[Bugfix][LoRA] Fix the bug when runs Qwen3-Reranker-0.6B with LoRA. (#7156)

### What this PR does / why we need it?
Fix the error that reports while initializing qwen3-reranker-0.6b model
with `--enable-lora`.
And add a testcase to verify the fix.

- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>

vllm_ascend/lora/utils.py

@@ -15,12 +15,14 @@ from vllm.lora.layers import (
     RowParallelLinearWithShardedLoRA,
     VocabParallelEmbeddingWithLoRA,
 )
+from vllm.lora.layers.replicated_linear import ReplicatedLinearWithLoRA
 from vllm.lora.layers.utils import _fully_sharded_can_replace, _not_fully_sharded_can_replace
 
 from vllm_ascend.ops.linear import (
     AscendColumnParallelLinear,
     AscendMergedColumnParallelLinear,
     AscendQKVParallelLinear,
+    AscendReplicatedLinear,
     AscendRowParallelLinear,
 )
 from vllm_ascend.ops.vocab_parallel_embedding import AscendVocabParallelEmbedding
@@ -103,6 +105,20 @@ class AscendMergedQKVParallelLinearWithLoRA(MergedQKVParallelLinearWithLoRA):
         return type(source_layer) is AscendQKVParallelLinear and len(packed_modules_list) == 3
 
 
+class AscendReplicatedLinearWithLoRA(ReplicatedLinearWithLoRA):
+    # ReplicatedLinear should always be replaced, regardless of the fully
+    # sharded LoRAs setting, because it is, by definition, copied per GPU.
+    @classmethod
+    def can_replace_layer(
+        cls,
+        source_layer: nn.Module,
+        lora_config: LoRAConfig,
+        packed_modules_list: list,
+        model_config: PretrainedConfig | None = None,
+    ) -> bool:
+        return type(source_layer) is AscendReplicatedLinear
+
+
 class AscendColumnParallelLinearWithShardedLoRA(ColumnParallelLinearWithShardedLoRA):
     @classmethod
     @_fully_sharded_can_replace
@@ -180,3 +196,4 @@ def refresh_all_lora_classes():
     vllm.lora.utils._all_lora_classes.add(AscendMergedQKVParallelLinearWithShardedLoRA)
     vllm.lora.utils._all_lora_classes.add(AscendQKVParallelLinearWithShardedLoRA)
     vllm.lora.utils._all_lora_classes.add(AscendRowParallelLinearWithShardedLoRA)
+    vllm.lora.utils._all_lora_classes.add(AscendReplicatedLinearWithLoRA)

vllm_ascend/ops/linear.py

@@ -433,7 +433,7 @@ class AscendReplicatedLinear(ReplicatedLinear):
         return_bias: bool = True,
         disable_tp: bool = False,
     ):
-        self.custom_op = get_replicated_op(disable_tp, prefix, self)
+        self.custom_op, self.tp_rank, self.tp_size = get_replicated_op(disable_tp, prefix, self)
         # If MergedReplicatedLinear, use output size of each partition.
         if hasattr(self, "output_sizes"):
             self.output_partition_sizes = self.output_sizes

vllm_ascend/ops/linear_op.py

@@ -734,11 +734,12 @@ def get_parallel_op(disable_tp, prefix, layer, direct):
     return None, get_tp_group().rank_in_group, get_tp_group().world_size
 
 
-def get_replicated_op(disable_tp, prefix, layer) -> CustomReplicatedOp | None:
+def get_replicated_op(disable_tp, prefix, layer) -> tuple[CustomReplicatedOp | None, int | None, int | None]:
     if disable_tp:
-        return None
+        return None, None, None
 
-    return CustomReplicatedOp(layer)
+    custom_op = CustomReplicatedOp(layer)
+    return custom_op, custom_op.tp_rank, custom_op.tp_size
 
 
 def is_moe_layer(prefix: str) -> bool: