[Feat] Support routing replay (#6696)

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

[Feat] Support routing replay
same as https://github.com/vllm-project/vllm-ascend/pull/6666
resubmit  because of DOC failure

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

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: liyongwen <1310439159@qq.com>
Signed-off-by: Li-Yongwen <63399187+Li-Yongwen@users.noreply.github.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/ut/patch/worker/patch_common/test_patch_routed_experts_capturer.py

@@ -0,0 +1,58 @@
+from unittest.mock import patch, MagicMock
+import uuid
+
+import torch
+
+from tests.ut.base import TestBase
+from vllm_ascend.patch.worker.patch_routed_experts_capturer import RoutedExpertsCapturer
+from vllm.config import ModelConfig, VllmConfig
+from vllm.config.parallel import ParallelConfig
+from transformers import PretrainedConfig
+from vllm.platforms import current_platform
+
+
+class MockVllmConfig:
+
+    def __init__(self):
+        self.model_config = MagicMock()
+        self.model_config.hf_text_config.num_hidden_layers = 1
+        self.model_config.hf_text_config.num_experts_per_tok = 1
+        self.parallel_config = MagicMock()
+        self.parallel_config.data_parallel_rank = 0
+        self.instance_id = uuid.uuid4().hex
+
+
+class TestPatchRoutedExpertsCapturer(TestBase):
+
+    def setUp(self):
+        RoutedExpertsCapturer.create()
+        self.capturer = RoutedExpertsCapturer.get_instance()
+        self.vllm_config = MockVllmConfig()
+
+    def test_init_buffer(self):
+        max_num_batched_tokens = 1
+        max_num_kv_tokens = 1
+        with patch(
+            target="vllm_ascend.patch.worker.patch_routed_experts_capturer.get_tensor_model_parallel_rank",
+            return_value=True
+        ):
+            current_platform.device_name = "cpu"
+            self.capturer.init_buffer(
+                max_num_batched_tokens=max_num_batched_tokens,
+                max_num_kv_tokens=max_num_kv_tokens,
+                vllm_config=self.vllm_config,
+            )
+            self.assertEqual(
+                self.capturer._device_buffer.shape,
+                (
+                    max_num_batched_tokens,
+                    self.vllm_config.model_config.hf_text_config.num_hidden_layers,
+                    self.vllm_config.model_config.hf_text_config.num_experts_per_tok,
+                )
+            )
+            self.assertEqual(self.capturer._device_buffer.dtype, torch.int32)
+            self.assertEqual(self.capturer._device_buffer.device.type, current_platform.device_name)
+
+    def tearDown(self):
+        self.capturer.clear_buffer()
+        self.capturer.cleanup()