v0.10.1rc1

tests/e2e/singlecard/test_vlm.py

@@ -0,0 +1,89 @@
+#
+# 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
+
+import pytest
+from vllm import SamplingParams
+from vllm.assets.audio import AudioAsset
+from vllm.assets.image import ImageAsset
+
+from tests.e2e.conftest import VllmRunner
+
+os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
+
+
+@pytest.mark.skip(reason="fix me")
+def test_multimodal_vl(prompt_template):
+    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 VllmRunner("Qwen/Qwen2.5-VL-3B-Instruct",
+                    max_model_len=4096,
+                    mm_processor_kwargs={
+                        "min_pixels": 28 * 28,
+                        "max_pixels": 1280 * 28 * 28,
+                        "fps": 1,
+                    },
+                    enforce_eager=True) as vllm_model:
+        vllm_model.generate_greedy(prompts=prompts,
+                                   images=images,
+                                   max_tokens=64)
+
+
+def test_multimodal_audio():
+    audio_prompt = "".join([
+        f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
+        for idx in range(2)
+    ])
+    question = "What sport and what nursery rhyme are referenced?"
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              "<|im_start|>user\n"
+              f"{audio_prompt}{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    mm_data = {
+        "audio": [
+            asset.audio_and_sample_rate for asset in
+            [AudioAsset("mary_had_lamb"),
+             AudioAsset("winning_call")]
+        ]
+    }
+    inputs = {"prompt": prompt, "multi_modal_data": mm_data}
+
+    sampling_params = SamplingParams(temperature=0.2,
+                                     max_tokens=10,
+                                     stop_token_ids=None)
+
+    with VllmRunner("Qwen/Qwen2-Audio-7B-Instruct",
+                    max_model_len=4096,
+                    max_num_seqs=5,
+                    dtype="bfloat16",
+                    limit_mm_per_prompt={"audio": 2},
+                    gpu_memory_utilization=0.9) as runner:
+        runner.generate(inputs, sampling_params=sampling_params)