forked from mrq/ai-voice-cloning
rewrote how AIVC gets training metrics (need to clean up later)
This commit is contained in:
parent
df0edacc60
commit
7f2da0f5fb
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@ -18,6 +18,7 @@ if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--mode', type=str, default='none', help='mode')
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args = parser.parse_args()
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args.opt = " ".join(args.opt) # absolutely disgusting
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@ -77,7 +78,7 @@ def train(yaml, launcher='none'):
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trainer.rank = torch.distributed.get_rank()
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torch.cuda.set_device(torch.distributed.get_rank())
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trainer.init(yaml, opt, launcher)
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trainer.init(yaml, opt, launcher, '')
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trainer.do_training()
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if __name__ == "__main__":
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195
src/utils.py
195
src/utils.py
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@ -594,6 +594,9 @@ class TrainingState():
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self.it = 0
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self.its = self.config['train']['niter']
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self.step = 0
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self.steps = 1
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self.epoch = 0
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self.epochs = int(self.its*self.batch_size/self.dataset_size)
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@ -653,13 +656,8 @@ class TrainingState():
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self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
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def load_statistics(self, update=False):
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if not os.path.isdir(f'{self.dataset_dir}/tb_logger/'):
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if not os.path.isdir(f'{self.dataset_dir}/'):
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return
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try:
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from tensorboard.backend.event_processing import event_accumulator
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use_tensorboard = True
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except Exception as e:
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use_tensorboard = False
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keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total', 'val_loss_text_ce', 'val_loss_mel_ce', 'learning_rate_gpt_0']
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infos = {}
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@ -669,32 +667,44 @@ class TrainingState():
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self.statistics['loss'] = []
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self.statistics['lr'] = []
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logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
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logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
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if update:
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logs = [logs[-1]]
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for log in logs:
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ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
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ea.Reload()
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with open(log, 'r', encoding="utf-8") as f:
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lines = f.readlines()
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scalars = ea.Tags()['scalars']
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for line in lines:
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if line.find('INFO: Training Metrics:') >= 0:
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data = line.split("INFO: Training Metrics:")[-1]
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info = json.loads(data)
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for key in keys:
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if key not in scalars:
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step = info['it']
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if update and step <= self.last_info_check_at:
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continue
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try:
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scalar = ea.Scalars(key)
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for s in scalar:
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if update and s.step <= self.last_info_check_at:
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continue
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highest_step = max( highest_step, s.step )
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target = 'lr' if key == "learning_rate_gpt_0" else 'loss'
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self.statistics[target].append( { "step": s.step, "value": s.value, "type": key } )
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if key == 'loss_gpt_total':
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self.losses.append( { "step": s.step, "value": s.value, "type": key } )
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except Exception as e:
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pass
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if 'lr' in info:
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self.statistics['lr'].append({'step': step, 'value': info['lr'], 'type': 'learning_rate_gpt_0'})
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if 'loss_text_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_text_ce'], 'type': 'loss_text_ce'})
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if 'loss_mel_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_mel_ce'], 'type': 'loss_mel_ce'})
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if 'loss_gpt_total' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'loss_gpt_total'})
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self.losses.append( self.statistics['loss'][-1] )
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elif line.find('INFO: Validation Metrics:') >= 0:
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data = line.split("INFO: Validation Metrics:")[-1]
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step = info['it']
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if update and step <= self.last_info_check_at:
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continue
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if 'loss_text_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'val_loss_text_ce'})
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if 'loss_mel_ce' in info:
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self.statistics['loss'].append({'step': step, 'value': info['loss_gpt_total'], 'type': 'val_loss_mel_ce'})
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self.last_info_check_at = highest_step
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@ -707,9 +717,8 @@ class TrainingState():
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models = sorted([ int(d[:-8]) for d in os.listdir(f'{self.dataset_dir}/models/') if d[-8:] == "_gpt.pth" ])
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states = sorted([ int(d[:-6]) for d in os.listdir(f'{self.dataset_dir}/training_state/') if d[-6:] == ".state" ])
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remove_models = models[:-keep]
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remove_states = states[:-keep]
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remove_models = models[:-2]
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remove_states = states[:-2]
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for d in remove_models:
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path = f'{self.dataset_dir}/models/{d}_gpt.pth'
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@ -727,8 +736,10 @@ class TrainingState():
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percent = 0
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message = None
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if line.find('Finished training') >= 0:
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self.killed = True
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# rip out iteration info
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if not self.training_started:
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elif not self.training_started:
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if line.find('Start training from epoch') >= 0:
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self.it_time_start = time.time()
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self.epoch_time_start = time.time()
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@ -745,83 +756,57 @@ class TrainingState():
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self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
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else:
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lapsed = False
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message = None
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if line.find('INFO: [epoch:') >= 0:
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info_line = line.split("INFO:")[-1]
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# to-do, actually validate this works, and probably kill training when it's found, the model's dead by this point
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if ': nan' in info_line and not self.nan_detected:
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self.nan_detected = self.it
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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+|[\d,]+)\b', info_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.replace(",", ""))
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self.load_statistics(update=True)
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should_return = True
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# INFO: Training Metrics: {"loss_text_ce": 4.308311939239502, "loss_mel_ce": 2.1610655784606934, "loss_gpt_total": 2.204148769378662, "lr": 0.0001, "it": 2, "step": 1, "steps": 1, "epoch": 1, "iteration_rate": 0.10700102965037028}
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if line.find('INFO: Training Metrics:') >= 0:
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data = line.split("INFO: Training Metrics:")[-1]
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self.info = json.loads(data)
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if 'epoch' in self.info:
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self.epoch = int(self.info['epoch'])
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if 'iter' in self.info:
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self.it = int(self.info['iter'])
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if 'it' in self.info:
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self.it = int(self.info['it'])
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if 'step' in self.info:
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self.step = int(self.info['step'])
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if 'steps' in self.info:
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self.steps = int(self.info['steps'])
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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 self.step == self.steps:
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lapsed = True
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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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if progress is not None:
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progress(percent, message)
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if 'lr' in self.info:
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self.statistics['lr'].append({'step': self.it, 'value': self.info['lr'], 'type': 'learning_rate_gpt_0'})
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if 'loss_text_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_text_ce'], 'type': 'loss_text_ce'})
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if 'loss_mel_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_mel_ce'], 'type': 'loss_mel_ce'})
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if 'loss_gpt_total' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'loss_gpt_total'})
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self.losses.append( self.statistics['loss'][-1] )
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if 'iteration_rate' in self.info:
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it_rate = self.info['iteration_rate']
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self.it_rate = f'{"{:.3f}".format(it_rate)}s/it' if it_rate >= 1 or it_rate == 0 else f'{"{:.3f}".format(1/it_rate)}it/s'
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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.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
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if self.epochs != self.its:
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self.metrics['step'].append(f"{self.it}/{self.its}")
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if self.steps > 1:
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self.metrics['step'].append(f"{self.step}/{self.steps}")
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self.metrics['step'] = ", ".join(self.metrics['step'])
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self.cleanup_old(keep=keep_x_past_checkpoints)
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should_return = True
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elif line.find('INFO: Validation Metrics:') >= 0:
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data = line.split("INFO: Validation Metrics:")[-1]
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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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per_cent = 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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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 == steps:
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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_taken = self.it_taken + 1
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if self.it_time_delta:
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try:
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rate = f'{"{:.3f}".format(self.it_time_delta)}s/it' if self.it_time_delta >= 1 or self.it_time_delta == 0 else f'{"{:.3f}".format(1/self.it_time_delta)}it/s'
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self.it_rate = rate
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except Exception as e:
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pass
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self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
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if self.epochs != self.its:
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self.metrics['step'].append(f"{self.it}/{self.its}")
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if steps > 1:
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self.metrics['step'].append(f"{step}/{steps}")
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self.metrics['step'] = ", ".join(self.metrics['step'])
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if 'loss_text_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'val_loss_text_ce'})
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if 'loss_mel_ce' in self.info:
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self.statistics['loss'].append({'step': self.it, 'value': self.info['loss_gpt_total'], 'type': 'val_loss_mel_ce'})
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should_return = True
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if lapsed:
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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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@ -850,24 +835,16 @@ class TrainingState():
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eta_hhmmss = "?"
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if self.eta_hhmmss:
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eta_hhmmss = self.eta_hhmmss
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else:
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try:
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eta = (self.its - self.it) * (self.it_time_deltas / self.it_taken)
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eta = str(timedelta(seconds=int(eta)))
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eta_hhmmss = eta
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except Exception as e:
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pass
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self.metrics['loss'] = []
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if 'learning_rate_gpt_0' in self.info:
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self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["learning_rate_gpt_0"])}')
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if 'lr' in self.info:
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self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
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if len(self.losses) > 0:
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self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
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if len(self.losses) >= 2:
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# """riemann sum""" but not really as this is for derivatives and not integrals
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deriv = 0
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accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
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loss_value = self.losses[-1]["value"]
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@ -1296,10 +1273,6 @@ def optimize_training_settings( **kwargs ):
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iterations = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
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if settings['epochs'] < settings['print_rate']:
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settings['print_rate'] = settings['epochs']
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messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {settings['print_rate']}")
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if settings['epochs'] < settings['save_rate']:
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settings['save_rate'] = settings['epochs']
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messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {settings['save_rate']}")
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@ -1355,14 +1328,11 @@ def save_training_settings( **kwargs ):
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iterations_per_epoch = settings['iterations'] / settings['epochs']
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settings['print_rate'] = int(settings['print_rate'] * iterations_per_epoch)
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settings['save_rate'] = int(settings['save_rate'] * iterations_per_epoch)
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settings['validation_rate'] = int(settings['validation_rate'] * iterations_per_epoch)
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iterations_per_epoch = int(iterations_per_epoch)
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if settings['print_rate'] < 1:
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settings['print_rate'] = 1
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if settings['save_rate'] < 1:
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settings['save_rate'] = 1
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if settings['validation_rate'] < 1:
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@ -1858,6 +1828,11 @@ def import_generate_settings(file="./config/generate.json"):
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res.update(settings)
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return res
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def reset_generation_settings():
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with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps({}, indent='\t') )
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return import_generate_settings()
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def read_generate_settings(file, read_latents=True):
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j = None
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latents = None
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12
src/webui.py
12
src/webui.py
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@ -152,14 +152,11 @@ def import_generate_settings_proxy( file=None ):
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res = []
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for k in GENERATE_SETTINGS_ARGS:
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res.append(settings[k] if k in settings else None)
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print(GENERATE_SETTINGS_ARGS)
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print(settings)
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print(res)
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return tuple(res)
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def reset_generation_settings_proxy():
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with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps({}, indent='\t') )
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return import_generate_settings_proxy()
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def compute_latents_proxy(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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compute_latents( voice=voice, voice_latents_chunks=voice_latents_chunks, progress=progress )
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return voice
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@ -442,7 +439,6 @@ def setup_gradio():
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TRAINING_SETTINGS["batch_size"] = gr.Number(label="Batch Size", value=128, precision=0)
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TRAINING_SETTINGS["gradient_accumulation_size"] = gr.Number(label="Gradient Accumulation Size", value=4, precision=0)
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with gr.Row():
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TRAINING_SETTINGS["print_rate"] = gr.Number(label="Print Frequency (in epochs)", value=5, precision=0)
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TRAINING_SETTINGS["save_rate"] = gr.Number(label="Save Frequency (in epochs)", value=5, precision=0)
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TRAINING_SETTINGS["validation_rate"] = gr.Number(label="Validation Frequency (in epochs)", value=5, precision=0)
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@ -665,7 +661,7 @@ def setup_gradio():
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)
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reset_generation_settings_button.click(
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fn=reset_generation_settings_proxy,
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fn=reset_generation_settings,
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inputs=None,
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outputs=generate_settings
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)
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