wehub-resource-sync
240 lines
10 KiB
Python
240 lines
10 KiB
Python
# Copyright (c) 2020 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 model import SentenceTransformer
|
|
|
|
from paddlenlp.data import Pad, Stack, Tuple
|
|
from paddlenlp.datasets import load_dataset
|
|
from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup
|
|
|
|
# fmt: off
|
|
parser = argparse.ArgumentParser()
|
|
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("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
|
|
parser.add_argument("--learning_rate", default=5e-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=3, 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.")
|
|
args = parser.parse_args()
|
|
# fmt: on
|
|
|
|
|
|
def set_seed(seed):
|
|
"""sets random seed"""
|
|
random.seed(seed)
|
|
np.random.seed(seed)
|
|
paddle.seed(seed)
|
|
|
|
|
|
@paddle.no_grad()
|
|
def evaluate(model, criterion, metric, data_loader):
|
|
"""
|
|
Given a dataset, it evals model and computes the metric.
|
|
|
|
Args:
|
|
model(obj:`paddle.nn.Layer`): A model to classify texts.
|
|
data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
|
|
criterion(obj:`paddle.nn.Layer`): It can compute the loss.
|
|
metric(obj:`paddle.metric.Metric`): The evaluation metric.
|
|
"""
|
|
model.eval()
|
|
metric.reset()
|
|
losses = []
|
|
for batch in data_loader:
|
|
query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids, labels = batch
|
|
logits = model(
|
|
query_input_ids=query_input_ids,
|
|
title_input_ids=title_input_ids,
|
|
query_token_type_ids=query_token_type_ids,
|
|
title_token_type_ids=title_token_type_ids,
|
|
)
|
|
loss = criterion(logits, labels)
|
|
losses.append(loss.numpy())
|
|
correct = metric.compute(logits, labels)
|
|
metric.update(correct)
|
|
accu = metric.accumulate()
|
|
print("eval loss: %.5f, accu: %.5f" % (np.mean(losses), accu))
|
|
model.train()
|
|
metric.reset()
|
|
|
|
|
|
def convert_example(example, tokenizer, max_seq_length=512, is_test=False):
|
|
"""
|
|
Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
|
|
by concatenating and adding special tokens. And creates a mask from the two sequences passed
|
|
to be used in a sequence-pair classification task.
|
|
|
|
A BERT sequence has the following format:
|
|
|
|
- single sequence: ``[CLS] X [SEP]``
|
|
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
|
|
|
|
A BERT sequence pair mask has the following format:
|
|
::
|
|
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
|
| first sequence | second sequence |
|
|
|
|
If only one sequence, only returns the first portion of the mask (0's).
|
|
|
|
|
|
Args:
|
|
example(obj:`list[str]`): List of input data, containing query, title and label if it have label.
|
|
tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
|
|
which contains most of the methods. Users should refer to the superclass for more information regarding methods.
|
|
max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
|
|
Sequences longer than this will be truncated, sequences shorter will be padded.
|
|
is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.
|
|
|
|
Returns:
|
|
query_input_ids(obj:`list[int]`): The list of query token ids.
|
|
query_token_type_ids(obj: `list[int]`): List of query sequence pair mask.
|
|
title_input_ids(obj:`list[int]`): The list of title token ids.
|
|
title_token_type_ids(obj: `list[int]`): List of title sequence pair mask.
|
|
label(obj:`numpy.array`, data type of int64, optional): The input label if not is_test.
|
|
"""
|
|
query, title = example["query"], example["title"]
|
|
|
|
query_encoded_inputs = tokenizer(text=query, max_seq_len=max_seq_length)
|
|
query_input_ids = query_encoded_inputs["input_ids"]
|
|
query_token_type_ids = query_encoded_inputs["token_type_ids"]
|
|
|
|
title_encoded_inputs = tokenizer(text=title, max_seq_len=max_seq_length)
|
|
title_input_ids = title_encoded_inputs["input_ids"]
|
|
title_token_type_ids = title_encoded_inputs["token_type_ids"]
|
|
|
|
if not is_test:
|
|
label = np.array([example["label"]], dtype="int64")
|
|
return query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids, label
|
|
else:
|
|
return query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids
|
|
|
|
|
|
def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
|
|
if trans_fn:
|
|
dataset = dataset.map(trans_fn)
|
|
|
|
shuffle = True if mode == "train" else False
|
|
if mode == "train":
|
|
batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
|
|
else:
|
|
batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
|
|
|
|
return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True)
|
|
|
|
|
|
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)
|
|
|
|
train_ds, dev_ds = load_dataset("lcqmc", splits=["train", "dev"])
|
|
|
|
pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
|
|
|
|
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), # query_input
|
|
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment
|
|
Pad(axis=0, pad_val=tokenizer.pad_token_id), # title_input
|
|
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # title_segment
|
|
Stack(dtype="int64"), # label
|
|
): [data for data in fn(samples)]
|
|
train_data_loader = create_dataloader(
|
|
train_ds, mode="train", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
|
|
)
|
|
dev_data_loader = create_dataloader(
|
|
dev_ds, mode="dev", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
|
|
)
|
|
|
|
model = SentenceTransformer(pretrained_model)
|
|
|
|
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_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"])]
|
|
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.loss.CrossEntropyLoss()
|
|
metric = paddle.metric.Accuracy()
|
|
|
|
global_step = 0
|
|
tic_train = time.time()
|
|
for epoch in range(1, args.epochs + 1):
|
|
for step, batch in enumerate(train_data_loader, start=1):
|
|
query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids, labels = batch
|
|
logits = model(
|
|
query_input_ids=query_input_ids,
|
|
title_input_ids=title_input_ids,
|
|
query_token_type_ids=query_token_type_ids,
|
|
title_token_type_ids=title_token_type_ids,
|
|
)
|
|
loss = criterion(logits, labels)
|
|
correct = metric.compute(logits, labels)
|
|
metric.update(correct)
|
|
acc = metric.accumulate()
|
|
|
|
global_step += 1
|
|
if global_step % 10 == 0 and rank == 0:
|
|
print(
|
|
"global step %d, epoch: %d, batch: %d, loss: %.5f, accu: %.5f, speed: %.2f step/s"
|
|
% (global_step, epoch, step, loss, acc, 10 / (time.time() - tic_train))
|
|
)
|
|
tic_train = time.time()
|
|
loss.backward()
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.clear_grad()
|
|
if global_step % 100 == 0 and rank == 0:
|
|
save_dir = os.path.join(args.save_dir, "model_%d" % global_step)
|
|
if not os.path.exists(save_dir):
|
|
os.makedirs(save_dir)
|
|
evaluate(model, criterion, metric, dev_data_loader)
|
|
save_param_path = os.path.join(save_dir, "model_state.pdparams")
|
|
paddle.save(model.state_dict(), save_param_path)
|
|
tokenizer.save_pretrained(save_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
do_train()
|