Intel® XPU Backend for Triton*

This is the development repository of Intel® XPU Backend for Triton*, a new Triton backend for Intel GPUs. Intel® XPU Backend for Triton* is a out of tree backend module for Triton used to provide best-in-class performance and productivity on any Intel GPUs for PyTorch and standalone usage.

Compatibility

Operating systems:
- Ubuntu 22.04
GPU Cards:
GPU Drivers:
- Latest Long Term Support (LTS) Release
- Latest Rolling Release
Toolchain:
- Latest Intel® Deep Learning Essentials

Note that Intel® XPU Backend for Triton* is not compatible with Intel® Extension for PyTorch* and Intel® oneAPI Base Toolkit*.

Quick Installation

Prerequisites

Latest Rolling Release or Long Term Support Release of GPU driver
Latest release of Intel® Deep Learning Essentials

Install PyTorch and Triton from nightly wheels

Currently, Intel® XPU Backend for Triton* requires a special version of PyTorch and both can be installed from nightly wheels. Navigate to the nightly wheels workflow, select the most recent successful run on the top of the page and download an artifact for the corresponding Python version. Extract the archive and in the extracted directory execute:

pip install torch-*.whl triton-*.whl

Before using Intel® XPU Backend for Triton* you need to initialize the toolchain. The default location is /opt/intel/oneapi (if installed as a root user) or ~/intel/oneapi (if installed as a regular user).

# replace /opt/intel/oneapi with the actual location of Intel® Deep Learning Essentials
source /opt/intel/oneapi/setvars.sh

Install from source

Prerequisites

Latest Rolling Release or Long Term Support Release of GPU driver
Latest release of Intel® Deep Learning Essentials

Compile PyTorch and Triton from source

Currently, Intel® XPU Backend for Triton* requires a special version of PyTorch and both need to be compiled at the same time.

Before compiling PyTorch and Intel® XPU Backend for Triton* you need to initialize the toolchain. The default location is /opt/intel/oneapi (if installed as a root user) or ~/intel/oneapi (if installed as a regular user).

# replace /opt/intel/oneapi with the actual location of Intel® Deep Learning Essentials
source /opt/intel/oneapi/setvars.sh

Clone this repository:

git clone https://github.com/intel/intel-xpu-backend-for-triton.git
cd intel-xpu-backend-for-triton

To avoid potential conflicts with installed packages it is recommended to create and activate a new Python virtual environment:

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

Compile and install PyTorch:

scripts/install-pytorch.sh --source

Compile and install Intel® XPU Backend for Triton*:

scripts/compile-triton.sh

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.

Find the version of LLVM that Triton builds against. Check cmake/llvm-hash.txt to see the current version.
Checkout LLVM at this revision to the directory llvm, which must be in the same directory as intel-xpu-backend-for-triton:
In the directory intel-xpu-backend-for-triton, build Triton with custom LLVM:
```
./scripts/compile-triton.sh --llvm --triton
```

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.
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.
VSCcode 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 python
- Get the full path to the compile_commands.json file produced by the build: find python/build -name 'compile_commands.json' | xargs readlink -f. You might get a full path similar to /Users/{username}/triton/python/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.

scripts/test-triton.sh

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.

Helpful environment variables

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/*
LLVM_IR_ENABLE_DUMP=1 dumps the IR before every pass run over the LLVM IR.
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").
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_REMARK enables the performance warnings that are emitted as remarks.

Usage Guide

Code Modifications

Intel® XPU Backend for Triton* requires a special version of PyTorch that can be built from sources or installed from nightly wheels.

Add import torch for xpu support.
Put the tensor and models to XPU by calling to('xpu').

This repository contains modified tutorials that must be used with Intel® XPU Backend for Triton*.

The following examples show modifications for the user code.

Example 1 : Triton Kernel

This example is a modified version of Vector Add triton kernel. Please refer to Vector Add for detailed comments and illustration about the code semantics.

Comparing to the original code, the following code modifies:

import torch
import triton
import triton.language as tl


@triton.jit
def add_kernel(
    x_ptr,
    y_ptr,
    output_ptr,
    n_elements,
    BLOCK_SIZE: tl.constexpr,
):
    pid = tl.program_id(axis=0)
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements
    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)

def add(x: torch.Tensor, y: torch.Tensor):
    # Put the tensor to xpu
    output = torch.empty_like(x).xpu()
    assert x.is_xpu and y.is_xpu and output.is_xpu
    n_elements = output.numel()
    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)

    return output

# For manual_seed, needs to use API for XPU
torch.xpu.manual_seed(0)
size = 512
# For tensors, needs to be put on XPU
x = torch.rand(size, device='xpu')
y = torch.rand(size, device='xpu')
output_torch = x + y
output_triton = add(x, y)
print(output_torch)
print(output_triton)
print(
    f'The maximum difference between torch and triton is '
    f'{torch.max(torch.abs(output_torch - output_triton))}'
)

Example 2 : End-to-End Model

Triton is transparent for end-to-end models. One could easily use torch.compile with inductor as backend by default. It will automatically generates triton kernel and gets benefit from it.

import torch
from torch._dynamo.testing import rand_strided

from torch.nn import *
class simpleModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # tensors inside model should be on xpu
        self.y = rand_strided((32, 8), (8, 1), device='xpu:0', dtype=torch.float32)

    def forward(self, x):
        z = x + self.y
        return z

# tensors passed to the model should be on xpu
x = rand_strided((32, 8), (8, 1), device='xpu:0', dtype=torch.float32)
xpu_model = simpleModel()
# Call torch.compile for optimization
optimized_mod = torch.compile(xpu_model)

graph_result = optimized_mod(x)

Performance Analysis Guide

There are several ways of doing performance analysis. We recommend using torch.profiler for end-to-end performance analysis and using Intel® VTune™ Profiler for more detailed kernel analysis. Note that the user needs to explicitly set TRITON_XPU_PROFILE=1 when the user needs to enable kernel profiling.

export TRITON_XPU_PROFILE=1

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.

License

MIT License. As found in LICENSE file.

Security

See Intel's Security Center for information on how to report a potential security issue or vulnerability.