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refactor selection and logging for DAdaptation

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Kohya S
2023-05-06 18:14:16 +09:00
parent 164a1978de
commit 2127907dd3
6 changed files with 28 additions and 98 deletions
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110
library/train_util.py
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@@ -2570,13 +2570,15 @@ def get_optimizer(args, trainable_params):
optimizer_class = torch.optim.SGD
optimizer = optimizer_class(trainable_params, lr=lr, nesterov=True, **optimizer_kwargs)
elif optimizer_type == "DAdaptation".lower() or optimizer_type == "DAdaptAdam".lower():
elif optimizer_type.startswith("DAdapt".lower()):
# DAdaptation family
# check dadaptation is installed
try:
import dadaptation
except ImportError:
raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
print(f"use D-Adaptation Adam optimizer | {optimizer_kwargs}")
# 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:
@@ -2596,96 +2598,24 @@ def get_optimizer(args, trainable_params):
f"when multiple learning rates are specified with dadaptation (e.g. for Text Encoder and U-Net), only the first one will take effect / D-Adaptation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}"
)
optimizer_class = dadaptation.DAdaptAdam
# set optimizer
if optimizer_type == "DAdaptation".lower() or optimizer_type == "DAdaptAdam".lower():
optimizer_class = dadaptation.DAdaptAdam
print(f"use D-Adaptation Adam optimizer | {optimizer_kwargs}")
elif optimizer_type == "DAdaptAdaGrad".lower():
optimizer_class = dadaptation.DAdaptAdaGrad
print(f"use D-Adaptation AdaGrad optimizer | {optimizer_kwargs}")
elif optimizer_type == "DAdaptAdan".lower():
optimizer_class = dadaptation.DAdaptAdan
print(f"use D-Adaptation Adan optimizer | {optimizer_kwargs}")
elif optimizer_type == "DAdaptSGD".lower():
optimizer_class = dadaptation.DAdaptSGD
print(f"use D-Adaptation SGD optimizer | {optimizer_kwargs}")
else:
raise ValueError(f"Unknown optimizer type: {optimizer_type}")
optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
elif optimizer_type == "DAdaptAdaGrad".lower():
try:
import dadaptation
except ImportError:
raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
print(f"use D-Adaptation AdaGrad optimizer | {optimizer_kwargs}")
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 dadaptation, 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 dadaptation (e.g. for Text Encoder and U-Net), only the first one will take effect / D-Adaptation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}"
)
optimizer_class = dadaptation.DAdaptAdaGrad
optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
elif optimizer_type == "DAdaptAdan".lower():
try:
import dadaptation
except ImportError:
raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
print(f"use D-Adaptation Adan optimizer | {optimizer_kwargs}")
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 dadaptation, 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 dadaptation (e.g. for Text Encoder and U-Net), only the first one will take effect / D-Adaptation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}"
)
optimizer_class = dadaptation.DAdaptAdan
optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
elif optimizer_type == "DAdaptSGD".lower():
try:
import dadaptation
except ImportError:
raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
print(f"use D-Adaptation SGD optimizer | {optimizer_kwargs}")
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 dadaptation, 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 dadaptation (e.g. for Text Encoder and U-Net), only the first one will take effect / D-Adaptation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}"
)
optimizer_class = dadaptation.DAdaptSGD
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: