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arguments.py
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arguments.py
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import os
from dataclasses import dataclass, field
from typing import Optional, List
from transformers import TrainingArguments
@dataclass
class ModelArguments:
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
target_model_path: str = field(
default=None,
metadata={"help": "Path to pretrained reranker target model"}
)
model_type: str = field(
default=None,
metadata={"help": "Name of the used model"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
# out projection
add_pooler: bool = field(default=False)
projection_in_dim: int = field(default=768)
projection_out_dim: int = field(default=768)
# for Jax training
dtype: Optional[str] = field(
default="float32",
metadata={
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one "
"of `[float32, float16, bfloat16]`. "
},
)
# neighbor masking
neighbor_mask_ratio: Optional[float] = field(
default=0,
metadata={
"help": "The probability of neighbor to be masked/corrupted during link prediction/neighbor-enhanced mlm pretraining"
}
)
pdebug: bool = field(default=False)
@dataclass
class DataArguments:
data_dir: str = field(
default=None, metadata={"help": "Path to data directory"}
)
train_dir: str = field(
default=None, metadata={"help": "Path to train directory"}
)
data_path: str = field(
default=None, metadata={"help": "Path to the single data file"}
)
train_path: str = field(
default=None, metadata={"help": "Path to single train file"}
)
eval_path: str = field(
default=None, metadata={"help": "Path to eval file"}
)
test_path: str = field(
default=None, metadata={"help": "Path to eval file"}
)
query_path: str = field(
default=None, metadata={"help": "Path to query file"}
)
corpus_path: str = field(
default=None, metadata={"help": "Path to corpus file"}
)
processed_data_path: str = field(
default=None, metadata={"help": "Path to processed data directory"}
)
data_cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the data downloaded from huggingface"}
)
dataset_name: str = field(
default=None, metadata={"help": "huggingface dataset name"}
)
passage_field_separator: str = field(default=' ')
dataset_proc_num: int = field(
default=12, metadata={"help": "number of proc used in dataset preprocess"}
)
hn_num: int = field(
default=4, metadata={"help": "number of negatives used"}
)
positive_passage_no_shuffle: bool = field(
default=False, metadata={"help": "always use the first positive passage"})
negative_passage_no_shuffle: bool = field(
default=False, metadata={"help": "always use the first negative passages"}
)
encode_in_path: List[str] = field(default=None, metadata={"help": "Path to data to encode"})
encode_is_qry: bool = field(default=False)
save_trec: bool = field(default=False)
encode_num_shard: int = field(default=1)
encode_shard_index: int = field(default=0)
max_len: int = field(
default=32,
metadata={
"help": "The maximum total input sequence length after tokenization for query. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
query_column_names: str = field(
default="id,text",
metadata={"help": "column names for the tsv data format"}
)
doc_column_names: str = field(
default="id,title,text",
metadata={"help": "column names for the tsv data format"}
)
# mlm pretrain
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
mlm_probability: Optional[float] = field(
default=0.15,
metadata={
"help": "The probability of token to be masked/corrupted during Mask Language Modeling"
}
)
# rerank
pos_rerank_num: int = field(default=5)
neg_rerank_num: int = field(default=15)
# coarse-grained node classification
class_num: int = field(default=10)
set_pad_id: bool = field(default=False, metadata={"help": "set the pad id to 0"})
def __post_init__(self):
pass
@dataclass
class DenseTrainingArguments(TrainingArguments):
negatives_x_device: bool = field(default=False, metadata={"help": "share negatives across devices"})
do_encode: bool = field(default=False, metadata={"help": "run the encoding loop"})
grad_cache: bool = field(default=False, metadata={"help": "Use gradient cache update"})
gc_q_chunk_size: int = field(default=4)
gc_p_chunk_size: int = field(default=32)
fix_bert: bool = field(default=False, metadata={"help": "fix BERT encoder during training or not"})
mlm_loss: bool = field(default=False, metadata={"help": "use mlm loss or not"})
mlm_weight: float = field(default=1, metadata={"help": "weight of mlm loss"})
warmup_ratio: float = field(default=0.1)
use_peft: bool = field(default=False, metadata={"help": "use PEFT or not"})
quantization: bool = field(default=False, metadata={"help": "use quantization or not"})
resume_training: bool = field(default=False, metadata={"help": "Resume training from a checkpoint"})
lora_alpha: Optional[int] = field(default=16, metadata={"help": "lora alpha"})
lora_rank: Optional[int] = field(default=8, metadata={"help": "lora rank"})
lora_dropout: Optional[float] = field(default=0.1, metadata={"help": "lora dropout"})
lora_bias: Optional[str] = field(default='none', metadata={"help": "Layers to add learnable bias"})
@dataclass
class PrivateTrainingArguments(TrainingArguments):
negatives_x_device: bool = field(default=False, metadata={"help": "share negatives across devices"})
do_encode: bool = field(default=False, metadata={"help": "run the encoding loop"})
grad_cache: bool = field(default=False, metadata={"help": "Use gradient cache update"})
gc_q_chunk_size: int = field(default=4)
gc_p_chunk_size: int = field(default=32)
fix_bert: bool = field(default=False, metadata={"help": "fix BERT encoder during training or not"})
mlm_loss: bool = field(default=False, metadata={"help": "use mlm loss or not"})
mlm_weight: float = field(default=1, metadata={"help": "weight of mlm loss"})
hub_strategy: Optional[str] = field(
default='end',
metadata={
"help": "Defines the scope of what is pushed to the Hub and when.r"
}
)
optim: Optional[str] = field(
default='adamw_torch',
metadata={
"help": "default optimizer"
}
)
learning_rate: Optional[float] = field(
default=1e-5,
metadata={"help": "The initial learning rate for AdamW optimizer"}
)
warmup_ratio: float = field(default=0.1)
start_eval: Optional[int] = field(
default=0,
metadata={"help": "step to start evaluation"}
)
early_stop: Optional[int] = field(
default=10,
metadata={"help": "step to stop training if no improvement"}
)
weight_decay: Optional[float] = field(
default=0.0,
metadata={"help": "Weight decay"}
)
seed: Optional[int] = field(
default=2024,
metadata={"help": "random seed"}
)
lora_alpha: Optional[int] = field(default=16, metadata={"help": "lora alpha"})
lora_rank: Optional[int] = field(default=8, metadata={"help": "lora rank"})
lora_dropout: Optional[float] = field(default=0, metadata={"help": "lora dropout"})
lora_bias: Optional[str] = field(default='none', metadata={"help": "Layers to add learnable bias"})
epsilon: Optional[float] = field(
default=-1.0,
metadata={
"help": "the privacy budget epsilon"
}
)
max_grad_norm: Optional[float] = field(
default=1.0,
metadata={
"help": "the maximum magnitude of L2 norms to perform gradient clipping"
}
)
max_pgrad_norm: Optional[float] = field(
default=1.0,
metadata={
"help": "the maximum magnitude of L2 norms to perform per-sample gradient clipping"
}
)
noise_scale: Optional[float] = field(
default=-1.0,
metadata={
"help": "the privacy budget noise_scale"
}
)
preclip: Optional[float] = field(
default=0.0,
metadata={"help": "preclip noise"}
)
neg_k: Optional[int] = field(
default=8,
metadata={"help": "number of negative samples"}
)
max_physical_batch_size: Optional[int] = field(
default=12,
metadata={"help": "max physcial batch size before splitting"}
)
dp_type: Optional[str] = field(
default='edge',
metadata={"help": "type of DP for relational data"}
)
use_peft: bool = field(default=False, metadata={"help": "use PEFT or not"})
quantization: bool = field(default=False, metadata={"help": "use quantization or not"})
resume_training: bool = field(default=False, metadata={"help": "Resume training from a checkpoint"})
@dataclass
class DenseEncodingArguments(TrainingArguments):
use_gpu: bool = field(default=False, metadata={"help": "Use GPU for encoding"})
encoded_save_path: str = field(default=None, metadata={"help": "where to save the encode"})
save_path: str = field(default=None, metadata={"help": "where to save the result file"})
retrieve_domain: str = field(default=None, metadata={"help": "name of the retrieve domain"})
source_domain: str = field(default=None, metadata={"help": "name of the source domain"})