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ecadbd9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | from dataclasses import dataclass, field, fields, asdict
from typing import Optional, List, Literal, Dict, Any, Union
from transformers import TrainingArguments, Trainer
# from omegaconf import OmegaConf
import sys
from smpeft import SamaConfig
@dataclass
class ModelConfig:
model_name: str = ""
dropout: float = 0.0
model_max_seq_length: int = field(default=512)
data_collator_mode: str=field(default='fixed', metadata={"help": "fixed or dynamic padding in DataCollator"})
# lambda_reg: float = field(default=1e-4, metadata={"help": "The control strength of regularity"})
adapter_path: Optional[str] = field(default=None)
merge_adapter_path: Optional[str] = field(default=None)
merge_output_path: Optional[str] = field(default=None)
@dataclass
class InferConfig:
datasets: List[str] = field(default_factory=lambda: ["boolq", "piqa", "social_i_qa", "hellaswag", "winogrande", "ARC-Easy", "ARC-Challenge", "openbookqa"])
is_json: bool = field(default=True)
infer_max_seq_length: int = field(default=1024)
@dataclass
class SamaConfig:
num_unique_blocks_L: int = field(default=8)
num_unique_blocks_R: int = field(default=-1)
# share_factor: int = field(default=2)
col_L: int = field(default=64)
row_R: int = field(default=64)
scaling: float = field(default=1.0)
task_type: str = "CAUSAL_LM"
target_modules: List[str] = field(default_factory=lambda: ["q_proj",])
drop_out: float = field(default=0.0)
@dataclass
class DataConfig:
dataset_name: str = 'math'
split_ratio: Union[int,float] = field(default=0.01)
path: str = "./nl_tasks/data/MetaMathQA-40K/MetaMathQA-40K.json"
dataset_split: str = field(default="train[:1000]", metadata={"help": "(`['train', 'test', 'eval']`):"})
adapter_names: List[Optional[str]] = field(default_factory=lambda: ["default"]) ###
dataset_field: List[str] = field(default_factory=list, metadata={"help": "Fields of dataset input and output."})
total_train_samples: int = field(default=2800)
total_test_samples: int = field(default=1200)
@dataclass
class TrainingOverride:
optim: str=field(default="adamw_torch") ##
eval_strategy: str=field(default='no')
per_device_train_batch_size: int=field(default=8) ##
per_device_eval_batch_size: int=field(default=8) ##
learning_rate: float = field(default=1e-05)
lr_scheduler_type: str = field(default='cosine')
warmup_steps: Union[int,float] = field(default=100)
gradient_checkpointing: bool = field(default=False)
gradient_accumulation_steps: int=field(default=1)
gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = field(
default_factory=lambda: {"use_reentrant": False}
)
output_dir: str = field(default="runs")
save_steps: float = field(default=0)
save_strategy: str = field(default='no')
save_total_limit: int = field(default=1)
# save_total_limit: int=field(default=1) No need any more
bf16: bool=field(default=False)
bf16_full_eval: bool=field(default=False)
save_safetensors: bool=field(default=False)
report_to: Union[None, str, list[str]]=field(default="none")
logging_steps: int=field(default=25) # we use int only
# logging_first_step: bool=field(default=False)
eval_steps: Union[None,int]=field(default=None) # we use int only f
eval_delay: Union[int,float]=field(default=0)
dataloader_num_workers: int = field(default=2)
dataloader_pin_memory: bool = field(default=True) ###
dataloader_persistent_workers: bool=field(default=True) ###
dataloader_prefetch_factor: int = field(default=1) ###
num_train_epochs: float = field(default=1.0)
max_steps: int=field(default=-1)
load_best_model_at_end: bool = field(default=True)
@dataclass
class MainConfig:
model: ModelConfig = field(default_factory=ModelConfig)
sama_adapter: SamaConfig = field(default_factory=SamaConfig)
data: DataConfig = field(default_factory=DataConfig)
trainer_args: TrainingOverride = field(default_factory=TrainingOverride)
infer: InferConfig = field(default_factory=InferConfig)
project_name: str = "llm_sama"
seed: int = 42
run_text: str=field(default='def')
# device: str = field(default='cpu')
@dataclass
class HFTrainingArguments(TrainingArguments):
extension: Optional[Dict[str, Any]] = field(
default=None,
metadata={"help": "Serialized MainConfig excluding training args"}
)
def convert_to_trainer_args(main_cfg: MainConfig) -> HFTrainingArguments:
"""
Maps MainConfig to MyTrainingArguments.
Logic:
1. Extract 'training' fields -> Pass to TrainingArguments constructor.
2. Pack 'model', 'data', etc. -> Put into 'extension'.
"""
KEY = "trainer_args"
# 1. Convert OmegaConf/Dataclass to pure Python dict
# resolve=True ensures variables like ${model.name} are interpolated
full_dict = asdict(main_cfg)
# 2. Extract Training Arguments
# These will be unpack **kwargs to initialize the parent TrainingArguments
train_args_dict = full_dict.pop(KEY)
# 3. The rest (model, data, seed) goes into extension
extension_payload = full_dict
# 4. Initialize MyTrainingArguments
# Note: We must ensure train_args_dict keys match TrainingArguments fields.
try:
args = HFTrainingArguments(**train_args_dict)
except TypeError as e:
print(f"Error: Your 'training' config contains keys unknown to HF TrainingArguments: {e}")
sys.exit(1)
# 5. Attach the extension
args.extension = extension_payload
return args
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