Source code for transformers.models.gpt_neo.configuration_gpt_neo

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""" GPT Neo model configuration """

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


logger = logging.get_logger(__name__)

GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


[docs]class GPTNeoConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.GPTNeoModel`. It is used to instantiate a GPT Neo 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 GPTNeo `gpt-neo-1.3B <https://huggingface.co/EleutherAI/gpt-neo-1.3B>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 50257): Vocabulary size of the GPT Neo model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.GPTNeoModel`. Vocabulary size of the model. Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.GPTNeoModel`. attention_types (:obj:`List`, `optional`, defaults to :obj:`[[["global", "local"], 12]]`): The type of attention for each layer in a :obj:`List` of the following format :obj:`[[["attention_type"], num_layerss]]` e.g. for a 24 layer model :obj:`[[["global"], 24]]` or :obj:`[[["global", "local"], 12]]` Choose the value of ``attention_type`` from :obj:`["global", "local"]` hidden_size (:obj:`int`, `optional`, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. num_layers (:obj:`int`, `optional`, defaults to 24): Number of hidden layers in the Transformer encoder. num_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (:obj:`int`, `optional`, defaults to 8192): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_new"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported. embed_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. max_position_embeddings (:obj:`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). type_vocab_size (:obj:`int`, `optional`, defaults to 2): The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.GPTNeoModel`. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5): The epsilon used by the layer normalization layers. use_cache (:obj:`bool`, `optional`, defaults to :obj:`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``. gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. Example:: >>> from transformers import GPTNeoModel, GPTNeoConfig >>> # Initializing a GPTNeo EleutherAI/gpt-neo-1.3B style configuration >>> configuration = GPTNeoConfig() >>> # Initializing a model from the EleutherAI/gpt-neo-1.3B style configuration >>> model = GPTNeoModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "gpt_neo" def __init__( self, vocab_size=50257, max_position_embeddings=2048, hidden_size=2048, num_layers=24, attention_types=[[["global", "local"], 12]], num_heads=16, intermediate_size=None, window_size=256, activation_function="gelu_new", resid_dropout=0.0, embed_dropout=0.0, attention_dropout=0.0, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, gradient_checkpointing=False, use_cache=True, bos_token_id=50256, eos_token_id=50256, **kwargs ): super().__init__(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.hidden_size = hidden_size self.num_layers = num_layers self.num_heads = num_heads self.intermediate_size = intermediate_size self.window_size = window_size self.activation_function = activation_function self.resid_dropout = resid_dropout self.embed_dropout = embed_dropout self.attention_dropout = attention_dropout self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels self.gradient_checkpointing = gradient_checkpointing self.use_cache = use_cache self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.attention_types = attention_types self.attention_layers = self.expand_attention_types_params(attention_types) if len(self.attention_layers) != self.num_layers: raise ValueError( "Configuration for convolutional module is incorrect." "It is required that `len(config.attention_layers)` == `config.num_layers`" f"but is `len(config.attention_layers) = {len(self.attention_layers)}`," f"`config.num_layers = {self.num_layers}`." "`config.attention_layers` is prepared using `config.attention_types`." "Please verify the value of `config.attention_types` argument." ) @staticmethod def expand_attention_types_params(attention_types): attentions = [] for item in attention_types: for _ in range(item[1]): attentions.extend(item[0]) return attentions @property def num_attention_heads(self): return self.num_heads @property def num_hidden_layers(self): return self.num_layers