1. Currently bmt.OpTransformerBlockList can only handle the hidden states returned by transformer block.

1. Recent released flash_atten implemented transformer block returns hidden_states as well as residual in order to fuse Dropout -> Add -> LN. Additionally, the above two will be passed to the next block as input;

   •  class Block(nn.Module):
          def forward(self, hidden_states: Tensor, residual: Optional[Tensor] = None,
                mixer_subset=None, mixer_kwargs=None):
              if self.prenorm:
                   ...
                  return hidden_states, residual
              ...

   • https://github.com/HazyResearch/flash-attention/blob/v1.0.4/flash_attn/modules/block.py#L172
2. Above case seemed not be considered by our bmt.OpTransformerBlockList and cannot be properly handled by us.
   • https://github.com/OpenBMB/BMTrain/blob/0.2.1/bmtrain/block_layer.py#L672

2. Request to support the above case which returns multiple values by a transformer block.