[Multimodal][Speculative Decoding]Eagle Eagle3 mm support, enablement on qwen2.5vl

[david6666666]

Purpose

follow #20788 Eagle Eagle3 mm support, enablement on qwen2.5vl
model is https://huggingface.co/Rayzl/qwen2.5-vl-7b-eagle3-sgl

Test Plan

vllm serve \
    /workspace/models/Qwen2.5-VL-7B-Instruct \
    --port 5580 --host 0.0.0.0 \
    --max-num-seqs 128 --dtype bfloat16 --max-model-len=32768  \
    --no-enable-prefix-caching --trust-remote-code -tp 4\
    --speculative-config '{"method": "eagle3", "model": "/workspace/models/qwen2.5-vl-7b-eagle3-sgl", "prefill_token_shift": false, "num_speculative_tokens": 3, "draft_tensor_parallel_size": 4, "max_model_len": 8192}' \
    --num-lookahead-slots=3 \
    --allowed-local-media-path /workspace/l00807937/ \
    --gpu-memory-utilization=0.93

python /workspace/eagle_mm/vllm-LJH/benchmarks/benchmark_serving.py \
  --backend openai-chat \
  --model /workspace/models/Qwen2.5-VL-7B-Instruct \
  --port 5580 \
  --host 127.0.0.1 \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
  --dataset-path /lmarena-ai/VisionArena-Chat \
  --hf-split train \
  --seed 40 \
  --save-result \
  --save-detailed \
  --result-dir $LOG_PATH \
  --result-filename vision_arena_outputs_$(date +"%Y%m%d_%H%M%S").json \
  2>&1 | tee $LOG_PATH/benchmark_VisionArena_${NUM_PROMPTS}reqs_$(date +"%Y%m%d_%H%M%S").log

Test Result

# eagle3
============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  53.18     
Total input tokens:                      92971     
Total generated tokens:                  3366      
Request throughput (req/s):              18.81     
Output token throughput (tok/s):         63.30     
Total Token throughput (tok/s):          1811.65   
---------------Time to First Token----------------
Mean TTFT (ms):                          966.79    
Median TTFT (ms):                        0.00      
P99 TTFT (ms):                           12999.41  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          3085.82   
Median TPOT (ms):                        1315.73   
P99 TPOT (ms):                           39158.27  
---------------Inter-token Latency----------------
Mean ITL (ms):                           739.84    
Median ITL (ms):                         647.41    
P99 ITL (ms):                            1607.11   
==================================================

# without eagle3
============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  90.38     
Total input tokens:                      92971     
Total generated tokens:                  112893    
Request throughput (req/s):              11.06     
Output token throughput (tok/s):         1249.05   
Total Token throughput (tok/s):          2277.68   
---------------Time to First Token----------------
Mean TTFT (ms):                          43495.63  
Median TTFT (ms):                        41275.69  
P99 TTFT (ms):                           87428.42  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          89.87     
Median TPOT (ms):                        92.47     
P99 TPOT (ms):                           173.62    
---------------Inter-token Latency----------------
Mean ITL (ms):                           129.99    
Median ITL (ms):                         94.06     
P99 ITL (ms):                            1487.70   
==================================================

# pytest
# command
pytest -s ./vllm-LJH/tests/v1/e2e/test_spec_decode.py -k qwen2.5_vl_eagle3
# result
tests/v1/e2e/test_spec_decode.py::test_eagle_correctness[FLASH_ATTN_VLLM_V1-qwen2.5_vl_eagle3]
tests/v1/e2e/test_spec_decode.py::test_eagle_correctness[FLASH_ATTN_VLLM_V1-qwen2.5_vl_eagle3]
================ 1 passed, 2 skipped, 16 deselected, 5 warnings in 75.59s (0:01:15) =================

(Optional) Documentation Update

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

[gemini-code-assist]

Code Review

This pull request adds support for Eagle and Eagle3 speculative decoding for the Qwen2.5-VL multimodal model. The changes include adding new model files for the Eagle and Eagle3 variants, updating model registries, and modifying tests. I found one issue related to a buggy condition in the weight loading logic for the Eagle model which could lead to incorrect behavior. My feedback addresses this.

[github-actions]

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[DarkLight1337]

Suggested change

print(f"model_setup={model_setup}")

[DarkLight1337]

cc @22quinn @morgendave can you help review?

[22quinn]

cc @22quinn @morgendave can you help review?

cc spec decode experts @zixi-qi @charlotte12l

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @david6666666.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork