Enhance readme for ddp cases in ldm tutorials (Project-MONAI#1857)

Enhance readme for ddp cases in ldm tutorials
Add amp argument in maisi diffusion training notebook as a workaround
for Project-MONAI#1858.

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Avoid including large-size files in the PR.
- [ ] Clean up long text outputs from code cells in the notebook.
- [ ] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

---------

Signed-off-by: YunLiu <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

generation/2d_ldm/README.md

@@ -57,6 +57,7 @@ torchrun \
     --master_addr=localhost --master_port=1234 \
     train_autoencoder.py -c ./config/config_train_32g.json -e ./config/environment.json -g ${NUM_GPUS_PER_NODE}
 ```
+Please note that during multi-GPU training, additional GPU memory may be required. Users might need to reduce the `batch_size` accordingly based on their available resources to ensure smooth training.
 
 <p align="center">
   <img src="./figs/train_recon.png" alt="autoencoder train curve" width="45%" >
@@ -88,6 +89,8 @@ torchrun \
     --master_addr=localhost --master_port=1234 \
     train_diffusion.py -c ./config/config_train_32g.json -e ./config/environment.json -g ${NUM_GPUS_PER_NODE}
 ```
+Please note that during multi-GPU training, additional GPU memory may be required. Users might need to reduce the `batch_size` accordingly based on their available resources to ensure smooth training.
+
 <p align="center">
   <img src="./figs/train_diffusion.png" alt="latent diffusion train curve" width="45%" >
 &nbsp; &nbsp; &nbsp; &nbsp;

generation/3d_ldm/README.md

@@ -61,6 +61,7 @@ torchrun \
     --master_addr=localhost --master_port=1234 \
     train_autoencoder.py -c ./config/config_train_32g.json -e ./config/environment.json -g ${NUM_GPUS_PER_NODE}
 ```
+Please note that during multi-GPU training, additional GPU memory may be required. Users might need to reduce the `batch_size` accordingly based on their available resources to ensure smooth training.
 
 <p align="center">
   <img src="./figs/train_recon.png" alt="autoencoder train curve" width="45%" >
@@ -87,6 +88,7 @@ torchrun \
     --master_addr=localhost --master_port=1234 \
     train_diffusion.py -c ./config/config_train_32g.json -e ./config/environment.json -g ${NUM_GPUS_PER_NODE}
 ```
+Please note that during multi-GPU training, additional GPU memory may be required. Users might need to reduce the `batch_size` accordingly based on their available resources to ensure smooth training.
 <p align="center">
   <img src="./figs/train_diffusion.png" alt="latent diffusion train curve" width="45%" >
 &nbsp; &nbsp; &nbsp; &nbsp;

generation/maisi/maisi_diff_unet_training_tutorial.ipynb

@@ -429,7 +429,9 @@
     "\n",
     "After all latent features have been created, we will initiate the multi-GPU script to train the latent diffusion model.\n",
     "\n",
-    "The image generation process utilizes the [DDPM scheduler](https://arxiv.org/pdf/2006.11239) with 1,000 iterative steps. The diffusion model is optimized using L1 loss and a decayed learning rate scheduler. The batch size for this process is set to 1."
+    "The image generation process utilizes the [DDPM scheduler](https://arxiv.org/pdf/2006.11239) with 1,000 iterative steps. The diffusion model is optimized using L1 loss and a decayed learning rate scheduler. The batch size for this process is set to 1.\n",
+    "\n",
+    "Please be aware that using the H100 GPU may occasionally result in random segmentation faults. To avoid this issue, you can disable AMP by setting the `--no_amp` flag."
    ]
   },
   {

generation/maisi/scripts/diff_model_train.py

@@ -24,7 +24,7 @@
 from torch.nn.parallel import DistributedDataParallel
 
 import monai
-from monai.data import ThreadDataLoader, partition_dataset
+from monai.data import DataLoader, partition_dataset
 from monai.transforms import Compose
 from monai.utils import first
 
@@ -50,7 +50,7 @@ def load_filenames(data_list_path: str) -> list:
 
 def prepare_data(
     train_files: list, device: torch.device, cache_rate: float, num_workers: int = 2, batch_size: int = 1
-) -> ThreadDataLoader:
+) -> DataLoader:
     """
     Prepare training data.
 
@@ -62,7 +62,7 @@ def prepare_data(
         batch_size (int): Mini-batch size.
 
     Returns:
-        ThreadDataLoader: Data loader for training.
+        DataLoader: Data loader for training.
     """
 
     def _load_data_from_file(file_path, key):
@@ -90,7 +90,7 @@ def _load_data_from_file(file_path, key):
         data=train_files, transform=train_transforms, cache_rate=cache_rate, num_workers=num_workers
     )
 
-    return ThreadDataLoader(train_ds, num_workers=6, batch_size=batch_size, shuffle=True)
+    return DataLoader(train_ds, num_workers=6, batch_size=batch_size, shuffle=True)
 
 
 def load_unet(args: argparse.Namespace, device: torch.device, logger: logging.Logger) -> torch.nn.Module:
@@ -124,14 +124,12 @@ def load_unet(args: argparse.Namespace, device: torch.device, logger: logging.Lo
     return unet
 
 
-def calculate_scale_factor(
-    train_loader: ThreadDataLoader, device: torch.device, logger: logging.Logger
-) -> torch.Tensor:
+def calculate_scale_factor(train_loader: DataLoader, device: torch.device, logger: logging.Logger) -> torch.Tensor:
     """
     Calculate the scaling factor for the dataset.
 
     Args:
-        train_loader (ThreadDataLoader): Data loader for training.
+        train_loader (DataLoader): Data loader for training.
         device (torch.device): Device to use for calculation.
         logger (logging.Logger): Logger for logging information.
 
@@ -181,7 +179,7 @@ def create_lr_scheduler(optimizer: torch.optim.Optimizer, total_steps: int) -> t
 def train_one_epoch(
     epoch: int,
     unet: torch.nn.Module,
-    train_loader: ThreadDataLoader,
+    train_loader: DataLoader,
     optimizer: torch.optim.Optimizer,
     lr_scheduler: torch.optim.lr_scheduler.PolynomialLR,
     loss_pt: torch.nn.L1Loss,
@@ -193,14 +191,15 @@ def train_one_epoch(
     device: torch.device,
     logger: logging.Logger,
     local_rank: int,
+    amp: bool = True,
 ) -> torch.Tensor:
     """
     Train the model for one epoch.
 
     Args:
         epoch (int): Current epoch number.
         unet (torch.nn.Module): UNet model.
-        train_loader (ThreadDataLoader): Data loader for training.
+        train_loader (DataLoader): Data loader for training.
         optimizer (torch.optim.Optimizer): Optimizer.
         lr_scheduler (torch.optim.lr_scheduler.PolynomialLR): Learning rate scheduler.
         loss_pt (torch.nn.L1Loss): Loss function.
@@ -212,6 +211,7 @@ def train_one_epoch(
         device (torch.device): Device to use for training.
         logger (logging.Logger): Logger for logging information.
         local_rank (int): Local rank for distributed training.
+        amp (bool): Use automatic mixed precision training.
 
     Returns:
         torch.Tensor: Training loss for the epoch.
@@ -237,7 +237,7 @@ def train_one_epoch(
 
         optimizer.zero_grad(set_to_none=True)
 
-        with autocast("cuda", enabled=True):
+        with autocast("cuda", enabled=amp):
             noise = torch.randn(
                 (num_images_per_batch, 4, images.size(-3), images.size(-2), images.size(-1)), device=device
             )
@@ -256,9 +256,13 @@ def train_one_epoch(
 
             loss = loss_pt(noise_pred.float(), noise.float())
 
-        scaler.scale(loss).backward()
-        scaler.step(optimizer)
-        scaler.update()
+        if amp:
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+        else:
+            loss.backward()
+            optimizer.step()
 
         lr_scheduler.step()
 
@@ -312,14 +316,18 @@ def save_checkpoint(
     )
 
 
-def diff_model_train(env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int) -> None:
+def diff_model_train(
+    env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, amp: bool = True
+) -> None:
     """
     Main function to train a diffusion model.
 
     Args:
         env_config_path (str): Path to the environment configuration file.
         model_config_path (str): Path to the model configuration file.
         model_def_path (str): Path to the model definition file.
+        num_gpus (int): Number of GPUs to use for training.
+        amp (bool): Use automatic mixed precision training.
     """
     args = load_config(env_config_path, model_config_path, model_def_path)
     local_rank, world_size, device = initialize_distributed(num_gpus)
@@ -357,7 +365,7 @@ def diff_model_train(env_config_path: str, model_config_path: str, model_def_pat
         )[local_rank]
 
     train_loader = prepare_data(
-        train_files, device, args.diffusion_unet_train["cache_rate"], args.diffusion_unet_train["batch_size"]
+        train_files, device, args.diffusion_unet_train["cache_rate"], batch_size=args.diffusion_unet_train["batch_size"]
     )
 
     unet = load_unet(args, device, logger)
@@ -392,6 +400,7 @@ def diff_model_train(env_config_path: str, model_config_path: str, model_def_pat
             device,
             logger,
             local_rank,
+            amp=amp,
         )
 
         loss_torch = loss_torch.tolist()
@@ -431,6 +440,7 @@ def diff_model_train(env_config_path: str, model_config_path: str, model_def_pat
         "--model_def", type=str, default="./configs/config_maisi.json", help="Path to model definition file"
     )
     parser.add_argument("--num_gpus", type=int, default=1, help="Number of GPUs to use for training")
+    parser.add_argument("--no_amp", dest="amp", action="store_false", help="Disable automatic mixed precision training")
 
     args = parser.parse_args()
-    diff_model_train(args.env_config, args.model_config, args.model_def, args.num_gpus)
+    diff_model_train(args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp)