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hyper_tune_train.py
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hyper_tune_train.py
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from torch.utils.data import DataLoader
import torch.nn as nn
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
import wandb
import yaml
import argparse
from tqdm import tqdm
import os
from set_seed import set_seed
import torchmetrics
import pprint
from models import *
from datasets import *
from utils import train_step, valid_step, EarlyStopping
from EDA import OutputEDA
Models = {"BERT": BERT_base_Model, "SBERT": SBERT_base_Model, "BERT_NLI": BERT_base_NLI_Model, "MLM": MLM_Model, "SimCSE": SimCSE}
Datasets = {"BERT": KorSTSDatasets_for_BERT, "SBERT": KorSTSDatasets, "BERT_NLI": KorNLIDatasets, "MLM": KorSTSDatasets_for_MLM, "SimCSE": KorSTSDatasets_for_SimCSE}
Criterions = {"MAE": nn.L1Loss, "MSE": nn.MSELoss, "BCE": nn.BCELoss, "NLL": nn.NLLLoss, "CE": nn.CrossEntropyLoss}
def main():
# 실행 위치 고정
# os.chdir(os.path.dirname(os.path.abspath(__file__)))
os.environ["TOKENIZERS_PARALLELISM"] = "False"
# 결과 재현성을 위한 랜덤 시드 고정.
set_seed(13)
parser = argparse.ArgumentParser(description='Training SBERT.')
parser.add_argument("--conf", type=str, default="sbert_config.yaml", help="config file path(.yaml)")
args = parser.parse_args()
with open(args.conf, "r") as f:
config = yaml.load(f, Loader=yaml.Loader)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = torch.device(device)
if not config["test_mode"]:
pj = "bert-mlm" if config['model_type'] == "MLM" else "sentence_bert"
run = wandb.init(project=pj, entity="nlp-13", config=config, name=config['log_name'], notes=config['notes'])
print("training on", device)
print('lr: ', wandb.config.lr)
print('epochs: ', wandb.config.epochs)
print('loss: ', wandb.config.loss)
print("batch_size: ", wandb.config.batch_size)
train_datasets = Datasets[config['model_type']](config['train_csv'], config['base_model'], config["stopword"])
valid_datasets = Datasets[config['model_type']](config['valid_csv'], config['base_model'], config["stopword"])
# EDA
outputEDA = OutputEDA(config["base_model"], config["log_name"])
# get pad_token_id.
collate_fn = Collate_fn(train_datasets.pad_id, config['model_type'])
# pair-bucket sampler
# train_seq_lengths = [(len(s1), len(s2)) for (s1, s2) in train_datasets.x]
# train_sampler = bucket_pair_indices(train_seq_lengths, batch_size=wandb.config.batch_size, max_pad_len=10)
train_loader = DataLoader(
train_datasets,
collate_fn=collate_fn,
shuffle=True,
batch_size=wandb.config.batch_size,
# batch_sampler=train_sampler
)
valid_loader = DataLoader(
valid_datasets,
collate_fn=collate_fn,
batch_size=wandb.config.batch_size
)
model = Models[config['model_type']](config["base_model"], config['dropout_prob'])
# #add token
# model.resize_vocab_len(train_datasets.len_added_token)
if not config["test_mode"]:
wandb.watch(model, log="all")
if os.path.exists(config["model_load_path"]):
try:
model.load_state_dict(torch.load(config["model_load_path"]))
except:
print("Weights dosen't match exactly with keys. So weights will loaded not strictly.")
model.load_state_dict(torch.load(config["model_load_path"]), strict=False)
print("weights loaded from", config["model_load_path"])
else:
print("no pretrained weights provided.")
model.to(device)
epochs = wandb.config.epochs
if config['model_type'] == "MLM":
criterion = Criterions[wandb.config.loss](ignore_index=0)
else:
criterion = Criterions[wandb.config.loss]()
# optimizer = Adam(params=model.parameters(), lr=config['lr'])
optimizer = Adam(params=model.parameters(), lr=wandb.config.lr)
if config['model_type'] in ["MLM", "SimCSE"]:
earlystopping = EarlyStopping(patience=config["early_stopping_patience"], verbose=True, mode="min")
scheduler = ReduceLROnPlateau(optimizer, 'min', factor=config["lr_scheduler_factor"],
patience=config["lr_scheduler_patience"], verbose=True)
else:
earlystopping = EarlyStopping(patience=config["early_stopping_patience"], verbose=True, mode="max")
scheduler = ReduceLROnPlateau(optimizer, 'max', factor=config["lr_scheduler_factor"],
patience=config["lr_scheduler_patience"], verbose=True)
pbar = tqdm(range(epochs))
# training code.
for epoch in pbar:
model.train()
for iter, data in enumerate(tqdm(train_loader)):
loss, score = train_step(data, config['model_type'], device, model, criterion, optimizer)
if not config["test_mode"]:
if config['model_type'] != "MLM":
wandb.log({"train_loss": loss, "train_pearson": score})
else:
wandb.log({"train_loss": loss, "train_PPL": score})
pbar.set_postfix({"train_loss": loss})
val_loss = 0
val_score = 0
model.eval()
with torch.no_grad():
model.eval()
for i, data in enumerate(tqdm(valid_loader)):
logits, loss, score = valid_step(data, config['model_type'], device, model, criterion, outputEDA)
val_loss += loss
val_score += score
val_loss /= (i+1)
val_score /= (i+1)
if not config["test_mode"]:
if config['model_type'] != "MLM":
wandb.log({"valid loss": val_loss, "valid_pearson": val_score})
else:
wandb.log({"valid loss": val_loss, "valid_PPL": val_score})
if config["watch_metrics"] == "loss":
earlystopping(val_loss)
scheduler.step(val_loss)
else:
earlystopping(val_score)
scheduler.step(val_score)
if earlystopping.best_epoch:
torch.save(model.state_dict(), config["model_save_path"])
print("model saved to ", config["model_save_path"])
if not config["test_mode"]:
outputEDA.save(epoch, val_score)
outputEDA.reset()
if earlystopping.earlystop:
break
if __name__ == "__main__":
sweep_config = {
'method':'random', # random: 임의의 값의 parameter 세트를 선택
'parameters':{
'lr':{
'distribution': 'uniform', # parameter를 설정하는 기준을 선택합니다. uniform은 연속적으로 균등한 값들을 선택합니다.
'min':1e-6, # 최소값을 설정합니다.
'max':1e-5 # 최대값을 설정합니다.
},
'epochs': {
'values': [9, 10, 11]
},
'loss':
{
'values': ['MAE', 'MSE']
},
'batch_size':
{
'values': [64, 32, 16]
}
},
'metric':{ # sweep_config의 metric은 최적화를 진행할 목표를 설정합니다.
'name':'valid_pearson', # pearson 점수가 최대화가 되는 방향으로 학습을 진행합니다.
'goal':'maximize',
'target': 0.999999
}
}
sweep_id = wandb.sweep(sweep_config)
wandb.agent(sweep_id=sweep_id, function = main, count=20)