[Perf] move quant before allgather in Allgather EP (#3420)

### What this PR does / why we need it?
move quant before allgather in Allgather EP, rely on
https://github.com/vllm-project/vllm-ascend/pull/3334

Deepseek R1 W8A8 performance on A2 with
`HCCL_ALGO="level0:NA;level1:pipeline"`:
| Seq length | Mean TTFT (ms) main | Mean TTFT (ms)  this PR |
|----------|----------|----------|
| 4k   |  375.21  | 364.99   |
| 16k  | 1465.23   | 1421.75  |
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.11.0
- vLLM main:
83f478bb19

---------

Signed-off-by: realliujiaxu <realliujiaxu@163.com>

vllm_ascend/ops/fused_moe/prepare_finalize.py

@@ -15,11 +15,13 @@
 # This file is a part of the vllm-ascend project.
 
 from abc import ABC, abstractmethod
+from enum import Enum
 from typing import Optional
 
 import torch
 import torch.distributed as dist
 import torch.nn as nn
+import torch_npu
 from vllm.distributed import tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import (
     get_dp_group, get_tensor_model_parallel_rank,
@@ -30,6 +32,12 @@ from vllm.model_executor.layers.fused_moe import FusedMoEConfig
 from vllm_ascend.utils import enable_sp
 
 
+class QuantType(Enum):
+    NONE = 0
+    W8A8 = 1
+    W4A8 = 2
+
+
 class PrepareAndFinalize(ABC):
     """
     Abstract base class for MoE (Mixture-of-Experts) tensor preparation and finalization
@@ -42,8 +50,11 @@ class PrepareAndFinalize(ABC):
                                      sizes, ranks, and communication settings.
     """
 
-    def __init__(self, moe_config: FusedMoEConfig):
+    def __init__(self,
+                 moe_config: FusedMoEConfig,
+                 quant_type: QuantType = QuantType.NONE):
         self.moe_config = moe_config
+        self.quant_type = quant_type
 
     @abstractmethod
     def prepare(
@@ -103,8 +114,10 @@ class PrepareAndFinalizeWithAll2All(PrepareAndFinalize):
     Will be used when num_tokens exceed mc2's limitation (512 tokens/rank).
     """
 
-    def __init__(self, moe_config: FusedMoEConfig):
-        super().__init__(moe_config)
+    def __init__(self,
+                 moe_config: FusedMoEConfig,
+                 quant_type: QuantType = QuantType.NONE):
+        super().__init__(moe_config, quant_type)
         self._restore_tp_across_dp()
 
     def _restore_tp_across_dp(self):
@@ -195,8 +208,10 @@ class PrepareAndFinalizeWithMC2(PrepareAndFinalizeWithAll2All):
     Relies on `mc2_mask` and `padded_num_tokens` from forward_context for alignment.
     """
 
-    def __init__(self, moe_config: FusedMoEConfig):
-        super().__init__(moe_config)
+    def __init__(self,
+                 moe_config: FusedMoEConfig,
+                 quant_type: QuantType = QuantType.NONE):
+        super().__init__(moe_config, quant_type)
         self._restore_tp_across_dp()
 
     def _restore_tp_across_dp(self):
@@ -316,11 +331,20 @@ class PrepareAndFinalizeWithAllGather(PrepareAndFinalize):
         router_logits: torch.Tensor,
     ) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor],
                Optional[torch.Tensor]]:
+        pertoken_scale = None
+        if self.quant_type == QuantType.W8A8:
+            hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(
+                hidden_states)
+            pertoken_scale = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
+                pertoken_scale, True, True)
         hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
             hidden_states, True, True)
         router_logits = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
             router_logits, True, True)
 
+        if pertoken_scale is not None:
+            return (hidden_states, pertoken_scale), router_logits, None, None
+
         return hidden_states, router_logits, None, None
 
     def _prepare_with_dp_group(