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RNA-MSM

Pre-trained model on non-coding RNA (ncRNA) with multi (homologous) sequence alignment using a masked language modeling (MLM) objective.

Disclaimer

This is an UNOFFICIAL implementation of the Multiple sequence alignment-based RNA language model and its application to structural inference by Yikun Zhang, Mei Lang, Jiuhong Jiang, Zhiqiang Gao, et al.

The OFFICIAL repository of RNA-MSM is at yikunpku/RNA-MSM.

Warning

The MultiMolecule team is aware of a potential risk in reproducing the results of RNA-MSM.

The original implementation of RNA-MSM used a custom tokenizer that does not append <eos> token to the end of the input sequence in consistent to MSA Transformer. This should not affect the performance of the model in most cases, but it can lead to unexpected behavior in some cases.

Please set eos_token = None in the tokenizer and set eos_token_id=None in the model configuration if you want the exact behavior of the original implementation.

See more at issue #10

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 RNA-MSM did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

RNA-MSM is a bert-style model. RNA-MSM follows the MSA architecture, where it uses a column-wise attention and a row-wise attention to reduce the computational complexity over conventional self-attention.

RNA-MSM is pre-trained on a large corpus of non-coding RNA sequences with multiple sequence alignment (MSA) 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
10 768 12 3072 95.92 21.66 10.57 1024

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

Masked Language Modeling

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/rnamsm")
>>> unmasker("gguc<mask>cucugguuagaccagaucugagccu")
[{'score': 0.25111356377601624,
  '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': 0.1200353354215622,
  'token': 14,
  'token_str': 'W',
  'sequence': 'G G U C W 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': 0.10132723301649094,
  'token': 15,
  'token_str': 'K',
  'sequence': 'G G U C K 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': 0.08383019268512726,
  'token': 18,
  'token_str': 'D',
  'sequence': 'G G U C D 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': 0.05737845227122307,
  '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'}]

RNA Secondary Structure Prediction

You can use this model directly with a pipeline for secondary structure prediction:

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>>> import multimolecule  # you must import multimolecule to register models
>>> from transformers import pipeline

>>> predictor = pipeline("rna-secondary-structure", model="multimolecule/rnamsm")
>>> predictor("ggucuc")
{'sequence': 'G G U C U C',
 'secondary_structure': '......',
 'contact_map': [[0.00261497194878757, 0.0022659720852971077, 0.0036333396565169096, 0.003973186947405338, 0.0034661777317523956, 0.0029443716630339622],
  [0.0022659730166196823, 0.002837304025888443, 0.003722205525264144, 0.0034382310695946217, 0.003498978214338422, 0.0030326189007610083],
  [0.0036333396565169096, 0.003722205525264144, 0.0026848132256418467, 0.002787571167573333, 0.0028246103320270777, 0.0030541368760168552],
  [0.003973186947405338, 0.0034382310695946217, 0.002787571167573333, 0.0028833637479692698, 0.0027405587024986744, 0.0029016658663749695],
  [0.0034661777317523956, 0.003498978214338422, 0.0028246103320270777, 0.0027405587024986744, 0.002930478658527136, 0.003173925681039691],
  [0.0029443716630339622, 0.0030326189007610083, 0.0030541368760168552, 0.0029016658663749695, 0.003173925681039691, 0.003476995974779129]]}

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, RnaMsmModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnamsm")
model = RnaMsmModel.from_pretrained("multimolecule/rnamsm")

text = "UAGCUUAUCAGACUGAUGUUG"
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, RnaMsmForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnamsm")
model = RnaMsmForSequencePrediction.from_pretrained("multimolecule/rnamsm")

text = "UAGCUUAUCAGACUGAUGUUG"
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 token 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, RnaMsmForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnamsm")
model = RnaMsmForNucleotidPrediction.from_pretrained("multimolecule/rnamsm")

text = "UAGCUUAUCAGACUGAUGUUG"
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, RnaMsmForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnamsm")
model = RnaMsmForContactPrediction.from_pretrained("multimolecule/rnamsm")

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

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

Training Details

RNA-MSM 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 RNA-MSM model was pre-trained on Rfam. The Rfam database is a collection of RNA sequence families of structural RNAs including non-coding RNA genes as well as cis-regulatory elements. RNA-MSM used Rfam 14.7 which contains 4,069 RNA families.

To avoid potential overfitting in structural inference, RNA-MSM excluded families with experimentally determined structures, such as ribosomal RNAs, transfer RNAs, and small nuclear RNAs. The final dataset contains 3,932 RNA families. The median value for the number of MSA sequences for these families by RNAcmap3 is 2,184.

To increase the number of homologous sequences, RNA-MSM used an automatic pipeline, RNAcmap3, for homolog search and sequence alignment. RNAcmap3 is a pipeline that combines the BLAST-N, INFERNAL, Easel, RNAfold and evolutionary coupling tools to generate homologous sequences.

RNA-MSM preprocessed all tokens by replacing “T”s with “U”s and substituting “R”, “Y”, “K”, “M”, “S”, “W”, “B”, “D”, “H”, “V”, “N” with “X”.

Note that RnaTokenizer will convert “T”s to “U”s for you, you may disable this behaviour by passing replace_T_with_U=False. RnaTokenizer does not perform other substitutions.

Training Procedure

Preprocessing

RNA-MSM 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.

Pre-training

The model was trained on 8 NVIDIA V100 GPUs with 32GiB memories.

  • Learning rate: 3e-4
  • Weight decay: 3e-4
  • Optimizer: Adam
  • Learning rate warm-up: 16,000 steps
  • Epochs: 300
  • Batch Size: 1
  • Dropout: 0.1

Citation

BibTeX:

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@article{zhang2023multiple,
    author = {Zhang, Yikun and Lang, Mei and Jiang, Jiuhong and Gao, Zhiqiang and Xu, Fan and Litfin, Thomas and Chen, Ke and Singh, Jaswinder and Huang, Xiansong and Song, Guoli and Tian, Yonghong and Zhan, Jian and Chen, Jie and Zhou, Yaoqi},
    title = "{Multiple sequence alignment-based RNA language model and its application to structural inference}",
    journal = {Nucleic Acids Research},
    volume = {52},
    number = {1},
    pages = {e3-e3},
    year = {2023},
    month = {11},
    abstract = "{Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.}",
    doi = {10.1093/nar/gkad1031},
    url = {https://doi.org/10.1093/nar/gkad1031},
    eprint = {https://academic.oup.com/nar/article-pdf/52/1/e3/55443207/gkad1031.pdf},
}

Contact

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

Please contact the authors of the RNA-MSM 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.rnamsm

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:

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>>> 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.codon = 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.codon:
            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)

RnaMsmConfig

Bases: PreTrainedConfig

This is the configuration class to store the configuration of a RnaMsmModel. It is used to instantiate a RnaMsm 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 RnaMsm yikunpku/RNA-MSM 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 RnaMsm model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [RnaMsmModel].

 26

hidden_size

int

Dimensionality of the encoder layers and the pooler layer.

 768

num_hidden_layers

int

Number of hidden layers in the Transformer encoder.

 10

num_attention_heads

int

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

 12

intermediate_size

int

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

 3072

hidden_act

str

The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "silu" and "gelu_new" are supported.

 'gelu'

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". 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.).

 'absolute'

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

head

HeadConfig | None

The configuration of the head.

 None

lm_head

MaskedLMHeadConfig | None

The configuration of the masked language model head.

 None

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmModel
>>> # Initializing a RNA-MSM multimolecule/rnamsm style configuration
>>> configuration = RnaMsmConfig()
>>> # Initializing a model (with random weights) from the multimolecule/rnamsm style configuration
>>> model = RnaMsmModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
Source code in multimolecule/models/rnamsm/configuration_rnamsm.py
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class RnaMsmConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`RnaMsmModel`][multimolecule.models.RnaMsmModel].
    It is used to instantiate a RnaMsm 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 RnaMsm
    [yikunpku/RNA-MSM](https://github.com/yikunpku/RNA-MSM) 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 RnaMsm model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`RnaMsmModel`].
        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_act:
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        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"`. 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`.
        head:
            The configuration of the head.
        lm_head:
            The configuration of the masked language model head.

    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmModel
        >>> # Initializing a RNA-MSM multimolecule/rnamsm style configuration
        >>> configuration = RnaMsmConfig()
        >>> # Initializing a model (with random weights) from the multimolecule/rnamsm style configuration
        >>> model = RnaMsmModel(configuration)
        >>> # Accessing the model configuration
        >>> configuration = model.config
    """

    model_type = "rnamsm"

    def __init__(
        self,
        vocab_size: int = 26,
        hidden_size: int = 768,
        num_hidden_layers: int = 10,
        num_attention_heads: int = 12,
        intermediate_size: int = 3072,
        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 = "absolute",
        is_decoder: bool = False,
        use_cache: bool = True,
        max_tokens_per_msa: int = 2**14,
        layer_type: str = "standard",
        attention_type: str = "standard",
        embed_positions_msa: bool = True,
        attention_bias: 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.max_tokens_per_msa = max_tokens_per_msa
        self.layer_type = layer_type
        self.attention_type = attention_type
        self.embed_positions_msa = embed_positions_msa
        self.attention_bias = attention_bias
        self.head = HeadConfig(**head) if head is not None else None
        self.lm_head = MaskedLMHeadConfig(**lm_head) if lm_head is not None else None

RnaMsmForContactPrediction

Bases: RnaMsmPreTrainedModel

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmForContactPrediction, RnaTokenizer
>>> config = RnaMsmConfig()
>>> model = RnaMsmForContactPrediction(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, 1])
>>> output["loss"]
tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
Source code in multimolecule/models/rnamsm/modeling_rnamsm.py
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class RnaMsmForContactPrediction(RnaMsmPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmForContactPrediction, RnaTokenizer
        >>> config = RnaMsmConfig()
        >>> model = RnaMsmForContactPrediction(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, 1])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
    """

    def __init__(self, config: RnaMsmConfig):
        super().__init__(config)
        self.rnamsm = RnaMsmModel(config)
        self.contact_head = ContactPredictionHead(config)
        self.head_config = self.contact_head.config
        self.require_attentions = self.contact_head.require_attentions

        # 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, ...] | RnaMsmContactPredictorOutput:
        if self.require_attentions:
            if output_attentions is False:
                warn("output_attentions must be True since prediction head requires attentions.")
            output_attentions = True
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rnamsm(
            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.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 RnaMsmContactPredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            col_attentions=outputs.col_attentions,
            row_attentions=outputs.row_attentions,
        )

RnaMsmForMaskedLM

Bases: RnaMsmPreTrainedModel

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmForMaskedLM, RnaTokenizer
>>> config = RnaMsmConfig()
>>> model = RnaMsmForMaskedLM(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/rnamsm/modeling_rnamsm.py
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class RnaMsmForMaskedLM(RnaMsmPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmForMaskedLM, RnaTokenizer
        >>> config = RnaMsmConfig()
        >>> model = RnaMsmForMaskedLM(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: RnaMsmConfig):
        super().__init__(config)
        self.rnamsm = RnaMsmModel(config, add_pooling_layer=False)
        self.lm_head = MaskedLMHead(config, weight=self.rnamsm.embeddings.word_embeddings.weight)

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

    def get_output_embeddings(self):
        return self.lm_head.decoder

    def set_output_embeddings(self, new_embeddings):
        self.lm_head.decoder = new_embeddings

    def forward(
        self,
        input_ids: Tensor | NestedTensor,
        attention_mask: Tensor | None = None,
        position_ids: 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, ...] | RnaMsmForMaskedLMOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rnamsm(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            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 RnaMsmForMaskedLMOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            col_attentions=outputs.col_attentions,
            row_attentions=outputs.row_attentions,
        )

RnaMsmForPreTraining

Bases: RnaMsmPreTrainedModel

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmForPreTraining, RnaTokenizer
>>> config = RnaMsmConfig()
>>> model = RnaMsmForPreTraining(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input, labels_lm=input["input_ids"])
>>> output["loss"]
tensor(..., grad_fn=<MeanBackward0>)
>>> output["logits_lm"].shape
torch.Size([1, 7, 26])
>>> output["logits_ss"].shape
torch.Size([1, 5, 5, 1])
Source code in multimolecule/models/rnamsm/modeling_rnamsm.py
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class RnaMsmForPreTraining(RnaMsmPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmForPreTraining, RnaTokenizer
        >>> config = RnaMsmConfig()
        >>> model = RnaMsmForPreTraining(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input, labels_lm=input["input_ids"])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<MeanBackward0>)
        >>> output["logits_lm"].shape
        torch.Size([1, 7, 26])
        >>> output["logits_ss"].shape
        torch.Size([1, 5, 5, 1])
    """

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

    def __init__(self, config: RnaMsmConfig):
        super().__init__(config)
        self.rnamsm = RnaMsmModel(config, add_pooling_layer=False)
        self.lm_head = MaskedLMHead(config, weight=self.rnamsm.embeddings.word_embeddings.weight)
        self.ss_head = ContactAttentionLinearHead(config, head_config=HeadConfig(output_name="row_attentions"))
        self.require_attentions = self.ss_head.require_attentions

        # 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,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels_lm: Tensor | None = None,
        labels_contact: Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> Tuple[Tensor, ...] | RnaMsmForPreTrainingOutput:
        if self.require_attentions:
            if output_attentions is False:
                warn("output_attentions must be True since prediction head requires attentions.")
            output_attentions = True
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rnamsm(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )

        output_lm = self.lm_head(outputs, labels=labels_lm)
        logits_lm, loss_lm = output_lm.logits, output_lm.loss

        output_ss = self.ss_head(outputs, attention_mask, input_ids, labels=labels_contact)
        logits_ss, loss_ss = output_ss.logits, output_ss.loss

        losses = tuple(l for l in (loss_lm, loss_ss) if l is not None)  # noqa: E741
        loss = torch.mean(torch.stack(losses)) if losses else None

        if not return_dict:
            output = outputs[2:]
            output = ((logits_ss, loss_ss) + output) if loss_ss is not None else ((logits_ss,) + output)
            output = ((logits_lm, loss_lm) + output) if loss_lm is not None else ((logits_lm,) + output)
            return ((loss,) + output) if loss is not None else output

        return RnaMsmForPreTrainingOutput(
            loss=loss,
            logits_lm=logits_lm,
            loss_lm=loss_lm,
            logits_ss=logits_ss,
            loss_ss=loss_ss,
            hidden_states=outputs.hidden_states,
            col_attentions=outputs.col_attentions,
            row_attentions=outputs.row_attentions,
        )

RnaMsmForSecondaryStructurePrediction

Bases: RnaMsmPreTrainedModel

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmForSecondaryStructurePrediction, RnaTokenizer
>>> config = RnaMsmConfig()
>>> model = RnaMsmForSecondaryStructurePrediction(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input)
>>> output["logits"].shape
torch.Size([1, 5, 5, 1])
Source code in multimolecule/models/rnamsm/modeling_rnamsm.py
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class RnaMsmForSecondaryStructurePrediction(RnaMsmPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmForSecondaryStructurePrediction, RnaTokenizer
        >>> config = RnaMsmConfig()
        >>> model = RnaMsmForSecondaryStructurePrediction(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input)
        >>> output["logits"].shape
        torch.Size([1, 5, 5, 1])
    """

    def __init__(self, config: RnaMsmConfig):
        super().__init__(config)
        self.rnamsm = RnaMsmModel(config, add_pooling_layer=False)
        self.ss_head = ContactAttentionLinearHead(config, head_config=HeadConfig(output_name="row_attentions"))
        self.require_attentions = self.ss_head.require_attentions

        # 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, ...] | RnaMsmContactPredictorOutput:
        if self.require_attentions:
            if output_attentions is False:
                warn("output_attentions must be True since prediction head requires attentions.")
            output_attentions = True
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rnamsm(
            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.ss_head(outputs, attention_mask, input_ids, labels=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 RnaMsmContactPredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            col_attentions=outputs.col_attentions,
            row_attentions=outputs.row_attentions,
        )

RnaMsmForSequencePrediction

Bases: RnaMsmPreTrainedModel

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmForSequencePrediction, RnaTokenizer
>>> config = RnaMsmConfig()
>>> model = RnaMsmForSequencePrediction(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, 1])
>>> output["loss"]
tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
Source code in multimolecule/models/rnamsm/modeling_rnamsm.py
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class RnaMsmForSequencePrediction(RnaMsmPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmForSequencePrediction, RnaTokenizer
        >>> config = RnaMsmConfig()
        >>> model = RnaMsmForSequencePrediction(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, 1])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
    """

    def __init__(self, config: RnaMsmConfig):
        super().__init__(config)
        self.rnamsm = RnaMsmModel(config)
        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,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        **kwargs,
    ) -> Tuple[Tensor, ...] | RnaMsmSequencePredictorOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rnamsm(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            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 RnaMsmSequencePredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            col_attentions=outputs.col_attentions,
            row_attentions=outputs.row_attentions,
        )

RnaMsmForTokenPrediction

Bases: RnaMsmPreTrainedModel

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmForTokenPrediction, RnaTokenizer
>>> config = RnaMsmConfig()
>>> model = RnaMsmForTokenPrediction(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, 1])
>>> output["loss"]
tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
Source code in multimolecule/models/rnamsm/modeling_rnamsm.py
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class RnaMsmForTokenPrediction(RnaMsmPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmForTokenPrediction, RnaTokenizer
        >>> config = RnaMsmConfig()
        >>> model = RnaMsmForTokenPrediction(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, 1])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
    """

    def __init__(self, config: RnaMsmConfig):
        super().__init__(config)
        self.rnamsm = RnaMsmModel(config)
        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,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        **kwargs,
    ) -> Tuple[Tensor, ...] | RnaMsmTokenPredictorOutput:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.rnamsm(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            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 RnaMsmTokenPredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            col_attentions=outputs.col_attentions,
            row_attentions=outputs.row_attentions,
        )

RnaMsmModel

Bases: RnaMsmPreTrainedModel

Examples:

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>>> from multimolecule import RnaMsmConfig, RnaMsmModel, RnaTokenizer
>>> config = RnaMsmConfig()
>>> model = RnaMsmModel(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, 768])
>>> output["pooler_output"].shape
torch.Size([1, 768])
Source code in multimolecule/models/rnamsm/modeling_rnamsm.py
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class RnaMsmModel(RnaMsmPreTrainedModel):
    """
    Examples:
        >>> from multimolecule import RnaMsmConfig, RnaMsmModel, RnaTokenizer
        >>> config = RnaMsmConfig()
        >>> model = RnaMsmModel(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, 768])
        >>> output["pooler_output"].shape
        torch.Size([1, 768])
    """

    def __init__(self, config: RnaMsmConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.pad_token_id = config.pad_token_id
        self.embeddings = RnaMsmEmbeddings(config)
        self.encoder = RnaMsmEncoder(config)
        self.pooler = RnaMsmPooler(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 forward(
        self,
        input_ids: Tensor | NestedTensor,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> Tuple[Tensor, ...] | RnaMsmModelOutputWithPooling:
        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 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)
        elif inputs_embeds is None:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        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_like(input_ids)
            )

        unsqueeze_input = input_ids.ndim == 2
        if unsqueeze_input:
            input_ids = input_ids.unsqueeze(1)
        if attention_mask.ndim == 2:
            attention_mask = attention_mask.unsqueeze(1)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        if unsqueeze_input:
            sequence_output = sequence_output.squeeze(1)
        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 RnaMsmModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            col_attentions=encoder_outputs.col_attentions,
            row_attentions=encoder_outputs.row_attentions,
        )

RnaMsmPreTrainedModel

Bases: PreTrainedModel

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

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

    config_class = RnaMsmConfig
    base_model_prefix = "rnamsm"
    supports_gradient_checkpointing = True
    _no_split_modules = ["RnaMsmLayer", "RnaMsmAxialLayer", "RnaMsmPkmLayer", "RnaMsmEmbeddings"]

    # 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) and module.elementwise_affine:
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)