WIP: Eager mode

Project.toml

@@ -5,8 +5,10 @@ version = "0.12.0"
 
 [deps]
 AutoHashEquals = "15f4f7f2-30c1-5605-9d31-71845cf9641f"
+CRC32c = "8bf52ea8-c179-5cab-976a-9e18b702a9bc"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"

examples/diffeq.jl

@@ -0,0 +1,8 @@
+using DifferentialEquations
+
+f(u,p,t)=1.01 .* u
+
+u0=constant(0.5)
+tspan=(0.0,1.0)
+prob=ODEProblem(f, u0, tspan)
+s=solve(prob)

examples/keras.jl

@@ -0,0 +1,12 @@
+using TensorFlow
+tf=TensorFlow
+m = tf.Sequential()
+
+tf.add(m, tf.Dense(3,10))
+tf.add(m, tf.Relu())
+tf.add(m, tf.Dense(10, 3))
+
+x=constant(randn(5,3))
+y=3x+5
+tf.compile(m, optimizer=tf.SGD(lr=1e-4), loss=tf.mse)
+tf.fit(m, x, y, n_epochs=200)

examples/neural_ode.jl

@@ -0,0 +1,8 @@
+using TensorFlow
+using DifferentialEquations
+
+model = tf.Sequential([tf.Dense(2, 1)])
+f(u, p, t) = model(u)
+problem = ODEProblem(f, u0=[0.5, 0.5], tspan=(0.0, 1.0))
+tf.compile(model, optimizer=tf.Adam(), loss=tf.diffeq_loss(problem, t=[0.0, 0.5, 1.0]))
+tf.fit(m, [1.0, 2.0, 5.0], n_epochs=100)

src/TensorFlow.jl

@@ -141,8 +141,23 @@ function deallocator(data, len, arg)
 
 end
 
+struct Context
+    attrs::Dict
+end
+
+Context() = Context(Dict())
+
+struct ContextStack
+    contexts::Vector{Context}
+end
+
+ContextStack() = ContextStack(Context[])
+
+global_context = ContextStack()
+
 function __init__()
     c_deallocator[] = @cfunction(deallocator, Cvoid, (Ptr{Cvoid}, Csize_t, Ptr{Cvoid}))
+    push!(global_context, default_context())
 end
 
 function load_python_process(;force_reload=false)
@@ -198,6 +213,7 @@ include("meta.jl")
 include("constants.jl")
 include("tensorflow_protos.jl")
 include("core.jl")
+include("eager.jl")
 include("run.jl")
 include("version.jl")
 include("ops.jl")
@@ -211,5 +227,7 @@ include("summary.jl")
 include("deprecated.jl")
 include("show.jl")
 include("generate_ops.jl")
+include("tape.jl")
+include("keras.jl")
 
 end

src/core.jl

@@ -521,6 +521,10 @@ mutable struct DeviceList
         end
         this
     end
+
+    function DeviceList(ptr, count)
+        new(ptr, count)
+    end
 end
 
 struct DeviceInfo
@@ -663,6 +667,8 @@ RawTensor(data::AbstractArray) = RawTensor(collect(data))
 
 RawTensor(t::RawTensor) = t
 
+Base.unsafe_convert(::Type{Ptr{Cvoid}}, t::RawTensor) = t.ptr
+
 function varint_encode(b::IO, n::Integer)
     while n ≥ 2^7
         write(b, UInt8(0b10000000 | (n & 0b1111111)))
@@ -803,7 +809,7 @@ function Base.sizeof(t::RawTensor)
     @tfcall(:TF_TensorByteSize, Csize_t, (Ptr{Cvoid},), t.ptr) |> Int
 end
 
-function set_device(node_desc, device::String)
+function set_device(node_desc, device)
     @tfcall(:TF_SetDevice, Cvoid,
         (Ptr{Cvoid}, Cstring),
         node_desc.ptr, device)
@@ -1168,7 +1174,7 @@ function load_proto(value::tensorflow.AttrValue)
         load_proto(value.list)
     elseif has_field(value, :_type)
         type_ = value._type
-        proto_type_map[type_]
+        get(proto_type_map, type_, Float32)  # wrong
     end
 end
 
@@ -1227,6 +1233,8 @@ function Tensor(op::Operation, value_index::Int)
     Tensor{get_output_type(base_tensor)}(op, value_index)
 end
 
+# Tensor constructors
+
 Tensor(op::Operation) = Tensor(op, 1)
 
 Tensor(value) = convert(Tensor, value)
@@ -1242,6 +1250,7 @@ Base.convert(::Type{Tensor{Any}}, value::Tensor{R}) where {R} = value
 
 Base.convert(::Type{Tensor{T}}, value) where {T} =  convert(Tensor{T}, constant(value))
 
+
 function operation_output_type(port::Port)
     @tfcall(:TF_OperationOutputType, TF_DataType, (Port,), port)
 end