update train_unconditional_ort.py (huggingface#1775)

* reflect changes

* run make style

Co-authored-by: Prathik Rao <[email protected]>
Co-authored-by: Prathik Rao <[email protected]@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>

Author: 3 people

examples/unconditional_image_generation/train_unconditional_ort.py

@@ -174,6 +174,16 @@ def parse_args():
     parser.add_argument(
         "--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
     )
+    parser.add_argument(
+        "--logger",
+        type=str,
+        default="tensorboard",
+        choices=["tensorboard", "wandb"],
+        help=(
+            "Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)"
+            " for experiment tracking and logging of model metrics and model checkpoints"
+        ),
+    )
     parser.add_argument(
         "--logging_dir",
         type=str,
@@ -195,7 +205,6 @@ def parse_args():
             "and an Nvidia Ampere GPU."
         ),
     )
-
     parser.add_argument(
         "--prediction_type",
         type=str,
@@ -206,6 +215,24 @@ def parse_args():
 
     parser.add_argument("--ddpm_num_steps", type=int, default=1000)
     parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
 
     args = parser.parse_args()
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
@@ -233,7 +260,7 @@ def main(args):
     accelerator = Accelerator(
         gradient_accumulation_steps=args.gradient_accumulation_steps,
         mixed_precision=args.mixed_precision,
-        log_with="tensorboard",
+        log_with=args.logger,
         logging_dir=logging_dir,
     )
 
@@ -321,6 +348,7 @@ def transforms(examples):
     model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
         model, optimizer, train_dataloader, lr_scheduler
     )
+    accelerator.register_for_checkpointing(lr_scheduler)
 
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
 
@@ -353,11 +381,34 @@ def transforms(examples):
         accelerator.init_trackers(run)
 
     global_step = 0
-    for epoch in range(args.num_epochs):
+    first_epoch = 0
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1]
+        accelerator.print(f"Resuming from checkpoint {path}")
+        accelerator.load_state(os.path.join(args.output_dir, path))
+        global_step = int(path.split("-")[1])
+        resume_global_step = global_step * args.gradient_accumulation_steps
+        first_epoch = resume_global_step // num_update_steps_per_epoch
+        resume_step = resume_global_step % num_update_steps_per_epoch
+
+    for epoch in range(first_epoch, args.num_epochs):
         model.train()
         progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
         progress_bar.set_description(f"Epoch {epoch}")
         for step, batch in enumerate(train_dataloader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % args.gradient_accumulation_steps == 0:
+                    progress_bar.update(1)
+                continue
+
             clean_images = batch["input"]
             # Sample noise that we'll add to the images
             noise = torch.randn(clean_images.shape).to(clean_images.device)
@@ -404,6 +455,12 @@ def transforms(examples):
                 progress_bar.update(1)
                 global_step += 1
 
+                if global_step % args.checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
             logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
             if args.use_ema:
                 logs["ema_decay"] = ema_model.decay
@@ -431,9 +488,11 @@ def transforms(examples):
 
                 # denormalize the images and save to tensorboard
                 images_processed = (images * 255).round().astype("uint8")
-                accelerator.trackers[0].writer.add_images(
-                    "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
-                )
+
+                if args.logger == "tensorboard":
+                    accelerator.get_tracker("tensorboard").add_images(
+                        "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
+                    )
 
             if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
                 # save the model