[rollout,vllm] fix: Add LoRA Loading to Async vLLM

verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py

@@ -50,7 +50,7 @@
 from tensordict import TensorDict
 from torch.distributed.device_mesh import DeviceMesh
 from vllm import LLM, SamplingParams
-from vllm.config import CompilationConfig, CompilationLevel
+from vllm.config import CompilationConfig, CompilationLevel, LoRAConfig
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.worker.worker_base import WorkerWrapperBase
@@ -479,10 +479,12 @@ def __init__(
         device_mesh: DeviceMesh,
     ):
         super().__init__(config, model_config, device_mesh)
-
         self.tokenizer = model_config.tokenizer
         self.inference_engine: WorkerWrapperBase = None
         self.address = self._init_zeromq()
+        self.lora_config = (
+            {"max_loras": 1, "max_lora_rank": model_config.lora_rank} if model_config.lora_rank > 0 else {}
+        )
 
         # https://github.com/vllm-project/vllm/issues/25171
         if config.layered_summon or config.expert_parallel_size > 1:
@@ -536,7 +538,6 @@ def _init_worker(self, all_kwargs: list[dict[str, Any]]):
         """Initialize worker engine."""
         if not torch.distributed.is_initialized():
             initialize_global_process_group_ray()
-
         all_kwargs[0]["rank"] = int(os.environ["RANK"])
         device_name = "NPU" if is_npu_available else "GPU"
         all_kwargs[0]["local_rank"] = (
@@ -545,6 +546,8 @@ def _init_worker(self, all_kwargs: list[dict[str, Any]]):
             else int(ray.get_runtime_context().get_accelerator_ids()[device_name][0])
         )
         self.vllm_config = all_kwargs[0]["vllm_config"]
+        if self.lora_config:
+            self.vllm_config.lora_config = LoRAConfig(**self.lora_config)
         self.inference_engine = WorkerWrapperBase(vllm_config=self.vllm_config)
         self.inference_engine.init_worker(all_kwargs)
 
@@ -582,11 +585,24 @@ async def update_weights(self, weights: Generator[tuple[str, torch.Tensor], None
         Args:
             weights: A generator that yields the name of the weight tensor and the tensor itself.
         """
-        from verl.utils.vllm.patch import patch_vllm_moe_model_weight_loader
+        peft_config, base_sync_done = kwargs.get("peft_config", None), kwargs.get("base_sync_done", False)
+        if peft_config and base_sync_done:
+            lora_int_id = int(time.time_ns() % 0x7FFFFFFF)
+            lora_reqest = TensorLoRARequest(
+                lora_name=f"{lora_int_id}",
+                lora_int_id=lora_int_id,
+                lora_path="simon_lora_path",
+                peft_config=asdict(peft_config),
+                lora_tensors=weights,
+            )
+            self.inference_engine.worker.add_lora(lora_reqest)
+            logger.info(f"vLLM load weights, loaded_params: {len(weights)}")
+        else:
+            from verl.utils.vllm.patch import patch_vllm_moe_model_weight_loader
 
-        model = self.inference_engine.worker.model_runner.model
-        patch_vllm_moe_model_weight_loader(model)
-        model.load_weights(weights)
+            model = self.inference_engine.worker.model_runner.model
+            patch_vllm_moe_model_weight_loader(model)
+            model.load_weights(weights)
 
     def generate_sequences(self, prompts: DataProto) -> DataProto:
         """Batch generate sequences in sync mode."""