def _run_eval(
self,
valid_worlds,
opt,
datatype,
max_exs=-1,
write_log=False,
extra_log_suffix="",
):
"""
Eval on validation/test data.
:param valid_world:
list of the pre-created validation worlds.
:param opt:
the options that specific the task, eval_task, etc
:param datatype:
the datatype to use, such as "valid" or "test"
:param bool write_log:
specifies to write metrics to file if the model_file is set
:param int max_exs:
limits the number of examples if max_exs > 0
"""
logging.info(f'running eval: {datatype}')
timer = Timer()
reports = []
max_exs_per_worker = max_exs / (len(valid_worlds) * num_workers())
for v_world in valid_worlds:
task_report = self._run_single_eval(opt, v_world,
max_exs_per_worker)
reports.append(task_report)
tasks = [world.getID() for world in valid_worlds]
named_reports = dict(zip(tasks, reports))
report = aggregate_named_reports(named_reports,
micro_average=self.opt.get(
'aggregate_micro', False))
# get the results from all workers
report = self._sync_metrics(report)
metrics = f'{datatype}:\n{nice_report(report)}\n'
logging.info(f'eval completed in {timer.time():.2f}s')
logging.report(metrics)
# write to file
if write_log and opt.get('model_file') and is_primary_worker():
# Write out metrics
with PathManager.open(
opt['model_file'] + extra_log_suffix + '.' + datatype,
'a') as f:
f.write(f'{metrics}\n')
return report
def eval_model(opt):
"""
Evaluates a model.
:param opt: tells the evaluation function how to run
:return: the final result of calling report()
"""
random.seed(42)
if 'train' in opt['datatype'] and 'evalmode' not in opt['datatype']:
raise ValueError(
'You should use --datatype train:evalmode if you want to evaluate on '
'the training set.')
# load model and possibly print opt
agent = create_agent(opt, requireModelExists=True)
agent.opt.log()
tasks = opt['task'].split(',')
reports = []
for task in tasks:
task_report = _eval_single_world(opt, agent, task)
reports.append(task_report)
logging.report(f"Report for {task}:\n{nice_report(task_report)}")
report = aggregate_named_reports(dict(zip(tasks, reports)),
micro_average=opt.get(
'aggregate_micro', False))
# print announcements and report
print_announcements(opt)
logging.info(
f'Finished evaluating tasks {tasks} using datatype {opt.get("datatype")}'
)
print(nice_report(report))
_save_eval_stats(opt, report)
return report
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_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
