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RiNALMo

Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective.

Disclaimer

This is an UNOFFICIAL implementation of the RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks by Rafael Josip Penić, et al.

The OFFICIAL repository of RiNALMo is at lbcb-sci/RiNALMo.

Tip

The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.

The team releasing RiNALMo did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

RiNALMo is a bert-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the Training Details section for more information on the training process.

Model Specification

Num Layers Hidden Size Num Heads Intermediate Size Num Parameters (M) FLOPs (G) MACs (G) Max Num Tokens
33 1280 20 5120 650.88 168.92 84.43 1022

Usage

The model file depends on the multimolecule library. You can install it using pip:

Bash
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pip install multimolecule

Direct Use

You can use this model directly with a pipeline for masked language modeling:

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>>> import multimolecule  # you must import multimolecule to register models
>>> from transformers import pipeline
>>> unmasker = pipeline("fill-mask", model="multimolecule/rinalmo")
>>> unmasker("gguc<mask>cucugguuagaccagaucugagccu")

[{'score': 0.3932918310165405,
  'token': 6,
  'token_str': 'A',
  'sequence': 'G G U C A C U C U G G U U A G A C C A G A U C U G A G C C U'},
 {'score': 0.2897723913192749,
  'token': 9,
  'token_str': 'U',
  'sequence': 'G G U C U C U C U G G U U A G A C C A G A U C U G A G C C U'},
 {'score': 0.15423105657100677,
  'token': 22,
  'token_str': 'X',
  'sequence': 'G G U C X C U C U G G U U A G A C C A G A U C U G A G C C U'},
 {'score': 0.12160095572471619,
  'token': 7,
  'token_str': 'C',
  'sequence': 'G G U C C C U C U G G U U A G A C C A G A U C U G A G C C U'},
 {'score': 0.0408296100795269,
  'token': 8,
  'token_str': 'G',
  'sequence': 'G G U C G C U C U G G U U A G A C C A G A U C U G A G C C U'}]

Downstream Use

Extract Features

Here is how to use this model to get the features of a given sequence in PyTorch:

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from multimolecule import RnaTokenizer, RiNALMoModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo")
model = RiNALMoModel.from_pretrained("multimolecule/rinalmo")

text = "UAGCUUAUCAGACUGAUGUUGA"
input = tokenizer(text, return_tensors="pt")

output = model(**input)

Sequence Classification / Regression

Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.

Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:

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import torch
from multimolecule import RnaTokenizer, RiNALMoForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo")
model = RiNALMoForSequencePrediction.from_pretrained("multimolecule/rinalmo")

text = "UAGCUUAUCAGACUGAUGUUGA"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])

output = model(**input, labels=label)

Token Classification / Regression

Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for nucleotide classification or regression.

Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:

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import torch
from multimolecule import RnaTokenizer, RiNALMoForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo")
model = RiNALMoForTokenPrediction.from_pretrained("multimolecule/rinalmo")

text = "UAGCUUAUCAGACUGAUGUUGA"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))

output = model(**input, labels=label)

Contact Classification / Regression

Note: This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.

Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:

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import torch
from multimolecule import RnaTokenizer, RiNALMoForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rinalmo")
model = RiNALMoForContactPrediction.from_pretrained("multimolecule/rinalmo")

text = "UAGCUUAUCAGACUGAUGUUGA"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))

output = model(**input, labels=label)

Training Details

RiNALMo used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.

Training Data

The RiNALMo model was pre-trained on a cocktail of databases including RNAcentral, Rfam, Ensembl Genome Browser, and Nucleotide. The training data contains 36 million unique ncRNA sequences.

To ensure sequence diversity in each training batch, RiNALMo clustered the sequences with MMSeqs2 into 17 million clusters and then sampled each sequence in the batch from a different cluster.

RiNALMo preprocessed all tokens by replacing “U”s with “T”s.

Note that during model conversions, “T” is replaced with “U”. RnaTokenizer will convert “T”s to “U”s for you, you may disable this behaviour by passing replace_T_with_U=False.

Training Procedure

Preprocessing

RiNALMo used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:

  • 15% of the tokens are masked.
  • In 80% of the cases, the masked tokens are replaced by <mask>.
  • In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
  • In the 10% remaining cases, the masked tokens are left as is.

PreTraining

The model was trained on 7 NVIDIA A100 GPUs with 80GiB memories.

  • Learning rate: 5e-5
  • Learning rate scheduler: cosine
  • Learning rate warm-up: 2,000 steps
  • Learning rate minimum: 1e-5
  • Epochs: 6
  • Batch Size: 1344
  • Dropout: 0.1

Citation

BibTeX:

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@article{penic2024rinalmo,
  title={RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks},
  author={Penić, Rafael Josip and Vlašić, Tin and Huber, Roland G. and Wan, Yue and Šikić, Mile},
  journal={arXiv preprint arXiv:2403.00043},
  year={2024}
}

Contact

Please use GitHub issues of MultiMolecule for any questions or comments on the model card.

Please contact the authors of the RiNALMo paper for questions or comments on the paper/model.

License

This model is licensed under the AGPL-3.0 License.

Text Only
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SPDX-License-Identifier: AGPL-3.0-or-later

multimolecule.models.rinalmo

RnaTokenizer

Bases: Tokenizer

Tokenizer for RNA sequences.

Parameters:

Name Type Description Default

alphabet

Alphabet | str | List[str] | None

alphabet to use for tokenization.

  • If is None, the standard RNA alphabet will be used.
  • If is a string, it should correspond to the name of a predefined alphabet. The options include
    • standard
    • extended
    • streamline
    • nucleobase
  • If is an alphabet or a list of characters, that specific alphabet will be used.
 None

nmers

int

Size of kmer to tokenize.

 1

codon

bool

Whether to tokenize into codons.

 False

replace_T_with_U

bool

Whether to replace T with U.

 True

do_upper_case

bool

Whether to convert input to uppercase.

 True

Examples:

Python Console Session
>>> from multimolecule import RnaTokenizer
>>> tokenizer = RnaTokenizer()
>>> tokenizer('<pad><cls><eos><unk><mask><null>ACGUNRYSWKMBDHV.X*-I')["input_ids"]
[1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 2]
>>> tokenizer('acgu')["input_ids"]
[1, 6, 7, 8, 9, 2]
>>> tokenizer('acgt')["input_ids"]
[1, 6, 7, 8, 9, 2]
>>> tokenizer = RnaTokenizer(replace_T_with_U=False)
>>> tokenizer('acgt')["input_ids"]
[1, 6, 7, 8, 3, 2]
>>> tokenizer = RnaTokenizer(nmers=3)
>>> tokenizer('uagcuuauc')["input_ids"]
[1, 83, 17, 64, 49, 96, 84, 22, 2]
>>> tokenizer = RnaTokenizer(codon=True)
>>> tokenizer('uagcuuauc')["input_ids"]
[1, 83, 49, 22, 2]
>>> tokenizer('uagcuuauca')["input_ids"]
Traceback (most recent call last):
ValueError: length of input sequence must be a multiple of 3 for codon tokenization, but got 10
Source code in multimolecule/tokenisers/rna/tokenization_rna.py
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class RnaTokenizer(Tokenizer):
    """
    Tokenizer for RNA sequences.

    Args:
        alphabet: alphabet to use for tokenization.

            - If is `None`, the standard RNA alphabet will be used.
            - If is a `string`, it should correspond to the name of a predefined alphabet. The options include
                + `standard`
                + `extended`
                + `streamline`
                + `nucleobase`
            - If is an alphabet or a list of characters, that specific alphabet will be used.
        nmers: Size of kmer to tokenize.
        codon: Whether to tokenize into codons.
        replace_T_with_U: Whether to replace T with U.
        do_upper_case: Whether to convert input to uppercase.

    Examples:
        >>> from multimolecule import RnaTokenizer
        >>> tokenizer = RnaTokenizer()
        >>> tokenizer('<pad><cls><eos><unk><mask><null>ACGUNRYSWKMBDHV.X*-I')["input_ids"]
        [1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 2]
        >>> tokenizer('acgu')["input_ids"]
        [1, 6, 7, 8, 9, 2]
        >>> tokenizer('acgt')["input_ids"]
        [1, 6, 7, 8, 9, 2]
        >>> tokenizer = RnaTokenizer(replace_T_with_U=False)
        >>> tokenizer('acgt')["input_ids"]
        [1, 6, 7, 8, 3, 2]
        >>> tokenizer = RnaTokenizer(nmers=3)
        >>> tokenizer('uagcuuauc')["input_ids"]
        [1, 83, 17, 64, 49, 96, 84, 22, 2]
        >>> tokenizer = RnaTokenizer(codon=True)
        >>> tokenizer('uagcuuauc')["input_ids"]
        [1, 83, 49, 22, 2]
        >>> tokenizer('uagcuuauca')["input_ids"]
        Traceback (most recent call last):
        ValueError: length of input sequence must be a multiple of 3 for codon tokenization, but got 10
    """

    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        alphabet: Alphabet | str | List[str] | None = None,
        nmers: int = 1,
        codon: bool = False,
        replace_T_with_U: bool = True,
        do_upper_case: bool = True,
        additional_special_tokens: List | Tuple | None = None,
        **kwargs,
    ):
        if codon and (nmers > 1 and nmers != 3):
            raise ValueError("Codon and nmers cannot be used together.")
        if codon:
            nmers = 3  # set to 3 to get correct vocab
        if not isinstance(alphabet, Alphabet):
            alphabet = get_alphabet(alphabet, nmers=nmers)
        super().__init__(
            alphabet=alphabet,
            nmers=nmers,
            codon=codon,
            replace_T_with_U=replace_T_with_U,
            do_upper_case=do_upper_case,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )
        self.replace_T_with_U = replace_T_with_U
        self.nmers = nmers
        self.condon = codon

    def _tokenize(self, text: str, **kwargs):
        if self.do_upper_case:
            text = text.upper()
        if self.replace_T_with_U:
            text = text.replace("T", "U")
        if self.condon:
            if len(text) % 3 != 0:
                raise ValueError(
                    f"length of input sequence must be a multiple of 3 for codon tokenization, but got {len(text)}"
                )
            return [text[i : i + 3] for i in range(0, len(text), 3)]
        if self.nmers > 1:
            return [text[i : i + self.nmers] for i in range(len(text) - self.nmers + 1)]  # noqa: E203
        return list(text)

RiNALMoConfig

Bases: PreTrainedConfig

This is the configuration class to store the configuration of a RiNALMoModel. It is used to instantiate a RiNALMo model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RiNALMo lbcb-sci/RiNALMo architecture.

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Parameters:

Name Type Description Default

vocab_size

int

Vocabulary size of the RiNALMo model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [RiNALMoModel].

 26

hidden_size

int

Dimensionality of the encoder layers and the pooler layer.

 1280

num_hidden_layers

int

Number of hidden layers in the Transformer encoder.

 33

num_attention_heads

int

Number of attention heads for each attention layer in the Transformer encoder.

 20

intermediate_size

int

Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.

 5120

hidden_dropout

float

The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

 0.1

attention_dropout

float

The dropout ratio for the attention probabilities.

 0.1

max_position_embeddings

int

The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

 1024

initializer_range

float

The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

 0.02

layer_norm_eps

float

The epsilon used by the layer normalization layers.

 1e-12

position_embedding_type

str

Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query", "rotary". For positional embeddings use "absolute". For more information on "relative_key", please refer to Self-Attention with Relative Position Representations (Shaw et al.). For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.).

 'rotary'

is_decoder

bool

Whether the model is used as a decoder or not. If False, the model is used as an encoder.

 False

use_cache

bool

Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.

 True

emb_layer_norm_before

bool

Whether to apply layer normalization after embeddings but before the main stem of the network.

 True

token_dropout

bool

When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.

 True

Examples:

Python Console Session
>>> from multimolecule import RiNALMoModel, RiNALMoConfig
>>> # Initializing a RiNALMo multimolecule/rinalmo style configuration
>>> configuration = RiNALMoConfig()
>>> # Initializing a model (with random weights) from the multimolecule/rinalmo style configuration
>>> model = RiNALMoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
Source code in multimolecule/models/rinalmo/configuration_rinalmo.py
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class RiNALMoConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`RiNALMoModel`][multimolecule.models.RiNALMoModel].
    It is used to instantiate a RiNALMo model according to the specified arguments, defining the model architecture.
    Instantiating a configuration with the defaults will yield a similar configuration to that of the RiNALMo
    [lbcb-sci/RiNALMo](https://github.com/lbcb-sci/RiNALMo) architecture.

    Configuration objects inherit from [`PreTrainedConfig`][multimolecule.models.PreTrainedConfig] and can be used to
    control the model outputs. Read the documentation from [`PreTrainedConfig`][multimolecule.models.PreTrainedConfig]
    for more information.

    Args:
        vocab_size:
            Vocabulary size of the RiNALMo model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`RiNALMoModel`].
        hidden_size:
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers:
            Number of hidden layers in the Transformer encoder.
        num_attention_heads:
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size:
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_dropout:
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout:
            The dropout ratio for the attention probabilities.
        max_position_embeddings:
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range:
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps:
            The epsilon used by the layer normalization layers.
        position_embedding_type:
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
            For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        is_decoder:
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        use_cache:
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        emb_layer_norm_before:
            Whether to apply layer normalization after embeddings but before the main stem of the network.
        token_dropout:
            When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.

    Examples:
        >>> from multimolecule import RiNALMoModel, RiNALMoConfig
        >>> # Initializing a RiNALMo multimolecule/rinalmo style configuration
        >>> configuration = RiNALMoConfig()
        >>> # Initializing a model (with random weights) from the multimolecule/rinalmo style configuration
        >>> model = RiNALMoModel(configuration)
        >>> # Accessing the model configuration
        >>> configuration = model.config
    """

    model_type = "rinalmo"

    def __init__(
        self,
        vocab_size: int = 26,
        hidden_size: int = 1280,
        num_hidden_layers: int = 33,
        num_attention_heads: int = 20,
        intermediate_size: int = 5120,
        hidden_act: str = "gelu",
        hidden_dropout: float = 0.1,
        attention_dropout: float = 0.1,
        max_position_embeddings: int = 1024,
        initializer_range: float = 0.02,
        layer_norm_eps: float = 1e-12,
        position_embedding_type: str = "rotary",
        is_decoder: bool = False,
        use_cache: bool = True,
        emb_layer_norm_before: bool = True,
        learnable_beta: bool = True,
        token_dropout: bool = True,
        head: HeadConfig | None = None,
        lm_head: MaskedLMHeadConfig | None = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.is_decoder = is_decoder
        self.use_cache = use_cache
        self.learnable_beta = learnable_beta
        self.token_dropout = token_dropout
        self.head = HeadConfig(**head if head is not None else {})
        self.lm_head = MaskedLMHeadConfig(**lm_head if lm_head is not None else {})
        self.emb_layer_norm_before = emb_layer_norm_before

RiNALMoForContactPrediction

Bases: RiNALMoPreTrainedModel

Examples:

Python Console Session
>>> from multimolecule import RiNALMoConfig, RiNALMoForContactPrediction, RnaTokenizer
>>> config = RiNALMoConfig()
>>> model = RiNALMoForContactPrediction(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input, labels=torch.randint(2, (1, 5, 5)))
>>> output["logits"].shape
torch.Size([1, 5, 5, 2])
>>> output["loss"]
tensor(..., grad_fn=<NllLossBackward0>)
Source code in multimolecule/models/rinalmo/modeling_rinalmo.py
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class RiNALMoForContactPrediction(RiNALMoPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RiNALMoConfig, RiNALMoForContactPrediction, RnaTokenizer
        >>> config = RiNALMoConfig()
        >>> model = RiNALMoForContactPrediction(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input, labels=torch.randint(2, (1, 5, 5)))
        >>> output["logits"].shape
        torch.Size([1, 5, 5, 2])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<NllLossBackward0>)
    """

    def __init__(self, config: RiNALMoConfig):
        super().__init__(config)
        self.rinalmo = RiNALMoModel(config, add_pooling_layer=True)
        self.contact_head = ContactPredictionHead(config)
        self.head_config = self.contact_head.config

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Tensor | NestedTensor,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        head_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> Tuple[Tensor, ...] | ContactPredictorOutput:
        if output_attentions is False:
            warn("output_attentions must be True for contact classification and will be ignored.")
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rinalmo(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=True,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )
        output = self.contact_head(outputs, attention_mask, input_ids, labels)
        logits, loss = output.logits, output.loss

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return ContactPredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

RiNALMoForMaskedLM

Bases: RiNALMoPreTrainedModel

Examples:

Python Console Session
>>> from multimolecule import RiNALMoConfig, RiNALMoForMaskedLM, RnaTokenizer
>>> config = RiNALMoConfig()
>>> model = RiNALMoForMaskedLM(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input, labels=input["input_ids"])
>>> output["logits"].shape
torch.Size([1, 7, 26])
>>> output["loss"]
tensor(..., grad_fn=<NllLossBackward0>)
Source code in multimolecule/models/rinalmo/modeling_rinalmo.py
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class RiNALMoForMaskedLM(RiNALMoPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RiNALMoConfig, RiNALMoForMaskedLM, RnaTokenizer
        >>> config = RiNALMoConfig()
        >>> model = RiNALMoForMaskedLM(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input, labels=input["input_ids"])
        >>> output["logits"].shape
        torch.Size([1, 7, 26])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<NllLossBackward0>)
    """

    _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]

    def __init__(self, config: RiNALMoConfig):
        super().__init__(config)
        if config.is_decoder:
            logger.warning(
                "If you want to use `RiNALMoForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )
        self.rinalmo = RiNALMoModel(config, add_pooling_layer=False)
        self.lm_head = MaskedLMHead(config)

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Tensor | NestedTensor,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        head_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        encoder_hidden_states: Tensor | None = None,
        encoder_attention_mask: Tensor | None = None,
        labels: Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> Tuple[Tensor, ...] | MaskedLMOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rinalmo(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )
        output = self.lm_head(outputs, labels)
        logits, loss = output.logits, output.loss

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return MaskedLMOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

RiNALMoForSequencePrediction

Bases: RiNALMoPreTrainedModel

Examples:

Python Console Session
>>> from multimolecule import RiNALMoConfig, RiNALMoForSequencePrediction, RnaTokenizer
>>> config = RiNALMoConfig()
>>> model = RiNALMoForSequencePrediction(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input, labels=torch.tensor([[1]]))
>>> output["logits"].shape
torch.Size([1, 2])
>>> output["loss"]
tensor(..., grad_fn=<NllLossBackward0>)
Source code in multimolecule/models/rinalmo/modeling_rinalmo.py
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class RiNALMoForSequencePrediction(RiNALMoPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RiNALMoConfig, RiNALMoForSequencePrediction, RnaTokenizer
        >>> config = RiNALMoConfig()
        >>> model = RiNALMoForSequencePrediction(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input, labels=torch.tensor([[1]]))
        >>> output["logits"].shape
        torch.Size([1, 2])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<NllLossBackward0>)
    """

    def __init__(self, config: RiNALMoConfig):
        super().__init__(config)
        self.rinalmo = RiNALMoModel(config, add_pooling_layer=True)
        self.sequence_head = SequencePredictionHead(config)
        self.head_config = self.sequence_head.config

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Tensor | NestedTensor,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        head_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> Tuple[Tensor, ...] | SequencePredictorOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rinalmo(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )
        output = self.sequence_head(outputs, labels)
        logits, loss = output.logits, output.loss

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequencePredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

RiNALMoForTokenPrediction

Bases: RiNALMoPreTrainedModel

Examples:

Python Console Session
>>> from multimolecule import RiNALMoConfig, RiNALMoForTokenPrediction, RnaTokenizer
>>> config = RiNALMoConfig()
>>> model = RiNALMoForTokenPrediction(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input, labels=torch.randint(2, (1, 5)))
>>> output["logits"].shape
torch.Size([1, 5, 2])
>>> output["loss"]
tensor(..., grad_fn=<NllLossBackward0>)
Source code in multimolecule/models/rinalmo/modeling_rinalmo.py
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class RiNALMoForTokenPrediction(RiNALMoPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RiNALMoConfig, RiNALMoForTokenPrediction, RnaTokenizer
        >>> config = RiNALMoConfig()
        >>> model = RiNALMoForTokenPrediction(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input, labels=torch.randint(2, (1, 5)))
        >>> output["logits"].shape
        torch.Size([1, 5, 2])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<NllLossBackward0>)
    """

    def __init__(self, config: RiNALMoConfig):
        super().__init__(config)
        self.rinalmo = RiNALMoModel(config, add_pooling_layer=True)
        self.token_head = TokenPredictionHead(config)
        self.head_config = self.token_head.config

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Tensor | NestedTensor,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        head_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> Tuple[Tensor, ...] | TokenPredictorOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rinalmo(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )
        output = self.token_head(outputs, attention_mask, input_ids, labels)
        logits, loss = output.logits, output.loss

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenPredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

RiNALMoModel

Bases: RiNALMoPreTrainedModel

Examples:

Python Console Session
>>> from multimolecule import RiNALMoConfig, RiNALMoModel, RnaTokenizer
>>> config = RiNALMoConfig()
>>> model = RiNALMoModel(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input)
>>> output["last_hidden_state"].shape
torch.Size([1, 7, 1280])
>>> output["pooler_output"].shape
torch.Size([1, 1280])
Source code in multimolecule/models/rinalmo/modeling_rinalmo.py
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class RiNALMoModel(RiNALMoPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RiNALMoConfig, RiNALMoModel, RnaTokenizer
        >>> config = RiNALMoConfig()
        >>> model = RiNALMoModel(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input)
        >>> output["last_hidden_state"].shape
        torch.Size([1, 7, 1280])
        >>> output["pooler_output"].shape
        torch.Size([1, 1280])
    """

    def __init__(self, config: RiNALMoConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.pad_token_id = config.pad_token_id
        self.embeddings = RiNALMoEmbeddings(config)
        self.encoder = RiNALMoEncoder(config)
        self.pooler = RiNALMoPooler(config) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def forward(
        self,
        input_ids: Tensor | NestedTensor,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        head_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        encoder_hidden_states: Tensor | None = None,
        encoder_attention_mask: Tensor | None = None,
        past_key_values: Tuple[Tuple[Tensor, Tensor, Tensor, Tensor], ...] | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> Tuple[Tensor, ...] | BaseModelOutputWithPoolingAndCrossAttentions:
        r"""
        Args:
            encoder_hidden_states:
                Shape: `(batch_size, sequence_length, hidden_size)`

                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
                the model is configured as a decoder.
            encoder_attention_mask:
                Shape: `(batch_size, sequence_length)`

                Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
                in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
            past_key_values:
                Tuple of length `config.n_layers` with each tuple having 4 tensors of shape
                `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)

                Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up
                decoding.

                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            use_cache:
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
        """
        if kwargs:
            warn(
                f"Additional keyword arguments `{', '.join(kwargs)}` are detected in "
                f"`{self.__class__.__name__}.forward`, they will be ignored.\n"
                "This is provided for backward compatibility and may lead to unexpected behavior."
            )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if self.config.is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        if isinstance(input_ids, NestedTensor):
            input_ids, attention_mask = input_ids.tensor, input_ids.mask
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        if input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device  # type: ignore[union-attr]

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if attention_mask is None:
            attention_mask = (
                input_ids.ne(self.pad_token_id)
                if self.pad_token_id is not None
                else torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
            )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )

forward

Python
1
forward(input_ids: Tensor | NestedTensor, attention_mask: Tensor | None = None, position_ids: Tensor | None = None, head_mask: Tensor | None = None, inputs_embeds: Tensor | NestedTensor | None = None, encoder_hidden_states: Tensor | None = None, encoder_attention_mask: Tensor | None = None, past_key_values: Tuple[Tuple[Tensor, Tensor, Tensor, Tensor], ...] | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, return_dict: bool | None = None, **kwargs) -> Tuple[Tensor, ...] | BaseModelOutputWithPoolingAndCrossAttentions

Parameters:

Name Type Description Default
encoder_hidden_states
Tensor | None

Shape: (batch_size, sequence_length, hidden_size)

Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.

 None
encoder_attention_mask
Tensor | None

Shape: (batch_size, sequence_length)

Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:

  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
 None
past_key_values
Tuple[Tuple[Tensor, Tensor, Tensor, Tensor], ...] | None

Tuple of length config.n_layers with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)

Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

 None
use_cache
bool | None

If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).

 None
Source code in multimolecule/models/rinalmo/modeling_rinalmo.py
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def forward(
    self,
    input_ids: Tensor | NestedTensor,
    attention_mask: Tensor | None = None,
    position_ids: Tensor | None = None,
    head_mask: Tensor | None = None,
    inputs_embeds: Tensor | NestedTensor | None = None,
    encoder_hidden_states: Tensor | None = None,
    encoder_attention_mask: Tensor | None = None,
    past_key_values: Tuple[Tuple[Tensor, Tensor, Tensor, Tensor], ...] | None = None,
    use_cache: bool | None = None,
    output_attentions: bool | None = None,
    output_hidden_states: bool | None = None,
    return_dict: bool | None = None,
    **kwargs,
) -> Tuple[Tensor, ...] | BaseModelOutputWithPoolingAndCrossAttentions:
    r"""
    Args:
        encoder_hidden_states:
            Shape: `(batch_size, sequence_length, hidden_size)`

            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask:
            Shape: `(batch_size, sequence_length)`

            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
            in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values:
            Tuple of length `config.n_layers` with each tuple having 4 tensors of shape
            `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)

            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up
            decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
            that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
            all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache:
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
            (see `past_key_values`).
    """
    if kwargs:
        warn(
            f"Additional keyword arguments `{', '.join(kwargs)}` are detected in "
            f"`{self.__class__.__name__}.forward`, they will be ignored.\n"
            "This is provided for backward compatibility and may lead to unexpected behavior."
        )
    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    if self.config.is_decoder:
        use_cache = use_cache if use_cache is not None else self.config.use_cache
    else:
        use_cache = False

    if isinstance(input_ids, NestedTensor):
        input_ids, attention_mask = input_ids.tensor, input_ids.mask
    if input_ids is not None and inputs_embeds is not None:
        raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
    if input_ids is not None:
        self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
        input_shape = input_ids.size()
    elif inputs_embeds is not None:
        input_shape = inputs_embeds.size()[:-1]
    else:
        raise ValueError("You have to specify either input_ids or inputs_embeds")

    batch_size, seq_length = input_shape
    device = input_ids.device if input_ids is not None else inputs_embeds.device  # type: ignore[union-attr]

    # past_key_values_length
    past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

    if attention_mask is None:
        attention_mask = (
            input_ids.ne(self.pad_token_id)
            if self.pad_token_id is not None
            else torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
        )

    # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
    # ourselves in which case we just need to make it broadcastable to all heads.
    extended_attention_mask: Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

    # If a 2D or 3D attention mask is provided for the cross-attention
    # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
    if self.config.is_decoder and encoder_hidden_states is not None:
        encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
        encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
        if encoder_attention_mask is None:
            encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
        encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
    else:
        encoder_extended_attention_mask = None

    # Prepare head mask if needed
    # 1.0 in head_mask indicate we keep the head
    # attention_probs has shape bsz x n_heads x N x N
    # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
    # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
    head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

    embedding_output = self.embeddings(
        input_ids=input_ids,
        position_ids=position_ids,
        attention_mask=attention_mask,
        inputs_embeds=inputs_embeds,
        past_key_values_length=past_key_values_length,
    )
    encoder_outputs = self.encoder(
        embedding_output,
        attention_mask=extended_attention_mask,
        head_mask=head_mask,
        encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_extended_attention_mask,
        past_key_values=past_key_values,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    sequence_output = encoder_outputs[0]
    pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

    if not return_dict:
        return (sequence_output, pooled_output) + encoder_outputs[1:]

    return BaseModelOutputWithPoolingAndCrossAttentions(
        last_hidden_state=sequence_output,
        pooler_output=pooled_output,
        past_key_values=encoder_outputs.past_key_values,
        hidden_states=encoder_outputs.hidden_states,
        attentions=encoder_outputs.attentions,
        cross_attentions=encoder_outputs.cross_attentions,
    )

RiNALMoPreTrainedModel

Bases: PreTrainedModel

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.

Source code in multimolecule/models/rinalmo/modeling_rinalmo.py
Python
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class RiNALMoPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = RiNALMoConfig
    base_model_prefix = "rinalmo"
    supports_gradient_checkpointing = True
    _no_split_modules = ["RiNALMoLayer", "RiNALMoEmbeddings"]

    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
    def _init_weights(self, module: nn.Module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)