pytorch-docs

name	pytorch-docs
description	Use when working with PyTorch APIs, tensors, autograd, torch.nn, torch.optim, torch.compile, export, distributed/RPC, CUDA/MPS/XPU backends, profiling, quantization, ONNX export, C++/LibTorch, or PyTorch runtime/debugging behavior.

PyTorch Docs

Official PyTorch docs synced from pytorch/pytorch/docs.

Use this skill for PyTorch framework APIs, tensor semantics, autograd, neural network modules, compilation/export, distributed training, device backends, profiling/debugging, quantization, ONNX, and LibTorch/C++ docs.

Hard Rules

• MUST search references/ before giving version-sensitive PyTorch API, runtime behavior, environment variable, backend, distributed, compile/export, or C++/LibTorch guidance.
• MUST distinguish core PyTorch framework APIs from adjacent libraries such as TorchVision, TorchAudio, TorchText, Lightning, Hugging Face Transformers, or ComfyUI.
• MUST call out backend scope when relevant: CPU, CUDA, ROCm/HIP, MPS, XPU, distributed, or C++.
• NEVER invent torch.* API names, environment variables, dispatch behavior, compile/export limitations, or distributed configuration without checking references.

Fast Lookup

rg -n "torch\.compile|torch.export|dynamo|inductor|AOT|graph break" skills/pytorch-docs/references
rg -n "autograd|tensor|nn\.Module|optimizer|DataLoader|distributed|RPC" skills/pytorch-docs/references
rg -n "CUDA|ROCm|HIP|MPS|XPU|environment variable|memory|profil" skills/pytorch-docs/references
rg -n "LibTorch|C\+\+|cpp|ONNX|quantization" skills/pytorch-docs/references

Reference Map

• references/source/ — main Python PyTorch docs and notes: tensors, autograd, modules, optimizers, distributed, compile/export, profiling, backend/runtime behavior.
• references/source/notes/ — deeper technical notes and backend/runtime caveats.
• references/source/user_guide/ — user guides for compilation, export, distributed, and runtime workflows.
• references/cpp/ and references/libtorch.rst — C++/LibTorch API docs.

Workflow

1. Identify whether the question is Python API, runtime/backend behavior, compile/export, distributed, quantization/ONNX, or C++/LibTorch.
2. Search the matching reference subtree with targeted rg terms.
3. Prefer documented API names, environment variables, warnings, and backend-specific limitations.
4. Route non-core ecosystem questions to more specific docs when PyTorch docs are not the source of truth.