9e65da990e
### What this PR does / why we need it?
cherry-pick #1501 from 0.9.1-dev to main
Currently, Ray is not compatible with ACL Graph, so we need to fall back
to eager mode when using the Ray backend.
co-authored: Yizhou Liu <liu_yizhou@outlook.com>
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
95 lines
3.2 KiB
Python
95 lines
3.2 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.
|
|
#
|
|
"""
|
|
Compare the outputs of vLLM with and without aclgraph.
|
|
|
|
Run `pytest tests/compile/test_aclgraph.py`.
|
|
"""
|
|
|
|
import pytest
|
|
import torch
|
|
from vllm import LLM, SamplingParams
|
|
|
|
from tests.e2e.conftest import VllmRunner
|
|
from tests.e2e.model_utils import check_outputs_equal
|
|
|
|
MODELS = [
|
|
"Qwen/Qwen2.5-0.5B-Instruct",
|
|
# TODO: REVERT ME when oom is fixed
|
|
# "vllm-ascend/Qwen3-30B-A3B-Puring"
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize("model", MODELS)
|
|
@pytest.mark.parametrize("max_tokens", [32])
|
|
def test_models_with_aclgraph(
|
|
model: str,
|
|
max_tokens: int,
|
|
) -> None:
|
|
prompts = [
|
|
"Hello, my name is", "The president of the United States is",
|
|
"The capital of France is", "The future of AI is"
|
|
]
|
|
|
|
sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
|
|
# TODO: change to use vllmrunner when the registry of custom op is solved
|
|
# while running pytest
|
|
vllm_model = LLM(model, max_model_len=1024)
|
|
vllm_aclgraph_outputs = vllm_model.generate(prompts, sampling_params)
|
|
del vllm_model
|
|
torch.npu.empty_cache()
|
|
|
|
vllm_model = LLM(model, enforce_eager=True, max_model_len=1024)
|
|
vllm_eager_outputs = vllm_model.generate(prompts, sampling_params)
|
|
del vllm_model
|
|
torch.npu.empty_cache()
|
|
|
|
vllm_aclgraph_outputs_list = []
|
|
for output in vllm_aclgraph_outputs:
|
|
vllm_aclgraph_outputs_list.append(
|
|
(output.outputs[0].index, output.outputs[0].text))
|
|
|
|
vllm_eager_outputs_list = []
|
|
for output in vllm_eager_outputs:
|
|
vllm_eager_outputs_list.append(
|
|
(output.outputs[0].index, output.outputs[0].text))
|
|
|
|
check_outputs_equal(
|
|
outputs_0_lst=vllm_eager_outputs_list,
|
|
outputs_1_lst=vllm_aclgraph_outputs_list,
|
|
name_0="vllm_eager_outputs",
|
|
name_1="vllm_aclgraph_outputs",
|
|
)
|
|
|
|
|
|
def test_deepseek_raises_error(monkeypatch: pytest.MonkeyPatch) -> None:
|
|
with monkeypatch.context() as m:
|
|
m.setenv("VLLM_USE_MODELSCOPE", "True")
|
|
with pytest.raises(NotImplementedError) as excinfo:
|
|
VllmRunner("deepseek-ai/DeepSeek-V2-Lite-Chat",
|
|
max_model_len=1024,
|
|
enforce_eager=False)
|
|
assert "ACL Graph does not support deepseek" in str(excinfo.value)
|
|
|
|
|
|
@pytest.mark.parametrize("model", MODELS)
|
|
def test_ray_backend_sets_no_compilation(model: str) -> None:
|
|
runner = VllmRunner(model,
|
|
enforce_eager=False,
|
|
distributed_executor_backend="ray")
|
|
assert runner.model.llm_engine.vllm_config.compilation_config.level == 0
|