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"""This example implements the GIT-Mol model
(https://arxiv.org/abs/2308.06911) using PyG.
"""
import argparse
import os.path as osp
import torch
from accelerate import Accelerator
from accelerate.utils import DistributedDataParallelKwargs
from torch.optim.lr_scheduler import StepLR
from tqdm import tqdm
from torch_geometric import seed_everything
from torch_geometric.datasets import GitMolDataset
from torch_geometric.llm.models import GITMol
from torch_geometric.loader import DataLoader
@torch.no_grad()
def eval(model, data_loader):
model.eval()
loss = 0
for batch in data_loader:
batch_loss = model(batch.x, batch.edge_index, batch.batch,
batch.edge_attr, batch.smiles, batch.image,
batch.caption)
loss += batch_loss.item() / len(data_loader)
return loss
def train(
num_epochs: int,
lr: float,
weight_decay: float,
batch_size: int,
checkpointing: bool,
):
# Load dataset ================================================
path = osp.dirname(osp.realpath(__file__))
path = osp.join(path, '..', '..', 'data', 'GITMol')
train_dataset = GitMolDataset(path, split=0)
val_dataset = GitMolDataset(path, split=1)
test_dataset = GitMolDataset(path, split=2)
seed_everything(42)
train_loader = DataLoader(train_dataset, batch_size=batch_size,
drop_last=True, pin_memory=True, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size,
drop_last=False, pin_memory=True, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=batch_size,
drop_last=False, pin_memory=True, shuffle=False)
# Create model ===============================================
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
device = accelerator.device
model = GITMol().to(device)
optimizer = torch.optim.AdamW(
[p for p in model.parameters() if p.requires_grad], lr=lr,
weight_decay=weight_decay)
scheduler = StepLR(optimizer, step_size=1, gamma=0.1)
model, optimizer, train_loader, scheduler = accelerator.prepare(
model, optimizer, train_loader, scheduler)
val_loader = accelerator.prepare_data_loader(val_loader,
device_placement=True)
test_loader = accelerator.prepare_data_loader(test_loader,
device_placement=True)
# Train and eval ============================================
best_epoch = 0
best_val_loss = float('inf')
for epoch in range(num_epochs):
# Train
model.train()
epoch_loss = 0
if epoch == 0:
print("Training beginning...")
epoch_str = f'Epoch: {epoch + 1}|{num_epochs}'
for batch in tqdm(train_loader, desc=epoch_str):
optimizer.zero_grad()
loss = model(batch.x, batch.edge_index, batch.batch,
batch.edge_attr, batch.smiles, batch.image,
batch.caption)
accelerator.backward(loss)
optimizer.step()
epoch_loss += loss.item()
train_loss = epoch_loss / len(train_loader)
# Eval
val_loss = eval(model, val_loader)
print(
f'{epoch_str}, Train loss: {train_loss:4f}, Val loss: {val_loss:4f}' # noqa: E501
)
if checkpointing and val_loss < best_val_loss:
best_val_loss = val_loss
best_epoch = epoch
torch.save(
{
'model_state_dict':
accelerator.unwrap_model(model).state_dict(),
'best_loss':
best_val_loss
},
f'gitmol_pretrain_epoch{best_epoch}_val_loss{best_val_loss:4f}_ckpt.pt' # noqa: E501
)
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
# Test
test_loss = eval(model, test_loader)
print(f'Test loss: {test_loss:4f}')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=3)
parser.add_argument('--lr', type=float, default=1e-5)
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument("--weight_decay", type=float, default=0.01)
parser.add_argument('--checkpointing', type=bool, default=True)
args = parser.parse_args()
train(
args.epochs,
args.lr,
args.weight_decay,
args.batch_size,
args.checkpointing,
)
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