def main(hparams):
dataset, tokenizer = get_dataset(hparams)
model = transformer(hparams)
optimizer = tf.keras.optimizers.Adam(CustomSchedule(hparams),
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
def loss_function(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, hparams.max_length - 1))
loss = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')(y_true, y_pred)
mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)
loss = tf.multiply(loss, mask)
return tf.reduce_mean(loss)
def accuracy(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, hparams.max_length - 1))
return tf.metrics.SparseCategoricalAccuracy()(y_true, y_pred)
model.compile(optimizer, loss=loss_function, metrics=[accuracy])
model.fit(dataset, epochs=hparams.epochs)
evaluate(hparams, model, tokenizer)
def model_fn(inp, tgt, mems, is_training):
inp = tf.transpose(inp, [1, 0])
tgt = tf.transpose(tgt, [1, 0])
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range,
maxval=FLAGS.init_range,
seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(
stddev=FLAGS.init_std,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std,
seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if FLAGS.proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
loss, new_mems = model.transformer(
dec_inp=inp,
target=tgt,
mems=mems,
n_token=n_token,
n_layer=FLAGS.n_layer,
d_model=FLAGS.d_model,
d_embed=FLAGS.d_embed,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=is_training,
mem_len=FLAGS.mem_len,
cutoffs=cutoffs,
div_val=FLAGS.div_val,
tie_projs=tie_projs,
input_perms=None,
target_perms=None,
head_target=None,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
untie_r=FLAGS.untie_r,
proj_same_dim=FLAGS.proj_same_dim)
# number of parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
if is_training:
all_vars = tf.trainable_variables()
return loss, new_mems, all_vars
else:
return loss, new_mems
def train(inputs, outputs, pre_train=False):
tf.keras.backend.clear_session()
dataset, VOCAB_SIZE, _ = get_dataset(inputs, outputs)
if pre_train:
model = tf.keras.models.load_model(config.MODEL_PATH)
else:
model = transformer(vocab_size=VOCAB_SIZE,
num_layers=config.NUM_LAYERS,
units=config.UNITS,
d_model=config.D_MODEL,
num_heads=config.NUM_HEADS,
dropout=config.DROPOUT)
learning_rate = model.CustomSchedule(config.D_MODEL)
optimizer = tf.keras.optimizers.Adam(learning_rate,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
model.compile(optimizer=optimizer,
loss=model.loss_function,
metrics=[model.accuracy])
model.fit(dataset, epochs=config.EPOCHS)
model.save(config.MODEL_PATH)
def model_fn(inp, tgt, mems, is_training):
inp = tf.transpose(inp, [1, 0])
tgt = tf.transpose(tgt, [1, 0])
initializer = tf.initializers.random_uniform(minval=-FLAGS.init_range,
maxval=FLAGS.init_range,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std, seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if FLAGS.proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
loss, new_mems, outputs = model.transformer(
dec_inp=inp,
target=tgt,
mems=mems,
n_token=n_token,
n_layer=FLAGS.n_layer,
d_model=FLAGS.d_model,
d_embed=FLAGS.d_embed,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=is_training,
mem_len=FLAGS.mem_len,
cutoffs=cutoffs,
div_val=FLAGS.div_val,
tie_projs=tie_projs,
input_perms=None,
target_perms=None,
head_target=None,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
use_tpu=False,
untie_r=FLAGS.untie_r,
proj_same_dim=FLAGS.proj_same_dim,
return_outputs=True)
if is_training:
all_vars = tf.trainable_variables()
grads = tf.gradients(loss, all_vars)
grads_and_vars = list(zip(grads, all_vars))
return loss, new_mems, grads_and_vars
return loss, new_mems, outputs
def main(params):
print("\n ...loading dataset\n")
dataset, test_dataset, tokenizer, meta = get_dataset(
params.max_samples,
params.max_length,
params.batch_size,
validation_split=params.validation_split)
print("\n ...creating model\n")
model = transformer(params.d_model, meta['vocab_size'], params.num_layers,
params.num_heads, params.dff, params.rate)
# saving model without compilation
model.save('model_untrained.h5')
optimizer = tf.keras.optimizers.Adam(CustomSchedule(params.d_model),
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
def loss_function(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, params.max_length - 1))
loss = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')(y_true, y_pred)
mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)
loss = tf.multiply(loss, mask)
return tf.reduce_mean(loss)
def accuracy(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, params.max_length - 1))
return tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
print("\n ...training model\n")
model.compile(optimizer, loss=loss_function, metrics=[accuracy])
history = model.fit(dataset,
epochs=params.epochs,
validation_data=test_dataset)
print("\nSaving model weights, tokenizer and meta data\n")
model.save('model_trained.h5')
tokenizer.save_to_file('tokenizer')
model.save_weights('model_weights.h5')
# saving history and meta using pickle
save_pickle(meta, 'meta')
save_pickle(history.history, 'history')
evaluate(model, tokenizer, meta)
def main(args):
# get datasets
dataset = data.get_dataset(args.dataset,
args.split,
image_size=args.image_size,
data_dir=args.data_dir,
is_training=True)
im_x = preprocess(dataset.x,
args.preprocessing_a,
image_size=args.image_size,
output_channels=args.num_channels)
im_y = preprocess(dataset.y,
args.preprocessing_b,
image_size=args.image_size)
im_batch_x, im_batch_y = data.create_batch([im_x, im_y],
batch_size=args.batch_size,
shuffle=args.shuffle,
queue_size=2,
min_queue_size=1)
# build models
transformed_x = model.transformer(im_batch_x,
output_channels=dataset.num_classes,
output_fn=None,
scope='model/AtoB')
transformed_y = model.transformer(im_batch_y,
output_channels=args.num_channels,
scope='model/BtoA')
cycled_x = model.transformer(tf.nn.softmax(transformed_x),
output_channels=args.num_channels,
scope='model/BtoA',
reuse=True)
cycled_y = model.transformer(transformed_y,
output_channels=dataset.num_classes,
output_fn=None,
scope='model/AtoB',
reuse=True)
# create loss functions
cycle_loss_x = tf.losses.absolute_difference(im_batch_x,
cycled_x,
scope='cycle_loss_x')
cycle_loss_y = tf.losses.softmax_cross_entropy(im_batch_y,
cycled_y,
scope='cycle_loss_y')
transform_loss_xy = tf.losses.absolute_difference(
im_batch_x, transformed_y, scope='transform_loss_xy')
transform_loss_yx = tf.losses.softmax_cross_entropy(
im_batch_y, transformed_x, scope='transform_loss_yx')
total_loss = cycle_loss_x + cycle_loss_y + transform_loss_xy + transform_loss_yx
optimizer = tf.train.AdamOptimizer(args.learning_rate, args.beta1,
args.beta2, args.epsilon)
inc_global_step = tf.assign_add(tf.train.get_or_create_global_step(), 1)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, inc_global_step)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_tensor = optimizer.minimize(total_loss)
# Set up train op to return loss
with tf.control_dependencies([train_tensor]):
train_op = tf.identity(total_loss, name='train_op')
# set up logging
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
summaries.add(tf.summary.histogram(variable.op.name, variable))
color_map = np.array(
list(map(lambda x: x.color,
labels[:dataset.num_classes]))).astype(np.float32)
segmentation_y = postprocess(tf.argmax(im_batch_y,
-1), 'segmentation_to_rgb',
dataset.num_classes, color_map)
segmentation_transformed_x = postprocess(tf.argmax(transformed_x, -1),
'segmentation_to_rgb',
dataset.num_classes, color_map)
segmentation_cycled_y = postprocess(tf.argmax(cycled_y,
-1), 'segmentation_to_rgb',
dataset.num_classes, color_map)
summaries.add(tf.summary.image('x', im_batch_x))
summaries.add(tf.summary.image('y', segmentation_y))
summaries.add(tf.summary.image('transformed_x',
segmentation_transformed_x))
summaries.add(tf.summary.image('transformed_y', transformed_y))
summaries.add(tf.summary.image('cycled_x', cycled_x))
summaries.add(tf.summary.image('cycled_y', segmentation_cycled_y))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries), name='summary_op')
# create train loop
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
saver = tf.train.Saver(var_list=tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope='model'))
checkpoint_path = os.path.join(args.output_dir, 'model.ckpt')
writer = tf.summary.FileWriter(args.output_dir)
with tf.Session() as sess:
# Tensorflow initializations
sess.run(tf.get_collection(tf.GraphKeys.TABLE_INITIALIZERS))
tf.train.start_queue_runners(sess=sess)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
last_log_time = 0
last_save_time = 0
for i in tqdm(range(args.num_batches)):
if last_log_time < time.time() - args.log_every_n_seconds:
last_log_time = time.time()
summary, loss_val, global_step = sess.run(
[summary_op, train_op,
tf.train.get_global_step()])
writer.add_summary(summary, global_step)
writer.flush()
else:
loss_val, global_step = sess.run(
[train_op, tf.train.get_global_step()])
if last_save_time < time.time() - args.save_every_n_seconds:
last_save_time = time.time()
saver.save(sess, checkpoint_path, global_step=global_step)
saver.save(sess, checkpoint_path, global_step=args.num_batches)
def model_fn(inpN, inpT, tgtN, tgtT, mems, is_training):
inpN = tf.transpose(inpN, [1, 0])
inpT = tf.transpose(inpT, [1, 0])
tgtN = tf.transpose(tgtN, [1, 0])
tgtT = tf.transpose(tgtT, [1, 0])
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range,
maxval=FLAGS.init_range,
seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(
stddev=FLAGS.init_std,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std,
seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if FLAGS.proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
lossN, lossT, new_mems, predictionN, predictionT = model.transformer(
inpN=inpN,
inpT=inpT,
targetsN=tgtN,
targetsT=tgtT,
mems=mems,
n_token_N=n_token_N,
n_token_T=n_token_T,
n_layer=FLAGS.n_layer,
d_model_N=FLAGS.d_model_N,
d_model_T=FLAGS.d_model_T,
d_embed_N=FLAGS.d_embed_N,
d_embed_T=FLAGS.d_embed_T,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=is_training,
mem_len=FLAGS.mem_len,
cutoffs=cutoffs,
div_val=FLAGS.div_val,
tie_projs=tie_projs,
input_perms=None,
target_perms=None,
head_target=None,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
use_tpu=False,
untie_r=FLAGS.untie_r,
proj_same_dim=FLAGS.proj_same_dim)
# number of parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
loss = tf.multiply(alpha, lossN) + tf.multiply(1 - alpha, lossT)
# format_str = '{{:<{0}s}}\t{{}}'.format(
# max([len(v.name) for v in tf.trainable_variables()]))
# for v in tf.trainable_variables():
# tf.logging.info(format_str.format(v.name, v.get_shape()))
if is_training:
all_vars = tf.trainable_variables()
grads = tf.gradients(loss, all_vars)
grads_and_vars = list(zip(grads, all_vars))
return lossN, lossT, loss, new_mems, grads_and_vars, predictionN, predictionT
else:
return lossN, lossT, loss, new_mems, predictionN, predictionT
def do_training(self, fleet, args):
"""
begin training.
Args:
fleet (Collective): Collective inherited base class Fleet
args (ArgumentParser): run args to config dist fleet.
Returns:
tuple: the value is train losses
"""
args = parse_args()
logging.info(args)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 4))
place = fluid.CUDAPlace(gpu_id)
dev_count = 1
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
args.num_trainers = fleet.worker_num()
args.trainer_id = fleet.worker_index()
args.run_params = json.loads(args.run_params)
dist_strategy = DistributedStrategy()
dist_strategy.enable_inplace = args.run_params['enable_inplace']
dist_strategy.fuse_all_reduce_ops = args.run_params[
'fuse_all_reduce_ops']
dist_strategy.nccl_comm_num = args.run_params['nccl_comm_num']
dist_strategy.use_local_sgd = args.run_params['use_local_sgd']
dist_strategy.mode = args.run_params["mode"]
dist_strategy.collective_mode = args.run_params["collective"]
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
sum_cost, avg_cost, predict, token_num, pyreader = transformer(
ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer,
ModelHyperParams.n_head,
ModelHyperParams.d_key,
ModelHyperParams.d_value,
ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid,
ModelHyperParams.prepostprocess_dropout,
ModelHyperParams.attention_dropout,
ModelHyperParams.relu_dropout,
ModelHyperParams.preprocess_cmd,
ModelHyperParams.postprocess_cmd,
ModelHyperParams.weight_sharing,
TrainTaskConfig.label_smooth_eps,
ModelHyperParams.bos_idx,
use_py_reader=args.use_py_reader,
is_test=False)
optimizer = fluid.optimizer.SGD(0.003)
if args.run_params["fp16"]:
optimizer = decorate(optimizer, init_loss_scaling=64.0)
optimizer = fleet.distributed_optimizer(optimizer,
strategy=dist_strategy)
optimizer.minimize(avg_cost, startup_program)
train_program = fleet.main_program
exe.run(startup_program)
train_data = prepare_data_generator(
args,
is_test=False,
count=dev_count,
pyreader=pyreader,
py_reader_provider_wrapper=py_reader_provider_wrapper)
loss_normalizer = -(
(1. - TrainTaskConfig.label_smooth_eps) * np.log(
(1. - TrainTaskConfig.label_smooth_eps)) +
TrainTaskConfig.label_smooth_eps *
np.log(TrainTaskConfig.label_smooth_eps /
(ModelHyperParams.trg_vocab_size - 1) + 1e-20))
step_idx = 0
init_flag = True
result_loss = []
result_ppl = []
train_info = []
for pass_id in six.moves.xrange(args.num_epochs):
pass_start_time = time.time()
if args.use_py_reader:
pyreader.start()
data_generator = None
else:
data_generator = train_data()
batch_id = 0
while True:
try:
feed_dict_list = prepare_feed_dict_list(
data_generator, init_flag, dev_count)
t1 = time.time()
outs = exe.run(program=train_program,
fetch_list=[sum_cost.name, token_num.name]
if step_idx % args.fetch_steps == 0 else [],
feed=feed_dict_list)
if step_idx % args.fetch_steps == 0:
sum_cost_val, token_num_val = np.array(
outs[0]), np.array(outs[1])
total_sum_cost = sum_cost_val.sum()
total_token_num = token_num_val.sum()
total_avg_cost = total_sum_cost / total_token_num
result_loss.append(total_avg_cost - loss_normalizer)
result_ppl.append(
np.exp([min(total_avg_cost, 100)]).item(0))
train_info.append(result_loss)
init_flag = False
batch_id += 1
step_idx += 1
if batch_id >= 5:
break
except (StopIteration, fluid.core.EOFException):
if args.use_py_reader:
pyreader.reset()
break
train_info = [round(i, 6) for i in train_info[0]]
return train_info
def train(args):
# priority: ENV > args > config
is_local = os.getenv("PADDLE_IS_LOCAL", "1")
if is_local == '0':
args.local = False
logging.info(args)
if args.device == 'CPU':
TrainTaskConfig.use_gpu = False
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
if training_role == "PSERVER" or (not TrainTaskConfig.use_gpu):
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
else:
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
dev_count = get_device_num()
# place = fluid.CUDAPlace(0)
# dev_count = fluid.core.get_cuda_device_count()
update_lr(TrainTaskConfig)
exe = fluid.Executor(place)
train_prog = fluid.Program()
startup_prog = fluid.Program()
if args.enable_ce:
train_prog.random_seed = 1000
startup_prog.random_seed = 1000
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
sum_cost, avg_cost, predict, token_num, pyreader = transformer(
ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer,
ModelHyperParams.n_head,
ModelHyperParams.d_key,
ModelHyperParams.d_value,
ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid,
ModelHyperParams.prepostprocess_dropout,
ModelHyperParams.attention_dropout,
ModelHyperParams.relu_dropout,
ModelHyperParams.preprocess_cmd,
ModelHyperParams.postprocess_cmd,
ModelHyperParams.weight_sharing,
TrainTaskConfig.label_smooth_eps,
use_py_reader=args.use_py_reader,
is_test=False)
optimizer = None
if args.sync:
lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
logging.info("before adam")
with fluid.default_main_program()._lr_schedule_guard():
learning_rate = lr_decay * TrainTaskConfig.learning_rate
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
else:
optimizer = fluid.optimizer.SGD(0.003)
optimizer.minimize(avg_cost)
if args.use_mem_opt:
pass
# fluid.memory_optimize(train_prog)
if args.local:
logging.info("local start_up:")
train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
avg_cost, token_num, predict, pyreader)
else:
print("This script cannot run in distributed mode.")
sys.exit(0)
if args.update_method == "nccl2":
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
port = os.getenv("PADDLE_PORT")
worker_ips = os.getenv("PADDLE_TRAINERS")
worker_endpoints = []
for ip in worker_ips.split(","):
worker_endpoints.append(':'.join([ip, port]))
trainers_num = len(worker_endpoints)
current_endpoint = os.getenv("POD_IP") + ":" + port
if trainer_id == 0:
logging.info("train_id == 0, sleep 60s")
time.sleep(60)
logging.info("trainers_num:{}".format(trainers_num))
logging.info("worker_endpoints:{}".format(worker_endpoints))
logging.info("current_endpoint:{}".format(current_endpoint))
append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
current_endpoint)
train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
avg_cost, token_num, predict, pyreader, trainers_num,
trainer_id)
return
port = os.getenv("PADDLE_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
logging.info("pserver_endpoints:{}".format(pserver_endpoints))
logging.info("current_endpoint:{}".format(current_endpoint))
logging.info("trainer_id:{}".format(trainer_id))
logging.info("pserver_ips:{}".format(pserver_ips))
logging.info("port:{}".format(port))
t = fluid.DistributeTranspiler()
t.transpile(trainer_id,
pservers=pserver_endpoints,
trainers=trainers,
program=train_prog,
startup_program=startup_prog)
if training_role == "PSERVER":
logging.info("distributed: pserver started")
current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
"PADDLE_PORT")
if not current_endpoint:
logging.critical("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
logging.info("distributed: trainer started")
trainer_prog = t.get_trainer_program()
train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
avg_cost, token_num, predict, pyreader)
else:
logging.critical(
"environment var TRAINER_ROLE should be TRAINER os PSERVER")
exit(1)
def main(args):
train_prog = fluid.Program()
startup_prog = fluid.Program()
train_prog.random_seed = 1000
startup_prog.random_seed = 1000
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
sum_cost, avg_cost, predict, token_num, pyreader = transformer(
ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer,
ModelHyperParams.n_head,
ModelHyperParams.d_key,
ModelHyperParams.d_value,
ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid,
ModelHyperParams.prepostprocess_dropout,
ModelHyperParams.attention_dropout,
ModelHyperParams.relu_dropout,
ModelHyperParams.preprocess_cmd,
ModelHyperParams.postprocess_cmd,
ModelHyperParams.weight_sharing,
TrainTaskConfig.label_smooth_eps,
use_py_reader=args.use_py_reader,
is_test=False)
lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
optimizer = fluid.optimizer.Adam(
learning_rate=lr_decay * TrainTaskConfig.learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(avg_cost)
if args.use_mem_opt:
fluid.memory_optimize(train_prog)
if TrainTaskConfig.use_gpu:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exe = fluid.Executor(place)
# Initialize the parameters.
if TrainTaskConfig.ckpt_path:
fluid.io.load_persistables(exe, TrainTaskConfig.ckpt_path)
else:
exe.run(startup_prog)
exec_strategy = fluid.ExecutionStrategy()
# For faster executor
exec_strategy.use_experimental_executor = True
exec_strategy.num_iteration_per_drop_scope = 5
build_strategy = fluid.BuildStrategy()
# Since the token number differs among devices, customize gradient scale to
# use token average cost among multi-devices. and the gradient scale is
# `1 / token_number` for average cost.
build_strategy.gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
train_exe = fluid.ParallelExecutor(
use_cuda=TrainTaskConfig.use_gpu,
loss_name=avg_cost.name,
main_program=train_prog,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
# the best cross-entropy value with label smoothing
loss_normalizer = -((1. - TrainTaskConfig.label_smooth_eps) * np.log(
(1. - TrainTaskConfig.label_smooth_eps
)) + TrainTaskConfig.label_smooth_eps *
np.log(TrainTaskConfig.label_smooth_eps / (
ModelHyperParams.trg_vocab_size - 1) + 1e-20))
train_data = prepare_data_generator(
args, is_test=False, count=dev_count, pyreader=pyreader)
if args.use_py_reader:
pyreader.start()
data_generator = None
else:
data_generator = train_data()
def run(iter_num):
reader_time = []
run_time = []
for step_idx in six.moves.xrange(iter_num):
try:
start_time = time.time()
feed_dict_list = prepare_feed_dict_list(data_generator,
init_flag, dev_count)
end_time = time.time()
reader_time.append(end_time - start_time)
start_time = time.time()
if args.use_parallel_exe:
outs = train_exe.run(
fetch_list=[sum_cost.name, token_num.name],
feed=feed_dict_list)
else:
outs = exe.run(program=train_prog,
fetch_list=[sum_cost.name, token_num.name],
feed=feed_dict_list[0]
if feed_dict_list is not None else None)
end_time = time.time()
run_time.append(end_time - start_time)
sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[
1])
# sum the cost from multi-devices
total_sum_cost = sum_cost_val.sum()
total_token_num = token_num_val.sum()
total_avg_cost = total_sum_cost / total_token_num
print("step_idx: %d, avg loss: %f, "
"normalized loss: %f, ppl: %f" %
(step_idx, total_avg_cost,
total_avg_cost - loss_normalizer,
np.exp([min(total_avg_cost, 100)])))
except (StopIteration, fluid.core.EOFException):
# The current pass is over.
if args.use_py_reader:
pyreader.reset()
pyreader.start()
return reader_time, run_time
@contextlib.contextmanager
def profile_context(profile=True):
if profile:
with profiler.profiler('All', 'total', '/tmp/profile_file'):
yield
else:
yield
# start-up
init_flag = True
run(5)
init_flag = False
# profiling
start = time.time()
# currently only support profiling on one device
with profile_context(args.profile_ops):
reader_time, run_time = run(args.iter_num)
end = time.time()
total_time = end - start
print(
"Total time: {0}, reader time: {1} s, run time: {2} s, step number: {3}".
format(total_time, np.sum(reader_time), np.sum(run_time),
args.iter_num))
import gradio as gr
import tensorflow as tf
import tensorflow_datasets as tfds
from model import transformer
from main import predict
import pickle
with open('pretrained_weights/meta.pickle', 'rb') as handle:
meta = pickle.load(handle)
tokenizer = tfds.features.text.SubwordTextEncoder.load_from_file(
'pretrained_weights/tokenizer')
model = transformer(d_model=256,
vocab_size=meta['vocab_size'],
num_layers=4,
num_heads=8,
dff=1024)
model.load_weights('pretrained_weights/transformer_weights.h5')
def chatbot(sentence):
print('\nQ:', sentence)
resutl = predict(model, tokenizer, sentence, meta)
print('\nA:', resutl)
return resutl
gr.Interface(chatbot, inputs="text", outputs="text").launch(share=True)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
tf.keras.backend.clear_session()
# Hyper-parameters
NUM_LAYERS = 4
D_MODEL = 312
NUM_HEADS = 8
UNITS = 768
DROPOUT = 0.2
model = transformer(
vocab_size=VOCAB_SIZE,
num_layers=NUM_LAYERS,
units=UNITS,
d_model=D_MODEL,
num_heads=NUM_HEADS,
dropout=DROPOUT)
def loss_function(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, preprocessor.MAX_LENGTH - 1))
loss = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')(y_true, y_pred)
mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)
loss = tf.multiply(loss, mask)
return tf.reduce_mean(loss)
def main():
"""
model train
"""
is_local = os.getenv("PADDLE_IS_LOCAL", "0")
if is_local == '0':
args.local = False
else:
args.local = True
# init
place = fluid.CUDAPlace(0) if args.device == 'GPU' else fluid.CPUPlace()
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
if training_role == "PSERVER":
place = fluid.CPUPlace()
exe = fluid.Executor(place)
sum_cost, avg_cost, predict, token_num = transformer(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
ModelHyperParams.src_pad_idx, ModelHyperParams.trg_pad_idx,
ModelHyperParams.pos_pad_idx)
warmup_steps = get_var("warmup_steps", value=TrainTaskConfig.warmup_steps)
d_model = get_var("d_model", value=ModelHyperParams.d_model)
lr_decay = fluid.layers\
.learning_rate_scheduler\
.noam_decay(d_model, warmup_steps)
optimizer = fluid.optimizer.Adam(
learning_rate = lr_decay,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimize_ops, params_grads = optimizer.minimize(avg_cost if TrainTaskConfig.use_avg_cost else sum_cost)
# Program to do validation.
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
inference_program = fluid.io.get_inference_program([avg_cost])
def test(exe):
test_total_cost = 0
test_total_token = 0
for batch_id, data in enumerate(test_reader()):
data_input = prepare_batch_input(
data, encoder_input_data_names + decoder_input_data_names[:-1] +
label_data_names, ModelHyperParams.eos_idx,
ModelHyperParams.eos_idx, ModelHyperParams.n_head,
ModelHyperParams.d_model)
test_sum_cost, test_token_num = exe.run(
inference_program,
feed=data_input,
fetch_list=[sum_cost, token_num],
use_program_cache=True)
test_total_cost += test_sum_cost
test_total_token += test_token_num
test_avg_cost = test_total_cost / test_total_token
test_ppl = np.exp([min(test_avg_cost, 100)])
return test_avg_cost, test_ppl
def train_loop(exe, trainer_prog):
for pass_id in xrange(args.pass_num):
ts = time.time()
total = 0
pass_start_time = time.time()
#print len(train_reader)
for batch_id, data in enumerate(train_reader):
#print len(data)
if len(data) != args.batch_size:
continue
total += len(data)
start_time = time.time()
data_input = prepare_batch_input(
data, encoder_input_data_names + decoder_input_data_names[:-1] +
label_data_names, ModelHyperParams.eos_idx,
ModelHyperParams.eos_idx, ModelHyperParams.n_head,
ModelHyperParams.d_model)
outs = exe.run(trainer_prog,
feed=data_input,
fetch_list=[sum_cost, avg_cost],
use_program_cache=True)
sum_cost_val, avg_cost_val = np.array(outs[0]), np.array(outs[1])
print("epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f, speed: %.2f" %
(pass_id, batch_id, sum_cost_val, avg_cost_val,
np.exp([min(avg_cost_val[0], 100)]),
len(data) / (time.time() - start_time)))
if args.test_save:
if batch_id == args.exit_batch_id:
print("batch_id: %d exit!" % batch_id)
break
# Validate and save the model for inference.
# val_avg_cost, val_ppl = test(exe)
val_avg_cost, val_ppl = 0,0
pass_end_time = time.time()
time_consumed = pass_end_time - pass_start_time
print("pass_id = %s time_consumed = %s val_avg_cost=%f val_ppl=%f speed: %.2f" % \
(str(pass_id), str(time_consumed), \
val_avg_cost, val_ppl, total / (time.time() - ts)))
fluid.io.save_inference_model(
os.path.join(args.model_path,
"pass_" + str(pass_id) + "_" + str(args.task_index) + ".infer.model"),
encoder_input_data_names + decoder_input_data_names[:-1],
[predict], exe)
if args.test_save:
break
if args.local:
# Initialize the parameters.
print("local start_up:")
exe.run(fluid.framework.default_startup_program())
#print(debuger.pprint_program_codes(fluid.framework.default_startup_program()))
for pos_enc_param_name in pos_enc_param_names:
#print("pos_enc_param_name:", pos_enc_param_name)
pos_enc_param = fluid.global_scope().find_var(
pos_enc_param_name).get_tensor()
pos_enc_param.set(
position_encoding_init(ModelHyperParams.max_length + 1,
ModelHyperParams.d_model), place)
#print "./nist06n/data-%d/part-*" % (args.task_index),
train_reader = data_util.DataLoader(
src_vocab_fpath="./thirdparty/nist06n/cn_30001.dict",
trg_vocab_fpath="./thirdparty/nist06n/en_30001.dict",
fpattern="./train/*" % (args.task_index),
batch_size=args.batch_size,
token_batch_size=TrainTaskConfig.token_batch_size,
sort_by_length=TrainTaskConfig.sort_by_length,
shuffle=True)
train_loop(exe, fluid.default_main_program())
else:
port = os.getenv("PADDLE_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
t = fluid.DistributeTranspiler()
t.transpile(
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
"PADDLE_PORT")
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
if args.save_graph:
block_no=0
for t in pserver_startup.blocks:
block_name="pserver_startup_block_%04d" % block_no
print block_name
print(debuger.draw_block_graphviz(t, path="./" + block_name+".dot"))
block_no+=1
block_no=0
for t in pserver_prog.blocks:
block_name="pserver_prog_block_%04d" % block_no
print(debuger.draw_block_graphviz(t, path="./" + block_name+".dot"))
block_no+=1
print "begin run"
exe.run(pserver_startup)#, save_program_to_file="./pserver_startup.desc")
exe.run(pserver_prog)#, save_program_to_file="./pserver_loop.desc")
elif training_role == "TRAINER":
# Parameter initialization
exe.run(fluid.default_startup_program())
#print("cluster start_up:")
for pos_enc_param_name in pos_enc_param_names:
#print("pos_enc_param_name:", pos_enc_param_name)
pos_enc_param = fluid.global_scope().find_var(
pos_enc_param_name).get_tensor()
pos_enc_param.set(
position_encoding_init(ModelHyperParams.max_length + 1,
ModelHyperParams.d_model), place)
train_reader = data_util.DataLoader(
src_vocab_fpath="./thirdparty/nist06n/cn_30001.dict",
trg_vocab_fpath="./thirdparty/nist06n/en_30001.dict",
fpattern="./train/part-*",
batch_size=args.batch_size,
token_batch_size=TrainTaskConfig.token_batch_size,
sort_by_length=TrainTaskConfig.sort_by_length,
shuffle=True)
trainer_prog = t.get_trainer_program()
train_loop(exe, trainer_prog)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
def train(args):
"""train start"""
logging.info(args)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
dev_count = 1
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
# For Distributed Training.
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
args.num_trainers = fleet.worker_num()
args.trainer_id = fleet.worker_index()
dist_strategy = DistributedStrategy()
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
sum_cost, avg_cost, predict, token_num, pyreader = transformer(
ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer,
ModelHyperParams.n_head,
ModelHyperParams.d_key,
ModelHyperParams.d_value,
ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid,
ModelHyperParams.prepostprocess_dropout,
ModelHyperParams.attention_dropout,
ModelHyperParams.relu_dropout,
ModelHyperParams.preprocess_cmd,
ModelHyperParams.postprocess_cmd,
ModelHyperParams.weight_sharing,
TrainTaskConfig.label_smooth_eps,
ModelHyperParams.bos_idx,
use_py_reader=args.use_py_reader,
is_test=False)
optimizer = None
if args.sync:
lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
with fluid.default_main_program()._lr_schedule_guard():
learning_rate = lr_decay * TrainTaskConfig.learning_rate
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
else:
optimizer = fluid.optimizer.SGD(0.003)
if args.use_fp16:
optimizer = decorate(optimizer,
init_loss_scaling=args.loss_scaling)
optimizer = fleet.distributed_optimizer(optimizer,
strategy=dist_strategy)
optimizer.minimize(avg_cost, startup_program)
train_program = fleet.main_program
orig_train_program = fleet._origin_program
train_loop(args, exe, train_program, orig_train_program, startup_program,
dev_count, sum_cost, avg_cost, token_num, predict, pyreader)
def get_logits(input_ids, mems, input_mask, target_mask):
"""Builds the graph for calculating the final logits"""
is_training = False
cutoffs = []
train_bin_sizes = []
eval_bin_sizes = []
proj_share_all_but_first = True
n_token = FLAGS.n_token
batch_size = FLAGS.batch_size
features = {"input": input_ids}
inp = tf.transpose(features["input"], [1, 0])
input_mask = tf.transpose(input_mask, [1, 0])
target_mask = tf.transpose(target_mask, [1, 0])
tgt = None
inp_perms, tgt_perms, head_tgt = None, None, None
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(minval=-FLAGS.init_range,
maxval=FLAGS.init_range,
seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(stddev=FLAGS.init_std,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std, seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
tf.logging.info("Vocab size : {}".format(n_token))
tf.logging.info("Batch size : {}".format(batch_size))
logits, new_mems = model.transformer(dec_inp=inp,
target=tgt,
mems=mems,
n_token=n_token,
n_layer=FLAGS.n_layer,
d_model=FLAGS.d_model,
d_embed=FLAGS.d_embed,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=0,
dropatt=0,
initializer=initializer,
is_training=is_training,
mem_len=FLAGS.seq_len +
FLAGS.max_decode_length,
cutoffs=cutoffs,
div_val=1,
tie_projs=tie_projs,
input_perms=inp_perms,
target_perms=tgt_perms,
head_target=head_tgt,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
use_tpu=FLAGS.use_tpu,
untie_r=FLAGS.untie_r,
proj_same_dim=True,
bidirectional_mask=FLAGS.bi_mask,
infer=True,
target_mask=target_mask,
input_mask=input_mask,
tgt_len=1)
return logits, new_mems
def main():
place = fluid.CUDAPlace(0) if TrainTaskConfig.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
sum_cost, avg_cost, predict, token_num = transformer(
ModelHyperParams.src_vocab_size + 0,
ModelHyperParams.trg_vocab_size + 0, ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer, ModelHyperParams.n_head,
ModelHyperParams.d_key, ModelHyperParams.d_value,
ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
ModelHyperParams.dropout, ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx)
lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps, place,
TrainTaskConfig.learning_rate)
optimizer = fluid.optimizer.Adam(learning_rate=lr_scheduler.learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(avg_cost if TrainTaskConfig.use_avg_cost else sum_cost)
train_data = paddle.batch(paddle.reader.shuffle(nist_data_provider.train(
"data", ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size),
buf_size=100000),
batch_size=TrainTaskConfig.batch_size)
# Initialize the parameters.
exe.run(fluid.framework.default_startup_program())
for pos_enc_param_name in pos_enc_param_names:
pos_enc_param = fluid.global_scope().find_var(
pos_enc_param_name).get_tensor()
pos_enc_param.set(
position_encoding_init(ModelHyperParams.max_length + 1,
ModelHyperParams.d_model), place)
for pass_id in xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
data_input = prepare_batch_input(
data, encoder_input_data_names +
decoder_input_data_names[:-1] + label_data_names,
ModelHyperParams.src_pad_idx, ModelHyperParams.trg_pad_idx,
ModelHyperParams.n_head, ModelHyperParams.d_model)
lr_scheduler.update_learning_rate(data_input)
outs = exe.run(fluid.framework.default_main_program(),
feed=data_input,
fetch_list=[sum_cost, avg_cost],
use_program_cache=True)
sum_cost_val, avg_cost_val = np.array(outs[0]), np.array(outs[1])
print("epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f" %
(pass_id, batch_id, sum_cost_val, avg_cost_val,
np.exp([min(avg_cost_val[0], 100)])))
pass_end_time = time.time()
time_consumed = pass_end_time - pass_start_time
print("pass_id = " + str(pass_id) + " time_consumed = " +
str(time_consumed))
fluid.io.save_inference_model(
os.path.join(TrainTaskConfig.model_dir,
"pass_" + str(pass_id) + ".infer.model"),
encoder_input_data_names + decoder_input_data_names[:-1],
[predict], exe)
def main(args):
# get datasets
source_dataset = data.get_dataset(args.source, args.split)
target_dataset = data.get_dataset(args.target, args.split)
im_s = preprocess(source_dataset.x,
args.preprocessing,
image_size=args.image_size,
output_channels=args.output_channels)
label_s = source_dataset.y
im_t = preprocess(target_dataset.x,
args.preprocessing,
image_size=args.image_size,
output_channels=args.output_channels)
label_t = target_dataset.y
im_batch_s, label_batch_s, im_batch_t, label_batch_t = data.create_batch(
[im_s, label_s, im_t, label_t],
batch_size=args.batch_size,
shuffle=args.shuffle)
# build models
transformed_s = model.transformer(im_batch_s, scope='model/s_to_t')
transformed_t = model.transformer(im_batch_t, scope='model/t_to_s')
cycled_s = model.transformer(transformed_s,
scope='model/t_to_s',
reuse=True)
cycled_t = model.transformer(transformed_t,
scope='model/s_to_t',
reuse=True)
# create loss functions
cycle_loss_s = tf.losses.absolute_difference(im_batch_s,
cycled_s,
scope='cycle_loss_s')
cycle_loss_t = tf.losses.absolute_difference(im_batch_t,
cycled_t,
scope='cycle_loss_t')
total_loss = cycle_loss_s + cycle_loss_t
optimizer = tf.train.AdamOptimizer(args.learning_rate, args.beta1,
args.beta2, args.epsilon)
inc_global_step = tf.assign_add(tf.train.get_or_create_global_step(), 1)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, inc_global_step)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_tensor = optimizer.minimize(total_loss)
# Set up train op to return loss
with tf.control_dependencies([train_tensor]):
train_op = tf.identity(total_loss, name='train_op')
# set up logging
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
summaries.add(tf.summary.histogram(variable.op.name, variable))
summaries.add(tf.summary.image('source', im_batch_s))
summaries.add(tf.summary.image('target', im_batch_t))
summaries.add(tf.summary.image('source_transformed', transformed_s))
summaries.add(tf.summary.image('target_transformed', transformed_t))
summaries.add(tf.summary.image('source_cycled', cycled_s))
summaries.add(tf.summary.image('target_cycled', cycled_t))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries), name='summary_op')
# create train loop
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
saver = tf.train.Saver(var_list=tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope='model'))
checkpoint_path = os.path.join(args.output_dir, 'model.ckpt')
writer = tf.summary.FileWriter(args.output_dir)
with tf.Session() as sess:
# Tensorflow initializations
sess.run(tf.get_collection(tf.GraphKeys.TABLE_INITIALIZERS))
tf.train.start_queue_runners(sess=sess)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
last_log_time = 0
last_save_time = 0
for i in tqdm(range(args.num_batches)):
if last_log_time < time.time() - args.log_every_n_seconds:
last_log_time = time.time()
summary, loss_val, global_step = sess.run(
[summary_op, train_op,
tf.train.get_global_step()])
writer.add_summary(summary, global_step)
writer.flush()
else:
loss_val, global_step = sess.run(
[train_op, tf.train.get_global_step()])
if last_save_time < time.time() - args.save_every_n_seconds:
last_save_time = time.time()
saver.save(sess, checkpoint_path, global_step=global_step)
saver.save(sess, checkpoint_path, global_step=args.num_batches)
def model_fn(inp, tgt, mems, is_training):
inp = tf.transpose(inp, [1, 0])
tgt = tf.transpose(tgt, [1, 0])
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range, maxval=FLAGS.init_range, seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(stddev=FLAGS.init_std,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std, seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if FLAGS.proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
reduce_loss = True
neg_log_probs, new_mems = model.transformer(
dec_inp=inp,
target=tgt,
mems=mems,
n_token=n_token,
n_layer=FLAGS.n_layer,
d_model=FLAGS.d_model,
d_embed=FLAGS.d_embed,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=is_training,
mem_len=FLAGS.mem_len,
cutoffs=cutoffs,
div_val=FLAGS.div_val,
tie_projs=tie_projs,
input_perms=None,
target_perms=None,
head_target=None,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
use_tpu=False,
untie_r=FLAGS.untie_r,
proj_same_dim=FLAGS.proj_same_dim,
reduce_loss=reduce_loss)
if reduce_loss:
loss = neg_log_probs
neg_log_probs = []
else:
loss = tf.reduce_mean(neg_log_probs)
# number of parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
# format_str = '{{:<{0}s}}\t{{}}'.format(
# max([len(v.name) for v in tf.trainable_variables()]))
# for v in tf.trainable_variables():
# tf.logging.info(format_str.format(v.name, v.get_shape()))
print("neg log loss", neg_log_probs)
if is_training:
all_vars = tf.trainable_variables()
grads = tf.gradients(loss, all_vars)
grads_and_vars = list(zip(grads, all_vars))
return loss, new_mems, grads_and_vars, neg_log_probs
else:
return loss, new_mems, neg_log_probs
self.d_model = d_model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
def __call__(self, step):
arg1 = tf.math.rsqrt(step)
arg2 = step * (self.warmup_steps**-1.5)
return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)
dataset, other_tuple = create_data()
model = transformer(vocab_size=other_tuple[0],
num_layers=NUM_LAYERS,
units=UNITS,
model=MODEL,
num_heads=NUM_HEADS,
dropout=DROPOUT)
learning_rate = CustomSchedule(d_model=128)
opt = tf.keras.optimizers.Adam(learning_rate,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
def accuracy(y_true, y_pred):
# first make sure both have the same length (b_size, MAX_LENGTH -1)
y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))
return tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
def train(args):
# priority: ENV > args > config
is_local = os.getenv("PADDLE_IS_LOCAL", "1")
if is_local == '0':
args.local = False
logging.info("args:{}".format(args))
if args.device == 'CPU':
TrainTaskConfig.use_gpu = False
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
if training_role == "PSERVER" or (not TrainTaskConfig.use_gpu):
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
else:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
exe = fluid.Executor(place)
if args.enable_ce:
fluid.default_startup_program().random_seed = 1000
sum_cost, avg_cost, predict, token_num = transformer(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps)
lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps,
TrainTaskConfig.learning_rate)
if args.local:
optimizer = fluid.optimizer.Adam(
learning_rate=lr_scheduler.learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(sum_cost)
elif args.sync == False:
optimizer = fluid.optimizer.SGD(0.003)
optimizer.minimize(sum_cost)
else:
lr_decay = fluid.layers\
.learning_rate_scheduler\
.noam_decay(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps)
optimizer = fluid.optimizer.Adam(learning_rate=lr_decay,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(sum_cost)
if args.local:
logging.info("local start_up:")
train_loop(exe, fluid.default_main_program(), dev_count, sum_cost,
avg_cost, lr_scheduler, token_num, predict)
else:
port = os.getenv("PADDLE_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
"PADDLE_PORT")
if not current_endpoint:
logging.critical("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
logging.info("psserver begin run")
#with open('pserver_startup.desc', 'w') as f:
# f.write(str(pserver_startup))
#with open('pserver_prog.desc', 'w') as f:
# f.write(str(pserver_prog))
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
with open('trainer_prog.desc', 'w') as f:
f.write(str(trainer_prog))
train_loop(exe, trainer_prog, dev_count, sum_cost, avg_cost,
lr_scheduler, token_num, predict)
else:
logging.info(
"environment var TRAINER_ROLE should be TRAINER os PSERVER")
def main(args):
# get datasets
dataset = data.get_dataset(args.dataset,
args.split,
image_size=args.image_size,
data_dir=args.data_dir,
is_training=True)
im_x = preprocess(dataset.x,
args.preprocessing_a,
image_size=args.image_size,
output_channels=args.num_channels)
im_y = preprocess(dataset.y,
args.preprocessing_b,
image_size=args.image_size)
# No need to use tf.train.batch
im_x = tf.expand_dims(im_x, 0)
im_y = tf.expand_dims(im_y, 0)
# build models
transformed_x = model.transformer(im_x,
output_channels=dataset.num_classes,
output_fn=None,
scope='model/AtoB')
transformed_y = model.transformer(im_y,
output_channels=args.num_channels,
scope='model/BtoA')
cycled_x = model.transformer(transformed_x,
output_channels=args.num_channels,
scope='model/BtoA',
reuse=True)
cycled_y = model.transformer(transformed_y,
output_channels=dataset.num_classes,
output_fn=None,
scope='model/AtoB',
reuse=True)
# Correct colors for outputting
color_map = np.array(
list(map(lambda x: x.color,
labels[:dataset.num_classes]))).astype(np.float32)
image_x = (im_x + 1.0) / 2.0
image_transformed_y = (transformed_y + 1.0) / 2.0
image_cycled_x = (cycled_x + 1.0) / 2.0
segmentation_y = postprocess(tf.argmax(im_y, -1), 'segmentation_to_rgb',
dataset.num_classes, color_map)
segmentation_transformed_x = postprocess(tf.argmax(transformed_x, -1),
'segmentation_to_rgb',
dataset.num_classes, color_map)
segmentation_cycled_y = postprocess(tf.argmax(cycled_y,
-1), 'segmentation_to_rgb',
dataset.num_classes, color_map)
saver = tf.train.Saver(var_list=tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope='model'))
with tf.Session() as sess:
# Tensorflow initializations
sess.run(tf.get_collection(tf.GraphKeys.TABLE_INITIALIZERS))
tf.train.start_queue_runners(sess=sess)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
saver.restore(sess, tf.train.latest_checkpoint(args.checkpoint_dir))
for i in tqdm(range(args.num_batches)):
x, y, x_t, y_t, x_c, y_c = sess.run([
image_x, segmentation_y, segmentation_transformed_x,
image_transformed_y, image_cycled_x, segmentation_cycled_y
])
plt.subplot(231)
plt.imshow(x[0])
plt.subplot(232)
plt.imshow(x_t[0])
plt.subplot(233)
plt.imshow(x_c[0])
plt.subplot(234)
plt.imshow(y[0])
plt.subplot(235)
plt.imshow(y_t[0])
plt.subplot(236)
plt.imshow(y_c[0])
plt.show()
parser = argparse.ArgumentParser()
parser.add_argument('--max_samples',
default=25000,
type=int,
help='maximum number of conversation pairs to use')
parser.add_argument('--max_length',
default=40,
type=int,
help='maximum sentence length')
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--num_layers', default=2, type=int)
parser.add_argument('--num_units', default=512, type=int)
parser.add_argument('--d_model', default=256, type=int)
parser.add_argument('--num_heads', default=8, type=int)
parser.add_argument('--dropout', default=0.1, type=float)
parser.add_argument('--activation', default='relu', type=str)
parser.add_argument('--epochs', default=20, type=int)
hparams = parser.parse_args()
dataset, tokenizer = get_dataset(hparams)
model = transformer(hparams)
model.load_weights('Test/cp.ckpt')
model.save("Test\model")
evaluate(hparams, model, tokenizer)
##########################################################################################
# experiment for n times
for exp_times in range(CONFIG['exp_times']):
SAVE_PATH = PJ('.', 'runs_test', DATASET, EXP_NAME, str(exp_times))
writer = SummaryWriter(PJ(SAVE_PATH))
# set experiment type: classifier / transformer
if CONFIG['type'] == "classifier":
model = classifier(backbone=CONFIG['model'],
k=CONFIG['k'], d=CONFIG['d'][CONFIG['concepts']][DATASET],
pretrained=CONFIG['pretrained'], freeze=CONFIG['freeze'])
elif CONFIG['type'] == "transformer":
model = transformer(backbone=CONFIG['model'], linear=CONFIG['linear'],
k=CONFIG['k'], d=CONFIG['d'][CONFIG['concepts']][DATASET],
pretrained=CONFIG['pretrained'], freeze=CONFIG['freeze'])
else:
assert False, "Must Assign the model type: classifier or transformer"
# load model weight
if CONFIG['load_model']:
print("Loading pretrained model")
state = torch.load(PJ(SAVE_PATH, 'best_result.pkl'))
# load model epoch
CONFIG['start_epoch'] = state['epoch']
assert CONFIG['end_epoch'] > CONFIG['start_epoch'], \
("The start epoch is {}, and the end epoch is smaller than start epoch.", state['epoch'])
# load model parameter
def model_fn(features, labels, mode, params):
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
batch_size = params["batch_size"]
mems = params["cache"]
inp = tf.transpose(features["inputs"], [1, 0])
tgt = tf.transpose(features["labels"], [1, 0])
bin_sizes = train_bin_sizes if is_training else eval_bin_sizes
if bin_sizes:
inp_perms = [tf.transpose(features["inp_mask"], [1, 0])]
tgt_perms = [tf.transpose(features["tgt_mask"], [1, 0])]
head_tgt = tf.transpose(features["head_labels"], [1, 0])
for b in range(len(bin_sizes)):
inp_perm = tf.transpose(features["inp_perm_{}".format(b)],
[1, 0, 2])
tgt_perm = tf.transpose(features["tgt_perm_{}".format(b)],
[1, 0, 2])
inp_perms.append(inp_perm)
tgt_perms.append(tgt_perm)
else:
inp_perms, tgt_perms, head_tgt = None, None, None
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range, maxval=FLAGS.init_range, seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(stddev=FLAGS.init_std,
seed=None)
proj_initializer = tf.initializers.random_normal(
stddev=FLAGS.proj_init_std, seed=None)
tie_projs = [False for _ in range(len(cutoffs) + 1)]
if FLAGS.proj_share_all_but_first:
for i in range(1, len(tie_projs)):
tie_projs[i] = True
tf.logging.info("Vocab size : {}".format(n_token))
tf.logging.info("Batch size : {}".format(batch_size))
loss, new_mems = model.transformer(dec_inp=inp,
target=tgt,
mems=mems,
n_token=n_token,
n_layer=FLAGS.n_layer,
d_model=FLAGS.d_model,
d_embed=FLAGS.d_embed,
n_head=FLAGS.n_head,
d_head=FLAGS.d_head,
d_inner=FLAGS.d_inner,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
initializer=initializer,
is_training=is_training,
mem_len=FLAGS.mem_len,
cutoffs=cutoffs,
div_val=FLAGS.div_val,
tie_projs=tie_projs,
input_perms=inp_perms,
target_perms=tgt_perms,
head_target=head_tgt,
same_length=FLAGS.same_length,
clamp_len=FLAGS.clamp_len,
use_tpu=FLAGS.use_tpu,
untie_r=FLAGS.untie_r,
proj_same_dim=FLAGS.proj_same_dim)
total_loss = tf.reduce_mean(loss)
if mode == tf.estimator.ModeKeys.EVAL:
if FLAGS.use_tpu:
with tf.colocate_with(total_loss):
total_loss = tf.contrib.tpu.cross_replica_sum(total_loss) \
/ FLAGS.num_hosts / FLAGS.num_core_per_host
metric_loss = tf.tile(tf.reshape(total_loss, [1, 1]),
[batch_size, 1])
eval_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=(metric_fn, [metric_loss]))
eval_spec.cache = new_mems
return eval_spec
# Configuring the optimization step.
global_step = tf.train.get_global_step()
# increase the learning rate linearly
if FLAGS.warmup_steps > 0:
warmup_lr = tf.to_float(global_step) / tf.to_float(FLAGS.warmup_steps) \
* FLAGS.learning_rate
else:
warmup_lr = 0.0
# number of parameters
num_params = np.sum(
[np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info("#params: {}".format(num_params))
# format_str = '{{:<{0}s}}\t{{}}'.format(
# max([len(v.name) for v in tf.trainable_variables()]))
# for v in tf.trainable_variables():
# tf.logging.info(format_str.format(v.name, v.get_shape()))
# decay the learning rate using the cosine schedule
decay_lr = tf.train.cosine_decay(
FLAGS.learning_rate,
global_step=global_step - FLAGS.warmup_steps,
decay_steps=FLAGS.train_steps - FLAGS.warmup_steps,
alpha=FLAGS.min_lr_ratio)
learning_rate = tf.where(global_step < FLAGS.warmup_steps, warmup_lr,
decay_lr)
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(
tf.train.AdamOptimizer(learning_rate=learning_rate))
#GradientDescentOptimizer
else:
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
grads_and_vars = optimizer.compute_gradients(total_loss)
gradients, variables = zip(*grads_and_vars)
clipped, _ = tf.clip_by_global_norm(gradients, FLAGS.clip)
train_op = optimizer.apply_gradients(
zip(clipped, variables), global_step=tf.train.get_global_step())
# Constucting TPUEstimatorSpec with cache.
train_spec = tf.contrib.tpu.TPUEstimatorSpec(mode=mode,
loss=total_loss,
train_op=train_op)
if FLAGS.mem_len < FLAGS.tgt_len:
new_mems = [new_mems[:FLAGS.mem_len] for mem_t in new_mems]
train_spec.cache = new_mems
return train_spec
def profile(args):
print args
if args.device == 'CPU':
TrainTaskConfig.use_gpu = False
if not TrainTaskConfig.use_gpu:
place = fluid.CPUPlace()
dev_count = multiprocessing.cpu_count()
else:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
exe = fluid.Executor(place)
sum_cost, avg_cost, predict, token_num = transformer(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps)
lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps,
TrainTaskConfig.learning_rate)
optimizer = fluid.optimizer.Adam(learning_rate=lr_scheduler.learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(sum_cost)
# Initialize the parameters.
if TrainTaskConfig.ckpt_path:
fluid.io.load_persistables(exe, TrainTaskConfig.ckpt_path)
lr_scheduler.current_steps = TrainTaskConfig.start_step
else:
exe.run(fluid.framework.default_startup_program())
# Disable all sorts for they will be done in the 1st batch.
train_data = reader.DataReader(
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
fpattern=args.train_file_pattern,
use_token_batch=args.use_token_batch,
batch_size=args.batch_size *
(1 if args.use_token_batch else dev_count),
pool_size=args.pool_size,
sort_type='none',
shuffle=False,
shuffle_batch=False,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
# count start and end tokens out
max_length=ModelHyperParams.max_length - 2,
clip_last_batch=False)
train_data = read_multiple(reader=train_data.batch_generator,
count=dev_count if args.use_token_batch else 1)
if dev_count > 1:
build_strategy = fluid.BuildStrategy()
build_strategy.gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
train_exe = fluid.ParallelExecutor(
use_cuda=TrainTaskConfig.use_gpu,
loss_name=sum_cost.name,
main_program=fluid.default_main_program(),
build_strategy=build_strategy)
print("Warming up ...")
train_loop(exe if dev_count == 1 else train_exe,
fluid.default_main_program(), False, 3, train_data, dev_count,
sum_cost, avg_cost, lr_scheduler, token_num, predict)
print("\nProfiling ...")
if dev_count == 1:
with profiler.profiler('All', 'total', '/tmp/profile_file'):
total_time, exec_time = train_loop(exe,
fluid.default_main_program(),
True, args.num_iters,
train_data, dev_count, sum_cost,
avg_cost, lr_scheduler,
token_num, predict)
else:
total_time, exec_time = train_loop(train_exe,
fluid.default_main_program(), True,
args.num_iters, train_data,
dev_count, sum_cost, avg_cost,
lr_scheduler, token_num, predict)
print("Elapsed time: total %f s, in executor %f s" %
(total_time, exec_time))
def test_context(exe, train_exe, dev_count):
# Context to do validation.
test_prog = fluid.Program()
startup_prog = fluid.Program()
if args.enable_ce:
test_prog.random_seed = 1000
startup_prog.random_seed = 1000
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
sum_cost, avg_cost, predict, token_num, pyreader = transformer(
ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer,
ModelHyperParams.n_head,
ModelHyperParams.d_key,
ModelHyperParams.d_value,
ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid,
ModelHyperParams.prepostprocess_dropout,
ModelHyperParams.attention_dropout,
ModelHyperParams.relu_dropout,
ModelHyperParams.preprocess_cmd,
ModelHyperParams.postprocess_cmd,
ModelHyperParams.weight_sharing,
TrainTaskConfig.label_smooth_eps,
use_py_reader=args.use_py_reader,
is_test=True)
test_prog = test_prog.clone(for_test=True)
test_data = prepare_data_generator(args,
is_test=True,
count=dev_count,
pyreader=pyreader)
exe.run(startup_prog)
test_exe = fluid.ParallelExecutor(use_cuda=TrainTaskConfig.use_gpu,
main_program=test_prog,
share_vars_from=train_exe)
def test(exe=test_exe, pyreader=pyreader):
test_total_cost = 0
test_total_token = 0
if args.use_py_reader:
pyreader.start()
data_generator = None
else:
data_generator = test_data()
while True:
try:
feed_dict_list = prepare_feed_dict_list(
data_generator, False, dev_count)
outs = test_exe.run(fetch_list=[sum_cost.name, token_num.name],
feed=feed_dict_list)
except (StopIteration, fluid.core.EOFException):
# The current pass is over.
if args.use_py_reader:
pyreader.reset()
break
sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[1])
test_total_cost += sum_cost_val.sum()
test_total_token += token_num_val.sum()
test_avg_cost = test_total_cost / test_total_token
test_ppl = np.exp([min(test_avg_cost, 100)])
return test_avg_cost, test_ppl
return test
def train(args):
is_local = os.getenv("PADDLE_IS_LOCAL", "1")
print is_local
if is_local == '0':
args.local = False
else:
args.local = True
print args
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
if training_role == "PSERVER":
place = fluid.CPUPlace()
else:
place = fluid.CUDAPlace(
0) if TrainTaskConfig.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
if TrainTaskConfig.use_gpu and training_role != "PSERVER":
dev_count = fluid.core.get_cuda_device_count()
sum_cost, avg_cost, predict, token_num = transformer(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
TrainTaskConfig.label_smooth_eps)
lr_decay = fluid.layers\
.learning_rate_scheduler\
.noam_decay(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps)
optimizer = fluid.optimizer.Adam(learning_rate=lr_decay,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(sum_cost)
def train_loop(exe, train_progm):
def read_multiple(reader,
count=dev_count if args.use_token_batch else 1,
clip_last=True):
"""
Stack data from reader for multi-devices.
"""
def __impl__():
res = []
for item in reader():
res.append(item)
if len(res) == count:
yield res
res = []
if len(res) == count:
yield res
elif not clip_last:
data = []
for item in res:
data += item
if len(data) > count:
inst_num_per_part = len(data) // count
yield [
data[inst_num_per_part * i:inst_num_per_part *
(i + 1)] for i in range(count)
]
return __impl__
def split_data(data, num_part=dev_count):
"""
Split data for each device.
"""
if len(data) == num_part:
return data
data = data[0]
inst_num_per_part = len(data) // num_part
return [
data[inst_num_per_part * i:inst_num_per_part * (i + 1)]
for i in range(num_part)
]
# Initialize the parameters.
if TrainTaskConfig.ckpt_path:
fluid.io.load_persistables(exe, TrainTaskConfig.ckpt_path)
lr_scheduler.current_steps = TrainTaskConfig.start_step
else:
print "init fluid.framework.default_startup_program"
exe.run(fluid.framework.default_startup_program())
train_data = reader.DataReader(
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
fpattern=args.train_file_pattern,
use_token_batch=args.use_token_batch,
batch_size=args.batch_size *
(1 if args.use_token_batch else dev_count),
pool_size=args.pool_size,
sort_type=args.sort_type,
shuffle=args.shuffle,
shuffle_batch=args.shuffle_batch,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
clip_last_batch=False)
train_data = read_multiple(reader=train_data.batch_generator)
build_strategy = fluid.BuildStrategy()
# Since the token number differs among devices, customize gradient scale to
# use token average cost among multi-devices. and the gradient scale is
# `1 / token_number` for average cost.
build_strategy.gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
#'''
train_exe = fluid.ParallelExecutor(use_cuda=TrainTaskConfig.use_gpu,
loss_name=sum_cost.name,
main_program=train_progm,
build_strategy=build_strategy)
#'''
def test_context():
# Context to do validation.
test_program = train_progm.clone()
with fluid.program_guard(test_program):
test_program = fluid.io.get_inference_program([avg_cost])
val_data = reader.DataReader(
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
fpattern=args.val_file_pattern,
use_token_batch=args.use_token_batch,
batch_size=args.batch_size *
(1 if args.use_token_batch else dev_count),
pool_size=args.pool_size,
sort_type=args.sort_type,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
clip_last_batch=False,
shuffle=False,
shuffle_batch=False)
test_exe = fluid.ParallelExecutor(use_cuda=TrainTaskConfig.use_gpu,
main_program=test_program,
share_vars_from=train_exe)
def test(exe=test_exe):
test_total_cost = 0
test_total_token = 0
test_data = read_multiple(reader=val_data.batch_generator)
for batch_id, data in enumerate(test_data()):
feed_list = []
for place_id, data_buffer in enumerate(split_data(data)):
data_input_dict, util_input_dict, _ = prepare_batch_input(
data_buffer, data_input_names, util_input_names,
ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
ModelHyperParams.n_head, ModelHyperParams.d_model)
feed_list.append(
dict(data_input_dict.items() +
util_input_dict.items()))
outs = exe.run(feed=feed_list,
fetch_list=[sum_cost.name, token_num.name])
sum_cost_val, token_num_val = np.array(outs[0]), np.array(
outs[1])
test_total_cost += sum_cost_val.sum()
test_total_token += token_num_val.sum()
test_avg_cost = test_total_cost / test_total_token
test_ppl = np.exp([min(test_avg_cost, 100)])
return test_avg_cost, test_ppl
return test
if args.val_file_pattern is not None:
test = test_context()
data_input_names = encoder_data_input_fields + decoder_data_input_fields[:
-1] + label_data_input_fields
util_input_names = encoder_util_input_fields + decoder_util_input_fields
init = False
for pass_id in xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
feed_list = []
total_num_token = 0
#lr_rate = lr_scheduler.update_learning_rate()
for place_id, data_buffer in enumerate(split_data(data)):
data_input_dict, util_input_dict, num_token = prepare_batch_input(
data_buffer, data_input_names, util_input_names,
ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
ModelHyperParams.n_head, ModelHyperParams.d_model)
total_num_token += num_token
feed_list.append(
dict(data_input_dict.items() +
util_input_dict.items()))
if not init:
for pos_enc_param_name in pos_enc_param_names:
pos_enc = position_encoding_init(
ModelHyperParams.max_length + 1,
ModelHyperParams.d_model)
feed_list[place_id][pos_enc_param_name] = pos_enc
for feed_dict in feed_list:
feed_dict[
sum_cost.name +
"@GRAD"] = 1. / total_num_token if TrainTaskConfig.use_avg_cost else np.asarray(
[1.], dtype="float32")
outs = train_exe.run(
fetch_list=[sum_cost.name, token_num.name], feed=feed_list)
#outs = exe.run(train_progm,fetch_list=[sum_cost.name, token_num.name],feed=feed_list[0])
sum_cost_val, token_num_val = np.array(outs[0]), np.array(
outs[1])
total_sum_cost = sum_cost_val.sum(
) # sum the cost from multi-devices
total_token_num = token_num_val.sum()
total_avg_cost = total_sum_cost / total_token_num
print(
"epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f"
% (pass_id, batch_id, total_sum_cost, total_avg_cost,
np.exp([min(total_avg_cost, 100)])))
init = True
# Validate and save the model for inference.
print("epoch: %d, " % pass_id +
("val avg loss: %f, val ppl: %f, " %
test() if args.val_file_pattern is not None else "") +
"consumed %fs" % (time.time() - pass_start_time))
fluid.io.save_persistables(
exe,
os.path.join(TrainTaskConfig.ckpt_dir,
"pass_" + str(pass_id) + ".checkpoint"))
fluid.io.save_inference_model(
os.path.join(TrainTaskConfig.model_dir,
"pass_" + str(pass_id) + ".infer.model"),
data_input_names[:-2] + util_input_names, [predict], exe)
if args.local:
print("local start_up:")
train_loop(exe, fluid.default_main_program())
else:
port = os.getenv("PADDLE_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
"PADDLE_PORT")
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
print "psserver begin run"
with open('pserver_startup', 'w') as f:
f.write(str(pserver_startup))
with open('pserver_prog', 'w') as f:
f.write(str(pserver_prog))
exe.run(pserver_startup
) #, save_program_to_file="./pserver_startup.desc")
exe.run(
pserver_prog) #, save_program_to_file="./pserver_loop.desc")
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
with open('trainer_prog', 'w') as f:
f.write(str(trainer_prog))
train_loop(exe, trainer_prog)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
def main():
args = parse_args()
place = fluid.CUDAPlace(0) if TrainTaskConfig.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
sum_cost, avg_cost, predict, token_num = transformer(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1, ModelHyperParams.n_layer,
ModelHyperParams.n_head, ModelHyperParams.d_key,
ModelHyperParams.d_value, ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid, ModelHyperParams.dropout,
TrainTaskConfig.label_smooth_eps)
lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
TrainTaskConfig.warmup_steps,
TrainTaskConfig.learning_rate)
optimizer = fluid.optimizer.Adam(learning_rate=lr_scheduler.learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
optimizer.minimize(sum_cost)
dev_count = fluid.core.get_cuda_device_count()
train_data = paddle.batch(paddle.dataset.wmt16.train(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size),
batch_size=TrainTaskConfig.batch_size)
# Program to do validation.
test_program = fluid.default_main_program().clone()
with fluid.program_guard(test_program):
test_program = fluid.io.get_inference_program([avg_cost])
val_data = paddle.batch(paddle.dataset.wmt16.validation(
ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size),
batch_size=TrainTaskConfig.batch_size)
def test(exe):
test_total_cost = 0
test_total_token = 0
test_data = read_multiple(reader=val_data, count=dev_count)
for batch_id, data in enumerate(test_data()):
feed_list = []
for place_id, data_buffer in enumerate(data):
data_input_dict, util_input_dict, _ = prepare_batch_input(
data_buffer, data_input_names, util_input_names,
ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
ModelHyperParams.n_head, ModelHyperParams.d_model)
feed_list.append(
dict(data_input_dict.items() + util_input_dict.items()))
outs = exe.run(feed=feed_list,
fetch_list=[sum_cost.name, token_num.name])
sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[1])
test_total_cost += sum_cost_val.sum()
test_total_token += token_num_val.sum()
test_avg_cost = test_total_cost / test_total_token
test_ppl = np.exp([min(test_avg_cost, 100)])
return test_avg_cost, test_ppl
# Initialize the parameters.
if TrainTaskConfig.ckpt_path:
fluid.io.load_persistables(exe, TrainTaskConfig.ckpt_path)
lr_scheduler.current_steps = TrainTaskConfig.start_step
else:
exe.run(fluid.framework.default_startup_program())
data_input_names = encoder_data_input_fields + decoder_data_input_fields[:
-1] + label_data_input_fields
util_input_names = encoder_util_input_fields + decoder_util_input_fields
train_exe = fluid.ParallelExecutor(use_cuda=TrainTaskConfig.use_gpu,
loss_name=sum_cost.name)
test_exe = fluid.ParallelExecutor(use_cuda=TrainTaskConfig.use_gpu,
main_program=test_program,
share_vars_from=train_exe)
init = False
train_data = read_multiple(reader=train_data, count=dev_count)
for pass_id in xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
feed_list = []
total_num_token = 0
lr_rate = lr_scheduler.update_learning_rate()
for place_id, data_buffer in enumerate(data):
data_input_dict, util_input_dict, num_token = prepare_batch_input(
data_buffer, data_input_names, util_input_names,
ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
ModelHyperParams.n_head, ModelHyperParams.d_model)
total_num_token += num_token
feed_list.append(
dict(data_input_dict.items() + util_input_dict.items() +
{lr_scheduler.learning_rate.name: lr_rate}.items()))
if not init:
for pos_enc_param_name in pos_enc_param_names:
tensor = position_encoding_init(
ModelHyperParams.max_length + 1,
ModelHyperParams.d_model)
feed_list[place_id][pos_enc_param_name] = tensor
for feed_dict in feed_list:
feed_dict[
sum_cost.name +
"@GRAD"] = 1. / total_num_token if TrainTaskConfig.use_avg_cost else np.asarray(
[1.], dtype="float32")
outs = train_exe.run(fetch_list=[sum_cost.name, token_num.name],
feed=feed_list)
sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[1])
total_sum_cost = sum_cost_val.sum(
) # sum the cost from multi devices
total_token_num = token_num_val.sum()
total_avg_cost = total_sum_cost / total_token_num
print("epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f" %
(pass_id, batch_id, total_sum_cost, total_avg_cost,
np.exp([min(total_avg_cost, 100)])))
init = True
pass_end_time = time.time()
# Validate and save the model for inference.
val_avg_cost, val_ppl = test(test_exe)
time_consumed = pass_end_time - pass_start_time
print("pass_id = " + str(pass_id) + " time_consumed = " +
str(time_consumed))
if pass_id == TrainTaskConfig.pass_num - 1:
if args.gpu_card_num == 1:
test_avg_ppl_kpi.add_record(np.array(val_ppl, dtype='float32'))
train_pass_duration_kpi.add_record(time_consumed)
test_avg_ppl_kpi.persist()
train_pass_duration_kpi.persist()
else:
test_avg_ppl_kpi_card4.add_record(
np.array(val_ppl, dtype='float32'))
train_pass_duration_kpi_card4.add_record(time_consumed)
test_avg_ppl_kpi_card4.persist()
train_pass_duration_kpi_card4.persist()
train_dataset = dset.ImageFolder(root=train_path)
test_dataset = dset.ImageFolder(root=test_path)
dataSet = Omniglot(train_dataset, transform=data_transforms, way=way)
testSet = Omniglot(test_dataset, transform=test_transforms, way=way)
testLoader = DataLoader(testSet, batch_size=32, shuffle=False, num_workers=16)
dataLoader = DataLoader(dataSet, batch_size=128,\
shuffle=False, num_workers=16)
loss_fn = torch.nn.CrossEntropyLoss(size_average=False)
# loss_fn = nn.DataParallel(loss_fn)
loss_fn.cuda()
net = transformer(way, img_size, N, d_model, d_k, h, drop_rate)
# net = nn.DataParallel(net)
net.cuda()
net.train()
train_loss = []
optimizer = torch.optim.Adam(net.parameters(),
lr=learning_rate,
betas=(0.9, 0.98),
eps=1e-9)
# optimizer = ScheduledOptim(optimizer, d_model, warmup_steps)
optimizer.zero_grad()
def right_error(output, truth):
