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ERNIE-RNA

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

Disclaimer

This is an UNOFFICIAL implementation of the ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations by Weijie Yin, Zhaoyu Zhang, Liang He, et al.

The OFFICIAL repository of ERNIE-RNA is at Bruce-ywj/ERNIE-RNA.

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

Model Details

ERNIE-RNA 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.

Variants

Model Specification

Num Layers Hidden Size Num Heads Intermediate Size Num Parameters (M) FLOPs (G) MACs (G) Max Num Tokens
12 768 12 3072 85.67 22.37 11.18 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

predictor = pipeline("fill-mask", model="multimolecule/ernierna")
output = predictor("gguc<mask>cucugguuagaccagaucugagccu")

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, ErnieRnaModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
model = ErnieRnaModel.from_pretrained("multimolecule/ernierna")

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, ErnieRnaForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
model = ErnieRnaForSequencePrediction.from_pretrained("multimolecule/ernierna")

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, ErnieRnaForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
model = ErnieRnaForTokenPrediction.from_pretrained("multimolecule/ernierna")

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, ErnieRnaForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ernierna")
model = ErnieRnaForContactPrediction.from_pretrained("multimolecule/ernierna")

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

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

Training Details

ERNIE-RNA 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 ERNIE-RNA model was pre-trained on RNAcentral. RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of Expert Databases representing a broad range of organisms and RNA types.

ERNIE-RNA applied CD-HIT (CD-HIT-EST) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral, resulting 25 million unique sequences. Sequences longer than 1024 nucleotides were subsequently excluded. The final dataset contains 20.4 million non-redundant RNA sequences. ERNIE-RNA preprocessed all tokens by replacing “T”s with “S”s.

Note that 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

ERNIE-RNA 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 24 NVIDIA V100 GPUs with 32GiB memories.

  • Learning rate: 1e-4
  • Learning rate warm-up: 20,000 steps
  • Weight decay: 0.01

Citation

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@article {Yin2024.03.17.585376,
    author = {Yin, Weijie and Zhang, Zhaoyu and He, Liang and Jiang, Rui and Zhang, Shuo and Liu, Gan and Zhang, Xuegong and Qin, Tao and Xie, Zhen},
    title = {ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations},
    elocation-id = {2024.03.17.585376},
    year = {2024},
    doi = {10.1101/2024.03.17.585376},
    publisher = {Cold Spring Harbor Laboratory},
    abstract = {With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA (Enhanced Representations with base-pairing restriction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.Competing Interest StatementOne patent based on the study was submitted by Z.X. and W.Y., which is entitled as "A Pre-training Approach for RNA Sequences and Its Applications"(application number, no 202410262527.5). The remaining authors declare no competing interests.},
    URL = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376},
    eprint = {https://www.biorxiv.org/content/early/2024/03/17/2024.03.17.585376.full.pdf},
    journal = {bioRxiv}
}

Note

The artifacts distributed in this repository are part of the MultiMolecule project. If you use MultiMolecule in your research, you must cite the MultiMolecule project as follows:

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@software{chen_2024_12638419,
  author    = {Chen, Zhiyuan and Zhu, Sophia Y.},
  title     = {MultiMolecule},
  doi       = {10.5281/zenodo.12638419},
  publisher = {Zenodo},
  url       = {https://doi.org/10.5281/zenodo.12638419},
  year      = 2024,
  month     = may,
  day       = 4
}

Contact

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

Please contact the authors of the ERNIE-RNA paper for questions or comments on the paper/model.

License

This model is licensed under the GNU Affero General Public License.

For additional terms and clarifications, please refer to our License FAQ.

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

multimolecule.models.ernierna

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)

ErnieRnaConfig

Bases: PreTrainedConfig

This is the configuration class to store the configuration of a ErnieRnaModel. It is used to instantiate an ErnieRna 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 ErnieRna Bruce-ywj/ERNIE-RNA 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 ErnieRna model. Defines the number of different tokens that can be represented by the input_ids passed when calling [ErnieRnaModel].

 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.

 12

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).

 1026

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

pairwise_alpha

float

Scaling factor for pairwise bias in the attention mechanism.

 0.8

position_embedding_type

str

Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query", "sinusoidal". 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.).

 'sinusoidal'

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

output_attention_biases

bool

Whether to return attention bias maps.

 False

add_cross_attention

bool

Whether to add cross-attention layers when the model is used as a decoder.

 False

Examples:

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>>> from multimolecule import ErnieRnaConfig, ErnieRnaModel
>>> # Initializing a ERNIE-RNA multimolecule/ernierna style configuration
>>> configuration = ErnieRnaConfig()
>>> # Initializing a model (with random weights) from the multimolecule/ernierna style configuration
>>> model = ErnieRnaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
Source code in multimolecule/models/ernierna/configuration_ernierna.py
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class ErnieRnaConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a
    [`ErnieRnaModel`][multimolecule.models.ErnieRnaModel]. It is used to instantiate an ErnieRna 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 ErnieRna [Bruce-ywj/ERNIE-RNA](https://github.com/Bruce-ywj/ERNIE-RNA)
    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 ErnieRna model. Defines the number of different tokens that can be represented by
            the `input_ids` passed when calling [`ErnieRnaModel`].
        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.
        pairwise_alpha:
            Scaling factor for pairwise bias in the attention mechanism.
        position_embedding_type:
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`,
            `"sinusoidal"`.
            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.
        output_attention_biases:
            Whether to return attention bias maps.
        add_cross_attention:
            Whether to add cross-attention layers when the model is used as a decoder.

    Examples:
        >>> from multimolecule import ErnieRnaConfig, ErnieRnaModel
        >>> # Initializing a ERNIE-RNA multimolecule/ernierna style configuration
        >>> configuration = ErnieRnaConfig()
        >>> # Initializing a model (with random weights) from the multimolecule/ernierna style configuration
        >>> model = ErnieRnaModel(configuration)
        >>> # Accessing the model configuration
        >>> configuration = model.config
    """

    model_type = "ernierna"

    def __init__(
        self,
        vocab_size: int = 26,
        hidden_size: int = 768,
        num_hidden_layers: int = 12,
        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 = 1026,
        initializer_range: float = 0.02,
        layer_norm_eps: float = 1e-12,
        position_embedding_type: str = "sinusoidal",
        pairwise_alpha: float = 0.8,
        is_decoder: bool = False,
        use_cache: bool = True,
        head: HeadConfig | None = None,
        lm_head: MaskedLMHeadConfig | None = None,
        output_attention_biases: bool = False,
        add_cross_attention: bool = False,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.type_vocab_size = 2
        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.pairwise_alpha = pairwise_alpha
        self.is_decoder = is_decoder
        self.use_cache = use_cache
        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
        self.output_attention_biases = output_attention_biases
        self.add_cross_attention = add_cross_attention

ErnieRnaForContactPrediction

Bases: ErnieRnaPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule import ErnieRnaConfig, ErnieRnaForContactPrediction, RnaTokenizer
>>> config = ErnieRnaConfig()
>>> model = ErnieRnaForContactPrediction(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/ernierna/modeling_ernierna.py
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class ErnieRnaForContactPrediction(ErnieRnaPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import ErnieRnaConfig, ErnieRnaForContactPrediction, RnaTokenizer
        >>> config = ErnieRnaConfig()
        >>> model = ErnieRnaForContactPrediction(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: ErnieRnaConfig):
        super().__init__(config)
        self.model = ErnieRnaModel(config, add_pooling_layer=False)
        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()

    @can_return_tuple
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Tuple[Tensor, ...] | ErnieRnaContactPredictorOutput:
        if self.require_attentions:
            output_attentions = kwargs.get("output_attentions", self.config.output_attentions)
            if output_attentions is False:
                warn("output_attentions must be True since prediction head requires attentions.")
            kwargs["output_attentions"] = True
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )
        output = self.contact_head(outputs, attention_mask, input_ids, labels)
        logits, loss = output.logits, output.loss

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

ErnieRnaForMaskedLM

Bases: ErnieRnaPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule import ErnieRnaConfig, ErnieRnaForMaskedLM, RnaTokenizer
>>> config = ErnieRnaConfig()
>>> model = ErnieRnaForMaskedLM(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/ernierna/modeling_ernierna.py
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class ErnieRnaForMaskedLM(ErnieRnaPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import ErnieRnaConfig, ErnieRnaForMaskedLM, RnaTokenizer
        >>> config = ErnieRnaConfig()
        >>> model = ErnieRnaForMaskedLM(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": "model.embeddings.word_embeddings.weight",
        "lm_head.decoder.bias": "lm_head.bias",
    }

    def __init__(self, config: ErnieRnaConfig):
        super().__init__(config)
        if config.is_decoder:
            warn(
                "If you want to use `ErnieRnaForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )
        self.model = ErnieRnaModel(config, add_pooling_layer=False)
        self.lm_head = MaskedLMHead(config)

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

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

    def set_output_embeddings(self, embeddings):
        self.lm_head.decoder = embeddings
        if hasattr(self.lm_head, "bias"):
            self.lm_head.bias = embeddings.bias

    @can_return_tuple
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        position_ids: 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,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Tuple[Tensor, ...] | ErnieRnaForMaskedLMOutput:
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            return_dict=True,
            **kwargs,
        )
        output = self.lm_head(outputs, labels)
        logits, loss = output.logits, output.loss

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

ErnieRnaForSecondaryStructurePrediction

Bases: ErnieRnaForPreTraining

Examples:

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>>> import torch
>>> from multimolecule import ErnieRnaConfig, ErnieRnaForSecondaryStructurePrediction, RnaTokenizer
>>> config = ErnieRnaConfig()
>>> model = ErnieRnaForSecondaryStructurePrediction(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input)
>>> output["logits_ss"].shape
torch.Size([1, 5, 5, 1])
Source code in multimolecule/models/ernierna/modeling_ernierna.py
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class ErnieRnaForSecondaryStructurePrediction(ErnieRnaForPreTraining):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import ErnieRnaConfig, ErnieRnaForSecondaryStructurePrediction, RnaTokenizer
        >>> config = ErnieRnaConfig()
        >>> model = ErnieRnaForSecondaryStructurePrediction(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input)
        >>> output["logits_ss"].shape
        torch.Size([1, 5, 5, 1])
    """

    def __init__(self, config: ErnieRnaConfig):
        super().__init__(config)
        self.ss_head = ErnieRnaSecondaryStructurePredictionHead(config)
        self.require_attention_biases = True

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

    @can_return_tuple
    def forward(  # type: ignore[override]  # pylint: disable=arguments-renamed
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels_lm: Tensor | None = None,
        labels_ss: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Tuple[Tensor, ...] | ErnieRnaForSecondaryStructurePredictorOutput:
        if self.require_attention_biases:
            output_attention_biases = kwargs.get("output_attention_biases", self.config.output_attention_biases)
            if output_attention_biases is False:
                warn("output_attention_biases must be True since prediction head requires attention biases.")
            kwargs["output_attention_biases"] = True
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )

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

        output_ss = self.ss_head(outputs, attention_mask, input_ids, labels_ss)
        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.tensor(losses)) if losses else None

        return ErnieRnaForSecondaryStructurePredictorOutput(
            loss=loss,
            logits_lm=logits_lm,
            loss_lm=loss_lm,
            logits_ss=logits_ss,
            loss_ss=loss_ss,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            attention_biases=outputs.attention_biases,
        )

ErnieRnaForSequencePrediction

Bases: ErnieRnaPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule import ErnieRnaConfig, ErnieRnaForSequencePrediction, RnaTokenizer
>>> config = ErnieRnaConfig()
>>> model = ErnieRnaForSequencePrediction(config)
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
>>> input = tokenizer("ACGUN", return_tensors="pt")
>>> output = model(**input)
>>> output["logits"].shape
torch.Size([1, 1])
Source code in multimolecule/models/ernierna/modeling_ernierna.py
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class ErnieRnaForSequencePrediction(ErnieRnaPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import ErnieRnaConfig, ErnieRnaForSequencePrediction, RnaTokenizer
        >>> config = ErnieRnaConfig()
        >>> model = ErnieRnaForSequencePrediction(config)
        >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/rna")
        >>> input = tokenizer("ACGUN", return_tensors="pt")
        >>> output = model(**input)
        >>> output["logits"].shape
        torch.Size([1, 1])
    """

    def __init__(self, config: ErnieRnaConfig):
        super().__init__(config)
        self.model = ErnieRnaModel(config)
        self.sequence_head = SequencePredictionHead(config)
        self.head_config = self.sequence_head.config

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

    @can_return_tuple
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Tuple[Tensor, ...] | ErnieRnaSequencePredictorOutput:
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )
        output = self.sequence_head(outputs, labels)
        logits, loss = output.logits, output.loss

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

ErnieRnaForTokenPrediction

Bases: ErnieRnaPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule import ErnieRnaConfig, ErnieRnaForTokenPrediction, RnaTokenizer
>>> config = ErnieRnaConfig()
>>> model = ErnieRnaForTokenPrediction(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/ernierna/modeling_ernierna.py
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class ErnieRnaForTokenPrediction(ErnieRnaPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import ErnieRnaConfig, ErnieRnaForTokenPrediction, RnaTokenizer
        >>> config = ErnieRnaConfig()
        >>> model = ErnieRnaForTokenPrediction(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: ErnieRnaConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = ErnieRnaModel(config, add_pooling_layer=False)
        self.token_head = TokenPredictionHead(config)
        self.head_config = self.token_head.config

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

    @can_return_tuple
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Tuple[Tensor, ...] | ErnieRnaTokenPredictorOutput:
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )
        output = self.token_head(outputs, attention_mask, input_ids, labels)
        logits, loss = output.logits, output.loss

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

ErnieRnaModel

Bases: ErnieRnaPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule import ErnieRnaConfig, ErnieRnaModel, RnaTokenizer
>>> config = ErnieRnaConfig()
>>> model = ErnieRnaModel(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/ernierna/modeling_ernierna.py
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class ErnieRnaModel(ErnieRnaPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import ErnieRnaConfig, ErnieRnaModel, RnaTokenizer
        >>> config = ErnieRnaConfig()
        >>> model = ErnieRnaModel(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])
    """

    pairwise_bias_map: Tensor

    def __init__(
        self, config: ErnieRnaConfig, add_pooling_layer: bool = True, tokenizer: PreTrainedTokenizer | None = None
    ):
        super().__init__(config)
        if tokenizer is None:
            tokenizer = AutoTokenizer.from_pretrained("multimolecule/rna")
        self.tokenizer = tokenizer
        self.pad_token_id = tokenizer.pad_token_id
        self.gradient_checkpointing = False
        self.vocab_size = len(self.tokenizer)
        if self.vocab_size != config.vocab_size:
            raise ValueError(
                f"Vocab size in tokenizer ({self.vocab_size}) does not match the one in config ({config.vocab_size})"
            )
        token_to_ids = self.tokenizer._token_to_id
        tokens = sorted(token_to_ids, key=token_to_ids.get)
        pairwise_bias_dict = get_pairwise_bias_dict(config.pairwise_alpha)
        self.register_buffer(
            "pairwise_bias_map",
            torch.tensor([[pairwise_bias_dict.get(f"{i}{j}", 0) for i in tokens] for j in tokens]),
            persistent=False,
        )
        self.pairwise_bias_proj = nn.Sequential(
            nn.Linear(1, config.num_attention_heads // 2),
            nn.GELU(),
            nn.Linear(config.num_attention_heads // 2, config.num_attention_heads),
        )
        self.embeddings = ErnieRnaEmbeddings(config)
        self.encoder = ErnieRnaEncoder(config)
        self.pooler = ErnieRnaPooler(config) if add_pooling_layer else None

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

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

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

    def get_pairwise_bias(
        self, input_ids: Tensor | NestedTensor, attention_mask: Tensor | NestedTensor | None = None
    ) -> Tensor | NestedTensor:
        if isinstance(input_ids, NestedTensor):
            input_ids = input_ids.tensor
        batch_size, seq_length = input_ids.shape

        # Broadcasting data indices to compute indices
        data_index_x = input_ids.unsqueeze(2).expand(batch_size, seq_length, seq_length)
        data_index_y = input_ids.unsqueeze(1).expand(batch_size, seq_length, seq_length)

        # Get bias from pairwise_bias_map
        if not self._inited:
            token_to_ids = self.tokenizer._token_to_id
            tokens = sorted(token_to_ids, key=token_to_ids.get)
            pairwise_bias_dict = get_pairwise_bias_dict(self.config.pairwise_alpha)
            self.register_buffer(
                "pairwise_bias_map",
                torch.tensor(
                    [[pairwise_bias_dict.get(f"{i}{j}", 0) for i in tokens] for j in tokens], device=input_ids.device
                ),
                persistent=False,
            )
            self._inited = True
        return self.pairwise_bias_map[data_index_x, data_index_y]

    @check_model_inputs
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        position_ids: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        encoder_hidden_states: Tensor | None = None,
        encoder_attention_mask: Tensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = None,
        cache_position: Tensor | None = None,
        output_attention_biases: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Tuple[Tensor, ...] | ErnieRnaModelOutputWithPoolingAndCrossAttentions:
        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`).
        """
        output_attention_biases = (
            output_attention_biases if output_attention_biases is not None else self.config.output_attention_biases
        )
        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 use_cache and past_key_values is None:
            past_key_values = (
                EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
                if encoder_hidden_states is not None or self.config.is_encoder_decoder
                else DynamicCache(config=self.config)
            )

        pairwise_bias = self.get_pairwise_bias(input_ids, attention_mask)
        attention_bias = self.pairwise_bias_proj(pairwise_bias.unsqueeze(-1)).transpose(1, 3)

        if isinstance(input_ids, NestedTensor) and attention_mask is None:
            attention_mask = input_ids.mask
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
        if input_ids is not None:
            device = input_ids.device
            seq_length = input_ids.shape[1]
        else:
            device = inputs_embeds.device  # type: ignore[union-attr]
            seq_length = inputs_embeds.shape[1]  # type: ignore[union-attr]

        # past_key_values_length
        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
        if cache_position is None:
            cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=device)

        if attention_mask is None and input_ids is not None and self.pad_token_id is not None:
            attention_mask = input_ids.ne(self.pad_token_id)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )
        attention_mask, encoder_attention_mask = self._create_attention_masks(
            attention_mask=attention_mask,
            encoder_attention_mask=encoder_attention_mask,
            embedding_output=embedding_output,
            encoder_hidden_states=encoder_hidden_states,
            cache_position=cache_position,
            past_key_values=past_key_values,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=attention_mask,
            attention_bias=attention_bias,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            output_attention_biases=output_attention_biases,
            position_ids=position_ids,
            **kwargs,
        )
        sequence_output = encoder_outputs.last_hidden_state
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        return ErnieRnaModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            attention_biases=encoder_outputs.attention_biases,
        )

    def _create_attention_masks(
        self,
        attention_mask,
        encoder_attention_mask,
        embedding_output,
        encoder_hidden_states,
        cache_position,
        past_key_values,
    ):
        if self.config.is_decoder:
            attention_mask = create_causal_mask(
                config=self.config,
                input_embeds=embedding_output,
                attention_mask=attention_mask,
                cache_position=cache_position,
                past_key_values=past_key_values,
            )
        else:
            attention_mask = create_bidirectional_mask(
                config=self.config, input_embeds=embedding_output, attention_mask=attention_mask
            )

        if encoder_attention_mask is not None:
            encoder_attention_mask = create_bidirectional_mask(
                config=self.config,
                input_embeds=embedding_output,
                attention_mask=encoder_attention_mask,
                encoder_hidden_states=encoder_hidden_states,
            )

        return attention_mask, encoder_attention_mask

forward

Python
forward(
    input_ids: Tensor | NestedTensor | None = None,
    attention_mask: Tensor | None = None,
    position_ids: Tensor | None = None,
    inputs_embeds: Tensor | NestedTensor | None = None,
    encoder_hidden_states: Tensor | None = None,
    encoder_attention_mask: Tensor | None = None,
    past_key_values: Cache | None = None,
    use_cache: bool | None = None,
    cache_position: Tensor | None = None,
    output_attention_biases: bool | None = None,
    **kwargs: Unpack[TransformersKwargs]
) -> (
    Tuple[Tensor, ...]
    | ErnieRnaModelOutputWithPoolingAndCrossAttentions
)

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
Cache | 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/ernierna/modeling_ernierna.py
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@check_model_inputs
def forward(
    self,
    input_ids: Tensor | NestedTensor | None = None,
    attention_mask: Tensor | None = None,
    position_ids: Tensor | None = None,
    inputs_embeds: Tensor | NestedTensor | None = None,
    encoder_hidden_states: Tensor | None = None,
    encoder_attention_mask: Tensor | None = None,
    past_key_values: Cache | None = None,
    use_cache: bool | None = None,
    cache_position: Tensor | None = None,
    output_attention_biases: bool | None = None,
    **kwargs: Unpack[TransformersKwargs],
) -> Tuple[Tensor, ...] | ErnieRnaModelOutputWithPoolingAndCrossAttentions:
    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`).
    """
    output_attention_biases = (
        output_attention_biases if output_attention_biases is not None else self.config.output_attention_biases
    )
    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 use_cache and past_key_values is None:
        past_key_values = (
            EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
            if encoder_hidden_states is not None or self.config.is_encoder_decoder
            else DynamicCache(config=self.config)
        )

    pairwise_bias = self.get_pairwise_bias(input_ids, attention_mask)
    attention_bias = self.pairwise_bias_proj(pairwise_bias.unsqueeze(-1)).transpose(1, 3)

    if isinstance(input_ids, NestedTensor) and attention_mask is None:
        attention_mask = input_ids.mask
    if (input_ids is None) ^ (inputs_embeds is not None):
        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
    if input_ids is not None:
        device = input_ids.device
        seq_length = input_ids.shape[1]
    else:
        device = inputs_embeds.device  # type: ignore[union-attr]
        seq_length = inputs_embeds.shape[1]  # type: ignore[union-attr]

    # past_key_values_length
    past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
    if cache_position is None:
        cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=device)

    if attention_mask is None and input_ids is not None and self.pad_token_id is not None:
        attention_mask = input_ids.ne(self.pad_token_id)

    embedding_output = self.embeddings(
        input_ids=input_ids,
        position_ids=position_ids,
        inputs_embeds=inputs_embeds,
        past_key_values_length=past_key_values_length,
    )
    attention_mask, encoder_attention_mask = self._create_attention_masks(
        attention_mask=attention_mask,
        encoder_attention_mask=encoder_attention_mask,
        embedding_output=embedding_output,
        encoder_hidden_states=encoder_hidden_states,
        cache_position=cache_position,
        past_key_values=past_key_values,
    )
    encoder_outputs = self.encoder(
        embedding_output,
        attention_mask=attention_mask,
        attention_bias=attention_bias,
        encoder_hidden_states=encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask,
        past_key_values=past_key_values,
        use_cache=use_cache,
        cache_position=cache_position,
        output_attention_biases=output_attention_biases,
        position_ids=position_ids,
        **kwargs,
    )
    sequence_output = encoder_outputs.last_hidden_state
    pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

    return ErnieRnaModelOutputWithPoolingAndCrossAttentions(
        last_hidden_state=sequence_output,
        pooler_output=pooled_output,
        past_key_values=encoder_outputs.past_key_values,
        attention_biases=encoder_outputs.attention_biases,
    )

ErnieRnaPreTrainedModel

Bases: PreTrainedModel

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

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

    config_class = ErnieRnaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _supports_attention_backend = True
    _can_record_outputs: dict[str, Any] | None = None
    _no_split_modules = ["ErnieRnaLayer", "ErnieRnaEmbeddings"]