[Feat][SP] Suport SP for VL MoE models (#7044)

### What this PR does / why we need it?

2nd PR for https://github.com/vllm-project/vllm-ascend/issues/5712,
extend SP to VL MoE models.


### Does this PR introduce _any_ user-facing change?
remove `sp_threshold` in additional config and reuse `sp_min_token_num`
from vLLM.


### How was this patch tested?
- Model: Qwen3-VL-30B-A3B, 
- TP4 DP2
- 100 reqs
- max concurrency 1

| Seq length | Mean TTFT (ms) main | Mean TTFT (ms) this PR |
|------------|---------------------|------------------------|
| 4k         | 429.40               | 323.3                  |
| 16k        | 1297.01              | 911.74                |

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: realliujiaxu <realliujiaxu@163.com>

vllm_ascend/compilation/passes/noop_elimination.py

@@ -0,0 +1,62 @@
+from collections.abc import Iterable
+
+import torch
+import torch.fx
+from torch import SymInt
+from torch.fx.experimental.symbolic_shapes import statically_known_true
+from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
+from vllm.logger import logger
+
+
+class NoOpEliminationPass(VllmInductorPass):
+    """Remove no-op view/reshape nodes after pattern rewrites."""
+
+    def __call__(self, graph: torch.fx.Graph) -> None:
+        fx_graph = graph.graph if hasattr(graph, "graph") else graph
+        removed = 0
+        for node in list(fx_graph.nodes):
+            if not self._is_view_like(node):
+                continue
+
+            input_node = node.args[0]
+            if not isinstance(input_node, torch.fx.Node):
+                continue
+
+            input_meta = input_node.meta.get("val")
+            output_meta = node.meta.get("val")
+            if input_meta is None or output_meta is None:
+                continue
+
+            input_shape = getattr(input_meta, "shape", None)
+            output_shape = getattr(output_meta, "shape", None)
+            if input_shape is None or output_shape is None:
+                continue
+
+            if self._all_dims_equivalent(input_shape, output_shape):
+                node.replace_all_uses_with(input_node)
+                fx_graph.erase_node(node)
+                removed += 1
+
+        logger.debug("NoOpEliminationPass removed %s no-op views", removed)
+
+    @staticmethod
+    def _is_view_like(node: torch.fx.Node) -> bool:
+        return (node.op == "call_method" and node.target in {"view", "reshape"}) or (
+            node.op == "call_function"
+            and node.target
+            in {
+                torch.ops.aten.view.default,
+                torch.ops.aten.reshape.default,
+            }
+        )
+
+    @staticmethod
+    def _dims_equivalent(dim: int | SymInt, i_dim: int | SymInt) -> bool:
+        return statically_known_true(dim == i_dim)  # type: ignore[no-any-return]
+
+    def _all_dims_equivalent(self, dims: Iterable[int | SymInt], i_dims: Iterable[int | SymInt]) -> bool:
+        dims_ = list(dims)
+        i_dims_ = list(i_dims)
+        if len(dims_) != len(i_dims_):
+            return False
+        return all(self._dims_equivalent(s, i_s) for s, i_s in zip(dims_, i_dims_))