578 lines
20 KiB
Python
578 lines
20 KiB
Python
"""
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This module contains the PinaDataModule class, which extends the
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LightningDataModule class to allow proper creation and management of
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different types of Datasets defined in PINA.
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"""
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import warnings
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from lightning.pytorch import LightningDataModule
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import torch
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from torch_geometric.data import Data
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from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
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from torch.utils.data.distributed import DistributedSampler
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from ..label_tensor import LabelTensor
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from .dataset import PinaDatasetFactory, PinaTensorDataset
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from ..collector import Collector
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class DummyDataloader:
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""" "
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Dummy dataloader used when batch size is None. It callects all the data
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in self.dataset and returns it when it is called a single batch.
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"""
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def __init__(self, dataset):
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"""
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Preprare a dataloader object which will return the entire dataset
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in a single batch. Depending on the number of GPUs, the dataset we
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have the following cases:
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- **Distributed Environment** (multiple GPUs):
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- Divides the dataset across processes using the rank and world
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size.
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- Fetches only the portion of data corresponding to the current
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process.
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- **Non-Distributed Environment** (single GPU):
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- Fetches the entire dataset.
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:param dataset: The dataset object to be processed.
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:type dataset: PinaDataset
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.. note:: This data loader is used when the batch size is None.
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"""
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if (
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torch.distributed.is_available()
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and torch.distributed.is_initialized()
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):
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rank = torch.distributed.get_rank()
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world_size = torch.distributed.get_world_size()
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if len(dataset) < world_size:
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raise RuntimeError(
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"Dimension of the dataset smaller than world size."
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" Increase the size of the partition or use a single GPU"
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)
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idx, i = [], rank
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while i < len(dataset):
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idx.append(i)
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i += world_size
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self.dataset = dataset.fetch_from_idx_list(idx)
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else:
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self.dataset = dataset.get_all_data()
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def __iter__(self):
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return self
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def __len__(self):
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return 1
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def __next__(self):
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return self.dataset
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class Collator:
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"""
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Class used to collate the batch
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"""
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def __init__(
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self, max_conditions_lengths, automatic_batching, dataset=None
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):
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"""
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Initialize the object, setting the collate function based on whether
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automatic batching is enabled or not.
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:param dict max_conditions_lengths: dict containing the maximum number
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of data points to consider in a single batch for each condition.
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:param PinaDataset dataset: The dataset where the data is stored.
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"""
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self.max_conditions_lengths = max_conditions_lengths
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# Set the collate function based on the batching strategy
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# collate_pina_dataloader is used when automatic batching is disabled
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# collate_torch_dataloader is used when automatic batching is enabled
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self.callable_function = (
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self._collate_torch_dataloader
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if automatic_batching
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else (self._collate_pina_dataloader)
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)
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self.dataset = dataset
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# Set the function which performs the actual collation
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if isinstance(self.dataset, PinaTensorDataset):
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# If the dataset is a PinaTensorDataset, use this collate function
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self._collate = self._collate_tensor_dataset
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else:
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# If the dataset is a PinaDataset, use this collate function
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self._collate = self._collate_graph_dataset
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def _collate_pina_dataloader(self, batch):
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"""
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Function used to create a batch when automatic batching is disabled.
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:param list(int) batch: List of integers representing the indices of
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the data points to be fetched.
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:return: Dictionary containing the data points fetched from the dataset.
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:rtype: dict
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"""
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# Call the fetch_from_idx_list method of the dataset
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return self.dataset.fetch_from_idx_list(batch)
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def _collate_torch_dataloader(self, batch):
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"""
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Function used to collate the batch
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"""
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batch_dict = {}
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if isinstance(batch, dict):
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return batch
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conditions_names = batch[0].keys()
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# Condition names
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for condition_name in conditions_names:
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single_cond_dict = {}
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condition_args = batch[0][condition_name].keys()
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for arg in condition_args:
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data_list = [
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batch[idx][condition_name][arg]
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for idx in range(
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min(
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len(batch),
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self.max_conditions_lengths[condition_name],
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)
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)
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]
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single_cond_dict[arg] = self._collate(data_list)
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batch_dict[condition_name] = single_cond_dict
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return batch_dict
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@staticmethod
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def _collate_tensor_dataset(data_list):
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"""
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Function used to collate the data when the dataset is a
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`PinaTensorDataset`.
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:param data_list: List of `torch.Tensor` or `LabelTensor` to be
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collated.
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:type data_list: list(torch.Tensor) | list(LabelTensor)
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:raises RuntimeError: If the data is not a `torch.Tensor` or a
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`LabelTensor`.
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:return: Batch of data
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:rtype: dict
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"""
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if isinstance(data_list[0], LabelTensor):
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return LabelTensor.stack(data_list)
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if isinstance(data_list[0], torch.Tensor):
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return torch.stack(data_list)
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raise RuntimeError("Data must be Tensors or LabelTensor ")
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def _collate_graph_dataset(self, data_list):
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"""
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Function used to collate the data when the dataset is a
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`PinaGraphDataset`.
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:param data_list: List of `Data` or `Graph` to be collated.
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:type data_list: list(Data) | list(Graph)
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:raises RuntimeError: If the data is not a `Data` or a `Graph`.
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:return: Batch of data
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:rtype: dict
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"""
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if isinstance(data_list[0], LabelTensor):
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return LabelTensor.cat(data_list)
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if isinstance(data_list[0], torch.Tensor):
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return torch.cat(data_list)
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if isinstance(data_list[0], Data):
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return self.dataset.create_batch(data_list)
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raise RuntimeError("Data must be Tensors or LabelTensor or pyG Data")
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def __call__(self, batch):
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"""
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Call the function to collate the batch, defined in __init__.
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:param batch: list of indices or list of retrieved data
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:type batch: list(int) | list(dict)
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:return: Dictionary containing the data points fetched from the dataset,
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collated.
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:rtype: dict
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"""
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return self.callable_function(batch)
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class PinaSampler:
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"""
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Class used to create the sampler instance.
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"""
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def __new__(cls, dataset, shuffle):
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"""
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Create the sampler instance, according to shuffle and whether the
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environment is distributed or not.
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:param PinaDataset dataset: The dataset to be sampled.
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:param bool shuffle: whether to shuffle the dataset or not before
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sampling.
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:return: The sampler instance.
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:rtype: torch.utils.data.Sampler
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"""
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if (
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torch.distributed.is_available()
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and torch.distributed.is_initialized()
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):
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sampler = DistributedSampler(dataset, shuffle=shuffle)
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else:
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if shuffle:
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sampler = RandomSampler(dataset)
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else:
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sampler = SequentialSampler(dataset)
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return sampler
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class PinaDataModule(LightningDataModule):
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"""
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This class extend LightningDataModule, allowing proper creation and
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management of different types of Datasets defined in PINA
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"""
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def __init__(
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self,
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problem,
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train_size=0.7,
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test_size=0.2,
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val_size=0.1,
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batch_size=None,
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shuffle=True,
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repeat=False,
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automatic_batching=None,
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num_workers=0,
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pin_memory=False,
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):
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"""
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Initialize the object, creating datasets based on the input problem.
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:param AbstractProblem problem: The problem containing the data on which
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to train/test the model.
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:param float train_size: Fraction or number of elements in the training
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split.
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:param float test_size: Fraction or number of elements in the test
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split.
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:param float val_size: Fraction or number of elements in the validation
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split.
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:param batch_size: The batch size used for training. If `None`, the
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entire dataset is used per batch.
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:type batch_size: int | None
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:param bool shuffle: Whether to shuffle the dataset before splitting.
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:param bool repeat: Whether to repeat the dataset indefinitely.
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:param automatic_batching: Whether to enable automatic batching.
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:param int num_workers: Number of worker threads for data loading.
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Default 0 (serial loading)
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:param bool pin_memory: Whether to use pinned memory for faster data
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transfer to GPU. (Default False)
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"""
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super().__init__()
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# Store fixed attributes
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self.batch_size = batch_size
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self.shuffle = shuffle
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self.repeat = repeat
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self.automatic_batching = automatic_batching
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if batch_size is None and num_workers != 0:
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warnings.warn(
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"Setting num_workers when batch_size is None has no effect on "
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"the DataLoading process."
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)
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self.num_workers = 0
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else:
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self.num_workers = num_workers
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if batch_size is None and pin_memory:
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warnings.warn(
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"Setting pin_memory to True has no effect when "
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"batch_size is None."
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)
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self.pin_memory = False
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else:
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self.pin_memory = pin_memory
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# Collect data
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collector = Collector(problem)
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collector.store_fixed_data()
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collector.store_sample_domains()
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# Check if the splits are correct
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self._check_slit_sizes(train_size, test_size, val_size)
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# Split input data into subsets
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splits_dict = {}
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if train_size > 0:
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splits_dict["train"] = train_size
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self.train_dataset = None
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else:
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self.train_dataloader = super().train_dataloader
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if test_size > 0:
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splits_dict["test"] = test_size
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self.test_dataset = None
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else:
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self.test_dataloader = super().test_dataloader
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if val_size > 0:
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splits_dict["val"] = val_size
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self.val_dataset = None
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else:
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self.val_dataloader = super().val_dataloader
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self.collector_splits = self._create_splits(collector, splits_dict)
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self.transfer_batch_to_device = self._transfer_batch_to_device
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def setup(self, stage=None):
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"""
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Perform the splitting of the dataset
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"""
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if stage == "fit" or stage is None:
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self.train_dataset = PinaDatasetFactory(
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self.collector_splits["train"],
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max_conditions_lengths=self.find_max_conditions_lengths(
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"train"
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),
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automatic_batching=self.automatic_batching,
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)
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if "val" in self.collector_splits.keys():
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self.val_dataset = PinaDatasetFactory(
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self.collector_splits["val"],
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max_conditions_lengths=self.find_max_conditions_lengths(
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"val"
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),
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automatic_batching=self.automatic_batching,
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)
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elif stage == "test":
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self.test_dataset = PinaDatasetFactory(
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self.collector_splits["test"],
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max_conditions_lengths=self.find_max_conditions_lengths("test"),
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automatic_batching=self.automatic_batching,
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)
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else:
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raise ValueError("stage must be either 'fit' or 'test'.")
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@staticmethod
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def _split_condition(condition_dict, splits_dict):
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len_condition = len(condition_dict["input"])
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lengths = [
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int(len_condition * length) for length in splits_dict.values()
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]
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remainder = len_condition - sum(lengths)
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for i in range(remainder):
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lengths[i % len(lengths)] += 1
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splits_dict = {
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k: max(1, v) for k, v in zip(splits_dict.keys(), lengths)
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}
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to_return_dict = {}
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offset = 0
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for stage, stage_len in splits_dict.items():
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to_return_dict[stage] = {
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k: v[offset : offset + stage_len]
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for k, v in condition_dict.items()
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if k != "equation"
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# Equations are NEVER dataloaded
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}
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if offset + stage_len >= len_condition:
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offset = len_condition - 1
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continue
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offset += stage_len
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return to_return_dict
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def _create_splits(self, collector, splits_dict):
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"""
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Create the dataset objects putting data
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"""
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# ----------- Auxiliary function ------------
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def _apply_shuffle(condition_dict, len_data):
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idx = torch.randperm(len_data)
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for k, v in condition_dict.items():
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if k == "equation":
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continue
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if isinstance(v, list):
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condition_dict[k] = [v[i] for i in idx]
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elif isinstance(v, LabelTensor):
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condition_dict[k] = LabelTensor(v.tensor[idx], v.labels)
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elif isinstance(v, torch.Tensor):
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condition_dict[k] = v[idx]
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else:
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raise ValueError(f"Data type {type(v)} not supported")
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# ----------- End auxiliary function ------------
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split_names = list(splits_dict.keys())
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dataset_dict = {name: {} for name in split_names}
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for (
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condition_name,
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condition_dict,
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) in collector.data_collections.items():
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len_data = len(condition_dict["input"])
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if self.shuffle:
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_apply_shuffle(condition_dict, len_data)
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for key, data in self._split_condition(
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condition_dict, splits_dict
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).items():
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dataset_dict[key].update({condition_name: data})
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return dataset_dict
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def _create_dataloader(self, split, dataset):
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shuffle = self.shuffle if split == "train" else False
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# Suppress the warning about num_workers.
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# In many cases, especially for PINNs,
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# serial data loading can outperform parallel data loading.
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warnings.filterwarnings(
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"ignore",
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message=(
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"The '(train|val|test)_dataloader' does not have many workers "
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"which may be a bottleneck."
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),
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module="lightning.pytorch.trainer.connectors.data_connector",
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)
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# Use custom batching (good if batch size is large)
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if self.batch_size is not None:
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sampler = PinaSampler(dataset, shuffle)
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if self.automatic_batching:
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collate = Collator(
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self.find_max_conditions_lengths(split),
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self.automatic_batching,
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dataset=dataset,
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)
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else:
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collate = Collator(
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None, self.automatic_batching, dataset=dataset
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)
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return DataLoader(
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dataset,
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self.batch_size,
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collate_fn=collate,
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sampler=sampler,
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num_workers=self.num_workers,
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)
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dataloader = DummyDataloader(dataset)
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dataloader.dataset = self._transfer_batch_to_device(
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dataloader.dataset, self.trainer.strategy.root_device, 0
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)
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self.transfer_batch_to_device = self._transfer_batch_to_device_dummy
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return dataloader
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def find_max_conditions_lengths(self, split):
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"""
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Define the maximum length of the conditions.
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:param split: The splits of the dataset.
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:type split: dict
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:return: The maximum length of the conditions.
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:rtype: dict
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"""
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max_conditions_lengths = {}
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for k, v in self.collector_splits[split].items():
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if self.batch_size is None:
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max_conditions_lengths[k] = len(v["input"])
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elif self.repeat:
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max_conditions_lengths[k] = self.batch_size
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else:
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max_conditions_lengths[k] = min(
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len(v["input"]), self.batch_size
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)
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return max_conditions_lengths
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def val_dataloader(self):
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"""
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Create the validation dataloader
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"""
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return self._create_dataloader("val", self.val_dataset)
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def train_dataloader(self):
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"""
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Create the training dataloader
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:return: The training dataloader
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:rtype: DataLoader
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"""
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return self._create_dataloader("train", self.train_dataset)
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def test_dataloader(self):
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"""
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Create the testing dataloader
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:return: The testing dataloader
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:rtype: DataLoader
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"""
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return self._create_dataloader("test", self.test_dataset)
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@staticmethod
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def _transfer_batch_to_device_dummy(batch, device, dataloader_idx):
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"""
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Transfer the batch to the device. This method is called in the
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training loop and is used to transfer the batch to the device.
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This method is used when the batch size is None: batch has already
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been transferred to the device.
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:param list(tuple) batch: list of tuple where the first element of the
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tuple is the condition name and the second element is the data.
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:param device: device to which the batch is transferred
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:type device: torch.device
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:param dataloader_idx: index of the dataloader
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:type dataloader_idx: int
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:return: The batch transferred to the device.
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:rtype: list(tuple)
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"""
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return batch
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def _transfer_batch_to_device(self, batch, device, dataloader_idx):
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"""
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|
Transfer the batch to the device. This method is called in the
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training loop and is used to transfer the batch to the device.
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|
:param dict batch: The batch to be transferred to the device.
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:param device: The device to which the batch is transferred.
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:type device: torch.device
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:param int dataloader_idx: The index of the dataloader.
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: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
|
|
"""
|
|
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
|