If you have been noticing that your locally installed LLM is slowing down when you try to include larger prompts. You may be interested in a new solution to improve the speed and performance of large language models in the form of StreamingLLM helps improve the speed and performance of you LLMs . Extending Llama 2 and Falcon up to 4 million tokens and providing a 22 times faster inference than your standard LLM.
Check out the video created below by AI Jason who explains more about StreamingLLM and how it can be used to improve performance of locally installed AI models. Exploring these challenges and explores potential solutions, focusing on a new research project that aims to increase the data input capacity and efficiency of LLMs.
One of the primary challenges in deploying LLMs in streaming applications is the extensive memory consumption during the decoding stage. This is due to the caching of Key and Value states (KV) of previous tokens. This issue is further compounded by the fact that popular LLMs, such as Llama-2, MPT, Falcon, and Pythia, cannot generalize to longer texts than the training sequence length. This limitation is primarily due to GPU memory constraints and the computational time required by the complex Transformer architecture used in these models.
A common solution to manage large data inputs is the use of Window attention. This approach involves caching only the most recent KVs, effectively limiting the amount of data that needs to be stored. However, this method has a significant drawback: it loses context about the removed tokens. When the text length surpasses the cache size, the performance of window attention deteriorates, leading to a loss of context and a decrease in the quality of the generated content.
StreamingLLM helps improve the speed of you LLMs
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This problem led researchers to observe an interesting phenomenon known as attention sink. They found that the model pays more attention to initial tokens than later ones, even if the initial tokens are not semantically important. This phenomenon, they discovered, could be leveraged to largely recover the performance of window attention.
Based on this analysis, the researchers introduced StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence length without any fine-tuning. This approach uses a combination of the first few tokens that have attention sink and a rolling cache of the latest tokens. This allows the LLM to maintain context about what has been discussed before, as well as recent conversation, effectively extending the effective context window.
The StreamingLLM approach has shown promising results, enabling LLMs to perform stable and efficient language modeling with up to 4 million tokens and more. In streaming settings, it outperforms the sliding window recomputation baseline by up to 22.2x speedup. This makes it particularly useful for applications such as long-form content generation and chatbots with long-term memory.
However, it’s important to note that StreamingLLM is not without its limitations. While it does maintain context about the beginning and end of a conversation, it still loses detailed context in the middle. This means it may not work well for summarizing large amounts of data, such as research papers.
The introduction of StreamingLLM and the concept of attention sink represent significant strides in overcoming the challenges of feeding unlimited data to LLMs. However, they are just one solution to the context limit problem. As the field of artificial intelligence continues to evolve, it’s likely that more creative concepts will emerge to further enhance the capacity and efficiency of LLMs.
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