chore: Refactor optimizer group

Author: kohya-ss

sdxl_train.py

@@ -357,27 +357,37 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
     accelerator.print("prepare optimizer, data loader etc.")
 
     if args.fused_optimizer_groups:
+        # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
+        # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each group of parameters.
+        # This balances memory usage and management complexity.
+
         # calculate total number of parameters
         n_total_params = sum(len(params["params"]) for params in params_to_optimize)
         params_per_group = math.ceil(n_total_params / args.fused_optimizer_groups)
 
-        # split params into groups
+        # split params into groups, keeping the learning rate the same for all params in a group
+        # this will increase the number of groups if the learning rate is different for different params (e.g. U-Net and text encoders)
         grouped_params = []
         param_group = []
         param_group_lr = -1
         for group in params_to_optimize:
             lr = group["lr"]
             for p in group["params"]:
+                # if the learning rate is different for different params, start a new group
                 if lr != param_group_lr:
                     if param_group:
                         grouped_params.append({"params": param_group, "lr": param_group_lr})
                         param_group = []
                     param_group_lr = lr
+
                 param_group.append(p)
+
+                # if the group has enough parameters, start a new group
                 if len(param_group) == params_per_group:
                     grouped_params.append({"params": param_group, "lr": param_group_lr})
                     param_group = []
                     param_group_lr = -1
+
         if param_group:
             grouped_params.append({"params": param_group, "lr": param_group_lr})
 
@@ -388,7 +398,6 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
             optimizers.append(optimizer)
         optimizer = optimizers[0]  # avoid error in the following code
 
-        print(len(grouped_params))
         logger.info(f"using {len(optimizers)} optimizers for fused optimizer groups")
 
     else:
@@ -420,6 +429,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     # lr schedulerを用意する
     if args.fused_optimizer_groups:
+        # prepare lr schedulers for each optimizer
         lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers]
         lr_scheduler = lr_schedulers[0]  # avoid error in the following code
     else:
@@ -472,6 +482,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
         optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
 
     if args.fused_backward_pass:
+        # use fused optimizer for backward pass: other optimizers will be supported in the future
         import library.adafactor_fused
 
         library.adafactor_fused.patch_adafactor_fused(optimizer)
@@ -488,16 +499,20 @@ def __grad_hook(tensor: torch.Tensor, param_group=param_group):
                     parameter.register_post_accumulate_grad_hook(__grad_hook)
 
     elif args.fused_optimizer_groups:
+        # prepare for additional optimizers and lr schedulers
         for i in range(1, len(optimizers)):
             optimizers[i] = accelerator.prepare(optimizers[i])
             lr_schedulers[i] = accelerator.prepare(lr_schedulers[i])
 
+        # counters are used to determine when to step the optimizer
         global optimizer_hooked_count
         global num_parameters_per_group
         global parameter_optimizer_map
+
         optimizer_hooked_count = {}
         num_parameters_per_group = [0] * len(optimizers)
         parameter_optimizer_map = {}
+
         for opt_idx, optimizer in enumerate(optimizers):
             for param_group in optimizer.param_groups:
                 for parameter in param_group["params"]:
@@ -511,7 +526,7 @@ def optimizer_hook(parameter: torch.Tensor):
                             optimizer_hooked_count[i] += 1
                             if optimizer_hooked_count[i] == num_parameters_per_group[i]:
                                 optimizers[i].step()
-                                optimizers[i].zero_grad()
+                                optimizers[i].zero_grad(set_to_none=True)
 
                         parameter.register_post_accumulate_grad_hook(optimizer_hook)
                         parameter_optimizer_map[parameter] = opt_idx
@@ -593,7 +608,7 @@ def optimizer_hook(parameter: torch.Tensor):
             current_step.value = global_step
 
             if args.fused_optimizer_groups:
-                optimizer_hooked_count = {i: 0 for i in range(len(optimizers))}
+                optimizer_hooked_count = {i: 0 for i in range(len(optimizers))}  # reset counter for each step
 
             with accelerator.accumulate(*training_models):
                 if "latents" in batch and batch["latents"] is not None:
@@ -725,14 +740,14 @@ def optimizer_hook(parameter: torch.Tensor):
                         accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
                     optimizer.step()
-                elif args.fused_optimizer_groups:
-                    for i in range(1, len(optimizers)):
-                        lr_schedulers[i].step()
-
-                lr_scheduler.step()
-
-                if not (args.fused_backward_pass or args.fused_optimizer_groups):
+                    lr_scheduler.step()
                     optimizer.zero_grad(set_to_none=True)
+                else:
+                    # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
+                    lr_scheduler.step()
+                    if args.fused_optimizer_groups:
+                        for i in range(1, len(optimizers)):
+                            lr_schedulers[i].step()
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients: