[Data] Apply DataProto to vLLM Inference & Align API with SGLang

[wheresmyhair]

Overview

• Apply DataProto to vllm inference pipeline, aligning its API with the sglang inferencer introduced in Unified data exchange protocol across modules #960. This unifies data exchange across inference engines and modernizes the vllm integration.
• Remove Ray dependency in vllm, paving the way for a Ray-less lmflow implementation.

Detailed Description

DataProto integration

• VLLMInferencer now returns DataProto instead of list[VLLMInferenceResultWithInput], with prompts in non_tensor_batch["inputs"] and generated text in non_tensor_batch["outputs"]
• prepare_inputs_for_inference creates DataProto for both sglang and vllm through a unified code path
• __vllm_inference in HFDecoderModel extracts prompts and sampling params from DataProto, converts to vllm.SamplingParams, and stores outputs back into the proto
• Inference results are saved/loaded as pickle via DataProto.save_to_disk / load_from_disk
• inference_results_path now accepts a directory — results are automatically saved as inference_results.pkl inside it

API alignment with sglang and modernization

• VLLMInferencer now mirrors SGLangInferencer
• Removed InferencerWithOffloading base class and all Ray-based distributed inference code -- vllm >= 0.8 supports data_parallel_size natively in vllm.LLM(), using a multiprocessing backend with no Ray dependency
• Added --inference_data_parallel_size argument
• Total GPUs used = tensor_parallel_size × data_parallel_size
• Removed use_beam_search from sampling params (dropped in vLLM V1), added deprecation warning
• Fixed deactivate_model_for_inference — old cleanup code referenced llm_engine.model_executor.driver_worker which no longer exists in V1
• Added --inference_max_model_len to cap context length (prompt and output) for models with large defaults
• Bumped vllm version constraint from >=0.4.3 to >=0.8.0 in setup.py

Files changed

File	Change
src/lmflow/pipeline/vllm_inferencer.py	Major rewrite: DataProto, aligned API, native DP
src/lmflow/models/hf_decoder_model.py	DataProto for vllm, unified prepare_inputs
src/lmflow/models/hf_model_mixin.py	DP, max_model_len, V1-compatible deactivation
src/lmflow/args.py	New args, dir-based results path
src/lmflow/pipeline/sglang_inferencer.py	Dir-based results path
src/lmflow/pipeline/utils/memory_safe_vllm_inference.py	Simplified to new API
examples/vllm_inference.py	Simplified to match sglang pattern
scripts/run_vllm_inference.sh	New script
scripts/run_sglang_inference.sh	Updated results path
setup.py	vllm >= 0.8.0
tests/pipeline/test_vllm_inferencer.py	New, 8 tests

Downstream impact

MemorySafeVLLMInferencer is updated to return DataProto. iterative_dpo_aligner.py consumes MemorySafeVLLMInferencer and will need a separate update to handle DataProto instead of list[VLLMInferenceResultWithInput].

Tests

• 6 unit tests pass (no GPU): sampling params parsing, DataProto save/load round-trip, DataProto repeat logic
• 2 GPU integration tests pass: full inference pipeline + save/load with Qwen3-0.6B on RTX 4090
• Run scripts/run_vllm_inference.sh end-to-end with target model