a3b5af8307
### What this PR does / why we need it? Add ut for torchair graph mode on DeepSeekV3 ### How was this patch tested? CI passed with new added test. --------- Signed-off-by: MengqingCao <cmq0113@163.com> Signed-off-by: Mengqing Cao <cmq0113@163.com>
81 lines
3.1 KiB
Python
81 lines
3.1 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.
|
||
# This file is a part of the vllm-ascend project.
|
||
#
|
||
"""Compare the short outputs of HF and vLLM when using greedy sampling.
|
||
|
||
Run `pytest tests/multicard/test_torchair_graph_mode.py`.
|
||
"""
|
||
import os
|
||
|
||
import pytest
|
||
|
||
from tests.conftest import VllmRunner
|
||
|
||
os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
|
||
|
||
|
||
@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
|
||
reason="torchair graph is not supported on v0")
|
||
def test_e2e_deepseekv3_with_torchair(monkeypatch: pytest.MonkeyPatch):
|
||
with monkeypatch.context() as m:
|
||
m.setenv("VLLM_USE_MODELSCOPE", "True")
|
||
m.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
|
||
|
||
example_prompts = [
|
||
"Hello, my name is",
|
||
"The president of the United States is",
|
||
"The capital of France is",
|
||
"The future of AI is",
|
||
]
|
||
dtype = "half"
|
||
max_tokens = 5
|
||
# torchair is only work without chunked-prefill now
|
||
with VllmRunner(
|
||
"vllm-ascend/DeepSeek-V3-Pruning",
|
||
dtype=dtype,
|
||
tensor_parallel_size=4,
|
||
distributed_executor_backend="mp",
|
||
additional_config={
|
||
"torchair_graph_config": {
|
||
"enabled": True,
|
||
},
|
||
"ascend_scheduler_config": {
|
||
"enabled": True,
|
||
},
|
||
"refresh": True,
|
||
},
|
||
enforce_eager=False,
|
||
) as vllm_model:
|
||
# use greedy sampler to make sure the generated results are fix
|
||
vllm_output = vllm_model.generate_greedy(example_prompts,
|
||
max_tokens)
|
||
# NOTE: vllm-ascend/DeepSeek-V3-Pruning is a random weight of
|
||
# DeepSeek-V3 with 2 hidden layers, thus the golden results seems
|
||
# inaccurate. This will only change if accuracy improves with the
|
||
# official weights of DeepSeek-V3.
|
||
golden_results = [
|
||
'Hello, my name is feasibility伸 spazio debtor添',
|
||
'The president of the United States is begg"""\n杭州风和 bestimm',
|
||
'The capital of France is frequentlyশามalinkAllowed',
|
||
'The future of AI is deleting俯احت怎么样了حراف',
|
||
]
|
||
|
||
assert len(golden_results) == len(vllm_output)
|
||
for i in range(len(vllm_output)):
|
||
assert golden_results[i] == vllm_output[i][1]
|
||
print(f"Generated text: {vllm_output[i][1]!r}")
|