# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # Copyright 2023 The vLLM team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This file is a part of the vllm-ascend project. # Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py # isort: skip_file import torch.nn as nn from vllm.config import CUDAGraphMode from vllm.v1.kv_cache_interface import KVCacheConfig from vllm_ascend.worker.model_runner_v1 import NPUModelRunner class XliteModelRunner(NPUModelRunner): def get_model(self) -> nn.Module: return self.model.unwrap() def load_model(self) -> None: super().load_model() from vllm_ascend.xlite.xlite import XliteWrapper self.model = XliteWrapper(self.model, self.vllm_config) def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None: super().initialize_kv_cache(kv_cache_config) self.model.register_kv_caches(self.kv_caches) def _should_build_dummy_attn_metadata( self, force_attention: bool = False, is_profile: bool = False, cudagraph_runtime_mode: CUDAGraphMode | None = None, ) -> bool: """ Override to build attention metadata during dummy_run when xlite is enable. For xlite, we need to build metadata during DP dummy_run to ensure all ranks have consistent metadata, even when some ranks have no requests. """ base_condition = super()._should_build_dummy_attn_metadata(force_attention, is_profile, cudagraph_runtime_mode) xlite_condition = self.ascend_config.xlite_graph_config.enabled and not is_profile return base_condition or xlite_condition