f99762eb25
### What this PR does / why we need it?
As a validation for #3664, add end-to-end tests to monitor the InternVL
model and ensure its continuous proper operation. This PR is only for
single-card. So the models that have more parameters than 8B like 78B
are needed to test using multi-cards.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
`pytest -sv tests/e2e/singlecard/multi-modal/test_internvl.py`
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
98 lines
3.3 KiB
Python
98 lines
3.3 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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from vllm_ascend.utils import vllm_version_is
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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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# skip testing InternVL3-8B and InternVL3_5-8B on 0.11.0 due to https://github.com/vllm-project/vllm-ascend/issues/3925.
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if vllm_version_is("0.11.0"):
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MODELS = [
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"OpenGVLab/InternVL2-8B",
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"OpenGVLab/InternVL2_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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