Lightstar/venv/Lib/site-packages/transformers/models/opt/configuration_opt.py

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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class OPTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT 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 OPT
    [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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*, defaults to 50272):
            Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`OPTModel`]
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        ffn_dim (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            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).
        do_layer_norm_before (`bool`, *optional*, defaults to `True`):
            Whether to perform layer normalization before the attention block.
        word_embed_proj_dim (`int`, *optional*):
            `word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to
            `hidden_size`.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
            details.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        enable_bias (`bool`, *optional*, defaults to `True`):
            Whether or not if the linear layers in the attention blocks should use the bias term.
        layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
            Whether or not if the layer norms should have learnable parameters.

    Example:

    ```python
    >>> from transformers import OPTConfig, OPTModel

    >>> # Initializing a OPT facebook/opt-large style configuration
    >>> configuration = OPTConfig()

    >>> # Initializing a model (with random weights) from the facebook/opt-large style configuration
    >>> model = OPTModel(configuration)

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

    model_type = "opt"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=50272,
        hidden_size=768,
        num_hidden_layers=12,
        ffn_dim=3072,
        max_position_embeddings=2048,
        do_layer_norm_before=True,
        _remove_final_layer_norm=False,
        word_embed_proj_dim=None,
        dropout=0.1,
        attention_dropout=0.0,
        num_attention_heads=12,
        activation_function="relu",
        layerdrop=0.0,
        init_std=0.02,
        use_cache=True,
        pad_token_id=1,
        bos_token_id=2,
        eos_token_id=2,
        enable_bias=True,
        layer_norm_elementwise_affine=True,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.num_attention_heads = num_attention_heads
        self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        self.ffn_dim = ffn_dim
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.layerdrop = layerdrop
        self.use_cache = use_cache
        self.do_layer_norm_before = do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        self.enable_bias = enable_bias
        self.layer_norm_elementwise_affine = layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        self._remove_final_layer_norm = _remove_final_layer_norm