Initial vLLM-omni repository structure

- Add complete directory structure for multimodal and non-autoregressive processing
- Include AsyncLLM, OmniEngine, and DiffusionEngine modules
- Add processors, schedulers, executors, and output processors
- Add API interfaces for Higress, Gradio, and ComfyUI
- Add configuration management and CLI interface
- Add comprehensive test structure and documentation
- Add pyproject.toml with dependencies and project configuration
- Add examples and documentation structure

tests/conftest.py

@@ -0,0 +1,118 @@
+"""
+Pytest configuration and fixtures for vLLM-omni tests.
+"""
+
+import pytest
+import asyncio
+from typing import AsyncGenerator, Generator
+from unittest.mock import Mock, AsyncMock
+
+import torch
+import numpy as np
+
+from vllm_omni.configs import load_config
+
+
+@pytest.fixture(scope="session")
+def event_loop() -> Generator[asyncio.AbstractEventLoop, None, None]:
+    """Create an instance of the default event loop for the test session."""
+    loop = asyncio.get_event_loop_policy().new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest.fixture
+async def mock_async_llm() -> AsyncMock:
+    """Mock AsyncLLM for testing."""
+    mock = AsyncMock()
+    mock.process.return_value = {"output": "test_output", "hidden_states": None}
+    return mock
+
+
+@pytest.fixture
+async def mock_omni_engine() -> AsyncMock:
+    """Mock OmniEngine for testing."""
+    mock = AsyncMock()
+    mock.execute.return_value = {"result": "test_result"}
+    return mock
+
+
+@pytest.fixture
+def sample_config() -> dict:
+    """Sample configuration for testing."""
+    return {
+        "general": {
+            "name": "vllm-omni-test",
+            "version": "0.1.0",
+            "debug": True,
+            "log_level": "DEBUG"
+        },
+        "model": {
+            "device": "cpu",
+            "dtype": "float32",
+            "max_model_len": 1024
+        },
+        "engines": {
+            "ar_engine": {"enabled": True, "max_batch_size": 32},
+            "diffusion_engine": {"enabled": True, "max_batch_size": 8}
+        }
+    }
+
+
+@pytest.fixture
+def sample_text_input() -> str:
+    """Sample text input for testing."""
+    return "Hello, world! This is a test input for vLLM-omni."
+
+
+@pytest.fixture
+def sample_image_input() -> np.ndarray:
+    """Sample image input for testing."""
+    return np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
+
+
+@pytest.fixture
+def sample_audio_input() -> np.ndarray:
+    """Sample audio input for testing."""
+    return np.random.randn(16000).astype(np.float32)  # 1 second at 16kHz
+
+
+@pytest.fixture
+def sample_multimodal_input() -> dict:
+    """Sample multimodal input for testing."""
+    return {
+        "text": "Describe this image",
+        "image": np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8),
+        "audio": np.random.randn(16000).astype(np.float32)
+    }
+
+
+@pytest.fixture
+def mock_torch_device() -> str:
+    """Mock torch device for testing."""
+    if torch.cuda.is_available():
+        return "cuda"
+    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        return "mps"
+    else:
+        return "cpu"
+
+
+# Pytest markers
+pytest_plugins = []
+
+
+def pytest_configure(config):
+    """Configure pytest with custom markers."""
+    config.addinivalue_line(
+        "markers", "unit: mark test as a unit test"
+    )
+    config.addinivalue_line(
+        "markers", "integration: mark test as an integration test"
+    )
+    config.addinivalue_line(
+        "markers", "benchmark: mark test as a benchmark test"
+    )
+    config.addinivalue_line(
+        "markers", "slow: mark test as slow running"
+    )