[GraphBolt] Async feature fetch refactor

python/dgl/graphbolt/feature_store.py

@@ -40,6 +40,7 @@ def read(self, ids: torch.Tensor = None):
 
     def read_async(self, ids: torch.Tensor):
         """Read the feature by index asynchronously.
+
         Parameters
         ----------
         ids : torch.Tensor
@@ -52,21 +53,25 @@ def read_async(self, ids: torch.Tensor):
             `read_async_num_stages(ids.device)`th invocation. The return result
             can be accessed by calling `.wait()`. on the returned future object.
             It is undefined behavior to call `.wait()` more than once.
+
         Example Usage
         --------
         >>> import dgl.graphbolt as gb
         >>> feature = gb.Feature(...)
         >>> ids = torch.tensor([0, 2])
-        >>> async_handle = feature.read_async(ids)
-        >>> for _ in range(feature.read_async_num_stages(ids.device)):
-        ...     future = next(async_handle)
+        >>> for stage, future in enumerate(feature.read_async(ids)):
+        ...     pass
+        >>> assert stage + 1 == feature.read_async_num_stages(ids.device)
         >>> result = future.wait()  # result contains the read values.
         """
         raise NotImplementedError
 
     def read_async_num_stages(self, ids_device: torch.device):
         """The number of stages of the read_async operation. See read_async
-        function for directions on its use.
+        function for directions on its use. This function is required to return
+        the number of yield operations when read_async is used with a tensor
+        residing on ids_device.
+
         Parameters
         ----------
         ids_device : torch.device

python/dgl/graphbolt/impl/cpu_cached_feature.py

@@ -83,6 +83,7 @@ def read(self, ids: torch.Tensor = None):
 
     def read_async(self, ids: torch.Tensor):
         """Read the feature by index asynchronously.
+
         Parameters
         ----------
         ids : torch.Tensor
@@ -95,14 +96,15 @@ def read_async(self, ids: torch.Tensor):
             `read_async_num_stages(ids.device)`th invocation. The return result
             can be accessed by calling `.wait()`. on the returned future object.
             It is undefined behavior to call `.wait()` more than once.
+
         Example Usage
         --------
         >>> import dgl.graphbolt as gb
         >>> feature = gb.Feature(...)
         >>> ids = torch.tensor([0, 2])
-        >>> async_handle = feature.read_async(ids)
-        >>> for _ in range(feature.read_async_num_stages(ids.device)):
-        ...     future = next(async_handle)
+        >>> for stage, future in enumerate(feature.read_async(ids)):
+        ...     pass
+        >>> assert stage + 1 == feature.read_async_num_stages(ids.device)
         >>> result = future.wait()  # result contains the read values.
         """
         policy = self._feature._policy
@@ -309,7 +311,10 @@ def wait():
 
     def read_async_num_stages(self, ids_device: torch.device):
         """The number of stages of the read_async operation. See read_async
-        function for directions on its use.
+        function for directions on its use. This function is required to return
+        the number of yield operations when read_async is used with a tensor
+        residing on ids_device.
+
         Parameters
         ----------
         ids_device : torch.device

python/dgl/graphbolt/impl/gpu_cached_feature.py

@@ -90,6 +90,7 @@ def read(self, ids: torch.Tensor = None):
 
     def read_async(self, ids: torch.Tensor):
         """Read the feature by index asynchronously.
+
         Parameters
         ----------
         ids : torch.Tensor
@@ -102,14 +103,15 @@ def read_async(self, ids: torch.Tensor):
             `read_async_num_stages(ids.device)`th invocation. The return result
             can be accessed by calling `.wait()`. on the returned future object.
             It is undefined behavior to call `.wait()` more than once.
+
         Example Usage
         --------
         >>> import dgl.graphbolt as gb
         >>> feature = gb.Feature(...)
         >>> ids = torch.tensor([0, 2])
-        >>> async_handle = feature.read_async(ids)
-        >>> for _ in range(feature.read_async_num_stages(ids.device)):
-        ...     future = next(async_handle)
+        >>> for stage, future in enumerate(feature.read_async(ids)):
+        ...     pass
+        >>> assert stage + 1 == feature.read_async_num_stages(ids.device)
         >>> result = future.wait()  # result contains the read values.
         """
         values, missing_index, missing_keys = self._feature.query(ids)
@@ -136,7 +138,10 @@ def wait():
 
     def read_async_num_stages(self, ids_device: torch.device):
         """The number of stages of the read_async operation. See read_async
-        function for directions on its use.
+        function for directions on its use. This function is required to return
+        the number of yield operations when read_async is used with a tensor
+        residing on ids_device.
+
         Parameters
         ----------
         ids_device : torch.device

python/dgl/graphbolt/impl/torch_based_feature_store.py

@@ -127,6 +127,7 @@ def read(self, ids: torch.Tensor = None):
 
     def read_async(self, ids: torch.Tensor):
         """Read the feature by index asynchronously.
+
         Parameters
         ----------
         ids : torch.Tensor
@@ -139,14 +140,15 @@ def read_async(self, ids: torch.Tensor):
             `read_async_num_stages(ids.device)`th invocation. The return result
             can be accessed by calling `.wait()`. on the returned future object.
             It is undefined behavior to call `.wait()` more than once.
+
         Example Usage
         --------
         >>> import dgl.graphbolt as gb
         >>> feature = gb.Feature(...)
         >>> ids = torch.tensor([0, 2])
-        >>> async_handle = feature.read_async(ids)
-        >>> for _ in range(feature.read_async_num_stages(ids.device)):
-        ...     future = next(async_handle)
+        >>> for stage, future in enumerate(feature.read_async(ids)):
+        ...     pass
+        >>> assert stage + 1 == feature.read_async_num_stages(ids.device)
         >>> result = future.wait()  # result contains the read values.
         """
         assert self._tensor.device.type == "cpu"
@@ -206,7 +208,10 @@ def wait():
 
     def read_async_num_stages(self, ids_device: torch.device):
         """The number of stages of the read_async operation. See read_async
-        function for directions on its use.
+        function for directions on its use. This function is required to return
+        the number of yield operations when read_async is used with a tensor
+        residing on ids_device.
+
         Parameters
         ----------
         ids_device : torch.device
@@ -405,6 +410,7 @@ def read(self, ids: torch.Tensor = None):
 
     def read_async(self, ids: torch.Tensor):
         """Read the feature by index asynchronously.
+
         Parameters
         ----------
         ids : torch.Tensor
@@ -417,14 +423,15 @@ def read_async(self, ids: torch.Tensor):
             `read_async_num_stages(ids.device)`th invocation. The return result
             can be accessed by calling `.wait()`. on the returned future object.
             It is undefined behavior to call `.wait()` more than once.
+
         Example Usage
         --------
         >>> import dgl.graphbolt as gb
         >>> feature = gb.Feature(...)
         >>> ids = torch.tensor([0, 2])
-        >>> async_handle = feature.read_async(ids)
-        >>> for _ in range(feature.read_async_num_stages(ids.device)):
-        ...     future = next(async_handle)
+        >>> for stage, future in enumerate(feature.read_async(ids)):
+        ...     pass
+        >>> assert stage + 1 == feature.read_async_num_stages(ids.device)
         >>> result = future.wait()  # result contains the read values.
         """
         assert torch.ops.graphbolt.detect_io_uring()
@@ -465,7 +472,10 @@ def wait():
 
     def read_async_num_stages(self, ids_device: torch.device):
         """The number of stages of the read_async operation. See read_async
-        function for directions on its use.
+        function for directions on its use. This function is required to return
+        the number of yield operations when read_async is used with a tensor
+        residing on ids_device.
+
         Parameters
         ----------
         ids_device : torch.device