[Feature] Support CUDA_VISIBLE_DEVICES and multiple tasks on one GPU (#148)

* [Feature] Support CUDA_VISIBLE_DEVICES and multiple tasks on one GPU

* Fix UT

* Update according to comments

opencompass/runners/local.py

 import os
 import os.path as osp
+import re
 import subprocess
 import time
 from concurrent.futures import ThreadPoolExecutor
@@ -26,6 +27,8 @@ class LocalRunner(BaseRunner):
         task (ConfigDict): Task type config.
         max_num_workers (int): Max number of workers to run in parallel.
             Defaults to 16.
+        max_workers_per_gpu (int): Max number of workers to run for one GPU.
+            Defaults to 1.
         debug (bool): Whether to run in debug mode.
         lark_bot_url (str): Lark bot url.
     """
@@ -34,9 +37,11 @@ class LocalRunner(BaseRunner):
                  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
 
     def launch(self, tasks: List[Dict[str, Any]]) -> List[Tuple[str, int]]:
         """Launch multiple tasks.
@@ -58,7 +63,20 @@ class LocalRunner(BaseRunner):
                 status.append((task_name, 0))
         else:
             import torch
-            gpus = np.ones(torch.cuda.device_count(), dtype=np.bool_)
+            if 'CUDA_VISIBLE_DEVICES' in os.environ:
+                all_gpu_ids = [
+                    int(i) for i in re.findall(
+                        r'(?<!-)\d+', os.getenv('CUDA_VISIBLE_DEVICES'))
+                ]
+            else:
+                all_gpu_ids = list(range(torch.cuda.device_count()))
+
+            if len(all_gpu_ids) > 0:
+                gpus = np.zeros(max(all_gpu_ids) + 1, dtype=np.uint)
+                gpus[all_gpu_ids] = self.max_workers_per_gpu
+            else:
+                gpus = np.array([], dtype=np.uint)
+
             pbar = tqdm(total=len(tasks))
             lock = Lock()
 
@@ -69,9 +87,9 @@ class LocalRunner(BaseRunner):
 
                 while True:
                     lock.acquire()
-                    if sum(gpus) >= num_gpus:
+                    if sum(gpus > 0) >= num_gpus:
                         gpu_ids = np.where(gpus)[0][:num_gpus]
-                        gpus[gpu_ids] = False
+                        gpus[gpu_ids] -= 1
                         lock.release()
                         break
                     lock.release()
@@ -87,7 +105,7 @@ class LocalRunner(BaseRunner):
                 pbar.update()
 
                 with lock:
-                    gpus[gpu_ids] = True
+                    gpus[gpu_ids] += 1
 
                 return res
 

run.py

@@ -98,6 +98,11 @@ def parse_args():
                         'in the config.',
                         type=int,
                         default=32)
+    parser.add_argument('--max-workers-per-gpu',
+                        help='Max task to run in parallel on one GPU. '
+                        'It will only be used in the local runner.',
+                        type=int,
+                        default=32)
     parser.add_argument(
         '--retry',
         help='Number of retries if the job failed when using slurm or dlc. '
@@ -337,6 +342,7 @@ def exec_infer_runner(tasks, args, cfg):
     else:
         runner = LocalRunner(task=dict(type='OpenICLInferTask'),
                              max_num_workers=args.max_num_workers,
+                             max_workers_per_gpu=args.max_workers_per_gpu,
                              debug=args.debug,
                              lark_bot_url=cfg['lark_bot_url'])
     runner(tasks)