change project to NNVM

nnvm/LICENSE

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nnvm/README.md

-# mxnngraph
-Prototype of graph optimizer and construction API for next generation core engine in MXNet and beyond
+# NNVM: Build deep learning system by parts
 
-## Goal
-- Construct graph easily
-- Pluggable inference, optimization algorithms
-- Can be used as input graph execution in various settings, specifically be able to support mxnet's symbolic execution API
+NNVM is not a deep learning library. It is a modular, lightweight library to
+help build deep learning libraries efficiently.
 
+## What is it
 
+While most deep learning systems offer end to end solutions,
+it is interesting to ask if we can actually assemble a deep learning system by parts.
+The goal is to enable hackers can customize optimizations, target platforms and set of operators they care about.
+We believe that the modular system is an interesting direction.
+The hope is that effective parts can be assembled together just like you assemble your own desktops.
+So the customized deep learning solution can be minimax, minimum in terms of dependencies,
+while maxiziming the users' need.
+
+NNVM offers one such part, it provides a generic to do generic
+computation graph optimization such as memory reduction, device allocation,
+operator fusion while being agnostic to the operator
+interface defintion and how operators are executed.
+NNVM is inspired by LLVM, aiming to be an intermediate representation library
+for neural nets and computation graphs in general.
+
+## Deep learning system by parts
+
+This is one way to divide the deep learning system into common parts.
+Each can be isolated to a modular part.
+
+- Computation graph definition, manipulation.
+- Computation graph intermediate optimization.
+- Computation graph execution.
+- Operator kernel libraries.
+- Imperative task scheduling and parallel task coordination.
+
+We hope that there will be more modular parts in the future,
+so system building can be fun and rewarding.
+
+## Links
+
+[MXNet](https://github.com/dmlc/mxnet) will be using NNVM as its intermediate
+representation layer for symbolic graphs.

nnvm/include/nngraph/base.h → nnvm/include/nnvm/base.h

 /*!
  *  Copyright (c) 2016 by Contributors
  * \file base.h
- * \brief Configuation of nngraph as well as basic data structure.
+ * \brief Configuation of nnvm as well as basic data structure.
  */
-#ifndef NNGRAPH_BASE_H_
-#define NNGRAPH_BASE_H_
+#ifndef NNVM_BASE_H_
+#define NNVM_BASE_H_
 
 #include <dmlc/base.h>
 #include <dmlc/any.h>
@@ -12,7 +12,7 @@
 #include <dmlc/registry.h>
 #include <dmlc/array_view.h>
 
-namespace nngraph {
+namespace nnvm {
 
 /*! \brief any type */
 using any = dmlc::any;
@@ -47,6 +47,6 @@ inline const T& get(const any& src) {
   return dmlc::get<T>(src);
 }
 
-}  // namespace nngraph
+}  // namespace nnvm
 
-#endif  // NNGRAPH_BASE_H_
+#endif  // NNVM_BASE_H_

nnvm/include/nngraph/graph.h → nnvm/include/nnvm/graph.h

 /*!
  *  Copyright (c) 2016 by Contributors
  * \file graph.h
- * \brief Configuation of nngraph as well as basic data structure.
+ * \brief Configuation of nnvm as well as basic data structure.
  */
-#ifndef NNGRAPH_GRAPH_H_
-#define NNGRAPH_GRAPH_H_
+#ifndef NNVM_GRAPH_H_
+#define NNVM_GRAPH_H_
 
 #include <vector>
 #include <string>
@@ -15,7 +15,7 @@
 #include "./base.h"
 #include "./node.h"
 
-namespace nngraph {
+namespace nnvm {
 
 /*!
  * \brief Symbolic computation graph.
@@ -98,6 +98,6 @@ inline void Graph::DFSVisit(FVisit fvisit) const {
       });
 }
 
-}  // namespace nngraph
+}  // namespace nnvm
 
-#endif  // NNGRAPH_GRAPH_H_
+#endif  // NNVM_GRAPH_H_

nnvm/include/nngraph/graph_attr_types.h → nnvm/include/nnvm/graph_attr_types.h

@@ -3,14 +3,14 @@
  * \file graph_attr_types.h
  * \brief Data structures that can appear in graph attributes.
  */
-#ifndef NNGRAPH_GRAPH_ATTR_TYPES_H_
-#define NNGRAPH_GRAPH_ATTR_TYPES_H_
+#ifndef NNVM_GRAPH_ATTR_TYPES_H_
+#define NNVM_GRAPH_ATTR_TYPES_H_
 
 #include <vector>
 #include <unordered_map>
 #include "./graph.h"
 
-namespace nngraph {
+namespace nnvm {
 
 /*!
  * \brief Auxililary data structure to index a graph.
@@ -39,7 +39,7 @@ struct IndexedGraph {
   /*! \brief Node data structure in IndexedGraph */
   struct Node {
     /*! \brief pointer to the source node */
-    const nngraph::Node* source;
+    const nnvm::Node* source;
     /*! \brief inputs to the node */
     array_view<NodeEntry> inputs;
     /*! \brief control flow dependencies to the node */
@@ -68,7 +68,7 @@ struct IndexedGraph {
    * \param e The entry to query for index.
    * \return the unique index.
    */
-  inline uint32_t entry_id(const nngraph::NodeEntry& e) const {
+  inline uint32_t entry_id(const nnvm::NodeEntry& e) const {
     return entry_rptr_[node_id(e.node.get())] + e.index;
   }
   /*!
@@ -76,7 +76,7 @@ struct IndexedGraph {
    * \param node The Node to query for index.
    * \return the node index.
    */
-  inline uint32_t node_id(const nngraph::Node* node) const {
+  inline uint32_t node_id(const nnvm::Node* node) const {
     return node2index_.at(node);
   }
   /*!
@@ -92,7 +92,7 @@ struct IndexedGraph {
    * \param node The pointer to the Node structure
    * \return const reference to the corresponding IndexedGraph::Node
    */
-  inline const Node& operator[](const nngraph::Node* node) const {
+  inline const Node& operator[](const nnvm::Node* node) const {
     return nodes_[node_id(node)];
   }
   /*! \return list of argument nodes */
@@ -113,7 +113,7 @@ struct IndexedGraph {
   // index to argument nodes
   std::vector<uint32_t> arg_nodes_;
   // mapping from node to index.
-  std::unordered_map<const nngraph::Node*, uint32_t> node2index_;
+  std::unordered_map<const nnvm::Node*, uint32_t> node2index_;
   // CSR pointer of node entries
   std::vector<size_t> entry_rptr_;
   // space to store input entries of each
@@ -122,6 +122,6 @@ struct IndexedGraph {
   std::vector<uint32_t> control_deps_;
 };
 
-}  // namespace nngraph
+}  // namespace nnvm
 
-#endif  // NNGRAPH_GRAPH_ATTR_TYPES_H_
+#endif  // NNVM_GRAPH_ATTR_TYPES_H_

nnvm/include/nngraph/node.h → nnvm/include/nnvm/node.h

 /*!
  *  Copyright (c) 2016 by Contributors
- * \file base.h
+ * \file node.h
  * \brief Graph node data structure.
  */
-#ifndef NNGRAPH_NODE_H_
-#define NNGRAPH_NODE_H_
+#ifndef NNVM_NODE_H_
+#define NNVM_NODE_H_
 
 #include <memory>
 #include <string>
@@ -13,7 +13,7 @@
 #include "./base.h"
 #include "./op.h"
 
-namespace nngraph {
+namespace nnvm {
 
 // Forward declare node.
 class Node;
@@ -93,6 +93,6 @@ inline uint32_t Node::num_outputs() const {
   }
 }
 
-}  // namespace nngraph
+}  // namespace nnvm
 
-#endif  // NNGRAPH_NODE_H_
+#endif  // NNVM_NODE_H_

nnvm/include/nngraph/op.h → nnvm/include/nnvm/op.h

@@ -3,8 +3,8 @@
  * \file op.h
  * \brief Operator information structor.
  */
-#ifndef NNGRAPH_OP_H_
-#define NNGRAPH_OP_H_
+#ifndef NNVM_OP_H_
+#define NNVM_OP_H_
 
 #include <string>
 #include <vector>
@@ -13,7 +13,7 @@
 #include <functional>
 #include "./base.h"
 
-namespace nngraph {
+namespace nnvm {
 
 // forward declarations
 class Node;
@@ -38,18 +38,18 @@ static const int kVarg = -1;
  *  // registeration of oeprators
  *  // NOTE that the attr function can register any
  *  // additional attributes to the operator
- *  NNGRAPH_REGISTER_OP(add)
+ *  NNVM_REGISTER_OP(add)
  *  .describe("add two inputs together")
  *  .set_num_inputs(2)
  *  .attr<OpKernel>("gpu_kernel", AddKernel);
  *
- *  NNGRAPH_REGISTER_OP(sub)
+ *  NNVM_REGISTER_OP(sub)
  *  .describe("substract one tensor from another")
  *  .set_num_inputs(2);
  *
  *  // Can call regster multiple times in different files
  *  // to register different part of information
- *  NNGRAPH_REGISTER_OP(sub)
+ *  NNVM_REGISTER_OP(sub)
  *  .attr<OpKernel>("gpu_kernel", SubKernel);
  *
  *  // get operators from registry.
@@ -124,7 +124,7 @@ class Op {
    *                       const std::vector<TShape>& ishapes) {
    *     // we can use the parsed version of param
    *     // without repeatively parsing the parameter
-   *     const SumParam& param = nngraph::get<SumParam>(attrs.parsed);
+   *     const SumParam& param = nnvm::get<SumParam>(attrs.parsed);
    *  }
    * \endcode
    */
@@ -234,36 +234,36 @@ class OpMap {
 };
 
 // internal macros to make
-#define NNGRAPH_STR_CONCAT_(__x, __y) __x##__y
-#define NNGRAPH_STR_CONCAT(__x, __y) NNGRAPH_STR_CONCAT_(__x, __y)
-#define NNGRAPH_REGISTER_VAR_DEF(OpName)                              \
-  static ::nngraph::Op & __make_ ## NNGraphOp ## _ ## OpName
+#define NNVM_STR_CONCAT_(__x, __y) __x##__y
+#define NNVM_STR_CONCAT(__x, __y) NNVM_STR_CONCAT_(__x, __y)
+#define NNVM_REGISTER_VAR_DEF(OpName)                              \
+  static ::nnvm::Op & __make_ ## NnvmOp ## _ ## OpName
 
 /*!
- * \def NNGRAPH_REGISTER_OP
+ * \def NNVM_REGISTER_OP
  * \brief Register
  * This macro must be used under namespace dmlc, and only used once in cc file.
  * \param OpName The name of registry
  *
  * \code
  *
- *  NNGRAPH_REGISTER_OP(add)
+ *  NNVM_REGISTER_OP(add)
  *  .describe("add two inputs together")
  *  .set_num_inputs(2)
  *  .attr<OpKernel>("gpu_kernel", AddKernel);
  *
  * \endcode
  */
-#define NNGRAPH_REGISTER_OP(OpName)                                     \
-  NNGRAPH_STR_CONCAT(NNGRAPH_REGISTER_VAR_DEF(OpName), __COUNTER__) =   \
-      ::dmlc::Registry<::nngraph::Op>::Get()->__REGISTER_OR_GET__(#OpName)
+#define NNVM_REGISTER_OP(OpName)                                     \
+  NNVM_STR_CONCAT(NNVM_REGISTER_VAR_DEF(OpName), __COUNTER__) =   \
+      ::dmlc::Registry<::nnvm::Op>::Get()->__REGISTER_OR_GET__(#OpName)
 
 // implementations of template functions after this.
 // member function of Op
 template<typename ValueType>
 inline const OpMap<ValueType>& Op::GetAttr(const std::string& key) {
   const any& ref = GetAttrMap(key);
-  return nngraph::get<OpMap<ValueType> >(ref);
+  return nnvm::get<OpMap<ValueType> >(ref);
 }
 
 template<typename ValueType>
@@ -282,7 +282,7 @@ inline Op& Op::attr(  // NOLINT(*)
           << " previously " << pmap->type().name()
           << " current " << typeid(OpMap<ValueType>).name();
       std::vector<std::pair<ValueType, int> >& vec =
-          nngraph::get<OpMap<ValueType> >(*pmap).data_;
+          nnvm::get<OpMap<ValueType> >(*pmap).data_;
       // resize the value type.
       vec.resize(index_ + 1,
                  std::make_pair(ValueType(), 0));
@@ -338,6 +338,6 @@ inline const ValueType& OpMap<ValueType>::operator[](const Op* op) const {
   return data_[idx].first;
 }
 
-}  // namespace nngraph
+}  // namespace nnvm
 
-#endif  // NNGRAPH_OP_H_
+#endif  // NNVM_OP_H_

nnvm/include/nngraph/pass.h → nnvm/include/nnvm/pass.h

@@ -3,15 +3,15 @@
  * \file pass.h
  * \brief Pass that can be applied to a graph.
  */
-#ifndef NNGRAPH_PASS_H_
-#define NNGRAPH_PASS_H_
+#ifndef NNVM_PASS_H_
+#define NNVM_PASS_H_
 
 #include <vector>
 #include <functional>
 #include "./base.h"
 #include "./graph.h"
 
-namespace nngraph {
+namespace nnvm {
 
 /*!
  * \brief A PassFunction is a basic "Operator on Graph"
@@ -90,12 +90,12 @@ struct PassFunctionReg
 };
 
 /*!
- * \def NNGRAPH_REGISTER_PASS
+ * \def NNVM_REGISTER_PASS
  * \brief Macro to register pass fuctions.
  *
  * \code
  * // example of registering a shape inference pass
- * NNGRAPH_REGISTER_PASS(InferShape)
+ * NNVM_REGISTER_PASS(InferShape)
  * .describe("Shape Inference function, generate graph attributes")
  * .provide_graph_attr("data_shape")
  * .depend_graph_attr("indexed_graph")
@@ -105,9 +105,9 @@ struct PassFunctionReg
  *   });
  * \endcode
  */
-#define NNGRAPH_REGISTER_PASS(name)                                     \
-  DMLC_REGISTRY_REGISTER(::nngraph::PassFunctionReg, PassFunctionReg, name)
+#define NNVM_REGISTER_PASS(name)                                     \
+  DMLC_REGISTRY_REGISTER(::nnvm::PassFunctionReg, PassFunctionReg, name)
 
-}  // namespace nngraph
+}  // namespace nnvm
 
-#endif  // NNGRAPH_PASS_H_
+#endif  // NNVM_PASS_H_

nnvm/include/nngraph/tuple.h → nnvm/include/nnvm/tuple.h

@@ -3,15 +3,15 @@
  * \file tuple.h
  * \brief Data structure Tuple and TShape to store dynamic sized shapes.
  */
-#ifndef NNGRAPH_TUPLE_H_
-#define NNGRAPH_TUPLE_H_
+#ifndef NNVM_TUPLE_H_
+#define NNVM_TUPLE_H_
 
 #include <vector>
 #include <type_traits>
 #include <algorithm>
 #include <iostream>
 
-namespace nngraph {
+namespace nnvm {
 
 /*! \brief data type to store array index */
 typedef uint32_t index_t;
@@ -329,6 +329,6 @@ class TShape : public Tuple<index_t> {
   }
 };
 
-}  // namespace nngraph
+}  // namespace nnvm
 
-#endif  // NNGRAPH_TUPLE_H_
+#endif  // NNVM_TUPLE_H_

nnvm/src/core/graph_attr_types.cc

@@ -3,10 +3,10 @@
  * \file graph_attr_types.cc
  * \brief Graph node data structure.
  */
-#include <nngraph/graph_attr_types.h>
+#include <nnvm/graph_attr_types.h>
 #include <limits>
 
-namespace nngraph {
+namespace nnvm {
 
 // implement constructor from graph
 IndexedGraph::IndexedGraph(const Graph &g) {
@@ -14,7 +14,7 @@ IndexedGraph::IndexedGraph(const Graph &g) {
   std::vector<size_t> inputs_rptr{0}, control_rptr{0};
 
   g.DFSVisit([this, &inputs_rptr, &control_rptr]
-             (const std::shared_ptr<nngraph::Node>& n) {
+             (const std::shared_ptr<nnvm::Node>& n) {
       CHECK_LT(nodes_.size(), std::numeric_limits<uint32_t>::max());
       uint32_t nid = static_cast<uint32_t>(nodes_.size());
       // nodes_
@@ -59,4 +59,4 @@ IndexedGraph::IndexedGraph(const Graph &g) {
   }
 }
 
-}  // namespace nngraph
+}  // namespace nnvm

nnvm/src/core/node.cc

@@ -3,9 +3,9 @@
  * \file node.cc
  * \brief Graph node data structure.
  */
-#include <nngraph/node.h>
+#include <nnvm/node.h>
 
-namespace nngraph {
+namespace nnvm {
 
 Node::~Node() {
   if (inputs.size() != 0) {
@@ -42,4 +42,4 @@ std::shared_ptr<Node> Node::Create() {
   return std::make_shared<Node>();
 }
 
-}  // namespace nngraph
+}  // namespace nnvm

nnvm/src/core/op.cc

@@ -3,18 +3,18 @@
  * \file op.cc
  * \brief Support for operator registry.
  */
-#include <nngraph/base.h>
-#include <nngraph/op.h>
+#include <nnvm/base.h>
+#include <nnvm/op.h>
 
 #include <atomic>
 #include <mutex>
 
 namespace dmlc {
 // enable registry
-DMLC_REGISTRY_ENABLE(nngraph::Op);
+DMLC_REGISTRY_ENABLE(nnvm::Op);
 }  // namespace dmlc
 
-namespace nngraph {
+namespace nnvm {
 
 // single manager of operator information.
 struct OpManager {
@@ -66,4 +66,4 @@ void Op::UpdateAttrMap(const std::string& key,
   updater(&value);
 }
 
-}  // namespace nngraph
+}  // namespace nnvm

nnvm/src/core/pass.cc

@@ -3,15 +3,15 @@
  * \file pass.cc
  * \brief Support for pass registry.
  */
-#include <nngraph/pass.h>
+#include <nnvm/pass.h>
 #include <algorithm>
 
 namespace dmlc {
 // enable registry
-DMLC_REGISTRY_ENABLE(nngraph::PassFunctionReg);
+DMLC_REGISTRY_ENABLE(nnvm::PassFunctionReg);
 }  // namespace dmlc
 
-namespace nngraph {
+namespace nnvm {
 
 const PassFunctionReg* FindPassDep(const std::string&attr_name) {
   for (auto* r : dmlc::Registry<PassFunctionReg>::List()) {
@@ -54,4 +54,4 @@ Graph ApplyPass(const Graph& src,
   return g;
 }
 
-}  // namespace nngraph
+}  // namespace nnvm

nnvm/src/example/operator.cc

 // Copyright (c) 2016 by Contributors
-// This is an example on how we can register operator information to NNGRAPH
+// This is an example on how we can register operator information to NNVM
 
-#include <nngraph/op.h>
+#include <nnvm/op.h>
 #include <utility>
 
-NNGRAPH_REGISTER_OP(add)
+NNVM_REGISTER_OP(add)
 .describe("add two data together")
 .set_num_inputs(2)
 .attr("inplace_pair", std::make_pair(0, 0));
 
 
-NNGRAPH_REGISTER_OP(add)
+NNVM_REGISTER_OP(add)
 .attr<std::string>("nick_name", "plus");

nnvm/src/test_main.cc

 // Copyright (c) 2016 by Contributors
-#include <nngraph/op.h>
-#include <nngraph/graph.h>
-#include <nngraph/tuple.h>
-#include <nngraph/graph_attr_types.h>
+#include <nnvm/op.h>
+#include <nnvm/graph.h>
+#include <nnvm/tuple.h>
+#include <nnvm/graph_attr_types.h>
 #include <string>
 
 void test_op() {
-  using namespace nngraph;
+  using namespace nnvm;
   auto add = Op::Get("add");
   auto nick = Op::GetAttr<std::string>("nick_name");
   LOG(INFO) << "nick=" << nick[add];
 }
 
 void test_tuple() {
-  using nngraph::Tuple;
-  using nngraph::TShape;
+  using nnvm::Tuple;
+  using nnvm::TShape;
   Tuple<int> x{1, 2, 3};
   Tuple<int> y{1, 2, 3, 5, 6};
   x = std::move(y);
@@ -27,7 +27,7 @@ void test_tuple() {
   std::istringstream is(os.str());
   is >> y;
   CHECK_EQ(x, y);
-  Tuple<nngraph::index_t> ss{1, 2, 3};
+  Tuple<nnvm::index_t> ss{1, 2, 3};
   TShape s = ss;
   s = std::move(ss);
   CHECK((s == TShape{1, 2, 3}));
@@ -35,8 +35,8 @@ void test_tuple() {
 
 
 void test_graph() {
-  nngraph::Graph g;
-  g.DFSVisit([](const std::shared_ptr<const nngraph::Node>& n){
+  nnvm::Graph g;
+  g.DFSVisit([](const std::shared_ptr<const nnvm::Node>& n){
     });
 }
 int main() {