def train_loop(args,
logger,
vocab,
train_progs,
infer_progs,
optimizer,
nccl2_num_trainers=1,
nccl2_trainer_id=0,
worker_endpoints=None):
train_prog, train_startup_prog, train_model = train_progs
infer_prog, infer_startup_prog, infer_model = infer_progs
# prepare device
place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
exe = Executor(place)
if not args.use_gpu:
place = fluid.CPUPlace()
import multiprocessing
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
else:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
if args.load_dir:
logger.info('load pretrained checkpoints from {}'.format(
args.load_dir))
fluid.io.load_persistables(exe, args.load_dir, main_program=train_prog)
elif args.load_pretraining_params:
logger.info('load pretrained params from {}'.format(
args.load_pretraining_params))
exe.run(train_startup_prog)
init_pretraining_params(exe,
args.load_pretraining_params,
main_program=train_prog)
else:
exe.run(train_startup_prog)
# prepare data
feed_list = [
train_prog.global_block().var(var_name)
for var_name in train_model.feed_order
]
feeder = fluid.DataFeeder(feed_list, place)
logger.info('Training the model...')
exe_strategy = fluid.parallel_executor.ExecutionStrategy()
parallel_executor = fluid.ParallelExecutor(loss_name=train_model.loss.name,
main_program=train_prog,
use_cuda=bool(args.use_gpu),
exec_strategy=exe_strategy,
num_trainers=nccl2_num_trainers,
trainer_id=nccl2_trainer_id)
logger.info("begin to load data")
train_data = data.BidirectionalLMDataset(args.train_path,
vocab,
test=(not args.shuffle),
shuffle_on_load=args.shuffle)
logger.info("finished load vocab")
# get train epoch size
log_interval = args.log_interval
total_time = 0.0
batch_size = args.batch_size
hidden_size = args.hidden_size
custom_samples_array = np.zeros(
(batch_size, args.num_steps, args.n_negative_samples_batch + 1),
dtype='int64')
custom_probabilities_array = np.zeros(
(batch_size, args.num_steps, args.n_negative_samples_batch + 1),
dtype='float32')
for i in range(batch_size):
for j in range(0, args.num_steps):
for k in range(0, args.n_negative_samples_batch + 1):
custom_samples_array[i][j][k] = k
custom_probabilities_array[i][j][k] = 1.0
start_time = time.time()
train_data_iter = lambda: train_data.iter_batches(batch_size * dev_count,
args.num_steps)
train_reader = read_multiple(train_data_iter, batch_size, dev_count)
total_num = 0
n_batch_loss = 0.0
n_batch_cnt = 0
last_hidden_values = np.zeros(
(dev_count, args.num_layers * 2 * batch_size * args.embed_size),
dtype='float32')
last_cell_values = np.zeros(
(dev_count, args.num_layers * 2 * batch_size * hidden_size),
dtype='float32')
n_tokens_per_batch = args.batch_size * args.num_steps
n_batches_per_epoch = int(args.all_train_tokens / n_tokens_per_batch)
n_batches_total = args.max_epoch * n_batches_per_epoch
begin_time = time.time()
for batch_id, batch_list in enumerate(train_reader(), 1):
if batch_id > n_batches_total:
break
feed_data = batch_reader(batch_list, args)
feed = list(feeder.feed_parallel(feed_data, dev_count))
for i in range(dev_count):
init_hidden_tensor = fluid.core.LoDTensor()
if args.use_gpu:
placex = fluid.CUDAPlace(i)
else:
placex = fluid.CPUPlace()
init_hidden_tensor.set(last_hidden_values[i], placex)
init_cell_tensor = fluid.core.LoDTensor()
init_cell_tensor.set(last_cell_values[i], placex)
feed[i]['init_hiddens'] = init_hidden_tensor
feed[i]['init_cells'] = init_cell_tensor
fetch_outs = parallel_executor.run(feed=feed,
fetch_list=[
train_model.loss.name,
train_model.last_hidden.name,
train_model.last_cell.name
],
return_numpy=False)
cost_train = np.array(fetch_outs[0]).mean()
last_hidden_values = np.array(fetch_outs[1])
last_hidden_values = last_hidden_values.reshape(
(dev_count, args.num_layers * 2 * batch_size * args.embed_size))
last_cell_values = np.array(fetch_outs[2])
last_cell_values = last_cell_values.reshape(
(dev_count, args.num_layers * 2 * batch_size * args.hidden_size))
total_num += args.batch_size * dev_count
n_batch_loss += np.array(fetch_outs[0]).sum()
n_batch_cnt += len(np.array(fetch_outs[0]))
if batch_id > 0 and batch_id % log_interval == 0:
smoothed_ppl = np.exp(n_batch_loss / n_batch_cnt)
ppl = np.exp(
np.array(fetch_outs[0]).sum() / len(np.array(fetch_outs[0])))
used_time = time.time() - begin_time
speed = log_interval / used_time
logger.info(
"[train] step:{}, loss:{:.3f}, ppl:{:.3f}, smoothed_ppl:{:.3f}, speed:{:.3f}"
.format(batch_id, n_batch_loss / n_batch_cnt, ppl,
smoothed_ppl, speed))
n_batch_loss = 0.0
n_batch_cnt = 0
begin_time = time.time()
if batch_id > 0 and batch_id % args.dev_interval == 0:
valid_ppl = eval(vocab, infer_progs, dev_count, logger, args)
logger.info("valid ppl {}".format(valid_ppl))
if batch_id > 0 and batch_id % args.save_interval == 0:
model_path = os.path.join(args.para_save_dir,
str(batch_id + epoch_id))
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(executor=exe,
dirname=model_path,
main_program=train_prog)
end_time = time.time()
total_time += end_time - start_time
epoch_id = int(batch_id / n_batches_per_epoch)
model_path = os.path.join(args.para_save_dir, str(epoch_id))
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(executor=exe,
dirname=model_path,
main_program=train_prog)
valid_ppl = eval(vocab, infer_progs, dev_count, logger, args)
logger.info("valid ppl {}".format(valid_ppl))
test_ppl = eval(vocab, infer_progs, dev_count, logger, args)
def eval(vocab, infer_progs, dev_count, logger, args):
infer_prog, infer_startup_prog, infer_model = infer_progs
feed_order = infer_model.feed_order
loss = infer_model.loss
# prepare device
place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
exe = Executor(place)
if not args.use_gpu:
place = fluid.CPUPlace()
import multiprocessing
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
else:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
total_loss = 0.0
total_cnt = 0
n_batch_cnt = 0
n_batch_loss = 0.0
val_feed_list = [
infer_prog.global_block().var(var_name) for var_name in feed_order
]
val_feeder = fluid.DataFeeder(val_feed_list, place)
dev_data = data.BidirectionalLMDataset(args.test_path,
vocab,
test=True,
shuffle_on_load=False)
dev_data_iter = lambda: dev_data.iter_batches(args.batch_size * dev_count,
args.num_steps)
dev_reader = read_multiple(dev_data_iter, args.batch_size, dev_count)
last_hidden_values = np.zeros(
(dev_count, args.num_layers * 2 * args.batch_size * args.embed_size),
dtype='float32')
last_cell_values = np.zeros(
(dev_count, args.num_layers * 2 * args.batch_size * args.hidden_size),
dtype='float32')
for batch_id, batch_list in enumerate(dev_reader(), 1):
feed_data = batch_reader(batch_list, args)
feed = list(val_feeder.feed_parallel(feed_data, dev_count))
for i in range(dev_count):
init_hidden_tensor = fluid.core.LoDTensor()
if args.use_gpu:
placex = fluid.CUDAPlace(i)
else:
placex = fluid.CPUPlace()
init_hidden_tensor.set(last_hidden_values[i], placex)
init_cell_tensor = fluid.core.LoDTensor()
init_cell_tensor.set(last_cell_values[i], placex)
feed[i]['init_hiddens'] = init_hidden_tensor
feed[i]['init_cells'] = init_cell_tensor
last_hidden_values = []
last_cell_values = []
for i in range(dev_count):
val_fetch_outs = exe.run(program=infer_prog,
feed=feed[i],
fetch_list=[
infer_model.loss.name,
infer_model.last_hidden.name,
infer_model.last_cell.name
],
return_numpy=False)
last_hidden_values.append(np.array(val_fetch_outs[1]))
last_cell_values.append(np.array(val_fetch_outs[2]))
total_loss += np.array(val_fetch_outs[0]).sum()
n_batch_cnt += len(np.array(val_fetch_outs[0]))
total_cnt += len(np.array(val_fetch_outs[0]))
n_batch_loss += np.array(val_fetch_outs[0]).sum()
last_hidden_values = np.array(last_hidden_values).reshape(
(dev_count,
args.num_layers * 2 * args.batch_size * args.embed_size))
last_cell_values = np.array(last_cell_values).reshape(
(dev_count,
args.num_layers * 2 * args.batch_size * args.hidden_size))
log_every_n_batch = args.log_interval
if log_every_n_batch > 0 and batch_id % log_every_n_batch == 0:
logger.info('Average dev loss from batch {} to {} is {}'.format(
batch_id - log_every_n_batch + 1, batch_id,
"%.10f" % (n_batch_loss / n_batch_cnt)))
n_batch_loss = 0.0
n_batch_cnt = 0
batch_offset = 0
ppl = np.exp(total_loss / total_cnt)
return ppl
