trtllm-codebase-exploration

To find existing forward dispatch paths in TensorRT-LLM, systematically map the module structure and class hierarchy, then trace forward methods to identify dispatch routing and state invariants. This avoids rewriting logic and prevents duplicated engineering effort.

TensorRT-LLM handles RoPE application and KV-cache management using fused kernels within the attention stack. You should trace the forward dispatch to recognize these accelerated behaviors and reuse the correct fused abstraction rather than reimplementing the logic.

The best way to modify attention modules is to first map the exact class layout and initialization attributes, read every forward method to understand dispatch routing, and then select the correct existing dispatcher or fused abstraction to prevent regressions.

You need to trace forward methods in TensorRT-LLM attention modules during new feature development, optimization work, or code modifications to identify expected inputs, satisfy state invariants, and locate existing handlers that already support your intended scenario.

You can and should use existing TensorRT-LLM utilities for fused-kernel capability checks. By reading the target class forward methods, you can identify the correct abstraction level and dispatch routing to reuse existing logic for your specific scenario.

Reimplementing logic in TensorRT-LLM wastes engineering effort because the codebase already contains forward methods and fused kernels for RoPE and KV-cache behavior. Duplicating this work leads to unnecessary churn, regressions, and missed reuse paths.