Support Qwen2.5 VL eagle3 #118
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- Add SupportsEagle3 interface to Llama4ForConditionalGeneration and Llama4ForCausalLM - Implement custom auxiliary hidden state layers (1, 23, 44) for Eagle3 speculative decoding - Enable multimodal input handling in Eagle3LlamaForCausalLM with text-only inference mode - Add proper dimension adaptation for auxiliary hidden states from multimodal verifiers - Implement dynamic Eagle3 auxiliary layer configuration from speculators config - Add GPU model runner method to read eagle_aux_hidden_state_layer_ids from draft config - Update auxiliary layer configuration logic to use speculative config dynamically - Simplify model implementations to provide fallback defaults This is the first successful implementation of Eagle3 speculative decoding with multimodal Llama4 models, supporting custom layer extraction and text-only drafter processing while leveraging multimodal context from auxiliary hidden states. The implementation now dynamically reads auxiliary layer configuration from the draft model's speculative config, eliminating hardcoded layer IDs.
- Fix aux_hidden_state_layers initialization syntax error in qwen2.py - Add missing return statement in qwen2_5_vl.py get_eagle3_aux_hidden_state_layers - Improve error handling with hasattr check instead of assert - Clean up method delegation to use direct return from language_model - Add fallback default auxiliary layers for Qwen2.5VL models These fixes enable Eagle3 speculative decoding support for Qwen2.5VL models. Successfully tested with Qwen2.5VL-7B + Eagle3 configuration.
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VLLM_USE_V1=1 \ CUDA_VISIBLE_DEVICES=6 \ python examples/offline_inference/spec_decode.py \ --method "eagle3" \ --tp 1 \ --print-output \ --model-dir "Qwen/Qwen2.5-VL-7B-Instruct" \ --eagle-dir "nm-testing/MOCK-UP-Eagle3ForQwen2.5VL7B" \ --dataset_name "hf" \ --dataset_path "philschmid/mt-bench" \ --num-spec-tokens 3Results: