diff --git a/src/utils.py b/src/utils.py
index c5aac42..8e87eb6 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -25,6 +25,7 @@ import torchaudio
import music_tag
import gradio as gr
import gradio.utils
+import pandas as pd
from datetime import datetime
from datetime import timedelta
@@ -435,13 +436,14 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
# superfluous, but it cleans up some things
class TrainingState():
- def __init__(self, config_path):
+ def __init__(self, config_path, keep_x_past_datasets=0):
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.dataset_dir = f"./training/{self.config['name']}/"
self.batch_size = self.config['datasets']['train']['batch_size']
self.dataset_path = self.config['datasets']['train']['path']
with open(self.dataset_path, 'r', encoding="utf-8") as f:
@@ -480,9 +482,67 @@ class TrainingState():
self.eta = "?"
self.eta_hhmmss = "?"
+ self.losses = {
+ 'iteration': [],
+ 'loss_gpt_total': []
+ }
+
+
+ self.load_losses()
+ self.cleanup_old(keep=keep_x_past_datasets)
+ self.spawn_process()
+
+ def spawn_process(self):
print("Spawning process: ", " ".join(self.cmd))
self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
+ def load_losses(self):
+ if not os.path.isdir(self.dataset_dir):
+ return
+
+ logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
+ infos = {}
+ for log in logs:
+ with open(log, 'r', encoding="utf-8") as f:
+ lines = f.readlines()
+ for line in lines:
+ 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+|[\d,]+)\b', line)
+ if not match or len(match) == 0:
+ continue
+
+ info = {}
+ for k, v in match:
+ info[k] = float(v.replace(",", ""))
+
+ if 'iter' in info:
+ it = info['iter']
+ infos[it] = info
+
+ for k in infos:
+ if 'loss_gpt_total' in infos[k]:
+ self.losses['iteration'].append(int(k))
+ self.losses['loss_gpt_total'].append(infos[k]['loss_gpt_total'])
+
+ def cleanup_old(self, keep=2):
+ if keep <= 0:
+ return
+
+ models = sorted([ int(d[:-8]) for d in os.listdir(f'{self.dataset_dir}/models/') if d[-8:] == "_gpt.pth" ])
+ states = sorted([ int(d[:-6]) for d in os.listdir(f'{self.dataset_dir}/training_state/') if d[-6:] == ".state" ])
+ remove_models = models[:-2]
+ remove_states = states[:-2]
+
+ for d in remove_models:
+ path = f'{self.dataset_dir}/models/{d}_gpt.pth'
+ print("Removing", path)
+ os.remove(path)
+ for d in remove_states:
+ path = f'{self.dataset_dir}/training_state/{d}.state'
+ print("Removing", path)
+ os.remove(path)
+
def parse(self, line, verbose=False, buffer_size=8, progress=None ):
self.buffer.append(f'{line}')
@@ -533,22 +593,7 @@ class TrainingState():
except Exception as e:
pass
- message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [ETA: {self.eta_hhmmss}] [{self.epoch_rate}, {self.it_rate}] {self.status}'
-
- """
- # I wanted frequently updated ETA, but I can't wrap my noggin around getting it to work on an empty belly
- # will fix later
-
- #self.eta = (self.its - self.it) * self.it_time_delta
- self.it_time_deltas = self.it_time_deltas + self.it_time_delta
- self.it_taken = self.it_taken + 1
- self.eta = (self.its - self.it) * (self.it_time_deltas / self.it_taken)
- try:
- eta = str(timedelta(seconds=int(self.eta)))
- self.eta_hhmmss = eta
- except Exception as e:
- pass
- """
+ message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [{self.epoch_rate}, {self.it_rate}] [Loss at it {self.losses["iteration"][-1]}: {self.losses["loss_gpt_total"][-1]}] [ETA: {self.eta_hhmmss}]'
if lapsed:
self.epoch = self.epoch + 1
@@ -578,15 +623,18 @@ class TrainingState():
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)
+ match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', line)
if match and len(match) > 0:
for k, v in match:
- self.info[k] = float(v)
+ 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']}"
- print(self.status)
- self.buffer.append(self.status)
+
+ self.losses['iteration'].append(self.it)
+ self.losses['loss_gpt_total'].append(self.info['loss_gpt_total'])
+
+ verbose = True
elif line.find('Saving models and training states') >= 0:
self.checkpoint = self.checkpoint + 1
@@ -598,11 +646,13 @@ class TrainingState():
print(f'{"{:.3f}".format(percent*100)}% {message}')
self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
+ self.cleanup_old()
+
self.buffer = self.buffer[-buffer_size:]
if verbose or not self.training_started:
return "".join(self.buffer)
-def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
+def run_training(config_path, verbose=False, buffer_size=8, 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"
@@ -614,7 +664,7 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
unload_whisper()
unload_voicefixer()
- training_state = TrainingState(config_path=config_path)
+ training_state = TrainingState(config_path=config_path, keep_x_past_datasets=keep_x_past_datasets)
for line in iter(training_state.process.stdout.readline, ""):
@@ -631,6 +681,18 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
#if return_code:
# raise subprocess.CalledProcessError(return_code, cmd)
+def get_training_losses():
+ global training_state
+ if not training_state or not training_state.losses:
+ return
+ return pd.DataFrame(training_state.losses)
+
+def update_training_dataplot():
+ global training_state
+ if not training_state or not training_state.losses:
+ return
+ return gr.LinePlot.update(value=pd.DataFrame(training_state.losses))
+
def reconnect_training(verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
global training_state
if not training_state or not training_state.process:
diff --git a/src/webui.py b/src/webui.py
index f479563..47a36f3 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -508,6 +508,15 @@ def setup_gradio():
training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
verbose_training = gr.Checkbox(label="Verbose Console Output")
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 Datasets", minimum=0, maximum=8, value=0)
+
+ training_loss_graph = gr.LinePlot(label="Loss Rates",
+ x="iteration",
+ y="loss_gpt_total",
+ title="Loss Rates",
+ width=600,
+ height=350
+ )
with gr.Tab("Settings"):
with gr.Row():
exec_inputs = []
@@ -720,8 +729,19 @@ def setup_gradio():
training_configs,
verbose_training,
training_buffer_size,
+ training_keep_x_past_datasets,
],
- outputs=training_output #console_output
+ outputs=[
+ training_output,
+ ],
+ )
+ training_output.change(
+ fn=update_training_dataplot,
+ inputs=None,
+ outputs=[
+ training_loss_graph,
+ ],
+ show_progress=False,
)
stop_training_button.click(stop_training,
inputs=None,