forked from mrq/ai-voice-cloning
added loss graph, because I'm going to experiment with cosine annealing LR and I need to view my loss
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a182df8f4e
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5460e191b0
143
src/utils.py
143
src/utils.py
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@ -627,7 +627,10 @@ class TrainingState():
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self.nan_detected = False
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self.last_info_check_at = 0
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self.statistics = []
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self.statistics = {
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'loss': [],
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'lr': [],
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}
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self.losses = []
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self.metrics = {
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'step': "",
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@ -637,7 +640,7 @@ class TrainingState():
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self.loss_milestones = [ 1.0, 0.15, 0.05 ]
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self.load_losses()
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self.load_statistics()
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if keep_x_past_checkpoints > 0:
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self.cleanup_old(keep=keep_x_past_checkpoints)
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if start:
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@ -649,7 +652,7 @@ class TrainingState():
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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 load_losses(self, update=False):
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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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return
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try:
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@ -658,69 +661,40 @@ class TrainingState():
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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']
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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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highest_step = self.last_info_check_at
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if not update:
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self.statistics = []
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self.statistics['loss'] = []
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self.statistics['lr'] = []
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if use_tensorboard:
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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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if update:
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logs = [logs[-1]]
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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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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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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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for key in keys:
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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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self.statistics.append( { "step": s.step, "value": s.value, "type": key } )
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scalars = ea.Tags()['scalars']
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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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for key in keys:
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if key not in scalars:
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continue
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else:
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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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with open(log, 'r', encoding="utf-8") as f:
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lines = f.readlines()
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for line in lines:
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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+|[\d,]+)\b', line)
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if not match or len(match) == 0:
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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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info = {}
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for k, v in match:
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info[k] = float(v.replace(",", ""))
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if 'iter' in info:
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it = info['iter']
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infos[it] = info
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for k in infos:
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if 'loss_gpt_total' in infos[k]:
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for key in keys:
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if update and int(k) <= 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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self.statistics.append({ "step": int(k), "value": infos[k][key], "type": key })
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if key == "loss_gpt_total":
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self.losses.append({ "step": int(k), "value": infos[k][key], "type": key })
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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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self.last_info_check_at = highest_step
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@ -784,7 +758,7 @@ class TrainingState():
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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_losses(update=True)
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self.load_statistics(update=True)
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should_return = True
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if 'epoch' in self.info:
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@ -1003,20 +977,26 @@ def run_training(config_path, verbose=False, keep_x_past_checkpoints=0, progress
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def update_training_dataplot(config_path=None):
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global training_state
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update = None
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losses = None
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lrs = None
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if not training_state:
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if config_path:
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training_state = TrainingState(config_path=config_path, start=False)
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if training_state.statistics:
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update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=600, height=350,)
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if len(training_state.statistics['loss']) > 0:
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losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['lr']) > 0:
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lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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del training_state
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training_state = None
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elif training_state.statistics:
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training_state.load_losses()
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update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=600, height=350,)
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else:
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training_state.load_statistics()
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if len(training_state.statistics['loss']) > 0:
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losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['lr']) > 0:
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lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=500, height=350,)
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return update
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return (losses, lrs)
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def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
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global training_state
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@ -1363,9 +1343,11 @@ def save_training_settings( **kwargs ):
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settings['iterations'] = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
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messages.append(f"For {settings['epochs']} epochs with {lines} lines, iterating for {settings['iterations']} steps")
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settings['print_rate'] = int(settings['print_rate'] * settings['iterations'] / settings['epochs'])
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settings['save_rate'] = int(settings['save_rate'] * settings['iterations'] / settings['epochs'])
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settings['validation_rate'] = int(settings['validation_rate'] * settings['iterations'] / settings['epochs'])
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iterations_per_epoch = int(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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settings['validation_batch_size'] = int(settings['batch_size'] / settings['gradient_accumulation_size'])
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@ -1407,16 +1389,31 @@ def save_training_settings( **kwargs ):
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elif isinstance(settings['learning_rate_schedule'],str):
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settings['learning_rate_schedule'] = json.loads(settings['learning_rate_schedule'])
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settings['learning_rate_schedule'] = schedule_learning_rate( settings['iterations'] / settings['epochs'], settings['learning_rate_schedule'] )
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settings['learning_rate_schedule'] = schedule_learning_rate( iterations_per_epoch, settings['learning_rate_schedule'] )
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learning_rate_schema.append(f" gen_lr_steps: {settings['learning_rate_schedule']}")
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learning_rate_schema.append(f" lr_gamma: 0.5")
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elif settings['learning_rate_scheme'] == "CosineAnnealingLR_Restart":
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learning_rate_schema.append(f" T_period: [120000, 120000, 120000]")
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learning_rate_schema.append(f" warmup: 10000")
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learning_rate_schema.append(f" eta_min: .01")
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learning_rate_schema.append(f" restarts: [140000, 280000]")
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learning_rate_schema.append(f" restart_weights: [.5, .25]")
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epochs = settings['epochs']
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restarts = int(epochs / 2)
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if 'learning_rate_period' not in settings:
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settings['learning_rate_period'] = [ iterations_per_epoch for x in range(epochs) ]
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if 'learning_rate_warmup' not in settings:
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settings['learning_rate_warmup'] = 0
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if 'learning_rate_min' not in settings:
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settings['learning_rate_min'] = 1e-07
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if 'learning_rate_restarts' not in settings:
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settings['learning_rate_restarts'] = [ iterations_per_epoch * (x+1) * 2 for x in range(restarts) ] # [52, 104, 156, 208]
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if 'learning_rate_restart_weights' not in settings:
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settings['learning_rate_restart_weights'] = [ ( restarts - x - 1 ) / restarts for x in range(restarts) ] # [.75, .5, .25, .125]
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settings['learning_rate_restart_weights'][-1] = settings['learning_rate_restart_weights'][-2] * 0.5
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learning_rate_schema.append(f" T_period: {settings['learning_rate_period']}")
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learning_rate_schema.append(f" warmup: !!float {settings['learning_rate_warmup']}")
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learning_rate_schema.append(f" eta_min: !!float {settings['learning_rate_min']}")
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learning_rate_schema.append(f" restarts: {settings['learning_rate_restarts']}")
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learning_rate_schema.append(f" restart_weights: {settings['learning_rate_restart_weights']}")
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settings['learning_rate_scheme'] = "\n".join(learning_rate_schema)
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"""
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39
src/webui.py
39
src/webui.py
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@ -430,21 +430,7 @@ def setup_gradio():
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with gr.Row():
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with gr.Column():
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training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_list())
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with gr.Row():
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refresh_configs = gr.Button(value="Refresh Configurations")
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training_loss_graph = gr.LinePlot(label="Training Metrics",
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x="step",
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y="value",
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title="Training Metrics",
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color="type",
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tooltip=['step', 'value', 'type'],
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width=600,
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height=350,
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)
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view_losses = gr.Button(value="View Losses")
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with gr.Column():
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refresh_configs = gr.Button(value="Refresh Configurations")
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training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
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@ -453,6 +439,27 @@ def setup_gradio():
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start_training_button = gr.Button(value="Train")
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stop_training_button = gr.Button(value="Stop")
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reconnect_training_button = gr.Button(value="Reconnect")
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with gr.Column():
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training_loss_graph = gr.LinePlot(label="Training Metrics",
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x="step",
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y="value",
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title="Training Metrics",
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color="type",
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tooltip=['step', 'value', 'type'],
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width=500,
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height=350,
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)
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training_lr_graph = gr.LinePlot(label="Training Metrics",
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x="step",
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y="value",
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title="Training Metrics",
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color="type",
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tooltip=['step', 'value', 'type'],
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width=500,
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height=350,
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)
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view_losses = gr.Button(value="View Losses")
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with gr.Tab("Settings"):
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with gr.Row():
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exec_inputs = []
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@ -650,6 +657,7 @@ def setup_gradio():
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inputs=None,
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outputs=[
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training_loss_graph,
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training_lr_graph,
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],
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show_progress=False,
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)
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@ -661,6 +669,7 @@ def setup_gradio():
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],
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outputs=[
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training_loss_graph,
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training_lr_graph,
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],
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)
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