apply ns

.vscode/launch.json

@@ -0,0 +1,36 @@
+{
+    // 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", "32",
+                "--checkpoint", "/root/kg_374/Relphormer/pretrain/output/FB15k-237/epoch=15-step=38899-Eval/hits10=0.96.ckpt",
+                "--pretrain", "0",
+                "--bce", "0",
+                "--check_val_every_n_epoch", "1",
+                "--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",
+            ]
+        }
+    ]
+}

lit_models/transformer.py

@@ -73,9 +73,54 @@ class TransformerLitModel(BaseLitModel):
     def forward(self, x):
         return self.model(x)
 
+    def create_negatives(self, batch):
+        negativeBatches = []
+        label = batch['label']
+
+        for i in range(label.shape[0]):
+            newBatch = {}
+            newBatch['attention_mask'] = None
+            newBatch['input_ids'] = torch.clone(batch['input_ids'])
+            newBatch['label'] = torch.zeros_like(batch['label'])
+            negativeBatches.append(newBatch)
+
+        entity_idx = []
+        self_label = []
+        for idx, l in enumerate(label):
+            decoded = self.decode([batch['input_ids'][idx]])[0].split(' ')
+            for j in range(1, len(decoded)):
+                if (decoded[j].startswith("[ENTITY_")):
+                    entity_idx.append(j)
+                    self_label.append(batch['input_ids'][idx][j])   
+                    break
+        
+        for idx, lbl in enumerate(label):
+            for i in range(label.shape[0]):
+                if (negativeBatches[idx]['input_ids'][i][entity_idx[i]] != lbl):
+                    negativeBatches[idx]['input_ids'][i][entity_idx[i]] = lbl
+                else:
+                    negativeBatches[idx]['input_ids'][i][entity_idx[i]] = self_label[i]
+                
+        return negativeBatches
+
     def training_step(self, batch, batch_idx):  # pylint: disable=unused-argument
         # embed();exit()
         # print(self.optimizers().param_groups[1]['lr'])
+
+        negativeBatches = self.create_negatives(batch)
+
+        loss = 0
+
+        for negativeBatch in negativeBatches:
+            label = negativeBatch.pop("label")
+            input_ids = batch['input_ids']
+            
+            logits = self.model(**negativeBatch, return_dict=True, distance_attention=None).logits
+            _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
+            bs = input_ids.shape[0]
+            mask_logits = logits[torch.arange(bs), mask_idx][:, self.entity_id_st:self.entity_id_ed]
+            loss += self.loss_fn(mask_logits, label)
+
         labels = batch.pop("labels")
         label = batch.pop("label")
         pos = batch.pop("pos")
@@ -110,9 +155,9 @@ class TransformerLitModel(BaseLitModel):
 
         assert mask_idx.shape[0] == bs, "only one mask in sequence!"
         if self.args.bce:
-            loss = self.loss_fn(mask_logits, labels)
+            loss += self.loss_fn(mask_logits, labels)
         else:
-            loss = self.loss_fn(mask_logits, label)
+            loss += self.loss_fn(mask_logits, label)
 
         if batch_idx == 0:
             print('\n'.join(self.decode(batch['input_ids'][:4])))

main.py

@@ -120,7 +120,7 @@ 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")
     
     if "EntityEmbedding" not in lit_model.__class__.__name__:
         trainer.fit(lit_model, datamodule=data)

pretrain/scripts/pretrain_fb15k-237.sh

@@ -1,13 +1,13 @@
-nohup python -u main.py --gpus "1" --max_epochs=16  --num_workers=32 \
+nohup python -u main.py --gpus "1," --max_epochs=16  --num_workers=32 \
    --model_name_or_path  bert-base-uncased \
    --accumulate_grad_batches 1 \
    --model_class BertKGC \
-   --batch_size 128 \
+   --batch_size 64 \
    --pretrain 1 \
    --bce 0 \
    --check_val_every_n_epoch 1 \
    --overwrite_cache \
-   --data_dir /kg_374/Relphormer/dataset/FB15k-237 \
+   --data_dir /root/kg_374/Relphormer/dataset/FB15k-237 \
    --eval_batch_size 256 \
    --max_seq_length 64 \
    --lr 1e-4 \

scripts/fb15k-237/fb15k-237.sh

@@ -1,13 +1,12 @@
-nohup python -u main.py --gpus "1" --max_epochs=16  --num_workers=32 \
+nohup python -u main.py --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 \
+   --batch_size 16 \
-   --checkpoint /kg_374/Relphormer/pretrain/output/FB15k-237/epoch=15-step=19299-Eval/hits10=0.96.ckpt \
+   --checkpoint /root/kg_374/Relphormer/pretrain/output/FB15k-237/epoch\=15-step\=38899-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 \