# Graph Basicstrue

This section describes the basic concepts you need to know when constructing a graph.

## Framework Bridgestrue

Frontends (or users who require the flexibility of constructing Ops directly) can utilize a set of graph construction functions to construct graphs.

A framework bridge constructs a function which is compiled/optimized by a sequence of graph transformations that replace subgraphs of the computation with more optimal subgraphs. Throughout this process, ops represent tensor operations.

## Tensorstrue

*Tensors* are maps from coordinates to scalar values, all of the same
type, called the *element type* of the tensor. Coordinates are tuples
of non-negative integers; all the coordinates for a tensor have the
same length, called the *rank* of the tensor. We often use
\(n\)-tensor for tensors with rank \(n\).

The shape of a tensor is a tuple of non-negative integers that
represents an exclusive upper bound for coordinate values. A tensor has an
element for every coordinate less than the shape, so the *size* of the tensor
is the product of the values in the shape.

An \(n\)-dimensional array is the usual implementation for a tensor, and the two terms are often used interchangeably, but a tensor could just as easily be represented by a function that returns 0 for every coordinate or a function that adds the elements of two other tensors at the same coordinate and returns that sum.

## Opstrue

A computation graph is a composition of tensor computations, called
`ops`

, which are nodes in the graph. In the graph, every op
*input* must be associated with an op *output*, and every op output
must have a fixed element type and shape to correspond with the
tensors used in the computation. Every op has zero or more inputs and
zero or more outputs. The outputs represent tensors that will be
provided during execution. Ops may also have additional attributes
that do not change during execution.

Every op is a Node, but not all nodes are ops. This is because pattern graphs are another kind of graph that includes ops combined with nodes that describe how to match subgraphs during graph optimization.

Constructed ops have element types and shapes for each of their outputs, which are determined during op construction from the element types and shapes associated with the inputs, as well as additional attributes of the ops. For example, tensor addition is defined for two tensors of the same shape and size and results in a tensor with the same element type and shape:

Here, \(X_I\) means the value of a coordinate \(I\) for the tensor \(X\). So the value of sum of two tensors is a tensor whose value at a coordinate is the sum of the elements are that coordinate for the two inputs. Unlike many frameowrks, it says nothing about storage or arrays.

An `Add`

op is used to represent an elementwise tensor sum. To
construct an Add op, each of the two inputs of the `Add`

must be
assigned some output of some already-created op. All outputs of
constructed ops have element types and shapes, so when the Add is
constructed, it verifies that the two input tensors have the same
element type and shape and then sets its output to have the same
element type and shape.

Since all nodes supplying outputs for inputs to a new node must exist before the new node can be created, it is impossible to construct a cyclic graph. Furthermore, type-checking is performed as the ops are constructed.

## Functionstrue

Ops are grouped together in a `Function`

, which describes a
computation that can be invoked on tensor arguments to compute tensor
results. When called by a bridge, the bridge provides tensors in the
form of row-major arrays for each argument and each computed
result. The same array can be used for more than one argument, but
each result must use a distinct array, and argument arrays cannot be
used as result arrays.

Function definition begins with creating one or more `Parameter`

ops, which represent the tensors that will be supplied as arguments to
the function. Parameters have no inputs and attributes for the
element type and shape of the tensor that will be provided as an
argument. The unique output of the `Parameter`

will have the
provided element type and shape.

A `Function`

has `Parameters`

, a vector of `Parameter`

ops,
where no `Parameter`

op may appear more than once in the vector. A
`Parameter`

op has no inputs and attributes for its shape and
element type; arrays passed to the function must have the same shape
and element type as the corresponding parameter. The `Function`

also has `Nodes`

, a vector of ops that are the results being
computed.

During execution, the output of the nth `Parameter`

op will be the tensor
corresponding to the array provided as the nth argument, and the outputs
of all result ops will be written into the result arrays in row-major
order.

## An Exampletrue

```
#include <memory>
#include <ngraph.hpp>
using ngraph;
// f(a, b, c) = (a + b) * c
void make_function()
{
// First construct the graph
Shape shape{32, 32};
auto a = std::make_shared<op::Parameter>(element::f32, shape);
auto b = std::make_shared<op::Parameter>(element::f32, shape);
auto c = std::make_shared<op::Parameter>(element::f32, shape);
auto t0 = std::make_shared<op::Add>(a, b);
auto t1 = std::make_shared<op::Multiply>(t0, c);
auto f = std::make_shared<Function>(Nodes{t1}, Parameters{a, b, c});
}
```

We use shared pointers for all ops. For each parameter, we need to element type and shape attributes. When the function is called, each argument must conform to the corresponding parameter element type and shape.

During typical graph construction, all ops have one output and some number of inputs, which makes it easy to construct the graph by assigning each unique output of a constructor argument node to an input of the op being constructed. For example, Add need to supply node outputs to each of its two inputs, which we supply from the unique outputs of the parameters a and b.

We do not perform any implicit element type coercion or shape conversion (such as broadcasts) since these can be framework-dependent, so all the shapes for the add and multiply must be the same. If there is a mismatch, the constructor will throw an exception.

After the graph is constructed, we create the function, passing the Function constructor the nodes that are results and the parameters that are arguments.