mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-10-15 08:08:09 +08:00
94 lines
4.4 KiB
Python
94 lines
4.4 KiB
Python
import json
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from typing import Literal, Optional
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from dataclasses import asdict, dataclass, field
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@dataclass
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class FinetuningArguments:
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r"""
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Arguments pertaining to which techniques we are going to fine-tuning with.
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"""
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finetuning_type: Optional[Literal["none", "freeze", "lora", "full"]] = field(
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default="lora",
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metadata={"help": "Which fine-tuning method to use."}
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)
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num_hidden_layers: Optional[int] = field(
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default=32,
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metadata={"help": "Number of decoder blocks in the model for partial-parameter (freeze) fine-tuning. \
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LLaMA choices: [\"32\", \"40\", \"60\", \"80\"], \
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LLaMA-2 choices: [\"32\", \"40\", \"80\"], \
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BLOOM choices: [\"24\", \"30\", \"70\"], \
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Falcon choices: [\"32\", \"60\"], \
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Baichuan choices: [\"32\", \"40\"] \
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Qwen choices: [\"32\"], \
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XVERSE choices: [\"40\"]"}
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)
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num_layer_trainable: Optional[int] = field(
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default=3,
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metadata={"help": "Number of trainable layers for partial-parameter (freeze) fine-tuning."}
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)
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name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
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default="mlp",
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metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \
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LLaMA choices: [\"mlp\", \"self_attn\"], \
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BLOOM & Falcon choices: [\"mlp\", \"self_attention\"], \
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Baichuan choices: [\"mlp\", \"self_attn\"], \
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Qwen choices: [\"mlp\", \"attn\"], \
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LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
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)
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lora_rank: Optional[int] = field(
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default=8,
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metadata={"help": "The intrinsic dimension for LoRA fine-tuning."}
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)
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lora_alpha: Optional[float] = field(
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default=32.0,
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metadata={"help": "The scale factor for LoRA fine-tuning (similar with the learning rate)."}
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)
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lora_dropout: Optional[float] = field(
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default=0.1,
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metadata={"help": "Dropout rate for the LoRA fine-tuning."}
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)
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lora_target: Optional[str] = field(
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default="q_proj,v_proj",
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metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \
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LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
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BLOOM & Falcon choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \
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Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
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Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
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LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."}
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)
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resume_lora_training: Optional[bool] = field(
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default=True,
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metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
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)
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dpo_beta: Optional[float] = field(
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default=0.1,
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metadata={"help": "The beta parameter for the DPO loss."}
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)
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def __post_init__(self):
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if isinstance(self.lora_target, str): # support custom target modules/layers of LoRA
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self.lora_target = [target.strip() for target in self.lora_target.split(",")]
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if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
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trainable_layer_ids = [self.num_hidden_layers - k - 1 for k in range(self.num_layer_trainable)]
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else: # fine-tuning the first n layers if num_layer_trainable < 0
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trainable_layer_ids = [k for k in range(-self.num_layer_trainable)]
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self.trainable_layers = ["{:d}.{}".format(idx, self.name_module_trainable) for idx in trainable_layer_ids]
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assert self.finetuning_type in ["none", "freeze", "lora", "full"], "Invalid fine-tuning method."
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def save_to_json(self, json_path: str):
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r"""Saves the content of this instance in JSON format inside `json_path`."""
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json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n"
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with open(json_path, "w", encoding="utf-8") as f:
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f.write(json_string)
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@classmethod
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def load_from_json(cls, json_path: str):
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r"""Creates an instance from the content of `json_path`."""
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with open(json_path, "r", encoding="utf-8") as f:
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text = f.read()
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return cls(**json.loads(text))
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