[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_v2.py

@@ -28,7 +28,6 @@
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
-import torch.distributed as dist
 import torch_npu
 import vllm.envs as envs
 from torch import nn
@@ -37,7 +36,7 @@ from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import (get_pp_group,
                               get_tensor_model_parallel_world_size,
-                              get_tp_group, tensor_model_parallel_all_reduce)
+                              get_tp_group)
 from vllm.distributed.parallel_state import get_dp_group
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -54,9 +53,9 @@ 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)
@@ -65,7 +64,6 @@ from vllm.model_executor.models.utils import (
     maybe_prefix)
 from vllm.sequence import IntermediateTensors
 
-import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.parallel_state import get_ep_group
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
@@ -74,8 +72,6 @@ from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
 from vllm_ascend.utils import (dispose_tensor, npu_stream_switch,
                                npu_wait_tensor)
 
-VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
-
 
 class CustomDeepseekV2SiluAndMul(SiluAndMul):
 
@@ -240,9 +236,8 @@ class CustomDeepseekV2MoE(nn.Module):
 
         ascend_config = get_ascend_config()
         self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
-        # NOTE: multistream only effective when `VLLM_ENABLE_MC2` is on
         self.enable_multistream_moe = \
-            ascend_config.torchair_graph_config.enable_multistream_moe and VLLM_ENABLE_MC2
+            ascend_config.torchair_graph_config.enable_multistream_moe
 
         self.gate = ReplicatedLinear(config.hidden_size,
                                      config.n_routed_experts,
@@ -312,22 +307,6 @@ class CustomDeepseekV2MoE(nn.Module):
             enable_force_load_balance = False
             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
-        use_separated_shared_experts = (self.shared_experts is not None
-                                        and not self.enable_multistream_moe)
-
-        if self.tp_size > 1:
-            if (VLLM_ENABLE_MC2
-                    and not is_prefill) or not (self.torchair_graph_enabled or
-                                                self.ep_group.world_size == 1):
-                if num_tokens < self.tp_size:
-                    hidden_states = nn.functional.pad(
-                        hidden_states, (0, 0, 0, self.tp_size - num_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)
@@ -338,34 +317,14 @@ class CustomDeepseekV2MoE(nn.Module):
             is_prefill=is_prefill,
             top_k=CustomDeepseekV2MoE.top_k,
             enable_force_load_balance=enable_force_load_balance,
-            shared_experts=(self.shared_experts
-                            if not use_separated_shared_experts else None),
+            shared_experts=self.shared_experts,
         )
 
-        if not isinstance(experts_hidden_states, tuple):
-            hidden_states = experts_hidden_states * self.routed_scaling_factor
-        else:
-            hidden_states = (
-                experts_hidden_states[0] * self.routed_scaling_factor +
-                experts_hidden_states[1])
+        hidden_states = (
+            experts_hidden_states[0] * self.routed_scaling_factor +
+            experts_hidden_states[1])
 
-        if self.tp_size > 1:
-            if (VLLM_ENABLE_MC2
-                    and not is_prefill) or not (self.torchair_graph_enabled or
-                                                self.ep_group.world_size == 1):
-                dist.all_gather(list(chunk_hidden_states), hidden_states,
-                                self.tp_group)
-                hidden_states = torch.cat(chunk_hidden_states, dim=0)
-                if num_tokens < self.tp_size:
-                    hidden_states = hidden_states[:num_tokens]
-            else:
-                hidden_states = tensor_model_parallel_all_reduce(hidden_states)
-
-        if use_separated_shared_experts:
-            hidden_states = hidden_states + self.shared_experts(
-                old_hidden_states)
-
-        return hidden_states.view(num_tokens, hidden_size)
+        return hidden_states
 
 
 class CustomDeepseekV2MLAAttention(DeepseekV2MLAAttention):