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Merge pull request #3842 from R-N/gradient-clipping

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Gradient clipping in train tab
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AUTOMATIC1111 2023-01-04 19:57:02 +03:00 committed by GitHub
commit 9092e1ca77
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4 changed files with 43 additions and 14 deletions
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23
modules/hypernetworks/hypernetwork.py
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@ -402,10 +402,8 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
shared.reload_hypernetworks()
return fn
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
from modules import images
@ -448,6 +446,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
return hypernetwork, filename
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
if clip_grad:
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
# dataset loading may take a while, so input validations and early returns should be done before this
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
@ -466,7 +468,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
shared.parallel_processing_allowed = False
shared.sd_model.cond_stage_model.to(devices.cpu)
shared.sd_model.first_stage_model.to(devices.cpu)
weights = hypernetwork.weights()
hypernetwork.train_mode()
@ -525,6 +527,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
if shared.state.interrupted:
break
if clip_grad:
clip_grad_sched.step(hypernetwork.step)
with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
if tag_drop_out != 0 or shuffle_tags:
@ -539,14 +544,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
_loss_step += loss.item()
scaler.scale(loss).backward()
# go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
# print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.7f}")
# scaler.unscale_(optimizer)
# print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
# torch.nn.utils.clip_grad_norm_(weights, max_norm=1.0)
# print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
if clip_grad:
clip_grad(weights, clip_grad_sched.learn_rate)
scaler.step(optimizer)
scaler.update()
hypernetwork.step += 1

11
modules/textual_inversion/learn_schedule.py
View File

@ -58,14 +58,19 @@ 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:

15
modules/textual_inversion/textual_inversion.py
View File

@ -251,8 +251,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
if save_model_every or create_image_every:
assert log_directory, "Log directory is empty"
def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
save_embedding_every = save_embedding_every or 0
create_image_every = create_image_every or 0
validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
@ -295,6 +294,11 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
return embedding, filename
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
None
if clip_grad:
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
# dataset loading may take a while, so input validations and early returns should be done before this
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
old_parallel_processing_allowed = shared.parallel_processing_allowed
@ -361,6 +365,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
if shared.state.interrupted:
break
if clip_grad:
clip_grad_sched.step(embedding.step)
with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
c = shared.sd_model.cond_stage_model(batch.cond_text)
@ -382,6 +389,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
# go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
if clip_grad:
clip_grad(embedding.vec, clip_grad_sched.learn_rate)
scaler.step(optimizer)
scaler.update()
embedding.step += 1

8
modules/ui.py
View File

@ -1290,6 +1290,10 @@ def create_ui():
with gr.Row():
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
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="0.1", show_label=False)
batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
@ -1402,6 +1406,8 @@ def create_ui():
training_width,
training_height,
steps,
clip_grad_mode,
clip_grad_value,
shuffle_tags,
tag_drop_out,
latent_sampling_method,
@ -1431,6 +1437,8 @@ def create_ui():
training_width,
training_height,
steps,
clip_grad_mode,
clip_grad_value,
shuffle_tags,
tag_drop_out,
latent_sampling_method,