ByteDance-Seed/triton

ByteDance-Seed/triton

Description: Development repository for the Triton language and compiler

Language: MLIR

License: MIT

Stars: 0

Forks: 9

Open issues: 4

Created: 2025-08-28T11:41:54Z

Pushed: 2026-05-25T17:46:27Z

Default branch: dist

Fork: yes

Parent repository: triton-lang/triton

Archived: no

README:

| `Documentation` | `Nightly Wheels` | |-------------------- | -------------------- | |  |  |

Triton

This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with higher flexibility than other existing DSLs.

The foundations of this project are described in the following MAPL2019 publication: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations. Please consider citing this work if you use Triton!

The official documentation contains installation instructions and tutorials. See also these third-party Triton puzzles, which can all be run using the Triton interpreter -- no GPU required.

Quick Installation

You can install the latest stable release of Triton from pip:

pip install triton

Binary wheels are available for CPython 3.9-3.13.

Install from source

git clone https://github.com/triton-lang/triton.git
cd triton

pip install -r python/requirements.txt # build-time dependencies
pip install -e .

Or with a virtualenv:

git clone https://github.com/triton-lang/triton.git
cd triton

python -m venv .venv --prompt triton
source .venv/bin/activate

pip install -r python/requirements.txt # build-time dependencies
pip install -e .

Building with a custom LLVM

Triton uses LLVM to generate code for GPUs and CPUs. Normally, the Triton build downloads a prebuilt LLVM, but you can also build LLVM from source and use that.

LLVM does not have a stable API, so the Triton build will not work at an arbitrary LLVM version.

1. Find the version of LLVM that Triton builds against. Check cmake/llvm-hash.txt to see the current version. For example, if it says: 49af6502c6dcb4a7f7520178bd14df396f78240c

This means that the version of Triton you have builds against LLVM 49af6502.

2. git checkout LLVM at this revision. Optionally, make additional modifications to LLVM.

3. Build LLVM. For example, you might run

$ cd $HOME/llvm-project # your clone of LLVM. $ mkdir build $ cd build $ cmake -G Ninja -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=ON ../llvm -DLLVM_ENABLE_PROJECTS="mlir;llvm;lld" -DLLVM_TARGETS_TO_BUILD="host;NVPTX;AMDGPU" $ ninja

4. Grab a snack, this will take a while.

5. Build Triton as above, but set the following environment variables.

Modify as appropriate to point to your LLVM build.

$ export LLVM_BUILD_DIR=$HOME/llvm-project/build

$ cd $ LLVM_INCLUDE_DIRS=$LLVM_BUILD_DIR/include \ LLVM_LIBRARY_DIR=$LLVM_BUILD_DIR/lib \ LLVM_SYSPATH=$LLVM_BUILD_DIR \ pip install -e .

Tips for building

• Set TRITON_BUILD_WITH_CLANG_LLD=true as an environment variable to use clang

and lld. lld in particular results in faster builds.

• Set TRITON_BUILD_WITH_CCACHE=true to build with ccache.

• Set TRITON_HOME=/some/path to change the location of the .triton

directory where Triton's cache is located and downloads are stored during the build. By default, this is the user's home directory. It can be changed anytime.

• If you're running out of memory when building Triton, specify the MAX_JOBS

environment variable (to the pip install -e . command) to limit the number of jobs.

• Pass --no-build-isolation to pip install to make nop builds faster.

Without this, every invocation of pip install uses a different symlink to cmake, and this forces ninja to rebuild most of the .a files.

• vscode intellisense has some difficulty figuring out how to build Triton's C++

(probably because, in our build, users don't invoke cmake directly, but instead use setup.py). Teach vscode how to compile Triton as follows.

• Do a local build. Run command pip install -e .
• Get the full path to the compile_commands.json file produced by the build:

find ./build -name 'compile_commands.json' | xargs readlink -f. You might get a full path similar to /Users/{username}/triton/build/cmake.macosx-11.1-arm64-cpython-3.12/compile_commands.json

• In vscode, install the

C/C++ extension, then open the command palette (Shift + Command + P on Mac, or Shift + Ctrl + P on Windows/Linux) and open C/C++: Edit Configurations (UI).

• Open "Advanced Settings" and paste the full path to

compile_commands.json into the "Compile Commands" textbox.

Running tests

There currently isn't a turnkey way to run all the Triton tests, but you can follow the following recipe.

# One-time setup. Note this will reinstall local Triton because torch
# overwrites it with the public version.
$ make dev-install

# To run all tests (requires a GPU)
$ make test

# Or, to run tests without a gpu
$ make test-nogpu

Tips for hacking

For detailed instructions on how to debug Triton's frontend, please refer to this tutorial. The following includes additional tips for hacking on Triton's backend.

Configuration knobs

See [python/triton/knobs.py](python/triton/knobs.py) for the full list of configuration knobs. You can set those knobs directly in python or use environment variables to control them. Below are some of the environment variables you can specify (see knobs.py for the full list):

• MLIR_ENABLE_DUMP=1 dumps the IR before every MLIR pass Triton runs, for all

kernels. Use MLIR_ENABLE_DUMP=kernelName to dump for a specific kernel only.

• Triton cache can interfere with the dump. In cases where MLIR_ENABLE_DUMP=1 does not work, try…