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run_utils.py
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run_utils.py
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import sys
import time
sys.stdout=sys.stderr
import numpy as np
import tensorflow as tf
from tensorflow.python.client import timeline
from language_model import LM
from common import CheckpointLoader
def run_train(dataset, hps, logdir, ps_device, task=0, master=""):
with tf.variable_scope("model"):
model = LM(hps, "train", ps_device)
stime = time.time()
print("Current time: %s" % stime)
print("ALL VARIABLES")
for v in tf.all_variables():
print("%s %s %s %s" % (v.name, v.get_shape(), v.dtype, v.device))
print("TRAINABLE VARIABLES")
for v in tf.trainable_variables():
print("%s %s %s %s" % (v.name, v.get_shape(), v.dtype, v.device))
print("LOCAL VARIABLES")
for v in tf.local_variables():
print("%s %s %s %s" % (v.name, v.get_shape(), v.dtype, v.device))
sv = tf.train.Supervisor(is_chief=(task == 0),
logdir=logdir,
summary_op=None, # Automatic summaries don't work with placeholders.
global_step=model.global_step,
save_summaries_secs=60*hps.save_summary_every_min,
save_model_secs=60*hps.save_model_every_min)
#save_summaries_secs=30,
#save_model_secs=120 * 5)
#config = tf.ConfigProto(allow_soft_placement=True,
# intra_op_parallelism_threads=2,
# inter_op_parallelism_threads=20)
config = tf.ConfigProto(allow_soft_placement=True)
with sv.managed_session(master, config=config) as sess:
# Slowly increase the number of workers during beginning of the training.
#while not sv.should_stop() and (time.time() - stime) < hps.max_time:
# step = int(sess.run(model.global_step))
# waiting_until_step = task * hps.num_delayed_steps
# if step >= waiting_until_step:
# break
# else:
# print("Current step is %d. Waiting until: %d" % (step, waiting_until_step))
# time.sleep(20.0)
local_step = 0
prev_global_step = sess.run(model.global_step)
cur_global_step = 0
prev_time = time.time()
data_iterator = dataset.iterate_forever(hps.batch_size * hps.num_gpus, hps.num_steps)
while not sv.should_stop() and (time.time() - stime) < hps.max_time:
fetches = [model.global_step, model.loss, model.train_op]
# Chief worker computes summaries every 100 steps.
should_compute_summary = (task == 0 and local_step % 100 == 0)
if should_compute_summary:
fetches += [model.summary_op]
#x, y, w = next(data_iterator)
x, y = next(data_iterator)
should_run_profiler = (hps.run_profiler and task == 0 and local_step % 1000 == 13)
if should_run_profiler:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
#fetched = sess.run(fetches, {model.x: x, model.y: y, model.w: w},
fetched = sess.run(fetches, {model.x: x, model.y: y},
options=run_options, run_metadata=run_metadata)
# Create the Timeline object, and write it to a json
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
print("Running profiler")
with open(logdir + "/timeline.json", 'w') as f:
f.write(ctf)
print("Finished profiling!")
else:
#fetched = sess.run(fetches, {model.x: x, model.y: y, model.w: w})
fetched = sess.run(fetches, {model.x: x, model.y: y})
cur_global_step = fetched[0]
local_step += 1
if should_compute_summary:
sv.summary_computed(sess, fetched[-1])
if local_step < 10 or local_step % 20 == 0:
cur_time = time.time()
num_words = hps.batch_size * hps.num_gpus * hps.num_steps
wps = (cur_global_step - prev_global_step) * num_words / (cur_time - prev_time)
prev_global_step = cur_global_step
print("Iteration %d, time = %.2fs, wps = %.0f, train loss = %.4f" % (
cur_global_step, cur_time - prev_time, wps, fetched[1]))
prev_time = cur_time
#save last model
sv._saver.save(sess, sv.save_path, cur_global_step)
sv.stop()
def run_eval(dataset, hps, logdir, mode, num_eval_steps):
with tf.variable_scope("model"):
hps.num_sampled = 0 # Always using full softmax at evaluation.
hps.keep_prob = 1.0
#model = LM(hps, "eval", "/cpu:0")
model = LM(hps, "eval", "/gpu:0")
if hps.average_params:
print("Averaging parameters for evaluation.")
saver = tf.train.Saver(model.avg_dict)
else:
saver = tf.train.Saver()
# Use only 4 threads for the evaluation.
#config = tf.ConfigProto(allow_soft_placement=True,
# intra_op_parallelism_threads=20,
# inter_op_parallelism_threads=1)
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
sw = tf.summary.FileWriter(logdir + "/" + mode, sess.graph)
ckpt_loader = CheckpointLoader(saver, model.global_step, logdir + "/train")
with sess.as_default():
while ckpt_loader.load_checkpoint():
global_step = ckpt_loader.last_global_step
data_iterator = dataset.iterate_once(hps.batch_size * hps.num_gpus, hps.num_steps)
#tf.initialize_local_variables().run()
tf.local_variables_initializer().run()
loss_nom = 0.0
loss_den = 0.0
#for i, (x, y, w) in enumerate(data_iterator):
for i, (x, y) in enumerate(data_iterator):
if i >= num_eval_steps and mode!="eval_full":
break
#loss = sess.run(model.loss, {model.x: x, model.y: y, model.w: w})
loss = sess.run(model.loss, {model.x: x, model.y: y})
loss_nom += loss
loss_den += 1 # ???
#loss_den += w.mean()
loss = loss_nom / loss_den
sys.stdout.write("%d: %.3f (%.3f) ... " % (i, loss, np.exp(loss)))
sys.stdout.flush()
sys.stdout.write("\n")
log_perplexity = loss_nom / loss_den
print("Results at %d: log_perplexity = %.3f perplexity = %.3f" % (
global_step, log_perplexity, np.exp(log_perplexity)))
summary = tf.Summary()
summary.value.add(tag='eval/log_perplexity', simple_value=log_perplexity)
summary.value.add(tag='eval/perplexity', simple_value=np.exp(log_perplexity))
sw.add_summary(summary, global_step)
sw.flush()
if mode == "eval_full":
break #we don't need to wait for other checkpoints in this mode