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# coding=utf-8
""" Hgrn configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class HgrnConfig(PretrainedConfig):
model_type = "hgrn"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
vocab_size=50272,
use_cache=True,
init_std=0.02,
# model config
decoder_embed_dim=1024,
decoder_layers=24,
add_bos_token=False,
act_fun="swish",
causal=True,
use_triton=False,
glu_act="swish",
glu_dim=2816,
bias=False,
norm_type="layernorm",
no_scale_embedding=False,
**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.add_bos_token = add_bos_token
self.act_fun = act_fun
self.causal = causal
self.use_triton = use_triton
self.glu_act = glu_act
self.glu_dim = glu_dim
self.bias = bias
self.norm_type = norm_type
self.no_scale_embedding = no_scale_embedding |