def print_parameters():
print('=' * 40)
print('Device information: ' + str(get_available_gpus()))
print('Here is the hyper parameter summary:')
print('Model type: ' + model_name)
if model_name == 'densenet':
print('Layers: ' + str(layers))
print('Growth: ' + str(growth))
if model_name == 'resnet':
print('Blocks: ' + str(blocks))
if model_name == 'se_resnet':
print('Blocks: ' + str(blocks))
print('SE_ratio: ' + str(se_ratio))
print('Keep ratio: ' + str(keep_ratio))
print('Total epoch: ' + str(epoches))
print('Initial learning rate: ' + str(init_lr))
print('Learning rate decay: ' + str(lr_dict))
print('Weight decay: ' + str(weight_decay))
print('Log folder: ' + run_dir)
print('=' * 40)
def train(config, sess):
assert (config.prior_model != None and (tf.train.checkpoint_exists(os.path.abspath(config.prior_model))) or (config.map_decay_c==0.0)), \
"MAP training requires a prior model file: Use command-line option --prior_model"
# Construct the graph, with one model replica per GPU
num_gpus = len(tf_utils.get_available_gpus())
num_replicas = max(1, num_gpus)
if config.loss_function == 'MRT':
assert config.gradient_aggregation_steps == 1
assert config.max_sentences_per_device == 0, "MRT mode does not support sentence-based split"
if config.max_tokens_per_device != 0:
assert (config.samplesN * config.maxlen <= config.max_tokens_per_device), "need to make sure candidates of a sentence could be " \
"feed into the model"
else:
assert num_replicas == 1, "MRT mode does not support sentence-based split"
assert (config.samplesN * config.maxlen <= config.token_batch_size), "need to make sure candidates of a sentence could be " \
"feed into the model"
logging.info('Building model...')
replicas = []
for i in range(num_replicas):
device_type = "GPU" if num_gpus > 0 else "CPU"
device_spec = tf.DeviceSpec(device_type=device_type, device_index=i)
with tf.device(device_spec):
with tf.variable_scope(tf.get_variable_scope(), reuse=(i>0)):
if config.model_type == "transformer":
model = TransformerModel(config)
else:
model = rnn_model.RNNModel(config)
replicas.append(model)
init = tf.zeros_initializer(dtype=tf.int32)
global_step = tf.get_variable('time', [], initializer=init, trainable=False)
if config.learning_schedule == "constant":
schedule = learning_schedule.ConstantSchedule(config.learning_rate)
elif config.learning_schedule == "transformer":
schedule = learning_schedule.TransformerSchedule(
global_step=global_step,
dim=config.state_size,
warmup_steps=config.warmup_steps)
elif config.learning_schedule == "warmup-plateau-decay":
schedule = learning_schedule.WarmupPlateauDecaySchedule(
global_step=global_step,
peak_learning_rate=config.learning_rate,
warmup_steps=config.warmup_steps,
plateau_steps=config.plateau_steps)
else:
logging.error('Learning schedule type is not valid: {}'.format(
config.learning_schedule))
sys.exit(1)
if config.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=schedule.learning_rate,
beta1=config.adam_beta1,
beta2=config.adam_beta2,
epsilon=config.adam_epsilon)
else:
logging.error('No valid optimizer defined: {}'.format(config.optimizer))
sys.exit(1)
if config.summary_freq:
summary_dir = (config.summary_dir if config.summary_dir is not None
else os.path.abspath(os.path.dirname(config.saveto)))
writer = tf.summary.FileWriter(summary_dir, sess.graph)
else:
writer = None
updater = ModelUpdater(config, num_gpus, replicas, optimizer, global_step,
writer)
if config.exponential_smoothing > 0.0:
smoothing = ExponentialSmoothing(config.exponential_smoothing)
saver, progress = model_loader.init_or_restore_variables(
config, sess, train=True)
global_step.load(progress.uidx, sess)
if config.sample_freq:
random_sampler = RandomSampler(
models=[replicas[0]],
configs=[config],
beam_size=1)
if config.beam_freq or config.valid_script is not None:
beam_search_sampler = BeamSearchSampler(
models=[replicas[0]],
configs=[config],
beam_size=config.beam_size)
#save model options
write_config_to_json_file(config, config.saveto)
text_iterator, valid_text_iterator = load_data(config)
_, _, num_to_source, num_to_target = util.load_dictionaries(config)
total_loss = 0.
n_sents, n_words = 0, 0
last_time = time.time()
logging.info("Initial uidx={}".format(progress.uidx))
# set epoch = 1 if print per-token-probability
if config.print_per_token_pro:
config.max_epochs = progress.eidx+1
for progress.eidx in range(progress.eidx, config.max_epochs):
logging.info('Starting epoch {0}'.format(progress.eidx))
for source_sents, target_sents in text_iterator:
if len(source_sents[0][0]) != config.factors:
logging.error('Mismatch between number of factors in settings ({0}), and number in training corpus ({1})\n'.format(config.factors, len(source_sents[0][0])))
sys.exit(1)
x_in, x_mask_in, y_in, y_mask_in = util.prepare_data(
source_sents, target_sents, config.factors, maxlen=None)
if x_in is None:
logging.info('Minibatch with zero sample under length {0}'.format(config.maxlen))
continue
write_summary_for_this_batch = config.summary_freq and ((progress.uidx % config.summary_freq == 0) or (config.finish_after and progress.uidx % config.finish_after == 0))
(factors, seqLen, batch_size) = x_in.shape
output = updater.update(
sess, x_in, x_mask_in, y_in, y_mask_in, num_to_target,
write_summary_for_this_batch)
if config.print_per_token_pro == False:
total_loss += output
else:
# write per-token probability into the file
f = open(config.print_per_token_pro, 'a')
for pro in output:
pro = str(pro) + '\n'
f.write(pro)
f.close()
n_sents += batch_size
n_words += int(numpy.sum(y_mask_in))
progress.uidx += 1
# Update the smoothed version of the model variables.
# To reduce the performance overhead, we only do this once every
# N steps (the smoothing factor is adjusted accordingly).
if config.exponential_smoothing > 0.0 and progress.uidx % smoothing.update_frequency == 0:
sess.run(fetches=smoothing.update_ops)
if config.disp_freq and progress.uidx % config.disp_freq == 0:
duration = time.time() - last_time
disp_time = datetime.now().strftime('[%Y-%m-%d %H:%M:%S]')
logging.info('{0} Epoch: {1} Update: {2} Loss/word: {3} Words/sec: {4} Sents/sec: {5}'.format(disp_time, progress.eidx, progress.uidx, total_loss/n_words, n_words/duration, n_sents/duration))
last_time = time.time()
total_loss = 0.
n_sents = 0
n_words = 0
if config.sample_freq and progress.uidx % config.sample_freq == 0:
x_small = x_in[:, :, :10]
x_mask_small = x_mask_in[:, :10]
y_small = y_in[:, :10]
samples = translate_utils.translate_batch(
sess, random_sampler, x_small, x_mask_small,
config.translation_maxlen, 0.0)
assert len(samples) == len(x_small.T) == len(y_small.T), \
(len(samples), x_small.shape, y_small.shape)
for xx, yy, ss in zip(x_small.T, y_small.T, samples):
source = util.factoredseq2words(xx, num_to_source)
target = util.seq2words(yy, num_to_target)
sample = util.seq2words(ss[0][0], num_to_target)
logging.info('SOURCE: {}'.format(source))
logging.info('TARGET: {}'.format(target))
logging.info('SAMPLE: {}'.format(sample))
if config.beam_freq and progress.uidx % config.beam_freq == 0:
x_small = x_in[:, :, :10]
x_mask_small = x_mask_in[:, :10]
y_small = y_in[:,:10]
samples = translate_utils.translate_batch(
sess, beam_search_sampler, x_small, x_mask_small,
config.translation_maxlen, config.normalization_alpha)
assert len(samples) == len(x_small.T) == len(y_small.T), \
(len(samples), x_small.shape, y_small.shape)
for xx, yy, ss in zip(x_small.T, y_small.T, samples):
source = util.factoredseq2words(xx, num_to_source)
target = util.seq2words(yy, num_to_target)
logging.info('SOURCE: {}'.format(source))
logging.info('TARGET: {}'.format(target))
for i, (sample_seq, cost) in enumerate(ss):
sample = util.seq2words(sample_seq, num_to_target)
msg = 'SAMPLE {}: {} Cost/Len/Avg {}/{}/{}'.format(
i, sample, cost, len(sample), cost/len(sample))
logging.info(msg)
if config.valid_freq and progress.uidx % config.valid_freq == 0:
if config.exponential_smoothing > 0.0:
sess.run(fetches=smoothing.swap_ops)
valid_ce = validate(sess, replicas[0], config,
valid_text_iterator)
sess.run(fetches=smoothing.swap_ops)
else:
valid_ce = validate(sess, replicas[0], config,
valid_text_iterator)
if (len(progress.history_errs) == 0 or
valid_ce < min(progress.history_errs)):
progress.history_errs.append(valid_ce)
progress.bad_counter = 0
save_non_checkpoint(sess, saver, config.saveto)
progress_path = '{0}.progress.json'.format(config.saveto)
progress.save_to_json(progress_path)
else:
progress.history_errs.append(valid_ce)
progress.bad_counter += 1
if progress.bad_counter > config.patience:
logging.info('Early Stop!')
progress.estop = True
break
if config.valid_script is not None:
if config.exponential_smoothing > 0.0:
sess.run(fetches=smoothing.swap_ops)
score = validate_with_script(sess, beam_search_sampler)
sess.run(fetches=smoothing.swap_ops)
else:
score = validate_with_script(sess, beam_search_sampler)
need_to_save = (score is not None and
(len(progress.valid_script_scores) == 0 or
score > max(progress.valid_script_scores)))
if score is None:
score = 0.0 # ensure a valid value is written
progress.valid_script_scores.append(score)
if need_to_save:
progress.bad_counter = 0
save_path = config.saveto + ".best-valid-script"
save_non_checkpoint(sess, saver, save_path)
write_config_to_json_file(config, save_path)
progress_path = '{}.progress.json'.format(save_path)
progress.save_to_json(progress_path)
if config.save_freq and progress.uidx % config.save_freq == 0:
saver.save(sess, save_path=config.saveto, global_step=progress.uidx)
write_config_to_json_file(config, "%s-%s" % (config.saveto, progress.uidx))
progress_path = '{0}-{1}.progress.json'.format(config.saveto, progress.uidx)
progress.save_to_json(progress_path)
if config.finish_after and progress.uidx % config.finish_after == 0:
logging.info("Maximum number of updates reached")
saver.save(sess, save_path=config.saveto, global_step=progress.uidx)
write_config_to_json_file(config, "%s-%s" % (config.saveto, progress.uidx))
progress.estop=True
progress_path = '{0}-{1}.progress.json'.format(config.saveto, progress.uidx)
progress.save_to_json(progress_path)
break
if progress.estop:
break
def create_parallel_optimization(model_fn,
input_fn,
n_outputs,
controller="/cpu:0"):
# This function is defined below; it returns a list of device ids like
# `['/gpu:0', '/gpu:1']`
devices = tf_utils.get_available_gpus()
# This list keeps track of the gradients per tower and the losses
tower_grads = []
losses = []
preds_list = []
labels_list = []
dropout_tensor = None
weights = []
# Get the current variable scope so we can reuse all variables we need once we get
# to the second iteration of the loop below
with tf.variable_scope(tf.get_variable_scope()) as outer_scope:
for i, id in enumerate(devices):
name = 'tower_{}'.format(i)
name = 'tower_{}'.format(i)
# Use the assign_to_device function to ensure that variables are created on the
# controller.
with tf.device(tf_utils.assign_to_device(
id, controller)), tf.name_scope(name):
# Compute loss and gradients, but don't apply them yet
loss, preds, labels, dropout_tensor = model_fn(
input_fn, n_outputs)
with tf.name_scope("compute_gradients"):
# `compute_gradients` returns a list of (gradient, variable) pairs
# grads = optimizer.compute_gradients(loss)
# g = tf.get_default_graph()
# ops = g.get_operations()
# for op in ge.filter_ops_from_regex(ops, "^tower_[01]/model/pool1/MaxPool"):
# tf.add_to_collection("checkpoints", op.outputs[0])
#grads=gradients(loss, tf.trainable_variables(),checkpoints="memory")
grads = tf.gradients(loss, tf.trainable_variables())
grads_and_vars = list(zip(grads, tf.trainable_variables()))
tower_grads.append(grads_and_vars)
losses.append(loss)
preds_list.append(preds)
labels_list.append(labels)
weights.append(tf.shape(preds)[0])
# After the first iteration, we want to reuse the variables.
outer_scope.reuse_variables()
with tf.name_scope("apply_gradients"), tf.device(controller):
# Note that what we are doing here mathematically is equivalent to returning the
# average loss over the towers and compute the gradients relative to that.
# Unfortunately, this would place all gradient-computations on one device, which is
# why we had to compute the gradients above per tower and need to average them here.
# This function is defined below; it takes the list of (gradient, variable) lists
# and turns it into a single (gradient, variables) list.
_gradients = tf_utils.weighted_average_gradients(tower_grads, weights)
total_bs = tf.reduce_sum(weights)
optimizer = tf.train.AdamOptimizer(
(parameters["learning_rate"] / 128) *
tf.cast(total_bs, tf.float32))
global_step = tf.train.get_or_create_global_step()
apply_gradient_op = optimizer.apply_gradients(_gradients, global_step)
avg_loss = tf.reduce_mean(losses)
concat_preds = tf.concat(preds_list, 0)
concat_labels = tf.concat(labels_list, 0)
return apply_gradient_op, avg_loss, concat_preds, concat_labels, dropout_tensor