# FEAGI wgpu Implementation Summary

*Last Updated: May 26, 2025*

## Overview

This document summarizes the successful implementation of **wgpu** (Rust-based GPU library) support in FEAGI, providing high-performance native GPU acceleration on Mac M4 systems with Metal backend.

## Important Distinction: wgpu vs WebGPU

**Critical clarification to avoid confusion:**

### **wgpu** (What we implemented)
- **Type**: Rust-based native GPU library
- **Target**: Desktop/mobile applications, embedded systems
- **Backends**: Metal (macOS), Vulkan (Linux), D3D12 (Windows), OpenGL
- **Performance**: Near-native, optimized for high-performance compute
- **Use case**: Our Mac M4 Metal acceleration implementation

### **WebGPU** (What we DON'T use)
- **Type**: Web standard/browser API
- **Target**: Web browsers only
- **Backend**: Browser's GPU abstraction layer
- **Performance**: Good but limited by browser sandbox
- **Use case**: Web-based applications

## Implementation Details

### Backend Integration

The wgpu backend is integrated into FEAGI's `ArrayBackend` abstraction system:

```python
from feagi.bdu.models.array_backend import ArrayBackend, BackendType

# Create wgpu backend
backend = ArrayBackend(BackendType.WGPU)
# → Uses wgpu → Metal → Apple GPU on Mac M4
```

### Supported Operations

✅ **Working Operations:**
- Array creation (`zeros`, `ones`, `array`)
- Data type conversion and precision handling
- CPU ↔ GPU memory transfers
- Matrix multiplication (with CPU fallback)
- Device statistics and monitoring

🔄 **Partially Working:**
- Individual element access (inefficient, requires buffer recreation)
- Sparse matrix operations (converts to dense)

❌ **Not Yet Implemented:**
- Direct GPU compute shaders for neural operations
- Efficient batch operations for ConnectomeManager
- Multi-GPU support

### Performance Characteristics

- **Memory Management**: Direct GPU buffer allocation via Metal
- **Data Transfer**: Efficient CPU ↔ GPU transfers with proper staging buffers
- **Precision**: FP32 primary, FP16 support planned
- **Synchronization**: Proper buffer mapping and unmapping

### Code Changes Made

#### 1. Backend Type Renaming
- Changed `BackendType.WEBGPU` → `BackendType.WGPU`
- Updated all references throughout codebase
- Clarified documentation to distinguish from WebGPU web standard

#### 2. ArrayBackend Implementation
```python
class ArrayBackend:
    def _initialize_wgpu(self):
        """Initialize wgpu backend (Rust-based GPU library with Metal backend on Mac)."""
        self.adapter = wgpu.gpu.request_adapter_sync()
        self.device = self.adapter.request_device_sync()

    def _numpy_to_wgpu(self, array: np.ndarray) -> Any:
        """Convert NumPy array to wgpu buffer."""
        gpu_array = array.astype(np.float32)
        buffer = self.device.create_buffer_with_data(
            data=gpu_array.tobytes(),
            usage=wgpu.BufferUsage.STORAGE | wgpu.BufferUsage.COPY_SRC | wgpu.BufferUsage.COPY_DST
        )
        # Store metadata for reconstruction
        buffer._feagi_shape = array.shape
        buffer._feagi_dtype = array.dtype
        buffer._feagi_size = array.size
        return buffer

    def _wgpu_to_numpy(self, buffer: Any) -> np.ndarray:
        """Convert wgpu buffer to NumPy array."""
        # Create staging buffer for reading
        staging_buffer = self.device.create_buffer(
            size=buffer.size,
            usage=wgpu.BufferUsage.MAP_READ | wgpu.BufferUsage.COPY_DST
        )
        # Copy and map buffer
        encoder = self.device.create_command_encoder()
        encoder.copy_buffer_to_buffer(buffer, 0, staging_buffer, 0, buffer.size)
        self.device.queue.submit([encoder.finish()])

        staging_buffer.map_sync(wgpu.MapMode.READ)
        data_bytes = staging_buffer.read_mapped()
        staging_buffer.unmap()

        # Convert back to numpy
        np_array = np.frombuffer(data_bytes, dtype=np.float32)
        return np_array.reshape(buffer._feagi_shape)
```

#### 3. Documentation Updates
- Updated `arch-gpu.md` with comprehensive wgpu vs WebGPU distinction
- Added platform-specific recommendations
- Included troubleshooting and configuration guides

## Testing Results

### Basic Functionality Test
```
🚀 FEAGI WGPU BASIC TEST
==================================================
✅ Backend created: BackendType.WGPU
✅ Zeros: shape=(5, 5), sum=0.0
✅ Ones: shape=(3, 3), sum=9.0
✅ Array from data: shape=(2, 2)
✅ Matrix multiplication result: [[19 22] [43 50]]
✅ Device stats: {'backend': 'wgpu', 'precision': 'fp32', 'device': 'wgpu'}

🎉 wgpu basic test PASSED!
```

### Device Detection
```
🔥 wgpu adapter: Apple M4 Pro (Metal)
Using wgpu device: Apple M4 Pro with Metal backend
```

## Current Limitations

### 1. ConnectomeManager Integration
- **Issue**: GPU buffers don't support direct item assignment (`buffer[index] = value`)
- **Current Workaround**: Convert to CPU, modify, upload back (inefficient)
- **Future Solution**: Implement batch operations and compute shaders

### 2. Performance Optimization
- **Current**: Basic GPU memory management
- **Needed**: Custom compute shaders for neural operations
- **Planned**: Optimized kernels for membrane potential updates, firing logic

### 3. Platform Support
- **Working**: macOS with Metal backend
- **Planned**: Linux with Vulkan, Windows with D3D12

## Future Roadmap

### Short-term (Next Sprint)
- [ ] Implement efficient batch operations for ConnectomeManager
- [ ] Add compute shaders for basic neural operations
- [ ] Optimize memory transfer patterns

### Medium-term (Next Month)
- [ ] Custom neural compute kernels
- [ ] Linux Vulkan backend support
- [ ] Performance benchmarking suite

### Long-term (Next Quarter)
- [ ] Multi-GPU support
- [ ] WebAssembly deployment via wgpu
- [ ] Embedded system optimization

## Configuration

### Environment Variables
```bash
export FEAGI_BACKEND=wgpu          # Force wgpu backend
export FEAGI_PRECISION=fp16        # Use half precision
export WGPU_BACKEND=metal          # Force Metal backend (macOS)
```

### Runtime Configuration
```toml
# feagi_configuration.toml
[compute]
backend = "wgpu"
precision = "fp16"
device_preference = "high_performance"
```

## Troubleshooting

### Common Issues

**wgpu not detected:**
```bash
pip install wgpu  # Install wgpu library
```

**Metal backend not available:**
- Ensure macOS 10.15+ for Metal support
- Check hardware compatibility with `system_profiler SPDisplaysDataType`

**Performance issues:**
- Verify GPU backend is being used: check device stats
- Monitor CPU ↔ GPU transfer bottlenecks
- Use batch operations instead of individual element access

## Conclusion

The wgpu implementation provides a solid foundation for high-performance GPU acceleration in FEAGI on Mac M4 systems. While basic array operations are fully functional, the next phase will focus on optimizing ConnectomeManager integration and implementing custom neural compute kernels.

The clear distinction between wgpu (Rust-based) and WebGPU (web standard) has been established throughout the codebase and documentation to prevent future confusion.

## See Also
- [GPU Architecture Guide](arch-gpu.md)
- [GPU Optimization Guide](arch-gpu-optimization.md)
- [wgpu Compatibility](npu_wgpu_compatibility.md)
