PoliBa-DeepLearning/NanoSocrates
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Fixed a bug where I took encoder embeddings rather than encoder output

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This commit is contained in:
Christian Risi 2025-10-09 11:37:21 +02:00
parent ba592c3480
commit 0158db2dce
1 changed files with 2 additions and 2 deletions
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Project_Model/Libs/Transformer/Models/TrainingModel.py
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@ -38,7 +38,7 @@ class TrainingModel(torch.nn.Module):
self.__detokener = DeToken(latent_space, vocabulary_size) self.__detokener = DeToken(latent_space, vocabulary_size)
def forward(self, args: tuple[torch.Tensor, torch.Tensor, torch.Tensor]): def forward(self, args: tuple[torch.Tensor, torch.Tensor, torch.Tensor]):
encoder_embedder_input, padding_tensor, decoder_embedder_input = args encoder_embedder_input, padding_tensor, decoder_embedder_input = args
encoder_tensor = self.__encoder_embedder(encoder_embedder_input) encoder_tensor = self.__encoder_embedder(encoder_embedder_input)
@ -47,7 +47,7 @@ class TrainingModel(torch.nn.Module):
encoder_output, _ = self.__encoder((encoder_tensor, padding_tensor)) encoder_output, _ = self.__encoder((encoder_tensor, padding_tensor))
decoder_output, _, _, _ = self.__decoder( decoder_output, _, _, _ = self.__decoder(
(decoder_tensor, encoder_tensor, encoder_tensor, None) (decoder_tensor, encoder_output, encoder_output, None)
) )
logits: torch.Tensor = self.__detokener(decoder_output) logits: torch.Tensor = self.__detokener(decoder_output)