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interrupt button

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Alex "mcmonkey" Goodwin 2023-03-27 10:43:01 -07:00
parent 8fc723fc95
commit 16ea4fc36d
1 changed files with 34 additions and 8 deletions
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36
modules/training.py
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@ -6,8 +6,10 @@ import transformers
from modules import ui, shared from modules import ui, shared
from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model, get_peft_model_state_dict from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model, get_peft_model_state_dict
WANT_INTERRUPT = False
CURRENT_STEPS = 0 CURRENT_STEPS = 0
MAX_STEPS = 0 MAX_STEPS = 0
CURRENT_GRADIENT_ACCUM = 1
def get_json_dataset(path: str): def get_json_dataset(path: str):
def get_set(): def get_set():
@ -39,15 +41,31 @@ def create_train_interface():
formatsFunction = get_json_dataset('training/formats') formatsFunction = get_json_dataset('training/formats')
format = gr.Dropdown(choices=formatsFunction(), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.') format = gr.Dropdown(choices=formatsFunction(), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.')
ui.create_refresh_button(format, lambda : None, lambda : {'choices': formatsFunction()}, 'refresh-button') ui.create_refresh_button(format, lambda : None, lambda : {'choices': formatsFunction()}, 'refresh-button')
with gr.Row():
startButton = gr.Button("Start LoRA Training") startButton = gr.Button("Start LoRA Training")
stopButton = gr.Button("Interrupt")
output = gr.Markdown(value="(...)") output = gr.Markdown(value="(...)")
startButton.click(do_train, [loraName, microBatchSize, batchSize, epochs, learningRate, loraRank, loraAlpha, loraDropout, cutoffLen, dataset, evalDataset, format], [output]) startEvent = startButton.click(do_train, [loraName, microBatchSize, batchSize, epochs, learningRate, loraRank, loraAlpha, loraDropout, cutoffLen, dataset, evalDataset, format], [output])
stopButton.click(doInterrupt, [], [], cancels=[], queue=False)
def doInterrupt():
global WANT_INTERRUPT
WANT_INTERRUPT = True
class Callbacks(transformers.TrainerCallback): class Callbacks(transformers.TrainerCallback):
def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs): def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
global CURRENT_STEPS, MAX_STEPS global CURRENT_STEPS, MAX_STEPS
CURRENT_STEPS = state.global_step CURRENT_STEPS = state.global_step * CURRENT_GRADIENT_ACCUM
MAX_STEPS = state.max_steps MAX_STEPS = state.max_steps * CURRENT_GRADIENT_ACCUM
if WANT_INTERRUPT:
control.should_epoch_stop = True
control.should_training_stop = True
def on_substep_end(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
global CURRENT_STEPS
CURRENT_STEPS += 1
if WANT_INTERRUPT:
control.should_epoch_stop = True
control.should_training_stop = True
def cleanPath(basePath: str, path: str): def cleanPath(basePath: str, path: str):
""""Strips unusual symbols and forcibly builds a path as relative to the intended directory.""" """"Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
@ -59,7 +77,8 @@ def cleanPath(basePath: str, path: str):
return f'{Path(basePath).absolute()}/{path}' return f'{Path(basePath).absolute()}/{path}'
def do_train(loraName: str, microBatchSize: int, batchSize: int, epochs: int, learningRate: float, loraRank: int, loraAlpha: int, loraDropout: float, cutoffLen: int, dataset: str, evalDataset: str, format: str): def do_train(loraName: str, microBatchSize: int, batchSize: int, epochs: int, learningRate: float, loraRank: int, loraAlpha: int, loraDropout: float, cutoffLen: int, dataset: str, evalDataset: str, format: str):
global CURRENT_STEPS, MAX_STEPS global WANT_INTERRUPT, CURRENT_STEPS, MAX_STEPS, CURRENT_GRADIENT_ACCUM
WANT_INTERRUPT = False
CURRENT_STEPS = 0 CURRENT_STEPS = 0
MAX_STEPS = 0 MAX_STEPS = 0
yield "Prepping..." yield "Prepping..."
@ -71,6 +90,7 @@ def do_train(loraName: str, microBatchSize: int, batchSize: int, epochs: int, le
if format is None: if format is None:
return "**Missing format choice input, cannot continue.**" return "**Missing format choice input, cannot continue.**"
gradientAccumulationSteps = batchSize // microBatchSize gradientAccumulationSteps = batchSize // microBatchSize
CURRENT_GRADIENT_ACCUM = gradientAccumulationSteps
actualLR = float(learningRate) actualLR = float(learningRate)
shared.tokenizer.pad_token = 0 shared.tokenizer.pad_token = 0
shared.tokenizer.padding_side = "left" shared.tokenizer.padding_side = "left"
@ -161,7 +181,9 @@ def do_train(loraName: str, microBatchSize: int, batchSize: int, epochs: int, le
startTime = time.perf_counter() startTime = time.perf_counter()
while thread.is_alive(): while thread.is_alive():
time.sleep(0.5) time.sleep(0.5)
if CURRENT_STEPS != lastStep: if WANT_INTERRUPT:
yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*"
elif CURRENT_STEPS != lastStep:
lastStep = CURRENT_STEPS lastStep = CURRENT_STEPS
timeElapsed = time.perf_counter() - startTime timeElapsed = time.perf_counter() - startTime
if timeElapsed <= 0: if timeElapsed <= 0:
@ -175,5 +197,9 @@ def do_train(loraName: str, microBatchSize: int, batchSize: int, epochs: int, le
yield f"Running... **{CURRENT_STEPS}** / **{MAX_STEPS}** ... {timerInfo}, `{timeElapsed:.1f}` seconds" yield f"Running... **{CURRENT_STEPS}** / **{MAX_STEPS}** ... {timerInfo}, `{timeElapsed:.1f}` seconds"
print("Training complete, saving...") print("Training complete, saving...")
loraModel.save_pretrained(loraName) loraModel.save_pretrained(loraName)
if WANT_INTERRUPT:
print("Training interrupted.")
yield f"Interrupted. Incomplete LoRA saved to `{loraName}`"
else:
print("Training complete!") print("Training complete!")
yield f"Done! LoRA saved to `{loraName}`" yield f"Done! LoRA saved to `{loraName}`"