[OPS]add split_qkv_tp_rmsnorm_rope ops (#7376)

### What this PR does / why we need it?
This PR introduces a new fused Triton kernel,
`split_qkv_tp_rmsnorm_rope` for Minimax-m2.5.

The implementation includes two Triton kernels:
1. `_split_qkv_and_compute_local_qk_var_kernel`: Splits the QKV input
and computes the local variance for RMSNorm.
2. `_apply_global_rmsnorm_kernel`: Applies global RMSNorm (considering
TP all-reduce for variance) and Neox-style RoPE.

### Does this PR introduce _any_ user-facing change?
Does not.

### How was this patch tested?
```python
pytest tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_split_qkv_tp_rmsnorm_rope.py
```
### Test Data
A3 TP16
基线  

| data       | TTFT(ms) | TPOT(ms) | TPS    |
|------------|---------:|---------:|-------:|
| 4k/1k@bs1  | 267.55   | 25.5     | 38.85  |
| 4k/1k@bs4  | 542.4    | 26.51    | 148.06 |

测试线

| data       | TTFT(ms) | TPOT(ms) | TPS    |
|------------|---------:|---------:|-------:|
| 4k/1k@bs1  | 234.64   | 20.96    | 47.24  |
| 4k/1k@bs4  | 508.36   | 22.16    | 176.69 |


- vLLM version: v0.17.0
- vLLM main:
4034c3d32e

Signed-off-by: xutianyi <xutianyi5@huawei.com>
Co-authored-by: xutianyi <xutianyi5@huawei.com>

vllm_ascend/patch/worker/patch_minimax_m2.py

@@ -28,6 +28,8 @@ from vllm.model_executor.layers.mamba.linear_attn import MiniMaxText01RMSNormTP
 from vllm.model_executor.models.minimax_m2 import MiniMaxM2Attention, MiniMaxM2Model, MiniMaxM2MoE
 from vllm.platforms import current_platform
 
+from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_slice
+
 FP8_DTYPES = tuple(
     getattr(torch, dtype_name)
     for dtype_name in (
@@ -172,3 +174,31 @@ def _patched_load_weights(
 
 
 MiniMaxM2Model.load_weights = _patched_load_weights
+
+
+def _patch_forward(
+    self,
+    positions: torch.Tensor,
+    hidden_states: torch.Tensor,
+) -> torch.Tensor:
+    qkv, _ = self.qkv_proj(hidden_states)
+    cos, sin = get_cos_and_sin_slice()
+    q, k, v = torch.ops.vllm.split_qkv_tp_rmsnorm_rope(
+        input=qkv,
+        q_weight=self.q_norm.weight,
+        k_weight=self.k_norm.weight,
+        q_hidden_size=self.q_size,
+        kv_hidden_size=self.kv_size,
+        head_dim=self.head_dim,
+        rotary_dim=getattr(self.rotary_emb, "rotary_dim", self.head_dim),
+        eps=self.q_norm.variance_epsilon,
+        tp_world=self.q_norm.tp_world,
+        cos=cos,
+        sin=sin,
+    )
+    attn_output = self.attn(q, k, v)
+    output, _ = self.o_proj(attn_output)
+    return output
+
+
+MiniMaxM2Attention.forward = _patch_forward