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finetune.py
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finetune.py
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import os
import time
import argparse
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn.model_selection import GroupKFold
from sklearn.preprocessing import MinMaxScaler
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from transformers import AdamW
from common import TARGETS, N_TARGETS
from utils.helpers import init_logger, init_seed
from utils.torch import to_numpy, to_device, to_cpu
from datasets import TextDataset, TransformerOutputDataset
from tokenization import tokenize
from learning import Learner
from one_cycle import OneCycleLR
from create_features import get_ohe_categorical_features
from evaluation import spearmanr_torch, get_cvs
from inference import infer
from train import models, pretrained_models, get_optimizer_param_groups
def get_model_outputs(model, loader, checkpoint_file, device, model_type='siamese'):
q_outputs, a_outputs = [], []
currently_deterministic = torch.backends.cudnn.deterministic
torch.backends.cudnn.deterministic = True
if checkpoint_file is not None:
checkpoint = torch.load(checkpoint_file)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
with torch.no_grad():
for i, (inputs, _) in enumerate(tqdm(loader)):
inputs = to_device(inputs, device)
if model_type == 'siamese':
batch_q_outputs = model.transformer(inputs[1], inputs[3])
batch_a_outputs = model.transformer(inputs[2], inputs[4])
if model_type == 'double':
batch_q_outputs = model.q_transformer(inputs[1], inputs[3])
batch_a_outputs = model.a_transformer(inputs[2], inputs[4])
q_outputs.append(to_cpu(batch_q_outputs))
a_outputs.append(to_cpu(batch_a_outputs))
q_outputs = torch.cat(q_outputs)
a_outputs = torch.cat(a_outputs)
torch.backends.cudnn.deterministic = currently_deterministic
return to_numpy(q_outputs), to_numpy(a_outputs)
def build_parser():
parser = argparse.ArgumentParser(
description='Perform second stage of training - finetuning.')
parser.add_argument('-model_name', type=str, default='siamese_roberta')
parser.add_argument('-checkpoint_dir', type=str, default='checkpoints/')
parser.add_argument('-log_dir', type=str, default='logs/')
parser.add_argument('-data_dir', type=str, default='data/')
return parser
if __name__=='__main__':
parser = build_parser()
args = parser.parse_args()
model_name = args.model_name
model_type = 'double' if model_name == 'double_albert' else 'siamese'
checkpoint_dir = args.checkpoint_dir
log_dir = args.log_dir
os.makedirs(checkpoint_dir, exist_ok=True)
os.makedirs(log_dir, exist_ok=True)
main_logger = init_logger(log_dir, f'finetune_main_{model_name}.log')
# Import data
test = pd.read_csv(f'{args.data_dir}test.csv')
train = pd.read_csv(f'{args.data_dir}train.csv')
# Min Max scale target after rank transformation
for col in TARGETS:
train[col] = train[col].rank(method="average")
train[TARGETS] = MinMaxScaler().fit_transform(train[TARGETS])
y = train[TARGETS].values
# Get model inputs
ids_train, seg_ids_train = tokenize(train, pretrained_model_str=pretrained_models[model_name])
cat_features_train, _ = get_ohe_categorical_features(train, test, 'category')
# Set training parameters
device = 'cuda'
num_workers = 10
n_folds = 10
lr = 1e-5
n_epochs = 10
bs = 2
grad_accum = 4
weight_decay = 0.01
loss_fn = nn.BCEWithLogitsLoss()
# Start training
init_seed()
folds = GroupKFold(n_splits=n_folds).split(
X=train['question_body'], groups=train['question_body'])
oofs = np.zeros((len(train), N_TARGETS))
main_logger.info(f'Start finetuning model {model_name}...')
for fold_id, (train_index, valid_index) in enumerate(folds):
main_logger.info(f'Fold {fold_id + 1} started at {time.ctime()}')
fold_logger = init_logger(log_dir, f'finetune_fold_{fold_id+1}_{model_name}.log')
loader = DataLoader(
TextDataset(cat_features_train, ids_train['question'], ids_train['answer'],
seg_ids_train['question'], seg_ids_train['answer'], np.arange(len(train)), y),
batch_size=bs, shuffle=False, num_workers=num_workers
)
model = models[model_name]()
checkpoint_file = f'{checkpoint_dir}{model_name}_fold_{fold_id+1}_best.pth'
# Get last hidden layer outputs from transformers
fold_logger.info(f'Precompute transformer outputs for model {model_name}...')
q_outputs, a_outputs = get_model_outputs(model, loader, checkpoint_file, device, model_type)
train_loader = DataLoader(
TransformerOutputDataset(cat_features_train, q_outputs, a_outputs, train_index, y),
batch_size=bs, shuffle=True, num_workers=num_workers
)
valid_loader = DataLoader(
TransformerOutputDataset(cat_features_train, q_outputs, a_outputs, valid_index, y),
batch_size=bs, shuffle=False, num_workers=num_workers, drop_last=False
)
# Train the head of the model
optimizer = AdamW(get_optimizer_param_groups(model.head, lr, weight_decay))
learner = Learner(
model.head,
optimizer,
train_loader,
valid_loader,
loss_fn,
device,
n_epochs,
f'{model_name}_head_fold_{fold_id + 1}',
checkpoint_dir,
scheduler=None,
metric_spec={'spearmanr': spearmanr_torch},
monitor_metric=True,
minimize_score=False,
logger=fold_logger,
grad_accum=grad_accum,
batch_step_scheduler=False,
eval_at_start=True
)
learner.train()
oofs[valid_index] = infer(learner.model, valid_loader, learner.best_checkpoint_file, device)
# Save tuned model in half precision (reduces memory making it easier to upload to Kaggle)
head_checkpoint_file = f'{checkpoint_dir}{model_name}_head_fold_{fold_id+1}_best.pth'
checkpoint = torch.load(head_checkpoint_file)
model.head.load_state_dict(checkpoint['model_state_dict'])
model.half()
tuned_checkpoint_file = f'{checkpoint_dir}{model_name}_tuned_fold_{fold_id+1}_best.pth'
torch.save({'model_state_dict': model.state_dict()}, tuned_checkpoint_file)
main_logger.info(f'Finished tuning {model_name}')
# Print CV scores
ix = np.where(train.groupby("question_body")["host"].transform("count")==1)[0] # unique question index
main_logger.info('CVs:')
main_logger.info(get_cvs(oofs, y, ix))
# Store OOFs
os.makedirs('oofs/', exist_ok=True)
pd.DataFrame(oofs, columns=TARGETS).to_csv(f'oofs/{model_name}_tuned_oofs.csv')