[CORE] Adding support for insertion of soft-tuned prompts (#4645)

Co-authored-by: Swapnil Parekh <swapnilp@ibm.com>
Co-authored-by: Joe G <joseph.granados@h2o.ai>
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>

vllm/spec_decode/draft_model_runner.py

@@ -4,7 +4,7 @@ import torch
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig)
+                         PromptAdapterConfig, SchedulerConfig)
 from vllm.logger import init_logger
 from vllm.sequence import (IntermediateTensors, SamplerOutput,
                            SequenceGroupMetadata)
@@ -48,6 +48,7 @@ class TP1DraftModelRunner(ModelRunner):
         kv_cache_dtype: Optional[str] = "auto",
         is_driver_worker: bool = False,
         multimodal_config: Optional[MultiModalConfig] = None,
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         return_hidden_states: bool = False,
     ):
         if return_hidden_states:
@@ -66,6 +67,7 @@ class TP1DraftModelRunner(ModelRunner):
             kv_cache_dtype=kv_cache_dtype,
             is_driver_worker=is_driver_worker,
             multimodal_config=multimodal_config,
+            prompt_adapter_config=prompt_adapter_config,
             return_hidden_states=return_hidden_states,
         )
 
@@ -136,6 +138,13 @@ class TP1DraftModelRunner(ModelRunner):
             self.set_active_loras(model_input.lora_requests,
                                   model_input.lora_mapping)
 
+        if self.prompt_adapter_config:
+            assert model_input.prompt_adapter_requests is not None
+            assert model_input.prompt_adapter_mapping is not None
+            self.set_active_prompt_adapters(
+                model_input.prompt_adapter_requests,
+                model_input.prompt_adapter_mapping)
+
         virtual_engine = model_input.virtual_engine
         outputs: List[SamplerOutput] = []
         for step in range(num_steps):

vllm/worker/cpu_model_runner.py

@@ -8,7 +8,7 @@ from torch import nn
 from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig)
+                         PromptAdapterConfig, SchedulerConfig)
 from vllm.logger import init_logger
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.model_loader import get_model
@@ -81,6 +81,7 @@ class CPUModelRunner(ModelRunnerBase[CPUModelInput]):
         lora_config: Optional[LoRAConfig],
         multimodal_config: Optional[MultiModalConfig],
         kv_cache_dtype: Optional[str] = "auto",
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         is_driver_worker: bool = False,
         *args,
         **kwargs,
@@ -94,6 +95,7 @@ class CPUModelRunner(ModelRunnerBase[CPUModelInput]):
         self.cache_config = cache_config
         self.lora_config = lora_config
         self.multimodal_config = multimodal_config
+        self.prompt_adapter_config = prompt_adapter_config
         self.load_config = load_config
         self.is_driver_worker = is_driver_worker
 

vllm/worker/cpu_worker.py

@@ -7,7 +7,7 @@ import torch.distributed
 from vllm.attention import get_attn_backend
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig)
+                         PromptAdapterConfig, SchedulerConfig)
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
 from vllm.logger import init_logger
@@ -133,6 +133,7 @@ class CPUWorker(LoraNotSupportedWorkerBase, LocalOrDistributedWorkerBase):
         lora_config: Optional[LoRAConfig] = None,
         multimodal_config: Optional[MultiModalConfig] = None,
         kv_cache_dtype: Optional[str] = "auto",
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         is_driver_worker: bool = False,
     ) -> None:
         self.model_config = model_config
@@ -145,6 +146,7 @@ class CPUWorker(LoraNotSupportedWorkerBase, LocalOrDistributedWorkerBase):
         self.rank = rank
         self.distributed_init_method = distributed_init_method
         self.lora_config = lora_config
+        self.prompt_adapter_config = prompt_adapter_config
         self.multimodal_config = multimodal_config
         self.is_driver_worker = is_driver_worker
         if self.is_driver_worker:
@@ -167,6 +169,7 @@ class CPUWorker(LoraNotSupportedWorkerBase, LocalOrDistributedWorkerBase):
             lora_config=self.lora_config,
             multimodal_config=self.multimodal_config,
             kv_cache_dtype=kv_cache_dtype,
+            prompt_adapter_config=self.prompt_adapter_config,
             is_driver_worker=is_driver_worker)
         # Uninitialized cache engine. Will be initialized by
         # initialize_cache.

vllm/worker/embedding_model_runner.py

@@ -5,7 +5,7 @@ import torch
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig)
+                         PromptAdapterConfig, SchedulerConfig)
 from vllm.logger import init_logger
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.pooling_params import PoolingParams
@@ -40,6 +40,7 @@ class EmbeddingModelRunner(
         lora_config: Optional[LoRAConfig],
         kv_cache_dtype: Optional[str] = "auto",
         is_driver_worker: bool = False,
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         multimodal_config: Optional[MultiModalConfig] = None,
     ):
         super().__init__(model_config,
@@ -51,6 +52,7 @@ class EmbeddingModelRunner(
                          lora_config=lora_config,
                          kv_cache_dtype=kv_cache_dtype,
                          is_driver_worker=is_driver_worker,
+                         prompt_adapter_config=prompt_adapter_config,
                          multimodal_config=multimodal_config)
 
     @torch.inference_mode()
@@ -71,6 +73,13 @@ class EmbeddingModelRunner(
             self.set_active_loras(model_input.lora_requests,
                                   model_input.lora_mapping)
 
+        if self.prompt_adapter_config:
+            assert model_input.prompt_adapter_requests is not None
+            assert model_input.prompt_adapter_mapping is not None
+            self.set_active_prompt_adapters(
+                model_input.prompt_adapter_requests,
+                model_input.prompt_adapter_mapping)
+
         # Currently cuda graph is only supported by the decode phase.
         assert model_input.attn_metadata is not None
         prefill_meta = model_input.attn_metadata.prefill_metadata

vllm/worker/model_runner.py

@@ -25,7 +25,7 @@ except ImportError:
 from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig)
+                         PromptAdapterConfig, SchedulerConfig)
 from vllm.distributed import get_pp_group
 from vllm.distributed.parallel_state import graph_capture
 from vllm.inputs import INPUT_REGISTRY
@@ -40,6 +40,10 @@ from vllm.model_executor.models.interfaces import (supports_lora,
                                                    supports_vision)
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensors,
                              MultiModalInputs)
+from vllm.prompt_adapter.layers import PromptAdapterMapping
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.prompt_adapter.worker_manager import (
+    LRUCacheWorkerPromptAdapterManager)
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import (IntermediateTensors, SamplerOutput,
                            SequenceGroupMetadata)
@@ -85,6 +89,8 @@ class ModelInputForGPU(ModelRunnerInputBase):
     lora_mapping: Optional["LoRAMapping"] = None
     lora_requests: Optional[Set[LoRARequest]] = None
     attn_metadata: Optional["AttentionMetadata"] = None
+    prompt_adapter_mapping: Optional[PromptAdapterMapping] = None
+    prompt_adapter_requests: Optional[Set[PromptAdapterRequest]] = None
     multi_modal_kwargs: Optional[Mapping[str, BatchedTensors]] = None
     request_ids_to_seq_ids: Optional[Dict[str, List[int]]] = None
     finished_requests_ids: Optional[List[str]] = None
@@ -97,6 +103,8 @@ class ModelInputForGPU(ModelRunnerInputBase):
             "lora_requests": self.lora_requests,
             "lora_mapping": self.lora_mapping,
             "multi_modal_kwargs": self.multi_modal_kwargs,
+            "prompt_adapter_mapping": self.prompt_adapter_mapping,
+            "prompt_adapter_requests": self.prompt_adapter_requests,
             "virtual_engine": self.virtual_engine,
             "request_ids_to_seq_ids": self.request_ids_to_seq_ids,
             "finished_requests_ids": self.finished_requests_ids,
@@ -133,6 +141,8 @@ class ModelInputForGPUWithSamplingMetadata(ModelInputForGPU):
             "lora_requests": self.lora_requests,
             "lora_mapping": self.lora_mapping,
             "multi_modal_kwargs": self.multi_modal_kwargs,
+            "prompt_adapter_mapping": self.prompt_adapter_mapping,
+            "prompt_adapter_requests": self.prompt_adapter_requests,
             "virtual_engine": self.virtual_engine,
             "request_ids_to_seq_ids": self.request_ids_to_seq_ids,
             "finished_requests_ids": self.finished_requests_ids,
@@ -172,6 +182,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         lora_config: Optional[LoRAConfig],
         kv_cache_dtype: Optional[str] = "auto",
         is_driver_worker: bool = False,
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         multimodal_config: Optional[MultiModalConfig] = None,
         return_hidden_states: bool = False,
     ):
@@ -183,6 +194,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         self.lora_config = lora_config
         self.load_config = load_config
         self.is_driver_worker = is_driver_worker
+        self.prompt_adapter_config = prompt_adapter_config
         self.multimodal_config = multimodal_config
         self.return_hidden_states = return_hidden_states
 
@@ -232,6 +244,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         self.model: nn.Module  # Set after load_model
         # Set after load_model.
         self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None
+        self.prompt_adapter_manager: LRUCacheWorkerPromptAdapterManager = None
 
         self.flashinfer_decode_workspace_buffer = None
         self.flashinfer_decode_wrapper = None
@@ -240,16 +253,14 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
 
     def load_model(self) -> None:
         with CudaMemoryProfiler() as m:
-            self.model = get_model(
-                model_config=self.model_config,
+            self.model = get_model(model_config=self.model_config,
                                    device_config=self.device_config,
                                    load_config=self.load_config,
                                    lora_config=self.lora_config,
                                    multimodal_config=self.multimodal_config,
                                    parallel_config=self.parallel_config,
                                    scheduler_config=self.scheduler_config,
-                cache_config=self.cache_config,
-            )
+                                   cache_config=self.cache_config)
 
         self.model_memory_usage = m.consumed_memory
         logger.info("Loading model weights took %.4f GB",
@@ -274,6 +285,15 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
             )
             self.model = self.lora_manager.create_lora_manager(self.model)
 
+        if self.prompt_adapter_config:
+            self.prompt_adapter_manager = LRUCacheWorkerPromptAdapterManager(
+                self.scheduler_config.max_num_seqs,
+                self.scheduler_config.max_num_batched_tokens, self.device,
+                self.prompt_adapter_config)
+            self.model = (
+                self.prompt_adapter_manager.create_prompt_adapter_manager(
+                    self.model))
+
         if self.kv_cache_dtype == "fp8" and is_hip():
             # Currently only ROCm accepts kv-cache scaling factors
             # via quantization_param_path and this will be deprecated
@@ -354,6 +374,9 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         lora_index_mapping: List[int] = []
         lora_prompt_mapping: List[int] = []
         lora_requests: Set[LoRARequest] = set()
+        prompt_adapter_index_mapping: List[int] = []
+        prompt_adapter_prompt_mapping: List[int] = []
+        prompt_adapter_requests: Set[PromptAdapterRequest] = set()
 
         seq_lens: List[int] = []
         prefill_seq_lens: List[int] = []
@@ -504,6 +527,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                 input_tokens.extend(tokens)
                 input_positions.extend(list(range(context_len, seq_len)))
                 lora_id = seq_group_metadata.lora_int_id
+                prompt_adapter_id = seq_group_metadata.prompt_adapter_id
 
                 if is_prompt:
                     assert len(seq_ids) == 1
@@ -534,6 +558,21 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                     mm_kwargs = self.multi_modal_input_mapper(mm_data)
                     multi_modal_inputs_list.append(mm_kwargs)
 
+                if prompt_adapter_id > 0 and is_prompt:
+                    prompt_adapter_requests.add(
+                        seq_group_metadata.prompt_adapter_request)
+
+                    num_tokens = seq_group_metadata.\
+                                            prompt_adapter_num_virtual_tokens
+                    pm = [prompt_adapter_id
+                          ] * num_tokens + [0] * (query_len - num_tokens)
+                    prompt_adapter_index_mapping += pm
+                    prompt_adapter_prompt_mapping.extend(
+                        [prompt_adapter_id] *
+                        (query_len if seq_group_metadata.sampling_params
+                         and seq_group_metadata.sampling_params.prompt_logprobs
+                         else 1))
+
                 is_profile_run = _is_block_tables_empty(
                     seq_group_metadata.block_tables)
                 if is_profile_run:
@@ -618,12 +657,11 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                 seq_lens.append(1)
                 block_tables.append([])
                 lora_index_mapping.append(0)
-
+                prompt_adapter_index_mapping.append(0)
                 if self.attn_backend.get_name() == "flashinfer":
                     last_paged_kv_indptr = paged_kv_indptr[-1]
                     paged_kv_indptr.append(last_paged_kv_indptr)
                     paged_kv_last_page_len.append(0)
-
             batch_size = graph_batch_size
             num_decode_tokens = batch_size
 
@@ -759,6 +797,14 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         else:
             lora_mapping = None
 
+        if self.prompt_adapter_config:
+            prompt_adapter_mapping = PromptAdapterMapping(
+                prompt_adapter_index_mapping,
+                prompt_adapter_prompt_mapping,
+            )
+        else:
+            prompt_adapter_mapping = None
+
         multi_modal_kwargs = MultiModalInputs.batch(multi_modal_inputs_list,
                                                     device=self.device)
         request_ids_to_seq_ids = {
@@ -776,7 +822,10 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
             lora_requests=lora_requests,
             multi_modal_kwargs=multi_modal_kwargs,
             request_ids_to_seq_ids=request_ids_to_seq_ids,
-            finished_requests_ids=finished_requests_ids)
+            finished_requests_ids=finished_requests_ids,
+            prompt_adapter_mapping=prompt_adapter_mapping,
+            prompt_adapter_requests=prompt_adapter_requests,
+        )
 
     @torch.inference_mode()
     def profile_run(self) -> None:
@@ -878,33 +927,67 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
     def remove_all_loras(self):
         if not self.lora_manager:
             raise RuntimeError("LoRA is not enabled.")
-        self.lora_manager.remove_all_loras()
+        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_loras(lora_requests, lora_mapping)
+        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_lora(lora_request)
+        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_lora(lora_id)
+        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_lora(lora_id)
+        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_loras()
+        return self.lora_manager.list_adapters()
+
+    def remove_all_prompt_adapters(self):
+        if not self.prompt_adapter_manager:
+            raise RuntimeError("PromptAdapter is not enabled.")
+        self.prompt_adapter_manager.remove_all_adapters()
+
+    def set_active_prompt_adapters(
+            self, prompt_adapter_requests: Set[PromptAdapterRequest],
+            prompt_adapter_mapping: PromptAdapterMapping) -> None:
+        if not self.prompt_adapter_manager:
+            raise RuntimeError("PromptAdapter is not enabled.")
+        self.prompt_adapter_manager.set_active_adapters(
+            prompt_adapter_requests, prompt_adapter_mapping)
+
+    def add_prompt_adapter(
+            self, prompt_adapter_request: PromptAdapterRequest) -> bool:
+        if not self.prompt_adapter_manager:
+            raise RuntimeError("PromptAdapter is not enabled.")
+        return self.prompt_adapter_manager.add_adapter(prompt_adapter_request)
+
+    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        if not self.prompt_adapter_manager:
+            raise RuntimeError("PromptAdapter is not enabled.")
+        return self.prompt_adapter_manager.remove_adapter(prompt_adapter_id)
+
+    def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        if not self.prompt_adapter_manager:
+            raise RuntimeError("PromptAdapter is not enabled.")
+        return self.prompt_adapter_manager.pin_adapter(prompt_adapter_id)
+
+    def list_prompt_adapters(self) -> Set[int]:
+        if not self.prompt_adapter_manager:
+            raise RuntimeError("PromptAdapter is not enabled.")
+        return self.prompt_adapter_manager.list_adapters()
 
     @torch.inference_mode()
     def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
@@ -1063,6 +1146,14 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                         )
                         self.set_active_loras(set(), lora_mapping)
 
+                    if self.prompt_adapter_config:
+                        prompt_adapter_mapping = PromptAdapterMapping(
+                            [-1] * batch_size,
+                            [-1] * batch_size,
+                        )
+                        self.set_active_prompt_adapters(
+                            set(), prompt_adapter_mapping)
+
                     graph_runner = CUDAGraphRunner(
                         self.model, self.attn_backend.get_name())
 
@@ -1189,6 +1280,13 @@ class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
             self.set_active_loras(model_input.lora_requests,
                                   model_input.lora_mapping)
 
+        if self.prompt_adapter_config:
+            assert model_input.prompt_adapter_requests is not None
+            assert model_input.prompt_adapter_mapping is not None
+            self.set_active_prompt_adapters(
+                model_input.prompt_adapter_requests,
+                model_input.prompt_adapter_mapping)
+
         if self.attn_backend.get_name() == "flashinfer":
             assert model_input.attn_metadata is not None
             assert model_input.input_tokens is not None

vllm/worker/worker.py

@@ -8,7 +8,8 @@ import torch.distributed
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig, SpeculativeConfig)
+                         PromptAdapterConfig, SchedulerConfig,
+                         SpeculativeConfig)
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment,
                               set_custom_all_reduce)
@@ -16,6 +17,7 @@ from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
 from vllm.platforms import current_platform
+from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sequence import ExecuteModelRequest
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.embedding_model_runner import EmbeddingModelRunner
@@ -45,6 +47,7 @@ class Worker(LocalOrDistributedWorkerBase):
         lora_config: Optional[LoRAConfig] = None,
         multimodal_config: Optional[MultiModalConfig] = None,
         speculative_config: Optional[SpeculativeConfig] = None,
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         is_driver_worker: bool = False,
         model_runner_cls: Optional[Type[GPUModelRunnerBase]] = None,
     ) -> None:
@@ -59,6 +62,7 @@ class Worker(LocalOrDistributedWorkerBase):
         self.distributed_init_method = distributed_init_method
         self.lora_config = lora_config
         self.load_config = load_config
+        self.prompt_adapter_config = prompt_adapter_config
         self.is_driver_worker = is_driver_worker
         if parallel_config and is_driver_worker:
             assert rank % parallel_config.tensor_parallel_size == 0, \
@@ -92,6 +96,7 @@ class Worker(LocalOrDistributedWorkerBase):
             lora_config=self.lora_config,
             kv_cache_dtype=self.cache_config.cache_dtype,
             is_driver_worker=is_driver_worker,
+            prompt_adapter_config=prompt_adapter_config,
             multimodal_config=multimodal_config,
             **speculative_args,
         )
@@ -296,6 +301,19 @@ class Worker(LocalOrDistributedWorkerBase):
     def list_loras(self) -> Set[int]:
         return self.model_runner.list_loras()
 
+    def add_prompt_adapter(
+            self, prompt_adapter_request: PromptAdapterRequest) -> bool:
+        return self.model_runner.add_prompt_adapter(prompt_adapter_request)
+
+    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        return self.model_runner.remove_lora(prompt_adapter_id)
+
+    def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        return self.model_runner.pin_prompt_adapter(prompt_adapter_id)
+
+    def list_prompt_adapters(self) -> Set[int]:
+        return self.model_runner.list_prompt_adapters()
+
     @property
     def max_model_len(self) -> int:
         return self.model_config.max_model_len

vllm/worker/xpu_model_runner.py

@@ -8,7 +8,7 @@ import torch.nn as nn
 from vllm.attention import get_attn_backend
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig)
+                         PromptAdapterConfig, SchedulerConfig)
 from vllm.distributed import broadcast_tensor_dict
 from vllm.inputs import INPUT_REGISTRY
 from vllm.logger import init_logger
@@ -88,6 +88,7 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
         lora_config: Optional[LoRAConfig],
         multimodal_config: Optional[MultiModalConfig],
         kv_cache_dtype: Optional[str] = "auto",
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         is_driver_worker: bool = False,
         *args,
         **kwargs,
@@ -98,6 +99,7 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
         self.lora_config = lora_config
         self.load_config = load_config
         self.cache_config = cache_config
+        self.prompt_adapter_config = prompt_adapter_config
         self.multimodal_config = multimodal_config
         self.is_driver_worker = is_driver_worker
 

vllm/worker/xpu_worker.py

@@ -10,7 +10,8 @@ import torch.distributed
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
-                         SchedulerConfig, SpeculativeConfig)
+                         PromptAdapterConfig, SchedulerConfig,
+                         SpeculativeConfig)
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
 from vllm.logger import init_logger
@@ -47,6 +48,7 @@ class XPUWorker(LoraNotSupportedWorkerBase, Worker):
         lora_config: Optional[LoRAConfig] = None,
         multimodal_config: Optional[MultiModalConfig] = None,
         speculative_config: Optional[SpeculativeConfig] = None,
+        prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         is_driver_worker: bool = False,
     ) -> None:
         assert device_config.device_type == "xpu"
@@ -63,6 +65,7 @@ class XPUWorker(LoraNotSupportedWorkerBase, Worker):
         self.rank = rank
         self.distributed_init_method = distributed_init_method
         self.lora_config = lora_config
+        self.prompt_adapter_config = prompt_adapter_config
         self.is_driver_worker = is_driver_worker
         if self.is_driver_worker:
             assert self.rank == 0, "The driver worker must have rank 0."