we can't record on pinned features so there is no need to record on t…

…he features.

python/dgl/graphbolt/feature_fetcher.py

@@ -171,7 +171,6 @@ def _read(self, data):
         if self.stream is not None:
             current_stream = torch.cuda.current_stream()
             self.stream.wait_stream(current_stream)
-            self.feature_store.record_stream(self.stream)
         with torch.cuda.stream(self.stream):
             data = self._read_data(data, current_stream)
             if self.stream is not None:

python/dgl/graphbolt/impl/torch_based_feature_store.py

@@ -208,11 +208,6 @@ def to(self, device):  # pylint: disable=invalid-name
             self2._tensor = self2._tensor.to(device)
         return self2
 
-    def record_stream(self, stream):
-        """Record the given stream in the Feature."""
-        if self._tensor.is_cuda:
-            self._tensor.record_stream(stream)
-
     def __repr__(self) -> str:
         ret = (
             "{Classname}(\n"
@@ -305,11 +300,6 @@ def to(self, device):  # pylint: disable=invalid-name
         self2._features = {k: v.to(device) for k, v in self2._features.items()}
         return self2
 
-    def record_stream(self, stream):
-        """Record the given stream in all the stored Features."""
-        for feature in self._features.values():
-            feature.record_stream(stream)
-
     def __repr__(self) -> str:
         ret = "{Classname}(\n" + "    {features}\n" + ")"
         features_str = textwrap.indent(str(self._features), "    ").strip()