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# Copyright (c) 2023 XiaoDuo AI. All rights reserved.
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from typing_extensions import Self
logger = logging.get_logger(__name__)
class XModelConfig(PretrainedConfig):
model_type = "xmodel"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=65280,
hidden_size=4096,
intermediate_size=None,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
hidden_act="silu",
max_position_embeddings=131072,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=500000.0,
rope_scaling=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
# self.intermediate_size = intermediate_size
if intermediate_size is None:
self.intermediate_size = find_multiple(int(8 * hidden_size / 3), 256)
else:
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.auto_map = {
"AutoConfig": "configuration_xmodel.XModelConfig",
"AutoModelForCausalLM": "modeling_xmodel.XModelForCausalLM"
}
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
@classmethod
def from_name(cls, name: str) -> Self:
return cls(**xmodel_configs[name])
xmodel_configs = {
"nano": dict(num_hidden_layers=6, num_attention_heads=6, num_key_value_heads=1, hidden_size=192),
"micro": dict(num_hidden_layers=6, num_attention_heads=6, num_key_value_heads=1, hidden_size=384),
"tiny": dict(num_hidden_layers=8, num_attention_heads=8, num_key_value_heads=2, hidden_size=512),
"small": dict(num_hidden_layers=12, num_attention_heads=12, num_key_value_heads=3, hidden_size=768),
# GPT-1 & Bert-Base
"medium": dict(num_hidden_layers=24, num_attention_heads=16, num_key_value_heads=4, hidden_size=1024), # Bert-Large
"large": dict(num_hidden_layers=24, num_attention_heads=16, num_key_value_heads=4, hidden_size=1536),
"xl": dict(num_hidden_layers=24, num_attention_heads=32, num_key_value_heads=4, hidden_size=2048), # GPT-2
"3B": dict(num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=4, hidden_size=2560),
"7B": dict(num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=32, hidden_size=4096),
"13B": dict(num_hidden_layers=40, num_attention_heads=40, num_key_value_heads=40, hidden_size=5120),
"34B": dict(num_hidden_layers=48, num_attention_heads=64, num_key_value_heads=8, hidden_size=8192),
"70B": dict(num_hidden_layers=80, num_attention_heads=64, num_key_value_heads=8, hidden_size=8192), # Llama
}
def find_multiple(n: int, k: int) -> int:
if n % k == 0:
return n
return n + k - (n % k)
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