factor out fake batching semantics

src/backend/mxnet/model.jl

@@ -1,4 +1,4 @@
-using Flux: batchone, unbatchone, rebatch
+using Flux: runrawbatched
 
 type AlterParam
   param
@@ -100,20 +100,19 @@ import Base: @get!
 
 executor(m::Model, input) = @get!(m.execs, input, executor(m.graph, input))
 
-function (m::Model)(x::Batch)
-  x′ = rawbatch(x)
-  m.last = exec = @mxerr m.graph.stacks executor(m, size(x′))
-  rebatch(exec(x′))
+function (m::Model)(x)
+  runrawbatched(x) do x
+    m.last = exec = @mxerr m.graph.stacks executor(m, size(x))
+    exec(x)
+  end
 end
 
-(m::Model)(x) = unbatchone(m(batchone(x)))
-
-function Flux.back!(m::Model, Δ::Batch, x::Batch)
-  m.last = exec = m.execs[size(rawbatch(x))]
-  rebatch(back!(exec, rawbatch(Δ)))
+function Flux.back!(m::Model, Δ, x)
+  runrawbatched(Δ, x) do Δ, x
+    m.last = exec = m.execs[size(x)]
+    back!(exec, Δ)
+  end
 end
-
-Flux.back!(m::Model, Δ, x) = first(Flux.back!(m, batchone(Δ), batchone(x)))
 
 Flux.update!(m::Model, η) = (update!(m.last, η); m)
 

src/compiler/code.jl

@@ -73,8 +73,7 @@ function process_type(ex)
   quote
     $(build_type(T, params))
     $(esc(:(Flux.runmodel(self::$T, $(args...)) = $(build_forward(body, args)))))
-    ($self::$(esc(T)))($(map(arg -> :($arg::Batch), args)...)) = rebatch(runmodel($self, $(map(x->:(rawbatch($x)), args)...)))
-    ($self::$(esc(T)))($(args...)) = unbatchone($self(map(batchone, ($(args...),))...))
+    ($self::$(esc(T)))($(args...)) = runrawbatched((xs...) -> runmodel($self, xs...), $(args...))
     $(esc(:(Flux.update!(self::$T, η)))) = ($(map(p -> :(update!($self.$p, η)), pnames)...);)
     $(esc(:(Flux.graph(self::$T)))) = $(DataFlow.constructor(mapconst(esc, makegraph(body, args))))
     nothing

src/compiler/interp.jl

@@ -34,9 +34,9 @@ end
 
 # TODO: batching should be secondary
 
-function interpmodel(m, args::Batch...)
+function interpmodel_(m, args...)
   ctx = Context(mux(iline, ilambda, iconst, iargs, ituple, interp))
-  rebatch(@ithrow interp(ctx, m, map(rawbatch, args)...))
+  interp(ctx, m, args...)
 end
 
-interpmodel(m, args...) = unbatchone(interpmodel(m, batchone(args)...))
+interpmodel(m, args...) = @ithrow runrawbatched((xs...) -> interpmodel_(m, xs...), args...)

src/dims/batching.jl

@@ -17,19 +17,6 @@ convert{T,S}(::Type{Batch{T,S}},storage::S) =
        Juno.trim(collect(b)))
 end
 
-# Convenience methods for batch size 1
-
-batchone(x) = Batch((x,))
-batchone(x::Batch) = x
-batchone(x::Tuple) = map(batchone, x)
-
-function unbatchone(xs::Batch)
-  @assert length(xs) == 1
-  return first(xs)
-end
-
-unbatchone(xs::Tuple) = map(unbatchone, xs)
-
 function rebatch(xs)
   dims = ndims(xs)-1
   T = Array{eltype(xs),dims}
@@ -37,8 +24,38 @@ function rebatch(xs)
   Batch{T,B}(xs)
 end
 
-rebatch(xs::Tuple) = map(rebatch, xs)
-
 convertel(T::Type, xs::Batch) =
   isa(eltype(eltype(xs)), T) ? xs :
     Batch(map(x->convertel(T, x), xs))
+
+# Add batching semantics to functions operating on raw arrays
+# TODO: remove this in favour of full batching semantics
+
+mapt(f, x) = f(x)
+mapt(f, xs::Tuple) = map(f, xs)
+
+batchone(x) = Batch((x,))
+batchone(x::Batch) = x
+
+function unbatchone(xs::Batch)
+  @assert length(xs) == 1
+  return first(xs)
+end
+
+isbatched(x) = false
+isbatched(x::Batch) = true
+isbatched(xs::Tuple) = any(isbatched, xs)
+
+batchify(xs) = isbatched(xs) ? (xs, true) : (mapt(batchone, xs), false)
+
+function runbatched(f, xs...)
+  # TODO: decide what to do with mixed inputs
+  xs, batched = batchify(xs)
+  ys = f(xs...)
+  batched ? ys : mapt(unbatchone, ys)
+end
+
+runrawbatched(f, xs...) =
+  runbatched((xs...) -> mapt(rebatch,
+                             f(mapt(rawbatch, xs)...)),
+             xs...)