[Fix] Fixed repeated loading of VLLM (#1051)

* [fix]Fixed the issue caused by the repeated loading of VLLM model during task segmentation.

* [fix] avoid TypeError: VLLM.__init__() got an unexpected keyword argument 'tokenizer_only'

* restore .pre-commit-config.yaml

* restore opencompass/tasks/openicl_infer.py

---------
Co-authored-by: IcyFeather <mengzhuo.happy@gmail.com>
Co-authored-by: Leymore <zfz-960727@163.com>

opencompass/models/vllm.py

@@ -49,6 +49,12 @@ class VLLM(BaseModel):
         model_kwargs = DEFAULT_MODEL_KWARGS.copy()
         if add_model_kwargs is not None:
             model_kwargs.update(add_model_kwargs)
+        import ray
+
+        if ray.is_initialized():
+            self.logger.info('shutdown ray instance to avoid '
+                             '"Calling ray.init() again" error.')
+            ray.shutdown()
         self.model = LLM(path, **model_kwargs)
 
     def generate(self, inputs: List[str], max_out_len: int,

opencompass/runners/local.py

@@ -46,12 +46,14 @@ class LocalRunner(BaseRunner):
         lark_bot_url (str): Lark bot url.
     """
 
-    def __init__(self,
-                 task: ConfigDict,
-                 max_num_workers: int = 16,
-                 debug: bool = False,
-                 max_workers_per_gpu: int = 1,
-                 lark_bot_url: str = None):
+    def __init__(
+        self,
+        task: ConfigDict,
+        max_num_workers: int = 16,
+        debug: bool = False,
+        max_workers_per_gpu: int = 1,
+        lark_bot_url: str = None,
+    ):
         super().__init__(task=task, debug=debug, lark_bot_url=lark_bot_url)
         self.max_num_workers = max_num_workers
         self.max_workers_per_gpu = max_workers_per_gpu
@@ -69,6 +71,7 @@ class LocalRunner(BaseRunner):
 
         status = []
         import torch
+
         if 'CUDA_VISIBLE_DEVICES' in os.environ:
             all_gpu_ids = [
                 int(i) for i in re.findall(r'(?<!-)\d+',
@@ -100,7 +103,18 @@ class LocalRunner(BaseRunner):
                     cmd = task.get_command(cfg_path=param_file, template=tmpl)
                     # run in subprocess if starts with torchrun etc.
                     if 'python3 ' in cmd or 'python ' in cmd:
-                        task.run()
+                        # If it is an infer type task do not reload if
+                        # the current model has already been loaded.
+                        if 'infer' in self.task_cfg.type.lower():
+                            # If a model instance already exists,
+                            # do not reload it.
+                            if hasattr(self, 'cur_model'):
+                                task.run(self.cur_model)
+                            else:
+                                task.run()
+                            self.cur_model = task.model
+                        else:
+                            task.run()
                     else:
                         subprocess.run(cmd, shell=True, text=True)
                 finally:

opencompass/tasks/openicl_infer.py

@@ -59,13 +59,17 @@ class OpenICLInferTask(BaseTask):
 
         return template.format(task_cmd=command)
 
-    def run(self):
+    def run(self, cur_model=None):
         self.logger.info(f'Task {task_abbr_from_cfg(self.cfg)}')
         for model_cfg, dataset_cfgs in zip(self.model_cfgs, self.dataset_cfgs):
             self.max_out_len = model_cfg.get('max_out_len', None)
             self.batch_size = model_cfg.get('batch_size', None)
             self.min_out_len = model_cfg.get('min_out_len', None)
-            self.model = build_model_from_cfg(model_cfg)
+            if cur_model:
+                self.model = cur_model
+            else:
+                self.model = build_model_from_cfg(model_cfg)
+                cur_model = self.model
 
             for dataset_cfg in dataset_cfgs:
                 self.model_cfg = model_cfg

opencompass/utils/build.py

@@ -22,4 +22,5 @@ def build_model_from_cfg(model_cfg: ConfigDict):
     model_cfg.pop('summarizer_abbr', None)
     model_cfg.pop('pred_postprocessor', None)
     model_cfg.pop('min_out_len', None)
+    model_cfg.pop('tokenizer_only', None)
     return MODELS.build(model_cfg)