>>>fromtorch.utils.cpp_extensionimportload>>>module=load(name='extension',sources=['extension.cpp','extension_kernel.cu'],extra_cflags=['-O2'],verbose=True) torch.utils.cpp_extension.load_inline(name,cpp_sources,cuda_sources=None,functions=None,extra_cflags=None,extra_cuda_cflags=None,extra_...
Sources may omit two required parts of a typical non-inline C++ extension: the necessary header includes, as well as the (pybind11) binding code. More precisely, strings passed tocpp_sourcesare first concatenated into a single.cppfile. This file is then prepended with#include <torch/extension....
编写C++或CUDA代码:根据需要进行优化的代码部分,编写对应的C++或CUDA代码,实现更高效的算法。 创建自定义扩展模块:使用torch.utils.cpp_extension或torch.utils.cpp_extension.load_inline函数创建自定义扩展模块。将之前编写的C++或CUDA代码与PyTorch相关的接口进行集成。 编译和安装扩展模块:通过编译和安装自定义扩展模块,...
torch.utils.cpp_extension.load(name, sources, extra_cflags=None, extra_cuda_cflags=None, extra_ldflags=None, extra_include_paths=None, build_directory=None, verbose=False) 即时加载(JIT)PyTorch C ++扩展。 为了加载扩展,会创建一个Ninja构建文件,该文件用于将指定的...
鉴于网课中教学案例使用的 api 是torch.utils.cpp_extension.load_inline(),我在这卖弄一下,给出load()的案例。以下出现的文件均在同一目录下。 首先我写了个 warp reduce softmax kernel,目标是跟torch.softmax来对比 // softmax.cu#include<cuda_runtime.h>#include<torch/types.h>template<typenameT>__de...
utils.cpp_extension.load( @@ -442,6 +462,80 @@ def test_inline_jit_compile_custom_op_cuda(self): z = torch.ops.inline_jit_extension_custom_op_cuda.cos_add(x, y) self.assertEqual(z, x.cos() + y.cos()) @unittest.skipIf(not TEST_XPU, "XPU not found") def test_inline_...
方式1(run_inline_v1.py): import torch from torch.utils.cpp_extension import load_inline cpp_src = """ #include <torch/extension.h> void printArray(torch::Tensor input) { int *ptr = (int *)input.data_ptr(); for(int i=0; i < input.numel(); i++) { printf("%d\\n", ptr[...
utils.cpp_extension.load_inline( 632 725 name="test_compilation_error_formatting", 633 - cpp_sources="int main() { return 0 }") 726 + cpp_sources="int main() { return 0 }") 634 727 pattern = r'.*(\\n|\\r).*' 635 728 self.assertNotRegex(str(e), pattern) 636 ...
utils.cpp_extension.load_inline( name='inline_jit_extension', cpp_sources=[cpp_source1, cpp_source2], verbose=True) x = torch.randn(4, 4) y = torch.randn(4, 4) z = module.sin_add(x, y) self.assertEqual(z, x.sin() + y.sin()) 浏览完整代码 来源:test_cpp_extensions.py ...
we’ll be exposing the warpPerspective function, which applies a perspective transformation to an image, from OpenCV to TorchScript as a custom operator. The first step is to write the implementation of our custom operator in C++. Let’s call the file for this implementationop.cppand make it...