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Model

helical.models.mamba2_mrna.Mamba2mRNA

Bases: HelicalRNAModel

Mamba2-mRNA Model.

The Mamba2-mRNA Model is a transformer-based model that can be used to extract mRNA embeddings from mRNA sequences. The model is based on the Mamba2 model, which is a transformer-based model trained on mRNA sequences. The model is available through this interface.

Example 
from helical.models.mamba2_mrna import Mamba2mRNA, Mamba2mRNAConfig
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

input_sequences = ["ACUG"*20, "AUGC"*20, "AUGC"*20, "ACUG"*20, "AUUG"*20]

mamba2_mrna_config = Mamba2mRNAConfig(batch_size=5, device=device)
mamba2_mrna = Mamba2mRNA(configurer=mamba2_mrna_config)

processed_input_data = mamba2_mrna.process_data(input_sequences)

embeddings = mamba2_mrna.get_embeddings(processed_input_data)
print(embeddings.shape)

Parameters:

Name Type Description Default
configurer Mamba2mRNAConfig

The configuration object for the Mamba2-mRNA model.

 default_configurer
Source code in helical/models/mamba2_mrna/model.py 
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class Mamba2mRNA(HelicalRNAModel):
    """Mamba2-mRNA Model.

    The Mamba2-mRNA Model is a transformer-based model that can be used to extract mRNA embeddings from mRNA sequences.
    The model is based on the Mamba2 model, which is a transformer-based model trained on mRNA sequences. The model is available through this interface.

    Example
    ----------
    ```python
    from helical.models.mamba2_mrna import Mamba2mRNA, Mamba2mRNAConfig
    import torch

    device = "cuda" if torch.cuda.is_available() else "cpu"

    input_sequences = ["ACUG"*20, "AUGC"*20, "AUGC"*20, "ACUG"*20, "AUUG"*20]

    mamba2_mrna_config = Mamba2mRNAConfig(batch_size=5, device=device)
    mamba2_mrna = Mamba2mRNA(configurer=mamba2_mrna_config)

    processed_input_data = mamba2_mrna.process_data(input_sequences)

    embeddings = mamba2_mrna.get_embeddings(processed_input_data)
    print(embeddings.shape)
    ```


    Parameters
    ----------
    configurer : Mamba2mRNAConfig
        The configuration object for the Mamba2-mRNA model.
    """

    default_configurer = Mamba2mRNAConfig()

    def __init__(self, configurer: Mamba2mRNAConfig = default_configurer):
        super().__init__()
        self.configurer = configurer
        self.config = configurer.config

        self.model = Mamba2Model.from_pretrained(self.config["model_name"])
        self.pretrained_config = Mamba2Config.from_pretrained(
            self.config["model_name"], trust_remote=True
        )
        self.tokenizer = CharTokenizer.from_pretrained(
            self.config["model_name"], trust_remote=True
        )
        self.model.to(self.config["device"])
        self.model.post_init()

        LOGGER.info("Mamba2-mRNA initialized successfully.")
        mode = "training" if self.model.training else "eval"
        LOGGER.info(
            f"'{self.config['model_name']}' model is in '{mode}' mode, on device '{next(self.model.parameters()).device.type}'."
        )

    def process_data(self, sequences: Union[list[str], DataFrame]) -> Dataset:
        """Process the mRNA sequences and return a Dataset object.

        Parameters
        ----------
        sequences : str or DataFrame
            The mRNA sequences. If a DataFrame is provided, it should have a column named "Sequence".

        Returns
        -------
        Dataset
            The dataset object.
        """
        LOGGER.info(f"Processing data for Mamba2-mRNA.")
        sequences = self.get_valid_rna_sequence(sequences)

        tokenized_sequences = self.tokenizer(
            sequences,
            return_tensors="pt",
            padding="max_length",
            truncation=True,
            max_length=self.config["input_size"],
            return_special_tokens_mask=True,
        )

        dataset = Dataset.from_dict(tokenized_sequences)

        LOGGER.info(f"Successfully preprocessed the data for Mamba2-mRNA.")
        return dataset

    def get_embeddings(self, dataset: Dataset) -> np.ndarray:
        """Get the embeddings for the mRNA sequences.

        Parameters
        ----------
        dataset : Dataset
            The dataset object.

        Returns
        -------
        np.ndarray
            The embeddings array.
        """
        LOGGER.info(f"Started getting embeddings:")
        dataloader = DataLoader(
            dataset,
            collate_fn=self._collate_fn,
            batch_size=self.config["batch_size"],
            shuffle=False,
        )
        embeddings = []

        progress_bar = tqdm(dataloader, desc="Getting embeddings")
        with torch.no_grad():
            for batch in progress_bar:
                input_ids = batch["input_ids"].to(self.config["device"])
                special_tokens_mask = batch["special_tokens_mask"].to(
                    self.config["device"]
                )
                attention_mask = batch["attention_mask"].to(self.config["device"])

                output = self.model(
                    input_ids,
                    special_tokens_mask=special_tokens_mask,
                    attention_mask=attention_mask,
                )

                last_hidden_states = output[0]

                if input_ids is not None:
                    batch_size, _ = input_ids.shape[:2]

                if self.pretrained_config.pad_token_id is None and batch_size > 1:
                    message = (
                        "Cannot handle batch sizes > 1 if no padding token is defined."
                    )
                    LOGGER.error(message)
                    raise ValueError(message)

                if self.pretrained_config.pad_token_id is None:
                    sequence_lengths = -1
                else:
                    if input_ids is not None:
                        sequence_lengths = (
                            torch.eq(input_ids, self.pretrained_config.pad_token_id)
                            .int()
                            .argmax(-1)
                            - 1
                        )
                        sequence_lengths = sequence_lengths % input_ids.shape[-1]
                        sequence_lengths = sequence_lengths.to(
                            last_hidden_states.device
                        )
                    else:
                        sequence_lengths = -1

                pooled_last_hidden_states = last_hidden_states[
                    torch.arange(batch_size, device=last_hidden_states.device),
                    sequence_lengths,
                ]

                # Take second last element from the output as last element is a special token
                embeddings.append(pooled_last_hidden_states.cpu().numpy())

                del batch
                del output

        LOGGER.info(f"Finished getting embeddings.")
        return np.concatenate(embeddings)

    def _collate_fn(self, batch):
        input_ids = torch.tensor([item["input_ids"] for item in batch])
        special_tokens_mask = torch.tensor(
            [item["special_tokens_mask"] for item in batch]
        )

        batch_dict = {
            "input_ids": input_ids,
            "special_tokens_mask": special_tokens_mask,
            "attention_mask": 1 - special_tokens_mask,
        }

        if "labels" in batch[0]:
            batch_dict["labels"] = torch.tensor([item["labels"] for item in batch])

        return BatchEncoding(batch_dict)

process_data(sequences)

Process the mRNA sequences and return a Dataset object.

Parameters:

Name Type Description Default
sequences str or DataFrame

The mRNA sequences. If a DataFrame is provided, it should have a column named "Sequence".

 required

Returns:

Type Description
Dataset

The dataset object.
Source code in helical/models/mamba2_mrna/model.py 
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def process_data(self, sequences: Union[list[str], DataFrame]) -> Dataset:
    """Process the mRNA sequences and return a Dataset object.

    Parameters
    ----------
    sequences : str or DataFrame
        The mRNA sequences. If a DataFrame is provided, it should have a column named "Sequence".

    Returns
    -------
    Dataset
        The dataset object.
    """
    LOGGER.info(f"Processing data for Mamba2-mRNA.")
    sequences = self.get_valid_rna_sequence(sequences)

    tokenized_sequences = self.tokenizer(
        sequences,
        return_tensors="pt",
        padding="max_length",
        truncation=True,
        max_length=self.config["input_size"],
        return_special_tokens_mask=True,
    )

    dataset = Dataset.from_dict(tokenized_sequences)

    LOGGER.info(f"Successfully preprocessed the data for Mamba2-mRNA.")
    return dataset

get_embeddings(dataset)

Get the embeddings for the mRNA sequences.

Parameters:

Name Type Description Default
dataset Dataset

The dataset object.

 required

Returns:

Type Description
ndarray

The embeddings array.
Source code in helical/models/mamba2_mrna/model.py 
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def get_embeddings(self, dataset: Dataset) -> np.ndarray:
    """Get the embeddings for the mRNA sequences.

    Parameters
    ----------
    dataset : Dataset
        The dataset object.

    Returns
    -------
    np.ndarray
        The embeddings array.
    """
    LOGGER.info(f"Started getting embeddings:")
    dataloader = DataLoader(
        dataset,
        collate_fn=self._collate_fn,
        batch_size=self.config["batch_size"],
        shuffle=False,
    )
    embeddings = []

    progress_bar = tqdm(dataloader, desc="Getting embeddings")
    with torch.no_grad():
        for batch in progress_bar:
            input_ids = batch["input_ids"].to(self.config["device"])
            special_tokens_mask = batch["special_tokens_mask"].to(
                self.config["device"]
            )
            attention_mask = batch["attention_mask"].to(self.config["device"])

            output = self.model(
                input_ids,
                special_tokens_mask=special_tokens_mask,
                attention_mask=attention_mask,
            )

            last_hidden_states = output[0]

            if input_ids is not None:
                batch_size, _ = input_ids.shape[:2]

            if self.pretrained_config.pad_token_id is None and batch_size > 1:
                message = (
                    "Cannot handle batch sizes > 1 if no padding token is defined."
                )
                LOGGER.error(message)
                raise ValueError(message)

            if self.pretrained_config.pad_token_id is None:
                sequence_lengths = -1
            else:
                if input_ids is not None:
                    sequence_lengths = (
                        torch.eq(input_ids, self.pretrained_config.pad_token_id)
                        .int()
                        .argmax(-1)
                        - 1
                    )
                    sequence_lengths = sequence_lengths % input_ids.shape[-1]
                    sequence_lengths = sequence_lengths.to(
                        last_hidden_states.device
                    )
                else:
                    sequence_lengths = -1

            pooled_last_hidden_states = last_hidden_states[
                torch.arange(batch_size, device=last_hidden_states.device),
                sequence_lengths,
            ]

            # Take second last element from the output as last element is a special token
            embeddings.append(pooled_last_hidden_states.cpu().numpy())

            del batch
            del output

    LOGGER.info(f"Finished getting embeddings.")
    return np.concatenate(embeddings)