diff --git a/docs/source/quantization.rst b/docs/source/quantization.rst index 0d520b0df95f5c..1ae196e453b3ff 100644 --- a/docs/source/quantization.rst +++ b/docs/source/quantization.rst @@ -86,7 +86,7 @@ Eager Mode Quantization is a beta feature. User needs to do fusion and specify w FX Graph Mode Quantization is a new automated quantization framework in PyTorch, and currently it's a prototype feature. It improves upon Eager Mode Quantization by adding support for functionals and automating the quantization process, although people might need to refactor the model to make the model compatible with FX Graph Mode Quantization (symbolically traceable with ``torch.fx``). Note that FX Graph Mode Quantization is not expected to work on arbitrary models since the model might not be symbolically traceable, we will integrate it into domain libraries like torchvision and users will be able to quantize models similar to the ones in supported domain libraries with FX Graph Mode Quantization. For arbitrary models we'll provide general guidelines, but to actually make it work, users might need to be familiar with ``torch.fx``, especially on how to make a model symbolically traceable. -New users of quantization are encouraged to try out FX Graph Mode Quantization first, if it does not work, user may try to follow the guideline of `using FX Graph Mode Quantization `_ or fall back to eager mode quantization. +New users of quantization are encouraged to try out FX Graph Mode Quantization first, if it does not work, user may try to follow the guideline of `using FX Graph Mode Quantization `_ or fall back to eager mode quantization. The following table compares the differences between Eager Mode Quantization and FX Graph Mode Quantization: