Boosting AI Model Inference: Three Proven Methods to Speed Up Your Models
As artificial intelligence (AI) continues to grow in popularity, so does the need for efficient inference. After all, who wants to wait minutes for a model to infer an image or provide an answer to a query? Fortunately, there are several methods to make AI model inference faster. Here are three main ways to speed up the inference process:
- Make it do inference faster
- Use a smaller model
- Run on better hardware
The process of making inference faster is called inference optimization. This includes optimizing the software code that runs the model, as well as optimizing the hardware that runs the software. Some common software optimizations include reducing redundant computations, improving the memory layout of data, and using better algorithms. Hardware optimizations include using specialized hardware, such as graphics processing units (GPUs) or tensor processing units (TPUs), that can perform matrix multiplication operations more quickly than traditional central processing units (CPUs).
Another approach to speed up inference is to use a smaller model. This process is called model compression. A smaller model has fewer parameters, and therefore requires less memory and fewer computations to run. There are several techniques for compressing models, but in this blog post, we’ll focus on the following four:
- Low-rank optimization
- Knowledge distillation
- Pruning
- Quantization
Low-rank optimization
The key idea behind low-rank optimization is to replace high-dimensional tensors with low-dimensional tensors. This technique has been used in models like MobileNet, which uses depthwise and pointwise convolutional filters. Another example is LORA, where large weight matrices are represented by low-rank trainable weight matrices. HuggingFace has implemented a new library called PEFT that allows easy LORA fine-tuning.
Knowledge distillation
Knowledge distillation is a technique where a smaller model (called a student model) is trained to mimic a larger model (called a teacher model). The student model is slightly less accurate than the teacher model, but it is much faster. This technique is useful when you have a large model that is accurate but too slow for deployment, and you want to create a smaller model that is still accurate enough for practical use. A popular knowledge distillation model would be DistilBERT that was trained by using BERT as the parent model.
Pruning
Pruning is a technique that has been used in decision trees for a long time. In the context of neural networks, pruning means removing entire nodes from the architecture or finding the least useful parameters and setting them to zero. This creates a more sparse network that tends to take up less space than dense networks. A popular pruning mechanism would be weight-decay. This algorithm adds a regularization term to the loss function that encourages small weights. During training, some weights may become very small and can be pruned without affecting the accuracy of the model.
Quantization
Quantization is probably the most popular method for compressing models. It involves using fewer bits to represent parameters. For example, you could convert parameters that are represented in 32-bit floating-point (FP32) format to 16-bit floating-point (FP16) format, or even to 8-bit fixed-point (INT8) format. This creates a smaller model and speeds up training and inference due to less precision in computation. Studies show that quantization affects accuracy negligibly, and it can be applied during model training or inference. You can use bitsandbytes for training quantization and algorithms like Nvidia TensorRT for inference quantization.
Conclusion
There are several ways to make AI model inference faster, including optimizing software and hardware, using a smaller model, and compressing models. Among the compression techniques, low-rank optimization, knowledge distillation, pruning, and quantization are some of the most effective methods. By using these techniques, you can create models that are smaller, faster, and still accurate enough for practical use.
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