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fixes

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mrq 2023-02-21 04:22:11 +00:00
parent bbc2d26289
commit b6f7aa6264
1 changed files with 17 additions and 22 deletions
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17
src/utils.py
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@ -415,7 +415,7 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
its = config['train']['niter']
checkpoint = 0
checkpoints = config['logger']['save_checkpoint_freq'] / its
checkpoints = its / config['logger']['save_checkpoint_freq']
buffer_size = 8
open_state = False
@ -443,19 +443,17 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
elif progress is not None:
if line.find(' 0%|') == 0:
open_state = True
it_time_start = time.time()
elif line.find('100%|') == 0 and open_state:
it_time_end = time.time()
open_state = False
it = it + 1
it_time_end = time.time()
it_time_delta = it_time_end-it_time_start
it_rate = f'[{"{:.3f}".format(it_time_delta)}s/it]' if it_time_delta >= 1 and it_time_delta != 0 else f'[{"{:.3f}".format(1/it_time_delta)}it/s]' # I doubt anyone will have it/s rates, but its here
it_time_start = time.time()
it_rate = f'[{"{:.3f}".format(it_time_delta)}s/it]' if it_time_delta >= 1 else f'[{"{:.3f}".format(1/it_time_delta)}it/s]' # I doubt anyone will have it/s rates, but its here
progress(it / float(its), f'[{it}/{its}] {it_rate} Training... {status}')
# try because I haven't tested this yet
try:
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)
@ -467,16 +465,13 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress
# it would be nice for losses to be shown at every step
if 'loss_gpt_total' in info:
status = f"Total loss at step {int(info['step'])}: {info['loss_gpt_total']}"
except Exception as e:
pass
if line.find('Saving models and training states') >= 0:
elif line.find('Saving models and training states') >= 0:
checkpoint = checkpoint + 1
progress(checkpoint / float(checkpoints), f'[{checkpoint}/{checkpoints}] Saving checkpoint...')
print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
if verbose:
if verbose or not training_started:
yield "".join(buffer[-buffer_size:])
training_process.stdout.close()