TastyPancakes/Flux.jl
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Minor fix for 5D support

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This commit is contained in:
Avik Pal 2018-06-28 14:21:17 +05:30
parent 681d8c4dfc
commit 5ccde88ce6
3 changed files with 4 additions and 8 deletions
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3
src/cuda/cuda.jl
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@ -3,8 +3,9 @@ module CUDA
using CuArrays using CuArrays
if CuArrays.cudnn_available() if CuArrays.cudnn_available()
include("cudnn.jl") CuParam{T,N} = Union{CuArray{T,N},TrackedArray{T,N,CuArray{T,N}}}
include("curnn.jl") include("curnn.jl")
include("cudnn.jl")
end end
end end

8
src/cuda/cudnn.jl
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@ -41,7 +41,7 @@ BNCache() = BNCache(nothing, nothing)
# CuDNN supports only 4D and 5D Tensors for BatchNorm Operations # CuDNN supports only 4D and 5D Tensors for BatchNorm Operations
# so use the native julia code when doing batchnorm on a 2D Array # so use the native julia code when doing batchnorm on a 2D Array
function batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T, 4}, function batchnorm(g::CuArray{T}, b::CuArray{T}, x::Union{CuArray{T, 4},CuArray{T,5}},
running_mean::CuArray{T}, running_var::CuArray{T}, momentum; running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
cache = nothing, alpha = T(1), beta = T(0), cache = nothing, alpha = T(1), beta = T(0),
eps = T(1e-5), training = true) where T<:Union{Float32, Float64} eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
@ -179,11 +179,7 @@ end
import ..Flux: Flux import ..Flux: Flux
import ..Tracker: track, back, @back, istracked, TrackedArray import ..Tracker: track, back, @back, istracked, TrackedArray
CuParam{T,N} = Union{CuArray{T,N},TrackedArray{T,N,CuArray{T,N}}} (BN::BatchNorm)(x::Union{CuParam{T,4},CuParam{T,5}}) where T<:Union{Float32, Float64} =
CuParam45{T} = Union{CuParam{T,4},CuParam{T,5}}
CuBatchNorm{T} = Flux.BatchNorm{<:Union{typeof(identity),typeof(relu)},<:CuParam{T,1},<:CuParam{T,1},<:T}
(BN::BatchNorm)(x::CuParam45{T}) =
batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ, BN.momentum; cache = nothing, alpha = 1, beta = 0, eps = BN.ϵ, training = BN.active) batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ, BN.momentum; cache = nothing, alpha = 1, beta = 0, eps = BN.ϵ, training = BN.active)
_batchnorm(g, b, x, running_mean, running_var, momentum, _batchnorm(g, b, x, running_mean, running_var, momentum,

1
src/cuda/curnn.jl
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@ -234,7 +234,6 @@ function copy_transpose!(dst::CuArray, src::CuArray)
return dst return dst
end end
CuParam{T,N} = Union{CuArray{T,N},TrackedArray{T,N,CuArray{T,N}}}
CuRNN{T} = Flux.RNNCell{<:Union{typeof(tanh),typeof(relu)},<:CuParam{T,2},<:CuParam{T,1}} CuRNN{T} = Flux.RNNCell{<:Union{typeof(tanh),typeof(relu)},<:CuParam{T,2},<:CuParam{T,1}}
CuGRU{T} = Flux.GRUCell{<:CuParam{T,2},<:CuParam{T,1}} CuGRU{T} = Flux.GRUCell{<:CuParam{T,2},<:CuParam{T,1}}
CuLSTM{T} = Flux.LSTMCell{<:CuParam{T,2},<:CuParam{T,1}} CuLSTM{T} = Flux.LSTMCell{<:CuParam{T,2},<:CuParam{T,1}}