[Hardware][CPU] Multi-LoRA implementation for the CPU backend (#11100)

Signed-off-by: Akshat Tripathi <akshat@krai.ai>
Signed-off-by: Oleg Mosalov <oleg@krai.ai>
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
Co-authored-by: Oleg Mosalov <oleg@krai.ai>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>
Co-authored-by: Isotr0py <2037008807@qq.com>

vllm/lora/punica_wrapper/punica_cpu.py

+from typing import Callable, Optional, Tuple, Union
+
+import torch
+
+from vllm.lora.ops.torch_ops import (bgmv_expand, bgmv_expand_slice,
+                                     bgmv_shrink, sgmv_expand,
+                                     sgmv_expand_slice, sgmv_shrink)
+
+from .punica_base import PunicaWrapperBase
+
+
+# The platforms that are compatible with the PyTorch-native implementation can
+# inherit this class
+class PunicaWrapperCPU(PunicaWrapperBase):
+    """
+    PunicaWrapperCPU is designed to manage and provide metadata for the punica 
+    kernel. The main function is to maintain the state information for 
+    Multi-LoRA, and to provide the interface for the pytorch punica ops.
+    """
+
+    def __init__(self, max_num_batched_tokens: int, max_batches: int,
+                 device: Union[torch.device, str], **kwargs):
+        PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
+                                   device)
+
+    def _shrink_prefill(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        scale: float,
+    ):
+        #No LoRA request, so return directly
+        if self.no_lora:
+            return
+        sgmv_shrink(
+            x,
+            w_t_all,
+            y,
+            *self.prefill_metadata,
+            scale,
+        )
+
+    def _shrink_decode(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        scale: float,
+    ):
+        bgmv_shrink(x, w_t_all, y, self.token_lora_indices, scale)
+
+    def _expand_prefill(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        add_inputs: bool,
+    ):
+        #No LoRA request, so return directly
+        if self.no_lora:
+            return
+        sgmv_expand(
+            x,
+            w_t_all,
+            y,
+            *self.prefill_metadata,
+            add_inputs,
+        )
+
+    def _expand_decode(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        add_inputs: bool,
+    ):
+        bgmv_expand(x, w_t_all, y, self.token_lora_indices, add_inputs)
+
+    def _expand_slice_prefill(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        y_offset: int,
+        y_slice_size: int,
+        add_inputs: bool,
+    ):
+        #No LoRA request, so return directly
+        if self.no_lora:
+            return
+        sgmv_expand_slice(
+            x,
+            w_t_all,
+            y,
+            *self.prefill_metadata,
+            y_offset,
+            y_slice_size,
+            add_inputs,
+        )
+
+    def _expand_slice_decode(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        y_offset: int,
+        y_slice_size: int,
+        add_inputs: bool,
+    ):
+        bgmv_expand_slice(x, w_t_all, y, self.token_lora_indices, y_offset,
+                          y_slice_size, add_inputs)
+
+    def _apply_expand(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        y_offset: int,
+        y_slice_size: int,
+        add_inputs: bool = True,
+    ):
+        """
+        Perform the ` y[:,y_offset:y_offset+y_slice_size]+=x@w_t_all` 
+        computation, which is suitable for the
+        GEMM of lora'b.
+        """
+
+        expand_slice_fun: Callable = (self._expand_slice_prefill
+                                      if self.is_prefill else
+                                      self._expand_slice_decode)
+        expand_slice_fun(y, x, w_t_all, y_offset, y_slice_size, add_inputs)
+
+    def _apply_shrink(self, y: torch.Tensor, x: torch.Tensor,
+                      w_t_all: torch.Tensor, scale: float):
+        """
+        Perform the ` y+=x@w_t_all` computation, which is suitable for the
+        GEMM of lora'a.
+        When `is_prefill is` true, it indicates that it is currently the
+        prefill stage, and the `_shrink_prefill` function should be called.
+        Otherwise, it is the decode stage, and the _shrink_decode function
+        should be called.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        shrink_fun: Callable = (self._shrink_prefill
+                                if self.is_prefill else self._shrink_decode)
+        shrink_fun(y, x, w_t_all, scale)
+        y = y.view_as(y_org)
+
+    def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
+                   x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...],
+                   scale: float, **kwargs):
+        """
+        Performs GEMM  for multiple slices of lora_a.
+        When `is_prefill is` true, it indicates that it is currently the
+        prefill stage, and the `_shrink_prefill` function should be called.
+        Otherwise, it is the decode stage, and the _shrink_decode function
+        should be called.
+            
+        Semantics:
+        for i in range(len(lora_a_stacked)):
+            y[i] += (x @ lora_a_stacked[i]) * scale
+        
+        Args:
+            y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors
+            x (torch.Tensor): Input tensor
+            lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights
+            scale (float): Scaling factor for the operation
+        """
+
+        x = x.view(-1, x.shape[-1])
+        # TODO fuse these kernels
+        for slice_idx in range(len(lora_a_stacked)):
+            self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx],
+                               scale)
+
+    def add_expand(self,
+                   y: torch.Tensor,
+                   x: Union[Tuple[torch.Tensor, ...], torch.Tensor],
+                   lora_b_stacked: Tuple[torch.Tensor, ...],
+                   lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
+                   output_slices: Tuple[int, ...],
+                   offset_start: int = 0,
+                   add_inputs=True,
+                   **kwargs) -> None:
+        """
+        Performs GEMM and bias addition for multiple slices of lora_b.
+      
+        Semantics:
+            for i in range(len(lora_b_stacked)):
+                slice = output_slices[i]
+                y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] + 
+                    lora_bias_stacked[i] 
+                offset += slice
+            
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors
+            lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight
+            lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): 
+                bias's weight
+            output_slices (Tuple[int, ...]): Every slice's size
+            add_inputs (bool):  Defaults to True.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        offset_left = offset_start
+        if lora_bias_stacked is not None:
+            self._apply_bias(self.token_lora_indices, y, output_slices,
+                             lora_bias_stacked)
+        for slice_idx in range(len(lora_b_stacked)):
+            self._apply_expand(
+                y,
+                x[slice_idx],
+                lora_b_stacked[slice_idx],
+                offset_left,
+                output_slices[slice_idx],
+                add_inputs=add_inputs,
+            )
+            offset_left += output_slices[slice_idx]
+        y = y.view_as(y_org)
+
+    def add_lora_embedding(self,
+                           y: torch.Tensor,
+                           x: torch.Tensor,
+                           lora_b_stacked: torch.Tensor,
+                           add_inputs: bool = True,
+                           **kwargs) -> None:
+        """
+        Applies lora  specifically for VocabParallelEmbeddingWithLoRA.
+
+        Semantics:
+            y += x @ lora_b_stacked
+
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (torch.Tensor): Input tensor.
+            lora_b_stacked (torch.Tensor): lora_b's weights.
+            add_inputs (bool): Default to True.
+        """
+
+        # Embedding layer only need expand op
+        expand_fun: Callable = (self._expand_prefill
+                                if self.is_prefill else self._expand_decode)
+        expand_fun(y, x, lora_b_stacked, add_inputs)
+
+    def add_lora_linear(self,
+                        y: torch.Tensor,
+                        x: torch.Tensor,
+                        lora_a_stacked: Tuple[torch.Tensor, ...],
+                        lora_b_stacked: Tuple[torch.Tensor, ...],
+                        lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
+                        scale: float,
+                        output_slices: Tuple[int, ...],
+                        *,
+                        buffer: Optional[Tuple[torch.Tensor, ...]] = None,
+                        **kwargs) -> None:
+        """
+        Applicable to linear-related lora. 
+
+        Semantics:
+            for i in range(len(lora_a_stacked)):
+                y[i] += (
+                    x[i].unsqueeze(0)
+                    @ lora_a_stacked[indices[i], layer_idx, :, :]
+                    @ lora_b_stacked[indices[i], layer_idx, :, :]
+                    * scale
+                    ).squeeze(0)+lora_bias_stacked[i]
+
+        Args:
+            y (torch.Tensor): Output tensor. Will be changed in-place.
+            x (torch.Tensor): Input tensor
+            lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight.
+            lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight.
+            lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias.
+            scale (float): Scaling factor.
+            output_slices (Tuple[int, ...]): Every slice's size.
+            buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None.
+        """
+
+        assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
+        if lora_bias_stacked is not None:
+            assert len(lora_bias_stacked) == len(output_slices)
+            y = self._apply_bias(self.token_lora_indices, y, output_slices,
+                                 lora_bias_stacked)
+
+        if buffer is None:
+            r = lora_b_stacked[0].size(-1)
+            # We set the buffer to be float32 by default, consistent with the
+            # triton op
+            buffer = tuple(
+                torch.zeros(
+                    (x.size(0), r), dtype=torch.float32, device=x.device)
+                for _ in range(len(output_slices)))
+        self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs)
+        self.add_expand(y,
+                        buffer,
+                        lora_b_stacked,
+                        None,
+                        output_slices,
+                        add_inputs=True,
+                        **kwargs)
+
+    def add_lora_logits(self,
+                        y: torch.Tensor,
+                        x: torch.Tensor,
+                        lora_a_stacked: torch.Tensor,
+                        lora_b_stacked: torch.Tensor,
+                        scale,
+                        *,
+                        buffer: Optional[torch.Tensor] = None,
+                        **kwargs) -> None:
+        """
+        Applies lora  specifically for LogitsProcessorWithLoRA.
+        
+        Semantics:
+            buffer = (x @ lora_a_stacked) * scale
+            y += buffer @ lora_b_stacked
+
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (torch.Tensor): Input tensor.
+            lora_a_stacked (torch.Tensor): lora_a's weights.
+            lora_b_stacked (torch.Tensor):lora_b's weights.
+            scale (float): Scaling factor.
+            buffer (Optional[torch.Tensor]):Default to None.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        x = x.view(-1, x.shape[-1])
+        r = lora_b_stacked.size(-1)
+        if buffer is None:
+            # We set the buffer to be float32 by default, consistent with the
+            # triton op
+            buffer = torch.zeros((x.size(0), r),
+                                 dtype=torch.float32,
+                                 device=x.device)
+        # LogitsProcessorWithLoRA always using bgmv.
+        bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale)
+        bgmv_expand(buffer,
+                    lora_b_stacked,
+                    y,
+                    self.sampler_indices,
+                    add_inputs=True)
+        y = y.view_as(y_org)

vllm/lora/punica_wrapper/punica_gpu.py

@@ -12,11 +12,11 @@ import torch
 from vllm.triton_utils import HAS_TRITON
 
 if HAS_TRITON:
-    from vllm.lora.ops.bgmv_expand import bgmv_expand
-    from vllm.lora.ops.bgmv_expand_slice import bgmv_expand_slice
-    from vllm.lora.ops.bgmv_shrink import bgmv_shrink
-    from vllm.lora.ops.sgmv_expand import sgmv_expand
-    from vllm.lora.ops.sgmv_shrink import sgmv_shrink
+    from vllm.lora.ops.triton_ops import bgmv_expand
+    from vllm.lora.ops.triton_ops import bgmv_expand_slice
+    from vllm.lora.ops.triton_ops import bgmv_shrink
+    from vllm.lora.ops.triton_ops import sgmv_expand
+    from vllm.lora.ops.triton_ops import sgmv_shrink
 
 from .punica_base import PunicaWrapperBase
 

vllm/lora/punica_wrapper/punica_selector.py

@@ -12,6 +12,11 @@ def get_punica_wrapper(*args, **kwargs) -> PunicaWrapperBase:
         from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU
         logger.info_once("Using PunicaWrapperGPU.")
         return PunicaWrapperGPU(*args, **kwargs)
+    elif current_platform.is_cpu():
+        # Lazy import to avoid ImportError
+        from vllm.lora.punica_wrapper.punica_cpu import PunicaWrapperCPU
+        logger.info_once("Using PunicaWrapperCPU.")
+        return PunicaWrapperCPU(*args, **kwargs)
     elif current_platform.is_hpu():
         # Lazy import to avoid ImportError
         from vllm.lora.punica_wrapper.punica_hpu import PunicaWrapperHPU

vllm/worker/cpu_model_runner.py

@@ -2,8 +2,8 @@ import dataclasses
 import weakref
 from collections import defaultdict
 from dataclasses import dataclass
-from typing import (TYPE_CHECKING, Any, Dict, List, Optional, Type, TypeVar,
-                    Union)
+from typing import (TYPE_CHECKING, Any, Dict, List, Optional, Set, Type,
+                    TypeVar, Union)
 
 import torch
 from torch import nn
@@ -12,10 +12,14 @@ from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.config import VllmConfig
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
+from vllm.lora.layers import LoRAMapping
+from vllm.lora.request import LoRARequest
+from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.model_loader import get_model
+from vllm.model_executor.models import supports_lora, supports_multimodal
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
                              MultiModalKwargs, MultiModalPlaceholderMap)
 from vllm.sequence import (IntermediateTensors, SequenceData,
@@ -49,6 +53,8 @@ class ModelInputForCPU(ModelRunnerInputBase):
     virtual_engine: Optional[int] = None
     seq_lens: Optional[List[int]] = None
     query_lens: Optional[List[int]] = None
+    lora_mapping: Optional["LoRAMapping"] = None
+    lora_requests: Optional[Set[LoRARequest]] = None
 
     def as_broadcastable_tensor_dict(
             self) -> Dict[str, Union[int, torch.Tensor]]:
@@ -57,6 +63,8 @@ class ModelInputForCPU(ModelRunnerInputBase):
             "input_positions": self.input_positions,
             "token_type_ids": self.token_type_ids,
             "multi_modal_kwargs": self.multi_modal_kwargs,
+            "lora_requests": self.lora_requests,
+            "lora_mapping": self.lora_mapping,
         }
         _add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
 
@@ -143,7 +151,11 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
                                 or runner.cache_config.enable_prefix_caching)
         self.model_input_cls = self.runner._model_input_cls
         self.attn_backend = self.runner.attn_backend
+        self.sliding_window = self.runner.sliding_window
+        self.block_size = self.runner.block_size
+        self.device = self.runner.device
         self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper
+        self.enable_lora = self.runner.lora_config is not None
         self.input_data = ModelInputForCPUBuilder.ModelInputData(
             self.runner.model_config.uses_mrope)
         self.att_metadata_builder = self.runner.attn_backend.get_builder_cls()(
@@ -183,15 +195,28 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
         attn_metadata = self.att_metadata_builder.build(
             input_data.seq_lens, input_data.query_lens, -1, -1)
 
-        return self.model_input_cls(
-            input_tokens=input_tokens,
+        is_prompt = (self.seq_group_metadata_list[0].is_prompt
+                     if self.seq_group_metadata_list else None)
+        # LoRA data.
+        lora_requests = set()
+        lora_mapping = None
+        if self.enable_lora:
+            lora_requests = set(seq.lora_request
+                                for seq in self.seq_group_metadata_list
+                                if seq.lora_request is not None)
+
+            lora_mapping = self._prepare_lora_input(
+                self.seq_group_metadata_list, is_prompt)
+
+        return self.model_input_cls(input_tokens=input_tokens,
                                     input_positions=input_positions,
                                     token_type_ids=token_type_ids,
                                     seq_lens=input_data.seq_lens,
                                     query_lens=input_data.query_lens,
                                     attn_metadata=attn_metadata,
                                     multi_modal_kwargs=multi_modal_kwargs,
-        )
+                                    lora_mapping=lora_mapping,
+                                    lora_requests=lora_requests)
 
     def _build_input_data(self):
         for seq_group_metadata in self.seq_group_metadata_list:
@@ -381,6 +406,24 @@ class ModelInputForCPUBuilder(ModelRunnerInputBuilderBase[ModelInputForCPU]):
             self.input_data.multi_modal_placeholder_maps[modality].extend(
                 placeholder_map)
 
+    def _prepare_lora_input(
+            self, seq_group_metadata_list: List[SequenceGroupMetadata],
+            is_prefill: bool) -> LoRAMapping:
+        index_mapping = []
+        prompt_mapping = []
+        for seq in seq_group_metadata_list:
+            lora_id = seq.lora_int_id
+            query_len = seq.token_chunk_size
+
+            index_mapping += [lora_id] * query_len
+            prompt_mapping += [lora_id] * (
+                query_len if seq.sampling_params
+                and seq.sampling_params.prompt_logprobs is not None else 1)
+
+        return LoRAMapping(index_mapping=tuple(index_mapping),
+                           prompt_mapping=tuple(prompt_mapping),
+                           is_prefill=is_prefill)
+
 
 class CPUModelRunnerBase(ModelRunnerBase[TModelInputForCPU]):
     """
@@ -431,10 +474,41 @@ class CPUModelRunnerBase(ModelRunnerBase[TModelInputForCPU]):
 
         # Lazy initialization.
         self.model: nn.Module  # Set after init_Model
+        # Set after load_model.
+        self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None
 
     def load_model(self) -> None:
         self.model = get_model(vllm_config=self.vllm_config)
 
+        if self.lora_config:
+            assert supports_lora(
+                self.model
+            ), f"{self.model.__class__.__name__} does not support LoRA yet."
+
+            if supports_multimodal(self.model):
+                logger.warning("Regarding multimodal models, vLLM currently "
+                               "only supports adding LoRA to language model.")
+
+            # It's necessary to distinguish between the max_position_embeddings
+            # of VLMs and LLMs.
+            if hasattr(self.model.config, "max_position_embeddings"):
+                max_pos_embeddings = self.model.config.max_position_embeddings
+            else:
+                max_pos_embeddings = (
+                    self.model.config.text_config.max_position_embeddings)
+
+            self.lora_manager = LRUCacheWorkerLoRAManager(
+                self.scheduler_config.max_num_seqs,
+                self.scheduler_config.max_num_batched_tokens,
+                self.vocab_size,
+                self.lora_config,
+                self.device,
+                self.model.embedding_modules,
+                self.model.embedding_padding_modules,
+                max_position_embeddings=max_pos_embeddings,
+            )
+            self.model = self.lora_manager.create_lora_manager(self.model)
+
     def _prepare_model_input_tensors(
         self,
         seq_group_metadata_list: List[SequenceGroupMetadata],
@@ -459,6 +533,37 @@ class CPUModelRunnerBase(ModelRunnerBase[TModelInputForCPU]):
     def vocab_size(self) -> int:
         return self.model_config.get_vocab_size()
 
+    def remove_all_loras(self):
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        self.lora_manager.remove_all_adapters()
+
+    def set_active_loras(self, lora_requests: Set[LoRARequest],
+                         lora_mapping: LoRAMapping) -> None:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        self.lora_manager.set_active_adapters(lora_requests, lora_mapping)
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.add_adapter(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.remove_adapter(lora_id)
+
+    def pin_lora(self, lora_id: int) -> bool:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.pin_adapter(lora_id)
+
+    def list_loras(self) -> Set[int]:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.list_adapters()
+
 
 class CPUModelRunner(CPUModelRunnerBase[ModelInputForCPUWithSamplingMetadata]):
     _model_input_cls: Type[ModelInputForCPUWithSamplingMetadata] = (
@@ -515,6 +620,12 @@ class CPUModelRunner(CPUModelRunnerBase[ModelInputForCPUWithSamplingMetadata]):
             raise ValueError(
                 "CPU worker does not support multi-step execution.")
 
+        if self.lora_config:
+            assert model_input.lora_requests is not None
+            assert model_input.lora_mapping is not None
+            self.set_active_loras(model_input.lora_requests,
+                                  model_input.lora_mapping)
+
         model_executable = self.model
 
         multimodal_kwargs = {}

vllm/worker/cpu_worker.py

 """A CPU worker class."""
-from typing import Dict, List, Optional, Tuple, Type
+from typing import Dict, List, Optional, Set, Tuple, Type
 
 import torch
 import torch.distributed
@@ -11,14 +11,14 @@ from vllm.config import (CacheConfig, DeviceConfig, ModelConfig,
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
 from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
 from vllm.sequence import ExecuteModelRequest
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, bind_kv_cache
 from vllm.worker.cpu_enc_dec_model_runner import CPUEncoderDecoderModelRunner
 from vllm.worker.cpu_model_runner import CPUModelRunner, CPUModelRunnerBase
 from vllm.worker.cpu_pooling_model_runner import CPUPoolingModelRunner
-from vllm.worker.worker_base import (LocalOrDistributedWorkerBase,
-                                     LoraNotSupportedWorkerBase, WorkerBase,
+from vllm.worker.worker_base import (LocalOrDistributedWorkerBase, WorkerBase,
                                      WorkerInput)
 
 logger = init_logger(__name__)
@@ -111,7 +111,7 @@ class CPUCacheEngine:
         return dtype_size * total
 
 
-class CPUWorker(LoraNotSupportedWorkerBase, LocalOrDistributedWorkerBase):
+class CPUWorker(LocalOrDistributedWorkerBase):
     """A worker class that executes (a partition of) the model on a CPU socket.
 
     Each worker is associated with a single CPU socket. The worker is 
@@ -266,6 +266,18 @@ class CPUWorker(LoraNotSupportedWorkerBase, LocalOrDistributedWorkerBase):
         # Initialize the cache.
         self._init_cache_engine()
 
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        return self.model_runner.add_lora(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        return self.model_runner.remove_lora(lora_id)
+
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.model_runner.pin_lora(lora_id)
+
+    def list_loras(self) -> Set[int]:
+        return self.model_runner.list_loras()
+
     def _validate_num_cpu_blocks(self, num_cpu_blocks: int) -> None:
         """Raise errors if the num_cpu_blocks is invalid.
         """