230 lines
8.5 KiB
Python
230 lines
8.5 KiB
Python
# Copyright (c) 2021 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 os
|
|
import sys
|
|
|
|
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append(__dir__)
|
|
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
|
|
|
|
import random
|
|
import time
|
|
import numpy as np
|
|
import paddle
|
|
|
|
from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
|
|
|
|
from xfun import XFUNDataset
|
|
from vqa_utils import parse_args, get_bio_label_maps, print_arguments, set_seed
|
|
from data_collator import DataCollator
|
|
from eval_re import evaluate
|
|
|
|
from ppocr.utils.logging import get_logger
|
|
|
|
|
|
def train(args):
|
|
logger = get_logger(log_file=os.path.join(args.output_dir, "train.log"))
|
|
rank = paddle.distributed.get_rank()
|
|
distributed = paddle.distributed.get_world_size() > 1
|
|
|
|
print_arguments(args, logger)
|
|
|
|
# Added here for reproducibility (even between python 2 and 3)
|
|
set_seed(args.seed)
|
|
|
|
label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
|
|
pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
|
|
|
|
# dist mode
|
|
if distributed:
|
|
paddle.distributed.init_parallel_env()
|
|
|
|
tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
|
|
if not args.resume:
|
|
model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
|
|
model = LayoutXLMForRelationExtraction(model, dropout=None)
|
|
logger.info('train from scratch')
|
|
else:
|
|
logger.info('resume from {}'.format(args.model_name_or_path))
|
|
model = LayoutXLMForRelationExtraction.from_pretrained(
|
|
args.model_name_or_path)
|
|
|
|
# dist mode
|
|
if distributed:
|
|
model = paddle.DataParallel(model)
|
|
|
|
train_dataset = XFUNDataset(
|
|
tokenizer,
|
|
data_dir=args.train_data_dir,
|
|
label_path=args.train_label_path,
|
|
label2id_map=label2id_map,
|
|
img_size=(224, 224),
|
|
max_seq_len=args.max_seq_length,
|
|
pad_token_label_id=pad_token_label_id,
|
|
contains_re=True,
|
|
add_special_ids=False,
|
|
return_attention_mask=True,
|
|
load_mode='all')
|
|
|
|
eval_dataset = XFUNDataset(
|
|
tokenizer,
|
|
data_dir=args.eval_data_dir,
|
|
label_path=args.eval_label_path,
|
|
label2id_map=label2id_map,
|
|
img_size=(224, 224),
|
|
max_seq_len=args.max_seq_length,
|
|
pad_token_label_id=pad_token_label_id,
|
|
contains_re=True,
|
|
add_special_ids=False,
|
|
return_attention_mask=True,
|
|
load_mode='all')
|
|
|
|
train_sampler = paddle.io.DistributedBatchSampler(
|
|
train_dataset, batch_size=args.per_gpu_train_batch_size, shuffle=True)
|
|
|
|
train_dataloader = paddle.io.DataLoader(
|
|
train_dataset,
|
|
batch_sampler=train_sampler,
|
|
num_workers=args.num_workers,
|
|
use_shared_memory=True,
|
|
collate_fn=DataCollator())
|
|
|
|
eval_dataloader = paddle.io.DataLoader(
|
|
eval_dataset,
|
|
batch_size=args.per_gpu_eval_batch_size,
|
|
num_workers=args.num_workers,
|
|
shuffle=False,
|
|
collate_fn=DataCollator())
|
|
|
|
t_total = len(train_dataloader) * args.num_train_epochs
|
|
|
|
# build linear decay with warmup lr sch
|
|
lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
|
|
learning_rate=args.learning_rate,
|
|
decay_steps=t_total,
|
|
end_lr=0.0,
|
|
power=1.0)
|
|
if args.warmup_steps > 0:
|
|
lr_scheduler = paddle.optimizer.lr.LinearWarmup(
|
|
lr_scheduler,
|
|
args.warmup_steps,
|
|
start_lr=0,
|
|
end_lr=args.learning_rate, )
|
|
grad_clip = paddle.nn.ClipGradByNorm(clip_norm=10)
|
|
optimizer = paddle.optimizer.Adam(
|
|
learning_rate=args.learning_rate,
|
|
parameters=model.parameters(),
|
|
epsilon=args.adam_epsilon,
|
|
grad_clip=grad_clip,
|
|
weight_decay=args.weight_decay)
|
|
|
|
# Train!
|
|
logger.info("***** Running training *****")
|
|
logger.info(" Num examples = {}".format(len(train_dataset)))
|
|
logger.info(" Num Epochs = {}".format(args.num_train_epochs))
|
|
logger.info(" Instantaneous batch size per GPU = {}".format(
|
|
args.per_gpu_train_batch_size))
|
|
logger.info(
|
|
" Total train batch size (w. parallel, distributed & accumulation) = {}".
|
|
format(args.per_gpu_train_batch_size *
|
|
paddle.distributed.get_world_size()))
|
|
logger.info(" Total optimization steps = {}".format(t_total))
|
|
|
|
global_step = 0
|
|
model.clear_gradients()
|
|
train_dataloader_len = len(train_dataloader)
|
|
best_metirc = {'f1': 0}
|
|
model.train()
|
|
|
|
train_reader_cost = 0.0
|
|
train_run_cost = 0.0
|
|
total_samples = 0
|
|
reader_start = time.time()
|
|
|
|
print_step = 1
|
|
|
|
for epoch in range(int(args.num_train_epochs)):
|
|
for step, batch in enumerate(train_dataloader):
|
|
train_reader_cost += time.time() - reader_start
|
|
train_start = time.time()
|
|
outputs = model(**batch)
|
|
train_run_cost += time.time() - train_start
|
|
# model outputs are always tuple in ppnlp (see doc)
|
|
loss = outputs['loss']
|
|
loss = loss.mean()
|
|
|
|
loss.backward()
|
|
optimizer.step()
|
|
optimizer.clear_grad()
|
|
# lr_scheduler.step() # Update learning rate schedule
|
|
|
|
global_step += 1
|
|
total_samples += batch['image'].shape[0]
|
|
|
|
if rank == 0 and step % print_step == 0:
|
|
logger.info(
|
|
"epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {:.6f}, lr: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
|
|
format(epoch, args.num_train_epochs, step,
|
|
train_dataloader_len, global_step,
|
|
np.mean(loss.numpy()),
|
|
optimizer.get_lr(), train_reader_cost / print_step, (
|
|
train_reader_cost + train_run_cost) / print_step,
|
|
total_samples / print_step, total_samples / (
|
|
train_reader_cost + train_run_cost)))
|
|
|
|
train_reader_cost = 0.0
|
|
train_run_cost = 0.0
|
|
total_samples = 0
|
|
|
|
if rank == 0 and args.eval_steps > 0 and global_step % args.eval_steps == 0 and args.evaluate_during_training:
|
|
# Log metrics
|
|
# Only evaluate when single GPU otherwise metrics may not average well
|
|
results = evaluate(model, eval_dataloader, logger)
|
|
if results['f1'] >= best_metirc['f1']:
|
|
best_metirc = results
|
|
output_dir = os.path.join(args.output_dir, "best_model")
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
if distributed:
|
|
model._layers.save_pretrained(output_dir)
|
|
else:
|
|
model.save_pretrained(output_dir)
|
|
tokenizer.save_pretrained(output_dir)
|
|
paddle.save(args,
|
|
os.path.join(output_dir, "training_args.bin"))
|
|
logger.info("Saving model checkpoint to {}".format(
|
|
output_dir))
|
|
logger.info("eval results: {}".format(results))
|
|
logger.info("best_metirc: {}".format(best_metirc))
|
|
reader_start = time.time()
|
|
|
|
if rank == 0:
|
|
# Save model checkpoint
|
|
output_dir = os.path.join(args.output_dir, "latest_model")
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
if distributed:
|
|
model._layers.save_pretrained(output_dir)
|
|
else:
|
|
model.save_pretrained(output_dir)
|
|
tokenizer.save_pretrained(output_dir)
|
|
paddle.save(args, os.path.join(output_dir, "training_args.bin"))
|
|
logger.info("Saving model checkpoint to {}".format(output_dir))
|
|
logger.info("best_metirc: {}".format(best_metirc))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
train(args)
|