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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 logging import basicConfig
import torch
from torch import nn
import json
from tqdm import tqdm
import os
import numpy as np
from transformers import BertTokenizer
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning import trainer, loggers
from torch.utils.data import Dataset, DataLoader
from transformers.optimization import get_linear_schedule_with_warmup
from transformers import BertForMaskedLM
from transformers import AutoConfig
from transformers.pipelines.base import Pipeline
from transformers import MegatronBertForMaskedLM
import argparse
import copy
from fengshen.utils.universal_checkpoint import UniversalCheckpoint
import warnings
from transformers import TextClassificationPipeline as HuggingfacePipe
class TCBertDataset(Dataset):
def __init__(self, data, tokenizer, args, prompt, label_classes):
super().__init__()
self.tokenizer = tokenizer
self.max_length = args.max_length
self.num_labels = args.num_labels
self.data = data
self.args = args
self.label_classes = label_classes
self.prompt = prompt
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.encode(self.data[index])
def encode(self, item, labeled=True):
if labeled:
ori_texta = self.prompt.format(item['label']) + item['content']
mask_texta = self.prompt.format("[MASK]" * len(item['label'])) + item['content']
# print('texta', texta)
labels = self.label_classes[item['label']]
ori_encode_dict = self.tokenizer.encode_plus(ori_texta,
max_length=self.max_length,
padding="longest",
truncation=True
)
mask_encode_dict = self.tokenizer.encode_plus(mask_texta,
max_length=self.max_length,
padding="longest",
truncation=True
)
ori_input_ids = torch.tensor(ori_encode_dict['input_ids']).long()
token_type_ids = torch.tensor(ori_encode_dict['token_type_ids']).long()
attention_mask = torch.tensor(ori_encode_dict['attention_mask']).float()
mask_input_ids = torch.tensor(mask_encode_dict['input_ids']).long()
mlmlabels = torch.where(mask_input_ids == self.tokenizer.mask_token_id, ori_input_ids, -100)
labels = torch.tensor(labels).long()
mlmlabels = torch.tensor(mlmlabels).long()
encoded = {
"sentence": item["content"],
"input_ids": mask_input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
"labels": labels,
"mlmlabels": mlmlabels,
}
else:
texta = self.prompt.format("[MASK]" * self.args.fixed_lablen) + item['content']
encode_dict = self.tokenizer.encode_plus(texta,
max_length=self.max_length,
padding="longest",
truncation=True
)
input_ids = encode_dict['input_ids']
token_type_ids = encode_dict['token_type_ids']
attention_mask = encode_dict['attention_mask']
encoded = {
"sentence": item["content"],
"input_ids": torch.tensor(input_ids).long(),
"token_type_ids": torch.tensor(token_type_ids).long(),
"attention_mask": torch.tensor(attention_mask).float(),
}
return encoded
class TCBertDataModel(pl.LightningDataModule):
@staticmethod
def add_data_specific_args(parent_args):
parser = parent_args.add_argument_group('TASK NAME DataModel')
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--batchsize', default=16, type=int)
parser.add_argument('--max_length', default=512, type=int)
parser.add_argument('--fixed_lablen', default=2, type=int)
return parent_args
def __init__(self, train_data, val_data, tokenizer, args, prompt, prompt_label):
super().__init__()
self.batchsize = args.batchsize
self.label_classes = self.get_label_classes(prompt_label)
args.num_labels = len(self.label_classes)
self.train_data = TCBertDataset(train_data, tokenizer, args, prompt, self.label_classes)
self.valid_data = TCBertDataset(val_data, tokenizer, args, prompt, self.label_classes)
def get_label_classes(self, prompt_label):
label_classes = {}
i = 0
for key in prompt_label.keys():
label_classes[key] = i
i+=1
print("label_classes:",label_classes)
return label_classes
def train_dataloader(self):
return DataLoader(self.train_data, shuffle=True, collate_fn=self.collate_fn, batch_size=self.batchsize, pin_memory=False)
def val_dataloader(self):
return DataLoader(self.valid_data, shuffle=False, collate_fn=self.collate_fn, batch_size=self.batchsize, pin_memory=False)
def collate_fn(self, batch):
'''
Aggregate a batch data.
batch = [ins1_dict, ins2_dict, ..., insN_dict]
batch_data = {'sentence':[ins1_sentence, ins2_sentence...], 'input_ids':[ins1_input_ids, ins2_input_ids...], ...}
'''
batch_data = {}
for key in batch[0]:
batch_data[key] = [example[key] for example in batch]
input_ids = batch_data['input_ids']
attention_mask = batch_data['attention_mask']
token_type_ids = batch_data["token_type_ids"]
labels = None
if 'labels' in batch_data:
labels = torch.LongTensor(batch_data['labels'])
mlmlabels = None
if 'mlmlabels' in batch_data:
mlmlabels = nn.utils.rnn.pad_sequence(batch_data['mlmlabels'],
batch_first=True,
padding_value=-100)
input_ids = nn.utils.rnn.pad_sequence(input_ids,
batch_first=True,
padding_value=0)
token_type_ids = nn.utils.rnn.pad_sequence(token_type_ids,
batch_first=True,
padding_value=0)
attention_mask = nn.utils.rnn.pad_sequence(attention_mask,
batch_first=True,
padding_value=0)
batch_data = {
"sentence":batch_data["sentence"],
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"labels": labels,
"mlmlabels":mlmlabels
}
return batch_data
class TCBertModel(nn.Module):
def __init__(self, pre_train_dir, nlabels):
super().__init__()
self.config = AutoConfig.from_pretrained(pre_train_dir)
print("pre_train_dir", pre_train_dir)
# if self.config.model_type == 'megatron-bert':
if "1.3B" in pre_train_dir:
self.bert = MegatronBertForMaskedLM.from_pretrained(pre_train_dir)
else:
self.bert = BertForMaskedLM.from_pretrained(pre_train_dir)
self.dropout = nn.Dropout(0.1)
self.nlabels = nlabels
self.linear_classifier = nn.Linear(self.config.hidden_size, self.nlabels)
def forward(self, input_ids, attention_mask, token_type_ids, mlmlabels=None):
outputs = self.bert(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
labels=mlmlabels,
output_hidden_states=True) # (bsz, seq, dim)
mlm_logits = outputs.logits
hidden_states = outputs.hidden_states[-1]
cls_logits = hidden_states[:,0]
cls_logits = self.dropout(cls_logits)
logits = self.linear_classifier(cls_logits)
return outputs.loss, logits, mlm_logits
class TCBertLitModel(pl.LightningModule):
@staticmethod
def add_model_specific_args(parent_args):
parser = parent_args.add_argument_group('BaseModel')
parser.add_argument('--learning_rate', default=1e-5, type=float)
parser.add_argument('--weight_decay', default=0.1, type=float)
parser.add_argument('--warmup', default=0.01, type=float)
parser.add_argument('--num_labels', default=2, type=int)
return parent_args
def __init__(self, args, model_path, nlabels):
super().__init__()
self.args = args
self.loss_fn = torch.nn.CrossEntropyLoss()
self.model = TCBertModel(model_path, nlabels)
def setup(self, stage) -> None:
if stage == 'fit':
num_gpus = self.trainer.gpus if self.trainer.gpus is not None else 0
self.total_step = int(self.trainer.max_epochs * self.num_data /
(max(1, num_gpus) * self.trainer.accumulate_grad_batches))
print('Total training step:', self.total_step)
def train_inputs(self, batch):
inputs = {
'input_ids': batch['input_ids'],
'attention_mask': batch['attention_mask'],
'token_type_ids': batch['token_type_ids'],
'mlmlabels': batch['mlmlabels']
}
return inputs
def training_step(self, batch, batch_idx):
labels = batch['labels']
batch = self.train_inputs(batch)
mlm_loss, logits, _= self.model(**batch)
if labels is not None:
cls_loss = self.loss_fn(logits, labels.view(-1))
loss = cls_loss + mlm_loss
ntotal = logits.size(0)
ncorrect = (logits.argmax(dim=-1) == labels).long().sum()
acc = ncorrect / ntotal
self.log('train_loss', loss, on_step=True, prog_bar=True)
self.log("train_acc", acc, on_step=True, prog_bar=True)
return loss
def validation_step(self, batch, batch_idx):
labels = batch['labels']
batch = self.train_inputs(batch)
mlm_loss, logits, _ = self.model(**batch)
predict = logits.argmax(dim=-1).cpu().tolist()
if labels is not None:
cls_loss = self.loss_fn(logits, labels.view(-1))
loss = cls_loss + mlm_loss
ntotal = logits.size(0)
ncorrect = int((logits.argmax(dim=-1) == labels).long().sum())
acc = ncorrect / ntotal
self.log('valid_loss', loss, on_step=True, prog_bar=True)
self.log("valid_acc", acc, on_step=True, prog_bar=True)
return int(ncorrect), int(ntotal)
def configure_optimizers(self):
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
paras = list(
filter(lambda p: p[1].requires_grad, self.named_parameters()))
paras = [{
'params':
[p for n, p in paras if not any(nd in n for nd in no_decay)],
'weight_decay': self.args.weight_decay
}, {
'params': [p for n, p in paras if any(nd in n for nd in no_decay)],
'weight_decay': 0.0
}]
optimizer = torch.optim.AdamW(paras, lr=self.args.learning_rate)
scheduler = get_linear_schedule_with_warmup(
optimizer, int(self.total_step * self.args.warmup),
self.total_step)
return [{
'optimizer': optimizer,
'lr_scheduler': {
'scheduler': scheduler,
'interval': 'step',
'frequency': 1
}
}]
class TCBertPredict:
def __init__(self, model, tokenizer, args, prompt, prompt_label):
self.tokenizer = tokenizer
self.args = args
self.data_model = TCBertDataModel(
[], [], tokenizer, args, prompt, prompt_label)
self.model = model
def predict_inputs(self, batch):
# Filter reduntant information(for example: 'sentence') that will be passed to model.forward()
inputs = {
'input_ids': batch['input_ids'].cuda(),
'attention_mask': batch['attention_mask'].cuda(),
'token_type_ids': batch['token_type_ids'].cuda(),
}
return inputs
def predict(self, batch_data):
batch = [self.data_model.train_data.encode(
sample, labeled=False) for sample in batch_data]
batch = self.data_model.collate_fn(batch)
batch = self.predict_inputs(batch)
_, logits, _ = self.model.model(**batch)
probs = torch.nn.functional.softmax(logits, dim=-1)
predicts = torch.argmax(probs, dim=-1).detach().cpu().numpy()
return predicts
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