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41846e205e
This PR sets up torch.func and populates it with the following APIs: - grad - grad_and_value - vjp - jvp - jacrev - jacfwd - hessian - functionalize - vmap It also renames all instances of `functorch` in the APIs for those docs to `torch.func`. We rewrite the `__module__` fields on some of the above APIs so that the APIs fit PyTorch's public api definition. - For an API to be public, it must have a `__module__` that points to a public PyTorch submodule. However, `torch._functorch.eager_transforms` is not public due to the leading underscore. - The solution is to rewrite `__module__` to point to where the API is exposed (torch.func). This is what both Numpy and JAX do for their APIs. - h/t pmeier in https://github.com/pytorch/pytorch/issues/90284#issuecomment-1348595246 for idea and code - The helper function, `exposed_in`, is confined to torch._functorch/utils for now because we're not completely sure if this should be the long-term solution. Implication for functorch.* APIs: - functorch.grad is the same object as torch.func.grad - this means that the functorch.grad docstring is actually the torch.func.grad docstring and will refer to torch.func instead of functorch. - This isn't really a problem since the plan on record is to deprecate functorch in favor of torch.func. We can fix these if we really want, but I'm not sure if a solution is worth maintaining. Test Plan: - view docs preview Future: - vmap should actually just be torch.vmap. This requires an extra step where I need to test internal callsites, so, I'm separating it into a different PR. - make_fx should be in torch.func to be consistent with `import functorch`. This one is a bit more of a headache to deal with w.r.t. public api, so going to deal with it separately. - beef up func.rst with everything else currently on the functorch documention website. func.rst is currently just an empty shell. Pull Request resolved: https://github.com/pytorch/pytorch/pull/91016 Approved by: https://github.com/samdow
154 lines
3.9 KiB
ReStructuredText
154 lines
3.9 KiB
ReStructuredText
.. PyTorch documentation master file, created by
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sphinx-quickstart on Fri Dec 23 13:31:47 2016.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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:github_url: https://github.com/pytorch/pytorch
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PyTorch documentation
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===================================
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PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
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Features described in this documentation are classified by release status:
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*Stable:* These features will be maintained long-term and there should generally
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be no major performance limitations or gaps in documentation.
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We also expect to maintain backwards compatibility (although
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breaking changes can happen and notice will be given one release ahead
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of time).
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*Beta:* These features are tagged as Beta because the API may change based on
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user feedback, because the performance needs to improve, or because
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coverage across operators is not yet complete. For Beta features, we are
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committing to seeing the feature through to the Stable classification.
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We are not, however, committing to backwards compatibility.
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*Prototype:* These features are typically not available as part of
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binary distributions like PyPI or Conda, except sometimes behind run-time
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flags, and are at an early stage for feedback and testing.
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Community
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community/*
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Developer Notes
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notes/*
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: torch.compile
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:hidden:
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dynamo/index
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dynamo/installation
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dynamo/get-started
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dynamo/guards-overview
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dynamo/custom-backends
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dynamo/deep-dive
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dynamo/troubleshooting
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dynamo/faq
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ir
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.. toctree::
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:maxdepth: 1
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:caption: Language Bindings
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cpp_index
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Javadoc <https://pytorch.org/javadoc/>
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torch::deploy <deploy>
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.. toctree::
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:glob:
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:maxdepth: 2
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:caption: Python API
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torch
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nn
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nn.functional
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tensors
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tensor_attributes
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tensor_view
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torch.amp <amp>
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torch.autograd <autograd>
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torch.library <library>
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cuda
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torch.backends <backends>
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torch.distributed <distributed>
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torch.distributed.algorithms.join <distributed.algorithms.join>
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torch.distributed.elastic <distributed.elastic>
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torch.distributed.fsdp <fsdp>
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torch.distributed.optim <distributed.optim>
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torch.distributed.tensor.parallel <distributed.tensor.parallel>
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torch.distributed.checkpoint <distributed.checkpoint>
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torch.distributions <distributions>
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torch._dynamo <_dynamo>
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torch.fft <fft>
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torch.func <func>
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futures
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fx
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torch.hub <hub>
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torch.jit <jit>
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torch.linalg <linalg>
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torch.monitor <monitor>
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torch.signal <signal>
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torch.special <special>
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torch.overrides
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torch.package <package>
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profiler
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nn.init
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onnx
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onnx_diagnostics
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optim
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complex_numbers
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ddp_comm_hooks
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pipeline
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quantization
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rpc
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torch.random <random>
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masked
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torch.nested <nested>
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sparse
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storage
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torch.testing <testing>
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torch.utils.benchmark <benchmark_utils>
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torch.utils.bottleneck <bottleneck>
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torch.utils.checkpoint <checkpoint>
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torch.utils.cpp_extension <cpp_extension>
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torch.utils.data <data>
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torch.utils.jit <jit_utils>
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torch.utils.dlpack <dlpack>
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torch.utils.mobile_optimizer <mobile_optimizer>
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torch.utils.model_zoo <model_zoo>
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torch.utils.tensorboard <tensorboard>
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type_info
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named_tensor
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name_inference
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torch.__config__ <config_mod>
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.. toctree::
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:maxdepth: 1
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:caption: Libraries
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torchaudio <https://pytorch.org/audio/stable>
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TorchData <https://pytorch.org/data>
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TorchRec <https://pytorch.org/torchrec>
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TorchServe <https://pytorch.org/serve>
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torchtext <https://pytorch.org/text/stable>
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torchvision <https://pytorch.org/vision/stable>
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PyTorch on XLA Devices <http://pytorch.org/xla/>
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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