Principle:Ggml org Ggml Computation Graph Construction
| Knowledge Sources | |
|---|---|
| Domains | ML_Infrastructure, Tensor_Computing |
| Last Updated | 2025-05-15 12:00 GMT |
Overview
Computation Graph Construction is the process of building directed acyclic graphs (DAGs) of tensor operations before any computation is executed.
Description
In GGML, tensor operations are not evaluated eagerly. Instead, the library follows a deferred computation model: when a caller invokes an operation such as matrix multiplication or element-wise addition, no arithmetic is performed. The operation is recorded as a node in an internal directed acyclic graph (DAG). Each node carries an operation type (e.g., GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_ROPE, GGML_OP_SOFT_MAX) together with pointers to its source tensors. The graph is only materialized and executed in a separate, subsequent step.
This separation of specification from execution solves several problems at once: it enables the runtime to analyse the full workload before committing resources, it opens the door to operation fusion and scheduling optimizations, and it guarantees a consistent, reproducible execution order across different hardware backends. GGML currently supports over 70 operation types, all of which participate in the same graph-building protocol.
Usage
Apply this principle whenever a program needs to compose multiple tensor operations into a single, optimizable unit of work. Build the graph first by calling operation helpers (e.g., ggml_mul_mat, ggml_add), then finalize the graph with ggml_build_forward_expand before dispatching it to a backend for execution.
Theoretical Basis
Computation Graph Construction rests on several well-established concepts from computer science and compiler theory:
Dataflow Graphs
A dataflow graph represents a computation as a directed graph where nodes are operations and edges are data dependencies. The GGML computation graph is exactly this: each node is a tensor operation, and each edge connects a producer tensor to a consumer operation. Because no node consumes its own output (directly or transitively), the graph is guaranteed to be acyclic — a DAG.
Lazy Evaluation
The deferred computation model is an instance of lazy evaluation. Rather than computing results at the point of definition, the library records what needs to be computed and defers how and when to a later stage. This mirrors the evaluation strategy found in functional programming languages (Haskell) and modern deep-learning frameworks (TensorFlow's graph mode, JAX's tracing).
Operation Fusion Opportunities
Once the full graph is available, a backend can identify sub-graphs that are profitably fused into a single kernel invocation. For example, a matrix multiplication immediately followed by an element-wise bias addition can be executed as a single fused GEMM+bias kernel, avoiding an intermediate memory round-trip. Graph-level visibility is a prerequisite for such optimizations.
Topological Ordering
Executing the graph requires a topological sort — a linear ordering of nodes such that every node appears after all of its dependencies. The recursive dependency walk performed during graph construction naturally produces this ordering, ensuring correct evaluation regardless of the order in which the user defined the operations.
Graph Structure
The graph distinguishes two categories of tensors:
- Nodes — Tensors that are the result of an operation (i.e., they have an op field other than GGML_OP_NONE). These are the interior vertices of the DAG.
- Leafs — Tensors that carry no operation: raw inputs, learned weights, and constants. These are the source vertices of the DAG with in-degree zero.
This partitioning allows the runtime to quickly identify which tensors require computation and which are ready to be consumed immediately.