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Learning rate sched syntax support for grad clipping

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This commit is contained in:
Muhammad Rizqi Nur 2022-10-28 17:16:23 +07:00
parent 1618df41ba
commit 16451ca573
4 changed files with 30 additions and 13 deletions
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13
modules/hypernetworks/hypernetwork.py
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@ -383,11 +383,15 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
ititial_step = hypernetwork.step or 0
if ititial_step > steps:
return hypernetwork, filename
clip_grad_mode_value = clip_grad_mode == "value"
clip_grad_mode_norm = clip_grad_mode == "norm"
clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
if clip_grad_enabled:
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
@ -407,6 +411,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
if shared.state.interrupted:
break
if clip_grad_enabled:
clip_grad_sched.step(hypernetwork.step)
with torch.autocast("cuda"):
c = stack_conds([entry.cond for entry in entries]).to(devices.device)
# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
@ -430,9 +437,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
if clip_grad_mode_value:
torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_value)
torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_sched.learn_rate)
elif clip_grad_mode_norm:
torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_value)
torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_sched.learn_rate)
optimizer.step()

11
modules/textual_inversion/learn_schedule.py
View file

@ -51,14 +51,19 @@ class LearnRateScheduler:
self.finished = False
def apply(self, optimizer, step_number):
def step(self, step_number):
if step_number <= self.end_step:
return
return False
try:
(self.learn_rate, self.end_step) = next(self.schedules)
except Exception:
except StopIteration:
self.finished = True
return False
return True
def apply(self, optimizer, step_number):
if not self.step(step_number):
return
if self.verbose:

12
modules/textual_inversion/textual_inversion.py
View file

@ -255,9 +255,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
ititial_step = embedding.step or 0
if ititial_step > steps:
return embedding, filename
clip_grad_mode_value = clip_grad_mode == "value"
clip_grad_mode_norm = clip_grad_mode == "norm"
clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
if clip_grad_enabled:
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
@ -273,6 +276,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
if shared.state.interrupted:
break
if clip_grad_enabled:
clip_grad_sched.step(embedding.step)
with torch.autocast("cuda"):
c = cond_model([entry.cond_text for entry in entries])
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
@ -285,9 +291,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
loss.backward()
if clip_grad_mode_value:
torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_value)
torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_sched.learn_rate)
elif clip_grad_mode_norm:
torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_value)
torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_sched.learn_rate)
optimizer.step()

7
modules/ui.py
View file

@ -1305,7 +1305,9 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Row():
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
with gr.Row():
clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="1.0", show_label=False)
batch_size = gr.Number(label='Batch size', value=1, precision=0)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
@ -1313,9 +1315,6 @@ def create_ui(wrap_gradio_gpu_call):
training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
steps = gr.Number(label='Max steps', value=100000, precision=0)
with gr.Row():
clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
clip_grad_value = gr.Number(value=1.0, show_label=False)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)