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283 | 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,
)
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