feat: Major optimization and robustness improvements (#31)

This release introduces significant performance optimizations, memory efficiency
improvements, and enhanced system robustness:

🚀 Performance Optimizations:
- Add three new fused CUDA kernels (rope_index, rot_pos_emb, get_window_index)
  for accelerated multimodal preprocessing
- Implement FSDP2 support for distributed training with improved memory efficiency
- Add Torch.compile integration for additional performance gains
- Optimize memory usage: reduce peak allocation from 48GB to 24GB on 8-GPU setup

🔧 System Robustness:
- Fix missing token position inputs in prediction pipeline
- Add type-robust negation operations in RoPE CUDA kernels (half/bfloat16 support)
- Fix dataset root parameter initialization in LeRobot data loader
- Enhanced error handling and input validation across fusion operators

📚 Documentation & Usability:
- Add comprehensive memory usage benchmarks and hardware recommendations
- Update citation format with proper arXiv reference
- Improve training configuration documentation with quick start guide
- Add detailed API documentation for new fusion operators

🛠️ Technical Details:
- Version bump to 1.0.1
- New CUDA kernels: rope_index.cu, rot_pos.cu, window_index.cu
- FSDP2 state dict loading with distribute_tensor support
- Enhanced multimodal RoPE with 3D position encoding
- Window attention optimization for Vision Transformers

Breaking Changes: None - all changes are backward compatible

csrc/README.md

@@ -1,6 +1,6 @@
 # Fusion Operators (CSRC)
 
-High-performance CUDA kernels for accelerating model training.
+High-performance CUDA kernels for accelerating model training, with specialized support for multimodal and MoE architectures.
 
 ## Operators
 
@@ -16,7 +16,12 @@ High-performance CUDA kernels for accelerating model training.
 ### Multimodal RoPE
 - `rope`: Rotary Position Embedding forward pass
 - `rope_bwd`: RoPE backward pass
-- Support for multimodal inputs with configurable sections
+- `rope_index`: Generates position indices for multimodal RoPE
+- `rot_pos_emb`: Fused rotary position embedding computation
+
+### Vision Transformer Optimization
+- `get_window_index`: Window attention index generation
+
 
 ## Acknowledgments