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/rot_pos.h

@@ -0,0 +1,6 @@
+#include <torch/extension.h>
+
+torch::Tensor fused_rot_pos_emb_cuda(
+    torch::Tensor inv_freq,
+    torch::Tensor grid_thw,
+    int spatial_merge_size);