Datamodule

SingleCellDataModule

Bases: MegatronDataModule

LightningDataModule wrapper of SingleCellDataset

Parameters:

Name	Type	Description	Default
data_path	Union[str, PosixPath]	Path to preprocessed single-cell data files	required
tokenizer	Tokenizer	Maps gene names to ids and vice-versa	required
collator		Used to batch samples	required
process_item		Function defining how each item should be processed	required
num_workers	int	Number of workers to use	10
num_mask_per_sample	int	Number of masked versions of a single sample to be returned by each worker	required
train_batch_size	int	Batch size for training	required
val_batch_size	int	Batch size for validation	required
include_unrecognized_vocab_in_dataset	bool	If set to True, a hard-check is performed to verify all gene identifers are in the user supplied tokenizer vocab. Defaults to False which means any gene identifier not in the user supplied tokenizer vocab will be excluded.	False

Attributes:

Name	Type	Description
cfg	Config	Configuration object
data_path	Union[str, PosixPath]	Path to preprocessed single-cell data files
median_dict	dict	Dictionary containing median values
tokenizer	Tokenizer	Tokenizer object
setup_called	bool	Flag indicating if the setup method has been called
dataset	SingleCellDataset	Single-cell dataset object

Source code in bionemo/geneformer/data/singlecell/datamodule.py

class SingleCellDataModule(MegatronDataModule):
    """LightningDataModule wrapper of `SingleCellDataset`

    Args:
        data_path (Union[str, PosixPath]): Path to preprocessed single-cell data files
        tokenizer (Tokenizer): Maps gene names to ids and vice-versa
        collator: Used to batch samples
        process_item: Function defining how each item should be processed
        num_workers (int): Number of workers to use
        num_mask_per_sample (int): Number of masked versions of a single sample to be returned by each worker
        train_batch_size (int): Batch size for training
        val_batch_size (int): Batch size for validation
        include_unrecognized_vocab_in_dataset (bool, optional): If set to True, a hard-check is performed to verify all gene identifers are in the user supplied tokenizer vocab. Defaults to False which means any gene identifier not in the user supplied tokenizer vocab will be excluded.

    Attributes:
        cfg (Config): Configuration object
        data_path (Union[str, PosixPath]): Path to preprocessed single-cell data files
        median_dict (dict): Dictionary containing median values
        tokenizer (Tokenizer): Tokenizer object
        setup_called (bool): Flag indicating if the setup method has been called
        dataset (SingleCellDataset): Single-cell dataset object

    """  # noqa: D415

    # Nothing says we cant pass in the dataset...
    def __init__(  # noqa: D107
        self,
        tokenizer: Tokenizer,
        median_dict: dict[str, float],
        train_dataset_path: str | Path | None = None,
        val_dataset_path: str | Path | None = None,
        test_dataset_path: str | Path | None = None,
        predict_dataset_path: str | Path | None = None,
        mask_prob: float = 0.15,
        mask_token_prob: float = 0.8,  # 80% mask token
        random_token_prob: float = 0.1,  # 10% random token, remaining 1-(mask+random) will be identity.
        seq_length: int = 2048,
        micro_batch_size: int = 4,
        global_batch_size: int = 8,
        rampup_batch_size: Optional[List[int]] = None,
        seed: int = 42,
        num_workers: int = 10,  # TODO can this be automatically set?
        persistent_workers: bool = True,
        pin_memory: bool = True,
        include_unrecognized_vocab_in_dataset: bool = False,
    ) -> None:
        super().__init__()
        if predict_dataset_path is None:
            assert (
                train_dataset_path is not None and val_dataset_path is not None and test_dataset_path is not None
            ), "Provide either predict_dataset_path or (train_dataset_path, val_dataset_path, and test_dataset_path)"
        elif train_dataset_path is None:
            assert (
                val_dataset_path is None and test_dataset_path is None
            ), "Provide either predict_dataset_path or (train_dataset_path, val_dataset_path, and test_dataset_path)"
            assert (
                predict_dataset_path is not None
            ), "Provide either predict_dataset_path or (train_dataset_path, val_dataset_path, and test_dataset_path)"
        self.data_path_predict = predict_dataset_path
        self.data_path_train = train_dataset_path
        self.data_path_val = val_dataset_path
        self.data_path_test = test_dataset_path
        self.tokenizer = tokenizer
        self.median_dict = median_dict
        self.max_len = seq_length
        self.mask_prob = mask_prob
        self.mask_token_prob = mask_token_prob
        self.random_token_prob = random_token_prob
        self.seed = seed
        self.num_workers = num_workers
        self.persistent_workers = persistent_workers
        self.pin_memory = pin_memory

        rng = np.random.default_rng(seed)
        if self.data_path_train is not None:
            assert self.data_path_val is not None and self.data_path_test is not None
            self._train_dataset_ori = SingleCellDataset(
                self.data_path_train,
                self.tokenizer,
                self.median_dict,
                self.max_len,
                mask_prob=self.mask_prob,
                mask_token_prob=self.mask_token_prob,
                random_token_prob=self.random_token_prob,
                seed=random_utils.get_seed_from_rng(rng),
                include_unrecognized_vocab_in_dataset=include_unrecognized_vocab_in_dataset,
            )
            self._val_dataset_ori = SingleCellDataset(
                self.data_path_val,
                self.tokenizer,
                self.median_dict,
                self.max_len,
                mask_prob=self.mask_prob,
                mask_token_prob=self.mask_token_prob,
                random_token_prob=self.random_token_prob,
                seed=random_utils.get_seed_from_rng(rng),
                include_unrecognized_vocab_in_dataset=include_unrecognized_vocab_in_dataset,
            )
            self._test_dataset_ori = SingleCellDataset(
                self.data_path_test,
                self.tokenizer,
                self.median_dict,
                self.max_len,
                mask_prob=self.mask_prob,
                mask_token_prob=self.mask_token_prob,
                random_token_prob=self.random_token_prob,
                seed=random_utils.get_seed_from_rng(rng),
                include_unrecognized_vocab_in_dataset=include_unrecognized_vocab_in_dataset,
            )
            self._predict_dataset_ori = None
        else:
            assert self.data_path_predict is not None
            self._predict_dataset_ori = SingleCellDataset(
                self.data_path_predict,
                self.tokenizer,
                self.median_dict,
                self.max_len,
                mask_prob=self.mask_prob,
                mask_token_prob=self.mask_token_prob,
                random_token_prob=self.random_token_prob,
                seed=random_utils.get_seed_from_rng(rng),
                include_unrecognized_vocab_in_dataset=include_unrecognized_vocab_in_dataset,
            )
            self._train_dataset_ori = None
            self._val_dataset_ori = None
            self._test_dataset_ori = None

        # This is needed here, or you need to specify it in the megatron adapter thing TODO name?
        #  Note that this sampler is sequential, meaning it does not do any shuffling. Let's wrap our data in a shuffler.
        if self.data_path_predict is not None:
            n_predict = len(self._predict_dataset_ori)
            self.data_sampler = MegatronDataSampler(
                seq_len=self.max_len,
                micro_batch_size=min(micro_batch_size, n_predict),
                global_batch_size=min(global_batch_size, n_predict),
                rampup_batch_size=rampup_batch_size,
                output_log=False,  # this is needed for predict step to work
            )
        else:
            self.data_sampler = MegatronDataSampler(
                seq_len=self.max_len,
                micro_batch_size=micro_batch_size,
                global_batch_size=global_batch_size,
                rampup_batch_size=rampup_batch_size,
            )

    def setup(self, stage: str = "") -> None:  # noqa: D102
        assert getattr(self, "trainer", None) is not None, "Please only call setup after trainer is attached."

        if self._train_dataset_ori is not None:
            assert self._val_dataset_ori is not None and self._test_dataset_ori is not None
            # Trainer API
            max_train_steps = self.trainer.max_steps
            if self.trainer.max_epochs > 1:
                logging.warning(
                    "Trainer is set to run for multiple epochs. This is not recommended due to the same shuffle being used in each. Instead set max_epochs to 1 and increase the number of max_steps."
                )
            assert max_train_steps > 0, "Please specify trainer.max_steps"

            num_train_samples = int(max_train_steps * self.data_sampler.global_batch_size)

            # This happens exactly once during setup.
            self._train_ds = MultiEpochDatasetResampler(
                self._train_dataset_ori,
                num_samples=num_train_samples,
                shuffle=True,
                seed=self.seed,
            )
            if self.trainer.limit_val_batches == 0:  # disable validation
                logging.info("Skip creating validation dataset because trainer.limit_val_batches=0.")
            else:
                num_val_samples = infer_num_samples(
                    limit_batches=self.trainer.limit_val_batches,
                    num_samples_in_dataset=len(self._val_dataset_ori),
                    global_batch_size=self.data_sampler.global_batch_size,
                    stage="val",
                )
                self._validation_ds = MultiEpochDatasetResampler(
                    self._val_dataset_ori,
                    num_samples=num_val_samples,
                    shuffle=False,
                    seed=self.seed,
                )
            if self.trainer.limit_test_batches == 0:  # disable testing
                logging.info("Skip creating test dataset because trainer.limit_test_batches=0.")

            else:
                num_test_samples = infer_num_samples(
                    limit_batches=self.trainer.limit_test_batches,
                    num_samples_in_dataset=len(self._test_dataset_ori),
                    global_batch_size=self.data_sampler.global_batch_size,
                    stage="test",
                )
                self._test_ds = MultiEpochDatasetResampler(
                    self._test_dataset_ori,
                    num_samples=num_test_samples,
                    shuffle=False,
                    seed=self.seed,
                )
        else:
            assert self._predict_dataset_ori is not None
            self._predict_ds = MultiEpochDatasetResampler(
                self._predict_dataset_ori,
                shuffle=False,
                seed=self.seed,
            )

    def train_dataloader(self) -> TRAIN_DATALOADERS:  # noqa: D102
        return self._create_dataloader(self._train_ds, mode="train")

    def val_dataloader(self) -> EVAL_DATALOADERS:  # noqa: D102
        return self._create_dataloader(self._validation_ds, mode="validation")

    def test_dataloader(self) -> EVAL_DATALOADERS:  # noqa: D102
        return self._create_dataloader(self._test_ds, mode="test")

    def predict_dataloader(self) -> EVAL_DATALOADERS:  # noqa: D102
        return self._create_dataloader(self._predict_ds, mode="predict", drop_last=False)

    def _create_dataloader(self, dataset, mode: Mode, **kwargs) -> WrappedDataLoader:
        """Create dataloader for train, validation, and test stages.

        Args:
            dataset: The dataset to create the dataloader for.
            mode: Stage of training, which is used to determined if consumed_samples in MegatronPretrainingSampler should be initialized to 0 (validation/test), or be set to the previous value from state_dict in case of checkpoint resumption (train).
            **kwargs: Additional arguments to pass to the dataloader.
        """
        self.update_init_global_step()
        return WrappedDataLoader(
            mode=mode,
            dataset=dataset,
            num_workers=self.num_workers,
            pin_memory=self.pin_memory,
            persistent_workers=self.persistent_workers,
            collate_fn=functools.partial(
                collate.bert_padding_collate_fn,
                padding_value=self.tokenizer.token_to_id(GeneTokenizer.pad_token),
                min_length=self.max_len,
                max_length=self.max_len,
            ),
            **kwargs,
        )

_create_dataloader(dataset, mode, **kwargs)

Create dataloader for train, validation, and test stages.

Parameters:

Name	Type	Description	Default
dataset		The dataset to create the dataloader for.	required
mode	Mode	Stage of training, which is used to determined if consumed_samples in MegatronPretrainingSampler should be initialized to 0 (validation/test), or be set to the previous value from state_dict in case of checkpoint resumption (train).	required
**kwargs		Additional arguments to pass to the dataloader.	{}

Source code in bionemo/geneformer/data/singlecell/datamodule.py

def _create_dataloader(self, dataset, mode: Mode, **kwargs) -> WrappedDataLoader:
    """Create dataloader for train, validation, and test stages.

    Args:
        dataset: The dataset to create the dataloader for.
        mode: Stage of training, which is used to determined if consumed_samples in MegatronPretrainingSampler should be initialized to 0 (validation/test), or be set to the previous value from state_dict in case of checkpoint resumption (train).
        **kwargs: Additional arguments to pass to the dataloader.
    """
    self.update_init_global_step()
    return WrappedDataLoader(
        mode=mode,
        dataset=dataset,
        num_workers=self.num_workers,
        pin_memory=self.pin_memory,
        persistent_workers=self.persistent_workers,
        collate_fn=functools.partial(
            collate.bert_padding_collate_fn,
            padding_value=self.tokenizer.token_to_id(GeneTokenizer.pad_token),
            min_length=self.max_len,
            max_length=self.max_len,
        ),
        **kwargs,
    )