EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

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

Browse Source 
### 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>
This commit is contained in:
paulyu12
2025-04-17 16:48:46 +08:00
committed by GitHub
parent 9935d45728
commit 697908f5cd
4 changed files with 484 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files

346
vllm_ascend/lora/punica_wrapper/punica_npu.py Normal file
View File

@@ -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)

4
vllm_ascend/platform.py
View File

@@ -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

136
vllm_ascend/worker/model_runner.py
View File

@@ -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

12
vllm_ascend/worker/worker.py
View File

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