69b817ed65
This PR added the unit test framework to enable ut for vLLM Ascend. Unit test runs on CPU machines. It'll be ran once lint check is passed the same as e2e test. For unit test, this PR created a new folder called `ut` under `tests` module. All the test file in `ut` should keep the same with the code in `vllm-ascend`. The file name should be start with `test_` prefix. For example, in this PR. the `test_ascend_config.py` is added for `ascend_config.py` test. A new fille `worker/test_worker_v1.py` is also added as the placeholder. This file should be the unit test for `vllm-ascend/worker/worker_v1.py`. Additional, a new `fake_weight` folder is added, it contains the config.json from `facebook/opt-125m`, so that the test will not always visit huggingface. TODO: We should add all the unit test file one by one in the future. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
# This file is a part of the vllm-ascend project.
|
|
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
|
|
# 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.
|
|
#
|
|
import multiprocessing
|
|
import os
|
|
|
|
import torch
|
|
from vllm.distributed.parallel_state import (get_world_group,
|
|
init_distributed_environment)
|
|
from vllm.utils import update_environment_variables
|
|
|
|
from vllm_ascend.distributed.device_communicators.pyhccl import \
|
|
PyHcclCommunicator
|
|
|
|
|
|
def distributed_run(fn, world_size):
|
|
number_of_processes = world_size
|
|
processes: list[multiprocessing.Process] = []
|
|
for i in range(number_of_processes):
|
|
env: dict[str, str] = {}
|
|
env['RANK'] = str(i)
|
|
env['LOCAL_RANK'] = str(i)
|
|
env['WORLD_SIZE'] = str(number_of_processes)
|
|
env['LOCAL_WORLD_SIZE'] = str(number_of_processes)
|
|
env['MASTER_ADDR'] = 'localhost'
|
|
env['MASTER_PORT'] = '12345'
|
|
p = multiprocessing.Process(target=fn, args=(env, ))
|
|
processes.append(p)
|
|
p.start()
|
|
|
|
for p in processes:
|
|
p.join()
|
|
|
|
for p in processes:
|
|
assert p.exitcode == 0
|
|
|
|
|
|
def worker_fn_wrapper(fn):
|
|
# `multiprocessing.Process` cannot accept environment variables directly
|
|
# so we need to pass the environment variables as arguments
|
|
# and update the environment variables in the function
|
|
def wrapped_fn(env):
|
|
update_environment_variables(env)
|
|
local_rank = os.environ['LOCAL_RANK']
|
|
device = torch.device(f"npu:{local_rank}")
|
|
torch.npu.set_device(device)
|
|
init_distributed_environment(backend="hccl")
|
|
fn()
|
|
|
|
return wrapped_fn
|
|
|
|
|
|
@worker_fn_wrapper
|
|
def worker_fn():
|
|
pynccl_comm = PyHcclCommunicator(get_world_group().cpu_group,
|
|
device=get_world_group().device)
|
|
tensor = torch.ones(16, 1024, 1024,
|
|
dtype=torch.float32).npu(pynccl_comm.rank)
|
|
tensor = pynccl_comm.all_reduce(tensor)
|
|
torch.npu.synchronize()
|
|
assert torch.all(tensor == pynccl_comm.world_size).cpu().item()
|
|
|
|
|
|
# def test_pyhccl():
|
|
# distributed_run(worker_fn, 2)
|
|
|
|
|
|
@worker_fn_wrapper
|
|
def broadcast_worker_fn():
|
|
# Test broadcast for every root rank.
|
|
# Essentially this is an all-gather operation.
|
|
pyhccl_comm = PyHcclCommunicator(get_world_group().cpu_group,
|
|
device=get_world_group().device)
|
|
recv_tensors = [
|
|
torch.empty(16,
|
|
1024,
|
|
1024,
|
|
dtype=torch.float32,
|
|
device=pyhccl_comm.device)
|
|
for i in range(pyhccl_comm.world_size)
|
|
]
|
|
recv_tensors[pyhccl_comm.rank] = torch.ones(
|
|
16, 1024, 1024, dtype=torch.float32,
|
|
device=pyhccl_comm.device) * pyhccl_comm.rank
|
|
|
|
for i in range(pyhccl_comm.world_size):
|
|
pyhccl_comm.broadcast(recv_tensors[i], src=i)
|
|
# the broadcast op might be launched in a different stream
|
|
# need to synchronize to make sure the tensor is ready
|
|
torch.npu.synchronize()
|
|
assert torch.all(recv_tensors[i] == i).cpu().item()
|
|
|
|
|
|
# def test_pyhccl_broadcast():
|
|
# distributed_run(broadcast_worker_fn, 4)
|