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common_gpu.h
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#ifndef CAFFE2_CORE_COMMON_GPU_H_
#define CAFFE2_CORE_COMMON_GPU_H_
#include <assert.h>
#include <cuda.h>
#include <cuda_runtime.h>
// Disable strict aliasing errors for CUDA 9.
// The cuda_fp16.h header in CUDA 9 RC triggers this diagnostic.
// It is included by cusparse.h as well, so guarding the
// inclusion of that header here is not enough.
#if CUDA_VERSION >= 9000
#ifdef __GNUC__
#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)
#pragma GCC diagnostic push
#endif
#pragma GCC diagnostic ignored "-Wstrict-aliasing"
#endif // __GNUC__
#endif // CUDA_VERSION >= 9000
#include <cublas_v2.h>
#include <curand.h>
#include <driver_types.h>
#include "caffe2/core/common.h"
#include "caffe2/core/logging.h"
#include "c10/cuda/CUDAMathCompat.h"
// Defines CAFFE2_CUDA_EXPORT and CAFFE2_CUDA_IMPORT. On Windows, this
// corresponds to different declarations (dllexport and dllimport). On
// Linux/Mac, it just resolves to the same "default visibility" setting.
#if defined(_MSC_VER)
#if defined(CAFFE2_BUILD_SHARED_LIBS)
#define CAFFE2_CUDA_EXPORT __declspec(dllexport)
#define CAFFE2_CUDA_IMPORT __declspec(dllimport)
#else
#define CAFFE2_CUDA_EXPORT
#define CAFFE2_CUDA_IMPORT
#endif
#else
#if defined(__GNUC__)
#define CAFFE2_CUDA_EXPORT __attribute__((__visibility__("default")))
#else
#define CAFFE2_CUDA_EXPORT
#endif
#define CAFFE2_CUDA_IMPORT CAFFE2_CUDA_EXPORT
#endif
// CAFFE2_CUDA_API is a macro that, depends on whether you are building the
// main caffe2 library or not, resolves to either CAFFE2_CUDA_EXPORT or
// CAFFE2_CUDA_IMPORT.
//
// This is used in e.g. Caffe2's protobuf files: when building the main library,
// it is defined as CAFFE2_CUDA_EXPORT to fix a Windows global-variable-in-dll
// issue, and for anyone dependent on Caffe2 it will be defined as
// CAFFE2_CUDA_IMPORT.
#ifdef CAFFE2_CUDA_BUILD_MAIN_LIB
#define CAFFE2_CUDA_API CAFFE2_CUDA_EXPORT
#else
#define CAFFE2_CUDA_API CAFFE2_CUDA_IMPORT
#endif
// This is a macro defined for cuda fp16 support. In default, cuda fp16 is
// supported by NVCC 7.5, but it is also included in the Tegra X1 platform with
// a (custom?) NVCC 7.0. As a result, we would normally just check the cuda
// version here, but would also allow a use to pass in the flag
// CAFFE_HAS_CUDA_FP16 manually.
#ifndef CAFFE_HAS_CUDA_FP16
#if CUDA_VERSION >= 7050
#define CAFFE_HAS_CUDA_FP16
#endif // CUDA_VERSION >= 7050
#endif // CAFFE_HAS_CUDA_FP16
#ifdef CAFFE_HAS_CUDA_FP16
#include <cuda_fp16.h>
#endif
// Re-enable strict aliasing diagnostic if it was disabled.
#if CUDA_VERSION >= 9000
#ifdef __GNUC__
#if __GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)
#pragma GCC diagnostic pop
#endif
#endif // __GNUC__
#endif // CUDA_VERSION >= 9000
/**
* The maximum number of GPUs that caffe2 recognizes.
*/
#define CAFFE2_COMPILE_TIME_MAX_GPUS 16
/**
* The maximum number of peers that each gpu can have when doing p2p setup.
* Currently, according to NVidia documentation, each device can support a
* system-wide maximum of eight peer connections.
* When Caffe2 sets up peer access resources, if we have more than 8 gpus,
* we will enable peer access in groups of 8.
*/
#define CAFFE2_CUDA_MAX_PEER_SIZE 8
namespace caffe2 {
#if CUDA_VERSION >= 9000
/**
* Empty class to identify TensorCore-based math
*/
class TensorCoreEngine {};
#endif
#if CUDA_VERSION >= 10000
#define CAFFE2_CUDA_PTRATTR_MEMTYPE type
#else
#define CAFFE2_CUDA_PTRATTR_MEMTYPE memoryType
#endif
/**
* A runtime function to report the cuda version that Caffe2 is built with.
*/
inline int CudaVersion() {
return CUDA_VERSION;
}
/**
* Returns the number of devices.
*/
CAFFE2_CUDA_API int NumCudaDevices();
/**
* Check if the current running session has a cuda gpu present.
*
* Note that this is different from having caffe2 built with cuda. Building
* Caffe2 with cuda only guarantees that this function exists. If there are no
* cuda gpus present in the machine, or there are hardware configuration
* problems like an insufficient driver, this function will still return false,
* meaning that there is no usable GPU present.
*
* In the open source build, it is possible that Caffe2's GPU code is
* dynamically loaded, and as a result a library could be only linked to the
* CPU code, but want to test if cuda is later available or not. In this case,
* one should use HasCudaRuntime() from common.h.
*/
inline bool HasCudaGPU() {
return NumCudaDevices() > 0;
}
/**
* Gets the current GPU id. This is a simple wrapper around cudaGetDevice().
*/
CAFFE2_CUDA_API int CaffeCudaGetDevice();
/**
* Gets the current GPU id. This is a simple wrapper around cudaGetDevice().
*/
CAFFE2_CUDA_API void CaffeCudaSetDevice(const int id);
/**
* Gets the GPU id that the current pointer is located at.
*/
CAFFE2_CUDA_API int GetGPUIDForPointer(const void* ptr);
/**
* Gets the device property for the given device. This function is thread safe.
*/
CAFFE2_CUDA_API const cudaDeviceProp& GetDeviceProperty(const int device);
/**
* Runs a device query function and prints out the results to LOG(INFO).
*/
CAFFE2_CUDA_API void DeviceQuery(const int deviceid);
/**
* Return a peer access pattern by returning a matrix (in the format of a
* nested vector) of boolean values specifying whether peer access is possible.
*
* This function returns false if anything wrong happens during the query of
* the GPU access pattern.
*/
CAFFE2_CUDA_API bool GetCudaPeerAccessPattern(vector<vector<bool>>* pattern);
/**
* Return the availability of TensorCores for math
*/
CAFFE2_CUDA_API bool TensorCoreAvailable();
/**
* Return a human readable cublas error string.
*/
CAFFE2_CUDA_API const char* cublasGetErrorString(cublasStatus_t error);
/**
* Return a human readable curand error string.
*/
CAFFE2_CUDA_API const char* curandGetErrorString(curandStatus_t error);
// CUDA: various checks for different function calls.
#define CUDA_ENFORCE(condition, ...) \
do { \
cudaError_t error = condition; \
CAFFE_ENFORCE_EQ( \
error, \
cudaSuccess, \
"Error at: ", \
__FILE__, \
":", \
__LINE__, \
": ", \
cudaGetErrorString(error), \
##__VA_ARGS__); \
} while (0)
#define CUDA_CHECK(condition) \
do { \
cudaError_t error = condition; \
CHECK(error == cudaSuccess) << cudaGetErrorString(error); \
} while (0)
#define CUDA_DRIVERAPI_ENFORCE(condition) \
do { \
CUresult result = condition; \
if (result != CUDA_SUCCESS) { \
const char* msg; \
cuGetErrorName(result, &msg); \
CAFFE_THROW("Error at: ", __FILE__, ":", __LINE__, ": ", msg); \
} \
} while (0)
#define CUDA_DRIVERAPI_CHECK(condition) \
do { \
CUresult result = condition; \
if (result != CUDA_SUCCESS) { \
const char* msg; \
cuGetErrorName(result, &msg); \
LOG(FATAL) << "Error at: " << __FILE__ << ":" << __LINE__ << ": " \
<< msg; \
} \
} while (0)
#define CUBLAS_ENFORCE(condition) \
do { \
cublasStatus_t status = condition; \
CAFFE_ENFORCE_EQ( \
status, \
CUBLAS_STATUS_SUCCESS, \
"Error at: ", \
__FILE__, \
":", \
__LINE__, \
": ", \
::caffe2::cublasGetErrorString(status)); \
} while (0)
#define CUBLAS_CHECK(condition) \
do { \
cublasStatus_t status = condition; \
CHECK(status == CUBLAS_STATUS_SUCCESS) \
<< ::caffe2::cublasGetErrorString(status); \
} while (0)
#define CURAND_ENFORCE(condition) \
do { \
curandStatus_t status = condition; \
CAFFE_ENFORCE_EQ( \
status, \
CURAND_STATUS_SUCCESS, \
"Error at: ", \
__FILE__, \
":", \
__LINE__, \
": ", \
::caffe2::curandGetErrorString(status)); \
} while (0)
#define CURAND_CHECK(condition) \
do { \
curandStatus_t status = condition; \
CHECK(status == CURAND_STATUS_SUCCESS) \
<< ::caffe2::curandGetErrorString(status); \
} while (0)
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
// CUDA_KERNEL_ASSERT is a macro that wraps an assert() call inside cuda
// kernels. This is not supported by Apple platforms so we special case it.
// See http://docs.nvidia.com/cuda/cuda-c-programming-guide/#assertion
#if defined(__APPLE__) || defined(__HIP_PLATFORM_HCC__)
#define CUDA_KERNEL_ASSERT(...)
#else // __APPLE__
#define CUDA_KERNEL_ASSERT(...) assert(__VA_ARGS__)
#endif // __APPLE__
// The following helper functions are here so that you can write a kernel call
// when you are not particularly interested in maxing out the kernels'
// performance. Usually, this will give you a reasonable speed, but if you
// really want to find the best performance, it is advised that you tune the
// size of the blocks and grids more reasonably.
// A legacy note: this is derived from the old good Caffe days, when I simply
// hard-coded the number of threads and wanted to keep backward compatibility
// for different computation capabilities.
// For more info on CUDA compute capabilities, visit the NVidia website at:
// http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities
// The number of cuda threads to use. Since work is assigned to SMs at the
// granularity of a block, 128 is chosen to allow utilizing more SMs for
// smaller input sizes.
constexpr int CAFFE_CUDA_NUM_THREADS = 128;
// The maximum number of blocks to use in the default kernel call. We set it to
// 4096 which would work for compute capability 2.x (where 65536 is the limit).
// This number is very carelessly chosen. Ideally, one would like to look at
// the hardware at runtime, and pick the number of blocks that makes most
// sense for the specific runtime environment. This is a todo item.
constexpr int CAFFE_MAXIMUM_NUM_BLOCKS = 4096;
constexpr int kCUDAGridDimMaxX = 2147483647;
constexpr int kCUDAGridDimMaxY = 65535;
constexpr int kCUDAGridDimMaxZ = 65535;
/**
* @brief Compute the number of blocks needed to run N threads.
*/
inline int CAFFE_GET_BLOCKS(const int N) {
return std::max(
std::min(
(N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS,
CAFFE_MAXIMUM_NUM_BLOCKS),
// Use at least 1 block, since CUDA does not allow empty block
1);
}
class DeviceGuard {
public:
explicit DeviceGuard(int newDevice) : previous_(CaffeCudaGetDevice()) {
if (previous_ != newDevice) {
CaffeCudaSetDevice(newDevice);
}
}
~DeviceGuard() noexcept {
CaffeCudaSetDevice(previous_);
}
private:
int previous_;
};
template <typename T, int N>
struct SimpleArray {
T data[N];
};
constexpr int kCUDATensorMaxDims = 8;
#define DISPATCH_FUNCTION_BY_VALUE_WITH_TYPE_1(val, Func, T, ...) \
do { \
CAFFE_ENFORCE_LE(val, kCUDATensorMaxDims); \
switch (val) { \
case 1: { \
Func<T, 1>(__VA_ARGS__); \
break; \
} \
case 2: { \
Func<T, 2>(__VA_ARGS__); \
break; \
} \
case 3: { \
Func<T, 3>(__VA_ARGS__); \
break; \
} \
case 4: { \
Func<T, 4>(__VA_ARGS__); \
break; \
} \
case 5: { \
Func<T, 5>(__VA_ARGS__); \
break; \
} \
case 6: { \
Func<T, 6>(__VA_ARGS__); \
break; \
} \
case 7: { \
Func<T, 7>(__VA_ARGS__); \
break; \
} \
case 8: { \
Func<T, 8>(__VA_ARGS__); \
break; \
} \
default: { \
break; \
} \
} \
} while (false)
#define DISPATCH_FUNCTION_BY_VALUE_WITH_TYPE_2(val, Func, T1, T2, ...) \
do { \
CAFFE_ENFORCE_LE(val, kCUDATensorMaxDims); \
switch (val) { \
case 1: { \
Func<T1, T2, 1>(__VA_ARGS__); \
break; \
} \
case 2: { \
Func<T1, T2, 2>(__VA_ARGS__); \
break; \
} \
case 3: { \
Func<T1, T2, 3>(__VA_ARGS__); \
break; \
} \
case 4: { \
Func<T1, T2, 4>(__VA_ARGS__); \
break; \
} \
case 5: { \
Func<T1, T2, 5>(__VA_ARGS__); \
break; \
} \
case 6: { \
Func<T1, T2, 6>(__VA_ARGS__); \
break; \
} \
case 7: { \
Func<T1, T2, 7>(__VA_ARGS__); \
break; \
} \
case 8: { \
Func<T1, T2, 8>(__VA_ARGS__); \
break; \
} \
default: { \
break; \
} \
} \
} while (false)
#define DISPATCH_FUNCTION_BY_VALUE_WITH_TYPE_3(val, Func, T1, T2, T3, ...) \
do { \
CAFFE_ENFORCE_LE(val, kCUDATensorMaxDims); \
switch (val) { \
case 1: { \
Func<T1, T2, T3, 1>(__VA_ARGS__); \
break; \
} \
case 2: { \
Func<T1, T2, T3, 2>(__VA_ARGS__); \
break; \
} \
case 3: { \
Func<T1, T2, T3, 3>(__VA_ARGS__); \
break; \
} \
case 4: { \
Func<T1, T2, T3, 4>(__VA_ARGS__); \
break; \
} \
case 5: { \
Func<T1, T2, T3, 5>(__VA_ARGS__); \
break; \
} \
case 6: { \
Func<T1, T2, T3, 6>(__VA_ARGS__); \
break; \
} \
case 7: { \
Func<T1, T2, T3, 7>(__VA_ARGS__); \
break; \
} \
case 8: { \
Func<T1, T2, T3, 8>(__VA_ARGS__); \
break; \
} \
default: { \
break; \
} \
} \
} while (false)
} // namespace caffe2
#endif // CAFFE2_CORE_COMMON_GPU_H_