Executing CUDA Kernels Concurrently From Multiple Processes

Enabling Inplace Input Mutation Optimizations In AOTInductor

AOTInductor Weight Streaming from PyTorch Models

Using torch._assert_async for PyTorch CUDA Model Development

Avoiding Weight Duplication In PyTorch Multi-Process Inference

where VS cond

Efficiently Using TorchRec KeyedJaggedTensor In GPU Systems

A Hybrid DeviceExecution Inference Engine from PyTorch

Exporting and Compiling Triton Kernels for AOTInductor

Eliminating PyTorch Model Data Memory Allocation for PyTorch Export Verification

Implementing PyTorch Custom Operations In C++ and CUDA Using torch.library

Graph Shape Inference with Symbolic Integers for Dynamic Shapes