Principle:Ggml org Ggml Graph Computation Execution
Metadata
| Field | Value |
|---|---|
| Page Type | Principle |
| Knowledge Sources | GGML |
| Domains | ML_Infrastructure, Hardware_Abstraction |
| Last Updated | 2025-05-15 12:00 GMT |
Overview
Executing a computation graph on hardware backends by following a deferred execution model: the caller constructs the full computation graph first, then submits it for execution in a single batch operation. This enables the runtime to optimize memory allocation, plan data movement, and dispatch work across heterogeneous backends before any computation begins.
Description
Graph computation execution in GGML follows the principle of lazy evaluation applied to tensor operations. Rather than executing each tensor operation eagerly as it is defined, GGML separates the specification of computation from its execution:
Build Phase
During the build phase, the caller constructs a directed acyclic graph (ggml_cgraph) of tensor operations using GGML's tensor API (e.g., ggml_mul_mat, ggml_add, ggml_relu). No computation occurs at this stage. Each API call merely records a node in the graph, capturing the operation type, input tensors, and output tensor metadata. The graph is a static description of the desired computation.
Execution Phase
Once the graph is fully constructed, the caller submits it to the backend scheduler for execution. The scheduler performs several steps:
- Allocation: If the graph has not been pre-allocated, the scheduler automatically allocates memory for all intermediate and output tensors. This allows the allocator to analyze the full graph and reuse memory where tensor lifetimes do not overlap.
- Graph splitting: The scheduler partitions the graph into splits, where each split is a contiguous subgraph that can be executed on a single backend (e.g., GPU, CPU). Tensors are assigned to backends based on priority rules and buffer locations.
- Data transfer: For splits that span different backends, the scheduler inserts copy operations to move tensor data between devices.
- Dispatch: Each split is dispatched to its assigned backend for execution. The backend translates the GGML operations into hardware-specific instructions (e.g., CUDA kernels, Metal shaders, CPU BLAS calls).
- Synchronization: After all splits have been dispatched, the scheduler synchronizes across all backends to ensure all computation has completed before returning control to the caller.
Benefits of Deferred Execution
The deferred execution model provides several optimization opportunities:
- Memory planning: By analyzing the full graph before execution, the allocator can compute tensor lifetimes and reuse buffers, significantly reducing peak memory usage compared to eager allocation.
- Backend dispatch: The scheduler can make globally optimal decisions about which operations to place on which backend, rather than making local per-operation decisions.
- Batch execution: Submitting the entire graph at once allows backends to use hardware-level batching mechanisms (e.g., CUDA graphs, Metal command buffers) for reduced launch overhead.
- Graph caching: When the same graph structure is executed repeatedly (as in inference loops), the allocation and splitting work can be cached, amortizing the overhead across iterations.
Usage
Graph computation execution is the core execution mechanism in any GGML-based application:
- LLM inference: Each token generation step builds a computation graph representing the forward pass through the transformer layers, then executes it. The graph structure is typically identical across iterations, enabling allocation reuse.
- Multi-GPU inference: The scheduler automatically splits the graph across multiple GPU backends based on tensor placement and backend priorities, enabling model parallelism without manual device management.
- Heterogeneous compute: Operations that lack GPU implementations are automatically offloaded to CPU backends, with the scheduler managing the necessary data transfers.
- Batch processing: Multiple inputs can be batched into a single graph and executed in one call, maximizing hardware utilization.
Theoretical Basis
Lazy Evaluation
The deferred execution model is an application of lazy evaluation from functional programming. Operations are recorded as thunks (unevaluated expressions) in the graph, and actual computation is deferred until the result is demanded (i.e., when the graph is submitted for execution). This allows the system to accumulate a complete picture of the computation before committing resources.
Graph-Based Computation
Representing computation as a directed acyclic graph (DAG) is a well-established pattern in numerical computing frameworks (TensorFlow, XLA, MLIR). The graph representation enables:
- Topological ordering: Ensuring operations execute in a valid dependency order.
- Lifetime analysis: Determining when each tensor is first produced and last consumed, enabling optimal memory reuse.
- Operator fusion: Identifying sequences of operations that can be fused into a single kernel (though GGML currently handles this at the backend level rather than as a graph transformation).
Batch Execution
Executing an entire graph as a unit rather than individual operations amortizes scheduling overhead and enables hardware-level optimizations. Modern GPU APIs (CUDA graphs, Vulkan command buffers) are specifically designed to benefit from batched submission of work, reducing CPU-GPU synchronization points and kernel launch overhead.