runnable v1
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@ -7,3 +7,4 @@ dataset/FB15k-237/masked_*.txt
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dataset/FB15k-237/cached_*.pkl
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**/__pycache__/
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**/.DS_Store
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nohup.out
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@ -79,6 +79,7 @@ class DataCollatorForSeq2Seq:
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label_pad_token_id: int = -100
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return_tensors: str = "pt"
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num_labels: int = 0
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args: Any = None
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def __call__(self, features, return_tensors=None):
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@ -105,7 +106,7 @@ class DataCollatorForSeq2Seq:
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if isinstance(l, int):
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new_labels[i][l] = 1
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else:
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if (l[0] != getNegativeEntityId()):
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if (l[0] != getNegativeEntityId(self.args)):
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for j in l:
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new_labels[i][j] = 1
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labels = new_labels
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@ -142,6 +143,7 @@ class KGC(BaseDataModule):
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padding="longest",
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max_length=self.args.max_seq_length,
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num_labels = len(entity_list),
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args=args
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)
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relations_tokens = self.processor.get_relations(args.data_dir)
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self.num_relations = len(relations_tokens)
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@ -110,7 +110,7 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
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model_name = my_args.model_name_or_path.split("/")[-1]
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is_pretrain = my_args.pretrain
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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")
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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")
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refresh = my_args.overwrite_cache
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if cache_filepath is not None and refresh is False:
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@ -137,6 +137,116 @@ def cache_results(_cache_fp, _refresh=False, _verbose=1):
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return wrapper_
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def cache_results_load_once(_cache_fp, _refresh=False, _verbose=1, _global_var=None):
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r"""
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=== USE IN TRAINING MODE ONLY ===
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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::
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import time
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import numpy as np
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from fastNLP import cache_results
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@cache_results('cache.pkl')
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def process_data():
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# 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
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time.sleep(1)
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return np.random.randint(10, size=(5,))
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start_time = time.time()
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print("res =",process_data())
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print(time.time() - start_time)
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start_time = time.time()
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print("res =",process_data())
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print(time.time() - start_time)
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# 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
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# Save cache to cache.pkl.
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# res = [5 4 9 1 8]
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# 1.0042750835418701
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# Read cache from cache.pkl.
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# res = [5 4 9 1 8]
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# 0.0040721893310546875
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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::
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# 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
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process_data(_cache_fp='cache2.pkl') # 完全不影响之前的‘cache.pkl'
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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
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'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]。
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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::
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process_data(_cache_fp='cache2.pkl', _refresh=True) # 这里强制重新生成一份对预处理的cache。
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# _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
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: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
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函数调用的时候传入_cache_fp这个参数。
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:param bool _refresh: 是否重新生成cache。
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:param int _verbose: 是否打印cache的信息。
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:return:
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"""
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def wrapper_(func):
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signature = inspect.signature(func)
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for key, _ in signature.parameters.items():
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if key in ('_cache_fp', '_refresh', '_verbose', '_global_var'):
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raise RuntimeError("The function decorated by cache_results cannot have keyword `{}`.".format(key))
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v = globals().get(_global_var, None)
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if (v is not None):
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return v
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def wrapper(*args, **kwargs):
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my_args = args[0]
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mode = "train"
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if '_cache_fp' in kwargs:
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cache_filepath = kwargs.pop('_cache_fp')
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assert isinstance(cache_filepath, str), "_cache_fp can only be str."
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else:
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cache_filepath = _cache_fp
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if '_refresh' in kwargs:
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refresh = kwargs.pop('_refresh')
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assert isinstance(refresh, bool), "_refresh can only be bool."
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else:
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refresh = _refresh
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if '_verbose' in kwargs:
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verbose = kwargs.pop('_verbose')
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assert isinstance(verbose, int), "_verbose can only be integer."
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else:
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verbose = _verbose
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refresh_flag = True
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model_name = my_args.model_name_or_path.split("/")[-1]
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is_pretrain = my_args.pretrain
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cache_filepath = os.path.join(my_args.data_dir, f"cached_{func.__name__}_{mode}_features{model_name}_pretrain{is_pretrain}.pkl")
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refresh = my_args.overwrite_cache
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if cache_filepath is not None and refresh is False:
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# load data
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if os.path.exists(cache_filepath):
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with open(cache_filepath, 'rb') as f:
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results = pickle.load(f)
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globals()[_global_var] = results
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if verbose == 1:
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logger.info("Read cache from {}.".format(cache_filepath))
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refresh_flag = False
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if refresh_flag:
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results = func(*args, **kwargs)
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if cache_filepath is not None:
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if results is None:
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raise RuntimeError("The return value is None. Delete the decorator.")
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with open(cache_filepath, 'wb') as f:
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pickle.dump(results, f)
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logger.info("Save cache to {}.".format(cache_filepath))
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return results
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return wrapper
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return wrapper_
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import argparse
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import csv
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@ -254,7 +364,7 @@ class _LiveState(type):
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class LiveState(metaclass=_LiveState):
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def __init__(self):
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self._pool_size = 16
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self._pool_size = 4
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self._deq = deque(maxlen=self._pool_size)
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def put(self, item):
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self._deq.append(item)
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@ -397,6 +507,9 @@ def solve(line, set_type="train", pretrain=1, max_triplet=32):
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for prev_ent in _prev:
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if (prev_ent == line[0] or prev_ent == line[2]):
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continue
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z = head_filter_entities["\t".join([prev_ent,line[1]])]
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if (len(z) == 0):
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z.append('[NEG]')
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@ -407,6 +520,8 @@ def solve(line, set_type="train", pretrain=1, max_triplet=32):
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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 \
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random.sample(masked_tail_neighbor["\t".join([prev_ent, line[1]])], max_triplet)
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if (len(masked_head_graph_list) == 0):
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masked_head_graph_list.append(['[NEG]', line[1], '[NEG]'])
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for item in masked_neg_graph_list:
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masked_neg_seq.add(item[0])
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@ -419,11 +534,11 @@ def solve(line, set_type="train", pretrain=1, max_triplet=32):
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masked_neg_seq = masked_neg_seq.difference({line[0]})
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masked_neg_seq = masked_neg_seq.difference({line[2]})
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masked_neg_seq = masked_neg_seq.difference({line[1]})
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masked_neg_seq = masked_neg_seq.difference(prev_ent)
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masked_neg_seq = masked_neg_seq.difference({prev_ent})
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masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[line[0]]})
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masked_neg_seq_id = masked_neg_seq_id.difference({rel2id[line[1]]})
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masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[line[2]]})
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masked_neg_seq_id = masked_neg_seq_id.difference(prev_ent)
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masked_neg_seq_id = masked_neg_seq_id.difference({ent2id[prev_ent]})
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# examples.append(
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# 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]]))
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# examples.append(
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@ -449,7 +564,7 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_nei
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global masked_head_neighbor
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global masked_tail_neighbor
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global rel2token
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global negativeEntity
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# global negativeEntity
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head_filter_entities = head
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tail_filter_entities = tail
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@ -461,7 +576,8 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_nei
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masked_head_neighbor = masked_head_neighbor_
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masked_tail_neighbor = masked_tail_neighbor_
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rel2token = rel2token_
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negativeEntity = ent2id['[NEG]']
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# negativeEntity = ent2id['[NEG]']
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print("Initialized negative entity ID")
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def delete_init(ent2text_):
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global ent2text
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@ -471,8 +587,10 @@ def getEntityIdByName(name):
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global ent2id
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return ent2id[name]
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def getNegativeEntityId():
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@cache_results_load_once(_cache_fp="./dataset", _global_var='negativeEntity')
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def getNegativeEntityId(args):
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global negativeEntity
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negativeEntity = ent2id['[NEG]']
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return negativeEntity
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@ -654,6 +772,8 @@ class KGProcessor(DataProcessor):
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threads = min(1, cpu_count())
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filter_init(head_filter_entities, tail_filter_entities,ent2text, rel2text, ent2id, ent2token, rel2id, masked_head_neighbor, masked_tail_neighbor, rel2token
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)
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# cache this
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getNegativeEntityId(args)
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if hasattr(args, "faiss_init") and args.faiss_init:
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annotate_ = partial(
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@ -81,13 +81,13 @@ class TransformerLitModel(BaseLitModel):
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pos = batch.pop("pos")
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try:
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en = batch.pop("en")
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self.print("__DEBUG__: en", en)
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# self.print("__DEBUG__: en", en)
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rel = batch.pop("rel")
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self.print("__DEBUG__: rel", rel)
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# self.print("__DEBUG__: rel", rel)
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except KeyError:
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pass
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input_ids = batch['input_ids']
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self.print("__DEBUG__: input_ids", input_ids)
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# self.print("__DEBUG__: input_ids", input_ids)
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distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance) - 1, distance) for i, distance in enumerate(batch['distance_attention'])])
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distance = batch.pop("distance_attention")
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@ -1,13 +1,12 @@
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nohup python -u main.py --gpus "1" --max_epochs=16 --num_workers=32 \
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nohup python -u main.py --gpus "2," --max_epochs=16 --num_workers=32 \
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--model_name_or_path bert-base-uncased \
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--accumulate_grad_batches 1 \
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--model_class BertKGC \
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--batch_size 64 \
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--checkpoint /kg_374/Relphormer/pretrain/output/FB15k-237/epoch=15-step=19299-Eval/hits10=0.96.ckpt \
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--checkpoint /kg_374/Relphormer/output/FB15k-237/epoch=1-Eval/hits10=Eval/hits1=0.47-Eval/hits1=0.22.ckpt \
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--pretrain 0 \
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--bce 0 \
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--check_val_every_n_epoch 1 \
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--overwrite_cache \
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--data_dir dataset/FB15k-237 \
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--eval_batch_size 128 \
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--max_seq_length 128 \
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