I am getting the following exception while building the ROM for a lineageos project and need some help diagnosing and resolving the issue:
In file included from device/lenovo/tb8504f/camera/QCamera2/util/QCameraDisplay.cpp:53:
In file included from system/core/libutils/include/utils/Looper.h:25:
bionic/libc/include/sys/epoll.h:67:8: error: redefinition of 'epoll_event'
struct epoll_event {
^
out/target/product/tb8504f/obj/KERNEL_OBJ/usr/include/linux/eventpoll.h:59:8: note: previous definition is here
struct epoll_event {
^
1 error generated.
Device tree:= https://github.com/darran-kelinske-fivestars/android_device_lenovo_tb8504f/tree/lineage-15.1
Vendor tree:= https://github.com/darran-kelinske-fivestars/android_vendor_lenovo_tb8504f/tree/lineage-15.1
Kernel source:= https://github.com/dazza5000/android_kernel_lenovo_msm8937/tree/tb8504f
ROM Source:= https://github.com/LineageOS/android
Command:
repo sync -j20 && source build/envsetup.sh && breakfast tb8504f && make -j20 | tee rom.log
Full log:
https://del.dog/qaridufuca
I fixed this by borrowing code from a different kernel version that includes the following check
#ifdef __KERNEL__
Full snippet below:
#ifdef __KERNEL__
struct epoll_event {
struct epoll_event {
__u32 events;
__u32 events;
__u64 data;
__u64 data;
## -73,4 +74,5 ## static inline void ep_take_care_of_epollwakeup(struct epoll_event *epev)
epev->events &= ~EPOLLWAKEUP;
epev->events &= ~EPOLLWAKEUP;
}
}
#endif
#endif
#endif /* __KERNEL__ */
Commit:
https://github.com/dazza5000/android_kernel_lenovo_msm8937/commit/ab8195cd2bc6f1c283d79568540d5792c9ed55c2
Related
I am trying to use swig to generate interface on some existing C++ methods. I am referring to the following link for practice to generate on c++ template methods
https://valelab4.ucsf.edu/svn/3rdpartypublic/swig/Examples/python/template/
File : example.h
// Some template definitions
template<class T> T max(T a, T b) { return a>b ? a : b; }
template<class T> class vector {
T *v;
int sz;
public:
vector(int _sz) {
v = new T[_sz];
sz = _sz;
}
T &get(int index) {
return v[index];
}
void set(int index, T &val) {
v[index] = val;
}
#ifdef SWIG
%extend {
T getitem(int index) {
return $self->get(index);
}
void setitem(int index, T val) {
$self->set(index,val);
}
}
#endif
};
File : example.i
%module example
%{
#include "example.h"
%}
/* Let's just grab the original header file here */
%include "example.h"
/* Now instantiate some specific template declarations */
%template(maxint) max<int>;
%template(maxdouble) max<double>;
%template(vecint) vector<int>;
%template(vecdouble) vector<double>;
setup.py
from distutils.core import setup, Extension
#name of module
name = "example"
#version of module
version = "1.0"
# specify the name of the extension and source files
# required to compile this
ext_modules = Extension(name='_example',sources=["example.i","example.h"])
setup(name=name,
version=version,
ext_modules=[ext_modules])
step 1: compile module
python setup.py build_ext
step 2: install in the current directory
python setup.py install --install-platlib=.
I am seeing the following errors as the output
python setup.py build_ext
running build_ext
building '_example' extension
swigging example.i to example_wrap.c
swig -python -o example_wrap.c example.i
example.h:5: Warning 301: class keyword used, but not in C++ mode.
example.h:5: Error: Syntax error - possibly a missing semicolon.
error: command 'swig' failed with exit status 1
Use the following in setup.py to pass the -c++ option to SWIG. Also remove example.h from sources or the compiler tries to compile the header. It's already included in the generated file.
ext_modules = Extension(name='_example', sources=["example.i"], swig_opts=['-c++'])
I downloaded the example from https://www.olcf.ornl.gov/tutorials/mixing-openacc-with-gpu-libraries/
The codes are given in the above mwntioned links
1) using pgcc
pgc++ -c cuFFT.cu
pgcc -acc -Mcudalib=cufft fft.c cufft.o
works perfectly fine
2) using pgc++
pgc++ -c cuFFT.cu
pgc++ -acc -Mcudalib=cufft fft.cpp (or .c samefiles) cufft.o
I get the following error
undefined reference to launchCUFFT(float*, int, void*)
pgacclnk: child process exit status 1: /usr/bin/ld
You're running into a C/C++ linking mismatch.
To get this to work in PGI 17.9 tools I had to:
rename cuFFT.cu to cuFFT.cpp
edit the malloc line in fft.c from:
float *data = malloc(2*n*sizeof(float));
to:
float *data = (float *)malloc(2*n*sizeof(float));
modify the declaration in fft.c from:
extern void launchCUFFT(float *d_data, int n, void *stream);
to:
extern "C" void launchCUFFT(float *d_data, int n, void *stream);
Issue the following compile commands:
$ pgc++ -c cuFFT.cpp
$ pgc++ -acc -Mcudalib=cufft fft.c cuFFT.o
When I try to call a CUDA kernel (a __global__ function) using a function pointer, everything appears to work just fine. However, if I forget to provide launch configuration when calling the kernel, NVCC will not result in an error or warning, but the program will compile and then crash if I attempt to run it.
__global__ void bar(float x) { printf("foo: %f\n", x); }
typedef void(*FuncPtr)(float);
void invoker(FuncPtr func)
{
func<<<1, 1>>>(1.0);
}
invoker(bar);
cudaDeviceSynchronize();
Compile and run the above. Everything will work just fine. Then, remove the kernel's launch configuration (i.e., <<<1, 1>>>). The code will compile just fine but it will crash when you try to run it.
Any idea what is going on? Is this a bug, or I am not supposed to pass around pointers of __global__ functions?
CUDA version: 8.0
OS version: Debian (Testing repo)
GPU: NVIDIA GeForce 750M
If we take a slightly more complex version of your repro, and look at the code emitted by the CUDA toolchain front-end, it becomes possible to see what is happening:
#include <cstdio>
__global__ void bar_func(float x) { printf("foo: %f\n", x); }
typedef void(*FuncPtr)(float);
void invoker(FuncPtr passed_func)
{
#ifdef NVCC_FAILS_HERE
bar_func(1.0);
#endif
bar_func<<<1,1>>>(1.0);
passed_func(1.0);
passed_func<<<1,1>>>(2.0);
}
So let's compile it a couple of ways:
$ nvcc -arch=sm_52 -c -DNVCC_FAILS_HERE invoker.cu
invoker.cu(10): error: a __global__ function call must be configured
i.e. the front-end can detect that bar_func is a global function and requires launch parameters. Another attempt:
$ nvcc -arch=sm_52 -c -keep invoker.cu
As you note, this produces no compile error. Let's look at what happened:
void bar_func(float x) ;
# 5 "invoker.cu"
typedef void (*FuncPtr)(float);
# 7 "invoker.cu"
void invoker(FuncPtr passed_func)
# 8 "invoker.cu"
{
# 12 "invoker.cu"
(cudaConfigureCall(1, 1)) ? (void)0 : (bar_func)((1.0));
# 13 "invoker.cu"
passed_func((2.0));
# 14 "invoker.cu"
(cudaConfigureCall(1, 1)) ? (void)0 : passed_func((3.0));
# 15 "invoker.cu"
}
The standard kernel invocation syntax <<<>>> gets expanded into an inline call to cudaConfigureCall, and then a host wrapper function is called. The host wrapper has the API internals required to launch the kernel:
void bar_func( float __cuda_0)
# 3 "invoker.cu"
{__device_stub__Z8bar_funcf( __cuda_0); }
void __device_stub__Z8bar_funcf(float __par0)
{
if (cudaSetupArgument((void *)(char *)&__par0, sizeof(__par0), (size_t)0UL) != cudaSuccess) return;
{ volatile static char *__f __attribute__((unused)); __f = ((char *)((void ( *)(float))bar_func));
(void)cudaLaunch(((char *)((void ( *)(float))bar_func)));
};
}
So the stub only handles arguments and launches the kernel via cudaLaunch. It doesn't handle launch configuration
The underlying reason for the crash (actually an undetected runtime API error) is that the kernel launch happens without a prior configuration. Obviously this happens because the CUDA front end (and C++ for that matter) can't do pointer introspection at compile time and detect that your function pointer is a stub function for calling a kernel.
I think the only way to describe this is a "limitation" of the runtime API and compiler. I wouldn't say what you are doing is wrong, but I would probably be using the driver API and explicitly managing the kernel launch myself in such a situation.
In jCuda one can load cuda files as PTX or CUBIN format and call(launch) __global__ functions (kernels) from Java.
With keeping that in mind, I want to develop a framework with JCuda that gets user's __device__ function in a .cu file at run-time, loads and runs it.
And I have already implemented a __global__ function, in which each thread finds out the start point of its related data, perform some computation, initialization and then call user's __device__ function.
Here is my kernel pseudo code:
extern "C" __device__ void userFunc(args);
extern "C" __global__ void kernel(){
// initialize
userFunc(args);
// rest of the kernel
}
And user's __device__ function:
extern "C" __device__ void userFunc(args){
// do something
}
And in Java side, here is the part that I load the modules(modules are made from ptx files which are successfully created from cuda files with this command: nvcc -m64 -ptx path/to/cudaFile -o cudaFile.ptx)
CUmodule kernelModule = new CUmodule(); // 1
CUmodule userFuncModule = new CUmodule(); // 2
cuModuleLoad(kernelModule, ptxKernelFileName); // 3
cuModuleLoad(userFuncModule, ptxUserFuncFileName); // 4
When I try to run it I got error at line 3 : CUDA_ERROR_NO_BINARY_FOR_GPU. After some searching I get that my ptx file has some syntax error. After running this suggested command:
ptxas -arch=sm_30 kernel.ptx
I got:
ptxas fatal : Unresolved extern function 'userFunc'
Even when I replace line 3 with 4 to load userFunc before kernel I get this error. I got stuck at this phase. Is this the correct way to load multiple modules that need to be linked together in JCuda? Or is it even possible?
Edit:
Second part of the question is here
The really short answer is: No, you can't load multiple modules into a context in the runtime API.
You can do what you want, but it requires explicit setup and execution of a JIT linking call. I have no idea how (or even whether) that has been implemented in JCUDA, but I can show you how to do it with the standard driver API. Hold on...
If you have a device function in one file, and a kernel in another, for example:
// test_function.cu
#include <math.h>
__device__ float mathop(float &x, float &y, float &z)
{
float res = sin(x) + cos(y) + sqrt(z);
return res;
}
and
// test_kernel.cu
extern __device__ float mathop(float & x, float & y, float & z);
__global__ void kernel(float *xvals, float * yvals, float * zvals, float *res)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
res[tid] = mathop(xvals[tid], yvals[tid], zvals[tid]);
}
You can compile them to PTX as usual:
$ nvcc -arch=sm_30 -ptx test_function.cu
$ nvcc -arch=sm_30 -ptx test_kernel.cu
$ head -14 test_kernel.ptx
//
// Generated by NVIDIA NVVM Compiler
//
// Compiler Build ID: CL-19324607
// Cuda compilation tools, release 7.0, V7.0.27
// Based on LLVM 3.4svn
//
.version 4.2
.target sm_30
.address_size 64
// .globl _Z6kernelPfS_S_S_
.extern .func (.param .b32 func_retval0) _Z6mathopRfS_S_
At runtime, your code must create a JIT link session, add each PTX to the linker session, then finalise the linker session. This will give you a handle to a compiled cubin image which can be loaded as a module as usual. The simplest possible driver API code to put this together looks like this:
#include <cstdio>
#include <cuda.h>
#define drvErrChk(ans) { drvAssert(ans, __FILE__, __LINE__); }
inline void drvAssert(CUresult code, const char *file, int line, bool abort=true)
{
if (code != CUDA_SUCCESS) {
fprintf(stderr, "Driver API Error %04d at %s %d\n", int(code), file, line);
exit(-1);
}
}
int main()
{
cuInit(0);
CUdevice device;
drvErrChk( cuDeviceGet(&device, 0) );
CUcontext context;
drvErrChk( cuCtxCreate(&context, 0, device) );
CUlinkState state;
drvErrChk( cuLinkCreate(0, 0, 0, &state) );
drvErrChk( cuLinkAddFile(state, CU_JIT_INPUT_PTX, "test_function.ptx", 0, 0, 0) );
drvErrChk( cuLinkAddFile(state, CU_JIT_INPUT_PTX, "test_kernel.ptx" , 0, 0, 0) );
size_t sz;
char * image;
drvErrChk( cuLinkComplete(state, (void **)&image, &sz) );
CUmodule module;
drvErrChk( cuModuleLoadData(&module, image) );
drvErrChk( cuLinkDestroy(state) );
CUfunction function;
drvErrChk( cuModuleGetFunction(&function, module, "_Z6kernelPfS_S_S_") );
return 0;
}
You should be able to compile and run this as posted and verify it works OK. It should serve as a template for a JCUDA implementation, if they have JIT linking support implemented.
I'm trying to parse JSON using Boost's property_tree parser and from C++11 code (my system is Debian Wheezy with gcc 4.7.2 and Boost 1.49). I tried the following code based on Serializing and deserializing json with boost:
#include <map>
#include <sstream>
#include <boost/property_tree/ptree.hpp>
#include <boost/property_tree/json_parser.hpp>
using boost::property_tree::ptree; using boost::property_tree::read_json; using boost::property_tree::write_json;
void example() {
// Write json.
ptree pt;
pt.put ("foo", "bar");
std::ostringstream buf;
write_json (buf, pt, false);
std::string json = buf.str(); // {"foo":"bar"}
// Read json.
ptree pt2;
std::istringstream is (json);
read_json (is, pt2);
std::string foo = pt2.get<std::string> ("foo");
}
If I compile this with g++ -std=c++03 -c' everything is fine. However, I also want to use C++11 features (which the code in the linked thread actually does!). But withg++ -std=c++11 -c' I get compile errors:
In file included from /usr/include/boost/property_tree/json_parser.hpp:14:0,
from test.cpp:4:
/usr/include/boost/property_tree/detail/json_parser_read.hpp: In instantiation of ‘void boost::property_tree::json_parser::context<Ptree>::a_literal_val::operator() (boost::property_tree::json_parser::context<Ptree>::It, boost::property_tree::json_parser::context<Ptree>::It) const [with Ptree = boost::property_tree::basic_ptree<std::basic_string<char>, std::basic_string<char> >; boost::property_tree::json_parser::context<Ptree>::It = __gnu_cxx::__normal_iterator<char*, std::vector<char, std::allocator<char> > >]’:
/usr/include/boost/spirit/home/classic/core/scanner/scanner.hpp:148:13: required from ‘static void boost::spirit::classic::attributed_action_policy<boost::spirit::classic::nil_t>::call(const ActorT&, boost::spirit::classic::nil_t, const IteratorT&, const IteratorT&) [with ActorT = boost::property_tree::json_parser::context<boost::property_tree::basic_ptree<std::basic_string<char>, std::basic_string<char> > >::a_literal_val; IteratorT = __gnu_cxx::__normal_iterator<char*, std::vector<char, std::allocator<char> > >]’
/usr/include/boost/spirit/home/classic/core/scanner/scanner.hpp:163:13: required from ‘void boost::spirit::classic::action_policy::do_action(const ActorT&, AttrT&, const IteratorT&, const IteratorT&) const [with ActorT = boost::property_tree::json_parser::context<boost::property_tree::basic_ptree<std::basic_string<char>, std::basic_string<char> > >::a_literal_val; AttrT = boost::spirit::classic::nil_t; IteratorT = __gnu_cxx::__normal_iterator<char*, std::vector<char, std::allocator<char> > >]’
...
test.cpp:20:1: required from here
/usr/include/boost/property_tree/detail/json_parser_read.hpp:105:17: error: no matching function for call to ‘boost::property_tree::basic_ptree<std::basic_string<char>, std::basic_string<char> >::push_back(std::pair<std::basic_string<char>, std::basic_string<char> >)’
/usr/include/boost/property_tree/detail/json_parser_read.hpp:105:17: note: candidate is:
In file included from /usr/include/boost/property_tree/ptree.hpp:516:0,
from test.cpp:3:
/usr/include/boost/property_tree/detail/ptree_implementation.hpp:362:9: note: boost::property_tree::basic_ptree<Key, Data, KeyCompare>::iterator boost::property_tree::basic_ptree<Key, Data, KeyCompare>::push_back(const value_type&) [with Key = std::basic_string<char>; Data = std::basic_string<char>; KeyCompare = std::less<std::basic_string<char> >; boost::property_tree::basic_ptree<Key, Data, KeyCompare>::value_type = std::pair<const std::basic_string<char>, boost::property_tree::basic_ptree<std::basic_string<char>, std::basic_string<char> > >]
/usr/include/boost/property_tree/detail/ptree_implementation.hpp:362:9: note: no known conversion for argument 1 from ‘std::pair<std::basic_string<char>, std::basic_string<char> >’ to ‘const value_type& {aka const std::pair<const std::basic_string<char>, boost::property_tree::basic_ptree<std::basic_string<char>, std::basic_string<char> > >&}’
How can I use Boost's read_json with C++11? Do I need a newer Boost version for this (i. e. install manually from source instead of using Wheezy's packaged one)? Is there something wrong in my code? Or is this simply not possible?
It is a known bug of older Boost versions.
You can fix it by applying the following patch:
--- json_parser_read.hpp 2013-09-01 03:55:57.000000000 +0400
+++ /usr/include/boost/property_tree/detail/json_parser_read.hpp 2013-09-01 03:56:21.000000000 +0400
## -102,7 +102,7 ##
void operator()(It b, It e) const
{
BOOST_ASSERT(c.stack.size() >= 1);
- c.stack.back()->push_back(std::make_pair(c.name, Str(b, e)));
+ c.stack.back()->push_back(std::make_pair(c.name, Ptree(Str(b, e))));
c.name.clear();
c.string.clear();
}
or with
sed -i -e 's/std::make_pair(c.name, Str(b, e))/std::make_pair(c.name, Ptree(Str(b, e)))/' json_parser_read.hpp