Mixture-of-Depths: Dynamically allocating compute in transformer-based language models (AI summary)

Key Points

1. The paper introduces the concept of Mixture-of-Depths (MoD) transformers, which allow dynamic allocation of compute in transformer-based language models by routing tokens to computational paths rather than engaging all tokens in compute uniformly.

2. MoD models are designed to learn to dynamically allocate compute efficiently, enabling models to match baseline performance for equivalent FLOPS and training times but with significantly fewer FLOPS per forward pass, resulting in faster training and improved efficiency.

3. The research explores the use of dynamic token-level routing decisions across the depth of the network, impacting both forward MLPs and multi-head attention. The paper also discusses the potential trade-offs between performance and speed in training MoD transformers.

4. The paper compares MoD transformers to conditional computation methods previously explored in the context of transformers, highlighting the benefits and hardware efficiency gains of MoD transformers, particularly in reducing memory footprint and FLOPS per forward pass.

5. In the implementation of MoD transformers, a static compute budget is set, and per-block routers emit scalar weights for each token, determining the tokens to participate in a block’s computations. The top-? scalar weights per sequence are then selected to determine the tokens participating in the block’s computations.

6. The paper describes routing around transformer blocks, illustrating the decision-making process for tokens to either engage in self-attention and MLP computations or bypass them through a residual connection. It also explores the implications of different routing schemes and their impact on FLOPS per forward pass.

7. The research provides empirical evidence of the effectiveness of MoD transformers, showing that by leveraging MoD, models can achieve lower loss and use more parameters while being faster to step during training on equivalent hardware.

8. The study also incorporates auto-regressive evaluation and explores the integration of MoD with Mixture-of-Experts (MoE) models, highlighting the performance improvements and compute savings offered through the MoD technique.

9. The paper discusses the potential extensions and applications of MoD transformers, suggesting their broader utility in tuning a model’s compute per forward pass and incorporating dynamic token-level routing to determine the types of computations available to the network.

Summary

The paper discusses a novel approach to dynamically allocating compute in transformer-based language models, allowing models to learn to allocate computing resources to specific positions in a sequence. The proposed method enforces a total compute budget by limiting the number of tokens that can participate in self-attention and MLP computations at each layer. Tokens to be processed are determined by a top-? routing mechanism, allowing for dynamic and context-sensitive compute expenditure at the token level. The authors demonstrate that models trained with this approach can efficiently allocate compute and guide performance improvements. The method, named Mixture-of-Depths (MoD), allows for trade-offs between model performance and speed by training models that achieve performance parity with reduced FLOPs and faster processing during post-training sampling.

The study emphasizes the importance of conditional computation in language modeling, aiming to reduce total compute by expending it only when needed. The MoD technique allocates compute based on the specific needs of tokens and sequences, with the potential to use smaller total compute budgets by not spending compute unnecessarily. The research also explores how MoD transformers can be integrated with Mixture-of-Experts (MoE) models, compounding the performance improvements offered by MoD with those of MoE. Additionally, the paper presents a detailed analysis of the MoD approach's impact on model efficiency, performance, and speed, highlighting the potential trade-offs and benefits of the proposed method.

Overall, the study demonstrates that MoD transformers offer a unique and efficient way to allocate compute resources in transformer-based language models, paving the way for improved model performance and speed gains while using fewer FLOPs per forward pass. The authors provide empirical evidence of the effectiveness of the MoD technique and offer insights into its potential applications and extensions in the field of conditional computation and model optimization.

Reference: https://arxiv.org/abs/2404.022...