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import os
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import torch
import nlp
from transformers import T5Tokenizer, BartTokenizer, HfArgumentParser
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task: str = field(
metadata={"help": "Which task 'qa', 'qg', 'e2e_qg', 'ans_ext', 'multi'. 'multi' means 'qa', 'qg', 'ans_ext' tasks"},
)
model_type: str = field(metadata={"help": "One of 't5', 'bart'"})
dataset_path: Optional[str] = field(
default="data/squad_multitask",
metadata={"help": "Path for dataset directory"},
)
train_file_name: Optional[str] = field(
default=None,
metadata={"help": "name for cached train dataset"},
)
valid_file_name: Optional[str] = field(
default=None,
metadata={"help": "name for cached valid dataset"},
)
valid_for_qg_only: bool = field(
default=False,
metadata={"help": "For multitask dataset valid split should contain only qg task or all tasks."}
)
qg_format: Optional[str] = field(
default='highlight_qg_format',
metadata={"help": "How to format inputs for que generation, 'highlight_qg_format' or 'prepend_qg_format'"},
)
max_source_length: Optional[int] = field(
default=512,
metadata={"help": "Max input length for the source text"},
)
max_target_length: Optional[int] = field(
default=32,
metadata={"help": "Max input length for the target text"},
)
class DataProcessor:
def __init__(self, tokenizer, model_type="t5", max_source_length=512, max_target_length=32):
self.tokenizer = tokenizer
self.max_source_length = max_source_length
self.max_target_length = max_target_length
self.model_type = model_type
self.hl_token = "<hl>"
if model_type == "t5":
self.sep_token = "<sep>"
elif model_type == "bart":
self.sep_token = "<sep>"
else:
self.sep_token = "[SEP]"
def process(self, dataset):
if self.model_type == "t5":
dataset = dataset.map(self._add_eos_examples)
dataset = dataset.map(self._add_special_tokens)
dataset = dataset.map(self._convert_to_features, batched=True)
return dataset
def _add_eos_examples(self, example):
example['source_text'] = example['source_text'] + " </s>"
example['target_text'] = example['target_text'] + " </s>"
return example
def _add_special_tokens(self, example):
example['source_text'] = example['source_text'].replace("{hl_token}", self.hl_token)
example['target_text'] = example['target_text'].replace("{sep_token}", self.sep_token)
return example
# tokenize the examples
def _convert_to_features(self, example_batch):
source_encoding = self.tokenizer.batch_encode_plus(
example_batch['source_text'],
max_length=self.max_source_length,
padding='max_length',
pad_to_max_length=True,
truncation=True,
)
target_encoding = self.tokenizer.batch_encode_plus(
example_batch['target_text'],
max_length=self.max_target_length,
padding='max_length',
pad_to_max_length=True,
truncation=True,
)
encodings = {
'source_ids': source_encoding['input_ids'],
'target_ids': target_encoding['input_ids'],
'attention_mask': source_encoding['attention_mask'],
}
return encodings
def filter_qa(example):
return example['task'] == 'qa'
def filter_qg(example):
return example['task'] == 'qg'
def filter_e2e_qg(example):
return example['task'] == 'e2e_qg'
def filter_ans_ext(example):
return example['task'] == 'ans_ext'
def filter_multi(example):
return example['task'] != 'e2e_qg'
TASK_TO_FILTER_FN = {
'qa': filter_qa,
'qg': filter_qg,
'e2e_qg': filter_e2e_qg,
'ans_ext': filter_ans_ext,
'multi': filter_multi
}
def main():
parser = HfArgumentParser((DataTrainingArguments,))
data_args = parser.parse_args_into_dataclasses()[0]
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO
)
if data_args.model_type == 't5':
tokenizer = T5Tokenizer.from_pretrained("t5-base")
else:
tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
tokenizer.add_tokens(['<sep>', '<hl>'])
train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION)
processor = DataProcessor(
tokenizer,
model_type=data_args.model_type,
max_source_length=data_args.max_source_length,
max_target_length=data_args.max_target_length
)
train_dataset = train_dataset.filter(TASK_TO_FILTER_FN[data_args.task])
if data_args.task == 'multi' and data_args.valid_for_qg_only:
logger.info("processing valid data only for qg task")
valid_dataset = valid_dataset.filter(filter_qg)
else:
valid_dataset = valid_dataset.filter(TASK_TO_FILTER_FN[data_args.task])
train_dataset = processor.process(train_dataset)
valid_dataset = processor.process(valid_dataset)
columns = ["source_ids", "target_ids", "attention_mask"]
train_dataset.set_format(type='torch', columns=columns)
valid_dataset.set_format(type='torch', columns=columns)
if data_args.train_file_name is None:
train_file_name = f"train_data_{data_args.task}_{data_args.qg_format}_{data_args.model_type}.pt"
train_path = os.path.join("data", train_file_name)
valid_file_name = f"valid_data_{data_args.task}_{data_args.qg_format}_{data_args.model_type}.pt"
valid_path = os.path.join("data", valid_file_name)
else:
train_path = os.path.join("data", data_args.train_file_name)
valid_path = os.path.join("data", data_args.valid_file_name)
torch.save(train_dataset, train_path)
logger.info(f"saved train dataset at {train_path}")
torch.save(valid_dataset, valid_path)
logger.info(f"saved validation dataset at {valid_path}")
tokenizer_path = f"{data_args.model_type}_qg_tokenizer"
if not os.path.exists(tokenizer_path):
os.mkdir(tokenizer_path)
tokenizer.save_pretrained(tokenizer_path)
logger.info(f"saved tokenizer at {tokenizer_path}")
if __name__ == "__main__":
main()
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