optimize batch sizes to be as evenly divisible as possible (noticed the calculated epochs mismatched the inputted epochs)

src/utils.py

@@ -608,8 +608,12 @@ def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate
 		mega_batch_factor = int(batch_size / 2)
 		messages.append(f"Mega batch factor is too large for the given batch size, clamping mega batch factor to: {mega_batch_factor}")
 
+	if batch_size % lines != 0:
+		nearest_slice = int(lines / batch_size) + 1
+		batch_size = int(lines / nearest_slice)
+		messages.append(f"Batch size not neatly divisible by dataset size, adjusting batch size to: {batch_size} ({nearest_slice} steps per epoch)")
+
 	iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
-	messages.append(f"For {epochs} epochs with {lines} lines, iterating for {iterations} steps")
 
 	if epochs < print_rate:
 		print_rate = epochs
@@ -623,8 +627,7 @@ def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate
 		resume_path = None
 		messages.append("Resume path specified, but does not exist. Disabling...")
 
-	learning_rate_schedule = schedule_learning_rate( iterations / epochs ) # faster learning schedule compared to just passing lines / batch_size due to truncating
-	messages.append(f"Suggesting best learning rate schedule for iterations: {learning_rate_schedule}")
+	messages.append(f"For {epochs} epochs with {lines} lines in batches of {batch_size}, iterating for {iterations} steps ({int(iterations / epochs)} steps per epoch)")
 
 	return (
 		batch_size,
@@ -637,12 +640,12 @@ def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate
 		messages
 	)
 
-def save_training_settings( iterations=None, batch_size=None, learning_rate=None, learning_rate_schedule=None, mega_batch_factor=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None ):
+def save_training_settings( iterations=None, batch_size=None, learning_rate=None, learning_rate_schedule=None, mega_batch_factor=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None ):	
 	settings = {
 		"iterations": iterations if iterations else 500,
 		"batch_size": batch_size if batch_size else 64,
 		"learning_rate": learning_rate if learning_rate else 1e-5,
-		"gen_lr_steps": learning_rate_schedule if learning_rate_schedule else [ 200, 300, 400, 500 ],
+		"gen_lr_steps": learning_rate_schedule if learning_rate_schedule else EPOCH_SCHEDULE,
 		"mega_batch_factor": mega_batch_factor if mega_batch_factor else 4,
 		"print_rate": print_rate if print_rate else 50,
 		"save_rate": save_rate if save_rate else 50,
@@ -656,6 +659,7 @@ def save_training_settings( iterations=None, batch_size=None, learning_rate=None
 		'pretrain_model_gpt': "pretrain_model_gpt: './models/tortoise/autoregressive.pth'" if not resume_path else "# pretrain_model_gpt: './models/tortoise/autoregressive.pth'"
 	}
 
+
 	if not output_name:
 		output_name = f'{settings["name"]}.yaml'
 

src/webui.py

@@ -212,6 +212,10 @@ def save_training_settings_proxy( epochs, batch_size, learning_rate, learning_ra
 	print_rate = int(print_rate * iterations / epochs)
 	save_rate = int(save_rate * iterations / epochs)
 
+	if not learning_rate_schedule:
+		learning_rate_schedule = EPOCH_SCHEDULE
+	learning_rate_schedule = schedule_learning_rate( iterations / epochs )
+
 	messages.append(save_training_settings(
 		iterations=iterations,
 		batch_size=batch_size,
@@ -355,8 +359,8 @@ def setup_gradio():
 				with gr.Row():
 					with gr.Column():
 						training_settings = [
-							gr.Number(label="Epochs", value=10, precision=0),
-							gr.Number(label="Batch Size", value=64, precision=0),
+							gr.Number(label="Epochs", value=500, precision=0),
+							gr.Number(label="Batch Size", value=128, precision=0),
 							gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
 							gr.Textbox(label="Learning Rate Schedule", placeholder=str(EPOCH_SCHEDULE)),
 							gr.Number(label="Mega Batch Factor", value=4, precision=0),
@@ -384,13 +388,13 @@ def setup_gradio():
 				with gr.Row():
 					with gr.Column():
 						training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_list())
-						verbose_training = gr.Checkbox(label="Verbose Training")
-						training_buffer_size = gr.Slider(label="Buffer Size", minimum=4, maximum=32, value=8)
 						refresh_configs = gr.Button(value="Refresh Configurations")
 						start_training_button = gr.Button(value="Train")
 						stop_training_button = gr.Button(value="Stop")
 					with gr.Column():
 						training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
+						verbose_training = gr.Checkbox(label="Verbose Console Output")
+						training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
 		with gr.Tab("Settings"):
 			with gr.Row():
 				exec_inputs = []