温州哪里有做网站,将一个网站拉入黑名单怎么做,wordpress设置只显标题,刚做的网站关键词就上来了参考内容#xff1a;pytorch添加C拓展简单实战编写及基本功能测试 文章目录 第一步#xff1a;编写 C 模块test.htest.cpp 第二步#xff1a;编写 setup.py第三步#xff1a;安装 C 模块第四步#xff1a;验证安装第五步#xff1a;C 模块使用test_cpp1.pytest_cpp2.py 运… 参考内容pytorch添加C拓展简单实战编写及基本功能测试 文章目录 第一步编写 C 模块test.htest.cpp 第二步编写 setup.py第三步安装 C 模块第四步验证安装第五步C 模块使用test_cpp1.pytest_cpp2.py 运行结果扩展阅读 编译安装前的文件目录 这里的 csrc 应该不是指 pytorch 项目中的 /torch/csrc csrc
├─ cpu
│ ├─ test.cpp
│ └─ test.h
└─ setup.py第一步编写 C 模块
test.h
#include torch/extension.h
#include vector// 前向传播
torch::Tensor Test_forward_cpu(const torch::Tensor inputA, const torch::Tensor inputB);// 反向传播
std::vectortorch::Tensor Test_backward_cpu(const torch::Tensor gradOutput);test.cpp
#include test.h// 前向传播
torch::Tensor Test_forward_cpu(const torch::Tensor x, const torch::Tensor y){AT_ASSERTM(x.sizes() y.sizes(), x must be the same size as y);torch::Tensor z torch::zeros(x.sizes());z 2 * x y;return z;
}// 反向传播
std::vectortorch::Tensor Test_backward_cpu(const torch::Tensor gradOutput){torch::Tensor gradOutputX 2 * gradOutput * torch::ones(gradOutput.sizes());torch::Tensor gradOutputY gradOutput * torch::ones(gradOutput.sizes());return {gradOutputX, gradOutputY};
}// pybind11 绑定
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m){m.def(forward, Test_forward_cpu, TEST forward);m.def(backward, Test_backward_cpu, TEST backward);
}第二步编写 setup.py
from setuptools import setup
import os
import glob
from torch.utils.cpp_extension import BuildExtension, CppExtension# 头文件目录
include_dirs os.path.dirname(os.path.abspath(__file__))
# 源代码目录
source_cpu glob.glob(os.path.join(include_dirs, cpu, *.cpp))setup(nametest_cpp, # 模块名称需要在 python 中调用version0.1,ext_modules[CppExtension(test_cpp, sourcessource_cpu, include_dirs[include_dirs]),],cmdclass{build_ext: BuildExtension}
)第三步安装 C 模块
在 csrc 文件夹下运行命令
python setup.py install第一次尝试的报错信息
/home/zjma/.conda/envs/debugtest/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!********************************************************************************Please avoid running setup.py directly.Instead, use pypa/build, pypa/installer or otherstandards-based tools.See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.********************************************************************************!!self.initialize_options()
/home/zjma/.conda/envs/debugtest/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: EasyInstallDeprecationWarning: easy_install command is deprecated.
!!********************************************************************************Please avoid running setup.py and easy_install.Instead, use pypa/build, pypa/installer or otherstandards-based tools.See https://github.com/pypa/setuptools/issues/917 for details.********************************************************************************!!self.initialize_options()参考 SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip 后得知是 setuptools 版本太高于是降低 setuptools 版本pip install setuptools58.2.0。
第二次尝试的运行结果
running install
running bdist_egg
running egg_info
writing test_cpp.egg-info/PKG-INFO
writing dependency_links to test_cpp.egg-info/dependency_links.txt
writing top-level names to test_cpp.egg-info/top_level.txt
reading manifest file test_cpp.egg-info/SOURCES.txt
writing manifest file test_cpp.egg-info/SOURCES.txt
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_ext
building test_cpp extension
creating /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8
creating /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home
creating /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home/zjma
creating /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home/zjma/pytorch_v1.13.1
creating /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home/zjma/pytorch_v1.13.1/csrc
creating /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home/zjma/pytorch_v1.13.1/csrc/cpu
Emitting ninja build file /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/build.ninja...
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBSN)
[1/1] c -MMD -MF /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home/zjma/pytorch_v1.13.1/csrc/cpu/test.o.d -pthread -B /home/zjma/.conda/envs/debugtest/compiler_compat -Wl,--sysroot/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/zjma/pytorch_v1.13.1/csrc -I/home/zjma/pytorch_v1.13.1/torch/include -I/home/zjma/pytorch_v1.13.1/torch/include/torch/csrc/api/include -I/home/zjma/pytorch_v1.13.1/torch/include/TH -I/home/zjma/pytorch_v1.13.1/torch/include/THC -I/home/zjma/.conda/envs/debugtest/include/python3.8 -c -c /home/zjma/pytorch_v1.13.1/csrc/cpu/test.cpp -o /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home/zjma/pytorch_v1.13.1/csrc/cpu/test.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE_gcc -DPYBIND11_STDLIB_libstdcpp -DPYBIND11_BUILD_ABI_cxxabi1016 -DTORCH_EXTENSION_NAMEtest_cpp -D_GLIBCXX_USE_CXX11_ABI1 -stdc14
cc1plus: warning: command-line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C
creating build/lib.linux-x86_64-3.8
g -pthread -shared -B /home/zjma/.conda/envs/debugtest/compiler_compat -L/home/zjma/.conda/envs/debugtest/lib -Wl,-rpath/home/zjma/.conda/envs/debugtest/lib -Wl,--no-as-needed -Wl,--sysroot/ /home/zjma/pytorch_v1.13.1/csrc/build/temp.linux-x86_64-3.8/home/zjma/pytorch_v1.13.1/csrc/cpu/test.o -L/home/zjma/pytorch_v1.13.1/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-3.8/test_cpp.cpython-38-x86_64-linux-gnu.so
creating build/bdist.linux-x86_64/egg
copying build/lib.linux-x86_64-3.8/test_cpp.cpython-38-x86_64-linux-gnu.so - build/bdist.linux-x86_64/egg
creating stub loader for test_cpp.cpython-38-x86_64-linux-gnu.so
byte-compiling build/bdist.linux-x86_64/egg/test_cpp.py to test_cpp.cpython-38.pyc
creating build/bdist.linux-x86_64/egg/EGG-INFO
copying test_cpp.egg-info/PKG-INFO - build/bdist.linux-x86_64/egg/EGG-INFO
copying test_cpp.egg-info/SOURCES.txt - build/bdist.linux-x86_64/egg/EGG-INFO
copying test_cpp.egg-info/dependency_links.txt - build/bdist.linux-x86_64/egg/EGG-INFO
copying test_cpp.egg-info/top_level.txt - build/bdist.linux-x86_64/egg/EGG-INFO
writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt
zip_safe flag not set; analyzing archive contents...
__pycache__.test_cpp.cpython-38: module references __file__
creating dist/test_cpp-0.1-py3.8-linux-x86_64.egg and adding build/bdist.linux-x86_64/egg to it
removing build/bdist.linux-x86_64/egg (and everything under it)
Processing test_cpp-0.1-py3.8-linux-x86_64.egg
removing /home/zjma/.conda/envs/debugtest/lib/python3.8/site-packages/test_cpp-0.1-py3.8-linux-x86_64.egg (and everything under it)
creating /home/zjma/.conda/envs/debugtest/lib/python3.8/site-packages/test_cpp-0.1-py3.8-linux-x86_64.egg
Extracting test_cpp-0.1-py3.8-linux-x86_64.egg to /home/zjma/.conda/envs/debugtest/lib/python3.8/site-packages
test-cpp 0.1 is already the active version in easy-install.pthInstalled /home/zjma/.conda/envs/debugtest/lib/python3.8/site-packages/test_cpp-0.1-py3.8-linux-x86_64.egg
Processing dependencies for test-cpp0.1
Finished processing dependencies for test-cpp0.1编译安装后的文件目录
csrc
├─ build
│ ├─ bdist.linux-x86_64
│ ├─ lib.linux-x86_64-3.8
│ │ └─ test_cpp.cpython-38-x86_64-linux-gnu.so
│ ├─ lib.linux-x86_64-cpython-38
│ │ └─ test_cpp.cpython-38-x86_64-linux-gnu.so
│ ├─ temp.linux-x86_64-3.8
│ │ ├─ .ninja_deps
│ │ ├─ .ninja_log
│ │ ├─ build.ninja
│ │ └─ home
│ └─ temp.linux-x86_64-cpython-38
│ ├─ .ninja_deps
│ ├─ .ninja_log
│ ├─ build.ninja
│ └─ home
├─ cpu
│ ├─ test.cpp
│ └─ test.h
├─ dist
│ └─ test_cpp-0.1-py3.8-linux-x86_64.egg
├─ setup.py
└─ test_cpp.egg-info├─ PKG-INFO├─ SOURCES.txt├─ dependency_links.txt└─ top_level.txt第四步验证安装
1、在虚拟环境的路径 /lib/python3.8/site-packages 下看到 test_cpp-0.1-py3.8-linux-x86_64.egg 文件 2、conda list 查看当前虚拟环境下已经安装的包 3、进入 python 的交互模式import test_cpp 后报错 import test_cpp
Traceback (most recent call last):File stdin, line 1, in module
ImportError: libc10.so: cannot open shared object file: No such file or directory参考 通过Python setup.py install的第三方包import时却无法导入是什么问题呢 - 神经的网络里挣扎的回答 - 知乎因为编译的 test_cpp 包需要依赖 torch 包导致无法导入。所以在 import test_cpp 前要先 import torch。
第五步C 模块使用
test_cpp1.py
import torch
import test_cpp
from torch.autograd import Functionclass TestFunction(Function):staticmethoddef forward(ctx, x, y):return test_cpp.forward(x, y)staticmethoddef backward(ctx, gradOutput):gradX, gradY test_cpp.backward(gradOutput)return gradX, gradYclass Test(torch.nn.Module):def __init__(self):super(Test, self).__init__()def forward(self, inputA, inputB):return TestFunction.apply(inputA, inputB)test_cpp2.py
import torch
from torch.autograd import Variablefrom test_cpp1 import Testx Variable(torch.Tensor([1,2,3]), requires_gradTrue)
y Variable(torch.Tensor([4,5,6]), requires_gradTrue)test Test()
z test(x, y)
z.sum().backward()print(x: , x)
print(y: , y)
print(z: , z)
print(x.grad: , x.grad)
print(y.grad: , y.grad)运行结果
/home/zjma/.conda/envs/debugtest/bin/python /home/zjma/PycharmProjects/pythonProject/test_cpp2.py
x: tensor([1., 2., 3.], requires_gradTrue)
y: tensor([4., 5., 6.], requires_gradTrue)
z: tensor([ 6., 9., 12.], grad_fnTestFunctionBackward)
x.grad: tensor([2., 2., 2.])
y.grad: tensor([1., 1., 1.])进程已结束退出代码为 0运行结果符合预期。
扩展阅读
pytorch之c/cuda拓展讲得很详细举的例子和上文基本一样但用到了CUDA很多内容可以扩展去看官方教程 相关内容的笔记后面可以复现一下 PyTorch进阶1C扩展pytorch 的C扩展
