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Merge pull request #178 from schmrlng/pull-request/e6f55641

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Convert OneHot CuArrays to dense CuArrays before passing to CUDNN methods
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Mike J Innes 2018-02-21 22:34:11 +00:00 committed by GitHub
commit e3b4b16e01
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2 changed files with 37 additions and 16 deletions
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4
src/cuda/cudnn.jl
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@ -325,6 +325,10 @@ function (m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x::CuParam{T}) where T <: Union
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))
function accum_transpose!(dst::CuArray, src::CuArray)
function kernel(dst, src)
I = @cuindex dst

49
test/cuda/cudnn.jl
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@ -4,28 +4,45 @@ info("Testing Flux/CUDNN")
@testset "RNN" begin
@testset for R in [RNN, GRU, LSTM]
x = param(rand(10,5))
cux = cu(x)
rnn = R(10, 5)
curnn = mapleaves(cu, rnn)
y = (rnn(x); rnn(x))
cuy = (curnn(cux); curnn(cux))
@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 = cu(x)
y = (rnn(x); rnn(x))
cuy = (curnn(cux); curnn(cux))
@test y.data ≈ collect(cuy.data)
@test haskey(Flux.CUDA.descs, curnn.cell)
@test y.data ≈ collect(cuy.data)
@test haskey(Flux.CUDA.descs, curnn.cell)
Δ = randn(size(y))
Δ = randn(size(y))
Flux.back!(y, Δ)
Flux.back!(cuy, cu(Δ))
Flux.back!(y, Δ)
Flux.back!(cuy, cu(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)
@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 = cu(ohx)
y = (rnn(ohx); rnn(ohx))
cuy = (curnn(cuohx); curnn(cuohx))
@test y.data ≈ collect(cuy.data)
end
end
end