doc: add proposal for cloud-edge speculative decoding strategy

Signed-off-by: Yu Fan <[email protected]>

doc: modify proposal

Signed-off-by: Yu Fan <[email protected]>

docs/proposals/algorithms/joint-inference/cloud-edge-speculative-decoding-for-llm.md

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+- [Cloud-edge collaborative speculative decoding for LLM based on KubeEdge-Ianvs](#cloud-edge-collaborative-speculative-decoding-for-llm-based-on-kubeedge-ianvs)
+  - [Motivation](#motivation)
+    - [Goals](#goals)
+  - [Proposal](#proposal)
+  - [Design Details](#design-details)
+    - [Overall Architecture](#overall-architecture)
+    - [Speculative Decoding Implementation](#speculative-decoding-implementation)
+  - [Roadmap](#roadmap)
+    - [October](#october)
+    - [November](#november)
+  - [References](#references)
+
+
+# Cloud-edge collaborative speculative decoding for LLM based on KubeEdge-Ianvs
+
+## Motivation
+
+The autoregressive decoding mode of LLM determines that LLM can only be decoded serially, which limits its inference speed.  Speculative decoding technique can be used to decode LLM in parallel with the help of draft model, so as to improve the inference speed of LLM without loss of accuracy. However, the speculative decoding technology of LLM does not consider the application in the cloud-edge distributed environment.
+
+This project aims to implement cloud-edge collaborative speculative decoding based on KubeEdge-Ianvs, an open source cloud-edge collaborative distributed machine learning platform, so as to further improve the LLM inference speed in cloud-edge environment.
+
+### Goals
+
+- Implement an example of cloud-edge collaborative speculative decoding based on KubeEdge-Ianvs platform.
+- (Optional) Propose a more efficient cloud-edge collaborative speculative decoding algorithm.
+
+## Proposal
+
+We propose KubeEdge-Ianvs to adopt the cloud-edge collaborative speculative decoding strategy to enhance LLM system efficiency according to emerging computational scenarios' needs.
+
+This proposal will utilize Sedna's Joint Inference interface and Ianvs's `query-routing` pipeline.
+
+## Design Details
+
+### Overall Architecture
+
+The architecture of this proposal is shown in the figure below. We leverage the existed *TestEnvManager*, *TestCaseController* and *StoryManager* in Ianvs.
+
+- In *TestEnvManager*, we plan to add `MT-Bench` as benchmark and *Accuracy*, *Latency*, *Throughput*, *Internal Token Latency* as metrics.
+- In *TestCaseController*, we plan to add a cloud-edge collaboration serving framework named *SpecDecLLM* in *Query Routing* pipeline.
+- In *StoryManager*, we plan to show Leaderboard and Test Report for users.
+
+<img src="./images/image-20241128535482354.png" alt="image-20241128535482354" style="zoom: 50%;" />
+
+The core progress is herited from the [Query Routing](https://github.com/kubeedge/ianvs/tree/main/examples/cloud-edge-collaborative-inference-for-llm).
+
+### Speculative Decoding Implementation
+
+Basically, there are two types of speculative decoding strategy.
+
+#### Draft-Model Mechanism
+
+Draft-Model Mechanism uses Small-scale LLM as a draft model to quickly generate tokens, which are then verified by LLM.
+
+The representative work includes:
+- [EAGLE](https://github.com/SafeAILab/EAGLE): Faster Inference of Language Models with Dynamic Draft Trees $^{3}$
+- [MEDUSA](https://github.com/FasterDecoding/Medusa): Simple LLMInference Acceleration Framework with Multiple Decoding Heads $^{4}$
+- ...
+
+<img src="./images/image-20241014224729431.png" alt="image-20241014224729431" style="zoom: 33%;" />
+
+#### No-Draft-Model Mechanism
+No-Draft-Model Mechanism doesn't require a draft model. It builds a Trie tree based on existing documents and also has them verified by LLM.
+
+The representative work includes:
+- [REST](https://github.com/FasterDecoding/REST): REST: Retrieval-Based Speculative Decoding $^{5}$
+- [LADE](https://github.com/hao-ai-lab/LookaheadDecoding): Break the Sequential Dependency of LLM Inference Using Lookahead Decoding $^{6}$
+- ...
+
+<img src="./images/image-20241014224258112.png" alt="image-20241014224258112" style="zoom: 50%;" />
+
+
+### Cloud Edge Collaboration Strategy
+
+The cloud-edge collaboration for speculative decoding can take many forms, such as:
+- The DraftModel is placed on the edge device and Large Model is placed in the cloud.
+- Trie tree is placed on the edge device while the Large Model remains in the cloud.
+
+However, these methods face some challenges in a cloud-edge architecture:
+- The edge device might lack sufficient resources, leading to slow model generation and rendering the Draft Model approach ineffective.
+- The cloud-edge system often face higher propagation delays, significantly higher than Internal Token Latency (ITL) (e.g., 50ms vs. 5ms), which may lead to a worse performance than the cloud-only system.
+
+To minimize communication as much as possible, we consider deploying both Draft Model or Trie Tree and Large Model together on the edge or on the cloud, achieving edge-cloud collaboration through [Query Routing](https://github.com/kubeedge/ianvs/tree/main/examples/cloud-edge-collaborative-inference-for-llm).
+
+The entire process is as follows:
+
+- Using a Hard Example Mining Module to judge a query is hard or not.
+  - If easy, send the query to the small model on Edge. 
+  - If hard, send the query to the Large Model on Cloud.
+- Both the small model and the large model can use speculative decoding mechanism to accelerate the inference process.
+    - Mechanism 1: Draft-Model Mechanism
+    - Mechanism 2: No-Draft-Model Mechanism
+
+## Roadmap
+
+### October
+
+- Submit a proposal and build a prototype of speculative decoding.
+
+### November
+
+- Implement an example using either the draft model strategy or the documents retrieval strategy.
+- PR and merge.
+
+## References
+
+[1] H. Xia *et al.*, “Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding.” arXiv, Feb. 20, 2024. Accessed: May 31, 2024.
+
+[2] He, Zhenyu, et al. "Rest: Retrieval-based speculative decoding." *Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*. 2024
+
+[3] Y. Li, F. Wei, C. Zhang, and H. Zhang, “EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Y. Al-Onaizan, M. Bansal, and Y.-N. Chen, Eds., Miami, Florida, USA: Association for Computational Linguistics, Nov. 2024, pp. 7421–7432.
+
+[4] T. Cai et al., “Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads,” Jun. 14, 2024, arXiv: arXiv:2401.10774. doi: 10.48550/arXiv.2401.10774.
+
+[5] Z. He, Z. Zhong, T. Cai, J. Lee, and D. He, “REST: Retrieval-Based Speculative Decoding,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), K. Duh, H. Gomez, and S. Bethard, Eds., Mexico City, Mexico: Association for Computational Linguistics, Jun. 2024, pp. 1582–1595. doi: 10.18653/v1/2024.naacl-long.88.
+
+
+[6] Y. Fu, P. Bailis, I. Stoica, and H. Zhang, “Break the Sequential Dependency of LLM Inference Using Lookahead Decoding,” in Proceedings of the 41st International Conference on Machine Learning, R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, and F. Berkenkamp, Eds., in Proceedings of Machine Learning Research, vol. 235. PMLR, Jul. 2024, pp. 14060–14079.