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first version of negative sampling

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This commit is contained in:
Cong Thanh Vu
2023-01-08 14:25:31 +00:00
parent c0d0be076f
commit fcfeae2bd3
10 changed files with 163 additions and 12 deletions
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38
.vscode/launch.json vendored Normal file
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@@ -0,0 +1,38 @@
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python: Current File",
"type": "python",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal",
"justMyCode": true,
"args": [
"--gpus", "1",
"--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",
"--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",
"--lr", "3e-5",
"--max_triplet", "64",
"--add_attn_bias", "True",
"--use_global_node", "True",
"--fast_dev_run", "True",
]
}
]
}

7
data/data_module.py
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@@ -10,7 +10,7 @@ from transformers.tokenization_utils_base import (BatchEncoding,
PreTrainedTokenizerBase)
from .base_data_module import BaseDataModule
from .processor import KGProcessor, get_dataset
from .processor import KGProcessor, get_dataset, getNegativeEntityId
import transformers
transformers.logging.set_verbosity_error()
@@ -105,8 +105,9 @@ class DataCollatorForSeq2Seq:
if isinstance(l, int):
new_labels[i][l] = 1
else:
for j in l:
new_labels[i][j] = 1
if (l[0] != getNegativeEntityId()):
for j in l:
new_labels[i][j] = 1
labels = new_labels
features = self.tokenizer.pad(

87
data/processor.py
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@@ -5,7 +5,7 @@ import contextlib
import sys
from collections import Counter
from multiprocessing import Pool
from multiprocessing import Pool, synchronize
from torch._C import HOIST_CONV_PACKED_PARAMS
from torch.utils.data import Dataset, Sampler, IterableDataset
from collections import defaultdict
@@ -235,6 +235,31 @@ class DataProcessor(object):
import copy
from collections import deque
import threading
class _LiveState(type):
_instances = {}
_lock = threading.Lock()
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
with cls._lock:
if cls not in cls._instances:
cls._instances[cls] = super(_LiveState, cls).__call__(*args, **kwargs)
return cls._instances[cls]
class LiveState(metaclass=_LiveState):
def __init__(self):
self._pool_size = 16
self._deq = deque(maxlen=self._pool_size)
def put(self, item):
self._deq.append(item)
def get(self):
return list(self._deq)
def solve_get_knowledge_store(line, set_type="train", pretrain=1):
"""
@@ -364,6 +389,53 @@ def solve(line, set_type="train", pretrain=1, max_triplet=32):
InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_head_seq), label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[line[1], line[2]], en_id = [rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]], text_d_id = list(masked_head_seq_id), graph_inf = masked_head_graph_list))
examples.append(
InputExample(guid=guid, text_a="[PAD]", text_b="[PAD]", text_c = "[MASK]", text_d = list(masked_tail_seq), label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[line[0], line[1]], en_id = [ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]], text_d_id = list(masked_tail_seq_id), graph_inf = masked_tail_graph_list))
liveState = LiveState()
_prev = liveState.get()
if (set_type == "train" and len(_prev) > 0):
for prev_ent in _prev:
z = head_filter_entities["\t".join([prev_ent,line[1]])]
if (len(z) == 0):
z.append('[NEG]')
z.append(line[2])
z.append(line[0])
masked_neg_seq = set()
masked_neg_seq_id = set()
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)
for item in masked_neg_graph_list:
masked_neg_seq.add(item[0])
masked_neg_seq.add(item[1])
masked_neg_seq.add(item[2])
masked_neg_seq_id.add(ent2id[item[0]])
masked_neg_seq_id.add(rel2id[item[1]])
masked_neg_seq_id.add(ent2id[item[2]])
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_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)
# 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(
# InputExample(guid=guid, text_a="[PAD] ", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[MASK]" +" " + ' '.join(text_a.split(' ')[:16]), text_d = masked_tail_seq, label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]]))
examples.append(
InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_neg_seq), label=lmap(lambda x: ent2id[x], z), real_label=ent2id[line[0]], en=[line[1], prev_ent], en_id = [rel2id[line[1]], ent2id[prev_ent]], rel=rel2id[line[1]], text_d_id = list(masked_neg_seq_id), graph_inf = masked_neg_graph_list))
examples.append(
InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_neg_seq), label=lmap(lambda x: ent2id[x], z), real_label=ent2id[line[2]], en=[line[1], prev_ent], en_id = [rel2id[line[1]], ent2id[prev_ent]], rel=rel2id[line[1]], text_d_id = list(masked_neg_seq_id), graph_inf = masked_neg_graph_list))
liveState.put(line[0])
liveState.put(line[2])
return examples
def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_neighbor_, masked_tail_neighbor_, rel2token_):
@@ -377,6 +449,7 @@ 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
head_filter_entities = head
tail_filter_entities = tail
@@ -388,11 +461,20 @@ 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]']
def delete_init(ent2text_):
global ent2text
ent2text = ent2text_
def getEntityIdByName(name):
global ent2id
return ent2id[name]
def getNegativeEntityId():
global negativeEntity
return negativeEntity
class KGProcessor(DataProcessor):
"""Processor for knowledge graph data set."""
@@ -443,6 +525,7 @@ class KGProcessor(DataProcessor):
"""Gets all entities in the knowledge graph."""
with open(self.entity_path, 'r') as f:
lines = f.readlines()
lines.append('[NEG]\t')
entities = []
for line in lines:
entities.append(line.strip().split("\t")[0])
@@ -469,6 +552,7 @@ class KGProcessor(DataProcessor):
ent2text_with_type = {}
with open(self.entity_path, 'r') as f:
ent_lines = f.readlines()
ent_lines.append('[NEG]\t')
for line in ent_lines:
temp = line.strip().split('\t')
try:
@@ -579,6 +663,7 @@ class KGProcessor(DataProcessor):
else:
annotate_ = partial(
solve,
set_type=set_type,
pretrain=self.args.pretrain,
max_triplet=self.args.max_triplet
)

11
lit_models/transformer.py
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@@ -81,10 +81,13 @@ class TransformerLitModel(BaseLitModel):
pos = batch.pop("pos")
try:
en = batch.pop("en")
self.print("__DEBUG__: en", en)
rel = batch.pop("rel")
self.print("__DEBUG__: rel", rel)
except KeyError:
pass
input_ids = batch['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")
@@ -382,13 +385,17 @@ class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel):
self.id2entity = {}
with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file:
cnt = 0
for line in file.readlines():
lines = file.readlines()
lines.append('[NEG]\t')
for line in lines:
e, d = line.strip().split("\t")
self.id2entity[cnt] = e
cnt += 1
self.id2entity_t = {}
with open("./dataset/FB15k-237/entity2text.txt", 'r') as file:
for line in file.readlines():
lines = file.readlines()
lines.append('[NEG]\t')
for line in lines:
e, d = line.strip().split("\t")
self.id2entity_t[e] = d
for k, v in self.id2entity.items():

6
main.py
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@@ -98,6 +98,7 @@ def main():
tokenizer = data.tokenizer
lit_model = litmodel_class(args=args, model=model, tokenizer=tokenizer, data_config=data.get_config())
print("__DEBUG__: Initialized")
if args.checkpoint:
lit_model.load_state_dict(torch.load(args.checkpoint, map_location="cpu")["state_dict"], strict=False)
@@ -120,9 +121,12 @@ def main():
callbacks = [early_callback, model_checkpoint]
# args.weights_summary = "full" # Print full summary of the model
trainer = pl.Trainer.from_argparse_args(args, callbacks=callbacks, logger=logger, default_root_dir="training/logs", accelerator="ddp")
trainer = pl.Trainer.from_argparse_args(args, callbacks=callbacks, logger=logger, default_root_dir="training/logs")
print('__DEBUG__: Init trainer')
if "EntityEmbedding" not in lit_model.__class__.__name__:
print('__DEBUG__: Fit trainer')
trainer.fit(lit_model, datamodule=data)
path = model_checkpoint.best_model_path
lit_model.load_state_dict(torch.load(path)["state_dict"], strict=False)

7
pretrain/data/data_module.py
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@@ -10,7 +10,7 @@ from transformers.tokenization_utils_base import (BatchEncoding,
PreTrainedTokenizerBase)
from .base_data_module import BaseDataModule
from .processor import KGProcessor, get_dataset
from .processor import KGProcessor, get_dataset, getNegativeEntityId
import transformers
transformers.logging.set_verbosity_error()
@@ -106,8 +106,9 @@ class DataCollatorForSeq2Seq:
if isinstance(l, int):
new_labels[i][l] = 1
else:
for j in l:
new_labels[i][j] = 1
if (l[0] != getNegativeEntityId()):
for j in l:
new_labels[i][j] = 1
labels = new_labels
features = self.tokenizer.pad(

11
pretrain/data/processor.py
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@@ -314,6 +314,7 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_):
global ent2id
global ent2token
global rel2id
global negativeEntity
head_filter_entities = head
tail_filter_entities = tail
@@ -322,11 +323,19 @@ def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_):
ent2id = ent2id_
ent2token = ent2token_
rel2id = rel2id_
negativeEntity = ent2id['[NEG]']
def delete_init(ent2text_):
global ent2text
ent2text = ent2text_
def getEntityIdByName(name):
global ent2id
return ent2id[name]
def getNegativeEntityId():
global negativeEntity
return negativeEntity
class KGProcessor(DataProcessor):
"""Processor for knowledge graph data set."""
@@ -377,6 +386,7 @@ class KGProcessor(DataProcessor):
"""Gets all entities in the knowledge graph."""
with open(self.entity_path, 'r') as f:
lines = f.readlines()
lines.append('[NEG]\t')
entities = []
for line in lines:
entities.append(line.strip().split("\t")[0])
@@ -403,6 +413,7 @@ class KGProcessor(DataProcessor):
ent2text_with_type = {}
with open(self.entity_path, 'r') as f:
ent_lines = f.readlines()
ent_lines.append('[NEG]\t')
for line in ent_lines:
temp = line.strip().split('\t')
try:

8
pretrain/lit_models/transformer.py
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@@ -364,13 +364,17 @@ class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel):
self.id2entity = {}
with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file:
cnt = 0
for line in file.readlines():
lines = file.readlines()
lines.append('[NEG]\t')
for line in lines:
e, d = line.strip().split("\t")
self.id2entity[cnt] = e
cnt += 1
self.id2entity_t = {}
with open("./dataset/FB15k-237/entity2text.txt", 'r') as file:
for line in file.readlines():
lines = file.readlines()
lines.append('[NEG]\t')
for line in lines:
e, d = line.strip().split("\t")
self.id2entity_t[e] = d
for k, v in self.id2entity.items():

0
pretrain/scripts/pretrain_fb15k-237.sh Normal file → Executable file
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0
scripts/fb15k-237/fb15k-237.sh Normal file → Executable file
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