- Embedding.forward() now preserves requires_grad from weight tensor
- PositionalEncoding.forward() uses Tensor addition (x + pos) instead of .data
- Critical for transformer input embeddings to have gradients
Both changes ensure gradient flows from loss back to embedding weights