FM / core

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core/

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core

Name	Size	Mode	Actions
allocator.h	136	0644	edit dl rm
blob.h	4168	0644	edit dl rm
blob_serialization.h	10791	0644	edit dl rm
blob_serializer_base.h	3905	0644	edit dl rm
blob_stats.h	1127	0644	edit dl rm
common.h	4329	0644	edit dl rm
common_cudnn.h	9893	0644	edit dl rm
common_gpu.h	21414	0644	edit dl rm
common_omp.h	156	0644	edit dl rm
context.h	6174	0644	edit dl rm
context_base.h	4382	0644	edit dl rm
context_gpu.h	11014	0644	edit dl rm
cudnn_wrappers.h	6956	0644	edit dl rm
db.h	9352	0644	edit dl rm
distributions_stubs.h	2161	0644	edit dl rm
event.h	12420	0644	edit dl rm
event_cpu.h	1192	0644	edit dl rm
export_c10_op_to_caffe2.h	9487	0644	edit dl rm
export_caffe2_op_to_c10.h	11101	0644	edit dl rm
flags.h	74	0644	edit dl rm
graph.h	5258	0644	edit dl rm
init.h	6496	0644	edit dl rm
logging.h	75	0644	edit dl rm
macros.h	3426	0644	edit dl rm
memonger.h	817	0644	edit dl rm
module.h	2473	0644	edit dl rm
net.h	4634	0644	edit dl rm
net_async_base.h	7397	0644	edit dl rm
net_async_scheduling.h	993	0644	edit dl rm
net_async_task.h	833	0644	edit dl rm
net_async_task_future.h	1925	0644	edit dl rm
net_async_task_graph.h	2253	0644	edit dl rm
net_async_tracing.h	5093	0644	edit dl rm
net_dag_utils.h	2146	0644	edit dl rm
net_parallel.h	2144	0644	edit dl rm
net_simple.h	2606	0644	edit dl rm
net_simple_refcount.h	2097	0644	edit dl rm
numa.h	72	0644	edit dl rm
observer.h	3809	0644	edit dl rm
operator.h	58872	0644	edit dl rm
operator_gradient.h	10222	0644	edit dl rm
operator_schema.h	18477	0644	edit dl rm
plan_executor.h	219	0644	edit dl rm
prof_dag_counters.h	2751	0644	edit dl rm
qtensor.h	6615	0644	edit dl rm
qtensor_serialization.h	2624	0644	edit dl rm
scope_guard.h	4675	0644	edit dl rm
static_tracepoint.h	398	0644	edit dl rm
static_tracepoint_elfx86.h	5555	0644	edit dl rm
stats.h	10365	0644	edit dl rm
storage.h	733	0644	edit dl rm
tensor.h	18668	0644	edit dl rm
tensor_impl.h	351	0644	edit dl rm
tensor_int8.h	450	0644	edit dl rm
test_utils.h	6285	0644	edit dl rm
timer.h	1218	0644	edit dl rm
transform.h	5741	0644	edit dl rm
types.h	2248	0644	edit dl rm
workspace.h	11305	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/core/common.h (4329B)

#ifndef CAFFE2_CORE_COMMON_H_
#define CAFFE2_CORE_COMMON_H_

#include <algorithm>
#include <cmath>
#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"

#include <c10/macros/Macros.h>

#include "c10/util/string_utils.h"

namespace caffe2 {

// 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.
#if defined(_MSC_VER) && !defined(__clang__)
#define CAFFE2_UNUSED __pragma(warning(suppress : 4100 4101))
#define CAFFE2_USED
#else
#define CAFFE2_UNUSED __attribute__((__unused__))
#define CAFFE2_USED __attribute__((__used__))
#endif //_MSC_VER

// Define alignment macro that is cross platform
#if defined(_MSC_VER) && !defined(__clang__)
#define CAFFE2_ALIGNED(x) __declspec(align(x))
#else
#define CAFFE2_ALIGNED(x) __attribute__((aligned(x)))
#endif

#if (defined _MSC_VER && !defined NOMINMAX)
#define NOMINMAX
#endif

#if defined(__has_cpp_attribute)
#if __has_cpp_attribute(nodiscard)
#define CAFFE2_NODISCARD [[nodiscard]]
#endif
#endif
#if !defined(CAFFE2_NODISCARD)
#define CAFFE2_NODISCARD
#endif

using std::make_unique;

#if defined(__ANDROID__) && !defined(__NDK_MAJOR__)
using ::round;
#else
using std::round;
#endif // defined(__ANDROID__) && !defined(__NDK_MAJOR__)

// 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 static_cast<Dst>(ptr);
#endif
}

// SkipIndices are used in operator_fallback_gpu.h and operator_fallback_mkl.h
// as utility 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;
  }
};

// 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 libtorch_gpu.so.
TORCH_API bool HasCudaRuntime();
TORCH_API bool HasHipRuntime();
namespace internal {
// Sets the Cuda Runtime flag that is used by HasCudaRuntime(). You should
// never use this function - it is only used by the Caffe2 gpu code to notify
// Caffe2 core that cuda runtime has been loaded.
TORCH_API void SetCudaRuntimeFlag();
TORCH_API void SetHipRuntimeFlag();
} // namespace internal
// Returns which setting Caffe2 was configured and built with (exported from
// CMake)
TORCH_API const std::map<string, string>& GetBuildOptions();

} // namespace caffe2

#endif // CAFFE2_CORE_COMMON_H_

#ifndef CAFFE2_CORE_COMMON_H_ #define CAFFE2_CORE_COMMON_H_ #include #include #include #include #include #include #include #include #include #include #ifdef __APPLE__ #include #endif #if defined(_MSC_VER) #include #else #include #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" #include #include "c10/util/string_utils.h" namespace caffe2 { // 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 using CaffeMap = std::map; // 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. #if defined(_MSC_VER) && !defined(__clang__) #define CAFFE2_UNUSED __pragma(warning(suppress : 4100 4101)) #define CAFFE2_USED #else #define CAFFE2_UNUSED __attribute__((__unused__)) #define CAFFE2_USED __attribute__((__used__)) #endif //_MSC_VER // Define alignment macro that is cross platform #if defined(_MSC_VER) && !defined(__clang__) #define CAFFE2_ALIGNED(x) __declspec(align(x)) #else #define CAFFE2_ALIGNED(x) __attribute__((aligned(x))) #endif #if (defined _MSC_VER && !defined NOMINMAX) #define NOMINMAX #endif #if defined(__has_cpp_attribute) #if __has_cpp_attribute(nodiscard) #define CAFFE2_NODISCARD [[nodiscard]] #endif #endif #if !defined(CAFFE2_NODISCARD) #define CAFFE2_NODISCARD #endif using std::make_unique; #if defined(__ANDROID__) && !defined(__NDK_MAJOR__) using ::round; #else using std::round; #endif // defined(__ANDROID__) && !defined(__NDK_MAJOR__) // dynamic cast reroute: if RTTI is disabled, go to reinterpret_cast template inline Dst dynamic_cast_if_rtti(Src ptr) { #ifdef __GXX_RTTI return dynamic_cast(ptr); #else return static_cast(ptr); #endif } // SkipIndices are used in operator_fallback_gpu.h and operator_fallback_mkl.h // as utility functions that marks input / output indices to skip when we use a // CPU operator as the fallback of GPU/MKL operator option. template class SkipIndices { private: template static inline bool ContainsInternal(const int i) { return (i == V); } template static inline bool ContainsInternal(const int i) { return (i == First) || ContainsInternal(i); } public: static inline bool Contains(const int i) { return ContainsInternal(i); } }; template <> class SkipIndices<> { public: static inline bool Contains(const int /*i*/) { return false; } }; // 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 libtorch_gpu.so. TORCH_API bool HasCudaRuntime(); TORCH_API bool HasHipRuntime(); namespace internal { // Sets the Cuda Runtime flag that is used by HasCudaRuntime(). You should // never use this function - it is only used by the Caffe2 gpu code to notify // Caffe2 core that cuda runtime has been loaded. TORCH_API void SetCudaRuntimeFlag(); TORCH_API void SetHipRuntimeFlag(); } // namespace internal // Returns which setting Caffe2 was configured and built with (exported from // CMake) TORCH_API const std::map& GetBuildOptions(); } // namespace caffe2 #endif // CAFFE2_CORE_COMMON_H_