功能:
Caffe的底層數據的切換(cpu模式和gpu模式),需要用到內存同步模塊。
源碼:頭文件
#ifndef CAFFE_SYNCEDMEM_HPP_
#define CAFFE_SYNCEDMEM_HPP_
#include <cstdlib>
#include "caffe/common.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
inline void CaffeMallocHost(void** ptr, size_t size) {
*ptr = malloc(size);
CHECK(*ptr) << "host allocation of size " << size << " failed";
}
inline void CaffeFreeHost(void* ptr) {
free(ptr);
}
/**
* @brief Manages memory allocation and synchronization between the host (CPU)
* and device (GPU).
*
* TODO(dox): more thorough description.
*/
class SyncedMemory {
public:
SyncedMemory()
: cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
own_cpu_data_(false) {}
explicit SyncedMemory(size_t size)
: cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
own_cpu_data_(false) {}
~SyncedMemory();
const void* cpu_data();//獲取cpu數據,返回void * 指針
void set_cpu_data(void* data);//用一個void * 指針修改指針
const void* gpu_data();//獲取gpu數據,返回void * 指針
void* mutable_cpu_data();//獲取可以更改cpu數據,返回void * 指針
void* mutable_gpu_data();//獲取可以更改gpu數據,返回void * 指針
enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };//enum枚舉值
SyncedHead head() { return head_; }//獲得枚舉值
size_t size() { return size_; }//獲得數據大小
private:
void to_cpu();//轉爲cpu模式
void to_gpu(); //轉爲gpu模式
void* cpu_ptr_;//指向cpu的指針
void* gpu_ptr_;//指向gpu的指指針
size_t size_; //大小
SyncedHead head_; //數據存放的位置,枚舉值之一
bool own_cpu_data_;//是否有cpu數據
DISABLE_COPY_AND_ASSIGN(SyncedMemory);
}; // class SyncedMemory
} // namespace caffe
#endif // CAFFE_SYNCEDMEM_HPP_
實現文件:
#include <cstring>
#include "caffe/common.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
//析構函數,調用caffe函數來釋放空間
SyncedMemory::~SyncedMemory() {
if (cpu_ptr_ && own_cpu_data_) {
CaffeFreeHost(cpu_ptr_);
}
//如果有gpu數據,也進行釋放。
#ifndef CPU_ONLY
if (gpu_ptr_) {
CUDA_CHECK(cudaFree(gpu_ptr_));
}
#endif // CPU_ONLY
}
// 根據head信息,選擇:1, 分類cpu空間,2.拷貝gpu數據值cpu
inline void SyncedMemory::to_cpu() {
switch (head_) {
case UNINITIALIZED:
CaffeMallocHost(&cpu_ptr_, size_);
caffe_memset(size_, 0, cpu_ptr_);
head_ = HEAD_AT_CPU;
own_cpu_data_ = true;
break;
case HEAD_AT_GPU:
#ifndef CPU_ONLY
if (cpu_ptr_ == NULL) {
CaffeMallocHost(&cpu_ptr_, size_);
own_cpu_data_ = true;
}
caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
head_ = SYNCED;
#else
NO_GPU;
#endif
break;
case HEAD_AT_CPU:
case SYNCED:
break;
}
}
// 根據head信息,選擇:1, 分類gpu空間,2.拷貝cpu數據值gpu
inline void SyncedMemory::to_gpu() {
#ifndef CPU_ONLY
switch (head_) {
case UNINITIALIZED:
CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
caffe_gpu_memset(size_, 0, gpu_ptr_);
head_ = HEAD_AT_GPU;
break;
case HEAD_AT_CPU:
if (gpu_ptr_ == NULL) {
CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
}
caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_);
head_ = SYNCED;
break;
case HEAD_AT_GPU:
case SYNCED:
break;
}
#else
NO_GPU;
#endif
}
//返回cpu指針 void * 類型
const void* SyncedMemory::cpu_data() {
to_cpu();
return (const void*)cpu_ptr_;
}
// 設置cpu數據,利用另外一個指針的數據來初始化
void SyncedMemory::set_cpu_data(void* data) {
CHECK(data);
if (own_cpu_data_) {
CaffeFreeHost(cpu_ptr_);
}
cpu_ptr_ = data;//直接重置指針,
head_ = HEAD_AT_CPU;
own_cpu_data_ = false;//設false
}
//獲得gpu指針
const void* SyncedMemory::gpu_data() {
#ifndef CPU_ONLY
to_gpu();
return (const void*)gpu_ptr_;
#else
NO_GPU;
#endif
}
//獲得可更改的cpu指針
void* SyncedMemory::mutable_cpu_data() {
to_cpu();
head_ = HEAD_AT_CPU;
return cpu_ptr_;
}
//獲得可更改的gpu指針
void* SyncedMemory::mutable_gpu_data() {
#ifndef CPU_ONLY
to_gpu();
head_ = HEAD_AT_GPU;
return gpu_ptr_;
#else
NO_GPU;
#endif
}
} // namespace caffe