def eval_during_train(n_token, cutoffs, ps_device, sess):
tf.logging.info("Reporting on valid during training")
eval_input_fn, eval_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split=FLAGS.eval_split, # train or valid
per_host_bsz=FLAGS.eval_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
num_batch = eval_record_info["num_batch"]
if FLAGS.max_eval_batch > 0:
num_batch = FLAGS.max_eval_batch
tf.logging.info("num of batches {}".format(num_batch))
eval_set = eval_input_fn({
"batch_size": FLAGS.eval_batch_size,
"data_dir": FLAGS.data_dir})
input_feed, label_feed = eval_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
per_core_bsz = FLAGS.eval_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems = [], [], []
def get_input_fn(split):
"""doc."""
assert split == "train" or split == "valid"
record_info_dir = os.path.join(FLAGS.record_info_dir, split)
batch_size = FLAGS.batch_size
input_fn, record_info_dict = data_utils.get_input_fn(
info_dir=record_info_dir,
split=split,
bsz_per_host=batch_size // FLAGS.num_hosts,
seq_len=FLAGS.seq_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
num_hosts=FLAGS.num_hosts,
num_core_per_host=FLAGS.num_core_per_host,
perm_size=FLAGS.perm_size,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
use_bfloat16=FLAGS.use_bfloat16,
num_predict=FLAGS.num_predict,
use_tpu=FLAGS.use_tpu,
bucket_uri=FLAGS.bucket_uri)
return input_fn, record_info_dict
def get_AlexNet_experiment(args):
"""
Function for creating an experiment using the AlexNet model on ImageNet
"""
train_input_fn = data_utils.get_input_fn(data_dir=os.path.join(
args.data_dir, 'train'),
num_epochs=args.num_epochs,
batch_size=args.batch_size,
shuffle=True)
val_input_fn = data_utils.get_input_fn(data_dir=os.path.join(
args.data_dir, 'val'),
num_epochs=1,
batch_size=2 * args.batch_size,
shuffle=False)
net = model.AlexNet(num_classes=1000, scope='ImageNet_AlexNet')
config = tf.contrib.learn.RunConfig(
log_device_placement=False,
gpu_memory_fraction=0.98,
tf_random_seed=1234,
save_summary_steps=50,
save_checkpoints_secs=300,
keep_checkpoint_max=10000,
keep_checkpoint_every_n_hours=10000,
log_step_count_steps=10,
)
estimator = tf.estimator.Estimator(model_fn=net.get_model_fn(),
model_dir=args.model_dir,
config=config,
params={'learning_rate': args.lr})
experiment = tf.contrib.learn.Experiment(estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=val_input_fn,
eval_metrics=None,
train_steps=None,
eval_steps=None,
train_monitors=[],
min_eval_frequency=1000,
eval_delay_secs=240)
return experiment
def get_simple_nn_experiment(args):
"""
Function for creating an experiment using the SimpleNN model on MNIST
"""
train_input_fn = data_utils.get_input_fn(data_dir=args.data_dir,
is_training=True,
num_epochs=args.num_epochs,
batch_size=args.batch_size,
shuffle=True,
normalize=args.normalize)
val_input_fn = data_utils.get_input_fn(data_dir=args.data_dir,
is_training=False,
num_epochs=1,
batch_size=2 * args.batch_size,
shuffle=False,
normalize=args.normalize)
simplecnn = model.SimpleMnistModel(num_classes=args.num_classes,
scope='SimpleMnist')
config = tf.estimator.RunConfig(keep_checkpoint_max=10000,
tf_random_seed=1234,
save_summary_steps=50,
save_checkpoints_secs=120)
estimator = tf.estimator.Estimator(model_fn=simplecnn.get_model_fn(),
model_dir=args.model_dir,
config=config,
params={'learning_rate': args.lr})
experiment = tf.contrib.learn.Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=val_input_fn,
eval_metrics=None,
train_steps=None,
eval_steps=None,
train_monitors=[],
min_eval_frequency=1,
)
return experiment
def test_data():
h = copy(hparams)
h.dataset = "unit_test"
(sequences, seq_lens), labels = get_input_fn("train", h)()
sess = tf.Session()
s, c, l = sess.run([sequences, seq_lens, labels])
t = Tokenizer.get_tokenizer(hparams)
decoded = t.decode(s)[0]
target = "aside from the terrific sea rescue _UNK of which there are very few i just did not care about any of the _UNK"
assert decoded == target.split()
assert l[0][0] == 0
def get_input_fn(split):
"""doc."""
assert split == "train"
batch_size = FLAGS.train_batch_size
input_fn, record_info_dict = data_utils.get_input_fn(
tfrecord_dir=FLAGS.record_info_dir,
split=split,
bsz_per_host=batch_size // FLAGS.num_hosts,
seq_len=FLAGS.seq_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
num_hosts=FLAGS.num_hosts,
num_core_per_host=FLAGS.num_core_per_host,
perm_size=FLAGS.perm_size,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
uncased=FLAGS.uncased,
num_passes=FLAGS.num_passes,
use_bfloat16=FLAGS.use_bfloat16,
num_predict=FLAGS.num_predict)
return input_fn, record_info_dict
def evaluate(n_token, cutoffs, ps_device):
# Get input function and model function
eval_input_fn, eval_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split=FLAGS.eval_split,
per_host_bsz=FLAGS.eval_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
num_batch = eval_record_info["num_batch"]
if FLAGS.max_eval_batch > 0:
num_batch = FLAGS.max_eval_batch
tf.logging.info("num of batches {}".format(num_batch))
# Create computational graph
eval_set = eval_input_fn({
"batch_size": FLAGS.eval_batch_size,
"data_dir": FLAGS.data_dir})
input_feed, label_feed = eval_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
per_core_bsz = FLAGS.eval_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems = [], [], []
for i in range(FLAGS.num_core_per_host):
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
mems_i = [tf.placeholder(tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)]
loss_i, new_mems_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=False,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
# sum losses across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
else:
loss = tower_losses[0]
# Evaluation loop
tower_mems_np = [
[np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model], dtype=np.float32)
for layer in range(FLAGS.n_layer)]
for core in range(FLAGS.num_core_per_host)
]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.eval_ckpt_path is None:
eval_ckpt_path = tf.train.latest_checkpoint(FLAGS.model_dir)
else:
eval_ckpt_path = FLAGS.eval_ckpt_path
tf.logging.info("Evaluate {}".format(eval_ckpt_path))
saver.restore(sess, eval_ckpt_path)
fetches = [loss, tower_new_mems, tf.size(label_feed)]
format_str = " >> processing batch {{:{0}d}}/{{:{0}d}} ..".format(
len(str(num_batch)))
total_loss, total_cnt = 0, 0
for step in range(num_batch):
if step % (num_batch // 10) == 0:
tf.logging.info(format_str.format(step, num_batch))
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, cnt_np = fetched[:3]
total_loss += loss_np * cnt_np
total_cnt += cnt_np
avg_loss = total_loss / total_cnt
tf.logging.info("| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
avg_loss, math.exp(avg_loss), avg_loss / math.log(2)))
def train(n_token, cutoffs, ps_device):
# os.environ['CUDA_VISIBLE_DEVICES'] = '2,3'
# Get input function and model function
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=FLAGS.train_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
tf.logging.info("num of batches {}".format(train_record_info["num_batch"]))
# Create computational graph
train_set = train_input_fn({
"batch_size": FLAGS.train_batch_size,
"data_dir": FLAGS.data_dir})
input_feed, label_feed = train_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
print_op = tf.print(inputs)
per_core_bsz = FLAGS.train_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
#todo review here
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
mems_i = [tf.placeholder(tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)]
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=True,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
# average losses and gradients across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
grads, all_vars = zip(*grads_and_vars)
# clip gradient
clipped, gnorm = tf.clip_by_global_norm(grads, FLAGS.clip)
grads_and_vars = list(zip(clipped, all_vars))
# configure the optimizer
global_step = tf.train.get_or_create_global_step()
# warmup stage: 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
# decay stage: 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)
# choose warmup or decay
learning_rate = tf.where(global_step < FLAGS.warmup_steps,
warmup_lr, decay_lr)
# get the train op
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
# Training loop
tower_mems_np = [
[np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model], dtype=np.float32)
for layer in range(FLAGS.n_layer)]
for core in range(FLAGS.num_core_per_host)
]
saver = tf.train.Saver()
tf.summary.scalar('learning_rate', learning_rate)
tf.summary.scalar('loss', loss)
# tf.summary.scalar('pplx', math.exp(curr_loss))
merged = tf.summary.merge_all()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
# todo 放在 此处是因为不用重复的创建trainer目录能显示变量
train_writer = tf.summary.FileWriter(os.path.join(FLAGS.model_dir, "log"), sess.graph)
if FLAGS.warm_start_path is not None:
tf.logging.info("warm start from {}".format(FLAGS.warm_start_path))
saver.restore(sess, FLAGS.warm_start_path)
fetches = [loss, tower_new_mems, global_step, gnorm, learning_rate, train_op]
total_loss, prev_step = 0., -1
while True:
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
#old
# fetched = sess.run(fetches, feed_dict=feed_dict)
# with tf.control_dependencies([print_op]):
summary, fetched = sess.run([merged, fetches], feed_dict=feed_dict)
loss_np, tower_mems_np, curr_step = fetched[:3]
total_loss += loss_np
if curr_step > 0 and curr_step % FLAGS.iterations == 0:
curr_loss = total_loss / (curr_step - prev_step)
tf.logging.info("[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(curr_step, fetched[-3], fetched[-2], curr_loss, math.exp(curr_loss), curr_loss / math.log(2)))
total_loss, prev_step = 0., curr_step
train_writer.add_summary(summary, curr_step)
if curr_step > 0 and curr_step % FLAGS.save_steps == 0:
save_path = os.path.join(FLAGS.model_dir, "model-{}.ckpt".format(curr_step))
saver.save(sess, save_path)
tf.logging.info("Model saved in path: {}".format(save_path))
if curr_step == FLAGS.train_steps:
train_writer.close()
break
def train(ps_device):
##### Get input function and model function
train_input_fn, record_info_dict = data_utils.get_input_fn(
tfrecord_dir=FLAGS.record_info_dir,
split="train",
bsz_per_host=FLAGS.train_batch_size,
seq_len=FLAGS.seq_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
num_hosts=1,
num_core_per_host=1, # set to one no matter how many GPUs
perm_size=FLAGS.perm_size,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
uncased=FLAGS.uncased,
num_passes=FLAGS.num_passes,
use_bfloat16=FLAGS.use_bfloat16,
num_predict=FLAGS.num_predict)
# for key, info in record_info_dict.items():
tf.compat.v1.logging.info("num of batches {}".format(record_info_dict["num_batch"]))
##### Create input tensors / placeholders
bsz_per_core = FLAGS.train_batch_size // FLAGS.num_core_per_host
params = {
"batch_size": FLAGS.train_batch_size # the whole batch
}
train_set = train_input_fn(params)
example = train_set.make_one_shot_iterator().get_next()
if FLAGS.num_core_per_host > 1:
examples = [{} for _ in range(FLAGS.num_core_per_host)]
for key in example.keys():
vals = tf.split(example[key], FLAGS.num_core_per_host, 0)
for device_id in range(FLAGS.num_core_per_host):
examples[device_id][key] = vals[device_id]
else:
examples = [example]
##### Create computational graph
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
# The mems for each tower is a dictionary
mems_i = {}
if FLAGS.mem_len:
mems_i["mems"] = create_mems_tf(bsz_per_core)
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
is_training=True,
features=examples[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
## average losses and gradients across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
## get train op
train_op, learning_rate, gnorm = model_utils.get_train_op(FLAGS, None,
grads_and_vars=grads_and_vars)
global_step = tf.train.get_global_step()
##### Training loop
# initialize mems
tower_mems_np = []
for i in range(FLAGS.num_core_per_host):
mems_i_np = {}
for key in tower_mems[i].keys():
mems_i_np[key] = initialize_mems_np(bsz_per_core)
tower_mems_np.append(mems_i_np)
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(allow_growth=True)
model_utils.init_from_checkpoint(FLAGS, global_vars=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
fetches = [loss, tower_new_mems, global_step, gnorm, learning_rate, train_op]
total_loss, prev_step = 0., -1
while True:
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for key in tower_mems_np[i].keys():
for m, m_np in zip(tower_mems[i][key], tower_mems_np[i][key]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, curr_step = fetched[:3]
total_loss += loss_np
if curr_step > 0 and curr_step % FLAGS.iterations == 0:
curr_loss = total_loss / (curr_step - prev_step)
tf.compat.v1.logging.info("[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
curr_step, fetched[-3], fetched[-2],
curr_loss, math.exp(curr_loss), curr_loss / math.log(2)))
total_loss, prev_step = 0., curr_step
if curr_step > 0 and curr_step % FLAGS.save_steps == 0:
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.compat.v1.logging.info("Model saved in path: {}".format(save_path))
if curr_step >= FLAGS.train_steps:
break
def main(unused_argv):
del unused_argv # Unused
tf.logging.set_verbosity(tf.logging.INFO)
# Get corpus info
corpus_info = data_utils.get_corpus_info(FLAGS.corpus_info_path)
n_token = corpus_info["vocab_size"]
cutoffs = corpus_info["cutoffs"][1:-1]
if FLAGS.save_steps == 0:
FLAGS.save_steps = None
if not FLAGS.do_eval_only:
# Get train input function
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=FLAGS.train_batch_size // FLAGS.num_hosts,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=FLAGS.num_hosts,
use_tpu=FLAGS.use_tpu)
train_bin_sizes = train_record_info["bin_sizes"]
num_train_batch = train_record_info["num_batch"]
# Get train cache function
train_cache_fn = get_cache_fn(FLAGS.mem_len)
else:
train_bin_sizes = []
num_train_batch = None
train_cache_fn = None
if FLAGS.do_eval or FLAGS.do_eval_only:
assert FLAGS.num_hosts == 1
# Get eval input function
eval_input_fn, eval_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split=FLAGS.eval_split,
per_host_bsz=FLAGS.eval_batch_size // FLAGS.num_hosts,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=FLAGS.num_hosts,
use_tpu=FLAGS.use_tpu)
eval_bin_sizes = eval_record_info["bin_sizes"]
num_eval_batch = eval_record_info["num_batch"]
if FLAGS.max_eval_batch > 0:
num_eval_batch = min(FLAGS.max_eval_batch, num_eval_batch)
# Get eval cache function
eval_cache_fn = get_cache_fn(FLAGS.mem_len)
model_fn = get_model_fn(n_token, cutoffs, train_bin_sizes,
eval_bin_sizes)
else:
eval_cache_fn = None
model_fn = get_model_fn(n_token, cutoffs, train_bin_sizes, [])
##### Create estimator
# TPU Configuration
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
per_host_input = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
session_config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations,
num_shards=FLAGS.num_core_per_host * FLAGS.num_hosts,
per_host_input_for_training=per_host_input),
keep_checkpoint_max=100000, # effectively save all checkpoints
save_checkpoints_secs=None,
save_checkpoints_steps=FLAGS.save_steps)
# warm start
warm_start_from = None
if FLAGS.warm_start_path is not None:
warm_start_from = tf.estimator.WarmStartSettings(
ckpt_to_initialize_from=FLAGS.warm_start_path)
# TPU Estimator
estimator = tpu_estimator.TPUEstimator(
model_fn=model_fn,
train_cache_fn=train_cache_fn,
eval_cache_fn=eval_cache_fn,
use_tpu=FLAGS.use_tpu,
config=run_config,
params={
"data_dir": FLAGS.data_dir,
"track_mean": FLAGS.track_mean
},
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
warm_start_from=warm_start_from)
if FLAGS.do_eval_only:
if FLAGS.eval_ckpt_path is not None:
ret = estimator.evaluate(input_fn=eval_input_fn,
steps=num_eval_batch,
checkpoint_path=FLAGS.eval_ckpt_path)
tf.logging.info("=" * 200)
log_str = "Eval results | "
for key, val in ret.items():
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
tf.logging.info("=" * 200)
else:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.model_dir)
eval_results = []
for eval_checkpoint in ckpt_state.all_model_checkpoint_paths:
if not exists(eval_checkpoint + ".index"): continue
global_step = int(eval_checkpoint.split("-")[-1])
if global_step < FLAGS.start_eval_steps or global_step > FLAGS.train_steps:
continue
ret = estimator.evaluate(input_fn=eval_input_fn,
steps=num_eval_batch,
checkpoint_path=eval_checkpoint)
eval_results.append(ret)
eval_results.sort(key=lambda x: x["perplexity"])
tf.logging.info("=" * 200)
log_str = "Best results | "
for key, val in eval_results[0].items():
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
tf.logging.info("=" * 200)
else:
if not FLAGS.do_eval:
estimator.train(input_fn=train_input_fn, steps=FLAGS.train_steps)
else:
for step in range(0, FLAGS.train_steps, num_train_batch):
train_steps = min(FLAGS.train_steps - step, num_train_batch)
estimator.train(input_fn=train_input_fn, steps=train_steps)
estimator.evaluate(input_fn=eval_input_fn,
steps=num_eval_batch)
def train(ps_device):
# Get input function and model function
train_input_fn, record_info_dict = data_utils.get_input_fn(
tfrecord_dir=FLAGS.record_info_dir,
split="train",
bsz_per_host=FLAGS.train_batch_size,
seq_len=FLAGS.seq_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
num_hosts=1,
num_core_per_host=1, # set to one no matter how many GPUs
perm_size=FLAGS.perm_size,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
uncased=FLAGS.uncased,
num_passes=FLAGS.num_passes,
use_bfloat16=FLAGS.use_bfloat16,
num_predict=FLAGS.num_predict)
# for key, info in record_info_dict.items():
tf.logging.info("num of batches {}".format(record_info_dict["num_batch"]))
# Create input tensors / placeholders
bsz_per_core = FLAGS.train_batch_size // FLAGS.num_core_per_host
params = {
"batch_size": FLAGS.train_batch_size # the whole batch
}
train_set = train_input_fn(params)
example = train_set.make_one_shot_iterator().get_next()
if FLAGS.num_core_per_host > 1:
examples = [{} for _ in range(FLAGS.num_core_per_host)]
for key in example.keys():
vals = tf.split(example[key], FLAGS.num_core_per_host, 0)
for device_id in range(FLAGS.num_core_per_host):
examples[device_id][key] = vals[device_id]
else:
examples = [example]
# Create computational graph
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
# The mems for each tower is a dictionary
mems_i = {}
if FLAGS.mem_len:
mems_i["mems"] = create_mems_tf(bsz_per_core)
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
is_training=True,
features=examples[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
# average losses and gradients across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
# get train op
train_op, learning_rate, gnorm = model_utils.get_train_op(FLAGS, None,
grads_and_vars=grads_and_vars)
global_step = tf.train.get_global_step()
# Training loop
# initialize mems
tower_mems_np = []
for i in range(FLAGS.num_core_per_host):
mems_i_np = {}
for key in tower_mems[i].keys():
mems_i_np[key] = initialize_mems_np(bsz_per_core)
tower_mems_np.append(mems_i_np)
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.97)#allow_growth=True)
model_utils.init_from_checkpoint(FLAGS, global_vars=True)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False,
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
sess.graph.finalize()
run_metadata = tf.RunMetadata()
options = tf.RunOptions(trace_level=tf.RunOptions.SOFTWARE_TRACE)
dot_rep = graph_to_dot(tf.get_default_graph())
# s = Source(dot_rep, filename="test.gv", format="PNG")
with open('profs/xln.dot', 'w') as fwr:
fwr.write(str(dot_rep))
operations_tensors = {}
operations_attributes = {}
operations_names = tf.get_default_graph().get_operations()
count1 = 0
count2 = 0
for operation in operations_names:
operation_name = operation.name
operations_info = tf.get_default_graph(
).get_operation_by_name(operation_name).values()
try:
operations_attributes[operation_name] = []
operations_attributes[operation_name].append(
operation.type)
operations_attributes[operation_name].append(tf.get_default_graph(
).get_tensor_by_name(operation_name + ':0').dtype._is_ref_dtype)
except:
pass
if len(operations_info) > 0:
if not (operations_info[0].shape.ndims is None):
operation_shape = operations_info[0].shape.as_list(
)
operation_dtype_size = operations_info[0].dtype.size
if not (operation_dtype_size is None):
operation_no_of_elements = 1
for dim in operation_shape:
if not(dim is None):
operation_no_of_elements = operation_no_of_elements * dim
total_size = operation_no_of_elements * operation_dtype_size
operations_tensors[operation_name] = total_size
else:
count1 = count1 + 1
else:
count1 = count1 + 1
operations_tensors[operation_name] = -1
# print('no shape_1: ' + operation_name)
# print('no shape_2: ' + str(operations_info))
# operation_namee = operation_name + ':0'
# tensor = tf.get_default_graph().get_tensor_by_name(operation_namee)
# print('no shape_3:' + str(tf.shape(tensor)))
# print('no shape:' + str(tensor.get_shape()))
else:
# print('no info :' + operation_name)
# operation_namee = operation.name + ':0'
count2 = count2 + 1
operations_tensors[operation_name] = -1
# try:
# tensor = tf.get_default_graph().get_tensor_by_name(operation_namee)
# print(tensor)
# print(tf.shape(tensor))
# except:
# print('no tensor: ' + operation_namee)
print(count1)
print(count2)
with open('./profs/tensors_sz_32.txt', 'w') as f:
for tensor, size in operations_tensors.items():
f.write('"' + tensor + '"::' + str(size) + '\n')
with open('./profs/operations_attributes.txt', 'w') as f:
for op, attrs in operations_attributes.items():
strr = op
for attr in attrs:
strr += '::' + str(attr)
strr += '\n'
f.write(strr)
fetches = [loss, tower_new_mems, global_step,
gnorm, learning_rate, train_op]
iter = 0
total_loss, prev_step = 0., -1
while True:
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for key in tower_mems_np[i].keys():
for m, m_np in zip(tower_mems[i][key], tower_mems_np[i][key]):
feed_dict[m] = m_np
if iter % 10 == 7 or iter == 0:
fetched = sess.run(fetches, feed_dict=feed_dict, options=options, run_metadata=run_metadata)
#if iter > 0:
profile(run_metadata, iter)
else:
t0 = time.time()
fetched = sess.run(fetches, feed_dict=feed_dict)
print(time.time() - t0)
if iter == 0:
mem_options = tf.profiler.ProfileOptionBuilder.time_and_memory()
mem_options["min_bytes"] = 0
mem_options["min_micros"] = 0
mem_options["output"] = 'file:outfile=./profs/mem.txt'
mem_options["select"] = ("bytes", "peak_bytes", "output_bytes",
"residual_bytes")
mem = tf.profiler.profile(
tf.Graph(), run_meta=run_metadata, cmd="scope", options=mem_options)
with open('profs/mem2.txt', 'w') as f:
f.write(str(mem))
iter += 1
loss_np, tower_mems_np, curr_step = fetched[:3]
total_loss += loss_np
if curr_step > 0 and curr_step % FLAGS.iterations == 0:
curr_loss = total_loss / (curr_step - prev_step)
tf.logging.info("[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
curr_step, fetched[-3], fetched[-2],
curr_loss, math.exp(curr_loss), curr_loss / math.log(2)))
total_loss, prev_step = 0., curr_step
if curr_step > 0 and curr_step % FLAGS.save_steps == 0:
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.logging.info("Model saved in path: {}".format(save_path))
if curr_step >= FLAGS.train_steps:
break
def train(ps_device):
##### Get input function and model function
train_input_fn, record_info_dict = data_utils.get_input_fn(
info_dir=os.path.join(FLAGS.record_info_dir, "train"),
split="train",
bsz_per_host=FLAGS.train_batch_size,
seq_len=FLAGS.seq_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
num_hosts=1,
num_core_per_host=1, # set to one no matter how many GPUs
perm_size=FLAGS.perm_size,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
use_bfloat16=FLAGS.use_bfloat16,
num_predict=FLAGS.num_predict)
valid_input_fn, record_info_dict_valid = data_utils.get_input_fn(
info_dir=os.path.join(FLAGS.record_info_dir, "valid"),
split="valid",
bsz_per_host=FLAGS.train_batch_size,
seq_len=FLAGS.seq_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
num_hosts=1,
num_core_per_host=1,
perm_size=FLAGS.perm_size,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
use_bfloat16=FLAGS.use_bfloat16,
num_predict=FLAGS.num_predict)
# for key, info in record_info_dict.items():
num_train_batches = record_info_dict["num_batch"]
tf.logging.info("num of train batches {}".format(
record_info_dict["num_batch"]))
tf.logging.info("num of validation batches {}".format(
record_info_dict_valid["num_batch"]))
##### Create input tensors / placeholders
bsz_per_core = FLAGS.train_batch_size // FLAGS.num_core_per_host
params = {
"batch_size": FLAGS.train_batch_size # the whole batch
}
train_set = train_input_fn(params)
valid_set = valid_input_fn(params)
t_iter = train_set.make_initializable_iterator()
example = t_iter.get_next()
v_iter = valid_set.make_initializable_iterator()
v_example = v_iter.get_next()
if FLAGS.num_core_per_host > 1:
# train set
examples = [{} for _ in range(FLAGS.num_core_per_host)]
for key in example.keys():
vals = tf.split(example[key], FLAGS.num_core_per_host, 0)
for device_id in range(FLAGS.num_core_per_host):
examples[device_id][key] = vals[device_id]
# validation set
v_examples = [{} for _ in range(FLAGS.num_core_per_host)]
for key in v_example.keys():
vals = tf.split(v_example[key], FLAGS.num_core_per_host, 0)
for device_id in range(FLAGS.num_core_per_host):
v_examples[device_id][key] = vals[device_id]
else:
examples = [example]
v_examples = [v_example]
##### Create computational graph
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
v_tower_mems, v_tower_losses, v_tower_new_mems = [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
# The mems for each tower is a dictionary
mems_i = {}
v_mems_i = {}
if FLAGS.mem_len:
mems_i["mems"] = create_mems_tf(bsz_per_core)
v_mems_i["mems"] = create_mems_tf(bsz_per_core)
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
is_training=True, features=examples[i], mems=mems_i)
v_loss_i, v_new_mems_i = single_core_graph(is_training=False,
features=v_examples[i],
mems=v_mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
v_tower_mems.append(v_mems_i)
v_tower_losses.append(v_loss_i)
v_tower_new_mems.append(v_new_mems_i)
## average losses and gradients across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
if len(v_tower_losses) > 1:
v_loss = tf.add_n(v_tower_losses) / len(v_tower_losses)
else:
v_loss = v_tower_losses[0]
## get train op
train_op, learning_rate, gnorm = model_utils.get_train_op(
FLAGS, None, num_train_batches, grads_and_vars=grads_and_vars)
global_step = tf.train.get_global_step()
##### Training loop
# initialize mems
tower_mems_np = []
v_tower_mems_np = []
for i in range(FLAGS.num_core_per_host):
mems_i_np = {}
v_mems_i_np = {}
for key in tower_mems[i].keys():
mems_i_np[key] = initialize_mems_np(bsz_per_core)
v_mems_i_np[key] = initialize_mems_np(bsz_per_core)
tower_mems_np.append(mems_i_np)
v_tower_mems_np.append(v_mems_i_np)
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(allow_growth=True)
model_utils.init_from_checkpoint(FLAGS, global_vars=True)
# Create performance summaries for Tensorboard logging
training_performance_summaries, valid_performance_summaries = tb.tensorboard_setup(
)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
# variables that are run in the session
fetches = [
loss, tower_new_mems, global_step, gnorm, learning_rate, train_op
]
v_fetches = [v_loss, v_tower_new_mems]
# Create writers for Tensorboard logging
info_dict = {
"id": FLAGS.run_id,
"n_layers": FLAGS.n_layers,
"d_model": FLAGS.d_model,
"n_heads": FLAGS.n_head
}
train_summary_writer, valid_summary_writer = tb.create_writers(
sess, info_dict, logging_dir=FLAGS.tb_logging_dir)
total_loss, prev_step = 0., -1
for i in range(FLAGS.epochs):
# Train loop
try:
sess.run(t_iter.initializer)
while True:
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for key in tower_mems_np[i].keys():
for m, m_np in zip(tower_mems[i][key],
tower_mems_np[i][key]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, curr_step = fetched[:3]
total_loss += loss_np
print(curr_step)
# Log training progress
if curr_step > 0 and curr_step % FLAGS.log_steps == 0:
curr_loss = total_loss / (curr_step - prev_step)
summ = tb.run_train(sess,
training_performance_summaries,
curr_loss)
train_summary_writer.add_summary(summ, curr_step)
tf.logging.info(
"[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".
format(curr_step, fetched[-3], fetched[-2],
curr_loss, math.exp(curr_loss),
curr_loss / math.log(2)))
total_loss, prev_step = 0., curr_step
# Save checkpoint
if curr_step > 0 and FLAGS.save_steps is not None and curr_step % FLAGS.save_steps == 0:
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.logging.info(
"Model saved in path: {}".format(save_path))
except tf.errors.OutOfRangeError:
pass
# Validation loop
try:
sess.run(v_iter.initializer)
v_total_loss, v_steps = 0., 0
while True:
v_feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for key in v_tower_mems_np[i].keys():
for m, m_np in zip(v_tower_mems[i][key],
v_tower_mems_np[i][key]):
v_feed_dict[m] = m_np
v_fetched = sess.run(v_fetches, feed_dict=v_feed_dict)
v_loss_np, v_tower_mems_np = v_fetched[:]
v_total_loss += v_loss_np
v_steps += 1
except tf.errors.OutOfRangeError:
val_loss = v_total_loss / v_steps
v_pplx = math.exp(val_loss)
tf.logging.info(
"Validation: [{}] | loss {:.2f} | pplx {:>7.2f}".format(
curr_step, val_loss, v_pplx))
summ_valid = tb.run_valid(sess, valid_performance_summaries,
val_loss, v_pplx)
valid_summary_writer.add_summary(summ_valid, curr_step)
tf.logging.info("------------ Epoch {} ------------".format(i))
def evaluate(n_token, cutoffs):
##### Get input function and model function
eval_input_fn, eval_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split=FLAGS.eval_split,
per_host_bsz=FLAGS.eval_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1)
meters = {}
warmup = 2
meters['eval_throughput'] = AverageMeter(warmup=warmup)
meters['eval_latency'] = AverageMeter(warmup=warmup, keep=True)
num_batch = eval_record_info["num_batch"]
if FLAGS.max_eval_batch > 0:
num_batch = FLAGS.max_eval_batch
tf.logging.info("num of batches {}".format(num_batch))
##### Create computational graph
eval_set = eval_input_fn({
"batch_size": FLAGS.eval_batch_size,
"data_dir": FLAGS.data_dir})
inputs, labels = eval_set.make_one_shot_iterator().get_next()
bsz = FLAGS.eval_batch_size
with tf.variable_scope(tf.get_variable_scope()):
mems = [tf.placeholder(tf.float32,
[FLAGS.mem_len, bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)]
loss, new_mems = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=False,
inp=inputs,
tgt=labels,
mems=mems)
target_tokens = tf.size(labels)
##### Evaluation loop
mems_np = [np.zeros([FLAGS.mem_len, bsz, FLAGS.d_model], dtype=np.float32)
for layer in range(FLAGS.n_layer)]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.eval_ckpt_path is None:
eval_ckpt_path = tf.train.latest_checkpoint(FLAGS.model_dir)
else:
eval_ckpt_path = FLAGS.eval_ckpt_path
tf.logging.info("Evaluate {}".format(eval_ckpt_path))
saver.restore(sess, eval_ckpt_path)
fetches = [loss, new_mems, target_tokens]
format_str = " >> processing batch {{:{0}d}}/{{:{0}d}}".format(
len(str(num_batch)))
total_loss, total_cnt, target_tokens = 0, 0, 0
start_time = time.time()
for step in range(num_batch):
feed_dict = {}
for m, m_np in zip(mems, mems_np):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, mems_np, tt = fetched
target_tokens += tt
cnt_np = 1
total_loss += loss_np * cnt_np
total_cnt += cnt_np
elapsed = time.time()-start_time
throughput = target_tokens / elapsed
latency = elapsed*1000
meters['eval_throughput'].update(throughput)
meters['eval_latency'].update(latency)
target_tokens = 0
if (step+1) % (num_batch // 10) == 0:
tf.logging.info(format_str.format(step+1, num_batch))
dllogger_data = {
'eval_latency': latency,
'eval_throughput': throughput,
}
dllogger.log(step=step+1, data=dllogger_data)
start_time = time.time()
avg_loss = total_loss / total_cnt
latency_data = np.array(meters['eval_latency'].vals)
tf.logging.info("Evaluating with: bs {}, math {} ".format(FLAGS.eval_batch_size, "fp16" if FLAGS.fp16 else "fp32"))
tf.logging.info("| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}, tok/s {:>6.1f}, ms/batch {:>4.2f}".format(
avg_loss, math.exp(avg_loss), avg_loss / math.log(2), meters['eval_throughput'].avg, meters['eval_latency'].avg))
summary = {
'eval_loss': avg_loss,
'eval_ppl': math.exp(avg_loss),
'eval_avg_throughput': meters['eval_throughput'].avg,
'eval_avg_latency': meters['eval_latency'].avg,
}
for p in FLAGS.percentiles:
p = int(p)
tf.logging.info("Latency {}%: {:>4.2f} ms".format(
p, np.percentile(latency_data, p)))
summary[f'eval_{p}%_latency'] = np.percentile(latency_data, p)
dllogger.log(step=tuple(), data=summary)
def train(n_token, cutoffs, rank, local_rank, size):
meters = {}
warmup = 2 + 12/size
meters['train_throughput'] = AverageMeter(warmup=warmup)
train_batch_size = FLAGS.train_batch_size // FLAGS.batch_chunk
##### Get input function and model function
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=train_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1)
tf.logging.info("num of batches {}".format(train_record_info["num_batch"]))
##### Create computational graph
train_set = train_input_fn({
"batch_size": train_batch_size,
"data_dir": FLAGS.data_dir})
inputs, labels = train_set.make_one_shot_iterator().get_next()
per_core_bsz = train_batch_size // FLAGS.num_core_per_host
with tf.variable_scope(tf.get_variable_scope()):
mems = [tf.Variable(tf.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model], tf.float32), trainable=False)
for _ in range(FLAGS.n_layer)]
loss, new_mems, all_vars = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=True,
inp=inputs,
tgt=labels,
mems=mems)
assign_mems = [mems[i].assign(new_mems[i]) for i in range(FLAGS.n_layer)]
target_tokens = tf.size(labels)
## configure the optimizer
global_step = tf.train.get_or_create_global_step()
# warmup stage: 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
# decay stage: 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)
# choose warmup or decay
learning_rate = tf.where(global_step < FLAGS.warmup_steps,
warmup_lr, decay_lr)
# get the train op
optimizer = lamb.LAMBOptimizer(learning_rate=learning_rate)
if FLAGS.horovod:
optimizer = hvd.DistributedOptimizer(optimizer, sparse_as_dense=True)
grads_and_vars = optimizer.compute_gradients(loss/FLAGS.batch_chunk, all_vars)
grads, all_vars = zip(*grads_and_vars)
accum_vars = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in all_vars]
in_progress = tf.get_variable(name="in_progress", shape=[], dtype=tf.bool, trainable=False,
initializer=tf.zeros_initializer)
accum_ops = tf.cond(in_progress,
lambda: [accum_vars[i].assign_add(grad) for i, grad in enumerate(grads)],
lambda: [accum_vars[i].assign(grad) for i, grad in enumerate(grads)])
with tf.control_dependencies(accum_ops + assign_mems):
acc_op = in_progress.assign(tf.ones_like(in_progress))
final_accum_vars = [accum_vars[i] + gv for i,gv in enumerate(grads)]
acc_clipped, acc_gnorm = tf.clip_by_global_norm(final_accum_vars, FLAGS.clip)
clipped, gnorm = tf.clip_by_global_norm(grads, FLAGS.clip)
acc_train_op = optimizer.apply_gradients(list(zip(acc_clipped, all_vars)), global_step)
grads_and_vars = list(zip(clipped, all_vars))
if FLAGS.jit_optimizer:
jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
with jit_scope():
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
else:
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
final_op = tf.group(train_op, assign_mems)
acc_final_op = tf.group(acc_train_op, assign_mems, in_progress.assign(tf.zeros_like(in_progress)))
##### Training loop
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(allow_growth = True, visible_device_list = str(local_rank))
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, gpu_options = gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.horovod:
sess.run(hvd.broadcast_global_variables(0))
accum = [acc_op, target_tokens]
fetches = [loss, global_step, target_tokens, learning_rate, final_op if FLAGS.batch_chunk == 1 else acc_final_op]
total_loss, prev_step, target_tokens = 0., -1, 0
start_time = time.time()
while True:
for i in range(FLAGS.batch_chunk-1):
_,tt = sess.run(accum)
target_tokens += tt
fetched = sess.run(fetches)
loss_np, curr_step, tt = fetched[:3]
total_loss += loss_np
target_tokens += tt
if curr_step > 0 and curr_step % FLAGS.log_interval == 0:
curr_loss = total_loss / (curr_step - prev_step)
throughput = target_tokens * size / (time.time()-start_time)
meters['train_throughput'].update(throughput)
if rank == 0:
tf.logging.info("step {} | lr {:8.9f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}, tok/s {:>6.0f}".format(
curr_step, fetched[-2],
curr_loss, math.exp(curr_loss), curr_loss / math.log(2), throughput))
dllogger_data = {
'lr': fetched[-1],
'train_loss': curr_loss,
'train_perplexity': math.exp(curr_loss),
'train_throughput': throughput,
}
dllogger.log(step=int(curr_step), data=dllogger_data)
total_loss, prev_step, target_tokens = 0., curr_step, 0
start_time = time.time()
if curr_step > 0 and curr_step % FLAGS.save_steps == 0 and rank == 0:
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.logging.info("Model saved in path: {}".format(save_path))
if curr_step == FLAGS.train_steps:
break
if rank == 0:
tf.logging.info("Training throughput: {:>6.0f} tok/s".format(meters['train_throughput'].avg))
summary = {
'train_throughput': meters['train_throughput'].avg,
}
dllogger.log(step=tuple(), data=summary)
def test(ps_device):
test_input_fn, record_info_dict_test = data_utils.get_input_fn(
info_dir=os.path.join(FLAGS.record_info_dir, "test"),
split="test",
bsz_per_host=FLAGS.test_batch_size,
seq_len=FLAGS.seq_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
num_hosts=1,
num_core_per_host=1,
perm_size=FLAGS.perm_size,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
use_bfloat16=FLAGS.use_bfloat16,
num_predict=FLAGS.num_predict)
tf.logging.info("num of test batches {}".format(record_info_dict_test["num_batch"]))
##### Create input tensors / placeholders
bsz_per_core = FLAGS.test_batch_size // FLAGS.num_core_per_host
params = {
"batch_size": FLAGS.test_batch_size # the whole batch
}
test_set = test_input_fn(params)
t_iter = test_set.make_initializable_iterator()
t_example = t_iter.get_next()
if FLAGS.num_core_per_host > 1:
# test set
t_examples = [{} for _ in range(FLAGS.num_core_per_host)]
for key in t_example.keys():
vals = tf.split(t_examples[key], FLAGS.num_core_per_host, 0)
for device_id in range(FLAGS.num_core_per_host):
t_examples[device_id][key] = vals[device_id]
else:
t_examples = [t_example]
##### Create computational graph
v_tower_mems, v_tower_losses, v_tower_new_mems = [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
# The mems for each tower is a dictionary
v_mems_i = {}
if FLAGS.mem_len:
v_mems_i["mems"] = create_mems_tf(bsz_per_core)
v_loss_i, v_new_mems_i = single_core_graph(
features=t_examples[i],
mems=v_mems_i)
v_tower_mems.append(v_mems_i)
v_tower_losses.append(v_loss_i)
v_tower_new_mems.append(v_new_mems_i)
## average losses and gradients across towers
if len(v_tower_losses) > 1:
v_loss = tf.add_n(v_tower_losses) / len(v_tower_losses)
else:
v_loss = v_tower_losses[0]
gpu_options = tf.GPUOptions(allow_growth=True)
model_utils.init_from_checkpoint(FLAGS, global_vars=True)
# Create performance summaries for Tensorboard logging
test_performance_summaries = tb.tensorboard_setup_test()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
# Create writers for Tensorboard logging
test_summary_writer = tb.create_test_writer(sess, logging_dir=FLAGS.tb_logging_dir)
# initialize mems
v_tower_mems_np = []
for i in range(FLAGS.num_core_per_host):
v_mems_i_np = {}
for key in v_tower_mems[i].keys():
v_mems_i_np[key] = initialize_mems_np(bsz_per_core)
v_tower_mems_np.append(v_mems_i_np)
v_fetches = [v_loss, v_tower_new_mems]
sess.run(t_iter.initializer)
v_total_loss = 0.
v_steps = 0
try:
while True:
v_feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for key in v_tower_mems_np[i].keys():
for m, m_np in zip(v_tower_mems[i][key], v_tower_mems_np[i][key]):
v_feed_dict[m] = m_np
v_fetched = sess.run(v_fetches, feed_dict=v_feed_dict)
v_loss_np, v_tower_mems_np = v_fetched[:]
v_total_loss += v_loss_np
v_steps += 1
print(v_steps)
except tf.errors.OutOfRangeError:
test_loss = v_total_loss/v_steps
t_pplx = math.exp(test_loss)
tf.logging.info("Test: loss {:.2f} | pplx {:>7.2f}".format(
test_loss, t_pplx))
summ_test = tb.run_test(sess, test_performance_summaries, test_loss, t_pplx)
test_summary_writer.add_summary(summ_test, 1)
def train(n_token, cutoffs, ps_device):
# get TF logger
log = logging.getLogger('tensorflow')
log.setLevel(logging.INFO)
# create formatter and add it to the handlers
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# create file handler which logs even debug messages
fh = logging.FileHandler('run_train.log')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
log.addHandler(fh)
##### Get input function and model function
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=FLAGS.train_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
tf.logging.info("num of batches {}".format(train_record_info["num_batch"]))
##### Create computational graph
train_set = train_input_fn({
"batch_size": FLAGS.train_batch_size,
"data_dir": FLAGS.data_dir
})
input_feed, label_feed = train_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
per_core_bsz = FLAGS.train_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
mems_i = [
tf.placeholder(tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)
]
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=True,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
## average losses and gradients across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
grads, all_vars = zip(*grads_and_vars)
## clip gradient
clipped, gnorm = tf.clip_by_global_norm(grads, FLAGS.clip)
grads_and_vars = list(zip(clipped, all_vars))
## configure the optimizer
global_step = tf.train.get_or_create_global_step()
# warmup stage: 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
# decay stage: 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)
# choose warmup or decay
learning_rate = tf.where(global_step < FLAGS.warmup_steps, warmup_lr,
decay_lr)
# get the train op
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
##### Training loop
tower_mems_np = [[
np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model],
dtype=np.float32) for layer in range(FLAGS.n_layer)
] for core in range(FLAGS.num_core_per_host)]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.warm_start_path is not None:
tf.logging.info("warm start from {}".format(FLAGS.warm_start_path))
saver.restore(sess, FLAGS.warm_start_path)
fetches = [
loss, tower_new_mems, global_step, gnorm, learning_rate, train_op
]
total_loss, prev_step = 0., -1
while True:
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, curr_step = fetched[:3]
total_loss += loss_np
if curr_step % 100 == 0:
print("Current step:", curr_step)
if curr_step > 0 and curr_step % FLAGS.iterations == 0:
curr_loss = total_loss / (curr_step - prev_step)
tf.logging.info(
"[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
curr_step, fetched[-3], fetched[-2], curr_loss,
math.exp(curr_loss), curr_loss / math.log(2)))
total_loss, prev_step = 0., curr_step
if curr_step > 0 and curr_step % FLAGS.save_steps == 0:
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.logging.info("Model saved in path: {}".format(save_path))
if curr_step == FLAGS.train_steps:
break
def dynamic_eval(n_token, cutoffs, ps_device):
##### Get input function and model function
if FLAGS.rms:
##using training data to collect gradient statistics
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=FLAGS.train_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
num_batch = train_record_info["num_batch"]
tf.logging.info("num of batches {}".format(num_batch))
##### Create computational graph
train_set = train_input_fn({
"batch_size": FLAGS.train_batch_size,
"data_dir": FLAGS.data_dir
})
input_feed, label_feed = train_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
per_core_bsz = FLAGS.train_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
mems_i = [
tf.placeholder(
tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)
]
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=True,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
## sum losses across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
global_step = tf.train.get_or_create_global_step()
optimizer = DynamicEvalOpt(learning_rate=FLAGS.learning_rate,
decay_rate=FLAGS.decay_rate,
eps=FLAGS.epsilon)
optimizer.gradstat = True
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
tower_mems_np = [[
np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model],
dtype=np.float32) for layer in range(FLAGS.n_layer)
] for core in range(FLAGS.num_core_per_host)]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.eval_ckpt_path is None:
eval_ckpt_path = tf.train.latest_checkpoint(FLAGS.model_dir)
else:
eval_ckpt_path = FLAGS.eval_ckpt_path
tf.logging.info("Evaluate {}".format(eval_ckpt_path))
saver.restore(sess, eval_ckpt_path)
fetches = [loss, tower_new_mems, tf.size(label_feed), train_op]
total_loss, prev_step = 0., -1
total_loss, total_cnt = 0, 0
format_str = " >> processing batch for gradient statistics {{:{0}d}}/{{:{0}d}} ..".format(
len(str(num_batch // 5000)))
## only small subset of training set used for gradient stats to save time
for step in range(num_batch // 5000):
if step % (num_batch // 50000) == 0:
tf.logging.info(format_str.format(step, num_batch // 5000))
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, cnt_np = fetched[:3]
total_loss += loss_np * cnt_np
total_cnt += cnt_np
avg_loss = total_loss / total_cnt
## tf.logging.info("| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
## avg_loss, math.exp(avg_loss), avg_loss / math.log(2)))
#####Done gradstat
###starting dynamic eval
eval_input_fn, eval_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split=FLAGS.eval_split,
per_host_bsz=FLAGS.eval_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_core_per_host,
num_hosts=1,
use_tpu=False)
num_batch = eval_record_info["num_batch"]
tf.logging.info("num of batches {}".format(num_batch))
##### Create computational graph
eval_set = eval_input_fn({
"batch_size": FLAGS.eval_batch_size,
"data_dir": FLAGS.data_dir
})
input_feed, label_feed = eval_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_core_per_host, 0)
labels = tf.split(label_feed, FLAGS.num_core_per_host, 0)
per_core_bsz = FLAGS.eval_batch_size // FLAGS.num_core_per_host
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_core_per_host):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
mems_i = [
tf.placeholder(tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)
]
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=True,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
## sum losses across towers
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
## configure the optimizer
global_step = tf.train.get_or_create_global_step()
if not FLAGS.rms:
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=FLAGS.learning_rate
) # DynamicEvalPS(learning_rate=FLAGS.learning_rate )
else:
optimizer.gradstat = False
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
##### Evaluation loop
tower_mems_np = [[
np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model],
dtype=np.float32) for layer in range(FLAGS.n_layer)
] for core in range(FLAGS.num_core_per_host)]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
if FLAGS.eval_ckpt_path is None:
eval_ckpt_path = tf.train.latest_checkpoint(FLAGS.model_dir)
else:
eval_ckpt_path = FLAGS.eval_ckpt_path
tf.logging.info("Evaluate {}".format(eval_ckpt_path))
saver.restore(sess, eval_ckpt_path)
fetches = [loss, tower_new_mems, tf.size(label_feed), train_op]
total_loss, prev_step = 0., -1
total_loss, total_cnt = 0, 0
format_str = " >> processing batch {{:{0}d}}/{{:{0}d}} ..".format(
len(str(num_batch)))
for step in range(num_batch // FLAGS.ratio):
if step % (num_batch // (10 * FLAGS.ratio)) == 0:
tf.logging.info(format_str.format(step, num_batch))
feed_dict = {}
for i in range(FLAGS.num_core_per_host):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, cnt_np = fetched[:3]
total_loss += loss_np * cnt_np
total_cnt += cnt_np
avg_loss = total_loss / total_cnt
tf.logging.info("| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
avg_loss, math.exp(avg_loss), avg_loss / math.log(2)))
def train_epoch(epoch, csv_logger, n_token, cutoffs):
ps_device = "/gpu:0"
train_input_fn, train_record_info = data_utils.get_input_fn(
record_info_dir=FLAGS.record_info_dir,
split="train",
per_host_bsz=FLAGS.train_batch_size,
tgt_len=FLAGS.tgt_len,
num_core_per_host=FLAGS.num_gpu,
num_hosts=1,
use_tpu=False)
tf.logging.info("-" * 30)
tf.logging.info("Starting epoch {}!".format(epoch))
tf.logging.info("num of batches {}".format(train_record_info["num_batch"]))
num_batch = train_record_info["num_batch"]
train_set = train_input_fn({
"batch_size": FLAGS.train_batch_size,
"data_dir": FLAGS.data_dir})
input_feed, label_feed = train_set.make_one_shot_iterator().get_next()
inputs = tf.split(input_feed, FLAGS.num_gpu, 0)
labels = tf.split(label_feed, FLAGS.num_gpu, 0)
per_core_bsz = FLAGS.train_batch_size // FLAGS.num_gpu
tower_mems, tower_losses, tower_new_mems, tower_grads_and_vars = [], [], [], []
for i in range(FLAGS.num_gpu):
reuse = True if i > 0 else None
with tf.device(assign_to_gpu(i, ps_device)), \
tf.variable_scope(tf.get_variable_scope(), reuse=reuse):
mems_i = [tf.placeholder(tf.float32,
[FLAGS.mem_len, per_core_bsz, FLAGS.d_model])
for _ in range(FLAGS.n_layer)]
loss_i, new_mems_i, grads_and_vars_i = single_core_graph(
n_token=n_token,
cutoffs=cutoffs,
is_training=True,
inp=inputs[i],
tgt=labels[i],
mems=mems_i)
tower_mems.append(mems_i)
tower_losses.append(loss_i)
tower_new_mems.append(new_mems_i)
tower_grads_and_vars.append(grads_and_vars_i)
if len(tower_losses) > 1:
loss = tf.add_n(tower_losses) / len(tower_losses)
grads_and_vars = average_grads_and_vars(tower_grads_and_vars)
else:
loss = tower_losses[0]
grads_and_vars = tower_grads_and_vars[0]
grads, all_vars = zip(*grads_and_vars)
clipped, gnorm = tf.clip_by_global_norm(grads, FLAGS.clip)
grads_and_vars = list(zip(clipped, all_vars))
global_step = tf.train.get_or_create_global_step()
total_steps = FLAGS.epochs * num_batch
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
decay_lr = tf.train.cosine_decay(
FLAGS.learning_rate,
global_step=global_step-FLAGS.warmup_steps,
decay_steps=total_steps-FLAGS.warmup_steps,
alpha=FLAGS.min_lr_ratio)
learning_rate = tf.where(global_step < FLAGS.warmup_steps,
warmup_lr, decay_lr)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.apply_gradients(grads_and_vars, global_step)
tower_mems_np = [
[np.zeros([FLAGS.mem_len, per_core_bsz, FLAGS.d_model], dtype=np.float32)
for layer in range(FLAGS.n_layer)]
for core in range(FLAGS.num_gpu)
]
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.global_variables_initializer())
latest_ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
if latest_ckpt is not None:
tf.logging.info("loading saved model from {}".format(latest_ckpt))
saver.restore(sess, latest_ckpt)
else:
tf.logging.info("No previously saved model. Starting from scratch!")
fetches = [loss, tower_new_mems, global_step, gnorm, learning_rate, train_op]
total_loss, prev_step = 0., -1
for ba in range(num_batch):
feed_dict = {}
for i in range(FLAGS.num_gpu):
for m, m_np in zip(tower_mems[i], tower_mems_np[i]):
feed_dict[m] = m_np
fetched = sess.run(fetches, feed_dict=feed_dict)
loss_np, tower_mems_np, curr_step = fetched[:3]
total_loss += loss_np
if curr_step > 0 and curr_step % FLAGS.iterations == 0:
curr_loss = total_loss / (curr_step - prev_step)
tf.logging.info("[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
curr_step, fetched[-3], fetched[-2],
curr_loss, math.exp(curr_loss), curr_loss / math.log(2)))
log_dict = {
'train_loss': curr_loss,
'train_ppl': math.exp(curr_loss),
'train_bpc': curr_loss / math.log(2),
'lr': fetched[-2],
'global_step': curr_step,
'epoch': epoch
}
csv_logger.writerow(log_dict)
total_loss, prev_step = 0., curr_step
if curr_step > 0 and curr_step % FLAGS.save_steps == 0:
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.logging.info("Finished Step : {}".format(curr_step))
tf.logging.info("Model saved in path: {}".format(save_path))
curr_loss = total_loss / (curr_step - prev_step)
tf.logging.info("[{}] | gnorm {:.2f} lr {:8.6f} "
"| loss {:.2f} | pplx {:>7.2f}, bpc {:>7.4f}".format(
curr_step, fetched[-3], fetched[-2],
curr_loss, math.exp(curr_loss), curr_loss / math.log(2)))
save_path = os.path.join(FLAGS.model_dir, "model.ckpt")
saver.save(sess, save_path)
tf.logging.info("Finished Epoch {}".format(curr_step))
tf.logging.info("Model saved in path: {}".format(save_path))
tf.logging.info("-" * 30)
