2be0fe2691
### What this PR does / why we need it? This patch adds support for the xlite graph wrapper to vllm_ascend. Xlite provides operator implementations of the transformer network on Ascend hardware. For details about xlite, please refer to the following link: https://gitee.com/openeuler/GVirt/blob/master/xlite/README.md The latest performance comparison data between xlite and the default aclgraph mode is as follows: ## Qwen3 32B TPS 910B3(A2) Online Inference Performance Comparison - aclgraph: main(c4a71fc6) - xlite-full: main(c4a71fc6) + xlite-full - xlite-decode-only: main(c4a71fc6) + xlite-decode-only - diff1: Performance comparison between xlite-full and aclgraph - diff2: Performance comparison between xlite-decode-only and aclgraph ### Does this PR introduce _any_ user-facing change? Enable the xlite graph mode by setting xlite_graph_config: --additional-config='{"xlite_graph_config": {"enabled": true}}' # Enabled for decode only --additional-config='{"xlite_graph_config": {"enabled": true, "full_mode": true}}' # Enabled for prefill and decode - vLLM version: v0.12.0 - vLLM main:ad32e3e19c--------- Signed-off-by: lulina <lina.lulina@huawei.com> Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
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# isort: skip_file
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import torch.nn as nn
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
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class XliteModelRunner(NPUModelRunner):
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def get_model(self) -> nn.Module:
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return self.model.unwrap()
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def load_model(self) -> None:
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super().load_model()
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from vllm_ascend.xlite.xlite import XliteWrapper
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self.model = XliteWrapper(self.model, self.vllm_config)
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def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
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super().initialize_kv_cache(kv_cache_config)
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self.model.register_kv_caches(self.kv_caches)
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