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arguments.py
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arguments.py
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from enum import Enum
import argparse
import dataclasses
from dataclasses import dataclass, field
from typing import Optional
import json
from transformers import HfArgumentParser, TrainingArguments
from tasks.utils import *
@dataclass
class BackdoorTrainingArguments(TrainingArguments):
removal: bool = field(
default=False,
metadata={
"help": "Will do backdoor removal"
}
)
max_steps: int = field(
default=0,
metadata={
"help": "Will do backdoor removal"
}
)
trigger_num: int = field(
metadata={
"help": "Number of trigger token: " + ", ".join(TASKS)
},
default=5
)
trigger_cand_num: int = field(
metadata={
"help": "Number of trigger candidates: for task:" + ", ".join(TASKS)
},
default=40
)
trigger_pos: str = field(
metadata={
"help": "Position trigger: for task:" + ", ".join(TASKS)
},
default="prefix"
)
trigger: str = field(
metadata={
"help": "Initial trigger: for task:" + ", ".join(TASKS)
},
default=None
)
poison_rate: float = field(
metadata={
"help": "Poison rate of backdooring for task:" + ", ".join(TASKS)
},
default=0.1
)
trigger_targeted: int = field(
metadata={
"help": "Poison rate of backdooring for task:" + ", ".join(TASKS)
},
default=0
)
trigger_acc_steps: int = field(
metadata={
"help": "Accumulate grad steps for task:" + ", ".join(TASKS)
},
default=32
)
backdoor: str = field(
metadata={
"help": "Type of backdooring for task:" + ", ".join(TASKS)
},
default="targeted"
)
backdoor_steps: int = field(
metadata={
"help": "Steps to conduct backdoor for task:" + ", ".join(TASKS)
},
default=200
)
warm_steps: int = field(
metadata={
"help": "Warmup steps for clean training for task:" + ", ".join(TASKS)
},
default=1000
)
clean_labels: str = field(
metadata={
"help": "Targeted label of backdooring for task:" + ", ".join(TASKS)
},
default=None
)
target_labels: str = field(
metadata={
"help": "Targeted label of backdooring for task:" + ", ".join(TASKS)
},
default=None
)
deepseed: bool = field(
metadata={
"help": "Targeted label of backdooring for task:" + ", ".join(TASKS)
},
default=False
)
use_checkpoint: str = field(
metadata={
"help": "Targeted label of backdooring for task:" + ", ".join(TASKS)
},
default=None
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.training_args
"""
task_name: str = field(
metadata={
"help": "The name of the task to train on: " + ", ".join(TASKS),
"choices": TASKS
}
)
dataset_name: str = field(
metadata={
"help": "The name of the dataset to use: " + ", ".join(DATASETS),
"choices": DATASETS
}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
max_seq_length: int = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=True, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
train_file: Optional[str] = field(
default=None, metadata={"help": "A csv or a json file containing the training data."}
)
validation_file: Optional[str] = field(
default=None, metadata={"help": "A csv or a json file containing the validation data."}
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "A csv or a json file containing the test data."}
)
template_id: Optional[int] = field(
default=0,
metadata={
"help": "The specific prompt string to use"
}
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
model_name_or_path_ori: str = field(
default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
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 huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
checkpoint: str = field(
metadata={"help": "checkpoint"},
default=None
)
autoprompt: bool = field(
default=False,
metadata={
"help": "Will use autoprompt during training"
}
)
prefix: bool = field(
default=False,
metadata={
"help": "Will use P-tuning v2 during training"
}
)
prompt_type: str = field(
default="p-tuning-v2",
metadata={
"help": "Will use prompt tuning during training"
}
)
prompt: bool = field(
default=False,
metadata={
"help": "Will use prompt tuning during training"
}
)
pre_seq_len: int = field(
default=4,
metadata={
"help": "The length of prompt"
}
)
prefix_projection: bool = field(
default=False,
metadata={
"help": "Apply a two-layer MLP head over the prefix embeddings"
}
)
prefix_hidden_size: int = field(
default=512,
metadata={
"help": "The hidden size of the MLP projection head in Prefix Encoder if prefix projection is used"
}
)
hidden_dropout_prob: float = field(
default=0.1,
metadata={
"help": "The dropout probability used in the models"
}
)
@dataclass
class QuestionAnwseringArguments:
n_best_size: int = field(
default=20,
metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
)
max_answer_length: int = field(
default=30,
metadata={
"help": "The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
},
)
version_2_with_negative: bool = field(
default=False, metadata={"help": "If true, some of the examples do not have an answer."}
)
null_score_diff_threshold: float = field(
default=0.0,
metadata={
"help": "The threshold used to select the null answer: if the best answer has a score that is less than "
"the score of the null answer minus this threshold, the null answer is selected for this example. "
"Only useful when `version_2_with_negative=True`."
},
)
def get_args():
"""Parse all the args."""
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, BackdoorTrainingArguments, QuestionAnwseringArguments))
args = parser.parse_args_into_dataclasses()
if args[2].backdoor == "clean":
args[2].poison_rate = 0.0
if args[2].trigger is not None:
raw_trigger = args[2].trigger.replace(" ", "").split(",")
trigger = [int(x) for x in raw_trigger]
else:
trigger = np.random.choice(20000, args[2].trigger_num, replace=False).tolist()
args[0].trigger = list([trigger])
args[2].trigger = list([trigger])
args[2].trigger_num = len(trigger)
label2ids = []
for k, v in json.loads(str(args[2].clean_labels)).items():
label2ids.append(v)
args[0].clean_labels = label2ids
args[2].clean_labels = label2ids
args[2].dataset_name = args[1].dataset_name
label2ids = []
for k, v in json.loads(str(args[2].target_labels)).items():
label2ids.append(v)
args[0].target_labels = label2ids
args[2].target_labels = label2ids
args[2].label_names = ["labels"]
print(f"-> clean label:{args[2].clean_labels}\n-> target label:{args[2].target_labels}")
return args