[V0.18.0][EPLB][BugFix] Fix moe_load precision in allgather (#7890)

### What this PR does / why we need it?
Fixed the bug of incorrect reshape usage.
For example:
ori_tensor: [[1, 2, 3], [4, 5, 6]]
after reshape:
[[1, 2], [3, 4], [5, 6]]
after permute:
[[1, 4], [2, 5], [3, 6]]
Now, we will directly use squeeze for a more intuitive understanding.
pr for main:
#7887 

### Does this PR introduce _any_ user-facing change?
The actual peak-to-average ratio has successfully decreased.

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

tests/ut/eplb/test_eplb_updator.py

@@ -0,0 +1,81 @@
+import unittest
+from unittest.mock import MagicMock, patch
+import torch
+from vllm_ascend.eplb.eplb_updator import EplbUpdator
+
+
+class TestEplbUpdatorComputeAndSetMoeLoad(unittest.TestCase):
+    def setUp(self):
+
+        # ====================== 1. Mock environment ======================
+        self.rank = 0
+        self.world_size = 4
+        self.device = torch.device("cpu")
+
+        # mock dist
+        p1 = patch("torch.distributed.get_rank", return_value=self.rank)
+        p2 = patch("torch.distributed.get_world_size", return_value=self.world_size)
+        self.addCleanup(p1.stop)
+        self.addCleanup(p2.stop)
+        p1.start()
+        p2.start()
+
+        # ====================== 2. Mock comm group ======================
+        self.mock_comm_group = MagicMock()
+
+        def mock_all_gather(tensor, dim):
+            gathered = torch.cat([tensor for _ in range(self.world_size)], dim=dim)
+            return gathered
+
+        self.mock_comm_group.all_gather = mock_all_gather
+
+        p3 = patch("vllm_ascend.eplb.eplb_updator.get_dynamic_eplb_group",
+                   return_value=self.mock_comm_group)
+        self.addCleanup(p3.stop)
+        p3.start()
+
+        # ====================== 3. Mock EplbUpdator ======================
+        self.eplb_config = MagicMock()
+        self.loader = MagicMock()
+        self.eplb_process = MagicMock()
+        self.process = MagicMock()
+        self.eplb_process.shared_dict = {}
+
+        self.updator = EplbUpdator(
+            eplb_config=self.eplb_config,
+            loader=self.loader,
+            eplb_process=self.eplb_process,
+            process=self.process
+        )
+
+        # ====================== 4. Mock adaptor ======================
+        self.adaptor = MagicMock()
+        self.adaptor.num_moe_layers = 4
+        self.adaptor.num_dense_layers = 2
+        self.mock_local_load = torch.randn(58, 100, 8, device=self.device)
+        self.adaptor.get_rank_expert_workload.return_value = self.mock_local_load
+
+        self.updator.set_adaptor(self.adaptor)
+
+    def test_compute_and_set_moe_load_normal(self):
+        self.updator.multi_stage = False
+
+        moe_load = self.updator.compute_and_set_moe_load()
+
+        self.assertEqual(moe_load.shape, (58, self.world_size, 100, 8))
+        self.assertTrue("moe_load" in self.updator.shared_dict)
+        self.assertEqual(moe_load.device.type, "cpu")
+        self.assertEqual(moe_load.shape[1], self.world_size)
+
+    def test_compute_and_set_moe_load_multi_stage(self):
+        self.updator.multi_stage = True
+
+        moe_load = self.updator.compute_and_set_moe_load()
+
+        self.assertEqual(moe_load.shape, (100, 58, self.world_size, 8))
+        self.assertTrue("moe_load" in self.updator.shared_dict)
+        self.assertEqual(moe_load.device.type, "cpu")
+
+
+if __name__ == '__main__':
+    unittest.main()

vllm_ascend/eplb/eplb_updator.py

@@ -131,10 +131,8 @@ class EplbUpdator:
         self.update_iteration()
 
     def compute_and_set_moe_load(self):
-        local_load = self.adaptor.get_rank_expert_workload()
-        moe_load = (
-            self.comm_group.all_gather(local_load, dim=0).reshape(-1, self.world_size, *local_load.shape[1:]).cpu()
-        )
+        local_load = self.adaptor.get_rank_expert_workload().unsqueeze(1)
+        moe_load = self.comm_group.all_gather(local_load, dim=1).cpu()
 
         if self.multi_stage:
             moe_load = moe_load.permute(2, 0, 1, 3)