gpt-neo / run_experiment.py
aliabd
full working demo
c6e7238
import atexit
import sacred
import argparse
import time
import math
import subprocess
import shutil
import os
import json
import threading
import requests
import glob
from configs import fetch_model_params
import socket
import subprocess
import queue
import sys
import signal
parser = argparse.ArgumentParser()
parser.add_argument('--tpu', type=str, required=True) # Name of TPU to train on, if any
parser.add_argument('--model', type=str, required=True) # JSON file that contains model parameters
parser.add_argument('--experiment_name', type=str, required=True) # name of experiment (will show up in omniboard)
parser.add_argument('--steps_per_checkpoint', type=int, default=5000)
parser.add_argument('--autostack', action="store_false")
parser.add_argument('--auto_layout', action="store_true")
parser.add_argument('--auto_layout_and_mesh_shape', action="store_true")
parser.add_argument('--new', action='store_true')
parser.add_argument('--test', action='store_true')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--predict', action='store_true')
parser.add_argument('--no_delete_tpu', action='store_true')
parser.add_argument('--initial_heartbeat_timeout', type=int, default=7200)
parser.add_argument('--heartbeat_timeout', type=int, default=1800) # kill and restart if nothing logged to tensorboard in this many seconds
args = parser.parse_args()
params = fetch_model_params(args.model)
ex = sacred.Experiment(args.experiment_name)
ex.observers.append(sacred.observers.QueuedMongoObserver(url='127.0.0.1:27017', db_name='db', username='user', password='password'))
def get_open_port(lo=8000, hi=8100):
for i in range(lo, hi):
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
if s.connect_ex(('localhost', i)) != 0:
return i
def train_thread(args, tpu, id, q):
print('starting training on', tpu)
# pass binary flags through
opts = ''
for flag in ['auto_layout', 'auto_layout_and_mesh_shape', 'new', 'test', 'predict', 'eval', ]:
if args.__getattribute__(flag):
opts += ' --' + flag
for flag in ['autostack', ]:
if not args.__getattribute__(flag):
opts += ' --' + flag
cmd = "python3 main.py --tpu {tpu} --model run_configs/config_{id}.json --steps_per_checkpoint {steps_per_checkpoint} {opts} --sacred_id {run_id}".format(tpu=tpu, id=id, steps_per_checkpoint=args.steps_per_checkpoint, opts=opts, run_id=id)
print('Running:', cmd)
proc = subprocess.Popen(cmd, shell=True)
# poll until it's exited
while proc.poll() is None:
time.sleep(60)
try:
nq, *nargs = q.get_nowait()
if nq == 'kill':
print('train thread recieved kill signal from logging thread')
# first send SIGTERM
proc.terminate()
time.sleep(60)
# if it still hasn't exited, we send SIGKILL
if proc.poll() is None:
print('SIGTERM not successful, sending SIGKILL')
proc.kill()
except queue.Empty:
pass
print('exited training!')
if proc.returncode == 0:
print('exited gracefully')
os.kill(os.getpid(), signal.SIGINT)
return
if args.no_delete_tpu:
print('recreate done, exiting train_thread - not killing tpu!')
return
print("Recreating {} in 60sec...".format(tpu))
time.sleep(60)
os.system("pu recreate {} --yes --retry 3600 --retry-randomness 1.5".format(tpu))
print('recreate done, exiting train_thread')
# clear out queue
while True:
try:
q.get_nowait()
print('dropped request in queue after pu recreate')
except queue.Empty:
break
def get_json(uri, params=None, timeout=15):
resp = requests.get(uri, params=params, timeout=timeout)
resp.raise_for_status()
return resp.json()
def get_tag_sets(base_uri):
j = get_json(f'{base_uri}/data/plugin/scalars/tags', {'experiment': ''})
assert isinstance(j, dict)
return {
run: j[run].keys()
for run in j.keys()
}
def get_scalar_data(base_uri, run, tag):
j = get_json(f'{base_uri}/data/plugin/scalars/scalars', {'experiment': '', 'run': run, 'tag': tag})
assert isinstance(j, list)
return j
def get_run_data(port):
base_uri = f'http://localhost:{port}/'
r = {}
try:
tag_sets = get_tag_sets(base_uri)
runs = tag_sets.keys()
if '.' in runs:
if 'loss' in tag_sets['.']:
r['loss'] = get_scalar_data(base_uri, '.', 'loss')
if 'eval' in runs:
if 'loss' in tag_sets['eval']:
r['val_loss'] = get_scalar_data(base_uri, 'eval', 'loss')
if 'eval_lambada' in runs:
if 'lambada_acc' in tag_sets['eval_lambada']:
r['lambada_acc'] = get_scalar_data(base_uri, 'eval_lambada', 'lambada_acc')
if 'lambada_log_ppl' in tag_sets['eval_lambada']:
r['lambada_ppl'] = [
[t, s, math.exp(lp)]
for [t, s, lp] in get_scalar_data(base_uri, 'eval_lambada', 'lambada_log_ppl')
]
except:
import traceback
traceback.print_exc()
return r
@ex.main
def main(_run):
print('Starting run', _run._id)
print('experiment main invoked with argv:', " ".join(sys.argv))
print('WARNING: please remember to remove old metric log files from the model directory.')
os.makedirs('run_configs', exist_ok=True)
shutil.copy(args.model if args.model.endswith('.json') else 'configs/{}.json'.format(args.model), 'run_configs/config_{}.json'.format(_run._id))
tensorboard_port = get_open_port()
print('Tensorboard at port:', tensorboard_port)
print('Tensorboard url: ', 'http://eleutherai.bmk.sh:'+ str(tensorboard_port))
os.system("screen -S tensorboard_{} -d -m bash -c 'tensorboard --logdir {} --port {} --bind_all --reload_multifile=true || tensorboard --logdir {} --port {} --reload_multifile=true'".format(_run._id, params["model_path"], tensorboard_port,params["model_path"], tensorboard_port,))
atexit.register(goodbye, _run._id)
curr_step = {}
seen_predictions = set()
heartbeat_timeout = args.initial_heartbeat_timeout * 2
while True:
last_tb_log_time = time.time()
start_time = time.time()
q = queue.Queue()
trainthd = threading.Thread(target=train_thread, args=(args, args.tpu, _run._id, q))
trainthd.start()
while trainthd.is_alive():
time.sleep(60)
if start_time + args.initial_heartbeat_timeout < time.time():
# after initial args.initial_heartbeat_timeout grace period, now we want to set the timeout threshold much lower
heartbeat_timeout = args.heartbeat_timeout
print('Polling tensorboard for metrics...')
data = get_run_data(tensorboard_port)
for k in data.keys():
for ts, step, val in data[k]:
if step <= curr_step.get(k, -1):
continue
_run.log_scalar(k, val, step)
if k == 'loss':
_run.log_scalar('tb_ts', ts, step)
print('Logged to sacred: step={},loss={},tb_ts={}'.format(step, val, ts))
# found something new, so logging!
last_tb_log_time = time.time()
curr_step[k] = step
for f in glob.glob('predictions_{}_*'.format(_run._id)):
if f in seen_predictions:
continue
print('collecting prediction file', f)
ex.add_artifact(f)
seen_predictions.add(f)
# collect eval metrics from jsonl
if os.path.exists(f'eval_{_run._id}.jsonl'):
with open(f'eval_{_run._id}.jsonl') as fh:
for line in fh:
ob = json.loads(line)
val_step = ob['global_step']
val_task = ob['task']
for metr in ob.keys():
k = 'fs.' + val_task + '.' + metr
if metr in ['task', 'global_step']: continue
if val_step <= curr_step.get(k, -1): continue
_run.log_scalar(k, ob[metr], val_step)
curr_step[k] = val_step
if time.time() - last_tb_log_time > heartbeat_timeout:
# the run hasn't logged in a while, so we restart it
q.put(('kill',))
# give training thread some time to do its thing and recreate tpu
while trainthd.is_alive():
print('logging thread waiting for killing stalled run and for tpu recreate to finish')
time.sleep(60)
# reset heartbeat timeout to initial
heartbeat_timeout = args.initial_heartbeat_timeout
last_tb_log_time = time.time()
if args.no_delete_tpu:
break
def goodbye(id):
print("You are now leaving the Python sector.")
print("Sie verlassen den pythonischen Sektor.")
os.system("screen -S tensorboard_{} -X quit".format(id))
if __name__ == '__main__':
for file in glob.glob("**/*", recursive=True):
if file.split('.')[-1] in ['py']:
print('Adding', file, 'to sacred')
ex.add_source_file(file)
ex.add_config({
'tpu_name': args.tpu,
**params
})
ex.run()