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# coding=utf-8
# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" 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)
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