forgot to add 'bs / gradient accum < 2 clamp validation logic

src/utils.py

@@ -506,6 +506,8 @@ class TrainingState():
 		with open(config_path, 'r') as file:
 			self.config = yaml.safe_load(file)
 
+		self.killed = False
+
 		self.dataset_dir = f"./training/{self.config['name']}/"
 		self.batch_size = self.config['datasets']['train']['batch_size']
 		self.dataset_path = self.config['datasets']['train']['path']
@@ -527,7 +529,6 @@ class TrainingState():
 		self.training_started = False
 
 		self.info = {}
-		self.status = "..."
 
 		self.epoch_rate = ""
 		self.epoch_time_start = 0
@@ -651,10 +652,12 @@ class TrainingState():
 			print("Removing", path)
 			os.remove(path)
 
-	def parse(self, line, verbose=False, buffer_size=8, keep_x_past_datasets=0, progress=None ):
+	def parse(self, line, verbose=False, keep_x_past_datasets=0, buffer_size=8, progress=None ):
 		self.buffer.append(f'{line}')
 
 		should_return = False
+		percent = 0
+		message = None
 
 		# rip out iteration info
 		if not self.training_started:
@@ -679,7 +682,7 @@ class TrainingState():
 				match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
 				if match and len(match) > 0:
 					match = match[0]
-					percent = int(match[0])/100.0
+					per_cent = int(match[0])/100.0
 					progressbar = match[1]
 					step = int(match[2])
 					steps = int(match[3])
@@ -698,15 +701,40 @@ class TrainingState():
 					self.it_time_end = time.time()
 					self.it_time_delta = self.it_time_end-self.it_time_start
 					self.it_time_start = time.time()
+					self.it_taken = self.it_taken + 1
 					try:
 						rate = f'{"{:.3f}".format(self.it_time_delta)}s/it' if self.it_time_delta >= 1 else f'{"{:.3f}".format(1/self.it_time_delta)}it/s'
 						self.it_rate = rate
 					except Exception as e:
 						pass
-					last_loss = ""
+
+					metric_step = [f"{self.epoch}/{self.epochs}", f"{self.it}/{self.its}", f"{step}/{steps}"]
+					metric_step = ", ".join(metric_step)
+
+					metric_rate = []
+					if self.epoch_rate:
+						metric_rate.append(self.epoch_rate)
+					if self.it_rate:
+						metric_rate.append(self.it_rate)
+					metric_rate = ", ".join(metric_rate)
+
+					eta_hhmmss = "?"
+					if self.eta_hhmmss:
+						eta_hhmmss = self.eta_hhmmss
+					else:
+						try:
+							eta = (self.its - self.it) * (self.it_time_deltas / self.it_taken)
+							eta = str(timedelta(seconds=int(eta)))
+							eta_hhmmss = eta
+						except Exception as e:
+							pass
+					
+					metric_loss = []
 					if len(self.losses) > 0:
-						last_loss = f'[Loss @ it. {self.losses[-1]["step"]}: {self.losses[-1]["value"]}]'
+						metric_loss.append(f'Loss: {"{:3f}".format(self.losses[-1]["value"])}')
-					message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [{self.epoch_rate}, {self.it_rate}] {last_loss} [ETA: {self.eta_hhmmss}]'
+					metric_loss = ", ".join(metric_loss)
+
+					message = f'[{metric_step}] [{metric_rate}] [{metric_loss}] [ETA: {eta_hhmmss}]'
 
 			if lapsed:
 				self.epoch = self.epoch + 1
@@ -741,16 +769,8 @@ class TrainingState():
 					for k, v in match:
 						self.info[k] = float(v.replace(",", ""))
 
-				if 'loss_gpt_total' in self.info:
-					self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
-					"""
-					self.losses.append({ "step": self.it, "value": self.info['loss_text_ce'], "type": "loss_text_ce" })
-					self.losses.append({ "step": self.it, "value": self.info['loss_mel_ce'], "type": "loss_mel_ce" })
-					self.losses.append({ "step": self.it, "value": self.info['loss_gpt_total'], "type": "loss_gpt_total" })
-					"""
-					should_return = True
-
 				self.load_losses(update=True)
+				should_return = True
 
 			elif line.find('Saving models and training states') >= 0:
 				self.checkpoint = self.checkpoint + 1
@@ -769,10 +789,18 @@ class TrainingState():
 			should_return = True
 
 		self.buffer = self.buffer[-buffer_size:]
-		if should_return:
-			return "".join(self.buffer)
 		
-def run_training(config_path, verbose=False, gpus=1, buffer_size=8, keep_x_past_datasets=0, progress=gr.Progress(track_tqdm=True)):
+		result = None
+		if should_return:
+			result = "".join(self.buffer) if not self.training_started else message
+
+		return (
+			result,
+			percent,
+			message,
+		)
+
+def run_training(config_path, verbose=False, gpus=1, keep_x_past_datasets=0, progress=gr.Progress(track_tqdm=True)):
 	global training_state
 	if training_state and training_state.process:
 		return "Training already in progress"
@@ -787,11 +815,10 @@ def run_training(config_path, verbose=False, gpus=1, buffer_size=8, keep_x_past_
 	training_state = TrainingState(config_path=config_path, keep_x_past_datasets=keep_x_past_datasets, gpus=gpus)
 
 	for line in iter(training_state.process.stdout.readline, ""):
-		
+		result, percent, message = training_state.parse( line=line, verbose=verbose, keep_x_past_datasets=keep_x_past_datasets, progress=progress )
-		res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, keep_x_past_datasets=keep_x_past_datasets, progress=progress )
 		print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
-		if res:
+		if result:
-			yield res
+			yield result
 
 	if training_state:
 		training_state.process.stdout.close()
@@ -824,15 +851,16 @@ def update_training_dataplot(config_path=None):
 
 	return update
 
-def reconnect_training(verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
+def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
 	global training_state
 	if not training_state or not training_state.process:
 		return "Training not in progress"
 
 	for line in iter(training_state.process.stdout.readline, ""):
-		res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress )
+		result, percent, message = training_state.parse( line=line, verbose=verbose, keep_x_past_datasets=keep_x_past_datasets, progress=progress )
-		if res:
+		print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
-			yield res
+		if result:
+			yield result
 
 def stop_training():
 	global training_state
@@ -845,6 +873,7 @@ def stop_training():
 	training_state.process.send_signal(signal.SIGINT)
 	return_code = training_state.process.wait()
 	training_state = None
+	print("Killed training process.")
 	return f"Training cancelled: {return_code}"
 
 def get_halfp_model_path():
@@ -966,8 +995,18 @@ def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learni
 	
 	if gradient_accumulation_size == 0:
 		gradient_accumulation_size = 1
+	
+	if batch_size / gradient_accumulation_size < 2:
+		gradient_accumulation_size = int(batch_size / 2)
+		if gradient_accumulation_size == 0:
+			gradient_accumulation_size = 1
+
+		messages.append(f"Gradient accumulation size is too large for a given batch size, clamping gradient accumulation size to: {gradient_accumulation_size}")
 	elif batch_size % gradient_accumulation_size != 0:
 		gradient_accumulation_size = int(batch_size / gradient_accumulation_size)
+		if gradient_accumulation_size == 0:
+			gradient_accumulation_size = 1
+
 		messages.append(f"Batch size is not evenly divisible by the gradient accumulation size, adjusting gradient accumulation size to: {gradient_accumulation_size}")
 
 	iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)

src/webui.py

@@ -535,9 +535,8 @@ def setup_gradio():
 						verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
 						
 						with gr.Row():
-							training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
 							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=1)
+							training_gpu_count = gr.Number(label="GPUs", value=1)
 						with gr.Row():
 							start_training_button = gr.Button(value="Train")
 							stop_training_button = gr.Button(value="Stop")
@@ -746,7 +745,6 @@ def setup_gradio():
 				training_configs,
 				verbose_training,
 				training_gpu_count,
-				training_buffer_size,
 				training_keep_x_past_datasets,
 			],
 			outputs=[
@@ -779,7 +777,6 @@ def setup_gradio():
 		reconnect_training_button.click(reconnect_training,
 			inputs=[
 				verbose_training,
-				training_buffer_size,
 			],
 			outputs=training_output #console_output
 		)