Efficient Prompt-Prompted Mixture of Experts for Large Language Model Generation

Temel Kavramlar

A novel training-free Mixture of Experts (MoE) method called GRIFFIN that selects unique feedforward experts at the sequence level to enable efficient generation across a variety of large language models with different non-ReLU activation functions, while preserving the original model's performance.

Özet

The paper introduces GRIFFIN, a novel training-free Mixture of Experts (MoE) method for efficient generation of large language models (LLMs). The key observation is that many trained LLMs naturally produce highly structured feedforward (FF) activation patterns within a sequence, a phenomenon called "flocking". GRIFFIN exploits this flocking behavior to select a subset of FF experts at the sequence level, without requiring any model training or architecture changes.
The paper makes the following key contributions:

GRIFFIN is a simple, training-free MoE method that can be applied to any pre-trained LLM, including those with non-ReLU activation functions.
By selecting unique FF experts per sequence, GRIFFIN maintains the original model's performance on a variety of classification and generation tasks, even when removing 50% of the FF parameters.
GRIFFIN achieves up to 1.25x speedup in latency on an NVIDIA L40 GPU compared to the full model, while preserving performance.
Extensive experiments demonstrate GRIFFIN's effectiveness, scalability and robustness across multiple LLMs and tasks.

The paper first observes the phenomenon of "flocking" in FF activations of many LLMs, where relative activation magnitudes are shared within a sequence. It then leverages this insight to design GRIFFIN, which selects the most relevant FF experts for each input sequence during the prompt phase, and uses those experts for the entire generation phase. This simple yet effective approach overcomes the limitations of existing MoE and pruning methods.

İstatistikler

In OPT-175B, fewer than 5% of neurons in FF blocks have nonzero values per token, meaning 95% of the compute in each FF block is wasted.
FF blocks typically consist of around two-thirds of the parameters in an LLM, making them a serious memory and compute bottleneck.

Alıntılar

"Flocking emerges in FF activations (inputs into the FF down projection) when we focus on a sequence's relative activation magnitudes instead of the absolute values."
"GRIFFIN does this by using a sequence's prompt to determine the experts to activate during generation, allowing it to overcome all of the aforementioned challenges."

Önemli Bilgiler Şuradan Elde Edildi

Prompt-prompted Mixture of Experts for Efficient LLM Generation

by Harry Dong,B... : arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01365.pdf

Daha Derin Sorular

How can the insights from GRIFFIN be extended to improve the efficiency of other components of LLMs beyond just the feedforward blocks?

The insights from GRIFFIN can be extended to improve the efficiency of other components of LLMs by considering the structured sparsity observed in activations. One way to extend these insights is to apply similar expert selection mechanisms to other components of LLMs, such as the attention mechanism. By identifying and utilizing the most relevant attention heads or layers based on the input sequence, it is possible to reduce computational costs and memory requirements while maintaining performance. Additionally, exploring the application of structured sparsity in other parts of the model, such as the transformer encoder or decoder layers, can lead to further efficiency gains. By leveraging the concept of flocking and the idea of selecting experts based on the input sequence, it is possible to optimize various components of LLMs for more efficient inference.

What are the potential drawbacks or limitations of relying on the prompt to determine the relevant experts for generation, and how could these be addressed?

Relying solely on the prompt to determine the relevant experts for generation may have some drawbacks and limitations. One potential limitation is that the effectiveness of the expert selection heavily depends on the quality and informativeness of the prompt. If the prompt does not adequately capture the key features of the input sequence, the selected experts may not be optimal for generating accurate outputs. Additionally, using the prompt for expert selection may introduce bias towards certain types of inputs, potentially limiting the model's generalization capabilities.
To address these limitations, one approach could be to incorporate additional context or information beyond just the prompt when selecting experts. This could involve considering a broader context window around the prompt or incorporating information from multiple parts of the input sequence. By expanding the scope of information used for expert selection, the model can make more informed decisions and improve the quality of generated outputs. Additionally, techniques like reinforcement learning or adaptive expert selection mechanisms could be explored to dynamically adjust the expert selection based on the input sequence characteristics during generation.

Given the observed structured sparsity in LLM activations, what other architectural or training innovations could be explored to further improve the efficiency and performance of these models?

Building on the observed structured sparsity in LLM activations, several architectural and training innovations could be explored to enhance the efficiency and performance of these models:

Sparse Transformer Architectures: Designing transformer architectures that explicitly leverage structured sparsity in activations to reduce computational complexity and memory requirements. This could involve incorporating sparse attention mechanisms or optimizing the model for efficient computation.

Dynamic Expert Selection: Developing dynamic expert selection mechanisms that adaptively choose experts based on the input sequence characteristics during both training and inference. This can help optimize the model's performance by focusing on relevant features while discarding irrelevant ones.

Hybrid Models: Exploring hybrid models that combine the strengths of sparse models with traditional dense models to achieve a balance between efficiency and performance. This could involve using sparse components for specific tasks or parts of the model where structured sparsity is most beneficial.

Regularization Techniques: Introducing regularization techniques tailored to exploit structured sparsity in activations, such as encouraging sparsity in specific parts of the model through regularization penalties or constraints during training.

By exploring these architectural and training innovations, it is possible to further enhance the efficiency and performance of LLMs while maintaining their effectiveness across a wide range of tasks and applications.