Development Roadmap (2024 Q4)

[Ying1123]

Here is the development roadmap for 2024 Q4. Contributions and feedback are welcome (Join Bi-weekly Development Meeting). Previous 2024 Q3 roadmap can be found in #634.

Performance

• Hide CPU overhead with overlapped scheduler (Faster overlap mode scheduler #1738, Enable overlap by default #2067)
• Support speculative decoding
  • Eagle Speculative EAGLE2 #2150
  • Reference-based. Reference speculative decoding #270
  • Medusa head [Feature] plan to support medusa? #859
  • Draft model based.
• Sparse Attention Support double sparsity #1459
• Faster grammar parsing library for constrained decoding [Performance] Support both xgrammar and outlines for constrained decoding #1752
• Multi-layer radix cache (GPU/CPU/Disk) @xiezhq-hermann
• Improve the performance of mixed chunked prefill. see a draft Rewrite mixed chunked prefill #1383
• Integrate CuDNN paged attention kernels

Parallelism

• Support sequence parallelism [Feature] Add initial support for sequence parallelism #1436. Related paper
• Support pipeline parallelism.
• Support expert parallelism + data parallelism for DeepSeek/MoE models. @ispobock
  • Data parallelism Support DP MLA #1970
  • Expert parallelism # [Feature] Expert parallelism support #1435
• Implement a better cache-aware load balancer for data parallelism. [router] cache-aware load-balancing router v1 #2114 [Feature] Cache-aware Data Parallel Router #1732 @ByronHsu @yichuan520030910320
• Overlap communication in tensor parallelsim. @ZhuohaoL
• Support disaggregated serving to separate prefill and decoding.

Hardware Coverage

• AMD optimizations. cc @HaiShaw
  • CK kernels
  • Setup CI (accuracy/performance) for AMD
• Intel XPU support.
  • [Feature, Hardware] Enable SGLang on XPU GPUs via PyTorch #1480
  • Add initial support for intel Gaudi accelerators #2121

Model Coverage

• Multi-modal models
  • Llama 3.2 Vision Llama3.2 vision model support #1551
  • QWen2-VL Support qwen2 vl model #1546
  • GLM 4V Add GLM-4v Multimodal Model support for SGLang #1641
  • VILA
  • Phi-vision
  • FishSpeech audio model support
  • Ultravox
• Language models
  • Mamba models @rahulbatra85 @HaiShaw
  • xLSTM
• Reward models
  • [Feature] Support reward model LxzGordon/URM-LLaMa-3.1-8B #1525
  • Gemma2 reward model support #1954

New features

• Integrate with LMCache https://github.com/LMCache/LMCache
• A padded batch mode to make results more deterministic

  sglang/docs/references/faq.md

  Line 3 in 8912b76

  ## The results are not deterministic, even with a temperature of 0

• Performance optimizations for multi-LoRA serving [LoRA, Performance] Add gemm expand triton kernel for multi-LoRA #1728

Quantization

@HaiShaw @zhyncs @ispobock

• Torchao integration Add llama implementation with no tensor parallel linears #1561
• Turbomind operators integration
• More CUTLASS mixed precision gemm integration
• KV cache quantization (more formats + scaling factor)

Server API

• Support directly taking embedding as inputs. [Feature] Generation Inputs: input_embeds #745
• Add APIs for using the inference engine in a single script without launching a separate server. See also examples.
  • Provide an offline engine API #1567
• Support endpoint other than OpenAI (Anthropic, Mistral) in the language frontend.
• Better APIs to support RL trainers, including https://github.com/huggingface/trl and https://github.com/OpenRLHF/OpenRLHF @zhaochenyang20
• Support generalized reward API (adding linear layers to any Causal LM to get the reward) https://github.com/OpenRLHF/OpenRLHF @zhaochenyang20

Observability

• Integrate Grafana / Prometheus
  • support prometheus metrics #1853 [WIP] Prometheus Metrics #1461

Others

• Notebook-style interactive tutorials. @zhaochenyang20
• Compiler mode optimizations for the language (e.g. support sending a full serialized SGL program to the server). @hnyls2002
• Memory pool refactor to better support mixing different attention layers (e.g., interleaved window attention). @Ying1123
• Make vLLM an optional dependency. @zhyncs @ByronHsu @yizhang2077 [Feature] Make vLLM optional in model code #1673

[fengyang95]

Are there any plans to optimize long context latency?

[lumiere-ml]

Hi，can I help for Multi-layer radix cache (GPU/CPU/Disk)？ Really insterested in that.

[tanzelin430]

Are there any plans to optimize long context latency?

I am interested in contributing to P-D split inference architechure and I have machines that support me to develop the architechure, if you guys got any related develop plans please let me know. Thank you @Ying1123 @zhyncs @fengyang95

[merrymercy]

@lumiere-ml @tanzelin430 Are you in the slack channel? We can follow up on that.

[zhyncs]

@lumiere-ml @tanzelin430 Are you in the slack channel? We can follow up on that.

@lumiere-ml @tanzelin430 Welcome to join our slack channel https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2ngly9muu-t37XiH87qvD~6rVBTkTEHw

[tanzelin430]

@lumiere-ml @tanzelin430 Are you in the slack channel? We can follow up on that.

@lumiere-ml @tanzelin430 Welcome to join our slack channel https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2ngly9muu-t37XiH87qvD~6rVBTkTEHw

thanks for invitation, I am in slack now. forward to collaberate with you

[lumiere-ml]

@lumiere-ml @tanzelin430 Are you in the slack channel? We can follow up on that.

@lumiere-ml @tanzelin430 Welcome to join our slack channel https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2ngly9muu-t37XiH87qvD~6rVBTkTEHw

Thanks for your invitation！

[Edenzzzz]

@lumiere-ml @tanzelin430 Are you in the slack channel? We can follow up on that.

@lumiere-ml @tanzelin430 Welcome to join our slack channel https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2ngly9muu-t37XiH87qvD~6rVBTkTEHw

Thanks for your invitation！

@lumiere-ml @zhyncs I'm also very interested, could you share which channel you're using to discuss?
Perhaps we can combine radix tree prefix matching with P-D disaggregation similar to Mooncake?

[mfdj2002]

If no one is actively working on supporting pipeline parallelism, I'm down to help

[Edenzzzz]

@mfdj2002 I think @CalvinXKY has expressed interest on slack, you can chat with him there

[merrymercy]

No one is working on pipeline parallelism. Feel free to contribute one.

[m0g1cian]

I recently completed a reward model implementation for RMs trained by LlamaFactory. Everything worked well but I've noticed a relatively small value diff in last hidden states between my SGLang implementation and the counterpart in TRL (resulting a ROC loss of ~0.3%)

Regardless, I think I can help with the task "Support generalized reward API (adding linear layers to any Causal LM to get the reward)"

[kuangdao]

i am interested in sequence parallelism, i want to know if the sequence parallelism will use the method of Context Parallelism for Scalable Million-Token Inference , thanks

[zhaochenyang20]

I recently completed a reward model implementation for RMs trained by LlamaFactory. Everything worked well but I’ve noticed a relatively small value diff in last hidden states between my SGLang implementation and the counterpart in TRL (resulting a ROC loss of ~0.3%)

Regardless, I think I can help with the task “Support generalized reward API (adding linear layers to any Causal LM to get the reward)”

Amazing, could you please send an Email with your wechat or other connection to zhaochenyang20@gmail.com

We can also discuss this on our Slack. find zhaochenyang20@gmail.com on sglang slack plz!

@m0g1cian

[trh11111]

I am also very interested in the scenario of PD disaggregation, and I hope to combine radix tree with PD disaggregation for some experiments. I saw that someone mentioned this in October. May I ask how the current development plan is progressing?

[zhaochenyang20]

I am also very interested in the scenario of PD disaggregation, and I hope to combine radix tree with PD disaggregation for some experiments. I saw that someone mentioned this in October. May I ask how the current development plan is progressing?

@trh11111 Yeah. We have new members joined our team work on this and PD disaggregation is the first-priority in our developmap for our next quoter.

[tanzelin430]

I am also very interested in the scenario of PD disaggregation, and I hope to combine radix tree with PD disaggregation for some experiments. I saw that someone mentioned this in October. May I ask how the current development plan is progressing?

Hi, I have just finish my graduation recruiment senson and am working on my ATC paper. I'll be soon looking into the development

[zhaochenyang20]

I am also very interested in the scenario of PD disaggregation, and I hope to combine radix tree with PD disaggregation for some experiments. I saw that someone mentioned this in October. May I ask how the current development plan is progressing?

Hi, I have just finish my graduation recruiment senson and am working on my ATC paper. I'll be soon looking into the development

@trh11111 if you feel interested in this part, could reach out to us on slack.

[mpjlu]

@lumiere-ml @tanzelin430 Are you in the slack channel? We can follow up on that.

@lumiere-ml @tanzelin430 Welcome to join our slack channel https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2ngly9muu-t37XiH87qvD~6rVBTkTEHw

how to join this slack channel

[zhyncs]

Hi @mpjlu https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2rtikx2pv-DUfPrhx2SaNAq~47YtV1XQ

[mpjlu]

Hi @mpjlu https://join.slack.com/t/sgl-fru7574/shared_invite/zt-2rtikx2pv-DUfPrhx2SaNAq~47YtV1XQ

Thanks