from typing import Optional, Union
import warnings
import torch
from torch_geometric.typing import InputNodes
from torch_geometric.data import Data, HeteroData, Dataset
from torch_geometric.loader.dataloader import DataLoader
from torch_geometric.loader.neighbor_loader import (NeighborSampler,
NeighborLoader,
get_input_node_type)
try:
from pytorch_lightning import LightningDataModule as PLLightningDataModule
no_pytorch_lightning = False
except (ImportError, ModuleNotFoundError):
PLLightningDataModule = object
no_pytorch_lightning = True
class LightningDataModule(PLLightningDataModule):
def __init__(self, has_val: bool, has_test: bool, **kwargs):
super().__init__()
if no_pytorch_lightning:
raise ModuleNotFoundError(
"No module named 'pytorch_lightning' found on this machine. "
"Run 'pip install pytorch_lightning' to install the library")
if not has_val:
self.val_dataloader = None
if not has_test:
self.test_dataloader = None
if 'shuffle' in kwargs:
warnings.warn(f"The 'shuffle={kwargs['shuffle']}' option is "
f"ignored in '{self.__class__.__name__}'. Remove it "
f"from the argument list to disable this warning")
del kwargs['shuffle']
if 'pin_memory' not in kwargs:
kwargs['pin_memory'] = True
if 'persistent_workers' not in kwargs:
kwargs['persistent_workers'] = kwargs.get('num_workers', 0) > 0
self.kwargs = kwargs
def prepare_data(self):
from pytorch_lightning.plugins import SingleDevicePlugin
from pytorch_lightning.plugins import DDPSpawnPlugin
plugin = self.trainer.training_type_plugin
if not isinstance(plugin, (SingleDevicePlugin, DDPSpawnPlugin)):
raise NotImplementedError(
f"'{self.__class__.__name__}' currently only supports "
f"'{SingleDevicePlugin.__name__}' and "
f"'{DDPSpawnPlugin.__name__}' training type plugins of "
f"'pytorch_lightning' (got '{plugin.__class__.__name__}')")
def __repr__(self) -> str:
return f'{self.__class__.__name__}({self._kwargs_repr(**self.kwargs)})'
[docs]class LightningDataset(LightningDataModule):
r"""Converts a set of :class:`~torch_geometric.data.Dataset` objects into a
:class:`pytorch_lightning.LightningDataModule` variant, which can be
automatically used as a :obj:`datamodule` for multi-GPU graph-level
training via `PyTorch Lightning <https://www.pytorchlightning.ai>`__.
:class:`LightningDataset` will take care of providing mini-batches via
:class:`~torch_geometric.loader.DataLoader`.
.. note::
Currently only the
:class:`pytorch_lightning.plugins.SingleDevicePlugin` and
:class:`pytorch_lightning.plugins.DDPSpawnPlugin` training type plugins
of `PyTorch Lightning <https://pytorch-lightning.readthedocs.io/en/
latest/guides/speed.html>`__ are supported in order to correctly
share data across all devices/processes:
.. code-block::
import pytorch_lightning as pl
trainer = pl.Trainer(strategy="ddp_spawn", gpus=4)
trainer.fit(model, datamodule)
Args:
train_dataset: (Dataset) The training dataset.
val_dataset: (Dataset, optional) The validation dataset.
(default: :obj:`None`)
test_dataset: (Dataset, optional) The test dataset.
(default: :obj:`None`)
batch_size (int, optional): How many samples per batch to load.
(default: :obj:`1`)
num_workers: How many subprocesses to use for data loading.
:obj:`0` means that the data will be loaded in the main process.
(default: :obj:`0`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.loader.DataLoader`.
"""
def __init__(
self,
train_dataset: Dataset,
val_dataset: Optional[Dataset] = None,
test_dataset: Optional[Dataset] = None,
batch_size: int = 1,
num_workers: int = 0,
**kwargs,
):
super().__init__(
has_val=val_dataset is not None,
has_test=test_dataset is not None,
batch_size=batch_size,
num_workers=num_workers,
**kwargs,
)
self.train_dataset = train_dataset
self.val_dataset = val_dataset
self.test_dataset = test_dataset
def dataloader(self, dataset: Dataset, shuffle: bool) -> DataLoader:
return DataLoader(dataset, shuffle=shuffle, **self.kwargs)
def train_dataloader(self) -> DataLoader:
""""""
from torch.utils.data import IterableDataset
shuffle = not isinstance(self.train_dataset, IterableDataset)
return self.dataloader(self.train_dataset, shuffle=shuffle)
def val_dataloader(self) -> DataLoader:
""""""
return self.dataloader(self.val_dataset, shuffle=False)
def test_dataloader(self) -> DataLoader:
""""""
return self.dataloader(self.test_dataset, shuffle=False)
def __repr__(self) -> str:
kwargs = kwargs_repr(train_dataset=self.train_dataset,
val_dataset=self.val_dataset,
test_dataset=self.test_dataset, **self.kwargs)
return f'{self.__class__.__name__}({kwargs})'
[docs]class LightningNodeData(LightningDataModule):
r"""Converts a :class:`~torch_geometric.data.Data` or
:class:`~torch_geometric.data.HeteroData` object into a
:class:`pytorch_lightning.LightningDataModule` variant, which can be
automatically used as a :obj:`datamodule` for multi-GPU node-level
training via `PyTorch Lightning <https://www.pytorchlightning.ai>`_.
:class:`LightningDataset` will take care of providing mini-batches via
:class:`~torch_geometric.loader.NeighborLoader`.
.. note::
Currently only the
:class:`pytorch_lightning.plugins.SingleDevicePlugin` and
:class:`pytorch_lightning.plugins.DDPSpawnPlugin` training type plugins
of `PyTorch Lightning <https://pytorch-lightning.readthedocs.io/en/
latest/guides/speed.html>`__ are supported in order to correctly
share data across all devices/processes:
.. code-block::
import pytorch_lightning as pl
trainer = pl.Trainer(strategy="ddp_spawn", gpus=4)
trainer.fit(model, datamodule)
Args:
data (Data or HeteroData): The :class:`~torch_geometric.data.Data` or
:class:`~torch_geometric.data.HeteroData` graph object.
input_train_nodes (torch.Tensor or str or (str, torch.Tensor)): The
indices of training nodes. If not given, will try to automatically
infer them from the :obj:`data` object. (default: :obj:`None`)
input_val_nodes (torch.Tensor or str or (str, torch.Tensor)): The
indices of validation nodes. If not given, will try to
automatically infer them from the :obj:`data` object.
(default: :obj:`None`)
input_test_nodes (torch.Tensor or str or (str, torch.Tensor)): The
indices of test nodes. If not given, will try to automatically
infer them from the :obj:`data` object. (default: :obj:`None`)
loader (str): The scalability technique to use (:obj:`"full"`,
:obj:`"neighbor"`). (default: :obj:`"neighbor"`)
batch_size (int, optional): How many samples per batch to load.
(default: :obj:`1`)
num_workers: How many subprocesses to use for data loading.
:obj:`0` means that the data will be loaded in the main process.
(default: :obj:`0`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.loader.NeighborLoader`.
"""
def __init__(
self,
data: Union[Data, HeteroData],
input_train_nodes: InputNodes = None,
input_val_nodes: InputNodes = None,
input_test_nodes: InputNodes = None,
loader: str = "neighbor",
batch_size: int = 1,
num_workers: int = 0,
**kwargs,
):
assert loader in ['full', 'neighbor']
if input_train_nodes is None:
input_train_nodes = infer_input_nodes(data, split='train')
if input_val_nodes is None:
input_val_nodes = infer_input_nodes(data, split='val')
if input_val_nodes is None:
input_val_nodes = infer_input_nodes(data, split='valid')
if input_test_nodes is None:
input_test_nodes = infer_input_nodes(data, split='test')
if loader == 'full' and batch_size != 1:
warnings.warn(f"Re-setting 'batch_size' to 1 in "
f"'{self.__class__.__name__}' for loader='full' "
f"(got '{batch_size}')")
batch_size = 1
if loader == 'full' and num_workers != 0:
warnings.warn(f"Re-setting 'num_workers' to 0 in "
f"'{self.__class__.__name__}' for loader='full' "
f"(got '{num_workers}')")
num_workers = 0
super().__init__(
has_val=input_val_nodes is not None,
has_test=input_test_nodes is not None,
batch_size=batch_size,
num_workers=num_workers,
**kwargs,
)
if loader == 'full':
if kwargs.get('pin_memory', False):
warnings.warn(f"Re-setting 'pin_memory' to 'False' in "
f"'{self.__class__.__name__}' for loader='full' "
f"(got 'True')")
self.kwargs['pin_memory'] = False
self.data = data
self.loader = loader
if loader == 'neighbor':
self.neighbor_sampler = NeighborSampler(
data=data,
num_neighbors=kwargs.get('num_neighbors', None),
replace=kwargs.get('replace', False),
directed=kwargs.get('directed', True),
input_node_type=get_input_node_type(input_train_nodes),
)
self.input_train_nodes = input_train_nodes
self.input_val_nodes = input_val_nodes
self.input_test_nodes = input_test_nodes
def prepare_data(self):
""""""
if self.loader == 'full':
if self.trainer.num_processes != 1 or self.trainer.num_gpus != 1:
raise ValueError(f"'{self.__class__.__name__}' with loader="
f"'full' requires training on a single GPU")
super().prepare_data()
def dataloader(self, input_nodes: InputNodes) -> DataLoader:
if self.loader == 'full':
warnings.filterwarnings('ignore', '.*does not have many workers.*')
warnings.filterwarnings('ignore', '.*data loading bottlenecks.*')
return torch.utils.data.DataLoader([self.data], shuffle=False,
collate_fn=lambda xs: xs[0],
**self.kwargs)
if self.loader == 'neighbor':
warnings.filterwarnings('ignore', '.*has `shuffle=True`.*')
return NeighborLoader(self.data, input_nodes=input_nodes,
neighbor_sampler=self.neighbor_sampler,
shuffle=True, **self.kwargs)
raise NotImplementedError
def train_dataloader(self) -> DataLoader:
""""""
return self.dataloader(self.input_train_nodes)
def val_dataloader(self) -> DataLoader:
""""""
return self.dataloader(self.input_val_nodes)
def test_dataloader(self) -> DataLoader:
""""""
return self.dataloader(self.input_test_nodes)
def __repr__(self) -> str:
kwargs = kwargs_repr(data=self.data, loader=self.loader, **self.kwargs)
return f'{self.__class__.__name__}({kwargs})'
###############################################################################
def infer_input_nodes(data: Union[Data, HeteroData], split: str) -> InputNodes:
attr_name: Optional[str] = None
if f'{split}_mask' in data:
attr_name = f'{split}_mask'
elif f'{split}_idx' in data:
attr_name = f'{split}_idx'
elif f'{split}_index' in data:
attr_name = f'{split}_index'
if attr_name is None:
return None
if isinstance(data, Data):
return data[attr_name]
if isinstance(data, HeteroData):
input_nodes_dict = data.collect(attr_name)
if len(input_nodes_dict) != 1:
raise ValueError(f"Could not automatically determine the input "
f"nodes of {data} since there exists multiple "
f"types with attribute '{attr_name}'")
return list(input_nodes_dict.items())[0]
return None
def kwargs_repr(**kwargs) -> str:
return ', '.join([f'{k}={v}' for k, v in kwargs.items() if v is not None])