add _post_process() implementation in pytorch_base.py to clean up distributed resource. (#45)

Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>

superbench/benchmarks/model_benchmarks/pytorch_base.py

@@ -9,7 +9,7 @@
 from torch.utils.data import DataLoader
 
 from superbench.common.utils import logger
-from superbench.benchmarks import Framework
+from superbench.benchmarks import Framework, ReturnCode
 from superbench.benchmarks.model_benchmarks.model_base import Optimizer, DistributedImpl, ModelBenchmark
 
 
@@ -161,6 +161,29 @@ def _create_optimizer(self):
 
         return True
 
+    def _postprocess(self):
+        """Postprocess/cleanup operations after the benchmarking.
+
+        Return:
+            True if _postprocess() succeed.
+        """
+        if not super()._postprocess():
+            return False
+
+        try:
+            if self._args.distributed_impl == DistributedImpl.DDP:
+                torch.distributed.destroy_process_group()
+        except BaseException as e:
+            self._result.set_return_code(ReturnCode.DISTRIBUTED_SETTING_DESTROY_FAILURE)
+            logger.error(
+                'Post process failed - model: {}, distributed implementation: {}, message: {}.'.format(
+                    self._name, self._args.distributed_impl, str(e)
+                )
+            )
+            return False
+
+        return True
+
     def _cal_params_count(self):
         """Calculate the parameters scale of the model.