def run(self):
# initialize cuda context
devices = self.devices if isinstance(self.devices,
list) else [self.devices]
self.devices = [torch.device(device) for device in devices]
if 'cuda' in self.devices[0].type:
torch.cuda.set_device(self.devices[0])
# seed the random number generators
for device in self.devices:
nn_util.init_random_seed(self.config['seed'], device)
agent_name = self.config.get('parser', 'vanilla')
self.agent = get_parser_agent_by_name(agent_name).build(
self.config, master='learner').to(self.devices[0]).train()
use_trainable_sketch_predictor = self.config.get(
'use_trainable_sketch_predictor', False)
if use_trainable_sketch_predictor:
assert len(self.devices) > 1
if 'cuda' in self.devices[1].type:
torch.cuda.set_device(self.devices[1])
self.sketch_predictor = SketchPredictor.build(
self.config).train().to(self.devices[1])
self.sketch_predictor_trainer = SketchPredictorTrainer(
self.sketch_predictor, self.config['max_train_step'], 0,
self.config)
self.train()
def run(self):
# initialize cuda context
self.device = torch.device(self.device)
if 'cuda' in self.device.type:
torch.cuda.set_device(self.device)
# seed the random number generators
nn_util.init_random_seed(self.config['seed'], self.device)
def get_train_shard_path(i):
return os.path.join(
self.config['train_shard_dir'], self.config['train_shard_prefix'] + str(i) + '.jsonl')
# create agent and set it to evaluation mode
if 'cuda' in str(self.device.type):
torch.cuda.set_device(self.device)
agent_name = self.config.get('parser', 'vanilla')
self.agent = get_parser_agent_by_name(agent_name).build(self.config, master=self.actor_id).to(self.device).eval()
# initialize sketch predictor
use_trainable_sketch_predictor = self.config.get('use_trainable_sketch_predictor', False)
if use_trainable_sketch_predictor:
self.sketch_predictor = SketchPredictorProxy()
self.sketch_predictor.initialize(self)
if self.config.get('actor_use_table_bert_proxy', False):
# initialize proxy
self.agent.encoder.bert_model.initialize(self)
# load environments
self.load_environments(
[
get_train_shard_path(i)
for i
in range(self.config['shard_start_id'], self.config['shard_end_id'])
],
example_ids=self.example_ids
)
if self.use_consistency_model:
print('Load consistency model', file=sys.stderr)
self.consistency_model = ConsistencyModel(QuestionSimilarityModel.load(self.config['question_similarity_model_path']),
self.shared_program_cache,
self.environments,
alpha=float(self.config['consistency_alpha']),
log_file=os.path.join(self.config['work_dir'], f'consistency_model_actor_{self.actor_id}.log'),
debug=self.actor_id == 0)
self.replay_buffer = ReplayBuffer(self.agent, self.shared_program_cache)
if self.config['load_saved_programs']:
self.replay_buffer.load(self.environments, self.config['saved_program_file'])
print(f'[Actor {self.actor_id}] loaded {self.replay_buffer.size} programs to buffer', file=sys.stderr)
self.train()
def run(self):
# initialize cuda context
devices = self.devices if isinstance(self.devices,
list) else [self.devices]
self.devices = [torch.device(device) for device in devices]
if 'cuda' in self.devices[0].type:
torch.cuda.set_device(self.devices[0])
# seed the random number generators
for device in self.devices:
nn_util.init_random_seed(self.config['seed'], device)
agent_name = self.config.get('parser', 'vanilla')
self.agent = get_parser_agent_by_name(agent_name).build(
self.config, master='learner').to(self.devices[0]).train()
model_path = os.path.join(self.config['work_dir'], 'model.best.bin')
if os.path.exists(model_path):
print("Loading from saved model.")
self.agent.load(model_path)
else:
print("No model found at %s." % model_path)
sys.stdout.flush()
# checkload s= input('Check if model loaded.')
use_trainable_sketch_predictor = self.config.get(
'use_trainable_sketch_predictor', False)
if use_trainable_sketch_predictor:
assert len(self.devices) > 1
if 'cuda' in self.devices[1].type:
torch.cuda.set_device(self.devices[1])
self.sketch_predictor = SketchPredictor.build(
self.config).train().to(self.devices[1])
self.sketch_predictor_trainer = SketchPredictorTrainer(
self.sketch_predictor, self.config['max_train_step'], 0,
self.config)
self.psi_queue.put(self.current_psi)
self.train()
def run(self):
# initialize cuda context
self.device = torch.device(self.device)
if 'cuda' in self.device.type:
torch.cuda.set_device(self.device)
# seed the random number generators
nn_util.init_random_seed(self.config['seed'], self.device)
agent_name = self.config.get('parser', 'vanilla')
self.agent = get_parser_agent_by_name(agent_name).build(
self.config, master='evaluator').to(self.device).eval()
self.load_environments()
summary_writer = SummaryWriter(
os.path.join(self.config['work_dir'], 'tb_log/dev'))
dev_scores = []
while True:
if self.check_and_load_new_model():
print(f'[Evaluator] evaluate model [{self.model_path}]',
file=sys.stderr)
t1 = time.time()
decode_results = self.agent.decode_examples(
self.environments,
beam_size=self.config['beam_size'],
batch_size=32)
eval_results = Evaluation.evaluate_decode_results(
self.environments, decode_results)
t2 = time.time()
print(
f'[Evaluator] step={self.get_global_step()}, result={repr(eval_results)}, took {t2 - t1}s',
file=sys.stderr)
summary_writer.add_scalar('eval/accuracy',
eval_results['accuracy'],
self.get_global_step())
summary_writer.add_scalar('eval/oracle_accuracy',
eval_results['oracle_accuracy'],
self.get_global_step())
dev_score = eval_results['accuracy']
if not dev_scores or max(dev_scores) < dev_score:
print(f'[Evaluator] save the current best model',
file=sys.stderr)
with open(os.path.join(self.config['work_dir'], 'dev.log'),
'w') as f:
f.write(json.dumps(eval_results))
self.agent.save(
os.path.join(self.config['work_dir'],
'model.best.bin'))
dev_scores.append(dev_score)
sys.stderr.flush()
time.sleep(2) # in seconds
