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e6ca8dee6f
- docs: Rewrite and expand docstrings for the GroupedQueryAttention class and all main methods (__init__, forward, _repeat_kv_heads, _create_sliding_window_mask):
- explained GQA architecture and motivation
- included mathematical formulas, step-by-step algorithms, usage examples
- added references to relevant scientific papers (Mistral, Llama 2, etc.)
- test: Add dedicated unit tests for GQA (output shape correctness, mask/window logic, KV head replication, RoPE processing, error and edge-cases)
- docs/test: Documentation and tests now fully reflect modern GQA usage and best practices for LLM architectures
This commit makes the implementation, usage, and theoretical underpinnings of GQA transparent and reproducible for researchers and engineers.