[Misc] Move lora patch file into lora module (#2797)

Cleanup useless file in patch module. Update the lora support list is OK
in vLLM Ascend, no need to patch vLLM


- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/lora/punica_npu.py

@@ -11,12 +11,14 @@ if is_310p():
                                          bgmv_shrink, sgmv_expand,
                                          sgmv_expand_slice, sgmv_shrink)
 else:
-    from vllm_ascend.lora.punica_wrapper.lora_ops import (
-        bgmv_expand, bgmv_expand_slice, bgmv_shrink, sgmv_expand,
-        sgmv_expand_slice, sgmv_shrink)
+    from vllm_ascend.lora.lora_ops import (bgmv_expand, bgmv_expand_slice,
+                                           bgmv_shrink, sgmv_expand,
+                                           sgmv_expand_slice, sgmv_shrink)
 
 from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
 
+from vllm_ascend.lora.utils import refresh_all_lora_classes
+
 
 # The platforms that are compatible with the PyTorch-native implementation can
 # inherit this class
@@ -31,6 +33,7 @@ class PunicaWrapperNPU(PunicaWrapperBase):
                  device: Union[torch.device, str], **kwargs):
         PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
                                    device)
+        refresh_all_lora_classes()
 
     def _shrink_prefill(
         self,

vllm_ascend/lora/utils.py

@@ -0,0 +1,77 @@
+from typing import Optional
+
+import vllm
+from torch import nn
+from transformers import PretrainedConfig
+from vllm.config import LoRAConfig
+from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
+                              MergedColumnParallelLinearWithLoRA,
+                              RowParallelLinearWithLoRA,
+                              VocabParallelEmbeddingWithLoRA)
+
+from vllm_ascend.ops.linear import (AscendColumnParallelLinear,
+                                    AscendMergedColumnParallelLinear,
+                                    AscendRowParallelLinear)
+from vllm_ascend.ops.vocab_parallel_embedding import \
+    AscendVocabParallelEmbedding
+
+
+class AscendColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
+
+    @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 AscendColumnParallelLinear
+
+
+class AscendMergedColumnParallelLinearWithLoRA(
+        MergedColumnParallelLinearWithLoRA):
+
+    @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 AscendMergedColumnParallelLinear
+
+
+class AscendRowParallelLinearWithLoRA(RowParallelLinearWithLoRA):
+
+    @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 AscendRowParallelLinear
+
+
+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
+
+
+def refresh_all_lora_classes():
+    vllm.lora.utils._all_lora_classes.add(AscendColumnParallelLinearWithLoRA)
+    vllm.lora.utils._all_lora_classes.add(
+        AscendMergedColumnParallelLinearWithLoRA)
+    vllm.lora.utils._all_lora_classes.add(AscendRowParallelLinearWithLoRA)
+    vllm.lora.utils._all_lora_classes.add(AscendVocabParallelEmbeddingWithLoRA)