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| """ Telechat configuration""" |
|
|
| from transformers.utils import logging |
| from transformers.configuration_utils import PretrainedConfig |
|
|
| logger = logging.get_logger(__name__) |
|
|
| class TelechatConfig(PretrainedConfig): |
| """ |
| Args: |
| vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model. |
| hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states. |
| ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states. |
| n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer |
| n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer. |
| 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. |
| apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks |
| hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout. |
| attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs |
| use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions. |
| training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning. |
| logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation. |
| embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm. |
| |
| """ |
|
|
| model_type = "telechat" |
| keys_to_ignore_at_inference = ["past_key_values"] |
| attribute_map = { |
| "num_hidden_layers": "n_layer", |
| "num_attention_heads": "n_head", |
| } |
|
|
| def __init__( |
| self, |
| vocab_size=160256, |
| hidden_size=4096, |
| n_layer=30, |
| n_head=32, |
| layer_norm_epsilon=1e-5, |
| initializer_range=0.02, |
| use_cache=True, |
| bos_token_id=1, |
| eos_token_id=2, |
| apply_residual_connection_post_layernorm=False, |
| hidden_dropout=0.0, |
| attention_dropout=0.0, |
| ffn_hidden_size=12288, |
| training_seqlen = 8192, |
| logn = True, |
| embed_layernorm = False, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| n_embed = kwargs.pop("n_embed", None) |
| self.hidden_size = hidden_size if n_embed is None else n_embed |
| self.n_layer = n_layer |
| self.n_head = n_head |
| self.layer_norm_epsilon = layer_norm_epsilon |
| self.initializer_range = initializer_range |
| self.use_cache = use_cache |
| self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm |
| self.hidden_dropout = hidden_dropout |
| self.attention_dropout = attention_dropout |
| self.bos_token_id = bos_token_id |
| self.eos_token_id = eos_token_id |
| self.logn = logn |
| self.ffn_hidden_size = ffn_hidden_size |
| self.training_seqlen = training_seqlen |
| self.embed_layernorm = embed_layernorm |
|
|
|
|
| super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
|
|
|
|
