高性能 ：OpenAI Triton Open-source GPU programming Language LINUX 环境配置
目录
- 配置triton环境
- cuda
- build-essential
- 带有pip的python环境
- 直接安装pip
- anaconda
- 安装 triton 环境
- pip install triton
- pip install torch
- 运行test示例
- vector-add.py
- launch.json
配置triton环境
cuda
- wget http://developer.download.nvidia.com/compute/cuda/11.0.2/local_installers/cuda_11.0.2_450.51.05_linux.run
- sudo sh cuda_11.0.2_450.51.05_linux.run
build-essential
sudo apt-get install build-essential:不安装可能会有报错，RuntimeError: Failed to find C compiler. Please specify via CC environment variable.
(tritonenv) cifi@cifi-666666:~/Downloads$ sudo apt-get install build-essential
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following additional packages will be installed:cpp-9 dpkg-dev fakeroot g++ g++-9 gcc gcc-10-base gcc-9 gcc-9-base libalgorithm-diff-perl libalgorithm-diff-xs-perl libalgorithm-merge-perl libasan5 libatomic1 libc-dev-bin libc6 libc6-dbg libc6-devlibcc1-0 libcrypt-dev libfakeroot libgcc-9-dev libgcc-s1 libgomp1 libitm1 liblsan0 libquadmath0 libstdc++-9-dev libstdc++6 libtsan0 libubsan1 linux-libc-dev make manpages-dev
Suggested packages:gcc-9-locales debian-keyring g++-multilib g++-9-multilib gcc-9-doc gcc-multilib autoconf automake libtool flex bison gcc-doc gcc-9-multilib glibc-doc libstdc++-9-doc make-doc
The following NEW packages will be installed:build-essential dpkg-dev fakeroot g++ g++-9 gcc gcc-9 libalgorithm-diff-perl libalgorithm-diff-xs-perl libalgorithm-merge-perl libasan5 libc-dev-bin libc6-dev libcrypt-dev libfakeroot libgcc-9-devlibitm1 liblsan0 libquadmath0 libstdc++-9-dev libtsan0 libubsan1 linux-libc-dev make manpages-dev
The following packages will be upgraded:cpp-9 gcc-10-base gcc-9-base libatomic1 libc6 libc6-dbg libcc1-0 libgcc-s1 libgomp1 libstdc++6
10 upgraded, 25 newly installed, 0 to remove and 395 not upgraded.
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直接安装pip
sudo apt install python3-pip
anaconda
wget https://repo.anaconda.com/archive/Anaconda3-2024.02-1-Linux-x86_64.sh，从网页https://www.anaconda.com/download/success下载速度可能更快bash Anaconda3-2024.02-1-Linux-x86_64.shconda create -n tritonenv python=3.8conda activate tritonenv
安装 triton 环境
pip install triton
pip install tritonhttps://triton-lang.org/main/getting-started/installation.html
(base) cifi@cifi-666666:~/Downloads$ conda activate tritonenv
(tritonenv) cifi@cifi-666666:~/Downloads$ pip install triton
Collecting tritonDownloading triton-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.3 kB)
Collecting filelock (from triton)Downloading filelock-3.16.1-py3-none-any.whl.metadata (2.9 kB)
Downloading triton-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (209.4 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 209.4/209.4 MB 8.1 kB/s eta 0:00:00
Downloading filelock-3.16.1-py3-none-any.whl (16 kB)
Installing collected packages: filelock, triton
Successfully installed filelock-3.16.1 triton-3.1.0
pip install torch
pip install torch
(tritonenv) cifi@cifi-666666:~/Downloads$ pip install torch
Collecting torchDownloading torch-2.4.1-cp38-cp38-manylinux1_x86_64.whl.metadata (26 kB)
Requirement already satisfied: filelock in /home/cifi/anaconda3/envs/tritonenv/lib/python3.8/site-packages (from torch) (3.16.1)
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Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
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Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-nccl-cu12==2.20.5 (from torch)Downloading nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)
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Installing collected packages: mpmath, typing-extensions, triton, sympy, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, MarkupSafe, fsspec, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, nvidia-cusolver-cu12, torchAttempting uninstall: tritonFound existing installation: triton 3.1.0Uninstalling triton-3.1.0:Successfully uninstalled triton-3.1.0
Successfully installed MarkupSafe-2.1.5 fsspec-2025.2.0 jinja2-3.1.5 mpmath-1.3.0 networkx-3.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.8.61 nvidia-nvtx-cu12-12.1.105 sympy-1.13.3 torch-2.4.1 triton-3.0.0 typing-extensions-4.12.2运行test示例
vector-add.py
# https://github.com/triton-lang/triton/blob/main/python/tutorials/01-vector-add.py
import torch
import triton
import triton.language as tl@triton.jit
def add_kernel(x_ptr, # *Pointer* to first input vector. 指向第一个输入向量的指针。y_ptr, # *Pointer* to second input vector. 指向第二个输入向量的指针。output_ptr, # *Pointer* to output vector. 指向输出向量的指针。n_elements, # Size of the vector. 向量的大小。BLOCK_SIZE: tl.constexpr, # Number of elements each program should process. 每个程序应处理的元素数量。# NOTE: `constexpr` so it can be used as a shape value. 注意：`constexpr` 因此它可以用作形状值。):# There are multiple 'programs' processing different data. We identify which program# 有多个“程序”处理不同的数据。需要确定是哪一个程序：pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0. 使用 1D 启动网格，因此轴为 0。# This program will process inputs that are offset from the initial data.# 该程序将处理相对初始数据偏移的输入。# For instance, if you had a vector of length 256 and block_size of 64, the programs would each access the elements [0:64, 64:128, 128:192, 192:256].# 例如，如果有一个长度为 256, 块大小为 64 的向量，程序将各自访问 [0:64, 64:128, 128:192, 192:256] 的元素。# Note that offsets is a list of pointers:# 注意 offsets 是指针列表：block_start = pid * BLOCK_SIZEoffsets = block_start + tl.arange(0, BLOCK_SIZE)# Create a mask to guard memory operations against out-of-bounds accesses.# 创建掩码以防止内存操作超出边界访问。mask = offsets < n_elements# Load x and y from DRAM, masking out any extra elements in case the input is not a multiple of the block size.# 从 DRAM 加载 x 和 y，如果输入不是块大小的整数倍，则屏蔽掉任何多余的元素。x = tl.load(x_ptr + offsets, mask=mask)y = tl.load(y_ptr + offsets, mask=mask)output = x + y# Write x + y back to DRAM.# 将 x + y 写回 DRAM。tl.store(output_ptr + offsets, output, mask=mask)def add(x: torch.Tensor, y: torch.Tensor):# We need to preallocate the output.# 需要预分配输出。output = torch.empty_like(x)assert x.is_cuda and y.is_cuda and output.is_cudan_elements = output.numel()# The SPMD launch grid denotes the number of kernel instances that run in parallel.# SPMD 启动网格表示并行运行的内核实例的数量。# It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int].# 它类似于 CUDA 启动网格。它可以是 Tuple[int]，也可以是 Callable(metaparameters) -> Tuple[int]。# In this case, we use a 1D grid where the size is the number of blocks:# 在这种情况下，使用 1D 网格，其中大小是块的数量：grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )# NOTE:# 注意：# - Each torch.tensor object is implicitly converted into a pointer to its first element.# - 每个 torch.tensor 对象都会隐式转换为其第一个元素的指针。# - `triton.jit`'ed functions can be indexed with a launch grid to obtain a callable GPU kernel.# - `triton.jit` 函数可以通过启动网格索引来获得可调用的 GPU 内核。# - Don't forget to pass meta-parameters as keywords arguments.# - 不要忘记以关键字参数传递元参数。add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)# We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still running asynchronously at this point.# 返回 z 的句柄，但由于 `torch.cuda.synchronize()` 尚未被调用，此时内核仍在异步运行。return outputtorch.manual_seed(0)
size = 98432
x = torch.rand(size, device='cuda')
y = torch.rand(size, device='cuda')
output_torch = x + y
output_triton = add(x, y)
print("Torch计算结果:")
print(output_torch)
print("Triton计算结果:")
print(output_triton)
print(f'Torch 和 Triton 之间的最大差异是'f'{torch.max(torch.abs(output_torch - output_triton))}')
launch.json
{// Use IntelliSense to learn about possible attributes.// Hover to view descriptions of existing attributes.// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387"version": "0.2.0","configurations": [{"name":"Python Debugger: Current File","type":"debugpy","request":"launch","program":"${file}","console":"integratedTerminal"},{"name": "Python Debugger: Current File","type": "debugpy","request": "launch","program": "${file}","python": "/home/cifi/anaconda3/envs/tritonenv/bin/python3","console": "integratedTerminal"}]
}
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