jax-triton

The jax-triton repository contains integrations between JAX and Triton.

Documentation can be found here.

This is not an officially supported Google product.

Quickstart

The main function of interest is jax_triton.triton_call for applying Triton functions to JAX arrays, including inside jax.jit-compiled functions. For example, we can define a kernel from the Triton tutorial:

import triton
import triton.language as tl


@triton.jit
def add_kernel(
    x_ptr,
    y_ptr,
    length,
    output_ptr,
    block_size: tl.constexpr,
):
  """Adds two vectors."""
  pid = tl.program_id(axis=0)
  block_start = pid * block_size
  offsets = block_start + tl.arange(0, block_size)
  mask = offsets < length
  x = tl.load(x_ptr + offsets, mask=mask)
  y = tl.load(y_ptr + offsets, mask=mask)
  output = x + y
  tl.store(output_ptr + offsets, output, mask=mask)

Then we can apply it to JAX arrays using jax_triton.triton_call:

import jax
import jax.numpy as jnp
import jax_triton as jt

def add(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
  out_shape = jax.ShapeDtypeStruct(shape=x.shape, dtype=x.dtype)
  block_size = 8
  return jt.triton_call(
      x,
      y,
      x.size,
      kernel=add_kernel,
      out_shape=out_shape,
      grid=(x.size // block_size,),
      block_size=block_size)

x_val = jnp.arange(8)
y_val = jnp.arange(8, 16)
print(add(x_val, y_val))
print(jax.jit(add)(x_val, y_val))

See the examples directory, especially fused_attention.py and the fused attention ipynb.

Installation

$ pip install jax-triton

You can either use a stable release of triton or a nightly release.

Make sure you have a CUDA-compatible jax installed. For example you could run:

$ pip install "jax[cuda12]"

Development

To develop jax-triton, you can clone the repo with:

$ git clone https://github.com/jax-ml/jax-triton.git

and do an editable install with:

$ cd jax-triton
$ pip install -e .

To run the jax-triton tests, you'll need pytest:

$ pip install pytest
$ pytest tests/