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train_ance.py
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# 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 argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from ance.model import SemanticIndexANCE
from data import (
convert_example,
create_dataloader,
get_latest_ann_data,
get_latest_checkpoint,
read_text_triplet,
)
from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup
from paddlenlp.utils.log import logger
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--save_dir", default='./checkpoints', type=str, help="The output directory where the model checkpoints will be written.")
parser.add_argument("--ann_data_dir", default='./ann_data', type=str, help="The output directory where the ann generated training data will be saved.")
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("--max_training_steps", default=1000000, type=int, help="The maximum total steps for training")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--output_emb_size", default=None, type=int, help="output_embedding_size")
parser.add_argument("--learning_rate", default=1e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--epochs", default=10, type=int, help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proportion 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=1000, help="random seed for initialization")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument('--save_steps', type=int, default=10000, help="Inteval steps to save checkpoint")
parser.add_argument("--train_set_file", type=str, required=True, help="The full path of train_set_file")
parser.add_argument("--margin", default=0.3, type=float, help="Margin for pair-wise margin_rank_loss")
args = parser.parse_args()
# fmt: on
def set_seed(seed):
"""sets random seed"""
random.seed(seed)
np.random.seed(seed)
paddle.seed(seed)
def do_train():
paddle.set_device(args.device)
rank = paddle.distributed.get_rank()
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args.seed)
pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
latest_checkpoint, latest_global_step = get_latest_checkpoint(args)
logger.info("get latest_checkpoint:{}".format(latest_checkpoint))
model = SemanticIndexANCE(pretrained_model, margin=args.margin, output_emb_size=args.output_emb_size)
if latest_checkpoint:
state_dict = paddle.load(latest_checkpoint)
model.set_dict(state_dict)
print("warmup from:{}".format(latest_checkpoint))
model = paddle.DataParallel(model)
tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")
trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # text_input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # text_segment
Pad(axis=0, pad_val=tokenizer.pad_token_id), # pos_sample_input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # pos_sample_segment
Pad(axis=0, pad_val=tokenizer.pad_token_id), # neg_sample_input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # neg_sample_segment
): [data for data in fn(samples)]
global_step = 0
while global_step < args.max_training_steps:
latest_ann_data, latest_ann_data_step = get_latest_ann_data(args.ann_data_dir)
if latest_ann_data_step == -1:
# No ann_data generated yet
latest_ann_data = args.train_set_file
logger.info("No ann_data generated yet, Use training_set:{}".format(args.train_set_file))
else:
# Using ann_data to training model
logger.info("Latest ann_data is ready for training: [{}]".format(latest_ann_data))
train_ds = load_dataset(read_text_triplet, data_path=latest_ann_data, lazy=False)
train_data_loader = create_dataloader(
train_ds, mode="train", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
)
num_training_steps = len(train_data_loader) * args.epochs
lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, args.warmup_proportion)
# 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"])]
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
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,
grad_clip=clip,
)
tic_train = time.time()
for epoch in range(1, args.epochs + 1):
for step, batch in enumerate(train_data_loader, start=1):
(
text_input_ids,
text_token_type_ids,
pos_sample_input_ids,
pos_sample_token_type_ids,
neg_sample_input_ids,
neg_sample_token_type_ids,
) = batch
loss = model(
text_input_ids=text_input_ids,
pos_sample_input_ids=pos_sample_input_ids,
neg_sample_input_ids=neg_sample_input_ids,
text_token_type_ids=text_token_type_ids,
pos_sample_token_type_ids=pos_sample_token_type_ids,
neg_sample_token_type_ids=neg_sample_token_type_ids,
)
global_step += 1
if global_step % 10 == 0 and rank == 0:
print(
"global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s, trainning_file: %s"
% (global_step, epoch, step, loss, 10 / (time.time() - tic_train), latest_ann_data)
)
tic_train = time.time()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.save_steps == 0 and rank == 0:
save_dir = os.path.join(args.save_dir, str(global_step))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_param_path = os.path.join(save_dir, "model_state.pdparams")
paddle.save(model.state_dict(), save_param_path)
tokenizer.save_pretrained(save_dir)
# Flag to indicate succeefully save model
succeed_flag_file = os.path.join(save_dir, "succeed_flag_file")
open(succeed_flag_file, "a").close()
if __name__ == "__main__":
do_train()