469 lines
16 KiB
Python
469 lines
16 KiB
Python
"""
|
|
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:
|
|
""" "
|
|
Dummy dataloader used when batch size is None. It callects all the data
|
|
in self.dataset and returns it when it is called a single batch.
|
|
"""
|
|
|
|
def __init__(self, dataset):
|
|
"""
|
|
param dataset: The dataset object to be processed.
|
|
:notes:
|
|
- **Distributed Environment**:
|
|
- Divides the dataset across processes using the
|
|
rank and world size.
|
|
- Fetches only the portion of data corresponding to
|
|
the current process.
|
|
- **Non-Distributed Environment**:
|
|
- Fetches the entire dataset.
|
|
"""
|
|
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
|
|
|
|
|
|
class Collator:
|
|
"""
|
|
Class used to collate the batch
|
|
"""
|
|
|
|
def __init__(self, max_conditions_lengths, dataset=None):
|
|
self.max_conditions_lengths = max_conditions_lengths
|
|
self.callable_function = (
|
|
self._collate_custom_dataloader
|
|
if max_conditions_lengths is None
|
|
else (self._collate_standard_dataloader)
|
|
)
|
|
self.dataset = dataset
|
|
if isinstance(self.dataset, PinaTensorDataset):
|
|
self._collate = self._collate_tensor_dataset
|
|
else:
|
|
self._collate = self._collate_graph_dataset
|
|
|
|
def _collate_custom_dataloader(self, batch):
|
|
return self.dataset.fetch_from_idx_list(batch)
|
|
|
|
def _collate_standard_dataloader(self, batch):
|
|
"""
|
|
Function used to collate the batch
|
|
"""
|
|
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):
|
|
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):
|
|
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_graph_batch(data_list)
|
|
raise RuntimeError("Data must be Tensors or LabelTensor or pyG Data")
|
|
|
|
def __call__(self, batch):
|
|
return self.callable_function(batch)
|
|
|
|
|
|
class PinaSampler:
|
|
"""
|
|
Class used to create the sampler instance.
|
|
"""
|
|
|
|
def __new__(cls, dataset, shuffle):
|
|
|
|
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
|
|
|
|
|
|
class PinaDataModule(LightningDataModule):
|
|
"""
|
|
This class extend 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, creating datasets based on the input problem.
|
|
|
|
:param problem: The problem defining the dataset.
|
|
:type problem: AbstractProblem
|
|
:param train_size: Fraction or number of elements in the training split.
|
|
:type train_size: float
|
|
:param test_size: Fraction or number of elements in the test split.
|
|
:type test_size: float
|
|
:param val_size: Fraction or number of elements in the validation split.
|
|
:type val_size: float
|
|
:param batch_size: Batch size used for training. If None, the entire
|
|
dataset is used per batch.
|
|
:type batch_size: int or None
|
|
:param shuffle: Whether to shuffle the dataset before splitting.
|
|
:type shuffle: bool
|
|
:param repeat: Whether to repeat the dataset indefinitely.
|
|
:type repeat: bool
|
|
:param automatic_batching: Whether to enable automatic batching.
|
|
:type automatic_batching: bool
|
|
:param num_workers: Number of worker threads for data loading.
|
|
Default 0 (serial loading)
|
|
:type num_workers: int
|
|
:param pin_memory: Whether to use pinned memory for faster data
|
|
transfer to GPU. (Default False)
|
|
:type pin_memory: bool
|
|
"""
|
|
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 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 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:
|
|
self.train_dataloader = super().train_dataloader
|
|
if test_size > 0:
|
|
splits_dict["test"] = test_size
|
|
self.test_dataset = None
|
|
else:
|
|
self.test_dataloader = super().test_dataloader
|
|
if val_size > 0:
|
|
splits_dict["val"] = val_size
|
|
self.val_dataset = None
|
|
else:
|
|
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
|
|
|
|
def setup(self, stage=None):
|
|
"""
|
|
Perform the splitting of the dataset
|
|
"""
|
|
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(condition_dict, splits_dict):
|
|
len_condition = len(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 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
|
|
"""
|
|
|
|
# ----------- 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):
|
|
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), dataset=dataset
|
|
)
|
|
else:
|
|
collate = Collator(None, 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
|
|
|
|
def find_max_conditions_lengths(self, split):
|
|
"""
|
|
Define the maximum length of the conditions.
|
|
|
|
:param split: The splits of the dataset.
|
|
:type split: dict
|
|
:return: The maximum length of the conditions.
|
|
: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
|
|
|
|
def val_dataloader(self):
|
|
"""
|
|
Create the validation dataloader
|
|
"""
|
|
return self._create_dataloader("val", self.val_dataset)
|
|
|
|
def train_dataloader(self):
|
|
"""
|
|
Create the training dataloader
|
|
"""
|
|
return self._create_dataloader("train", self.train_dataset)
|
|
|
|
def test_dataloader(self):
|
|
"""
|
|
Create the testing dataloader
|
|
"""
|
|
return self._create_dataloader("test", self.test_dataset)
|
|
|
|
@staticmethod
|
|
def _transfer_batch_to_device_dummy(batch, device, dataloader_idx):
|
|
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.
|
|
"""
|
|
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
|
|
"""
|
|
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):
|
|
"""
|
|
# TODO
|
|
"""
|
|
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 = self.test_dataset.input
|
|
return to_return
|