mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
Default hidden visibility (#10752)
Summary: Flipping to hidden visibility one more time. Let's see what fails. cc mingzhe09088 pjh5 Yangqing Pull Request resolved: https://github.com/pytorch/pytorch/pull/10752 Reviewed By: ezyang Differential Revision: D9526343 Pulled By: orionr fbshipit-source-id: c0e9c29270e95e1b2e21c598095f720c199e1e52
This commit is contained in:
Orion Reblitz-Richardson
Facebook Github Bot
parent
92ff070b83
commit
4cb968fb77
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@ -2,11 +2,13 @@
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#include <atomic>
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#include "ATen/core/ATenGeneral.h"
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namespace at {
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// base class for refcounted things, allows for collects of generic
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// refcounted objects that include tensors
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struct Retainable {
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struct AT_API Retainable {
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Retainable(): refcount(1), weak_refcount(1) {}
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void retain() {
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++refcount;
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@ -4,3 +4,5 @@
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// TODO: Merge the *_API macros.
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#define AT_API AT_CORE_API
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#define AT_EXPORT AT_CORE_EXPORT
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#define AT_IMPORT AT_CORE_IMPORT
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@ -19,7 +19,7 @@ namespace at {
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namespace detail {
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// Obtains the base name from a full path.
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std::string StripBasename(const std::string& full_path);
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AT_CORE_API std::string StripBasename(const std::string& full_path);
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inline std::ostream& _str(std::ostream& ss) {
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return ss;
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@ -56,7 +56,7 @@ inline std::string str(const char* c_str) {
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}
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/// Represents a location in source code (for debugging).
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struct SourceLocation {
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struct AT_CORE_API SourceLocation {
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const char* function;
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const char* file;
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uint32_t line;
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@ -12,21 +12,33 @@
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#ifdef _WIN32
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#if !defined(AT_CORE_STATIC_WINDOWS)
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// TODO: unfiy the controlling macros.
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#if defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS)
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#define AT_CORE_API __declspec(dllexport)
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#else // defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS)
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#define AT_CORE_API __declspec(dllimport)
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#endif // defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS)
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#define AT_CORE_EXPORT __declspec(dllexport)
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#define AT_CORE_IMPORT __declspec(dllimport)
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#else // !defined(AT_CORE_STATIC_WINDOWS)
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#define AT_CORE_API
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#define AT_CORE_EXPORT
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#define AT_CORE_IMPORT
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#endif // !defined(AT_CORE_STATIC_WINDOWS)
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#else // _WIN32
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#if defined(__GNUC__)
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#define AT_CORE_API __attribute__((__visibility__("default")))
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#define AT_CORE_EXPORT __attribute__((__visibility__("default")))
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#else // defined(__GNUC__)
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#define AT_CORE_EXPORT
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#endif // defined(__GNUC__)
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#define AT_CORE_IMPORT AT_CORE_EXPORT
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#endif // _WIN32
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// AT_CORE_API is a macro that, depends on whether you are building the
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// main library or not, resolves to either AT_CORE_EXPORT or
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// AT_CORE_IMPORT.
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//
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// TODO: unify the controlling macros.
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#if defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS)
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#define AT_CORE_API AT_CORE_EXPORT
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#else // defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS)
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#define AT_CORE_API AT_CORE_IMPORT
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#endif // defined(CAFFE2_BUILD_MAIN_LIBS) || defined(ATen_cpu_EXPORTS) || defined(caffe2_EXPORTS)
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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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#define AT_DISABLE_COPY_AND_ASSIGN(classname) \
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@ -1,5 +1,6 @@
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#pragma once
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#include <ATen/core/ATenGeneral.h>
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#include <ATen/core/Error.h>
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#include <atomic>
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#include <stdexcept>
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@ -32,7 +33,7 @@ namespace c10 {
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// tells us if the object was allocated by us. If it wasn't, no
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// intrusive_ptr for you!
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class intrusive_ptr_target {
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class AT_CORE_API intrusive_ptr_target {
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// Note [Weak references for intrusive refcounting]
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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// Here's the scheme:
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@ -113,7 +114,7 @@ class intrusive_ptr_target {
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namespace detail {
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template <class TTarget>
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struct intrusive_target_default_null_type final {
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struct AT_CORE_EXPORT intrusive_target_default_null_type final {
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static constexpr TTarget* singleton() noexcept {
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return nullptr;
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}
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@ -126,7 +127,7 @@ class weak_intrusive_ptr;
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template <
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class TTarget,
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class NullType = detail::intrusive_target_default_null_type<TTarget>>
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class intrusive_ptr final {
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class AT_CORE_EXPORT intrusive_ptr final {
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private:
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static_assert(
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std::is_base_of<intrusive_ptr_target, TTarget>::value,
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@ -415,7 +416,7 @@ inline bool operator!=(
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template <
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typename TTarget,
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class NullType = detail::intrusive_target_default_null_type<TTarget>>
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class weak_intrusive_ptr final {
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class AT_CORE_EXPORT weak_intrusive_ptr final {
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private:
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static_assert(
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std::is_base_of<intrusive_ptr_target, TTarget>::value,
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@ -797,13 +798,13 @@ namespace std {
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// To allow intrusive_ptr and weak_intrusive_ptr inside std::unordered_map or
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// std::unordered_set, we need std::hash
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template <class TTarget, class NullType>
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struct hash<c10::intrusive_ptr<TTarget, NullType>> {
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struct AT_CORE_EXPORT hash<c10::intrusive_ptr<TTarget, NullType>> {
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size_t operator()(const c10::intrusive_ptr<TTarget, NullType>& x) const {
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return std::hash<TTarget*>()(x.get());
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}
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};
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template <class TTarget, class NullType>
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struct hash<c10::weak_intrusive_ptr<TTarget, NullType>> {
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struct AT_CORE_EXPORT hash<c10::weak_intrusive_ptr<TTarget, NullType>> {
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size_t operator()(const c10::weak_intrusive_ptr<TTarget, NullType>& x) const {
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return std::hash<TTarget*>()(x._unsafe_get_target());
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}
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@ -207,6 +207,7 @@ target_link_libraries(caffe2_protos PUBLIC protobuf::libprotobuf)
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# Compile exposed libraries.
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list(APPEND Caffe2_CPU_SRCs $<TARGET_OBJECTS:c10>)
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add_library(caffe2 ${Caffe2_CPU_SRCS})
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target_compile_options(caffe2 PRIVATE "-fvisibility=hidden")
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caffe2_interface_library(caffe2_protos caffe2_protos_whole)
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target_link_libraries(caffe2 PRIVATE caffe2_protos_whole)
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if (${CAFFE2_LINK_LOCAL_PROTOBUF})
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@ -229,13 +230,6 @@ else()
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target_compile_options(caffe2 INTERFACE "$<$<COMPILE_LANGUAGE:CXX>:-std=c++11>")
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endif()
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# Note(jiayq): This is not complete yet, but in the end we will need to deal with
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# explicit hidden visibility.
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# This line is here so that when testing build, we can enable it to properly test
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# annotation of public symbols. When finally doing proper build with all symbols
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# annotated, we will enable this line and have it wrapped with gcc/clang checks.
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# target_compile_options(caffe2 PRIVATE "-fvisibility=hidden")
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target_compile_options(caffe2 PRIVATE "-DCAFFE2_BUILD_MAIN_LIB")
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if (MSVC AND NOT BUILD_SHARED_LIBS)
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# Note [Supporting both static and dynamic libraries on Window]
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@ -12,6 +12,7 @@
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#ifndef NOM_GRAPH_GRAPH_H
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#define NOM_GRAPH_GRAPH_H
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#include "caffe2/core/common.h"
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#include "nomnigraph/Support/Common.h"
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#include <algorithm>
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@ -42,7 +43,7 @@ class Node;
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// \brief Edge within a Graph.
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template <typename T, typename... U>
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class Edge : public StorageType<U...> {
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class CAFFE2_API Edge : public StorageType<U...> {
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public:
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using NodeRef = typename Graph<T, U...>::NodeRef;
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Edge(NodeRef tail, NodeRef head, U... args)
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@ -76,7 +77,7 @@ class Edge : public StorageType<U...> {
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// \brief Node within a Graph.
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template <typename T, typename... U>
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class Node : public StorageType<T>, public Notifier<Node<T, U...>> {
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class CAFFE2_API Node : public StorageType<T>, public Notifier<Node<T, U...>> {
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public:
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using NodeRef = typename Graph<T, U...>::NodeRef;
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using EdgeRef = typename Graph<T, U...>::EdgeRef;
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@ -155,7 +156,7 @@ class Node : public StorageType<T>, public Notifier<Node<T, U...>> {
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/// for example.
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///
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template <typename T, typename... U>
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class Subgraph {
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class CAFFE2_API Subgraph {
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public:
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Subgraph() {
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DEBUG_PRINT("Creating instance of Subgraph: %p\n", this);
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@ -222,7 +223,7 @@ class Subgraph {
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/// Everything is owned by the graph to simplify storage concerns.
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///
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template <typename T, typename... U>
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class Graph {
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class CAFFE2_API Graph {
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public:
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using SubgraphType = Subgraph<T, U...>;
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using NodeRef = Node<T, U...>*;
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@ -1,13 +1,14 @@
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#ifndef NOM_REPRESENTATIONS_COMPILER_H
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#define NOM_REPRESENTATIONS_COMPILER_H
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#include "caffe2/core/common.h"
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#include "nomnigraph/Graph/Graph.h"
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#include "nomnigraph/Support/Casting.h"
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namespace nom {
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namespace repr {
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class Value {
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class CAFFE2_API Value {
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public:
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enum class ValueKind { Value, Instruction, Data };
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Value(ValueKind K) : Kind(K) {}
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@ -21,10 +22,10 @@ class Value {
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const ValueKind Kind;
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};
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class Data : public Value {
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class CAFFE2_API Data : public Value {
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public:
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Data() : Value(ValueKind::Data) {}
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static bool classof(const Value* V) {
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CAFFE2_API static bool classof(const Value* V) {
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return V->getKind() == ValueKind::Data;
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}
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virtual ~Data() = default;
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@ -40,7 +41,7 @@ class Data : public Value {
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size_t Version = 0;
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};
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class Instruction : public Value {
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class CAFFE2_API Instruction : public Value {
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public:
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/// \brief All the different types of execution.
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enum class Opcode {
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@ -53,7 +54,7 @@ class Instruction : public Value {
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};
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Instruction() : Value(ValueKind::Instruction), Op(Opcode::Generic) {}
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Instruction(Opcode op) : Value(ValueKind::Instruction), Op(op) {}
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static bool classof(const Value* V) {
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CAFFE2_API static bool classof(const Value* V) {
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return V->getKind() == ValueKind::Instruction;
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}
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virtual ~Instruction() = default;
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@ -65,7 +66,7 @@ class Instruction : public Value {
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Opcode Op;
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};
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class Terminator : public Instruction {
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class CAFFE2_API Terminator : public Instruction {
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public:
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Terminator(Instruction::Opcode op) : Instruction(op) {}
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@ -79,17 +80,17 @@ class Terminator : public Instruction {
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}
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};
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class Branch : public Terminator {
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class CAFFE2_API Branch : public Terminator {
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public:
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Branch() : Terminator(Instruction::Opcode::Branch) {}
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};
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class Return : public Terminator {
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class CAFFE2_API Return : public Terminator {
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public:
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Return() : Terminator(Instruction::Opcode::Return) {}
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};
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class Phi : public Instruction {
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class CAFFE2_API Phi : public Instruction {
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public:
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Phi() : Instruction(Instruction::Opcode::Phi) {}
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};
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@ -1,6 +1,7 @@
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#ifndef NOM_REPRESENTATIONS_CONTROLFLOW_H
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#define NOM_REPRESENTATIONS_CONTROLFLOW_H
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#include "caffe2/core/common.h"
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#include "nomnigraph/Graph/Graph.h"
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#include "nomnigraph/Representations/Compiler.h"
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@ -13,7 +14,7 @@ namespace repr {
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/// of the data flow graph as well as an ordering on instruction
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/// execution. Basic blocks are used for control flow analysis.
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template <typename T, typename... U>
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class BasicBlock {
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class CAFFE2_API BasicBlock {
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public:
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using NodeRef = typename Subgraph<T, U...>::NodeRef;
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BasicBlock() {}
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@ -91,7 +92,7 @@ class BasicBlock {
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using Program = Graph<Value>;
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template <typename G>
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struct ControlFlowGraphImpl {
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struct CAFFE2_API ControlFlowGraphImpl {
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// Hack to help debugging in case this class is misused.
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static_assert(
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sizeof(ControlFlowGraphImpl),
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@ -101,7 +102,7 @@ struct ControlFlowGraphImpl {
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};
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template <typename T, typename... U>
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struct ControlFlowGraphImpl<Graph<T, U...>> {
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struct CAFFE2_API ControlFlowGraphImpl<Graph<T, U...>> {
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using type = Graph<std::unique_ptr<BasicBlock<T, U...>>, int>;
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using bbType = BasicBlock<T, U...>;
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};
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@ -111,7 +112,7 @@ struct ControlFlowGraphImpl<Graph<T, U...>> {
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///
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/// \note G Must be of type Graph<T, U...>.
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template <typename G>
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class ControlFlowGraph : public ControlFlowGraphImpl<G>::type {
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class CAFFE2_API ControlFlowGraph : public ControlFlowGraphImpl<G>::type {
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public:
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// This is for C++11 compatibility, otherwise we could use "using"
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ControlFlowGraph() {}
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@ -130,7 +131,7 @@ using BasicBlockType = typename ControlFlowGraphImpl<G>::bbType;
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/// \brief Converts graph to SSA representation. Modifies the graph
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/// by inserting versions and phi nodes.
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template <typename Phi, typename G>
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void addSSA(G* dfg, ControlFlowGraph<G>* cfg) {
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CAFFE2_API void addSSA(G* dfg, ControlFlowGraph<G>* cfg) {
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static_assert(
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std::is_base_of<Instruction, Phi>::value,
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"Phi type must be derived from Instruction.");
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@ -12,6 +12,7 @@
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#ifndef NOM_REPRESENTATIONS_NEURALNET_H
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#define NOM_REPRESENTATIONS_NEURALNET_H
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#include "caffe2/core/common.h"
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#include "nomnigraph/Graph/Graph.h"
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#include "nomnigraph/Representations/Compiler.h"
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#include "nomnigraph/Representations/ControlFlow.h"
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@ -40,7 +41,7 @@ class NeuralNetData;
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/// a saved void* pointer for external use. Derived classes
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/// add richer semantics to the annotation and it is encouraged
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/// to use them.
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class Annotation {
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class CAFFE2_API Annotation {
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public:
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enum class AnnotationKind { Generic, Caffe2 };
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@ -59,7 +60,7 @@ class Annotation {
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const AnnotationKind Kind;
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};
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class NeuralNetOperator : public Instruction {
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class CAFFE2_API NeuralNetOperator : public Instruction {
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public:
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/// Discriminator for LLVM-style RTTI (isa<>)
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enum class NNKind {
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@ -132,7 +133,7 @@ class NeuralNetOperator : public Instruction {
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std::unique_ptr<Annotation> ExtraAnnotation;
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};
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class NeuralNetData : public Data {
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class CAFFE2_API NeuralNetData : public Data {
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public:
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/// Discriminator for LLVM-style RTTI (isa<>)
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enum class NNDataKind { Generic, Tensor };
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@ -156,7 +157,7 @@ class NeuralNetData : public Data {
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size_t Version = 0;
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};
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class Tensor : public NeuralNetData {
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class CAFFE2_API Tensor : public NeuralNetData {
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public:
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enum class DataType { Generic, Float, Half, Int8 };
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enum class Layout { Generic, NCHW, NHWC };
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@ -165,7 +166,7 @@ class Tensor : public NeuralNetData {
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: NeuralNetData(NNDataKind::Tensor),
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name_(name),
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type_(DataType::Generic) {}
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static bool classof(const NeuralNetData* D) {
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CAFFE2_API static bool classof(const NeuralNetData* D) {
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return D->getKind() == NNDataKind::Tensor;
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}
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@ -192,10 +193,10 @@ class Tensor : public NeuralNetData {
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};
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#define NOMNIGRAPH_DEFINE_NN_RTTI(op) \
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static bool classof(const NeuralNetOperator* N) { \
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CAFFE2_API static bool classof(const NeuralNetOperator* N) { \
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return N->getKind() == NNKind::op; \
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} \
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static bool classof(const Value* N) { \
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CAFFE2_API static bool classof(const Value* N) { \
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if (isa<NeuralNetOperator>(N)) { \
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return dyn_cast<NeuralNetOperator>(N)->getKind() == NNKind::op; \
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} \
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|
|
@ -204,21 +205,21 @@ class Tensor : public NeuralNetData {
|
|||
|
||||
#include "nomnigraph/Generated/OpClasses.h"
|
||||
|
||||
class While : public NeuralNetOperator {
|
||||
class CAFFE2_API While : public NeuralNetOperator {
|
||||
public:
|
||||
While() : NeuralNetOperator(NNKind::While, Opcode::Branch) {}
|
||||
NOMNIGRAPH_DEFINE_NN_RTTI(While);
|
||||
~While() {}
|
||||
};
|
||||
|
||||
class NNPhi : public NeuralNetOperator {
|
||||
class CAFFE2_API NNPhi : public NeuralNetOperator {
|
||||
public:
|
||||
NNPhi() : NeuralNetOperator(NNKind::NNPhi, Opcode::Phi) {}
|
||||
NOMNIGRAPH_DEFINE_NN_RTTI(NNPhi);
|
||||
~NNPhi() {}
|
||||
};
|
||||
|
||||
class GenericOperator : public NeuralNetOperator {
|
||||
class CAFFE2_API GenericOperator : public NeuralNetOperator {
|
||||
public:
|
||||
GenericOperator() : NeuralNetOperator(NNKind::GenericOperator) {}
|
||||
GenericOperator(std::string name)
|
||||
|
|
@ -240,7 +241,7 @@ using NNGraph = nom::Graph<std::unique_ptr<nom::repr::Value>>;
|
|||
using NNSubgraph = nom::Subgraph<std::unique_ptr<nom::repr::Value>>;
|
||||
using NNCFGraph = nom::repr::ControlFlowGraph<NNGraph>;
|
||||
|
||||
struct NNModule {
|
||||
struct CAFFE2_API NNModule {
|
||||
NNGraph dataFlow;
|
||||
NNCFGraph controlFlow;
|
||||
std::unordered_set<NNGraph::NodeRef> inputs;
|
||||
|
|
@ -259,7 +260,7 @@ template <bool B, class T = void>
|
|||
using enable_if_t = typename std::enable_if<B, T>::type;
|
||||
|
||||
template <typename T, typename U>
|
||||
struct inheritedFrom {
|
||||
struct CAFFE2_API inheritedFrom {
|
||||
static constexpr bool value =
|
||||
std::is_base_of<U, T>::value && !std::is_same<U, T>::value;
|
||||
};
|
||||
|
|
@ -267,14 +268,14 @@ struct inheritedFrom {
|
|||
// This is just a way to fix issues when the isa<> implementation
|
||||
// can't automatically downcast.
|
||||
template <typename T, typename N, typename = void>
|
||||
struct is_impl {
|
||||
struct CAFFE2_API is_impl {
|
||||
inline static bool impl(N n) {
|
||||
return isa<T>(n->data());
|
||||
}
|
||||
};
|
||||
|
||||
template <typename T, typename N>
|
||||
struct is_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetOperator>::value>> {
|
||||
struct CAFFE2_API is_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetOperator>::value>> {
|
||||
inline static bool impl(N n) {
|
||||
if (!isa<NeuralNetOperator>(n->data().get())) {
|
||||
return false;
|
||||
|
|
@ -285,7 +286,7 @@ struct is_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetOperator>::value>> {
|
|||
};
|
||||
|
||||
template <typename T, typename N>
|
||||
struct is_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetData>::value>> {
|
||||
struct CAFFE2_API is_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetData>::value>> {
|
||||
inline static bool impl(N n) {
|
||||
if (!isa<NeuralNetData>(n->data().get())) {
|
||||
return false;
|
||||
|
|
@ -303,14 +304,14 @@ inline bool is(N n) {
|
|||
// This is just a way to fix issues when the dyn_cast<> implementation
|
||||
// can't automatically downcast.
|
||||
template <typename T, typename N, typename = void>
|
||||
struct get_impl {
|
||||
struct CAFFE2_API get_impl {
|
||||
inline static T* impl(N n) {
|
||||
return dyn_cast<T>(n->data().get());
|
||||
}
|
||||
};
|
||||
|
||||
template <typename T, typename N>
|
||||
struct get_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetOperator>::value>> {
|
||||
struct CAFFE2_API get_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetOperator>::value>> {
|
||||
inline static T* impl(N n) {
|
||||
if (!is<T>(n)) {
|
||||
assert(0 && "Cannot get type from node");
|
||||
|
|
@ -322,7 +323,7 @@ struct get_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetOperator>::value>> {
|
|||
};
|
||||
|
||||
template <typename T, typename N>
|
||||
struct get_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetData>::value>> {
|
||||
struct CAFFE2_API get_impl<T, N, enable_if_t<inheritedFrom<T, NeuralNetData>::value>> {
|
||||
inline static T* impl(N n) {
|
||||
if (!is<T>(n)) {
|
||||
assert(0 && "Cannot get type from node");
|
||||
|
|
@ -339,7 +340,7 @@ inline T* get(N n) {
|
|||
}
|
||||
|
||||
template <typename T, typename G>
|
||||
std::vector<typename G::NodeRef> nodeIterator(G& g) {
|
||||
CAFFE2_API std::vector<typename G::NodeRef> nodeIterator(G& g) {
|
||||
std::vector<typename G::NodeRef> out;
|
||||
for (auto node : g.getMutableNodes()) {
|
||||
if (!is<T>(node)) {
|
||||
|
|
@ -351,7 +352,7 @@ std::vector<typename G::NodeRef> nodeIterator(G& g) {
|
|||
}
|
||||
|
||||
template <typename T, typename G>
|
||||
std::vector<std::pair<T*, typename G::NodeRef>> dataIterator(G& g) {
|
||||
CAFFE2_API std::vector<std::pair<T*, typename G::NodeRef>> dataIterator(G& g) {
|
||||
std::vector<std::pair<T*, typename G::NodeRef>> out;
|
||||
for (auto node : g.getMutableNodes()) {
|
||||
if (!is<T>(node)) {
|
||||
|
|
@ -364,7 +365,7 @@ std::vector<std::pair<T*, typename G::NodeRef>> dataIterator(G& g) {
|
|||
}
|
||||
|
||||
template <typename T, typename... Args>
|
||||
void insertOp(
|
||||
CAFFE2_API void insertOp(
|
||||
NNGraph& g,
|
||||
NNGraph::NodeRef a,
|
||||
NNGraph::NodeRef b,
|
||||
|
|
@ -394,7 +395,7 @@ void insertOp(
|
|||
}
|
||||
|
||||
template <typename NewT, typename OldT>
|
||||
NNGraph::NodeRef convertNode(NNGraph& g, NNGraph::NodeRef node) {
|
||||
CAFFE2_API NNGraph::NodeRef convertNode(NNGraph& g, NNGraph::NodeRef node) {
|
||||
assert(is<OldT>(node) && "Cannot get type from node.");
|
||||
|
||||
NeuralNetOperator* nnOpPtr =
|
||||
|
|
@ -410,21 +411,21 @@ NNGraph::NodeRef convertNode(NNGraph& g, NNGraph::NodeRef node) {
|
|||
}
|
||||
|
||||
/// NeuralNetData specific helpers.
|
||||
bool hasProducer(NNGraph::NodeRef n);
|
||||
NNGraph::NodeRef getProducer(NNGraph::NodeRef n);
|
||||
bool hasConsumer(NNGraph::NodeRef n);
|
||||
std::vector<NNGraph::NodeRef> getConsumers(NNGraph::NodeRef n);
|
||||
CAFFE2_API bool hasProducer(NNGraph::NodeRef n);
|
||||
CAFFE2_API NNGraph::NodeRef getProducer(NNGraph::NodeRef n);
|
||||
CAFFE2_API bool hasConsumer(NNGraph::NodeRef n);
|
||||
CAFFE2_API std::vector<NNGraph::NodeRef> getConsumers(NNGraph::NodeRef n);
|
||||
|
||||
bool hasInputs(NNGraph::NodeRef n);
|
||||
std::vector<NNGraph::NodeRef> getInputs(NNGraph::NodeRef n);
|
||||
std::vector<NNGraph::NodeRef> getOutputs(NNGraph::NodeRef n);
|
||||
CAFFE2_API bool hasInputs(NNGraph::NodeRef n);
|
||||
CAFFE2_API std::vector<NNGraph::NodeRef> getInputs(NNGraph::NodeRef n);
|
||||
CAFFE2_API std::vector<NNGraph::NodeRef> getOutputs(NNGraph::NodeRef n);
|
||||
|
||||
void coalesceInsertedDataDependencies(repr::NNModule* m);
|
||||
CAFFE2_API void coalesceInsertedDataDependencies(repr::NNModule* m);
|
||||
|
||||
template <NNGraph* G>
|
||||
struct NodeHelper {};
|
||||
struct CAFFE2_API NodeHelper {};
|
||||
|
||||
struct NNNodeMatchCriteria {
|
||||
struct CAFFE2_API NNNodeMatchCriteria {
|
||||
const std::function<bool(NNGraph::NodeRef)> predicate;
|
||||
const std::string debugString;
|
||||
|
||||
|
|
@ -454,22 +455,22 @@ using NNMatchNode = nom::matcher::MatchNode<NNNodeMatchCriteria>;
|
|||
// Commonly used criteria.
|
||||
|
||||
// The node has a single output and the output has a single consumer.
|
||||
NNNodeMatchCriteria criteriaSingleOutputAndConsumer();
|
||||
CAFFE2_API NNNodeMatchCriteria criteriaSingleOutputAndConsumer();
|
||||
// The node has a unique consumer (there may be multiple edges from output
|
||||
// to the single consumer).
|
||||
NNNodeMatchCriteria criteriaSingleConsumer();
|
||||
CAFFE2_API NNNodeMatchCriteria criteriaSingleConsumer();
|
||||
|
||||
template <typename NodeType>
|
||||
NNNodeMatchCriteria matchOp(const std::string& debugString = "matchOp") {
|
||||
CAFFE2_API NNNodeMatchCriteria matchOp(const std::string& debugString = "matchOp") {
|
||||
return NNNodeMatchCriteria(
|
||||
[](NNGraph::NodeRef nodeRef) { return is<NodeType>(nodeRef); },
|
||||
debugString);
|
||||
}
|
||||
|
||||
NNNodeMatchCriteria matchTensor();
|
||||
CAFFE2_API NNNodeMatchCriteria matchTensor();
|
||||
|
||||
template <typename NodeType>
|
||||
NNNodeMatchCriteria matchOp(
|
||||
CAFFE2_API NNNodeMatchCriteria matchOp(
|
||||
const std::function<bool(const NodeType&)> predicate,
|
||||
const std::string& debugString = "matchOpWithPredicate") {
|
||||
return NNNodeMatchCriteria(
|
||||
|
|
@ -481,8 +482,8 @@ NNNodeMatchCriteria matchOp(
|
|||
debugString);
|
||||
};
|
||||
|
||||
struct NNNodeMatch {
|
||||
static bool isMatch(
|
||||
struct CAFFE2_API NNNodeMatch {
|
||||
CAFFE2_API static bool isMatch(
|
||||
const NNGraph::NodeRef& node,
|
||||
const NNNodeMatchCriteria& criteria) {
|
||||
return criteria.predicate(node);
|
||||
|
|
@ -495,7 +496,7 @@ using NNSubgraphMatcher =
|
|||
// This helper method makes it easy to create matching criteria in NNGraph.
|
||||
// For example, operatorSubgraph(opMatch, ...) will refer to a tree like this:
|
||||
// ... -> opMatch -> opMatch_Output
|
||||
NNMatchGraph::NodeRef operatorSubgraph(
|
||||
CAFFE2_API NNMatchGraph::NodeRef operatorSubgraph(
|
||||
NNMatchGraph& g,
|
||||
const NNNodeMatchCriteria& root,
|
||||
const std::vector<NNMatchGraph::NodeRef>& childrenCriteria = {},
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ using DataPtr = std::shared_ptr<void>;
|
|||
class StorageImpl;
|
||||
using Storage = std::shared_ptr<StorageImpl>;
|
||||
|
||||
class StorageImpl {
|
||||
class CAFFE2_API StorageImpl {
|
||||
public:
|
||||
StorageImpl() = delete;
|
||||
StorageImpl(const StorageImpl&) = delete;
|
||||
|
|
|
|||
|
|
@ -882,7 +882,7 @@ class CAFFE2_API TensorImpl : public c10::intrusive_ptr_target {
|
|||
}
|
||||
};
|
||||
|
||||
class UndefinedTensorImpl final : public TensorImpl {
|
||||
class CAFFE2_API UndefinedTensorImpl final : public TensorImpl {
|
||||
UndefinedTensorImpl() : TensorImpl(CPU){};
|
||||
|
||||
public:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
#pragma once
|
||||
|
||||
#include "caffe2/core/common.h"
|
||||
#include "onnx/onnx_pb.h"
|
||||
|
||||
#include <set>
|
||||
|
|
@ -13,7 +14,7 @@ using ::ONNX_NAMESPACE::AttributeProto;
|
|||
using ::ONNX_NAMESPACE::NodeProto;
|
||||
|
||||
// \brief This class generates unique dummy names
|
||||
class DummyName {
|
||||
class CAFFE2_API DummyName {
|
||||
public:
|
||||
std::string NewDummyName();
|
||||
|
||||
|
|
@ -70,7 +71,7 @@ inline AttributeProto MakeAttribute(
|
|||
return attr;
|
||||
}
|
||||
|
||||
NodeProto MakeNode(
|
||||
CAFFE2_API NodeProto MakeNode(
|
||||
const std::string& type,
|
||||
const std::vector<std::string>& inputs,
|
||||
const std::vector<std::string>& outputs,
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ public:
|
|||
return &OpDef;
|
||||
}
|
||||
|
||||
static bool classof(const Annotation *A) {
|
||||
CAFFE2_API static bool classof(const Annotation *A) {
|
||||
return A->getKind() == AnnotationKind::Caffe2;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ namespace opt {
|
|||
|
||||
using namespace nom;
|
||||
|
||||
void sinkMaxPool(nom::repr::NNModule* nn) {
|
||||
CAFFE2_EXPORT void sinkMaxPool(nom::repr::NNModule* nn) {
|
||||
for (auto max_pool_node :
|
||||
repr::nn::nodeIterator<repr::MaxPool>(nn->dataFlow)) {
|
||||
if (repr::nn::getInputs(max_pool_node).size() != 1) {
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ using PredictorParameters = std::map<std::string, std::shared_ptr<Blob>>;
|
|||
/**
|
||||
* Stores parameters nessasary for creating a PredictorInterface object.
|
||||
*/
|
||||
struct PredictorConfig {
|
||||
struct CAFFE2_API PredictorConfig {
|
||||
// A map of parameter name to Tensor object. Predictor is supposed to
|
||||
// guarantee constness of all these Tensor objects.
|
||||
std::shared_ptr<PredictorParameters> parameters;
|
||||
|
|
@ -40,14 +40,14 @@ struct PredictorConfig {
|
|||
std::shared_ptr<Workspace> ws;
|
||||
};
|
||||
|
||||
Workspace makeWorkspace(std::shared_ptr<PredictorParameters> parameters);
|
||||
CAFFE2_API Workspace makeWorkspace(std::shared_ptr<PredictorParameters> parameters);
|
||||
|
||||
PredictorConfig makePredictorConfig(
|
||||
CAFFE2_API PredictorConfig makePredictorConfig(
|
||||
const MetaNetDef& net,
|
||||
Workspace* parent = nullptr,
|
||||
bool run_init = true);
|
||||
|
||||
PredictorConfig makePredictorConfig(
|
||||
CAFFE2_API PredictorConfig makePredictorConfig(
|
||||
const NetDef& init_net,
|
||||
const NetDef& run_net,
|
||||
Workspace* parent = nullptr,
|
||||
|
|
|
|||
|
|
@ -253,8 +253,8 @@ struct TORCH_API Variable : public at::Tensor {
|
|||
// Variable::Impl
|
||||
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
struct Variable::Impl : public at::TensorImpl {
|
||||
TORCH_API explicit Impl(
|
||||
struct TORCH_API Variable::Impl : public at::TensorImpl {
|
||||
explicit Impl(
|
||||
at::Tensor data,
|
||||
bool requires_grad = false,
|
||||
Edge gradient_edge = Edge());
|
||||
|
|
@ -367,7 +367,7 @@ struct Variable::Impl : public at::TensorImpl {
|
|||
/// same version_counter. The grad_fn field of the Variable may become stale
|
||||
/// due to in-place modifications of the shared data. Accesses should go
|
||||
/// through get_grad_fn(). All other fields are always valid.
|
||||
struct Variable::ViewImpl : public Variable::Impl {
|
||||
struct TORCH_API Variable::ViewImpl : public Variable::Impl {
|
||||
ViewImpl(Variable base, at::Tensor data, Edge gradient_edge);
|
||||
|
||||
/// Gets the up-to-date grad_fn. If the shared data or base was modified, we
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ template <typename T>
|
|||
using Shared = c10::intrusive_ptr<T>;
|
||||
|
||||
// string
|
||||
struct ConstantString : c10::intrusive_ptr_target {
|
||||
struct TORCH_API ConstantString : c10::intrusive_ptr_target {
|
||||
private:
|
||||
const std::string str_;
|
||||
public:
|
||||
|
|
@ -34,7 +34,7 @@ struct ConstantString : c10::intrusive_ptr_target {
|
|||
|
||||
// non-mutable list
|
||||
template<typename Elem>
|
||||
struct ConstantList : c10::intrusive_ptr_target {
|
||||
struct TORCH_API ConstantList : c10::intrusive_ptr_target {
|
||||
private:
|
||||
std::vector<Elem> elements_;
|
||||
public:
|
||||
|
|
@ -67,7 +67,7 @@ using DoubleList = ConstantList<double>;
|
|||
#define TORCH_FORALL_TAGS(_) \
|
||||
_(None) _(Tensor) _(Double) _(Int) _(Tuple) _(IntList) _(DoubleList) _(String) _(TensorList)
|
||||
|
||||
struct IValue {
|
||||
struct TORCH_API IValue {
|
||||
IValue()
|
||||
: payload(0)
|
||||
, tag(Tag::None)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user