def eval(data):
# when eval the batch_size set to 1
eval_data_iter = reader.get_data_iter(data, config.batch_size,
config.num_steps)
total_loss = 0.0
iters = 0
init_hidden, init_cell = generate_init_data()
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed = prepare_input(
batch, init_hidden, init_cell, epoch_id=0, with_lr=False)
fetch_outs = exe.run(
program=inference_program,
feed=input_data_feed,
fetch_list=[loss.name, last_hidden.name, last_cell.name],
use_program_cache=False)
cost_eval = np.array(fetch_outs[0])
init_hidden = np.array(fetch_outs[1])
init_cell = np.array(fetch_outs[2])
total_loss += cost_eval
iters += config.num_steps
ppl = np.exp(total_loss / iters)
return ppl
def eval(data):
# when eval the batch_size set to 1
eval_data_iter = reader.get_data_iter(data, batch_size, num_steps)
total_loss = 0.0
iters = 0
init_hidden = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed = prepare_input(batch,
init_hidden,
init_cell,
epoch_id,
with_lr=False)
fetch_outs = exe.run(
inference_program,
feed=input_data_feed,
fetch_list=[loss.name, last_hidden.name, last_cell.name],
use_program_cache=True)
cost_train = np.array(fetch_outs[0])
init_hidden = np.array(fetch_outs[1])
init_cell = np.array(fetch_outs[2])
total_loss += cost_train
iters += num_steps
ppl = np.exp(total_loss / iters)
return ppl
def eval(model, data):
print("begion to eval")
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
model.eval()
train_data_iter = reader.get_data_iter(data, batch_size, num_steps)
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, 1))
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(
x, y, init_hidden, init_cell)
out_loss = dy_loss.numpy()
init_hidden_data = last_hidden.numpy()
init_cell_data = last_cell.numpy()
total_loss += out_loss
iters += num_steps
print("eval finished")
ppl = np.exp(total_loss / iters)
print("ppl ", batch_id, ppl[0])
if args.ce:
print("kpis\ttest_ppl\t%0.3f" % ppl[0])
def eval(model, data):
print("begin to eval")
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
model.eval()
train_data_iter = reader_decorator(
reader.get_data_iter(data, batch_size, num_steps))
eval_data_loader = fluid.io.DataLoader.from_generator(capacity=200)
eval_data_loader.set_batch_generator(train_data_iter, places=place)
for batch_id, batch in enumerate(eval_data_loader):
x, y = batch
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(
x, y, init_hidden, init_cell)
out_loss = dy_loss.numpy()
init_hidden_data = last_hidden.numpy()
init_cell_data = last_cell.numpy()
total_loss += out_loss
iters += num_steps
print("eval finished")
ppl = np.exp(total_loss / iters)
print("ppl ", batch_id, ppl[0])
def data_gen():
data_iter_size = config.batch_size
train_batches = reader.get_data_iter(train_data, data_iter_size,
config.num_steps)
for batch in train_batches:
x, y = batch
x = x.reshape((-1, config.num_steps, 1))
y = y.reshape((-1, 1))
yield x, y
def data_gen():
data_iter_size = batch_size // device_count
train_batches = reader.get_data_iter(train_data,
data_iter_size, num_steps)
for batch in train_batches:
x, y = batch
x = x.reshape((-1, num_steps, 1))
y = y.reshape((-1, 1))
yield x, y
def fetch_loss_grad(x):
data_iter = reader.get_data_iter(x, 1)
for batch_id, batch in enumerate(data_iter):
input_data_feed = prepare_input(batch)
result = exe.run(test_program,
fetch_list=fetch_list,
feed=input_data_feed)
loss = result[0]
grad = result[1]
return loss, grad
def eval(data, epoch_id=0):
model.eval()
eval_data_iter = reader.get_data_iter(data, batch_size, mode='eval')
total_loss = 0.0
word_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, word_num = prepare_input(
batch, epoch_id)
loss = model(input_data_feed)
total_loss += loss * batch_size
word_count += word_num
ppl = np.exp(total_loss.numpy() / word_count)
model.train()
return ppl
def train():
startup_program = fluid.default_startup_program()
main_program = fluid.default_main_program()
raw_data = reader.raw_data('fra.txt', num_samples=num_samples)
train_data = raw_data[0]
data_vars = raw_data[1]
model = BaseModel(hidden_size=latent_dim,
src_vocab_size=data_vars['num_encoder_tokens'],
tar_vocab_size=data_vars['num_decoder_tokens'],
batch_size=batch_size,
batch_first=True)
loss = model.build_graph()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(loss)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
ce_ppl = []
for epoch_id in range(num_epochs):
print("epoch ", epoch_id)
train_data_iter = reader.get_data_iter(train_data, batch_size)
total_loss = 0
word_count = 0.0
for batch_id, batch in enumerate(train_data_iter):
input_data_feed, word_num = prepare_input(batch, epoch_id=epoch_id)
fetch_outs = exe.run(feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=True)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += word_num
if batch_id > 0 and batch_id % batch_size == 0:
print(" ppl", batch_id, np.exp(total_loss / word_count))
ce_ppl.append(np.exp(total_loss / word_count))
total_loss = 0.0
word_count = 0.0
def train_an_epoch(epoch_id, batch_times):
# get train epoch size
log_interval = get_log_interval(len(train_data))
train_data_iter = reader.get_data_iter(
train_data, config.batch_size * device_count, config.num_steps)
total_loss = 0
iters = 0
init_hidden, init_cell = generate_init_data()
for batch_id, batch in enumerate(train_data_iter):
input_data_feed = prepare_input(batch,
init_hidden=init_hidden,
init_cell=init_cell,
epoch_id=epoch_id,
with_lr=True,
device_count=device_count)
batch_start_time = time.time()
fetch_outs = exe.run(train_program,
feed=input_data_feed,
fetch_list=[
loss.name, "learning_rate",
last_hidden.name, last_cell.name
],
use_program_cache=True)
batch_time = time.time() - batch_start_time
batch_times.append(batch_time)
cost_train = np.array(fetch_outs[0])
lr = np.array(fetch_outs[1])
init_hidden = np.array(fetch_outs[2])
init_cell = np.array(fetch_outs[3])
total_loss += cost_train
iters += config.num_steps
if batch_id > 0 and batch_id % log_interval == 0:
ppl = np.exp(total_loss / iters)
print(
"-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f, lr: %.5f"
% (epoch_id, batch_id, batch_time, ppl[0], lr[0]))
# profiler tools for benchmark
if args.profile and batch_id == log_interval:
profiler.reset_profiler()
elif args.profile and batch_id == (log_interval + 5):
break
ppl = np.exp(total_loss / iters)
return ppl
def train_an_epoch(epoch_id, batch_times):
# get train epoch size
log_interval = get_log_interval(len(train_data), batch_size)
train_data_iter = reader.get_data_iter(train_data, batch_size,
num_steps)
total_loss = 0
iters = 0
for batch_id, batch in enumerate(train_data_iter):
if batch_id == 0:
init_hidden, init_cell = get_init_data()
else:
init_hidden = None
init_cell = None
input_data_feed = prepare_input(batch,
init_hidden=init_hidden,
init_cell=init_cell,
epoch_id=epoch_id,
device_count=device_count)
batch_start_time = time.time()
fetch_outs = exe.run(train_program,
feed=input_data_feed,
fetch_list=[loss.name, "learning_rate"],
use_program_cache=True)
batch_time = time.time() - batch_start_time
batch_times.append(batch_time)
cost_train = np.array(fetch_outs[0])
lr = np.array(fetch_outs[1])
total_loss += cost_train
iters += num_steps
if batch_id > 0 and batch_id % log_interval == 0:
ppl = np.exp(total_loss / iters)
print(
"-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f, lr: %.5f"
% (epoch_id, batch_id, batch_time, ppl[0], lr[0]))
if args.profile:
if batch_id == 1:
profiler.reset_profiler()
elif batch_id >= 11:
break
ppl = np.exp(total_loss / iters)
return ppl
def eval(sess, data):
if args.inference_only:
sess.run(init)
batch_times = []
start_time = time.time()
eval_loss = 0.0
eval_iters = 0
eval_data_iter = reader.get_data_iter(data, batch_size, num_steps)
init_h = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_c = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
for batch in eval_data_iter:
x, y = batch
feed_dict = {}
feed_dict[feeding_list[0]] = x
feed_dict[feeding_list[1]] = y
feed_dict[feeding_list[2]] = init_h
feed_dict[feeding_list[3]] = init_c
batch_start_time = time.time()
output = sess.run([cost, final_h, final_c], feed_dict)
batch_times.append(time.time() - batch_start_time)
train_cost = output[0]
init_h = output[1]
init_c = output[2]
eval_loss += train_cost
eval_iters += num_steps
ppl = np.exp(eval_loss / eval_iters)
eval_time_total = time.time() - start_time
eval_time_run = np.sum(batch_times)
if args.inference_only:
print(
"Eval batch_size: %d; Time (total): %.5f s; Time (only run): %.5f s; ppl: %.5f"
% (batch_size, eval_time_total, eval_time_run, ppl))
return ppl, eval_time_total
def eval(model, data):
print("begion to eval")
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
model.eval()
train_data_iter = reader.get_data_iter(data, batch_size, num_steps)
init_hidden = paddle.to_tensor(data=init_hidden_data,
dtype=None,
place=None,
stop_gradient=True)
accum_num_recall = 0.0
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, num_steps, 1))
x = paddle.to_tensor(data=x_data,
dtype=None,
place=None,
stop_gradient=True)
y = paddle.to_tensor(data=y_data,
dtype=None,
place=None,
stop_gradient=True)
dy_loss, last_hidden, acc = ptb_model(x, y, init_hidden)
out_loss = dy_loss.numpy()
acc_ = acc.numpy()[0]
accum_num_recall += acc_
if batch_id % 1 == 0:
print("batch_id:%d recall@20:%.4f" %
(batch_id, accum_num_recall / (batch_id + 1)))
init_hidden = last_hidden
total_loss += out_loss
iters += num_steps
print("eval finished")
ppl = np.exp(total_loss / iters)
print("recall@20 ", accum_num_recall / (batch_id + 1))
if args.ce:
print("kpis\ttest_ppl\t%0.3f" % ppl[0])
def data_gen():
data_iter_size = config.batch_size // device_count
train_batches = reader.get_data_iter(train_data, data_iter_size,
config.num_steps)
for batch in train_batches:
x, y = batch
x = x.reshape((-1, config.num_steps, 1))
y = y.reshape((-1, 1))
if args.rnn_model == "lod":
x = to_lodtensor(x.reshape((-1, 1)), place, [
range(0, (data_iter_size + 1) * config.num_steps,
config.num_steps)
])
y = to_lodtensor(y.reshape((-1, 1)), place, [
range(0, (data_iter_size + 1) * config.num_steps,
config.num_steps)
])
yield x, y
def eval(data, epoch_id=0):
eval_data_iter = reader.get_data_iter(data, batch_size, mode='eval')
total_loss = 0.0
word_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, word_num = prepare_input(batch,
epoch_id,
with_lr=False)
fetch_outs = exe.run(inference_program,
feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += word_num
ppl = np.exp(total_loss / word_count)
return ppl
def eval(data):
eval_data_iter = reader.get_data_iter(data, batch_size, mode='eval')
total_loss = 0.0
word_count = 0.0
batch_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, src_word_num, dec_word_sum = prepare_input(batch)
fetch_outs = exe.run(inference_program,
feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += dec_word_sum
batch_count += batch_size
nll = total_loss / batch_count
ppl = np.exp(total_loss / word_count)
return nll, ppl
def train():
ce_time = []
ce_ppl = []
max_epoch = args.max_epoch
kl_w = args.kl_start
lr_w = args.learning_rate
best_valid_nll = 1e100 # +inf
best_epoch_id = -1
decay_cnt = 0
max_decay = args.max_decay
decay_factor = 0.5
decay_ts = 2
steps_not_improved = 0
for epoch_id in range(max_epoch):
start_time = time.time()
if args.enable_ce:
train_data_iter = reader.get_data_iter(train_data,
batch_size,
args.sort_cache,
args.cache_num,
enable_ce=True)
else:
train_data_iter = reader.get_data_iter(train_data, batch_size,
args.sort_cache,
args.cache_num)
total_loss = 0
total_rec_loss = 0
total_kl_loss = 0
word_count = 0.0
batch_count = 0.0
batch_times = []
for batch_id, batch in enumerate(train_data_iter):
batch_start_time = time.time()
kl_w = min(1.0, kl_w + anneal_r)
kl_weight = kl_w
input_data_feed, src_word_num, dec_word_sum = prepare_input(
batch, kl_weight, lr_w)
fetch_outs = exe.run(
program=train_program,
feed=input_data_feed,
fetch_list=[loss.name, kl_loss.name, rec_loss.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
kl_cost_train = np.array(fetch_outs[1])
rec_cost_train = np.array(fetch_outs[2])
total_loss += cost_train * batch_size
total_rec_loss += rec_cost_train * batch_size
total_kl_loss += kl_cost_train * batch_size
word_count += dec_word_sum
batch_count += batch_size
batch_end_time = time.time()
batch_time = batch_end_time - batch_start_time
batch_times.append(batch_time)
if batch_id > 0 and batch_id % 200 == 0:
print("-- Epoch:[%d]; Batch:[%d]; Time: %.4f s; "
"kl_weight: %.4f; kl_loss: %.4f; rec_loss: %.4f; "
"nll: %.4f; ppl: %.4f" %
(epoch_id, batch_id, batch_time, kl_w,
total_kl_loss / batch_count, total_rec_loss /
batch_count, total_loss / batch_count,
np.exp(total_loss / word_count)))
ce_ppl.append(np.exp(total_loss / word_count))
end_time = time.time()
epoch_time = end_time - start_time
ce_time.append(epoch_time)
print(
"\nTrain epoch:[%d]; Epoch Time: %.4f; avg_time: %.4f s/step\n"
% (epoch_id, epoch_time, sum(batch_times) / len(batch_times)))
val_nll, val_ppl = eval(valid_data)
print("dev ppl", val_ppl)
test_nll, test_ppl = eval(test_data)
print("test ppl", test_ppl)
if val_nll < best_valid_nll:
best_valid_nll = val_nll
steps_not_improved = 0
best_nll = test_nll
best_ppl = test_ppl
best_epoch_id = epoch_id
save_path = os.path.join(args.model_path,
"epoch_" + str(best_epoch_id),
"checkpoint")
print("save model {}".format(save_path))
fluid.save(main_program, save_path)
else:
steps_not_improved += 1
if steps_not_improved == decay_ts:
old_lr = lr_w
lr_w *= decay_factor
steps_not_improved = 0
new_lr = lr_w
print('-----\nchange lr, old lr: %f, new lr: %f\n-----' %
(old_lr, new_lr))
dir_name = args.model_path + "/epoch_" + str(best_epoch_id)
fluid.load(main_program, dir_name, exe)
decay_cnt += 1
if decay_cnt == max_decay:
break
print('\nbest testing nll: %.4f, best testing ppl %.4f\n' %
(best_nll, best_ppl))
if args.enable_ce:
card_num = get_cards()
_ppl = 0
_time = 0
try:
_time = ce_time[-1]
_ppl = ce_ppl[-1]
except:
print("ce info error")
print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
print("kpis\ttrain_ppl_card%s\t%f" % (card_num, _ppl))
def train_ptb_lm():
args = parse_args()
model_type = args.model_type
vocab_size = 10000
if model_type == "test":
num_layers = 1
batch_size = 2
hidden_size = 10
num_steps = 3
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 1
max_epoch = 1
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "small":
num_layers = 2
batch_size = 20
hidden_size = 200
num_steps = 20
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 4
max_epoch = 13
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "medium":
num_layers = 2
batch_size = 20
hidden_size = 650
num_steps = 35
init_scale = 0.05
max_grad_norm = 5.0
epoch_start_decay = 6
max_epoch = 39
dropout = 0.5
lr_decay = 0.8
base_learning_rate = 1.0
elif model_type == "large":
num_layers = 2
batch_size = 20
hidden_size = 1500
num_steps = 35
init_scale = 0.04
max_grad_norm = 10.0
epoch_start_decay = 14
max_epoch = 55
dropout = 0.65
lr_decay = 1.0 / 1.15
base_learning_rate = 1.0
else:
print("model type not support")
return
with fluid.dygraph.guard(core.CUDAPlace(0)):
if args.ce:
print("ce mode")
seed = 33
np.random.seed(seed)
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
max_epoch = 1
ptb_model = PtbModel("ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale,
dropout=dropout)
dy_param_updated = dict()
dy_param_init = dict()
dy_loss = None
last_hidden = None
last_cell = None
data_path = args.data_path
print("begin to load data")
ptb_data = reader.get_ptb_data(data_path)
print("finished load data")
train_data, valid_data, test_data = ptb_data
batch_len = len(train_data) // batch_size
total_batch_size = (batch_len - 1) // num_steps
log_interval = total_batch_size // 20
bd = []
lr_arr = [1.0]
for i in range(1, max_epoch):
bd.append(total_batch_size * i)
new_lr = base_learning_rate * (lr_decay**max(
i + 1 - epoch_start_decay, 0.0))
lr_arr.append(new_lr)
sgd = SGDOptimizer(learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr_arr))
def eval(model, data):
print("begion to eval")
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
model.eval()
train_data_iter = reader.get_data_iter(data, batch_size, num_steps)
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, 1))
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(
x, y, init_hidden, init_cell)
out_loss = dy_loss.numpy()
init_hidden_data = last_hidden.numpy()
init_cell_data = last_cell.numpy()
total_loss += out_loss
iters += num_steps
print("eval finished")
ppl = np.exp(total_loss / iters)
print("ppl ", batch_id, ppl[0])
if args.ce:
print("kpis\ttest_ppl\t%0.3f" % ppl[0])
grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(max_grad_norm)
for epoch_id in range(max_epoch):
ptb_model.train()
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
train_data_iter = reader.get_data_iter(train_data, batch_size,
num_steps)
start_time = time.time()
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, 1))
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(
x, y, init_hidden, init_cell)
out_loss = dy_loss.numpy()
init_hidden_data = last_hidden.numpy()
init_cell_data = last_cell.numpy()
dy_loss.backward()
sgd.minimize(dy_loss, grad_clip=grad_clip)
ptb_model.clear_gradients()
total_loss += out_loss
iters += num_steps
if batch_id > 0 and batch_id % log_interval == 0:
ppl = np.exp(total_loss / iters)
print(epoch_id, "ppl ", batch_id, ppl[0],
sgd._global_learning_rate().numpy())
print("one ecpoh finished", epoch_id)
print("time cost ", time.time() - start_time)
ppl = np.exp(total_loss / iters)
print("ppl ", epoch_id, ppl[0])
if args.ce:
print("kpis\ttrain_ppl\t%0.3f" % ppl[0])
eval(ptb_model, test_data)
def train_ptb_lm():
args = parse_args()
# check if set use_gpu=True in paddlepaddle cpu version
model_check.check_cuda(args.use_gpu)
# check if paddlepaddle version is satisfied
model_check.check_version()
model_type = args.model_type
vocab_size = 37484
if model_type == "test":
num_layers = 1
batch_size = 2
hidden_size = 10
num_steps = 4
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 1
max_epoch = 1
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "small":
num_layers = 2
batch_size = 20
hidden_size = 200
num_steps = 20
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 4
max_epoch = 2
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "gru4rec":
num_layers = 1
batch_size = 500
hidden_size = 100
num_steps = 10
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 10
max_epoch = 5
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 0.05
elif model_type == "medium":
num_layers = 2
batch_size = 20
hidden_size = 650
num_steps = 35
init_scale = 0.05
max_grad_norm = 5.0
epoch_start_decay = 6
max_epoch = 39
dropout = 0.5
lr_decay = 0.8
base_learning_rate = 1.0
elif model_type == "large":
num_layers = 2
batch_size = 20
hidden_size = 1500
num_steps = 35
init_scale = 0.04
max_grad_norm = 10.0
epoch_start_decay = 14
max_epoch = 55
dropout = 0.65
lr_decay = 1.0 / 1.15
base_learning_rate = 1.0
else:
print("model type not support")
return
with fluid.dygraph.guard(core.CUDAPlace(0)):
if args.ce:
print("ce mode")
seed = 33
np.random.seed(seed)
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
max_epoch = 1
ptb_model = PtbModel("ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale,
dropout=dropout)
if args.init_from_pretrain_model:
if not os.path.exists(args.init_from_pretrain_model + '.pdparams'):
print(args.init_from_pretrain_model)
raise Warning("The pretrained params do not exist.")
return
fluid.load_dygraph(args.init_from_pretrain_model)
print("finish initing model from pretrained params from %s" %
(args.init_from_pretrain_model))
dy_param_updated = dict()
dy_param_init = dict()
dy_loss = None
last_hidden = None
data_path = args.data_path
print("begin to load data")
ptb_data = reader.get_ptb_data(data_path)
print("finished load data")
train_data, valid_data, test_data = ptb_data
batch_len = len(train_data) // batch_size
total_batch_size = (batch_len - 1) // num_steps
print("total_batch_size:", total_batch_size)
log_interval = total_batch_size // 20
bd = []
lr_arr = [base_learning_rate]
for i in range(1, max_epoch):
bd.append(total_batch_size * i)
new_lr = base_learning_rate * (lr_decay**max(
i + 1 - epoch_start_decay, 0.0))
lr_arr.append(new_lr)
sgd = AdagradOptimizer(parameter_list=ptb_model.parameters(),
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr_arr))
print("parameters:--------------------------------")
for para in ptb_model.parameters():
print(para.name)
print("parameters:--------------------------------")
def eval(model, data):
print("begion to eval")
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
model.eval()
train_data_iter = reader.get_data_iter(data, batch_size, num_steps)
init_hidden = to_variable(init_hidden_data)
accum_num_recall = 0.0
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, num_steps, 1))
x = to_variable(x_data)
y = to_variable(y_data)
dy_loss, last_hidden, acc = ptb_model(x, y, init_hidden)
out_loss = dy_loss.numpy()
acc_ = acc.numpy()[0]
accum_num_recall += acc_
if batch_id % 1 == 0:
print("batch_id:%d recall@20:%.4f" %
(batch_id, accum_num_recall / (batch_id + 1)))
init_hidden = last_hidden
total_loss += out_loss
iters += num_steps
print("eval finished")
ppl = np.exp(total_loss / iters)
print("recall@20 ", accum_num_recall / (batch_id + 1))
if args.ce:
print("kpis\ttest_ppl\t%0.3f" % ppl[0])
grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(max_grad_norm)
for epoch_id in range(max_epoch):
ptb_model.train()
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
train_data_iter = reader.get_data_iter(train_data, batch_size,
num_steps)
init_hidden = to_variable(init_hidden_data)
start_time = time.time()
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, num_steps, 1))
x = to_variable(x_data)
y = to_variable(y_data)
dy_loss, last_hidden, acc = ptb_model(x, y, init_hidden)
out_loss = dy_loss.numpy()
acc_ = acc.numpy()[0]
init_hidden = last_hidden
dy_loss.backward()
sgd.minimize(dy_loss, grad_clip=grad_clip)
ptb_model.clear_gradients()
total_loss += out_loss
iters += num_steps
if batch_id > 0 and batch_id % 100 == 1:
ppl = np.exp(total_loss / iters)
print(
"-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, acc: %.5f, lr: %.5f"
% (epoch_id, batch_id, ppl[0], acc_,
sgd._global_learning_rate().numpy()))
print("one ecpoh finished", epoch_id)
print("time cost ", time.time() - start_time)
ppl = np.exp(total_loss / iters)
print("-- Epoch:[%d]; ppl: %.5f" % (epoch_id, ppl[0]))
if args.ce:
print("kpis\ttrain_ppl\t%0.3f" % ppl[0])
save_model_dir = os.path.join(args.save_model_dir, str(epoch_id),
'params')
fluid.save_dygraph(ptb_model.state_dict(), save_model_dir)
print("Saved model to: %s.\n" % save_model_dir)
eval(ptb_model, test_data)
def train(sess):
sess.run(init)
if args.profile:
profiler_step = 0
profiler = model_analyzer.Profiler(graph=sess.graph)
run_options = tf.RunOptions(trace_level = tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
total_time = 0.0
epoch_times = []
for epoch_id in xrange(max_epoch):
batch_times = []
epoch_start_time = time.time()
train_data_iter = reader.get_data_iter( train_data, batch_size, num_steps)
# assign lr, update the learning rate
new_lr_1 = base_learning_rate * ( lr_decay ** max(epoch_id + 1 - epoch_start_decay, 0.0) )
sess.run( lr_update, {new_lr: new_lr_1})
total_loss = 0.0
iters = 0
batch_len = len(train_data) // batch_size
epoch_size = ( batch_len - 1 ) // num_steps
if args.profile:
log_fre = 1
else:
log_fre = epoch_size // 10
init_h = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32')
init_c = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32')
count = 0.0
for batch_id, batch in enumerate(train_data_iter):
x,y = batch
feed_dict = {}
feed_dict[feeding_list[0]] = x
feed_dict[feeding_list[1]] = y
feed_dict[feeding_list[2]] = init_h
feed_dict[feeding_list[3]] = init_c
batch_start_time = time.time()
if args.profile:
output = sess.run([cost, final_h, final_c, train_op], feed_dict, options=run_options, run_metadata=run_metadata)
profiler.add_step(step=profiler_step, run_meta=run_metadata)
profiler_step = profiler_step + 1
if batch_id >= 10:
break
else:
output = sess.run([cost, final_h, final_c, train_op], feed_dict)
batch_time = time.time() - batch_start_time
batch_times.append(batch_time)
train_cost = output[0]
init_h = output[1]
init_c = output[2]
total_loss += train_cost
iters += num_steps
count = count + 1
if batch_id > 0 and batch_id % log_fre == 0:
ppl = np.exp( total_loss / iters )
print("-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f, lr: %.5f" % (epoch_id, batch_id, batch_time, ppl, new_lr_1))
ppl = np.exp(total_loss / iters)
epoch_time = time.time() - epoch_start_time
epoch_times.append(epoch_time)
total_time += epoch_time
print("\nTrain epoch:[%d]; epoch Time: %.5f s; ppl: %.5f; avg_time: %.5f steps/s\n"
% (epoch_id, epoch_time, ppl, (batch_id + 1) / sum(batch_times)))
valid_ppl, _ = eval(sess, valid_data)
print("Valid ppl: %.5f" % valid_ppl)
test_ppl, test_time = eval(sess, test_data)
print("Test Time (total): %.5f, ppl: %.5f" % (test_time, test_ppl))
if args.profile:
profile_op_opt_builder = option_builder.ProfileOptionBuilder()
profile_op_opt_builder.select(['micros','occurrence'])
profile_op_opt_builder.order_by('micros')
profile_op_opt_builder.with_max_depth(50)
profiler.profile_operations(profile_op_opt_builder.build())
def main():
args = parse_args()
print(args)
num_layers = args.num_layers
src_vocab_size = args.src_vocab_size
tar_vocab_size = args.tar_vocab_size
batch_size = args.batch_size
dropout = args.dropout
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
with fluid.dygraph.guard(place):
#args.enable_ce = True
if args.enable_ce:
fluid.default_startup_program().random_seed = 102
fluid.default_main_program().random_seed = 102
np.random.seed(102)
random.seed(102)
# Training process
if args.attention:
model = AttentionModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
else:
model = BaseModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
gloabl_norm_clip = GradientClipByGlobalNorm(max_grad_norm)
lr = args.learning_rate
opt_type = args.optimizer
if opt_type == "sgd":
optimizer = fluid.optimizer.SGD(lr,
parameter_list=model.parameters(),
grad_clip=gloabl_norm_clip)
elif opt_type == "adam":
optimizer = fluid.optimizer.Adam(lr,
parameter_list=model.parameters(),
grad_clip=gloabl_norm_clip)
else:
print("only support [sgd|adam]")
raise Exception("opt type not support")
train_data_prefix = args.train_data_prefix
eval_data_prefix = args.eval_data_prefix
test_data_prefix = args.test_data_prefix
vocab_prefix = args.vocab_prefix
src_lang = args.src_lang
tar_lang = args.tar_lang
print("begin to load data")
raw_data = reader.raw_data(src_lang, tar_lang, vocab_prefix,
train_data_prefix, eval_data_prefix,
test_data_prefix, args.max_len)
print("finished load data")
train_data, valid_data, test_data, _ = raw_data
def prepare_input(batch, epoch_id=0):
src_ids, src_mask, tar_ids, tar_mask = batch
res = {}
src_ids = src_ids.reshape((src_ids.shape[0], src_ids.shape[1]))
in_tar = tar_ids[:, :-1]
label_tar = tar_ids[:, 1:]
in_tar = in_tar.reshape((in_tar.shape[0], in_tar.shape[1]))
label_tar = label_tar.reshape(
(label_tar.shape[0], label_tar.shape[1], 1))
inputs = [src_ids, in_tar, label_tar, src_mask, tar_mask]
return inputs, np.sum(tar_mask)
# get train epoch size
def eval(data, epoch_id=0):
model.eval()
eval_data_iter = reader.get_data_iter(data,
batch_size,
mode='eval')
total_loss = 0.0
word_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, word_num = prepare_input(batch, epoch_id)
loss = model(input_data_feed)
total_loss += loss * batch_size
word_count += word_num
ppl = np.exp(total_loss.numpy() / word_count)
model.train()
return ppl
ce_time = []
ce_ppl = []
max_epoch = args.max_epoch
for epoch_id in range(max_epoch):
epoch_start = time.time()
model.train()
if args.enable_ce:
train_data_iter = reader.get_data_iter(train_data,
batch_size,
enable_ce=True)
else:
train_data_iter = reader.get_data_iter(train_data, batch_size)
total_loss = 0
word_count = 0.0
batch_times = []
total_reader_cost = 0.0
interval_time_start = time.time()
batch_start = time.time()
for batch_id, batch in enumerate(train_data_iter):
batch_reader_end = time.time()
total_reader_cost += batch_reader_end - batch_start
input_data_feed, word_num = prepare_input(batch,
epoch_id=epoch_id)
word_count += word_num
loss = model(input_data_feed)
loss.backward()
optimizer.minimize(loss)
model.clear_gradients()
total_loss += loss * batch_size
total_loss_value = total_loss.numpy()
batch_times.append(time.time() - batch_start)
if batch_id > 0 and batch_id % 100 == 0:
print(
"-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, batch_cost: %.5f sec, reader_cost: %.5f sec, ips: %.5f words/sec"
% (epoch_id, batch_id,
np.exp(total_loss_value / word_count),
(time.time() - interval_time_start) / 100,
total_reader_cost / 100, word_count /
(time.time() - interval_time_start)))
ce_ppl.append(np.exp(total_loss_value / word_count))
total_loss = 0.0
word_count = 0.0
total_reader_cost = 0.0
interval_time_start = time.time()
batch_start = time.time()
train_epoch_cost = time.time() - epoch_start
print(
"\nTrain epoch:[%d]; epoch_cost: %.5f sec; avg_batch_cost: %.5f s/step\n"
% (epoch_id, train_epoch_cost,
sum(batch_times) / len(batch_times)))
ce_time.append(train_epoch_cost)
dir_name = os.path.join(args.model_path, "epoch_" + str(epoch_id))
print("begin to save", dir_name)
paddle.fluid.save_dygraph(model.state_dict(), dir_name)
print("save finished")
dev_ppl = eval(valid_data)
print("dev ppl", dev_ppl)
test_ppl = eval(test_data)
print("test ppl", test_ppl)
if args.enable_ce:
card_num = get_cards()
_ppl = 0
_time = 0
try:
_time = ce_time[-1]
_ppl = ce_ppl[-1]
except:
print("ce info error")
print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
print("kpis\ttrain_ppl_card%s\t%f" % (card_num, _ppl))
def eval(data):
if args.inference_only and args.init_params_path:
dirname = args.init_params_path
filename = None
if not os.path.isdir(args.init_params_path):
dirname = os.path.dirname(args.init_params_path)
filename = os.path.basename(args.init_params_path)
fluid.io.load_persistables(exe,
dirname,
main_program=main_program,
filename=filename)
print("Load parameters from: %s." % args.init_params_path)
batch_times = []
start_time = time.time()
# when eval the batch_size set to 1
eval_data_iter = reader.get_data_iter(data, batch_size, num_steps)
total_loss = 0.0
iters = 0
init_hidden = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed = prepare_input(batch,
init_hidden,
init_cell,
epoch_id=0,
with_lr=False)
batch_start_time = time.time()
# eval should not run the grad op and change the parameters.
# use Executor to eval
fetch_outs = exe.run(
program=inference_program,
feed=input_data_feed,
fetch_list=[loss.name, last_hidden.name, last_cell.name],
use_program_cache=True)
batch_times.append(time.time() - batch_start_time)
cost_train = np.array(fetch_outs[0])
init_hidden = np.array(fetch_outs[1])
init_cell = np.array(fetch_outs[2])
total_loss += cost_train
iters += num_steps
ppl = np.exp(total_loss / iters)
eval_time_total = time.time() - start_time
eval_time_run = np.sum(batch_times)
# Benchmark
if args.inference_only:
print("\n======== Benchmark Result ========")
print(
"Eval batch_size: %d; Time (total): %.5f s; Time (only run): %.5f s; ppl: %.5f"
% (batch_size, eval_time_total, eval_time_run, ppl[0]))
print("")
# Save the inference model for C++ inference purpose
fluid.io.save_inference_model(save_model_dir,
feed_order,
[loss, last_hidden, last_cell],
exe,
main_program=inference_program,
model_filename="model",
params_filename="params")
print("Save inference model to: %s." % save_model_dir)
return ppl
def train_an_epoch(epoch_id, batch_times):
# get train epoch size
num_batchs = len(train_data) // batch_size
epoch_size = (num_batchs - 1) // num_steps
if args.profile:
log_interval = 1
else:
log_interval = max(1, epoch_size // 10)
data_iter_size = batch_size
if device_count > 1 and args.parallel:
data_iter_size = batch_size * device_count
train_data_iter = reader.get_data_iter(train_data, data_iter_size,
num_steps)
total_loss = 0
iters = 0
if device_count > 1 and args.parallel:
init_hidden = np.zeros(
(num_layers * device_count, batch_size, hidden_size),
dtype='float32')
init_cell = np.zeros(
(num_layers * device_count, batch_size, hidden_size),
dtype='float32')
else:
init_hidden = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
for batch_id, batch in enumerate(train_data_iter):
input_data_feed = prepare_input(batch,
init_hidden,
init_cell,
epoch_id=epoch_id,
device_count=device_count)
batch_start_time = time.time()
fetch_outs = exe.run(train_program,
feed=input_data_feed,
fetch_list=[
loss.name, last_hidden.name,
last_cell.name, "learning_rate"
],
use_program_cache=True)
batch_time = time.time() - batch_start_time
batch_times.append(batch_time)
cost_train = np.array(fetch_outs[0])
init_hidden = np.array(fetch_outs[1])
init_cell = np.array(fetch_outs[2])
lr = np.array(fetch_outs[3])
total_loss += cost_train
iters += num_steps
if batch_id > 0 and batch_id % log_interval == 0:
ppl = np.exp(total_loss / iters)
print(
"-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f, lr: %.5f"
% (epoch_id, batch_id, batch_time, ppl[0], lr[0]))
if args.profile:
if batch_id == 1:
profiler.reset_profiler()
elif batch_id >= 11:
break
ppl = np.exp(total_loss / iters)
return ppl
def train():
model = BaseModel(batch_size=batch_size, maxlen=n_frames)
loss, acc, output, no_grad_set = model.build_graph()
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Adadelta(0.001)
optimizer.minimize(loss, no_grad_set=no_grad_set)
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
log_writter = LogWriter(log_path, sync_cycle=10)
with log_writter.mode("train") as logger:
log_train_loss = logger.scalar(tag="train_loss")
log_train_acc = logger.scalar(tag="train_acc")
with log_writter.mode("validation") as logger:
log_valid_loss = logger.scalar(tag="validation_loss")
log_valid_acc = logger.scalar(tag="validation_acc")
def prepare_input(batch):
x, y, x_seqlen = batch
res = {}
res['input'] = np.array(x).astype("float32")
res['input_seqlen'] = np.array(x_seqlen).astype("int64")
res['label'] = np.array(y).astype("float32")
return res
# (samples, seq, width, height, pixel)
noisy_movies, shifted_movies = reader.generate_movies(n_samples, n_frames)
data = noisy_movies[:1000], shifted_movies[:1000]
train_data, validation_data = split(data, validation_split)
step_id = 0
for epoch_id in range(max_epoch):
start_time = time.time()
print("epoch id", epoch_id)
valid_data_iter = reader.get_data_iter(validation_data, batch_size)
train_data_iter = reader.get_data_iter(train_data, batch_size)
# train
total_loss = 0
batch_id = 0
for batch in train_data_iter:
input_data_feed = prepare_input(batch)
fetch_outs = exe.run(program=main_program,
feed=input_data_feed,
fetch_list=[loss.name, acc.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
acc_train = fetch_outs[1]
total_loss += cost_train
if batch_id > 0 and batch_id % 5 == 0:
log_train_loss.add_record(step_id, total_loss)
log_train_acc.add_record(step_id, acc_train)
step_id += 1
print("current loss: %.7f, for batch %d" % (total_loss, batch_id))
total_loss = 0.0
batch_id += 1
# validate
total_loss = 0
total_acc = 0
batch_id = 0
for batch in valid_data_iter:
input_data_feed = prepare_input(batch)
fetch_outs = exe.run(program=inference_program,
feed=input_data_feed,
fetch_list=[loss.name, acc.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
acc_train = fetch_outs[1]
total_loss += cost_train
batch_id += 1
log_valid_loss.add_record(epoch_id, total_loss)
log_valid_acc.add_record(epoch_id, total_acc / batch_id)
print("validation loss: %.7f" % (total_loss))
fluid.io.save_inference_model(
dirname=params_path,
feeded_var_names=['input', 'input_seqlen'],
target_vars=[loss, acc],
executor=exe)
def train_ptb_lm():
args = parse_args()
# check if set use_gpu=True in paddlepaddle cpu version
model_check.check_cuda(args.use_gpu)
place = core.CPUPlace()
if args.use_gpu == True:
place = core.CUDAPlace(0)
# check if paddlepaddle version is satisfied
model_check.check_version()
model_type = args.model_type
vocab_size = 10000
if model_type == "test":
num_layers = 1
batch_size = 2
hidden_size = 10
num_steps = 3
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 1
max_epoch = 1
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "small":
num_layers = 2
batch_size = 20
hidden_size = 200
num_steps = 20
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 4
max_epoch = 13
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "medium":
num_layers = 2
batch_size = 20
hidden_size = 650
num_steps = 35
init_scale = 0.05
max_grad_norm = 5.0
epoch_start_decay = 6
max_epoch = 39
dropout = 0.5
lr_decay = 0.8
base_learning_rate = 1.0
elif model_type == "large":
num_layers = 2
batch_size = 20
hidden_size = 1500
num_steps = 35
init_scale = 0.04
max_grad_norm = 10.0
epoch_start_decay = 14
max_epoch = 55
dropout = 0.65
lr_decay = 1.0 / 1.15
base_learning_rate = 1.0
else:
print("model type not support")
return
with fluid.dygraph.guard(place):
if args.ce:
print("ce mode")
seed = 33
np.random.seed(seed)
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
max_epoch = 1
ptb_model = PtbModel(
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale,
dropout=dropout)
if args.init_from_pretrain_model:
if not os.path.exists(args.init_from_pretrain_model + '.pdparams'):
print(args.init_from_pretrain_model)
raise Warning("The pretrained params do not exist.")
return
fluid.load_dygraph(args.init_from_pretrain_model)
print("finish initing model from pretrained params from %s" %
(args.init_from_pretrain_model))
dy_param_updated = dict()
dy_param_init = dict()
dy_loss = None
last_hidden = None
last_cell = None
data_path = args.data_path
print("begin to load data")
ptb_data = reader.get_ptb_data(data_path)
print("finished load data")
train_data, valid_data, test_data = ptb_data
batch_len = len(train_data) // batch_size
total_batch_size = (batch_len - 1) // num_steps
log_interval = 200
bd = []
lr_arr = [1.0]
for i in range(1, max_epoch):
bd.append(total_batch_size * i)
new_lr = base_learning_rate * (lr_decay**
max(i + 1 - epoch_start_decay, 0.0))
lr_arr.append(new_lr)
sgd = SGDOptimizer(learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr_arr), parameter_list=ptb_model.parameters())
def eval(model, data):
print("begin to eval")
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
init_cell_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
model.eval()
train_data_iter = reader.get_data_iter(data, batch_size, num_steps)
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, num_steps, 1))
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
init_cell)
out_loss = dy_loss.numpy()
init_hidden_data = last_hidden.numpy()
init_cell_data = last_cell.numpy()
total_loss += out_loss
iters += num_steps
print("eval finished")
ppl = np.exp(total_loss / iters)
print("ppl ", batch_id, ppl[0])
if args.ce:
print("kpis\ttest_ppl\t%0.3f" % ppl[0])
grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(max_grad_norm)
for epoch_id in range(1):
ptb_model.train()
total_loss = 0.0
iters = 0.0
init_hidden_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
init_cell_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
train_data_iter = reader.get_data_iter(train_data, batch_size,
num_steps)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
start_time = time.time()
start = time.time()
for batch_id, batch in enumerate(train_data_iter):
x_data, y_data = batch
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, num_steps, 1))
x = to_variable(x_data)
y = to_variable(y_data)
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
init_cell)
init_hidden = last_hidden
init_cell = last_cell
init_hidden.stop_gradient = True
init_cell.stop_gradient = True
out_loss = dy_loss.numpy()
dy_loss.backward()
sgd.minimize(dy_loss, grad_clip=grad_clip)
ptb_model.clear_gradients()
total_loss += out_loss
iters += num_steps
if batch_id > 0 and batch_id % log_interval == 0:
ppl = np.exp(total_loss / iters)
print("-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, lr: %.5f, loss: %.5f" %
(epoch_id, batch_id, ppl[0],
sgd._global_learning_rate().numpy(), out_loss))
end = time.time()
print("One epoch cost {}".format(end - start))
print("one epoch finished", epoch_id)
print("time cost ", time.time() - start_time)
ppl = np.exp(total_loss / iters)
print("-- Epoch:[%d]; ppl: %.5f" % (epoch_id, ppl[0]))
if batch_size <= 20 and epoch_id == 0 and ppl[0] > 1000:
# for bad init, after first epoch, the loss is over 1000
# no more need to continue
print("Parameters are randomly initialized and not good this time because the loss is over 1000 after the first epoch.")
print("Abort this training process and please start again.")
return
if args.ce:
print("kpis\ttrain_ppl\t%0.3f" % ppl[0])
save_model_dir = os.path.join(args.save_model_dir,
str(epoch_id), 'params')
fluid.save_dygraph(ptb_model.state_dict(), save_model_dir)
print("Saved model to: %s.\n" % save_model_dir)
eval(ptb_model, valid_data)
eval(ptb_model, test_data)
def train():
args = parse_args()
model_type = args.model_type
rnn_model = args.rnn_model
logger = logging.getLogger("lm")
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if args.enable_ce:
fluid.default_startup_program().random_seed = SEED
if args.log_path:
file_handler = logging.FileHandler(args.log_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.info('Running with args : {}'.format(args))
vocab_size = 10000
if model_type == "test":
num_layers = 1
batch_size = 2
hidden_size = 10
num_steps = 3
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 1
max_epoch = 1
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "small":
num_layers = 2
batch_size = 20
hidden_size = 200
num_steps = 20
init_scale = 0.1
max_grad_norm = 5.0
epoch_start_decay = 4
max_epoch = 13
dropout = 0.0
lr_decay = 0.5
base_learning_rate = 1.0
elif model_type == "medium":
num_layers = 2
batch_size = 20
hidden_size = 650
num_steps = 35
init_scale = 0.05
max_grad_norm = 5.0
epoch_start_decay = 6
max_epoch = 39
dropout = 0.5
lr_decay = 0.8
base_learning_rate = 1.0
elif model_type == "large":
num_layers = 2
batch_size = 20
hidden_size = 1500
num_steps = 35
init_scale = 0.04
max_grad_norm = 10.0
epoch_start_decay = 14
max_epoch = 55
dropout = 0.65
lr_decay = 1.0 / 1.15
base_learning_rate = 1.0
else:
print("model type not support")
return
# Training process
loss, last_hidden, last_cell, feed_order = lm_model.lm_model(
hidden_size,
vocab_size,
batch_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale,
dropout=dropout,
rnn_model=rnn_model)
# clone from default main program and use it as the validation program
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(
clip_norm=max_grad_norm))
learning_rate = fluid.layers.create_global_var(name="learning_rate",
shape=[1],
value=1.0,
dtype='float32',
persistable=True)
optimizer = fluid.optimizer.SGD(learning_rate=learning_rate)
optimizer.minimize(loss)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
data_path = args.data_path
print("begin to load data")
raw_data = reader.ptb_raw_data(data_path)
print("finished load data")
train_data, valid_data, test_data, _ = raw_data
def prepare_input(batch, init_hidden, init_cell, epoch_id=0, with_lr=True):
x, y = batch
new_lr = base_learning_rate * (lr_decay**max(
epoch_id + 1 - epoch_start_decay, 0.0))
lr = np.ones((1), dtype='float32') * new_lr
res = {}
x = x.reshape((-1, num_steps, 1))
y = y.reshape((-1, 1))
res['x'] = x
res['y'] = y
res['init_hidden'] = init_hidden
res['init_cell'] = init_cell
if with_lr:
res['learning_rate'] = lr
return res
def eval(data):
# when eval the batch_size set to 1
eval_data_iter = reader.get_data_iter(data, batch_size, num_steps)
total_loss = 0.0
iters = 0
init_hidden = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed = prepare_input(batch,
init_hidden,
init_cell,
epoch_id,
with_lr=False)
fetch_outs = exe.run(
inference_program,
feed=input_data_feed,
fetch_list=[loss.name, last_hidden.name, last_cell.name],
use_program_cache=True)
cost_train = np.array(fetch_outs[0])
init_hidden = np.array(fetch_outs[1])
init_cell = np.array(fetch_outs[2])
total_loss += cost_train
iters += num_steps
ppl = np.exp(total_loss / iters)
return ppl
# get train epoch size
batch_len = len(train_data) // batch_size
epoch_size = (batch_len - 1) // num_steps
log_interval = epoch_size // 10
total_time = 0.0
for epoch_id in range(max_epoch):
start_time = time.time()
print("epoch id", epoch_id)
train_data_iter = reader.get_data_iter(train_data, batch_size,
num_steps)
total_loss = 0
init_hidden = None
init_cell = None
#debug_para(fluid.framework.default_main_program(), parallel_executor)
total_loss = 0
iters = 0
init_hidden = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
init_cell = np.zeros((num_layers, batch_size, hidden_size),
dtype='float32')
for batch_id, batch in enumerate(train_data_iter):
input_data_feed = prepare_input(batch,
init_hidden,
init_cell,
epoch_id=epoch_id)
fetch_outs = exe.run(feed=input_data_feed,
fetch_list=[
loss.name, last_hidden.name,
last_cell.name, 'learning_rate'
],
use_program_cache=True)
cost_train = np.array(fetch_outs[0])
init_hidden = np.array(fetch_outs[1])
init_cell = np.array(fetch_outs[2])
lr = np.array(fetch_outs[3])
total_loss += cost_train
iters += num_steps
if batch_id > 0 and batch_id % log_interval == 0:
ppl = np.exp(total_loss / iters)
print("ppl ", batch_id, ppl[0], lr[0])
ppl = np.exp(total_loss / iters)
if epoch_id == 0 and ppl[0] > 1000:
# for bad init, after first epoch, the loss is over 1000
# no more need to continue
return
end_time = time.time()
total_time += end_time - start_time
print("train ppl", ppl[0])
if epoch_id == max_epoch - 1 and args.enable_ce:
card_num = get_cards()
print("ptblm\tlstm_language_model_%s_duration_card%d\t%s" %
(args.rnn_model, card_num, total_time / max_epoch))
print("ptblm\tlstm_language_model_%s_loss_card%d\t%s" %
(args.rnn_model, card_num, ppl[0]))
model_path = os.path.join("model_new/", 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=main_program)
valid_ppl = eval(valid_data)
print("valid ppl", valid_ppl[0])
test_ppl = eval(test_data)
print("test ppl", test_ppl[0])
def train():
args = parse_args()
num_layers = args.num_layers
src_vocab_size = args.src_vocab_size
tar_vocab_size = args.tar_vocab_size
batch_size = args.batch_size
dropout = args.dropout
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
if args.enable_ce:
fluid.default_main_program().random_seed = 102
framework.default_startup_program().random_seed = 102
# Training process
if args.attention:
model = AttentionModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
else:
model = BaseModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
loss = model.build_graph()
# clone from default main program and use it as the validation program
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(
clip_norm=max_grad_norm))
lr = args.learning_rate
opt_type = args.optimizer
if opt_type == "sgd":
optimizer = fluid.optimizer.SGD(lr)
elif opt_type == "adam":
optimizer = fluid.optimizer.Adam(lr)
else:
print("only support [sgd|adam]")
raise Exception("opt type not support")
optimizer.minimize(loss)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
train_data_prefix = args.train_data_prefix
eval_data_prefix = args.eval_data_prefix
test_data_prefix = args.test_data_prefix
vocab_prefix = args.vocab_prefix
src_lang = args.src_lang
tar_lang = args.tar_lang
print("begin to load data")
raw_data = reader.raw_data(src_lang, tar_lang, vocab_prefix,
train_data_prefix, eval_data_prefix,
test_data_prefix, args.max_len)
print("finished load data")
train_data, valid_data, test_data, _ = raw_data
def prepare_input(batch, epoch_id=0, with_lr=True):
src_ids, src_mask, tar_ids, tar_mask = batch
res = {}
src_ids = src_ids.reshape((src_ids.shape[0], src_ids.shape[1], 1))
in_tar = tar_ids[:, :-1]
label_tar = tar_ids[:, 1:]
in_tar = in_tar.reshape((in_tar.shape[0], in_tar.shape[1], 1))
label_tar = label_tar.reshape(
(label_tar.shape[0], label_tar.shape[1], 1))
res['src'] = src_ids
res['tar'] = in_tar
res['label'] = label_tar
res['src_sequence_length'] = src_mask
res['tar_sequence_length'] = tar_mask
return res, np.sum(tar_mask)
# get train epoch size
def eval(data, epoch_id=0):
eval_data_iter = reader.get_data_iter(data, batch_size, mode='eval')
total_loss = 0.0
word_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, word_num = prepare_input(batch,
epoch_id,
with_lr=False)
fetch_outs = exe.run(inference_program,
feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += word_num
ppl = np.exp(total_loss / word_count)
return ppl
ce_time = []
ce_ppl = []
max_epoch = args.max_epoch
for epoch_id in range(max_epoch):
start_time = time.time()
print("epoch id", epoch_id)
if args.enable_ce:
train_data_iter = reader.get_data_iter(train_data,
batch_size,
enable_ce=True)
else:
train_data_iter = reader.get_data_iter(train_data, batch_size)
total_loss = 0
word_count = 0.0
for batch_id, batch in enumerate(train_data_iter):
input_data_feed, word_num = prepare_input(batch, epoch_id=epoch_id)
fetch_outs = exe.run(feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=True)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += word_num
if batch_id > 0 and batch_id % 100 == 0:
print("ppl", batch_id, np.exp(total_loss / word_count))
ce_ppl.append(np.exp(total_loss / word_count))
total_loss = 0.0
word_count = 0.0
end_time = time.time()
time_gap = end_time - start_time
ce_time.append(time_gap)
dir_name = args.model_path + "/epoch_" + str(epoch_id)
print("begin to save", dir_name)
fluid.io.save_params(exe, dir_name)
print("save finished")
dev_ppl = eval(valid_data)
print("dev ppl", dev_ppl)
test_ppl = eval(test_data)
print("test ppl", test_ppl)
if args.enable_ce:
card_num = get_cards()
_ppl = 0
_time = 0
try:
_time = ce_time[-1]
_ppl = ce_ppl[-1]
except:
print("ce info error")
print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
print("kpis\ttrain_ppl_card%s\t%f" % (card_num, _ppl))
def train():
def prepare_input(batch):
src_ids, label = batch
res = {}
res['src'] = src_ids
res['label'] = label
return res
# Set parameters:
# ngram_range = 2 will add bi-grams features
ngram_range = 2
max_features = 20000
maxlen = 400
batch_size = 32
embedding_dims = 50
epochs = 5
print('Loading data...')
all_data = reader.raw_data(num_words=max_features)
x_train, y_train, x_test, y_test = all_data
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Average train sequence length: {}'.format(
np.mean(list(map(len, x_train)), dtype=int)))
print('Average test sequence length: {}'.format(
np.mean(list(map(len, x_test)), dtype=int)))
if ngram_range > 1:
print('Adding {}-gram features'.format(ngram_range))
# Create set of unique n-gram from the training set.
ngram_set = set()
for input_list in x_train:
for i in range(2, ngram_range + 1):
set_of_ngram = create_ngram_set(input_list, ngram_value=i)
ngram_set.update(set_of_ngram)
# Dictionary mapping n-gram token to a unique integer.
# Integer values are greater than max_features in order
# to avoid collision with existing features.
start_index = max_features + 1
token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}
indice_token = {token_indice[k]: k for k in token_indice}
# max_features is the highest integer that could be found in the dataset.
max_features = np.max(list(indice_token.keys())) + 1
# Augmenting x_train and x_test with n-grams features
x_train = add_ngram(x_train, token_indice, ngram_range)
x_test = add_ngram(x_test, token_indice, ngram_range)
print('Average train sequence length: {}'.format(
np.mean(list(map(len, x_train)), dtype=int)))
print('Average test sequence length: {}'.format(
np.mean(list(map(len, x_test)), dtype=int)))
print('Pad sequences (samples x time)')
x_train = reader.pad_sequences(x_train, maxlen=maxlen)
x_test = reader.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
all_data = x_train, y_train, x_test, y_test
print('Build model...')
model = BaseModel(max_features=max_features)
loss, acc = model.build_graph()
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Adam(0.01)
optimizer.minimize(loss)
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
for epoch_id in range(epochs):
start_time = time.time()
print("epoch id", epoch_id)
train_data_iter = reader.get_data_iter(all_data, batch_size)
total_loss = 0
total_acc = 0
batch_id = 0
for batch in train_data_iter:
input_data_feed = prepare_input(batch)
fetch_outs = exe.run(feed=input_data_feed,
fetch_list=[loss.name, acc.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
acc_train = np.array(fetch_outs[1])
total_loss += cost_train
total_acc += acc_train
if batch_id > 0 and batch_id % 10 == 0:
print("current loss: %.3f, current acc: %.3f for step %d" %
(total_loss, total_acc * 0.1, batch_id))
total_loss = 0.0
total_acc = 0.0
batch_id += 1
test_data_iter = reader.get_data_iter(all_data, batch_size, mode='test')
all_acc = []
for batch in test_data_iter:
input_data_feed = prepare_input(batch)
fetch_outs = exe.run(program=inference_program,
feed=input_data_feed,
fetch_list=[loss.name, acc.name],
use_program_cache=False)
all_acc.append(fetch_outs[1])
all_acc = np.array(all_acc).astype("float32")
print("test acc: %.3f" % all_acc.mean())
def train():
ce_time = []
ce_ppl = []
max_epoch = args.max_epoch
for epoch_id in range(max_epoch):
start_time = time.time()
if args.enable_ce:
train_data_iter = reader.get_data_iter(train_data,
batch_size,
enable_ce=True)
else:
train_data_iter = reader.get_data_iter(train_data, batch_size)
total_loss = 0
word_count = 0.0
batch_times = []
time_interval = 0.0
batch_start_time = time.time()
epoch_word_count = 0.0
total_reader_cost = 0.0
batch_read_start = time.time()
for batch_id, batch in enumerate(train_data_iter):
input_data_feed, word_num = prepare_input(batch,
epoch_id=epoch_id)
word_count += word_num
total_reader_cost += time.time() - batch_read_start
fetch_outs = exe.run(program=CompiledProgram,
feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=True)
cost_train = np.mean(fetch_outs[0])
# print(cost_train)
total_loss += cost_train * batch_size
batch_end_time = time.time()
batch_time = batch_end_time - batch_start_time
batch_times.append(batch_time)
time_interval += batch_time
epoch_word_count += word_num
if batch_id > 0 and batch_id % 100 == 0:
print(
"-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f; reader cost: %0.5f s; ips: %0.5f tokens/sec"
% (epoch_id, batch_id, batch_time,
np.exp(total_loss / word_count), total_reader_cost /
100, word_count / time_interval))
ce_ppl.append(np.exp(total_loss / word_count))
total_loss = 0.0
word_count = 0.0
time_interval = 0.0
total_reader_cost = 0.0
# profiler tools
if args.profile and epoch_id == 0 and batch_id == 100:
profiler.reset_profiler()
elif args.profile and epoch_id == 0 and batch_id == 105:
return
batch_start_time = time.time()
batch_read_start = time.time()
end_time = time.time()
epoch_time = end_time - start_time
ce_time.append(epoch_time)
print(
"\nTrain epoch:[%d]; Epoch Time: %.5f; avg_time: %.5f s/step; ips: %0.5f tokens/sec\n"
% (epoch_id, epoch_time, sum(batch_times) / len(batch_times),
epoch_word_count / sum(batch_times)))
if not args.profile:
save_path = os.path.join(args.model_path,
"epoch_" + str(epoch_id),
"checkpoint")
print("begin to save", save_path)
fluid.save(train_program, save_path)
print("save finished")
dev_ppl = eval(valid_data)
print("dev ppl", dev_ppl)
test_ppl = eval(test_data)
print("test ppl", test_ppl)
if args.enable_ce:
card_num = get_cards()
_ppl = 0
_time = 0
try:
_time = ce_time[-1]
_ppl = ce_ppl[-1]
except:
print("ce info error")
print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
print("kpis\ttrain_ppl_card%s\t%f" % (card_num, _ppl))
def train():
raw_data, raw_data_test = reader.get_lt5_data()
model = BaseModel(fine_tune=False)
loss, acc, output = model.build_graph()
main_program = fluid.default_main_program()
test_program = main_program.clone(for_test=True)
optimizer = fluid.optimizer.Adadelta(0.01)
optimizer.minimize(loss)
place = fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
def prepare_input(batch, epoch_id=0):
x, y = batch
res = {}
res['img'] = np.array(x).astype("float32") / 255
res['label'] = np.array(y).astype("int64")
return res
def train_test(test_batch):
total_acc = []
input_data_feed = prepare_input(test_batch)
fetch_outs = exe.run(program=test_program,
feed=input_data_feed,
fetch_list=[acc.name],
use_program_cache=True)
acc_train = np.array(fetch_outs[0])
total_acc.append(acc_train)
print("test avg acc: {0:.2%}".format(np.mean(total_acc)))
for epoch_id in range(epochs):
print("epoch id", epoch_id)
train_data_iter = reader.get_data_iter(raw_data, batch_size)
test_data_iter = reader.get_data_iter(raw_data_test, batch_size)
data_iter = zip(train_data_iter, test_data_iter)
total_loss = 0
total_acc = []
for batch_id, batch in enumerate(data_iter):
batch_train, batch_test = batch
input_data_feed = prepare_input(batch_train)
fetch_outs = exe.run(program=main_program,
feed=input_data_feed,
fetch_list=[loss.name, acc.name],
use_program_cache=True)
cost_train = np.array(fetch_outs[0])
acc_train = np.array(fetch_outs[1])
total_loss += cost_train * batch_size
total_acc.append(acc_train)
print("train total loss: ", total_loss, np.mean(total_acc))
train_test(batch_test)
print()
shutil.rmtree(temp_model_path, ignore_errors=True)
os.makedirs(temp_model_path)
fluid.io.save_params(executor=exe, dirname=temp_model_path)
