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torch.compile is the goto mechanism to increase performance of PyTorch models of all shapes and forms.
While it is widely understood how to change the computation by manipulating the FX trace representation, it becomes a much more general tool by also transforming model and input expectations (the guards): This enables model-changing transformations like quantization and distributed without needing to adapt the model to it.
We take a deep dive into the torch.compile internals to see what's going on under the hood and how we can hook into the gears to enable distributed (starting from a single-GPU model) and quantization. In this quest, marvel at the interplay between PyTorch's Python code, the Pyton interpreter and PyTorch's C++ code that enable the Dynamo frontend of torch.compile and then use a big hammer to use it in unexpected ways. Building on our experience with Lightning Thunder, an experimental compiler for PyTorch models, we propose a transform mechanism taking care of compute, model, and weights.
Thomas Viehmann does PyTorch and Optimization at Lightning AI, PyTorch contributor since 2017, founded MathInf GmbH in 2018, co-authored of “Deep Learning with PyTorch” in 2020.
Tuesday April 7, 2026 14:45 - 15:10 CEST Master Stage
