added graph to chart loss_gpt_total rate, added option to prune X number of previous models/states, something else

This commit is contained in:
mrq 2023-02-28 01:01:50 +00:00
parent 6925ec731b
commit bc0d9ab3ed
2 changed files with 106 additions and 24 deletions

View File

@ -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:

View File

@ -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,