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import argparse
import os
import torch
import yaml
from tqdm import tqdm
from transformers import get_cosine_schedule_with_warmup
# from model_nested import NerFilteredSemiCRF
from .model import GLiNER
from .modules.run_evaluation import get_for_all_path, sample_train_data
from .save_load import save_model, load_model
import json
# train function
def train(model, optimizer, train_data, num_steps=1000, eval_every=100, log_dir="logs", warmup_ratio=0.1,
train_batch_size=8, device='cuda'):
model.train()
# initialize data loaders
train_loader = model.create_dataloader(train_data, batch_size=train_batch_size, shuffle=True)
pbar = tqdm(range(num_steps))
if warmup_ratio < 1:
num_warmup_steps = int(num_steps * warmup_ratio)
else:
num_warmup_steps = int(warmup_ratio)
scheduler = get_cosine_schedule_with_warmup(
optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_steps
)
iter_train_loader = iter(train_loader)
for step in pbar:
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:
loss = model(x) # Forward pass
except:
continue
# check if loss is nan
if torch.isnan(loss):
continue
loss.backward() # Compute gradients
optimizer.step() # Update parameters
scheduler.step() # Update learning rate schedule
optimizer.zero_grad() # Reset gradients
description = f"step: {step} | epoch: {step // len(train_loader)} | loss: {loss.item():.2f}"
if (step + 1) % eval_every == 0:
current_path = os.path.join(log_dir, f'model_{step + 1}')
save_model(model, current_path)
#val_data_dir = "/gpfswork/rech/ohy/upa43yu/NER_datasets" # can be obtained from "https://drive.google.com/file/d/1T-5IbocGka35I7X3CE6yKe5N_Xg2lVKT/view"
#get_for_all_path(model, step, log_dir, val_data_dir) # you can remove this comment if you want to evaluate the model
model.train()
pbar.set_description(description)
def create_parser():
parser = argparse.ArgumentParser(description="Span-based NER")
parser.add_argument("--config", type=str, default="config.yaml", help="Path to config file")
parser.add_argument('--log_dir', type=str, default='logs', help='Path to the log directory')
return parser
def load_config_as_namespace(config_file):
with open(config_file, 'r') as f:
config_dict = yaml.safe_load(f)
return argparse.Namespace(**config_dict)
if __name__ == "__main__":
# parse args
parser = create_parser()
args = parser.parse_args()
# load config
config = load_config_as_namespace(args.config)
config.log_dir = args.log_dir
try:
with open(config.train_data, 'r') as f:
data = json.load(f)
except:
data = sample_train_data(config.train_data, 10000)
if config.prev_path != "none":
model = load_model(config.prev_path)
model.config = config
else:
model = GLiNER(config)
if torch.cuda.is_available():
model = model.cuda()
lr_encoder = float(config.lr_encoder)
lr_others = float(config.lr_others)
optimizer = torch.optim.AdamW([
# encoder
{'params': model.token_rep_layer.parameters(), 'lr': lr_encoder},
{'params': model.rnn.parameters(), 'lr': lr_others},
# projection layers
{'params': model.span_rep_layer.parameters(), 'lr': lr_others},
{'params': model.prompt_rep_layer.parameters(), 'lr': lr_others},
])
device = 'cuda' if torch.cuda.is_available() else 'cpu'
train(model, optimizer, data, num_steps=config.num_steps, eval_every=config.eval_every,
log_dir=config.log_dir, warmup_ratio=config.warmup_ratio, train_batch_size=config.train_batch_size,
device=device)
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