Principle:Ollama Ollama ML Backend Abstraction
| Knowledge Sources | |
|---|---|
| Domains | ML Framework, Abstraction |
| Last Updated | 2025-02-15 00:00 GMT |
Overview
The ML Backend Abstraction defines a common interface layer that decouples model inference logic from the underlying compute framework, allowing Ollama to support multiple backends such as GGML and MLX through a unified API surface.
Core Concepts
Backend Interface
The core abstraction is the Backend interface which defines the contract every ML backend must fulfill. This includes model loading (Load), tensor access (Get), compute context creation (NewContext), device enumeration (BackendDevices), and memory management (Close, BackendMemory). By programming against this interface rather than a concrete implementation, model architectures remain portable across backends.
Tensor Abstraction
The Tensor interface provides a comprehensive set of operations including arithmetic (Add, Sub, Mul, Div), matrix multiplication (Mulmat), activation functions (GELU, SILU, RELU, Sigmoid), normalization (LayerNorm, RMSNorm), convolution, and shape manipulation (Reshape, Permute, Slice). Each backend implements these operations using its native compute primitives, whether that is GGML's CPU/CUDA kernels or MLX's Metal shaders.
Context and Compute Graph
The Context interface abstracts the compute graph construction and execution model. Backends create tensors, build a computation graph through lazy operations, and then execute the graph via Compute or Forward. This lazy evaluation pattern allows backends to optimize the full computation graph before execution, enabling kernel fusion and memory planning.
Backend Registration
Backends register themselves at initialization time via RegisterBackend, providing a factory function keyed by name (e.g., "ggml"). The NewBackend function dispatches to the registered factory, currently defaulting to the GGML backend. This registry pattern enables compile-time backend selection and future extension to additional frameworks.
Implementation Notes
The abstraction layer is defined in ml/backend.go with the GGML implementation registered under ml/backend/ggml/. Model architectures in model/models/ are written against the ml.Backend and ml.Tensor interfaces, making them backend-agnostic. The ml.ScaledDotProductAttention interface provides an optional fused attention optimization that backends can implement for improved performance.