Dynamo

torch.compile is PyTorch's model compilation tool. It inspects a model before execution and converts as much of its computation as possible into an optimized region, allowing compiler transformations such as kernel fusion, operation reordering, and memory planning. Python scalar reads such as `.item()` can cause graph breaks that fragment these regions and force synchronization between Python and the accelerator, reducing available optimization and potentially slowing execution.

Triton is a kernel language that PyTorch Inductor commonly generates for GPU targets. In the torch.compile pipeline, it is part of the stage that generates optimized code for PyTorch models.

NVIDIA Dynamo is a developer toolkit from NVIDIA for managing distributed key–value (KV) context caches used in large‑language‑model inference. It provides KV‑aware routing, cache offloading, and prefill/decode disaggregation to move KV cache and related activation/state across GPU clusters, reducing memory pressure and supporting scalable LLM serving. It is intended for integration into GPU‑based inference and serving stacks.

AOT Autograd is a PyTorch compilation component used as the second stage of torch.compile. It prepares a captured graph for a backend by decomposing high-level operations and capturing the backward graphs needed for training.

Compilation for Dataflow is an IBM course on Coursera about the compiler stages a PyTorch model passes through before running on an accelerator. It covers how compiler decisions affect execution, including graph breaks, synchronization caused by Python scalar reads, and the resulting loss of compiler optimization in compiled workloads.