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import json
import os
import re
import random
from tqdm import tqdm
from transformers import (
get_cosine_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_constant_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_inverse_sqrt_schedule,
)
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from transformers.trainer import (
is_sagemaker_mp_enabled,
get_parameter_names,
ALL_LAYERNORM_LAYERS,
)
from transformers import AutoTokenizer
from gliner import GLiNER, GLiNERConfig
from gliner.data_processing import SpanProcessor, TokenProcessor, SpanBiEncoderProcessor, TokenBiEncoderProcessor
from gliner.data_processing.tokenizer import WordsSplitter
from gliner.data_processing.collator import DataCollatorWithPadding, DataCollator
from gliner.utils import load_config_as_namespace
from gliner.evaluation import get_for_all_path
def save_top_k_checkpoints(model: GLiNER, save_path: str, checkpoint: int, top_k: int = 5):
"""
Save the top-k checkpoints (latest k checkpoints) of a model and tokenizer.
Parameters:
model (GLiNER): The model to save.
save_path (str): The directory path to save the checkpoints.
top_k (int): The number of top checkpoints to keep. Defaults to 5.
"""
# Save the current model and tokenizer
if isinstance(model, DDP):
model.module.save_pretrained(os.path.join(save_path, str(checkpoint)))
else:
model.save_pretrained(os.path.join(save_path, str(checkpoint)))
# List all files in the directory
files = os.listdir(save_path)
# Filter files to keep only the model checkpoints
checkpoint_folders = [file for file in files if re.search(r'model_\d+', file)]
# Sort checkpoint files by modification time (latest first)
checkpoint_folders.sort(key=lambda x: os.path.getmtime(os.path.join(save_path, x)), reverse=True)
# Keep only the top-k checkpoints
for checkpoint_folder in checkpoint_folders[top_k:]:
checkpoint_folder = os.path.join(save_path, checkpoint_folder)
checkpoint_files = [os.path.join(checkpoint_folder, f) for f in os.listdir(checkpoint_folder)]
for file in checkpoint_files:
os.remove(file)
os.rmdir(os.path.join(checkpoint_folder))
class Trainer:
def __init__(self, config, allow_distributed, compile_model=False, device='cuda'):
self.config = config
self.lr_encoder = float(self.config.lr_encoder)
self.lr_others = float(self.config.lr_others)
self.weight_decay_encoder = float(self.config.weight_decay_encoder)
self.weight_decay_other = float(self.config.weight_decay_other)
self.compile_model = compile_model
self.device = device
self.model_config = GLiNERConfig(**vars(config))
tokenizer = AutoTokenizer.from_pretrained(config.model_name)
if config.labels_encoder is None:
self.model_config.class_token_index=len(tokenizer)
tokenizer.add_tokens([self.model_config.ent_token, self.model_config.sep_token])
self.model_config.vocab_size = len(tokenizer)
self.allow_distributed = allow_distributed
self.optimizer = None
def setup_distributed(self, rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12356'
torch.cuda.set_device(rank)
dist.init_process_group("nccl", rank=rank, world_size=world_size)
def cleanup_distributed(self):
dist.destroy_process_group()
def create_optimizer(self, opt_model, **optimizer_kwargs):
"""
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
"""
if self.optimizer is None:
decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
decay_parameters = [name for name in decay_parameters if "bias" not in name]
if self.lr_others is not None:
encoder_parameters = [name for name, _ in opt_model.named_parameters() if "token_rep_layer" in name]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in encoder_parameters and p.requires_grad)
],
"weight_decay": self.weight_decay_other,
"lr": self.lr_others,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in encoder_parameters and p.requires_grad)
],
"weight_decay": 0.0,
"lr": self.lr_others,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in encoder_parameters and p.requires_grad)
],
"weight_decay": self.weight_decay_encoder,
"lr": self.lr_encoder,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in encoder_parameters and p.requires_grad)
],
"weight_decay": 0.0,
"lr": self.lr_encoder,
},
]
else:
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
],
"weight_decay": self.weight_decay_encoder,
"lr": self.lr_encoder,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
],
"weight_decay": 0.0,
"lr": self.lr_encoder,
},
]
self.optimizer = torch.optim.AdamW(optimizer_grouped_parameters, **optimizer_kwargs)
return self.optimizer
def setup_model_and_optimizer(self, rank=None, device=None):
if device is None:
device = self.device
if self.config.prev_path is not None:
model = GLiNER.from_pretrained(self.config.prev_path).to(device)
model.config = self.model_config
else:
model = GLiNER(self.model_config).to(device)
if self.config.labels_encoder is None:
model.resize_token_embeddings([self.model_config.ent_token, self.model_config.sep_token],
set_class_token_index = False,
add_tokens_to_tokenizer=False)
if rank is not None:
model = DDP(model, device_ids=[rank], output_device=rank, find_unused_parameters=False)
if self.config.labels_encoder is None:
model.module.resize_token_embeddings([self.model_config.ent_token, self.model_config.sep_token],
set_class_token_index = False,
add_tokens_to_tokenizer=False)
optimizer = self.create_optimizer(model.model)
if self.compile_model:
model.compile_for_training()
return model, optimizer
def create_dataloader(self, dataset, data_processor, sampler=None, shuffle=True):
# dataset = GLiNERDataset(dataset, config = self.config, data_processor=self.data_processor)
# collator = DataCollatorWithPadding(self.config)
collator = DataCollator(self.config, data_processor=data_processor, prepare_labels=True)
data_loader = DataLoader(dataset, batch_size=self.config.train_batch_size, num_workers=12,
shuffle=shuffle, collate_fn=collator, sampler=sampler)
return data_loader
def train_dist(self, rank, world_size, dataset):
# Init distributed process group
self.setup_distributed(rank, world_size)
device = f'cuda:{rank}'
model, optimizer = self.setup_model_and_optimizer(rank, device=device)
sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=True, drop_last=False)
train_loader = self.create_dataloader(dataset, model.data_processor, sampler=sampler, shuffle=False)
num_steps = self.config.num_steps // world_size
self.train(model=model, optimizer=optimizer, train_loader=train_loader,
num_steps=num_steps, device=device, rank=rank)
self.cleanup_distributed()
def init_scheduler(self, scheduler_type, optimizer, num_warmup_steps, num_steps):
if scheduler_type == "cosine":
scheduler = get_cosine_schedule_with_warmup(
optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_steps
)
elif scheduler_type == "linear":
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_steps
)
elif scheduler_type == "constant":
scheduler = get_constant_schedule_with_warmup(
optimizer,
num_warmup_steps=num_warmup_steps,
)
elif scheduler_type == "polynomial":
scheduler = get_polynomial_decay_schedule_with_warmup(
optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_steps
)
elif scheduler_type == "inverse_sqrt":
scheduler = get_inverse_sqrt_schedule(
optimizer,
num_warmup_steps=num_warmup_steps,
)
else:
raise ValueError(
f"Invalid scheduler_type value: '{scheduler_type}' \n Supported scheduler types: 'cosine', 'linear', 'constant', 'polynomial', 'inverse_sqrt'"
)
return scheduler
def train(self, model, optimizer, train_loader, num_steps, device='cuda', rank=None):
model.train()
pbar = tqdm(range(num_steps))
warmup_ratio = self.config.warmup_ratio
eval_every = self.config.eval_every
save_total_limit = self.config.save_total_limit
log_dir = self.config.log_dir
val_data_dir = self.config.val_data_dir
num_warmup_steps = int(num_steps * warmup_ratio) if warmup_ratio < 1 else int(warmup_ratio)
scheduler = self.init_scheduler(self.config.scheduler_type, optimizer, num_warmup_steps, num_steps)
iter_train_loader = iter(train_loader)
scaler = torch.cuda.amp.GradScaler()
for step in pbar:
optimizer.zero_grad()
try:
x = next(iter_train_loader)
except StopIteration:
iter_train_loader = iter(train_loader)
x = next(iter_train_loader)
for k, v in x.items():
if isinstance(v, torch.Tensor):
x[k] = v.to(device)
try:
with torch.cuda.amp.autocast(dtype=torch.float16):
loss = model(alpha = self.config.loss_alpha,
gamma = self.config.loss_gamma,
label_smoothing = self.config.label_smoothing,
reduction = self.config.loss_reduction,
**x).loss
if torch.isnan(loss).any():
print("Warning: NaN loss detected")
continue
scaler.scale(loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), self.config.max_grad_norm)
scaler.step(optimizer)
scaler.update()
scheduler.step()
del x
torch.cuda.empty_cache()
except Exception as e:
print(f"Error: {e}")
del x
torch.cuda.empty_cache()
continue
description = f"step: {step} | epoch: {step // len(train_loader)} | loss: {loss.item():.2f}"
pbar.set_description(description)
if (step + 1) % eval_every == 0:
if rank is None or rank == 0:
checkpoint = f'model_{step + 1}'
save_top_k_checkpoints(model, log_dir, checkpoint, save_total_limit)
if val_data_dir != "none":
get_for_all_path(model, step, log_dir, val_data_dir)
model.train()
def run(self):
with open(self.config.train_data, 'r') as f:
data = json.load(f)
random.shuffle(data)
if torch.cuda.device_count() > 1 and self.allow_distributed:
world_size = torch.cuda.device_count()
mp.spawn(self.train_dist, args=(world_size, data), nprocs=world_size, join=True)
else:
model, optimizer = self.setup_model_and_optimizer()
train_loader = self.create_dataloader(data, model.data_processor, shuffle=True)
self.train(model, optimizer, train_loader, num_steps=self.config.num_steps, device=self.device)
def create_parser():
parser = argparse.ArgumentParser(description="Span-based NER")
parser.add_argument("--config", type=str, default="configs/config.yaml", help="Path to config file")
parser.add_argument('--log_dir', type=str, default='logs', help='Path to the log directory')
parser.add_argument('--allow_distributed', type=bool, default=False,
help='Whether to allow distributed training if there are more than one GPU available')
parser.add_argument('--compile_model', type=bool, default=False,
help='Whether to apply torch.compile to a modell or not')
return parser
if __name__ == "__main__":
parser = create_parser()
args = parser.parse_args()
config = load_config_as_namespace(args.config)
config.log_dir = args.log_dir
trainer = Trainer(config, allow_distributed=args.allow_distributed,
compile_model = args.compile_model,
device='cuda' if torch.cuda.is_available() else 'cpu')
trainer.run() |