136ea9ff56
### What this PR does / why we need it?
Currently, there are two paths to judge the chip type in code,
`get_ascend_soc_version` use `get_soc_version` api in torch_npu, and
`is_310p` `use _build_info.__soc_version__`, which generate when
install. We need to unify the two paths.
We need to unify these codes based on the following points:
1. We need to ensure consistency in chip type judgment between compiling
and running states;
2. In compiling state, we need chip type to complete op's compilation,
but in running state, we only need device
type(910B/910_93/310P/910_95/etc) to make code branch judgement;
3. In compiling state, torch_npu may not have been installed yet, so we
can't use torch_npu's api.
Based on the above points, we have made the following changes:
1. When user set env `SOC_VERSION`, use it; when not set, query
soc_version by `npu-smi`;
2. generate device_type based on soc_version when compiling, and write
`__device_type__` instead of `__soc_version__` in `_build_info.py`;
3. In running state, use `__device_type__` to judge code branch.
### Does this PR introduce _any_ user-facing change?
When not set env `SOC_VERSION`, it will not be `ASCEND910B1` by default,
we will query soc_version by `npu-smi`. And env `SOC_VERSION` must be in
the list `soc_to_device` in `setup.py`.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
116 lines
4.0 KiB
Python
116 lines
4.0 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 torch
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import vllm.envs as envs_vllm
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from vllm.config import ParallelConfig
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from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
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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.VLLM_DP_MASTER_PORT if envs_vllm.VLLM_DP_MASTER_PORT else answer
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return port
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ParallelConfig.get_next_dp_init_port = parallel_config_get_dp_port
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class NullHandle:
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def __init__(self):
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pass
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def wait(self):
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pass
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def communication_adaptation_310p():
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def broadcast310p_wrapper(fn):
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def broadcast310p(tensor, src, group=None, async_op=False):
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if tensor.device == torch.device('cpu'):
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return fn(tensor, src, group, async_op)
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rank = torch.distributed.get_rank(group)
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world_size = torch.distributed.get_world_size(group)
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tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
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tensor_list[rank] = tensor
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torch.distributed.all_gather(tensor_list, tensor, group=group)
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tensor[...] = tensor_list[src]
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if async_op:
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return NullHandle()
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else:
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return None
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return broadcast310p
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torch.distributed.broadcast = broadcast310p_wrapper(
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torch.distributed.broadcast)
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torch.distributed.distributed_c10d.broadcast = broadcast310p_wrapper(
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torch.distributed.distributed_c10d.broadcast)
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def all_reduce_wrapper_310p(fn):
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def all_reduce(
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tensor,
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op=torch.distributed.ReduceOp.SUM,
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group=None,
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async_op=False,
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):
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if tensor.dtype != torch.int64:
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return fn(tensor, op, group, async_op)
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rank = torch.distributed.get_rank(group)
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world_size = torch.distributed.get_world_size(group)
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tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
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tensor_list[rank] = tensor
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torch.distributed.all_gather(tensor_list, tensor, group=group)
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if op == torch.distributed.ReduceOp.SUM:
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return torch.stack(tensor_list).sum(0)
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elif op == torch.distributed.ReduceOp.MAX:
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return torch.tensor(
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torch.stack(tensor_list).cpu().numpy().max(0),
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device=tensor.device,
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)
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else:
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raise RuntimeError(f"not implement op {op}")
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return all_reduce
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torch.distributed.all_reduce = all_reduce_wrapper_310p(
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torch.distributed.all_reduce)
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torch.distributed.distributed_c10d.all_reduce = all_reduce_wrapper_310p(
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torch.distributed.distributed_c10d.all_reduce)
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if get_ascend_device_type() == AscendDeviceType._310P:
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communication_adaptation_310p()
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