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# coding=utf-8
# Copyright 2021 The IDEA 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.
from fengshen.models.zen1.tokenization import BertTokenizer
from fengshen.models.zen1.modeling import ZenForSequenceClassification
from fengshen.models.zen1.ngram_utils import ZenNgramDict
from pytorch_lightning.callbacks import LearningRateMonitor
import csv
from dataclasses import dataclass
import logging
import math
import numpy as np
import os
from tqdm import tqdm
import json
import torch
import pytorch_lightning as pl
from random import shuffle
import argparse
from pytorch_lightning.callbacks import ModelCheckpoint
from torch.utils.data import Dataset, DataLoader
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids, label_id, ngram_ids, ngram_positions, ngram_lengths,
ngram_tuples, ngram_seg_ids, ngram_masks):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.ngram_ids = ngram_ids
self.ngram_positions = ngram_positions
self.ngram_lengths = ngram_lengths
self.ngram_tuples = ngram_tuples
self.ngram_seg_ids = ngram_seg_ids
self.ngram_masks = ngram_masks
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_examples(self, data_path, mode):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
# if sys.version_info[0] == 2:
# line = list(unicode(cell, 'utf-8') for cell in line)
lines.append(line)
return lines
@classmethod
def _read_json(cls, input_file):
"""Reads a jsonl file."""
with open(input_file, "r", encoding="utf-8") as f:
lines = f.readlines()
samples = []
for line in tqdm(lines):
data = json.loads(line)
samples.append(data)
return samples
class TnewsProcessor(DataProcessor):
"""Processor for the tnews data set (HIT version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_json(os.path.join(data_dir, "train.json")), "train")
def get_examples(self, data_path, mode):
return self._create_examples(
self._read_json(data_path),
set_type=mode
)
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
# if i == 0:
# continue
guid = "%s-%s" % (set_type, i)
# text_a = line[0]
text_a = line['sentence']
label = line['label'] if 'label' in line.keys() else None
examples.append(
InputExample(guid=guid, text_a=text_a, label=label))
return examples
class OcnliProcessor(DataProcessor):
"""Processor for the ocnli or cmnli data set (HIT version)."""
def get_examples(self, data_path, mode):
return self._create_examples(
self._read_json(data_path),
set_type=mode
)
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
# if i == 0:
# continue
guid = "%s-%s" % (set_type, i)
# text_a = line[0]
text_a = line['sentence1']
text_b = line['sentence2']
label = line['label'] if 'label' in line.keys() else None
# 特殊处理，cmnli有label为-的
if label == '-':
label = None
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class IflytekProcessor(DataProcessor):
"""Processor for the iflytek data set (HIT version)."""
def get_examples(self, data_path, mode):
return self._create_examples(
self._read_json(data_path),
set_type=mode
)
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
# if i == 0:
# continue
guid = "%s-%s" % (set_type, i)
# text_a = line[0]
text_a = line['sentence']
label = line['label'] if 'label' in line.keys() else None
examples.append(
InputExample(guid=guid, text_a=text_a, label=label))
return examples
def convert_examples_to_features(examples, label_map, max_seq_length, tokenizer, ngram_dict):
"""Loads a data file into a list of `InputBatch`s."""
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(examples)))
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:(max_seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
segment_ids = [0] * len(tokens)
if tokens_b:
tokens += tokens_b + ["[SEP]"]
segment_ids += [1] * (len(tokens_b) + 1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
padding = [0] * (max_seq_length - len(input_ids))
input_ids += padding
input_mask += padding
segment_ids += padding
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
# ----------- code for ngram BEGIN-----------
ngram_matches = []
# Filter the word segment from 2 to 7 to check whether there is a word
for p in range(2, 8):
for q in range(0, len(tokens) - p + 1):
character_segment = tokens[q:q + p]
# j is the starting position of the word
# i is the length of the current word
character_segment = tuple(character_segment)
if character_segment in ngram_dict.ngram_to_id_dict:
ngram_index = ngram_dict.ngram_to_id_dict[character_segment]
ngram_matches.append([ngram_index, q, p, character_segment])
shuffle(ngram_matches)
# max_word_in_seq_proportion = max_word_in_seq
max_word_in_seq_proportion = math.ceil((len(tokens) / max_seq_length) * ngram_dict.max_ngram_in_seq)
if len(ngram_matches) > max_word_in_seq_proportion:
ngram_matches = ngram_matches[:max_word_in_seq_proportion]
ngram_ids = [ngram[0] for ngram in ngram_matches]
ngram_positions = [ngram[1] for ngram in ngram_matches]
ngram_lengths = [ngram[2] for ngram in ngram_matches]
ngram_tuples = [ngram[3] for ngram in ngram_matches]
ngram_seg_ids = [0 if position < (len(tokens_a) + 2) else 1 for position in ngram_positions]
ngram_mask_array = np.zeros(ngram_dict.max_ngram_in_seq, dtype=np.bool)
ngram_mask_array[:len(ngram_ids)] = 1
# record the masked positions
ngram_positions_matrix = np.zeros(shape=(max_seq_length, ngram_dict.max_ngram_in_seq), dtype=np.int32)
for i in range(len(ngram_ids)):
ngram_positions_matrix[ngram_positions[i]:ngram_positions[i] + ngram_lengths[i], i] = 1.0
# Zero-pad up to the max word in seq length.
padding = [0] * (ngram_dict.max_ngram_in_seq - len(ngram_ids))
ngram_ids += padding
ngram_lengths += padding
ngram_seg_ids += padding
# ----------- code for ngram END-----------
label_id = label_map[example.label] if example.label is not None else 0
features.append(
InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
ngram_ids=ngram_ids,
ngram_positions=ngram_positions_matrix,
ngram_lengths=ngram_lengths,
ngram_tuples=ngram_tuples,
ngram_seg_ids=ngram_seg_ids,
ngram_masks=ngram_mask_array))
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
class TaskDataset(Dataset):
def __init__(self, data_path, processor, mode='train'):
super().__init__()
self.data = self.load_data(data_path, processor, mode)
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.data[index]
def load_data(self, data_path, processor, mode):
if mode == "train":
examples = processor.get_examples(data_path, mode)
elif mode == "test":
examples = processor.get_examples(data_path, mode)
elif mode == "dev":
examples = processor.get_examples(data_path, mode)
return examples
@dataclass
class TaskCollator:
args = None
tokenizer = None
ngram_dict = None
label2id = None
def __call__(self, samples):
features = convert_examples_to_features(samples, self.label2id, self.args.max_seq_length, self.tokenizer, self.ngram_dict)
# logger.info(" Num examples = %d", len(samples))
input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
ngram_ids = torch.tensor([f.ngram_ids for f in features], dtype=torch.long)
ngram_positions = torch.tensor([f.ngram_positions for f in features], dtype=torch.long)
# ngram_lengths = torch.tensor([f.ngram_lengths for f in features], dtype=torch.long)
# ngram_seg_ids = torch.tensor([f.ngram_seg_ids for f in features], dtype=torch.long)
# ngram_masks = torch.tensor([f.ngram_masks for f in features], dtype=torch.long)
return {
'input_ids': input_ids,
'input_ngram_ids': ngram_ids,
'ngram_position_matrix': ngram_positions,
'attention_mask': input_mask,
'token_type_ids': segment_ids,
'labels': label_ids,
}
# return default_collate(sample_list)
class TaskDataModel(pl.LightningDataModule):
@staticmethod
def add_data_specific_args(parent_args):
parser = parent_args.add_argument_group('TASK NAME DataModel')
parser.add_argument('--data_dir', default='./data', type=str)
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--train_data', default='train.json', type=str)
parser.add_argument('--valid_data', default='dev.json', type=str)
parser.add_argument('--test_data', default='test.json', type=str)
parser.add_argument('--train_batchsize', default=16, type=int)
parser.add_argument('--valid_batchsize', default=32, type=int)
parser.add_argument('--max_seq_length', default=128, type=int)
parser.add_argument('--texta_name', default='text', type=str)
parser.add_argument('--textb_name', default='sentence2', type=str)
parser.add_argument('--label_name', default='label', type=str)
parser.add_argument('--id_name', default='id', type=str)
parser.add_argument('--dataset_name', default=None, type=str)
parser.add_argument('--vocab_file',
type=str, default=None,
help="Vocabulary mapping/file BERT was pretrainined on")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument('--task_name', default='tnews', type=str)
return parent_args
def __init__(self, args):
super().__init__()
self.train_batchsize = args.train_batchsize
self.valid_batchsize = args.valid_batchsize
self.collator = TaskCollator()
self.collator.args = args
self.collator.tokenizer = BertTokenizer.from_pretrained(args.pretrained_model_path, do_lower_case=args.do_lower_case)
self.collator.ngram_dict = ZenNgramDict.from_pretrained(args.pretrained_model_path, tokenizer=self.collator.tokenizer)
processors = {
'afqmc': OcnliProcessor,
'tnews': TnewsProcessor,
'ocnli': OcnliProcessor,
'cmnli': OcnliProcessor,
'iflytek': IflytekProcessor,
}
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
if args.dataset_name is None:
self.label2id, self.id2label = self.load_schema(os.path.join(
args.data_dir, args.train_data), args)
self.train_data = TaskDataset(os.path.join(
args.data_dir, args.train_data), processor, mode='train')
self.valid_data = TaskDataset(os.path.join(
args.data_dir, args.valid_data), processor, mode='dev')
self.test_data = TaskDataset(os.path.join(
args.data_dir, args.test_data), processor, mode='test')
self.collator.label2id = self.label2id
else:
import datasets
ds = datasets.load_dataset(args.dataset_name)
self.train_data = ds['train']
self.valid_data = ds['validation']
self.test_data = ds['test']
self.save_hyperparameters(args)
def train_dataloader(self):
return DataLoader(self.train_data, shuffle=True, batch_size=self.train_batchsize, pin_memory=False,
collate_fn=self.collator)
def val_dataloader(self):
return DataLoader(self.valid_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
collate_fn=self.collator)
def predict_dataloader(self):
return DataLoader(self.test_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
collate_fn=self.collator)
def load_schema(self, data_path, args):
with open(data_path, 'r', encoding='utf8') as f:
lines = f.readlines()
label_list = []
for line in tqdm(lines):
data = json.loads(line)
labels = data[args.label_name] if args.label_name in data.keys(
) else 0
if labels not in label_list:
label_list.append(labels)
label2id, id2label = {}, {}
for i, k in enumerate(label_list):
label2id[k] = i
id2label[i] = k
return label2id, id2label
class LitModel(pl.LightningModule):
@staticmethod
def add_model_specific_args(parent_args):
parser = parent_args.add_argument_group('BaseModel')
parser.add_argument('--num_labels', default=2, type=int)
return parent_args
def __init__(self, args):
super().__init__()
self.model = ZenForSequenceClassification.from_pretrained(args.pretrained_model_path, num_labels=args.num_labels)
self.save_hyperparameters(args)
def setup(self, stage) -> None:
if stage == 'fit':
train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
# Calculate total steps
if self.trainer.max_epochs > 0:
world_size = self.trainer.world_size
tb_size = self.hparams.train_batchsize * max(1, world_size)
ab_size = self.trainer.accumulate_grad_batches
self.total_steps = (len(train_loader.dataset) *
self.trainer.max_epochs // tb_size) // ab_size
else:
self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches
print('Total steps: {}' .format(self.total_steps))
def training_step(self, batch, batch_idx):
loss, logits = self.model(**batch)
acc = self.comput_metrix(logits, batch['labels'])
self.log('train_loss', loss)
self.log('train_acc', acc)
return loss
def comput_metrix(self, logits, labels):
y_pred = torch.argmax(logits, dim=-1)
y_pred = y_pred.view(size=(-1,))
y_true = labels.view(size=(-1,)).float()
corr = torch.eq(y_pred, y_true)
acc = torch.sum(corr.float())/labels.size()[0]
return acc
def validation_step(self, batch, batch_idx):
loss, logits = self.model(**batch)
acc = self.comput_metrix(logits, batch['labels'])
self.log('val_loss', loss)
self.log('val_acc', acc)
def predict_step(self, batch, batch_idx):
output = self.model(**batch)
return output.logits
def configure_optimizers(self):
from fengshen.models.model_utils import configure_optimizers
return configure_optimizers(self)
class TaskModelCheckpoint:
@staticmethod
def add_argparse_args(parent_args):
parser = parent_args.add_argument_group('BaseModel')
parser.add_argument('--monitor', default='train_loss', type=str)
parser.add_argument('--mode', default='min', type=str)
parser.add_argument('--dirpath', default='./log/', type=str)
parser.add_argument(
'--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str)
parser.add_argument('--save_top_k', default=3, type=float)
parser.add_argument('--every_n_train_steps', default=100, type=float)
parser.add_argument('--save_weights_only', default=True, type=bool)
return parent_args
def __init__(self, args):
self.callbacks = ModelCheckpoint(monitor=args.monitor,
save_top_k=args.save_top_k,
mode=args.mode,
every_n_train_steps=args.every_n_train_steps,
save_weights_only=args.save_weights_only,
dirpath=args.dirpath,
filename=args.filename)
def save_test(data, args, data_model):
with open(args.output_save_path, 'w', encoding='utf-8') as f:
idx = 0
for i in range(len(data)):
batch = data[i]
for sample in batch:
tmp_result = dict()
label_id = np.argmax(sample.numpy())
tmp_result['id'] = data_model.test_data.data[idx]['id']
tmp_result['label'] = data_model.id2label[label_id]
json_data = json.dumps(tmp_result, ensure_ascii=False)
f.write(json_data+'\n')
idx += 1
print('save the result to '+args.output_save_path)
def main():
total_parser = argparse.ArgumentParser("TASK NAME")
total_parser.add_argument('--pretrained_model_path', default='', type=str)
total_parser.add_argument('--output_save_path',
default='./predict.json', type=str)
# * Args for data preprocessing
total_parser = TaskDataModel.add_data_specific_args(total_parser)
# * Args for training
total_parser = pl.Trainer.add_argparse_args(total_parser)
total_parser = TaskModelCheckpoint.add_argparse_args(total_parser)
# * Args for base model
from fengshen.models.model_utils import add_module_args
total_parser = add_module_args(total_parser)
total_parser = LitModel.add_model_specific_args(total_parser)
args = total_parser.parse_args()
checkpoint_callback = TaskModelCheckpoint(args).callbacks
lr_monitor = LearningRateMonitor(logging_interval='step')
trainer = pl.Trainer.from_argparse_args(args,
callbacks=[checkpoint_callback, lr_monitor]
)
data_model = TaskDataModel(args)
model = LitModel(args)
trainer.fit(model, data_model)
if __name__ == "__main__":
main()
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