[cherry-pick] static EPLB fix bug, add unit test to v0.9.1-dev (#1667)

### What this PR does / why we need it?

[cherry-pick master->
0.9.1-dev](#1186)
1.add static EPLB unit test
2.fix bug: Tensor cannot be directly judged by if statements
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
Run the unit test.

Signed-off-by: songshanhu07 <1763685535@qq.com>

Author: songshanhu07

tests/ut/ops/test_expert_load_balancer.py

@@ -0,0 +1,147 @@
+# fused moe ops test will hit the infer_schema error, we need add the patch
+# here to make the test pass.
+import vllm_ascend.patch.worker.patch_common.patch_utils  # type: ignore[import]  # isort: skip  # noqa
+
+import json
+import unittest
+from typing import List, TypedDict
+from unittest import mock
+
+import torch
+
+from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
+
+
+class Device(TypedDict):
+    device_id: int
+    device_expert: List[int]
+
+
+class Layer(TypedDict):
+    layer_id: int
+    device_count: int
+    device_list: List[Device]
+
+
+class MockData(TypedDict):
+    moe_layer_count: int
+    layer_list: List[Layer]
+
+
+MOCK_DATA: MockData = {
+    "moe_layer_count":
+    1,
+    "layer_list": [{
+        "layer_id":
+        0,
+        "device_count":
+        2,
+        "device_list": [{
+            "device_id": 0,
+            "device_expert": [7, 2, 0, 3, 5]
+        }, {
+            "device_id": 1,
+            "device_expert": [6, 1, 4, 7, 2]
+        }]
+    }]
+}
+
+
+class TestExpertLoadBalancer(unittest.TestCase):
+
+    def setUp(self):
+        json_file = "expert_map.json"
+        with open(json_file, 'w') as f:
+            json.dump(MOCK_DATA, f)
+
+        self.expert_load_balancer = ExpertLoadBalancer(json_file,
+                                                       global_expert_num=8)
+
+    def test_init(self):
+
+        self.assertIsInstance(self.expert_load_balancer.expert_map_tensor,
+                              torch.Tensor)
+        self.assertEqual(self.expert_load_balancer.layers_num,
+                         MOCK_DATA["moe_layer_count"])
+        self.assertEqual(self.expert_load_balancer.ranks_num,
+                         MOCK_DATA["layer_list"][0]["device_count"])
+
+    def test_generate_index_dicts(self):
+        tensor_2d = torch.tensor([[7, 2, 0, 3, 5], [6, 1, 4, 7, 2]])
+        result = self.expert_load_balancer.generate_index_dicts(tensor_2d)
+        expected_result = [{
+            7: 0,
+            2: 1,
+            0: 2,
+            3: 3,
+            5: 4
+        }, {
+            6: 5,
+            1: 6,
+            4: 7,
+            7: 8,
+            2: 9
+        }]
+        self.assertEqual(result, expected_result)
+
+    def test_generate_expert_placement_map(self):
+        expert_placement_map = self.expert_load_balancer.generate_expert_placement_map(
+        )
+        self.assertEqual(expert_placement_map.shape,
+                         (self.expert_load_balancer.layers_num,
+                          self.expert_load_balancer.ranks_num, 8))
+        self.assertTrue(torch.all(expert_placement_map >= -1))
+
+    def test_generate_log2phy_expert_map(self):
+        layer_id = 0
+        log2phy_map = self.expert_load_balancer.generate_log2phy_expert_map(
+            layer_id)
+        self.assertEqual(log2phy_map.shape,
+                         (self.expert_load_balancer.ranks_num, 8))
+        self.assertTrue(torch.all(log2phy_map >= -1))
+
+    @mock.patch("torch_npu.npu._lazy_init")
+    @mock.patch("torch.npu.current_device", return_value="cpu")
+    def test_get_rank_placement_map(self, mock_current_device, mock_lazy_init):
+        layer_id = 0
+        rank_id = 0
+        rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
+            layer_id, rank_id)
+        self.assertEqual(rank_local_expert_num, 5)
+        expected_tensor = torch.tensor([2, -1, 1, 3, -1, 4, -1, 0],
+                                       dtype=torch.int32).to(
+                                           rank_expert_map.device)
+        self.assertTrue(rank_expert_map.equal(expected_tensor))
+
+        rank_id = 1
+        rank_local_expert_num, rank_expert_map = self.expert_load_balancer.get_rank_placement_map(
+            layer_id, rank_id)
+        expected_tensor = torch.tensor([-1, 1, 4, -1, 2, -1, 0, 3],
+                                       dtype=torch.int32).to(
+                                           rank_expert_map.device)
+        self.assertTrue(rank_expert_map.equal(expected_tensor))
+
+    def test_get_rank_log2phy_map(self):
+        layer_id = 0
+        rank_id = 0
+        log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
+            layer_id, rank_id)
+        expected_tensor = torch.tensor([2, 6, 1, 3, 7, 4, 5, 0],
+                                       dtype=torch.int32).to(
+                                           log2phy_map.device)
+        self.assertTrue(log2phy_map.equal(expected_tensor))
+
+        rank_id = 1
+        log2phy_map = self.expert_load_balancer.get_rank_log2phy_map(
+            layer_id, rank_id)
+        expected_tensor = torch.tensor([2, 6, 9, 3, 7, 4, 5, 8],
+                                       dtype=torch.int32).to(
+                                           log2phy_map.device)
+        self.assertTrue(log2phy_map.equal(expected_tensor))
+
+    def test_get_global_redundant_expert_num(self):
+        redundant_expert_num = self.expert_load_balancer.get_global_redundant_expert_num(
+        )
+        expected_redundant_expert_num = len(MOCK_DATA["layer_list"][0]["device_list"][0]["device_expert"]) * \
+                                        MOCK_DATA["layer_list"][0]["device_count"] - 8
+        self.assertEqual(redundant_expert_num, expected_redundant_expert_num)

vllm_ascend/quantization/w8a8_dynamic.py

@@ -217,7 +217,7 @@ def fused_experts_with_mc2(
     dynamic_scale_for_share: Optional[Any] = None,
     mc2_mask: Optional[torch.Tensor] = None,
 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
-    if log2phy:
+    if log2phy is not None:
         topk_ids = log2phy[topk_ids]
     quant_mode = 2
     ep_group = get_ep_group()
@@ -352,7 +352,7 @@ def fused_experts_with_all2all(hidden_states: torch.Tensor,
                                global_redundant_expert_num: int = 0,
                                w1_scale_bias: torch.Tensor = None,
                                w2_scale_bias: torch.Tensor = None):
-    if log2phy:
+    if log2phy is not None:
         topk_ids = log2phy[topk_ids]
     original_shape = hidden_states.shape
     if len(original_shape) == 3: