Support for Prodigy(Dadapt variety for Dylora) (#585)

* Update train_util.py for DAdaptLion

* Update train_README-zh.md for dadaptlion

* Update train_README-ja.md for DAdaptLion

* add DAdatpt V3

* Alignment

* Update train_util.py for experimental

* Update train_util.py V3

* Update train_README-zh.md

* Update train_README-ja.md

* Update train_util.py fix

* Update train_util.py

* support Prodigy

* add lower

docs/train_README-ja.md

@@ -622,6 +622,7 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - DAdaptAdanIP : 引数は同上
     - DAdaptLion : 引数は同上
     - DAdaptSGD : 引数は同上
+    - Prodigy : https://github.com/konstmish/prodigy
     - AdaFactor : [Transformers AdaFactor](https://huggingface.co/docs/transformers/main_classes/optimizer_schedules)
     - 任意のオプティマイザ
 

docs/train_README-zh.md

@@ -555,9 +555,10 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - DAdaptAdam : 参数同上
     - DAdaptAdaGrad : 参数同上
     - DAdaptAdan : 参数同上
-    - DAdaptAdanIP : 引数は同上
+    - DAdaptAdanIP : 参数同上
     - DAdaptLion : 参数同上
     - DAdaptSGD : 参数同上
+    - Prodigy : https://github.com/konstmish/prodigy
     - AdaFactor : [Transformers AdaFactor](https://huggingface.co/docs/transformers/main_classes/optimizer_schedules)
     - 任何优化器
 

fine_tune.py

@@ -397,7 +397,7 @@ def train(args):
             current_loss = loss.detach().item()  # 平均なのでbatch sizeは関係ないはず
             if args.logging_dir is not None:
                 logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
-                if args.optimizer_type.lower().startswith("DAdapt".lower()):  # tracking d*lr value
+                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy":  # tracking d*lr value
                     logs["lr/d*lr"] = (
                         lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
                     )

library/train_util.py

@@ -2808,6 +2808,38 @@ def get_optimizer(args, trainable_params):
 
         optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
+    elif optimizer_type == "Prodigy".lower():
+        # Prodigy
+        # check Prodigy is installed
+        try:
+            import prodigyopt
+        except ImportError:
+            raise ImportError("No Prodigy / Prodigy がインストールされていないようです")
+
+        # check lr and lr_count, and print warning
+        actual_lr = lr
+        lr_count = 1
+        if type(trainable_params) == list and type(trainable_params[0]) == dict:
+            lrs = set()
+            actual_lr = trainable_params[0].get("lr", actual_lr)
+            for group in trainable_params:
+                lrs.add(group.get("lr", actual_lr))
+            lr_count = len(lrs)
+
+        if actual_lr <= 0.1:
+            print(
+                f"learning rate is too low. If using Prodigy, set learning rate around 1.0 / 学習率が低すぎるようです。1.0前後の値を指定してください: lr={actual_lr}"
+            )
+            print("recommend option: lr=1.0 / 推奨は1.0です")
+        if lr_count > 1:
+            print(
+                f"when multiple learning rates are specified with Prodigy (e.g. for Text Encoder and U-Net), only the first one will take effect / Prodigyで複数の学習率を指定した場合（Text EncoderとU-Netなど）、最初の学習率のみが有効になります: lr={actual_lr}"
+            )
+
+        print(f"use Prodigy optimizer | {optimizer_kwargs}")
+        optimizer_class = prodigyopt.Prodigy
+        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+
     elif optimizer_type == "Adafactor".lower():
         # 引数を確認して適宜補正する
         if "relative_step" not in optimizer_kwargs:

train_db.py

@@ -384,7 +384,7 @@ def train(args):
             current_loss = loss.detach().item()
             if args.logging_dir is not None:
                 logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
-                if args.optimizer_type.lower().startswith("DAdapt".lower()):  # tracking d*lr value
+                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():  # tracking d*lr value
                     logs["lr/d*lr"] = (
                         lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
                     )

train_network.py

@@ -57,7 +57,7 @@ def generate_step_logs(
             logs["lr/textencoder"] = float(lrs[0])
             logs["lr/unet"] = float(lrs[-1])  # may be same to textencoder
 
-        if args.optimizer_type.lower().startswith("DAdapt".lower()):  # tracking d*lr value of unet.
+        if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():  # tracking d*lr value of unet.
             logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
     else:
         idx = 0
@@ -67,7 +67,7 @@ def generate_step_logs(
 
         for i in range(idx, len(lrs)):
             logs[f"lr/group{i}"] = float(lrs[i])
-            if args.optimizer_type.lower().startswith("DAdapt".lower()):
+            if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
                 logs[f"lr/d*lr/group{i}"] = (
                     lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
                 )

train_textual_inversion.py

@@ -476,7 +476,7 @@ def train(args):
             current_loss = loss.detach().item()
             if args.logging_dir is not None:
                 logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
-                if args.optimizer_type.lower().startswith("DAdapt".lower()):  # tracking d*lr value
+                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():  # tracking d*lr value
                     logs["lr/d*lr"] = (
                         lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
                     )

train_textual_inversion_XTI.py

@@ -515,7 +515,7 @@ def train(args):
             current_loss = loss.detach().item()
             if args.logging_dir is not None:
                 logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
-                if args.optimizer_type.lower().startswith("DAdapt".lower()):  # tracking d*lr value
+                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():  # tracking d*lr value
                     logs["lr/d*lr"] = (
                         lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
                     )