Papers
Topics
Authors
Recent
Search
2000 character limit reached

Challenges in Deploying Long-Context Transformers: A Theoretical Peak Performance Analysis

Published 14 May 2024 in cs.LG, cs.AI, cs.CL, and cs.DC | (2405.08944v1)

Abstract: Transformer-based long context generative models power emerging AI applications like hour-long video understanding and project-level coding agent. Deploying long context transformers (e.g., 100K to 10M tokens) is prohibitively expensive compared to short context (e.g., 4K tokens) model variants. Reducing the cost of long-context transformers is becoming a pressing research and engineering challenge starting from the year of 2024. This work describes a concurrent programming framework for quantitatively analyzing the efficiency challenges in serving multiple long-context requests under limited size of GPU high-bandwidth memory (HBM) regime. We give a detailed analysis of how all additional computational costs, compared to 4K context, trace back to \textit{one single source: the large size of the KV cache}. We use a 34B GPT-3.5 level model of 50K context on A100 NVLink as a running example, and describe how its large KV cache causes four types of deployment challenges: (1) prefilling long inputs takes much longer compute time and GPU memory than short inputs; (2) after prefilling, the large KV cache residing on the GPU HBM substantially restricts the number of concurrent users being served; (3) during decoding, repeatedly reading the KV cache from HBM to SM largely increases latency; (4) when KV cache memory overflows, swapping it from HBM to DDR causes significant context switching latency. We use this framework to analyze existing works and identify possibilities of combining them to build end-to-end systems. Overall, this work offers a foundational framework for analyzing long context transformer deployment and identifies directions towards reducing the inference cost of 1M context to be as cheap as 4K.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (31)
  1. Gqa: Training generalized multi-query transformer models from multi-head checkpoints. arXiv preprint arXiv:2305.13245, 2023a.
  2. Colt5: Faster long-range transformers with conditional computation. arXiv preprint arXiv:2303.09752, 2023b.
  3. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023.
  4. Longalign: A recipe for long context alignment of large language models. arXiv preprint arXiv:2401.18058, 2024.
  5. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318, 2023.
  6. Cohere. Command r+. 2024. URL https://huggingface.co/CohereForAI/c4ai-command-r-plus.
  7. DeepSeek-AI. Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model. 2024. URL https://api.semanticscholar.org/CorpusID:269613809.
  8. Layer skip: Enabling early exit inference and self-speculative decoding. arXiv preprint arXiv:2404.16710, 2024.
  9. Data engineering for scaling language models to 128k context. arXiv preprint arXiv:2402.10171, 2024.
  10. Model tells you what to discard: Adaptive KV cache compression for LLMs. In Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@NeurIPS 2023), 2023. URL https://openreview.net/forum?id=e9D2STGwLJ.
  11. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023.
  12. Longt5: Efficient text-to-text transformer for long sequences. arXiv preprint arXiv:2112.07916, 2021.
  13. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  14. Mixtral of experts. arXiv preprint arXiv:2401.04088, 2024.
  15. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020.
  16. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th Symposium on Operating Systems Principles, pages 611–626, 2023.
  17. Fast inference from transformers via speculative decoding. In International Conference on Machine Learning, pages 19274–19286. PMLR, 2023.
  18. Snapkv: Llm knows what you are looking for before generation. arXiv preprint arXiv:2404.14469, 2024.
  19. Infinite-llm: Efficient llm service for long context with distattention and distributed kvcache. arXiv preprint arXiv:2401.02669, 2024.
  20. Kivi: A tuning-free asymmetric 2bit quantization for kv cache. arXiv preprint arXiv:2402.02750, 2024.
  21. Dynamic memory compression: Retrofitting llms for accelerated inference. arXiv preprint arXiv:2403.09636, 2024.
  22. Confident adaptive language modeling. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=uLYc4L3C81A.
  23. Triforce: Lossless acceleration of long sequence generation with hierarchical speculative decoding. arXiv preprint arXiv:2404.11912, 2024a.
  24. You only cache once: Decoder-decoder architectures for language models. 2024b. URL https://api.semanticscholar.org/CorpusID:269626143.
  25. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.
  26. Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. In Anna Korhonen, David Traum, and Lluís Màrquez, editors, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5797–5808, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1580. URL https://aclanthology.org/P19-1580.
  27. Retrieval head mechanistically explains long-context factuality. arXiv preprint arXiv:2404.15574, 2024.
  28. Memorizing transformers. arXiv preprint arXiv:2203.08913, 2022.
  29. Yi: Open foundation models by 01. ai. arXiv preprint arXiv:2403.04652, 2024.
  30. Wkvquant: Quantizing weight and key/value cache for large language models gains more. arXiv preprint arXiv:2402.12065, 2024.
  31. H2o: Heavy-hitter oracle for efficient generative inference of large language models. Advances in Neural Information Processing Systems, 36, 2024.
Citations (11)
View on Semantic Scholar

Summary

  • The paper demonstrates that the primary challenge is managing a large KV cache, which directly limits user concurrency and increases latency.
  • It applies a concurrent programming framework to quantitatively analyze efficiency bottlenecks on a 50K token model using an A100 GPU.
  • Findings reveal that prefilling time and GPU memory demands dramatically increase for long contexts, highlighting areas for KV cache optimization.

Efficient Deployment of Long-Context Transformers

Introduction

In the rapidly evolving world of AI applications, we encounter a unique challenge: deploying long-context transformers efficiently. These models, which process huge contexts like entire books or extensive code repositories, are invaluable for applications demanding vast input data. Yet, their deployment is prohibitively expensive compared to their short-context counterparts. This article will break down a paper that tackles this pressing issue, offering both insights and practical solutions.

Challenges in Deploying Long-Context Transformers

Deploying a long-context transformer involves addressing several unique challenges. The crux of the problem lies in the KV cache—an internal storage used by transformers to keep track of key-value pairs necessary for generating outputs. Here's a breakdown of the key challenges:

  1. Prefilling Latency: Long inputs take significantly more time and GPU memory to preprocess compared to short inputs.
  2. Concurrency: The large KV cache uses up considerable GPU high-bandwidth memory (HBM), limiting the number of concurrent user sessions.
  3. Decoding Latency: Repeatedly reading from the KV cache during output generation increases latency.
  4. Context Switching: When memory overflows, swapping KV cache data between GPU and CPU adds significant delay.

Quantitative Analysis Using a Concurrent Programming Framework

To tackle these challenges, the paper introduces a concurrent programming framework. This framework quantitatively analyzes the efficiency bottlenecks in serving multiple long-context requests under the constraints of limited GPU HBM. By examining a model with a 50K token context length on an A100 GPU, the framework breaks down the primary challenges:

  • Concurrency Bound: The level of concurrency is directly limited by the size of the GPU HBM.
  • Compute Bound Prefilling: The time taken to prefill the input is bounded by the GPU's floating point operations per second (FLOPS).
  • Memory Bound Decoding: The delay in decoding sequences is constrained by the HBM bandwidth.
  • PCIE Bound Context Switching: The latency in switching contexts is restricted by the PCIE bandwidth between GPU HBM and CPU DDR memory.

Numerical Results and Performance Metrics

The analysis shows stark differences between short and long-context models. For a 34B model with a 4K context, the KV cache requires only 0.91 GB, compared to a massive 22.8 GB for a 100K context model. This discrepancy highlights the significant increase in memory and computational costs associated with long contexts.

Key Findings:

  • Prefilling for 50K tokens can take around 14.1 seconds, compared to just 0.89 seconds for 4K tokens.
  • Concurrent user support drops from about 20 users (4K context) to only 1 user (50K context) per GPU.
  • Decoding latency slightly increases with longer contexts, while context switching overhead also significantly rises.

Implications and Future Directions

This paper provides a foundational framework that identifies the principal source of inefficiency: the size of the KV cache. Practical implications include:

  • Optimizing Memory Use: Methods to compress the KV cache without losing information could democratize access to long-context models.
  • Prefilling and Decoding: Innovations that reduce the prefilling and decoding latencies are crucial. For instance, reducing the KV cache size from 100K to 1GB will greatly impact cost efficiency.

Moving forward, integrating multiple existing approaches to build an end-to-end optimized system is promising. Collaborative efforts could yield substantial advancements, making the deployment of long-context transformers as cost-effective as their short-context counterparts.

Conclusion

In summary, deploying long-context transformers efficiently poses several significant challenges rooted in the size of the KV cache. The concurrent programming framework provided in the paper offers a detailed analysis of these issues and identifies key areas for optimization. As the AI community continues to innovate, working towards compressing the KV cache and improving both prefilling and decoding processes will be crucial steps in enabling widespread use of these powerful models.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Authors (1)

  1. Yao Fu 

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 11 tweets with 1139 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free