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This PR fixes typo and URL (`http -> https`) in `rst` files under `docs` directory Pull Request resolved: https://github.com/pytorch/pytorch/pull/92762 Approved by: https://github.com/H-Huang
56 lines
2.2 KiB
ReStructuredText
56 lines
2.2 KiB
ReStructuredText
torch.func
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==========
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.. currentmodule:: torch.func
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torch.func, previously known as "functorch", is
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`JAX-like <https://github.com/google/jax>`_ composable function transforms for PyTorch.
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.. note::
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This library is currently in `beta <https://pytorch.org/blog/pytorch-feature-classification-changes/#beta>`_.
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What this means is that the features generally work (unless otherwise documented)
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and we (the PyTorch team) are committed to bringing this library forward. However, the APIs
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may change under user feedback and we don't have full coverage over PyTorch operations.
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If you have suggestions on the API or use-cases you'd like to be covered, please
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open an GitHub issue or reach out. We'd love to hear about how you're using the library.
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What are composable function transforms?
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----------------------------------------
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- A "function transform" is a higher-order function that accepts a numerical function
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and returns a new function that computes a different quantity.
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- :mod:`torch.func` has auto-differentiation transforms (``grad(f)`` returns a function that
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computes the gradient of ``f``), a vectorization/batching transform (``vmap(f)``
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returns a function that computes ``f`` over batches of inputs), and others.
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- These function transforms can compose with each other arbitrarily. For example,
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composing ``vmap(grad(f))`` computes a quantity called per-sample-gradients that
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stock PyTorch cannot efficiently compute today.
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Why composable function transforms?
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-----------------------------------
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There are a number of use cases that are tricky to do in PyTorch today:
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- computing per-sample-gradients (or other per-sample quantities)
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- running ensembles of models on a single machine
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- efficiently batching together tasks in the inner-loop of MAML
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- efficiently computing Jacobians and Hessians
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- efficiently computing batched Jacobians and Hessians
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Composing :func:`vmap`, :func:`grad`, and :func:`vjp` transforms allows us to express the above without designing a separate subsystem for each.
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This idea of composable function transforms comes from the `JAX framework <https://github.com/google/jax>`_.
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Read More
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---------
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.. toctree::
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:maxdepth: 2
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func.whirlwind_tour
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func.api
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func.ux_limitations
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func.migrating
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