101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
import os
|
|
from dataclasses import dataclass, field
|
|
from typing import Optional, List
|
|
|
|
from transformers import TrainingArguments
|
|
|
|
|
|
def default_list() -> List[int]:
|
|
return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']
|
|
|
|
|
|
@dataclass
|
|
class ModelArguments:
|
|
"""
|
|
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
|
"""
|
|
|
|
model_name_or_path: str = field(
|
|
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
|
)
|
|
config_name: Optional[str] = field(
|
|
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
|
)
|
|
tokenizer_name: Optional[str] = field(
|
|
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
|
)
|
|
# cache_dir: Optional[str] = field(
|
|
# default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
|
# )
|
|
use_lora: bool = field(
|
|
default=True,
|
|
metadata={"help": "If passed, will use LORA (low-rank parameter-efficient training) to train the model."}
|
|
)
|
|
lora_rank: int = field(
|
|
default=64,
|
|
metadata={"help": "The rank of lora."}
|
|
)
|
|
lora_alpha: float = field(
|
|
default=16,
|
|
metadata={"help": "The alpha parameter of lora."}
|
|
)
|
|
lora_dropout: float = field(
|
|
default=0.1,
|
|
metadata={"help": "The dropout rate of lora modules."}
|
|
)
|
|
target_modules: List[str] = field(
|
|
default_factory=default_list
|
|
)
|
|
save_merged_lora_model: bool = field(
|
|
default=False,
|
|
metadata={"help": "If passed, will merge the lora modules and save the entire model."}
|
|
)
|
|
use_flash_attn: bool = field(
|
|
default=True,
|
|
metadata={"help": "If passed, will use flash attention to train the model."}
|
|
)
|
|
use_slow_tokenizer: bool = field(
|
|
default=False,
|
|
metadata={"help": "If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library)."}
|
|
)
|
|
token: str = field(
|
|
default=""
|
|
)
|
|
cache_dir: str = field(
|
|
default="./LMs"
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class DataArguments:
|
|
cache_path: str = field(
|
|
default='./data_dir'
|
|
)
|
|
|
|
train_data: str = field(
|
|
default='./toy_finetune_data.jsonl', metadata={"help": "Path to train data"}
|
|
)
|
|
|
|
max_example_num_per_dataset: int = field(
|
|
default=100000000, metadata={"help": "the max number of examples for each dataset"}
|
|
)
|
|
|
|
cutoff_len: int = field(
|
|
default=512,
|
|
metadata={
|
|
"help": "The maximum total input sequence length after tokenization for passage. Sequences longer "
|
|
"than this will be truncated, sequences shorter will be padded."
|
|
},
|
|
)
|
|
|
|
remove_stop_words: bool = field(
|
|
default=False
|
|
)
|
|
|
|
def __post_init__(self):
|
|
if not os.path.exists(self.train_data):
|
|
raise FileNotFoundError(f"cannot find file: {self.train_data}, please set a true path")
|
|
|
|
@dataclass
|
|
class PretrainTrainingArguments(TrainingArguments):
|
|
mask: bool = field(default=True, metadata={"help": "mask the input part"}) |