[Bugfix][LoRA][Operator] Fix LoRA custom operators accuracy issue (#2672)

### What this PR does / why we need it?
Fix the LoRA accuracy issue that introduced by custom AscendC operator
"bgmv_shrink, sgmv_shrink, bgmv_expand, sgmv_epand".

The bug details are: 
- In the kernel function, if you want to call GlobalTensor.GetSize
method, you have to pass the second parameter of bufferSize when you
call GlobalTensor.SetGlobalBuffer first.
- Or GlobalTensor.GetSize method will return a random value.
- You can refer to [this
doc](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/81RC1alpha002/apiref/ascendcopapi/atlasascendc_api_07_00024.html).

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
pytest -sv tests/e2e/singlecard/test_ilama_lora.py
pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py

- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a

---------

Signed-off-by: paulyu12 <paulyu0307@gmail.com>
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: paulyu12 <paulyu0307@gmail.com>

vllm_ascend/patch/worker/patch_common/__init__.py

@@ -18,4 +18,5 @@
 import vllm_ascend.patch.worker.patch_common.patch_distributed  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_linear  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_logits  # noqa
+import vllm_ascend.patch.worker.patch_common.patch_lora_embedding  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_minicpm  # noqa

vllm_ascend/patch/worker/patch_common/patch_lora_embedding.py

@@ -0,0 +1,29 @@
+from typing import Optional
+
+import vllm
+from torch import nn
+from transformers import PretrainedConfig
+from vllm.config import LoRAConfig
+from vllm.lora.layers import VocabParallelEmbeddingWithLoRA
+from vllm.lora.utils import _all_lora_classes
+
+from vllm_ascend.ops.vocab_parallel_embedding import \
+    AscendVocabParallelEmbedding
+
+
+class AscendVocabParallelEmbeddingWithLoRA(VocabParallelEmbeddingWithLoRA):
+
+    @classmethod
+    def can_replace_layer(
+        cls,
+        source_layer: nn.Module,
+        lora_config: LoRAConfig,
+        packed_modules_list: list,
+        model_config: Optional[PretrainedConfig],
+    ) -> bool:
+        return type(source_layer) is AscendVocabParallelEmbedding
+
+
+# Patch for lora register_model issue after overriding VocabParallelEmbedding class (#2515)
+_all_lora_classes.add(AscendVocabParallelEmbeddingWithLoRA)
+vllm.lora.utils._all_lora_classes = _all_lora_classes