[RWKV7] Remove in-place operations and add gradient checkpointing for v_first

[Triang-jyed-driung]

1. The attention layer needs to return v_first, see https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v5/src/model.py#L885.
2. No in-place operations for v_first, see https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v5/src/model.py#L870.
3. This design of RWKV-7 should be treated with care. https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v5/src/model.py#L1273

Rationale 1: In-place operations harm gradient computation. A possible error looks like:

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [4, 192, 768]], which is output 0 of torch::autograd::CopyBackwards, is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!

Rationale 2: This requires an additional parameter returned for RWKV7Attention. However, given the nature of the value embeddings, one expects RWKV7Attention at the 0th layer, even if the model is hybrid architecture. Instead of forcing to unify the interface and return values which may cause undefined behavior, an additional value to unpack would sure raise the awareness of hybrid architecture builders.