def _save_train_stats(self, suffix=None):
if not is_primary_worker():
# never do IO as a non-primary worker
return
fn = self.opt.get('model_file', None)
if not fn:
return
if suffix:
fn += suffix
fn += '.trainstats'
with PathManager.open(fn, 'w') as f:
json.dump(
{
'parleys': self.parleys,
'train_time': self.train_time.time(),
'train_steps': self._train_steps,
'total_epochs': self._total_epochs,
'train_reports': self.train_reports,
'valid_reports': self.valid_reports,
'best_valid': self.best_valid,
'impatience': self.impatience,
'final_valid_report': dict_report(self.final_valid_report),
'final_test_report': dict_report(self.final_test_report),
'final_extra_valid_report': dict_report(
self.final_extra_valid_report
),
},
f,
indent=4,
)
def log_final(self, setting, report):
report = dict_report(report)
report = {
f'{k}/{setting}': v
for k, v in report.items() if isinstance(v, numbers.Number)
}
for key, value in report.items():
self.run.summary[key] = value
def _run_opt_get_report(self, opt):
script = TestTodWorldScript(opt)
script.run()
def get_episode_report(goal, episode_metric):
metrics_dict = dict_report(episode_metric.report())
metrics_dict["goal"] = goal
return metrics_dict
return (
dict_report(script.world.report()),
[get_episode_report(g, e) for g, e in script.episode_metrics],
)
def _check_metrics_correct(self, script, opt):
"""
Last argument is only relevant for the max_turn test.
"""
max_rounds = opt[test_agents.TEST_NUM_ROUNDS_OPT_KEY]
max_episodes = opt[test_agents.TEST_NUM_EPISODES_OPT_KEY]
episode_metrics = script.episode_metrics
for episode_idx, episode in enumerate(episode_metrics):
goal, episode_metric = episode
episode_metric = dict_report(episode_metric.report())
self.assertAlmostEqual(
episode_metric["all_goals_hit"],
not test_agents.episode_has_broken_api_turn(
episode_idx, max_rounds),
)
broken_episodes = sum([
test_agents.episode_has_broken_api_turn(i, max_rounds)
for i in range(max_episodes)
])
report = dict_report(script.world.report())
self.assertAlmostEqual(
report["all_goals_hit"],
float(max_episodes - broken_episodes) / max_episodes,
)
def log(self):
"""
Output a training log entry.
"""
opt = self.opt
if opt['display_examples']:
print(self.world.display() + '\n~~')
logs = []
# get report
train_report = self.world.report()
train_report = self._sync_metrics(train_report)
self.world.reset_metrics()
train_report_trainstats = dict_report(train_report)
train_report_trainstats['total_epochs'] = self._total_epochs
train_report_trainstats['total_exs'] = self._total_exs
train_report_trainstats['parleys'] = self.parleys
train_report_trainstats['train_steps'] = self._train_steps
train_report_trainstats['train_time'] = self.train_time.time()
self.train_reports.append(train_report_trainstats)
# time elapsed
logs.append(f'time:{self.train_time.time():.0f}s')
logs.append(f'total_exs:{self._total_exs}')
logs.append(f'total_steps:{self._train_steps}')
if self._total_epochs >= 0:
# only if it's unbounded
logs.append(f'epochs:{self._total_epochs:.2f}')
time_left = self._compute_eta(
self._total_epochs, self.train_time.time(), self._train_steps
)
if time_left is not None:
logs.append(f'time_left:{max(0,time_left):.0f}s')
log = '{}\n{}\n'.format(' '.join(logs), nice_report(train_report))
logging.info(log)
self.log_time.reset()
self._last_log_steps = 0
if opt['tensorboard_log'] and is_primary_worker():
self.tb_logger.log_metrics('train', self.parleys, train_report)
if opt['wandb_log'] and is_primary_worker():
self.wb_logger.log_metrics('train', self.parleys, train_report)
return train_report
def log_metrics(self, setting, step, report):
"""
Log all metrics to W&B.
:param setting:
One of train/valid/test. Will be used as the title for the graph.
:param step:
Number of parleys
:param report:
The report to log
"""
report = dict_report(report)
report = {
f'{k}/{setting}': v
for k, v in report.items() if isinstance(v, numbers.Number)
}
report['custom_step'] = step
self.run.log(report)
def test_token_stats(self):
from parlai.scripts.token_stats import TokenStats
from parlai.core.metrics import dict_report
results = dict_report(TokenStats.main(task='integration_tests:multiturn'))
assert results == {
'exs': 2000,
'max': 16,
'mean': 7.5,
'min': 1,
'p01': 1,
'p05': 1,
'p10': 1,
'p25': 4,
'p50': 7.5,
'p75': 11.5,
'p90': 16,
'p95': 16,
'p99': 16,
'p@128': 1,
}
def validate(self):
"""
Perform a validation run, checking whether we should stop training.
:return: boolean indicating whether training should stop
:rtype: bool
"""
opt = self.opt
if self.valid_worlds is None:
# we need to load the world now
self.valid_worlds = load_eval_worlds(self.agent, opt, 'valid')
# run evaluation on valid set
valid_report = self._run_eval(
self.valid_worlds, opt, 'valid', opt['validation_max_exs']
)
v = dict_report(valid_report)
v['train_time'] = self.train_time.time()
v['parleys'] = self.parleys
v['train_steps'] = self._train_steps
v['total_exs'] = self._total_exs
v['total_epochs'] = self._total_epochs
self.valid_reports.append(v)
# logging
if opt['tensorboard_log'] and is_primary_worker():
valid_report['total_exs'] = self._total_exs
self.tb_logger.log_metrics('valid', self.parleys, valid_report)
# flush on a validation
self.tb_logger.flush()
if opt['wandb_log'] and is_primary_worker():
valid_report['total_exs'] = self._total_exs
self.wb_logger.log_metrics('valid', self.parleys, valid_report)
# send valid metrics to agent if the agent wants them
if hasattr(self.agent, 'receive_metrics'):
self.agent.receive_metrics(valid_report)
# check which metric to look at
new_valid = valid_report[opt['validation_metric']]
if isinstance(new_valid, Metric):
new_valid = new_valid.value()
# check if this is the best validation so far
if (
self.best_valid is None
or self.valid_optim * new_valid > self.valid_optim * self.best_valid
):
logging.success(
'new best {}: {:.4g}{}'.format(
opt['validation_metric'],
new_valid,
' (previous best was {:.4g})'.format(self.best_valid)
if self.best_valid is not None
else '',
)
)
self.best_valid = new_valid
self.impatience = 0
if opt.get('model_file'):
logging.info(f"saving best valid model: {opt['model_file']}")
self.save_model()
self.saved = True
if (
opt['validation_metric_mode'] == 'max'
and self.best_valid >= opt['validation_cutoff']
) or (
opt['validation_metric_mode'] == 'min'
and self.best_valid <= opt['validation_cutoff']
):
logging.info('task solved! stopping.')
return True
else:
self.impatience += 1
logging.report(
'did not beat best {}: {} impatience: {}'.format(
opt['validation_metric'], round(self.best_valid, 4), self.impatience
)
)
self.validate_time.reset()
# saving
if opt.get('model_file') and opt.get('save_after_valid'):
logging.info(f"saving model checkpoint: {opt['model_file']}.checkpoint")
self.save_model('.checkpoint')
# check if we are out of patience
if (
opt['validation_patience'] > 0
and self.impatience >= opt['validation_patience']
):
logging.info('ran out of patience! stopping training.')
return True
return False
def save_conversations(
cls,
act_list,
datapath,
opt,
save_keys='all',
context_ids='context',
self_chat=False,
**kwargs,
):
"""
Write Conversations to file from an act list.
Conversations assume the act list is of the following form: a list of episodes,
each of which is comprised of a list of act pairs (i.e. a list dictionaries
returned from one parley)
"""
to_save = cls._get_path(datapath)
context_ids = context_ids.split(',')
# save conversations
speakers = []
with PathManager.open(to_save, 'w') as f:
for ep in act_list:
if not ep:
continue
convo = {
'dialog': [],
'context': [],
'metadata_path': Metadata._get_path(to_save),
}
for act_pair in ep:
new_pair = []
for ex in act_pair:
ex_id = ex.get('id')
if ex_id in context_ids:
context = True
else:
context = False
if ex_id not in speakers:
speakers.append(ex_id)
# set turn
turn = {}
if save_keys != 'all':
save_keys_lst = save_keys.split(',')
else:
save_keys_lst = ex.keys()
for key in save_keys_lst:
turn[key] = ex.get(key, '')
if key == 'metrics':
turn[key] = dict_report(turn[key])
turn['id'] = ex_id
if not context:
new_pair.append(turn)
else:
convo['context'].append(turn)
if new_pair:
convo['dialog'].append(new_pair)
json_convo = json.dumps(convo,
default=lambda v: '<not serializable>')
f.write(json_convo + '\n')
logging.info(f'Conversations saved to file: {to_save}')
# save metadata
Metadata.save_metadata(to_save,
opt,
self_chat=self_chat,
speakers=speakers,
**kwargs)
def get_episode_report(goal, episode_metric):
metrics_dict = dict_report(episode_metric.report())
metrics_dict["goal"] = goal
return metrics_dict
def _save_outputs(self, opt, world, logger, episode_metrics):
if is_distributed(): # flatten everything intelligently if need be
world_report = aggregate_unnamed_reports(
all_gather_list(world.report()))
episode_metrics_unflattened = all_gather_list(episode_metrics)
flattened = []
for rank_elem in episode_metrics_unflattened:
for elem in rank_elem:
flattened.append(elem)
episode_metrics = flattened
else:
world_report = world.report()
logging.report("Final report:\n" + nice_report(world_report))
report = dict_report(world_report)
def get_episode_report(goal, episode_metric):
metrics_dict = dict_report(episode_metric.report())
metrics_dict["goal"] = goal
return metrics_dict
report["tod_metrics"] = [
get_episode_report(g, e) for g, e in episode_metrics
]
if "report_filename" in opt and opt["report_filename"] is not None:
if len(world_report) == 0:
logging.warning("Report is empty; not saving report")
report_fname = f"{opt['report_filename']}.json"
# Save report
if not is_distributed() or is_primary_worker():
with PathManager.open(report_fname, "w") as f:
logging.info(f"Saving model report to {report_fname}")
json.dump({"opt": opt, "report": report}, f, indent=4)
f.write("\n") # for jq
if "world_logs" in opt and opt["world_logs"] is not None:
if is_distributed(): # Save separately, then aggregate together
rank = get_rank()
log_outfile_part = (
f"{opt['world_logs']}_{opt['save_format']}_{rank}.jsonl")
logger.write(log_outfile_part,
world,
file_format=opt["save_format"])
sync_object(None)
if is_primary_worker():
log_outfile = f"{opt['world_logs']}_{opt['save_format']}.jsonl"
log_outfile_metadata = (
f"{opt['world_logs']}_{opt['save_format']}.metadata")
with open(log_outfile, "w+") as outfile:
for rank in range(num_workers()):
log_outfile_part = (
f"{opt['world_logs']}_{opt['save_format']}_{rank}.jsonl"
)
with open(log_outfile_part) as infile:
for line in infile:
json_blob = json.loads(line.strip())
if (
len(json_blob["dialog"]) < 2
): # skip when we don't have generation
continue
json_blob[
"metadata_path"] = log_outfile_metadata
outfile.write(json.dumps(json_blob))
outfile.write("\n")
log_output_part_metadata = f"{opt['world_logs']}_{opt['save_format']}_{rank}.metadata"
if rank == 0:
copyfile(log_output_part_metadata,
log_outfile_metadata),
os.remove(log_outfile_part)
os.remove(log_output_part_metadata)
else:
log_outfile = f"{opt['world_logs']}_{opt['save_format']}.jsonl"
logger.write(log_outfile,
world,
file_format=opt["save_format"])
return report
