321 lines
11 KiB
Python
321 lines
11 KiB
Python
"""DataLoader module for PinaDataset."""
|
|
|
|
import itertools
|
|
from functools import partial
|
|
import torch
|
|
from torch.utils.data import DataLoader
|
|
from torch.utils.data.distributed import DistributedSampler
|
|
from torch.utils.data.sampler import SequentialSampler
|
|
|
|
|
|
class DummyDataloader:
|
|
"""
|
|
DataLoader that returns the entire dataset in a single batch.
|
|
"""
|
|
|
|
def __init__(self, dataset, device=None):
|
|
"""
|
|
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``.
|
|
"""
|
|
# Handle distributed environment
|
|
if PinaSampler.is_distributed():
|
|
# Get rank and world size
|
|
rank = torch.distributed.get_rank()
|
|
world_size = torch.distributed.get_world_size()
|
|
# Ensure dataset is large enough
|
|
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"
|
|
)
|
|
# Split dataset among processes
|
|
idx, i = [], rank
|
|
while i < len(dataset):
|
|
idx.append(i)
|
|
i += world_size
|
|
else:
|
|
idx = list(range(len(dataset)))
|
|
self.dataset = dataset.getitem_from_list(idx)
|
|
self.device = device
|
|
self.dataset = (
|
|
{k: v.to(self.device) for k, v in self.dataset.items()}
|
|
if self.device
|
|
else self.dataset
|
|
)
|
|
|
|
def __iter__(self):
|
|
"""
|
|
Iterate over the dataloader.
|
|
"""
|
|
return self
|
|
|
|
def __len__(self):
|
|
"""
|
|
Return the length of the dataloader, which is always 1.
|
|
:return: The length of the dataloader.
|
|
:rtype: int
|
|
"""
|
|
return 1
|
|
|
|
def __next__(self):
|
|
"""
|
|
Return the entire dataset as a single batch.
|
|
:return: The entire dataset.
|
|
:rtype: dict
|
|
"""
|
|
return self.dataset
|
|
|
|
|
|
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=True):
|
|
"""
|
|
Instantiate and initialize the sampler.
|
|
|
|
:param PinaDataset dataset: The dataset from which to sample.
|
|
:return: The sampler instance.
|
|
:rtype: :class:`torch.utils.data.Sampler`
|
|
"""
|
|
|
|
if cls.is_distributed():
|
|
sampler = DistributedSampler(dataset, shuffle=shuffle)
|
|
else:
|
|
if shuffle:
|
|
sampler = torch.utils.data.RandomSampler(dataset)
|
|
else:
|
|
sampler = SequentialSampler(dataset)
|
|
return sampler
|
|
|
|
@staticmethod
|
|
def is_distributed():
|
|
"""
|
|
Check if the sampler is distributed.
|
|
:return: True if the sampler is distributed, False otherwise.
|
|
:rtype: bool
|
|
"""
|
|
return (
|
|
torch.distributed.is_available()
|
|
and torch.distributed.is_initialized()
|
|
)
|
|
|
|
|
|
def _collect_items(batch):
|
|
"""
|
|
Helper function to collect items from a batch of graph data samples.
|
|
:param batch: List of graph data samples.
|
|
"""
|
|
to_return = {name: [] for name in batch[0].keys()}
|
|
for sample in batch:
|
|
for k, v in sample.items():
|
|
to_return[k].append(v)
|
|
return to_return
|
|
|
|
|
|
def collate_fn_custom(batch, dataset):
|
|
"""
|
|
Override the default collate function to handle datasets without automatic
|
|
batching.
|
|
:param batch: List of indices from the dataset.
|
|
:param dataset: The PinaDataset instance (must be provided).
|
|
"""
|
|
return dataset.getitem_from_list(batch)
|
|
|
|
|
|
def collate_fn_default(batch, stack_fn):
|
|
"""
|
|
Default collate function that simply returns the batch as is.
|
|
:param batch: List of data samples.
|
|
"""
|
|
to_return = _collect_items(batch)
|
|
return {k: stack_fn[k](v) for k, v in to_return.items()}
|
|
|
|
|
|
class PinaDataLoader:
|
|
"""
|
|
Custom DataLoader for PinaDataset.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dataset_dict,
|
|
batch_size,
|
|
num_workers=0,
|
|
shuffle=False,
|
|
batching_mode="common_batch_size",
|
|
device=None,
|
|
):
|
|
"""
|
|
Initialize the PinaDataLoader.
|
|
:param dict dataset_dict: A dictionary mapping dataset names to their
|
|
respective PinaDataset instances.
|
|
:param int batch_size: The batch size for the dataloader.
|
|
:param int num_workers: Number of worker processes for data loading.
|
|
:param bool shuffle: Whether to shuffle the data at every epoch.
|
|
:param str batching_mode: The batching mode to use. Options are
|
|
"common_batch_size", "separate_conditions", and "proportional".
|
|
:param device: The device to which the data should be moved.
|
|
"""
|
|
self.dataset_dict = dataset_dict
|
|
self.batch_size = batch_size
|
|
self.num_workers = num_workers
|
|
self.shuffle = shuffle
|
|
self.batching_mode = batching_mode.lower()
|
|
self.device = device
|
|
|
|
# Batch size None means we want to load the entire dataset in a single
|
|
# batch
|
|
if batch_size is None:
|
|
batch_size_per_dataset = {
|
|
split: None for split in dataset_dict.keys()
|
|
}
|
|
else:
|
|
# Compute batch size per dataset
|
|
if batching_mode in ["common_batch_size", "separate_conditions"]:
|
|
# (the sum of the batch sizes is equal to
|
|
# n_conditions * batch_size)
|
|
batch_size_per_dataset = {
|
|
split: batch_size for split in dataset_dict.keys()
|
|
}
|
|
elif batching_mode == "propotional":
|
|
# batch sizes is equal to the specified batch size)
|
|
batch_size_per_dataset = self._compute_batch_size()
|
|
|
|
# Creaete a dataloader per dataset
|
|
self.dataloaders = {
|
|
split: self._create_dataloader(
|
|
dataset, batch_size_per_dataset[split]
|
|
)
|
|
for split, dataset in dataset_dict.items()
|
|
}
|
|
|
|
def _compute_batch_size(self):
|
|
"""
|
|
Compute an appropriate batch size for the given dataset.
|
|
"""
|
|
# Compute number of elements per dataset
|
|
elements_per_dataset = {
|
|
dataset_name: len(dataset)
|
|
for dataset_name, dataset in self.dataset_dict.items()
|
|
}
|
|
# Compute the total number of elements
|
|
total_elements = sum(el for el in elements_per_dataset.values())
|
|
# Compute the portion of each dataset
|
|
portion_per_dataset = {
|
|
name: el / total_elements
|
|
for name, el in elements_per_dataset.items()
|
|
}
|
|
# Compute batch size per dataset. Ensure at least 1 element per
|
|
# dataset.
|
|
batch_size_per_dataset = {
|
|
name: max(1, int(portion * self.batch_size))
|
|
for name, portion in portion_per_dataset.items()
|
|
}
|
|
# Adjust batch sizes to match the specified total batch size
|
|
tot_el_per_batch = sum(el for el in batch_size_per_dataset.values())
|
|
if self.batch_size > tot_el_per_batch:
|
|
difference = self.batch_size - tot_el_per_batch
|
|
while difference > 0:
|
|
for k, v in batch_size_per_dataset.items():
|
|
if difference == 0:
|
|
break
|
|
if v > 1:
|
|
batch_size_per_dataset[k] += 1
|
|
difference -= 1
|
|
if self.batch_size < tot_el_per_batch:
|
|
difference = tot_el_per_batch - self.batch_size
|
|
while difference > 0:
|
|
for k, v in batch_size_per_dataset.items():
|
|
if difference == 0:
|
|
break
|
|
if v > 1:
|
|
batch_size_per_dataset[k] -= 1
|
|
difference -= 1
|
|
return batch_size_per_dataset
|
|
|
|
def _create_dataloader(self, dataset, batch_size):
|
|
"""
|
|
Create the dataloader for the given dataset.
|
|
|
|
:param PinaDataset dataset: The dataset for which to create the
|
|
dataloader.
|
|
:param int batch_size: The batch size for the dataloader.
|
|
:return: The created dataloader.
|
|
:rtype: :class:`torch.utils.data.DataLoader`
|
|
"""
|
|
# If batch size is None, use DummyDataloader
|
|
if batch_size is None or batch_size >= len(dataset):
|
|
return DummyDataloader(dataset, device=self.device)
|
|
|
|
# Determine the appropriate collate function
|
|
if not dataset.automatic_batching:
|
|
collate_fn = partial(collate_fn_custom, dataset=dataset)
|
|
else:
|
|
collate_fn = partial(collate_fn_default, stack_fn=dataset.stack_fn)
|
|
|
|
# Create and return the dataloader
|
|
return DataLoader(
|
|
dataset,
|
|
batch_size=batch_size,
|
|
collate_fn=collate_fn,
|
|
num_workers=self.num_workers,
|
|
sampler=PinaSampler(dataset, shuffle=self.shuffle),
|
|
)
|
|
|
|
def __len__(self):
|
|
"""
|
|
Return the length of the dataloader.
|
|
:return: The length of the dataloader.
|
|
:rtype: int
|
|
"""
|
|
# If separate conditions, return sum of lengths of all dataloaders
|
|
# else, return max length among dataloaders
|
|
if self.batching_mode == "separate_conditions":
|
|
return sum(len(dl) for dl in self.dataloaders.values())
|
|
return max(len(dl) for dl in self.dataloaders.values())
|
|
|
|
def __iter__(self):
|
|
"""
|
|
Iterate over the dataloader.
|
|
:return: Yields batches from the dataloader.
|
|
:rtype: dict
|
|
"""
|
|
if self.batching_mode == "separate_conditions":
|
|
for split, dl in self.dataloaders.items():
|
|
for batch in dl:
|
|
yield {split: batch}
|
|
return
|
|
|
|
iterators = {
|
|
split: itertools.cycle(dl) for split, dl in self.dataloaders.items()
|
|
}
|
|
|
|
for _ in range(len(self)):
|
|
batch_dict = {}
|
|
for split, it in iterators.items():
|
|
|
|
# Iterate through each dataloader and get the next batch
|
|
batch = next(it, None)
|
|
# Check if batch is None (in case of uneven lengths)
|
|
if batch is None:
|
|
return
|
|
|
|
batch_dict[split] = batch
|
|
yield batch_dict
|