f2990f7741
### What this PR does / why we need it?
Added an E2E test case for the scenario of enabling a static kernel for
npugraph_ex, monitoring its compilation and unloading process.
Also fixed the previously existing spelling errors
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: chencangtao <chencangtao@huawei.com>
Co-authored-by: chencangtao <chencangtao@huawei.com>
168 lines
7.7 KiB
Python
168 lines
7.7 KiB
Python
#
|
||
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||
# 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 pytest
|
||
import os
|
||
|
||
from tests.e2e.singlecard.utils import (PROMPTS_LONG, PROMPTS_SHORT,
|
||
LLMTestCase, gen_and_valid)
|
||
|
||
CASE_QWEN_ACLGRAPH = LLMTestCase(
|
||
model="Qwen/Qwen3-0.6B",
|
||
prompts=PROMPTS_SHORT,
|
||
golden_answers=[
|
||
" Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I want to know if there are any",
|
||
' the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president',
|
||
' Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of',
|
||
' not just a technological frontier but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and'
|
||
],
|
||
)
|
||
|
||
CASE_DS_ACLGRAPH = LLMTestCase(
|
||
model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
|
||
quantization="ascend",
|
||
prompts=PROMPTS_SHORT,
|
||
golden_answers=[
|
||
'\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2',
|
||
' a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the',
|
||
' Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art',
|
||
' here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of'
|
||
],
|
||
)
|
||
|
||
CASE_QWEN_FULL_DECODE_ONLY = LLMTestCase(
|
||
model="Qwen/Qwen3-0.6B",
|
||
prompts=PROMPTS_LONG,
|
||
golden_answers=[
|
||
' \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the',
|
||
" \n\nTo solve this problem, we can use the following approach: Let $P$ be the perimeter of the square. Then, the expected value of the area",
|
||
' \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations $x^2 +'
|
||
])
|
||
|
||
CASE_DS_FULL_DECODE_ONLY = LLMTestCase(
|
||
model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
|
||
quantization="ascend",
|
||
prompts=PROMPTS_LONG,
|
||
golden_answers=[
|
||
"\n\nSelect an assignment template",
|
||
"\n\nI'm not sure how to approach this problem. I'm not sure if I should use the law of total probability or if I should use",
|
||
"\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations"
|
||
])
|
||
|
||
CASE_QWEN_EX = LLMTestCase(
|
||
model="Qwen/Qwen3-0.6B",
|
||
prompts=PROMPTS_LONG,
|
||
golden_answers=[
|
||
' \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the',
|
||
" \n\nTo solve this problem, we can use the following approach: Let $P$ be the perimeter of the square. Then, the expected value of the area",
|
||
' \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can'
|
||
])
|
||
|
||
CASE_DS_EX = LLMTestCase(model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
|
||
quantization="ascend",
|
||
prompts=PROMPTS_LONG,
|
||
golden_answers=[
|
||
"\n\nSelect an assignment template",
|
||
"\n\nI'm not sure how to approach this problem. I'm not sure if I should use the law of total probability or if I should use",
|
||
"\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations"
|
||
])
|
||
|
||
@pytest.mark.parametrize("cur_case", [CASE_QWEN_ACLGRAPH, CASE_DS_ACLGRAPH])
|
||
def test_piecewise_res_consistency(cur_case: LLMTestCase):
|
||
runner_kwargs = {
|
||
"model_name": cur_case.model,
|
||
"max_model_len": 1024,
|
||
"cudagraph_capture_sizes": [1, 2, 4, 8],
|
||
"quantization": cur_case.quantization,
|
||
}
|
||
gen_and_valid(runner_kwargs=runner_kwargs,
|
||
prompts=cur_case.prompts,
|
||
sampling_params=cur_case.sampling_params,
|
||
golden_answers=cur_case.golden_answers)
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"cur_case", [CASE_QWEN_FULL_DECODE_ONLY, CASE_DS_FULL_DECODE_ONLY])
|
||
def test_full_decode_only_res_consistency(cur_case: LLMTestCase, monkeypatch):
|
||
monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False)
|
||
runner_kwargs = {
|
||
"model_name": cur_case.model,
|
||
"max_model_len": 1024,
|
||
"compilation_config": {
|
||
"cudagraph_capture_sizes": [4, 8, 32, 64],
|
||
"cudagraph_mode": "FULL_DECODE_ONLY"
|
||
},
|
||
"quantization": cur_case.quantization,
|
||
}
|
||
gen_and_valid(runner_kwargs=runner_kwargs,
|
||
prompts=cur_case.prompts,
|
||
sampling_params=cur_case.sampling_params,
|
||
golden_answers=cur_case.golden_answers)
|
||
|
||
@pytest.mark.parametrize("cur_case", [CASE_QWEN_EX, CASE_DS_EX])
|
||
def test_npugraph_ex_res_consistency(cur_case: LLMTestCase, monkeypatch):
|
||
monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False)
|
||
runner_kwargs = {
|
||
"model_name": cur_case.model,
|
||
"quantization": cur_case.quantization,
|
||
"max_model_len": 1024,
|
||
"compilation_config": {
|
||
"cudagraph_capture_sizes": [4, 8, 32, 64],
|
||
"cudagraph_mode": "FULL_DECODE_ONLY"
|
||
},
|
||
"additional_config": {
|
||
"npugraph_ex_config": {
|
||
"enable": True
|
||
}
|
||
},
|
||
}
|
||
gen_and_valid(runner_kwargs=runner_kwargs,
|
||
prompts=cur_case.prompts,
|
||
sampling_params=cur_case.sampling_params,
|
||
golden_answers=cur_case.golden_answers)
|
||
|
||
# The accuracy has already been verified in the previous test case.
|
||
# This test case is used to check whether the functionality works properly
|
||
# after enabling the static kernel and whether it is uninstalled as expected.
|
||
@pytest.mark.parametrize("cur_case", [CASE_QWEN_EX])
|
||
def test_npugraph_ex_with_static_kernel(cur_case: LLMTestCase, monkeypatch):
|
||
monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False)
|
||
runner_kwargs = {
|
||
"model_name": cur_case.model,
|
||
"quantization": cur_case.quantization,
|
||
"max_model_len": 1024,
|
||
"compilation_config": {
|
||
"cudagraph_capture_sizes": [4, 8],
|
||
"cudagraph_mode": "FULL_DECODE_ONLY"
|
||
},
|
||
"additional_config": {
|
||
"npugraph_ex_config": {
|
||
"enable": True,
|
||
"enable_static_kernel": True,
|
||
}
|
||
},
|
||
}
|
||
gen_and_valid(runner_kwargs=runner_kwargs,
|
||
prompts=cur_case.prompts,
|
||
sampling_params=cur_case.sampling_params,
|
||
golden_answers=cur_case.golden_answers)
|
||
|
||
# Check whether the static kernel is properly uninstall
|
||
ascend_home_path = os.environ["ASCEND_HOME_PATH"]
|
||
static_kernel_install_path = os.path.join(ascend_home_path, 'opp/static_kernel/ai_core')
|
||
assert not os.path.exists(static_kernel_install_path)
|