[Feature] Support DSA-CP for Hybrid scenario (#5702)

Signed-off-by: zzhx1 <zzh_201018@outlook.com>

### What this PR does / why we need it?
> Extracted from PR #5513
Based on the Sharded-CP feature PR:#4702;
RFC:https://github.com/vllm-project/vllm/issues/30055

### Support FULL_DECODE_ONLY Mode under PD-Mixed Scenario:
Extends DSA-CP to handle the FULL_DECODE_ONLY execution mode when
running in a prefill-decode mixed (PD-mixed) serving environment,
improving throughput and resource utilization for decode-intensive
workloads.
**In pure prefill nodes:**
- Both q_proj and o_proj are sharded across world ranks, using
**broadcast** for weights distribution.

**In PD-mixed nodes (supporting both prefill and decode):**

- q_proj is fully replicated (not sharded) to avoid communication
overhead during decoding.
- o_proj Using the original TP `RowParallelLinear` method to store
weights

**During prefill execution:**
- o_proj forwards through all_gather to collect weights, reconstructing
the complete o_proj weights on each card.

**During decode (graph replay phase):**
- Additional all_to_all (before o_proj) and reduce_scatter (after
o_proj) are introduced to enable sequence-parallel output aggregation
while maintaining correctness under SFA CP.

### benchmark:
- TTFT increased by **527%**
- TPOT increased by **180%**

<img width="1550" height="938" alt="image"
src="https://github.com/user-attachments/assets/9b7a03d8-a3db-4a99-8923-6e5bfcfecf72"
/>


### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Signed-off-by: zzhxx <zhangzihang23@mails.ucas.ac.cn>
Co-authored-by: clrs97 <524936896@qq.com>

vllm_ascend/distributed/parallel_state.py

@@ -7,7 +7,7 @@ from vllm.distributed.parallel_state import (GroupCoordinator, get_tp_group,
                                              init_model_parallel_group)
 
 from vllm_ascend.ascend_config import get_ascend_config
-from vllm_ascend.utils import enable_dsa_cp, flashcomm2_enable
+from vllm_ascend.utils import enable_dsa_cp_with_layer_shard, flashcomm2_enable
 
 # Currently, mc2 op need their own group coordinator.
 _MC2: Optional[GroupCoordinator] = None
@@ -238,7 +238,7 @@ def init_ascend_model_parallel(parallel_config: ParallelConfig, ):
                 FC2_group_ranks = torch.tensor(
                     flashcomm2_otp_group_ranks).squeeze(0)
             _SHARD_WEIGHT = create_shard_weight_group(FC2_group_ranks)
-        elif enable_dsa_cp():
+        elif enable_dsa_cp_with_layer_shard():
             # For dsa_cp, all shard layers are replicated.
             _SHARD_WEIGHT = create_shard_weight_group(None)
         else: