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Svetlana Karslioglu 19da3da3d3 Restructure C++ docs (#174096 )

The fix-cpp-build branch is a major restructuring that:

- Drops Exhale (which auto-generated API docs but caused OOM in CI) in favor of hand-written RST pages using Breathe directives
- Reorganizes notes into a hierarchical API reference with proper categories
- Adds new content: XPU docs, stable ABI docs, DeviceGuard/Stream docs
- Always runs Doxygen instead of gating on an env var

Pull Request resolved: https://github.com/pytorch/pytorch/pull/174096
Approved by: https://github.com/janeyx99, https://github.com/seemethere, https://github.com/albanD

2026-04-07 17:24:38 +00:00

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Linear layers in PyTorch C++ — Linear, Bilinear, and Flatten modules.	PyTorch, C++, Linear, Bilinear, Flatten, fully connected, dense layer

Linear Layers

Linear layers apply affine transformations to input data: y = xW^T + b. They are the building blocks of fully-connected networks and are used for feature transformation, classification heads, and projection layers.

Linear: Standard fully-connected layer
Bilinear: Bilinear transformation of two inputs
Identity: Pass-through layer (useful for skip connections)
Flatten/Unflatten: Reshape tensors between convolutional and linear layers

Linear

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Example:

auto linear = torch::nn::Linear(torch::nn::LinearOptions(784, 256).bias(true));
auto output = linear->forward(input);  // input: [N, 784]

Bilinear

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Identity

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Flatten

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Unflatten

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