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run_token_cls.py
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run_token_cls.py
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# 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 os
import random
import time
from functools import partial
from datasets import load_dataset
import paddle
from paddle.io import DataLoader
from paddlenlp.transformers import LinearDecayWithWarmup
from paddlenlp.transformers import AutoModelForTokenClassification, AutoTokenizer
from paddlenlp.metrics import ChunkEvaluator
from paddlenlp.data import DataCollatorForTokenClassification
from paddlenlp.utils.log import logger
parser = argparse.ArgumentParser()
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model_name_or_path",
default=None,
type=str,
required=True)
parser.add_argument("--task_name",
default="msra_ner",
type=str,
choices=["msra_ner"],
help="The named entity recognition datasets.")
parser.add_argument(
"--output_dir",
default="best_msra_ner_model",
type=str,
help=
"The output directory where the model predictions and 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=8,
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("--do_train",
action='store_true',
help="Whether to train.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to predict.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs."
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--logging_steps",
type=int,
default=100,
help="Log every X updates steps.")
parser.add_argument("--save_steps",
type=int,
default=100,
help="Save checkpoint every X updates steps.")
parser.add_argument("--seed",
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--device",
default="gpu",
type=str,
choices=["cpu", "gpu", "xpu"],
help="The device to select to train the model, is must be cpu/gpu/xpu.")
args = parser.parse_args()
return args
@paddle.no_grad()
def evaluate(model, loss_fct, metric, data_loader, label_num, mode="valid"):
model.eval()
metric.reset()
avg_loss, precision, recall, f1_score = 0, 0, 0, 0
for batch in data_loader:
logits = model(batch['input_ids'], batch['token_type_ids'])
loss = loss_fct(logits, batch['labels'])
avg_loss = paddle.mean(loss)
preds = logits.argmax(axis=2)
num_infer_chunks, num_label_chunks, num_correct_chunks = metric.compute(
batch['seq_len'], preds, batch['labels'])
metric.update(num_infer_chunks.numpy(), num_label_chunks.numpy(),
num_correct_chunks.numpy())
precision, recall, f1_score = metric.accumulate()
print("%s: eval loss: %f, precision: %f, recall: %f, f1: %f" %
(mode, avg_loss, precision, recall, f1_score))
model.train()
return f1_score
def run(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
raw_datasets = load_dataset(args.task_name)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
train_ds = raw_datasets['train']
column_names = train_ds.column_names
label_list = train_ds.features['ner_tags'].feature.names
label_num = len(label_list)
batchify_fn = DataCollatorForTokenClassification(tokenizer=tokenizer)
# Define the model netword and its loss
model = AutoModelForTokenClassification.from_pretrained(
args.model_name_or_path, num_classes=label_num)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
def tokenize_and_align_labels(examples, no_entity_id=0):
tokenized_inputs = tokenizer(
examples['tokens'],
max_seq_len=args.max_seq_length,
# We use this argument because the texts in our dataset are lists of words (with a label for each word).
is_split_into_words=True,
return_length=True)
labels = []
for i, label in enumerate(examples['ner_tags']):
label_ids = label
if len(tokenized_inputs['input_ids'][i]) - 2 < len(label_ids):
label_ids = label_ids[:len(tokenized_inputs['input_ids'][i]) -
2]
label_ids = [no_entity_id] + label_ids + [no_entity_id]
label_ids += [no_entity_id] * (
len(tokenized_inputs['input_ids'][i]) - len(label_ids))
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs
test_ds = raw_datasets['test']
test_ds = test_ds.select(range(len(test_ds) - 1))
test_ds = test_ds.map(tokenize_and_align_labels,
batched=True,
remove_columns=column_names)
test_data_loader = DataLoader(dataset=test_ds,
collate_fn=batchify_fn,
num_workers=0,
batch_size=args.batch_size,
return_list=True)
if args.do_train:
train_ds = train_ds.select(range(len(train_ds) - 1))
train_ds = train_ds.map(tokenize_and_align_labels,
batched=True,
remove_columns=column_names)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True, drop_last=True)
train_data_loader = DataLoader(dataset=train_ds,
collate_fn=batchify_fn,
num_workers=0,
batch_sampler=train_batch_sampler,
return_list=True)
num_training_steps = args.max_steps if args.max_steps > 0 else len(
train_data_loader) * args.num_train_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,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
loss_fct = paddle.nn.loss.CrossEntropyLoss()
metric = ChunkEvaluator(label_list=label_list)
global_step = 0
best_f1 = 0.0
last_step = args.num_train_epochs * len(train_data_loader)
tic_train = time.time()
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_data_loader):
global_step += 1
logits = model(batch['input_ids'], batch['token_type_ids'])
loss = loss_fct(logits, batch['labels'])
avg_loss = paddle.mean(loss)
if global_step % args.logging_steps == 0:
print(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch, step, avg_loss,
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
avg_loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.save_steps == 0 or global_step == num_training_steps:
if paddle.distributed.get_rank() == 0:
f1 = evaluate(model, loss_fct, metric, test_data_loader,
label_num, "test")
if f1 > best_f1:
best_f1 = f1
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= num_training_steps:
print("best_f1: ", best_f1)
return
print("best_f1: ", best_f1)
if args.do_eval:
eval_data_loader = DataLoader(dataset=test_ds,
collate_fn=batchify_fn,
num_workers=0,
batch_size=args.batch_size,
return_list=True)
# Define the model netword and its loss
model = AutoModelForTokenClassification.from_pretrained(
args.model_name_or_path, num_classes=label_num)
loss_fct = paddle.nn.loss.CrossEntropyLoss()
metric = ChunkEvaluator(label_list=label_list)
model.eval()
metric.reset()
for step, batch in enumerate(eval_data_loader):
logits = model(batch["input_ids"], batch["token_type_ids"])
loss = loss_fct(logits, batch["labels"])
avg_loss = paddle.mean(loss)
preds = logits.argmax(axis=2)
num_infer_chunks, num_label_chunks, num_correct_chunks = metric.compute(
batch["length"], preds, batch["labels"])
metric.update(num_infer_chunks.numpy(), num_label_chunks.numpy(),
num_correct_chunks.numpy())
precision, recall, f1_score = metric.accumulate()
print("eval loss: %f, precision: %f, recall: %f, f1: %f" %
(avg_loss, precision, recall, f1_score))
def print_arguments(args):
"""print arguments"""
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == "__main__":
args = parse_args()
print_arguments(args)
run(args)