set validation to save rate and validation file if exists (need to test later)

models/.template.yaml

@@ -122,7 +122,7 @@ train:
   niter: ${iterations}
   warmup_iter: -1
   mega_batch_factor: ${gradient_accumulation_size}
-  val_freq: ${iterations}
+  val_freq: ${validation_rate}
 
   ema_enabled: false # I really don't think EMA matters
 

src/utils.py

@@ -1228,10 +1228,7 @@ def schedule_learning_rate( iterations, schedule=EPOCH_SCHEDULE ):
 
 def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, resume_path, half_p, bnb, workers, source_model, voice ):
 	name = f"{voice}-finetune"
-	dataset_name = f"{voice}-train"
 	dataset_path = f"./training/{voice}/train.txt"
-	validation_name = f"{voice}-val"
-	validation_path = f"./training/{voice}/train.txt"
 
 	with open(dataset_path, 'r', encoding="utf-8") as f:
 		lines = len(f.readlines())
@@ -1303,7 +1300,7 @@ def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learni
 		messages
 	)
 
-def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weight=None, learning_rate_schedule=None, batch_size=None, gradient_accumulation_size=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, half_p=None, bnb=None, workers=None, source_model=None ):
+def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weight=None, learning_rate_schedule=None, batch_size=None, gradient_accumulation_size=None, print_rate=None, save_rate=None, validation_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None, half_p=None, bnb=None, workers=None, source_model=None ):
 	if not source_model:
 		source_model = f"./models/tortoise/autoregressive{'_half' if half_p else ''}.pth"
 
@@ -1320,6 +1317,7 @@ def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weig
 		"dataset_path": dataset_path if dataset_path else "./training/finetune/train.txt",
 		"validation_name": validation_name if validation_name else "finetune",
 		"validation_path": validation_path if validation_path else "./training/finetune/train.txt",
+		'validation_rate': validation_rate if validation_rate else iterations,
 
 		"text_ce_lr_weight": text_ce_lr_weight if text_ce_lr_weight else 0.01,
 

src/webui.py

@@ -300,7 +300,7 @@ def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, lear
 	dataset_name = f"{voice}-train"
 	dataset_path = f"./training/{voice}/train.txt"
 	validation_name = f"{voice}-val"
-	validation_path = f"./training/{voice}/train.txt"
+	validation_path = f"./training/{voice}/validation.txt"
 
 	with open(dataset_path, 'r', encoding="utf-8") as f:
 		lines = len(f.readlines())
@@ -312,6 +312,16 @@ def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, lear
 
 	print_rate = int(print_rate * iterations / epochs)
 	save_rate = int(save_rate * iterations / epochs)
+	validation_rate = save_rate
+
+	if iterations % save_rate != 0:
+		adjustment = int(iterations / save_rate) * save_rate
+		messages.append(f"Iteration rate is not evenly divisible by save rate, adjusting: {iterations} => {adjustment}")
+		iterations = adjustment
+
+	if not os.path.exists(validation_path):
+		validation_rate = iterations
+		validation_path = dataset_path
 
 	if not learning_rate_schedule:
 		learning_rate_schedule = EPOCH_SCHEDULE
@@ -329,6 +339,7 @@ def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, lear
 		gradient_accumulation_size=gradient_accumulation_size,
 		print_rate=print_rate,
 		save_rate=save_rate,
+		validation_rate=validation_rate,
 		name=name,
 		dataset_name=dataset_name,
 		dataset_path=dataset_path,
@@ -559,7 +570,7 @@ def setup_gradio():
 						verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
 						
 						with gr.Row():
-							training_keep_x_past_checkpoints = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
+							training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
 							training_gpu_count = gr.Number(label="GPUs", value=get_device_count())
 						with gr.Row():
 							start_training_button = gr.Button(value="Train")
@@ -777,7 +788,7 @@ def setup_gradio():
 				training_configs,
 				verbose_training,
 				training_gpu_count,
-				training_keep_x_past_checkpoints,
+				training_keep_x_past_datasets,
 			],
 			outputs=[
 				training_output,