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https://github.com/zebrajr/pytorch.git
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Summary: Closes https://github.com/caffe2/caffe2/pull/404 Differential Revision: D4943280 Pulled By: Yangqing fbshipit-source-id: c0988598d8ccb8329feac88382686324b90d4d46
208 lines
6.0 KiB
C++
208 lines
6.0 KiB
C++
#ifndef CAFFE2_CORE_COMMON_H_
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#define CAFFE2_CORE_COMMON_H_
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#include <algorithm>
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#include <map>
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#include <memory>
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#include <numeric>
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#include <set>
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#include <sstream>
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#include <string>
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#include <type_traits>
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#include <vector>
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#ifdef __APPLE__
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#include <TargetConditionals.h>
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#endif
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#if defined(_MSC_VER)
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#include <io.h>
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#else
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#include <unistd.h>
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#endif
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// Macros used during the build of this caffe2 instance. This header file
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// is automatically generated by the cmake script during build.
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#include "caffe2/core/macros.h"
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// Caffe2 version. The plan is to increment the minor version every other week
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// as a track point for bugs, until we find a proper versioning cycle.
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#define CAFFE2_VERSION_MAJOR 0
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#define CAFFE2_VERSION_MINOR 6
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#define CAFFE2_VERSION_PATCH 0
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#define CAFFE2_VERSION \
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(CAFFE2_VERSION_MAJOR * 10000 + CAFFE2_VERSION_MINOR * 100 + \
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CAFFE2_VERSION_PATCH)
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namespace caffe2 {
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// Data type for caffe2 Index/Size. We use size_t to be safe here as well as for
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// large matrices that are common in sparse math.
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typedef int64_t TIndex;
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// Note(Yangqing): NVCC does not play well with unordered_map on some platforms,
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// forcing us to use std::map instead of unordered_map. This may affect speed
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// in some cases, but in most of the computation code we do not access map very
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// often, so it should be fine for us. I am putting a CaffeMap alias so we can
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// change it more easily if things work out for unordered_map down the road.
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template <typename Key, typename Value>
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using CaffeMap = std::map<Key, Value>;
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// using CaffeMap = std::unordered_map;
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// Using statements for common classes that we refer to in caffe2 very often.
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// Note that we only place it inside caffe2 so the global namespace is not
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// polluted.
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/* using override */
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using std::set;
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using std::string;
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using std::unique_ptr;
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using std::vector;
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// Just in order to mark things as not implemented. Do not use in final code.
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#define CAFFE_NOT_IMPLEMENTED CAFFE_THROW("Not Implemented.")
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// suppress an unused variable.
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#define UNUSED_VARIABLE __attribute__((unused))
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// Disable the copy and assignment operator for a class. Note that this will
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// disable the usage of the class in std containers.
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#ifndef DISABLE_COPY_AND_ASSIGN
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#define DISABLE_COPY_AND_ASSIGN(classname) \
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private: \
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classname(const classname&) = delete; \
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classname& operator=(const classname&) = delete
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#endif
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// Define enabled when building for iOS or Android devices
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#if !defined(CAFFE2_MOBILE)
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#if defined(__ANDROID__)
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#define CAFFE2_ANDROID 1
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#define CAFFE2_MOBILE 1
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#elif (defined(__APPLE__) && \
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(TARGET_IPHONE_SIMULATOR || TARGET_OS_SIMULATOR || TARGET_OS_IPHONE))
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#define CAFFE2_IOS 1
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#define CAFFE2_MOBILE 1
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#elif (defined(__APPLE__) && TARGET_OS_MAC)
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#define CAFFE2_IOS 1
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#define CAFFE2_MOBILE 0
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#else
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#define CAFFE2_MOBILE 0
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#endif // ANDROID / IOS / MACOS
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#endif // CAFFE2_MOBILE
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// Define alignment macro that is cross platform
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#if defined(_MSC_VER)
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#define CAFFE2_ALIGNED(x) __declspec(align(x))
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#else
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#define CAFFE2_ALIGNED(x) __attribute__((aligned(x)))
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#endif
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/**
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* Macro for marking functions as having public visibility.
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* Ported from folly/CPortability.h
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*/
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#ifndef __GNUC_PREREQ
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#if defined __GNUC__ && defined __GNUC_MINOR__
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#define __GNUC_PREREQ(maj, min) \
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((__GNUC__ << 16) + __GNUC_MINOR__ >= ((maj) << 16) + (min))
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#else
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#define __GNUC_PREREQ(maj, min) 0
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#endif
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#endif
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#if defined(__GNUC__)
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#if __GNUC_PREREQ(4, 9)
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#define CAFFE2_EXPORT [[gnu::visibility("default")]]
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#else
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#define CAFFE2_EXPORT __attribute__((__visibility__("default")))
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#endif
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#else
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#define CAFFE2_EXPORT
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#endif
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// make_unique is a C++14 feature. If we don't have 14, we will emulate
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// its behavior. This is copied from folly/Memory.h
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#if __cplusplus >= 201402L || \
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(defined __cpp_lib_make_unique && __cpp_lib_make_unique >= 201304L) || \
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(defined(_MSC_VER) && _MSC_VER >= 1900)
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/* using override */ using std::make_unique;
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#else
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template<typename T, typename... Args>
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typename std::enable_if<!std::is_array<T>::value, std::unique_ptr<T>>::type
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make_unique(Args&&... args) {
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return std::unique_ptr<T>(new T(std::forward<Args>(args)...));
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}
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// Allows 'make_unique<T[]>(10)'. (N3690 s20.9.1.4 p3-4)
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template<typename T>
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typename std::enable_if<std::is_array<T>::value, std::unique_ptr<T>>::type
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make_unique(const size_t n) {
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return std::unique_ptr<T>(new typename std::remove_extent<T>::type[n]());
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}
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// Disallows 'make_unique<T[10]>()'. (N3690 s20.9.1.4 p5)
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template<typename T, typename... Args>
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typename std::enable_if<
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std::extent<T>::value != 0, std::unique_ptr<T>>::type
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make_unique(Args&&...) = delete;
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#endif
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// to_string implementation for Android related stuff.
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#ifndef __ANDROID__
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using std::to_string;
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#else
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template <typename T>
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std::string to_string(T value)
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{
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std::ostringstream os;
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os << value;
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return os.str();
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}
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#endif
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// dynamic cast reroute: if RTTI is disabled, go to reinterpret_cast
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template <typename Dst, typename Src>
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inline Dst dynamic_cast_if_rtti(Src ptr) {
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#ifdef __GXX_RTTI
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return dynamic_cast<Dst>(ptr);
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#else
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return reinterpret_cast<Dst>(ptr);
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#endif
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}
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// SkipIndices are used in operator_fallback_gpu.h and operator_fallback_mkl.h
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// as utilty functions that marks input / output indices to skip when we use a
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// CPU operator as the fallback of GPU/MKL operator option.
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template <int... values>
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class SkipIndices {
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private:
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template <int V>
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static inline bool ContainsInternal(const int i) {
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return (i == V);
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}
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template <int First, int Second, int... Rest>
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static inline bool ContainsInternal(const int i) {
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return (i == First) && ContainsInternal<Second, Rest...>(i);
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}
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public:
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static inline bool Contains(const int i) {
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return ContainsInternal<values...>(i);
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}
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};
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template <>
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class SkipIndices<> {
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public:
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static inline bool Contains(const int i) {
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return false;
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}
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};
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} // namespace caffe2
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#endif // CAFFE2_CORE_COMMON_H_
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