Refer to this tutorial and the general documentation for more details. This release adds weight normalization ( weight_norm), orthogonal parametrization (matrix constraints and part of pruning) and more flexibility when creating your own parametrization. Nn.Module parametrizaton, a feature that allows users to parametrize any parameter or buffer of an nn.Module without modifying the nn.Module itself, is available in stable. Refer to this documentation for more details. The module has 30 operations, including gamma, Bessel, and (Gauss) error functions. (Stable) torch.specialĪ torch.special module, analogous to SciPy’s special module, is now available in stable. You can learn more about FX in the official documentation and GitHub examples of program transformations implemented using torch.fx. This toolkit aims to support a subset of Python language semantics-rather than the whole Python language-to facilitate ease of implementation of transforms.
It is a toolkit for pass writers to facilitate Python-to-Python transformation of functions and nn.Module instances. Frontend APIs (Stable) Python code transformations with FXįX provides a Pythonic platform for transforming and lowering PyTorch programs.
This release is composed of over 3,400 commits since 1.9, made by 426 contributors. We are excited to announce the release of PyTorch 1.10.
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