# Pattern Matcher¶

The nGraph Compiler is an optimizing compiler. As such, it provides a way to capture a given function graph and perform a series of optimization passes over that graph. The result is a semantically-equivalent graph that, when executed using any backend, has optimizations inherent at the hardware level: superior runtime characteristics to increase training performance or reduce inference latency.

class Matcher

Matcher matches (compares) two graphs.

Public Functions

Matcher(const std::shared_ptr<Node> &pattern_node, const std::string &name, bool strict_mode)

Constructs a Matcher object.

Parameters
• pattern_node: is a pattern sub graph that will be matched against input graphs
• name: is a string which is used for logging and disabling a matcher
• strict_mode: forces a matcher to consider shapes and ET of nodes

bool match(const std::shared_ptr<Node> &graph_node)

Matches a pattern to graph_node.

Parameters
• graph_node: is an input graph to be matched against

bool match(const std::shared_ptr<Node> &graph_node, const PatternMap &previous_matches)

Matches a pattern to graph_node.

Parameters
• graph_node: is an input graph to be matched against
• previous_matches: contains previous mappings from labels to nodes to use

## Fusion¶

There are several ways to describe what happens when we capture and translate the framework’s output of ops into an nGraph graph. Fusion is the term we shall use in our documentation; the action also can be described as: combining, folding, squashing, collapsing, or merging of graph functions.

Optimization passes may include algebraic simplifications, domain-specific simplifications, and fusion. Most passes share the same mode of operation (or the same operational structure) and consist of various stages (each one a step) where a developer can experiment with the intercepted or dynamic graph. These steps may be cycled or recycled as needed:

1. Locate a list of potentially-transformable subgraphs in the given graph.
2. Transform the selected candidates into semantically-equivalent subgraphs that execute faster, or with less memory (or both).
3. Verify that the optimization pass performs correctly, with any or all expected transformations, with the NGRAPH_SERIALIZE_TRACING option, which serializes a graph in the json format after a pass.
4. Measure and evaluate your performance improvements with NGRAPH_CPU_TRACING, which produces timelines compatible with chrome://tracing.

Optimizations can be experimented upon without using any backend by registering a pass with pass manager (Manager), calling run_passes on a function, and then inspecting the transformed graph.

Optimization passes can be programmed ahead of time if you know or can predict what your graph will look like when it’s ready to be executed (in other words: which ops can be automatically translated into nGraph Core ops).

The Interpreter is simply a backend providing reference implementations of ngraph ops in C++, with the focus on simplicity over performance.

### Example¶

Let us first consider a simple example. A user would like to execute a graph that describes the following arithmetic expression:

$$a + b * 1$$ or $$Add(a, Mul(b, 1))$$

In the above expressions, 1 is an identity element; any element multiplied by the identity element is equal to itself. In other words, the original expression $$a + b * 1$$ is exactly equivalent to the expression $$a + b$$, so we can eliminate this extra multiplication step.

The writer of an optimization pass which uses algebraic simplification would probably want to first locate all multiplication expressions where multiplicands are multiplied by 1 (for stage 1) and to then replace, those expressions with just their multiplicands (for stage 2).

To make the work of an optimization pass writer easier, the nGraph Library includes facilities that enable the finding of relevant candidates using pattern matching (via pattern/matcher.hpp), and the transforming of the original graph into an optimized version (via pass/graph_rewrite.hpp).