Large Language Models (LLMs) such as Generative Pretrained Transformers (GPT) and its family have revolutionized the fields of artificial intelligence and machine learning. These models help computers understand human language and make predictions about what people might say or write.
Software developers train GPT AIs on vast amounts of text data and use complex algorithms to process language in a way that mimics human thought.
Due to its versatility, GPT-like technologies have many applications, from chatbots and virtual assistants to automated translations and content generation.
Despite the importance of these models to the future of AI and ML, it is still a challenge to deploy them on a large scale mainly because of their colossal size and related computational costs.
However, this might soon be a thing of the past, according to a recent academic paper titled: “Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning.”
The authors of this research paper, Elias Frantar of IST Austria, and Dan Alistarh of IST Austria & Neural Magic demonstrate for the first time that it is possible to prune up to a minimum of 50% sparsity in only one shot. Moreover, this can be achieved without the need to retrain GPT models, while still maintaining minimal loss of accuracy.
Researchers Design SparseGPT Specifically To Prune GPT-Family Models
One of the best-performing models, GPT-175B comprises 175 billion parameters, which cumulatively amount to 320GB of disk space in half-precision (FP16) format. Therefore, GPT-175B models would call for not less than five A100 GPUs with 80GB of memory each for every inference.
“It is therefore natural that there has been significant interest in reducing these costs via model compression,” the researcher said in the study.
Although there have been other GPT compression approaches, they concentrated on quantization [3,47,46,9] which refers to the reduction of the numerical depiction of isolated weights.
To ensure that model compression was effective and efficient in making large AI models smaller and more efficient, the researchers turned to Pruning, a technique involving removing parts of the model that aren’t as important or necessary.
Pruning has been around for a while and has proven to be effective for various applications, such as image recognition and smaller-scale language models.
However, there’s a catch. The most successful pruning techniques require extensive retraining of the model to regain the accuracy lost when removing parts of it.
This retraining process can take a lot of time and resources, especially for massive AI models like GPT-3, which boasts billions of parameters. Some alternative one-shot pruning methods don’t need retraining, but they are too resource-intensive to be applied to GPT-3-sized models.
How is SparseGPT Different from Other GPT Compression Approaches
Frantar and Alistarh are proposing a shift to Sparse-GPT as the only one-shot pruning technique with the capacity to function efficiently in larger language models with a scale between 10 and 100 billion parameters.
Sparse-GPT accomplishes this “by reducing the pruning problem to an extremely large-scale instance of sparse regression,” the research said. “It is based on a new approximate sparse regression solver, used to solve a layer-wise compression problem, which is efficient enough to execute in a few hours on the largest openly-available GPT models (175B parameters), using a single GPU.”
SparseGPT can efficiently reduce the model’s size by up to 60% without any loss in performance or quality – a fantastic achievement when applied to the largest publicly-available language models, like OPT-175B and BLOOM-176B.
The technology manages to keep errors virtually non-existent by measuring its effectiveness using perplexity and zero-shot accuracy.
If implemented, we could see a substantial reduction in the cost of GPT-AIs in addition to achieving faster speed, and leaner AI systems without compromising the incredible language abilities we’ve come to expect from them!
SparseGPT can make large AI models like the 175-billion-parameter OPT family more efficient by selectively removing up to 60% of their layers without significantly affecting their performance.
In comparison, the current method, Magnitude Pruning, can only handle up to 10% sparsity before the performance starts to decline and completely fails beyond 30% sparsity.
SparseGPT has been designed to successfully apply more hardware-friendly sparsity patterns, like 2:4 and 4:8, which makes it even more useful for enhancing the efficiency of AI models.
This cutting-edge method opens up new possibilities for improving AI performance while reducing resource demands. However, Frantar and Alistarh acknowledged that “these patterns tend to lose additional accuracy relative to the dense baseline, especially for the smaller models, these sparsity patterns can be directly exploited to obtain computational speedups.”
Nevertheless, the study demonstrated that the sparsity induced by Sparse-GPT is able to compound when used along with additional compression obtained through other quantization approaches.
“One interesting fact is that our method is entirely local, in the sense that it relies solely on weight updates designed to preserve the input-output relationship for each layer, which are computed without any global gradient information,” the researchers explained.
Despite their commendable achievement, Frantar and Alistarh have committed to fine-tuning the technique for large-scale models to improve its accuracy and efficiency. They believe that it is possible to achieve 80-90% sparsity with progressive pruning and fine-tuning.
As part of their continued research, they will explore the potential application of Sparse-GPT techniques in reducing the computational burden of pre-training gargantuan models.
The main goal is to investigate the effectiveness of these approaches during the training process, which could have significant implications for the development of more streamlined and efficient models.
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This article was written for Business 2 Community by John Isige.
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