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config.py
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config.py
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from typing import Literal, Optional, List, Union
from dataclasses import asdict, dataclass, field
from transformers import Seq2SeqTrainingArguments
import torch
# 预训练: pretrain
# 模型指令微调: sft_train
# 奖励模型训练: rm_train
# PPO模型强化训练: ppo_train
# DPO模型强化训练: dpo_train
# 网页端测试模型: web_inference
# 终端模型交互: terminal_inference
# 融合模型: merge_lora_model
# 打印模型参数: show_model_info
# 存储量化的模型: save_quantized_model
# 模型效果测试及评估: sft_batch_test
# 奖励模型效果测试及评估: rm_batch_test
# 扩充词表: expand_vocab
@dataclass
class WorkingMode:
mode: str = field(
default='web_inference',
metadata={
# 工作模式
'help': 'Working mode.',
'choices': ['pretrain', 'sft_train', 'rm_train', 'ppo_train',
'dpo_train', 'web_inference', 'terminal_inference',
'merge_lora_model', 'show_model_info', 'save_quantized_model',
'sft_batch_test', 'rm_batch_test', 'expand_vocab'],
}
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
"""
model_type: str = field(
default='chatglm',
metadata={
# 模型类型
'help': 'Model type.',
'choices': ['chatglm', 'qwen', 'llama', 'falcon', 'baichuan', 'aquila',
'internlm', 'moss', 'bloom', 'rwkv', 'xverse', 'mistral', 'yi'],
}
)
model_path: str = field(
default='/home/llm_models/ChatGLM/ChatGLM3-6B',
metadata={
# 从huggingface.co/models上下载的模型保存到本地的路径。
'help': 'Local path to pretrained model or model identifier from huggingface.co/models.'
}
)
checkpoint_dir: Optional[str] = field(
default=None,
metadata={
# 保存下载的或者自己训练的adapter增量模型的地方,在RLHF时候,此处需要填写指令微调后模型所在的文件地址(如果有)。
'help': 'Path to save the (delta) model checkpoints as well as the configurations automatically.',
}
)
reward_model_checkpoint: str = field(
default='checkpoint/rm',
metadata={
# 在使用PPO做RLHF时候,此处需要填写奖励模型所在的文件地址
'help': 'The checkpoint of reward model.'
}
)
cache_dir: Optional[str] = field(
default=None,
metadata={
# 存储从huggingface上下载的临时的模型文件,一般不用管。
'help': 'Where do you want to store the pretrained models downloaded from huggingface.co',
},
)
use_fast_tokenizer: Optional[bool] = field(
default=False,
metadata={
# 是否使用fast tokenizer,该参数只在llama类模型生效。
'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.',
}
)
use_ntk: Optional[str] = field(
default='linear',
metadata={
# 是否使用NTK(高频外推,低频内插)方法扩大模型的输入长度,支持ntk rope和ntk alibi,训练的时候添加可能没啥效果。
# linear: 简单的把旋转位置编码扩大n倍的长度,项目里面用你定义的max_input_token和原始模型的最大长度输入来决定
# dynamic: 根据推理文本的长度动态的调整缩放系数
'help': 'Whether to use NTK method to expand the token length of model input.',
'choices': ['linear', 'dynamic'],
}
)
use_flash_attn: Optional[bool] = field(
default=False,
metadata={
# 是否使用Flash Attention。
# Huggingface官方支持了LLama、Falcon和Mistral的Flash Attention,它将根据你安装的版本进行调用flash attention或者flash attention2。
# 目前支持LLama、Falcon和Mistral,他们正在适配更多的模型:https://github.com/huggingface/transformers/issues/26350
'help': 'Whether to use Flash attention.',
}
)
use_attention_sink: Optional[bool] = field(
default=False,
metadata={
# 使用StreamingLLM中的window attention
# 目前支持falcon, mistral, qwen, llama
'help': 'Whether to use window attention(Streaming LLM).',
}
)
attention_sink_size: Optional[int] = field(
default=4,
metadata={
# 该参数在使用StreamingLLM生效
# 用作注意力的初始token数量。这些token始终包含在注意力的KV缓存中。
'help': 'The number of initial tokens to use as the attention sink. '
'These tokens are always included in the Attention Sink KV Cache.',
}
)
attention_sink_window_size: Optional[int] = field(
default=1020,
metadata={
# 使用StreamingLLM生效
# 该参数在滑动窗口的大小,即在注意力KV缓存中包含的“最近token”的数量。较大的窗口大小会消耗更多的内存。
'help': 'The size of the sliding window, i.e. the number of "recent tokens" to include in the Attention Sink KV Cache. '
'A larger window size costs more memory.',
}
)
resize_emb: Optional[str] = field(
default=None,
metadata={
# 使用随机方法初始化重新设置embedding大小并且修改LLM最后的全连接层
'help': 'Whether to resize embedding and modify the output dim of last linear of LLM.',
'choices': ['random'],
}
)
padding_side: Optional[str] = field(
default='left',
metadata={
# 有些模型该参数由相应的tokenizer_config.json文件提供,没有的要自己提供。
'help': 'Padding side.',
'choices': ['left', 'right'],
}
)
torch_dtype: Optional[str] = field(
default='float16',
metadata={
# 如果全参进行模型训练,需要使用float32(混合精度训练fp16打开的时候此处也是设置float32,训练的时候优化器会自动转换模型参数为fp16)
# 推理或者其他方式训练可选择bfloat16或者float16
'help': "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, "
"the dtype will be automatically derived from the model's weights.",
'choices': ['auto', 'bfloat16', 'float16', 'float32'],
}
)
quantization: Optional[str] = field(
default='bnb',
metadata={
# 如果使用qlora只能选择bnb,两种量化方式区别不大。
'help': 'The specific model version to use (can be a branch name, tag name or commit id).',
'choices': ['cpm', 'bnb'],
}
)
quantization_bit: Optional[int] = field(
default=None,
metadata={
# 使用8bit量化还是4bit量化?
'help': 'The number of bits to quantize the model.',
'choices': [4, 8],
}
)
quantization_type: Optional[Literal['fp4', 'nf4']] = field(
default='nf4',
metadata={
# 默认就好
'help': 'Quantization data type to use in int4 training.',
'choices': ['fp4', 'nf4']
}
)
double_quantization: Optional[bool] = field(
default=True,
metadata={
# 默认就好
'help': 'Whether to use double quantization in int4 training or not.',
}
)
gradio_port: Optional[int] = field(
default=7777,
metadata={
# 使用web_inference进行交互时候,网页的端口号。
'help': 'The port id of gradio.'
}
)
quantized_or_merged_output_dir: Optional[str] = field(
default=None,
metadata={
# 当你想保存量化后的模型或者融合后的模型时,处理后的模型保存的地址。
'help': 'Path to save the quantized or merged model checkpoints as well as the configurations manually.',
}
)
save_path_after_vocab_expansion: Optional[str] = field(
default='auto',
metadata={
# 扩充词表后新模型保存的路径,默认auto,即为原文件夹中新建一个子文件夹
'help': 'The path to save the new model after expanding the vocab.'
}
)
def __post_init__(self):
if self.torch_dtype in ('auto', None):
self.torch_dtype = self.torch_dtype
else:
self.torch_dtype = getattr(torch, self.torch_dtype)
if self.quantization_bit is not None:
assert self.quantization_bit in [4, 8], 'We only accept 4-bit or 8-bit quantization.'
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and evaluation.
"""
train_file_dir: Optional[str] = field(
default='datasets/finetune/example/train',
metadata={
# 训练集保存的路径。
'help': 'The train json data file folder.'
}
)
validation_file_dir: Optional[str] = field(
default='datasets/finetune/example/eval',
metadata={
# 验证集保存的路径。
'help': 'The evaluation json file folder.'
}
)
test_file: Optional[str] = field(
default='datasets/finetune/example/test',
metadata={
# 测试集保存的路径。
'help': 'The test file.'
}
)
dev_ratio: Optional[float] = field(
default=0,
metadata={
# 如果要验证模型结果,但是又没有数据集,愿意从训练集拿多少比例的数据给验证集?
'help': 'Proportion of the dataset to include in the development set, should be between 0.0 and 1.0.'
}
)
prompt_template: Optional[str] = field(
default='chatglm3',
metadata={
# 选择对应模型的模板prompt,一般Chat模型的出品方都会有一个固定的prompt,这部分很重要,预测训练阶段都需要根据chat模型的要求修改
'help': 'Which template to use for constructing prompts in training and inference.',
'choices': ['default', 'alpaca', 'vicuna', 'belle', 'linly', 'ziya', 'aquila', 'firefly',
'openbuddy', 'internlm', 'baichuan', 'baichuan2', 'chatglm', 'qwen', 'moss',
'linksoul', 'xverse', 'tigerbot', 'flagalpha', 'chatglm3', 'orca', 'yi']
}
)
overwrite_cache: Optional[bool] = field(
default=True,
metadata={
# 是否重写本地保存的huggingface下载的临时模型文件
'help': 'Overwrite the cached training and evaluation sets.'
}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={
# 处理数据的时候进程中的worker数,默认就好。
'help': 'The number of processes to use for the preprocessing.'
}
)
max_input_token: int = field(
default=2048,
metadata={
# 模型接受的最大输入的token数,一般来说如果基座使用了NTK的方法后,可以把输入调得更大,平时的时候使用基座模型的规定的最大长度就好
'help': 'Max token of model input.'
}
)
ignore_pad_token_for_loss: Optional[bool] = field(
default=True,
metadata={
# 是否让label里面的padding部分不参与计算。
'help': 'Whether to ignore the tokens corresponding to padded labels in the loss computation or not.'
}
)
corpus_path_for_expansion: Optional[str] = field(
default='datasets/expand_vocab',
metadata={
# 用于扩充词表的语料所在路径,必须是【包含文本的路径】或【单个文本】
'help': "The corpus path for vocab's expansion."
}
)
@dataclass
class TrainingArguments(Seq2SeqTrainingArguments):
fine_tuning_type: Optional[str] = field(
default='lora',
metadata={
# 可选用的训练方式
'help': 'Which fine-tuning method to use.',
'choices': ['full', 'lora', 'adalora', 'prompt_tuning', 'p_tuning', 'prefix_tuning']
}
)
use_firefly_loss: bool = field(
default=True,
metadata={
# 多轮对话的Firefly的loss函数集成:https://mp.weixin.qq.com/s/nhogoWnzl3nrs_77r38_UA
'help': 'Whether to use firefly loss.'
}
)
output_dir: str = field(
default='checkpoint/sft',
metadata={
# 这是存放训练之后保存模型文件所在的文件夹
# 继承于transformers的TrainingArguments
'help': 'The output directory where the model predictions and checkpoints will be written.'
}
)
do_train: bool = field(
default=True,
metadata={
# 进行训练
# 继承于transformers的TrainingArguments
'help': 'Whether to run training.'
}
)
do_eval: bool = field(
default=True,
metadata={
# 跑验证集
# 继承于transformers的TrainingArguments
'help': 'Whether to run eval on the dev set.'
}
)
predict_with_generate: bool = field(
default=True,
metadata={
# 生成时使用seq2seq方式
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'
}
)
num_train_epochs: float = field(
default=5.0,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Total number of training epochs to perform.'
}
)
per_device_train_batch_size: Optional[int] = field(
default=2,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Batch size per GPU/TPU core/CPU for training.'
}
)
per_device_eval_batch_size: Optional[int] = field(
default=2,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Batch size per GPU/TPU core/CPU for evaluation.'
}
)
resume_from_checkpoint: Optional[Union[str, bool]] = field(
default=True,
metadata={
# 断点续训
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Continue train model from your checkpoint.'
}
)
gradient_accumulation_steps: Optional[int] = field(
default=2,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Number of updates steps to accumulate before performing a backward/update pass.'
}
)
gradient_checkpointing: bool = field(
default=True,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'If True, use gradient checkpointing to save memory at the expense of slower backward pass.'
}
)
optim: Optional[str] = field(
default='adamw_torch',
metadata={
# 默认就好,继承于transformers的Seq2SeqTrainingArguments
'help': 'The optimizer to use.',
'choices': ['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_apex_fused', 'adamw_anyprecision']
}
)
lr_scheduler_type: Optional[str] = field(
default='cosine',
metadata={
# 默认就好,继承于transformers的Seq2SeqTrainingArguments
'help': 'The scheduler type to use.'
}
)
learning_rate: float = field(
default=1e-3,
metadata={
# 设置训练时候的学习率
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'The initial learning rate for AdamW.'
}
)
warmup_steps: int = field(
default=0,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Linear warmup over warmup_steps.'
}
)
warmup_ratio: float = field(
default=0.0,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Linear warmup over warmup_ratio fraction of total steps.'
}
)
fp16: bool = field(
default=True,
metadata={
# 开启fp16混合精度训练
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Whether to use fp16 (mixed) precision instead of 32-bit'
},
)
weight_decay: float = field(
default=0.0,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Weight decay for AdamW if we apply some.'
}
)
evaluation_strategy: str = field(
default='no',
metadata={
# 默认就好
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'The evaluation strategy to use.'
}
)
eval_steps: Optional[float] = field(
default=None,
metadata={
# 默认就好
# 继承于transformers的Seq2SeqTrainingArguments
'help': (
'Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`.'
'If smaller than 1, will be interpreted as ratio of total training steps.'
)
}
)
save_steps: float = field(
default=1000,
metadata={
# 默认就好
# 继承于transformers的Seq2SeqTrainingArguments
'help': (
'Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`.'
'If smaller than 1, will be interpreted as ratio of total training steps.'
)
},
)
save_strategy: str = field(
default='steps',
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'The checkpoint save strategy to use.'
}
)
save_total_limit: Optional[int] = field(
default=None,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Limit the total amount of checkpoints. Deletes the older checkpoints in the output_dir. '
'Default is unlimited checkpoints'
}
)
overwrite_output_dir: bool = field(
default=False,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Overwrite the content of the output directory. '
'Use this to continue training if output_dir points to a checkpoint directory.'
}
)
ddp_timeout: Optional[int] = field(
default=1800,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Overrides the default timeout for distributed training (value should be given in seconds).'
},
)
deepspeed: Optional[str] = field(
default=None,
metadata={
# 如果使用deepspeed进行训练,此处填写deepspeed的配置
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) '
'or an already loaded json file as a dict'
}
)
report_to: Optional[List[str]] = field(
default=None,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'The list of integrations to report the results and logs to.'
}
)
logging_strategy: str = field(
default='steps',
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'The logging strategy to use.'
}
)
logging_steps: float = field(
default=10,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': (
'Log every X updates steps. Should be an integer or a float in range `[0,1)`.'
'If smaller than 1, will be interpreted as ratio of total training steps.'
)
},
)
logging_first_step: bool = field(
default=False,
metadata={
# 继承于transformers的Seq2SeqTrainingArguments
'help': 'Log the first global_step'
}
)
noise_alpha: Optional[float] = field(
default=0,
metadata={
# 使用NEFTune对模型进行Noise Tune,https://arxiv.org/abs/2310.05914
'help': 'Whether to use Noisy Embedding Fine Tuning, if you want using it, set noise_alpha > 0.'
},
)
# 下面都是peft的设置参数
# Lora:
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.'
}
)
# AdaLora:
adalora_beta: Optional[float] = field(
default=0.85,
metadata={
'help': 'The hyperparameter of EMA for sensitivity smoothing and quantification.'
}
)
adalora_init_r: Optional[int] = field(
default=12,
metadata={
'help': 'The initial rank for each incremental matrix.'
}
)
adalora_tinit: Optional[int] = field(
default=200,
metadata={
'help': 'The steps of initial fine-tuning warmup.'
}
)
adalora_tfinal: Optional[int] = field(
default=1000,
metadata={
'help': 'The step of final fine-tuning.'
}
)
adalora_delta_t: Optional[int] = field(
default=10,
metadata={
'help': 'The time internval between two budget allocations.'
}
)
lora_bias: Optional[str] = field(
default='none',
metadata={
'help': "Bias type for Lora. Can be 'none', 'all' or 'lora_only'",
'choices': ['none', 'all', 'lora_only']
}
)
lora_target: Optional[str] = field(
default='query_key_value',
metadata={
'help': "Name(s) of target modules to use cpm Quantize. Use comma to separate multiple modules.\
ChatGLM choices: [\"query_key_value\", \"self_attention.dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"], \
Falcon choices: [\"query_key_value\", \"self_attention.dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"], \
BLOOM choices: [\"query_key_value\", \"self_attention.dense\", \"dense_h_to_4h\", \"dense_4h_to_h\"],\
LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"down_proj\", \"up_proj\"],\
InternLM choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"] \
Aquila choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"down_proj\", \"up_proj\"] \
Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"] \
Qwen choices: [\"c_attn\", \"c_proj\", \"w1\", \"w2\"] \
Xverse choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"down_proj\", \"up_proj\"] \
yi choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"down_proj\", \"up_proj\"] \
Mistral choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"down_proj\", \"up_proj\"]"
}
)
# prompt_tuning:
num_virtual_tokens: Optional[int] = field(
default=20,
metadata={
'help': 'Number of virtual tokens.'
}
)
prompt_encoder_hidden_size: Optional[int] = field(
default=128,
metadata={
'help': 'The hidden size of the prompt encoder'
}
)
# 下面都是RLHF的设置参数
seed: Optional[int] = field(
default=0,
metadata={
'help': 'the seed'
}
)
init_kl_coef: Optional[float] = field(
default=0.2,
metadata={
'help': 'Initial KL penalty coefficient (used for adaptive and linear control)'
}
)
adap_kl_ctrl: Optional[bool] = field(
default=True, metadata={
'help': 'Use adaptive KL control, otherwise linear'
}
)
target_kl: Optional[float] = field(
default=0.1,
metadata={
'help': 'The kl target for early stopping'
}
)
ppo_epochs: Optional[int] = field(
default=4,
metadata={
'help': 'Number of optimisation epochs per batch of samples'
}
)
ppo_steps: Optional[int] = field(
default=16,
metadata={
'help': 'Number of training steps'
}
)
dpo_beta: Optional[float] = field(
default=0.1,
metadata={
'help': 'The beta factor in DPO loss. Higher beta means less divergence from the initial policy.'
}
)
log_with: Optional[str] = field(
default='wandb',
metadata={
'help': "Log with either 'wandb' or 'tensorboard', check https://huggingface.co/docs/accelerate/usage_guides/tracking for more details"
},
)
# 下面是扩充词表的部分具体参数
vocab_size: Optional[int] = field(
default=8000,
metadata={
# 指定训练得到的词表大小(实际去重清洗后会稍小)
'help': 'Specify the size of the trained vocabulary (it will actually be smaller).'
}
)
max_sentence_length: Optional[int] = field(
default=24000,
metadata={
# 指定输入句子的最大长度,以字节为单位
'help': 'Specifies the maximum length of the input sentence(in bytes).'
}
)
expand_mode: Optional[str] = field(
default="inject",
metadata={
# 决定扩充词表的方式
# inject: 直接注入一个分隔好的词表 txt/tsv 文件,每个词占一行
# train: 从一个语料文本训练词表
'help': 'Ways to expand the vocabulary.',
'choices': ['inject', 'train']
}
)
@dataclass
class GeneratingArguments:
"""
Arguments pertaining to specify the decoding parameters.
这里都是模型做生成时候的配置,在需要预测阶段(比如webui使用的时候)和RLHF-PPO阶段的时候需要配置
"""
do_sample: Optional[bool] = field(
default=True,
metadata={'help': 'Whether or not to use sampling, use greedy decoding otherwise.'}
)
temperature: Optional[float] = field(
default=0.95,
metadata={'help': 'The value used to modulate the next token probabilities.'}
)
top_p: Optional[float] = field(
default=0.7,
metadata={
'help': 'The smallest set of most probable tokens with '
'probabilities that add up to top_p or higher are kept.'}
)
top_k: Optional[int] = field(
default=50,
metadata={'help': 'The number of highest probability vocabulary tokens to keep for top-k filtering.'}
)
num_beams: Optional[int] = field(
default=1,
metadata={'help': 'Number of beams for beam search. 1 means no beam search.'}
)
max_length: Optional[int] = field(
default=None,
metadata={'help': 'The whole numbers of output tokens, including the number of tokens in the prompt.'}
)
max_new_tokens: Optional[int] = field(
default=512,
metadata={'help': 'The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.'}
)
repetition_penalty: Optional[float] = field(
default=1.0,
metadata={'help': 'The parameter for repetition penalty. 1.0 means no penalty.'}
)
def to_dict(self):
args = asdict(self)
if args.get('max_new_tokens', None):
args.pop('max_length', None)
return args