blob: 4bd2235b693d0e1b0c157b787ff2ae2f9e89648e [file] [log] [blame]
#ifndef CAFFE2_CORE_COMMON_H_
#define CAFFE2_CORE_COMMON_H_
#include <algorithm>
#include <map>
#include <memory>
#include <numeric>
#include <set>
#include <sstream>
#include <string>
#include <type_traits>
#include <vector>
#ifdef __APPLE__
#include <TargetConditionals.h>
#endif
#if defined(_MSC_VER)
#include <io.h>
#else
#include <unistd.h>
#endif
// Macros used during the build of this caffe2 instance. This header file
// is automatically generated by the cmake script during build.
#include "caffe2/core/macros.h"
// Caffe2 version. The plan is to increment the minor version every other week
// as a track point for bugs, until we find a proper versioning cycle.
#define CAFFE2_VERSION_MAJOR 0
#define CAFFE2_VERSION_MINOR 6
#define CAFFE2_VERSION_PATCH 0
#define CAFFE2_VERSION \
(CAFFE2_VERSION_MAJOR * 10000 + CAFFE2_VERSION_MINOR * 100 + \
CAFFE2_VERSION_PATCH)
namespace caffe2 {
// Data type for caffe2 Index/Size. We use size_t to be safe here as well as for
// large matrices that are common in sparse math.
typedef int64_t TIndex;
// Note(Yangqing): NVCC does not play well with unordered_map on some platforms,
// forcing us to use std::map instead of unordered_map. This may affect speed
// in some cases, but in most of the computation code we do not access map very
// often, so it should be fine for us. I am putting a CaffeMap alias so we can
// change it more easily if things work out for unordered_map down the road.
template <typename Key, typename Value>
using CaffeMap = std::map<Key, Value>;
// using CaffeMap = std::unordered_map;
// Using statements for common classes that we refer to in caffe2 very often.
// Note that we only place it inside caffe2 so the global namespace is not
// polluted.
/* using override */
using std::set;
using std::string;
using std::unique_ptr;
using std::vector;
// Just in order to mark things as not implemented. Do not use in final code.
#define CAFFE_NOT_IMPLEMENTED CAFFE_THROW("Not Implemented.")
// suppress an unused variable.
#ifdef _MSC_VER
#define CAFFE2_UNUSED
#define CAFFE2_USED
#else
#define CAFFE2_UNUSED __attribute__((__unused__))
#define CAFFE2_USED __attribute__((__used__))
#endif //_MSC_VER
// Disable the copy and assignment operator for a class. Note that this will
// disable the usage of the class in std containers.
#ifndef DISABLE_COPY_AND_ASSIGN
#define DISABLE_COPY_AND_ASSIGN(classname) \
private: \
classname(const classname&) = delete; \
classname& operator=(const classname&) = delete
#endif
// Define enabled when building for iOS or Android devices
#if !defined(CAFFE2_MOBILE)
#if defined(__ANDROID__)
#define CAFFE2_ANDROID 1
#define CAFFE2_MOBILE 1
#elif (defined(__APPLE__) && \
(TARGET_IPHONE_SIMULATOR || TARGET_OS_SIMULATOR || TARGET_OS_IPHONE))
#define CAFFE2_IOS 1
#define CAFFE2_MOBILE 1
#elif (defined(__APPLE__) && TARGET_OS_MAC)
#define CAFFE2_IOS 1
#define CAFFE2_MOBILE 0
#else
#define CAFFE2_MOBILE 0
#endif // ANDROID / IOS / MACOS
#endif // CAFFE2_MOBILE
// Define alignment macro that is cross platform
#if defined(_MSC_VER)
#define CAFFE2_ALIGNED(x) __declspec(align(x))
#else
#define CAFFE2_ALIGNED(x) __attribute__((aligned(x)))
#endif
/**
* Macro for marking functions as having public visibility.
* Ported from folly/CPortability.h
*/
#ifndef __GNUC_PREREQ
#if defined __GNUC__ && defined __GNUC_MINOR__
#define __GNUC_PREREQ(maj, min) \
((__GNUC__ << 16) + __GNUC_MINOR__ >= ((maj) << 16) + (min))
#else
#define __GNUC_PREREQ(maj, min) 0
#endif
#endif
#if defined(__GNUC__)
#if __GNUC_PREREQ(4, 9)
#define CAFFE2_EXPORT [[gnu::visibility("default")]]
#else
#define CAFFE2_EXPORT __attribute__((__visibility__("default")))
#endif
#else
#define CAFFE2_EXPORT
#endif
// make_unique is a C++14 feature. If we don't have 14, we will emulate
// its behavior. This is copied from folly/Memory.h
#if __cplusplus >= 201402L || \
(defined __cpp_lib_make_unique && __cpp_lib_make_unique >= 201304L) || \
(defined(_MSC_VER) && _MSC_VER >= 1900)
/* using override */ using std::make_unique;
#else
template<typename T, typename... Args>
typename std::enable_if<!std::is_array<T>::value, std::unique_ptr<T>>::type
make_unique(Args&&... args) {
return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
}
// Allows 'make_unique<T[]>(10)'. (N3690 s20.9.1.4 p3-4)
template<typename T>
typename std::enable_if<std::is_array<T>::value, std::unique_ptr<T>>::type
make_unique(const size_t n) {
return std::unique_ptr<T>(new typename std::remove_extent<T>::type[n]());
}
// Disallows 'make_unique<T[10]>()'. (N3690 s20.9.1.4 p5)
template<typename T, typename... Args>
typename std::enable_if<
std::extent<T>::value != 0, std::unique_ptr<T>>::type
make_unique(Args&&...) = delete;
#endif
// to_string implementation for Android related stuff.
#ifndef __ANDROID__
using std::to_string;
using std::stoi;
#else
template <typename T>
std::string to_string(T value)
{
std::ostringstream os;
os << value;
return os.str();
}
inline int stoi(const string& str)
{
std::stringstream ss;
int n = 0;
ss << str;
ss >> n;
return n;
}
#endif
// dynamic cast reroute: if RTTI is disabled, go to reinterpret_cast
template <typename Dst, typename Src>
inline Dst dynamic_cast_if_rtti(Src ptr) {
#ifdef __GXX_RTTI
return dynamic_cast<Dst>(ptr);
#else
return reinterpret_cast<Dst>(ptr);
#endif
}
// SkipIndices are used in operator_fallback_gpu.h and operator_fallback_mkl.h
// as utilty functions that marks input / output indices to skip when we use a
// CPU operator as the fallback of GPU/MKL operator option.
template <int... values>
class SkipIndices {
private:
template <int V>
static inline bool ContainsInternal(const int i) {
return (i == V);
}
template <int First, int Second, int... Rest>
static inline bool ContainsInternal(const int i) {
return (i == First) && ContainsInternal<Second, Rest...>(i);
}
public:
static inline bool Contains(const int i) {
return ContainsInternal<values...>(i);
}
};
template <>
class SkipIndices<> {
public:
static inline bool Contains(const int /*i*/) {
return false;
}
};
// A global variable to mark if Caffe2 has cuda linked to the current runtime.
// Do not directly use this variable, but instead use the HasCudaRuntime()
// function below.
extern bool g_caffe2_has_cuda_linked;
// HasCudaRuntime() tells the program whether the binary has Cuda runtime
// linked. This function should not be used in static initialization functions
// as the underlying boolean variable is going to be switched on when one
// loads libcaffe2_gpu.so.
inline bool HasCudaRuntime() {
return g_caffe2_has_cuda_linked;
}
} // namespace caffe2
#endif // CAFFE2_CORE_COMMON_H_