Principle:Onnx Onnx Operator Node Construction
| Knowledge Sources | |
|---|---|
| Domains | Model_Construction, Computation_Graph |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A construction mechanism for defining individual computation operations as nodes within a directed acyclic computation graph.
Description
In the ONNX intermediate representation, a computation graph is composed of nodes, where each node represents a single operation (such as matrix multiplication, convolution, or activation). Each node is defined by an operator type selected from the ONNX operator specification, a set of named input edges, a set of named output edges, and optional attributes that parameterize the operation. Nodes are the atomic units of computation in the graph: they consume tensors produced by previous nodes (or graph inputs) and produce tensors consumed by subsequent nodes (or graph outputs).
This principle addresses the need to express arbitrary neural network operations in a standardized, runtime-agnostic format that can be serialized, validated, and executed by any ONNX-compliant runtime.
Usage
Use this principle whenever constructing individual computation steps in an ONNX model. Operator nodes are created after tensor specifications are defined and before the graph is assembled. Each node must reference valid input/output edge names that connect to other nodes or graph inputs/outputs.
Theoretical Basis
An ONNX operator node is defined as:
Failed to parse (syntax error): {\displaystyle \text{Node} = (\text{op\_type}, \text{inputs}, \text{outputs}, \text{attributes}) }
Where:
- op_type selects an operator from the ONNX specification (e.g., "Relu", "Conv", "MatMul")
- inputs is an ordered list of edge names consuming tensors from the graph
- outputs is an ordered list of edge names producing tensors into the graph
- attributes are compile-time constants parameterizing the operator (e.g., kernel_shape for Conv)
Pseudo-code:
# Abstract node construction
node = NodeProto()
node.op_type = operator_name
node.inputs = [input_edge_1, input_edge_2, ...]
node.outputs = [output_edge_1, ...]
node.attributes = {key: value for each operator parameter}