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You can no longer try out a new attention variant by writing a few PyTorch operators – you often need to write a new custom kernel

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Join us at PyTorch Conference North America · Oct 20-21 · San Jose, CA

https://pytorch.org/blog/flexattention/

You can no longer try out a new attention variant by writing a few PyTorch operators – you often need to write a new custom kernel

Training references — Torchvision 0.27 documentation -->

https://docs.pytorch.org/vision/stable/training_references.html

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Overloading Torch-TensorRT Converters with Custom Converters — Torch

https://docs.pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/converter_overloading.html

Overloading Torch-TensorRT Converters with Custom Converters# If for some reason you want to change the conversion behavior of a specific PyTorch operation to TensorRT, you can do so by writing a custom converter and overloading Torch-TensorRT’s

NLP From Scratch: Translation with a Sequence to Sequence Network and

https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html

NLP From Scratch: Translation with a Sequence to Sequence Network and Attention — PyTorch Tutorials 2

torch.compiler.keep_tensor_guards_unsafe — PyTorch main documentation

https://docs.pytorch.org/docs/main/generated/torch.compiler.keep_tensor_guards_unsafe.html

Skip to main content Back to top Ctrl+K Install PyTorch User Guide PyTorch Main Components torch.compiler torch.export Developer Notes Accelerator Integration Reference API torch torch.nn torch.nn.functional torch.Tensor