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* add CUDACUDNN extension * why test is complaining about cuDNN not a dependency? * try require cuDNN in Flux * news and cleanup * require Main * relax test GroupedConvTranspose gpu test
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,9 @@ | ||
| using Flux, CUDA | ||
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| BN = BatchNorm(3) |> gpu; | ||
| x = randn(2, 2, 3, 4) |> gpu; | ||
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| NNlib.batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; | ||
| alpha=1, beta=0, eps=BN.ϵ, | ||
| training=Flux._isactive(BN, x)) | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,49 @@ | ||
| module FluxCUDAExt | ||
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| using Flux | ||
| import Flux: _cuda | ||
| using Flux: FluxCPUAdaptor, FluxCUDAAdaptor, fmap | ||
| using CUDA | ||
| using NNlib | ||
| using Zygote | ||
| using ChainRulesCore | ||
| using Random | ||
| using Adapt | ||
| import Adapt: adapt_storage | ||
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| const USE_CUDA = Ref{Union{Nothing, Bool}}(nothing) | ||
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| function check_use_cuda() | ||
| if !isnothing(USE_CUDA[]) | ||
| return | ||
| end | ||
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| USE_CUDA[] = CUDA.functional() | ||
| if !USE_CUDA[] | ||
| @info """ | ||
| The CUDA function is being called but CUDA.jl is not functional. | ||
| Defaulting back to the CPU. (No action is required if you want to run on the CPU). | ||
| """ maxlog=1 | ||
| end | ||
| return | ||
| end | ||
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| ChainRulesCore.@non_differentiable check_use_cuda() | ||
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| include("functor.jl") | ||
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| function __init__() | ||
| Flux.CUDA_LOADED[] = true | ||
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| try | ||
| Base.require(Main, :cuDNN) | ||
| catch | ||
| @warn """Package cuDNN not found in current path. | ||
| - Run `import Pkg; Pkg.add(\"cuDNN\")` to install the cuDNN package, then restart julia. | ||
| - If cuDNN is not installed, some Flux functionalities will not be available when running on the GPU. | ||
| """ | ||
| end | ||
| end | ||
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| end |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,32 @@ | ||
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| adapt_storage(to::FluxCUDAAdaptor, x) = CUDA.cu(x) | ||
| adapt_storage(to::FluxCUDAAdaptor, x::Zygote.FillArrays.AbstractFill) = CUDA.cu(collect(x)) | ||
| adapt_storage(to::FluxCUDAAdaptor, x::Random.TaskLocalRNG) = CUDA.default_rng() | ||
| adapt_storage(to::FluxCUDAAdaptor, x::CUDA.RNG) = x | ||
| adapt_storage(to::FluxCUDAAdaptor, x::AbstractRNG) = | ||
| error("Cannot map RNG of type $(typeof(x)) to GPU. GPU execution only supports Random.default_rng().") | ||
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| # TODO: figure out the correct design for OneElement | ||
| adapt_storage(to::FluxCUDAAdaptor, x::Zygote.OneElement) = CUDA.cu(collect(x)) | ||
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| adapt_storage(to::FluxCPUAdaptor, x::T) where T <: CUDA.CUSPARSE.CUDA.CUSPARSE.AbstractCuSparseMatrix = adapt(Array, x) | ||
| adapt_storage(to::FluxCPUAdaptor, x::CUDA.RNG) = Random.default_rng() | ||
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| function ChainRulesCore.rrule(::typeof(Adapt.adapt_storage), to::FluxCPUAdaptor, x::CUDA.AbstractGPUArray) | ||
| adapt_storage(to, x), dx -> (NoTangent(), NoTangent(), adapt_storage(FluxCUDAAdaptor(), unthunk(dx))) | ||
| end | ||
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| ChainRulesCore.rrule(::typeof(adapt), a::FluxCPUAdaptor, x::AnyCuArray) = | ||
| adapt(a, x), Δ -> (NoTangent(), NoTangent(), adapt(FluxCUDAAdaptor(), unthunk(Δ))) | ||
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| ChainRulesCore.rrule(::typeof(adapt), a::FluxCUDAAdaptor, x::AnyCuArray) = | ||
| adapt(a, x), Δ -> (NoTangent(), NoTangent(), Δ) | ||
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| ChainRulesCore.rrule(::typeof(adapt), a::FluxCUDAAdaptor, x::AbstractArray) = | ||
| adapt(a, x), Δ -> (NoTangent(), NoTangent(), adapt(FluxCPUAdaptor(), unthunk(Δ))) | ||
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| function _cuda(x) | ||
| check_use_cuda() | ||
| USE_CUDA[] || return x | ||
| fmap(x -> Adapt.adapt(FluxCUDAAdaptor(), x), x; exclude=Flux._isleaf) | ||
| end |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1 @@ | ||
| rng_from_array(::CuArray) = CUDA.default_rng() |
34 changes: 25 additions & 9 deletions
34
src/cuda/cudnn.jl → ext/FluxCUDAcuDNNExt/FluxCUDAcuDNNExt.jl
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,21 +1,37 @@ | ||
| import NNlibCUDA: batchnorm, ∇batchnorm | ||
| module FluxCUDAcuDNNExt | ||
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| using Flux | ||
| using CUDA, cuDNN | ||
| using NNlib | ||
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| const USE_CUDNN = Ref{Union{Nothing, Bool}}(nothing) | ||
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| function check_use_cudnn() | ||
| if !isnothing(USE_CUDNN[]) | ||
| return | ||
| end | ||
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| USE_CUDNN[] = cuDNN.has_cudnn() | ||
| if !USE_CUDNN[] | ||
| @warn """ | ||
| cuDNN.jl didn't found libcudnn, some Flux functionality will not be available. | ||
| """ maxlog=1 | ||
| end | ||
| return | ||
| end | ||
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| function (BN::Flux.BatchNorm)(x::Union{CuArray{T,2},CuArray{T,4},CuArray{T,5}}, | ||
| cache=nothing) where T<:Union{Float32, Float64} | ||
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| @assert BN.affine "BatchNorm: only affine=true supported on gpu" | ||
| @assert BN.track_stats "BatchNorm: only track_stats=true supported on gpu" | ||
| @assert length(BN.β) == size(x, ndims(x)-1) "BatchNorm: input has wrong number of channels" | ||
| return BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; | ||
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| return BN.λ.(NNlib.batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; | ||
| cache=cache, alpha=1, beta=0, eps=BN.ϵ, | ||
| training=Flux._isactive(BN, x))) | ||
| end | ||
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| function ChainRulesCore.rrule(::typeof(batchnorm), g, b, x, running_mean, running_var, momentum; kw...) | ||
| y = batchnorm(g, b, x, running_mean, running_var, momentum; kw...) | ||
| function batchnorm_pullback(Δ) | ||
| grad = ∇batchnorm(g, b, x, unthunk(Δ), running_mean, running_var, momentum; kw...) | ||
| (NoTangent(), grad..., NoTangent(), NoTangent(), NoTangent()) | ||
| end | ||
| y, batchnorm_pullback | ||
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| end |
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