def export_model(FLAGS,
init_checkpoint,
checkpoint_dir,
export_dir,
**kargs):
config = model_config_parser(FLAGS)
opt_config = Bunch({})
anneal_config = Bunch({})
model_io_config = Bunch({"fix_lm":False})
# with tf.gfile.Open(FLAGS.label_id, "r") as frobj:
# label_dict = json.load(frobj)
num_classes = int(FLAGS.num_classes)
def get_receiver_features():
receiver_tensors = {
"input_ids":tf.placeholder(tf.int32, [None, FLAGS.max_length], name='input_ids'),
"segment_ids":tf.placeholder(tf.int32, [None, FLAGS.max_length], name='segment_ids'),
"input_mask":tf.placeholder(tf.int32, [None, FLAGS.max_length], name='input_mask'),
"input_ori_ids":tf.placeholder(tf.int32, [None, FLAGS.max_length], name='input_ori_ids'),
"context":tf.placeholder(tf.int32, [None, None], name='context'),
}
return receiver_tensors
def serving_input_receiver_fn():
receiver_features = get_receiver_features()
print(receiver_features, "==input receiver_features==")
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(receiver_features)()
return input_fn
model_fn = model_fn_builder(config, num_classes, init_checkpoint,
model_reuse=None,
load_pretrained="yes",
opt_config=opt_config,
model_io_config=model_io_config,
exclude_scope="",
not_storage_params=[],
target=kargs.get("input_target", ""),
output_type="estimator",
checkpoint_dir=checkpoint_dir,
num_storage_steps=100,
task_index=0,
anneal_config=anneal_config,
**kargs)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=checkpoint_dir)
export_dir = estimator.export_savedmodel(export_dir,
serving_input_receiver_fn,
checkpoint_path=init_checkpoint)
print("===Succeeded in exporting saved model==={}".format(export_dir))
def export_model(FLAGS, init_checkpoint, checkpoint_dir, export_dir, **kargs):
config = model_config_parser(FLAGS)
opt_config = Bunch({})
anneal_config = Bunch({})
model_io_config = Bunch({"fix_lm": False})
with tf.gfile.Open(FLAGS.label_id, "r") as frobj:
label_dict = json.load(frobj)
num_classes = len(label_dict["id2label"])
def serving_input_receiver_fn():
receiver_features = data_interface_server(FLAGS)
print(receiver_features, "==input receiver_features==")
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(
receiver_features)()
return input_fn
model_fn_builder = model_fn_interface(FLAGS)
model_fn = model_fn_builder(config,
num_classes,
init_checkpoint,
model_reuse=None,
load_pretrained=FLAGS.load_pretrained,
opt_config=opt_config,
model_io_config=model_io_config,
exclude_scope="",
not_storage_params=[],
target=kargs.get("input_target", ""),
output_type="estimator",
checkpoint_dir=checkpoint_dir,
num_storage_steps=100,
task_index=0,
anneal_config=anneal_config,
**kargs)
estimator = tf.estimator.Estimator(model_fn=model_fn,
model_dir=checkpoint_dir)
export_dir = estimator.export_savedmodel(export_dir,
serving_input_receiver_fn,
checkpoint_path=init_checkpoint)
print("===Succeeded in exporting saved model==={}".format(export_dir))
def export_model(FLAGS, init_checkpoint, checkpoint_dir, export_dir, **kargs):
config = model_config_parser(FLAGS)
opt_config = Bunch({})
anneal_config = Bunch({})
model_io_config = Bunch({"fix_lm": False})
def serving_input_receiver_fn():
receiver_features = data_interface_server(FLAGS)
print(receiver_features, "==input receiver_features==")
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(
receiver_features)()
return input_fn
ss = np.random.random((2, 10))
p = tf.constant(ss)
sess = tf.Session()
print(sess.run(p), ss)
model_fn = model_fn_builder(config,
2,
init_checkpoint,
model_reuse=None,
load_pretrained=FLAGS.load_pretrained,
opt_config=opt_config,
model_io_config=model_io_config,
exclude_scope="",
not_storage_params=[],
target=kargs.get("input_target", ""),
output_type="estimator",
checkpoint_dir=checkpoint_dir,
num_storage_steps=100,
task_index=0,
anneal_config=anneal_config,
**kargs)
estimator = tf.estimator.Estimator(model_fn=model_fn,
model_dir=checkpoint_dir)
export_dir = estimator.export_savedmodel(export_dir,
serving_input_receiver_fn,
checkpoint_path=init_checkpoint)
print("===Succeeded in exporting saved model==={}".format(export_dir))
def train_eval_fn(FLAGS, worker_count, task_index, is_chief, target,
init_checkpoint, train_file, dev_file, checkpoint_dir,
is_debug, **kargs):
graph = tf.Graph()
with graph.as_default():
import json
# config = model_config_parser(FLAGS)
if FLAGS.if_shard == "0":
train_size = FLAGS.train_size
epoch = int(FLAGS.epoch / worker_count)
elif FLAGS.if_shard == "1":
train_size = int(FLAGS.train_size / worker_count)
epoch = FLAGS.epoch
else:
train_size = int(FLAGS.train_size / worker_count)
epoch = FLAGS.epoch
multi_task_config = Bunch(
json.load(tf.gfile.Open(FLAGS.multi_task_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)
num_eval_steps = int(FLAGS.eval_size / FLAGS.batch_size)
if is_debug == "0":
num_storage_steps = 190
num_eval_steps = 100
num_train_steps = 200
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, "=============")
opt_config = Bunch({
"init_lr": FLAGS.init_lr,
"num_train_steps": num_train_steps,
"num_warmup_steps": num_warmup_steps,
"worker_count": worker_count,
"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"),
"grad_clip": kargs.get("grad_clip", "global_norm"),
"clip_norm": kargs.get("clip_norm", 1.0)
})
anneal_config = Bunch({
"initial_value": 1.0,
"num_train_steps": num_train_steps
})
model_io_config = Bunch({"fix_lm": False})
if FLAGS.opt_type == "hvd" and hvd:
checkpoint_dir = checkpoint_dir if task_index == 0 else None
else:
checkpoint_dir = checkpoint_dir
print("==checkpoint_dir==", checkpoint_dir, is_chief)
model_config_dict = {}
num_labels_dict = {}
init_checkpoint_dict = {}
load_pretrained_dict = {}
exclude_scope_dict = {}
not_storage_params_dict = {}
target_dict = {}
task_type_dict = {}
model_type_lst = []
label_dict = {}
for task_type in FLAGS.multi_task_type.split(","):
print("==task type==", task_type)
model_config_dict[task_type] = model_config_parser(
Bunch(multi_task_config[task_type]))
num_labels_dict[task_type] = multi_task_config[task_type][
"num_labels"]
init_checkpoint_dict[task_type] = os.path.join(
FLAGS.buckets, multi_task_config[task_type]["init_checkpoint"])
load_pretrained_dict[task_type] = multi_task_config[task_type][
"load_pretrained"]
exclude_scope_dict[task_type] = multi_task_config[task_type][
"exclude_scope"]
not_storage_params_dict[task_type] = multi_task_config[task_type][
"not_storage_params"]
target_dict[task_type] = multi_task_config[task_type]["target"]
task_type_dict[task_type] = multi_task_config[task_type][
"task_type"]
label_dict[task_type] = json.load(
open(
os.path.join(FLAGS.buckets,
multi_task_config[task_type]["label_id"])))
model_train_fn = multitask_model_fn(
model_config_dict,
num_labels_dict,
task_type_dict,
init_checkpoint_dict,
load_pretrained_dict=load_pretrained_dict,
opt_config=opt_config,
model_io_config=model_io_config,
exclude_scope_dict=exclude_scope_dict,
not_storage_params_dict=not_storage_params_dict,
target_dict=target_dict,
output_type="sess",
checkpoint_dir=checkpoint_dir,
num_storage_steps=num_storage_steps,
anneal_config=anneal_config,
task_layer_reuse=None,
model_type_lst=model_type_lst,
**kargs)
eval_model_fn = {}
for task_type in FLAGS.multi_task_type.split(","):
eval_task_type_dict = {}
model_config_dict[task_type] = model_config_parser(
Bunch(multi_task_config[task_type]))
num_labels_dict[task_type] = multi_task_config[task_type][
"num_labels"]
init_checkpoint_dict[task_type] = os.path.join(
FLAGS.buckets, multi_task_config[task_type]["init_checkpoint"])
load_pretrained_dict[task_type] = multi_task_config[task_type][
"load_pretrained"]
exclude_scope_dict[task_type] = multi_task_config[task_type][
"exclude_scope"]
not_storage_params_dict[task_type] = multi_task_config[task_type][
"not_storage_params"]
target_dict[task_type] = multi_task_config[task_type]["target"]
eval_task_type_dict[task_type] = multi_task_config[task_type][
"task_type"]
eval_model_fn[task_type] = multitask_model_fn(
model_config_dict,
num_labels_dict,
eval_task_type_dict,
init_checkpoint_dict,
load_pretrained_dict=load_pretrained_dict,
opt_config=opt_config,
model_io_config=model_io_config,
exclude_scope_dict=exclude_scope_dict,
not_storage_params_dict=not_storage_params_dict,
target_dict=target_dict,
output_type="sess",
checkpoint_dir=checkpoint_dir,
num_storage_steps=num_storage_steps,
anneal_config=anneal_config,
task_layer_reuse=True,
model_type_lst=model_type_lst,
multi_task_config=multi_task_config,
**kargs)
print("==succeeded in building model==")
def eval_metric_fn(features, eval_op_dict, task_type):
logits = eval_op_dict["logits"][task_type]
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".format(task_type)], tf.int32))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
return {
"accuracy": accuracy,
"loss": eval_op_dict["loss"][task_type],
"pred_label": pred_label,
"label_ids": features["{}_label_ids".format(task_type)]
}
def train_metric_fn(features, train_op_dict):
return train_op_dict
name_to_features = data_interface(FLAGS, multi_task_config,
FLAGS.multi_task_type.split(","))
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)
return example
params = Bunch({})
params.epoch = epoch
params.batch_size = FLAGS.batch_size
if kargs.get("parse_type", "parse_single") == "parse_single":
train_file_lst = [
multi_task_config[task_type]["train_result_file"]
for task_type in FLAGS.multi_task_type.split(",")
]
print(train_file_lst)
train_features = tf_data_utils.train_input_fn(
train_file_lst,
_decode_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
eval_features_dict = {}
for task_type in FLAGS.multi_task_type.split(","):
name_to_features = data_interface(
FLAGS, {task_type: multi_task_config[task_type]})
eval_features_dict[task_type] = tf_data_utils.eval_input_fn(
multi_task_config[task_type]["dev_result_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":
train_file_lst = [
multi_task_config[task_type]["train_result_file"]
for task_type in FLAGS.multi_task_type.split(",")
]
train_file_path_lst = [
os.path.join(FLAGS.buckets, train_file)
for train_file in train_file_lst
]
train_features = tf_data_utils.train_batch_input_fn(
train_file_path_lst,
_decode_batch_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
eval_features_dict = {}
for task_type in FLAGS.multi_task_type.split(","):
name_to_features = data_interface(
FLAGS, {task_type: multi_task_config[task_type]},
[task_type_dict])
dev_file_path = os.path.join(
FLAGS.buckets,
multi_task_config[task_type]["dev_result_file"])
eval_features_dict[
task_type] = tf_data_utils.eval_batch_input_fn(
dev_file_path,
_decode_batch_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
train_op_dict = model_train_fn(train_features, [],
tf.estimator.ModeKeys.TRAIN)
train_dict = train_metric_fn(train_features, train_op_dict["train"])
eval_dict = {}
for task_type in eval_features_dict:
eval_features = eval_features_dict[task_type]
eval_op_dict = eval_model_fn[task_type](eval_features, [],
tf.estimator.ModeKeys.EVAL)
eval_dict_tmp = eval_metric_fn(eval_features, eval_op_dict["eval"],
task_type)
eval_dict[task_type] = eval_dict_tmp
print("==succeeded in building data and model==")
print(train_op_dict)
def task_eval(eval_dict, sess, eval_total_dict):
eval_result = sess.run(eval_dict)
for key in eval_result:
if key not in eval_total_dict:
if key in ["pred_label", "label_ids"]:
eval_total_dict[key] = []
eval_total_dict[key].extend(eval_result[key])
if key in ["accuracy", "loss"]:
eval_total_dict[key] = 0.0
eval_total_dict[key] += eval_result[key]
else:
if key in ["pred_label", "label_ids"]:
eval_total_dict[key].extend(eval_result[key])
if key in ["accuracy", "loss"]:
eval_total_dict[key] += eval_result[key]
def task_metric(eval_dict, label_dict):
label_id = eval_dict["label_ids"]
pred_label = eval_dict["pred_label"]
label_dict_id = sorted(list(label_dict["id2label"].keys()))
print(len(label_id), len(pred_label), len(set(label_id)))
accuracy = accuracy_score(label_id, pred_label)
print("==accuracy==", accuracy)
if len(label_dict["id2label"]) < 10:
result = classification_report(label_id,
pred_label,
target_names=[
label_dict["id2label"][key]
for key in label_dict_id
],
digits=4)
print(result, task_index)
eval_total_dict["classification_report"] = result
print("==classification report==")
def eval_fn(eval_dict, sess):
i = 0
total_accuracy = 0
eval_total_dict = {}
for task_type in eval_dict:
eval_total_dict[task_type] = {}
while True:
try:
for task_type in eval_dict:
task_eval(eval_dict[task_type], sess,
eval_total_dict[task_type])
i += 1
if np.mod(i, num_eval_steps) == 0:
break
except tf.errors.OutOfRangeError:
print("End of dataset")
break
for task_type in eval_total_dict:
task_metric(eval_total_dict[task_type], label_dict[task_type])
return eval_total_dict
def train_fn(train_op_dict, sess):
i = 0
cnt = 0
loss_dict = {}
monitoring_train = []
monitoring_eval = []
while True:
try:
[train_result] = sess.run([train_op_dict])
for key in train_result:
if key == "train_op":
continue
else:
if key == "loss":
for task_type in train_result[key]:
loss_dict[task_type][
"loss"] += train_result[key][task_type]
else:
try:
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]
except:
continue
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 = eval_fn(eval_dict, sess)
monitoring_eval.append(eval_finial_dict)
for key in loss_dict:
loss_dict[key] = 0.0
cnt = 0
if is_debug == "0":
if i == num_train_steps:
break
except tf.errors.OutOfRangeError:
print("==Succeeded in training model==")
break
return {"eval": monitoring_eval, "train": monitoring_train}
print("start training")
hooks = []
hooks.extend(train_op_dict["hooks"])
if FLAGS.opt_type == "ps" or FLAGS.opt_type == "ps_sync":
sess_config = tf.ConfigProto(allow_soft_placement=False,
log_device_placement=False)
print("==create monitored training session==", FLAGS.opt_type,
is_chief)
sess = tf.train.MonitoredTrainingSession(
master=target,
is_chief=is_chief,
config=kargs.get("sess_config", sess_config),
hooks=hooks,
checkpoint_dir=checkpoint_dir,
save_checkpoint_steps=num_storage_steps)
elif FLAGS.opt_type == "pai_soar" and pai:
sess_config = tf.ConfigProto(allow_soft_placement=False,
log_device_placement=False)
sess = tf.train.MonitoredTrainingSession(
master=target,
is_chief=is_chief,
config=kargs.get("sess_config", sess_config),
hooks=hooks,
checkpoint_dir=checkpoint_dir,
save_checkpoint_steps=num_storage_steps)
elif FLAGS.opt_type == "hvd" and hvd:
sess_config = tf.ConfigProto(allow_soft_placement=False,
log_device_placement=False)
sess_config.gpu_options.allow_growth = False
sess_config.gpu_options.visible_device_list = str(hvd.local_rank())
sess = tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_dir,
hooks=hooks,
config=sess_config,
save_checkpoint_steps=num_storage_steps)
else:
print("==single sess==")
sess_config = tf.ConfigProto(allow_soft_placement=False,
log_device_placement=False)
sess = tf.train.MonitoredTrainingSession(
config=sess_config,
hooks=hooks,
checkpoint_dir=checkpoint_dir,
save_checkpoint_steps=num_storage_steps)
print("==begin to train and eval==")
monitoring_info = train_fn(train_dict, sess)
if task_index == 0:
start_time = time.time()
print("===========begin to eval============")
eval_finial_dict = eval_fn(eval_dict, sess)
end_time = time.time()
print("==total forward time==", end_time - start_time)
def train_eval_fn(FLAGS, worker_count, task_index, is_chief, target,
init_checkpoint, train_file, dev_file, checkpoint_dir,
is_debug, **kargs):
graph = tf.Graph()
with graph.as_default():
import json
# config = json.load(open(FLAGS.config_file, "r"))
# config = Bunch(config)
# config.use_one_hot_embeddings = True
# config.scope = "bert"
# config.dropout_prob = 0.1
# config.label_type = "single_label"
# config.model = FLAGS.model_type
config = model_config_parser(FLAGS)
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
init_lr = FLAGS.init_lr
distillation_dict = json.load(tf.gfile.Open(FLAGS.distillation_config))
distillation_config = Bunch(
json.load(tf.gfile.Open(FLAGS.multi_task_config)))
warmup_ratio = config.get('warmup', 0.1)
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)
if is_debug == "0":
num_storage_steps = 2
num_eval_steps = 10
num_train_steps = 10
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)
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
})
anneal_config = Bunch({
"initial_value": 1.0,
"num_train_steps": num_train_steps
})
model_io_config = Bunch({"fix_lm": False})
model_io_fn = model_io.ModelIO(model_io_config)
num_classes = FLAGS.num_classes
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
print("==checkpoint_dir==", checkpoint_dir, is_chief)
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"]
model_fn = distillation_model_fn(
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,
output_type="estimator",
distillation_config=distillation_dict,
**kargs)
name_to_features = data_interface(FLAGS)
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)
def train_eval_fn(FLAGS, worker_count, task_index, is_chief, target,
init_checkpoint, train_file, dev_file, checkpoint_dir,
is_debug, **kargs):
graph = tf.Graph()
with graph.as_default():
import json
# config = model_config_parser(FLAGS)
print(FLAGS.train_size)
if FLAGS.if_shard == "0":
train_size = FLAGS.train_size
epoch = int(FLAGS.epoch / worker_count)
elif FLAGS.if_shard == "1":
train_size = int(FLAGS.train_size / worker_count)
epoch = FLAGS.epoch
else:
train_size = int(FLAGS.train_size / worker_count)
epoch = FLAGS.epoch
multi_task_config = Bunch(
json.load(tf.gfile.Open(FLAGS.multi_task_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)
num_eval_steps = int(FLAGS.eval_size / FLAGS.batch_size)
if is_debug == "0":
num_storage_steps = 190
num_eval_steps = 100
num_train_steps = 200
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, "=============")
opt_config = Bunch({
"init_lr": kargs.get("init_lr", 5e-5) / worker_count,
"num_train_steps": num_train_steps,
"num_warmup_steps": num_warmup_steps,
"worker_count": worker_count,
"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"),
"grad_clip": kargs.get("grad_clip", "global_norm"),
"clip_norm": kargs.get("clip_norm", 1.0)
})
anneal_config = Bunch({
"initial_value": 1.0,
"num_train_steps": num_train_steps
})
model_io_config = Bunch({"fix_lm": False})
if FLAGS.opt_type == "hvd" and hvd:
checkpoint_dir = checkpoint_dir if task_index == 0 else None
else:
checkpoint_dir = checkpoint_dir
print("==checkpoint_dir==", checkpoint_dir, is_chief)
model_config_dict = {}
num_labels_dict = {}
init_checkpoint_dict = {}
load_pretrained_dict = {}
exclude_scope_dict = {}
not_storage_params_dict = {}
target_dict = {}
task_type_dict = {}
model_type_lst = []
label_dict = {}
for task_type in FLAGS.multi_task_type.split(","):
print("==task type==", task_type)
model_config_dict[task_type] = model_config_parser(
Bunch(multi_task_config[task_type]))
num_labels_dict[task_type] = multi_task_config[task_type][
"num_labels"]
init_checkpoint_dict[task_type] = os.path.join(
FLAGS.buckets, multi_task_config[task_type]["init_checkpoint"])
load_pretrained_dict[task_type] = multi_task_config[task_type][
"load_pretrained"]
exclude_scope_dict[task_type] = multi_task_config[task_type][
"exclude_scope"]
not_storage_params_dict[task_type] = multi_task_config[task_type][
"not_storage_params"]
target_dict[task_type] = multi_task_config[task_type]["target"]
task_type_dict[task_type] = multi_task_config[task_type][
"task_type"]
label_dict[task_type] = json.load(
tf.gfile.Open(
os.path.join(FLAGS.buckets,
multi_task_config[task_type]["label_id"])))
model_fn = multitask_model_fn(
model_config_dict,
num_labels_dict,
task_type_dict,
init_checkpoint_dict,
load_pretrained_dict=load_pretrained_dict,
opt_config=opt_config,
model_io_config=model_io_config,
exclude_scope_dict=exclude_scope_dict,
not_storage_params_dict=not_storage_params_dict,
target_dict=target_dict,
output_type="estimator",
checkpoint_dir=checkpoint_dir,
num_storage_steps=num_storage_steps,
anneal_config=anneal_config,
task_layer_reuse=None,
model_type_lst=model_type_lst,
**kargs)
print("==succeeded in building model==")
name_to_features = data_interface(FLAGS, multi_task_config,
FLAGS.multi_task_type.split(","))
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)
return example
params = Bunch({})
params.epoch = epoch
params.batch_size = FLAGS.batch_size
if kargs.get("parse_type", "parse_single") == "parse_single":
train_file_lst = [
multi_task_config[task_type]["train_result_file"]
for task_type in FLAGS.multi_task_type.split(",")
]
print(train_file_lst)
train_features = lambda: tf_data_utils.all_reduce_multitask_train_input_fn(
train_file_lst,
_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":
train_file_lst = [
multi_task_config[task_type]["train_result_file"]
for task_type in FLAGS.multi_task_type.split(",")
]
train_file_path_lst = [
os.path.join(FLAGS.buckets, train_file)
for train_file in train_file_lst
]
print(train_file_path_lst)
print("==apply train batch==")
train_features = lambda: tf_data_utils.all_reduce_train_batch_input_fn(
train_file_path_lst,
_decode_batch_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
print("==succeeded in building data and model==")
print("start training")
train_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,
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==")
model_estimator.train(input_fn=train_features,
max_steps=num_train_steps,
hooks=train_hooks)
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))
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_and_evaluate(model_estimator, train_spec,
eval_spec)
train_end_time = time.time()
print("==training time==", train_end_time - train_being_time)
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)
def eval_fn(FLAGS, worker_count, task_index, is_chief, target, init_checkpoint,
train_file, dev_file, checkpoint_dir, is_debug, **kargs):
graph = tf.Graph()
with graph.as_default():
import json
# config = model_config_parser(FLAGS)
if FLAGS.if_shard == "0":
train_size = FLAGS.train_size
epoch = int(FLAGS.epoch / worker_count)
elif FLAGS.if_shard == "1":
train_size = int(FLAGS.train_size / worker_count)
epoch = FLAGS.epoch
else:
train_size = int(FLAGS.train_size / worker_count)
epoch = FLAGS.epoch
multi_task_config = Bunch(
json.load(tf.gfile.Open(FLAGS.multi_task_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)
num_eval_steps = int(FLAGS.eval_size / FLAGS.batch_size)
if is_debug == "0":
num_storage_steps = 190
num_eval_steps = 100
num_train_steps = 200
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, "=============")
opt_config = Bunch({
"init_lr": kargs.get("init_lr", 1e-5) / worker_count,
"num_train_steps": num_train_steps,
"num_warmup_steps": num_warmup_steps,
"worker_count": worker_count,
"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"),
"grad_clip": kargs.get("grad_clip", "global_norm"),
"clip_norm": kargs.get("clip_norm", 1.0)
})
anneal_config = Bunch({
"initial_value": 1.0,
"num_train_steps": num_train_steps
})
model_io_config = Bunch({"fix_lm": False})
if FLAGS.opt_type == "hvd" and hvd:
checkpoint_dir = checkpoint_dir if task_index == 0 else None
else:
checkpoint_dir = checkpoint_dir
print("==checkpoint_dir==", checkpoint_dir, is_chief)
model_config_dict = {}
num_labels_dict = {}
init_checkpoint_dict = {}
load_pretrained_dict = {}
exclude_scope_dict = {}
not_storage_params_dict = {}
target_dict = {}
task_type_dict = {}
model_type_lst = []
label_dict = {}
eval_model_fn = {}
for task_type in FLAGS.multi_task_type.split(","):
eval_task_type_dict = {}
model_config_dict[task_type] = model_config_parser(
Bunch(multi_task_config[task_type]))
num_labels_dict[task_type] = multi_task_config[task_type][
"num_labels"]
init_checkpoint_dict[task_type] = os.path.join(
FLAGS.buckets, multi_task_config[task_type]["init_checkpoint"])
print(
init_checkpoint_dict[task_type], task_type, "===",
os.path.join(FLAGS.buckets,
multi_task_config[task_type]["init_checkpoint"]))
load_pretrained_dict[task_type] = multi_task_config[task_type][
"load_pretrained"]
exclude_scope_dict[task_type] = multi_task_config[task_type][
"exclude_scope"]
not_storage_params_dict[task_type] = multi_task_config[task_type][
"not_storage_params"]
target_dict[task_type] = multi_task_config[task_type]["target"]
eval_task_type_dict[task_type] = multi_task_config[task_type][
"task_type"]
label_dict[task_type] = json.load(
tf.gfile.Open(
os.path.join(FLAGS.buckets,
multi_task_config[task_type]["label_id"])))
eval_model_fn[task_type] = multitask_model_fn(
model_config_dict,
num_labels_dict,
eval_task_type_dict,
init_checkpoint_dict,
load_pretrained_dict=load_pretrained_dict,
opt_config=opt_config,
model_io_config=model_io_config,
exclude_scope_dict=exclude_scope_dict,
not_storage_params_dict=not_storage_params_dict,
target_dict=target_dict,
output_type="sess",
checkpoint_dir=checkpoint_dir,
num_storage_steps=num_storage_steps,
anneal_config=anneal_config,
task_layer_reuse=False,
model_type_lst=model_type_lst,
**kargs)
print(init_checkpoint_dict, "==init_checkpoint==")
print("==succeeded in building model==")
def eval_metric_fn(features, eval_op_dict, task_type):
logits = eval_op_dict["logits"][task_type]
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".format(task_type)], tf.int32))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
return {
"accuracy": accuracy,
"loss": eval_op_dict["loss"][task_type],
"pred_label": pred_label,
"label_ids": features["{}_label_ids".format(task_type)]
}
name_to_features = data_interface(FLAGS, multi_task_config,
FLAGS.multi_task_type.split(","))
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)
return example
params = Bunch({})
params.epoch = 0
params.batch_size = FLAGS.batch_size
if kargs.get("parse_type", "parse_single") == "parse_single":
eval_features_dict = {}
for task_type in FLAGS.multi_task_type.split(","):
name_to_features = data_interface(
FLAGS, {task_type: multi_task_config[task_type]},
[task_type])
eval_features_dict[task_type] = tf_data_utils.eval_input_fn(
multi_task_config[task_type]["dev_result_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":
eval_features_dict = {}
for task_type in FLAGS.multi_task_type.split(","):
name_to_features = data_interface(
FLAGS, {task_type: multi_task_config[task_type]},
[task_type])
dev_file_path = os.path.join(
FLAGS.buckets,
multi_task_config[task_type]["test_result_file"])
eval_features_dict[
task_type] = tf_data_utils.eval_batch_input_fn(
dev_file_path,
_decode_batch_record,
name_to_features,
params,
if_shard=FLAGS.if_shard,
worker_count=worker_count,
task_index=task_index)
eval_dict = {}
for task_type in eval_features_dict:
eval_features = eval_features_dict[task_type]
eval_op_dict = eval_model_fn[task_type](eval_features, [],
tf.estimator.ModeKeys.EVAL)
eval_dict_tmp = eval_metric_fn(eval_features, eval_op_dict["eval"],
task_type)
eval_dict[task_type] = eval_dict_tmp
print(eval_dict)
print("==succeeded in building data and model==")
def task_eval(eval_dict, sess, eval_total_dict):
eval_result = sess.run(eval_dict)
for key in eval_result:
if key not in eval_total_dict:
if key in ["pred_label", "label_ids"]:
eval_total_dict[key] = []
eval_total_dict[key].extend(eval_result[key])
if key in ["accuracy", "loss"]:
eval_total_dict[key] = 0.0
eval_total_dict[key] += eval_result[key]
else:
if key in ["pred_label", "label_ids"]:
eval_total_dict[key].extend(eval_result[key])
if key in ["accuracy", "loss"]:
eval_total_dict[key] += eval_result[key]
def task_metric(eval_dict, label_dict, eval_total_dict):
label_id = eval_dict["label_ids"]
pred_label = eval_dict["pred_label"]
label_dict_id = sorted(list(label_dict["id2label"].keys()))
print(len(label_id), len(pred_label), len(set(label_id)))
accuracy = accuracy_score(label_id, pred_label)
print("==accuracy==", accuracy)
if len(label_dict["id2label"]) < 10:
result = classification_report(label_id,
pred_label,
target_names=[
label_dict["id2label"][key]
for key in label_dict_id
],
digits=4)
print(result, task_index)
eval_total_dict["classification_report"] = result
print("==classification report==")
def eval_fn(eval_dict, sess):
i = 0
total_accuracy = 0
eval_total_dict = {}
for task_type in eval_dict:
eval_total_dict[task_type] = {}
while True:
try:
for task_type in eval_dict:
task_eval(eval_dict[task_type], sess,
eval_total_dict[task_type])
i += 1
except tf.errors.OutOfRangeError:
print("End of dataset")
break
for task_type in eval_total_dict:
task_metric(eval_total_dict[task_type], label_dict[task_type],
eval_total_dict[task_type])
return eval_total_dict
print("start evaluating")
sess_config = tf.ConfigProto(allow_soft_placement=False,
log_device_placement=False)
sess = tf.Session(config=sess_config)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
print("==begin to train and eval==")
start_time = time.time()
eval_finial_dict = eval_fn(eval_dict, sess)
end_time = time.time()
print("==forward time==", end_time - start_time)
return eval_finial_dict
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
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
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
})
model_io_config = Bunch({"fix_lm": False})
model_io_fn = model_io.ModelIO(model_io_config)
num_classes = FLAGS.num_classes
checkpoint_dir = checkpoint_dir
if FLAGS.model_type == 'bert':
model_fn_builder = classifier_model_fn_builder
tf.logging.info("****** bert mlm ******")
elif FLAGS.model_type == 'bert_seq':
model_fn_builder = classifier_seq_model_fn_builder
tf.logging.info("****** bert seq as gpt ******")
elif FLAGS.model_type == 'gated_cnn_seq':
model_fn_builder = gatedcnn_model_fn_builder
tf.logging.info("****** gated cnn seq ******")
else:
model_fn_builder = classifier_model_fn_builder
tf.logging.info("****** bert mlm ******")
model_fn = model_fn_builder(
config,
num_classes,
init_checkpoint,
model_reuse=None,
load_pretrained=FLAGS.load_pretrained,
model_io_config=model_io_config,
opt_config=opt_config,
model_io_fn=model_io_fn,
# exclude_scope=kargs.get('exclude_scope', ""),
not_storage_params=[],
target=kargs.get("input_target", ""),
num_train_steps=num_train_steps,
use_tpu=True,
**kargs)
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.random_generator == "1":
input_fn_builder = tf_data_utils.electra_input_fn_builder
tf.logging.info(
"***** Running random sample input fn builder *****")
elif FLAGS.random_generator == "2":
input_fn_builder = tf_data_utils.bert_seq_input_fn_builder
tf.logging.info("***** Running bert seq input fn builder *****")
elif FLAGS.random_generator == "3":
input_fn_builder = tf_data_utils.bert_mnli_input_fn_builder
tf.logging.info("***** Running bert seq input fn builder *****")
elif FLAGS.random_generator == "4":
input_fn_builder = tf_data_utils.gatedcnn_pretrain_input_fn_builder_v1
tf.logging.info("***** Running gatedcnn input fn builder *****")
else:
input_fn_builder = tf_data_utils.input_fn_builder
tf.logging.info(
"***** Running fixed sample input fn builder *****")
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.batch_size)
input_features = 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 = 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])))
def export_api():
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)))
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] = "yes"
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"]
def serving_input_receiver_fn():
receiver_features = {}
print(receiver_features, "==input receiver_features==")
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(
receiver_features)()
return input_fn
model_fn = 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)
estimator = tf.estimator.Estimator(model_fn=model_fn,
model_dir=checkpoint_dir)
export_dir = estimator.export_savedmodel(export_dir,
serving_input_receiver_fn,
checkpoint_path=init_checkpoint)
print("===Succeeded in exporting saved model==={}".format(export_dir))