a1f142b7ad
There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import os
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# Set spawn method before any torch/NPU imports to avoid fork issues
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os.environ.setdefault('VLLM_WORKER_MULTIPROC_METHOD', 'spawn')
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import pytest
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from vllm.assets.image import ImageAsset
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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MODELS = [
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"OpenGVLab/InternVL2-8B",
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"OpenGVLab/InternVL2_5-8B",
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"OpenGVLab/InternVL3-8B",
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"OpenGVLab/InternVL3_5-8B",
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]
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@pytest.mark.parametrize("model", MODELS)
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def test_internvl_basic(model: str):
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"""Test basic InternVL2 inference with single image."""
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# Load test image
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image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
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# InternVL uses chat template format
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# Format: <|im_start|>user\n<image>\nQUESTION<|im_end|>\n<|im_start|>assistant\n
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questions = [
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"What is the content of this image?",
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"Describe this image in detail.",
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]
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# Build prompts with InternVL2 chat template
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prompts = [
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f"<|im_start|>user\n<image>\n{q}<|im_end|>\n<|im_start|>assistant\n"
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for q in questions
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]
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images = [image] * len(prompts)
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outputs = {}
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for enforce_eager, mode in [(False, "eager"), (True, "graph")]:
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with VllmRunner(
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model,
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max_model_len=8192,
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limit_mm_per_prompt={"image": 4},
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enforce_eager=enforce_eager,
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dtype="bfloat16",
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) as vllm_model:
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generated_outputs = vllm_model.generate_greedy(
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prompts=prompts,
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images=images,
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max_tokens=128,
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)
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assert len(generated_outputs) == len(prompts), \
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f"Expected {len(prompts)} outputs, got {len(generated_outputs)} in {mode} mode"
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for i, (_, output_str) in enumerate(generated_outputs):
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assert output_str, \
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f"{mode.capitalize()} mode output {i} should not be empty. Prompt: {prompts[i]}"
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assert len(output_str.strip()) > 0, \
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f"{mode.capitalize()} mode Output {i} should have meaningful content"
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outputs[mode] = generated_outputs
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eager_outputs = outputs["eager"]
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graph_outputs = outputs["graph"]
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check_outputs_equal(outputs_0_lst=eager_outputs,
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outputs_1_lst=graph_outputs,
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name_0="eager mode",
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name_1="graph mode")
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