[feat]: oproj tensor parallelism in pure DP and graph-mode scenarios. (#2167)

### What this PR does / why we need it?
This PR introduces Oproj matrix tensor model parallel to achieve
decreasing of memory consumption. It only support graph mode in pure DP
scenario.

In deepseek r1 w8a8 PD disagregated Decode instance, using pure DP, with
oproj_tensor_parallel_size = 8, we have 1 ms TPOT increasing, saved 5.8
GB NPU memory per RANK. We got best performance when
oproj_tensor_parallel_size=4 without TPOT increasing.

performance data:
<img width="1442" height="442" alt="image"
src="https://github.com/user-attachments/assets/83270fc5-868a-4387-b0a9-fac29b4a376d"
/>

### Does this PR introduce _any_ user-facing change?
This PR introduces one new config in `additional_config`.
| Name | Effect | Required | Type | Constraints |
| :---------------------------- |
:--------------------------------------- | :------- | :--- |
:----------------- |
| oproj_tensor_parallel_size | Split the o_proj matrix along the row
dimension (head num * head dim) into oproj_tensor_parallel_size pieces.
| No | int | default value is None, once this value is set, the feature
will be enabled, head num * head dim must be divisible by this value. |

example

`--additional_config={"oproj_tensor_parallel_size": 8}`

### How was this patch tested?


- vLLM version: v0.10.1.1
- vLLM main:
eddaafc1c7

---------

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

vllm_ascend/torchair/models/torchair_deepseek_v2.py

@@ -74,7 +74,7 @@ from vllm_ascend.quantization.quant_config import AscendLinearMethod
 from vllm_ascend.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE
 from vllm_ascend.torchair.quantization.torchair_w8a8_dynamic import \
     TorchairAscendW8A8DynamicLinearMethod
-from vllm_ascend.utils import dispose_tensor, npu_prefetch
+from vllm_ascend.utils import dispose_tensor, npu_prefetch, oproj_tp_enable
 
 
 class TorchairDeepseekV2SiluAndMul(SiluAndMul):
@@ -514,11 +514,18 @@ class TorchairDeepseekV2MLAAttention(DeepseekV2MLAAttention):
             bias=False,
             quant_config=quant_config,
             prefix=f"{prefix}.kv_b_proj")
-        if (config.n_routed_experts is not None
-                and self.debug_layer_idx >= config.first_k_dense_replace
-                and self.debug_layer_idx % config.moe_layer_freq == 0
-                and (ascend_config.torchair_graph_config.enable_multistream_moe
-                     or self.enable_shared_expert_dp)):
+
+        if oproj_tp_enable():
+            self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim,
+                                            self.hidden_size,
+                                            bias=False,
+                                            quant_config=quant_config,
+                                            prefix=f"{prefix}.o_proj")
+        elif (config.n_routed_experts is not None
+              and self.debug_layer_idx >= config.first_k_dense_replace
+              and self.debug_layer_idx % config.moe_layer_freq == 0
+              and (ascend_config.torchair_graph_config.enable_multistream_moe
+                   or self.enable_shared_expert_dp)):
             self.o_proj = TorchairDeepseekV2RowParallelLinearReplaceAllreduce(
                 self.num_heads * self.v_head_dim,
                 self.hidden_size,