Principle:Onnx Onnx Graph Assembly
| Knowledge Sources | |
|---|---|
| Domains | Model_Construction, Computation_Graph |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A composition mechanism that assembles individual operator nodes, input/output specifications, and constant initializers into a complete directed acyclic computation graph.
Description
Graph assembly is the step where individual components of an ONNX model are composed into a coherent computation graph (GraphProto). The graph is a directed acyclic graph (DAG) where nodes represent operations, edges represent data flow between operations, and initializers provide constant tensor values (such as trained weights). The graph also carries its own input/output declarations (ValueInfoProto) and optional intermediate value information for shape inference.
This principle solves the problem of organizing isolated computation nodes into a structure that defines execution order, data dependencies, and the external interface of the computation. Without graph assembly, individual nodes have no defined relationship to each other.
Usage
Use this principle after creating tensor specifications (inputs/outputs) and operator nodes. Graph assembly is required before building a model, as every ONNX model must contain exactly one main graph. The assembled graph defines the complete computation to be executed by a runtime.
Theoretical Basis
A computation graph is formally defined as:
Where:
- V = set of operator nodes (NodeProto)
- E = set of directed edges (implicit via input/output name matching)
- I = set of input specifications (ValueInfoProto)
- O = set of output specifications (ValueInfoProto)
- W = set of constant initializers (TensorProto)
Pseudo-code:
# Abstract graph assembly
graph = GraphProto()
graph.name = graph_name
graph.nodes = [node_1, node_2, ..., node_n] # topological order
graph.inputs = [input_spec_1, input_spec_2, ...]
graph.outputs = [output_spec_1, ...]
graph.initializers = [weight_tensor_1, ...] # constant values