def main(args):
if args.distribute:
distribute.enable_distributed_training()
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
if distribute.rank() == 0:
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
# Build model
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
# Create global step
global_step = tf.train.get_or_create_global_step()
dtype = tf.float16 if args.half else None
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer, regularizer, dtype), features,
params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
loss = loss + tf.losses.get_regularization_loss()
if distribute.rank() == 0:
print_variables()
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("loss", loss)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
opt = optimizers.MultiStepOptimizer(opt, params.update_cycle)
if args.half:
opt = optimizers.LossScalingOptimizer(opt, params.loss_scale)
# Optimization
grads_and_vars = opt.compute_gradients(
loss, colocate_gradients_with_ops=True)
if params.clip_grad_norm:
grads, var_list = list(zip(*grads_and_vars))
grads, _ = tf.clip_by_global_norm(grads, params.clip_grad_norm)
grads_and_vars = zip(grads, var_list)
train_op = opt.apply_gradients(grads_and_vars, global_step=global_step)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Hooks
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"source": tf.shape(features["source"]),
"target": tf.shape(features["target"])
},
every_n_iter=1)
]
broadcast_hook = distribute.get_broadcast_hook()
if broadcast_hook:
train_hooks.append(broadcast_hook)
if distribute.rank() == 0:
# Add hooks
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
train_hooks.append(
hooks.MultiStepHook(tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver),
step=params.update_cycle))
if eval_input_fn is not None:
train_hooks.append(
hooks.MultiStepHook(hooks.EvaluationHook(
lambda f: inference.create_inference_graph([model], f,
params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
session_config(params),
device_list=params.device_list,
max_to_keep=params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps),
step=params.update_cycle))
checkpoint_dir = params.output
else:
checkpoint_dir = None
restore_op = restore_variables(args.checkpoint)
def restore_fn(step_context):
step_context.session.run(restore_op)
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_dir,
hooks=train_hooks,
save_checkpoint_secs=None,
config=session_config(params)) as sess:
# Restore pre-trained variables
sess.run_step_fn(restore_fn)
while not sess.should_stop():
sess.run(train_op)
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
#import ipdb; ipdb.set_trace()
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset_c2f_4layers.get_training_input_and_c2f_label(
params.input, params.c2f_input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
update_cycle = params.update_cycle
features, init_op = cache.cache_features(features, update_cycle)
# Build model
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer, regularizer), features,
params.device_list)
if len(sharded_losses) > 1:
losses_mle, losses_l1, losses_l2, losses_l3, losses_l4 = sharded_losses
loss_mle = tf.add_n(losses_mle) / len(losses_mle)
loss_l1 = tf.add_n(losses_l1) / len(losses_l1)
loss_l2 = tf.add_n(losses_l2) / len(losses_l2)
loss_l3 = tf.add_n(losses_l3) / len(losses_l3)
loss_l4 = tf.add_n(losses_l4) / len(losses_l4)
else:
loss_mle, loss_l1, loss_l2, loss_l3, loss_l4 = sharded_losses[0]
loss = loss_mle + (loss_l1 + loss_l2 + loss_l3 + loss_l4
) / 4.0 + tf.losses.get_regularization_loss()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
loss, ops = optimize.create_train_op(loss, opt, global_step, params)
restore_op = restore_variables(args.checkpoint)
#import ipdb; ipdb.set_trace()
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset_c2f_4layers.sort_and_zip_files(files)
eval_input_fn = dataset_c2f_4layers.get_evaluation_input
else:
eval_input_fn = None
# Add hooks
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
multiplier = tf.convert_to_tensor([update_cycle, 1])
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss_mle": loss_mle,
"loss_l1": loss_l1,
"loss_l2": loss_l2,
"loss_l3": loss_l3,
"loss_l4": loss_l4,
"source": tf.shape(features["source"]) * multiplier,
"target": tf.shape(features["target"]) * multiplier
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver)
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: inference.create_inference_graph([model], f,
params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
#def restore_fn(step_context):
# step_context.session.run(restore_op)
#def step_fn(step_context):
# # Bypass hook calls
# step_context.session.run([init_op, ops["zero_op"]])
# for i in range(update_cycle - 1):
# step_context.session.run(ops["collect_op"])
# return step_context.run_with_hooks(ops["train_op"])
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
# Restore pre-trained variables
sess._tf_sess().run(restore_op)
while not sess.should_stop():
sess._tf_sess().run([init_op, ops["zero_op"]])
for i in range(update_cycle - 1):
sess._tf_sess().run(ops["collect_op"])
sess.run(ops["train_op"])
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
# Build model
initializer = get_initializer(params)
model = model_cls(params)
if params.MRT:
assert params.batch_size == 1
features = mrt_utils.get_MRT(features, params, model)
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer), features, params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=loss,
global_step=global_step,
learning_rate=learning_rate,
clip_gradients=params.clip_grad_norm or None,
optimizer=opt,
colocate_gradients_with_ops=True)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Add hooks
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"source": tf.shape(features["source"]),
"target": tf.shape(features["target"])
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=tf.train.Saver(max_to_keep=params.keep_checkpoint_max,
sharded=False))
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: search.create_inference_graph(
model.get_evaluation_func(), f, params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
while not sess.should_stop():
sess.run(train_op)
def main(args):
if args.distribute:
distribute.enable_distributed_training()
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
if not args.distribute or distribute.rank() == 0:
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
assert 'r2l' in params.input[2]
# Build Graph
use_all_devices(params)
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.abd_get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
update_cycle = params.update_cycle
features, init_op = cache.cache_features(features, update_cycle)
# Build model
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
# Create global step
global_step = tf.train.get_or_create_global_step()
dtype = tf.float16 if args.fp16 else None
if args.distribute:
training_func = model.get_training_func(initializer, regularizer,
dtype)
loss = training_func(features)
else:
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer, regularizer, dtype),
features, params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
loss = loss + tf.losses.get_regularization_loss()
# Print parameters
if not args.distribute or distribute.rank() == 0:
print_variables()
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
loss, ops = optimize.create_train_op(
loss, opt, global_step,
distribute.all_reduce if args.distribute else None, args.fp16,
params)
restore_op = restore_variables(args.checkpoint)
# Validation
if params.validation and params.references[0]:
files = params.validation + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.abd_get_evaluation_input
else:
eval_input_fn = None
# Add hooks
multiplier = tf.convert_to_tensor([update_cycle, 1])
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"source": tf.shape(features["source"]) * multiplier,
"target": tf.shape(features["target"]) * multiplier
},
every_n_iter=1)
]
if args.distribute:
train_hooks.append(distribute.get_broadcast_hook())
config = session_config(params)
if not args.distribute or distribute.rank() == 0:
# Add hooks
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
train_hooks.append(
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver))
if eval_input_fn is not None:
if not args.distribute or distribute.rank() == 0:
train_hooks.append(
hooks.EvaluationHook(
lambda f: inference.create_inference_graph([model], f,
params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
def restore_fn(step_context):
step_context.session.run(restore_op)
def step_fn(step_context):
# Bypass hook calls
step_context.session.run([init_op, ops["zero_op"]])
for i in range(update_cycle - 1):
step_context.session.run(ops["collect_op"])
return step_context.run_with_hooks(ops["train_op"])
# Create session, do not use default CheckpointSaverHook
if not args.distribute or distribute.rank() == 0:
checkpoint_dir = params.output
else:
checkpoint_dir = None
with tf.train.MonitoredTrainingSession(checkpoint_dir=checkpoint_dir,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
# Restore pre-trained variables
sess.run_step_fn(restore_fn)
while not sess.should_stop():
sess.run_step_fn(step_fn)
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model) # a model class
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# print(params.vocabulary)
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
parsing_features = dataset.get_training_input(params.parsing_input,
params,
problem='parsing')
amr_features = dataset.get_training_input(params.amr_input,
params,
problem='amr')
else:
parsing_features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
amr_features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
update_cycle = params.update_cycle
parsing_features, parsing_init_op = cache.cache_features(
parsing_features, update_cycle)
amr_features, amr_init_op = cache.cache_features(
amr_features, update_cycle)
# Build model
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Multi-GPU setting
# parsing_sharded_losses, amr_sharded_losses = parallel.parallel_model(
# model.get_training_func(initializer, regularizer),
# [parsing_features, amr_features],
# params.device_list
# )
# parsing_loss, amr_loss = model.get_training_func(initializer, regularizer)([parsing_features, amr_features])
# with tf.variable_scope("shared_decode_variable") as scope:
# parsing_loss = model.get_training_func(initializer, regularizer, problem='parsing')(parsing_features)
# scope.reuse_variables()
# amr_loss = model.get_training_func(initializer, regularizer, problem='amr')(amr_features)
#with tf.variable_scope("encoder_shared", reuse=True):
print(params.layer_postprocess)
with tf.variable_scope("encoder_shared",
initializer=initializer,
regularizer=regularizer,
reuse=tf.AUTO_REUSE):
parsing_encoder_output = model.get_encoder_out(
parsing_features, "train", params)
amr_encoder_output = model.get_encoder_out(amr_features, "train",
params)
with tf.variable_scope("parsing_decoder",
initializer=initializer,
regularizer=regularizer):
parsing_loss = model.get_decoder_out(parsing_features,
parsing_encoder_output,
"train",
params,
problem="parsing")
with tf.variable_scope("amr_decoder",
initializer=initializer,
regularizer=regularizer):
amr_loss = model.get_decoder_out(amr_features,
amr_encoder_output,
"train",
params,
problem="amr")
parsing_loss = parsing_loss + tf.losses.get_regularization_loss()
amr_loss = amr_loss + tf.losses.get_regularization_loss()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
parsing_loss, parsing_ops = optimize.create_train_op(parsing_loss,
opt,
global_step,
params,
problem="parsing")
amr_loss, amr_ops = optimize.create_train_op(amr_loss,
opt,
global_step,
params,
problem="amr")
restore_op = restore_variables(args.checkpoint)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Add hooks
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
multiplier = tf.convert_to_tensor([update_cycle, 1])
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(parsing_loss),
tf.train.NanTensorHook(amr_loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"parsing_loss": parsing_loss,
"amr_loss": amr_loss,
"parsing_source":
tf.shape(parsing_features["source"]) * multiplier,
"parsing_target":
tf.shape(parsing_features["target"]) * multiplier,
"amr_source":
tf.shape(amr_features["source"]) * multiplier,
"amr_target": tf.shape(amr_features["target"]) * multiplier
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver)
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: inference.create_inference_graph([model], f,
params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params, flag='target'),
lambda x: decode_target_ids(x, params, flag='source'),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
def restore_fn(step_context):
step_context.session.run(restore_op)
def parsing_step_fn(step_context):
# Bypass hook calls
step_context.session.run([parsing_init_op, parsing_ops["zero_op"]
]) # if params.cycle==1 do nothing
for i in range(update_cycle - 1):
step_context.session.run(parsing_ops["collect_op"])
return step_context.run_with_hooks(parsing_ops["train_op"])
def amr_step_fn(step_context):
# Bypass hook calls
step_context.session.run([amr_init_op, amr_ops["zero_op"]])
for i in range(update_cycle - 1):
step_context.session.run(amr_ops["collect_op"])
return step_context.run_with_hooks(amr_ops["train_op"])
def step_fn(step_context):
# Bypass hook calls
return step_context.run_with_hooks(parsing_ops["train_op"])
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
# Restore pre-trained variables
sess.run_step_fn(restore_fn)
step = 0
while not sess.should_stop():
if step % 2 == 0:
sess.run_step_fn(parsing_step_fn)
else:
sess.run_step_fn(amr_step_fn)
step += 1
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params)
features, init_op = cache.cache_features(features, params.update_cycle)
# Build model
initializer = get_initializer(params)
model = model_cls(params)
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer), features, params.device_list)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
loss, ops = optimize.create_train_op(loss, opt, global_step, params)
restore_op = restore_variables(args.checkpoint)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Add hooks
save_vars = tf.trainable_variables() + [global_step]
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook({
"step": global_step,
"loss": loss,
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False))
]
config = session_config(params)
# gpu allow growth
config.gpu_options.allow_growth = True
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: inference.create_inference_graph(
[model.get_inference_func()], f, params),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
sess.run(restore_op)
while not sess.should_stop():
# Bypass hook calls
utils.session_run(sess, [init_op, ops["zero_op"]])
for i in range(params.update_cycle):
utils.session_run(sess, ops["collect_op"])
utils.session_run(sess, ops["scale_op"])
sess.run(ops["train_op"])
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
export_params(params.output, "params.json", params)
export_params(params.output, "%s.json" % args.model,
collect_params(params, model_cls.get_parameters()))
with tf.Graph().as_default():
features = dataset.get_final_training_input(params.input, params)
update_cycle = params.update_cycle
features, init_op = cache.cache_features(features, update_cycle)
initializer = get_initializer(params)
regularizer = tf.contrib.layers.l1_l2_regularizer(
scale_l1=params.scale_l1, scale_l2=params.scale_l2)
model = model_cls(params)
global_step = tf.train.get_or_create_global_step()
model_fn = model.get_training_func(initializer, regularizer)
class_loss, attention_loss = model_fn(features)
attention_loss = attention_loss * params.lamda
loss = class_loss + attention_loss + tf.losses.get_regularization_loss(
)
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
if params.optimizer == "Adam":
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "LazyAdam":
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
elif params.optimizer == "SGD":
opt = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise RuntimeError("Optimizer %s not supported" % params.optimizer)
loss, ops = optimize.create_train_op(loss, opt, global_step, params)
restore_op = restore_variables(args.checkpoint)
if params.validation:
eval_sorted_keys, eval_inputs = dataset.read_eval_input_file(
params.validation)
eval_input_fn = dataset.get_predict_input
else:
eval_input_fn = None
save_vars = tf.trainable_variables() + [global_step]
saver = tf.train.Saver(
var_list=save_vars if params.only_save_trainable else None,
max_to_keep=params.keep_checkpoint_max,
sharded=False)
tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
multiplier = tf.convert_to_tensor([update_cycle, 1])
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"class_loss": class_loss,
"attention_loss": attention_loss,
"text": tf.shape(features["text"]) * multiplier,
"aspect": tf.shape(features["aspect"]) * multiplier,
"polarity": tf.shape(features["polarity"]) * multiplier
},
every_n_iter=1),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=saver)
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: inference.create_predict_graph([model], f, params
),
lambda: eval_input_fn(eval_inputs, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps))
def restore_fn(step_context):
step_context.session.run(restore_op)
def step_fn(step_context):
step_context.session.run([init_op, ops["zero_op"]])
for i in range(update_cycle - 1):
step_context.session.run(ops["collect_op"])
return step_context.run_with_hooks(ops["train_op"])
with tf.train.MonitoredTrainingSession(checkpoint_dir=params.output,
hooks=train_hooks,
save_checkpoint_secs=None,
config=config) as sess:
sess.run_step_fn(restore_fn)
while not sess.should_stop():
sess.run_step_fn(step_fn)
