Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time

Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time

Transformers are powerful tools in language model applications but often suffer from slow inference times due to their complex architecture, where each layer applies attention mechanisms that are computationally intensive. This latency is particularly problematic in applications like chatbots, where fast response times are crucial.

Introduction to Sparsity in Transformers

The Deja Vu method addresses these inefficiencies by introducing sparsity into the Transformer's architecture. Traditional Transformers utilize a large number of neurons and attention heads, resulting in dense network layers. Sparsity within a Transformer implies using only a fraction of these neurons and attention heads per input, significantly reducing the number of parameters that need to be loaded and activated during operation.

Concept of Deja Vu

The key innovation of Deja Vu lies in its approach to making Large Language Models (LLMs) sparse. Rather than activating a fixed percentage of parameters across all inputs, Deja Vu employs what is termed as "contextual sparsity." This technique dynamically adjusts which neurons and attention heads are activated based on the specific input being processed. This method ensures that while the network becomes lighter and faster, it does not lose its capacity for in-context learning, which is essential for maintaining performance.

Challenges with Implementing Sparsity

However, there are challenges associated with implementing sparsity in Transformers:

  • Hardware Compatibility: Modern GPUs are optimized for dense networks. Sparse networks, which contain a high proportion of zero-valued elements, do not utilize these optimizations effectively, potentially negating the benefits of sparsity.
  • Retraining Costs: Converting a dense Transformer model to a sparse one typically requires extensive retraining, which can be resource-intensive.
  • Static vs. Dynamic Sparsity: Static sparsity patterns, where certain neurons are permanently deactivated regardless of input, can impair the model’s learning capabilities. Deja Vu's contextual sparsity avoids this pitfall by ensuring sparsity adapts to the input, preserving the model's ability to perform in-context learning.

Empirical Results and Applications

Deja Vu has been shown to double the speed of the Transformer implementations from NVIDIA and achieve speeds six times faster than popular models available on Hugging Face, though these improvements are not applicable to vision Transformers. Additionally, Deja Vu integrates a Mixture of Experts approach to select among multiple MLP variants, enhancing decision-making within the model.

Conclusion

Deja Vu represents a significant step forward in making Transformers more efficient without compromising their functional capabilities. By reducing load times and enhancing responsiveness through contextual sparsity, Deja Vu offers a promising avenue for improving Transformer-based applications across various domains.


https://arxiv.org/abs/2310.17157


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