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Customize loader arguments for evaluation in LightningDataModule #6450

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merged 5 commits into from
Jan 17, 2023
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Customize loader arguments for evaluation in LightningDataModule #6450

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rusty1s Jan 17, 2023
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update
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@rusty1s
rusty1s committed Jan 17, 2023
commit 5fc30e3a91f3f41d3c6a7f8c016c9586c8eb7e61
11 changes: 6 additions & 5 deletions test/data/lightning/test_datamodule.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,11 +160,11 @@ def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.01)


# @onlyCUDA
# @onlyFullTest
@onlyCUDA
@onlyFullTest
@withPackage('pytorch_lightning')
@pytest.mark.parametrize('loader', ['neighbor'])
@pytest.mark.parametrize('strategy_type', [None])
@pytest.mark.parametrize('loader', ['full', 'neighbor'])
@pytest.mark.parametrize('strategy_type', [None, 'ddp_spawn'])
def test_lightning_node_data(get_dataset, strategy_type, loader):
import pytorch_lightning as pl

Expand Down Expand Up @@ -198,11 +198,12 @@ def test_lightning_node_data(get_dataset, strategy_type, loader):
max_epochs=5, log_every_n_steps=1)
datamodule = LightningNodeData(data, loader=loader, batch_size=batch_size,
num_workers=num_workers, **kwargs)
return

old_x = data.x.clone().cpu()
assert str(datamodule) == (f'LightningNodeData(data={data_repr}, '
f'loader={loader}, batch_size={batch_size}, '
f'num_workers={num_workers}, '
f'num_neighbors=[5], '
f'pin_memory={loader != "full"}, '
f'persistent_workers={loader != "full"})')
trainer.fit(model, datamodule)
Expand Down
187 changes: 138 additions & 49 deletions torch_geometric/data/lightning/datamodule.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,6 +112,8 @@ class LightningDataset(LightningDataModule):
(default: :obj:`None`)
test_dataset (Dataset, optional): The test dataset.
(default: :obj:`None`)
pred_dataset (Dataset, optional): The prediction dataset.
(default: :obj:`None`)
batch_size (int, optional): How many samples per batch to load.
(default: :obj:`1`)
num_workers (int): How many subprocesses to use for data loading.
Expand All @@ -125,6 +127,7 @@ def __init__(
train_dataset: Dataset,
val_dataset: Optional[Dataset] = None,
test_dataset: Optional[Dataset] = None,
pred_dataset: Optional[Dataset] = None,
batch_size: int = 1,
num_workers: int = 0,
**kwargs,
Expand All @@ -140,6 +143,7 @@ def __init__(
self.train_dataset = train_dataset
self.val_dataset = val_dataset
self.test_dataset = test_dataset
self.pred_dataset = pred_dataset

def dataloader(self, dataset: Dataset, **kwargs) -> DataLoader:
return DataLoader(dataset, **kwargs)
Expand Down Expand Up @@ -170,6 +174,14 @@ def test_dataloader(self) -> DataLoader:

return self.dataloader(self.test_dataset, shuffle=False, **kwargs)

def predict_dataloader(self) -> DataLoader:
""""""
kwargs = copy.copy(self.kwargs)
kwargs.pop('sampler', None)
kwargs.pop('batch_sampler', None)

return self.dataloader(self.pred_dataset, shuffle=False, **kwargs)

def __repr__(self) -> str:
kwargs = kwargs_repr(train_dataset=self.train_dataset,
val_dataset=self.val_dataset,
Expand Down Expand Up @@ -251,7 +263,7 @@ class LightningNodeData(LightningDataModule):
:obj:`0` means that the data will be loaded in the main process.
(default: :obj:`0`)
eval_loader_kwargs (Dict[str, Any], optional): Custom keyword arguments
that override the :class:`~torch_geometric.loader.NeighborLoader`
that override the :class:`torch_geometric.loader.NeighborLoader`
configuration during evaluation. (default: :obj:`None`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.loader.NeighborLoader`.
Expand Down Expand Up @@ -363,7 +375,7 @@ def __init__(
# The user wants to override certain values during evaluation, so
# we shallow-copy the sampler and update its attributes.

if hasattr(self.neighbor_sampler):
if hasattr(self, 'neighbor_sampler'):
self.eval_neighbor_sampler = copy.copy(self.neighbor_sampler)

eval_sampler_kwargs, self.eval_loader_kwargs = split_kwargs(
Expand All @@ -376,7 +388,7 @@ def __init__(
self.eval_loader_kwargs = copy.copy(self.loader_kwargs)
self.eval_loader_kwargs.update(eval_loader_kwargs)
else:
if hasattr(self.neighbor_sampler):
if hasattr(self, 'neighbor_sampler'):
self.eval_neighbor_sampler = self.neighbor_sampler

self.eval_loader_kwargs = self.loader_kwargs
Expand Down Expand Up @@ -488,10 +500,6 @@ def test_dataloader(self) -> DataLoader:

def predict_dataloader(self) -> DataLoader:
""""""
kwargs = copy.copy(self.kwargs)
kwargs.pop('sampler', None)
kwargs.pop('batch_sampler', None)

return self.dataloader(
self.input_pred_nodes,
self.input_pred_time,
Expand Down Expand Up @@ -543,9 +551,9 @@ class LightningLinkData(LightningDataModule):
input_train_edges (Tensor or EdgeType or Tuple[EdgeType, Tensor]):
The training edges. (default: :obj:`None`)
input_train_labels (torch.Tensor, optional):
The labels of train edges. (default: :obj:`None`)
The labels of training edges. (default: :obj:`None`)
input_train_time (torch.Tensor, optional): The timestamp
of train edges. (default: :obj:`None`)
of training edges. (default: :obj:`None`)
input_val_edges (Tensor or EdgeType or Tuple[EdgeType, Tensor]):
The validation edges. (default: :obj:`None`)
input_val_labels (torch.Tensor, optional):
Expand All @@ -555,9 +563,15 @@ class LightningLinkData(LightningDataModule):
input_test_edges (Tensor or EdgeType or Tuple[EdgeType, Tensor]):
The test edges. (default: :obj:`None`)
input_test_labels (torch.Tensor, optional):
The labels of train edges. (default: :obj:`None`)
The labels of test edges. (default: :obj:`None`)
input_test_time (torch.Tensor, optional): The timestamp
of test edges. (default: :obj:`None`)
input_pred_edges (Tensor or EdgeType or Tuple[EdgeType, Tensor]):
The prediction edges. (default: :obj:`None`)
input_pred_labels (torch.Tensor, optional):
The labels of prediction edges. (default: :obj:`None`)
input_pred_time (torch.Tensor, optional): The timestamp
of prediction edges. (default: :obj:`None`)
loader (str): The scalability technique to use (:obj:`"full"`,
:obj:`"neighbor"`). (default: :obj:`"neighbor"`)
link_sampler (BaseSampler, optional): A custom sampler object to
Expand All @@ -568,6 +582,10 @@ class LightningLinkData(LightningDataModule):
num_workers (int): How many subprocesses to use for data loading.
:obj:`0` means that the data will be loaded in the main process.
(default: :obj:`0`)
eval_loader_kwargs (Dict[str, Any], optional): Custom keyword arguments
that override the
:class:`torch_geometric.loader.LinkNeighborLoader` configuration
during evaluation. (default: :obj:`None`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.loader.LinkNeighborLoader`.
"""
Expand All @@ -583,10 +601,14 @@ def __init__(
input_test_edges: InputEdges = None,
input_test_labels: OptTensor = None,
input_test_time: OptTensor = None,
input_pred_edges: InputEdges = None,
input_pred_labels: OptTensor = None,
input_pred_time: OptTensor = None,
loader: str = "neighbor",
link_sampler: Optional[BaseSampler] = None,
batch_size: int = 1,
num_workers: int = 0,
eval_loader_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
):
if link_sampler is not None:
Expand All @@ -610,6 +632,16 @@ def __init__(
f"(got '{num_workers}')")
num_workers = 0

if loader == 'full' and kwargs.get('sampler') is not None:
warnings.warn("'sampler' option is not supported for "
"loader='full'")
kwargs.pop('sampler', None)

if loader == 'full' and kwargs.get('batch_sampler') is not None:
warnings.warn("'batch_sampler' option is not supported for "
"loader='full'")
kwargs.pop('sampler', None)

super().__init__(
has_val=input_val_edges is not None,
has_test=input_test_edges is not None,
Expand All @@ -628,27 +660,56 @@ def __init__(
self.data = data
self.loader = loader

# Determine sampler and loader arguments ##############################

if loader in ['neighbor', 'link_neighbor']:
sampler_args = dict(inspect.signature(NeighborSampler).parameters)
sampler_args.pop('data')
sampler_args.pop('share_memory')
sampler_kwargs = {
key: kwargs.get(key, param.default)
for key, param in sampler_args.items()
}
self.neighbor_sampler = NeighborSampler(
data=data,
share_memory=num_workers > 0,
**sampler_kwargs,
# Define a new `NeighborSampler` that can be re-used across
# different data loaders.
sampler_kwargs, self.loader_kwargs = split_kwargs(
self.kwargs,
NeighborSampler,
)
elif link_sampler is not None:
sampler_kwargs.setdefault('share_memory', num_workers > 0)

# TODO Consider renaming to `self.link_sampler`
self.neighbor_sampler = NeighborSampler(data, **sampler_kwargs)

elif link_sampler is not None:
_, self.loader_kwargs = split_kwargs(
self.kwargs,
link_sampler.__class__,
)
self.neighbor_sampler = link_sampler

if getattr(self, 'neighbor_sampler', None) is not None:
cls = self.neighbor_sampler.__class__
for param in inspect.signature(cls).parameters:
self.kwargs.pop(param, None)
else:
self.loader_kwargs = self.kwargs

# Determine validation sampler and loader arguments ###################

if eval_loader_kwargs is not None:
# The user wants to override certain values during evaluation, so
# we shallow-copy the sampler and update its attributes.

if hasattr(self, 'neighbor_sampler'):
self.eval_neighbor_sampler = copy.copy(self.neighbor_sampler)

eval_sampler_kwargs, self.eval_loader_kwargs = split_kwargs(
eval_loader_kwargs,
self.neighbor_sampler.__class__,
)
for key, value in eval_sampler_kwargs.items():
setattr(self.eval_neighbor_sampler, key, value)
else:
self.eval_loader_kwargs = copy.copy(self.loader_kwargs)
self.eval_loader_kwargs.update(eval_loader_kwargs)
else:
if hasattr(self, 'neighbor_sampler'):
self.eval_neighbor_sampler = self.neighbor_sampler

self.eval_loader_kwargs = self.loader_kwargs

self.eval_loader_kwargs.pop('sampler', None)
self.eval_loader_kwargs.pop('batch_sampler', None)

self.input_train_edges = input_train_edges
self.input_train_labels = input_train_labels
Expand All @@ -659,11 +720,15 @@ def __init__(
self.input_test_edges = input_test_edges
self.input_test_labels = input_test_labels
self.input_test_time = input_test_time
self.input_pred_edges = input_pred_edges
self.input_pred_labels = input_pred_labels
self.input_pred_time = input_pred_time

# Can be overriden to set input indices of the `LinkLoader`:
self.input_train_id: OptTensor = None
self.input_val_id: OptTensor = None
self.input_test_id: OptTensor = None
self.input_pred_id: OptTensor = None

def prepare_data(self):
""""""
Expand All @@ -687,26 +752,28 @@ def dataloader(
input_labels: OptTensor = None,
input_time: OptTensor = None,
input_id: OptTensor = None,
link_sampler: Optional[BaseSampler] = None,
**kwargs,
) -> DataLoader:
if self.loader == 'full':
warnings.filterwarnings('ignore', '.*does not have many workers.*')
warnings.filterwarnings('ignore', '.*data loading bottlenecks.*')

kwargs['shuffle'] = True
kwargs.pop('sampler', None)
kwargs.pop('batch_sampler', None)

return torch.utils.data.DataLoader(
[self.data],
collate_fn=lambda xs: xs[0],
**kwargs,
)

else:
if link_sampler is None:
warnings.warn("No 'link_sampler' specified. Falling back to "
"using the default training sampler.")
link_sampler = self.neighbor_sampler

return LinkLoader(
self.data,
link_sampler=self.neighbor_sampler,
link_sampler=link_sampler,
edge_label_index=input_edges,
edge_label=input_labels,
edge_label_time=input_time,
Expand All @@ -716,32 +783,54 @@ def dataloader(

def train_dataloader(self) -> DataLoader:
""""""
shuffle = (self.kwargs.get('sampler', None) is None
and self.kwargs.get('batch_sampler', None) is None)
shuffle = self.kwargs.get('sampler', None) is None
shuffle &= self.kwargs.get('batch_sampler', None) is None

return self.dataloader(self.input_train_edges, self.input_train_labels,
self.input_train_time, self.input_train_id,
shuffle=shuffle, **self.kwargs)
return self.dataloader(
self.input_train_edges,
self.input_train_labels,
self.input_train_time,
self.input_train_id,
link_sampler=getattr(self, 'neighbor_sampler', None),
shuffle=shuffle,
**self.loader_kwargs,
)

def val_dataloader(self) -> DataLoader:
""""""
kwargs = copy.copy(self.kwargs)
kwargs.pop('sampler', None)
kwargs.pop('batch_sampler', None)

return self.dataloader(self.input_val_edges, self.input_val_labels,
self.input_val_time, self.input_val_id,
shuffle=False, **kwargs)
return self.dataloader(
self.input_val_edges,
self.input_val_labels,
self.input_val_time,
self.input_val_id,
link_sampler=getattr(self, 'eval_neighbor_sampler', None),
shuffle=False,
**self.eval_loader_kwargs,
)

def test_dataloader(self) -> DataLoader:
""""""
kwargs = copy.copy(self.kwargs)
kwargs.pop('sampler', None)
kwargs.pop('batch_sampler', None)
return self.dataloader(
self.input_test_edges,
self.input_test_labels,
self.input_test_time,
self.input_test_id,
link_sampler=getattr(self, 'eval_neighbor_sampler', None),
shuffle=False,
**self.eval_loader_kwargs,
)

return self.dataloader(self.input_test_edges, self.input_test_labels,
self.input_test_time, self.input_test_id,
shuffle=False, **kwargs)
def predict_dataloader(self) -> DataLoader:
""""""
return self.dataloader(
self.input_pred_edges,
self.input_pred_labels,
self.input_pred_time,
self.input_pred_id,
link_sampler=getattr(self, 'eval_neighbor_sampler', None),
shuffle=False,
**self.eval_loader_kwargs,
)

def __repr__(self) -> str:
kwargs = kwargs_repr(data=self.data, loader=self.loader, **self.kwargs)
Expand Down