![[The AI Show Episode 142]: ChatGPT’s New Image Generator, Studio Ghibli Craze and Backlash, Gemini 2.5, OpenAI Academy, 4o Updates, Vibe Marketing & xAI Acquires X](https://www.marketingaiinstitute.com/hubfs/ep%20142%20cover.png)
![[DEALS] The Premium Learn to Code Certification Bundle (97% off) & Other Deals Up To 98% Off – Offers End Soon!](https://www.javacodegeeks.com/wp-content/uploads/2012/12/jcg-logo.jpg){kind=logo}
![From drop-out to software architect with Jason Lengstorf [Podcast #167]](https://cdn.hashnode.com/res/hashnode/image/upload/v1743796461357/f3d19cd7-e6f5-4d7c-8bfc-eb974bc8da68.png?#)
.png?#)
.jpg?#)
_Christophe_Coat_Alamy.jpg?#)
 (1).webp?#)
![Rapidus in Talks With Apple as It Accelerates Toward 2nm Chip Production [Report]](https://www.iclarified.com/images/news/96937/96937/96937-640.jpg)
torch.compile vs torch.export
based on pytorch 2.6 torch.compile() and torch.export both leverage the PT2 stack, but serve different purposes. torch.compile torch.compile() is a JIT compiler that falls back to the Python runtime for untraceable parts, offering flexibility. torch.export In contrast, torch.export is an AOT compiler that captures the full graph, errors on untraceable code, and produces portable, low-level (ATen) graphs ideal for deployment. Internally torch.compile(): Uses TorchDynamo for tracing and AOT Autograd for optimization, with torch.fx as the graph backbone, enabling runtime flexibility. torch.export(): Relies on TorchDynamo for broad bytecode tracing, AOT Autograd for ATen lowering, and torch.fx for graph representation, ensuring a standalone graph. Comparison Table Feature torch.compile() torch.export() Compilation JIT AOT Graph Capture Partial (w/ breaks) Full (errors if untraceable) Untraceable Handling Python runtime fallback Requires rewrite/info Graph Output Flexible, runtime-tied Portable, ATen-level Python Support High (via fallback) Broad (bytecode tracing)
based on pytorch 2.6
torch.compile() and torch.export both leverage the PT2 stack, but serve different purposes.
torch.compile
torch.compile() is a JIT compiler that falls back to the Python runtime for untraceable parts, offering flexibility.
torch.export
In contrast, torch.export is an AOT compiler that captures the full graph, errors on untraceable code, and produces portable, low-level (ATen) graphs ideal for deployment.
Internally
- torch.compile(): Uses TorchDynamo for tracing and AOT Autograd for optimization, with torch.fx as the graph backbone, enabling runtime flexibility.
- torch.export(): Relies on TorchDynamo for broad bytecode tracing, AOT Autograd for ATen lowering, and torch.fx for graph representation, ensuring a standalone graph.
Comparison Table
| Feature | torch.compile() |
torch.export() |
|---|---|---|
| Compilation | JIT | AOT |
| Graph Capture | Partial (w/ breaks) | Full (errors if untraceable) |
| Untraceable Handling | Python runtime fallback | Requires rewrite/info |
| Graph Output | Flexible, runtime-tied | Portable, ATen-level |
| Python Support | High (via fallback) | Broad (bytecode tracing) |
