[2/N][Feat] Add MC2 communication method for MoE layers (#2469)

### What this PR does / why we need it?
This method replaces the previous all-gather approach for small numbers
of tokens.

The key changes include:
- A new `AscendFusedMoE` layer that handles token splitting, local
computation, and final aggregation via all-gather.
- Logic in the model runner to dynamically select between the new MC2
method and the existing all-gather method based on the number of input
tokens.
- Sharding the MoE communication mask across tensor-parallel ranks.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
Test case fixed.


- vLLM version: v0.10.1.1
- vLLM main:
b00e69f8ca

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/ascend_forward_context.py

@@ -11,7 +11,6 @@ from vllm.forward_context import (BatchDescriptor, get_forward_context,
                                   set_forward_context)
 
 import vllm_ascend.envs as envs_ascend
-from vllm_ascend.distributed.moe_comm_method import MoECommMethod
 
 
 class FusedMoEState(Enum):
@@ -57,7 +56,7 @@ def set_ascend_forward_context(
         with_prefill: bool = True,
         in_profile_run: bool = False,
         reserved_mc2_mask: Optional[torch.Tensor] = None,
-        moe_comm_method: Optional[MoECommMethod] = None,
+        moe_comm_method: str = "",
         num_actual_tokens: Optional[int] = None,
         aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
         batch_descriptor: Optional[BatchDescriptor] = None):
@@ -75,7 +74,7 @@ def set_ascend_forward_context(
             batch_descriptor=batch_descriptor,
     ):
         forward_context = get_forward_context()
-        forward_context.moe_comm_method = moe_comm_method
+        forward_context.moe_comm_method_name = moe_comm_method + "commimpl"
         forward_context.with_prefill = with_prefill
         ep_size = (get_ep_group().world_size if
                    vllm_config.parallel_config.enable_expert_parallel else 1)