Worked on the ROCm/TransformerEngine repository to address precision issues in FP8 recomputation for quantized transformer workloads. Focused on deep learning and GPU computing, the developer implemented a fix in Python that clones amax_history and scale within the FP8GlobalStateManager when updating forward paths. This approach prevents unintended mutations to scaling factors, thereby eliminating numerical drift and improving inference reliability. The solution enhanced performance optimization by ensuring that updated buffers are used rather than direct references, reducing debugging time and increasing accuracy in FP8 computations. The work reflects a careful, detail-oriented approach to state management in high-performance transformer systems.