Sync from v0.13

tests/models/multimodal/generation/test_audioflamingo3.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The vLLM team.
+# Copyright 2025 NVIDIA CORPORATION and the HuggingFace Inc. team. All rights
+# reserved.
+#
+# 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.
+
+import json
+import os
+
+import pytest
+
+from tests.models.registry import HF_EXAMPLE_MODELS
+from vllm import LLM, SamplingParams
+
+MODEL_NAME = "nvidia/audio-flamingo-3-hf"
+
+
+def get_fixture_path(filename):
+    return os.path.join(
+        os.path.dirname(__file__), "../../fixtures/audioflamingo3", filename
+    )
+
+
+@pytest.fixture(scope="module")
+def llm():
+    # Check if the model is supported by the current transformers version
+    model_info = HF_EXAMPLE_MODELS.get_hf_info("AudioFlamingo3ForConditionalGeneration")
+    model_info.check_transformers_version(on_fail="skip")
+
+    try:
+        llm = LLM(
+            model=MODEL_NAME,
+            trust_remote_code=True,
+            dtype="bfloat16",
+            enforce_eager=True,
+            limit_mm_per_prompt={"audio": 1},
+        )
+        return llm
+    except Exception as e:
+        pytest.skip(f"Failed to load model {MODEL_NAME}: {e}")
+
+
+def test_single_generation(llm):
+    fixture_path = get_fixture_path("expected_results_single.json")
+    if not os.path.exists(fixture_path):
+        pytest.skip(f"Fixture not found: {fixture_path}")
+
+    with open(fixture_path) as f:
+        expected = json.load(f)
+
+    audio_url = "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/Why_do_we_ask_questions_converted.wav"
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "audio_url", "audio_url": {"url": audio_url}},
+                {"type": "text", "text": "Transcribe the input speech."},
+            ],
+        }
+    ]
+
+    sampling_params = SamplingParams(temperature=0.0, max_tokens=128)
+
+    outputs = llm.chat(
+        messages=messages,
+        sampling_params=sampling_params,
+    )
+    generated_text = outputs[0].outputs[0].text.strip()
+
+    expected_text = expected["transcriptions"][0]
+
+    assert expected_text in generated_text or generated_text in expected_text
+
+
+def test_batched_generation(llm):
+    fixture_path = get_fixture_path("expected_results_batched.json")
+    if not os.path.exists(fixture_path):
+        pytest.skip(f"Fixture not found: {fixture_path}")
+
+    with open(fixture_path) as f:
+        expected = json.load(f)
+
+    items = [
+        {
+            "audio_url": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/dogs_barking_in_sync_with_the_music.wav",
+            "question": "What is surprising about the relationship "
+            "between the barking and the music?",
+            "expected_idx": 0,
+        },
+        {
+            "audio_url": "https://huggingface.co/datasets/nvidia/AudioSkills/resolve/main/assets/Ch6Ae9DT6Ko_00-04-03_00-04-31.wav",
+            "question": (
+                "Why is the philosopher's name mentioned in the lyrics? "
+                "(A) To express a sense of nostalgia "
+                "(B) To indicate that language cannot express clearly, "
+                "satirizing the inversion of black and white in the world "
+                "(C) To add depth and complexity to the lyrics "
+                "(D) To showcase the wisdom and influence of the philosopher"
+            ),
+            "expected_idx": 1,
+        },
+    ]
+
+    conversations = []
+    for item in items:
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "audio_url", "audio_url": {"url": item["audio_url"]}},
+                    {"type": "text", "text": item["question"]},
+                ],
+            }
+        ]
+        conversations.append(messages)
+
+    sampling_params = SamplingParams(temperature=0.0, max_tokens=128)
+
+    outputs = llm.chat(
+        messages=conversations,
+        sampling_params=sampling_params,
+    )
+
+    for i, output in enumerate(outputs):
+        generated_text = output.outputs[0].text.strip()
+        expected_text = expected["transcriptions"][i]
+
+        assert expected_text in generated_text or generated_text in expected_text