huge success

README.md

@@ -16,4 +16,19 @@ Please consult [the wiki](https://git.ecker.tech/mrq/ai-voice-cloning/wiki) for
 
 ## Bug Reporting
 
-If you run into any problems, please refer to the [issues you may encounter](https://git.ecker.tech/mrq/ai-voice-cloning/wiki/Issues) wiki page first. Please don't hesitate to submit an issue.
+If you run into any problems, please refer to the [issues you may encounter](https://git.ecker.tech/mrq/ai-voice-cloning/wiki/Issues) wiki page first. Please don't hesitate to submit an issue.
+
+## Changelogs
+
+Below will be a rather-loose changelogss, as I don't think I have a way to chronicle them outside of commit messages:
+
+### `2023.02.22`
+
+* greatly reduced VRAM consumption through the use of [TimDettmers/bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
+* cleaned up section of code that handled parsing output from training script
+* added button to reconnect to the training script's output (sometimes skips a line to update, but it's better than nothing)
+* actually update submodules from the update script (somehow forgot to pass `--remote`)
+
+### `Before 2023.02.22`
+
+Refer to commit logs.

dlas

@@ -1 +1 @@
-Subproject commit 6c284ef8ec4c4769de3181d90ac96ff63581ef55
+Subproject commit 0ef8ab6872813d1021d4d75e82b63377d28f5a06

models/.template.yaml

@@ -2,7 +2,7 @@ name: ${name}
 model: extensibletrainer
 scale: 1
 gpu_ids: [0] # <-- unless you have multiple gpus, use this
-start_step: -1
+start_step: 0
 checkpointing_enabled: true  # <-- Gradient checkpointing. Enable for huge GPU memory savings. Disable for distributed training.
 fp16: ${float16} # might want to check this out
 wandb: false  # <-- enable to log to wandb. tensorboard logging is always enabled.

setup-cuda.bat

@@ -9,5 +9,8 @@ python -m pip install -r .\dlas\requirements.txt
 python -m pip install -r .\tortoise-tts\requirements.txt
 python -m pip install -r .\requirements.txt
 python -m pip install -e .\tortoise-tts\
+
+copy .\dlas\bitsandbytes_windows\* .\venv\Lib\site-packages\bitsandbytes\. /Y
+
 deactivate
 pause

src/train.py

@@ -1,8 +1,8 @@
-import torch
-import argparse
-
 import os
 import sys
+import argparse
+
+
 
 # this is some massive kludge that only works if it's called from a shell and not an import/PIP package
 # it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell
@@ -19,6 +19,17 @@ sys.path.insert(0, './dlas/')
 # don't even really bother trying to get DLAS PIP'd
 # without kludge, it'll have to be accessible as `codes` and not `dlas`
 
+import torch_intermediary
+# could just move this auto-toggle into the MITM script
+try:
+    import bitsandbytes as bnb
+    torch_intermediary.OVERRIDE_ADAM = True
+    torch_intermediary.OVERRIDE_ADAMW = True
+except Exception as e:
+    torch_intermediary.OVERRIDE_ADAM = False
+    torch_intermediary.OVERRIDE_ADAMW = False
+
+import torch
 from codes import train as tr
 from utils import util, options as option
 

src/utils.py

@@ -17,6 +17,7 @@ import urllib.request
 import signal
 import gc
 import subprocess
+import yaml
 
 import tqdm
 import torch
@@ -26,6 +27,7 @@ import gradio as gr
 import gradio.utils
 
 from datetime import datetime
+from datetime import timedelta
 
 from tortoise.api import TextToSpeech, MODELS, get_model_path
 from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
@@ -42,7 +44,7 @@ tts_loading = False
 webui = None
 voicefixer = None
 whisper_model = None
-training_process = None
+training_state = None
 
 
 def generate(
@@ -434,8 +436,88 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
 
 	return voice
 
+# superfluous, but it cleans up some things
+class TrainingState():
+	def __init__(self, config_path, buffer_size=8):
+		self.cmd = ['train.bat', config_path] if os.name == "nt" else ['bash', './train.sh', config_path]
+
+		# parse config to get its iteration
+		with open(config_path, 'r') as file:
+			self.config = yaml.safe_load(file)
+
+		self.it = 0
+		self.its = self.config['train']['niter']
+
+		self.checkpoint = 0
+		self.checkpoints = int(self.its / self.config['logger']['save_checkpoint_freq'])
+
+		self.buffer = []
+
+		self.open_state = False
+		self.training_started = False
+
+		self.info = {}
+		self.status = ""
+
+		self.it_rate = ""
+		self.it_time_start = 0
+		self.it_time_end = 0
+		self.eta = "?"
+
+		print("Spawning process: ", " ".join(self.cmd))
+		self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
+
+	def parse(self, line, verbose=False, buffer_size=8, progress=None):
+		self.buffer.append(f'{line}')
+
+		# rip out iteration info
+		if not self.training_started:
+			if line.find('Start training from epoch') >= 0:
+				self.it_time_start = time.time()
+				self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
+				
+				match = re.findall(r'iter: ([\d,]+)', line)
+				if match and len(match) > 0:
+					self.it = int(match[0].replace(",", ""))
+		elif progress is not None:
+			if line.find('  0%|') == 0:
+				self.open_state = True
+			elif line.find('100%|') == 0 and self.open_state:
+				self.open_state = False
+				self.it = self.it + 1
+
+				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_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]' # I doubt anyone will have it/s rates, but its here
+				self.eta = (self.its - self.it) * self.it_time_delta
+				self.eta_hhmmss = str(timedelta(seconds=int(self.eta)))
+
+				progress(self.it / float(self.its), f'[{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.it_rate} Training... {self.status}')
+
+			if line.find('INFO: [epoch:') >= 0:
+				# easily rip out our stats...
+				match = re.findall(r'\b([a-z_0-9]+?)\b: ([0-9]\.[0-9]+?e[+-]\d+)\b', line)
+				if match and len(match) > 0:
+					for k, v in match:
+						self.info[k] = float(v)
+				
+				# ...and returns our loss rate
+				# it would be nice for losses to be shown at every step
+				if 'loss_gpt_total' in self.info:
+					# self.info['step'] returns the steps, not iterations, so we won't even bother ripping the reported step count, as iteration count won't get ripped from the regex
+					self.status = f"Total loss at iteration {self.it}: {self.info['loss_gpt_total']}"
+			elif line.find('Saving models and training states') >= 0:
+				self.checkpoint = self.checkpoint + 1
+				progress(self.checkpoint / float(self.checkpoints), f'[{self.checkpoint}/{self.checkpoints}] Saving checkpoint...')
+
+		if verbose or not self.training_started:
+			return "".join(self.buffer[-buffer_size:])
+
 def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
-	global training_process
+	global training_state
+	if training_state and training_state.process:
+		return "Training already in progress"
 	
 	# I don't know if this is still necessary, as it was bitching at me for not doing this, despite it being in a separate process
 	torch.multiprocessing.freeze_support()
@@ -444,90 +526,38 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
 	unload_whisper()
 	unload_voicefixer()
 
-	cmd = ['train.bat', config_path] if os.name == "nt" else ['bash', './train.sh', config_path]
-	print("Spawning process: ", " ".join(cmd))
-	training_process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
-
-	# parse config to get its iteration
-	import yaml
-	with open(config_path, 'r') as file:
-		config = yaml.safe_load(file)
-
-	it = 0
-	its = config['train']['niter']
-
-	checkpoint = 0
-	checkpoints = its / config['logger']['save_checkpoint_freq']
-
-	buffer_size = 8
-	open_state = False
-	training_started = False
-
-	yield " ".join(cmd)
-	
-	info = {}
-	buffer = []
-	infos = []
-	yields = True
-	status = ""
-
-	it_rate = ""
-	it_time_start = 0
-	it_time_end = 0
-
-	for line in iter(training_process.stdout.readline, ""):
-		buffer.append(f'{line}')
-
-		# rip out iteration info
-		if not training_started:
-			if line.find('Start training from epoch') >= 0:
-				training_started = True
-				
-				match = re.findall(r'iter: ([\d,]+)', line)
-				if match and len(match) > 0:
-					it = int(match[0].replace(",", ""))
-		elif progress is not None:
-			if line.find('  0%|') == 0:
-				open_state = True
-			elif line.find('100%|') == 0 and open_state:
-				open_state = False
-				it = it + 1
-
-				it_time_end = time.time()
-				it_time_delta = it_time_end-it_time_start
-				it_time_start = time.time()
-				it_rate = f'[{"{:.3f}".format(it_time_delta)}s/it]' if it_time_delta >= 1 else f'[{"{:.3f}".format(1/it_time_delta)}it/s]' # I doubt anyone will have it/s rates, but its here
-
-				progress(it / float(its), f'[{it}/{its}] {it_rate} Training... {status}')
-
-			if line.find('INFO: [epoch:') >= 0:
-				# easily rip out our stats...
-				match = re.findall(r'\b([a-z_0-9]+?)\b: ([0-9]\.[0-9]+?e[+-]\d+)\b', line)
-				if match and len(match) > 0:
-					for k, v in match:
-						info[k] = float(v)
-				
-				# ...and returns our loss rate
-				# it would be nice for losses to be shown at every step
-				if 'loss_gpt_total' in info:
-					status = f"Total loss at step {int(info['step'])}: {info['loss_gpt_total']}"
-			elif line.find('Saving models and training states') >= 0:
-				checkpoint = checkpoint + 1
-				progress(checkpoint / float(checkpoints), f'[{checkpoint}/{checkpoints}] Saving checkpoint...')
+	training_state = TrainingState(config_path=config_path, buffer_size=buffer_size)
 
+	for line in iter(training_state.process.stdout.readline, ""):
 		print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
+		
+		res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress )
+		if res:
+			yield res
 
-		if verbose or not training_started:
-			yield "".join(buffer[-buffer_size:])
-
-	training_process.stdout.close()
-	return_code = training_process.wait()
-	training_process = None
+	training_state.process.stdout.close()
+	return_code = training_state.process.wait()
+	output = "".join(training_state.buffer[-buffer_size:])
+	training_state = None
 	
 	#if return_code:
 	#	raise subprocess.CalledProcessError(return_code, cmd)
 
-	return "".join(buffer[-buffer_size:])
+	return output
+
+def reconnect_training(config_path, verbose=False, buffer_size=8, 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 )
+		if res:
+			yield res
+			
+	output = "".join(training_state.buffer[-buffer_size:])
+
+	return output
 
 def stop_training():
 	global training_process

src/webui.py

@@ -410,6 +410,7 @@ def setup_gradio():
 						refresh_configs = gr.Button(value="Refresh Configurations")
 						start_training_button = gr.Button(value="Train")
 						stop_training_button = gr.Button(value="Stop")
+						reconnect_training_button = gr.Button(value="Reconnect")
 					with gr.Column():
 						training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
 						verbose_training = gr.Checkbox(label="Verbose Console Output")
@@ -614,6 +615,13 @@ def setup_gradio():
 			inputs=None,
 			outputs=training_output #console_output
 		)
+		reconnect_training_button.click(reconnect_training,
+			inputs=[
+				verbose_training,
+				training_buffer_size,
+			],
+			outputs=training_output #console_output
+		)
 		prepare_dataset_button.click(
 			prepare_dataset_proxy,
 			inputs=dataset_settings,

update.bat

@@ -1,5 +1,5 @@
 git pull
-git submodule update
+git submodule update --remote
 
 python -m venv venv
 call .\venv\Scripts\activate.bat

update.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 git pull
-git submodule update
+git submodule update --remote
 
 python3 -m venv venv
 source ./venv/bin/activate