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from dataclasses import dataclass, field
import json
import transformers
from utils.supported_models import MODEL_HF_PATH, MODEL_FAMILIES
@dataclass
class ModelArguments:
model_id: str = field(default="llava-1.5-7b")
model_local_path: Optional[str] = field(default=None)
cut_embed_dim: Optional[int] = field(
default=3072,
metadata={"help": "cut off embed dim for tbstars model"},
)
parscale_n: Optional[int] = field(
default=2,
metadata={"help": "Use knn for partial fc"},
)
parscale_n_tokens: Optional[int] = field(
default=42,
metadata={"help": "parscale_n_tokens"},
)
parscale_attn_smooth: Optional[float] = field(
default=0.01,
metadata={"help": "parscale_attn_smooth"},
)
parscale_layer: Optional[int] = field(
default=1,
metadata={"help": "when to begin the parscale"},
)
perceiver_num_heads: Optional[int] = field(
default=32,
metadata={"help": "qformer_head"},
)
perceiver_num_queries: Optional[int] = field(
default=144,
metadata={"help": "qformer_dim"},
)
lora_load: Optional[bool] = field(
default=False,
metadata={"help": "if loading the lora pth"},
)
lora_pth: Optional[str] = field(
default='',
metadata={"help": "lora pth"},
)
def __post_init__(self):
assert self.model_id in MODEL_HF_PATH, f"Unknown model_id: {self.model_id}"
self.model_hf_path: str = MODEL_HF_PATH[self.model_id]
assert self.model_id in MODEL_FAMILIES, f"Unknown model_id: {self.model_id}"
self.model_family_id: str = MODEL_FAMILIES[self.model_id]
if not self.model_local_path:
self.model_local_path = self.model_hf_path
@dataclass
class DataArguments:
data_path: str = field(
default=None, metadata={"help": "Path to the training data json file."}
)
eval_data_path: Optional[str] = field(
default=None, metadata={"help": "Path to the evaluation data json file."}
)
image_folder: Optional[str] = field(default=None)
video_folder: Optional[str] = field(default=None)
num_frames: Optional[int] = field(default=8)
user_key: Optional[str] = field(default="human")
assistant_key: Optional[str] = field(default="gpt")
image_data_path: str = field(
default=None, metadata={"help": "Path to the image data json file."}
)
text_data_path: str = field(
default=None, metadata={"help": "Path to the text data json file."}
)
query_data_path: str = field(
default=None, metadata={"help": "Path to the query data json file."}
)
cand_pool_path: str = field(
default=None, metadata={"help": "Path to the cand pool data json file."}
)
instructions_path: str = field(
default=None, metadata={"help": "Path to the instructions data json file."}
)
rerank_data_path: str = field(
default=None, metadata={"help": "Path to the rerank data json file."}
)
image_path_prefix: str = field(
default=None, metadata={"help": "Path to the image files."}
)
tables: str = field(
default=None, metadata={"help": "Odps Tables."}
)
tables_eval: str = field(
default=None, metadata={"help": "Odps Tables for eval."}
)
max_lines: int = field(
default=-1, metadata={"help": "Max data lines."}
)
max_lines_eval: int = field(
default=-1, metadata={"help": "Max data lines of eval."}
)
image_type: str = field(
default="crop", metadata={"help": "eval instruction."}
)
text_require: Optional[bool] = field(
default=False,
metadata={"help": "requrei text or not"},
)
@dataclass
class TrainingArguments(transformers.TrainingArguments):
model_max_length: int = field(
default=1024,
metadata={
"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
eval_json: str = field(
default='eval_results.jsonl', metadata={"help": "json file to save eval result"}
)
sub_loss: Optional[bool] = field(
default=True,
metadata={"help": "if use subspace loss"},
)
sub_loss_weight: Optional[float] = field(
default=1,
metadata={"help": "sub loss weight"},
)
margin_value: Optional[float] = field(
default=1,
metadata={"help": "margin value of triplet loss"},
)
triplet_weight: Optional[float] = field(
default=1,
metadata={"help": "margin value of triplet loss"},
)
club_lr: float = field(
default=1e-5, metadata={"help": "lambda for uniform loss"}
)
club_weight_decay: float = field(
default=0.01, metadata={"help": "lambda for uniform loss"}
)
club_flag: bool = field(
default=False, metadata={"help": "lambda for uniform loss"}
)
club_iters: int = field(
default=5, metadata={"help": "lambda for uniform loss"}
)
club_weight: float = field(
default=0.0001, metadata={"help": "lambda for uniform loss"}
)
club_target: bool = field(
default=False, metadata={"help": "if regulation for target"}
)
club_warmup_iters: int = field(
default=10, metadata={"help": "warm_up_iters for club"}
)
use_flash_attn: bool = False
train_vision_encoder: bool = False
train_vision_projector: bool = False
train_llm: bool = False
train_parscale: bool = False
vision_projector_lr: float = None
def __post_init__(self):
super().__post_init__()
if self.resume_from_checkpoint == "False":
self.resume_from_checkpoint = False
elif self.resume_from_checkpoint == "True":
self.resume_from_checkpoint = True
self.remove_unused_columns = False
@dataclass
class LoraArguments:
use_lora: bool = True
use_vision_lora: bool = True
q_lora: bool = False
lora_r: int = 64
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_weight_path: str = ""
lora_bias: str = "none"
vision_lora_r: int = 16
vision_lora_alpha: int = 16
@dataclass
class FCArguments:
r"""
Arguments pertaining to which techniques we are going to fine-tuning with.
"""
stage: Optional[Literal["classify_infer_crop", "classify_infer", "pt", "sft", "rm", "ppo", "retrieval", "reranker", "retrieval_infer_query", "retrieval_infer_item", "retrieval_hard_negative", "classify"]] = field(
default="classify",
metadata={"help": "Which stage will be performed in training."},
)
embed_dim: Optional[int] = field(
default=512,
metadata={"help": "embed dimension of output token"},
)
temperature: Optional[float] = field(
default=0.02,
metadata={"help": "temperature of the infonce loss"},
)
neg_gather_size: Optional[int] = field(
default=1,
metadata={
"help": "how many gpus share negatives in the infonce loss. "
"1 = only in-device batch; 8 = share within a node; "
"-1 or world_size = share across all gpus. "
"candidate pool = 2 * neg_gather_size * per_device_train_batch_size"
},
)
classify_num: Optional[int] = field(
default=100000,
metadata={"help": "Num of classification"},
)
classify_sample_rate: Optional[float] = field(
default=0.05,
metadata={"help": "Sample rate for partial fc"},
)
classify_use_knn: Optional[bool] = field(
default=True,
metadata={"help": "Use knn for partial fc"},
)
classify_global_knn: Optional[bool] = field(
default=False,
metadata={"help": "Use knn for partial fc"},
)
classify_knn_topk: Optional[int] = field(
default=30,
metadata={"help": "Knn topk for partial fc"},
)
classify_knn_chunk_size: Optional[int] = field(
default=5000,
metadata={"help": "knn chunk size for sim matrix calc: only open when global_knn"},
)
classify_knn_ratio: Optional[float] = field(
default=0.5,
metadata={"help": "Knn rate for partial fc"},
)
classify_knn_weight_cover_rate: Optional[float] = field(
default=0.3,
metadata={"help": "Knn rate for partial fc"},
)
classify_knn_update_after_save: Optional[bool] = field(
default=True,
metadata={"help": "knn_update_after_save"},
)
classify_loss_type: Optional[str] = field(
default="arcface",
metadata={"help": "loss function for partial fc"},
)
classify_ckpt_part: Optional[bool] = field(
default=True,
metadata={"help": "partial fc ckpt save load partly"},
)
classify_freeze_backbone: Optional[bool] = field(
default=False,
metadata={"help": "freeze backbone, only train partial fc"},
)
model_name_or_path_pfc: Optional[str] = field(
default="/data/oss_bucket_0/guijiang/outputs/tongkuan/lamra_qwen2_5_fc_debug",
metadata={"help": "pth to fc pth"},
)
model_name_or_path_proj: Optional[str] = field(
default="/data/oss_bucket_0/guijiang/outputs/tongkuan/lamra_qwen2_5_fc_debug",
metadata={"help": "pth to fc pth"},
)
proj_name: Optional[Literal["load_add_projector", "load_mlp_projector"]] = field(
default="load_add_projector",
metadata={"help": "Which stage will be performed in training."},
)
pfc_trainable: Optional[bool] = field(
default=True,
metadata={"help": "train pfc or not"},
)
def save_to_json(self, json_path: str):
r"""Saves the content of this instance in JSON format inside `json_path`."""
json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n"
with open(json_path, "w", encoding="utf-8") as f:
f.write(json_string)
@classmethod
def load_from_json(cls, json_path: str):
r"""Creates an instance from the content of `json_path`."""
with open(json_path, "r", encoding="utf-8") as f:
text = f.read()
return cls(**json.loads(text))
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