""" Moss model configuration""" from transformers.utils import logging from transformers.configuration_utils import PretrainedConfig logger = logging.get_logger(__name__) class MossConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MossModel`]. It is used to instantiate a Moss 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 Moss [fnlp/moss-moon-003-base](https://huggingface.co/fnlp/moss-moon-003-base) 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 107008): Vocabulary size of the Moss model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MossModel`]. n_positions (`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). n_embd (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. rotary_dim (`int`, *optional*, defaults to 64): Number of dimensions in the embedding that Rotary Position Embedding is applied to. n_inner (`int`, *optional*, defaults to None): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd activation_function (`str`, *optional*, defaults to `"gelu_new"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers. initializer_range (`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). Example: ```python >>> from modeling_moss import MossModel >>> from configuration_moss import MossConfig >>> # Initializing a moss-moon-003-base configuration >>> configuration = MossConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = MossModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "moss" attribute_map = { "max_position_embeddings": "n_positions", "hidden_size": "n_embd", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=107008, n_positions=2048, n_ctx=2048, n_embd=4096, n_layer=28, n_head=16, rotary_dim=64, n_inner=None, activation_function="gelu_new", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, layer_norm_epsilon=1e-5, initializer_range=0.02, use_cache=True, bos_token_id=106028, eos_token_id=106068, tie_word_embeddings=False, wbits=32, groupsize=128, **kwargs, ): self.vocab_size = vocab_size self.n_ctx = n_ctx self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.n_inner = n_inner self.rotary_dim = rotary_dim self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.use_cache = use_cache self.wbits = wbits self.groupsize = groupsize self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__( bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs )