[MOE][Bugfix] Cancel H2D for expert_map (#7000)

### What this PR does / why we need it?
If expert_map is on the device, there may be occasional repeated answers
in long output scenarios.

dsv3.2-exp-w8a8
No garbled characters are displayed in the output.
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2025 | ef2f4f | accuracy | gen | 60.00 |

- vLLM version: v0.16.0
- vLLM main:
15d76f74e2

Signed-off-by: shenchuxiaofugui <1311027364@qq.com>

vllm_ascend/eplb/core/eplb_utils.py

@@ -22,6 +22,7 @@ from collections import defaultdict
 import numpy as np
 import torch
 from vllm.logger import logger
+from vllm.model_executor.layers.fused_moe.layer import determine_expert_map
 
 
 def expert_file_to_tensor(expert_map_path, layer_id):
@@ -60,6 +61,11 @@ def init_eplb_config(eplb_config, layer_id, moe_config):
     global_placement = None
     eplb_enable = eplb_config.dynamic_eplb
     n_redundant = eplb_config.num_redundant_experts if eplb_enable else 0
+
+    if ep_size == 1:
+        assert not eplb_enable, "EPLB must used in expert parallelism."
+        return None, None, None, n_redundant
+
     if expert_map_path:
         if not (os.path.exists(expert_map_path) and os.access(expert_map_path, os.R_OK)):
             raise ValueError("Invalid EPLB path")
@@ -71,13 +77,13 @@ def init_eplb_config(eplb_config, layer_id, moe_config):
                 raise ValueError("Eplb supports only w8a8_dynamic quantization.")
         else:
             eplb_enable = False
+    elif not eplb_enable:
+        _, expert_map, _ = determine_expert_map(ep_size, moe_config.ep_rank, n_experts)
+        return None, expert_map, None, 0
 
     if global_placement is None:
         global_placement = generate_global_placement(n_experts, ep_size, n_redundant)
 
-    if ep_size == 1:
-        assert not eplb_enable, "EPLB must used in expert parallelism."
-        return None, None, None, n_redundant
     global_expert_map = []
     for rankid in range(ep_size):
         expert_map = torch.full((n_experts,), -1, dtype=torch.int32)
@@ -85,7 +91,7 @@ def init_eplb_config(eplb_config, layer_id, moe_config):
         expert_map[local_placement] = torch.arange(local_placement.shape[0], dtype=torch.int32)
         global_expert_map.append(expert_map)
         if rankid == moe_config.ep_rank:
-            local_expert_map = expert_map.npu()
+            local_expert_map = expert_map
     log2phy = generate_log2phy_map(global_expert_map, moe_config.ep_rank).npu() if eplb_enable else None
 
     return torch.stack(global_expert_map), local_expert_map, log2phy, n_redundant