def worker(args, queue, worker_id):
#lm = config.lm
if args.partial_lm:
lm = build_language_model(args.num_props, new=args.gen_lm, _all=False)
else:
lm = load_language_model(_all=False, new=args.gen_lm)
if args.data_sampling > 0:
load_proof_steps_into_lm(lm, ['train'], args.data_sampling)
#lm = load_language_model(_all=False, new=args.gen_lm)
#load_proof_steps_into_lm(lm, ['train'], args.data_sampling)
config = get_config(lm)
device_idx = args.num_gpus - 1 - (worker_id // args.num_workers)
if args.random_generate:
device_idx = 0
device_id = args.gpu_list[device_idx]
print ('build a worker on gpu %d' % (device_id))
torch.cuda.set_device(device_id)
args.device = torch.device('cuda:'+str(device_id))
interface = None if args.random_generate else interface_lm.LMInterface(args, lm)
generator = Constructor(args, config, interface)
generator.initialize()
_logger = log.get_logger('worker%d'%(worker_id), args, append=True)
_logger.info('worker %d initialize', worker_id)
tt = 0
cnt = 0
while True:
t = time.time()
if args.random_generate:
expr = generator.random_generate()
else:
expr = generator.parameterized_generate()
tt += time.time() - t
if expr is not None:
if args.task == 'pred' and len(expr.prop.e) > 0:
queue.put(generator.encode_pred_tasks([expr]))
if args.task == 'gen' and len(expr.unconstrained) > 0:
queue.put(generator.encode_gen_tasks([expr]))
#if not (args.task == 'gen' and len(expr.unconstrained) == 0):
# if args.task == 'pred':
# queue.put(generator.encode_pred_tasks([expr]))
# else:
# queue.put(generator.encode_gen_tasks([expr]))
if len(generator.expressions_list) > args.num_cons_exprs+generator.num_initial_expr:
generator.reinitialize_expressions()
_logger.info('worker %d initialize', worker_id)
if cnt == 5000:
_logger.info('worker %d generate time per expr %s seconds', worker_id, tt/cnt)
cnt = 0
tt = 0
cnt += 1
def worker_pre(args, queue, batch_size, ii):
_logger = log.get_logger('worker_pre', args, append=True)
_logger.info('worker_pre initialize')
if args.partial_lm:
lm = build_language_model(args.num_props, new=args.gen_lm, _all=False)
else:
lm = load_language_model(_all=False, new=args.gen_lm, iset=args.iset)
config = get_config(lm)
#exprs = load_exprs(args.expr_list[0], lm)
#interface = interface_lm.LMInterface(args, lm)
#generator = Constructor(args, config)
#generator.initialize_prop()
#generator.expressions_list = exprs
#for e in exprs:
# generator.expressions[e.id] = e
#generator.num_initial_expr = len(generator.expressions)
#generator.initialize_searcher()
#_logger.info('initialize generator with %d exprs', generator.num_initial_expr)
fl = os.listdir(args.exprs_pre)
if True:
if args.data_sampling > 0:
exprs = load_exprs(args.expr_list[0], lm)
generator = Constructor(args, config)
generator.initialize_prop()
generator.expressions_list = exprs
for e in exprs:
generator.expressions[e.id] = e
generator.num_initial_expr = len(generator.expressions)
generator.initialize_searcher()
else:
generator = Constructor(args, config)
generator.initialize()
#generator.reinitialize_expressions()
print ('--loading pre exprs--')
exprs_pre = load_exprs(os.path.join(args.exprs_pre, fl[ii]), lm)
print ('--done--')
#if args.train_from_queue:
# exprs_pre = exprs_pre[:300000]
generator.expressions_list += exprs_pre
for e in exprs_pre:
generator.expressions[e.id] = e
_logger.info('load %d exprs' % (len(generator.expressions)))
i = 0
while True:
#for i in range(len(exprs_pre)//batch_size//5):
_exprs = []
while len(_exprs) < batch_size:
expr = random.choice(exprs_pre)
if args.task == 'gen' and len(expr.unconstrained) == 0:
continue
if args.task == 'pred' and len(expr.prop.e) == 0:
continue
_exprs.append(expr)
if args.task == 'pred':
data = generator.encode_pred_tasks(_exprs)
else:
data = generator.encode_gen_tasks(_exprs)
#print (i, data)
queue.put(data)
i += 1
if i >= len(exprs_pre)//batch_size//5 and (not args.train_from_queue) and (not args.cons_pre_one):
break
print ('finish processing current exprs')
