TastyPancakes/Flux.jl
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param offsets

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This commit is contained in:
Mike J Innes 2018-01-26 18:29:29 +00:00
parent af0c5523ff
commit 0b886507dc
1 changed files with 28 additions and 2 deletions
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30
src/cuda/cudnn.jl
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@ -1,5 +1,5 @@
using CuArrays.CUDNN: @check, libcudnn, cudnnStatus_t, libcudnn_handle, using CuArrays.CUDNN: @check, libcudnn, cudnnStatus_t, libcudnn_handle,
cudnnDataType, TensorDesc cudnnDataType, TensorDesc, FilterDesc
mutable struct DropoutDesc mutable struct DropoutDesc
ptr::Ptr{Void} ptr::Ptr{Void}
@ -80,5 +80,31 @@ function rnnParamSize(r::RNNDesc)
size = Csize_t[0] size = Csize_t[0]
@check ccall((:cudnnGetRNNParamsSize, libcudnn), cudnnStatus_t, (Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Csize_t},Cint), @check ccall((:cudnnGetRNNParamsSize, libcudnn), cudnnStatus_t, (Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Csize_t},Cint),
libcudnn_handle[], r, TensorDesc(r.T, (1,r.input,1)), size, cudnnDataType(r.T)) libcudnn_handle[], r, TensorDesc(r.T, (1,r.input,1)), size, cudnnDataType(r.T))
return Int(size[]) return Int(size[])÷sizeof(r.T)
end
# 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 rnnMatrixOffset(r::RNNDesc, w::CuArray, param; layer = 1)
ptr = [C_NULL]
desc = FilterDesc(CuArrays.CUDNN.createFilterDesc())
@check ccall((:cudnnGetRNNLinLayerMatrixParams,libcudnn), cudnnStatus_t, (Ptr{Void},Ptr{Void},Cint,Ptr{Void},Ptr{Void},Ptr{Void},Cint,Ptr{Void},Ptr{Ptr{Void}}),
libcudnn_handle[], r, layer-1, TensorDesc(r.T, (1,r.input,1)), FilterDesc(reshape(w, 1, 1, :)), w, param-1, desc, ptr)
offset = ptr[]-Base.cconvert(Ptr{Void},w).ptr
CuArrays.CUDNN.free(desc)
return Int(offset)÷sizeof(r.T)
end
function rnnBiasOffset(r::RNNDesc, w::CuArray, param; layer = 1)
ptr = [C_NULL]
desc = FilterDesc(CuArrays.CUDNN.createFilterDesc())
@check ccall((:cudnnGetRNNLinLayerBiasParams,libcudnn), cudnnStatus_t, (Ptr{Void},Ptr{Void},Cint,Ptr{Void},Ptr{Void},Ptr{Void},Cint,Ptr{Void},Ptr{Ptr{Void}}),
libcudnn_handle[], r, layer-1, TensorDesc(r.T, (1,r.input,1)), FilterDesc(reshape(w, 1, 1, :)), w, param-1, desc, ptr)
offset = ptr[]-Base.cconvert(Ptr{Void},w).ptr
dims = size(desc)
CuArrays.CUDNN.free(desc)
return Int(offset)÷sizeof(r.T)
end end