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208 changes: 10 additions & 198 deletions nnvm/LICENSE
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43 changes: 37 additions & 6 deletions nnvm/README.md
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# 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.
12 changes: 6 additions & 6 deletions nnvm/include/nngraph/base.h → nnvm/include/nnvm/base.h
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/*!
* 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>
#include <dmlc/logging.h>
#include <dmlc/registry.h>
#include <dmlc/array_view.h>

namespace nngraph {
namespace nnvm {

/*! \brief any type */
using any = dmlc::any;
Expand Down Expand Up @@ -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_
12 changes: 6 additions & 6 deletions nnvm/include/nngraph/graph.h → nnvm/include/nnvm/graph.h
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/*!
* 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>
Expand All @@ -15,7 +15,7 @@
#include "./base.h"
#include "./node.h"

namespace nngraph {
namespace nnvm {

/*!
* \brief Symbolic computation graph.
Expand Down Expand Up @@ -98,6 +98,6 @@ inline void Graph::DFSVisit(FVisit fvisit) const {
});
}

} // namespace nngraph
} // namespace nnvm

#endif // NNGRAPH_GRAPH_H_
#endif // NNVM_GRAPH_H_
20 changes: 10 additions & 10 deletions nnvm/include/nngraph/graph_attr_types.h → nnvm/include/nnvm/graph_attr_types.h
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* \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.
Expand Down Expand Up @@ -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 */
Expand Down Expand Up @@ -68,15 +68,15 @@ 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;
}
/*!
* \brief Get the corresponding node id for a given Node in the 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);
}
/*!
Expand All @@ -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 */
Expand All @@ -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
Expand All @@ -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_
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