ATen: Tensor Library

ATen (A Tensor Library) is the foundational tensor and mathematical operation library on which all of PyTorch is built. It provides the core Tensor class and hundreds of mathematical operations that work on tensors.

When to use ATen directly:

• When writing low-level tensor operations or custom kernels
• When you need direct access to tensor data and metadata
• When working with the PyTorch internals or extending PyTorch

Basic usage:

#include <ATen/ATen.h>

// Create tensors
at::Tensor a = at::ones({2, 3});
at::Tensor b = at::randn({2, 3});

// Perform operations
at::Tensor c = a + b;
at::Tensor d = at::matmul(a.t(), b);

// Move to GPU
if (at::cuda::is_available()) {
    at::Tensor gpu_tensor = c.to(at::kCUDA);
}

For most applications, prefer using the higher-level torch:: namespace (see {doc}../nn/index, {doc}../optim/index) which provides a more user-friendly API.

Header Files

The following headers are part of the ATen public API:

• ATen/ATen.h - Main ATen header
• ATen/Backend.h - Backend enumeration
• ATen/core/Tensor.h - Tensor class
• ATen/core/ivalue.h - IValue type (see {doc}../c10/index)
• ATen/core/ScalarType.h - Data type definitions
• ATen/TensorOptions.h - Tensor creation options
• ATen/Scalar.h - Scalar type
• ATen/Layout.h - Tensor layout
• ATen/DeviceGuard.h - Device context management
• ATen/native/TensorShape.h - Tensor shape operations
• ATen/cuda/CUDAContext.h - CUDA context (see {doc}../cuda/index)
• ATen/cudnn/Descriptors.h - cuDNN descriptors
• ATen/mkl/Descriptors.h - MKL descriptors

The core `at::Tensor` class is defined in a generated header file
(`TensorBody.h`) that only exists after building PyTorch. The documentation
below describes the API manually.

ATen Categories

:maxdepth: 1

tensor
creation
indexing
accessors