move towards abstract interpreter model

src/backend/tensorflow/graph.jl

@@ -1,15 +1,12 @@
-import Base: @get!
+using Base: @get!
-import DataFlow: Constant, postwalk, value, inputs, constant
+using DataFlow: Constant, constant, Context, interpret, interptuple
-import TensorFlow: RawTensor
+using TensorFlow: RawTensor
 
 # TODO: implement Julia's type promotion rules
 
-cvalue(x) = x
+node(x::Tensor) = x
-cvalue(c::Constant) = c.value
+node(x::Tuple) = map(node, x)
-cvalue(v::Vertex) = cvalue(value(v))
+node(x::Number) = TensorFlow.constant(Float32(x))
-
-graph(x::Tensor) = x
-graph(x::Number) = TensorFlow.constant(Float32(x))
 
 graph(::typeof(*), args...) = *(args...)
 graph(::typeof(.*), args...) = .*(args...)
@@ -30,39 +27,32 @@ graph(::Input, x) = x
 graph(p::MaxPool, x) =
   nn.max_pool(x, [1, p.size..., 1], [1, p.stride..., 1], "VALID")
 
-graph(::DataFlow.Group, xs...) = (xs...,)
-
-graph(params::Associative, c::Conv2D, x) =
-  nn.conv2d(x, graph(params, c.filter), [1,c.stride...,1], "VALID")
-
 graph(op::Op, xs...) = op.f(xs...)
-Flux.shape(op::Op, d...) = op.shape(d...)
 
-graph{T<:AArray}(params::Associative, p::Flux.Param{T}) =
+interp(ctx, c::Conv2D, x) =
-  @get!(params, p, Variable(p.x))
+  nn.conv2d(interpret(ctx, x), interp(ctx, Constant(c.filter)), [1,c.stride...,1], "VALID")
 
-function graph(params::Associative, v::IVertex, args...)
+interp(ctx, c::Constant) = node(c.value)
-  # TODO: check number of arguments
-  v = spliceinputs(v, map(constant, args)...) |> detuple
-  postwalk(v) do v
-    vertex(graph(params, cvalue(v), cvalue.(inputs(v))...))
-  end |> value
-end
 
-function graph(params::Associative, model, args...)
+interp{T<:AArray}(ctx, p::Constant{Flux.Param{T}}) =
+  haskey(ctx[:params], p.value) ?
+     ctx[:params][p.value] :
+    (ctx[:params][p.value] = Variable(p.value.x))
+
+function interp(ctx, model, args...)
   g = Flux.graph(model)
-  g == nothing && return graph(model, args...)
+  g == nothing && return graph(model, interpret(ctx, args)...)
   DataFlow.iscyclic(g) && error("This model has a cycle; try unrolling it first.")
-  graph(params, g, args...)
+  interpret(ctx, g, interpret(ctx, args)...)
 end
 
 function tograph(model, args...)
-  params = Dict{Flux.Param,Tensor}()
+  ctx = Context(interptuple(interp), params = ObjectIdDict())
-  g = graph(params, model, args...)
+  out = interp(ctx, model, map(constant, args)...)
-  return params, g
+  return ctx[:params], out
 end
 
-TensorFlow.Tensor(m::Flux.Model, args...) = graph(Dict(), m, args...)
+TensorFlow.Tensor(m::Flux.Model, args...) = tograph(m, args...)[2]
 
 RawTensor(data::Union{Batch,Seq}) = RawTensor(rawbatch(data))
 

src/backend/tensorflow/recurrent.jl

@@ -12,7 +12,7 @@ function makesession(model::Flux.Unrolled)
   input = placeholder(Float32)
   inputs = TensorFlow.unpack(input, num = model.steps, axis = 1)
   instates = [placeholder(Float32) for _ in model.state]
-  params, (outstates, outputs) = tograph(model.graph, cgroup(instates...), cgroup(inputs...))
+  params, (outstates, outputs) = tograph(model, cgroup(instates...), cgroup(inputs...))
   output = TensorFlow.pack(outputs, axis = 1)
   run(sess, initialize_all_variables())
   sess, params, (instates, input), (outstates, output)

src/backend/tensorflow/tensorflow.jl

@@ -1,7 +1,7 @@
 module TF
 
 using ..Flux, DataFlow, TensorFlow, Juno
-import Flux: accuracy, spliceinputs, detuple
+import Flux: accuracy
 
 export tf
 
@@ -12,6 +12,8 @@ end
 
 Op(f) = Op(f, (d...) -> nothing)
 
+Flux.shape(op::Op, d...) = op.shape(d...)
+
 include("graph.jl")
 include("model.jl")
 include("recurrent.jl")