96fa7ff63b
### What this PR does / why we need it? 1. Fix rank set in DP scenario. The new poc version of torch-npu support setting `ASCEND_RT_VISIBLE_DEVICES` dynamically, thus we could use the rank set in `DPEngineCoreProc` directly instead of calculating local rank across dp by hand in the patched `_init_data_parallel` Closes: https://github.com/vllm-project/vllm-ascend/issues/1170 2. Bump torch-npu version to 2.5.1.post1.dev20250528 Closes: https://github.com/vllm-project/vllm-ascend/pull/1242 Closes: https://github.com/vllm-project/vllm-ascend/issues/1232 ### How was this patch tested? CI passed with new added test. --------- Signed-off-by: MengqingCao <cmq0113@163.com> Signed-off-by: Icey <1790571317@qq.com> Co-authored-by: Icey <1790571317@qq.com>
84 lines
2.8 KiB
Python
84 lines
2.8 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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#
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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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# Adapted from vllm/model_executor/models/qwen2_vl.py
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# This file is a part of the vllm-ascend project.
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import vllm
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import vllm.distributed
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import vllm.envs as envs
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from torch.distributed import ProcessGroup
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from vllm.config import ParallelConfig
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from vllm.distributed.utils import \
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stateless_init_torch_distributed_process_group
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def ascend_destroy_model_parallel():
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"""Set the groups to none and destroy them."""
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from vllm.distributed.parallel_state import _DP, _PP, _TP
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if _TP:
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_TP.destroy()
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_TP = None
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if _PP:
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_PP.destroy()
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_PP = None
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if _DP:
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_DP.destroy()
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_DP = None
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from vllm_ascend.distributed.parallel_state import \
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destory_ascend_model_parallel
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destory_ascend_model_parallel()
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def parallel_config_get_dp_port(self) -> int:
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"""
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We might need to initialize process groups in multiple
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processes that is related to data parallelism,
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e.g. both in the worker and in the engine, which
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can live in different processes. To avoid port conflicts, we
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increment the port number each time we need to initialize a
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new process group related to data parallelism.
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"""
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answer = self.data_parallel_master_port
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self.data_parallel_master_port += 1
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# NOTE: Get port from envs directly when using torchrun
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port = envs.VLLM_DP_MASTER_PORT if envs.VLLM_DP_MASTER_PORT else answer
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return port
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def stateless_init_dp_group(self) -> "ProcessGroup":
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# TODO(Yizhou): Currently we have to set the backend to gloo
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# because in vllm.config.ParallelConfig.has_unfinished_dp the
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# device is set to cpu. We need to fix this in the future.
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# We need to compare the performance of gloo and hccl and then
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# decide which one to use.
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dp_group = stateless_init_torch_distributed_process_group(
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self.data_parallel_master_ip,
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self.get_next_dp_init_port(),
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self.data_parallel_rank,
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self.data_parallel_size,
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backend="gloo")
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return dp_group
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vllm.distributed.parallel_state.destroy_model_parallel = ascend_destroy_model_parallel
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ParallelConfig.get_next_dp_init_port = parallel_config_get_dp_port
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ParallelConfig.stateless_init_dp_group = stateless_init_dp_group
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