Merge #874
874: Move CUDNN wrappers to CuArrays r=MikeInnes a=MikeInnes Co-authored-by: Tim Besard <tim.besard@gmail.com> Co-authored-by: Mike Innes <mike.j.innes@gmail.com>
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@ -105,10 +105,12 @@ uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
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version = "4.0.0"
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[[CuArrays]]
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deps = ["AbstractFFTs", "Adapt", "CUDAapi", "CUDAdrv", "CUDAnative", "GPUArrays", "LinearAlgebra", "MacroTools", "NNlib", "Printf", "Random", "Requires", "SparseArrays", "TimerOutputs"]
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git-tree-sha1 = "46b48742a84bb839e74215b7e468a4a1c6ba30f9"
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deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "LinearAlgebra", "MacroTools", "NNlib", "Printf", "Random", "Requires", "SparseArrays", "TimerOutputs"]
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git-tree-sha1 = "45683305171430978c17f496969dc9b6d3094a51"
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repo-rev = "master"
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repo-url = "https://github.com/JuliaGPU/CuArrays.jl.git"
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uuid = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
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version = "1.2.1"
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version = "1.3.0"
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[[DataAPI]]
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git-tree-sha1 = "8903f0219d3472543fc4b2f5ebaf675a07f817c0"
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@ -11,7 +11,6 @@ Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
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CuArrays = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
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DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
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Juno = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
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NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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@ -3,6 +3,7 @@ module CUDA
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using ..CuArrays
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if CuArrays.libcudnn !== nothing # TODO: use CuArrays.has_cudnn()
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using CuArrays: CUDNN
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include("curnn.jl")
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include("cudnn.jl")
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else
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@ -1,199 +1,5 @@
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using CuArrays: libcudnn
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using CuArrays.CUDNN: @check, handle, cudnnStatus_t, cudnnTensorDescriptor_t,
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cudnnBatchNormMode_t, cudnnHandle_t, cudnnDataType, TensorDesc, FilterDesc
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import CuArrays.CUDAdrv: CuPtr, CU_NULL
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using LinearAlgebra
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mutable struct DropoutDesc
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ptr::Ptr{Nothing}
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states::CuVector{UInt8}
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end
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Base.unsafe_convert(::Type{Ptr{Nothing}}, dd::DropoutDesc) = dd.ptr
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function DropoutDesc(ρ::Real; seed::Integer=0)
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d = [C_NULL]
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s = Csize_t[0]
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@check ccall((:cudnnCreateDropoutDescriptor,libcudnn), cudnnStatus_t, (Ptr{Ptr{Nothing}},), d)
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@check ccall((:cudnnDropoutGetStatesSize,libcudnn),cudnnStatus_t,(Ptr{Nothing},Ptr{Csize_t}),handle(),s)
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states = CuArray{UInt8}(undef, s[]) # TODO: can we drop this when ρ=0?
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desc = DropoutDesc(d[], states)
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@check ccall((:cudnnSetDropoutDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},Ptr{Nothing},Cfloat,CuPtr{Nothing},Csize_t,Culonglong),
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desc,handle(),ρ,states,length(states),seed)
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finalizer(desc) do x
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@check ccall((:cudnnDestroyDropoutDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x)
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end
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return desc
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end
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const BATCHNORM_SPATIAL = 1
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const BATCHNORM_ACTIVATION = 0
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const BATCHNORM_MIN_EPS = 1e-5
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@inline _wsize(y) = (map(_ -> 1, size(y)[1:end-2])..., size(y)[end-1], 1)
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@inline _reddims(y) = (collect(1:ndims(y)-2)..., ndims(y))
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mutable struct BNCache
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mean
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ivar
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end
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BNCache() = BNCache(nothing, nothing)
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# NOTE: CuDNN supports only 4D and 5D Tensors for BatchNorm Operations
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# so reshape a 2D Tensor into 4D
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batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T, 2},
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running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
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cache = nothing, alpha = T(1), beta = T(0),
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eps = T(1e-5), training = true) where T<:Union{Float32, Float64} =
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dropdims(batchnorm(g, b, reshape(x, 1, 1, size(x, 1), size(x, 2)), running_mean, running_var, momentum,
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cache = cache, alpha = alpha, beta = beta, eps = eps, training = training), dims = (1, 2))
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function batchnorm(g::CuArray{T}, b::CuArray{T}, x::Union{CuArray{T, 4},CuArray{T,5}},
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running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
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cache = nothing, alpha = T(1), beta = T(0),
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eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
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y = similar(x)
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cudnnBNForward!(y, g, b, x, running_mean, running_var, momentum, cache = cache,
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alpha = alpha, beta = beta, eps = eps, training = training)
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y
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end
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function cudnnBNForward!(y::CuArray{T}, g::CuArray{T}, b::CuArray{T}, x::CuArray{T},
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running_mean::CuArray{T}, running_var::CuArray{T},
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momentum; cache = nothing,
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alpha = T(1), beta = T(0),
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eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
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dims = _wsize(x)
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if eps < BATCHNORM_MIN_EPS
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# warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS)
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eps = BATCHNORM_MIN_EPS
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end
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xd = TensorDesc(x)
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yd = TensorDesc(y)
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gd = TensorDesc(T, dims)
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if training
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if cache !== nothing
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mean = zeros(CuArray{T}, dims...)
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ivar = ones(CuArray{T}, dims...)
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else
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mean = CU_NULL
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ivar = CU_NULL
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end
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@check ccall((:cudnnBatchNormalizationForwardTraining, libcudnn), cudnnStatus_t,
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(cudnnHandle_t,cudnnBatchNormMode_t,
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Ptr{T}, Ptr{T},
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Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T}, CuPtr{T},
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Cdouble, CuPtr{T}, CuPtr{T},
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Cdouble, CuPtr{T}, CuPtr{T}),
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handle(), BATCHNORM_SPATIAL,
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Ref(T(alpha)), Ref(T(beta)),
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xd, x,
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yd, y,
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gd, g, b,
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momentum, running_mean, running_var,
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eps, mean, ivar)
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if cache !== nothing
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cache.mean = mean
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cache.ivar = ivar
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end
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else
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@check ccall((:cudnnBatchNormalizationForwardInference, libcudnn), cudnnStatus_t,
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(Ptr{cudnnHandle_t},cudnnBatchNormMode_t,
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Ptr{T}, Ptr{T},
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Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T}, CuPtr{T},
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CuPtr{T}, CuPtr{T},
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Cdouble),
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handle(), BATCHNORM_SPATIAL,
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Ref(T(alpha)), Ref(T(beta)),
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xd, x,
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yd, y,
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gd, g, b,
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running_mean, running_var,
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eps)
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end
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end
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function ∇batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T, 2}, dy::CuArray{T, 2},
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running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
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cache = nothing, eps = T(1e-5), alpha = T(1),
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beta = T(0), training = true) where T<:Union{Float32, Float64}
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dg, db, dx = ∇batchnorm(g, b, reshape(x, 1, 1, size(x, 1), size(x, 2)), reshape(dy, 1, 1, size(dy, 1),
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size(dy, 2)), running_mean, running_var, momentum, cache = cache, eps = eps,
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alpha = alpha, beta = beta, training = training)
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(dg, db, dropdims(dx, dims = (1, 2)))
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end
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function ∇batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T}, dy::CuArray{T},
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running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
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cache = nothing, eps = T(1e-5), alpha = T(1),
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beta = T(0), training = true) where T<:Union{Float32, Float64}
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dg = similar(g)
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db = similar(b)
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dx = similar(x)
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cudnnBNBackward!(dg, g, db, dx, x, dy, running_mean, running_var, T(momentum),
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training = training, cache = cache, eps = eps, alpha = alpha, beta = beta)
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(dg, db, dx)
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end
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function cudnnBNBackward!(dg::CuArray{T}, g::CuArray{T}, db::CuArray{T},
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dx::CuArray{T}, x::CuArray{T}, dy::CuArray{T},
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running_mean::CuArray{T}, running_var::CuArray{T},
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momentum; cache = nothing, eps = T(1e-5),
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alpha = T(1), beta = T(0),
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dalpha = T(1), dbeta = T(0), training = true) where T<:Union{Float32, Float64}
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if training
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xd = TensorDesc(x)
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dyd = TensorDesc(dy)
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dxd = TensorDesc(dx)
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gd = TensorDesc(T, _wsize(x))
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if cache !== nothing
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mean, ivar = cache.mean, cache.ivar
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info("mean and ivar are fetched from the cache")
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else
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mean, ivar = CU_NULL, CU_NULL
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end
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if eps < BATCHNORM_MIN_EPS
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eps = BATCHNORM_MIN_EPS
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end
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@check ccall((:cudnnBatchNormalizationBackward, libcudnn), cudnnStatus_t,
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(cudnnHandle_t,cudnnBatchNormMode_t,
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Ptr{T}, Ptr{T},
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Ptr{T}, Ptr{T},
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Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T}, CuPtr{T}, CuPtr{T},
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Cdouble, CuPtr{T}, CuPtr{T}),
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handle(), BATCHNORM_SPATIAL,
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Ref(T(alpha)), Ref(T(beta)),
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Ref(T(dalpha)), Ref(T(dbeta)),
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xd, x,
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dyd, dy,
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dxd, dx,
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gd, g, dg, db,
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eps, mean, ivar)
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else
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ivar = 1 ./ sqrt.(reshape(running_var, _wsize(x)) .+ eps)
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dx .= dy .* reshape(g, _wsize(x)) .* ivar
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dg .= squeeze(sum(dy .* (x .- reshape(running_mean, _wsize(x))) .* ivar, _reddims(dy)), dims = (1,2,4))
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db .= squeeze(sum(dy, _reddims(dy)), dims = (1,2,4))
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end
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end
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# Flux Interface
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import ..Flux: data
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import CuArrays.CUDNN: batchnorm, ∇batchnorm
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(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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BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; cache = cache, alpha = 1, beta = 0, eps = BN.ϵ, training = Flux.istraining()))
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@ -1,273 +1,26 @@
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using CuArrays: libcudnn
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using CuArrays.CUDNN: @check, cudnnStatus_t, cudnnTensorDescriptor_t,
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cudnnBatchNormMode_t, cudnnHandle_t, cudnnDataType, TensorDesc, FilterDesc
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import CuArrays.CUDAdrv: CuPtr, CU_NULL
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using LinearAlgebra
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const RNN_RELU = 0 # Stock RNN with ReLu activation
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const RNN_TANH = 1 # Stock RNN with tanh activation
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const LSTM = 2 # LSTM with no peephole connections
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const GRU = 3 # Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1)
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const LINEAR_INPUT = 0
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const SKIP_INPUT = 1
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const UNIDIRECTIONAL = 0
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const BIDIRECTIONAL = 1
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const RNN_ALGO_STANDARD = 0
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const RNN_ALGO_PERSIST_STATIC = 1
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const RNN_ALGO_PERSIST_DYNAMIC = 2
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# param layout:
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# RNN: [weight, bias] × [input, hidden]
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# GRU: [weight, bias] × [input, hidden] × [reset, update, newmem]
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# LSTM: [weight, bias] × [input, hidden] × [input, forget, newmem, output]
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function params(w::CuVector, input, hidden, n = 1)
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slice(offset, shape) = reshape(view(w, offset.+(1:prod(shape))), shape)
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wx = slice(0, (input, hidden*n))
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wh = slice(length(wx), (hidden, hidden*n))
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bias = view(w, length(wx)+length(wh) .+ (1:hidden*n))
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(wx, wh), bias
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end
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mutable struct RNNDesc{T}
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mode::Int
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input::Int
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hidden::Int
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params::CuVector{T}
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weights::NTuple{2,CuMatrix{T}}
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bias::CuVector{T}
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ptr::Ptr{Nothing}
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end
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Base.unsafe_convert(::Type{Ptr{Nothing}}, d::RNNDesc) = d.ptr
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function rnnParamSize(T, r, input)
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size = Csize_t[0]
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@check ccall((:cudnnGetRNNParamsSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Ptr{Nothing},Ptr{Csize_t},Cint),
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handle(), r, TensorDesc(T, (1,input,1)), size, cudnnDataType(T))
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return Int(size[])÷sizeof(T)
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end
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ngates(mode) = [1, 1, 4, 3][mode+1]
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ngates(r::RNNDesc) = ngates(r.mode)
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function RNNDesc{T}(mode::Int, input::Int, hidden::Int; layers = 1) where T
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d = [C_NULL]
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@check ccall((:cudnnCreateRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Ptr{Nothing}},),d)
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dropoutDesc = DropoutDesc(0)
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inputMode = LINEAR_INPUT
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direction = UNIDIRECTIONAL
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algo = RNN_ALGO_STANDARD
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@check ccall((:cudnnSetRNNDescriptor_v6,libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Cint,Ptr{Nothing},Cint,Cint,Cint,Cint,Cint),
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handle(),d[],hidden,layers,dropoutDesc,inputMode,direction,mode,algo,cudnnDataType(T))
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w = CuArrays.zeros(T, rnnParamSize(T, d[], input))
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# TODO: avoid reserve allocation here
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rd = RNNDesc{T}(mode, input, hidden, w, params(w, input, hidden, ngates(mode))..., d[])
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finalizer(rd) do x
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@check ccall((:cudnnDestroyRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x)
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end
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return rd
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end
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function rnnWorkspaceSize(r::RNNDesc, seqlen, xdesc)
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size = Csize_t[0]
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@check ccall((:cudnnGetRNNWorkspaceSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Ptr{Ptr{Nothing}},Ptr{Csize_t}),
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handle(), r, seqlen, xdesc, size)
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return Int(size[])
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end
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const workspace = Ref{Union{Nothing,CuVector{UInt8}}}(nothing)
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function getworkspace(bytes)
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if workspace[] === nothing || length(workspace[]) < bytes
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workspace[] = CuVector{UInt8}(undef, bytes)
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end
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workspace[]
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end
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getworkspace(r::RNNDesc, seqlen, xdesc) =
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getworkspace(rnnWorkspaceSize(r, seqlen, xdesc))
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function rnnTrainingReserveSize(r::RNNDesc, seqlen, xdesc)
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size = Csize_t[0]
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@check ccall((:cudnnGetRNNTrainingReserveSize,libcudnn), cudnnStatus_t, (Ptr{Nothing}, Ptr{Nothing}, Cint, Ptr{Ptr{Nothing}}, Ptr{Csize_t}),
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handle(), r, seqlen, xdesc, size)
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return Int(size[])
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end
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function cudnnRNNForward(rnn::RNNDesc{T}, seqlen, xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
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workspace, reserve=nothing) where T
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if reserve == nothing
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@check ccall((:cudnnRNNForwardInference, libcudnn), cudnnStatus_t,
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(Ptr{Nothing}, Ptr{Nothing}, Cint,
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Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
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Ptr{Nothing}, CuPtr{T},
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CuPtr{Nothing}, Csize_t),
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handle(), rnn, seqlen,
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xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
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workspace, length(workspace))
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else
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@check ccall((:cudnnRNNForwardTraining, libcudnn), cudnnStatus_t,
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(Ptr{Nothing}, Ptr{Nothing}, Cint,
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Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
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CuPtr{Nothing}, Csize_t, CuPtr{Nothing}, Csize_t),
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handle(), rnn, seqlen,
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xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
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workspace, length(workspace), reserve, length(reserve))
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end
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end
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xDesc(x) = [TensorDesc(eltype(x), (1, size(x, 1), size(x, 2)))]
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|
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hDesc(h::Nothing) = C_NULL, CU_NULL
|
||||
hDesc(x::Integer) = (@assert x == 0; hDesc(nothing))
|
||||
function hDesc(h::CuArray)
|
||||
TensorDesc(eltype(h), (size(h, 1), size(h, 2), 1)), h
|
||||
end
|
||||
|
||||
# TODO: can we just manipulate strides here?
|
||||
# TODO: should use repmat, but this isn't implemented.
|
||||
hBatch(x::AbstractVector, h::CuVector) = h
|
||||
hBatch(x::AbstractMatrix, h::CuVector) = h .* CuArrays.ones(1, size(x, 2))
|
||||
hBatch(x::AbstractMatrix, h::CuMatrix) = h .* CuArrays.ones(1, size(h,2) == 1 ? size(x,2) : 1)
|
||||
|
||||
function forward(rnn::RNNDesc{T}, x::CuArray{T}, h_::CuArray{T}, c_ = nothing, train = Val{false}) where T
|
||||
h = hBatch(x, h_)
|
||||
c = c_ == nothing ? nothing : hBatch(x, c_)
|
||||
@assert size(x, 1) == rnn.input
|
||||
@assert size(h, 1) == rnn.hidden
|
||||
@assert size(x, 2) == size(h, 2)
|
||||
seqLength = 1
|
||||
xdesc = xDesc(x)
|
||||
y = x isa AbstractVector ? similar(x, rnn.hidden) : similar(x, rnn.hidden, size(x, 2))
|
||||
ho = similar(h)
|
||||
ydesc = xDesc(y)
|
||||
workspace = getworkspace(rnn, seqLength, xdesc)
|
||||
reserve = train == Val{true} ?
|
||||
CuVector{UInt8}(undef, rnnTrainingReserveSize(rnn, seqLength, xdesc)) :
|
||||
nothing
|
||||
co = c == nothing ? c : similar(c)
|
||||
cudnnRNNForward(rnn, seqLength,
|
||||
xdesc, x,
|
||||
hDesc(h)...,
|
||||
hDesc(c)...,
|
||||
FilterDesc(T, (1, 1, length(rnn.params))), rnn.params,
|
||||
ydesc, y,
|
||||
hDesc(ho)...,
|
||||
hDesc(co)...,
|
||||
workspace, reserve)
|
||||
result = c == nothing ? (y, ho) : (y, ho, co)
|
||||
return train == Val{true} ? (reserve, result) : result
|
||||
end
|
||||
|
||||
forwardTrain(rnn::RNNDesc{T}, x::CuArray{T}, h::CuArray{T}, c = nothing) where T =
|
||||
forward(rnn, x, h, c, Val{true})
|
||||
|
||||
function cudnnRNNBackwardData(rnn::RNNDesc{T}, seqlen, yd, y, dyd, dy, dhod, dho, dcod, dco,
|
||||
wd, w, hd, h, cd, c, dxd, dx, dhd, dh, dcd, dc, ws, rs) where T
|
||||
@check ccall((:cudnnRNNBackwardData,libcudnn),cudnnStatus_t,
|
||||
(Ptr{Nothing}, Ptr{Nothing}, Cint,
|
||||
Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
|
||||
Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing},
|
||||
CuPtr{T}, Ptr{Ptr{Nothing}}, CuPtr{T}, Ptr{Nothing}, CuPtr{T}, Ptr{Nothing}, CuPtr{T},
|
||||
CuPtr{Nothing}, Csize_t, CuPtr{Nothing}, Csize_t),
|
||||
handle(), rnn, seqlen, yd, y, dyd, dy, dhod, dho, dcod, dco,
|
||||
wd, w, hd, h, cd, c, dxd, dx, dhd, dh, dcd, dc, ws, length(ws), rs, length(rs))
|
||||
end
|
||||
|
||||
function backwardData(rnn::RNNDesc{T}, y, dy_, dho, dco, h, c, reserve) where T
|
||||
# Same as above, any more efficient way?
|
||||
dy = dy_ isa Integer ? zero(y) : dy_
|
||||
yd = xDesc(y)
|
||||
dx = y isa AbstractVector ? similar(dy, rnn.input) : similar(dy, rnn.input, size(dy, 2))
|
||||
dh = similar(h)
|
||||
dc = c == nothing ? nothing : similar(c)
|
||||
cudnnRNNBackwardData(rnn, 1,
|
||||
yd, y, yd, dy, hDesc(dho)..., hDesc(dco)...,
|
||||
FilterDesc(T, (1, 1, length(rnn.params))), rnn.params,
|
||||
hDesc(h)..., hDesc(c)..., xDesc(dx), dx, hDesc(dh)..., hDesc(dc)...,
|
||||
workspace[], reserve)
|
||||
return c == nothing ? (dx, dh) : (dx, dh, dc)
|
||||
end
|
||||
|
||||
backwardData(rnn, y, dy, dho, hx, reserve) =
|
||||
backwardData(rnn, y, dy, dho, nothing, hx, nothing, reserve)
|
||||
|
||||
function cudnnRNNBackwardWeights(rnn::RNNDesc{T}, seqlen, xd, x, hd, h, yd, y, dwd, dw,
|
||||
workspace, reserve) where T
|
||||
@check ccall((:cudnnRNNBackwardWeights,libcudnn), cudnnStatus_t,
|
||||
(Ptr{Nothing}, Ptr{Nothing}, Cint, # handle, rnnDesc, seqLength
|
||||
Ptr{Ptr{Nothing}}, CuPtr{T}, #x
|
||||
Ptr{Nothing}, CuPtr{T}, #hx
|
||||
Ptr{Ptr{Nothing}}, CuPtr{T}, #y
|
||||
CuPtr{Nothing}, Csize_t, #ws
|
||||
Ptr{Nothing}, CuPtr{T}, #dw
|
||||
CuPtr{Nothing}, Csize_t), #rs
|
||||
handle(), rnn, seqlen, xd, x, hd, h, yd, y,
|
||||
workspace, length(workspace), dwd, dw, reserve, length(reserve))
|
||||
end
|
||||
|
||||
function backwardWeights(rnn::RNNDesc{T}, x, h, y, reserve) where T
|
||||
dw = zero(rnn.params)
|
||||
cudnnRNNBackwardWeights(rnn, 1,
|
||||
xDesc(x), x, hDesc(h)..., xDesc(y), y,
|
||||
FilterDesc(T, (1, 1, length(dw))), dw,
|
||||
workspace[], reserve)
|
||||
return params(dw, rnn.input, rnn.hidden, ngates(rnn))
|
||||
end
|
||||
|
||||
# Interface
|
||||
|
||||
import ..Flux: Flux, relu
|
||||
using CuArrays.CUDAnative
|
||||
using CuArrays: @cuindex, cudims
|
||||
|
||||
function LinearAlgebra.copy_transpose!(dst::CuArray, src::CuArray)
|
||||
function kernel(dst, src)
|
||||
I = @cuindex dst
|
||||
dst[I...] = src[reverse(I)...]
|
||||
return
|
||||
end
|
||||
blk, thr = cudims(dst)
|
||||
@cuda blocks=blk threads=thr kernel(dst, src)
|
||||
return dst
|
||||
end
|
||||
|
||||
CuRNN{T} = Flux.RNNCell{<:Union{typeof(tanh),typeof(relu)},<:CuArray{T,2},<:CuArray{T,1}}
|
||||
CuGRU{T} = Flux.GRUCell{<:CuArray{T,2},<:CuArray{T,1}}
|
||||
CuLSTM{T} = Flux.LSTMCell{<:CuArray{T,2},<:CuArray{T,1}}
|
||||
CuRNNs{T} = Union{CuRNN{T},CuGRU{T},CuLSTM{T}}
|
||||
|
||||
function copyparams!(m::CuRNNs, d::RNNDesc)
|
||||
Wi, Wh = d.weights
|
||||
copy_transpose!(Wi, m.Wi)
|
||||
copy_transpose!(Wh, m.Wh)
|
||||
copy_transpose!(d.bias, m.b)
|
||||
return
|
||||
end
|
||||
|
||||
function RNNDesc(m::CuRNNs{T}) where T
|
||||
function CUDNN.RNNDesc(m::CuRNNs{T}) where T
|
||||
h, i = length(m.h), size(m.Wi, 2)
|
||||
mode = m isa CuRNN ?
|
||||
(m.σ == tanh ? RNN_TANH : RNN_RELU) :
|
||||
m isa CuGRU ? GRU : LSTM
|
||||
r = RNNDesc{T}(mode, i, h)
|
||||
(m.σ == tanh ? CUDNN.CUDNN_RNN_TANH : CUDNN.CUDNN_RNN_RELU) :
|
||||
m isa CuGRU ? CUDNN.CUDNN_GRU : CUDNN.CUDNN_LSTM
|
||||
r = CUDNN.RNNDesc{T}(mode, i, h)
|
||||
return r
|
||||
end
|
||||
|
||||
const descs = WeakKeyDict()
|
||||
|
||||
function desc(rnn)
|
||||
d = haskey(descs, rnn) ? descs[rnn] : (descs[rnn] = RNNDesc(rnn))
|
||||
copyparams!(rnn, d)
|
||||
d = haskey(descs, rnn) ? descs[rnn] : (descs[rnn] = CUDNN.RNNDesc(rnn))
|
||||
CUDNN.setweights!(d, rnn.Wi, rnn.Wh, rnn.b)
|
||||
return d
|
||||
end
|
||||
|
||||
@ -275,17 +28,17 @@ import Zygote
|
||||
using Zygote: @adjoint
|
||||
|
||||
function (m::CuRNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
|
||||
y, h′ = forward(desc(m), x, h)
|
||||
y, h′ = CUDNN.forward(desc(m), x, h)
|
||||
return h′, y
|
||||
end
|
||||
|
||||
function (m::CuGRU{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
|
||||
y, h′ = forward(desc(m), x, h)
|
||||
y, h′ = CUDNN.forward(desc(m), x, h)
|
||||
return h′, y
|
||||
end
|
||||
|
||||
function (m::CuLSTM{T})(h::NTuple{2,CuArray{T}}, x::CuArray{T}) where T <: Union{Float32,Float64}
|
||||
y, h′, c′ = forward(desc(m), x, h[1], h[2])
|
||||
y, h′, c′ = CUDNN.forward(desc(m), x, h[1], h[2])
|
||||
return (h′, c′), y
|
||||
end
|
||||
|
||||
@ -303,7 +56,7 @@ unbroadcast(x::AbstractArray, Δ) =
|
||||
coerce_cuda(x::Union{CuArray,Nothing}) = x
|
||||
coerce_cuda(x::Tuple) = coerce_cuda.(x)
|
||||
|
||||
coerce_cuda(x) = x .+ CuArrays.fill(0)
|
||||
coerce_cuda(x::AbstractArray) = x .+ CuArrays.fill(0)
|
||||
|
||||
function struct_grad!(cx::Zygote.Context, x, x̄)
|
||||
for f in fieldnames(typeof(x))
|
||||
@ -316,28 +69,23 @@ end
|
||||
|
||||
for RNN in (CuRNN, CuGRU)
|
||||
@eval @adjoint function (m::$RNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
|
||||
reserve, (y, ho) = forwardTrain(desc(m), x, h)
|
||||
(y, ho), back = CUDNN.pullback(desc(m), x, h)
|
||||
(ho, y), function (Δ)
|
||||
dho, dy = coerce_cuda(Δ)
|
||||
h_ = hBatch(x, h)
|
||||
dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve)
|
||||
(dWi, dWh), db = backwardWeights(descs[m], x, h_, y, reserve)
|
||||
dm = struct_grad!(__context__, m, (σ=nothing,Wi=transpose(dWi),Wh=transpose(dWh),b=db,h=nothing))
|
||||
(dm, unbroadcast(h, dh), dx)
|
||||
dho, dy = coerce_cuda(Δ) # Support FillArrays etc.
|
||||
m̄ = back(dy, dho)
|
||||
dm = struct_grad!(__context__, m, (σ=nothing,Wi=transpose(m̄.Wi),Wh=transpose(m̄.Wh),b=m̄.b,h=nothing))
|
||||
(dm, unbroadcast(h, m̄.h), m̄.x)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
@adjoint function (m::CuLSTM)((h, c)::Tuple{CuArray{T},CuArray{T}}, x::CuArray{T}) where T <: Union{Float32,Float64}
|
||||
reserve, (y, ho, co) = forwardTrain(desc(m), x, h, c)
|
||||
(y, ho, co), back = CUDNN.pullback(desc(m), x, h, c)
|
||||
((ho, co), y), function (Δ)
|
||||
dhc, dy = coerce_cuda(Δ)
|
||||
dhc, dy = coerce_cuda(Δ) # Support FillArrays etc.
|
||||
dho, dco = dhc === nothing ? (nothing, nothing) : dhc
|
||||
h_ = hBatch(x, h)
|
||||
c_ = hBatch(x, c)
|
||||
dx, dh, dc = backwardData(descs[m], y, dy, dho, dco, h_, c_, reserve)
|
||||
(dWi, dWh), db = backwardWeights(descs[m], x, h_, y, reserve)
|
||||
dm = struct_grad!(__context__, m, (Wi=transpose(dWi),Wh=transpose(dWh),b=db,h=nothing,c=nothing))
|
||||
(dm, (unbroadcast(h, dh), unbroadcast(c, dc)), dx)
|
||||
m̄ = back(dy, dho, dco)
|
||||
dm = struct_grad!(__context__, m, (σ=nothing,Wi=transpose(m̄.Wi),Wh=transpose(m̄.Wh),b=m̄.b,h=nothing,c=nothing))
|
||||
(dm, (unbroadcast(h, m̄.h), unbroadcast(c, m̄.c)), m̄.x)
|
||||
end
|
||||
end
|
||||
|
||||
@ -52,9 +52,7 @@ end
|
||||
end
|
||||
|
||||
if CuArrays.libcudnn != nothing
|
||||
@info "Testing Flux/CUDNN"
|
||||
include("cudnn.jl")
|
||||
if !haskey(ENV, "CI_DISABLE_CURNN_TEST")
|
||||
include("curnn.jl")
|
||||
end
|
||||
@info "Testing Flux/CUDNN"
|
||||
include("cudnn.jl")
|
||||
include("curnn.jl")
|
||||
end
|
||||
|
||||
@ -22,8 +22,8 @@ end
|
||||
rand(10, batch_size)
|
||||
cux = gpu(x)
|
||||
|
||||
y, back = pullback((r, x) -> (r(x)), rnn, x)
|
||||
cuy, cuback = pullback((r, x) -> (r(x)), curnn, cux)
|
||||
y, back = pullback((r, x) -> r(x), rnn, x)
|
||||
cuy, cuback = pullback((r, x) -> r(x), curnn, cux)
|
||||
|
||||
@test y ≈ collect(cuy)
|
||||
@test haskey(Flux.CUDA.descs, curnn.cell)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user