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Merge pull request #294 from avik-pal/cudnn_batchnorm

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Wrapper for CuDNN BatchNorm
This commit is contained in:
Mike J Innes 2018-11-27 23:51:32 +00:00 committed by GitHub
commit dd154ca049
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GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 624 additions and 367 deletions
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6
src/cuda/cuda.jl
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@ -7,6 +7,12 @@ if !applicable(CuArray{UInt8}, undef, 1)
end
if CuArrays.libcudnn != nothing
if isdefined(CuArrays, :libcudnn_handle)
handle() = CuArrays.libcudnn_handle[]
else
handle() = CuArrays.CUDNN.handle()
end
include("curnn.jl")
include("cudnn.jl")
else
@warn("CUDNN is not installed, some functionality will not be available.")

483
src/cuda/cudnn.jl
View File

@ -1,13 +1,8 @@
using .CuArrays.CUDNN: @check, libcudnn, cudnnStatus_t,
cudnnDataType, TensorDesc, FilterDesc
using .CuArrays.CUDNN: @check, libcudnn, cudnnStatus_t, cudnnTensorDescriptor_t,
cudnnBatchNormMode_t, cudnnHandle_t, cudnnDataType, TensorDesc, FilterDesc
import ..Flux: data
using LinearAlgebra
if isdefined(CuArrays, :libcudnn_handle)
handle() = CuArrays.libcudnn_handle[]
else
handle() = CuArrays.CUDNN.handle()
end
mutable struct DropoutDesc
ptr::Ptr{Nothing}
states::CuVector{UInt8}
@ -30,324 +25,204 @@ function DropoutDesc(ρ::Real; seed::Integer=0)
return desc
end
const RNN_RELU = 0 # Stock RNN with ReLu activation
const RNN_TANH = 1 # Stock RNN with tanh activation
const LSTM = 2 # LSTM with no peephole connections
const GRU = 3 # Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1)
const BATCHNORM_SPATIAL = 1
const BATCHNORM_ACTIVATION = 0
const BATCHNORM_MIN_EPS = 1e-5
const LINEAR_INPUT = 0
const SKIP_INPUT = 1
@inline _wsize(y) = (map(_ -> 1, size(y)[1:end-2])..., size(y)[end-1], 1)
const UNIDIRECTIONAL = 0
const BIDIRECTIONAL = 1
@inline _reddims(y) = (collect(1:ndims(y)-2)..., ndims(y))
const RNN_ALGO_STANDARD = 0
const RNN_ALGO_PERSIST_STATIC = 1
const RNN_ALGO_PERSIST_DYNAMIC = 2
# param layout:
# RNN: [weight, bias] × [input, hidden]
# GRU: [weight, bias] × [input, hidden] × [reset, update, newmem]
# LSTM: [weight, bias] × [input, hidden] × [input, forget, newmem, output]
function params(w::CuVector, input, hidden, n = 1)
slice(offset, shape) = reshape(view(w, offset.+(1:prod(shape))), shape)
wx = slice(0, (input, hidden*n))
wh = slice(length(wx), (hidden, hidden*n))
bias = view(w, length(wx)+length(wh) .+ (1:hidden*n))
(wx, wh), bias
mutable struct BNCache
mean
ivar
end
mutable struct RNNDesc{T}
mode::Int
input::Int
hidden::Int
params::CuVector{T}
weights::NTuple{2,CuMatrix{T}}
bias::CuVector{T}
ptr::Ptr{Nothing}
BNCache() = BNCache(nothing, nothing)
# NOTE: CuDNN supports only 4D and 5D Tensors for BatchNorm Operations
# so reshape a 2D Tensor into 4D
batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T, 2},
running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
cache = nothing, alpha = T(1), beta = T(0),
eps = T(1e-5), training = true) where T<:Union{Float32, Float64} =
dropdims(batchnorm(g, b, reshape(x, 1, 1, size(x, 1), size(x, 2)), running_mean, running_var, momentum,
cache = cache, alpha = alpha, beta = beta, eps = eps, training = training), dims = (1, 2))
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;
cache = nothing, alpha = T(1), beta = T(0),
eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
y = similar(x)
cudnnBNForward!(y, g, b, x, running_mean, running_var, momentum, cache = cache,
alpha = alpha, beta = beta, eps = eps, training = training)
y
end
Base.unsafe_convert(::Type{Ptr{Nothing}}, d::RNNDesc) = d.ptr
function rnnParamSize(T, r, input)
size = Csize_t[0]
@check ccall((:cudnnGetRNNParamsSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Ptr{Nothing},Ptr{Csize_t},Cint),
handle(), r, TensorDesc(T, (1,input,1)), size, cudnnDataType(T))
return Int(size[])÷sizeof(T)
end
ngates(mode) = [1, 1, 4, 3][mode+1]
ngates(r::RNNDesc) = ngates(r.mode)
function RNNDesc{T}(mode::Int, input::Int, hidden::Int; layers = 1) where T
d = [C_NULL]
@check ccall((:cudnnCreateRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Ptr{Nothing}},),d)
dropoutDesc = DropoutDesc(0)
inputMode = LINEAR_INPUT
direction = UNIDIRECTIONAL
algo = RNN_ALGO_STANDARD
@check ccall((:cudnnSetRNNDescriptor_v6,libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Cint,Ptr{Nothing},Cint,Cint,Cint,Cint,Cint),
handle(),d[],hidden,layers,dropoutDesc,inputMode,direction,mode,algo,cudnnDataType(T))
w = cuzeros(T, rnnParamSize(T, d[], input))
# TODO: avoid reserve allocation here
rd = RNNDesc{T}(mode, input, hidden, w, params(w, input, hidden, ngates(mode))..., d[])
finalizer(rd) do x
@check ccall((:cudnnDestroyRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x)
function cudnnBNForward!(y::CuArray{T}, g::CuArray{T}, b::CuArray{T}, x::CuArray{T},
running_mean::CuArray{T}, running_var::CuArray{T},
momentum; cache = nothing,
alpha = T(1), beta = T(0),
eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
dims = _wsize(x)
if eps < BATCHNORM_MIN_EPS
# warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS)
eps = BATCHNORM_MIN_EPS
end
return rd
end
xd = TensorDesc(x)
yd = TensorDesc(y)
gd = TensorDesc(T, dims)
function rnnWorkspaceSize(r::RNNDesc, seqlen, xdesc)
size = Csize_t[0]
@check ccall((:cudnnGetRNNWorkspaceSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Ptr{Ptr{Nothing}},Ptr{Csize_t}),
handle(), r, seqlen, xdesc, size)
return Int(size[])
end
if training
const workspace = [CuVector{UInt8}(undef, 1)]
if cache !== nothing
mean = zeros(CuArray{T}, dims...)
ivar = ones(CuArray{T}, dims...)
else
mean = C_NULL
ivar = C_NULL
end
getworkspace(bytes) =
length(workspace[]) ≥ bytes ?
workspace[] :
(workspace[] = CuVector{UInt8}(undef, bytes))
getworkspace(r::RNNDesc, seqlen, xdesc) =
getworkspace(rnnWorkspaceSize(r, seqlen, xdesc))
function rnnTrainingReserveSize(r::RNNDesc, seqlen, xdesc)
size = Csize_t[0]
@check ccall((:cudnnGetRNNTrainingReserveSize,libcudnn), cudnnStatus_t, (Ptr{Nothing}, Ptr{Nothing}, Cint, Ptr{Ptr{Nothing}}, Ptr{Csize_t}),
handle(), r, seqlen, xdesc, size)
return Int(size[])
end
function cudnnRNNForward(rnn::RNNDesc{T}, seqlen, xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
workspace, reserve=nothing) where T
if reserve == nothing
@check ccall((:cudnnRNNForwardInference, libcudnn), cudnnStatus_t,
(Ptr{Nothing}, Ptr{Nothing}, Cint,
Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T},
@check ccall((:cudnnBatchNormalizationForwardTraining, libcudnn), cudnnStatus_t,
(cudnnHandle_t,cudnnBatchNormMode_t,
Ptr{T}, Ptr{T},
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Csize_t),
handle(), rnn, seqlen,
xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
workspace, length(workspace))
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T}, Ptr{T},
Cdouble, Ptr{T}, Ptr{T},
Cdouble, Ptr{T}, Ptr{T}),
handle(), BATCHNORM_SPATIAL,
Ref(T(alpha)), Ref(T(beta)),
xd, x,
yd, y,
gd, g, b,
momentum, running_mean, running_var,
eps, mean, ivar)
if cache !== nothing
cache.mean = mean
cache.ivar = ivar
end
else
@check ccall((:cudnnRNNForwardTraining, libcudnn), cudnnStatus_t,
(Ptr{Nothing}, Ptr{Nothing}, Cint,
Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Csize_t, Ptr{Nothing}, Csize_t),
handle(), rnn, seqlen,
xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
workspace, length(workspace), reserve, length(reserve))
@check ccall((:cudnnBatchNormalizationForwardInference, libcudnn), cudnnStatus_t,
(Ptr{cudnnHandle_t},cudnnBatchNormMode_t,
Ptr{T}, Ptr{T},
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T}, Ptr{T},
Ptr{T}, Ptr{T},
Cdouble),
handle(), BATCHNORM_SPATIAL,
Ref(T(alpha)), Ref(T(beta)),
xd, x,
yd, y,
gd, g, b,
running_mean, running_var,
eps)
end
end
xDesc(x) = [TensorDesc(eltype(x), (1, size(x, 1), size(x, 2)))]
hDesc(h::Nothing) = C_NULL, C_NULL
hDesc(x::Integer) = (@assert x == 0; hDesc(nothing))
function hDesc(h::CuArray)
TensorDesc(eltype(h), (size(h, 1), size(h, 2), 1)), h
function ∇batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T, 2}, dy::CuArray{T, 2},
running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
cache = nothing, eps = T(1e-5), alpha = T(1),
beta = T(0), training = true) where T<:Union{Float32, Float64}
dg, db, dx = ∇batchnorm(g, b, reshape(x, 1, 1, size(x, 1), size(x, 2)), reshape(dy, 1, 1, size(dy, 1),
size(dy, 2)), running_mean, running_var, momentum, cache = cache, eps = eps,
alpha = alpha, beta = beta, training = training)
(dg, db, dropdims(dx, dims = (1, 2)))
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 .* cuones(1, size(x, 2))
hBatch(x::AbstractMatrix, h::CuMatrix) = h .* cuones(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
function ∇batchnorm(g::CuArray{T}, b::CuArray{T}, x::CuArray{T}, dy::CuArray{T},
running_mean::CuArray{T}, running_var::CuArray{T}, momentum;
cache = nothing, eps = T(1e-5), alpha = T(1),
beta = T(0), training = true) where T<:Union{Float32, Float64}
dg = similar(g)
db = similar(b)
dx = similar(x)
cudnnBNBackward!(dg, g, db, dx, x, dy, running_mean, running_var, T(momentum),
training = training, cache = cache, eps = eps, alpha = alpha, beta = beta)
(dg, db, dx)
end
forwardTrain(rnn::RNNDesc{T}, x::CuArray{T}, h::CuArray{T}, c = nothing) where T =
forward(rnn, x, h, c, Val{true})
function cudnnBNBackward!(dg::CuArray{T}, g::CuArray{T}, db::CuArray{T},
dx::CuArray{T}, x::CuArray{T}, dy::CuArray{T},
running_mean::CuArray{T}, running_var::CuArray{T},
momentum; cache = nothing, eps = T(1e-5),
alpha = T(1), beta = T(0),
dalpha = T(1), dbeta = T(0), training = true) where T<:Union{Float32, Float64}
if training
xd = TensorDesc(x)
dyd = TensorDesc(dy)
dxd = TensorDesc(dx)
gd = TensorDesc(T, _wsize(x))
if cache !== nothing
mean, ivar = cache.mean, cache.ivar
info("mean and ivar are fetched from the cache")
else
mean, ivar = C_NULL, C_NULL
end
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}}, Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing},
Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Csize_t, Ptr{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
if eps < BATCHNORM_MIN_EPS
eps = BATCHNORM_MIN_EPS
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}}, Ptr{T}, #x
Ptr{Nothing}, Ptr{T}, #hx
Ptr{Ptr{Nothing}}, Ptr{T}, #y
Ptr{Nothing}, Csize_t, #ws
Ptr{Nothing}, Ptr{T}, #dw
Ptr{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
import ..Tracker: TrackedArray
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
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}}
CuGRU{T} = Flux.GRUCell{<:CuParam{T,2},<:CuParam{T,1}}
CuLSTM{T} = Flux.LSTMCell{<:CuParam{T,2},<:CuParam{T,1}}
CuRNNs{T} = Union{CuRNN{T},CuGRU{T},CuLSTM{T}}
function copyparams!(m::CuRNNs, d::RNNDesc)
Wi, Wh = d.weights
copy_transpose!(Wi, Flux.data(m.Wi))
copy_transpose!(Wh, Flux.data(m.Wh))
copy_transpose!(d.bias, Flux.data(m.b))
return
end
function 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)
return r
end
const descs = WeakKeyDict()
function desc(rnn)
d = haskey(descs, rnn) ? descs[rnn] : (descs[rnn] = RNNDesc(rnn))
copyparams!(rnn, d)
return d
end
import Flux.Tracker
import Flux.Tracker: data, istracked, track, unbroadcast, @grad, nobacksies
istrain(m::CuRNNs, args...) = any(x -> x isa TrackedArray, (m.Wi, m.Wh, m.b, args...))
function (m::CuRNN{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64}
result = istrain(m, h, x) ?
track(m, x, h, m.Wi, m.Wh, m.b) :
forward(desc(m), x, h)
return result[2], result[1]
end
function (m::CuGRU{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64}
result = istrain(m, h, x) ?
track(m, x, h, m.Wi, m.Wh, m.b) :
forward(desc(m), x, h)
return result[2], result[1]
end
function (m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x::CuParam{T}) where T <: Union{Float32,Float64}
result = istrain(m, h, x) ?
track(m, x, h[1], h[2], m.Wi, m.Wh, m.b) :
forward(desc(m), x, h[1], h[2])
return (result[2], result[3]), result[1]
end
(m::CuRNN{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
(m::CuGRU{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
(m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
@grad function (m::Union{CuRNN,CuGRU})(x, h, Wi, Wh, b)
reserve, result = forwardTrain(desc(m), data(x), data(h))
result, function (Δ)
y, ho = result
dy, dho = Δ
h_ = hBatch(x, data(h))
dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve)
(dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve)
nobacksies(:RNN, (dx, unbroadcast(h, dh), transpose(dWi), transpose(dWh), db))
@check ccall((:cudnnBatchNormalizationBackward, libcudnn), cudnnStatus_t,
(cudnnHandle_t,cudnnBatchNormMode_t,
Ptr{T}, Ptr{T},
Ptr{T}, Ptr{T},
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T}, Ptr{T}, Ptr{T},
Cdouble, Ptr{T}, Ptr{T}),
handle(), BATCHNORM_SPATIAL,
Ref(T(alpha)), Ref(T(beta)),
Ref(T(dalpha)), Ref(T(dbeta)),
xd, x,
dyd, dy,
dxd, dx,
gd, g, dg, db,
eps, mean, ivar)
else
ivar = 1 ./ sqrt.(reshape(running_var, _wsize(x)) .+ eps)
dx .= dy .* reshape(g, _wsize(x)) .* ivar
dg .= squeeze(sum(dy .* (x .- reshape(running_mean, _wsize(x))) .* ivar, _reddims(dy)), dims = (1,2,4))
db .= squeeze(sum(dy, _reddims(dy)), dims = (1,2,4))
end
end
@grad function (m::CuLSTM)(x, h, c, Wi, Wh, b)
reserve, result = forwardTrain(desc(m), data.((x, h, c))...)
result, function (Δ)
y, ho = result
dy, dho, dco = Δ
h_ = hBatch(x, data(h))
c_ = hBatch(x, data(c))
dx, dh, dc = backwardData(descs[m], y, dy, dho, dco, h_, c_, reserve)
(dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve)
nobacksies(:RNN,
(dx, unbroadcast(h, dh), unbroadcast(c, dc),
transpose(dWi), transpose(dWh), db))
end
end
# Flux Interface
(BN::Flux.BatchNorm)(x::Union{CuParam{T,2},CuParam{T,4},CuParam{T,5}}, cache = nothing) where T<:Union{Float32, Float64} =
batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; cache = cache, alpha = 1, beta = 0, eps = BN.ϵ, training = BN.active)
batchnorm(g::TrackedArray, b::TrackedArray, x::TrackedArray, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::TrackedArray, b::TrackedArray, x::CuArray{T}, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::TrackedArray, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::CuArray{T}, b::TrackedArray, x::CuArray{T}, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::CuArray{T}, b::TrackedArray, x::TrackedArray, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::TrackedArray, b::CuArray{T}, x::CuArray{T}, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::CuArray{T}, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
@grad batchnorm(g, b, x, running_mean, running_var, momentum; kw...) =
batchnorm(data.((g, b, x))..., running_mean, running_var, momentum; kw...), Δ -> (nobacksies(:batchnorm, ∇batchnorm(data.((g, b, x, Δ))..., running_mean, running_var, momentum; kw...))..., nothing, nothing, nothing)

325
src/cuda/curnn.jl Normal file
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@ -0,0 +1,325 @@
using .CuArrays.CUDNN: @check, libcudnn, cudnnStatus_t, cudnnTensorDescriptor_t,
cudnnBatchNormMode_t, cudnnHandle_t, cudnnDataType, TensorDesc, FilterDesc
using LinearAlgebra
const RNN_RELU = 0 # Stock RNN with ReLu activation
const RNN_TANH = 1 # Stock RNN with tanh activation
const LSTM = 2 # LSTM with no peephole connections
const GRU = 3 # Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1)
const LINEAR_INPUT = 0
const SKIP_INPUT = 1
const UNIDIRECTIONAL = 0
const BIDIRECTIONAL = 1
const RNN_ALGO_STANDARD = 0
const RNN_ALGO_PERSIST_STATIC = 1
const RNN_ALGO_PERSIST_DYNAMIC = 2
# param layout:
# RNN: [weight, bias] × [input, hidden]
# GRU: [weight, bias] × [input, hidden] × [reset, update, newmem]
# LSTM: [weight, bias] × [input, hidden] × [input, forget, newmem, output]
function params(w::CuVector, input, hidden, n = 1)
slice(offset, shape) = reshape(view(w, offset.+(1:prod(shape))), shape)
wx = slice(0, (input, hidden*n))
wh = slice(length(wx), (hidden, hidden*n))
bias = view(w, length(wx)+length(wh) .+ (1:hidden*n))
(wx, wh), bias
end
mutable struct RNNDesc{T}
mode::Int
input::Int
hidden::Int
params::CuVector{T}
weights::NTuple{2,CuMatrix{T}}
bias::CuVector{T}
ptr::Ptr{Nothing}
end
Base.unsafe_convert(::Type{Ptr{Nothing}}, d::RNNDesc) = d.ptr
function rnnParamSize(T, r, input)
size = Csize_t[0]
@check ccall((:cudnnGetRNNParamsSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Ptr{Nothing},Ptr{Csize_t},Cint),
handle(), r, TensorDesc(T, (1,input,1)), size, cudnnDataType(T))
return Int(size[])÷sizeof(T)
end
ngates(mode) = [1, 1, 4, 3][mode+1]
ngates(r::RNNDesc) = ngates(r.mode)
function RNNDesc{T}(mode::Int, input::Int, hidden::Int; layers = 1) where T
d = [C_NULL]
@check ccall((:cudnnCreateRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Ptr{Nothing}},),d)
dropoutDesc = DropoutDesc(0)
inputMode = LINEAR_INPUT
direction = UNIDIRECTIONAL
algo = RNN_ALGO_STANDARD
@check ccall((:cudnnSetRNNDescriptor_v6,libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Cint,Ptr{Nothing},Cint,Cint,Cint,Cint,Cint),
handle(),d[],hidden,layers,dropoutDesc,inputMode,direction,mode,algo,cudnnDataType(T))
w = cuzeros(T, rnnParamSize(T, d[], input))
# TODO: avoid reserve allocation here
rd = RNNDesc{T}(mode, input, hidden, w, params(w, input, hidden, ngates(mode))..., d[])
finalizer(rd) do x
@check ccall((:cudnnDestroyRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x)
end
return rd
end
function rnnWorkspaceSize(r::RNNDesc, seqlen, xdesc)
size = Csize_t[0]
@check ccall((:cudnnGetRNNWorkspaceSize, libcudnn), cudnnStatus_t, (Ptr{Nothing},Ptr{Nothing},Cint,Ptr{Ptr{Nothing}},Ptr{Csize_t}),
handle(), r, seqlen, xdesc, size)
return Int(size[])
end
const workspace = [CuVector{UInt8}(undef, 1)]
getworkspace(bytes) =
length(workspace[]) ≥ bytes ?
workspace[] :
(workspace[] = CuVector{UInt8}(undef, bytes))
getworkspace(r::RNNDesc, seqlen, xdesc) =
getworkspace(rnnWorkspaceSize(r, seqlen, xdesc))
function rnnTrainingReserveSize(r::RNNDesc, seqlen, xdesc)
size = Csize_t[0]
@check ccall((:cudnnGetRNNTrainingReserveSize,libcudnn), cudnnStatus_t, (Ptr{Nothing}, Ptr{Nothing}, Cint, Ptr{Ptr{Nothing}}, Ptr{Csize_t}),
handle(), r, seqlen, xdesc, size)
return Int(size[])
end
function cudnnRNNForward(rnn::RNNDesc{T}, seqlen, xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
workspace, reserve=nothing) where T
if reserve == nothing
@check ccall((:cudnnRNNForwardInference, libcudnn), cudnnStatus_t,
(Ptr{Nothing}, Ptr{Nothing}, Cint,
Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Csize_t),
handle(), rnn, seqlen,
xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
workspace, length(workspace))
else
@check ccall((:cudnnRNNForwardTraining, libcudnn), cudnnStatus_t,
(Ptr{Nothing}, Ptr{Nothing}, Cint,
Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Csize_t, Ptr{Nothing}, Csize_t),
handle(), rnn, seqlen,
xd, x, hd, h, cd, c, wd, w, yd, y, hod, ho, cod, co,
workspace, length(workspace), reserve, length(reserve))
end
end
xDesc(x) = [TensorDesc(eltype(x), (1, size(x, 1), size(x, 2)))]
hDesc(h::Nothing) = C_NULL, C_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 .* cuones(1, size(x, 2))
hBatch(x::AbstractMatrix, h::CuMatrix) = h .* cuones(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}}, Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing},
Ptr{T}, Ptr{Ptr{Nothing}}, Ptr{T}, Ptr{Nothing}, Ptr{T}, Ptr{Nothing}, Ptr{T},
Ptr{Nothing}, Csize_t, Ptr{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}}, Ptr{T}, #x
Ptr{Nothing}, Ptr{T}, #hx
Ptr{Ptr{Nothing}}, Ptr{T}, #y
Ptr{Nothing}, Csize_t, #ws
Ptr{Nothing}, Ptr{T}, #dw
Ptr{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
import ..Tracker: TrackedArray
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
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}}
CuGRU{T} = Flux.GRUCell{<:CuParam{T,2},<:CuParam{T,1}}
CuLSTM{T} = Flux.LSTMCell{<:CuParam{T,2},<:CuParam{T,1}}
CuRNNs{T} = Union{CuRNN{T},CuGRU{T},CuLSTM{T}}
function copyparams!(m::CuRNNs, d::RNNDesc)
Wi, Wh = d.weights
copy_transpose!(Wi, Flux.data(m.Wi))
copy_transpose!(Wh, Flux.data(m.Wh))
copy_transpose!(d.bias, Flux.data(m.b))
return
end
function 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)
return r
end
const descs = WeakKeyDict()
function desc(rnn)
d = haskey(descs, rnn) ? descs[rnn] : (descs[rnn] = RNNDesc(rnn))
copyparams!(rnn, d)
return d
end
import Flux.Tracker
import Flux.Tracker: data, istracked, track, unbroadcast, @grad, nobacksies
istrain(m::CuRNNs, args...) = any(x -> x isa TrackedArray, (m.Wi, m.Wh, m.b, args...))
function (m::CuRNN{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64}
result = istrain(m, h, x) ?
track(m, x, h, m.Wi, m.Wh, m.b) :
forward(desc(m), x, h)
return result[2], result[1]
end
function (m::CuGRU{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64}
result = istrain(m, h, x) ?
track(m, x, h, m.Wi, m.Wh, m.b) :
forward(desc(m), x, h)
return result[2], result[1]
end
function (m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x::CuParam{T}) where T <: Union{Float32,Float64}
result = istrain(m, h, x) ?
track(m, x, h[1], h[2], m.Wi, m.Wh, m.b) :
forward(desc(m), x, h[1], h[2])
return (result[2], result[3]), result[1]
end
(m::CuRNN{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
(m::CuGRU{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
(m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x))
@grad function (m::Union{CuRNN,CuGRU})(x, h, Wi, Wh, b)
reserve, result = forwardTrain(desc(m), data(x), data(h))
result, function (Δ)
y, ho = result
dy, dho = Δ
h_ = hBatch(x, data(h))
dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve)
(dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve)
nobacksies(:RNN, (dx, unbroadcast(h, dh), transpose(dWi), transpose(dWh), db))
end
end
@grad function (m::CuLSTM)(x, h, c, Wi, Wh, b)
reserve, result = forwardTrain(desc(m), data.((x, h, c))...)
result, function (Δ)
y, ho = result
dy, dho, dco = Δ
h_ = hBatch(x, data(h))
c_ = hBatch(x, data(c))
dx, dh, dc = backwardData(descs[m], y, dy, dho, dco, h_, c_, reserve)
(dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve)
nobacksies(:RNN,
(dx, unbroadcast(h, dh), unbroadcast(c, dc),
transpose(dWi), transpose(dWh), db))
end
end

20
src/layers/normalise.jl
View File

@ -44,7 +44,6 @@ end
_testmode!(a::Dropout, test) = (a.active = !test)
"""
LayerNorm(h::Integer)
A [normalisation layer](https://arxiv.org/pdf/1607.06450.pdf) designed to be
@ -86,7 +85,6 @@ See [Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf).
Example:
```julia
m = Chain(
Dense(28^2, 64),
@ -101,14 +99,14 @@ mutable struct BatchNorm{F,V,W,N}
β::V # bias
γ::V # scale
μ::W # moving mean
σ::W # moving std
σ²::W # moving std
ϵ::N
momentum::N
active::Bool
end
BatchNorm(chs::Integer, λ = identity;
initβ = (i) -> zeros(i), initγ = (i) -> ones(i), ϵ = 1e-8, momentum = .1) =
initβ = (i) -> zeros(i), initγ = (i) -> ones(i), ϵ = 1e-5, momentum = .1) =
BatchNorm(λ, param(initβ(chs)), param(initγ(chs)),
zeros(chs), ones(chs), ϵ, momentum, true)
@ -124,31 +122,31 @@ function (BN::BatchNorm)(x)
if !BN.active
μ = reshape(BN.μ, affine_shape...)
σ = reshape(BN.σ, affine_shape...)
σ² = reshape(BN.σ², affine_shape...)
else
T = eltype(x)
ϵ = data(convert(T, BN.ϵ))
axes = [1:dims-2; dims] # axes to reduce along (all but channels axis)
μ = mean(x, dims = axes)
σ = sqrt.(mean((x .- μ).^2, dims = axes) .+ ϵ)
σ² = sum((x .- μ) .^ 2, dims = axes) ./ m
# update moving mean/std
mtm = data(convert(T, BN.momentum))
BN.μ = (1 - mtm) .* BN.μ .+ mtm .* dropdims(data(μ), dims = (axes...,))
BN.σ = (1 - mtm) .* BN.σ .+ mtm .* dropdims(data(σ), dims = (axes...,)) .* m ./ (m - 1)
BN.μ = (1 - mtm) .* BN.μ .+ mtm .* reshape(data(μ), :)
BN.σ² = ((1 - mtm) .* BN.σ² .+ mtm .* reshape(data(σ²), :) .* m ./ (m - 1))
end
let λ = BN.λ
λ.(reshape(γ, affine_shape...) .* ((x .- μ) ./ σ) .+ reshape(β, affine_shape...))
λ.(reshape(γ, affine_shape...) .* ((x .- μ) ./ sqrt.(σ² .+ BN.ϵ)) .+ reshape(β, affine_shape...))
end
end
children(BN::BatchNorm) =
(BN.λ, BN.β, BN.γ, BN.μ, BN.σ, BN.ϵ, BN.momentum, BN.active)
(BN.λ, BN.β, BN.γ, BN.μ, BN.σ², BN.ϵ, BN.momentum, BN.active)
mapchildren(f, BN::BatchNorm) = # e.g. mapchildren(cu, BN)
BatchNorm(BN.λ, f(BN.β), f(BN.γ), f(BN.μ), f(BN.σ), BN.ϵ, BN.momentum, BN.active)
BatchNorm(BN.λ, f(BN.β), f(BN.γ), f(BN.μ), f(BN.σ²), BN.ϵ, BN.momentum, BN.active)
_testmode!(BN::BatchNorm, test) = (BN.active = !test)

6
test/cuda/cuda.jl
View File

@ -36,4 +36,8 @@ Flux.back!(sum(l))
end
CuArrays.libcudnn != nothing && include("cudnn.jl")
if CuArrays.libcudnn != nothing
@info "Testing Flux/CUDNN"
include("cudnn.jl")
include("curnn.jl")
end

80
test/cuda/cudnn.jl
View File

@ -1,48 +1,48 @@
using Flux, CuArrays, Test
using Flux, Flux.Tracker, CuArrays, Test
using Flux.Tracker: TrackedArray, data
@info "Testing Flux/CUDNN"
@testset "CUDNN BatchNorm" begin
@testset "4D Input" begin
x = TrackedArray(Float64.(collect(reshape(1:12, 2, 2, 3, 1))))
m = BatchNorm(3)
cx = gpu(x)
cm = gpu(m)
@testset "RNN" begin
@testset for R in [RNN, GRU, LSTM]
rnn = R(10, 5)
curnn = mapleaves(gpu, rnn)
@testset for batch_size in (1, 5)
Flux.reset!(rnn)
Flux.reset!(curnn)
x = batch_size == 1 ?
param(rand(10)) :
param(rand(10,batch_size))
cux = gpu(x)
y = (rnn(x); rnn(x))
cuy = (curnn(cux); curnn(cux))
y = m(x)
cy = cm(cx)
@test y.data ≈ collect(cuy.data)
@test haskey(Flux.CUDA.descs, curnn.cell)
@test cy isa TrackedArray{Float32,4,CuArray{Float32,4}}
Δ = randn(size(y))
@test cpu(data(cy)) ≈ data(y)
Flux.back!(y, Δ)
Flux.back!(cuy, gpu(Δ))
g = rand(size(y)...)
Flux.back!(y, g)
Flux.back!(cy, gpu(g))
@test x.grad ≈ collect(cux.grad)
@test rnn.cell.Wi.grad ≈ collect(curnn.cell.Wi.grad)
@test rnn.cell.Wh.grad ≈ collect(curnn.cell.Wh.grad)
@test rnn.cell.b.grad ≈ collect(curnn.cell.b.grad)
@test rnn.cell.h.grad ≈ collect(curnn.cell.h.grad)
if isdefined(rnn.cell, :c)
@test rnn.cell.c.grad ≈ collect(curnn.cell.c.grad)
end
Flux.reset!(rnn)
Flux.reset!(curnn)
ohx = batch_size == 1 ?
Flux.onehot(rand(1:10), 1:10) :
Flux.onehotbatch(rand(1:10, batch_size), 1:10)
cuohx = gpu(ohx)
y = (rnn(ohx); rnn(ohx))
cuy = (curnn(cuohx); curnn(cuohx))
@test y.data ≈ collect(cuy.data)
@test m.γ.grad ≈ cpu(cm.γ.grad)
@test m.β.grad ≈ cpu(cm.β.grad)
@test x.grad ≈ cpu(x.grad)
end
@testset "2D Input" begin
x = TrackedArray(Float64.(collect(reshape(1:12, 3, 4))))
m = BatchNorm(3)
cx = gpu(x)
cm = gpu(m)
y = m(x)
cy = cm(cx)
@test cy isa TrackedArray{Float32,2,CuArray{Float32,2}}
@test cpu(data(cy)) ≈ data(y)
g = rand(size(y)...)
Flux.back!(y, g)
Flux.back!(cy, gpu(g))
@test m.γ.grad ≈ cpu(cm.γ.grad)
@test m.β.grad ≈ cpu(cm.β.grad)
@test x.grad ≈ cpu(x.grad)
end
end
end

46
test/cuda/curnn.jl Normal file
View File

@ -0,0 +1,46 @@
using Flux, CuArrays, Test
@testset "RNN" begin
@testset for R in [RNN, GRU, LSTM]
rnn = R(10, 5)
curnn = mapleaves(gpu, rnn)
@testset for batch_size in (1, 5)
Flux.reset!(rnn)
Flux.reset!(curnn)
x = batch_size == 1 ?
param(rand(10)) :
param(rand(10,batch_size))
cux = gpu(x)
y = (rnn(x); rnn(x))
cuy = (curnn(cux); curnn(cux))
@test y.data ≈ collect(cuy.data)
@test haskey(Flux.CUDA.descs, curnn.cell)
Δ = randn(size(y))
Flux.back!(y, Δ)
Flux.back!(cuy, gpu(Δ))
@test x.grad ≈ collect(cux.grad)
@test rnn.cell.Wi.grad ≈ collect(curnn.cell.Wi.grad)
@test rnn.cell.Wh.grad ≈ collect(curnn.cell.Wh.grad)
@test rnn.cell.b.grad ≈ collect(curnn.cell.b.grad)
@test rnn.cell.h.grad ≈ collect(curnn.cell.h.grad)
if isdefined(rnn.cell, :c)
@test rnn.cell.c.grad ≈ collect(curnn.cell.c.grad)
end
Flux.reset!(rnn)
Flux.reset!(curnn)
ohx = batch_size == 1 ?
Flux.onehot(rand(1:10), 1:10) :
Flux.onehotbatch(rand(1:10, batch_size), 1:10)
cuohx = gpu(ohx)
y = (rnn(ohx); rnn(ohx))
cuy = (curnn(cuohx); curnn(cuohx))
@test y.data ≈ collect(cuy.data)
end
end
end

23
test/layers/normalisation.jl
View File

@ -1,4 +1,5 @@
using Flux: testmode!
using Flux.Tracker: data
@testset "Dropout" begin
x = [1.,2.,3.]
@ -28,7 +29,8 @@ using Flux: testmode!
end
@testset "BatchNorm" begin
let m = BatchNorm(2), x = param([1 2; 3 4; 5 6]')
let m = BatchNorm(2), x = param([1 3 5;
2 4 6])
@test m.β.data == [0, 0] # initβ(2)
@test m.γ.data == [1, 1] # initγ(2)
@ -53,29 +55,30 @@ end
# .1 * 4 + 0 = .4
@test m.μ ≈ reshape([0.3, 0.4], 2, 1)
# julia> .1 .* std(x, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]
# julia> .1 .* var(x, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]
# 2×1 Array{Float64,2}:
# 1.14495
# 1.14495
@test m.σ ≈ .1 .* std(x.data, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]
# 1.3
# 1.3
@test m.σ² ≈ .1 .* var(x.data, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.]
testmode!(m)
@test !m.active
x′ = m(x).data
@test x′[1] ≈ (1 .- 0.3) / 1.1449489742783179
@test isapprox(x′[1], (1 .- 0.3) / sqrt(1.3), atol = 1.0e-5)
end
# with activation function
let m = BatchNorm(2, σ), x = param([1 2; 3 4; 5 6]')
let m = BatchNorm(2, sigmoid), x = param([1 3 5;
2 4 6])
@test m.active
m(x)
testmode!(m)
@test !m.active
x′ = m(x).data
@test x′[1] ≈ σ((1 - 0.3) / 1.1449489742783179)
y = m(x).data
@test isapprox(y, data(sigmoid.((x .- m.μ) ./ sqrt.(m.σ² .+ m.ϵ))), atol = 1.0e-7)
end
let m = BatchNorm(2), x = param(reshape(1:6, 3, 2, 1))
@ -85,7 +88,7 @@ end
end
let m = BatchNorm(2), x = param(reshape(1:12, 2, 3, 2, 1))
y = reshape(permutedims(x, [3, 1, 2, 4]), 2, :)
y = reshape(permutedims(x, [3, 1, 2, 4]), 2, :)
y = permutedims(reshape(m(y), 2, 2, 3, 1), [2, 3, 1, 4])
@test m(x) == y
end

2
test/runtests.jl
View File

@ -13,7 +13,7 @@ if Base.JLOptions().check_bounds == 1
exit()
end
using Flux, Test, Random
using Flux, Test, Random, Statistics
using Random
Random.seed!(0)