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Triton Conference 2025
The 3rd Triton Developer Conference took place on October 21, 2025 at the Microsoft Silicon Valley Campus in Mountain View, California.
Conference Materials
Conference recordings and materials are now available online:
For previous conference materials, see:
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 3.10-3.14.
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 and use LLVM from source.
LLVM does not have a stable API, so the Triton build will not work at an
arbitrary LLVM version.
For convenience, use the following command to build LLVM and install Triton with the custom LLVM:
make dev-install-llvm
Alternatively, follow these steps to build LLVM from source manually.
Find the version of LLVM that Triton builds against. Check the llvm_hash
field in cmake/llvm-info.json to see the current version. For example, if it
says:
"llvm_hash": "49af6502c6dcb4a7f7520178bd14df396f78240c"
This means that the version of Triton you have builds against
LLVM 49af6502.
git checkout LLVM at this revision. Optionally, make additional
modifications to LLVM.
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;clang" -DLLVM_TARGETS_TO_BUILD="host;NVPTX;AMDGPU"
$ ninja
Grab a snack, this will take a while.
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 <triton install>
$ 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.
The build system creates a compile_commands.json file under the Triton repo
directory. This file is used by VSCode IntelliSense and clangd to provide
code completion and other features for C++ code.
If IntelliSense does not work, you can try the following steps:
- 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 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 cleaning your triton cache: rm -r ~/.triton/cache/*.
MLIR_DUMP_PATH specifies where MLIR_ENABLE_DUMP will dump to. If unset will dump to stderr.
LLVM_IR_ENABLE_DUMP=1 dumps the IR before every pass run over the LLVM IR.
TRITON_REPRODUCER_PATH=<reproducer_path> will generate an MLIR reproducer file
at <reproducer_path> before each MLIR compiler stage. If any of the stages fail,
<reproducer_path> will be a local MLIR reproducer captured right before the failing pass.
TRITON_INTERPRET=1 uses the Triton interpreter instead of running on the
GPU. You can insert Python breakpoints in your kernel code!
TRITON_ENABLE_LLVM_DEBUG=1 passes -debug to LLVM, printing a lot of
debugging information to stdout. If this is too noisy, run with just
TRITON_LLVM_DEBUG_ONLY instead to limit the output.
- An alternative way to reduce output noisiness is running with
LLVM_IR_ENABLE_DUMP=1, extract the IR before the LLVM pass of interest, and
then run LLVM's opt standalone, perhaps passing -debug-only=foo on the
command line.
TRITON_LLVM_DEBUG_ONLY=<comma-separated> is the equivalent of LLVM's
-debug-only command-line option. This limits the LLVM debug output to
specific pass or component names (which are specified using #define DEBUG_TYPE throughout LLVM and Triton) in order to allow the debug output to
be less noisy. TRITON_LLVM_DEBUG_ONLY allows for one or more comma
separated values to be specified (eg
TRITON_LLVM_DEBUG_ONLY="tritongpu-remove-layout-conversions" or
TRITON_LLVM_DEBUG_ONLY="tritongpu-remove-layout-conversions,regalloc").
TRITON_ENABLE_ASAN=1 invokes the LLVM address sanitizer for
memory leak and out of bounds access detection. Currently only supported on the AMD
backend. This must be run using the ASAN libraries documented here.
- When enabling the address sanitizer it is recommended to disable various memory caching strategies
both within the ROCm stack and PyTorch. This will give the address sanitizer the best chance at finding the
memory fault where it originates. See this test for more details.
USE_IR_LOC={ttir,ttgir} reparses the IR such that the location information
will be the line number of the IR file with that particular extension,
instead of line number of the python file. This can provide a direct mapping
from the IR to llir/ptx. When used with performance tools, it can provide a
breakdown on IR instructions.
TRITON_PRINT_AUTOTUNING=1 prints out the best autotuning config and total time
spent for each kernel after autotuning is complete.
DISABLE_LLVM_OPT will disable llvm optimizations for make_llir and make_ptx
if its value is true when parsing as Bool. Otherwise, it will be parsed as a list
of flags to disable llvm optimizations. One usage case is
DISABLE_LLVM_OPT="disable-lsr"
Loop strength reduction is known to cause up to 10% performance changes for
certain kernels with register pressure.
TRITON_ALWAYS_COMPILE=1 forces to compile kernels regardless of cache hit.
MLIR_ENABLE_TIMING dumps the timing information for each MLIR pass.
LLVM_ENABLE_TIMING dumps the timing information for each LLVM pass.
TRITON_DEFAULT_FP_FUSION overrides the default behavior of allowing fp fusion (mul+add->fma).
MLIR_ENABLE_DIAGNOSTICS=<comma-separated> controls diagnostic emission in MLIR.
Options are: warnings, remarks, stacktraces, operations.
Use comma-separated values to customize output. For example,
MLIR_ENABLE_DIAGNOSTICS=remarks,operations enables remarks and IR operations,
while MLIR_ENABLE_DIAGNOSTICS=warnings,stacktraces enables warnings with
stacktraces. By default, only errors are shown. Setting warnings includes
errors and warnings; remarks includes errors, warnings, and remarks.
MLIR_ENABLE_REMARK is deprecated. Please use MLIR_ENABLE_DIAGNOSTICS=remarks.
TRITON_KERNEL_DUMP enables the dumping of the IR from each compilation stage and the final ptx/amdgcn.
TRITON_DUMP_DIR specifies the directory to save the dumped IR and ptx/amdgcn when TRITON_KERNEL_DUMP is set to 1.
TRITON_KERNEL_OVERRIDE enables the override of the compiled kernel with a user-specified IR/ptx/amdgcn at the beginning of each compilation stage.
TRITON_OVERRIDE_DIR specifies the directory from which to load the IR/ptx/amdgcn files when TRITON_KERNEL_OVERRIDE is set to 1.
TRITON_F32_DEFAULT sets the default input precision of tl.dot when using 32-bit floats, which can be either ieee, tf32, or tf32x3.
TRITON_FRONT_END_DEBUGGING=1 disables exception wrapping when an error occurs in the compiler frontend, allowing the full stack trace to be seen.
TRITON_DISABLE_LINE_INFO=1 removes all line information from the module.
PTXAS_OPTIONS passes additional command-line options to the PTX assembler ptxas (only on NVIDIA).
LLVM_EXTRACT_DI_LOCAL_VARIABLES emit full debug info, allowing for eval of values in gpu debuggers (ie cuda-gdb, rocm-gdb etc)
TRITON_DEFAULT_BACKEND=<backend> optionally sets the default backend used by Triton when
constructing the active driver (i.e., triton.runtime.driver.active).
[!NOTE]
Some of these environment variables don't have a knob in knobs.py-- those are only relevant to the C++ layer(s), hence they don't exist in the python layer.
Kernel Override Steps
export TRITON_ALWAYS_COMPILE=1
export TRITON_KERNEL_DUMP=1
export TRITON_DUMP_DIR=<dump_dir>
export TRITON_KERNEL_OVERRIDE=1
export TRITON_OVERRIDE_DIR=<override_dir>
# Step 1: Run the kernel once to dump kernel's IRs and ptx/amdgcn in $TRITON_DUMP_DIR
# Step 2: Copy $TRITON_DUMP_DIR/<kernel_hash> to $TRITON_OVERRIDE_DIR
# Step 3: Delete the stages that you do not want to override and modify the stage you do want to override
# Step 4: Run the kernel again to see the overridden result
Compiler Pipeline Inspection Steps
To introspect the pipeline add_stages, before running your kernels, simply set
the add_stages_inspection_hook like so:
def inspect_stages(_self, stages, options, language, capability):
# inspect or modify add_stages here
triton.knobs.runtime.add_stages_inspection_hook = inspect_stages
Examples of how to use this for out of tree plugin passes is here
Changelog
Version 2.0 is out! New features include:
- Many, many bug fixes
- Performance improvements
- Backend rewritten to use MLIR
- Support for kernels that contain back-to-back matmuls (e.g., flash attention)
Contributing
Community contributions are more than welcome, whether it be to fix bugs or to add new features at github. For more detailed instructions, please visit our contributor's guide.
Compatibility
Supported Platforms:
Supported Hardware:
- NVIDIA GPUs (Compute Capability 8.0+)
- AMD GPUs (ROCm 6.2+)
- Under development: CPUs
Development Container (Dev Container)
Dev Containers for the Triton project are available from
the triton-dev-containers repository.
Key Benefits:
- Consistency: All developers can work with the same development
environment, ensuring uniform behavior across different systems.
- Isolation: The container prevents potential conflicts with software
installed on your local machine.
- Portability: Easily share the development environment with team members,
minimizing onboarding time and setup issues.
How to Use the Dev Container:
For detailed instructions on how to use the dev containers, please see
the dev container user guide.
Triton 是一种语言和编译器，用于编写高效的自定义深度学习 GPU 内核，目标是比 CUDA 开发效率更高，同时比其他现有的领域专用语言（DSL）更灵活。