"""
This module contains the PinaDataModule class, which extends the
LightningDataModule class to allow proper creation and management of
different types of Datasets defined in PINA.
"""
import warnings
from lightning.pytorch import LightningDataModule
import torch
from torch_geometric.data import Data
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from ..label_tensor import LabelTensor
from .dataset import PinaDatasetFactory, PinaTensorDataset
from ..collector import Collector
class DummyDataloader:
def __init__(self, dataset):
"""
Prepare a dataloader object that returns the entire dataset in a single
batch. Depending on the number of GPUs, the dataset is managed
as follows:
- **Distributed Environment** (multiple GPUs): Divides dataset across
processes using the rank and world size. Fetches only portion of
data corresponding to the current process.
- **Non-Distributed Environment** (single GPU): Fetches the entire
dataset.
:param PinaDataset dataset: The dataset object to be processed.
.. note::
This dataloader is used when the batch size is ``None``.
"""
if (
torch.distributed.is_available()
and torch.distributed.is_initialized()
):
rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
if len(dataset) < world_size:
raise RuntimeError(
"Dimension of the dataset smaller than world size."
" Increase the size of the partition or use a single GPU"
)
idx, i = [], rank
while i < len(dataset):
idx.append(i)
i += world_size
self.dataset = dataset.fetch_from_idx_list(idx)
else:
self.dataset = dataset.get_all_data()
def __iter__(self):
return self
def __len__(self):
return 1
def __next__(self):
return self.dataset
[docs]
class Collator:
"""
This callable class is used to collate the data points fetched from the
dataset. The collation is performed based on the type of dataset used and
on the batching strategy.
"""
def __init__(
self, max_conditions_lengths, automatic_batching, dataset=None
):
"""
Initialize the object, setting the collate function based on whether
automatic batching is enabled or not.
:param dict max_conditions_lengths: ``dict`` containing the maximum
number of data points to consider in a single batch for
each condition.
:param bool automatic_batching: Whether automatic PyTorch batching is
enabled or not. For more information, see the
:class:`~pina.data.data_module.PinaDataModule` class.
:param PinaDataset dataset: The dataset where the data is stored.
"""
self.max_conditions_lengths = max_conditions_lengths
# Set the collate function based on the batching strategy
# collate_pina_dataloader is used when automatic batching is disabled
# collate_torch_dataloader is used when automatic batching is enabled
self.callable_function = (
self._collate_torch_dataloader
if automatic_batching
else (self._collate_pina_dataloader)
)
self.dataset = dataset
# Set the function which performs the actual collation
if isinstance(self.dataset, PinaTensorDataset):
# If the dataset is a PinaTensorDataset, use this collate function
self._collate = self._collate_tensor_dataset
else:
# If the dataset is a PinaDataset, use this collate function
self._collate = self._collate_graph_dataset
def _collate_pina_dataloader(self, batch):
"""
Function used to create a batch when automatic batching is disabled.
:param list[int] batch: List of integers representing the indices of
the data points to be fetched.
:return: Dictionary containing the data points fetched from the dataset.
:rtype: dict
"""
# Call the fetch_from_idx_list method of the dataset
return self.dataset.fetch_from_idx_list(batch)
def _collate_torch_dataloader(self, batch):
"""
Function used to collate the batch
:param list[dict] batch: List of retrieved data.
:return: Dictionary containing the data points fetched from the dataset,
collated.
:rtype: dict
"""
batch_dict = {}
if isinstance(batch, dict):
return batch
conditions_names = batch[0].keys()
# Condition names
for condition_name in conditions_names:
single_cond_dict = {}
condition_args = batch[0][condition_name].keys()
for arg in condition_args:
data_list = [
batch[idx][condition_name][arg]
for idx in range(
min(
len(batch),
self.max_conditions_lengths[condition_name],
)
)
]
single_cond_dict[arg] = self._collate(data_list)
batch_dict[condition_name] = single_cond_dict
return batch_dict
@staticmethod
def _collate_tensor_dataset(data_list):
"""
Function used to collate the data when the dataset is a
:class:`~pina.data.dataset.PinaTensorDataset`.
:param data_list: Elements to be collated.
:type data_list: list[torch.Tensor] | list[LabelTensor]
:return: Batch of data.
:rtype: dict
:raises RuntimeError: If the data is not a :class:`torch.Tensor` or a
:class:`~pina.label_tensor.LabelTensor`.
"""
if isinstance(data_list[0], LabelTensor):
return LabelTensor.stack(data_list)
if isinstance(data_list[0], torch.Tensor):
return torch.stack(data_list)
raise RuntimeError("Data must be Tensors or LabelTensor ")
def _collate_graph_dataset(self, data_list):
"""
Function used to collate data when the dataset is a
:class:`~pina.data.dataset.PinaGraphDataset`.
:param data_list: Elememts to be collated.
:type data_list: list[Data] | list[Graph]
:return: Batch of data.
:rtype: dict
:raises RuntimeError: If the data is not a
:class:`~torch_geometric.data.Data` or a :class:`~pina.graph.Graph`.
"""
if isinstance(data_list[0], LabelTensor):
return LabelTensor.cat(data_list)
if isinstance(data_list[0], torch.Tensor):
return torch.cat(data_list)
if isinstance(data_list[0], Data):
return self.dataset.create_batch(data_list)
raise RuntimeError(
"Data must be Tensors or LabelTensor or pyG "
"torch_geometric.data.Data"
)
def __call__(self, batch):
"""
Perform the collation of data fetched from the dataset. The behavoior
of the function is set based on the batching strategy during class
initialization.
:param batch: List of retrieved data or sampled indices.
:type batch: list[int] | list[dict]
:return: Dictionary containing colleted data fetched from the dataset.
:rtype: dict
"""
return self.callable_function(batch)
[docs]
class PinaSampler:
"""
This class is used to create the sampler instance based on the shuffle
parameter and the environment in which the code is running.
"""
def __new__(cls, dataset, shuffle):
"""
Instantiate and initialize the sampler.
:param PinaDataset dataset: The dataset from which to sample.
:param bool shuffle: Whether to shuffle the dataset.
:return: The sampler instance.
:rtype: :class:`torch.utils.data.Sampler`
"""
if (
torch.distributed.is_available()
and torch.distributed.is_initialized()
):
sampler = DistributedSampler(dataset, shuffle=shuffle)
else:
if shuffle:
sampler = RandomSampler(dataset)
else:
sampler = SequentialSampler(dataset)
return sampler
[docs]
class PinaDataModule(LightningDataModule):
"""
This class extends :class:`~lightning.pytorch.core.LightningDataModule`,
allowing proper creation and management of different types of datasets
defined in PINA.
"""
def __init__(
self,
problem,
train_size=0.7,
test_size=0.2,
val_size=0.1,
batch_size=None,
shuffle=True,
repeat=False,
automatic_batching=None,
num_workers=0,
pin_memory=False,
):
"""
Initialize the object and creating datasets based on the input problem.
:param AbstractProblem problem: The problem containing the data on which
to create the datasets and dataloaders.
:param float train_size: Fraction of elements in the training split. It
must be in the range [0, 1].
:param float test_size: Fraction of elements in the test split. It must
be in the range [0, 1].
:param float val_size: Fraction of elements in the validation split. It
must be in the range [0, 1].
:param int batch_size: The batch size used for training. If ``None``,
the entire dataset is returned in a single batch.
Default is ``None``.
:param bool shuffle: Whether to shuffle the dataset before splitting.
Default ``True``.
:param bool repeat: If ``True``, in case of batch size larger than the
number of elements in a specific condition, the elements are
repeated until the batch size is reached. If ``False``, the number
of elements in the batch is the minimum between the batch size and
the number of elements in the condition. Default is ``False``.
:param automatic_batching: If ``True``, automatic PyTorch batching
is performed, which consists of extracting one element at a time
from the dataset and collating them into a batch. This is useful
when the dataset is too large to fit into memory. On the other hand,
if ``False``, the items are retrieved from the dataset all at once
avoind the overhead of collating them into a batch and reducing the
``__getitem__`` calls to the dataset. This is useful when the
dataset fits into memory. Avoid using automatic batching when
``batch_size`` is large. Default is ``False``.
:param int num_workers: Number of worker threads for data loading.
Default ``0`` (serial loading).
:param bool pin_memory: Whether to use pinned memory for faster data
transfer to GPU. Default ``False``.
:raises ValueError: If at least one of the splits is negative.
:raises ValueError: If the sum of the splits is different from 1.
.. seealso::
For more information on multi-process data loading, see:
https://pytorch.org/docs/stable/data.html#multi-process-data-loading
For details on memory pinning, see:
https://pytorch.org/docs/stable/data.html#memory-pinning
"""
super().__init__()
# Store fixed attributes
self.batch_size = batch_size
self.shuffle = shuffle
self.repeat = repeat
self.automatic_batching = automatic_batching
# If batch size is None, num_workers has no effect
if batch_size is None and num_workers != 0:
warnings.warn(
"Setting num_workers when batch_size is None has no effect on "
"the DataLoading process."
)
self.num_workers = 0
else:
self.num_workers = num_workers
# If batch size is None, pin_memory has no effect
if batch_size is None and pin_memory:
warnings.warn(
"Setting pin_memory to True has no effect when "
"batch_size is None."
)
self.pin_memory = False
else:
self.pin_memory = pin_memory
# Collect data
collector = Collector(problem)
collector.store_fixed_data()
collector.store_sample_domains()
# Check if the splits are correct
self._check_slit_sizes(train_size, test_size, val_size)
# Split input data into subsets
splits_dict = {}
if train_size > 0:
splits_dict["train"] = train_size
self.train_dataset = None
else:
# Use the super method to create the train dataloader which
# raises NotImplementedError
self.train_dataloader = super().train_dataloader
if test_size > 0:
splits_dict["test"] = test_size
self.test_dataset = None
else:
# Use the super method to create the train dataloader which
# raises NotImplementedError
self.test_dataloader = super().test_dataloader
if val_size > 0:
splits_dict["val"] = val_size
self.val_dataset = None
else:
# Use the super method to create the train dataloader which
# raises NotImplementedError
self.val_dataloader = super().val_dataloader
self.collector_splits = self._create_splits(collector, splits_dict)
self.transfer_batch_to_device = self._transfer_batch_to_device
[docs]
def setup(self, stage=None):
"""
Create the dataset objects for the given stage.
If the stage is "fit", the training and validation datasets are created.
If the stage is "test", the testing dataset is created.
:param str stage: The stage for which to perform the dataset setup.
:raises ValueError: If the stage is neither "fit" nor "test".
"""
if stage == "fit" or stage is None:
self.train_dataset = PinaDatasetFactory(
self.collector_splits["train"],
max_conditions_lengths=self.find_max_conditions_lengths(
"train"
),
automatic_batching=self.automatic_batching,
)
if "val" in self.collector_splits.keys():
self.val_dataset = PinaDatasetFactory(
self.collector_splits["val"],
max_conditions_lengths=self.find_max_conditions_lengths(
"val"
),
automatic_batching=self.automatic_batching,
)
elif stage == "test":
self.test_dataset = PinaDatasetFactory(
self.collector_splits["test"],
max_conditions_lengths=self.find_max_conditions_lengths("test"),
automatic_batching=self.automatic_batching,
)
else:
raise ValueError("stage must be either 'fit' or 'test'.")
@staticmethod
def _split_condition(single_condition_dict, splits_dict):
"""
Split the condition into different stages.
:param dict single_condition_dict: The condition to be split.
:param dict splits_dict: The dictionary containing the number of
elements in each stage.
:return: A dictionary containing the split condition.
:rtype: dict
"""
len_condition = len(single_condition_dict["input"])
lengths = [
int(len_condition * length) for length in splits_dict.values()
]
remainder = len_condition - sum(lengths)
for i in range(remainder):
lengths[i % len(lengths)] += 1
splits_dict = {
k: max(1, v) for k, v in zip(splits_dict.keys(), lengths)
}
to_return_dict = {}
offset = 0
for stage, stage_len in splits_dict.items():
to_return_dict[stage] = {
k: v[offset : offset + stage_len]
for k, v in single_condition_dict.items()
if k != "equation"
# Equations are NEVER dataloaded
}
if offset + stage_len >= len_condition:
offset = len_condition - 1
continue
offset += stage_len
return to_return_dict
def _create_splits(self, collector, splits_dict):
"""
Create the dataset objects putting data in the correct splits.
:param Collector collector: The collector object containing the data.
:param dict splits_dict: The dictionary containing the number of
elements in each stage.
:return: The dictionary containing the dataset objects.
:rtype: dict
"""
# ----------- Auxiliary function ------------
def _apply_shuffle(condition_dict, len_data):
idx = torch.randperm(len_data)
for k, v in condition_dict.items():
if k == "equation":
continue
if isinstance(v, list):
condition_dict[k] = [v[i] for i in idx]
elif isinstance(v, LabelTensor):
condition_dict[k] = LabelTensor(v.tensor[idx], v.labels)
elif isinstance(v, torch.Tensor):
condition_dict[k] = v[idx]
else:
raise ValueError(f"Data type {type(v)} not supported")
# ----------- End auxiliary function ------------
split_names = list(splits_dict.keys())
dataset_dict = {name: {} for name in split_names}
for (
condition_name,
condition_dict,
) in collector.data_collections.items():
len_data = len(condition_dict["input"])
if self.shuffle:
_apply_shuffle(condition_dict, len_data)
for key, data in self._split_condition(
condition_dict, splits_dict
).items():
dataset_dict[key].update({condition_name: data})
return dataset_dict
def _create_dataloader(self, split, dataset):
""" "
Create the dataloader for the given split.
:param str split: The split on which to create the dataloader.
:param str dataset: The dataset to be used for the dataloader.
:return: The dataloader for the given split.
:rtype: torch.utils.data.DataLoader
"""
shuffle = self.shuffle if split == "train" else False
# Suppress the warning about num_workers.
# In many cases, especially for PINNs,
# serial data loading can outperform parallel data loading.
warnings.filterwarnings(
"ignore",
message=(
"The '(train|val|test)_dataloader' does not have many workers "
"which may be a bottleneck."
),
module="lightning.pytorch.trainer.connectors.data_connector",
)
# Use custom batching (good if batch size is large)
if self.batch_size is not None:
sampler = PinaSampler(dataset, shuffle)
if self.automatic_batching:
collate = Collator(
self.find_max_conditions_lengths(split),
self.automatic_batching,
dataset=dataset,
)
else:
collate = Collator(
None, self.automatic_batching, dataset=dataset
)
return DataLoader(
dataset,
self.batch_size,
collate_fn=collate,
sampler=sampler,
num_workers=self.num_workers,
)
dataloader = DummyDataloader(dataset)
dataloader.dataset = self._transfer_batch_to_device(
dataloader.dataset, self.trainer.strategy.root_device, 0
)
self.transfer_batch_to_device = self._transfer_batch_to_device_dummy
return dataloader
[docs]
def find_max_conditions_lengths(self, split):
"""
Define the maximum length for each conditions.
:param dict split: The split of the dataset.
:return: The maximum length per condition.
:rtype: dict
"""
max_conditions_lengths = {}
for k, v in self.collector_splits[split].items():
if self.batch_size is None:
max_conditions_lengths[k] = len(v["input"])
elif self.repeat:
max_conditions_lengths[k] = self.batch_size
else:
max_conditions_lengths[k] = min(
len(v["input"]), self.batch_size
)
return max_conditions_lengths
[docs]
def val_dataloader(self):
"""
Create the validation dataloader.
:return: The validation dataloader
:rtype: torch.utils.data.DataLoader
"""
return self._create_dataloader("val", self.val_dataset)
[docs]
def train_dataloader(self):
"""
Create the training dataloader
:return: The training dataloader
:rtype: torch.utils.data.DataLoader
"""
return self._create_dataloader("train", self.train_dataset)
[docs]
def test_dataloader(self):
"""
Create the testing dataloader
:return: The testing dataloader
:rtype: torch.utils.data.DataLoader
"""
return self._create_dataloader("test", self.test_dataset)
@staticmethod
def _transfer_batch_to_device_dummy(batch, device, dataloader_idx):
"""
Transfer the batch to the device. This method is used when the batch
size is None: batch has already been transferred to the device.
:param list[tuple] batch: List of tuple where the first element of the
tuple is the condition name and the second element is the data.
:param torch.device device: Device to which the batch is transferred.
:param int dataloader_idx: Index of the dataloader.
:return: The batch transferred to the device.
:rtype: list[tuple]
"""
return batch
def _transfer_batch_to_device(self, batch, device, dataloader_idx):
"""
Transfer the batch to the device. This method is called in the
training loop and is used to transfer the batch to the device.
:param dict batch: The batch to be transferred to the device.
:param torch.device device: The device to which the batch is
transferred.
:param int dataloader_idx: The index of the dataloader.
:return: The batch transferred to the device.
:rtype: list[tuple]
"""
batch = [
(
k,
super(LightningDataModule, self).transfer_batch_to_device(
v, device, dataloader_idx
),
)
for k, v in batch.items()
]
return batch
@staticmethod
def _check_slit_sizes(train_size, test_size, val_size):
"""
Check if the splits are correct. The splits sizes must be positive and
the sum of the splits must be 1.
:param float train_size: The size of the training split.
:param float test_size: The size of the testing split.
:param float val_size: The size of the validation split.
:raises ValueError: If at least one of the splits is negative.
:raises ValueError: If the sum of the splits is different
from 1.
"""
if train_size < 0 or test_size < 0 or val_size < 0:
raise ValueError("The splits must be positive")
if abs(train_size + test_size + val_size - 1) > 1e-6:
raise ValueError("The sum of the splits must be 1")
@property
def input(self):
"""
Return all the input points coming from all the datasets.
:return: The input points for training.
:rtype: dict
"""
to_return = {}
if hasattr(self, "train_dataset") and self.train_dataset is not None:
to_return["train"] = self.train_dataset.input
if hasattr(self, "val_dataset") and self.val_dataset is not None:
to_return["val"] = self.val_dataset.input
if hasattr(self, "test_dataset") and self.test_dataset is not None:
to_return["test"] = self.test_dataset.input
return to_return
