hook up backward passes

src/cuda/cudnn.jl

@@ -105,7 +105,7 @@ getworkspace(bytes) =
     (workspace[] = CuVector{UInt8}(bytes))
 
 getworkspace(r::RNNDesc, seqlen, xdesc) =
-  getworkspace(rnnWorkspaceSize(rnn, seqlen, xdesc))
+  getworkspace(rnnWorkspaceSize(r, seqlen, xdesc))
 
 function rnnTrainingReserveSize(r::RNNDesc, seqlen, xdesc)
   size = Csize_t[0]
@@ -145,6 +145,7 @@ end
 xDesc(x) = [TensorDesc(eltype(x), (1, size(x, 1), size(x, 2)))]
 
 hDesc(h::Void) = 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
@@ -272,19 +273,72 @@ function desc(rnn)
   return d
 end
 
+import Flux.Tracker: data, isleaf, istracked, track, back_, @back
+
+struct RNNCall{R}
+  rnn::R
+end
+
+(c::RNNCall)(args...) = forward(desc(c.rnn), args..., train = true)
+
 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}
-  y, h = forward(desc(m), Flux.data(x), Flux.data(h), train = istrain(m, h, x))
+  result = istrain(m, h, x) ?
-  return h, y
+    track(RNNCall(m), x, h) :
+    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}
-  y, h = forward(desc(m), Flux.data(x), Flux.data(h), train = istrain(m, h, x))
+  result = istrain(m, h, x) ?
-  return h, y
+    track(RNNCall(m), x, h) :
+    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}
-  y, h, c = forward(desc(m), Flux.data(x), Flux.data.(h)..., train = istrain(m, h, x))
+  result = istrain(m, h, x) ?
-  return (h, c), y
+    track(RNNCall(m), x, h[1], h[2]) :
+    forward(desc(m), x, h[1], h[2])
+  return (result[2], result[3]), result[1]
+end
+
+function accum_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)
+  return dst
+end
+
+function back_(m::RNNCall{<:Union{CuRNN,CuGRU}}, y_, Δ, x, h)
+  y, ho = y_
+  dy, dho = Δ
+  dx, dh = backwardData(descs[m.rnn], y, dy, dho, data(h))
+  @back(x, dx)
+  @back(h, dh)
+  (dWi, dWh), db = backwardWeights(descs[m.rnn], data(x), data(h), y)
+  # We don't have to make this assumption, it's just slightly more complex.
+  @assert all(isleaf.((m.rnn.Wi, m.rnn.Wh, m.rnn.b)))
+  istracked(m.rnn.Wi) && accum_transpose!(m.rnn.Wi.grad, dWi)
+  istracked(m.rnn.Wh) && accum_transpose!(m.rnn.Wh.grad, dWh)
+  istracked(m.rnn.b) && accum_transpose!(m.rnn.b.grad, db)
+end
+
+function back_(m::RNNCall{<:CuLSTM}, y_, Δ, x, h, c)
+  y, ho, co = y_
+  dy, dho, dco = Δ
+  dx, dh, dc = backwardData(descs[m.rnn], y, dy, dho, dco, data(h), data(c))
+  (dWi, dWh), db = backwardWeights(descs[m.rnn], data(x), data(h), y)
+  @back(x, dx)
+  @back(h, dh)
+  @back(c, dc)
+  @assert all(isleaf.((m.rnn.Wi, m.rnn.Wh, m.rnn.b)))
+  istracked(m.rnn.Wi) && accum_transpose!(m.rnn.Wi.grad, dWi)
+  istracked(m.rnn.Wh) && accum_transpose!(m.rnn.Wh.grad, dWh)
+  istracked(m.rnn.b) && accum_transpose!(m.rnn.b.grad, db)
 end