Deja Vu: Advancing Efficiency in Transformers through Contextual Sparsity (version 2)

 Deja Vu: Advancing Efficiency in Transformers through Contextual Sparsity (v2)

Transformers have revolutionized language processing but often face criticism for slow inference times, especially in real-time applications like chatbots. These delays primarily stem from the dense and computationally intensive attention mechanisms each layer of a transformer employs. To address these inefficiencies, the Deja Vu method introduces a strategic sparsity into the architecture of transformers, which significantly accelerates processing by reducing the number of active parameters during inference.

Introduction to Sparsity in Transformers

Traditionally, transformers utilize a large number of neurons and attention heads, leading to heavily populated network layers. Deja Vu challenges this norm by selectively activating only a fraction of these elements based on the specific demands of the input. This approach not only lightens the network but also ensures that the essential capacity for in-context learning is not compromised.

Contextual Sparsity: A Core Innovation

Deja Vu's innovation lies in its unique method of "contextual sparsity." Unlike static sparsity patterns, which deactivate a fixed percentage of neurons irrespective of the input, contextual sparsity dynamically adjusts which neurons and attention heads are turned on or off depending on the specific input being processed. This adaptability helps maintain the model's learning capabilities and performance while significantly boosting its efficiency.

Overcoming Challenges with Dynamic Sparsity

Implementing sparsity in transformers is not without challenges:

  • Hardware Compatibility: Modern GPUs are better optimized for dense networks. Sparse models, with many zero-valued elements, often fail to utilize GPU optimizations effectively, which can diminish the intended performance gains.
  • Retraining Costs: Transitioning from a dense to a sparse transformer model typically requires substantial retraining, which can be resource-intensive.
  • Static vs. Dynamic Sparsity: While static sparsity can undermine a model’s ability to adapt and learn from varying contexts, Deja Vu circumvents this issue by ensuring that sparsity configurations are input-responsive, preserving robust in-context learning.

Empirical Success and Practical Applications

Empirical results show that Deja Vu can double the speed of NVIDIA's transformer implementations and reach speeds up to six times faster than popular models hosted on platforms like Hugging Face. These improvements are particularly noted in language models and do not extend to vision transformers. Deja Vu also incorporates a Mixture of Experts to optimally select from multiple MLP variants, enhancing the model's decision-making processes.

Conclusion

Deja Vu marks a substantial advancement in transformer technology, making these powerful models more suitable for real-time applications. By reducing load times and enhancing responsiveness through contextual sparsity, Deja Vu not only preserves but enhances the functional capabilities of transformers across various domains.

paper: https://arxiv.org/abs/2310.17157

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