Principle:Ggml org Llama cpp Computation Graph Building
| Knowledge Sources | |
|---|---|
| Domains | Computation_Graph |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Computation Graph Building is the principle of constructing the directed acyclic graph of tensor operations that represents a model's forward pass.
Description
This principle covers the construction of GGML computation graphs that encode the sequence of tensor operations for a model's forward pass. Each model architecture defines its own graph building logic that maps input tokens through embedding lookups, attention layers, feed-forward networks, and output projections into logit tensors. The graph is built dynamically for each batch and then dispatched to compute backends for execution.
Usage
Apply this principle when adding support for new model architectures, optimizing the computation graph for specific hardware backends, or debugging inference correctness by inspecting the graph structure.
Theoretical Basis
GGML uses a define-by-run computation graph model where tensor operations are recorded into a graph data structure as they are called. Each node in the graph represents either a tensor (data) or an operation (computation). The graph captures data dependencies between operations, enabling the backend scheduler to optimize execution order, allocate temporary buffers, and parallelize independent operations across devices. The graph is rebuilt for each forward pass to accommodate variable batch sizes and sequence lengths, but the graph structure itself is deterministic for a given architecture and input shape.