forked from camenduru/ai-voice-cloning
csome adjustments to the training output parser, now updates per iteration for really large batches (like the one I'm doing for a dataset size of 19420)
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d5d8821a9d
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8b4da29d5f
123
src/utils.py
123
src/utils.py
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@ -470,14 +470,21 @@ class TrainingState():
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self.epoch_rate = ""
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self.epoch_time_start = 0
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self.epoch_time_end = 0
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self.it_rate = ""
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self.it_time_start = 0
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self.it_time_end = 0
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self.last_step = 0
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self.eta = "?"
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self.eta_hhmmss = "?"
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print("Spawning process: ", " ".join(self.cmd))
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self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
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def parse(self, line, verbose=False, buffer_size=8, progress=None):
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self.buffer.append(f'{line}')
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def parse(self, line, verbose=False, buffer_size=8, progress=None, owner=True):
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if owner:
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self.buffer.append(f'{line}')
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# rip out iteration info
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if not self.training_started:
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@ -492,47 +499,97 @@ class TrainingState():
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if match and len(match) > 0:
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self.it = int(match[0].replace(",", ""))
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else:
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if line.find('%|') > 0 and not self.open_state:
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self.open_state = True
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elif line.find('100%|') == 0 and self.open_state:
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self.open_state = False
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self.epoch = self.epoch + 1
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lapsed = line.find('100%|') == 0 and self.open_state
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self.epoch_time_end = time.time()
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self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
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self.epoch_time_start = time.time()
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self.epoch_rate = f'[{"{:.3f}".format(self.epoch_time_delta)}s/epoch]' if self.epoch_time_delta >= 1 else f'[{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s]' # I doubt anyone will have it/s rates, but its here
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self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
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self.eta_hhmmss = str(timedelta(seconds=int(self.eta)))
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if line.find('%|') > 0:
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match = re.findall(r' +?(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
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if match and len(match) > 0:
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match = match[0]
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percent = int(match[0])/100.0
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progressbar = match[1]
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step = int(match[2])
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steps = int(match[3])
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elapsed = match[4]
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until = match[5]
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rate = match[6]
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epoch_percent = self.epoch / float(self.epochs)
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if owner:
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last_step = self.last_step
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self.last_step = step
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if last_step < step:
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self.it = self.it + (step - last_step)
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if last_step > step and step == 0:
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lapsed = True
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self.it_time_end = time.time()
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self.it_time_delta = self.it_time_end-self.it_time_start
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self.it_time_start = time.time()
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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]'
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self.eta = (self.its - self.it) * self.it_time_delta
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self.eta_hhmmss = str(timedelta(seconds=int(self.eta)))
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message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
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if progress is not None:
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progress(epoch_percent, message)
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if owner:
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# print(f'{"{:.3f}".format(percent*100)}% {message}')
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self.buffer.append(f'[{"{:.3f}".format(epoch_percent*100)}% / {"{:.3f}".format(percent*100)}%] {message}')
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if line.find('%|') > 0 and not self.open_state:
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if owner:
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self.open_state = True
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elif lapsed:
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if owner:
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self.open_state = False
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self.epoch = self.epoch + 1
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self.it = int(self.epoch * (self.dataset_size / self.batch_size))
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self.epoch_time_end = time.time()
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self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
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self.epoch_time_start = time.time()
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self.epoch_rate = f'[{"{:.3f}".format(self.epoch_time_delta)}s/epoch]' if self.epoch_time_delta >= 1 else f'[{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s]' # I doubt anyone will have it/s rates, but its here
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self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
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self.eta_hhmmss = str(timedelta(seconds=int(self.eta)))
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percent = self.epoch / float(self.epochs)
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message = f'[{self.epoch}/{self.epochs}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} {self.status}'
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print(f'{"{:.3f}".format(percent*100)}% {message}')
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message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
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if progress is not None:
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progress(percent, message)
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self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
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if owner:
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print(f'{"{:.3f}".format(percent*100)}% {message}')
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self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
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if line.find('INFO: [epoch:') >= 0:
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# easily rip out our stats...
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match = re.findall(r'\b([a-z_0-9]+?)\b: ([0-9]\.[0-9]+?e[+-]\d+)\b', line)
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if match and len(match) > 0:
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for k, v in match:
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self.info[k] = float(v)
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if 'loss_gpt_total' in self.info:
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self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
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print(self.status)
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self.buffer.append(self.status)
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if owner:
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# easily rip out our stats...
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match = re.findall(r'\b([a-z_0-9]+?)\b: ([0-9]\.[0-9]+?e[+-]\d+)\b', line)
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if match and len(match) > 0:
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for k, v in match:
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self.info[k] = float(v)
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if 'loss_gpt_total' in self.info:
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self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
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print(self.status)
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self.buffer.append(self.status)
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elif line.find('Saving models and training states') >= 0:
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self.checkpoint = self.checkpoint + 1
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if owner:
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self.checkpoint = self.checkpoint + 1
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percent = self.checkpoint / float(self.checkpoints)
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message = f'[{self.checkpoint}/{self.checkpoints}] Saving checkpoint...'
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print(f'{"{:.3f}".format(percent*100)}% {message}')
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if progress is not None:
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progress(percent, message)
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self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
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if owner:
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print(f'{"{:.3f}".format(percent*100)}% {message}')
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self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
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self.buffer = self.buffer[-buffer_size:]
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if owner:
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self.buffer = self.buffer[-buffer_size:]
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if verbose or not self.training_started:
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return "".join(self.buffer)
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@ -552,7 +609,7 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
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for line in iter(training_state.process.stdout.readline, ""):
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res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress )
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res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress, owner=True )
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print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
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if res:
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yield res
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@ -565,13 +622,13 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
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#if return_code:
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# raise subprocess.CalledProcessError(return_code, cmd)
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def reconnect_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
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def reconnect_training(verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
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global training_state
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if not training_state or not training_state.process:
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return "Training not in progress"
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for line in iter(training_state.process.stdout.readline, ""):
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res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress )
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res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress, owner=True )
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if res:
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yield res
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