data.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os

import paddle

from paddlenlp.utils.log import logger


def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
 if trans_fn:
 dataset = dataset.map(trans_fn)
 shuffle = True if mode == "train" else False
 if mode == "train":
 batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
 else:
 batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)

 return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True)


def convert_example(example, tokenizer, max_seq_length=512, pad_to_max_seq_len=False):
 """
 Builds model inputs from a sequence.

 A BERT sequence has the following format:

 - single sequence: ``[CLS] X [SEP]``

 Args:
 example(obj:`list(str)`): The list of text to be converted to ids.
 tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
 which contains most of the methods. Users should refer to the superclass for more information regarding methods.
 max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
 Sequences longer than this will be truncated, sequences shorter will be padded.
 is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.

 Returns:
 input_ids(obj:`list[int]`): The list of query token ids.
 token_type_ids(obj: `list[int]`): List of query sequence pair mask.
 """

 result = []
 for key, text in example.items():
 encoded_inputs = tokenizer(text=text, max_seq_len=max_seq_length, pad_to_max_seq_len=pad_to_max_seq_len)
 input_ids = encoded_inputs["input_ids"]
 token_type_ids = encoded_inputs["token_type_ids"]
 result += [input_ids, token_type_ids]
 return result


def read_text_pair(data_path):
 """Reads data."""
 with open(data_path, "r", encoding="utf-8") as f:
 for line in f:
 data = line.rstrip().split("\t")
 if len(data) != 2:
 continue
 yield {"text_a": data[0], "text_b": data[1]}


def read_text_triplet(data_path):
 """Reads data."""
 with open(data_path, "r", encoding="utf-8") as f:
 for line in f:
 data = line.rstrip().split("\t")
 if len(data) != 3:
 continue
 yield {"text": data[0], "pos_sample": data[1], "neg_sample": data[2]}


# ANN - active learning ------------------------------------------------------
def get_latest_checkpoint(args):
 """
 Return: (latest_checkpint_path, global_step)
 """
 if not os.path.exists(args.save_dir):
 return args.init_from_ckpt, 0

 subdirectories = list(next(os.walk(args.save_dir))[1])

 def valid_checkpoint(checkpoint):
 chk_path = os.path.join(args.save_dir, checkpoint)
 scheduler_path = os.path.join(chk_path, "model_state.pdparams")
 succeed_flag_file = os.path.join(chk_path, "succeed_flag_file")
 return os.path.exists(scheduler_path) and os.path.exists(succeed_flag_file)

 trained_steps = [int(s) for s in subdirectories if valid_checkpoint(s)]

 if len(trained_steps) > 0:
 return os.path.join(args.save_dir, str(max(trained_steps)), "model_state.pdparams"), max(trained_steps)

 return args.init_from_ckpt, 0


# ANN - active learning ------------------------------------------------------
def get_latest_ann_data(ann_data_dir):
 if not os.path.exists(ann_data_dir):
 return None, -1

 subdirectories = list(next(os.walk(ann_data_dir))[1])

 def valid_checkpoint(step):
 ann_data_file = os.path.join(ann_data_dir, step, "new_ann_data")
 # succed_flag_file is an empty file that indicates ann data has been generated
 succeed_flag_file = os.path.join(ann_data_dir, step, "succeed_flag_file")
 return os.path.exists(succeed_flag_file) and os.path.exists(ann_data_file)

 ann_data_steps = [int(s) for s in subdirectories if valid_checkpoint(s)]

 if len(ann_data_steps) > 0:
 latest_ann_data_file = os.path.join(ann_data_dir, str(max(ann_data_steps)), "new_ann_data")
 logger.info("Using lateset ann_data_file:{}".format(latest_ann_data_file))
 return latest_ann_data_file, max(ann_data_steps)

 logger.info("no new ann_data, return (None, -1)")
 return None, -1


def gen_id2corpus(corpus_file):
 id2corpus = {}
 with open(corpus_file, "r", encoding="utf-8") as f:
 for idx, line in enumerate(f):
 id2corpus[idx] = line.rstrip()
 return id2corpus


def gen_text_file(similar_text_pair_file):
 text2similar_text = {}
 texts = []
 with open(similar_text_pair_file, "r", encoding="utf-8") as f:
 for line in f:
 splited_line = line.rstrip().split("\t")
 if len(splited_line) != 2:
 continue

 text, similar_text = line.rstrip().split("\t")

 if not text or not similar_text:
 continue

 text2similar_text[text] = similar_text
 texts.append({"text": text})
 return texts, text2similar_text