Merge pull request #2 from MooreThreads/xd/cases

Update doc to add integrated projects

Author: yeahdongcn

README.md

@@ -283,6 +283,152 @@ if torchada.is_gpu_device(tensor):
 
 This is a fundamental limitation because `device.type` is a C-level property that cannot be patched from Python. Downstream projects that check `device.type == "cuda"` need to be patched to use `torchada.is_gpu_device()` or check for both types: `device.type in ("cuda", "musa")`.
 
+## Real-World Integrations
+
+torchada has been successfully integrated into several popular PyTorch-based projects. Below are examples demonstrating the typical integration patterns.
+
+### Integrated Projects
+
+| Project | Category | PR | Status |
+|---------|----------|--------|--------|
+| [ComfyUI](https://github.com/comfyanonymous/ComfyUI) | Image/Video Generation | [#11618](https://github.com/comfyanonymous/ComfyUI/pull/11618) | Open |
+| [LightLLM](https://github.com/ModelTC/LightLLM) | LLM Inference | [#1162](https://github.com/ModelTC/LightLLM/pull/1162) | Open |
+| [Xinference](https://github.com/xorbitsai/inference) | Model Serving | [#4425](https://github.com/xorbitsai/inference/pull/4425) | ✅ Merged |
+| [LightX2V](https://github.com/ModelTC/LightX2V) | Image/Video Generation | [#678](https://github.com/ModelTC/LightX2V/pull/678) | ✅ Merged |
+
+### Integration Patterns
+
+#### Pattern 1: Early Import with Platform Detection
+
+The most common pattern is to import `torchada` early in the application lifecycle:
+
+```python
+# In __init__.py or main entry point
+from your_app.device_utils import is_musa
+
+if is_musa():
+    import torchada  # noqa: F401
+
+# Platform detection function
+def is_musa():
+    import torch
+    return hasattr(torch.version, "musa") and torch.version.musa is not None
+```
+
+This pattern is used by **LightLLM** and **LightX2V**.
+
+#### Pattern 2: Add to Dependencies
+
+Add `torchada` to your project's dependencies:
+
+```python
+# pyproject.toml
+dependencies = [
+    "torchada>=0.1.11",
+]
+
+# Or requirements.txt
+torchada>=0.1.11
+```
+
+#### Pattern 3: Device Availability Check
+
+Create a device availability function that checks for MUSA:
+
+```python
+def is_musa_available() -> bool:
+    try:
+        import torch
+        import torch_musa  # noqa: F401
+        import torchada  # noqa: F401
+        return torch.musa.is_available()
+    except ImportError:
+        return False
+
+def get_available_device():
+    if torch.cuda.is_available():
+        return "cuda"
+    elif is_musa_available():
+        return "musa"
+    return "cpu"
+```
+
+This pattern is used by **Xinference**.
+
+#### Pattern 4: Platform-Specific Feature Flags
+
+Enable features based on platform capabilities:
+
+```python
+import torchada
+
+musa_available = hasattr(torch, "musa") and torch.musa.is_available()
+
+def is_musa():
+    return musa_available
+
+# Enable NVIDIA-like optimizations on MUSA
+if is_nvidia() or is_musa():
+    ENABLE_PYTORCH_ATTENTION = True
+    NUM_STREAMS = 2  # Async weight offloading
+    MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.9
+```
+
+This pattern is used by **ComfyUI**.
+
+#### Pattern 5: Platform Device Classes
+
+For projects with a device abstraction layer:
+
+```python
+from your_platform.base.nvidia import CudaDevice
+from your_platform.registry import PLATFORM_DEVICE_REGISTER
+
+@PLATFORM_DEVICE_REGISTER("musa")
+class MusaDevice(CudaDevice):
+    name = "cuda"  # Use CUDA APIs (redirected by torchada)
+
+    @staticmethod
+    def is_available() -> bool:
+        try:
+            import torch
+            import torchada  # noqa: F401
+            return hasattr(torch, "musa") and torch.musa.is_available()
+        except ImportError:
+            return False
+```
+
+This pattern is used by **LightX2V**.
+
+### Common Integration Steps
+
+1. **Add dependency**: Add `torchada>=0.1.11` to your project dependencies
+
+2. **Import early**: Import `torchada` before using any `torch.cuda` APIs
+   ```python
+   import torchada  # Apply patches
+   import torch
+   ```
+
+3. **Add platform detection**: Create `is_musa()` function for platform-specific code
+   ```python
+   def is_musa():
+       return hasattr(torch.version, "musa") and torch.version.musa is not None
+   ```
+
+4. **Update feature flags**: Include MUSA in capability checks
+   ```python
+   if is_nvidia() or is_musa():
+       # Enable GPU-specific features
+   ```
+
+5. **Handle device type checks**: Use `torchada.is_gpu_device()` or check both types
+   ```python
+   # Instead of: device.type == "cuda"
+   # Use: device.type in ("cuda", "musa")
+   # Or: torchada.is_gpu_device(device)
+   ```
+
 ## Architecture
 
 torchada uses a decorator-based patch registration system: