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import os
import numpy as np
import torch
import torch.nn as nn
import wandb.filesync
import yaml
from torch.optim import AdamW
from tqdm import tqdm
import wandb
from celeba import create_dataloader
from model.transformer import DIT
from model.vae import VAE
from scheduler.linear_scheduler import LinearNoiseScheduler
device = "cuda" if torch.cuda.is_available() else "cpu"
def train(args):
with open(args.config_path, "r") as file:
try:
config = yaml.safe_load(file)
except yaml.YAMLError as e:
print(f"Error in loading yaml: {e}")
train_config = config["train_params"]
dit_config = config["dit_params"]
dataset_config = config["dataset_params"]
diffusion_params = config["diffusion_params"]
vae_config = config["autoencoder_params"]
wandb.init(
project="diffusion-transformer",
name=f"{train_config['task_name']}_dit_training",
config={
"train_config": train_config,
"dit_config": dit_config,
"dataset_config": dataset_config,
"diffusion_params": diffusion_params,
"vae_config": vae_config,
"device": device,
},
tags=["dit", "diffusion", "transformer"],
)
dataloader = create_dataloader(dataset_config["im_path"])
scheduler = LinearNoiseScheduler(
diffusion_params["num_timesteps"],
diffusion_params["beta_start"],
diffusion_params["beta_end"],
)
im_size = dataset_config["im_size"] // 2 ** sum(vae_config["down_sample"])
model = DIT(
im_size=im_size, im_channels=vae_config["z_channels"], config=dit_config
).to(device)
model.train()
wandb.watch(model, log="all", log_freq=100)
if os.path.exists(
os.path.join(train_config["task_name"], train_config["dit_ckpt_name"])
):
checkpoint = torch.load(
os.path.join(train_config["task_name"], train_config["dit_ckpt_name"]),
map_location=device,
)
optimizer = AdamW(model.parameters(), lr=train_config["dit_lr"])
model.load_state_dict(checkpoint["dit"])
start_epoch = checkpoint["epoch"]
step_count = checkpoint["step_count"]
optimizer.load_state_dict(checkpoint["optimizer"])
print(f"Resuming from epoch {start_epoch}, step {step_count}")
wandb.log({"resumed_from_epoch": start_epoch, "resumed_from_step": step_count})
else:
step_count = 0
start_epoch = 0
optimizer = AdamW(model.parameters(), lr=train_config["dit_lr"])
if not os.path.exists(
os.path.join(
train_config["task_name"], train_config["vae_autoencoder_ckpt_name"]
)
):
print("No VAE checkpoint found, VAE checkpoint needed")
wandb.finish()
return
else:
vae = VAE(dataset_config["im_channels"], vae_config).to(device)
vae.load_state_dict(
torch.load(
os.path.join(
train_config["task_name"], train_config["vae_autoencoder_ckpt_name"]
),
map_location=device,
)
)
vae.eval()
for param in vae.parameters():
param.requires_grad = False
print("VAE checkpoint loaded")
# Log model architecture
wandb.log(
{
"model_parameters": sum(p.numel() for p in model.parameters()),
"trainable_parameters": sum(
p.numel() for p in model.parameters() if p.requires_grad
),
}
)
num_epochs = train_config["dit_epochs"]
accu_steps = train_config["dit_acc_steps"]
criterion = nn.MSELoss()
for epoch in range(start_epoch, num_epochs):
losses = []
for im in tqdm(dataloader):
im = im.float().to(device)
step_count += 1
with torch.no_grad():
im, _ = vae.encode(im)
noise = torch.randn_like(im).to(device)
t = torch.randint(0, diffusion_params["num_timesteps"], (im.shape[0],)).to(
device
)
noisy_im = scheduler.add_noise(im, noise, t)
pred = model(noisy_im, t)
loss = criterion(pred, noise)
losses.append(loss.item())
loss = loss / accu_steps
loss.backward()
if step_count % 10 == 0: # Log every 10 steps
wandb.log(
{
"batch_loss": loss.item() * accu_steps,
"learning_rate": optimizer.param_groups[0]["lr"],
"step_count": step_count,
"epoch": epoch,
}
)
if step_count % accu_steps == 0:
optimizer.step()
optimizer.zero_grad()
optimizer.step()
optimizer.zero_grad()
wandb.log(
{
"epoch": epoch,
"epoch_loss_std": np.std(losses),
"learning_rate": optimizer.param_groups[0]["lr"],
}
)
print(f"Epoch {epoch}: Loss: {np.mean(losses)}")
torch.save(
{
"dit": model.state_dict(),
"epoch": epoch + 1,
"step": step_count,
"optimizer": optimizer.state_dict(),
},
os.path.join(train_config["task_name"], train_config["dit_ckpt_name"]),
)
if (epoch + 1) % 5 == 0: # Save every 5 epochs
artifact = wandb.Artifact(
f"dit_model_epoch_{epoch + 1}",
type="model",
description=f"DIT model checkpoint at epoch {epoch + 1}",
)
artifact.add_file(
os.path.join(train_config["task_name"], train_config["dit_ckpt_name"]),
)
wandb.log_artifact(artifact)
final_artifact = wandb.Artifact(
"dit_model_final", type="model", description="Final DIT model checkpoint"
)
final_artifact.add_file(
os.path.join(train_config["task_name"], train_config["dit_ckpt_name"])
)
wandb.log_artifact(final_artifact)
print("Done Training")
wandb.finish()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Arguments for dit training")
parser.add_argument(
"--config", dest="config_path", default="celeba/config.yaml", type=str
)
args = parser.parse_args()
train(args)
|