PyTorch Conference Europe 2026

CUDA streams are a widely-used method for parallelizing GPU computation on NVIDIA GPUs. They have long been requested by our users and enable multiple key capabilities - overlapping communication and compute kernels, training on multiple batches in parallel and parallelizing kernels, all of which are needed for achieving SOTA training performance. Another key capability is activation offloading - this can be applied to any model to prevent OOMs by asynchronously storing activations in cpu memory until they are needed by the model.

Before this work, torch.compile previously would graph break on CUDA stream contexts, which can be costly for models that utilize streams. Although workarounds exist (e.g. wrapping stream manipulation into custom ops), these solutions add complexity and create friction in the user experience. By enabling seamless CUDA stream support in PT2, we allow our users to leverage the familiar eager APIs for stream assignment and synchronization directly within torch.compile. This not only simplifies the workflow but also ensures that models using custom streaming patterns can run efficiently out-of-the-box without manual intervention or code restructuring.