Building a Fast LLM Inference Engine with C++ and CUDA from Scratch

Dec. 12, 2024

In this article, we dive into building an LLM inference engine using C++ and CUDA without relying on external libraries. The goal is to understand the full stack of LLM inference-from CUDA kernels to model architecture-and optimize performance for running fast on a single prompt on consumer devices.

Why Build It from Scratch?

Building an LLM inference engine from scratch offers several benefits:

• Deep Understanding: You get a thorough grasp of how different optimizations affect inference speed.
• Control and Flexibility: Without libraries, you have complete control over every aspect of the implementation.
• Performance Optimization: Custom solutions can be highly optimized for specific hardware and use cases.

LLM Inference Overview

1. Recap: LLM Architectures and Inference

LLMs (Large Language Models) are typically based on transformer architectures, which consist of multiple layers of self-attention mechanisms and feed-forward neural networks. The inference process involves:

• Tokenization: Converting input text into tokens.
• Embedding: Mapping tokens to high-dimensional vectors.
• Transformer Layers: Applying attention and feed-forward operations.
• Decoding: Generating output tokens.

2. Inference on the CPU

Before diving into GPU optimization, let's look at CPU inference:

• Multithreading: Utilizing multiple CPU cores can significantly speed up matrix multiplications and other operations.
• Weight Quantization and SIMD: Reducing weight precision (e.g., from float32 to int8) and using SIMD (Single Instruction Multiple Data) instructions can further boost performance.

3. Inference on the GPU

GPUs are highly parallel and can handle large matrix operations much faster than CPUs. Here’s how we optimize for GPUs:

3.1 A Naive Port to CUDA

• Initial Setup: Translating CPU code to CUDA involves managing memory transfers between host and device.
• Basic Kernel: Implementing basic matrix multiplication kernels using CUDA.

3.2 Better Matmuls

• Optimized Kernels: Using more efficient matrix multiplication algorithms like cuBLAS or custom kernels.
• Tiling: Dividing large matrices into smaller tiles to optimize memory access patterns.

3.3 Fusing and Even Better Matmuls

• Fusion Techniques: Combining multiple operations (e.g., matmul + bias addition) into a single kernel to reduce overhead.
• Advanced Optimizations: Using techniques like mixed precision (float16 for computations, float32 for accumulation).

3.4 Attention and Long Context Generation

• Efficient Attention Mechanisms: Implementing efficient self-attention algorithms that handle long context sequences.
• Memory Management: Managing memory efficiently to avoid out-of-memory errors during long sequence generation.

3.5 KV Quantization and Compiler Gotchas

• KV Cache Optimization: Using quantized key-value (KV) caches to reduce memory usage and improve performance.
• Compiler Optimizations: Understanding and leveraging compiler optimizations, such as loop unrolling and inline functions.

What’s Next?

Future work includes:

• Further Optimizations: Exploring more advanced techniques like dynamic parallelism and asynchronous data transfers.
• Model Support: Extending the engine to support a wider range of LLM architectures.
• User-Friendly Interfaces: Developing user-friendly interfaces for easier deployment and usage.

Acknowledgements

• calm: Much of my implementation is inspired by Arseny Kapoulkine’s inference engine. This project was kicked off by understanding calm and its optimizations.
• llama2.c: Parts of the CPU implementation are influenced by Andrej Karpathy's work.