Bob/serialize2 (#267)

* add json lib 2.1.1

* add json serialization of graph

* cleanup

* enhance unit test

* remove FunctionProvider class and replace with virtual get_function() in Node

* remove json code from element_type

* move serialize to be directly in the ngraph namespace. cleanup header file.

* add cname check to element::operator==

* add using json = nlohmann::json

src/ngraph/CMakeLists.txt

@@ -88,15 +88,29 @@ set (SRC
     runtime/tensor_view.cpp
     runtime/tuple.cpp
     runtime/utils.cpp
+    serializer.cpp
     shape.cpp
     types/element_type.cpp
     types/type.cpp
     util.cpp
     )
 
+message(STATUS  ${CMAKE_CURRENT_SOURCE_DIR}/ops)
+file(GLOB_RECURSE OPS "${CMAKE_CURRENT_SOURCE_DIR}/ops" "${CMAKE_CURRENT_SOURCE_DIR}/ops/*.hpp")
+foreach(OP ${OPS})
+    file(STRINGS ${OP} OP_CLASS REGEX "class [A-Za-z0-9_]+ :")
+    foreach(LINE ${OP_CLASS})
+        string(REGEX REPLACE ".*class ([A-Za-z0-9_]+) : public ([A-Za-z0-9_]+).*" "\\1:\\2" CLASS_FOUND ${LINE})
+        set(OP_CLASS_LIST ${OP_CLASS_LIST} ${CLASS_FOUND})
+   endforeach(LINE ${OP_CLASS})
+endforeach()
+message(STATUS "${CMAKE_CURRENT_BINARY_DIR}/ops_list.txt")
+string(REPLACE ";" "\n" OP_CLASS_LINES "${OP_CLASS_LIST}")
+file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/ops_list.txt" "${OP_CLASS_LINES}")
+
 # find_program (GRAPHVIZ dot)
 # message (STATUS "graphviz '${GRAPHVIZ}'")
-find_package(Graphviz)
+find_package(Graphviz QUIET)
 if (GRAPHVIZ_FOUND)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DGRAPHVIZ_FOUND")
 endif()

src/ngraph/function.hpp

@@ -40,6 +40,7 @@ namespace ngraph
                  const std::string& name = "");
 
         std::shared_ptr<Node> get_result() { return m_result; }
+        std::shared_ptr<const Node> get_result() const { return m_result; }
         const std::vector<std::shared_ptr<op::Parameter>>& get_parameters() const
         {
             return m_parameters;

src/ngraph/json.hpp

 // clang-format off
+#pragma clang diagnostic ignored "-Weverything"
+
 /*
     __ _____ _____ _____
  __|  |   __|     |   | |  JSON for Modern C++

src/ngraph/node.cpp

@@ -181,6 +181,11 @@ std::shared_ptr<Node> Node::backprop_node(const std::shared_ptr<Node>& x,
     return adjoints_it->second.get(x);
 }
 
+std::shared_ptr<Function> Node::get_function() const
+{
+    return nullptr;
+}
+
 namespace ngraph
 {
     ostream& operator<<(ostream& out, const Node& node)

src/ngraph/node.hpp

@@ -111,6 +111,8 @@ namespace ngraph
         virtual std::shared_ptr<Node>
             copy_with_new_args(const std::vector<std::shared_ptr<Node>>& new_args) const = 0;
 
+        virtual std::shared_ptr<Function> get_function() const;
+
     protected:
         std::string m_node_type;
         Nodes m_arguments;

src/ngraph/ops/function_call.hpp

@@ -45,7 +45,7 @@ namespace ngraph
         /// | Backend | Status             |
         /// | ------- | ------------------ |
         /// | NGVM    | Fully implemented. |
-        class FunctionCall : public ngraph::Node
+        class FunctionCall : public Node
         {
         public:
             /// \brief Constructs a function call operation.
@@ -62,7 +62,7 @@ namespace ngraph
             }
 
             /// \return The function to be called.
-            std::shared_ptr<Function> get_function() const { return m_function; }
+            std::shared_ptr<Function> get_function() const override { return m_function; }
         protected:
             std::shared_ptr<Function> m_function;
         };

src/ngraph/ops/get_tuple_element.hpp

@@ -20,8 +20,6 @@ namespace ngraph
 {
     namespace op
     {
-        class Node;
-
         /// \brief Operation to get an element from a tuple.
         ///
         /// ## Parameters
@@ -47,7 +45,7 @@ namespace ngraph
         /// | Backend | Status             |
         /// | ------- | ------------------ |
         /// | NGVM    | Fully implemented. |
-        class GetTupleElement : public ngraph::Node
+        class GetTupleElement : public Node
         {
         public:
             /// \brief Constructs a get-tuple-element operation.

src/ngraph/ops/reduce.hpp

@@ -111,10 +111,7 @@ namespace ngraph
             }
 
             /// \return The function to use for reduction.
-            std::shared_ptr<Function> get_reduction_function() const
-            {
-                return m_reduction_function;
-            }
+            std::shared_ptr<Function> get_function() const override { return m_reduction_function; }
             /// \return The axis positions (0-based) to be eliminated through reduction.
             const AxisSet& get_reduction_axes() const { return m_reduction_axes; }
         protected:

src/ngraph/ops/tuple.hpp

@@ -39,7 +39,7 @@ namespace ngraph
         /// | Backend | Status             |
         /// | ------- | ------------------ |
         /// | NGVM    | Fully implemented. |
-        class Tuple : public ngraph::Node
+        class Tuple : public Node
         {
         public:
             /// \brief Constructs a tuple construction operation.

src/ngraph/pass/manager.cpp

@@ -16,12 +16,12 @@
 #include <memory>
 
 #include "ngraph/function.hpp"
-#include "ngraph/log.hpp"
 #include "ngraph/node.hpp"
 #include "ngraph/ops/function_call.hpp"
 #include "ngraph/ops/reduce.hpp"
 #include "ngraph/pass/manager.hpp"
 #include "ngraph/pass/pass.hpp"
+#include "ngraph/util.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -38,30 +38,11 @@ void ngraph::pass::Manager::initialize_default_passes()
 {
 }
 
-static void find_functions(shared_ptr<Function> f, set<shared_ptr<Function>>& funcs)
-{
-    funcs.insert(f);
-
-    for (shared_ptr<Node> node : f->get_ops())
-    {
-        shared_ptr<op::FunctionCall> fc = dynamic_pointer_cast<op::FunctionCall>(node);
-        if (fc)
-        {
-            find_functions(fc->get_function(), funcs);
-        }
-        shared_ptr<op::Reduce> reduce = dynamic_pointer_cast<op::Reduce>(node);
-        if (reduce)
-        {
-            find_functions(reduce->get_reduction_function(), funcs);
-        }
-    }
-}
-
 void ngraph::pass::Manager::run_passes(shared_ptr<Function> func)
 {
     // find all functions
     set<shared_ptr<Function>> tfs;
-    find_functions(func, tfs);
+    traverse_functions(func, [&](shared_ptr<Function> f) { tfs.insert(f); });
     get_state().set_functions(tfs);
 
     vector<shared_ptr<Function>> fs;

src/ngraph/runtime/cpu/emitter.cpp

@@ -1001,7 +1001,7 @@ void Emitter::EmitReduce(const ngraph::Node* n,
                          const std::vector<TensorViewInfo>& outputs)
 {
     auto reduce = static_cast<const op::Reduce*>(n);
-    auto reduction_function = reduce->get_reduction_function();
+    auto reduction_function = reduce->get_function();
 
     auto reductee_type = reduce->get_arguments().at(0)->get_value_type();
     auto reductee_tensor_view_type = dynamic_pointer_cast<const TensorViewType>(reductee_type);

src/ngraph/runtime/cpu/external_function.cpp

@@ -248,9 +248,9 @@ using namespace ngraph::runtime::cpu::eigen;
             {
                 for (descriptor::Tensor* tensor : node->liveness_new_list)
                 {
-                    TU << tensor->get_element_type() << "* " << tensor->get_name() << " = ("
-                       << tensor->get_element_type() << "*)(memory_handler.get_ptr("
-                       << tensor->get_pool_offset() << "));\n";
+                    TU << tensor->get_element_type().c_type_string() << "* " << tensor->get_name()
+                       << " = (" << tensor->get_element_type().c_type_string()
+                       << "*)(memory_handler.get_ptr(" << tensor->get_pool_offset() << "));\n";
                 }
             }
             TU << "\n";

src/ngraph/runtime/ngvm/external_function.cpp

@@ -740,7 +740,7 @@ ExternalFunction::OpMap& ExternalFunction::get_op_map()
         REGISTER_TO_OP_MAP(op::Reduce)
         {
             auto reduce = static_cast<const op::Reduce*>(n);
-            auto reduction_function = reduce->get_reduction_function();
+            auto reduction_function = reduce->get_function();
 
             std::shared_ptr<ExternalFunction> external;
 

src/ngraph/serializer.cpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// ----------------------------------------------------------------------------
+
+#include "ngraph/serializer.hpp"
+#include "ngraph/ops/abs.hpp"
+#include "ngraph/ops/acos.hpp"
+#include "ngraph/ops/add.hpp"
+#include "ngraph/ops/asin.hpp"
+#include "ngraph/ops/atan.hpp"
+#include "ngraph/ops/broadcast.hpp"
+#include "ngraph/ops/ceiling.hpp"
+#include "ngraph/ops/concatenate.hpp"
+#include "ngraph/ops/constant.hpp"
+#include "ngraph/ops/convert.hpp"
+#include "ngraph/ops/cos.hpp"
+#include "ngraph/ops/cosh.hpp"
+#include "ngraph/ops/divide.hpp"
+#include "ngraph/ops/dot.hpp"
+#include "ngraph/ops/equal.hpp"
+#include "ngraph/ops/exp.hpp"
+#include "ngraph/ops/floor.hpp"
+#include "ngraph/ops/function_call.hpp"
+#include "ngraph/ops/get_tuple_element.hpp"
+#include "ngraph/ops/greater.hpp"
+#include "ngraph/ops/greater_eq.hpp"
+#include "ngraph/ops/less.hpp"
+#include "ngraph/ops/less_eq.hpp"
+#include "ngraph/ops/log.hpp"
+#include "ngraph/ops/maximum.hpp"
+#include "ngraph/ops/minimum.hpp"
+#include "ngraph/ops/multiply.hpp"
+#include "ngraph/ops/negative.hpp"
+#include "ngraph/ops/not_equal.hpp"
+#include "ngraph/ops/power.hpp"
+#include "ngraph/ops/reduce.hpp"
+#include "ngraph/ops/remainder.hpp"
+#include "ngraph/ops/reshape.hpp"
+#include "ngraph/ops/select.hpp"
+#include "ngraph/ops/sign.hpp"
+#include "ngraph/ops/sin.hpp"
+#include "ngraph/ops/sinh.hpp"
+#include "ngraph/ops/slice.hpp"
+#include "ngraph/ops/subtract.hpp"
+#include "ngraph/ops/sum.hpp"
+#include "ngraph/ops/tan.hpp"
+#include "ngraph/ops/tanh.hpp"
+#include "ngraph/ops/tuple.hpp"
+#include "ngraph/util.hpp"
+
+using namespace ngraph;
+using namespace std;
+using json = nlohmann::json;
+
+std::shared_ptr<ngraph::Function>
+    read_function(const json&, std::unordered_map<std::string, std::shared_ptr<Function>>&);
+
+json write(const ngraph::Function&);
+json write(const ngraph::Node&);
+json write(const ngraph::element::Type&);
+
+// This stupidity is caused by the fact that we do not pass element types
+// by value but by reference even though they can be compared. There is no reason to pass
+// them by reference EVERYWERE but here we are...
+const element::Type& to_ref(const element::Type& t)
+{
+    if (t == element::boolean)
+    {
+        return element::boolean;
+    }
+    if (t == element::f32)
+    {
+        return element::f32;
+    }
+    if (t == element::f64)
+    {
+        return element::f64;
+    }
+    if (t == element::i8)
+    {
+        return element::i8;
+    }
+    if (t == element::i16)
+    {
+        return element::i16;
+    }
+    if (t == element::i32)
+    {
+        return element::i32;
+    }
+    if (t == element::i64)
+    {
+        return element::i64;
+    }
+    if (t == element::u8)
+    {
+        return element::u8;
+    }
+    if (t == element::u16)
+    {
+        return element::u16;
+    }
+    if (t == element::u32)
+    {
+        return element::u32;
+    }
+    if (t == element::u64)
+    {
+        return element::u64;
+    }
+    throw runtime_error("type not valid");
+}
+
+static json write_element_type(const ngraph::element::Type& n)
+{
+    json j;
+    j["bitwidth"] = n.bitwidth();
+    j["is_real"] = n.is_real();
+    j["is_signed"] = n.is_signed();
+    j["c_type_string"] = n.c_type_string();
+    return j;
+}
+
+static const element::Type& read_element_type(const json& j)
+{
+    size_t bitwidth = j.at("bitwidth").get<size_t>();
+    bool is_real = j.at("is_real").get<bool>();
+    bool is_signed = j.at("is_signed").get<bool>();
+    string c_type_string = j.at("c_type_string").get<string>();
+
+    return to_ref(element::Type(bitwidth, is_real, is_signed, c_type_string));
+}
+
+string ngraph::serialize(shared_ptr<ngraph::Function> func)
+{
+    json j;
+    vector<json> functions;
+    traverse_functions(func,
+                       [&](shared_ptr<ngraph::Function> f) { functions.push_back(write(*f)); });
+    for (auto it = functions.rbegin(); it != functions.rend(); it++)
+    {
+        j.push_back(*it);
+    }
+
+    return j.dump();
+}
+
+shared_ptr<ngraph::Function> ngraph::deserialize(istream& in)
+{
+    json js = json::array();
+    shared_ptr<Function> rc;
+    in >> js;
+    unordered_map<string, shared_ptr<Function>> function_map;
+    for (json func : js)
+    {
+        shared_ptr<Function> f = read_function(func, function_map);
+        if (rc == nullptr)
+        {
+            rc = f;
+        }
+    }
+
+    return rc;
+}
+
+json write(const Function& f)
+{
+    json function;
+    function["name"] = f.get_name();
+    function["result_type"] = write_element_type(f.get_result_type()->get_element_type());
+    function["result_shape"] = f.get_result_type()->get_shape();
+    for (auto param : f.get_parameters())
+    {
+        function["parameters"].push_back(param->get_name());
+    }
+    function["result"].push_back(f.get_result()->get_name());
+
+    list<shared_ptr<Node>> result_list;
+    {
+        deque<Node*> independent_nodes;
+        unordered_map<const Node*, size_t> node_depencency_count;
+        unordered_map<Node*, shared_ptr<Node>> node_map;
+
+        traverse_nodes(const_cast<Function*>(&f), [&](shared_ptr<Node> node) {
+            node_map[node.get()] = node;
+            node_depencency_count[node.get()] = node->get_arguments().size();
+            if (node->get_arguments().size() == 0)
+            {
+                independent_nodes.push_back(node.get());
+            }
+        });
+
+        while (independent_nodes.size() > 0)
+        {
+            auto independent_node = independent_nodes.front();
+            result_list.push_back(node_map[independent_node]);
+            independent_nodes.pop_front();
+
+            for (auto user : independent_node->users())
+            {
+                node_depencency_count[user] -= 1;
+                size_t count = node_depencency_count[user];
+                if (count == 0)
+                {
+                    independent_nodes.push_back(user);
+                }
+            }
+        }
+    }
+
+    json nodes;
+    for (shared_ptr<Node> node : result_list)
+    {
+        nodes.push_back(write(*node));
+    }
+    function["ops"] = nodes;
+    return function;
+}
+
+shared_ptr<ngraph::Function>
+    read_function(const json& func_js, unordered_map<string, shared_ptr<Function>>& function_map)
+{
+    shared_ptr<ngraph::Function> rc;
+
+    string func_name = func_js.at("name").get<string>();
+    vector<string> func_result = func_js.at("result").get<vector<string>>();
+    vector<string> func_parameters = func_js.at("parameters").get<vector<string>>();
+    const element::Type& result_type = read_element_type(func_js.at("result_type"));
+    vector<size_t> result_shape = func_js.at("result_shape").get<vector<size_t>>();
+    unordered_map<string, shared_ptr<Node>> node_map;
+    for (json node_js : func_js.at("ops"))
+    {
+        string node_name = node_js.at("name").get<string>();
+        string node_op = node_js.at("op").get<string>();
+        const element::Type& node_etype = read_element_type(node_js.at("element_type"));
+        vector<string> node_inputs = node_js.at("inputs").get<vector<string>>();
+        vector<string> node_outputs = node_js.at("outputs").get<vector<string>>();
+        shared_ptr<Node> node;
+        shared_ptr<Function> function_ptr = nullptr;
+        vector<shared_ptr<Node>> args;
+        for (const string& name : node_inputs)
+        {
+            args.push_back(node_map.at(name));
+        }
+
+        vector<string> known_nodes;
+        for (auto x : node_map)
+        {
+            known_nodes.push_back(x.first);
+        }
+
+        if (node_op == "Abs")
+        {
+            node = make_shared<op::Abs>(args[0]);
+        }
+        else if (node_op == "Acos")
+        {
+            node = make_shared<op::Acos>(args[0]);
+        }
+        else if (node_op == "Add")
+        {
+            node = make_shared<op::Add>(args[0], args[1]);
+        }
+        else if (node_op == "Asin")
+        {
+            node = make_shared<op::Asin>(args[0]);
+        }
+        else if (node_op == "Atan")
+        {
+            node = make_shared<op::Atan>(args[0]);
+        }
+        else if (node_op == "Broadcast")
+        {
+            auto shape = node_js.at("shape").get<vector<size_t>>();
+            auto axes = node_js.at("axes").get<set<size_t>>();
+            node = make_shared<op::Broadcast>(args[0], shape, axes);
+        }
+        else if (node_op == "Ceiling")
+        {
+            node = make_shared<op::Ceiling>(args[0]);
+        }
+        else if (node_op == "Concat")
+        {
+            auto axis = node_js.at("axis").get<size_t>();
+            node = make_shared<op::Concat>(args, axis);
+        }
+        else if (node_op == "Constant")
+        {
+            auto shape = node_js.at("shape").get<vector<size_t>>();
+            auto value = node_js.at("value").get<vector<string>>();
+            node = make_shared<op::Constant>(node_etype, shape, value);
+        }
+        else if (node_op == "Convert")
+        {
+            auto target_type = read_element_type(node_js.at("target_type"));
+            node = make_shared<op::Convert>(args[0], target_type);
+        }
+        else if (node_op == "Cos")
+        {
+            node = make_shared<op::Cos>(args[0]);
+        }
+        else if (node_op == "Cosh")
+        {
+            node = make_shared<op::Cosh>(args[0]);
+        }
+        else if (node_op == "Divide")
+        {
+            node = make_shared<op::Divide>(args[0], args[1]);
+        }
+        else if (node_op == "Dot")
+        {
+            node = make_shared<op::Dot>(args[0], args[1]);
+        }
+        else if (node_op == "Equal")
+        {
+            node = make_shared<op::Equal>(args[0], args[1]);
+        }
+        else if (node_op == "Exp")
+        {
+            node = make_shared<op::Exp>(args[0]);
+        }
+        else if (node_op == "Floor")
+        {
+            node = make_shared<op::Floor>(args[0]);
+        }
+        else if (node_op == "FunctionCall")
+        {
+            string function_name = node_js.at("function").get<string>();
+            shared_ptr<Function> f_ptr = function_map.at(function_name);
+            node = make_shared<op::FunctionCall>(f_ptr, args);
+        }
+        // else if (node_op == "GetTupleElement")
+        // {
+        //     node = make_shared<op::GetTupleElement>(args[0]);
+        // }
+        else if (node_op == "Greater")
+        {
+            node = make_shared<op::Greater>(args[0], args[1]);
+        }
+        else if (node_op == "GreaterEq")
+        {
+            node = make_shared<op::GreaterEq>(args[0], args[1]);
+        }
+        else if (node_op == "Less")
+        {
+            node = make_shared<op::Less>(args[0], args[1]);
+        }
+        else if (node_op == "LessEq")
+        {
+            node = make_shared<op::LessEq>(args[0], args[1]);
+        }
+        else if (node_op == "Log")
+        {
+            node = make_shared<op::Log>(args[0]);
+        }
+        else if (node_op == "Maximum")
+        {
+            node = make_shared<op::Maximum>(args[0], args[1]);
+        }
+        else if (node_op == "Minimum")
+        {
+            node = make_shared<op::Minimum>(args[0], args[1]);
+        }
+        else if (node_op == "Multiply")
+        {
+            node = make_shared<op::Multiply>(args[0], args[1]);
+        }
+        else if (node_op == "Negative")
+        {
+            node = make_shared<op::Negative>(args[0]);
+        }
+        else if (node_op == "NotEqual")
+        {
+            node = make_shared<op::NotEqual>(args[0], args[1]);
+        }
+        else if (node_op == "Parameter")
+        {
+            auto shape = node_js.at("shape");
+            node = make_shared<op::Parameter>(node_etype, shape);
+        }
+        else if (node_op == "Power")
+        {
+            node = make_shared<op::Power>(args[0], args[1]);
+        }
+        else if (node_op == "Reduce")
+        {
+            auto reduction_axes = node_js.at("reduction_axes").get<set<size_t>>();
+            node = make_shared<op::Reduce>(args[0], args[1], function_ptr, reduction_axes);
+        }
+        else if (node_op == "Remainder")
+        {
+            node = make_shared<op::Remainder>(args[0], args[1]);
+        }
+        else if (node_op == "Reshape")
+        {
+            auto input_order = node_js.at("input_order").get<vector<size_t>>();
+            auto output_shape = node_js.at("output_shape").get<vector<size_t>>();
+            node = make_shared<op::Reshape>(args[0], input_order, output_shape);
+        }
+        else if (node_op == "Select")
+        {
+            node = make_shared<op::Select>(args[0], args[1], args[2]);
+        }
+        else if (node_op == "Sign")
+        {
+            node = make_shared<op::Sign>(args[0]);
+        }
+        else if (node_op == "Sin")
+        {
+            node = make_shared<op::Sin>(args[0]);
+        }
+        else if (node_op == "Sinh")
+        {
+            node = make_shared<op::Sinh>(args[0]);
+        }
+        else if (node_op == "Slice")
+        {
+            auto lower_bounds = node_js.at("lower_bounds").get<vector<size_t>>();
+            auto upper_bounds = node_js.at("upper_bounds").get<vector<size_t>>();
+            auto step = node_js.at("step").get<vector<size_t>>();
+            node = make_shared<op::Slice>(args[0], lower_bounds, upper_bounds, step);
+        }
+        else if (node_op == "Subtract")
+        {
+            node = make_shared<op::Subtract>(args[0], args[1]);
+        }
+        else if (node_op == "Sum")
+        {
+            auto reduction_axes = node_js.at("reduction_axes").get<set<size_t>>();
+            node = make_shared<op::Sum>(args[0], reduction_axes);
+        }
+        else if (node_op == "Tan")
+        {
+            node = make_shared<op::Tan>(args[0]);
+        }
+        else if (node_op == "Tanh")
+        {
+            node = make_shared<op::Tanh>(args[0]);
+        }
+        else if (node_op == "Tuple")
+        {
+            node = make_shared<op::Tuple>(args);
+        }
+        else
+        {
+            stringstream ss;
+            ss << "unsupported op " << node_op;
+            throw runtime_error(ss.str());
+        }
+        node_map[node_name] = node;
+    }
+
+    auto result = node_map.at(func_result[0]);
+    std::vector<std::shared_ptr<op::Parameter>> params;
+    for (auto param_name : func_parameters)
+    {
+        params.push_back(dynamic_pointer_cast<op::Parameter>(node_map.at(param_name)));
+    }
+    auto rt = make_shared<TensorViewType>(result_type, result_shape);
+    rc = make_shared<Function>(result, rt, params, func_name);
+    function_map[func_name] = rc;
+
+    return rc;
+}
+
+json write(const Node& n)
+{
+    json node;
+    node["name"] = n.get_name();
+    node["op"] = n.description();
+    node["element_type"] = write_element_type(n.get_element_type());
+    json inputs = json::array();
+    json outputs = json::array();
+    for (const descriptor::Input& input : n.get_inputs())
+    {
+        inputs.push_back(input.get_output().get_node()->get_name());
+    }
+    for (const descriptor::Output& output : n.get_outputs())
+    {
+        outputs.push_back(output.get_node()->get_name());
+    }
+    node["inputs"] = inputs;
+    node["outputs"] = outputs;
+
+    string node_op = n.description();
+    if (node_op == "Abs")
+    {
+    }
+    else if (node_op == "Acos")
+    {
+    }
+    else if (node_op == "Add")
+    {
+    }
+    else if (node_op == "Asin")
+    {
+    }
+    else if (node_op == "Atan")
+    {
+    }
+    else if (node_op == "Broadcast")
+    {
+        auto tmp = dynamic_cast<const op::Broadcast*>(&n);
+        node["axes"] = tmp->get_broadcast_axes();
+        node["shape"] = tmp->get_broadcast_shape();
+    }
+    else if (node_op == "Ceiling")
+    {
+    }
+    else if (node_op == "Concat")
+    {
+        auto tmp = dynamic_cast<const op::Concat*>(&n);
+        node["axis"] = tmp->get_concatenation_axis();
+    }
+    else if (node_op == "Constant")
+    {
+        auto tmp = dynamic_cast<const op::Constant*>(&n);
+        node["value"] = tmp->get_value_strings();
+        node["shape"] = tmp->get_shape();
+    }
+    else if (node_op == "Convert")
+    {
+        auto tmp = dynamic_cast<const op::Convert*>(&n);
+        node["target_type"] = write_element_type(tmp->get_convert_element_type());
+    }
+    else if (node_op == "Cos")
+    {
+    }
+    else if (node_op == "Cosh")
+    {
+    }
+    else if (node_op == "Divide")
+    {
+    }
+    else if (node_op == "Dot")
+    {
+    }
+    else if (node_op == "Equal")
+    {
+    }
+    else if (node_op == "Exp")
+    {
+    }
+    else if (node_op == "Floor")
+    {
+    }
+    else if (node_op == "FunctionCall")
+    {
+        node["function"] = n.get_function()->get_name();
+    }
+    else if (node_op == "GetTupleElement")
+    {
+    }
+    else if (node_op == "Greater")
+    {
+    }
+    else if (node_op == "GreaterEq")
+    {
+    }
+    else if (node_op == "Less")
+    {
+    }
+    else if (node_op == "LessEq")
+    {
+    }
+    else if (node_op == "Log")
+    {
+    }
+    else if (node_op == "Maximum")
+    {
+    }
+    else if (node_op == "Minimum")
+    {
+    }
+    else if (node_op == "Multiply")
+    {
+    }
+    else if (node_op == "Negative")
+    {
+    }
+    else if (node_op == "NotEqual")
+    {
+    }
+    else if (node_op == "Parameter")
+    {
+        auto tmp = dynamic_cast<const op::Parameter*>(&n);
+        node["shape"] = tmp->get_shape();
+    }
+    else if (node_op == "Power")
+    {
+    }
+    else if (node_op == "Reduce")
+    {
+        auto tmp = dynamic_cast<const op::Reduce*>(&n);
+        node["function"] = tmp->get_function()->get_name();
+        node["reduction_axes"] = tmp->get_reduction_axes();
+    }
+    else if (node_op == "Remainder")
+    {
+    }
+    else if (node_op == "Reshape")
+    {
+        auto tmp = dynamic_cast<const op::Reshape*>(&n);
+        node["input_order"] = tmp->get_input_order();
+        node["output_shape"] = tmp->get_output_shape();
+    }
+    else if (node_op == "Select")
+    {
+    }
+    else if (node_op == "Sign")
+    {
+    }
+    else if (node_op == "Sin")
+    {
+    }
+    else if (node_op == "Sinh")
+    {
+    }
+    else if (node_op == "Slice")
+    {
+        auto tmp = dynamic_cast<const op::Slice*>(&n);
+        node["lower_bounds"] = tmp->get_lower_bounds();
+        node["upper_bounds"] = tmp->get_upper_bounds();
+        node["step"] = tmp->get_step();
+    }
+    else if (node_op == "Subtract")
+    {
+    }
+    else if (node_op == "Sum")
+    {
+        auto tmp = dynamic_cast<const op::Sum*>(&n);
+        node["reduction_axes"] = tmp->get_reduction_axes();
+    }
+    else if (node_op == "Tan")
+    {
+    }
+    else if (node_op == "Tanh")
+    {
+    }
+    else if (node_op == "Tuple")
+    {
+    }
+
+    return node;
+}

src/ngraph/serializer.hpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// ----------------------------------------------------------------------------
+
+#pragma once
+
+#include <memory>
+#include <unordered_map>
+
+#include "ngraph/function.hpp"
+#include "ngraph/json.hpp"
+#include "ngraph/node.hpp"
+
+namespace ngraph
+{
+    std::string serialize(std::shared_ptr<ngraph::Function>);
+    std::shared_ptr<ngraph::Function> deserialize(std::istream&);
+}

src/ngraph/types/element_type.cpp

@@ -21,7 +21,7 @@
 
 using namespace ngraph;
 
-const element::Type element::boolean(8, false, false, "bool");
+const element::Type element::boolean(8, false, false, "char");
 const element::Type element::f32(32, true, true, "float");
 const element::Type element::f64(64, true, true, "double");
 const element::Type element::i8(8, false, true, "int8_t");
@@ -39,7 +39,6 @@ element::Type::Type(size_t bitwidth, bool is_real, bool is_signed, const std::st
     , m_is_signed{is_signed}
     , m_cname{cname}
 {
-    assert(m_bitwidth % 8 == 0);
 }
 
 const std::string& element::Type::c_type_string() const
@@ -53,13 +52,35 @@ bool element::Type::operator==(const element::Type& other) const
            m_is_signed == other.m_is_signed && m_cname == other.m_cname;
 }
 
+bool element::Type::operator<(const Type& other) const
+{
+    size_t v1 = m_bitwidth << 2;
+    v1 |= (m_is_real ? 2 : 0);
+    v1 |= (m_is_signed ? 1 : 0);
+
+    size_t v2 = other.m_bitwidth << 2;
+    v2 |= (other.m_is_real ? 2 : 0);
+    v2 |= (other.m_is_signed ? 1 : 0);
+
+    return v1 < v2;
+}
+
 size_t element::Type::size() const
 {
     return std::ceil(static_cast<float>(m_bitwidth) / 8.0f);
 }
 
+size_t element::Type::hash() const
+{
+    size_t h1 = std::hash<size_t>{}(m_bitwidth);
+    size_t h2 = std::hash<bool>{}(m_is_real);
+    size_t h3 = std::hash<bool>{}(m_is_signed);
+    return h1 ^ ((h2 ^ (h3 << 1)) << 1);
+}
+
 std::ostream& element::operator<<(std::ostream& out, const element::Type& obj)
 {
-    out << obj.m_cname;
+    out << "element::Type(" << obj.m_bitwidth << ", " << obj.m_is_real << ", " << obj.m_is_signed
+        << ")";
     return out;
 }

src/ngraph/types/element_type.hpp

@@ -47,23 +47,20 @@ namespace ngraph
 
         class Type
         {
-            Type(const Type&) = delete;
-            Type& operator=(const Type&) = delete;
-
         public:
-            virtual ~Type() {}
+            Type() = delete;
+            Type(const Type&) = default;
             Type(size_t bitwidth, bool is_real, bool is_signed, const std::string& cname);
-
+            virtual ~Type() {}
             const std::string& c_type_string() const;
             size_t size() const;
-            size_t hash() const
-            {
-                std::hash<std::string> h;
-                return h(m_cname);
-            }
-
+            size_t hash() const;
+            bool is_real() const { return m_is_real; }
+            bool is_signed() const { return m_is_signed; }
+            size_t bitwidth() const { return m_bitwidth; }
             bool operator==(const Type& other) const;
             bool operator!=(const Type& other) const { return !(*this == other); }
+            bool operator<(const Type& other) const;
             friend std::ostream& operator<<(std::ostream&, const Type&);
 
         private:

src/ngraph/util.cpp

@@ -145,7 +145,6 @@ void ngraph::traverse_nodes(std::shared_ptr<ngraph::Function> p,
 }
 
 void ngraph::traverse_nodes(ngraph::Function* p, std::function<void(shared_ptr<Node>)> f)
-
 {
     std::unordered_set<shared_ptr<Node>> instances_seen;
     deque<shared_ptr<Node>> stack;
@@ -172,6 +171,34 @@ void ngraph::traverse_nodes(ngraph::Function* p, std::function<void(shared_ptr<N
     }
 }
 
+void ngraph::traverse_functions(std::shared_ptr<ngraph::Function> p,
+                                std::function<void(shared_ptr<Function>)> f)
+{
+    std::unordered_set<shared_ptr<Function>> instances_seen;
+    deque<shared_ptr<Function>> stack;
+
+    stack.push_front(p);
+
+    while (stack.size() > 0)
+    {
+        shared_ptr<Function> func = stack.front();
+        if (instances_seen.find(func) == instances_seen.end())
+        {
+            instances_seen.insert(func);
+            f(func);
+        }
+        stack.pop_front();
+        for (shared_ptr<Node> op : func->get_ops())
+        {
+            shared_ptr<Function> fp = op->get_function();
+            if (fp)
+            {
+                stack.push_front(fp);
+            }
+        }
+    }
+}
+
 void ngraph::free_nodes(shared_ptr<Function> p)
 {
     std::deque<Node*> sorted_list;

src/ngraph/util.hpp

@@ -18,6 +18,7 @@
 #include <chrono>
 #include <functional>
 #include <iostream>
+#include <list>
 #include <map>
 #include <memory>
 #include <sstream>
@@ -239,8 +240,9 @@ namespace ngraph
     }
 
     void traverse_nodes(Function* p, std::function<void(std::shared_ptr<Node>)> f);
-
     void traverse_nodes(std::shared_ptr<Function> p, std::function<void(std::shared_ptr<Node>)> f);
+    void traverse_functions(std::shared_ptr<Function> p,
+                            std::function<void(std::shared_ptr<Function>)> f);
 
     void free_nodes(std::shared_ptr<Function>);
 } // end namespace ngraph

test/backend_test.in.cpp

@@ -14,9 +14,10 @@
 
 #include <algorithm>
 #include <cinttypes>
+#include <cmath>
+
 #include "gtest/gtest.h"
 
-#include <cmath>
 #include "ngraph/log.hpp"
 #include "ngraph/ngraph.hpp"
 

test/copy.cpp

@@ -358,7 +358,7 @@ TEST(copy, reduce)
 
     ASSERT_TRUE(nullptr != new_node);
     ASSERT_TRUE(new_args == new_node->get_arguments());
-    ASSERT_TRUE(f == node_cast->get_reduction_function());
+    ASSERT_TRUE(f == node_cast->get_function());
     ASSERT_TRUE(axes == node_cast->get_reduction_axes());
 }
 

test/element_type.cpp

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // ----------------------------------------------------------------------------
 
+#include <map>
+
 #include "gtest/gtest.h"
 
 #include "ngraph/types/element_type.hpp"
@@ -33,3 +35,50 @@ TEST(element_type, from)
     EXPECT_EQ(element::from<uint32_t>(), element::u32);
     EXPECT_EQ(element::from<uint64_t>(), element::u64);
 }
+
+TEST(element_type, mapable)
+{
+    std::map<element::Type, std::string> test_map;
+
+    test_map.insert({element::f32, "float"});
+}
+
+TEST(element_type, size)
+{
+    {
+        element::Type t1{1, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{2, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{3, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{4, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{5, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{6, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{7, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{2, false, false, ""};
+        EXPECT_EQ(1, t1.size());
+    }
+    {
+        element::Type t1{9, false, false, ""};
+        EXPECT_EQ(2, t1.size());
+    }
+}

test/serialize.cpp

@@ -12,4 +12,88 @@
 // See the License for the specific language governing permissions and
 // ----------------------------------------------------------------------------
 
+#include <fstream>
+#include <sstream>
+
 #include "gtest/gtest.h"
+
+#include "ngraph/json.hpp"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/serializer.hpp"
+#include "ngraph/util.hpp"
+
+using namespace std;
+using namespace ngraph;
+
+template <typename T>
+static void copy_data(shared_ptr<runtime::TensorView> tv, const vector<T>& data)
+{
+    size_t data_size = data.size() * sizeof(T);
+    tv->write(data.data(), 0, data_size);
+}
+
+TEST(serialize, main)
+{
+    // First create "f(A,B,C) = (A+B)*C".
+    auto shape = Shape{2, 2};
+    auto A = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto B = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto C = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto rt_f = make_shared<TensorViewType>(element::Float32::element_type(), shape);
+    auto f = make_shared<Function>((A + B) * C, rt_f, op::Parameters{A, B, C}, "f");
+
+    // Now make "g(X,Y,Z) = f(X,Y,Z) + f(X,Y,Z)"
+    auto X = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Y = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Z = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto rt_g = make_shared<TensorViewType>(element::Float32::element_type(), shape);
+    auto g = make_shared<Function>(make_shared<op::FunctionCall>(f, Nodes{X, Y, Z}) +
+                                       make_shared<op::FunctionCall>(f, Nodes{X, Y, Z}),
+                                   rt_g,
+                                   op::Parameters{X, Y, Z},
+                                   "g");
+
+    // Now make "h(X,Y,Z) = g(X,Y,Z) + g(X,Y,Z)"
+    auto X1 = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Y1 = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Z1 = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto rt_h = make_shared<TensorViewType>(element::Float32::element_type(), shape);
+    auto h = make_shared<Function>(make_shared<op::FunctionCall>(g, Nodes{X1, Y1, Z1}) +
+                                       make_shared<op::FunctionCall>(g, Nodes{X1, Y1, Z1}),
+                                   rt_h,
+                                   op::Parameters{X1, Y1, Z1},
+                                   "h");
+
+    string js = serialize(h);
+
+    {
+        ofstream f("serialize_function.js");
+        f << js;
+    }
+
+    istringstream in(js);
+    shared_ptr<Function> sfunc = deserialize(in);
+
+    // Now call g on some test vectors.
+    auto manager = runtime::Manager::get("CPU");
+    auto external = manager->compile(sfunc);
+    auto backend = manager->allocate_backend();
+    auto cf = backend->make_call_frame(external);
+
+    auto x = backend->make_primary_tensor_view(element::Float32::element_type(), shape);
+    copy_data(x, vector<float>{1, 2, 3, 4});
+    auto y = backend->make_primary_tensor_view(element::Float32::element_type(), shape);
+    copy_data(y, vector<float>{5, 6, 7, 8});
+    auto z = backend->make_primary_tensor_view(element::Float32::element_type(), shape);
+    copy_data(z, vector<float>{9, 10, 11, 12});
+    auto result = backend->make_primary_tensor_view(element::Float32::element_type(), shape);
+
+    cf->call({x, y, z}, {result});
+    EXPECT_EQ((vector<float>{54, 80, 110, 144}), result->get_vector<float>());
+
+    cf->call({y, x, z}, {result});
+    EXPECT_EQ((vector<float>{54, 80, 110, 144}), result->get_vector<float>());
+
+    cf->call({x, z, y}, {result});
+    EXPECT_EQ((vector<float>{50, 72, 98, 128}), result->get_vector<float>());
+}

test/util.cpp

@@ -18,6 +18,7 @@
 
 #include "gtest/gtest.h"
 
+#include "ngraph/function.hpp"
 #include "ngraph/ngraph.hpp"
 #include "ngraph/util.hpp"
 #include "util/all_close.hpp"
@@ -202,3 +203,40 @@ TEST(util, all_close)
     EXPECT_FALSE(ngraph::test::all_close<float>(c, a, .05f, 0));
     EXPECT_TRUE(ngraph::test::all_close<float>(c, a, .11f, 0));
 }
+
+TEST(util, traverse_functions)
+{
+    // First create "f(A,B,C) = (A+B)*C".
+    auto shape = Shape{2, 2};
+    auto A = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto B = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto C = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto rt_f = make_shared<TensorViewType>(element::Float32::element_type(), shape);
+    auto f = make_shared<Function>((A + B) * C, rt_f, op::Parameters{A, B, C}, "f");
+
+    // Now make "g(X,Y,Z) = f(X,Y,Z) + f(X,Y,Z)"
+    auto X = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Y = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Z = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto rt_g = make_shared<TensorViewType>(element::Float32::element_type(), shape);
+    auto g = make_shared<Function>(make_shared<op::FunctionCall>(f, Nodes{X, Y, Z}) +
+                                       make_shared<op::FunctionCall>(f, Nodes{X, Y, Z}),
+                                   rt_g,
+                                   op::Parameters{X, Y, Z},
+                                   "g");
+
+    // Now make "h(X,Y,Z) = g(X,Y,Z) + g(X,Y,Z)"
+    auto X1 = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Y1 = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto Z1 = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto rt_h = make_shared<TensorViewType>(element::Float32::element_type(), shape);
+    auto h = make_shared<Function>(make_shared<op::FunctionCall>(g, Nodes{X1, Y1, Z1}) +
+                                       make_shared<op::FunctionCall>(g, Nodes{X1, Y1, Z1}),
+                                   rt_h,
+                                   op::Parameters{X1, Y1, Z1},
+                                   "h");
+
+    vector<Function*> functions;
+    traverse_functions(h, [&](shared_ptr<Function> fp) { functions.push_back(fp.get()); });
+    ASSERT_EQ(3, functions.size());
+}