Optimizing your LLM in production

Note: This blog post is also available as a documentation page on Transformers.

Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however:

• To exhibit near-human text understanding and generation capabilities, LLMs currently require to be composed of billions of parameters (see Kaplan et al, Wei et. al). This consequently amplifies the memory demands for inference.
• In many real-world tasks, LLMs need to be given extensive contextual information. This necessitates the model's capability to manage very long input sequences during inference.

The crux of these challenges lies in augmenting the computational and memory capabilities of LLMs, especially when handling expansive input sequences.

In this blog post, we will go over the most effective techniques at the time of writing this blog post to tackle these challenges for efficient LLM deployment:

1. Lower Precision: Research has shown that operating at reduced numerical precision, namely 8-bit and 4-bit, can achieve computational advantages without a considerable decline in model performance.

2. Flash Attention: Flash Attention is a variation of the attention algorithm that not only provides a more memory-efficient approach but also realizes increased efficiency due to optimized GPU memory utilization.

3. Architectural Innovations: Considering that LLMs are always deployed in the same way during inference, namely autoregressive text generation with a long input context, specialized model architectures have been proposed that allow for more efficient inference. The most important advancement in model architectures hereby are Alibi, Rotary embeddings, Multi-Query Attention (MQA) and Grouped-Query-Attention (GQA).

1. Harnessing the Power of Lower Precision

!pip install transformers accelerate bitsandbytes optimum