Add ability to replace oot ops when using lora

vllm/lora/layers/column_parallel_linear.py

@@ -9,6 +9,7 @@
 from vllm.config.lora import LoRAConfig
 from vllm.distributed import tensor_model_parallel_all_gather
 from vllm.distributed.utils import divide
+from vllm.model_executor.custom_op import maybe_get_oot_by_class
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     MergedColumnParallelLinear,
@@ -155,9 +156,9 @@ def can_replace_layer(
         packed_modules_list: list,
         model_config: PretrainedConfig | None = None,
     ) -> bool:
-        if type(source_layer) is ColumnParallelLinear:
+        if type(source_layer) is maybe_get_oot_by_class(ColumnParallelLinear):
             return True
-        if type(source_layer) is MergedColumnParallelLinear:
+        if type(source_layer) is maybe_get_oot_by_class(MergedColumnParallelLinear):
             if len(packed_modules_list) != 1:
                 return False
             # Exclude layers with 3+ output sizes - those are handled by
@@ -606,7 +607,7 @@ def can_replace_layer(
     ) -> bool:
         # Support MergedColumnParallelLinear with 3 or more slices
         # (2 slices are handled by MergedColumnParallelLinearWithLoRA)
-        if type(source_layer) is not MergedColumnParallelLinear:
+        if type(source_layer) is not maybe_get_oot_by_class(MergedColumnParallelLinear):
             return False
 
         # If packed_modules_list has 3+ items, use this class

vllm/lora/layers/replicated_linear.py

@@ -7,6 +7,7 @@
 from transformers import PretrainedConfig
 
 from vllm.config.lora import LoRAConfig
+from vllm.model_executor.custom_op import maybe_get_oot_by_class
 from vllm.model_executor.layers.linear import ReplicatedLinear
 
 from .base_linear import BaseLinearLayerWithLoRA
@@ -55,7 +56,7 @@ def can_replace_layer(
         packed_modules_list: list,
         model_config: PretrainedConfig | None = None,
     ) -> bool:
-        return type(source_layer) is ReplicatedLinear
+        return type(source_layer) is maybe_get_oot_by_class(ReplicatedLinear)
 
     def slice_lora_a(
         self, lora_a: torch.Tensor | list[torch.Tensor | None]

vllm/lora/layers/row_parallel_linear.py

@@ -11,6 +11,7 @@
     split_tensor_along_last_dim,
     tensor_model_parallel_all_reduce,
 )
+from vllm.model_executor.custom_op import maybe_get_oot_by_class
 from vllm.model_executor.layers.linear import RowParallelLinear
 from vllm.platforms import current_platform
 
@@ -89,7 +90,7 @@ def can_replace_layer(
         packed_modules_list: list,
         model_config: PretrainedConfig | None = None,
     ) -> bool:
-        return type(source_layer) is RowParallelLinear
+        return type(source_layer) is maybe_get_oot_by_class(RowParallelLinear)
 
 
 # The following layer is based on the tensor parallelism strategy given in

vllm/lora/layers/vocal_parallel_embedding.py

@@ -7,6 +7,7 @@
 from transformers import PretrainedConfig
 
 from vllm.config.lora import LoRAConfig
+from vllm.model_executor.custom_op import maybe_get_oot_by_class
 from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from vllm.platforms import current_platform
 
@@ -132,7 +133,7 @@ def can_replace_layer(
         packed_modules_list: list,
         model_config: PretrainedConfig | None = None,
     ) -> bool:
-        return type(source_layer) is VocabParallelEmbedding
+        return type(source_layer) is maybe_get_oot_by_class(VocabParallelEmbedding)
 
     @property
     def weight(self):

vllm/model_executor/custom_op.py

@@ -22,10 +22,11 @@
 op_registry_oot: dict[str, type["CustomOp"] | type["PluggableLayer"]] = {}
 
 
-def get_oot_class_by_name(class_name: str) -> type | None:
+def maybe_get_oot_by_class(class_type: type) -> type:
+    class_name = class_type.__name__
     if class_name in op_registry_oot:
         return op_registry_oot[class_name]
-    return None
+    return class_type
 
 
 class PluggableLayer(nn.Module):

vllm/model_executor/layers/attention/mm_encoder_attention.py

@@ -6,7 +6,7 @@
 import torch
 
 from vllm.logger import init_logger
-from vllm.model_executor.custom_op import CustomOp, get_oot_class_by_name
+from vllm.model_executor.custom_op import CustomOp, maybe_get_oot_by_class
 from vllm.model_executor.models.vision import get_vit_attn_backend
 from vllm.utils.math_utils import round_up
 from vllm.v1.attention.backends.fa_utils import get_flash_attn_version
@@ -125,7 +125,7 @@ def maybe_compute_seq_lens(
         cu_seqlens: np.ndarray,
         device: torch.device,
     ) -> torch.Tensor | None:
-        if (oot_class := get_oot_class_by_name(cls.__name__)) is not None:
+        if (oot_class := maybe_get_oot_by_class(cls)) is not cls:
             return oot_class.maybe_compute_seq_lens(attn_backend, cu_seqlens, device)  # type: ignore[attr-defined]
 
         if attn_backend != AttentionBackendEnum.FLASHINFER:
@@ -149,7 +149,7 @@ def maybe_recompute_cu_seqlens(
         tp_size: int,
         device: torch.device,
     ) -> torch.Tensor:
-        if (oot_class := get_oot_class_by_name(cls.__name__)) is not None:
+        if (oot_class := maybe_get_oot_by_class(cls)) is not cls:
             return oot_class.maybe_recompute_cu_seqlens(  # type: ignore[attr-defined]
                 attn_backend, cu_seqlens, hidden_size, tp_size, device
             )