mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 13:43:53 +08:00
787010a637
V1 is enabled by default, no need to set it by hand now. This PR remove
the useless setting in example and tests
- vLLM version: v0.9.2
- vLLM main:
9ad0a4588b
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
130 lines
4.7 KiB
Python
130 lines
4.7 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.
|
|
# This file is a part of the vllm-ascend project.
|
|
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
|
|
#
|
|
"""Compare the short outputs of HF and vLLM when using greedy sampling.
|
|
|
|
Run `pytest tests/test_offline_inference.py`.
|
|
"""
|
|
import os
|
|
from unittest.mock import patch
|
|
|
|
import pytest
|
|
import vllm # noqa: F401
|
|
from modelscope import snapshot_download # type: ignore[import-untyped]
|
|
from vllm import SamplingParams
|
|
from vllm.assets.image import ImageAsset
|
|
|
|
import vllm_ascend # noqa: F401
|
|
from tests.e2e.conftest import VllmRunner
|
|
|
|
MODELS = [
|
|
"Qwen/Qwen2.5-0.5B-Instruct",
|
|
"Qwen/Qwen3-0.6B-Base",
|
|
]
|
|
MULTIMODALITY_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
|
|
|
|
QUANTIZATION_MODELS = [
|
|
"vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8",
|
|
]
|
|
os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
|
|
|
|
|
|
@pytest.mark.parametrize("model", MODELS)
|
|
@pytest.mark.parametrize("dtype", ["half", "float16"])
|
|
@pytest.mark.parametrize("max_tokens", [5])
|
|
def test_models(model: str, dtype: str, max_tokens: int) -> None:
|
|
# 5042 tokens for gemma2
|
|
# gemma2 has alternating sliding window size of 4096
|
|
# we need a prompt with more than 4096 tokens to test the sliding window
|
|
prompt = "The following numbers of the sequence " + ", ".join(
|
|
str(i) for i in range(1024)) + " are:"
|
|
example_prompts = [prompt]
|
|
|
|
with VllmRunner(model,
|
|
max_model_len=8192,
|
|
dtype=dtype,
|
|
enforce_eager=True,
|
|
gpu_memory_utilization=0.7) as vllm_model:
|
|
vllm_model.generate_greedy(example_prompts, max_tokens)
|
|
|
|
|
|
@pytest.mark.parametrize("model", QUANTIZATION_MODELS)
|
|
@pytest.mark.parametrize("max_tokens", [5])
|
|
def test_quantization_models(model: str, max_tokens: int) -> None:
|
|
prompt = "The following numbers of the sequence " + ", ".join(
|
|
str(i) for i in range(1024)) + " are:"
|
|
example_prompts = [prompt]
|
|
|
|
# NOTE: Using quantized model repo id from modelscope encounters an issue,
|
|
# this pr (https://github.com/vllm-project/vllm/pull/19212) fix the issue,
|
|
# after it is being merged, there's no need to download model explicitly.
|
|
model_path = snapshot_download(model)
|
|
|
|
with VllmRunner(model_path,
|
|
max_model_len=8192,
|
|
enforce_eager=True,
|
|
dtype="auto",
|
|
gpu_memory_utilization=0.7,
|
|
quantization="ascend") as vllm_model:
|
|
vllm_model.generate_greedy(example_prompts, max_tokens)
|
|
|
|
|
|
@pytest.mark.parametrize("model", MULTIMODALITY_MODELS)
|
|
def test_multimodal(model, prompt_template, vllm_runner):
|
|
image = ImageAsset("cherry_blossom") \
|
|
.pil_image.convert("RGB")
|
|
img_questions = [
|
|
"What is the content of this image?",
|
|
"Describe the content of this image in detail.",
|
|
"What's in the image?",
|
|
"Where is this image taken?",
|
|
]
|
|
images = [image] * len(img_questions)
|
|
prompts = prompt_template(img_questions)
|
|
with vllm_runner(model,
|
|
max_model_len=4096,
|
|
mm_processor_kwargs={
|
|
"min_pixels": 28 * 28,
|
|
"max_pixels": 1280 * 28 * 28,
|
|
"fps": 1,
|
|
}) as vllm_model:
|
|
vllm_model.generate_greedy(prompts=prompts,
|
|
images=images,
|
|
max_tokens=64)
|
|
|
|
|
|
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION": "1"})
|
|
def test_models_topk() -> None:
|
|
example_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=5,
|
|
temperature=0.0,
|
|
top_k=50,
|
|
top_p=0.9)
|
|
|
|
with VllmRunner("Qwen/Qwen2.5-0.5B-Instruct",
|
|
max_model_len=8192,
|
|
dtype="float16",
|
|
enforce_eager=True,
|
|
gpu_memory_utilization=0.7) as vllm_model:
|
|
vllm_model.generate(example_prompts, sampling_params)
|