fef18b60bc
Refactor E2E CI to make it clear and faster
1. remove some uesless e2e test
2. remove some uesless function
3. Make sure all test runs with VLLMRunner to avoid oom error
4. Make sure all ops test end with torch.empty_cache to avoid oom error
5. run the test one by one to avoid resource limit error
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
97 lines
3.1 KiB
Python
97 lines
3.1 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 gc
|
|
|
|
import torch
|
|
from vllm import SamplingParams
|
|
from vllm.utils import GiB_bytes
|
|
|
|
from tests.e2e.conftest import VllmRunner
|
|
from tests.e2e.utils import fork_new_process_for_each_test
|
|
from vllm_ascend.device_allocator.camem import CaMemAllocator
|
|
|
|
|
|
@fork_new_process_for_each_test
|
|
def test_basic_camem():
|
|
# some tensors from default memory pool
|
|
shape = (1024, 1024)
|
|
x = torch.empty(shape, device='npu:0')
|
|
x.zero_()
|
|
|
|
# some tensors from custom memory pool
|
|
allocator = CaMemAllocator.get_instance()
|
|
with allocator.use_memory_pool():
|
|
# custom memory pool
|
|
y = torch.empty(shape, device='npu:0')
|
|
y.zero_()
|
|
y += 1
|
|
z = torch.empty(shape, device='npu:0')
|
|
z.zero_()
|
|
z += 2
|
|
|
|
# they can be used together
|
|
output = x + y + z
|
|
assert torch.allclose(output, torch.ones_like(output) * 3)
|
|
|
|
free_bytes = torch.npu.mem_get_info()[0]
|
|
allocator.sleep()
|
|
free_bytes_after_sleep = torch.npu.mem_get_info()[0]
|
|
assert free_bytes_after_sleep > free_bytes
|
|
allocator.wake_up()
|
|
|
|
# they can be used together
|
|
output = x + y + z
|
|
assert torch.allclose(output, torch.ones_like(output) * 3)
|
|
|
|
gc.collect()
|
|
torch.npu.empty_cache()
|
|
torch.npu.reset_peak_memory_stats()
|
|
|
|
|
|
@fork_new_process_for_each_test
|
|
def test_end_to_end():
|
|
free, total = torch.npu.mem_get_info()
|
|
used_bytes_baseline = total - free # in case other process is running
|
|
|
|
prompt = "How are you?"
|
|
sampling_params = SamplingParams(temperature=0, max_tokens=10)
|
|
|
|
with VllmRunner("Qwen/Qwen3-0.6B",
|
|
enforce_eager=True,
|
|
enable_sleep_mode=True) as runner:
|
|
|
|
output = runner.model.generate(prompt, sampling_params)
|
|
# the benefit of `llm.sleep(level=2)` is mainly CPU memory usage,
|
|
# which is difficult to measure in the test. therefore, we only
|
|
# test sleep level 1 here.
|
|
runner.model.sleep(level=1)
|
|
|
|
free_gpu_bytes_after_sleep, total = torch.npu.mem_get_info()
|
|
used_bytes = total - free_gpu_bytes_after_sleep - used_bytes_baseline
|
|
# now the memory usage should be less than the model weights
|
|
# (0.5B model, 1GiB weights)
|
|
assert used_bytes < 1 * GiB_bytes
|
|
|
|
runner.model.wake_up()
|
|
output2 = runner.model.generate(prompt, sampling_params)
|
|
|
|
# cmp output
|
|
assert output[0].outputs[0].text == output2[0].outputs[0].text
|