c9-b65b8de01458"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
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I had to make cuDNN a strong dependence because it seems that there is no way to make using CUDA also trigger the loading of cuDNN from the extension. This is not ideal but the alternatives seem to be worse:

  • have the extension triggered by using CUDA, cuDNN
  • keep NNlibCUDA as a separate package

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Is there some other option?

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Making it a strong dep is a non-starter because cuDNN directly depends on CUDA.jl. I would say we go with option 1, but there are a couple variations on it we could also consider:

  1. Make only cuDNN a weak dep and access CUDA.jl through cuDNN.CUDA.
  2. Create separate extensions for CUDA.jl and cuDNN. Then someone can choose to only load the former if they don't need e.g. conv functionality. This doesn't make anything easier so we should probably consider it after an extension is in place.

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I also think we should go with using CUDA, cuDNN. This will also carry over to Flux's usage.
I'm annoyed by portability issues for scripts. Ideally, it should be possible to run the same script on any machine without any edit and using the appropriate device, so something like:

using Flux

gpu_backend = ... # Some hardware detection utility or Preferences.jl magic?

 if gpu_backend == "cuda"
  using CUDA, cuDNN
elseif gpu_backend == "amdgpu"
  using AMDGPU
elseif gpu_backend == "metal"
  using Metal
end

Even better, the whole loading should be conditionally done by flux itself, but I don't think this can be done without hard dependencies.

Anyways, since this is a crucial decision, let's also ask @mcabbott and @darsnack if they are ok with having the CUDA extension here be triggered by using CUDA, cuDNN.

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I really don't like using Flux, CUDA, cuDNN, it seems a huge shame to have to load two packages, one of which is a super-obscure internal thing. I mean I don't even know where it lives, https://www.google.com/search?q=cuDNN.jl leads me only to one abandoned 5 years ago.

It's a huge shame to give up on using Flux, CUDA as the interface. I understand that the default use of new package extensions does not allow us to then load cuDNN. I wonder if we should seriously consider either hacks for now (can Requires or something like it load cuDNN?) or finding out if upstream can be changed e.g. to unify cuDNN into CUDA.

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Some packages might want to expose GPU-accelerated functionality without users having to depend on either CUDA or CUDNN. With Preferences, the user environment would then need to include CUDA (i.e. in the Project.toml) in order to set the CUDNN preference.

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Does JuliaPackaging/Preferences.jl#24 work for package extension dependencies? The example seems to imply that packages can set preferences for their dependencies.

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I'm not sure. In any case, that example also shows how the active project needs to have A as a hard dependency, either in [deps] or in [extras], which is the point I was making above.

That said, although I'm happy to consider alternative ways of loading CUDNN-like functionality, I don't see it happening soon. Without a first-class language feature and using Preferences.jl, it would require users to import CUDA.jl to enable the CUDNN features, which IMO is mostly the same as having them do using cuDNN. And even with a first-class feature where, say, packages could express in their Project.toml which features they request of a package, it doesn't seem clear how that would interact with package extensions (what if a user has CUDA.jl but not CUDA.jl+CUDNN, etc).

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By sheer coincidence, I noticed JuliaPackaging/Preferences.jl#53 was reported today.

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FWIW, I'm currently too busy working on other things to consider reworking the CUDA.jl/cuDNN.jl situation again (especially now that it just stabilized a bit after introducing JLLs), but I'm not opposed to changes. So if anybody would want to explore a different mechanism for shipping CUDA libraries, feel free to open an issue or PR.

GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Expand All @@ -16,19 +17,25 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[weakdeps]
AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

[extensions]
NNlibAMDGPUExt = "AMDGPU"
NNlibCUDAExt = "CUDA"

[compat]
AMDGPU = "0.4.8"
Adapt = "2, 3.2"
Atomix = "0.1"
ChainRulesCore = "1.13"
CUDA = "4"
cuDNN = "1"
GPUArraysCore = "0.1"
KernelAbstractions = "0.9.2"
Requires = "0.5, 1.0"
julia = "1.6"
julia = "1.9"

[extras]
AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -16,4 +16,4 @@ This package provides a library of functions useful for neural networks, such as

For use with automatic differentiation, this package defines gradients using [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl). These will be seen by various packages including [Zygote.jl](https://github.com/FluxML/Zygote.jl).

To use these functions with [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) you will need [NNlibCUDA.jl](https://github.com/FluxML/NNlibCUDA.jl) as well.
GPU support is provided whenever the corresponding package (e.g. [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) or [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl)) is loaded.
3 changes: 1 addition & 2 deletions docs/src/index.md
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For use with automatic differentiation, this package defines gradients using [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl). These will be seen by various packages including [Zygote.jl](https://github.com/FluxML/Zygote.jl).

To use these functions with [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) you will need [NNlibCUDA.jl](https://github.com/FluxML/NNlibCUDA.jl) as well.
For [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl) you will need to load it and NNlib in the same Julia session.
To use these functions with [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) or [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl) you will need to load them and NNlib in the same Julia session.
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2 changes: 1 addition & 1 deletion ext/NNlibCUDA/src/NNlibCUDA.jl → ext/NNlibCUDAExt/NNlibCUDAExt.jl
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module NNlibCUDA
module NNlibCUDAExt

using NNlib
using CUDA, cuDNN
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# CTC loss moved from Flux.jl to NNlib + NNlibCUDA
# CTC loss moved from Flux.jl to NNlib

import NNlib: ctc_loss, ctc_alpha, ∇ctc_loss

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## Activation functions
#
# Some of activation functions have its wrapper function for GPU in NNlibCUDA.jl.
# Some of activation functions have its wrapper function for GPU in NNlibCUDAExt.jl.
# https://github.com/JuliaGPU/CuArrays.jl/issues/614

ACTIVATIONS = [
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# CTC loss moved from Flux.jl to NNlib + NNlibCUDA
# CTC loss moved from Flux.jl to NNlib

## CPU implementation

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### Deprecated while v0.7 was latest

function ∇softmax(Δ, x; dims = 1)
# This 2-arg version recomputes the forward pass, which is slow.
# Removed from use in 0.7, but only prints a warning during 0.8:
Base.depwarn("`∇softmax(Δ, x)` without `y = softmax(x)` argument is deprecated, as this is inefficient, please use `∇softmax_data(dy, y)`", :∇softmax)
∇softmax(Δ, x, softmax(x; dims); dims)
end
∇softmax!(Δ, x; dims = 1) = Δ .= ∇softmax(Δ, x; dims)
∇softmax!(out, Δ, x; dims = 1) = out .= ∇softmax(Δ, x; dims)

function ∇logsoftmax(Δ, x; dims = 1)
Base.depwarn("`∇logsoftmax(Δ, x)` without `y = logsoftmax(x)` argument is deprecated, please use `∇logsoftmax_data(dy, y)`", :∇logsoftmax)
∇logsoftmax(Δ, x, logsoftmax(x; dims); dims)
end
∇logsoftmax!(Δ, x; dims = 1) = Δ .= ∇logsoftmax(Δ, x; dims)
∇logsoftmax!(out, Δ, x; dims = 1) = out .= ∇logsoftmax(Δ, x; dims)


### Deprecated while v0.8 was latest

export ∇softmax,
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Expand Up @@ -158,5 +158,5 @@ _rng_from_array(::AbstractArray) = Random.default_rng()
@non_differentiable _rng_from_array(::Any)

# This exists because `rand!(default_rng(), CUDA.rand(3))` ignores the RNG,
# and Flux would prefer an error. NNlibCUDA will overload it to produce that.
# and Flux would prefer an error. NNlibCUDAExt will overload it to produce that.
_rng_compat_array(::AbstractRNG, ::AbstractArray) = nothing
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Expand Up @@ -380,15 +380,6 @@ function ∇upsample_linear_kernel!(
return dx
end

# Compatibility layer for old versions of NNlibCUDA.
# TODO Can be removed from NNlib 0.9.
upsample_linear_wcn!(y, x) = upsample_linear_kernel!(y, x)
upsample_bilinear_whcn!(y, x) = upsample_linear_kernel!(y, x)
upsample_trilinear_whdcn!(y, x) = upsample_linear_kernel!(y, x)
∇upsample_linear_wcn!(y, x) = ∇upsample_linear_kernel!(y, x)
∇upsample_bilinear_whcn!(y, x) = ∇upsample_linear_kernel!(y, x)
∇upsample_trilinear_whdcn!(y, x) = ∇upsample_linear_kernel!(y, x)

# Linear (CPU): parallelization along channel x batch dimensions.

@kernel function _upsample_linear_kernel!(::CPU, y::T, x::T, rwidth, align::Val{A}) where {
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@@ -1,13 +1,11 @@
[deps]
Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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Expand Up @@ -14,8 +14,8 @@ end
end
end

# Broadcasting over complex CuArray works without NNlibCUDA, this test checks that
# NNlibCUDA does not cause such operations to take a fast path which does not support
# Broadcasting over complex CuArray works without NNlibCUDAExt, this test checks that
# NNlibCUDAExt does not cause such operations to take a fast path which does not support
# complex numbers (e.g. cuDNN)
@testset "complex" begin
f(x) = tanh.(x)
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27 changes: 27 additions & 0 deletions test/ext_cuda/batchnorm.jl
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@testset "Batchnorm" begin
v = CUDA.rand(Float32, 2)
m = CUDA.rand(Float32, 2, 5)

@testset for training in (true, false), track_stats in (true, false)
kws = (training=training, track_stats=track_stats)

# Normal
batchnorm(v, v, m, v, v, 1.0; kws...)
∇batchnorm(v, v, m, m, v, v, 1.0; kws...)

# No affine
batchnorm(nothing, nothing, m, v, v, 1.0; kws...)
∇batchnorm(nothing, nothing, m, m, v, v, 1.0; kws...)

# No tracking
batchnorm(v, v, m, nothing, nothing, 1.0; kws...)
∇batchnorm(v, v, m, m, nothing, nothing, 1.0; kws...)

# Both or neither tracked or affine params must be set
for (α, β) in ((v, nothing), (nothing, v))
@test_throws MethodError batchnorm(α, β, m, v, v, 1.0; kws...)
@test_throws MethodError ∇batchnorm(α, β, m, m, v, v, 1.0; kws...)
@test_throws ArgumentError batchnorm(v, v, m, α, β, 1.0; kws...)
end
end
end
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