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89b38b0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 | from dataclasses import dataclass, field
from typing import Optional
try:
from accelerate.utils import ParallelismConfig as _PC
except Exception:
class _PC:
pass
import transformers.training_args as _ta
if not hasattr(_ta, "ParallelismConfig"):
_ta.ParallelismConfig = _PC
from transformers import TrainingArguments as HFTrainingArguments
from trl import DPOConfig as DPOConfigTRL
from trl import GRPOConfig as GRPOConfigTRL
@dataclass
class ModelArguments:
model_id: Optional[str] = field(default="Qwen/Qwen2-VL-7B-Instruct")
@dataclass
class CLSArguments(HFTrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
adam_beta1: float = field(default=0.9)
adam_beta2: float = field(default=0.999)
adam_epsilon: float = field(default=1e-8)
freeze_vision_tower: bool = field(default=False)
freeze_llm: bool = field(default=False)
freeze_merger: bool = field(default=False)
disable_flash_attn2: bool = field(default=False)
unfreeze_topk_llm: int = 0
unfreeze_topk_vision: int = 0
mlp_head_dim: Optional[int] = field(default=0)
mlp_head_dropout: Optional[float] = field(default=0.0)
loss_type : str = field(
default="cross_entropy",
metadata={"help": "Loss type to use. Should be one of `cross_entropy`, `focal_loss`, `class_balanced_cross_entropy`, or `class_balanced_focal_loss`."}
)
focal_alpha: Optional[str] = field(
default=None,
metadata={"help": "Focal Loss alpha value. If None use CrossEntropyLoss. ex '1.0,7.5'"}
)
focal_gamma: float = field(
default=0.0,
metadata={"help": "Focal Loss gamma value"}
)
num_labels: int = field(
default=2,
metadata={"help": "Number of labels for classification."}
)
class_balanced_beta: float = field(
default=0.999,
metadata={"help": "Beta value for Class Balanced Loss. If 0.0, use standard CrossEntropyLoss."}
)
early_stopping_patience: int = field(
default=0,
metadata={"help": "Number of epochs with no improvement after which training will be stopped."}
)
early_stopping_threshold: float = field(
default=0.0,
metadata={"help": "Minimum change in the monitored quantity to qualify as an improvement."}
)
max_seq_length: int = field(
default=32768, # This is the default value of the qwen2-vl model
metadata={
"help":
"Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
double_quant: bool = field(
default=True,
metadata={"help": "Compress the quantization statistics through double quantization."}
)
quant_type: str = field(
default="nf4",
metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
)
bits: int = field(
default=16,
metadata={"help": "How many bits to use."}
)
lora_enable: bool = False
vision_lora: bool = False
use_dora: bool = False
lora_rank: int = 64
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_weight_path: str = ""
lora_bias: str = "none"
vision_lr: Optional[float] = None
merger_lr: Optional[float] = None
head_lr: Optional[float] = None
lora_namespan_exclude: str = field(default=None, metadata={"help": "List of namespan to exclude for LoRA"})
num_lora_modules: int = -1
use_liger_kernel: bool = True
@dataclass
class TrainingArguments(HFTrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
adam_beta1: float = field(default=0.9)
adam_beta2: float = field(default=0.999)
adam_epsilon: float = field(default=1e-8)
freeze_vision_tower: bool = field(default=False)
freeze_llm: bool = field(default=False)
freeze_merger: bool = field(default=False)
disable_flash_attn2: bool = field(default=False)
unfreeze_topk_llm: int = 0
unfreeze_topk_vision: int = 0
max_seq_length: int = field(
default=32768, # This is the default value of the qwen2-vl model
metadata={
"help":
"Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
double_quant: bool = field(
default=True,
metadata={"help": "Compress the quantization statistics through double quantization."}
)
quant_type: str = field(
default="nf4",
metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
)
bits: int = field(
default=16,
metadata={"help": "How many bits to use."}
)
lora_enable: bool = False
vision_lora: bool = False
use_dora: bool = False
lora_rank: int = 64
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_weight_path: str = ""
lora_bias: str = "none"
vision_lr: Optional[float] = None
merger_lr: Optional[float] = None
lora_namespan_exclude: str = field(default=None, metadata={"help": "List of namespan to exclude for LoRA"})
num_lora_modules: int = -1
use_liger_kernel: bool = True
# Generation-based evaluation settings
generation_max_new_tokens: int = field(
default=512,
metadata={"help": "Maximum number of new tokens to generate during evaluation."}
)
@dataclass
class DPOArguments(DPOConfigTRL):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
adam_beta1: float = field(default=0.9)
adam_beta2: float = field(default=0.999)
adam_epsilon: float = field(default=1e-8)
freeze_vision_tower: bool = field(default=False)
freeze_llm: bool = field(default=False)
freeze_merger: bool = field(default=False)
disable_flash_attn2: bool = field(default=False)
unfreeze_topk_llm: int = 0
unfreeze_topk_vision: int = 0
max_seq_length: int = field(
default=32768, # This is the default value of the qwen2-vl model
metadata={
"help":
"Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
double_quant: bool = field(
default=True,
metadata={"help": "Compress the quantization statistics through double quantization."}
)
quant_type: str = field(
default="nf4",
metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
)
bits: int = field(
default=16,
metadata={"help": "How many bits to use."}
)
lora_enable: bool = False
vision_lora: bool = False
use_dora: bool = False
lora_rank: int = 64
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_weight_path: str = ""
lora_bias: str = "none"
vision_lr: Optional[float] = None
merger_lr: Optional[float] = None
lora_namespan_exclude: str = field(default=None, metadata={"help": "List of namespan to exclude for LoRA"})
num_lora_modules: int = -1
use_liger_loss: bool = True
beta: float = field(
default=0.1,
metadata={"help": "The beta value for DPO."}
)
precompute_ref_log_probs: bool = field(
default=False,
metadata={"help": "Whether to precompute the reference log probabilities."}
)
dpo_loss:str = field(
default="sigmoid",
metadata={"help": "The type of DPO loss to use."}
)
@dataclass
class GRPOArguments(GRPOConfigTRL):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
adam_beta1: float = field(default=0.9)
adam_beta2: float = field(default=0.999)
adam_epsilon: float = field(default=1e-8)
freeze_vision_tower: bool = field(default=False)
freeze_llm: bool = field(default=False)
freeze_merger: bool = field(default=False)
disable_flash_attn2: bool = field(default=False)
unfreeze_topk_llm: int = 0
unfreeze_topk_vision: int = 0
double_quant: bool = field(
default=True,
metadata={"help": "Compress the quantization statistics through double quantization."}
)
quant_type: str = field(
default="nf4",
metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
)
bits: int = field(
default=16,
metadata={"help": "How many bits to use."}
)
lora_enable: bool = False
vision_lora: bool = False
use_dora: bool = False
lora_rank: int = 64
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_weight_path: str = ""
lora_bias: str = "none"
vision_lr: Optional[float] = None
merger_lr: Optional[float] = None
lora_namespan_exclude: str = field(default=None, metadata={"help": "List of namespan to exclude for LoRA"})
num_lora_modules: int = -1
beta: float = field(
default=0.04,
metadata={
"help": "KL coefficient. If `0.0`, the reference model is not loaded, reducing memory usage and improving "
"training speed, but may be numerically unstable for long training runs."
},
)
temperature: float = 0.9
top_p: float = 1.0
top_k: int = 50
min_p: Optional[float] = None
repetition_penalty: float = 1.0
max_completion_length: int = 256
max_prompt_length: int = 512
use_liger_loss: bool = True
@dataclass
class DataArguments:
data_path: str = field(
default=None, metadata={"help": "Path to the training data."}
)
eval_path: str= field(
default=None, metadata={"help": "Path to the evaluation data."}
)
eval_image_folder: Optional[str] = field(
default=None, metadata={"help": "Path to the evaluation image data."}
)
lazy_preprocess: bool = False
image_folder: Optional[str] = field(default=None)
image_min_pixels: Optional[int] = field(default=3136)
image_max_pixels: Optional[int] = field(default=12845056)
video_min_pixels: Optional[int] = field(default=100352)
video_max_pixels: Optional[int] = field(default=602112)
image_resized_width: int = field(default=None)
image_resized_height: int = field(default=None)
video_resized_width: int = field(default=None)
video_resized_height: int = field(default=None)
fps: Optional[int] = field(default=None, metadata={"help": "Frames per second for video data."})
nframes: Optional[int] = field(default=None, metadata={"help": "Number of frames for video data."}) |