[BugFix]Fix eplb problems when using dynamic eplb. (#3364)

### What this PR does / why we need it?
When using dynamic eplb,it will be blocking by nz tensor.We fix these
prolems by clone src tensor and recv tensor.

### Does this PR introduce any user-facing change?

### How was this patch tested?
Qwen3_moe in A3.

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>

vllm_ascend/ops/moe/moe_comm_method.py

@@ -150,7 +150,8 @@ class MoECommMethod(ABC):
                                        with_quant=use_int8_w8a8
                                        or use_int4_w4a8,
                                        fusion=use_int8_w8a8,
-                                       need_trans=need_trans)
+                                       need_trans=need_trans,
+                                       dynamic_eplb=dynamic_eplb)
 
         final_hidden_states = self.token_dispatcher.token_combine(
             hidden_states=mlp_output)

vllm_ascend/ops/moe/moe_mlp.py

@@ -63,7 +63,8 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
                     dynamic_scale: torch.Tensor = None,
                     w1_scale_bias: torch.Tensor = None,
                     w2_scale_bias: torch.Tensor = None,
-                    fusion: bool = False) -> torch.Tensor:
+                    fusion: bool = False,
+                    dynamic_eplb: bool = False) -> torch.Tensor:
     if dynamic_scale is None:
         unquantized_hidden_states = hidden_states
         hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(
@@ -79,7 +80,7 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
 
     is_mc2 = get_forward_context().moe_comm_type == MoECommType.MC2
     if w1_scale_bias is None and is_mc2:
-        if fusion:
+        if fusion and not dynamic_eplb:
             # gmm1: gate_up_proj & act_fn: swiglu
             hidden_states, swiglu_out_scale, _ = torch_npu.npu_grouped_matmul_swiglu_quant(
                 x=hidden_states,
@@ -134,7 +135,7 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
             # TODO w4a8 scene: dynamic acquisition of dtype in the future
             _output_dtype = torch.bfloat16
 
-        if fusion:
+        if fusion and not dynamic_eplb:
             # gmm1: gate_up_proj & act_fn: swiglu
             hidden_states, swiglu_out_scale, _ = torch_npu.npu_grouped_matmul_swiglu_quant(
                 x=hidden_states,
@@ -229,7 +230,8 @@ def unified_apply_mlp(hidden_states: torch.Tensor,
                       topk_scales: Optional[torch.Tensor] = None,
                       with_quant: bool = False,
                       fusion: bool = False,
-                      need_trans: bool = True) -> torch.Tensor:
+                      need_trans: bool = True,
+                      dynamic_eplb: bool = False) -> torch.Tensor:
     if with_quant:
         return quant_apply_mlp(hidden_states=hidden_states,
                                w1=w1,
@@ -241,7 +243,8 @@ def unified_apply_mlp(hidden_states: torch.Tensor,
                                group_list_type=group_list_type,
                                w1_scale_bias=w1_scale_bias,
                                w2_scale_bias=w2_scale_bias,
-                               fusion=fusion)
+                               fusion=fusion,
+                               dynamic_eplb=dynamic_eplb)
     else:
         return unquant_apply_mlp(hidden_states=hidden_states,
                                  w1=w1,