[refactor] Refactoring AscendFusedMoE (#1229)

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### What this PR does / why we need it?
This PR is used for resolved [issue
1147](https://github.com/vllm-project/vllm-ascend/issues/1147)
1. Move fused_moe code into one file `fused_moe.py`.
2. Integrate branch conditions into function `get_fused_moe_state`.
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### Does this PR introduce _any_ user-facing change?
1. This PR has removed the env `VLLM_ENABLE_MC2`, because I think this
env is useless, we can make judgments based on the current scenario
without this env, it will only increase complexity.
2. This PR has removed the env `USING_LCCL_COM`, because this env has
already expired.
3. `additional_config.expert_tensor_parallel_size` has already expired,
and now we also use parameter `enable_expert_parallel`, consistent with
the vLLM.
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Signed-off-by: zzzzwwjj <1183291235@qq.com>

vllm_ascend/models/deepseek_dbo.py

@@ -51,9 +51,9 @@ from vllm.model_executor.layers.sampler import get_sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.models.deepseek_v2 import \
-    DeepseekV2ForCausalLM  # ruff: noqa: E501
+    DeepseekV2ForCausalLM  # noqa: E501
 from vllm.model_executor.models.deepseek_v2 import \
-    yarn_get_mscale  # ruff: noqa: E501
+    yarn_get_mscale  # noqa: E501
 from vllm.model_executor.models.deepseek_v2 import (DeepseekV2Attention,
                                                     DeepseekV2DecoderLayer,
                                                     DeepseekV2MLAAttention)
@@ -79,7 +79,6 @@ from vllm_ascend.ops.fused_moe import AscendFusedMoE
 from vllm_ascend.utils import dispose_tensor
 
 VLLM_ASCEND_ENABLE_DBO: bool = envs_ascend.VLLM_ASCEND_ENABLE_DBO
-VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
 
 
 class CustomDeepseekDBOMLP(CustomDeepseekV2MLP):
@@ -189,26 +188,8 @@ class CustomDeepseekDBOMoE(nn.Module):
             if hasattr(attn_metadata, 'with_prefill_across_dp'):
                 is_prefill = is_prefill or attn_metadata.with_prefill_across_dp
 
-        num_tokens, hidden_size = hidden_states.shape
-
         old_hidden_states = hidden_states.clone()
 
-        if self.tp_size > 1:
-            if envs_ascend.VLLM_ENABLE_MC2 and not is_prefill:
-                chunks = torch.chunk(hidden_states, self.tp_size, dim=0)
-                hidden_states = chunks[self.tp_rank]
-            elif not self.torchair_graph_enabled:
-                num_padding_tokens = (self.tp_size -
-                                      num_tokens % self.tp_size) % self.tp_size
-                # Pad hidden_states to make it divisible by tp_size to avoid cross-ring AllGatherV on 910B2C
-                if num_padding_tokens > 0:
-                    hidden_states = nn.functional.pad(
-                        hidden_states, (0, 0, 0, num_padding_tokens))
-                chunk_hidden_states = torch.tensor_split(hidden_states,
-                                                         self.tp_size,
-                                                         dim=0)
-                hidden_states = chunk_hidden_states[self.tp_rank]
-
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
 
@@ -220,33 +201,13 @@ class CustomDeepseekDBOMoE(nn.Module):
             enable_force_load_balance=enable_force_load_balance,
         ) * self.routed_scaling_factor
 
-        if self.tp_size > 1:
-            if self.torchair_graph_enabled:
-                if envs_ascend.VLLM_ENABLE_MC2 and not is_prefill:
-                    final_hidden_states = torch.zeros(
-                        [num_tokens, hidden_size],
-                        dtype=self.params_dtype,
-                        device="npu")
-                    dist.all_gather_into_tensor(final_hidden_states,
-                                                hidden_states, self.tp_group)
-                    hidden_states = final_hidden_states
-                else:
-                    hidden_states = tensor_model_parallel_all_reduce(
-                        hidden_states)
-            else:
-                dist.all_gather(list(chunk_hidden_states), hidden_states,
-                                self.tp_group)
-                hidden_states = torch.cat(chunk_hidden_states, dim=0)
-                if num_padding_tokens > 0:
-                    hidden_states = hidden_states[:-num_padding_tokens]
-
         if self.n_shared_experts is not None:
             shared_output = self.shared_experts(old_hidden_states)
 
         if shared_output is not None:
             hidden_states = hidden_states + shared_output
 
-        return hidden_states.view(num_tokens, hidden_size)
+        return hidden_states
 
     # ----------------------------------------- TBO-related --------------------------------------------
     def _forward_ms_op_shared_expert(