CuRNN updates

src/cuda/curnn.jl

@@ -22,10 +22,10 @@ const RNN_ALGO_PERSIST_DYNAMIC = 2
 # LSTM: [weight, bias] × [input, hidden] × [input, forget, newmem, output]
 
 function params(w::CuVector, input, hidden, n = 1)
-  slice(offset, shape) = reshape(w[offset+(1:prod(shape))], shape)
+  slice(offset, shape) = reshape(w[offset.+(1:prod(shape))], shape)
   wx = slice(0, (input, hidden*n))
   wh = slice(length(wx), (hidden, hidden*n))
-  bias = w[length(wx)+length(wh) + (1:hidden*n)]
+  bias = w[length(wx)+length(wh) .+ (1:hidden*n)]
   (wx, wh), bias
 end
 
@@ -65,8 +65,9 @@ function RNNDesc{T}(mode::Int, input::Int, hidden::Int; layers = 1) where 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, x ->
-    @check ccall((:cudnnDestroyRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x))
+  finalizer(rd) do x
+    @check ccall((:cudnnDestroyRNNDescriptor,libcudnn),cudnnStatus_t,(Ptr{Nothing},),x)
+  end
   return rd
 end
 
@@ -220,17 +221,17 @@ end
 
 import ..Flux: Flux, relu
 import ..Tracker: TrackedArray
-using CUDAnative
-using CuArrays: @cuindex, cudims
+using .CuArrays.CUDAnative
+using .CuArrays: @cuindex, cudims
 
-function copy_transpose!(dst::CuArray, src::CuArray)
+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 (blk, thr) kernel(dst, src)
+  @cuda blocks=blk threads=thr kernel(dst, src)
   return dst
 end
 
@@ -303,7 +304,7 @@ end
     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(size(h), dh), dWi.', dWh.', db))
+    nobacksies(:RNN, (dx, unbroadcast(size(h), dh), transpose(dWi), transpose(dWh), db))
   end
 end
 
@@ -318,6 +319,6 @@ end
     (dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve)
     nobacksies(:RNN,
       (dx, unbroadcast(size(h), dh), unbroadcast(size(c), dc),
-       dWi.', dWh.', db))
+       transpose(dWi), transpose(dWh), db))
   end
 end