PyTorch documentation

The PyTorch documentation provides comprehensive guidance and resources for users looking to leverage the PyTorch framework for deep learning projects. It covers a wide range of topics, including installation instructions, tutorials for beginners and advanced users, detailed API references, best practices for model training, and troubleshooting tips. The documentation aims to support developers in effectively utilizing PyTorch’s capabilities for building neural networks, implementing machine learning algorithms, and conducting research in the field of artificial intelligence. With a focus on practicality and accessibility, the PyTorch documentation serves as a valuable resource for both newcomers and experienced practitioners in the deep learning community.

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Migrating from functorch to torch.func

torch.func, previously known as “functorch”, is JAX-like composable function transforms for PyTorch. functorch started as an out-of-tree library over at the pytorch/functorch repository. Our goal has ...

UX Limitations

PyTorch documentation

torch.func, like JAX , has restrictions around what can be transformed. In general, JAX’s limitations are that transforms only work with pure functions: that is, functions where the output is complete...

Patching Batch Norm

PyTorch documentation

What’s happening? Batch Norm requires in-place updates to running_mean and running_var of the same size as the input. Functorch does not support inplace update to a regular tensor that takes in a batc...

torch.func.replace_all_batch_norm_modules_

PyTorch documentation

In place updates root by setting the running_mean and running_var to be None and setting track_running_stats to be False for any nn.BatchNorm module in root Module

torch.func.stack_module_state

PyTorch documentation

Prepares a list of torch.nn.Modules for ensembling with vmap() . Given a list of M nn.Modules of the same class, returns two dictionaries that stack all of their parameters and buffers together, index...

POE0004:operator-supported-in-newer-opset-version

PyTorch documentation

Operator is supported in newer opset version. Example:

torch.func.functional_call

PyTorch documentation

Performs a functional call on the module by replacing the module parameters and buffers with the provided ones. Note If the module has active parametrizations, passing a value in the parameters_and_bu...

POE0003:missing-standard-symbolic-function

PyTorch documentation

Missing symbolic function for standard PyTorch operator, cannot translate node to ONNX.

torch.func.functionalize

PyTorch documentation

functionalize is a transform that can be used to remove (intermediate) mutations and aliasing from a function, while preserving the function’s semantics. functionalize(func) returns a new function wit...

POE0002:missing-custom-symbolic-function

PyTorch documentation

Missing symbolic function for custom PyTorch operator, cannot translate node to ONNX.

torch.signal.windows.nuttall

PyTorch documentation

Computes the minimum 4-term Blackman-Harris window according to Nuttall. where z_n = 2 π n/ M . The window is normalized to 1 (maximum value is 1). However, the 1 doesn’t appear if M is even and sym i...

torch.func.hessian

PyTorch documentation

Computes the Hessian of func with respect to the arg(s) at index argnum via a forward-over-reverse strategy. The forward-over-reverse strategy (composing jacfwd(jacrev(func)) ) is a good default for g...