Add New lr scheduler

library/train_util.py

@@ -42,7 +42,8 @@
 from torchvision import transforms
 from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection
 import transformers
-from diffusers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION
+from diffusers.optimization import SchedulerType as DiffusersSchedulerType, TYPE_TO_SCHEDULER_FUNCTION as DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION
+from transformers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION
 from diffusers import (
     StableDiffusionPipeline,
     DDPMScheduler,
@@ -2971,6 +2972,20 @@ def add_sd_models_arguments(parser: argparse.ArgumentParser):
 
 
 def add_optimizer_arguments(parser: argparse.ArgumentParser):
+    def int_or_float(value):
+        if value.endswith('%'):
+            try:
+                return float(value[:-1]) / 100.0
+            except ValueError:
+                raise argparse.ArgumentTypeError(f"Value '{value}' is not a valid percentage")
+        try:
+            float_value = float(value)
+            if float_value >= 1:
+                return int(value)
+            return float(value)
+        except ValueError:
+            raise argparse.ArgumentTypeError(f"'{value}' is not an int or float")
+
     parser.add_argument(
         "--optimizer_type",
         type=str,
@@ -3023,9 +3038,15 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
     )
     parser.add_argument(
         "--lr_warmup_steps",
-        type=int,
+        type=int_or_float,
+        default=0,
+        help="Int number of steps for the warmup in the lr scheduler (default is 0) or float with ratio of train steps / 学習率のスケジューラをウォームアップするステップ数（デフォルト0）",
+    )
+    parser.add_argument(
+        "--lr_decay_steps",
+        type=int_or_float,
         default=0,
-        help="Number of steps for the warmup in the lr scheduler (default is 0) / 学習率のスケジューラをウォームアップするステップ数（デフォルト0）",
+        help="Int number of steps for the decay in the lr scheduler (default is 0) or float with ratio of train steps",
     )
     parser.add_argument(
         "--lr_scheduler_num_cycles",
@@ -3045,6 +3066,18 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
         help="Combines backward pass and optimizer step to reduce VRAM usage. Only available in SDXL"
         + " / バックワードパスとオプティマイザステップを組み合わせてVRAMの使用量を削減します。SDXLでのみ有効",
     )
+    parser.add_argument(
+        "--lr_scheduler_timescale",
+        type=int,
+        default=None,
+        help="Inverse sqrt timescale for inverse sqrt scheduler,defaults to `num_warmup_steps`",
+    )
+    parser.add_argument(
+        "--lr_scheduler_min_lr_ratio",
+        type=float,
+        default=None,
+        help="The minimum learning rate as a ratio of the initial learning rate for cosine with min lr scheduler and warmup decay scheduler",
+    )
 
 
 def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool):
@@ -4292,10 +4325,14 @@ def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
     Unified API to get any scheduler from its name.
     """
     name = args.lr_scheduler
-    num_warmup_steps: Optional[int] = args.lr_warmup_steps
     num_training_steps = args.max_train_steps * num_processes  # * args.gradient_accumulation_steps
+    num_warmup_steps: Optional[int] = int(args.lr_warmup_steps * num_training_steps) if isinstance(args.lr_warmup_steps, float) else args.lr_warmup_steps
+    num_decay_steps: Optional[int] = int(args.lr_decay_steps * num_training_steps) if isinstance(args.lr_decay_steps, float) else args.lr_decay_steps
+    num_stable_steps = num_training_steps - num_warmup_steps - num_decay_steps
     num_cycles = args.lr_scheduler_num_cycles
     power = args.lr_scheduler_power
+    timescale = args.lr_scheduler_timescale
+    min_lr_ratio = args.lr_scheduler_min_lr_ratio
 
     lr_scheduler_kwargs = {}  # get custom lr_scheduler kwargs
     if args.lr_scheduler_args is not None and len(args.lr_scheduler_args) > 0:
@@ -4331,13 +4368,13 @@ def wrap_check_needless_num_warmup_steps(return_vals):
         # logger.info(f"adafactor scheduler init lr {initial_lr}")
         return wrap_check_needless_num_warmup_steps(transformers.optimization.AdafactorSchedule(optimizer, initial_lr))
 
-    name = SchedulerType(name)
-    schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
+    name = SchedulerType(name) or DiffusersSchedulerType(name)
+    schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] or DIFFUSERS_TYPE_TO_SCHEDULER_FUNCTION[name]
 
     if name == SchedulerType.CONSTANT:
         return wrap_check_needless_num_warmup_steps(schedule_func(optimizer, **lr_scheduler_kwargs))
 
-    if name == SchedulerType.PIECEWISE_CONSTANT:
+    if name == DiffusersSchedulerType.PIECEWISE_CONSTANT:
         return schedule_func(optimizer, **lr_scheduler_kwargs)  # step_rules and last_epoch are given as kwargs
 
     # All other schedulers require `num_warmup_steps`
@@ -4347,6 +4384,9 @@ def wrap_check_needless_num_warmup_steps(return_vals):
     if name == SchedulerType.CONSTANT_WITH_WARMUP:
         return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **lr_scheduler_kwargs)
 
+    if name == SchedulerType.INVERSE_SQRT:
+        return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, timescale=timescale, **lr_scheduler_kwargs)
+
     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
         raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
@@ -4365,7 +4405,31 @@ def wrap_check_needless_num_warmup_steps(return_vals):
             optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power, **lr_scheduler_kwargs
         )
 
-    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, **lr_scheduler_kwargs)
+    if name == SchedulerType.COSINE_WITH_MIN_LR:
+        return schedule_func(
+            optimizer, 
+            num_warmup_steps=num_warmup_steps, 
+            num_training_steps=num_training_steps, 
+            num_cycles=num_cycles / 2,
+            min_lr_rate=min_lr_ratio, 
+            **lr_scheduler_kwargs,
+        )
+
+    # All other schedulers require `num_decay_steps`
+    if num_decay_steps is None:
+        raise ValueError(f"{name} requires `num_decay_steps`, please provide that argument.")
+    if name == SchedulerType.WARMUP_STABLE_DECAY:
+        return schedule_func(
+            optimizer, 
+            num_warmup_steps=num_warmup_steps, 
+            num_stable_steps=num_stable_steps, 
+            num_decay_steps=num_decay_steps, 
+            num_cycles=num_cycles / 2, 
+            min_lr_ratio=min_lr_ratio if min_lr_ratio is not None else 0.0,
+            **lr_scheduler_kwargs,
+        )
+
+    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_decay_steps=num_decay_steps, **lr_scheduler_kwargs)
 
 
 def prepare_dataset_args(args: argparse.Namespace, support_metadata: bool):

requirements.txt

@@ -1,5 +1,5 @@
-accelerate==0.25.0
-transformers==4.36.2
+accelerate==0.30.0
+transformers==4.41.2
 diffusers[torch]==0.25.0
 ftfy==6.1.1
 # albumentations==1.3.0
@@ -16,7 +16,7 @@ altair==4.2.2
 easygui==0.98.3
 toml==0.10.2
 voluptuous==0.13.1
-huggingface-hub==0.20.1
+huggingface-hub==0.23.3
 # for Image utils
 imagesize==1.4.1
 # for BLIP captioning