Guide the agent through implementing a correct Triton kernel that applies Rotary Position Embeddings (RoPE) to query and key tensors before attention. This covers the two incompatible layout conventions (GPT-NeoX/HuggingFace-LLaMA vs GPT-J/original-paper), pre-computed cos/sin table consumption, per-token position handling for continuous batching, partial-RoPE masking, and the precision discipline required to keep cos/sin in fp32 while applying to fp16/bf16 activations. RoPE is the dominant positional encoding in LLaMA, Mistral, Qwen, Gemma, GPT-NeoX, and most decoder-only LLMs trained after 2022, so getting this kernel right is load-bearing for inference correctness.
