Accelerating Large Language Models: Techniques for Faster Inference

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By Next Solution Lab on 2024-07-14 21:30:38

Introduction:

Large Language Models (LLM) are machine learning models that can comprehend and generate human text. They are exceptional in language-related tasks, but due to their huge size, the computational resource requirement is also huge. As large language models (LLMs) continue to grow in size and complexity, achieving faster inference speeds has become a critical challenge. In this blog post, we'll explore three advanced techniques that can significantly enhance the inference speed of LLMs: Quantization, Flash Attention, and Flash Attention 2. Each of these methods addresses different aspects of model optimization, providing a comprehensive toolkit for developers aiming to deploy LLMs more efficiently

1. Quantization:

Quantization is a technique that reduces the precision of the numbers used in computations, allowing models to run faster and use less memory. By lowering the bit-width of the weights and activations, quantization can drastically improve inference speed without substantial losses in accuracy.

From the above figure, we can see how we can reduce the memory requirement to a factor of 4 by 

using quantization. In most scenarios, reducing the number of bits doesn’t affect the performance much but it should always be experimented with to see which types of quantization suit the best.

Types of Quantization:

None (32-bit floating point): This is the default precision for most LLMs, providing the highest accuracy but at the cost of slower inference speeds and higher memory usage.

16-bit (FP16/BF16): Reduces the precision to 16 bits, which can halve the memory usage and potentially double the speed. FP16 (half-precision floating point) and BF16 (bfloat16) are popular choices, with BF16 offering a better trade-off between range and precision.

8-bit (INT8): Further reduces the precision to 8 bits, offering significant speedups and memory savings. This level of quantization often requires fine-tuning to maintain model accuracy.

4-bit (INT4): The most aggressive form of quantization, using only 4 bits. While it offers substantial performance gains, maintaining accuracy can be challenging and often requires specialized techniques and hardware support.

Depending on the type of quantization, it can reduce memory and increase inference time in LLM. If we reduce the number of bits, then we’ll require less memory, but it may also reduce performance. So quantization should be treated as a hyper-parameter that needs to be tuned depending on a specific task.

2. Flash Attention:

Flash Attention is an advanced technique designed to optimize the attention mechanism in transformers, which is a major bottleneck in terms of computational complexity and memory usage.

Key Features of Flash Attention:

Memory-efficient: Flash Attention uses a technique called "memory-efficient attention" to reduce the memory footprint during training and inference. By computing attention scores more efficiently, it significantly cuts down on memory usage.

Speed improvements: By reducing the memory overhead, Flash Attention allows for faster computation of the attention scores, leading to quicker inference times.

Flash Attention achieves these improvements by reorganizing the computation of attention weights and values, minimizing the need for storing intermediate results, and reducing the overall memory bandwidth requirements.

3. Flash Attention 2:

Building on the success of Flash Attention, Flash Attention 2 introduces further optimizations to push the performance envelope even further.

Enhancements in Flash Attention 2:

Improved kernel fusion: Flash Attention 2 enhances the kernel fusion techniques used in Flash Attention, combining multiple operations into a single kernel call. This reduces the overhead associated with launching multiple kernels and improves computational efficiency.

Parallel computation: The new version introduces more sophisticated parallel computation strategies, better utilizing modern hardware architectures like GPUs and TPUs. This results in even greater speeds, especially on large-scale models.

Optimized memory access patterns: Flash Attention 2 further refines memory access patterns, ensuring that data is fetched and processed in the most efficient manner possible. This minimizes latency and maximizes throughput.

These improvements make Flash Attention 2 an attractive option for deploying state-of-the-art LLMs in production environments where inference speed is a critical factor.

4. Comparison:

To compare which of these techniques are most effective in terms of speed, all of them were used on the same input. A Japanese LLM Elyza-7B was used for this task, and the same 20 inputs were used throughout the experiment. It should also be noted that performance was calculated manually by a human. Memory requirements and inference speed were both considered in this experiment.

Findings:

4-bit quantization reduces the amount of memory requirement.

 ‘Throughput’ (Tokens generated per second) is a better indicator of speed than ‘Average Time’ / ‘Total Time’

Quantization didn’t affect much ‘Performance’ (as none quantization and 8-bit quantization had similar performance). But in 4-bit quantization, performance was slightly worse.

No quantization (16 bits) with both Flash Attention 1 and 2 or only Flash Attention 2 was the fastest configuration with pretty decent accuracy.

Even if the performance was worse than the best configuration, 4-bit quantization with Flash Attention also showed a promising result as the memory requirement is extremely low.

This result isn’t fixed but depends on the task, model, and given input.

Conclusion:

Optimizing the inference speed of large language models is crucial for their practical deployment in real-world applications. Techniques like Quantization, Flash Attention, and Flash Attention 2 offer powerful tools to achieve this goal. There are a lot of other techniques for increasing the speed of LLM, like ‘PyTorch scaled dot product attention’, 'BetterTransformer', and 'Optimum', that wasn’t used in this experiment. So it’s always the best idea to try everything and see which one works the best for that specific task. By leveraging these methods, developers can deploy LLMs that are not only faster but also more memory-efficient, enabling a wider range of applications and use cases.

Whether you're deploying LLMs on cloud infrastructure, edge devices, or mobile platforms, these techniques can help you overcome the challenges of inference speed and make the most of your computational resources. As the field continues to evolve, staying updated with the latest advancements in model optimization will be key to maintaining competitive performance in AI-driven applications.

Reference:

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness [https://arxiv.org/pdf/2205.14135]

QLORA: Efficient Finetuning of Quantized LLMs [https://arxiv.org/pdf/2305.14314]

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning [https://arxiv.org/pdf/2307.08691]

Hugging Face GPU Inference Guide:  [https://huggingface.co/docs/transformers/en/perf_infer_gpu_one#bettertransformer]