AOTInductor, a PyTorch compiler backend, supports in-place input mutations in optimized inference engines, contrary to the assumption that it relies solely on functionalization. The key is to explicit…
A new GitHub example demonstrates how to store AOTInductor model weights outside the shared library and update them at inference runtime in a thread-safe manner, addressing the lack of documentation f…
PyTorch's multiprocessing module provides a CUDA Inter-Process Communication (IPC) API that enables sharing model weights across multiple processes for inference, avoiding duplication in GPU VRAM. The…
PyTorch users can now implement custom operations in C++ and CUDA for use in both Python and C++ inference programs, with automatic device dispatch between CPU and CUDA implementations. The approach s…
PyTorch AOTInductor now compiles exported programs with hybrid CPU-GPU execution plans into a single executable package, eliminating the need to manually split models into separate device sub-models. …