first version of negative sampling

.vscode/launch.json

@@ -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",
+            ]
+        }
+    ]
+}

data/data_module.py

@@ -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:
+                    if (l[0] != getNegativeEntityId()):
-                        new_labels[i][j] = 1
+                        for j in l:
+                            new_labels[i][j] = 1
             labels = new_labels
 
         features = self.tokenizer.pad(

data/processor.py

@@ -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
             )

lit_models/transformer.py

@@ -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():

main.py

@@ -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)

pretrain/data/data_module.py

@@ -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:
+                    if (l[0] != getNegativeEntityId()):
-                        new_labels[i][j] = 1
+                        for j in l:
+                            new_labels[i][j] = 1
             labels = new_labels
 
         features = self.tokenizer.pad(

pretrain/data/processor.py

@@ -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:

pretrain/lit_models/transformer.py

@@ -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():