# Burn Torch Backend [Burn](https://github.com/tracel-ai/burn) Torch backend [![Current Crates.io Version](https://img.shields.io/crates/v/burn-tch.svg)](https://crates.io/crates/burn-tch) [![license](https://shields.io/badge/license-MIT%2FApache--2.0-blue)](https://github.com/tracel-ai/burn-tch/blob/master/README.md) This crate provides a Torch backend for [Burn](https://github.com/tracel-ai/burn) utilizing the [`tch-rs`](https://github.com/LaurentMazare/tch-rs) crate, which offers a Rust interface to the [PyTorch](https://pytorch.org/) C++ API. The backend supports CPU (multithreaded), [CUDA](https://pytorch.org/docs/stable/notes/cuda.html) (multiple GPUs), and [MPS](https://pytorch.org/docs/stable/notes/mps.html) devices (MacOS). ## Installation [`tch-rs`](https://github.com/LaurentMazare/tch-rs) requires the C++ PyTorch library (LibTorch) to be available on your system. By default, the CPU distribution is installed for LibTorch v2.2.0 as required by `tch-rs`.
CUDA To install the latest compatible CUDA distribution, set the `TORCH_CUDA_VERSION` environment variable before the `tch-rs` dependency is retrieved with `cargo`. ```shell export TORCH_CUDA_VERSION=cu121 ``` On Windows: ```powershell $Env:TORCH_CUDA_VERSION = "cu121" ``` For example, running the validation sample for the first time could be done with the following commands: ```shell export TORCH_CUDA_VERSION=cu121 cargo run --bin cuda --release ``` **Important:** make sure your driver version is compatible with the selected CUDA version. A CUDA Toolkit installation is not required since LibTorch ships with the appropriate CUDA runtimes. Having the latest driver version is recommended, but you can always take a look at the [toolkit driver version table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#id4) or [minimum required driver version](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#minor-version-compatibility) (limited feature-set, might not work with all operations).

Once your installation is complete, you should be able to build/run your project. You can also validate your installation by running the appropriate `cpu`, `cuda` or `mps` sample as below. ```shell cargo run --bin cpu --release cargo run --bin cuda --release cargo run --bin mps --release ``` _Note: no MPS distribution is available for automatic download at this time, please check out the [manual instructions](#metal-mps)._ ### Manual Download To install `tch-rs` with a different LibTorch distribution, you will have to manually download the desired LibTorch distribution. The instructions are detailed in the sections below for each platform. | Compute Platform | CPU | GPU | Linux | MacOS | Windows | Android | iOS | WASM | | :------------------------ | :----------------------------: | :-: | :---: | :---: | :-----: | :-----: | :-: | :--: | | [CPU](#cpu) | Yes | No | Yes | Yes | Yes | Yes | Yes | No | | [CUDA](#cuda) | Yes [[1]](#cpu-sup) | Yes | Yes | No | Yes | No | No | No | | [Metal (MPS)](#metal-mps) | No | Yes | No | Yes | No | No | No | No | | Vulkan | Yes | Yes | Yes | Yes | Yes | Yes | No | No | [1] The LibTorch CUDA distribution also comes with CPU support. #### CPU
🐧 Linux First, download the LibTorch CPU distribution. ```shell wget -O libtorch.zip https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcpu.zip unzip libtorch.zip ``` Then, point to that installation using the `LIBTORCH` and `LD_LIBRARY_PATH` environment variables before building `burn-tch` or a crate which depends on it. ```shell export LIBTORCH=/absolute/path/to/libtorch/ export LD_LIBRARY_PATH=/absolute/path/to/libtorch/lib:$LD_LIBRARY_PATH ```

🍎 Mac First, download the LibTorch CPU distribution. ```shell wget -O libtorch.zip https://download.pytorch.org/libtorch/cpu/libtorch-macos-x86_64-2.2.0.zip unzip libtorch.zip ``` Then, point to that installation using the `LIBTORCH` and `DYLD_LIBRARY_PATH` environment variables before building `burn-tch` or a crate which depends on it. ```shell export LIBTORCH=/absolute/path/to/libtorch/ export DYLD_LIBRARY_PATH=/absolute/path/to/libtorch/lib:$DYLD_LIBRARY_PATH ```

🪟 Windows First, download the LibTorch CPU distribution. ```powershell wget https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-2.2.0%2Bcpu.zip -OutFile libtorch.zip Expand-Archive libtorch.zip ``` Then, set the `LIBTORCH` environment variable and append the library to your path as with the PowerShell commands below before building `burn-tch` or a crate which depends on it. ```powershell $Env:LIBTORCH = "/absolute/path/to/libtorch/" $Env:Path += ";/absolute/path/to/libtorch/" ```

#### CUDA LibTorch 2.2.0 currently includes binary distributions with CUDA 11.8 or 12.1 runtimes. The manual installation instructions are detailed below. **CUDA 11.8**
🐧 Linux First, download the LibTorch CUDA 11.8 distribution. ```shell wget -O libtorch.zip https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu118.zip unzip libtorch.zip ``` Then, point to that installation using the `LIBTORCH` and `LD_LIBRARY_PATH` environment variables before building `burn-tch` or a crate which depends on it. ```shell export LIBTORCH=/absolute/path/to/libtorch/ export LD_LIBRARY_PATH=/absolute/path/to/libtorch/lib:$LD_LIBRARY_PATH ``` **Note:** make sure your CUDA installation is in your `PATH` and `LD_LIBRARY_PATH`.

🪟 Windows First, download the LibTorch CUDA 11.8 distribution. ```powershell wget https://download.pytorch.org/libtorch/cu118/libtorch-win-shared-with-deps-2.2.0%2Bcu118.zip -OutFile libtorch.zip Expand-Archive libtorch.zip ``` Then, set the `LIBTORCH` environment variable and append the library to your path as with the PowerShell commands below before building `burn-tch` or a crate which depends on it. ```powershell $Env:LIBTORCH = "/absolute/path/to/libtorch/" $Env:Path += ";/absolute/path/to/libtorch/" ```

**CUDA 12.1**
🐧 Linux First, download the LibTorch CUDA 12.1 distribution. ```shell wget -O libtorch.zip https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu121.zip unzip libtorch.zip ``` Then, point to that installation using the `LIBTORCH` and `LD_LIBRARY_PATH` environment variables before building `burn-tch` or a crate which depends on it. ```shell export LIBTORCH=/absolute/path/to/libtorch/ export LD_LIBRARY_PATH=/absolute/path/to/libtorch/lib:$LD_LIBRARY_PATH ``` **Note:** make sure your CUDA installation is in your `PATH` and `LD_LIBRARY_PATH`.

🪟 Windows First, download the LibTorch CUDA 12.1 distribution. ```powershell wget https://download.pytorch.org/libtorch/cu121/libtorch-win-shared-with-deps-2.2.0%2Bcu121.zip -OutFile libtorch.zip Expand-Archive libtorch.zip ``` Then, set the `LIBTORCH` environment variable and append the library to your path as with the PowerShell commands below before building `burn-tch` or a crate which depends on it. ```powershell $Env:LIBTORCH = "/absolute/path/to/libtorch/" $Env:Path += ";/absolute/path/to/libtorch/" ```

#### Metal (MPS) There is no official LibTorch distribution with MPS support at this time, so the easiest alternative is to use a PyTorch installation. This requires a Python installation. _Note: MPS acceleration is available on MacOS 12.3+._ ```shell pip install torch==2.2.0 export LIBTORCH_USE_PYTORCH=1 export DYLD_LIBRARY_PATH=/path/to/pytorch/lib:$DYLD_LIBRARY_PATH ``` ## Example Usage For a simple example, check out any of the test programs in [`src/bin/`](./src/bin/). Each program sets the device to use and and performs a simple elementwise addition. For a more complete example using the `tch` backend, take a loot at the [Burn mnist example](https://github.com/tracel-ai/burn/tree/main/examples/mnist).