add complete disk implementation

python/dgl/graphbolt/impl/torch_based_feature_store.py

@@ -396,7 +396,43 @@ def read_async(self, ids: torch.Tensor = None):
             The read feature future.
         """
         assert torch.ops.graphbolt.detect_io_uring()
-        yield self._ondisk_npy_array.index_select(ids)
+        if ids.is_cuda:
+            ids_device = ids.device
+            current_stream = torch.cuda.current_stream()
+            device_to_host_stream = get_device_to_host_uva_stream()
+            device_to_host_stream.wait_stream(current_stream)
+            with torch.cuda.stream(device_to_host_stream):
+                ids.record_stream(torch.cuda.current_stream())
+                ids = ids.to(self._tensor.device, non_blocking=True)
+                ids_copy_event = torch.cuda.Event()
+                ids_copy_event.record()
+
+            yield  # first stage is done.
+
+            ids_copy_event.synchronize()
+            values = self._ondisk_npy_array.index_select(ids)
+            yield
+
+            host_to_device_stream = get_device_to_host_uva_stream()
+            with torch.cuda.stream(host_to_device_stream):
+                values_cuda = values.wait().to(ids_device, non_blocking=True)
+                values_cuda.record_stream(current_stream)
+                values_copy_event = torch.cuda.Event()
+                values_copy_event.record()
+
+            class _Waiter:
+                @staticmethod
+                def wait():
+                    values_copy_event.wait()
+                    return values_cuda
+
+            yield _Waiter()
+        else:
+            yield self._ondisk_npy_array.index_select(ids)
+
+    def read_async_num_stages(self, ids_device: torch.device):
+        """The number of stages of the read_async operation"""
+        return 3 if ids_device.type == "cuda" else 1
 
     def size(self):
         """Get the size of the feature.