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[GraphBolt] Add read_async to gb.Feature. [1] #7546

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merged 3 commits into from
Jul 20, 2024
Merged

[GraphBolt] Add read_async to gb.Feature. [1] #7546

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995de9f
[GraphBolt] Add `read_async` to `gb.Feature`.
mfbalin Jul 20, 2024
c321141
linting.
mfbalin Jul 20, 2024
507bbd4
Merge branch 'master' into gb_read_async_API
mfbalin Jul 20, 2024
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40 changes: 40 additions & 0 deletions python/dgl/graphbolt/feature_store.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,46 @@ def read(self, ids: torch.Tensor = None):
"""
raise NotImplementedError

def read_async(self, ids: torch.Tensor):
"""Read the feature by index asynchronously.
Parameters
----------
ids : torch.Tensor
The index of the feature. Only the specified indices of the
feature are read.
Returns
-------
A generator object.
The returned generator object returns a future on
`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)
>>> result = future.wait() # result contains the read values.
"""
raise NotImplementedError

def read_async_num_stages(self, ids_device: torch.device):
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If you already returned an async_handle, why do you need to define read_async_num_stages?
Consider merging?

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Because read_async returns a generator object. There is no way to also return how many stages are in it.

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You can always override the generator object to provide an extra field?

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e.g. implement the len function of the object?

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How can I do that?

"""The number of stages of the read_async operation. See read_async
function for directions on its use.
Parameters
----------
ids_device : torch.device
The device of the ids parameter passed into read_async.
Returns
-------
int
The number of stages of the read_async operation.
"""
raise NotImplementedError

def size(self):
"""Get the size of the feature.

Expand Down
40 changes: 40 additions & 0 deletions python/dgl/graphbolt/impl/cpu_cached_feature.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,6 +79,46 @@ def read(self, ids: torch.Tensor = None):
self._feature.replace(missing_keys, missing_values)
return values

def read_async(self, ids: torch.Tensor):
"""Read the feature by index asynchronously.
Parameters
----------
ids : torch.Tensor
The index of the feature. Only the specified indices of the
feature are read.
Returns
-------
A generator object.
The returned generator object returns a future on
`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)
>>> 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.
Parameters
----------
ids_device : torch.device
The device of the ids parameter passed into read_async.
Returns
-------
int
The number of stages of the read_async operation.
"""
raise NotImplementedError

def size(self):
"""Get the size of the feature.

Expand Down
40 changes: 40 additions & 0 deletions python/dgl/graphbolt/impl/gpu_cached_feature.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -88,6 +88,46 @@ def read(self, ids: torch.Tensor = None):
self._feature.replace(missing_keys, missing_values)
return values

def read_async(self, ids: torch.Tensor):
"""Read the feature by index asynchronously.
Parameters
----------
ids : torch.Tensor
The index of the feature. Only the specified indices of the
feature are read.
Returns
-------
A generator object.
The returned generator object returns a future on
`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)
>>> 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.
Parameters
----------
ids_device : torch.device
The device of the ids parameter passed into read_async.
Returns
-------
int
The number of stages of the read_async operation.
"""
raise NotImplementedError

def size(self):
"""Get the size of the feature.

Expand Down
80 changes: 80 additions & 0 deletions python/dgl/graphbolt/impl/torch_based_feature_store.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -121,6 +121,46 @@ def read(self, ids: torch.Tensor = None):
return self._tensor
return index_select(self._tensor, ids)

def read_async(self, ids: torch.Tensor):
"""Read the feature by index asynchronously.
Parameters
----------
ids : torch.Tensor
The index of the feature. Only the specified indices of the
feature are read.
Returns
-------
A generator object.
The returned generator object returns a future on
`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)
>>> 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.
Parameters
----------
ids_device : torch.device
The device of the ids parameter passed into read_async.
Returns
-------
int
The number of stages of the read_async operation.
"""
raise NotImplementedError

def size(self):
"""Get the size of the feature.

Expand Down Expand Up @@ -303,6 +343,46 @@ def read(self, ids: torch.Tensor = None):
else:
return index_select(self._tensor, ids)

def read_async(self, ids: torch.Tensor):
"""Read the feature by index asynchronously.
Parameters
----------
ids : torch.Tensor
The index of the feature. Only the specified indices of the
feature are read.
Returns
-------
A generator object.
The returned generator object returns a future on
`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)
>>> 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.
Parameters
----------
ids_device : torch.device
The device of the ids parameter passed into read_async.
Returns
-------
int
The number of stages of the read_async operation.
"""
raise NotImplementedError

def size(self):
"""Get the size of the feature.
Returns
Expand Down
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