File size: 2,326 Bytes
bc6e7dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
# Copyright 2024 OpenNLPLab
#
# 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.
# coding=utf-8
""" Transnormer configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class TransnormerConfig(PretrainedConfig):
model_type = "transnormer"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
vocab_size=64000,
use_cache=True,
init_std=0.02,
# model config
decoder_embed_dim=1024,
decoder_layers=24,
decoder_attention_heads=8,
no_scale_embedding=False,
add_bos_token=False,
norm_type="simplermsnorm",
linear_use_lrpe_list=[],
hidden_dim=1024,
linear_act_fun="silu",
glu_dim=2816,
bias=False,
gate_dim=16,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
# hf origin
self.vocab_size = vocab_size
self.use_cache = use_cache
self.init_std = init_std
# add
self.decoder_embed_dim = decoder_embed_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.no_scale_embedding = no_scale_embedding
self.add_bos_token = add_bos_token
self.norm_type = norm_type
self.linear_use_lrpe_list = linear_use_lrpe_list
self.hidden_dim = hidden_dim
self.linear_act_fun = linear_act_fun
self.glu_dim = glu_dim
self.bias = bias
self.gate_dim = gate_dim
|