Made model Batch ready

Project_Model/Libs/Transformer/Classes/DeToken.py

@@ -0,0 +1,19 @@
+import torch
+
+
+class DeToken(torch.nn.Module):
+
+    def __init__(self, embedding_size: int, vocabulary_size: int) -> None:
+        super().__init__()
+
+        self.__linear = torch.nn.Linear(embedding_size, vocabulary_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        # 1) Go from latent space to vocabularu space
+        x = self.__linear(x)
+
+        # 2) Go to logits
+        x = torch.softmax(x, 2)
+
+        return x

Project_Model/Libs/Transformer/Classes/Decoder.py

@@ -35,22 +35,28 @@ class Decoder(nn.Module):
         )
         self.__layer_norm_3 = nn.LayerNorm(embedding_dimension)
 
-
+    def forward(
-
+        self,
-    def forward(self, x, k_x, v_x, padding_mask = None): #-> list[torch.Tensor]:  # k_x = v_x . While x_q = x
+        args: tuple[
+            torch.Tensor,
+            torch.Tensor,
+            torch.Tensor,
+            torch.Tensor
+        ]
+    ):  # -> list[torch.Tensor]:  # k_x = v_x . While x_q = x
+        # WARNING: args is needed to have sequential
+        x, k_x, v_x, padding_mask = args
 
         # build of attention mask
         attention_mask = get_causal_attention_mask(x.size(1))
 
         # 1) Masked Attention
         MASKED_ATTENTION = self.__masked_attention(
-            x, x, x, key_padding_mask=padding_mask, attn_mask=attention_mask
+            x, x, x, key_padding_mask=padding_mask, attention_mask=attention_mask
         )
 
         # 2) Dropout
-        DROPPED_MASKED_ATTENTION = self.__dropout(
+        DROPPED_MASKED_ATTENTION = self.__dropout(MASKED_ATTENTION)
-            MASKED_ATTENTION
-        )
         del MASKED_ATTENTION
 
         # 3) Residual Connection
@@ -61,7 +67,9 @@ class Decoder(nn.Module):
         x = self.__layer_norm_1(x)
 
         # 5) Encoder–decoder (cross) attention
-        CROSS_ATTENTION = self.__cross_attention(x, k_x, v_x, key_padding_mask=padding_mask)
+        CROSS_ATTENTION = self.__cross_attention(
+            x, k_x, v_x, key_padding_mask=padding_mask
+        )
 
         # 6) Dropout
         DROPPED_CROSS_ATTENTION = self.__dropout(CROSS_ATTENTION)
@@ -88,7 +96,7 @@ class Decoder(nn.Module):
         # 12) Layer Normalization
         x = self.__layer_norm_3(x)
 
-        return x, k_x, v_x, padding_mask
+        return (x, k_x, v_x, padding_mask)
 
 
 # use eval to disable dropout ecc

Project_Model/Libs/Transformer/Classes/Encoder.py

@@ -1,3 +1,4 @@
+import torch
 import torch.nn as nn
 from Project_Model.Libs.Transformer.Classes.FeedForwardNetwork import FeedForwardNetwork
 from Project_Model.Libs.Transformer.Classes.TorchMultiHeadAttention import (
@@ -29,14 +30,17 @@ class Encoder(
             embedding_dimension
         )  # norm of second "Add and Normalize"
         self.__dropout = nn.Dropout(0.1)  # ...
-        pass
 
-    def forward(self, x, padding_mask = None):
+
+    def forward(self, args: tuple[torch.Tensor, torch.Tensor]):
+        # WARNING: args is needed to have sequential
+        x, padding_mask = args
+
         # -> ATTENTION -> dropout -> add and normalize -> FF -> dropout -> add and normalize ->
         # Attention with Residual Connection    [ input + self-attention]
 
         # 1) Multi Head Attention
-        ATTENTION = self.__attention(x, x, x,key_padding_mask= padding_mask)
+        ATTENTION = self.__attention(x, x, x, key_padding_mask=padding_mask)
 
         # 2) Dropout
         DROPPED_ATTENTION = self.__dropout(ATTENTION)
@@ -62,7 +66,7 @@ class Encoder(
         # 8) Layer Normalization
         x = self.__layer_norm_2(x)
 
-        return x,padding_mask
+        return (x, padding_mask)
 
 
 # use eval to disable dropout ecc

Project_Model/Libs/Transformer/Classes/TorchMultiHeadAttention.py

@@ -8,17 +8,16 @@ class TorchMultiHeadAttention(nn.Module):
         self,
         embedding_dimension: int,
         number_of_attention_heads: int,
-        dropout: float = 0.0,
+        dropout: float = 0.0
     ):
         super().__init__()
-        self.attention = nn.MultiheadAttention(
+        self.attention = torch.nn.MultiheadAttention(
             embedding_dimension,
-            number_of_attention_heads,
+            num_heads=number_of_attention_heads,
             dropout=dropout,
             batch_first=True,
         )
 
-
     def forward(
         self,
         x_q: torch.Tensor,