Introduction
Introduction
ggml is a machine learning (ML) library written in C and C++ with a focus on Transformer inference. The project is open-source and is being actively developed by a growing community. In this article, we will provide an introductory overview of ggml, covering the key concepts, a simple usage example, and an example using a backend.
ggml: A Low-Level Machine Learning Library
ggml: A Low-Level Machine Learning Library
ggml is a machine learning library that allows developers to build and run their own machine learning models. The library is designed to be highly efficient and flexible, making it suitable for a wide range of applications. ggml is particularly useful for developers who want to build custom machine learning models that are optimized for specific use cases.
Key Concepts
Key Concepts
Before diving into the details of ggml, it’s essential to understand some key concepts. ggml is a low-level library, and as such, it requires a good understanding of computer science and programming.
- ggml_context: A "container" that holds objects such as tensors, graphs, and optionally data.
- ggml_cgraph: Represents a computational graph. Think of it as the "order of computation" that will be transferred to the backend.
- ggml_backend: Represents an interface for executing computation graphs. There are many types of backends: CPU (default), CUDA, Metal (Apple Silicon), Vulkan, RPC, etc.
- ggml_backend_buffer_type: Represents a buffer type. Think of it as a "memory allocator" connected to each backend.
- ggml_backend_buffer: Represents a buffer allocated by buffer_type. Remember: a buffer can hold the data of multiple tensors.
Simple Example
Simple Example
In this example, we will go through the steps to replicate the code we ran in the "Getting Started" section. We need to create 2 matrices, multiply them, and get the result.
Printing the Computational Graph
Printing the Computational Graph
The ggml_cgraph represents the computational graph, which defines the order of operations that will be executed by the backend. Printing the graph can be a helpful debugging tool, especially when working with more complex models and computations.
Conclusion
Conclusion
This article has provided an introductory overview of ggml, covering the key concepts, a simple usage example, and an example using a backend. While we’ve covered the basics, there is much more to explore when it comes to ggml. In upcoming articles, we’ll dive deeper into other ggml-related subjects, such as the GGUF format, quantization, and how the different backends are organized and utilized.
Frequently Asked Questions
Frequently Asked Questions
question 1: What is ggml?
ggml is a machine learning library written in C and C++ with a focus on Transformer inference.
question 2: Is ggml open-source?
Yes, ggml is an open-source library.
question 3: What are the key concepts in ggml?
The key concepts in ggml include ggml_context, ggml_cgraph, ggml_backend, ggml_backend_buffer_type, and ggml_backend_buffer.
question 4: Can I use ggml with different backends?
Yes, ggml supports multiple backends, including CPU, CUDA, Metal (Apple Silicon), Vulkan, RPC, etc.
question 5: What are the benefits of using ggml?
The benefits of using ggml include high efficiency, flexibility, and customizability.
