[Platform][Worker][ModelRunner] Add LoRA & Multi-LoRA support (#521)

### What this PR does / why we need it?
According to this RFC [[RFC]: Join the MultiLora and MultiLora Dynammic
Serving feature develop
#396](https://github.com/vllm-project/vllm-ascend/issues/396) and this
[vLLM Ascend Roadmap Q2 2025
#448](https://github.com/vllm-project/vllm-ascend/issues/448), we pull
request relavant code to support (1) Multi-LoRA and (2) Multi-LoRA
Dynamic Serving.

LoRA reference is here: [LoRA
reference](https://docs.vllm.ai/en/latest/features/lora.html)

### Does this PR introduce _any_ user-facing change?

Following openai HTTP apis will be supported:
/v1/load_lora_adapter
/v1/unload_lora_adapter

### How was this patch tested?
git clone https://github.com/vllm-project/vllm.git
cd vllm/examples/offline_inference/ && python3 multilora_inference.py

---------

Signed-off-by: paulyu <paulyu0307@gmail.com>
Co-authored-by: paulyu <paulyu0307@gmail.com>

vllm_ascend/lora/punica_wrapper/punica_npu.py

@@ -0,0 +1,346 @@
+# SPDX-License-Identifier: Apache-2.0
+
+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 vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
+
+
+# The platforms that are compatible with the PyTorch-native implementation can
+# inherit this class
+class PunicaWrapperNPU(PunicaWrapperBase):
+    """
+    PunicaWrapperNPU 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_ascend/platform.py

@@ -141,6 +141,10 @@ class NPUPlatform(Platform):
             return "vllm_ascend.attention.attention.AscendMLAAttentionBackend"
         return "vllm_ascend.attention.attention.AscendAttentionBackend"
 
+    @classmethod
+    def get_punica_wrapper(cls) -> str:
+        return "vllm_ascend.lora.punica_wrapper.punica_npu.PunicaWrapperNPU"
+
     @classmethod
     def get_current_memory_usage(cls,
                                  device: Optional[torch.types.Device] = None

vllm_ascend/worker/model_runner.py

@@ -38,11 +38,13 @@ from vllm.inputs import INPUT_REGISTRY, InputRegistry
 from vllm.logger import 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, SamplingMetadataCache
 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.model_loader.tensorizer import TensorizerConfig
+from vllm.model_executor.models import supports_lora, supports_multimodal
 from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
                              MultiModalKwargs, MultiModalPlaceholderMap,
@@ -79,6 +81,8 @@ class ModelInputForNPU(ModelRunnerInputBase):
     token_types: Optional[torch.Tensor] = None
     seq_lens: Optional[List[int]] = None
     query_lens: Optional[List[int]] = None
+    lora_mapping: Optional["LoRAMapping"] = None
+    lora_requests: Optional[Set[LoRARequest]] = None
     attn_metadata: Optional["AttentionMetadata"] = None
     multi_modal_kwargs: Optional[BatchedTensorInputs] = None
     request_ids_to_seq_ids: Optional[Dict[str, List[int]]] = None
@@ -93,6 +97,8 @@ class ModelInputForNPU(ModelRunnerInputBase):
         tensor_dict = {
             "input_tokens": self.input_tokens,
             "input_positions": self.input_positions,
+            "lora_requests": self.lora_requests,
+            "lora_mapping": self.lora_mapping,
             "multi_modal_kwargs": self.multi_modal_kwargs,
             "virtual_engine": self.virtual_engine,
             "request_ids_to_seq_ids": self.request_ids_to_seq_ids,
@@ -139,6 +145,8 @@ class ModelInputForNPUWithSamplingMetadata(ModelInputForNPU):
         tensor_dict = {
             "input_tokens": self.input_tokens,
             "input_positions": self.input_positions,
+            "lora_requests": self.lora_requests,
+            "lora_mapping": self.lora_mapping,
             "multi_modal_kwargs": self.multi_modal_kwargs,
             "virtual_engine": self.virtual_engine,
             "request_ids_to_seq_ids": self.request_ids_to_seq_ids,
@@ -181,6 +189,9 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
             self.query_lens[0] = 0  # type: ignore
             self.context_lens[0] = 0  # type: ignore
             self.curr_sliding_window_blocks[0] = 0  # type: ignore
+            self.lora_index_mapping.clear()  # type: ignore
+            self.lora_prompt_mapping.clear()  # type: ignore
+            self.lora_requests.clear()  # type: ignore
 
         def __init__(
             self,
@@ -211,6 +222,11 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
             # The current sliding window block.
             curr_sliding_window_blocks: Optional[List[int]] = None,
 
+            # LoRA inputs.
+            lora_index_mapping: Optional[List[List[int]]] = None,
+            lora_prompt_mapping: Optional[List[List[int]]] = None,
+            lora_requests: Optional[Set[LoRARequest]] = None,
+
             # Multi-modal inputs.
             multi_modal_kwargs: Optional[MultiModalKwargs] = None,
             multi_modal_placeholder_maps: Optional[Dict[
@@ -291,6 +307,19 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
                         for seq_id in range(len(self.seq_ids)):
                             self.curr_sliding_window_blocks[seq_id] = 0
 
+                    if lora_index_mapping:
+                        self.lora_index_mapping = lora_index_mapping
+                    else:
+                        self.lora_index_mapping.clear()
+                    if lora_prompt_mapping:
+                        self.lora_prompt_mapping = lora_prompt_mapping
+                    else:
+                        self.lora_prompt_mapping.clear()
+                    if lora_requests:
+                        self.lora_requests = lora_requests
+                    else:
+                        self.lora_requests.clear()
+
             else:
                 self.input_tokens = input_tokens or []
                 self.input_positions = input_positions or []
@@ -303,6 +332,10 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
                 self.curr_sliding_window_blocks = \
                     curr_sliding_window_blocks or []
 
+                self.lora_index_mapping = lora_index_mapping or []
+                self.lora_prompt_mapping = lora_prompt_mapping or []
+                self.lora_requests = lora_requests or set()
+
             self.multi_modal_kwargs = multi_modal_kwargs
             self.multi_modal_placeholder_maps = multi_modal_placeholder_maps
             self.prefix_cache_hit = prefix_cache_hit
@@ -325,6 +358,9 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
             self.context_lens = [0] * self.n_seqs
             self.curr_sliding_window_blocks = [0] * self.n_seqs
 
+            self.lora_index_mapping = []
+            self.lora_prompt_mapping = []
+
     def __init__(self,
                  runner,
                  finished_requests_ids: Optional[List[str]] = None):
@@ -335,6 +371,7 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
             self._compute_lens,
             self._compute_for_prefix_cache_hit,
             self._compute_for_sliding_window,
+            self._compute_lora_input,
         ]
         # Compute functions for each sequence group.
         # WARNING: The order of the functions matters!
@@ -348,6 +385,7 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
         self.scheduler_config = self.runner.scheduler_config
         self.sliding_window = self.runner.sliding_window
         self.block_size = self.runner.block_size
+        self.enable_lora = self.runner.lora_config is not None
         self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper
         self.finished_requests_ids = finished_requests_ids
         self.decode_only = True
@@ -512,6 +550,25 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
         # Attention metadata.
         attn_metadata = self.attn_metadata_builder.build(seq_lens, query_lens)
 
+        # LoRA data.
+        lora_requests = set()
+        lora_mapping = None
+        if self.enable_lora:
+            lora_requests = set(r for data in self.inter_data_list
+                                for r in data.lora_requests)
+            lora_index_mapping = flatten_2d_lists([
+                flatten_2d_lists(inter_data.lora_index_mapping)
+                for inter_data in self.inter_data_list
+            ])
+            lora_prompt_mapping = flatten_2d_lists([
+                flatten_2d_lists(inter_data.lora_prompt_mapping)
+                for inter_data in self.inter_data_list
+            ])
+            lora_mapping = LoRAMapping(
+                **dict(index_mapping=lora_index_mapping,
+                       prompt_mapping=lora_prompt_mapping,
+                       is_prefill=not self.decode_only))
+
         # Multi-modal data.
         multi_modal_kwargs_list = [
             data.multi_modal_kwargs for data in self.inter_data_list
@@ -525,6 +582,8 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
             attn_metadata=attn_metadata,
             seq_lens=seq_lens,
             query_lens=query_lens,
+            lora_mapping=lora_mapping,
+            lora_requests=lora_requests,
             multi_modal_kwargs=multi_modal_kwargs,
             request_ids_to_seq_ids=request_ids_to_seq_ids,
             finished_requests_ids=self.finished_requests_ids)
@@ -663,6 +722,25 @@ class ModelInputForNPUBuilder(ModelRunnerInputBuilderBase[ModelInputForNPU]):
             seq_idx] = curr_sliding_window_block
         inter_data.seq_lens[seq_idx] = sliding_seq_len
 
+    def _compute_lora_input(self, inter_data: InterDataForSeqGroup,
+                            seq_idx: int,
+                            seq_group_metadata: SequenceGroupMetadata):
+        """If LoRA is enabled, compute LoRA index and prompt mapping."""
+        if not self.enable_lora:
+            return
+        lora_id = seq_group_metadata.lora_int_id
+        if lora_id > 0:
+            inter_data.lora_requests.add(seq_group_metadata.lora_request)
+        query_len = inter_data.query_lens[seq_idx]
+        inter_data.lora_index_mapping.append([lora_id] * query_len)
+        sampling_params = seq_group_metadata.sampling_params
+        if sampling_params and sampling_params.prompt_logprobs is not None:
+            inter_data.lora_prompt_mapping.append([lora_id] * query_len)
+        elif not self.chunked_prefill_enabled or seq_group_metadata.do_sample:
+            inter_data.lora_prompt_mapping.append([lora_id])
+        else:
+            inter_data.lora_prompt_mapping.append([])
+
     def _compute_multi_modal_input(self, inter_data: InterDataForSeqGroup,
                                    seq_group_metadata: SequenceGroupMetadata):
         """If multi-modal data is given, add it to the input."""
@@ -789,6 +867,8 @@ class NPUModelRunnerBase(ModelRunnerBase[TModelInputForNPU]):
 
         # Lazy initialization
         self.model: nn.Module  # Set after load_model
+        # Set after load_model.
+        self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None
 
         set_cpu_offload_max_bytes(
             int(self.cache_config.cpu_offload_gb * 1024**3))
@@ -818,6 +898,32 @@ class NPUModelRunnerBase(ModelRunnerBase[TModelInputForNPU]):
         logger.info("Loading model weights took %.4f GB",
                     self.model_memory_usage / float(2**30))
 
+        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 save_sharded_state(
         self,
         path: str,
@@ -967,23 +1073,35 @@ class NPUModelRunnerBase(ModelRunnerBase[TModelInputForNPU]):
             return
 
     def remove_all_loras(self):
-        raise RuntimeError("LoRA is not supported on NPU now.")
+        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:
-        raise RuntimeError("LoRA is not supported on NPU now.")
+        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:
-        raise RuntimeError("LoRA is not supported on NPU now.")
+        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:
-        raise RuntimeError("LoRA is not supported on NPU now.")
+        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:
-        raise RuntimeError("LoRA is not supported on NPU now.")
+        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]:
-        raise RuntimeError("LoRA is not supported on NPU now.")
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.list_adapters()
 
     def remove_all_prompt_adapters(self):
         raise RuntimeError("PromptAdapter is not supported on NPU now.")
@@ -1086,6 +1204,12 @@ class NPUModelRunner(NPUModelRunnerBase[ModelInputForNPUWithSamplingMetadata]):
         if num_steps > 1:
             raise ValueError("num_steps > 1 is not supported in ModelRunner")
 
+        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)
+
         self.attn_state.begin_forward(model_input)
 
         assert model_input.attn_metadata is not None

vllm_ascend/worker/worker.py

@@ -404,20 +404,16 @@ class NPUWorker(LocalOrDistributedWorkerBase):
         return output
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
-        raise NotImplementedError(
+        return self.model_runner.add_lora(lora_request)
-            "LoRA is not implemented for NPU backend currently.")
 
     def remove_lora(self, lora_id: int) -> bool:
-        raise NotImplementedError(
+        return self.model_runner.remove_lora(lora_id)
-            "LoRA is not implemented for NPU backend currently.")
 
     def pin_lora(self, lora_id: int) -> bool:
-        raise NotImplementedError(
+        return self.model_runner.pin_lora(lora_id)
-            "LoRA is not implemented for NPU backend currently.")
 
     def list_loras(self) -> Set[int]:
-        raise NotImplementedError(
+        return self.model_runner.list_loras()
-            "LoRA is not implemented for NPU backend currently.")
 
     def add_prompt_adapter(
             self, prompt_adapter_request: PromptAdapterRequest) -> bool: