Versions [RSS]	2.0.0.0, 2.0.0.1, 2.0.1.0, 2.0.1.1, 2.0.1.2, 2.0.1.3, 2.0.1.5, 2.0.1.6, 2.0.1.7, 2.0.1.8, 2.0.1.9, 2.0.1.10
Dependencies	async (>=2.2.5 && <2.3), base (>=4.7 && <5), bytestring (>=0.11.5 && <0.13), containers (>=0.6.7 && <0.8), inline-c (>=0.9.1.10 && <0.10), inline-c-cpp (>=0.5.0.2 && <0.6.0.0), libtorch-ffi-helper (>=2.0.0 && <2.1), safe-exceptions (>=0.1.7 && <0.2), sysinfo (>=0.1.1 && <0.2), template-haskell (>=2.20.0 && <2.24), text (>=2.0.2 && <2.2) [details]
License	BSD-3-Clause
Copyright	2018 Austin Huang
Author	Austin Huang
Maintainer	hasktorch@gmail.com
Category	Codegen
Home page	https://github.com/hasktorch/hasktorch#readme
Uploaded	by junjihashimoto at 2025年12月03日T15:19:30Z
Distributions	NixOS:2.0.1.9
Reverse Dependencies	1 direct, 3 indirect [details]
Downloads	271 total (29 in the last 30 days)
Rating	(no votes yet) [estimated by Bayesian average]
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Status	Docs available [build log] Last success reported on 2025年12月03日 [all 1 reports]

libtorch-ffi

This package provides FFI bindings to PyTorch's libtorch C++ library.

Setup

The package automatically downloads and configures libtorch during the build process. You can customize the setup using environment variables.

Environment Variables

LIBTORCH_VERSION

• Default: 2.5.0
• Description: Specifies the version of libtorch to download and use
• Example: export LIBTORCH_VERSION=2.5.0

LIBTORCH_HOME

• Default: XDG cache directory (~/.cache/libtorch on Linux/macOS)
• Description: Base directory where libtorch will be downloaded and stored
• Example: export LIBTORCH_HOME=/opt/libtorch

LIBTORCH_CUDA_VERSION

• Default: cpu
• Description: CUDA version for GPU support
• Options:
  • cpu - CPU-only version (default)
  • cu117 - CUDA 11.7
  • cu118 - CUDA 11.8
  • cu121 - CUDA 12.1
  • Any other CUDA version string supported by PyTorch
• Example: export LIBTORCH_CUDA_VERSION=cu118

LIBTORCH_SKIP_DOWNLOAD

• Default: Not set
• Description: When set (to any value), skips the automatic download of libtorch
• Use case: When you have libtorch already installed system-wide
• Example: export LIBTORCH_SKIP_DOWNLOAD=1

Directory Structure

The downloaded libtorch is stored in a platform-specific directory structure:

$LIBTORCH_HOME/
└── <version>/
 └── <platform>/
 └── <cuda-flavor>/
 ├── lib/
 ├── include/
 └── .ok

Where:

• <version> is the libtorch version (e.g., 2.5.0)
• <platform> is one of:
  • macos-arm64 - macOS on Apple Silicon
  • macos-x86_64 - macOS on Intel
  • linux-x86_64 - Linux on x86_64
• <cuda-flavor> is the CUDA version (e.g., cpu, cu118)

Build Process

1. Pre-configuration: The package checks if it's running in a Nix sandbox. If not, it proceeds with the download process.

2. Download: If libtorch is not found in the cache directory, it will be automatically downloaded from PyTorch's official servers.

3. Configuration: The build system automatically:

   • Adds the libtorch library directory to the library search path
   • Adds the include directories for C++ headers
   • Sets up proper runtime library paths (rpath) for dynamic linking
   • On macOS, adds the -ld_classic flag for compatibility

Platform-Specific Notes

macOS

• Uses rpath for dynamic library loading
• Automatically adds -ld_classic flag for linker compatibility
• Supports both Apple Silicon (arm64) and Intel (x86_64) architectures
• Since libtorch-ffi's rpath is propagated, it doesn't matter whether hasktorch is a static link or a shared link

Linux

• Uses rpath for dynamic library loading
• Supports x86_64 architecture
• Multiple CUDA versions available for GPU support
• Since libtorch-ffi's rpath is not propagated, hasktorch must be a shared link

Linking Configuration

Due to rpath propagation differences between platforms, Linux requires shared linking. Add the following configuration:

For Cabal (cabal.project)

shared: True
executable-dynamic: True

For Stack (stack.yaml)

configure-options:
 $targets:
 - --enable-executable-dynamic
 - --enable-shared

Nix Support

The package detects when it's being built in a Nix sandbox and skips the automatic download. In this case, libtorch should be provided through Nix derivation inputs.

Troubleshooting

1. Download failures: Check your internet connection and ensure the PyTorch download servers are accessible.

2. Missing libraries: The .ok marker file indicates a successful download. If this file is missing but the directory exists, delete the directory and let the setup download again.

3. CUDA version mismatch: Ensure your system CUDA version matches the LIBTORCH_CUDA_VERSION you've specified.

4. Custom libtorch installation: Set LIBTORCH_SKIP_DOWNLOAD=1 and ensure your system's libtorch is properly configured in your build environment.

Example Usage

# Use CPU-only version
cabal build libtorch-ffi
# Use CUDA 11.8 version
export LIBTORCH_CUDA_VERSION=cu118
cabal build libtorch-ffi
# Use a specific version
export LIBTORCH_VERSION=2.4.0
cabal build libtorch-ffi
# Use existing system libtorch
export LIBTORCH_SKIP_DOWNLOAD=1
cabal build libtorch-ffi