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