Fix lora requests when dp with vllm (#2433)

* fix: use lora_request for data parallel vllm evals

* fix(docs): include type hint

* chore: lint, et pre-commit al

---------
Co-authored-by: Chris Kerwell Gresla <chris@wafer.systems>

lm_eval/models/vllm_causallms.py

@@ -239,17 +239,25 @@ class VLLM(TemplateLM):
             # but then tensor_parallel breaks
             @ray.remote
             def run_inference_one_model(
-                model_args: dict, sampling_params, requests: List[List[int]]
+                model_args: dict,
+                sampling_params,
+                requests: List[List[int]],
+                lora_request: LoRARequest,
             ):
                 llm = LLM(**model_args)
                 return llm.generate(
-                    prompt_token_ids=requests, sampling_params=sampling_params
+                    prompt_token_ids=requests,
+                    sampling_params=sampling_params,
+                    lora_request=lora_request,
                 )
 
             # dispatch requests to all self.data_parallel_size workers, in interleaved fashion
             # interleaved important to balance context lengths across workers
             requests = [list(x) for x in distribute(self.data_parallel_size, requests)]
-            inputs = ((self.model_args, sampling_params, req) for req in requests)
+            inputs = (
+                (self.model_args, sampling_params, req, self.lora_request)
+                for req in requests
+            )
             object_refs = [run_inference_one_model.remote(*x) for x in inputs]
             results = ray.get(object_refs)
             # Invoke ray.shutdown() to prevent hang-ups if subsequent calls required.
@@ -257,19 +265,12 @@ class VLLM(TemplateLM):
             # flatten results
             return undistribute(results)
 
-        if self.lora_request is not None:
-            outputs = self.model.generate(
-                prompt_token_ids=requests,
-                sampling_params=sampling_params,
-                use_tqdm=True if self.batch_size == "auto" else False,
-                lora_request=self.lora_request,
-            )
-        else:
-            outputs = self.model.generate(
-                prompt_token_ids=requests,
-                sampling_params=sampling_params,
-                use_tqdm=True if self.batch_size == "auto" else False,
-            )
+        outputs = self.model.generate(
+            prompt_token_ids=requests,
+            sampling_params=sampling_params,
+            use_tqdm=True if self.batch_size == "auto" else False,
+            lora_request=self.lora_request,
+        )
         return outputs
 
     def loglikelihood_rolling(