95 lines
4.3 KiB
Python
95 lines
4.3 KiB
Python
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# coding=utf-8
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# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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 Telechat2Config(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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self.num_key_value_heads= kwargs.pop("num_key_value_heads", None)
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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