[CI] Fix broken CI (#1889)

1. vLLM commit
45badd05d0
changed the pooling check logic which broken vLLM Ascend.
2. vLLM commit
3e04107d97
requires higher version of transformers. The transformers version bug
has been fixed by
e936e401de.
We can safe to remove the version limit now.
3. vLLM commit
217937221b
added a new input `enable_eplb` for FusedMoe Ops

This PR fix the broken CI.


- vLLM version: v0.9.2
- vLLM main:
6a971ed692

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/ops/common_fused_moe.py

@@ -40,23 +40,26 @@ def unquantized_fused_moe_init_func(self, *args, **kwargs):
 
 
 def forward_oot(
-    self,
-    layer: torch.nn.Module,
-    x: torch.Tensor,
-    use_grouped_topk: bool,
-    top_k: int,
-    router_logits: torch.Tensor,
-    renormalize: bool,
-    topk_group: Optional[int] = None,
-    num_expert_group: Optional[int] = None,
-    custom_routing_function: Optional[Callable] = None,
-    scoring_func: str = "softmax",
-    e_score_correction_bias: Optional[torch.Tensor] = None,
-    global_num_experts: Optional[int] = None,
-    expert_map: Optional[torch.Tensor] = None,
-    apply_router_weight_on_input: bool = False,
-    activation: str = "silu",
-) -> torch.Tensor:
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        use_grouped_topk: bool,
+        top_k: int,
+        router_logits: torch.Tensor,
+        renormalize: bool,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        global_num_experts: Optional[int] = None,
+        expert_map: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None) -> torch.Tensor:
 
     if SELECT_GATING_TOPK_SOTFMAX_EXPERTS:
         topk_weights, topk_ids = select_gating_top_k_softmax_experts(