Support blocks with multiple inputs (#574)

Author: seanmor5

lib/axon.ex

@@ -746,17 +746,26 @@ defmodule Axon do
   """
   @doc type: :special
   def block(fun, opts \\ []) when is_function(fun) do
+    {:arity, arity} = Function.info(fun, :arity)
     opts = Keyword.validate!(opts, [:name, :meta])
     block_id = System.unique_integer([:positive, :monotonic])
 
-    fn inputs ->
+    block_fun(arity, fn inputs ->
       layer(:block, List.wrap(inputs),
         op_name: :block,
         name: opts[:name],
         meta: opts[:meta],
         block_fun: fun,
         block_id: block_id
       )
+    end)
+  end
+
+  for i <- 0..128 do
+    args = Macro.generate_arguments(i, __MODULE__)
+
+    defp block_fun(unquote(i), callback) do
+      fn unquote_splicing(args) -> callback.(unquote(args)) end
     end
   end
 

lib/axon/compiler.ex

@@ -606,17 +606,17 @@ defmodule Axon.Compiler do
          %Axon.Node{
            id: id,
            op: :block,
-           parent: [parent],
+           parent: parents,
            opts: [block_fun: block_fun, block_id: block_id],
            name: name_fn
          },
          nodes,
          cache_and_counts,
          config
        ) do
-    {[parent_id], {cache, op_counts, block_cache, model_state_meta}} =
+    {parent_ids, {cache, op_counts, block_cache, model_state_meta}} =
       Enum.map_reduce(
-        [parent],
+        parents,
         cache_and_counts,
         &to_model_funs(&1, nodes, &2, config)
       )
@@ -627,7 +627,8 @@ defmodule Axon.Compiler do
           {funs, name, block_cache, op_counts}
 
         %{} ->
-          funs = build(block_fun.(Axon.input("subgraph")), debug?: config.debug?)
+          inputs = Enum.with_index(parents, fn _, i -> Axon.input("subgraph#{i}") end)
+          funs = build(apply(block_fun, inputs), debug?: config.debug?)
           name = name_fn.(:block, op_counts)
           op_counts = Map.update(op_counts, :block, 1, fn x -> x + 1 end)
           {funs, name, Map.put(block_cache, block_id, {funs, name}), op_counts}
@@ -637,9 +638,9 @@ defmodule Axon.Compiler do
       # Recurse graph inputs and invoke cache to get parent results,
       # state, and result_cache and then apply dtype policy and hooks
       # to each input
-      {[layer_input], {state, result_cache, none?}} =
+      {layer_inputs, {state, result_cache, none?}} =
         Enum.map_reduce(
-          [parent_id],
+          parent_ids,
           {state, result_cache, false},
           fn parent_id, {state, result_cache, none?} ->
             {layer_input, {state, result_cache}} =
@@ -663,7 +664,13 @@ defmodule Axon.Compiler do
         {%Axon.None{}, {state, result_cache}}
       else
         block_params = params[block_name] || %{}
-        result = apply(block_predict_fun, [Axon.ModelState.new(block_params), layer_input])
+
+        inputs =
+          layer_inputs
+          |> Enum.with_index()
+          |> Map.new(fn {input, i} -> {"subgraph#{i}", input} end)
+
+        result = apply(block_predict_fun, [Axon.ModelState.new(block_params), inputs])
 
         {out_result, out_state} =
           case result do
@@ -685,8 +692,8 @@ defmodule Axon.Compiler do
     end
 
     init_fun = fn template, cache, result_cache, fn_stacktrace, keys ->
-      {[parent_shape], {parent_params, result_cache, none?}} =
-        Enum.map_reduce([parent_id], {%{}, result_cache, false}, fn
+      {parent_shapes, {parent_params, result_cache, none?}} =
+        Enum.map_reduce(parent_ids, {%{}, result_cache, false}, fn
           parent_id, {params, result_cache, none?} ->
             {parent_shape, {params, result_cache}} =
               call_init_cache(
@@ -706,8 +713,12 @@ defmodule Axon.Compiler do
       if none? do
         {%Axon.None{}, {parent_params, result_cache}}
       else
-        template = Nx.broadcast(0.0, parent_shape)
-        block_params = apply(block_init_fun, [template, Axon.ModelState.empty()])
+        templates =
+          parent_shapes
+          |> Enum.with_index()
+          |> Map.new(fn {shape, i} -> {"subgraph#{i}", Nx.broadcast(0.0, shape)} end)
+
+        block_params = apply(block_init_fun, [templates, Axon.ModelState.empty()])
 
         params =
           if block_params == %{} do

test/axon/compiler_test.exs

@@ -5330,6 +5330,43 @@ defmodule CompilerTest do
       input = random({1, 1})
       assert_equal(predict_fn.(params, input), actual_predict_fn.(input, k, b))
     end
+
+    test "works with multiple block inputs" do
+      block =
+        Axon.block(fn x, y ->
+          dense = Axon.block(&Axon.dense(&1, 4))
+          Axon.add(dense.(y), dense.(x))
+        end)
+
+      input1 = Axon.input("input1")
+      input2 = Axon.input("input2")
+
+      model = block.(input1, input2) |> Axon.dense(1)
+
+      {init_fn, predict_fn} = Axon.build(model)
+
+      actual_predict_fn = fn %{"input1" => x, "input2" => y}, k1, b1, k2, b2 ->
+        x = Axon.Layers.dense(x, k1, b1)
+        y = Axon.Layers.dense(y, k1, b1)
+
+        x
+        |> Nx.add(y)
+        |> Axon.Layers.dense(k2, b2)
+      end
+
+      input = %{"input1" => Nx.tensor([[0.5]]), "input2" => Nx.tensor([[0.75]])}
+
+      assert %ModelState{
+               data: %{
+                 "block_0" => %{
+                   "block_0" => %{"dense_0" => %{"kernel" => k1, "bias" => b1}}
+                 },
+                 "dense_0" => %{"kernel" => k2, "bias" => b2}
+               }
+             } = params = init_fn.(input, ModelState.empty())
+
+      assert_equal(predict_fn.(params, input), actual_predict_fn.(input, k1, b1, k2, b2))
+    end
   end
 
   describe "initializers" do