def __init__(self, parser):
opt = parser.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt)
# Create model and assign it to the specified task
self.agent = create_agent(opt)
self.world = create_task(opt, self.agent)
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.max_num_epochs = opt['num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt['max_train_time'] if opt['max_train_time'] > 0 else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 else float('inf')
self.val_every_n_secs = opt['validation_every_n_secs'] if opt['validation_every_n_secs'] > 0 else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt['save_every_n_secs'] > 0 else float('inf')
self.best_valid = 0
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
def eval_model(opt, parser, printargs=True):
# Create model and assign it to the specified task
agent = create_agent(opt)
world = create_task(opt, agent)
# Show arguments after loading model
parser.opt = agent.opt
if (printargs):
parser.print_args()
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = Timer()
tot_time = 0
# Show some example dialogs:
for _ in range(int(opt['num_examples'])):
world.parley()
if opt['display_examples']:
print("---")
print(world.display() + "\n~~")
if log_time.time() > log_every_n_secs:
tot_time += log_time.time()
print(str(int(tot_time)) + "s elapsed: " + str(world.report()))
log_time.reset()
if world.epoch_done():
print("EPOCH DONE")
break
print(world.report())
world.shutdown()
def main():
# Get command line arguments
parser = ParlaiParser(True, True)
parser.add_argument('-d', '--display-examples', type='bool', default=False)
parser.add_argument('-e', '--num-epochs', default=1)
parser.add_argument('-mtt',
'--max-train-time',
type=float,
default=float('inf'))
opt = parser.parse_args()
# Create model and assign it to the specified task
agent = create_agent(opt)
world = create_task(opt, agent)
train_time = Timer()
print("[training...]")
for i in range(opt['num_epochs'] * len(world)):
world.parley()
if opt['display_examples']:
print(world.display() + "\n~~")
if train_time.time() > opt['max_train_time']:
print("[max_train_time elapsed: " + str(train_time.time()) + "]")
break
world.shutdown()
if opt['model_file']:
agent.save(opt['model_file'])
run_eval(agent, opt, 'valid')
run_eval(agent, opt, 'test')
def main(opt):
# Check options
assert ('pretrained_model' in opt)
assert (opt['datatype'] in {'test', 'valid'})
# Load document reader
doc_reader = DocReaderAgent(opt)
# Log params
logger.info(
'[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v) for k, v in doc_reader.opt.items()]))
logger.info('[ Running validation... ]')
valid_world = create_task(opt, doc_reader)
valid_time = Timer()
for _ in valid_world:
valid_world.parley()
metrics = valid_world.report()
if 'tasks' in metrics:
for task, t_metrics in metrics['tasks'].items():
logger.info('task = %s | EM = %.4f | F1 = %.4f | exs = %d | ' %
(task, t_metrics['accuracy'], t_metrics['f1'],
t_metrics['total']))
logger.info('Overall EM = %.4f | exs = %d' %
(metrics['accuracy'], metrics['total']))
else:
logger.info('EM = %.4f | F1 = %.4f | exs = %d' %
(metrics['accuracy'], metrics['f1'], metrics['total']))
logger.info('[ Done. Time = %.2f (s) ]' % valid_time.time())
def validate(opt, agent, n_iter):
opt = copy.deepcopy(opt)
opt['datatype'] = 'valid'
valid_world = create_task(opt, agent)
logger.info('[ Running validation... ]')
valid_time = Timer()
for _ in valid_world:
valid_world.parley()
metrics = valid_world.report()
if 'tasks' in metrics:
for task, t_metrics in metrics['tasks'].items():
logger.info('[valid] task = %s | iter = %d | exs = %d | ' %
(task, n_iter, t_metrics['total']) +
'EM = %.4f | F1 = %.4f' %
(t_metrics['accuracy'], t_metrics['f1']))
logger.info('[valid] iter = %d | overall EM = %.4f | exs = %d' %
(n_iter, metrics['accuracy'], metrics['total']))
else:
logger.info(
'[valid] iter = %d | EM = %.4f | F1 = %.4f | exs = %d' %
(n_iter, metrics['accuracy'], metrics['f1'], metrics['total'])
)
logger.info('[ Done. Time = %.2f (s) ]' % valid_time.time())
return metrics[opt['valid_metric']]
def __init__(self, opt):
if isinstance(opt, ParlaiParser):
opt = opt.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file_transmitter') and opt.get('model_file_receiver'):
opt['dict_file'] = opt['model_file_transmitter'] + '_' + opt['model_file_receiver'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt, skip_if_built=False)
# Create model and assign it to the specified task
print("[ create meta-agent ... ]")
self.agent = create_agent(opt)
print("[ create agent A ... ]")
shared = self.agent.share()
self.agent_a = create_agent_from_shared(shared)
self.agent_a.set_id(suffix=' A')
print("[ create agent B ... ]")
self.agent_b = create_agent_from_shared(shared)
# self.agent_b = create_agent(opt)
self.agent_b.set_id(suffix=' B')
# self.agent_a.copy(self.agent, 'transmitter')
# self.agent_b.copy(self.agent, 'transmitter')
self.world = create_selfplay_world(opt, [self.agent_a, self.agent_b])
# TODO: if batch, it is also not parallel
# self.world = BatchSelfPlayWorld(opt, self_play_world)
self.train_time = Timer()
self.train_dis_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys_episode = 0
self.max_num_epochs = opt['num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt['max_train_time'] if opt['max_train_time'] > 0 else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 else float('inf')
self.train_dis_every_n_secs = opt['train_display_every_n_secs'] if opt['train_display_every_n_secs'] > 0 else float('inf')
self.val_every_n_secs = opt['validation_every_n_secs'] if opt['validation_every_n_secs'] > 0 else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt['save_every_n_secs'] > 0 else float('inf')
self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
self.best_valid = None
if opt.get('model_file_transmitter') and os.path.isfile(opt['model_file_transmitter'] + '.best_valid'):
with open(opt['model_file_transmitter'] + ".best_valid", 'r') as f:
x = f.readline()
self.best_valid = float(x)
f.close()
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
if opt['tensorboard_log'] is True:
self.writer = TensorboardLogger(opt)
def main(opt):
# Build dictionary from task data
if 'pretrained_model' in opt:
dictionary = None
else:
dictionary = build_dict(opt)
# Build document reader
doc_reader = DocReaderAgent(opt, word_dict=dictionary)
# Log params
logger.info('[ Created with options: ] %s' %
''.join(['\n{}\t{}'.format(k, v)
for k, v in doc_reader.opt.items()]))
# Build training world once
opt['datatype'] = 'train'
train_world = create_task(opt, doc_reader)
train_time = Timer()
# Keep track of best model + how long since the last improvement
best_valid = 0
impatience = 0
logger.info("[ Ok, let's go... ]")
iteration = 0
while impatience < opt['patience']:
# Train...
logger.info('[ Training for %d iters... ]' % opt['train_interval'])
train_time.reset()
for _ in range(opt['train_interval']):
train_world.parley()
logger.info('[ Done. Time = %.2f (s) ]' % train_time.time())
# ...validate!
valid_metric = validate(opt, doc_reader, iteration)
if valid_metric > best_valid:
logger.info(
'[ Best eval %d: %s = %.4f (old = %.4f) ]' %
(iteration, opt['valid_metric'], valid_metric, best_valid)
)
best_valid = valid_metric
impatience = 0
if 'model_file' in opt:
doc_reader.save(opt['model_file'])
if valid_metric == 1:
logger.info('[ Task solved! Stopping. ]')
break
else:
impatience += 1
iteration += 1
def __init__(self, parser):
opt = parser.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict.build_dict(opt)
# Create model and assign it to the specified task
self.agent = create_agent(opt)
self.world = create_task(opt, self.agent)
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.total_episodes = 0
self.total_epochs = 0
self.max_num_epochs = opt[
'num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt[
'max_train_time'] if opt['max_train_time'] > 0 else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt[
'log_every_n_secs'] > 0 else float('inf')
self.val_every_n_secs = opt['validation_every_n_secs'] if opt[
'validation_every_n_secs'] > 0 else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt[
'save_every_n_secs'] > 0 else float('inf')
self.best_valid = 0
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
def __init__(self, opt, agents=None, shared=None):
self.id = opt['task']
self.opt = copy.deepcopy(opt)
if shared:
# Create agents based on shared data.
self.agents = create_agents_from_shared(shared['agents'])
else:
# Add passed in agents to world directly.
self.agents = agents
self.max_exs = None
self.total_exs = 0
self.total_epochs = 0
self.total_parleys = 0
self.time = Timer()
def __init__(self, opt):
if isinstance(opt, ParlaiParser):
print(
'[ Deprecated Warning: TrainLoop should be passed opt not Parser ]'
)
opt = opt.parse_args()
# Possibly load from checkpoint
if opt['load_from_checkpoint'] and opt.get(
'model_file') and os.path.isfile(opt['model_file'] +
'.checkpoint'):
opt['init_model'] = opt['model_file'] + '.checkpoint'
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
# If data built via pytorch data teacher, we need to load prebuilt dict
if opt.get('pytorch_teacher_task'):
opt['dict_file'] = get_pyt_dict_file(opt)
elif opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt, skip_if_built=True)
# Create model and assign it to the specified task
self.agent = create_agent(opt)
self.world = create_task(opt, self.agent)
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.max_num_epochs = opt[
'num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt['max_train_time'] if opt['max_train_time'] > 0 \
else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 \
else float('inf')
self.val_every_n_secs = \
opt['validation_every_n_secs'] if opt['validation_every_n_secs'] > 0 \
else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt['save_every_n_secs'] \
> 0 else float('inf')
self.val_every_n_epochs = \
opt['validation_every_n_epochs'] if opt['validation_every_n_epochs'] > 0 \
else float('inf')
self.last_valid_epoch = 0
self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
self.best_valid = None
if opt.get('model_file') and os.path.isfile(opt['model_file'] +
'.best_valid'):
with open(opt['model_file'] + ".best_valid", 'r') as f:
x = f.readline()
self.best_valid = float(x)
f.close()
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
if opt['tensorboard_log'] is True:
self.writer = TensorboardLogger(opt)
def eval_ppl(opt):
"""Evaluates the the perplexity and f1 of a model (and hits@1 if model has
ranking enabled.
"""
dict_agent = build_dict()
# create agents
agent = create_agent(opt)
world = create_task(opt, [agent, dict_agent],
default_world=PerplexityWorld)
world.dict = dict_agent
# set up logging
log_time = Timer()
tot_time = 0
while not world.epoch_done():
world.parley() # process an example
if log_time.time() > 1: # log every 1 sec
tot_time += log_time.time()
report = world.report()
print('{}s elapsed, {}%% complete, {}'.format(
int(tot_time),
round_sigfigs(report['total'] / world.num_examples() * 100, 2),
report))
log_time.reset()
if world.epoch_done():
print('EPOCH DONE')
tot_time += log_time.time()
final_report = world.report()
print('{}s elapsed: {}'.format(int(tot_time), final_report))
def detect(opt, printargs=None, print_parser=None):
"""Checks a task for offensive language.
"""
if print_parser is not None:
if print_parser is True and isinstance(opt, ParlaiParser):
print_parser = opt
elif print_parser is False:
print_parser = None
random.seed(42)
# Create model and assign it to the specified task
agent = create_agent(opt, requireModelExists=True)
world = create_task(opt, agent)
bad = OffensiveLanguageDetector()
if print_parser:
# Show arguments after loading model
print_parser.opt = agent.opt
print_parser.print_args()
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = Timer()
tot_time = 0
# Show some example dialogs:
cnt = 0
while not world.epoch_done():
world.parley()
offensive = False
for a in world.acts:
if bad.contains_offensive_language(a.get('text', '')):
offensive = True
labels = a.get('labels', a.get('eval_labels', ''))
for l in labels:
if bad.contains_offensive_language(l):
offensive = True
if offensive:
if opt['display_examples']:
print(world.display() + "\n~~")
cnt += 1
if log_time.time() > log_every_n_secs:
tot_time += log_time.time()
report = world.report()
log = {'total': report['total']}
log['done'] = report['total'] / world.num_examples()
if log['done'] > 0:
log['eta'] = int(tot_time / log['done'] - tot_time)
z = '%.2f' % (100 * log['done'])
log['done'] = str(z) + '%'
log['offenses'] = cnt
print(str(int(tot_time)) + "s elapsed: " + str(log))
log_time.reset()
if world.epoch_done():
print("EPOCH DONE")
print(
str(cnt) + " offensive messages found out of " +
str(world.num_examples()) + " messages.")
return world.report()
def eval_model(opt, parser, printargs=True):
# Create model and assign it to the specified task
agent = create_agent(opt)
world = create_task(opt, agent)
# Show arguments after loading model
parser.opt = agent.opt
if (printargs):
parser.print_args()
log_every_n_secs = opt[
'log_every_n_secs'] if opt['log_every_n_secs'] > 0 else float('inf')
log_time = Timer()
tot_time = 0
# Show some example dialogs:
for _ in range(int(opt['num_examples'])):
world.parley()
if opt['display_examples']:
print("---")
print(world.display() + "\n~~")
if log_time.time() > log_every_n_secs:
tot_time += log_time.time()
print(str(int(tot_time)) + "s elapsed: " + str(world.report()))
log_time.reset()
if world.epoch_done():
print("EPOCH DONE")
break
print(world.report())
world.shutdown()
def __init__(self, parser):
opt = parser.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict.build_dict(opt)
# Create model and assign it to the specified task
self.agent = create_agent(opt)
self.world = create_task(opt, self.agent)
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
print('[ training... ]')
self.parleys = 0
self.total_exs = 0
self.total_episodes = 0
self.total_epochs = 0
self.max_exs = None
self.max_parleys = None
self.world_num_exs = self.world.num_examples()
if self.world_num_exs is not None:
self.max_exs = opt['num_epochs'] * self.world_num_exs
self.max_parleys = math.ceil(self.max_exs / opt['batchsize'])
self.best_valid = 0
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
def __init__(self, opt, shared=None):
super().__init__(opt, shared)
if not shared:
# don't enter this loop for shared instantiations
self.path = opt['model_file']
# loss
self.loss_time = Timer()
self.losses = []
self.save_loss_every_n_secs = opt['save_loss_every_n_secs']
# attention
if opt['task'] == "babi:All1k":
self.tasks = {'babi:Task1k:' + str(i): 0 for i in range(1, 21)}
elif opt['task'] == "babi:All10k":
self.tasks = {
'babi:Task10k:' + str(i): 0
for i in range(1, 21)
}
else:
self.tasks = {task: 0 for task in opt['task'].split(',')}
self.attention_weights = {task: [] for task in self.tasks.keys()}
self.save_attention_exs = opt['save_attention_exs']
def __init__(self, opt, agents=None, shared=None):
self.id = opt['task']
self.opt = copy.deepcopy(opt)
if shared:
# Create agents based on shared data.
self.agents = create_agents_from_shared(shared['agents'])
else:
# Add passed in agents to world directly.
self.agents = agents
self.max_exs = None
self.total_exs = 0
self.total_epochs = 0
self.total_parleys = 0
self.time = Timer()
def eval_model(opt, printargs=None, print_parser=None):
"""Evaluates a model.
Arguments:
opt -- tells the evaluation function how to run
print_parser -- if provided, prints the options that are set within the
model after loading the model
"""
if printargs is not None:
print('[ Deprecated Warning: eval_model no longer uses `printargs` ]')
print_parser = printargs
if print_parser is not None:
if print_parser is True and isinstance(opt, ParlaiParser):
print_parser = opt
elif print_parser is False:
print_parser = None
if isinstance(opt, ParlaiParser):
print(
'[ Deprecated Warning: eval_model should be passed opt not Parser ]'
)
opt = opt.parse_args()
random.seed(42)
# Create model and assign it to the specified task
agent = create_agent(opt, requireModelExists=True)
world = create_task(opt, agent)
if print_parser:
# Show arguments after loading model
print_parser.opt = agent.opt
print_parser.print_args()
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = Timer()
tot_time = 0
# Show some example dialogs:
cnt = 0
while not world.epoch_done():
cnt += 1
world.parley()
if opt['display_examples']:
print("---")
print(world.display() + "\n~~")
if log_time.time() > log_every_n_secs:
tot_time += log_time.time()
print(str(int(tot_time)) + "s elapsed: " + str(world.report()))
log_time.reset()
if opt['num_examples'] > 0 and cnt >= opt['num_examples']:
break
if world.epoch_done():
print("EPOCH DONE")
report = world.report()
print(report)
return report
def __init__(self, parser):
opt = parser.parse_args()
# Possibly load from checkpoint
if opt['load_from_checkpoint'] and opt.get(
'model_file') and os.path.isfile(opt['model_file'] +
'.checkpoint'):
opt['init_model'] = opt['model_file'] + '.checkpoint'
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt)
# Create model and assign it to the specified task
self.agent = create_agent(opt)
self.world = create_task(opt, self.agent)
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.max_num_epochs = opt[
'num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt[
'max_train_time'] if opt['max_train_time'] > 0 else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt[
'log_every_n_secs'] > 0 else float('inf')
self.val_every_n_secs = opt['validation_every_n_secs'] if opt[
'validation_every_n_secs'] > 0 else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt[
'save_every_n_secs'] > 0 else float('inf')
self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
self.best_valid = None
if opt.get('model_file') and os.path.isfile(opt['model_file'] +
'.best_valid'):
with open(opt['model_file'] + ".best_valid", 'r') as f:
x = f.readline()
self.best_valid = float(x)
f.close()
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
def run_eval(valid_worlds, opt, datatype, max_exs=-1, write_log=False):
"""
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
"""
if valid_worlds is None:
# This isn't the primary worker, so we can just skip evaluation
return None
print('[ running eval: ' + datatype + ' ]')
timer = Timer()
reports = []
for v_world in valid_worlds:
task_report = _run_single_eval(opt, v_world,
max_exs / len(valid_worlds))
reports.append(task_report)
tasks = [world.opt['task'] for world in valid_worlds]
report = aggregate_task_reports(reports,
tasks,
micro=opt.get('aggregate_micro', True))
metrics = f'{datatype}:{report}'
print(f'[ eval completed in {timer.time():.2f}s ]')
print(metrics)
# write to file
if write_log and opt.get('model_file'):
# Write out metrics
f = open(opt['model_file'] + '.' + datatype, 'a+')
f.write(metrics + '\n')
f.close()
return report
def eval_ppl(opt, build_dict):
"""Evaluates the the perplexity of a model.
See the documentation for this file for more info.
:param opt: option dict
:param build_dict: function for building official dictionary.
note that this function does not use the opt passed into eval_ppl,
but rather should have hardcoded settings for its dictionary.
"""
dict_agent = build_dict()
# create agents
agent = create_agent(opt)
world = create_task(opt, [agent, dict_agent], default_world=PerplexityWorld)
# set up logging
log_time = Timer()
tot_time = 0
while not world.epoch_done():
world.parley() # process an example
if log_time.time() > 1: # log every 1 sec
tot_time += log_time.time()
report = world.report()
print('{}s elapsed, {}%% complete, {}'.format(
int(tot_time),
round_sigfigs(report['total'] / world.num_examples() * 100, 3),
report))
log_time.reset()
print('EPOCH DONE')
tot_time += log_time.time()
final_report = world.report()
print('{}s elapsed: {}'.format(int(tot_time), final_report))
print("============================")
print("FINAL PPL: " +str(final_report['ppl']))
if final_report.get('ppl', 0) == float('inf'):
print('Note: you got inf perplexity. Consider adding (or raising) the '
'minimum probability you assign to each possible word. If you '
'assign zero probability to the correct token in the evaluation '
'vocabulary, you get inf probability immediately.')
def eval_model(parser, printargs=True):
random.seed(42)
opt = parser.parse_args(print_args=False)
nomodel = False
# check to make sure the model file exists
if opt.get('model_file') is None:
nomodel = True
elif not os.path.isfile(opt['model_file']):
raise RuntimeError('WARNING: Model file does not exist, check to make '
'sure it is correct: {}'.format(opt['model_file']))
# Create model and assign it to the specified task
agent = create_agent(opt)
if nomodel and hasattr(agent, 'load'):
# double check that we didn't forget to set model_file on loadable model
raise RuntimeError('Stopping evaluation because model_file unset but '
'model has a `load` function.')
world = create_task(opt, agent)
# Show arguments after loading model
parser.opt = agent.opt
if (printargs):
parser.print_args()
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = Timer()
tot_time = 0
# Show some example dialogs:
cnt = 0
while not world.epoch_done():
cnt += 1
world.parley()
if opt['display_examples']:
print("---")
print(world.display() + "\n~~")
if log_time.time() > log_every_n_secs:
tot_time += log_time.time()
print(str(int(tot_time)) + "s elapsed: " + str(world.report()))
log_time.reset()
if opt['num_examples'] > 0 and cnt >= opt['num_examples']:
break
if world.epoch_done():
print("EPOCH DONE")
report = world.report()
print(report)
return report
def eval_ppl(opt):
"""Evaluates the the perplexity and f1 of a model (and hits@1 if model has
ranking enabled.
"""
dict_agent = build_dict()
# create agents
agent = create_agent(opt)
world = create_task(opt, [agent, dict_agent],
default_world=PerplexityWorld)
world.dict = dict_agent
# set up logging
log_time = Timer()
tot_time = 0
while not world.epoch_done():
world.parley() # process an example
if log_time.time() > 1: # log every 1 sec
tot_time += log_time.time()
report = world.report()
print('{}s elapsed, {}%% complete, {}'.format(
int(tot_time),
round_sigfigs(report['total'] / world.num_examples() * 100, 3),
report))
log_time.reset()
if world.epoch_done():
print('EPOCH DONE')
tot_time += log_time.time()
final_report = world.report()
print('{}s elapsed: {}'.format(int(tot_time), final_report))
print("============================")
print("FINAL PPL: " + str(final_report['ppl']))
if final_report.get('ppl', 0) == float('inf'):
print('Note: you got inf perplexity. Consider adding (or raising) the '
'minimum probability you assign to each possible word. If you '
'assign zero probability to the correct token in the evaluation '
'vocabulary, you get inf probability immediately.')
def __init__(self, opt):
# if python is called from a non-interactive shell, like a bash script,
# it will by-default ignore SIGINTs, and KeyboardInterrupt exceptions are
# not produced. This line brings them back
signal.signal(signal.SIGINT, signal.default_int_handler)
if isinstance(opt, ParlaiParser):
print(
'[ Deprecated Warning: TrainLoop should be passed opt not Parser ]'
)
opt = opt.parse_args()
# Possibly load from checkpoint
trainstats_suffix = '.trainstats' # we might load training statistics from here
if opt['load_from_checkpoint'] and opt.get(
'model_file') and os.path.isfile(opt['model_file'] +
'.checkpoint'):
opt['init_model'] = opt['model_file'] + '.checkpoint'
trainstats_suffix = '.checkpoint.trainstats'
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
# If data built via pytorch data teacher, we need to load prebuilt dict
if opt.get('pytorch_teacher_task'):
opt['dict_file'] = get_pyt_dict_file(opt)
elif opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt, skip_if_built=True)
# Create model and assign it to the specified task
self.agent = create_agent(opt) #specify model such as seq2seq
self.world = create_task(opt, self.agent) # bacthworld or other world
# set up timers
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.max_num_epochs = opt[
'num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt['max_train_time'] if opt['max_train_time'] > 0 \
else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 \
else float('inf')
self.val_every_n_secs = \
opt['validation_every_n_secs'] if opt['validation_every_n_secs'] > 0 \
else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt['save_every_n_secs'] \
> 0 else float('inf')
self.val_every_n_epochs = \
opt['validation_every_n_epochs'] if opt['validation_every_n_epochs'] > 0 \
else float('inf')
# smart defaults for --validation-metric-mode
if opt['validation_metric'] in {'loss', 'ppl', 'mean_rank'}:
opt['validation_metric_mode'] = 'min'
elif opt['validation_metric'] in {
'accuracy', 'hits@1', 'hits@5', 'f1', 'bleu'
}:
opt['validation_metric_mode'] = 'max'
if opt.get('validation_metric_mode') is None:
opt['validation_metric_mode'] = 'max'
self.last_valid_epoch = 0
self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
self.best_valid = None
if opt.get('model_file') and os.path.isfile(opt['model_file'] +
'.best_valid'):
with open(opt['model_file'] + ".best_valid", 'r') as f:
x = f.readline()
self.best_valid = float(x)
f.close()
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
# we may have been preempted, make sure we note that amount
self._preempted_epochs = 0.0
if (opt.get('model_file')
and os.path.isfile(opt['model_file'] + trainstats_suffix)):
# looks like we were preempted. make sure we load up our total
# training stats, etc
with open(opt['model_file'] + trainstats_suffix) as ts:
obj = json.load(ts)
self._preempted_epochs = obj.get('total_epochs', 0)
self.train_time.total = obj.get('train_time', 0)
self.impatience = obj.get('impatience', 0)
if opt['tensorboard_log'] is True:
self.writer = TensorboardLogger(opt)
class TrainLoop():
def __init__(self, opt):
# if python is called from a non-interactive shell, like a bash script,
# it will by-default ignore SIGINTs, and KeyboardInterrupt exceptions are
# not produced. This line brings them back
signal.signal(signal.SIGINT, signal.default_int_handler)
if isinstance(opt, ParlaiParser):
print(
'[ Deprecated Warning: TrainLoop should be passed opt not Parser ]'
)
opt = opt.parse_args()
# Possibly load from checkpoint
trainstats_suffix = '.trainstats' # we might load training statistics from here
if opt['load_from_checkpoint'] and opt.get(
'model_file') and os.path.isfile(opt['model_file'] +
'.checkpoint'):
opt['init_model'] = opt['model_file'] + '.checkpoint'
trainstats_suffix = '.checkpoint.trainstats'
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
# If data built via pytorch data teacher, we need to load prebuilt dict
if opt.get('pytorch_teacher_task'):
opt['dict_file'] = get_pyt_dict_file(opt)
elif opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt, skip_if_built=True)
# Create model and assign it to the specified task
self.agent = create_agent(opt) #specify model such as seq2seq
self.world = create_task(opt, self.agent) # bacthworld or other world
# set up timers
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.max_num_epochs = opt[
'num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt['max_train_time'] if opt['max_train_time'] > 0 \
else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 \
else float('inf')
self.val_every_n_secs = \
opt['validation_every_n_secs'] if opt['validation_every_n_secs'] > 0 \
else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt['save_every_n_secs'] \
> 0 else float('inf')
self.val_every_n_epochs = \
opt['validation_every_n_epochs'] if opt['validation_every_n_epochs'] > 0 \
else float('inf')
# smart defaults for --validation-metric-mode
if opt['validation_metric'] in {'loss', 'ppl', 'mean_rank'}:
opt['validation_metric_mode'] = 'min'
elif opt['validation_metric'] in {
'accuracy', 'hits@1', 'hits@5', 'f1', 'bleu'
}:
opt['validation_metric_mode'] = 'max'
if opt.get('validation_metric_mode') is None:
opt['validation_metric_mode'] = 'max'
self.last_valid_epoch = 0
self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
self.best_valid = None
if opt.get('model_file') and os.path.isfile(opt['model_file'] +
'.best_valid'):
with open(opt['model_file'] + ".best_valid", 'r') as f:
x = f.readline()
self.best_valid = float(x)
f.close()
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
# we may have been preempted, make sure we note that amount
self._preempted_epochs = 0.0
if (opt.get('model_file')
and os.path.isfile(opt['model_file'] + trainstats_suffix)):
# looks like we were preempted. make sure we load up our total
# training stats, etc
with open(opt['model_file'] + trainstats_suffix) as ts:
obj = json.load(ts)
self._preempted_epochs = obj.get('total_epochs', 0)
self.train_time.total = obj.get('train_time', 0)
self.impatience = obj.get('impatience', 0)
if opt['tensorboard_log'] is True:
self.writer = TensorboardLogger(opt)
def save_model(self, suffix=None):
if not is_primary_worker():
# never do IO as a non-primary worker
return
if not self.opt.get('model_file'):
# nothing to save to, just exit
return
fn = self.opt['model_file']
if suffix:
fn += suffix
while True:
# don't ever let a ctrl-c interrupt saving
try:
self.agent.save(fn)
self._save_train_stats(suffix)
break
except KeyboardInterrupt:
pass
def _save_train_stats(self, suffix=None):
fn = self.opt['model_file']
if suffix:
fn += suffix
fn += '.trainstats'
with open(fn, 'w') as f:
json.dump(
{
'train_time':
self.train_time.time(),
'total_epochs':
(self._preempted_epochs +
num_workers() * self.world.get_total_epochs()),
'impatience':
self.impatience,
}, f)
def validate(self):
opt = self.opt
if self.valid_world is None:
# we need to load the world now
self.valid_world = _maybe_load_eval_world(self.agent, opt, 'valid')
# run evaluation on valid set
valid_report = sync_object(
run_eval(self.valid_world, opt, 'valid', opt['validation_max_exs'],
True))
# logging
if opt['tensorboard_log'] is True and is_primary_worker():
self.writer.add_metrics('valid', int(self.train_time.time()),
valid_report)
# saving
if (opt.get('model_file') and opt.get('save_after_valid')
and is_primary_worker()):
print("[ saving model checkpoint: " + opt['model_file'] +
".checkpoint ]")
self.save_model('.checkpoint')
# 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
if '/' in opt['validation_metric']:
# if you are multitasking and want your validation metric to be
# a metric specific to a subtask, specify your validation metric
# as -vmt subtask/metric
subtask = opt['validation_metric'].split('/')[0]
validation_metric = opt['validation_metric'].split('/')[1]
new_valid = valid_report['tasks'][subtask][validation_metric]
else:
new_valid = valid_report[opt['validation_metric']]
# 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):
print('[ new best {}: {}{} ]'.format(
opt['validation_metric'], new_valid,
' (previous best was {})'.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') and is_primary_worker():
print("[ saving best valid model: " + opt['model_file'] + " ]")
self.save_model()
print("[ saving best valid metric: " + opt['model_file'] +
".best_valid ]")
save_best_valid(opt['model_file'], self.best_valid)
self.saved = True
if (opt['validation_metric'] == 'accuracy'
and self.best_valid >= opt['validation_cutoff']):
print('[ task solved! stopping. ]')
return True
else:
self.impatience += 1
print('[ did not beat best {}: {} impatience: {} ]'.format(
opt['validation_metric'], round(self.best_valid, 4),
self.impatience))
self.validate_time.reset()
# check if we are out of patience
if (opt['validation_patience'] > 0
and self.impatience >= opt['validation_patience']):
print('[ ran out of patience! stopping training. ]')
return True
return False
def _average_dicts(self, all_versions):
# instead of a list-of-dicts with like keys, make a dict-of-lists with
# keys to reduce
to_reduce = {}
for d in all_versions:
for k, v in d.items():
to_reduce.setdefault(k, []).append(v)
# now perform the reduction
finalized = {}
for k, values in to_reduce.items():
if k == 'exs' or k == 'total_skipped_batches':
# sum across workers
finalized[k] = np.sum(values)
elif isinstance(values[0], dict):
# do the same procedure recursively
finalized[k] = self._average_dicts(values)
else:
# all other cases, take the mean across the workers
finalized[k] = np.mean(values)
return finalized
def _sync_training_metrics(self, metrics):
"""
Sync training metrics across workers. A handful of special cases are handled
as exceptions, and the remaining metrics are simply averaged across workers.
"""
if not is_distributed():
# nothing special needed
return metrics
all_versions = all_gather_list(metrics)
return self._average_dicts(all_versions)
def _nice_format(self, dictionary):
rounded = {}
for k, v in dictionary.items():
if isinstance(v, dict):
rounded[k] = self._nice_format(v)
elif isinstance(v, float):
rounded[k] = round_sigfigs(v, 4)
else:
rounded[k] = v
return rounded
def _compute_eta(self, epochs_completed, time_elapsed):
"""
Computes the estimated seconds remaining in training.
:param float epochs_completed: number of epochs already completed.
:param float time_elapsed: total time spent already, in seconds.
:return: ETA in seconds, or None if not computable
"""
# start off with no estimate
eta = None
# Determine time_left and num_epochs
max_epochs = self.opt.get('num_epochs', 0)
if max_epochs > 0 and epochs_completed > 0:
epoch_progress = epochs_completed / max_epochs
eta = (1 - epoch_progress) * time_elapsed / epoch_progress
max_training_time = self.opt.get('max_training_time', -1)
if max_training_time > 0:
time_left = max_training_time - time_elapsed
if eta is None or time_left < eta:
eta = time_left
return eta
def log(self):
opt = self.opt
if opt['display_examples']:
print(self.world.display() + '\n~~')
logs = []
# get report
train_report = self._sync_training_metrics(self.world.report())
self.world.reset_metrics()
# time elapsed
logs.append('time:{}s'.format(np.floor(self.train_time.time())))
logs.append('total_exs:{}'.format(self._total_exs))
if self._total_epochs >= 0:
# only if it's unbounded
logs.append('epochs:{}'.format(round(self._total_epochs, 2)))
time_left = self._compute_eta(self._total_epochs,
self.train_time.time())
if time_left is not None:
logs.append('time_left:{}s'.format(max(0, np.ceil(time_left))))
log = '[ {} ] {}'.format(' '.join(logs),
self._nice_format(train_report))
print(log)
self.log_time.reset()
if opt['tensorboard_log'] is True and is_primary_worker():
self.writer.add_metrics('train', self._total_exs, train_report)
def train(self):
if is_distributed():
warn_once(
"Distributed training outputs average-per-worker metrics during "
"training, and may be slightly distorted. Validation/test are "
"unadulterated.")
opt = self.opt
world = self.world
with world:
while True:
# do one example / batch of examples
world.parley()
self.parleys += 1
# print(world.display())
# get the total training examples done, compute epochs
self._total_epochs = (
self._preempted_epochs +
num_workers() * self.world.get_total_epochs())
exs_per_epoch = self.world.num_examples()
self._total_exs = int(
np.round(self._total_epochs * exs_per_epoch))
# and use the primary worker's timings for everything
train_time, log_time, validate_time = sync_object(
(self.train_time.time(), self.log_time.time(),
self.validate_time.time()))
# check counters and timers
if self._total_epochs >= self.max_num_epochs:
self.log()
print(
'[ num_epochs completed:{} time elapsed:{}s ]'.format(
self.max_num_epochs, train_time))
break
if train_time > self.max_train_time:
print('[ max_train_time elapsed:{}s ]'.format(train_time))
break
if log_time > self.log_every_n_secs:
self.log()
if (validate_time > self.val_every_n_secs
or self._total_epochs - self.last_valid_epoch >=
self.val_every_n_epochs):
stop_training = self.validate()
self.last_valid_epoch = self._total_epochs
if stop_training:
break
if (self.save_time.time() > self.save_every_n_secs
and opt.get('model_file') and is_primary_worker()):
print("[ saving model checkpoint: {}.checkpoint".format(
opt['model_file']))
self.save_model('.checkpoint')
self.save_time.reset()
if not self.saved and is_primary_worker():
# save agent
self.save_model()
elif opt.get('model_file'):
# reload best validation model
self.agent = create_agent(opt)
valid_world = _maybe_load_eval_world(self.agent, opt, 'valid')
v_report = run_eval(valid_world, opt, 'valid', write_log=True)
test_world = _maybe_load_eval_world(self.agent, opt, 'test')
t_report = run_eval(test_world, opt, 'test', write_log=True)
if valid_world:
valid_world.shutdown()
if test_world:
test_world.shutdown()
return v_report, t_report
def eval_ppl(opt, build_dict=None, dict_file=None):
"""Evaluates the the perplexity of a model.
This uses a dictionary which implements the following functions:
- tokenize(text): splits string up into list of tokens
- __in__(text): checks whether dictionary contains a token
- keys(): returns an iterator over all tokens in the dictionary
:param opt: option dict
:param build_dict: function which returns a dictionary class implementing
the functions above.
:param dict_file: file used when loading the dictionary class set via the
"dictionary_class" argument (defaults to
parlai.core.dict:DictionaryAgent).
Either build_dict or dict_file must be set (both default to None) to
determine the dictionary used for the evaluation.
"""
if not build_dict and not dict_file:
raise RuntimeError('eval_ppl script either needs a dictionary build '
'function or a dictionary file.')
if build_dict:
dict_agent = build_dict()
else:
dict_opt = copy.deepcopy(opt)
dict_opt['model'] = dict_opt.get(
'dictionary_class', 'parlai.core.dict:DictionaryAgent'
)
dict_opt['model_file'] = dict_file
if 'override' in dict_opt:
del dict_opt['override']
dict_agent = create_agent(dict_opt, requireModelExists=True)
# create agents
agent = create_agent(opt)
world = create_task(opt, [agent, dict_agent], default_world=PerplexityWorld)
# set up logging
log_time = Timer()
tot_time = 0
while not world.epoch_done():
world.parley() # process an example
if log_time.time() > 1: # log every 1 sec
tot_time += log_time.time()
report = world.report()
print('{}s elapsed, {}%% complete, {}'.format(
int(tot_time),
round_sigfigs(report['exs'] / world.num_examples() * 100, 3),
report))
log_time.reset()
print('EPOCH DONE')
tot_time += log_time.time()
final_report = world.report()
print('{}s elapsed: {}'.format(int(tot_time), final_report))
print("============================")
print("FINAL PPL: " + str(final_report['ppl']))
if final_report.get('ppl', 0) == float('inf'):
print('Note: you got inf perplexity. Consider adding (or raising) the '
'minimum probability you assign to each possible word. If you '
'assign zero probability to the correct token in the evaluation '
'vocabulary, you get inf probability immediately.')
def main():
# Get command line arguments
parser = ParlaiParser(True, True)
train = parser.add_argument_group('Training Loop Arguments')
train.add_argument('-et',
'--evaltask',
help=('task to use for valid/test (defaults to the ' +
'one used for training if not set)'))
train.add_argument('-d', '--display-examples', type='bool', default=False)
train.add_argument('-e', '--num-epochs', type=float, default=-1)
train.add_argument('-ttim', '--max-train-time', type=float, default=-1)
train.add_argument('-ltim', '--log-every-n-secs', type=float, default=2)
train.add_argument('-lparl',
'--log-every-n-parleys',
type=int,
default=100)
train.add_argument('-vtim',
'--validation-every-n-secs',
type=float,
default=-1)
train.add_argument('-vparl',
'--validation-every-n-parleys',
type=int,
default=-1)
train.add_argument('-vme',
'--validation-max-exs',
type=int,
default=-1,
help='max examples to use during validation (default ' +
'-1 uses all)')
train.add_argument(
'-vp',
'--validation-patience',
type=int,
default=5,
help=('number of iterations of validation where result ' +
'does not improve before we stop training'))
train.add_argument(
'-vmt',
'--validation-metric',
default='accuracy',
help='key into report table for selecting best validation')
train.add_argument('-dbf',
'--dict-build-first',
type='bool',
default=True,
help='build dictionary first before training agent')
opt = parser.parse_args()
# Set logging
logger = logging.getLogger('DrQA')
logger.setLevel(logging.INFO)
fmt = logging.Formatter('%(asctime)s: %(message)s', '%m/%d/%Y %I:%M:%S %p')
console = logging.StreamHandler()
console.setFormatter(fmt)
logger.addHandler(console)
if 'log_file' in opt:
logfile = logging.FileHandler(opt['log_file'], 'w')
logfile.setFormatter(fmt)
logger.addHandler(logfile)
logger.info('[ COMMAND: %s ]' % ' '.join(sys.argv))
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
logger.info("[ building dictionary first... ]")
build_dict.build_dict(opt)
# Create model and assign it to the specified task
agent = create_agent(opt)
world = create_task(opt, agent)
train_time = Timer()
validate_time = Timer()
log_time = Timer()
logger.info('[ training... ]')
parleys = 0
total_exs = 0
max_exs = opt['num_epochs'] * len(world)
max_parleys = math.ceil(max_exs / opt['batchsize'])
best_valid = 0
impatience = 0
saved = False
valid_world = None
best_accuracy = 0
while True:
world.parley()
parleys += 1
if opt['num_epochs'] > 0 and parleys >= max_parleys:
logger.info('[ num_epochs completed: {} ]'.format(
opt['num_epochs']))
break
if opt['max_train_time'] > 0 and train_time.time(
) > opt['max_train_time']:
logger.info('[ max_train_time elapsed: {} ]'.format(
train_time.time()))
break
# instead of every_n_secs, use n_parleys
# if opt['log_every_n_secs'] > 0 and log_time.time() > opt['log_every_n_secs']:
if opt['log_every_n_parleys'] > 0 and parleys % opt[
'log_every_n_parleys'] == 0:
if opt['display_examples']:
logger.info(world.display() + '\n~~')
logs = []
# time elapsed
logs.append('time:{}s'.format(math.floor(train_time.time())))
logs.append('parleys:{}'.format(parleys))
# get report and update total examples seen so far
if hasattr(agent, 'report'):
train_report = agent.report()
agent.reset_metrics()
else:
train_report = world.report()
world.reset_metrics()
if hasattr(train_report, 'get') and train_report.get('total'):
total_exs += train_report['total']
logs.append('total_exs:{}'.format(total_exs))
# check if we should log amount of time remaining
time_left = None
if opt['num_epochs'] > 0:
exs_per_sec = train_time.time() / total_exs
time_left = (max_exs - total_exs) * exs_per_sec
if opt['max_train_time'] > 0:
other_time_left = opt['max_train_time'] - train_time.time()
if time_left is not None:
time_left = min(time_left, other_time_left)
else:
time_left = other_time_left
if time_left is not None:
logs.append('time_left:{}s'.format(math.floor(time_left)))
# join log string and add full metrics report to end of log
log = '[ {} ] {}'.format(' '.join(logs), train_report)
logger.info(log)
log_time.reset()
# instead of every_n_secs, use n_parleys
# if (opt['validation_every_n_secs'] > 0 and
# validate_time.time() > opt['validation_every_n_secs']):
if (opt['validation_every_n_parleys'] > 0
and parleys % opt['validation_every_n_parleys'] == 0):
#if True :
valid_report, valid_world = run_eval(agent,
opt,
'valid',
opt['validation_max_exs'],
logger=logger)
#if False :
if valid_report[opt['validation_metric']] > best_accuracy:
best_accuracy = valid_report[opt['validation_metric']]
impatience = 0
logger.info('[ new best accuracy: ' + str(best_accuracy) +
' ]')
world.save_agents()
saved = True
if best_accuracy == 1:
logger.info('[ task solved! stopping. ]')
break
#if True:
else:
opt['learning_rate'] *= 0.5
agent.model.set_lrate(opt['learning_rate'])
logger.info('[ Decrease learning_rate %.2e]' %
opt['learning_rate'])
impatience += 1
logger.info(
'[ did not beat best accuracy: {} impatience: {} ]'.format(
round(best_accuracy, 4), impatience))
validate_time.reset()
if opt['validation_patience'] > 0 and impatience >= opt[
'validation_patience']:
logger.info('[ ran out of patience! stopping training. ]')
break
if opt['learning_rate'] < pow(10, -6):
logger.info(
'[ learning_rate < pow(10,-6) ! stopping training. ]')
break
world.shutdown()
if not saved:
world.save_agents()
else:
# reload best validation model
agent = create_agent(opt)
run_eval(agent, opt, 'valid', write_log=True, logger=logger)
run_eval(agent, opt, 'test', write_log=True, logger=logger)
def main(parser):
opt = parser.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict.build_dict(opt)
# Create model and assign it to the specified task
agent = create_agent(opt)
world = create_task(opt, agent)
train_time = Timer()
validate_time = Timer()
log_time = Timer()
print('[ training... ]')
parleys = 0
total_exs = 0
max_exs = opt['num_epochs'] * len(world)
max_parleys = math.ceil(max_exs / opt['batchsize'])
best_valid = 0
impatience = 0
saved = False
valid_world = None
with world:
while True:
world.parley()
parleys += 1
if opt['num_epochs'] > 0 and parleys >= max_parleys:
print('[ num_epochs completed: {} ]'.format(opt['num_epochs']))
break
if opt['max_train_time'] > 0 and train_time.time() > opt['max_train_time']:
print('[ max_train_time elapsed: {} ]'.format(train_time.time()))
break
if opt['log_every_n_secs'] > 0 and log_time.time() > opt['log_every_n_secs']:
if opt['display_examples']:
print(world.display() + '\n~~')
logs = []
# time elapsed
logs.append('time:{}s'.format(math.floor(train_time.time())))
logs.append('parleys:{}'.format(parleys))
# get report and update total examples seen so far
if hasattr(agent, 'report'):
train_report = agent.report()
agent.reset_metrics()
else:
train_report = world.report()
world.reset_metrics()
if hasattr(train_report, 'get') and train_report.get('total'):
total_exs += train_report['total']
logs.append('total_exs:{}'.format(total_exs))
# check if we should log amount of time remaining
time_left = None
if opt['num_epochs'] > 0:
exs_per_sec = train_time.time() / total_exs
time_left = (max_exs - total_exs) * exs_per_sec
if opt['max_train_time'] > 0:
other_time_left = opt['max_train_time'] - train_time.time()
if time_left is not None:
time_left = min(time_left, other_time_left)
else:
time_left = other_time_left
if time_left is not None:
logs.append('time_left:{}s'.format(math.floor(time_left)))
# join log string and add full metrics report to end of log
log = '[ {} ] {}'.format(' '.join(logs), train_report)
print(log)
log_time.reset()
if (opt['validation_every_n_secs'] > 0 and
validate_time.time() > opt['validation_every_n_secs']):
valid_report, valid_world = run_eval(
agent, opt, 'valid', opt['validation_max_exs'],
valid_world=valid_world)
if valid_report[opt['validation_metric']] > best_valid:
best_valid = valid_report[opt['validation_metric']]
impatience = 0
print('[ new best {}: {} ]'.format(
opt['validation_metric'], best_valid))
world.save_agents()
saved = True
if opt['validation_metric'] == 'accuracy' and best_valid > 99.5:
print('[ task solved! stopping. ]')
break
else:
impatience += 1
print('[ did not beat best {}: {} impatience: {} ]'.format(
opt['validation_metric'], round(best_valid, 4),
impatience))
validate_time.reset()
if opt['validation_patience'] > 0 and impatience >= opt['validation_patience']:
print('[ ran out of patience! stopping training. ]')
break
if not saved:
# save agent
world.save_agents()
elif opt.get('model_file'):
# reload best validation model
agent = create_agent(opt)
run_eval(agent, opt, 'valid', write_log=True)
run_eval(agent, opt, 'test', write_log=True)
class World(object):
"""Empty parent providing null definitions of API functions for Worlds.
All children can override these to provide more detailed functionality."""
def __init__(self, opt, agents=None, shared=None):
self.id = opt['task']
self.opt = copy.deepcopy(opt)
if shared:
# Create agents based on shared data.
self.agents = create_agents_from_shared(shared['agents'])
else:
# Add passed in agents to world directly.
self.agents = agents
self.max_exs = None
self.total_exs = 0
self.total_epochs = 0
self.total_parleys = 0
self.time = Timer()
def parley(self):
"""The main method, that does one step of actions for the agents
in the world. This is empty in the base class.
"""
pass
def getID(self):
"""Return the name of the world, typically the task the world encodes."""
return self.id
def display(self):
"""Returns a string describing the current state of the world.
Useful for monitoring and debugging.
By default, display the messages between the agents."""
if not hasattr(self, 'acts'):
return ''
return display_messages(self.acts)
def episode_done(self):
"""Whether the episode is done or not."""
return False
def epoch_done(self):
"""Whether the epoch is done or not.
Not all worlds have the notion of an epoch, but this is useful
for fixed training, validation or test sets.
"""
return False
def share(self):
shared_data = {}
shared_data['world_class'] = type(self)
shared_data['opt'] = self.opt
shared_data['agents'] = self._share_agents()
return shared_data
def _share_agents(self):
"""Create shared data for agents so other classes can create the same
agents without duplicating the data (i.e. sharing parameters).
"""
if not hasattr(self, 'agents'):
return None
shared_agents = [a.share() for a in self.agents]
return shared_agents
def get_agents(self):
"""Return the list of agents."""
return self.agents
def get_acts(self):
"""Return the last act of each agent."""
return self.acts
def get_time(self):
"""Return total training time"""
return self.time.time()
def get_total_exs(self):
"""Return total amount of examples seen by world."""
return self.total_exs
def get_total_epochs(self):
"""Return total amount of epochs on which the world has trained."""
return self.total_epochs
def __enter__(self):
"""Empty enter provided for use with ``with`` statement.
e.g:
.. code-block:: python
with World() as world:
for n in range(10):
n.parley()
"""
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
"""After ``with`` statement, call shutdown."""
silent_exit = isinstance(exc_value, KeyboardInterrupt)
self.shutdown()
return silent_exit
def num_examples(self):
return 0
def num_episodes(self):
return 0
def reset(self):
for a in self.agents:
a.reset()
self.max_exs = None
self.total_exs = 0
self.total_epochs = 0
self.total_parleys = 0
self.time.reset()
def reset_metrics(self):
for a in self.agents:
a.reset_metrics()
def shutdown(self):
"""Perform any cleanup, if appropriate."""
pass
def update_counters(self):
"""Update how many epochs have completed"""
self.total_parleys += 1
if self.max_exs is None:
if ('num_epochs' in self.opt and self.opt['num_epochs'] > 0):
self.max_exs = self.num_examples(
) * self.opt['num_epochs'] if self.num_examples() else -1
else:
self.max_exs = -1
# when we know the size of the data
if self.max_exs > 0:
self.total_epochs = self.total_parleys * self.opt.get(
'batchsize', 1) / self.num_examples()
# when we do not know the size of the data
else:
if self.epoch_done():
self.total_epochs += 1
def main():
# Get command line arguments
parser = ParlaiParser(True, True)
train = parser.add_argument_group('Training Loop Arguments')
train.add_argument('-et', '--evaltask',
help=('task to use for valid/test (defaults to the '
'one used for training if not set)'))
train.add_argument('-d', '--display-examples',
type='bool', default=False)
train.add_argument('-e', '--num-epochs', type=float, default=-1)
train.add_argument('-ttim', '--max-train-time',
type=float, default=-1)
train.add_argument('-ltim', '--log-every-n-secs',
type=float, default=2)
train.add_argument('-vtim', '--validation-every-n-secs',
type=float, default=-1)
train.add_argument('-vme', '--validation-max-exs',
type=int, default=-1,
help='max examples to use during validation (default '
'-1 uses all)')
train.add_argument('-vp', '--validation-patience',
type=int, default=5,
help=('number of iterations of validation where result'
' does not improve before we stop training'))
train.add_argument('-vmt', '--validation-metric', default='accuracy',
help='key into report table for selecting best '
'validation')
train.add_argument('-dbf', '--dict-build-first',
type='bool', default=True,
help='build dictionary first before training agent')
opt = parser.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict.build_dict(opt)
# Create model and assign it to the specified task
agent = create_agent(opt)
world = create_task(opt, agent)
train_time = Timer()
validate_time = Timer()
log_time = Timer()
print('[ training... ]')
parleys = 0
total_exs = 0
max_exs = opt['num_epochs'] * len(world)
max_parleys = math.ceil(max_exs / opt['batchsize'])
best_valid = 0
impatience = 0
saved = False
valid_world = None
while True:
world.parley()
parleys += 1
if opt['num_epochs'] > 0 and parleys >= max_parleys:
print('[ num_epochs completed: {} ]'.format(opt['num_epochs']))
break
if opt['max_train_time'] > 0 and train_time.time() > opt['max_train_time']:
print('[ max_train_time elapsed: {} ]'.format(train_time.time()))
break
if opt['log_every_n_secs'] > 0 and log_time.time() > opt['log_every_n_secs']:
if opt['display_examples']:
print(world.display() + '\n~~')
logs = []
# time elapsed
logs.append('time:{}s'.format(math.floor(train_time.time())))
logs.append('parleys:{}'.format(parleys))
# get report and update total examples seen so far
if hasattr(agent, 'report'):
train_report = agent.report()
agent.reset_metrics()
else:
train_report = world.report()
world.reset_metrics()
if hasattr(train_report, 'get') and train_report.get('total'):
total_exs += train_report['total']
logs.append('total_exs:{}'.format(total_exs))
# check if we should log amount of time remaining
time_left = None
if opt['num_epochs'] > 0:
exs_per_sec = train_time.time() / total_exs
time_left = (max_exs - total_exs) * exs_per_sec
if opt['max_train_time'] > 0:
other_time_left = opt['max_train_time'] - train_time.time()
if time_left is not None:
time_left = min(time_left, other_time_left)
else:
time_left = other_time_left
if time_left is not None:
logs.append('time_left:{}s'.format(math.floor(time_left)))
# join log string and add full metrics report to end of log
log = '[ {} ] {}'.format(' '.join(logs), train_report)
print(log)
log_time.reset()
if (opt['validation_every_n_secs'] > 0 and
validate_time.time() > opt['validation_every_n_secs']):
valid_report, valid_world = run_eval(
agent, opt, 'valid', opt['validation_max_exs'],
valid_world=valid_world)
if valid_report[opt['validation_metric']] > best_valid:
best_valid = valid_report[opt['validation_metric']]
impatience = 0
print('[ new best {}: {} ]'.format(
opt['validation_metric'], best_valid))
world.save_agents()
saved = True
if opt['validation_metric'] == 'accuracy' and best_valid == 1:
print('[ task solved! stopping. ]')
break
else:
impatience += 1
print('[ did not beat best {}: {} impatience: {} ]'.format(
opt['validation_metric'], round(best_valid, 4),
impatience))
validate_time.reset()
if opt['validation_patience'] > 0 and impatience >= opt['validation_patience']:
print('[ ran out of patience! stopping training. ]')
break
world.shutdown()
if not saved:
world.save_agents()
else:
# reload best validation model
agent = create_agent(opt)
run_eval(agent, opt, 'valid', write_log=True)
run_eval(agent, opt, 'test', write_log=True)
def test_timer(self):
t = Timer()
elapsed = t.stop().time()
assert elapsed > 0
same = t.time()
assert elapsed == same
t.resume()
time.sleep(0.1)
more = t.time()
assert more > elapsed
other = Timer()
less = other.reset().time()
assert less > 0
assert less < t.time()
def __init__(self, opt, shared=None):
opt = copy.deepcopy(opt)
super().__init__(opt, shared)
self.use_cuda = not opt['no_cuda'] and torch.cuda.is_available()
self.is_combine_attr = (hasattr(self, 'other_task_datafiles')
and self.other_task_datafiles)
self.random_policy = opt.get('random_policy', False)
self.count_sample = opt.get('count_sample', False)
self.anti = opt.get('anti', False)
if self.random_policy:
random.seed(17)
if not shared:
if not self.stream and opt.get('pace_by', 'sample') == 'bucket':
score_list = [episode[0][2] for episode in self.data.data]
assert score_list == sorted(score_list)
num_buckets = opt.get('num_buckets',
int(self.num_episodes() / 10))
lb_indices = [
int(len(score_list) * i / num_buckets)
for i in range(num_buckets)
]
lbs = [score_list[idx] for idx in lb_indices]
bucket_ids = [
self.sort_into_bucket(ctrl_val, lbs)
for ctrl_val in score_list
]
bucket_cnt = [0 for _ in range(num_buckets)]
for i in range(num_buckets):
bucket_cnt[i] = bucket_ids.count(i)
self.bucket_cnt = bucket_cnt
self.lastYs = [None] * self.bsz
# build multiple task data
self.tasks = [self.data]
if self.is_combine_attr:
print('[ build multiple task data ... ]')
for datafile in self.other_task_datafiles:
task_opt = copy.deepcopy(opt)
task_opt['datafile'] = datafile
self.tasks.append(
DialogData(task_opt,
data_loader=self.setup_data,
cands=self.label_candidates()))
print('[ build multiple task data done! ]')
# record the selections of each subtasks
self.subtasks = opt['subtasks'].split(':')
self.subtask_counter = OrderedDict()
self.p_selections = OrderedDict()
self.c_selections = OrderedDict()
for t in self.subtasks:
self.subtask_counter[t] = 0
self.p_selections[t] = []
self.c_selections[t] = []
if self.count_sample and not self.stream:
self.sample_counter = OrderedDict()
for idx, t in enumerate(self.subtasks):
self.sample_counter[t] = [
0 for _ in self.tasks[idx].data
]
# setup the tensorboard log
if opt['tensorboard_log_teacher'] is True:
opt['tensorboard_tag'] = 'task'
teacher_metrics = 'reward,policy_loss,critic_loss,mean_advantage_reward,action_ent'.split(
',')
opt['tensorboard_metrics'] = ','.join(
opt['tensorboard_metrics'].split(',') + teacher_metrics)
self.writer = TensorboardLogger(opt)
else:
self.lastYs = shared['lastYs']
self.tasks = shared['tasks']
if not self.stream and opt.get('pace_by', 'sample') == 'bucket':
self.bucket_cnt = shared['bucket_cnt']
if 'writer' in shared:
self.writer = shared['writer']
if 'subtask_counter' in shared:
self.subtask_counter = shared['subtask_counter']
if 'p_selections' in shared:
self.p_selections = shared['p_selections']
if 'c_selections' in shared:
self.c_selections = shared['c_selections']
# build the policy net, criterion and optimizer here
self.state_dim = 32 + len(self.tasks) # hand-craft features
self.action_dim = len(self.tasks)
if not shared:
self.policy = PolicyNet(self.state_dim, self.action_dim)
self.critic = CriticNet(self.state_dim, self.action_dim)
init_teacher = get_init_teacher(opt, shared)
if init_teacher is not None:
# load teacher parameters if available
print('[ Loading existing teacher params from {} ]'
''.format(init_teacher))
states = self.load(init_teacher)
else:
states = {}
else:
self.policy = shared['policy']
self.critic = shared['critic']
states = shared['states']
if (
# only build an optimizer if we're training
'train' in opt.get('datatype', '') and
# and this is the main model
shared is None):
# for policy net
self.optimizer = self.init_optim(
[p for p in self.policy.parameters() if p.requires_grad],
lr=opt['learningrate_teacher'],
optim_states=states.get('optimizer'),
saved_optim_type=states.get('optimizer_type'))
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer,
'min',
factor=0.8, # 0.5 --> 0.8
patience=5, # 3 -- > 5
verbose=True)
if 'lr_scheduler' in states:
self.scheduler.load_state_dict(states['lr_scheduler'])
# for critic net
self.optimizer_critic = self.init_optim(
[p for p in self.critic.parameters() if p.requires_grad],
lr=opt['learningrate_teacher_critic'],
optim_states=states.get('optimizer_critic'),
saved_optim_type=states.get('optimizer_type'))
self.scheduler_critic = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer_critic,
'min',
factor=0.8, # 0.5 --> 0.8
patience=5, # 3 -- > 5
verbose=True)
if 'lr_scheduler_critic' in states:
self.scheduler_critic.load_state_dict(
states['lr_scheduler_critic'])
self.critic_criterion = torch.nn.SmoothL1Loss()
self.reward_metric = opt.get('reward_metric', 'total_metric')
self.reward_metric_mode = opt.get('reward_metric_mode', 'max')
self.prev_prev_valid_report = states[
'prev_prev_valid_report'] if 'prev_prev_valid_report' in states else None
self.prev_valid_report = states[
'prev_valid_report'] if 'prev_valid_report' in states else None
self.current_valid_report = states[
'current_valid_report'] if 'current_valid_report' in states else None
self.saved_actions = states[
'saved_actions'] if 'saved_actions' in states else OrderedDict()
self.saved_state_actions = states[
'saved_state_actions'] if 'saved_state_actions' in states else OrderedDict(
)
if self.use_cuda:
for k, v in self.saved_actions.items():
self.saved_actions[k] = v.cuda()
for k, v in self.saved_state_actions.items():
self.saved_state_actions[k] = v.cuda()
self._number_teacher_updates = states[
'_number_teacher_updates'] if '_number_teacher_updates' in states else 0
# enable the batch_act
self.use_batch_act = self.bsz > 1
self.T = self.opt.get('T', 1000)
self.c0 = self.opt.get('c0', 0.01)
self.p = self.opt.get('p', 2)
# setup the timer
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 \
else float('inf')
self.action_log_time = Timer()
self.move_to_cuda()
class DefaultTeacher(FbDialogTeacher):
def __init__(self, opt, shared=None):
opt = copy.deepcopy(opt)
super().__init__(opt, shared)
self.use_cuda = not opt['no_cuda'] and torch.cuda.is_available()
self.is_combine_attr = (hasattr(self, 'other_task_datafiles')
and self.other_task_datafiles)
self.random_policy = opt.get('random_policy', False)
self.count_sample = opt.get('count_sample', False)
self.anti = opt.get('anti', False)
if self.random_policy:
random.seed(17)
if not shared:
if not self.stream and opt.get('pace_by', 'sample') == 'bucket':
score_list = [episode[0][2] for episode in self.data.data]
assert score_list == sorted(score_list)
num_buckets = opt.get('num_buckets',
int(self.num_episodes() / 10))
lb_indices = [
int(len(score_list) * i / num_buckets)
for i in range(num_buckets)
]
lbs = [score_list[idx] for idx in lb_indices]
bucket_ids = [
self.sort_into_bucket(ctrl_val, lbs)
for ctrl_val in score_list
]
bucket_cnt = [0 for _ in range(num_buckets)]
for i in range(num_buckets):
bucket_cnt[i] = bucket_ids.count(i)
self.bucket_cnt = bucket_cnt
self.lastYs = [None] * self.bsz
# build multiple task data
self.tasks = [self.data]
if self.is_combine_attr:
print('[ build multiple task data ... ]')
for datafile in self.other_task_datafiles:
task_opt = copy.deepcopy(opt)
task_opt['datafile'] = datafile
self.tasks.append(
DialogData(task_opt,
data_loader=self.setup_data,
cands=self.label_candidates()))
print('[ build multiple task data done! ]')
# record the selections of each subtasks
self.subtasks = opt['subtasks'].split(':')
self.subtask_counter = OrderedDict()
self.p_selections = OrderedDict()
self.c_selections = OrderedDict()
for t in self.subtasks:
self.subtask_counter[t] = 0
self.p_selections[t] = []
self.c_selections[t] = []
if self.count_sample and not self.stream:
self.sample_counter = OrderedDict()
for idx, t in enumerate(self.subtasks):
self.sample_counter[t] = [
0 for _ in self.tasks[idx].data
]
# setup the tensorboard log
if opt['tensorboard_log_teacher'] is True:
opt['tensorboard_tag'] = 'task'
teacher_metrics = 'reward,policy_loss,critic_loss,mean_advantage_reward,action_ent'.split(
',')
opt['tensorboard_metrics'] = ','.join(
opt['tensorboard_metrics'].split(',') + teacher_metrics)
self.writer = TensorboardLogger(opt)
else:
self.lastYs = shared['lastYs']
self.tasks = shared['tasks']
if not self.stream and opt.get('pace_by', 'sample') == 'bucket':
self.bucket_cnt = shared['bucket_cnt']
if 'writer' in shared:
self.writer = shared['writer']
if 'subtask_counter' in shared:
self.subtask_counter = shared['subtask_counter']
if 'p_selections' in shared:
self.p_selections = shared['p_selections']
if 'c_selections' in shared:
self.c_selections = shared['c_selections']
# build the policy net, criterion and optimizer here
self.state_dim = 32 + len(self.tasks) # hand-craft features
self.action_dim = len(self.tasks)
if not shared:
self.policy = PolicyNet(self.state_dim, self.action_dim)
self.critic = CriticNet(self.state_dim, self.action_dim)
init_teacher = get_init_teacher(opt, shared)
if init_teacher is not None:
# load teacher parameters if available
print('[ Loading existing teacher params from {} ]'
''.format(init_teacher))
states = self.load(init_teacher)
else:
states = {}
else:
self.policy = shared['policy']
self.critic = shared['critic']
states = shared['states']
if (
# only build an optimizer if we're training
'train' in opt.get('datatype', '') and
# and this is the main model
shared is None):
# for policy net
self.optimizer = self.init_optim(
[p for p in self.policy.parameters() if p.requires_grad],
lr=opt['learningrate_teacher'],
optim_states=states.get('optimizer'),
saved_optim_type=states.get('optimizer_type'))
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer,
'min',
factor=0.8, # 0.5 --> 0.8
patience=5, # 3 -- > 5
verbose=True)
if 'lr_scheduler' in states:
self.scheduler.load_state_dict(states['lr_scheduler'])
# for critic net
self.optimizer_critic = self.init_optim(
[p for p in self.critic.parameters() if p.requires_grad],
lr=opt['learningrate_teacher_critic'],
optim_states=states.get('optimizer_critic'),
saved_optim_type=states.get('optimizer_type'))
self.scheduler_critic = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer_critic,
'min',
factor=0.8, # 0.5 --> 0.8
patience=5, # 3 -- > 5
verbose=True)
if 'lr_scheduler_critic' in states:
self.scheduler_critic.load_state_dict(
states['lr_scheduler_critic'])
self.critic_criterion = torch.nn.SmoothL1Loss()
self.reward_metric = opt.get('reward_metric', 'total_metric')
self.reward_metric_mode = opt.get('reward_metric_mode', 'max')
self.prev_prev_valid_report = states[
'prev_prev_valid_report'] if 'prev_prev_valid_report' in states else None
self.prev_valid_report = states[
'prev_valid_report'] if 'prev_valid_report' in states else None
self.current_valid_report = states[
'current_valid_report'] if 'current_valid_report' in states else None
self.saved_actions = states[
'saved_actions'] if 'saved_actions' in states else OrderedDict()
self.saved_state_actions = states[
'saved_state_actions'] if 'saved_state_actions' in states else OrderedDict(
)
if self.use_cuda:
for k, v in self.saved_actions.items():
self.saved_actions[k] = v.cuda()
for k, v in self.saved_state_actions.items():
self.saved_state_actions[k] = v.cuda()
self._number_teacher_updates = states[
'_number_teacher_updates'] if '_number_teacher_updates' in states else 0
# enable the batch_act
self.use_batch_act = self.bsz > 1
self.T = self.opt.get('T', 1000)
self.c0 = self.opt.get('c0', 0.01)
self.p = self.opt.get('p', 2)
# setup the timer
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 \
else float('inf')
self.action_log_time = Timer()
self.move_to_cuda()
def move_to_cuda(self):
if self.use_cuda:
self.policy.cuda()
self.critic.cuda()
@classmethod
def optim_opts(self):
"""
Fetch optimizer selection.
By default, collects everything in torch.optim, as well as importing:
- qhm / qhmadam if installed from github.com/facebookresearch/qhoptim
Override this (and probably call super()) to add your own optimizers.
"""
# first pull torch.optim in
optims = {
k.lower(): v
for k, v in optim.__dict__.items()
if not k.startswith('__') and k[0].isupper()
}
try:
import apex.optimizers.fused_adam as fused_adam
optims['fused_adam'] = fused_adam.FusedAdam
except ImportError:
pass
try:
# https://openreview.net/pdf?id=S1fUpoR5FQ
from qhoptim.pyt import QHM, QHAdam
optims['qhm'] = QHM
optims['qhadam'] = QHAdam
except ImportError:
# no QHM installed
pass
return optims
def init_optim(self, params, lr, optim_states=None, saved_optim_type=None):
"""
Initialize optimizer with teacher parameters.
:param params:
parameters from the teacher
:param optim_states:
optional argument providing states of optimizer to load
:param saved_optim_type:
type of optimizer being loaded, if changed will skip loading
optimizer states
"""
opt = self.opt
# set up optimizer args
kwargs = {'lr': lr}
if opt.get('momentum_teacher') > 0 and opt['optimizer_teacher'] in [
'sgd', 'rmsprop', 'qhm'
]:
# turn on momentum for optimizers that use it
kwargs['momentum'] = opt['momentum_teacher']
if opt['optimizer_teacher'] == 'sgd' and opt.get(
'nesterov_teacher', True):
# for sgd, maybe nesterov
kwargs['nesterov'] = opt.get('nesterov_teacher', True)
elif opt['optimizer_teacher'] == 'qhm':
# qhm needs a nu
kwargs['nu'] = opt.get('nus_teacher', (0.7, ))[0]
elif opt['optimizer_teacher'] == 'adam':
# turn on amsgrad for adam
# amsgrad paper: https://openreview.net/forum?id=ryQu7f-RZ
kwargs['amsgrad'] = True
elif opt['optimizer_teacher'] == 'qhadam':
# set nus for qhadam
kwargs['nus'] = opt.get('nus_teacher', (0.7, 1.0))
if opt['optimizer_teacher'] in [
'adam', 'sparseadam', 'adamax', 'qhadam'
]:
# set betas for optims that use it
kwargs['betas'] = opt.get('betas_teacher', (0.9, 0.999))
optim_class = self.optim_opts()[opt['optimizer_teacher']]
optimizer = optim_class(params, **kwargs)
if optim_states:
if saved_optim_type != opt['optimizer_teacher']:
print('WARNING: not loading optim state since optim class '
'changed.')
else:
try:
optimizer.load_state_dict(optim_states)
except ValueError:
print('WARNING: not loading optim state since model '
'params changed.')
if self.use_cuda:
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
return optimizer
def load(self, path):
"""
Return opt and teacher states.
TODO: load behaviors should be consistent with function state_dict().
"""
states = torch.load(path, map_location=lambda cpu, _: cpu)
if 'policy' in states:
self.policy.load_state_dict(states['policy'])
if 'critic' in states:
self.critic.load_state_dict(states['critic'])
if 'optimizer' in states and hasattr(self, 'optimizer'):
self.optimizer.load_state_dict(states['optimizer'])
if 'optimizer_critic' in states and hasattr(self, 'optimizer_critic'):
self.optimizer_critic.load_state_dict(states['optimizer_critic'])
return states
def share(self):
shared = super().share()
if hasattr(self, 'bucket_cnt'):
shared['bucket_cnt'] = self.bucket_cnt
shared['tasks'] = self.tasks
shared['policy'] = self.policy
shared['critic'] = self.critic
shared['states'] = {
'optimizer_type': self.opt['optimizer_teacher'],
'prev_prev_valid_report': self.prev_prev_valid_report,
'prev_valid_report': self.prev_valid_report,
'current_valid_report': self.current_valid_report,
'saved_actions': self.saved_actions,
'saved_state_actions': self.saved_state_actions,
}
if hasattr(self, 'writer'):
shared['writer'] = self.writer
if hasattr(self, 'subtask_counter'):
shared['subtask_counter'] = self.subtask_counter
if hasattr(self, 'p_selections'):
shared['p_selections'] = self.p_selections
if hasattr(self, 'c_selections'):
shared['c_selections'] = self.c_selections
return shared
@staticmethod
def sort_into_bucket(val, bucket_lbs):
"""
Returns the highest bucket such that val >= lower bound for that bucket.
Inputs:
val: float. The value to be sorted into a bucket.
bucket_lbs: list of floats, sorted ascending.
Returns:
bucket_id: int in range(num_buckets); the bucket that val belongs to.
"""
num_buckets = len(bucket_lbs)
for bucket_id in range(num_buckets - 1, -1, -1): # iterate descending
lb = bucket_lbs[bucket_id]
if val >= lb:
return bucket_id
raise ValueError('val %f is not >= any of the lower bounds: %s' %
(val, bucket_lbs))
def pace_function(self, states, sum_num, T=1000, c0=0.01, p=2):
train_step = states['train_step']
progress = self.root_p_pace(train_step, T, c0, p)
return int(sum_num * progress)
@staticmethod
def root_p_pace(timestep, T=1000, c0=0.01, p=2):
root_p = math.pow(
timestep * (1 - math.pow(c0, p)) / T + math.pow(c0, p), 1.0 / p)
return min(1.0, root_p)
def act(self, observation=None, task_idx=0):
"""Send new dialog message."""
if not hasattr(self, 'epochDone'):
# reset if haven't yet
self.reset()
# get next example, action is episode_done dict if already out of exs
action, self.epochDone = self.next_example(observation=observation,
task_idx=task_idx)
action['id'] = self.getID()
# remember correct answer if available
self.lastY = action.get('labels', action.get('eval_labels', None))
if ((not self.datatype.startswith('train')
or 'evalmode' in self.datatype) and 'labels' in action):
# move labels to eval field so not used for training
# but this way the model can use the labels for perplexity or loss
action = action.copy()
labels = action.pop('labels')
if not self.opt.get('hide_labels', False):
action['eval_labels'] = labels
return action
def _cry_for_missing_in_obs(self, something):
raise RuntimeError(
"{} is needed to include in observations to build states!".format(
something))
def _build_states(self, observations):
for key in ['train_step', 'train_report', 'loss_desc', 'prob_desc']:
if key not in observations[0]:
self._cry_for_missing_in_obs(key)
train_step = observations[0]['train_step'] # scala
train_step = min(train_step / self.T, 1)
train_report = observations[0]['train_report']
nll_loss = train_report.get('nll_loss', 0) / 10 # scala
loss_desc = observations[0]['loss_desc']
loss_desc = F.normalize(loss_desc, p=2, dim=-1)
prob_desc = observations[0]['prob_desc']
prob_desc = F.normalize(prob_desc, p=2, dim=-1)
if hasattr(self, 'subtask_counter'):
subtask_progress = self.subtask_counter.values()
max_min = max(subtask_progress) - min(subtask_progress)
subtask_progress = [
(item - min(subtask_progress)) / max_min if max_min > 0 else 0
for item in subtask_progress
]
else:
subtask_progress = [0]
subtask_progress = torch.FloatTensor(subtask_progress)
if self.use_cuda:
subtask_progress = subtask_progress.cuda()
prev_valid_report = self.prev_valid_report
if prev_valid_report is None:
prev_valid_report = {}
bleu = prev_valid_report.get('bleu', 0)
valid_nll_loss = prev_valid_report.get('nll_loss', 0) / 10
dist_1_ratio = prev_valid_report.get('dist_1_ratio', 0)
dist_2_ratio = prev_valid_report.get('dist_2_ratio', 0)
dist_3_ratio = prev_valid_report.get('dist_3_ratio', 0)
embed_avg = prev_valid_report.get('embed_avg', 0)
embed_greedy = prev_valid_report.get('embed_greedy', 0)
embed_extrema = prev_valid_report.get('embed_extrema', 0)
embed_coh = prev_valid_report.get('embed_coh', 0)
intra_dist_1 = prev_valid_report.get('intra_dist_1', 0) / 10
intra_dist_2 = prev_valid_report.get('intra_dist_2', 0) / 10
intra_dist_3 = prev_valid_report.get('intra_dist_3', 0) / 10
response_length = prev_valid_report.get(
'response_length', 0) / self.opt.get('label_truncate', 100)
# sent_entropy_uni = prev_valid_report.get('sent_entropy_uni', 0) / 100
# sent_entropy_bi = prev_valid_report.get('sent_entropy_bi', 0) / 100
# sent_entropy_tri = prev_valid_report.get('sent_entropy_tri', 0) / 100
word_entropy_uni = prev_valid_report.get('word_entropy_uni', 0) / 100
word_entropy_bi = prev_valid_report.get('word_entropy_bi', 0) / 100
word_entropy_tri = prev_valid_report.get('word_entropy_tri', 0) / 100
states = torch.FloatTensor([
train_step,
nll_loss,
bleu,
valid_nll_loss,
dist_1_ratio,
dist_2_ratio,
dist_3_ratio,
embed_avg,
embed_greedy,
embed_extrema,
embed_coh,
intra_dist_1,
intra_dist_2,
intra_dist_3,
response_length,
# sent_entropy_uni, sent_entropy_bi, sent_entropy_tri,
word_entropy_uni,
word_entropy_bi,
word_entropy_tri
])
if self.use_cuda:
states = states.cuda()
states = torch.cat([states, loss_desc, prob_desc, subtask_progress],
dim=-1).unsqueeze(dim=0)
return states
def __uniform_weights(self):
w = 1 / len(self.tasks)
weights = torch.FloatTensor([w] * len(self.tasks))
if self.use_cuda:
weights = weights.cuda()
return weights.unsqueeze(dim=0)
def __load_training_batch(self, observations):
if observations and len(
observations) > 0 and observations[0] and self.is_combine_attr:
if not self.random_policy:
with torch.no_grad():
current_states = self._build_states(observations)
action_probs = self.policy(current_states)
if self.action_log_time.time() > self.log_every_n_secs and len(
self.tasks) > 1:
with torch.no_grad():
# log the action distributions
action_p = ','.join([
str(round_sigfigs(x, 4))
for x in action_probs[0].data.tolist()
])
log = '[ {} {} ]'.format('Action probs:', action_p)
print(log)
self.action_log_time.reset()
sample_from = Categorical(action_probs[0])
action = sample_from.sample()
train_step = observations[0]['train_step']
self.saved_actions[train_step] = sample_from.log_prob(action)
self.saved_state_actions[train_step] = torch.cat(
[current_states, action_probs], dim=1)
selected_task = action.item()
self.subtask_counter[self.subtasks[selected_task]] += 1
probs = action_probs[0].tolist()
selection_report = {}
for idx, t in enumerate(self.subtasks):
selection_report['p_{}'.format(t)] = probs[idx]
self.p_selections[t].append(probs[idx])
selection_report['c_{}'.format(
t)] = self.subtask_counter[t]
self.c_selections[t].append(self.subtask_counter[t])
self.writer.add_metrics(setting='Teacher/task_selection',
step=train_step,
report=selection_report)
else:
selected_task = random.choice(range(len(self.tasks)))
self.subtask_counter[self.subtasks[selected_task]] += 1
else:
selected_task = 0
return self.__load_batch(observations, task_idx=selected_task)
def __load_batch(self, observations, task_idx=0):
if observations is None:
observations = [None] * self.bsz
bsz = len(observations)
batch = []
# Sample from multiple tasks using the policy net
for idx in range(bsz):
batch.append(self.act(observations[idx], task_idx=task_idx))
return batch
def batch_act(self, observations):
"""
Returns an entire batch of examples instead of just one.
"""
if not hasattr(self, 'epochDone'):
# reset if haven't yet
self.reset()
if self.opt['datatype'] == 'train':
batch = self.__load_training_batch(observations)
else:
batch = self.__load_batch(observations)
# pad batch
if len(batch) < self.bsz:
batch += [{
'episode_done': True,
'id': self.getID()
}] * (self.bsz - len(batch))
# remember correct answer if available (for padding, None)
for i, ex in enumerate(batch):
if 'labels' in ex:
labels = ex['labels']
self.lastYs[i] = labels
if not self.datatype.startswith(
'train') or 'evalmode' in self.datatype:
del ex['labels']
if not self.opt.get('hide_labels', False):
ex['eval_labels'] = labels
else:
self.lastYs[i] = ex.get('eval_labels', None)
return batch
def next_example(self, observation=None, task_idx=0):
"""
Returns the next example.
If there are multiple examples in the same episode, returns the next
one in that episode. If that episode is over, gets a new episode index
and returns the first example of that episode.
"""
if self.stream:
action, epoch_done = self.tasks[task_idx].get()
else:
if self.episode_done:
self.episode_idx = self.next_episode_idx()
self.entry_idx = 0
else:
self.entry_idx += 1
if self.episode_idx >= self.num_episodes():
return {'episode_done': True}, True
if observation is None or self.opt['datatype'] != 'train':
# The first step of the training or validation mode
sampled_episode_idx = self.episode_idx
sampled_entry_idx = self.entry_idx
else:
# --------------- pick the sample according to the pace function -----------
pace_by = self.opt.get('pace_by', 'sample')
if pace_by == 'sample':
sum_num = self.num_episodes()
elif pace_by == 'bucket':
sum_num = len(self.bucket_cnt)
else:
raise ValueError('pace_by must be {} or {}!'.format(
'sample', 'bucket'))
states4pace_func = observation
if hasattr(self, 'subtask_counter'):
states4pace_func = {
'train_step':
self.subtask_counter[self.subtasks[task_idx]]
}
threshold = self.pace_function(states4pace_func, sum_num,
self.T, self.c0, self.p)
if pace_by == 'sample':
stop_step = threshold
elif pace_by == 'bucket':
stop_step = sum(self.bucket_cnt[:threshold])
else:
raise ValueError('pace_by must be {} or {}!'.format(
'sample', 'bucket'))
stop_step = self.num_episodes(
) if stop_step > self.num_episodes() else stop_step
# sampled_episode_idx = random.choice(list(range(self.num_episodes()))[:stop_step])
sampled_episode_idx = np.random.choice(stop_step)
sampled_entry_idx = 0 # make sure the episode only contains one entry
if self.anti:
sampled_episode_idx = self.num_episodes(
) - 1 - sampled_episode_idx
if self.count_sample:
self.sample_counter[
self.subtasks[task_idx]][sampled_episode_idx] += 1
ex = self.get(sampled_episode_idx,
sampled_entry_idx,
task_idx=task_idx)
if observation is None or self.opt['datatype'] != 'train':
self.episode_done = ex.get('episode_done', False)
if (not self.random and self.episode_done
and self.episode_idx + self.opt.get("batchsize", 1) >=
self.num_episodes()):
epoch_done = True
else:
epoch_done = False
else:
# in the setting of curriculum leaning, samples are not uniformly
# picked from the training set, so, the epoch records here make no sense.
epoch_done = False
action = ex
return action, epoch_done
def get(self, episode_idx, entry_idx=0, task_idx=0):
return self.tasks[task_idx].get(episode_idx, entry_idx)[0]
def update_params(self):
self._number_teacher_updates += 1
if self.opt.get('gradient_clip_teacher', -1) > 0:
torch.nn.utils.clip_grad_norm_(self.policy.parameters(),
self.opt['gradient_clip_teacher'])
self.optimizer.step()
def update_critic_params(self):
if self.opt.get('gradient_clip_teacher', -1) > 0:
torch.nn.utils.clip_grad_norm_(self.critic.parameters(),
self.opt['gradient_clip_teacher'])
self.optimizer_critic.step()
def receive_metrics(self, metrics_dict):
if self.is_combine_attr and not self.random_policy:
assert self.reward_metric in metrics_dict, '{} is not in the metrics_dict!'.format(
self.reward_metric)
self.prev_prev_valid_report = self.prev_valid_report
self.prev_valid_report = self.current_valid_report
self.current_valid_report = metrics_dict
delt_reward = None
if self.prev_prev_valid_report and self.prev_valid_report and self.current_valid_report:
delt_reward1 = self.current_valid_report[
self.reward_metric] - self.prev_valid_report[
self.reward_metric]
delt_reward0 = self.prev_valid_report[
self.reward_metric] - self.prev_prev_valid_report[
self.reward_metric]
if self.reward_metric_mode == 'min':
delt_reward1 = -delt_reward1
delt_reward0 = -delt_reward0
delt_reward = delt_reward1 / (delt_reward0 + 1e-6) - 1
if delt_reward and len(self.saved_actions) > 0 and len(
self.saved_state_actions) > 0:
reward = torch.clamp(torch.FloatTensor([delt_reward]), -10, 10)
if self.use_cuda:
reward = reward.cuda()
with torch.no_grad():
batch_state_actions = torch.cat(list(
self.saved_state_actions.values()),
dim=0)
if self.use_cuda:
batch_state_actions = batch_state_actions.cuda()
estimate_rewards = self.critic(
batch_state_actions).squeeze()
advantages = reward - estimate_rewards
# rescale the rewards by ranking
episode_len = len(advantages)
ranks = torch.FloatTensor(
list(
reversed(
ss.rankdata(advantages.cpu(),
method='dense')))).unsqueeze(dim=1)
rescaled_rewards = torch.sigmoid(
12 * (0.5 - ranks / episode_len))
rescaled_rewards = [r.item() for r in rescaled_rewards]
policy_loss = []
idx = 0
for model_train_step, log_prob in self.saved_actions.items():
policy_loss.append(-log_prob.unsqueeze(dim=0) *
rescaled_rewards[idx])
idx += 1
policy_loss = torch.cat(policy_loss).sum()
# regularization term regarding action distribution
bsz = batch_state_actions.size(0)
action_probs = torch.cat(list(
self.saved_state_actions.values()),
dim=0).narrow(1, self.state_dim,
self.action_dim)
action_ent = torch.sum(
-action_probs * torch.log(action_probs)) / bsz
self.policy.train()
self.optimizer.zero_grad()
policy_loss = policy_loss + self.opt.get('reg_action',
0.001) * (-action_ent)
policy_loss.backward()
self.update_params()
# lr_scheduler step on teacher loss
policy_loss_item = policy_loss.item()
if self.opt.get('optimizer_teacher', '') == 'sgd':
self.scheduler.step(policy_loss_item)
# training on the critic
self.critic.train()
self.optimizer_critic.zero_grad()
batch_values = self.critic(batch_state_actions)
critic_target = torch.FloatTensor(bsz, 1)
critic_target = critic_target.fill_(reward.item())
if self.use_cuda:
critic_target = critic_target.cuda()
critic_loss = self.critic_criterion(batch_values,
critic_target)
critic_loss.backward()
self.update_critic_params()
critic_loss_item = critic_loss.item()
if self.opt.get('optimizer_teacher', '') == 'sgd':
self.scheduler_critic.step(critic_loss_item)
# log something
print(
'[ reward: {}; mean_advantage_reward: {}; policy loss: {};'
' critic loss: {}; action ent: {}; episode length: {} ]'.
format(reward.item(), np.mean(advantages.tolist()),
policy_loss_item, critic_loss_item,
action_ent.item(), len(self.saved_actions)))
report = {
'reward': reward.item(),
'mean_advantage_reward': np.mean(advantages.tolist()),
'policy_loss': policy_loss_item,
'critic_loss': critic_loss_item,
'action_ent': action_ent.item(),
}
self.writer.add_metrics(setting='Teacher/receive_metrics',
step=self._number_teacher_updates,
report=report)
# clear history actions
self.saved_actions.clear()
self.saved_state_actions.clear()
def state_dict(self):
"""
Get the state dict for saving
TODO: save more teacher-related states for reloading
"""
states = {}
if hasattr(self, 'policy'): # save model params
if hasattr(self.policy, 'module'):
# did we wrap in a DistributedDataParallel
states['policy'] = self.policy.module.state_dict()
else:
states['policy'] = self.policy.state_dict()
if hasattr(self, 'critic'): # save model params
if hasattr(self.critic, 'module'):
# did we wrap in a DistributedDataParallel
states['critic'] = self.critic.module.state_dict()
else:
states['critic'] = self.critic.state_dict()
if hasattr(self, 'optimizer'): # save optimizer params
states['optimizer'] = self.optimizer.state_dict()
states['optimizer_type'] = self.opt['optimizer_teacher']
if hasattr(self, 'optimizer_critic'):
states['optimizer_critic'] = self.optimizer_critic.state_dict()
if getattr(self, 'scheduler', None):
states['lr_scheduler'] = self.scheduler.state_dict()
if getattr(self, 'scheduler_critic', None):
states['lr_scheduler_critic'] = self.scheduler_critic.state_dict()
states['prev_prev_valid_report'] = self.prev_prev_valid_report
states['prev_valid_report'] = self.prev_valid_report
states['current_valid_report'] = self.current_valid_report
states['saved_actions'] = self.saved_actions
states['saved_state_actions'] = self.saved_state_actions
states['_number_teacher_updates'] = self._number_teacher_updates
return states
def save(self, path=None):
if path:
teacher_path = path
else:
model_file = self.opt.get('model_file', None)
if model_file:
teacher_path = model_file + '.teacher'
else:
teacher_path = None
if teacher_path:
states = self.state_dict()
if states:
with open(teacher_path, 'wb') as write:
torch.save(states, write)
# save opt file
with open(teacher_path + '.opt', 'w',
encoding='utf-8') as handle:
json.dump(self.opt, handle)
# for convenience of working with jq, make sure there's a newline
handle.write('\n')
if self.count_sample:
# save sample count info
for task_name, task_val in self.sample_counter.items():
with open(teacher_path +
'.sample_count.{}'.format(task_name),
'w',
encoding='utf-8') as f:
f.write('\n'.join([str(item) for item in task_val]))
self.write_selections('p_selections', teacher_path)
self.write_selections('c_selections', teacher_path)
def write_selections(self, selections, teacher_path):
if hasattr(self, selections):
with open(teacher_path + '.{}'.format(selections),
'w',
encoding='utf-8') as f:
f.write('\t'.join(self.subtasks))
f.write('\n')
for idx in range(
len(getattr(self, selections)[self.subtasks[0]])):
p_line = []
for t in self.subtasks:
p_line.append(str(getattr(self, selections)[t][idx]))
f.write('\t'.join(p_line))
f.write('\n')
class TrainLoop():
def __init__(self, opt):
if isinstance(opt, ParlaiParser):
print(
'[ Deprecated Warning: TrainLoop should be passed opt not Parser ]'
)
opt = opt.parse_args()
# Possibly load from checkpoint
if opt['load_from_checkpoint'] and opt.get(
'model_file') and os.path.isfile(opt['model_file'] +
'.checkpoint'):
opt['init_model'] = opt['model_file'] + '.checkpoint'
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt, skip_if_built=True)
# Create model and assign it to the specified task
self.agent = create_agent(opt)
self.world = create_task(opt, self.agent)
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.max_num_epochs = opt[
'num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt[
'max_train_time'] if opt['max_train_time'] > 0 else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt[
'log_every_n_secs'] > 0 else float('inf')
self.val_every_n_secs = opt['validation_every_n_secs'] if opt[
'validation_every_n_secs'] > 0 else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt[
'save_every_n_secs'] > 0 else float('inf')
self.val_every_n_epochs = opt['validation_every_n_epochs'] if opt[
'validation_every_n_epochs'] > 0 else float('inf')
self.last_valid_epoch = 0
self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
self.best_valid = None
if opt.get('model_file') and os.path.isfile(opt['model_file'] +
'.best_valid'):
with open(opt['model_file'] + ".best_valid", 'r') as f:
x = f.readline()
self.best_valid = float(x)
f.close()
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
if opt['tensorboard_log'] is True:
self.writer = TensorboardLogger(opt)
def validate(self):
opt = self.opt
# run evaluation on valid set
valid_report, self.valid_world = run_eval(self.agent,
opt,
'valid',
opt['validation_max_exs'],
valid_world=self.valid_world)
# logging
if opt['tensorboard_log'] is True:
self.writer.add_metrics('valid',
int(math.floor(self.train_time.time())),
valid_report)
# saving
if opt.get('model_file') and opt.get('save_after_valid'):
print("[ saving model checkpoint: " + opt['model_file'] +
".checkpoint ]")
self.agent.save(opt['model_file'] + '.checkpoint')
# 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
if '/' in opt['validation_metric']:
# if you are multitasking and want your validation metric to be
# a metric specific to a subtask, specify your validation metric
# as -vmt subtask/metric
subtask = opt['validation_metric'].split('/')[0]
validation_metric = opt['validation_metric'].split('/')[1]
new_valid = valid_report['tasks'][subtask][validation_metric]
else:
new_valid = valid_report[opt['validation_metric']]
# 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:
print('[ new best {}: {}{} ]'.format(
opt['validation_metric'], new_valid,
' (previous best was {})'.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'):
print("[ saving best valid model: " + opt['model_file'] + " ]")
self.agent.save(opt['model_file'])
print("[ saving best valid metric: " + opt['model_file'] +
".best_valid ]")
save_best_valid(opt['model_file'], self.best_valid)
self.saved = True
if opt['validation_metric'] == 'accuracy' and self.best_valid >= opt[
'validation_cutoff']:
print('[ task solved! stopping. ]')
return True
else:
self.impatience += 1
print('[ did not beat best {}: {} impatience: {} ]'.format(
opt['validation_metric'], round(self.best_valid, 4),
self.impatience))
self.validate_time.reset()
# check if we are out of patience
if opt['validation_patience'] > 0 and self.impatience >= opt[
'validation_patience']:
print('[ ran out of patience! stopping training. ]')
return True
return False
def log(self):
opt = self.opt
if opt['display_examples']:
print(self.world.display() + '\n~~')
logs = []
# get report
train_report = self.world.report(compute_time=True)
self.world.reset_metrics()
# time elapsed
logs.append('time:{}s'.format(math.floor(self.train_time.time())))
total_exs = self.world.get_total_exs()
logs.append('total_exs:{}'.format(total_exs))
exs_per_ep = self.world.num_examples()
if exs_per_ep:
logs.append('epochs:{}'.format(round(total_exs / exs_per_ep, 2)))
if 'time_left' in train_report:
logs.append('time_left:{}s'.format(
math.floor(train_report.pop('time_left', ""))))
log = '[ {} ] {}'.format(' '.join(logs), train_report)
print(log)
self.log_time.reset()
if opt['tensorboard_log'] is True:
self.writer.add_metrics('train', int(logs[1].split(":")[1]),
train_report)
def train(self):
opt = self.opt
world = self.world
with world:
while True:
# do one example / batch of examples
world.parley()
self.parleys += 1
# check counters and timers
if world.get_total_epochs() >= self.max_num_epochs:
self.log()
print(
'[ num_epochs completed:{} time elapsed:{}s ]'.format(
self.max_num_epochs, self.train_time.time()))
break
if self.train_time.time() > self.max_train_time:
print('[ max_train_time elapsed:{}s ]'.format(
self.train_time.time()))
break
if self.log_time.time() > self.log_every_n_secs:
self.log()
if self.validate_time.time() > self.val_every_n_secs:
stop_training = self.validate()
if stop_training:
break
if world.get_total_epochs(
) - self.last_valid_epoch >= self.val_every_n_epochs:
stop_training = self.validate()
self.last_valid_epoch = world.get_total_epochs()
if stop_training:
break
if self.save_time.time() > self.save_every_n_secs and opt.get(
'model_file'):
print("[ saving model checkpoint: " + opt['model_file'] +
".checkpoint ]")
self.agent.save(opt['model_file'] + '.checkpoint')
self.save_time.reset()
if not self.saved:
# save agent
self.agent.save(opt['model_file'])
elif opt.get('model_file'):
# reload best validation model
self.agent = create_agent(opt)
v_report, v_world = run_eval(self.agent, opt, 'valid', write_log=True)
t_report, t_world = run_eval(self.agent, opt, 'test', write_log=True)
v_world.shutdown()
t_world.shutdown()
return v_report, t_report
def main():
# Get command line arguments
parser = ParlaiParser(True, True)
train = parser.add_argument_group('Training Loop Arguments')
train.add_argument('-et', '--evaltask',
help=('task to use for valid/test (defaults to the ' +
'one used for training if not set)'))
train.add_argument('-d', '--display-examples',
type='bool', default=False)
train.add_argument('-e', '--num-epochs', type=int, default=-1)
train.add_argument('-ttim', '--max-train-time',
type=float, default=-1)
train.add_argument('-ltim', '--log-every-n-secs',
type=float, default=2)
train.add_argument('-vtim', '--validation-every-n-secs',
type=float, default=-1)
train.add_argument('-vme', '--validation-max-exs',
type=int, default=-1,
help='max examples to use during validation (default ' +
'-1 uses all)')
train.add_argument('-vp', '--validation-patience',
type=int, default=5,
help=('number of iterations of validation where result '
+ 'does not improve before we stop training'))
train.add_argument('-dbf', '--dict-build-first',
type='bool', default=True,
help='build dictionary first before training agent')
opt = parser.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
build_dict.build_dict(opt)
# Create model and assign it to the specified task
agent = create_agent(opt)
world = create_task(opt, agent)
train_time = Timer()
validate_time = Timer()
log_time = Timer()
print('[ training... ]')
total_exs = 0
max_exs = opt['num_epochs'] * len(world)
best_accuracy = 0
impatience = 0
saved = False
while True:
world.parley()
if opt['num_epochs'] > 0 and total_exs >= max_exs:
print('[ num_epochs completed: {} ]'.format(opt['num_epochs']))
break
if opt['max_train_time'] > 0 and train_time.time() > opt['max_train_time']:
print('[ max_train_time elapsed: {} ]'.format(train_time.time()))
break
if opt['log_every_n_secs'] > 0 and log_time.time() > opt['log_every_n_secs']:
if opt['display_examples']:
print(world.display() + '\n~~')
logs = []
# time elapsed
logs.append('time:{}s'.format(math.floor(train_time.time())))
# get report and update total examples seen so far
if hasattr(agent, 'report'):
train_report = agent.report()
agent.reset_metrics()
else:
train_report = world.report()
world.reset_metrics()
total_exs += train_report['total']
logs.append('total_exs:{}'.format(total_exs))
# check if we should log amount of time remaining
time_left = None
if opt['num_epochs'] > 0:
exs_per_sec = train_time.time() / total_exs
time_left = (max_exs - total_exs) * exs_per_sec
if opt['max_train_time'] > 0:
other_time_left = opt['max_train_time'] - train_time.time()
if time_left is not None:
time_left = min(time_left, other_time_left)
else:
time_left = other_time_left
if time_left is not None:
logs.append('time_left:{}s'.format(math.floor(time_left)))
# join log string and add full metrics report to end of log
log = '[ {} ] {}'.format(' '.join(logs), train_report)
print(log)
log_time.reset()
if (opt['validation_every_n_secs'] > 0 and
validate_time.time() > opt['validation_every_n_secs']):
valid_report = run_eval(agent, opt, 'valid', True, opt['validation_max_exs'])
if valid_report['accuracy'] > best_accuracy:
best_accuracy = valid_report['accuracy']
impatience = 0
print('[ new best accuracy: ' + str(best_accuracy) + ' ]')
if opt['model_file']:
agent.save(opt['model_file'])
saved = True
if best_accuracy == 1:
print('[ task solved! stopping. ]')
break
else:
impatience += 1
print('[ did not beat best accuracy: {} impatience: {} ]'.format(
round(best_accuracy, 4), impatience))
validate_time.reset()
if opt['validation_patience'] > 0 and impatience >= opt['validation_patience']:
print('[ ran out of patience! stopping training. ]')
break
world.shutdown()
if not saved:
if opt['model_file']:
agent.save(opt['model_file'])
else:
# reload best validation model
agent = create_agent(opt)
run_eval(agent, opt, 'valid')
run_eval(agent, opt, 'test')
class TrainLoop():
def __init__(self, opt):
if isinstance(opt, ParlaiParser):
opt = opt.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get(
'model_file_transmitter') and opt.get(
'model_file_receiver'):
opt['dict_file'] = opt['model_file_transmitter'] + '_' + opt[
'model_file_receiver'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt, skip_if_built=False)
# Create model and assign it to the specified task
print("[ create meta-agent ... ]")
self.agent = create_agent(opt)
print("[ create agent A ... ]")
shared = self.agent.share()
self.agent_a = create_agent_from_shared(shared)
self.agent_a.set_id(suffix=' A')
print("[ create agent B ... ]")
self.agent_b = create_agent_from_shared(shared)
# self.agent_b = create_agent(opt)
self.agent_b.set_id(suffix=' B')
# self.agent_a.copy(self.agent, 'transmitter')
# self.agent_b.copy(self.agent, 'transmitter')
self.world = create_selfplay_world(opt, [self.agent_a, self.agent_b])
# TODO: if batch, it is also not parallel
# self.world = BatchSelfPlayWorld(opt, self_play_world)
self.train_time = Timer()
self.train_dis_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys_episode = 0
self.max_num_epochs = opt[
'num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt[
'max_train_time'] if opt['max_train_time'] > 0 else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt[
'log_every_n_secs'] > 0 else float('inf')
self.train_dis_every_n_secs = opt['train_display_every_n_secs'] if opt[
'train_display_every_n_secs'] > 0 else float('inf')
self.val_every_n_secs = opt['validation_every_n_secs'] if opt[
'validation_every_n_secs'] > 0 else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt[
'save_every_n_secs'] > 0 else float('inf')
self.valid_optim = 1 if opt['validation_metric_mode'] == 'max' else -1
self.best_valid = None
if opt.get('model_file_transmitter') and os.path.isfile(
opt['model_file_transmitter'] + '.best_valid'):
with open(opt['model_file_transmitter'] + ".best_valid", 'r') as f:
x = f.readline()
self.best_valid = float(x)
f.close()
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
if opt['tensorboard_log'] is True:
self.writer = TensorboardLogger(opt)
def validate(self):
opt = self.opt
valid_report, self.valid_world = run_eval(self.agent,
opt,
'valid',
opt['validation_max_exs'],
valid_world=self.valid_world)
if opt['tensorboard_log'] is True:
self.writer.add_metrics('valid', self.parleys_episode,
valid_report)
if opt.get('model_file_transmitter') and opt.get('save_after_valid'):
print("[ saving transmitter checkpoint: " +
opt['model_file_transmitter'] + ".checkpoint ]")
self.agent.save(component='transmitter')
# if opt.get('model_file_receiver') and opt.get('save_after_valid'):
# print("[ saving receiver checkpoint: " + opt['model_file_receiver'] + ".checkpoint ]")
# self.agent.save(component='receiver')
if hasattr(self.agent, 'receive_metrics'):
self.agent.receive_metrics(valid_report)
if '/' in opt['validation_metric']:
# if you are multitasking and want your validation metric to be
# a metric specific to a subtask, specify your validation metric
# as -vmt subtask/metric
subtask = opt['validation_metric'].split('/')[0]
validation_metric = opt['validation_metric'].split('/')[1]
new_valid = valid_report['tasks'][subtask][validation_metric]
else:
new_valid = valid_report[opt['validation_metric']]
if self.best_valid is None or self.valid_optim * new_valid > self.valid_optim * self.best_valid:
print('[ new best {}: {}{} ]'.format(
opt['validation_metric'], new_valid,
' (previous best was {})'.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'):
print("[ saving best valid model: " + opt['model_file'] + " ]")
# the fine-tuned transmitter part is actually what we want for PSquare bot
self.agent.save()
print("[ saving best valid metric: " + opt['model_file'] +
".best_valid ]")
save_best_valid(opt['model_file'], self.best_valid)
self.saved = True
if opt['validation_metric'] == 'accuracy' and self.best_valid >= opt[
'validation_cutoff']:
print('[ task solved! stopping. ]')
return True
else:
self.impatience += 1
print('[ did not beat best {}: {} impatience: {} ]'.format(
opt['validation_metric'], round(self.best_valid, 4),
self.impatience))
self.validate_time.reset()
if 0 < opt['validation_patience'] <= self.impatience:
print('[ ran out of patience! stopping training. ]')
return True
return False
def log(self):
opt = self.opt
if opt['display_examples']:
print(self.world.display() + '\n~~')
logs = []
# get report
train_report = self.world.report()
self.world.reset_metrics()
# time elapsed
logs.append('time:{}s'.format(math.floor(self.train_time.time())))
logs.append('parleys:{}'.format(self.parleys_episode))
if 'time_left' in train_report:
logs.append('time_left:{}s'.format(
math.floor(train_report.pop('time_left', ""))))
if 'num_epochs' in train_report:
logs.append('num_epochs:{}'.format(
train_report.pop('num_epochs', '')))
log = '[ {} ] {}'.format(' '.join(logs), train_report)
print(log)
self.log_time.reset()
if opt['tensorboard_log'] is True:
self.writer.add_metrics('train', self.parleys_episode,
train_report)
def train(self):
# print('#### Validating at {} training episode '.format(self.parleys_episode))
# self.validate()
opt = self.opt
world = self.world
with world:
while True:
self.parleys_episode += 1
if self.parleys_episode % 100 == 0:
print('#### Training {} episode '.format(
self.parleys_episode))
if self.train_dis_time.time() > self.train_dis_every_n_secs:
is_display = True
# clear to zero
self.train_dis_time.reset()
else:
is_display = False
world.parley_episode(is_training=True, is_display=is_display)
if world.get_total_epochs() >= self.max_num_epochs:
self.log()
print(
'[ num_epochs completed:{} time elapsed:{}s ]'.format(
self.max_num_epochs, self.train_time.time()))
break
if self.train_time.time() > self.max_train_time:
print('[ max_train_time elapsed:{}s ]'.format(
self.train_time.time()))
break
if self.log_time.time() > self.log_every_n_secs:
self.log()
if self.validate_time.time() > self.val_every_n_secs:
print('#### Validating at {} training episode '.format(
self.parleys_episode))
stop_training = self.validate()
if stop_training:
break
if self.save_time.time() > self.save_every_n_secs:
if opt.get('model_file_transmitter'):
print("[ saving transmitter checkpoint: " +
opt['model_file_transmitter'] + ".checkpoint ]")
self.agent.save(opt['model_file_transmitter'] +
'.checkpoint',
component='transmitter')
if opt.get('model_file_receiver'):
print("[ saving receiver checkpoint: " +
opt['model_file_receiver'] + ".checkpoint ]")
self.agent.save(opt['model_file_receiver'] +
'.checkpoint',
component='receiver')
self.save_time.reset()
if not self.saved:
# save agent
# self.agent.save(component='transmitter')
self.agent.save()
# self.agent.save(component='receiver') # TODO: API for save all components
elif opt.get('model_file_transmitter') and opt.get(
'model_file_receiver'
): # TODO: check if both components are necessary
# reload best validation model
self.agent = create_agent(opt)
v_report, v_world = run_eval(self.agent, opt, 'valid', write_log=True)
t_report, t_world = run_eval(self.agent, opt, 'test', write_log=True)
v_world.shutdown()
t_world.shutdown()
return v_report, t_report
class TrainLoop():
def __init__(self, parser):
opt = parser.parse_args()
# Possibly build a dictionary (not all models do this).
if opt['dict_build_first'] and 'dict_file' in opt:
if opt['dict_file'] is None and opt.get('model_file'):
opt['dict_file'] = opt['model_file'] + '.dict'
print("[ building dictionary first... ]")
build_dict(opt)
# Create model and assign it to the specified task
self.agent = create_agent(opt)
self.world = create_task(opt, self.agent)
self.train_time = Timer()
self.validate_time = Timer()
self.log_time = Timer()
self.save_time = Timer()
print('[ training... ]')
self.parleys = 0
self.max_num_epochs = opt['num_epochs'] if opt['num_epochs'] > 0 else float('inf')
self.max_train_time = opt['max_train_time'] if opt['max_train_time'] > 0 else float('inf')
self.log_every_n_secs = opt['log_every_n_secs'] if opt['log_every_n_secs'] > 0 else float('inf')
self.val_every_n_secs = opt['validation_every_n_secs'] if opt['validation_every_n_secs'] > 0 else float('inf')
self.save_every_n_secs = opt['save_every_n_secs'] if opt['save_every_n_secs'] > 0 else float('inf')
self.best_valid = 0
self.impatience = 0
self.saved = False
self.valid_world = None
self.opt = opt
def validate(self):
opt = self.opt
valid_report, self.valid_world = run_eval(
self.agent, opt, 'valid', opt['validation_max_exs'],
valid_world=self.valid_world)
if valid_report[opt['validation_metric']] > self.best_valid:
self.best_valid = valid_report[opt['validation_metric']]
self.impatience = 0
print('[ new best {}: {} ]'.format(
opt['validation_metric'], self.best_valid))
self.world.save_agents()
self.saved = True
if opt['validation_metric'] == 'accuracy' and self.best_valid >= opt['validation_cutoff']:
print('[ task solved! stopping. ]')
return True
else:
self.impatience += 1
print('[ did not beat best {}: {} impatience: {} ]'.format(
opt['validation_metric'], round(self.best_valid, 4),
self.impatience))
self.validate_time.reset()
if opt['validation_patience'] > 0 and self.impatience >= opt['validation_patience']:
print('[ ran out of patience! stopping training. ]')
return True
return False
def log(self):
opt = self.opt
if opt['display_examples']:
print(self.world.display() + '\n~~')
logs = []
# get report
if hasattr(self.agent, 'report'):
train_report = self.agent.report()
self.agent.reset_metrics()
else:
train_report = self.world.report(compute_time=True)
self.world.reset_metrics()
# time elapsed
logs.append('time:{}s'.format(math.floor(self.train_time.time())))
logs.append('parleys:{}'.format(self.parleys))
if 'time_left' in train_report:
logs.append('time_left:{}s'.format(
math.floor(train_report.pop('time_left', ""))))
if 'num_epochs' in train_report:
logs.append('num_epochs:{}'.format(
train_report.pop('num_epochs', '')))
log = '[ {} ] {}'.format(' '.join(logs), train_report)
print(log)
self.log_time.reset()
def train(self):
opt = self.opt
world = self.world
with world:
while True:
world.parley()
self.parleys += 1
if world.get_total_epochs() >= self.max_num_epochs:
self.log()
print('[ num_epochs completed:{} time elapsed:{}s ]'.format(
self.max_num_epochs, self.train_time.time()))
break
if self.train_time.time() > self.max_train_time:
print('[ max_train_time elapsed:{}s ]'.format(self.train_time.time()))
break
if self.log_time.time() > self.log_every_n_secs:
self.log()
if self.validate_time.time() > self.val_every_n_secs:
stop_training = self.validate()
if stop_training:
break
if self.save_time.time() > self.save_every_n_secs:
print("[ saving model: " + opt['model_file'] + " ]")
world.save_agents()
self.save_time.reset()
if not self.saved:
# save agent
world.save_agents()
elif opt.get('model_file'):
# reload best validation model
self.agent = create_agent(opt)
_rep, wrld = run_eval(self.agent, opt, 'valid', write_log=True)
wrld.shutdown() # may need to shut down threads, remote connections
_rep, wrld = run_eval(self.agent, opt, 'test', write_log=True)
wrld.shutdown() # may need to shut down threads, remote connections
class World(object):
"""Empty parent providing null definitions of API functions for Worlds.
All children can override these to provide more detailed functionality."""
def __init__(self, opt, agents=None, shared=None):
self.id = opt['task']
self.opt = copy.deepcopy(opt)
if shared:
# Create agents based on shared data.
self.agents = create_agents_from_shared(shared['agents'])
else:
# Add passed in agents to world directly.
self.agents = agents
self.max_exs = None
self.total_exs = 0
self.total_epochs = 0
self.total_parleys = 0
self.time = Timer()
def parley(self):
"""The main method, that does one step of actions for the agents
in the world. This is empty in the base class.
"""
pass
def getID(self):
"""Return the name of the world, typically the task the world encodes."""
return self.id
def display(self):
"""Returns a string describing the current state of the world.
Useful for monitoring and debugging.
By default, display the messages between the agents."""
if not hasattr(self, 'acts'):
return ''
return display_messages(self.acts)
def episode_done(self):
"""Whether the episode is done or not."""
return False
def epoch_done(self):
"""Whether the epoch is done or not.
Not all worlds have the notion of an epoch, but this is useful
for fixed training, validation or test sets.
"""
return False
def share(self):
shared_data = {}
shared_data['world_class'] = type(self)
shared_data['opt'] = self.opt
shared_data['agents'] = self._share_agents()
return shared_data
def _share_agents(self):
"""Create shared data for agents so other classes can create the same
agents without duplicating the data (i.e. sharing parameters).
"""
if not hasattr(self, 'agents'):
return None
shared_agents = [a.share() for a in self.agents]
return shared_agents
def get_agents(self):
"""Return the list of agents."""
return self.agents
def get_acts(self):
"""Return the last act of each agent."""
return self.acts
def get_time(self):
"""Return total training time"""
return self.time.time()
def get_total_exs(self):
"""Return total amount of examples seen by world."""
return self.total_exs
def get_total_epochs(self):
"""Return total amount of epochs on which the world has trained."""
return self.total_epochs
def __enter__(self):
"""Empty enter provided for use with ``with`` statement.
e.g:
.. code-block:: python
with World() as world:
for n in range(10):
n.parley()
"""
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
"""After ``with`` statement, call shutdown."""
silent_exit = isinstance(exc_value, KeyboardInterrupt)
self.shutdown()
return silent_exit
def num_examples(self):
return 0
def num_episodes(self):
return 0
def reset(self):
for a in self.agents:
a.reset()
self.max_exs = None
self.total_exs = 0
self.total_epochs = 0
self.total_parleys = 0
self.time.reset()
def reset_metrics(self):
for a in self.agents:
a.reset_metrics()
def save_agents(self):
"""Saves all of the agents in the world by calling their respective
save() methods.
"""
for a in self.agents:
a.save()
def shutdown(self):
"""Perform any cleanup, if appropriate."""
pass
def update_counters(self):
"""Update how many epochs have completed"""
self.total_parleys += 1
if self.max_exs is None:
if ('num_epochs' in self.opt and self.opt['num_epochs'] > 0):
self.max_exs = self.num_examples() * self.opt['num_epochs'] if self.num_examples() else -1
else:
self.max_exs = -1
# when we know the size of the data
if self.max_exs > 0:
self.total_epochs = self.total_parleys * self.opt.get('batchsize', 1) / self.num_examples()
# when we do not know the size of the data
else:
if self.epoch_done():
self.total_epochs += 1
