[Model] DeepSeek-V3 Enhancements

[simon-mo]

This issue tracks follow up enhancements after initial support for the Deepseek V3 model. Please feel free to chime in and contribute!

• Follow up [Model] [Quantization] Support deepseek_v3 w8a8 fp8 block-wise quantization #11523: enhance testing with shapes of production models and run it regularly on H100.
  • Solving via cutlas blockwise quantization kernels.
• Follow up Deepseek v3 #11502:
  • Test and enable torch.compile
  • Refactor MoEMethodBase to unify and clean up the extra arguments of scoring_func and e_correction_bias
  • Kernel tuning for 8xH200, MI300x, H100 (TP16 and TP8PP2 case)
    • Use https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_moe.py, but adapt it for the w8a8 fused moe kernel.
  • CUDA Graph support
• MLA [WIP] Deepseek V2 MLA #10927 @simon-mo
• Support nextn prediction heads (EAGLE style prediction heads)
  • Original PR for EAGLE support [Speculative Decoding] EAGLE Implementation with Top-1 proposer #6830 Perf [Performance]: vllm Eagle performance is worse than expected #9565 Discussion [Usage]: how to use EAGLE on vLLM? #11126 Docs [Doc]Add documentation for using EAGLE in vLLM #11417
• Support expert parallelism for MoE.
• Support data parallelism for MLA.

[july8023]

If I want to deploy deepseek 600B use vllm and RTX4090, are there any restrictions? How many RTX 4090 do I need at least?

[fsaudm]

Is inference with A100s supported? How about quantization??

[mphilippnv]

Deepseek v3 doesn't appear to support pipeline parallelism. I get this error when attempting to deploy to 2 8x H100 nodes:

NotImplementedError: Pipeline parallelism is only supported for the following  architectures: ['AquilaForCausalLM', 'AquilaModel', 'DeepseekV2ForCausalLM', 'GPT2LMHeadModel', 'InternLM2ForCausalLM', 'InternLMForCausalLM', 'InternVLChatModel', 'JAISLMHeadModel', 'LlamaForCausalLM', 'LLaMAForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'NemotronForCausalLM', 'Phi3ForCausalLM', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'QWenLMHeadModel', 'Qwen2VLForConditionalGeneration'].

I'm using --tensor-parallel-size 8 --pipeline-parallel-size 2

[simon-mo]

@july8023 It should work on 4090, generally the models takes about 600GB memory, then you want about 100-300GB for KV cache so feel free to plan around that.
@fsaudm A100s are not supported because this models requires FP8 tensor cores.
@mphilippnv which version of vLLM are you using? You might need to update to v0.6.6 or higher.

[fsaudm]

@simon-mo right, A100s don't support fp8. Would the arg --dtype bfloat16 suffice? If not, I found the bf16 version in Huggingface, any insights on whether that would work?

[simon-mo]

The model currently does not support --dtype bfloat16 because it is natively trained in fp8. Can you point me to the bf16 version?

[fsaudm]

@simon-mo on HF: https://huggingface.co/opensourcerelease/DeepSeek-V3-bf16/tree/main

, on the official repo they provide a script to cast fp8 to bf16, but of course you can't do it on A100s... my guess is a good soul did it and uploaded it to HF. In the repo, see 6.

https://github.com/deepseek-ai/DeepSeek-V3

[simon-mo]

vLLM does support this bf16 model on A100. It looks like the config.json properly removed quantization_config so it would already.

[mphilippnv]

@july8023 It should work on 4090, generally the models takes about 600GB memory, then you want about 100-300GB for KV cache so feel free to plan around that. @fsaudm A100s are not supported because this models requires FP8 tensor cores. @mphilippnv which version of vLLM are you using? You might need to update to v0.6.6 or higher.

Using v0.6.6

EDIT: Apologies, I was using 0.6.2. Redeploying helm chart with 0.6.6.post1. Will see how it goes.

[fsaudm]

Any knowledge of a working example of serving deepseekv3 on A100s with vLLM? I'll try later, but any hints or help is very much appreciated