fix(pt): replace in-place nlist masking to enable CUDA graph capture (#5433)

## Summary

- Replace `nlist[nlist == -1] = 0` in `DescrptBlockRepformers.forward`
(DPA-2) and `DescrptBlockRepflows.forward` (DPA-3, two occurrences) with
`nlist = torch.where(nlist == -1, 0, nlist)`.
- TorchScript was lowering the indexed-assignment into an `index_put_`
whose value was `torch.tensor(0, device=...)` — a per-call scalar
allocation that triggers a CPU↔GPU sync and is forbidden during CUDA
stream capture (`cudaErrorStreamCaptureUnsupported`). This made
`forward_lower` of DPA-2 / DPA-3 models non-capturable from the LAMMPS
C++ plugin, blocking the ~8–12× CUDA-graph speedup reported in #5432.
- `aten::where` with a Python scalar embeds the literal in the kernel
without allocating a device tensor, so the resulting frozen IR is
graph-capturable. The pattern matches what `se_atten.py` and
`se_t_tebd.py` already use.

## Notes

- `nlist` / `a_nlist` were already rebound to local copies via
`torch.where(exclude_mask != 0, ...)` earlier in both functions, so
dropping the in-place mutation has no observable effect on callers.
- Existing deployed `.pth` files won't benefit until re-exported (their
TorchScript IR is frozen).
- The reporter also flagged that the `comm_dict` ghost-atom path in
`forward_lower` may still contain other CPU↔GPU sync points needed for
full LAMMPS capture; that audit is out of scope here.

Closes #5432.

## Test plan

- [x] `pytest source/tests/pt/model/test_dpa3.py` — passes (exercises
`torch.jit.script` on the DPA-3 descriptor → covers patched
`repflows.py` lines)
- [x] `pytest source/tests/pt/model/test_dpa2.py` — passes (numerical
consistency of DPA-2 descriptor → covers patched `repformers.py` line)
- [x] Inline `torch.jit.script(DescrptDPA2(...))` succeeds (verifies new
`torch.where(... == -1, 0, nlist)` form scripts cleanly)
- [ ] CI: full `test_jit.py` and consistency suites

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **Refactor**
* Optimized internal tensor operations in descriptor modules for
improved performance and consistency.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Co-authored-by: Han Wang <wang_han@iapcm.ac.cn>

Author: wanghan-iapcm

deepmd/pt/model/descriptor/repflows.py

@@ -496,8 +496,8 @@ def forward(
         a_sw = a_sw.masked_fill(~a_nlist_mask, 0.0)
         # set all padding positions to index of 0
         # if the a neighbor is real or not is indicated by nlist_mask
-        nlist[nlist == -1] = 0
-        a_nlist[a_nlist == -1] = 0
+        nlist = torch.where(nlist == -1, 0, nlist)
+        a_nlist = torch.where(a_nlist == -1, 0, a_nlist)
 
         # get node embedding
         # [nframes, nloc, tebd_dim]

deepmd/pt/model/descriptor/repformers.py

@@ -457,7 +457,7 @@ def forward(
 
         # set all padding positions to index of 0
         # if the a neighbor is real or not is indicated by nlist_mask
-        nlist[nlist == -1] = 0
+        nlist = torch.where(nlist == -1, 0, nlist)
         # nb x nall x ng1
         if comm_dict is None:
             assert mapping is not None