Update of the batcher to resolve a bug in the 4th construction

Project_Model/Libs/Batch/Classes/Batcher.py

@@ -170,13 +170,54 @@ class Batcher:
         X = []
         Y = []
         for rdf in batch["RDFs"]:
+            # here first truncate to max_lenght
+            rdf = rdf[: self.__max_length] # truncator that uses "eot" so no problem
             x, y = self._completation_task_token_truncator(
                 rdf, 0.5, continue_triple_token, eot, minibatch_seed
             )
             X.append(x)
             Y.append(y)
-        return self.__normalization(X, Y)
+        return self.__token_cmpletation_task_special_normalization(X, Y)
 
+    def __token_cmpletation_task_special_normalization(self, X: list[list[int]], Y: list[list[int]]
+    ) -> tuple[list[list[int]], list[list[int]], list[list[int]], list[list[int]]]:
+        
+        def continue_rdf_padding(sequence: list[int], pad_token: int):
+            for i, x in enumerate(sequence):
+                if x == pad_token:
+                    i = i+1 # continueRDF will be excluded by the mask
+                    # fill the tail with True and stop
+                    return [False] * i + [True] * (len(sequence) - i)
+            return [False] * len(sequence)  # no pad token found
+
+        pad_token = self._tokenizer.encode(SpecialToken.PAD.value)[0]
+        end_token = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)[0]
+        continue_rdf = self._tokenizer.encode(SpecialToken.CONTINUE_RDF.value)[0]
+        out_X = []
+        padding_X = []
+        out_Y = []
+        padding_Y = []
+
+        for x in X:
+            out_x, _ = normalize_sequence(
+                x, self.__max_length, pad_token, end_token, True
+            )
+            out_X.append(out_x)
+            # padding_X.append(padding_x)
+            special_padding = continue_rdf_padding(out_x,continue_rdf)
+            padding_X.append(special_padding)
+
+        for y in Y:
+            out_y, padding_y = normalize_sequence(
+                y, self.__max_length, pad_token, end_token, True
+            )
+            out_Y.append(out_y)
+            # special padding
+            # special_padding = continue_rdf_padding(out_y,continue_rdf)
+            # padding_Y.append(special_padding)
+            padding_Y.append(padding_Y)
+
+        return out_X, out_Y, padding_X, padding_Y
 
 if __name__ == "__main__":
 
@@ -194,6 +235,6 @@ if __name__ == "__main__":
 
     prova = "<ABS>Cactus Flower is a 1969 American screwball comedy film directed by Gene Saks, and starring Walter Matthau, Ingrid Bergman and Goldie Hawn, who won an Academy Award for her performance.The screenplay was adapted by I. A. L. Diamond from the 1965 Broadway play of the same title written by Abe Burrows, which, in turn, is based on the French play Fleur de cactus by Pierre Barillet and Jean-Pierre Gredy. Cactus Flower was the ninth highest-grossing film of 1969."
     print(TOKENANO.encode(prova))
-    batcher = Batcher(DATASET_PATH, TOKENANO, MASKER)
+    batcher = Batcher(DATASET_PATH,256, TOKENANO, MASKER)
     for batch in batcher.batch(8):
         print(batch)

Project_Model/Libs/Transformer/Classes/Decoder.py

@@ -20,7 +20,6 @@ class Decoder(nn.Module):
         super().__init__()
 
 
-
         self.__masked_attention = MultiHeadAttention(
             embedding_dimension, number_of_attention_heads, dropout=0.1
         )
@@ -58,7 +57,7 @@ class Decoder(nn.Module):
         # build of attention mask
         # TODO: create a prefix causal mask if needed
         if decoder_only:
-            attention_mask = get_prefix_causal_mask_from_padding_mask(x.size(1),tgt_padding_mask,self.__attention_heads)
+            attention_mask = get_prefix_causal_mask_from_padding_mask(x.size(1),src_padding_mask,self.__attention_heads) # the correct is tgt however ...
         else:
             attention_mask = get_causal_attention_mask(x.size(1))
 

Project_Model/Libs/Transformer/Utils/attention_mask.py

@@ -24,5 +24,7 @@ def get_prefix_causal_mask_from_padding_mask(seq_len:int, prefix_mask, att_heads
     prefix_causal_mask = prefix_causal_mask.repeat_interleave(att_heads, dim=0)     # B*H,T,T
     return prefix_causal_mask
 
+#def get_prefix_causal_mask():
+#    continue_rdf =