[train_unconditional] fix gradient accumulation. (#308)

fix grad accum

examples/unconditional_image_generation/train_unconditional.py

 import argparse
+import math
 import os
 
 import torch
@@ -29,6 +30,7 @@ logger = get_logger(__name__)
 def main(args):
     logging_dir = os.path.join(args.output_dir, args.logging_dir)
     accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
         mixed_precision=args.mixed_precision,
         log_with="tensorboard",
         logging_dir=logging_dir,
@@ -105,6 +107,8 @@ def main(args):
         model, optimizer, train_dataloader, lr_scheduler
     )
 
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+
     ema_model = EMAModel(model, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay)
 
     if args.push_to_hub:
@@ -117,7 +121,7 @@ def main(args):
     global_step = 0
     for epoch in range(args.num_epochs):
         model.train()
-        progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)
+        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):
             clean_images = batch["input"]
@@ -146,13 +150,16 @@ def main(args):
                     ema_model.step(model)
                 optimizer.zero_grad()
 
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
                 progress_bar.update(1)
+                global_step += 1
+
             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
             progress_bar.set_postfix(**logs)
             accelerator.log(logs, step=global_step)
-            global_step += 1
         progress_bar.close()
 
         accelerator.wait_for_everyone()