|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| """ Telechat configuration"""
|
|
|
| from packaging import version
|
| from collections import OrderedDict
|
| from transformers.utils import is_torch_available, logging
|
| from transformers.configuration_utils import PretrainedConfig
|
| from typing import TYPE_CHECKING, Any, List, Mapping, Optional
|
|
|
| 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)
|
|
|
|
|
