我正在尝试在本文档之后松散地向 TensorFlow 添加一个新操作。不同之处在于我正在尝试实现基于 GPU 的操作。我要添加的操作是来自此处的 cuda 操作（cuda_op.py、cuda_op_kernel.cc、cuda_op_kernel.cu.cc）。我正在尝试在 tensorflow 之外编译这些并tf.load_op_library用于将它们拉入。我做了一些更改，所以这里是我的文件：
cuda_op_kernel.cc
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
using namespace tensorflow; // NOLINT(build/namespaces)
REGISTER_OP("AddOne")
.Input("input: int32")
.Output("output: int32")
.SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
c->set_output(0, c->input(0));
return Status::OK();
});
void AddOneKernelLauncher(const int* in, const int N, int* out);
class AddOneOp : public OpKernel {
public:
explicit AddOneOp(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
// Grab the input tensor
const Tensor& input_tensor = context->input(0);
auto input = input_tensor.flat<int32>();
// Create an output tensor
Tensor* output_tensor = NULL;
OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
&output_tensor));
auto output = output_tensor->template flat<int32>();
// Set all but the first element of the output tensor to 0.
const int N = input.size();
// Call the cuda kernel launcher
AddOneKernelLauncher(input.data(), N, output.data());
}
};
REGISTER_KERNEL_BUILDER(Name("AddOne").Device(DEVICE_GPU), AddOneOp);
cuda_op_kernel.cu
#define EIGEN_USE_GPU
#include <cuda.h>
#include <stdio.h>
__global__ void AddOneKernel(const int* in, const int N, int* out) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
out[i] = in[i] + 1;
}
}
void AddOneKernelLauncher(const int* in, const int N, int* out) {
AddOneKernel<<<32, 256>>>(in, N, out);
cudaError_t cudaerr = cudaDeviceSynchronize();
if (cudaerr != cudaSuccess)
printf("kernel launch failed with error \"%s\".\n", cudaGetErrorString(cudaerr));
}
CMakeLists.txt
cmake_minimum_required(VERSION 3.5)
#found from running python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())'
include_directories(/usr/local/lib/python3.5/dist-packages/tensorflow/include)
find_package(CUDA)
#set flags based on tutorial
set (CMAKE_CXX_FLAGS "--std=c++11 -fPIC -O2 -D_GLIBCXX_USE_CXX11_ABI=0")
#pass flags to c++ compiler
SET(CUDA_PROPAGATE_HOST_FLAGS ON)
#create library
cuda_add_library(
cuda_op SHARED
src/cuda_op_kernel.cu
src/cuda_op_kernel.cc
OPTIONS -gencode=arch=compute_20,code=sm_20)
#copy test file to build folder
configure_file(src/test.py test.py COPYONLY)
测试.py
import tensorflow as tf
mod = tf.load_op_library('./libcuda_op.so')
with tf.Session() as sess:
start = [5,4,3,2,1]
print(start)
print(mod.add_one(start).eval())
我能够test.py成功编译和运行，但输出始终是[0 0 0 0 0]. 如果我AddOneKernel<<<32, 256>>>(in, N, out);用for (int i = 0; i < N; i++) out[i] = in[i] + 1;和DEVICE_GPU替换DEVICE_CPU，则运算输出正确的值[6 5 4 3 2]（完全相同CMakeList.txt）。
知道如何获得要返回的正确值吗？