[Refactor][MoE] remove redundant code after refactoring fused_moe (#2612)

### What this PR does / why we need it?
There are a lot of redundant codes related to moe here, and the
structure is not very clear.
We did the following things：

we have placed the relatively independent code related to apply_mlp into
a separate file;
removed the environment variables of alltoall_buffer and alltoall_seq.
Remove the code related to alltoall_buffer and alltoall_seq, and retain
the sole TokenDispatcher inheritance class.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e&ut

- vLLM version: v0.10.1.1
- vLLM main:
4071c76cf3

---------

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>

tests/ut/torchair/ops/test_torchair_fused_moe.py

@@ -353,8 +353,7 @@ class TestTorchairAscendUnquantizedFusedMoEMethod:
             else:
                 assert result.shape == x.shape
 
-    @pytest.mark.parametrize("others_param",
-                             [[16, False], [1, True], [1, False], [4, False]])
+    @pytest.mark.parametrize("others_param", [16, 1, 4])
     def test_apply_with_expert_map(self, moe_method, mock_dist_env,
                                    mock_moe_env, others_param):
         """
@@ -363,13 +362,11 @@ class TestTorchairAscendUnquantizedFusedMoEMethod:
         3 test use_select_experts and fused_experts_with_all2all
         4 test use_select_experts and fused_experts
         """
-        ep_size, alltoall_buffer = others_param
+        ep_size = others_param
         is_prefill = False
         forward_context = MagicMock(
             fused_moe_state=_get_fused_moe_state(ep_size, is_prefill, True))
-        with patch("vllm_ascend.torchair.ops.torchair_fused_moe.MOE_ALL2ALL_BUFFER",
-                   alltoall_buffer), \
-             patch("vllm_ascend.torchair.ops.torchair_fused_moe.get_forward_context", return_value=forward_context), \
+        with patch("vllm_ascend.torchair.ops.torchair_fused_moe.get_forward_context", return_value=forward_context), \
              patch("vllm_ascend.torchair.ops.torchair_fused_moe.get_ascend_soc_version", return_value=AscendSocVersion.A3):
             expert_map = torch.tensor([0, 1, 2, -1, -1, -1, -1, -1])
             moe_method.ep_size = ep_size
@@ -377,8 +374,6 @@ class TestTorchairAscendUnquantizedFusedMoEMethod:
             if ep_size == 1:
                 x = x.view(-1, 2)
             router_logits = torch.randn(8, 8)
-            if alltoall_buffer:
-                moe_method.max_model_len = 1
             layer = MagicMock()
             layer.w13_weight = torch.randn(8, 16, 1)
             layer.w2_weight = torch.randn(16, 8, 1)