| from dataclasses import dataclass, field |
| from typing import Optional |
|
|
| from transformers import TrainingArguments |
|
|
|
|
| @dataclass |
| class ModelArgs: |
| |
| model_name_or_path: str = field( |
| default="meta-llama/Llama-2-7b-chat-hf", |
| metadata={ |
| "help": "Path to pretrained model or model identifier from huggingface.co/models" |
| }, |
| ) |
| super_tokenizer_name_or_path: str = field( |
| default="/share/ninglu_shao/code/PluginTransformer/data/outputs/90k_0104+8-longalpaca_0106/super_tokenizer", |
| metadata={ |
| "help": "Path to pretrained model or model identifier from huggingface.co/models" |
| }, |
| ) |
| |
| super_tokenizer_num_hidden_layers: int = field( |
| default=8, |
| metadata={"help": "Encoder model's layer num."}, |
| ) |
| is_model_frozen: bool = field( |
| default=True, |
| metadata={"help": "Freeze or not decoder model."}, |
| ) |
| use_flash_attention_2: bool = field( |
| default=True, |
| metadata={"help": "Use flash attention 2 or not."}, |
| ) |
| dtype: str = field( |
| default="bf16", |
| ) |
| device_map: Optional[str] = field( |
| default=None, |
| ) |
|
|
|
|
| @dataclass |
| class DataArgs: |
| |
| dataset_list: str = field( |
| default="wikipedia", |
| metadata={"help": "Path of dataset"}, |
| ) |
| dataset_save_dir: str = field( |
| default="/share/ninglu_shao/data/PluginTransformer", |
| metadata={"help": "The path to save dataset."}, |
| ) |
|
|
| def __post_init__(self): |
| self.dataset_list = [dataset.strip() for dataset in self.dataset_list.split(",")] |
|
|
|
|
| @dataclass |
| class TrainingArgs(TrainingArguments): |
| |
| output_dir: str = field( |
| default="outputs/test_4", |
| metadata={ |
| "help": "The output directory where the model predictions and checkpoints will be written." |
| }, |
| ) |
| overwrite_output_dir: bool = field( |
| default=False, |
| metadata={"help": "If True, overwrite the content of the output directory."}, |
| ) |
| |
| learning_rate: float = field( |
| default=1e-4, |
| metadata={"help": "The initial learning rate for optimizer."}, |
| ) |
| warmup_ratio: float = field( |
| default=0.1, |
| metadata={"help": "The ratio of warmup steps for optimizer."}, |
| ) |
| num_train_epochs: float = field( |
| default=1, |
| metadata={"help": "Total number of training epochs to perform."}, |
| ) |
| per_device_train_batch_size: int = field( |
| default=8, |
| metadata={"help": "The batch size per GPU/TPU core/CPU for training."}, |
| ) |
| |
| dataloader_num_workers: int = field( |
| default=32, |
| metadata={"help": "Number of subprocesses to use for data loading."}, |
| ) |
| remove_unused_columns: bool = field( |
| default=False, |
| metadata={ |
| "help": "Whether or not to automatically remove the columns unused by the model forward method." |
| }, |
| ) |
| |
| save_strategy: str = field( |
| default="steps", |
| metadata={"help": "The checkpoint save strategy to adopt during training."}, |
| ) |
| save_steps: int = field( |
| default=500, |
| metadata={"help": "Saving frequency according to saving strategy"}, |
| ) |
| save_total_limit: int = field( |
| default=None, |
| metadata={"help": "How many checkpoints to keep in the output_dir."}, |
| ) |
| logging_steps: int = field( |
| default=10, |
| metadata={"help": "Logging frequency according to logging strategy."}, |
| ) |
| |
| fp16: bool = field( |
| default=False, |
| metadata={ |
| "help": "Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training." |
| }, |
| ) |
| bf16: bool = field( |
| default=True, |
| metadata={ |
| "help": "Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training." |
| }, |
| ) |
| |
| |
| |
| |
| |
| |
|
|
|
|
| @dataclass |
| class GenerationArgs: |
| do_sample: bool = field( |
| default=False, |
| metadata={"help": "Sample when decoding?"}, |
| ) |
| num_return_sequences: int = field( |
| default=1, |
| metadata={"help": "How many sequences to generate?"}, |
| ) |
| max_length: int = field( |
| default=1024, |
| metadata={"help": "Maximum length."}, |
| ) |
