wehub-resource-sync
199 lines
8.6 KiB
Python
199 lines
8.6 KiB
Python
# Copyright (c) 2022 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 argparse
|
|
import functools
|
|
import os
|
|
import random
|
|
import time
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from metric import MetricReport
|
|
from paddle.io import BatchSampler, DataLoader, DistributedBatchSampler
|
|
from utils import evaluate, preprocess_function, read_local_dataset
|
|
|
|
from paddlenlp.data import DataCollatorWithPadding
|
|
from paddlenlp.datasets import load_dataset
|
|
from paddlenlp.transformers import (
|
|
AutoModelForSequenceClassification,
|
|
AutoTokenizer,
|
|
LinearDecayWithWarmup,
|
|
)
|
|
from paddlenlp.utils.log import logger
|
|
|
|
# fmt: off
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--device', default="gpu", help="Select which device to train model, defaults to gpu.")
|
|
parser.add_argument("--dataset_dir", required=True, default=None, type=str, help="Local dataset directory should include train.txt, dev.txt and label.txt")
|
|
parser.add_argument("--save_dir", default="./checkpoint", type=str, help="The output directory where the model checkpoints will be written.")
|
|
parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
|
|
parser.add_argument('--model_name', default="ernie-3.0-medium-zh", help="Select model to train, defaults to ernie-3.0-medium-zh.",
|
|
choices=["ernie-1.0-large-zh-cw", "ernie-3.0-xbase-zh", "ernie-3.0-base-zh", "ernie-3.0-medium-zh", "ernie-3.0-micro-zh", "ernie-3.0-mini-zh", "ernie-3.0-nano-zh", "ernie-2.0-base-en", "ernie-2.0-large-en", "ernie-m-base", "ernie-m-large"])
|
|
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
|
|
parser.add_argument("--learning_rate", default=3e-5, type=float, help="The initial learning rate for Adam.")
|
|
parser.add_argument("--epochs", default=10, type=int, help="Total number of training epochs to perform.")
|
|
parser.add_argument('--early_stop', action='store_true', help='Epoch before early stop.')
|
|
parser.add_argument('--early_stop_nums', type=int, default=3, help='Number of epoch before early stop.')
|
|
parser.add_argument("--logging_steps", default=5, type=int, help="The interval steps to logging.")
|
|
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
|
|
parser.add_argument('--warmup', action='store_true', help="whether use warmup strategy")
|
|
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup steps over the training process.")
|
|
parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
|
|
parser.add_argument("--seed", type=int, default=3, help="random seed for initialization")
|
|
parser.add_argument("--train_file", type=str, default="train.txt", help="Train dataset file name")
|
|
parser.add_argument("--dev_file", type=str, default="dev.txt", help="Dev dataset file name")
|
|
parser.add_argument("--label_file", type=str, default="label.txt", help="Label file name")
|
|
args = parser.parse_args()
|
|
# fmt: on
|
|
|
|
|
|
def set_seed(seed):
|
|
"""
|
|
Sets random seed
|
|
"""
|
|
random.seed(seed)
|
|
np.random.seed(seed)
|
|
paddle.seed(seed)
|
|
os.environ["PYTHONHASHSEED"] = str(seed)
|
|
|
|
|
|
def args_saving():
|
|
argsDict = args.__dict__
|
|
with open(os.path.join(args.save_dir, "setting.txt"), "w") as f:
|
|
f.writelines("------------------ start ------------------" + "\n")
|
|
for eachArg, value in argsDict.items():
|
|
f.writelines(eachArg + " : " + str(value) + "\n")
|
|
f.writelines("------------------- end -------------------")
|
|
|
|
|
|
def train():
|
|
"""
|
|
Training a multi label classification model
|
|
"""
|
|
|
|
if not os.path.exists(args.save_dir):
|
|
os.makedirs(args.save_dir)
|
|
args_saving()
|
|
set_seed(args.seed)
|
|
paddle.set_device(args.device)
|
|
|
|
rank = paddle.distributed.get_rank()
|
|
if paddle.distributed.get_world_size() > 1:
|
|
paddle.distributed.init_parallel_env()
|
|
|
|
# load and preprocess dataset
|
|
label_list = {}
|
|
with open(os.path.join(args.dataset_dir, args.label_file), "r", encoding="utf-8") as f:
|
|
for i, line in enumerate(f):
|
|
l = line.strip()
|
|
label_list[l] = i
|
|
train_ds = load_dataset(
|
|
read_local_dataset, path=os.path.join(args.dataset_dir, args.train_file), label_list=label_list, lazy=False
|
|
)
|
|
dev_ds = load_dataset(
|
|
read_local_dataset, path=os.path.join(args.dataset_dir, args.dev_file), label_list=label_list, lazy=False
|
|
)
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
|
|
trans_func = functools.partial(
|
|
preprocess_function, tokenizer=tokenizer, max_seq_length=args.max_seq_length, label_nums=len(label_list)
|
|
)
|
|
train_ds = train_ds.map(trans_func)
|
|
dev_ds = dev_ds.map(trans_func)
|
|
# batchify dataset
|
|
collate_fn = DataCollatorWithPadding(tokenizer)
|
|
if paddle.distributed.get_world_size() > 1:
|
|
train_batch_sampler = DistributedBatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)
|
|
else:
|
|
train_batch_sampler = BatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)
|
|
dev_batch_sampler = BatchSampler(dev_ds, batch_size=args.batch_size, shuffle=False)
|
|
train_data_loader = DataLoader(dataset=train_ds, batch_sampler=train_batch_sampler, collate_fn=collate_fn)
|
|
dev_data_loader = DataLoader(dataset=dev_ds, batch_sampler=dev_batch_sampler, collate_fn=collate_fn)
|
|
|
|
# define model
|
|
model = AutoModelForSequenceClassification.from_pretrained(args.model_name, num_classes=len(label_list))
|
|
if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
|
|
state_dict = paddle.load(args.init_from_ckpt)
|
|
model.set_dict(state_dict)
|
|
model = paddle.DataParallel(model)
|
|
|
|
num_training_steps = len(train_data_loader) * args.epochs
|
|
lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, args.warmup_steps)
|
|
|
|
# Generate parameter names needed to perform weight decay.
|
|
# All bias and LayerNorm parameters are excluded.
|
|
decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])]
|
|
optimizer = paddle.optimizer.AdamW(
|
|
learning_rate=lr_scheduler,
|
|
parameters=model.parameters(),
|
|
weight_decay=args.weight_decay,
|
|
apply_decay_param_fun=lambda x: x in decay_params,
|
|
)
|
|
|
|
criterion = paddle.nn.BCEWithLogitsLoss()
|
|
metric = MetricReport()
|
|
|
|
global_step = 0
|
|
best_f1_score = 0
|
|
early_stop_count = 0
|
|
tic_train = time.time()
|
|
|
|
for epoch in range(1, args.epochs + 1):
|
|
|
|
if args.early_stop and early_stop_count >= args.early_stop_nums:
|
|
logger.info("Early stop!")
|
|
break
|
|
|
|
for step, batch in enumerate(train_data_loader, start=1):
|
|
|
|
labels = batch.pop("labels")
|
|
logits = model(**batch)
|
|
loss = criterion(logits, labels)
|
|
|
|
loss.backward()
|
|
optimizer.step()
|
|
if args.warmup:
|
|
lr_scheduler.step()
|
|
optimizer.clear_grad()
|
|
|
|
global_step += 1
|
|
if global_step % args.logging_steps == 0 and rank == 0:
|
|
logger.info(
|
|
"global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s"
|
|
% (global_step, epoch, step, loss, args.logging_steps / (time.time() - tic_train))
|
|
)
|
|
tic_train = time.time()
|
|
|
|
early_stop_count += 1
|
|
micro_f1_score, macro_f1_score = evaluate(model, criterion, metric, dev_data_loader)
|
|
|
|
save_best_path = args.save_dir
|
|
if not os.path.exists(save_best_path):
|
|
os.makedirs(save_best_path)
|
|
|
|
# save models
|
|
if macro_f1_score > best_f1_score:
|
|
early_stop_count = 0
|
|
best_f1_score = macro_f1_score
|
|
model._layers.save_pretrained(save_best_path)
|
|
tokenizer.save_pretrained(save_best_path)
|
|
logger.info("Current best macro f1 score: %.5f" % (best_f1_score))
|
|
logger.info("Final best macro f1 score: %.5f" % (best_f1_score))
|
|
logger.info("Save best macro f1 text classification model in %s" % (args.save_dir))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
train()
|