Need Forwarding with state.

[lxianl455]

Translation: When training, it runs without state: def forward(self, idx: torch.Tensor) -> torch.Tensor: x = self.token_embedding(idx) x = self.emb_dropout(x) x = self.xlstm_block_stack(x) logits = self.lm_head(x) return logits

Can you give a “forward with state” version? def forward(self, idx: torch.Tensor, state) -> torch.Tensor: x = self.token_embedding(idx) x = self.emb_dropout(x) x = self.xlstm_block_stack(x, state) logits = self. lm_head(x) return logits

[sieusaoml]

Translation: When training, it runs without state: def forward(self, idx: torch.Tensor) -> torch.Tensor: x = self.token_embedding(idx) x = self.emb_dropout(x) x = self.xlstm_block_stack(x) logits = self.lm_head(x) return logits

Can you give a “forward with state” version? def forward(self, idx: torch.Tensor, state) -> torch.Tensor: x = self.token_embedding(idx) x = self.emb_dropout(x) x = self.xlstm_block_stack(x, state) logits = self. lm_head(x) return logits

https://github.com/sieusaoml/xLSTM-custom-block a custom block xlstm of my

[lxianl455]

Yes, I want to do something similar. But in the code, is it only sLSTM that can be initialized with the previous hidden state? Can't mLSTM be initialized with the previous state?

[hiimbach]

The step() method and the forward() method of mLSTMLayer use different type of conv1d forward, so I think if you want to use hidden state, you need to use step() token by token instead of forward all of tokens at the same time.

[lxianl455]

Yes, I am not asking to forward all of the tokens at the same time. In fact, my original model was an LSTM, which processes each token in a loop. I just want to replace this LSTM with xLSTM. But it seems that 'step' is used during inference, right? May I ask if it can backpropagate normally during training? Will the inplace operations lead to backpropagation errors?

[sieusaoml]

mLSTMLayer can be used with the previous hidden state, but backpropagate gradient in my test with context_lenght=1 has an error