[EPLB] Display the expert hotness comparison before and after eplb. (#6877)

### What this PR does / why we need it?
To intuitively show the effect of the eplb algorithm, we print the
expert heat before and after eplb.

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

### How was this patch tested?

![Snipaste_2026-02-28_17-23-42](https://github.com/user-attachments/assets/db1dadd1-cf96-44da-af34-57d41ccf412f)


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

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

docs/source/user_guide/feature_guide/eplb_swift_balancer.md

@@ -66,7 +66,7 @@ vllm serve Qwen/Qwen3-235B-A22 \
   --tensor-parallel-size 16 \
   --enable-expert-parallel \
   --additional-config '{
-    "expert_map_path": "/path/to/eplb.json"
+    "eplb_config": {"expert_map_path": "/path/to/eplb.json"}
   }'
 ```
 

vllm_ascend/eplb/core/eplb_worker.py

@@ -17,6 +17,7 @@
 from multiprocessing import Process, Queue
 from typing import Any
 
+import numpy as np
 import torch
 import torch.distributed as dist
 from vllm.logger import logger
@@ -60,6 +61,16 @@ class EplbWorker:
         old_placement = self.global2local(self.old_expert_maps, self.num_local_experts)
         _, _, new_placement = self.calculate_rebalance_experts(load_info, old_placement)
 
+        if self.rank_id == 0:
+            hotness = self._calculate_hotness(old_placement, load_info)
+            current_mean, current_max = self._compute_imbalance(old_placement, hotness)
+            update_mean, update_max = self._compute_imbalance(new_placement, hotness)
+            logger.info(
+                "[Expert Hotness] Current: mean={:.3f}, max={:.3f}, Updated: mean={:.3f}, max={:.3f}".format(
+                    current_mean, current_max, update_mean, update_max
+                )
+            )
+
         if not torch.is_tensor(new_placement):
             new_placement = torch.tensor(new_placement)
         self.check_expert_placement(old_placement, new_placement)
@@ -251,6 +262,36 @@ class EplbWorker:
 
         return list(zip(send_all, recv_all, maps, log2phy_all, layer_ids))
 
+    @staticmethod
+    def _compute_imbalance(deployment_all_layer, hotness_all_layer: np.ndarray):
+        imbalance_list = []
+        deployment_all_layer = np.array(deployment_all_layer)
+        for deployment, hotness in zip(deployment_all_layer, hotness_all_layer):
+            counts = np.bincount(deployment.reshape(-1), minlength=hotness.shape[0])
+
+            unit_hotness = np.divide(hotness, counts, out=np.zeros_like(hotness, dtype=float), where=counts != 0)
+
+            stage_load = unit_hotness[deployment].sum(-1)
+            stage_par = stage_load.max() / stage_load.mean()
+            imbalance_list.append(stage_par)
+
+        max_val = max(imbalance_list)
+        mean_val = sum(imbalance_list) / len(imbalance_list)
+        return mean_val, max_val
+
+    @staticmethod
+    def _calculate_hotness(deployment_all_layer, moe_load_all_layer):
+        hotnesses = []
+        num_of_expert = deployment_all_layer.shape[1] * deployment_all_layer.shape[2]
+        for deployment, rank_load in zip(deployment_all_layer, moe_load_all_layer.numpy()):
+            hotness = np.zeros(num_of_expert, dtype=rank_load.dtype)
+            deployment_flat = deployment.ravel()
+            rank_load_flat = rank_load.ravel()
+            np.add.at(hotness, deployment_flat, rank_load_flat)
+            hotnesses.append(hotness)
+
+        return np.array(hotnesses)
+
 
 class EplbProcess:
     def __init__(self, shared_dict, policy_type: int = 0, enable_d2d: bool = True):

vllm_ascend/eplb/eplb_updator.py

@@ -34,7 +34,6 @@ class EplbUpdator:
         self.eplb_loader = loader
         self.eplb_process = eplb_process
         self.shared_dict = self.eplb_process.shared_dict
-        self.moe_imbalance_dict: dict[int, float] = {}
         self.comm_group = get_dynamic_eplb_group()
 
     def set_adaptor(self, adaptor: VllmEplbAdaptor):
@@ -137,44 +136,8 @@ class EplbUpdator:
         self.shared_dict["moe_load"] = moe_load.cpu()
         logger.debug(f"[ModelRunner] Updated shared_dict['moe_load'] shape={moe_load.shape}")
 
-        if dist.get_rank() == 0:
-            self.compute_moe_imbalance(moe_load)
-            self.summarize_moe_imbalance()
-
         return moe_load
 
-    def compute_moe_imbalance(self, moe_load: torch.Tensor):
-        self.moe_imbalance_dict.clear()
-
-        layer_card_load = moe_load.sum(dim=-1).cpu().float()
-
-        for layer_idx in range(layer_card_load.size(0)):
-            layer_load = layer_card_load[layer_idx]
-
-            mean_load = layer_load.mean().item()
-            max_load = layer_load.max().item()
-
-            moe_load_imbalance = max_load / (mean_load + 1e-6)
-
-            logger.debug(f"[ModelRunner][MOE_load_stats][Layer {layer_idx}] PAR={moe_load_imbalance:.4f}")
-
-            self.moe_imbalance_dict[layer_idx] = moe_load_imbalance
-
-    def summarize_moe_imbalance(self):
-        values = list(self.moe_imbalance_dict.values())
-        if not values:
-            logger.info("[MOE_load_stats] No data available.")
-            return
-
-        avg_imbalance = sum(values) / len(values)
-        max_imbalance = max(values)
-        min_imbalance = min(values)
-
-        logger.info(
-            f"[ModelRunner][MOE_load_stats] Peak-to-Average-Ratio: "
-            f"Mean={avg_imbalance:.4f}, Max={max_imbalance:.4f}, Min={min_imbalance:.4f}"
-        )
-
     def warm_up_eplb(self):
         self.shared_dict["expert_maps"] = self.adaptor.get_global_expert_map()
         self.compute_and_set_moe_load()