Spaces:
Runtime error
Runtime error
File size: 4,746 Bytes
b87a3ce |
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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
import json
from typing import Literal, Optional
from dataclasses import asdict, dataclass, field
@dataclass
class FinetuningArguments:
r"""
Arguments pertaining to which techniques we are going to fine-tuning with.
"""
finetuning_type: Optional[Literal["lora", "freeze", "full", "none"]] = field(
default="lora",
metadata={"help": "Which fine-tuning method to use."}
)
num_hidden_layers: Optional[int] = field(
default=32,
metadata={"help": "Number of decoder blocks in the model for partial-parameter (freeze) fine-tuning. \
LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
LLaMA-2 choices: [\"32\", \"40\", \"80\"], \
BLOOM choices: [\"24\", \"30\", \"70\"], \
Falcon choices: [\"32\", \"60\"], \
Baichuan choices: [\"32\", \"40\"] \
Qwen choices: [\"32\"], \
XVERSE choices: [\"40\"], \
ChatGLM2 choices: [\"28\"]"}
)
num_layer_trainable: Optional[int] = field(
default=3,
metadata={"help": "Number of trainable layers for partial-parameter (freeze) fine-tuning."}
)
name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
default="mlp",
metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \
LLaMA choices: [\"mlp\", \"self_attn\"], \
BLOOM & Falcon & ChatGLM2 choices: [\"mlp\", \"self_attention\"], \
Baichuan choices: [\"mlp\", \"self_attn\"], \
Qwen choices: [\"mlp\", \"attn\"], \
LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
)
lora_rank: Optional[int] = field(
default=8,
metadata={"help": "The intrinsic dimension for LoRA fine-tuning."}
)
lora_alpha: Optional[float] = field(
default=32.0,
metadata={"help": "The scale factor for LoRA fine-tuning (similar with the learning rate)."}
)
lora_dropout: Optional[float] = field(
default=0.1,
metadata={"help": "Dropout rate for the LoRA fine-tuning."}
)
lora_target: Optional[str] = field(
default=None,
metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
BLOOM & Falcon & ChatGLM2 choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \
Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
)
resume_lora_training: Optional[bool] = field(
default=True,
metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
)
ppo_score_norm: Optional[bool] = field(
default=False,
metadata={"help": "Use score normalization in PPO Training."}
)
dpo_beta: Optional[float] = field(
default=0.1,
metadata={"help": "The beta parameter for the DPO loss."}
)
def __post_init__(self):
if isinstance(self.lora_target, str): # support custom target modules/layers of LoRA
self.lora_target = [target.strip() for target in self.lora_target.split(",")]
if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = [self.num_hidden_layers - k - 1 for k in range(self.num_layer_trainable)]
else: # fine-tuning the first n layers if num_layer_trainable < 0
trainable_layer_ids = [k for k in range(-self.num_layer_trainable)]
self.trainable_layers = ["{:d}.{}".format(idx, self.name_module_trainable) for idx in trainable_layer_ids]
assert self.finetuning_type in ["lora", "freeze", "full", "none"], "Invalid fine-tuning method."
def save_to_json(self, json_path: str):
r"""Saves the content of this instance in JSON format inside `json_path`."""
json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n"
with open(json_path, "w", encoding="utf-8") as f:
f.write(json_string)
@classmethod
def load_from_json(cls, json_path: str):
r"""Creates an instance from the content of `json_path`."""
with open(json_path, "r", encoding="utf-8") as f:
text = f.read()
return cls(**json.loads(text))
|