Developed and delivered XLA autocast support within the gradient checkpointing utility for the pytorch/xla repository, enabling conditional mixed-precision execution during the forward pass of checkpointed layers. This feature allows PyTorch models running on XLA devices to leverage mixed-precision computations, reducing memory usage during training without compromising flexibility. The work involved updating autocast APIs and integrating them into the checkpointing workflow, ensuring seamless compatibility with existing PyTorch and XLA infrastructure. Implementation was carried out using Python and Shell, with a focus on gradient checkpointing and mixed-precision techniques to optimize performance and resource utilization for large-scale machine learning workloads.