lama/modules/gpt_connector.py [114:129]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if try_cuda:
            self.try_cuda()
        src_tensor_list, dst_tensor_list, masked_indices_list, _ = zip(*[
            self.__get_input_tensors(sentences) for sentences in sentences_list
        ])

        src_tensor_batch = torch.nn.utils.rnn.pad_sequence(
            src_tensor_list, batch_first=True)

        # The model uses shared embedding space for tokens and positions. More
        # precisely, the first len(vocab) indidices are reseved for words, the
        # last n_special symbols are reserved for special symbols and the rest
        # is used for positions. Softmax and embedding matrices are shared and
        # as result some of output "symbols" correspond to positions. To fix
        # that we have to manually remove logits for positions.
        with torch.no_grad():
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



lama/modules/gpt_connector.py [143:153]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if try_cuda:
            self.try_cuda()

        src_tensor_list, dst_tensor_list, masked_indices_list, _ = zip(*[
            self.__get_input_tensors(sentences) for sentences in sentences_list
        ])

        src_tensor_batch = torch.nn.utils.rnn.pad_sequence(
            src_tensor_list, batch_first=True)

        with torch.no_grad():
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -