Retention Networks Explained

Literature Review

"Attention Is All You Need" (Vaswani et al., 2017)

Introduced the Transformer model, focusing solely on attention mechanisms, eliminating the need for recurrence and convolutions.
Achieved state-of-the-art results in machine translation, proving more parallelizable and efficient in training.

"Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention" (Katharopoulos et al., 2020)

Proposed linear transformers to reduce complexity from O(N^2) to O(N), making them significantly faster for long sequences.
Maintained performance comparable to standard transformers while being up to 4000x faster in autoregressive prediction.

"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness" (Dao et al., 2022)

Introduced FlashAttention, an IO-aware attention algorithm optimizing memory usage between GPU memory levels.
Enabled faster Transformer training and improved performance on various tasks, including long-sequence challenges.

"Retentive Network: A Successor to Transformer for Large Language Models" (Sun et al., 2023)

Proposed RETNET, a new architecture for large language models, achieving efficient training, low-cost inference, and strong performance.
Introduced a retention mechanism supporting various computation paradigms, positioning it as a successor to the Transformer.

Biography

Yutao Sun	Li Dong	Shaohan Huang	Shuming Ma
Research intern in Microsoft Research Lab - Asia PhD student in Tsinghua University, Beijing Research interest in LLM backbone & applications, and long sequence modeling and inference	Principal Researcher, Microsoft Research Lab - Asia PhD in Informatics from The University of Edinburgh, Scotland Research interest in Human Language Technologies and Natural Language Computing	Senior Researcher at the Natural Language Computing group at Microsoft Research Lab - Asia MS in Computer Science at Beihang University, Beijing Recently worked on a paper called: Language is not all you need	Researcher at the Natural Language Computing group at Microsoft Research Lab - Asia Master’s and Bachelor’s degrees from Peking University, with a focus on natural language processing Published 30+ papers on large-scale pre-trained LMs at top conferences (ICML, ICLR, ACL, EMNLP)
Yuqing Xia	Jilong Xue	Furu Wei	Jianyong Wang
Researcher at the Natural Language Computing group at Microsoft Research Lab - Asia Ph.D. in Biology from Peking University in 2019 Interested in using AI to empower natural science research	Principal Researcher in System Research Group of Microsoft Research Asia (MSRA) Jilong received his Ph.D. in Computer Science from Peking University Builds AI frameworks and compilers to bridge and optimize hardware for AI applications	Partner Research Manager at Microsoft Research Asia Previously a Staff Researcher at IBM Research - China Ph.D. in computer science from Wuhan University in 2009 Worked with Li Dong on several Microsoft research papers	Professor in the Department of Computer Science and Technology at Tsinghua University Supported by various organizations Research focuses on various data mining algorithms

Methodology and Architecture

RetNet is a novel architecture designed to address some of the limitations of the Transformer model, particularly in the context of large language models. Here's an overview of its methodology and architecture:

Methodology:

Objective: The primary goal of RetNet is to achieve training parallelism, efficient long-sequence modeling, Transformer-comparable performance, and low-cost inference simultaneously.
Addressing the 'Impossible Triangle': The paper identifies the challenge of achieving training parallelism, good performance, and low inference cost simultaneously in Transformer models, referring to this challenge as the "impossible triangle."
Comparison with Existing Models: RetNet is compared extensively with the Transformer and its variants, focusing on aspects like scaling curves, in-context learning, and inference cost.

Figure 1: Impossible Triangle

Figure 2: Comparison of Existing Models

Architecture:

Multi-Scale Retention Mechanism: RetNet introduces a multi-scale retention mechanism as a substitute for the multi-head attention mechanism used in Transformers. This mechanism supports three computation paradigms:

Parallel Representation: Facilitates training parallelism, fully utilizing GPU devices for efficient training.
Recurrent Representation: Enables efficient O(1) inference in terms of memory and computation, significantly reducing deployment cost and latency. This representation also simplifies implementation by eliminating the need for key-value cache tricks.

Figure 3.1: Retention Equation

Figure 3.2: Retention Equation in Sequential Form

Figure 4: Parallel and Sequential Representation of Retention

Performance and Efficiency:

Inference Efficiency: RetNet demonstrates length-invariant inference cost. For instance, with a 7B model and 8k sequence length, RetNet decodes 8.4 times faster and saves 70% of memory compared to Transformers.
Training Efficiency: During training, RetNet achieves 25-50% memory saving and 7 times acceleration compared to the standard Transformer. It also shows advantages over highly-optimized variants like FlashAttention.

Performance and Efficiency

Figure 5: Inference Cost

Figure 6: Model Size

Figure 7: Performance Comparison between Different Transformers and RetNet

Hence, RetNet's architecture is designed to overcome the limitations of the Transformer model by introducing a novel retention mechanism that supports multiple computation paradigms, leading to improvements in training efficiency, inference cost, and scalability.

Social Impact

Since RetNets function in a manner similar to transformers, their societal impact overlaps:

Environmental Concerns: Everytime a series of 25 prompts are asked to ChatGPT, it consumes approximately the equivalent of 500ml (16oz) of water. But by using RetNets, we can reduce the inference costs significantly, hence reducing its environmental impact.
Bias: It can inadvertently perpetuate biases present in its training data. Addressing these biases is crucial for ensuring fairness and ethical use of AI.
Inclusivity: These technologies can significantly enhance accessibility for people with disabilities, offering tools for communication, navigation, and interaction that were previously unavailable.
Privacy Concerns: Most Language models are trained on a larger scale data scraped from the internet, which might also include user data. There is always a risk of privacy violations as adversarial prompts can lead to data leaks.
Impact on Employment: Automation and AI are already leading to displacement in certain sectors, but it's also creating new opportunities in tech and other industries. The social impact of this is still underway.

Industry Applications

Jobs in General: Using a language model as a knowledge base increases the productivity of workers manifold.
Finance: In the finance sector, these technologies can enhance fraud detection and improve customer service through intelligent chatbots.
Retail and E-commerce: AI can personalize shopping experiences, summarize reviews, and recommend products to users.

The above are the examples that I can think of, but I believe it will be beneficial to almost every industry in some way or the other.

Follow-on Research

Model Efficiency and Validity: Follow-on research has to be carried out on the scalability, efficiency, and validity of this architecture. Only one RetNet model is shown in the paper as a 7B parameter model.
Cross-disciplinary Applications: Exploring the application of these AI models in various fields such as climate science, astrophysics, and materials science is a burgeoning area of research.
More applications of retention can be studied and how it can affect the field currently dominated by transformers can be explored.

Peer-Review

Summary: The Retentive Network (RETNET), introduced in this study, is a proposed foundational architecture for large language models. It claims to achieve training parallelism, low-cost inference, and robust performance. The paper explores the interplay between recurrence and attention, leading to the development of a retention mechanism for sequence modeling. Notably, the parallel representation fosters training parallelism, while the recurrent representation facilitates low-cost O(1) inference, enhancing decoding throughput, latency, and GPU memory efficiency without compromising on performance.

Strengths:

The paper is well written and easy to understand.
It introduces a novel concept with the potential to significantly enhance future large language models (LLMs).
The research includes experiments comparing RetNet's performance with traditional transformers.

Weaknesses:

The model has been tested and compared only for a single size, leaving some uncertainty about its scalability.
The evaluation of model performance relies solely on perplexity, which may not fully capture the model's capabilities.
Since it was published by Microsoft, they could've released a working implementation to test it out, similar to Chat GPT.

Limitations:

The paper does not explore the scalability of RETNET for significantly larger models. A broader range of model sizes, extending beyond the 13B range, would provide a more comprehensive understanding of its performance scalability.

Overall Rating: 6 (Weak Accept)

The field is rapidly evolving, and competitors will take any advantage when available. If RetNet was truly revolutionary, we'd already be seeing LLMs based on it. It's been around 5 months, and it hasn't gotten a lot of traction as of yet.