d80b0cca5d
### What this PR does / why we need it?
Fix test on pyhccl to 2 cards
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
0d0cc9e150
Signed-off-by: MengqingCao <cmq0113@163.com>
122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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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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#
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import multiprocessing
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import os
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import torch
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from vllm.distributed.parallel_state import (get_world_group,
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init_distributed_environment)
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from vllm.utils import update_environment_variables
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from tests.e2e.conftest import cleanup_dist_env_and_memory
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from vllm_ascend.distributed.device_communicators.pyhccl import \
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PyHcclCommunicator
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os.environ["TOKENIZERS_PARALLELISM"] = "true"
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multiprocessing.set_start_method("spawn", force=True)
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def _worker_entry(env, fn):
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# `multiprocessing.Process` cannot accept environment variables directly
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# so we need to pass the environment variables as arguments
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# and update the environment variables in the function
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update_environment_variables(env)
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rank = int(os.environ['RANK'])
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local_rank = int(os.environ['LOCAL_RANK'])
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word_size = int(os.environ['WORLD_SIZE'])
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distributed_init_method = "tcp://localhost:12345"
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device = torch.device(f"npu:{local_rank}")
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torch.npu.set_device(device)
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init_distributed_environment(
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world_size=word_size,
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rank=rank,
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distributed_init_method=distributed_init_method,
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local_rank=local_rank,
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backend="hccl")
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fn()
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cleanup_dist_env_and_memory()
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def distributed_run(fn, world_size):
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number_of_processes = world_size
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processes: list[multiprocessing.Process] = []
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for i in range(number_of_processes):
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env: dict[str, str] = {}
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env['RANK'] = str(i)
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env['LOCAL_RANK'] = str(i)
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env['WORLD_SIZE'] = str(number_of_processes)
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env['LOCAL_WORLD_SIZE'] = str(number_of_processes)
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p = multiprocessing.Process(target=_worker_entry, args=(env, fn))
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processes.append(p)
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p.start()
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for p in processes:
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p.join()
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for p in processes:
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assert p.exitcode == 0
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def worker_fn():
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pynccl_comm = PyHcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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tensor = torch.ones(16, 1024, 1024,
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dtype=torch.float32).npu(pynccl_comm.rank)
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tensor = pynccl_comm.all_reduce(tensor)
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torch.npu.synchronize()
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assert torch.all(tensor == pynccl_comm.world_size).cpu().item()
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def test_pyhccl():
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distributed_run(worker_fn, 2)
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def broadcast_worker_fn():
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# Test broadcast for every root rank.
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# Essentially this is an all-gather operation.
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pyhccl_comm = PyHcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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recv_tensors = [
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torch.empty(16,
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1024,
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1024,
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dtype=torch.float32,
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device=pyhccl_comm.device)
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for i in range(pyhccl_comm.world_size)
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]
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recv_tensors[pyhccl_comm.rank] = torch.ones(
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16, 1024, 1024, dtype=torch.float32,
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device=pyhccl_comm.device) * pyhccl_comm.rank
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for i in range(pyhccl_comm.world_size):
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pyhccl_comm.broadcast(recv_tensors[i], src=i)
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# the broadcast op might be launched in a different stream
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# need to synchronize to make sure the tensor is ready
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torch.npu.synchronize()
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assert torch.all(recv_tensors[i] == i).cpu().item()
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def test_pyhccl_broadcast():
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distributed_run(broadcast_worker_fn, 2)
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