runnable v1
This commit is contained in:
parent
fcfeae2bd3
commit
45cd8e1396
1
.gitignore
vendored
1
.gitignore
vendored
@ -7,3 +7,4 @@ dataset/FB15k-237/masked_*.txt
|
||||
dataset/FB15k-237/cached_*.pkl
|
||||
**/__pycache__/
|
||||
**/.DS_Store
|
||||
nohup.out
|
||||
|
@ -79,6 +79,7 @@ class DataCollatorForSeq2Seq:
|
||||
label_pad_token_id: int = -100
|
||||
return_tensors: str = "pt"
|
||||
num_labels: int = 0
|
||||
args: Any = None
|
||||
|
||||
def __call__(self, features, return_tensors=None):
|
||||
|
||||
@ -105,7 +106,7 @@ class DataCollatorForSeq2Seq:
|
||||
if isinstance(l, int):
|
||||
new_labels[i][l] = 1
|
||||
else:
|
||||
if (l[0] != getNegativeEntityId()):
|
||||
if (l[0] != getNegativeEntityId(self.args)):
|
||||
for j in l:
|
||||
new_labels[i][j] = 1
|
||||
labels = new_labels
|
||||
@ -142,6 +143,7 @@ class KGC(BaseDataModule):
|
||||
padding="longest",
|
||||
max_length=self.args.max_seq_length,
|
||||
num_labels = len(entity_list),
|
||||
args=args
|
||||
)
|
||||
relations_tokens = self.processor.get_relations(args.data_dir)
|
||||
self.num_relations = len(relations_tokens)
|
||||
|
@ -110,7 +110,7 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
|
||||
|
||||
model_name = my_args.model_name_or_path.split("/")[-1]
|
||||
is_pretrain = my_args.pretrain
|
||||
cache_filepath = os.path.join(my_args.data_dir, f"cached_{mode}_features{model_name}_pretrain{is_pretrain}_faiss{my_args.faiss_init}_seqlength{my_args.max_seq_length}_{my_args.litmodel_class}.pkl")
|
||||
cache_filepath = os.path.join(my_args.data_dir, f"cached_{func.__name__}_{mode}_features{model_name}_pretrain{is_pretrain}_faiss{my_args.faiss_init}_seqlength{my_args.max_seq_length}_{my_args.litmodel_class}.pkl")
|
||||
refresh = my_args.overwrite_cache
|
||||
|
||||
if cache_filepath is not None and refresh is False:
|
||||
@ -137,6 +137,116 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
|
||||
|
||||
return wrapper_
|
||||
|
||||
def cache_results_load_once(_cache_fp, _refresh=False, _verbose=1, _global_var=None):
|
||||
r"""
|
||||
=== USE IN TRAINING MODE ONLY ===
|
||||
cache_results是fastNLP中用于cache数据的装饰器。通过下面的例子看一下如何使用::
|
||||
|
||||
import time
|
||||
import numpy as np
|
||||
from fastNLP import cache_results
|
||||
|
||||
@cache_results('cache.pkl')
|
||||
def process_data():
|
||||
# 一些比较耗时的工作,比如读取数据,预处理数据等,这里用time.sleep()代替耗时
|
||||
time.sleep(1)
|
||||
return np.random.randint(10, size=(5,))
|
||||
|
||||
start_time = time.time()
|
||||
print("res =",process_data())
|
||||
print(time.time() - start_time)
|
||||
|
||||
start_time = time.time()
|
||||
print("res =",process_data())
|
||||
print(time.time() - start_time)
|
||||
|
||||
# 输出内容如下,可以看到两次结果相同,且第二次几乎没有花费时间
|
||||
# Save cache to cache.pkl.
|
||||
# res = [5 4 9 1 8]
|
||||
# 1.0042750835418701
|
||||
# Read cache from cache.pkl.
|
||||
# res = [5 4 9 1 8]
|
||||
# 0.0040721893310546875
|
||||
|
||||
可以看到第二次运行的时候,只用了0.0001s左右,是由于第二次运行将直接从cache.pkl这个文件读取数据,而不会经过再次预处理::
|
||||
|
||||
# 还是以上面的例子为例,如果需要重新生成另一个cache,比如另一个数据集的内容,通过如下的方式调用即可
|
||||
process_data(_cache_fp='cache2.pkl') # 完全不影响之前的‘cache.pkl'
|
||||
|
||||
上面的_cache_fp是cache_results会识别的参数,它将从'cache2.pkl'这里缓存/读取数据,即这里的'cache2.pkl'覆盖默认的
|
||||
'cache.pkl'。如果在你的函数前面加上了@cache_results()则你的函数会增加三个参数[_cache_fp, _refresh, _verbose]。
|
||||
上面的例子即为使用_cache_fp的情况,这三个参数不会传入到你的函数中,当然你写的函数参数名也不可能包含这三个名称::
|
||||
|
||||
process_data(_cache_fp='cache2.pkl', _refresh=True) # 这里强制重新生成一份对预处理的cache。
|
||||
# _verbose是用于控制输出信息的,如果为0,则不输出任何内容;如果为1,则会提醒当前步骤是读取的cache还是生成了新的cache
|
||||
|
||||
:param str _cache_fp: 将返回结果缓存到什么位置;或从什么位置读取缓存。如果为None,cache_results没有任何效用,除非在
|
||||
函数调用的时候传入_cache_fp这个参数。
|
||||
:param bool _refresh: 是否重新生成cache。
|
||||
:param int _verbose: 是否打印cache的信息。
|
||||
:return:
|
||||
"""
|
||||
|
||||
def wrapper_(func):
|
||||
signature = inspect.signature(func)
|
||||
for key, _ in signature.parameters.items():
|
||||
if key in ('_cache_fp', '_refresh', '_verbose', '_global_var'):
|
||||
raise RuntimeError("The function decorated by cache_results cannot have keyword `{}`.".format(key))
|
||||
v = globals().get(_global_var, None)
|
||||
if (v is not None):
|
||||
return v
|
||||
|
||||
def wrapper(*args, **kwargs):
|
||||
|
||||
my_args = args[0]
|
||||
mode = "train"
|
||||
if '_cache_fp' in kwargs:
|
||||
cache_filepath = kwargs.pop('_cache_fp')
|
||||
assert isinstance(cache_filepath, str), "_cache_fp can only be str."
|
||||
else:
|
||||
cache_filepath = _cache_fp
|
||||
if '_refresh' in kwargs:
|
||||
refresh = kwargs.pop('_refresh')
|
||||
assert isinstance(refresh, bool), "_refresh can only be bool."
|
||||
else:
|
||||
refresh = _refresh
|
||||
if '_verbose' in kwargs:
|
||||
verbose = kwargs.pop('_verbose')
|
||||
assert isinstance(verbose, int), "_verbose can only be integer."
|
||||
else:
|
||||
verbose = _verbose
|
||||
refresh_flag = True
|
||||
|
||||
model_name = my_args.model_name_or_path.split("/")[-1]
|
||||
is_pretrain = my_args.pretrain
|
||||
cache_filepath = os.path.join(my_args.data_dir, f"cached_{func.__name__}_{mode}_features{model_name}_pretrain{is_pretrain}.pkl")
|
||||
refresh = my_args.overwrite_cache
|
||||
|
||||
if cache_filepath is not None and refresh is False:
|
||||
# load data
|
||||
if os.path.exists(cache_filepath):
|
||||
with open(cache_filepath, 'rb') as f:
|
||||
results = pickle.load(f)
|
||||
globals()[_global_var] = results
|
||||
if verbose == 1:
|
||||
logger.info("Read cache from {}.".format(cache_filepath))
|
||||
refresh_flag = False
|
||||
|
||||
if refresh_flag:
|
||||
results = func(*args, **kwargs)
|
||||
if cache_filepath is not None:
|
||||
if results is None:
|
||||
raise RuntimeError("The return value is None. Delete the decorator.")
|
||||
with open(cache_filepath, 'wb') as f:
|
||||
pickle.dump(results, f)
|
||||
logger.info("Save cache to {}.".format(cache_filepath))
|
||||
|
||||
return results
|
||||
|
||||
return wrapper
|
||||
|
||||
return wrapper_
|
||||
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
@ -254,7 +364,7 @@ class _LiveState(type):
|
||||
class LiveState(metaclass=_LiveState):
|
||||
|
||||
def __init__(self):
|
||||
self._pool_size = 16
|
||||
self._pool_size = 4
|
||||
self._deq = deque(maxlen=self._pool_size)
|
||||
def put(self, item):
|
||||
self._deq.append(item)
|
||||
@ -397,6 +507,9 @@ def solve(line, set_type="train", pretrain=1, max_triplet=32):
|
||||
|
||||
for prev_ent in _prev:
|
||||
|
||||
if (prev_ent == line[0] or prev_ent == line[2]):
|
||||
continue
|
||||
|
||||
z = head_filter_entities["\t".join([prev_ent,line[1]])]
|
||||
if (len(z) == 0):
|
||||
z.append('[NEG]')
|
||||
@ -407,6 +520,8 @@ def solve(line, set_type="train", pretrain=1, max_triplet=32):
|
||||
|
||||
masked_neg_graph_list = masked_tail_neighbor.get("\t".join([prev_ent, line[1]]), []) if len(masked_tail_neighbor.get("\t".join([prev_ent, line[1]]), [])) < max_triplet else \
|
||||
random.sample(masked_tail_neighbor["\t".join([prev_ent, line[1]])], max_triplet)
|
||||
if (len(masked_head_graph_list) == 0):
|
||||
masked_head_graph_list.append(['[NEG]', line[1], '[NEG]'])
|
||||
|
||||
for item in masked_neg_graph_list:
|
||||
masked_neg_seq.add(item[0])
|
||||
@ -419,11 +534,11 @@ def solve(line, set_type="train", pretrain=1, max_triplet=32):
|
||||
masked_neg_seq = masked_neg_seq.difference({line[0]})
|
||||
masked_neg_seq = masked_neg_seq.difference({line[2]})
|
||||
masked_neg_seq = masked_neg_seq.difference({line[1]})
|
||||
masked_neg_seq = masked_neg_seq.difference(prev_ent)
|
||||
masked_neg_seq = masked_neg_seq.difference({prev_ent})
|
||||
masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[line[0]]})
|
||||
masked_neg_seq_id = masked_neg_seq_id.difference({rel2id[line[1]]})
|
||||
masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[line[2]]})
|
||||
masked_neg_seq_id = masked_neg_seq_id.difference(prev_ent)
|
||||
masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[prev_ent]})
|
||||
# examples.append(
|
||||
# InputExample(guid=guid, text_a="[MASK]", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[PAD]" + " " + ' '.join(text_c.split(' ')[:16]), text_d = masked_head_seq, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]]))
|
||||
# examples.append(
|
||||
@ -449,7 +564,7 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_nei
|
||||
global masked_head_neighbor
|
||||
global masked_tail_neighbor
|
||||
global rel2token
|
||||
global negativeEntity
|
||||
# global negativeEntity
|
||||
|
||||
head_filter_entities = head
|
||||
tail_filter_entities = tail
|
||||
@ -461,7 +576,8 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_nei
|
||||
masked_head_neighbor = masked_head_neighbor_
|
||||
masked_tail_neighbor = masked_tail_neighbor_
|
||||
rel2token = rel2token_
|
||||
negativeEntity = ent2id['[NEG]']
|
||||
# negativeEntity = ent2id['[NEG]']
|
||||
print("Initialized negative entity ID")
|
||||
|
||||
def delete_init(ent2text_):
|
||||
global ent2text
|
||||
@ -471,8 +587,10 @@ def getEntityIdByName(name):
|
||||
global ent2id
|
||||
return ent2id[name]
|
||||
|
||||
def getNegativeEntityId():
|
||||
@cache_results_load_once(_cache_fp="./dataset", _global_var='negativeEntity')
|
||||
def getNegativeEntityId(args):
|
||||
global negativeEntity
|
||||
negativeEntity = ent2id['[NEG]']
|
||||
return negativeEntity
|
||||
|
||||
|
||||
@ -654,6 +772,8 @@ class KGProcessor(DataProcessor):
|
||||
threads = min(1, cpu_count())
|
||||
filter_init(head_filter_entities, tail_filter_entities,ent2text, rel2text, ent2id, ent2token, rel2id, masked_head_neighbor, masked_tail_neighbor, rel2token
|
||||
)
|
||||
# cache this
|
||||
getNegativeEntityId(args)
|
||||
|
||||
if hasattr(args, "faiss_init") and args.faiss_init:
|
||||
annotate_ = partial(
|
||||
|
@ -81,13 +81,13 @@ class TransformerLitModel(BaseLitModel):
|
||||
pos = batch.pop("pos")
|
||||
try:
|
||||
en = batch.pop("en")
|
||||
self.print("__DEBUG__: en", en)
|
||||
# self.print("__DEBUG__: en", en)
|
||||
rel = batch.pop("rel")
|
||||
self.print("__DEBUG__: rel", rel)
|
||||
# self.print("__DEBUG__: rel", rel)
|
||||
except KeyError:
|
||||
pass
|
||||
input_ids = batch['input_ids']
|
||||
self.print("__DEBUG__: input_ids", input_ids)
|
||||
# self.print("__DEBUG__: input_ids", input_ids)
|
||||
|
||||
distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance) - 1, distance) for i, distance in enumerate(batch['distance_attention'])])
|
||||
distance = batch.pop("distance_attention")
|
||||
|
@ -1,13 +1,12 @@
|
||||
nohup python -u main.py --gpus "1" --max_epochs=16 --num_workers=32 \
|
||||
nohup python -u main.py --gpus "2," --max_epochs=16 --num_workers=32 \
|
||||
--model_name_or_path bert-base-uncased \
|
||||
--accumulate_grad_batches 1 \
|
||||
--model_class BertKGC \
|
||||
--batch_size 64 \
|
||||
--checkpoint /kg_374/Relphormer/pretrain/output/FB15k-237/epoch=15-step=19299-Eval/hits10=0.96.ckpt \
|
||||
--checkpoint /kg_374/Relphormer/output/FB15k-237/epoch=1-Eval/hits10=Eval/hits1=0.47-Eval/hits1=0.22.ckpt \
|
||||
--pretrain 0 \
|
||||
--bce 0 \
|
||||
--check_val_every_n_epoch 1 \
|
||||
--overwrite_cache \
|
||||
--data_dir dataset/FB15k-237 \
|
||||
--eval_batch_size 128 \
|
||||
--max_seq_length 128 \
|
||||
|
Loading…
Reference in New Issue
Block a user