config_nuclm.py

# coding=utf-8
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"""ESM model configuration"""

from dataclasses import asdict, dataclass
from typing import Optional

from transformers import PretrainedConfig
from transformers import logging


logger = logging.get_logger(__name__)

# TODO Update this


class EsmConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM 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 ESM
    [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.

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


    Args:
        vocab_size (`int`, *optional*):
            Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ESMModel`].
        mask_token_id (`int`, *optional*):
            The index of the mask token in the vocabulary. This must be included in the config because of the
            "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
        pad_token_id (`int`, *optional*):
            The index of the padding token in the vocabulary. This must be included in the config because certain parts
            of the ESM code use this instead of the attention mask.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        activation_function (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
            "relu", and "silu" are supported.
        gated_linear_unit (`bool`, *optional*, defaults to `False`):
            Whether to use gated linear units in the encoder layers. If `True`, it will be a combination of gated
            linear units and the activation function.
        max_position_embeddings (`int`, *optional*, defaults to 1026):
            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 (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
            For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        is_decoder (`bool`, *optional*, defaults to `False`):
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        emb_layer_norm_before (`bool`, *optional*):
            Whether to apply layer normalization after embeddings but before the main stem of the network.
        token_dropout (`bool`, defaults to `False`):
            When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.

    Examples:

    ```python
    >>> from transformers import EsmModel, EsmConfig

    >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()

    >>> # Initializing a model from the configuration >>> model = ESMModel(configuration)

    >>> # Accessing the model configuration >>> configuration = model.config
    ```"""

    model_type = "esm"

    def __init__(
        self,
        vocab_size=None,
        mask_token_id=None,
        pad_token_id=None,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        activation_function="gelu",
        gated_linear_unit=False,
        max_position_embeddings=1026,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        position_embedding_type="absolute",
        use_cache=True,
        emb_layer_norm_before=None,
        token_dropout=False,
        is_folding_model=False,
        esmfold_config=None,
        vocab_list=None,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, mask_token_id=mask_token_id, **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_dropout_prob = hidden_dropout_prob
        self.activation_function = activation_function
        self.gated_linear_unit = gated_linear_unit
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        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.use_cache = use_cache
        self.emb_layer_norm_before = emb_layer_norm_before
        self.token_dropout = token_dropout
        self.is_folding_model = is_folding_model
        self.esmfold_config = None
        self.vocab_list = None

    def to_dict(self):
        """
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = super().to_dict()
        return output