387ce1cc5b
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? add new e2e tests case for aclgraph memory to v0.11.0 ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? ut Signed-off-by: lilinsiman <lilinsiman@gmail.com>
101 lines
4.1 KiB
Python
101 lines
4.1 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 multiprocessing
|
|
import os
|
|
from unittest.mock import patch
|
|
|
|
import pytest
|
|
import torch
|
|
from modelscope import snapshot_download # type: ignore
|
|
from vllm import SamplingParams
|
|
|
|
from tests.e2e.conftest import VllmRunner
|
|
from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
|
|
|
|
MODELS = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
|
|
|
|
|
|
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
|
|
reason="aclgraph only support on v1")
|
|
@pytest.mark.parametrize("model", MODELS)
|
|
@pytest.mark.parametrize("max_tokens", [4])
|
|
@patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1"})
|
|
def test_aclgraph_mem_use(model: str, max_tokens: int) -> None:
|
|
del os.environ["VLLM_WORKER_MULTIPROC_METHOD"]
|
|
capture_called = multiprocessing.Value("i", 0) # int, 0 or 1
|
|
capture_mem_before = multiprocessing.Value("q", -1) # long long (64-bit)
|
|
capture_mem_after = multiprocessing.Value("q", -1) # long long
|
|
|
|
def capture_model_wrapper(original_method):
|
|
|
|
def wrapped(self):
|
|
mem_before = torch.npu.mem_get_info()[0] # free memory
|
|
result = original_method(self)
|
|
mem_after = torch.npu.mem_get_info()[0]
|
|
with capture_called.get_lock():
|
|
capture_called.value = 1
|
|
capture_mem_before.value = mem_before
|
|
capture_mem_after.value = mem_after
|
|
return result
|
|
|
|
return wrapped
|
|
|
|
original_capture = NPUModelRunner._capture_model
|
|
|
|
with patch.object(NPUModelRunner,
|
|
'_capture_model',
|
|
new=capture_model_wrapper(original_capture)):
|
|
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)
|
|
if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
|
|
vllm_model = VllmRunner(snapshot_download(model),
|
|
max_model_len=1024,
|
|
quantization="ascend")
|
|
else:
|
|
vllm_model = VllmRunner(snapshot_download(model))
|
|
_ = vllm_model.generate(prompts, sampling_params)
|
|
|
|
assert capture_called.value == 1, "_capture_model was not called during test"
|
|
assert capture_mem_before.value != -1, "capture_mem_before not set"
|
|
assert capture_mem_after.value != -1, "capture_mem_after not set"
|
|
|
|
print("capture_mem_before =", capture_mem_before.value)
|
|
print("capture_mem_after =", capture_mem_after.value)
|
|
|
|
mem_used_by_capture = capture_mem_before.value - capture_mem_after.value
|
|
# Empirical observation: capturing ACL graphs for Qwen3-0.6B uses ~0.20 GiB of NPU memory.
|
|
# DeepSeek-V2-Lite-W8A8 uses ~0.68 GiB of NPU memory
|
|
# a 1.3x tolerance is applied to account for runtime variance.
|
|
if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
|
|
baseline_capture_mem = 0.68
|
|
capture_mem_tolerance = 1.5
|
|
else:
|
|
baseline_capture_mem = 0.20
|
|
capture_mem_tolerance = 1.3
|
|
max_capture_mem_gib = baseline_capture_mem * capture_mem_tolerance
|
|
max_mem_expected = max_capture_mem_gib * (1024**3)
|
|
assert mem_used_by_capture < max_mem_expected, (
|
|
f"_capture_model used more memory than expected. "
|
|
f"Used: {mem_used_by_capture / (1024**3):.2f} GiB, "
|
|
f"Expected: < {max_capture_mem_gib:.2f} GiB")
|
|
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = 'spawn'
|