def main(_):
graph = tf.Graph()
with graph.as_default():
sess_config = tf.ConfigProto()
import random
name_to_features = {
"input_ids":
tf.FixedLenFeature([FLAGS.max_length], tf.int64),
"input_mask":
tf.FixedLenFeature([FLAGS.max_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([FLAGS.max_length], tf.int64),
"masked_lm_positions":
tf.FixedLenFeature([FLAGS.max_predictions_per_seq], tf.int64),
"masked_lm_ids":
tf.FixedLenFeature([FLAGS.max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([FLAGS.max_predictions_per_seq], tf.float32),
"label_ids":
tf.FixedLenFeature([], tf.int64),
}
params = Bunch({})
params.epoch = 1
params.batch_size = FLAGS.batch_size
def parse_folder(path):
files = os.listdir(path)
output = []
for file_name in files:
output.append(os.path.join(path, file_name))
random.shuffle(output)
return output
print(params["batch_size"], "===batch size===")
jd_test = parse_folder(FLAGS.train_result_file)
input_fn = tf_data_utils.train_input_fn(jd_test, tf_data_utils._decode_record, name_to_features, params)
sess = tf.Session(config=sess_config)
init_op = tf.group(
tf.local_variables_initializer())
sess.run(init_op)
i = 0
cnt = 0
while True:
try:
features = sess.run(input_fn)
i += 1
cnt += 1
except tf.errors.OutOfRangeError:
print("End of dataset")
break
print(i*FLAGS.batch_size)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
for name in ["input_ids", "input_mask", "segment_ids"]:
example[name] = tf.reshape(example[name], [-1, max_seq_length])
return example
params = Bunch({})
params.epoch = FLAGS.epoch
params.batch_size = FLAGS.batch_size
train_features = tf_data_utils.train_input_fn(FLAGS.train_file,
_decode_record,
name_to_features, params)
eval_features = tf_data_utils.eval_input_fn(FLAGS.dev_file, _decode_record,
name_to_features, params)
[train_op, train_loss, train_per_example_loss,
train_logits] = model_train_fn(train_features, [],
tf.estimator.ModeKeys.TRAIN)
[_, eval_loss, eval_per_example_loss,
eval_logits] = model_eval_fn(eval_features, [],
tf.estimator.ModeKeys.EVAL)
result = metric_fn(eval_features, eval_logits, eval_loss)
model_io_fn.set_saver()
init_op = tf.group(tf.global_variables_initializer(),
def train_eval_fn(FLAGS, init_checkpoint, train_file, dev_file, checkpoint_dir,
**kargs):
graph = tf.Graph()
with graph.as_default():
import json
config = model_config_parser(FLAGS)
train_size = int(FLAGS.train_size)
init_lr = FLAGS.init_lr
distillation_config = Bunch(
json.load(tf.gfile.Open(FLAGS.multi_task_config)))
if FLAGS.use_tpu:
warmup_ratio = config.get('warmup', 0.1)
num_train_steps = int(train_size / FLAGS.batch_size * FLAGS.epoch)
num_warmup_steps = int(num_train_steps * warmup_ratio)
print('==num warmup steps==', num_warmup_steps)
print(" model type {}".format(FLAGS.model_type))
print(num_train_steps, num_warmup_steps, "=============",
kargs.get('num_gpus', 1), '==number of gpus==')
tf.logging.info("***** Running evaluation *****")
tf.logging.info("***** train steps : %d", num_train_steps)
max_eval_steps = int(int(FLAGS.eval_size) / FLAGS.batch_size)
clip_norm_scale = 1.0
lr_scale = 1.0
lr = init_lr
checkpoint_dir = checkpoint_dir
opt_config = Bunch({
"init_lr":
lr,
"num_train_steps":
num_train_steps,
"num_warmup_steps":
num_warmup_steps,
"train_op":
kargs.get("train_op", "adam"),
"decay":
kargs.get("decay", "no"),
"warmup":
kargs.get("warmup", "no"),
"clip_norm":
config.get("clip_norm", 1.0),
"grad_clip":
config.get("grad_clip", "global_norm"),
"use_tpu":
1
})
else:
warmup_ratio = config.get('warmup', 0.1)
worker_count = kargs.get('worker_count', 1)
task_index = kargs.get('task_index', 0)
is_chief = kargs.get('is_chief', 0)
if FLAGS.if_shard == "0":
train_size = FLAGS.train_size
epoch = int(FLAGS.epoch / worker_count)
elif FLAGS.if_shard == "1":
print("==number of gpus==", kargs.get('num_gpus', 1))
train_size = int(FLAGS.train_size / worker_count /
kargs.get('num_gpus', 1))
# train_size = int(FLAGS.train_size)
epoch = FLAGS.epoch
else:
train_size = int(FLAGS.train_size / worker_count)
epoch = FLAGS.epoch
num_train_steps = int(train_size / FLAGS.batch_size * epoch)
if config.get('ln_type', 'postln') == 'postln':
num_warmup_steps = int(num_train_steps * warmup_ratio)
elif config.get('ln_type', 'preln') == 'postln':
num_warmup_steps = 0
else:
num_warmup_steps = int(num_train_steps * warmup_ratio)
print('==num warmup steps==', num_warmup_steps)
num_storage_steps = min(
[int(train_size / FLAGS.batch_size), 10000])
if num_storage_steps <= 100:
num_storage_steps = 500
num_eval_steps = int(FLAGS.eval_size / FLAGS.batch_size)
print(
"num_train_steps {}, num_eval_steps {}, num_storage_steps {}".
format(num_train_steps, num_eval_steps, num_storage_steps))
print(" model type {}".format(FLAGS.model_type))
print(num_train_steps, num_warmup_steps, "=============",
kargs.get('num_gpus', 1), '==number of gpus==')
if worker_count * kargs.get("num_gpus", 1) >= 2:
clip_norm_scale = 1.0
lr_scale = 0.8
else:
clip_norm_scale = 1.0
lr_scale = 1.0
lr = init_lr * worker_count * kargs.get("num_gpus", 1) * lr_scale
if lr >= 1e-3:
lr = 1e-3
print('==init lr==', lr)
if FLAGS.opt_type == "hvd" and hvd:
checkpoint_dir = checkpoint_dir if task_index == 0 else None
elif FLAGS.opt_type == "all_reduce":
checkpoint_dir = checkpoint_dir
elif FLAGS.opt_type == "collective_reduce":
checkpoint_dir = checkpoint_dir if task_index == 0 else None
elif FLAGS.opt_type == "ps" or FLAGS.opt_type == "ps_sync":
checkpoint_dir = checkpoint_dir if task_index == 0 else None
opt_config = Bunch({
"init_lr":
lr,
"num_train_steps":
num_train_steps,
"num_warmup_steps":
num_warmup_steps,
"worker_count":
worker_count,
"gpu_count":
worker_count * kargs.get("num_gpus", 1),
"opt_type":
FLAGS.opt_type,
"is_chief":
is_chief,
"train_op":
kargs.get("train_op", "adam"),
"decay":
kargs.get("decay", "no"),
"warmup":
kargs.get("warmup", "no"),
"clip_norm":
config.get("clip_norm", 1.0),
"grad_clip":
config.get("grad_clip", "global_norm"),
"epoch":
FLAGS.epoch,
"strategy":
FLAGS.distribution_strategy,
"use_tpu":
0
})
model_io_config = Bunch({"fix_lm": False})
model_io_fn = model_io.ModelIO(model_io_config)
num_classes = FLAGS.num_classes
model_config_dict = {}
num_labels_dict = {}
init_checkpoint_dict = {}
load_pretrained_dict = {}
exclude_scope_dict = {}
not_storage_params_dict = {}
target_dict = {}
for task_type in FLAGS.multi_task_type.split(","):
print("==task type==", task_type)
model_config_dict[task_type] = model_config_parser(
Bunch(distillation_config[task_type]))
print(task_type, distillation_config[task_type],
'=====task model config======')
num_labels_dict[task_type] = distillation_config[task_type][
"num_labels"]
init_checkpoint_dict[task_type] = os.path.join(
FLAGS.buckets,
distillation_config[task_type]["init_checkpoint"])
load_pretrained_dict[task_type] = distillation_config[task_type][
"load_pretrained"]
exclude_scope_dict[task_type] = distillation_config[task_type][
"exclude_scope"]
not_storage_params_dict[task_type] = distillation_config[
task_type]["not_storage_params"]
target_dict[task_type] = distillation_config[task_type]["target"]
tf.logging.info("***** use tpu ***** %s", str(FLAGS.use_tpu))
model_fn = classifier_model_fn_builder(
model_config_dict,
num_labels_dict,
init_checkpoint_dict,
load_pretrained_dict,
model_io_config=model_io_config,
opt_config=opt_config,
exclude_scope_dict=exclude_scope_dict,
not_storage_params_dict=not_storage_params_dict,
target_dict=target_dict,
use_tpu=FLAGS.use_tpu,
**kargs)
if FLAGS.use_tpu:
from data_generator import tf_data_utils
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=True,
model_fn=model_fn,
config=kargs.get('run_config', {}),
train_batch_size=FLAGS.batch_size,
eval_batch_size=FLAGS.batch_size)
tf.logging.info("****** do train ******* %s", str(FLAGS.do_train))
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.batch_size)
input_features = tf_data_utils.electra_input_fn_builder(
train_file,
FLAGS.max_length,
FLAGS.max_predictions_per_seq,
True,
num_cpu_threads=4)
estimator.train(input_fn=input_features,
max_steps=num_train_steps)
else:
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.batch_size)
eval_input_fn = tf_data_utils.electra_input_fn_builder(
input_files=dev_file,
max_seq_length=FLAGS.max_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
tf.logging.info("***** Begining Running evaluation *****")
result = estimator.evaluate(input_fn=eval_input_fn,
steps=max_eval_steps)
output_eval_file = os.path.join(checkpoint_dir,
"eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
else:
from data_generator import distributed_tf_data_utils as tf_data_utils
name_to_features = {
"input_ids":
tf.FixedLenFeature([FLAGS.max_length], tf.int64),
"input_mask":
tf.FixedLenFeature([FLAGS.max_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([FLAGS.max_length], tf.int64),
"input_ori_ids":
tf.FixedLenFeature([FLAGS.max_length], tf.int64),
"masked_lm_positions":
tf.FixedLenFeature([FLAGS.max_predictions_per_seq], tf.int64),
"masked_lm_ids":
tf.FixedLenFeature([FLAGS.max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([FLAGS.max_predictions_per_seq],
tf.float32),
"next_sentence_labels":
tf.FixedLenFeature([], tf.int64),
}
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example.
"""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def _decode_batch_record(record, name_to_features):
example = tf.parse_example(record, name_to_features)
# for name in list(example.keys()):
# t = example[name]
# if t.dtype == tf.int64:
# t = tf.to_int32(t)
# example[name] = t
return example
params = Bunch({})
params.epoch = FLAGS.epoch
params.batch_size = FLAGS.batch_size
if kargs.get("run_config", None):
if kargs.get("parse_type", "parse_single") == "parse_single":
train_features = lambda: tf_data_utils.all_reduce_train_input_fn(
train_file,
_decode_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
eval_features = lambda: tf_data_utils.all_reduce_eval_input_fn(
dev_file,
_decode_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
elif kargs.get("parse_type", "parse_single") == "parse_batch":
print("==apply parse example==")
train_features = lambda: tf_data_utils.all_reduce_train_batch_input_fn(
train_file,
_decode_batch_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
eval_features = lambda: tf_data_utils.all_reduce_eval_batch_input_fn(
dev_file,
_decode_batch_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
else:
train_features = lambda: tf_data_utils.train_input_fn(
train_file,
_decode_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
eval_features = lambda: tf_data_utils.eval_input_fn(
dev_file,
_decode_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
train_hooks = []
eval_hooks = []
sess_config = tf.ConfigProto(allow_soft_placement=False,
log_device_placement=False)
if FLAGS.opt_type == "ps" or FLAGS.opt_type == "ps_sync":
print("==no need for hook==")
elif FLAGS.opt_type == "pai_soar" and pai:
print("no need for hook")
elif FLAGS.opt_type == "hvd" and hvd:
sess_config.gpu_options.allow_growth = True
sess_config.gpu_options.visible_device_list = str(
hvd.local_rank())
print("==no need fo hook==")
else:
print("==no need for hooks==")
if kargs.get("run_config", None):
run_config = kargs.get("run_config", None)
run_config = run_config.replace(
save_checkpoints_steps=num_storage_steps)
print("==run config==", run_config.save_checkpoints_steps)
else:
run_config = tf.estimator.RunConfig(
model_dir=checkpoint_dir,
save_checkpoints_steps=num_storage_steps,
session_config=sess_config)
if kargs.get("profiler", "profiler") == "profiler":
if checkpoint_dir:
hooks = tf.train.ProfilerHook(
save_steps=100,
save_secs=None,
output_dir=os.path.join(checkpoint_dir, "profiler"),
)
train_hooks.append(hooks)
print("==add profiler hooks==")
model_estimator = tf.estimator.Estimator(model_fn=model_fn,
model_dir=checkpoint_dir,
config=run_config)
train_being_time = time.time()
tf.logging.info("==training distribution_strategy=={}".format(
kargs.get("distribution_strategy", "MirroredStrategy")))
if kargs.get("distribution_strategy",
"MirroredStrategy") == "MirroredStrategy":
print("==apply single machine multi-card training==")
train_spec = tf.estimator.TrainSpec(input_fn=train_features,
max_steps=num_train_steps)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_features,
steps=num_eval_steps)
model_estimator.train(input_fn=train_features,
max_steps=num_train_steps,
hooks=train_hooks)
# tf.estimator.train(model_estimator, train_spec)
train_end_time = time.time()
print("==training time==", train_end_time - train_being_time)
tf.logging.info("==training time=={}".format(train_end_time -
train_being_time))
eval_results = model_estimator.evaluate(input_fn=eval_features,
steps=num_eval_steps)
print(eval_results)
elif kargs.get("distribution_strategy", "MirroredStrategy") in [
"ParameterServerStrategy", "CollectiveAllReduceStrategy"
]:
print("==apply multi-machine machine multi-card training==")
try:
print(os.environ['TF_CONFIG'], "==tf_run_config==")
except:
print("==not tf config==")
train_spec = tf.estimator.TrainSpec(input_fn=train_features,
max_steps=num_train_steps)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_features,
steps=num_eval_steps)
# tf.estimator.train(model_estimator, train_spec) # tf 1.12 doesn't need evaluate
tf.estimator.train_and_evaluate(model_estimator, train_spec,
eval_spec)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
for name in ["input_ids", "input_mask", "segment_ids"]:
example[name] = tf.reshape(example[name], [-1, max_seq_length])
return example
params = Bunch({})
params.epoch = 5
params.batch_size = 6
train_features = tf_data_utils.train_input_fn(
"/data/xuht/concat/data/train.tfrecords", _decode_record,
name_to_features, params)
eval_features = tf_data_utils.eval_input_fn(
"/data/xuht/concat/data/test.tfrecords", _decode_record,
name_to_features, params)
[train_op, train_loss, train_per_example_loss,
train_logits] = model_train_fn(train_features, [],
tf.estimator.ModeKeys.TRAIN)
[_, eval_loss, eval_per_example_loss,
eval_logits] = model_eval_fn(eval_features, [],
tf.estimator.ModeKeys.EVAL)
result = metric_fn(eval_features, eval_logits, eval_loss)
model_io_fn.set_saver()
def main(_):
graph = tf.Graph()
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
with graph.as_default():
import json
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu_id
sess = tf.Session()
config = json.load(open(FLAGS.config_file, "r"))
student_config = Bunch(config)
student_config.use_one_hot_embeddings = True
student_config.scope = "student/bert"
student_config.dropout_prob = 0.1
student_config.label_type = "single_label"
student_config.init_checkpoint = FLAGS.student_init_checkpoint
temperature = 2.0 # 1.0
distill_ratio = 0.7
true_label_ratio = 0.3
student_config.temperature = temperature
student_config.distill_ratio = distill_ratio
student_config.num_hidden_layers = 6
json.dump(student_config, open(FLAGS.model_output+"/student_config.json", "w"))
teacher_config = Bunch(config)
teacher_config.use_one_hot_embeddings = True
teacher_config.scope = "teacher/bert"
teacher_config.dropout_prob = 0.1
teacher_config.label_type = "single_label"
teacher_config.init_checkpoint = FLAGS.teacher_init_checkpoint
json.dump(teacher_config, open(FLAGS.model_output+"/teacher_config.json", "w"))
model_config_dict = {"student":student_config, "teacher":teacher_config}
init_checkpoint_dict = {"student":FLAGS.student_init_checkpoint,
"teacher":FLAGS.teacher_init_checkpoint}
num_train_steps = int(
FLAGS.train_size / FLAGS.batch_size * FLAGS.epoch)
num_warmup_steps = int(num_train_steps * 0.1)
num_storage_steps = int(FLAGS.train_size / FLAGS.batch_size)
print(num_train_steps, num_warmup_steps, "=============")
opt_config = Bunch({"init_lr":1e-5,
"num_train_steps":num_train_steps,
"num_warmup_steps":num_warmup_steps})
model_io_config = Bunch({"fix_lm":False})
model_io_fn = model_io.ModelIO(model_io_config)
num_choice = FLAGS.num_classes
max_seq_length = FLAGS.max_length
model_train_fn = distillation.distillation_model_fn(
model_config_dict=model_config_dict,
num_labels=num_choice,
init_checkpoint_dict=init_checkpoint_dict,
model_reuse=None,
load_pretrained={"teacher":True, "student":True},
model_io_fn=model_io_fn,
model_io_config=model_io_config,
opt_config=opt_config,
student_input_name=["a", "b"],
teacher_input_name=["a", "b"],
unlabel_input_name=["ua", "ub"],
temperature=temperature,
exclude_scope_dict={"student":"student", "teacher":"teacher"},
not_storage_params=["adam_m", "adam_v"],
distillation_weight={"label":distill_ratio,
"unlabel":distill_ratio,
"true_label":true_label_ratio},
if_distill_unlabeled=False
)
model_eval_fn = distillation.distillation_model_fn(
model_config_dict=model_config_dict,
num_labels=num_choice,
init_checkpoint_dict=init_checkpoint_dict,
model_reuse=True,
load_pretrained={"teacher":True, "student":True},
model_io_fn=model_io_fn,
model_io_config=model_io_config,
opt_config=opt_config,
student_input_name=["a", "b"],
teacher_input_name=["a", "b"],
unlabel_input_name=["ua", "ub"],
temperature=temperature,
exclude_scope_dict={"student":"student", "teacher":"teacher"},
not_storage_params=["adam_m", "adam_v"],
distillation_weight={"label":distill_ratio,
"unlabel":distill_ratio,
"true_label":true_label_ratio},
if_distill_unlabeled=False
)
def metric_fn(features, logits, loss):
print(logits.get_shape(), "===logits shape===")
pred_label = tf.argmax(logits, axis=-1, output_type=tf.int32)
prob = tf.nn.softmax(logits)
accuracy = correct = tf.equal(
tf.cast(pred_label, tf.int32),
tf.cast(features["label_ids"], tf.int32)
)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
return {"accuracy":accuracy, "loss":loss, "pred_label":pred_label, "label_ids":features["label_ids"]}
name_to_features = {
"input_ids_a":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_mask_a":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids_a":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_ids_b":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_mask_b":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids_b":
tf.FixedLenFeature([max_seq_length], tf.int64),
"label_ids":
tf.FixedLenFeature([], tf.int64),
}
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example.
"""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
params = Bunch({})
params.epoch = FLAGS.epoch
params.batch_size = FLAGS.batch_size
# train_features = tf_data_utils.train_input_fn("/data/xuht/wsdm19/data/train.tfrecords",
# _decode_record, name_to_features, params)
# eval_features = tf_data_utils.eval_input_fn("/data/xuht/wsdm19/data/dev.tfrecords",
# _decode_record, name_to_features, params)
train_features = tf_data_utils.train_input_fn(FLAGS.train_file,
_decode_record, name_to_features, params)
eval_features = tf_data_utils.eval_input_fn(FLAGS.dev_file,
_decode_record, name_to_features, params)
[train_op, train_loss, train_per_example_loss, train_logits] = model_train_fn(train_features, [], tf.estimator.ModeKeys.TRAIN)
[_, eval_loss, eval_per_example_loss, eval_logits] = model_eval_fn(eval_features, [], tf.estimator.ModeKeys.EVAL)
result = metric_fn(eval_features, eval_logits, eval_loss)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
def eval_fn(result):
i = 0
total_accuracy = 0
label, label_id = [], []
while True:
try:
eval_result = sess.run(result)
total_accuracy += eval_result["accuracy"]
label_id.extend(eval_result["label_ids"])
label.extend(eval_result["pred_label"])
i += 1
except tf.errors.OutOfRangeError:
print("End of dataset")
break
f1 = f1_score(label_id, label, average="macro")
accuracy = accuracy_score(label_id, label)
print("test accuracy accuracy {} {} f1 {}".format(total_accuracy/i,
accuracy, f1))
return total_accuracy/ i, f1
def train_fn(op, loss):
i = 0
cnt = 0
total_loss = 0.0
while True:
try:
[_, train_loss] = sess.run([op, loss])
total_loss += train_loss
i += 1
cnt += 1
if np.mod(i, num_storage_steps) == 0:
print(total_loss/cnt)
# model_io_fn.save_model(sess, "/data/xuht/wsdm19/data/model_11_15_focal_loss/oqmrc_{}.ckpt".format(int(i/8000)))
model_io_fn.save_model(sess, FLAGS.model_output+"/oqmrc_{}.ckpt".format(int(i/num_storage_steps)))
print("==successful storing model=={}".format(int(i/num_storage_steps)))
total_loss = 0
cnt = 0
except tf.errors.OutOfRangeError:
break
print("===========begin to train============")
train_fn(train_op, train_loss)
print("===========begin to eval============")
accuracy, f1 = eval_fn(result)
print("==accuracy {} f1 {}==".format(accuracy, f1))
# model_io_fn.save_model(sess, "/data/xuht/wsdm19/data/model_11_15_focal_loss/oqmrc.ckpt")
model_io_fn.save_model(sess, FLAGS.model_output+"/oqmrc.ckpt")
def main(_):
hvd.init()
sess_config = tf.ConfigProto()
sess_config.gpu_options.visible_device_list = str(hvd.local_rank())
graph = tf.Graph()
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
with graph.as_default():
import json
# config = json.load(open("/data/xuht/bert/chinese_L-12_H-768_A-12/bert_config.json", "r"))
config = json.load(open(FLAGS.config_file, "r"))
init_checkpoint = FLAGS.init_checkpoint
print("===init checkoutpoint==={}".format(init_checkpoint))
config = Bunch(config)
config.use_one_hot_embeddings = True
config.scope = "bert"
config.dropout_prob = 0.1
config.label_type = "single_label"
config.lm_ratio = 0.0
config.task_ratio = 1.0
json.dump(config, open(FLAGS.model_output+"/config.json", "w"))
init_lr = 1e-5
if FLAGS.if_shard == "0":
train_size = FLAGS.train_size
epoch = int(FLAGS.epoch / hvd.size())
elif FLAGS.if_shard == "1":
train_size = int(FLAGS.train_size/hvd.size())
epoch = FLAGS.epoch
sess = tf.Session(config=sess_config)
num_train_steps = int(
train_size / FLAGS.batch_size * epoch)
num_warmup_steps = int(num_train_steps * 0.1)
num_storage_steps = int(train_size / FLAGS.batch_size)
print(num_train_steps, num_warmup_steps, "=============")
opt_config = Bunch({"init_lr":init_lr/(hvd.size()),
"num_train_steps":num_train_steps,
"num_warmup_steps":num_warmup_steps})
model_io_config = Bunch({"fix_lm":False})
model_io_fn = model_io.ModelIO(model_io_config)
optimizer_fn = optimizer.Optimizer(opt_config)
num_choice = FLAGS.num_classes
max_seq_length = FLAGS.max_length
max_predictions_per_seq = FLAGS.max_predictions_per_seq
model_train_fn = classifier_fn.classifier_model_fn_builder(config,
num_choice, init_checkpoint,
reuse=None,
load_pretrained=True,
model_io_fn=model_io_fn,
optimizer_fn=optimizer_fn,
model_io_config=model_io_config,
opt_config=opt_config)
model_eval_fn = classifier_fn.classifier_model_fn_builder(config,
num_choice, init_checkpoint,
reuse=True,
load_pretrained=True,
model_io_fn=model_io_fn,
optimizer_fn=optimizer_fn,
model_io_config=model_io_config,
opt_config=opt_config)
name_to_features = {
"input_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_mask":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"masked_lm_positions":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_ids":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
"label_ids":
tf.FixedLenFeature([], tf.int64),
}
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example.
"""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
params = Bunch({})
params.epoch = epoch
params.batch_size = FLAGS.batch_size
def parse_folder(path):
files = os.listdir(path)
output = []
for file_name in files:
output.append(os.path.join(path, file_name))
random.shuffle(output)
return output
train_features = tf_data_utils.train_input_fn(
parse_folder(FLAGS.train_file),
_decode_record, name_to_features, params)
train_dict = model_train_fn(train_features, [], tf.estimator.ModeKeys.TRAIN)
eval_features = tf_data_utils.eval_input_fn(
parse_folder(FLAGS.dev_file),
_decode_record, name_to_features, params)
eval_dict = model_eval_fn(eval_features, [], tf.estimator.ModeKeys.EVAL)
model_io_fn.set_saver()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
sess.run(hvd.broadcast_global_variables(0))
def eval_fn(op_dict):
i = 0
eval_total_dict = {}
while True:
try:
eval_result = sess.run(op_dict)
for key in eval_result:
if key in ["probabilities", "label_ids"]:
if key in eval_total_dict:
eval_total_dict[key].extend(eval_result[key])
else:
eval_total_dict[key] = []
eval_total_dict[key].extend(eval_result[key])
i += 1
except tf.errors.OutOfRangeError:
print("End of dataset")
break
for key in eval_result:
if key not in ["probabilities", "label_ids"]:
eval_total_dict[key] = eval_result[key]
label_id = eval_total_dict["label_ids"]
label = np.argmax(np.array(eval_total_dict["probabilities"]), axis=-1)
macro_f1 = f1_score(label_id, label, average="macro")
micro_f1 = f1_score(label_id, label, average="micro")
accuracy = accuracy_score(label_id, label)
print("test accuracy {} macro_f1 score {} micro_f1 {} masked_lm_accuracy {} sentence_f {}".format(accuracy,
macro_f1, micro_f1,
eval_total_dict["masked_lm_accuracy"],
eval_total_dict["sentence_f"]))
return eval_total_dict
def run_eval(steps):
import _pickle as pkl
eval_features = tf_data_utils.eval_input_fn(
parse_folder(FLAGS.dev_file),
_decode_record, name_to_features, params)
eval_dict = model_eval_fn(eval_features, [], tf.estimator.ModeKeys.EVAL)
sess.run(tf.local_variables_initializer())
eval_finial_dict = eval_fn(eval_dict)
if hvd.rank() == 0:
pkl.dump(eval_finial_dict, open(FLAGS.model_output+"/eval_dict_{}.pkl".format(steps), "wb"))
return eval_finial_dict
def train_fn(op_dict):
i = 0
cnt = 0
loss_dict = {}
monitoring_train = []
monitoring_eval = []
while True:
try:
train_result = sess.run(op_dict)
for key in train_result:
if key == "train_op":
continue
else:
if np.isnan(train_result[key]):
print(train_loss, "get nan loss")
break
else:
if key in loss_dict:
loss_dict[key] += train_result[key]
else:
loss_dict[key] = train_result[key]
i += 1
cnt += 1
if np.mod(i, num_storage_steps) == 0:
string = ""
for key in loss_dict:
tmp = key + " " + str(loss_dict[key]/cnt) + "\t"
string += tmp
print(string)
monitoring_train.append(loss_dict)
eval_finial_dict = run_eval(int(i/num_storage_steps))
monitoring_eval.append(eval_finial_dict)
for key in loss_dict:
loss_dict[key] = 0.0
if hvd.rank() == 0:
model_io_fn.save_model(sess, FLAGS.model_output+"/model_{}.ckpt".format(int(i/num_storage_steps)))
print("==successful storing model=={}".format(int(i/num_storage_steps)))
cnt = 0
except tf.errors.OutOfRangeError:
if hvd.rank() == 0:
import _pickle as pkl
pkl.dump({"train":monitoring_train,
"eval":monitoring_eval}, open(FLAGS.model_output+"/monitoring.pkl", "wb"))
break
print("===========begin to train============")
train_fn(train_dict)
if hvd.rank() == 0:
model_io_fn.save_model(sess, FLAGS.model_output+"/model.ckpt")
print("===========begin to eval============")
eval_finial_dict = run_eval("final")
