blob: 92d30827314f8620251b0a407768b1b6ba4f78fb [file] [log] [blame]
#include <ATen/cudnn/Handle.h>
#include <ATen/cuda/detail/DeviceThreadHandles.h>
#include <c10/cuda/CUDAStream.h>
namespace at { namespace native {
namespace {
void createCuDNNHandle(cudnnHandle_t *handle) {
AT_CUDNN_CHECK(cudnnCreate(handle));
}
void destroyCuDNNHandle(cudnnHandle_t handle) {
// this is because of something dumb in the ordering of
// destruction. Sometimes atexit, the cuda context (or something)
// would already be destroyed by the time this gets destroyed. It
// happens in fbcode setting. @colesbury and I decided to not destroy
// the handle as a workaround.
// - @soumith
#ifdef NO_CUDNN_DESTROY_HANDLE
#else
cudnnDestroy(handle);
#endif
}
using CudnnPoolType = at::cuda::DeviceThreadHandlePool<cudnnHandle_t, createCuDNNHandle, destroyCuDNNHandle>;
} // namespace
cudnnHandle_t getCudnnHandle() {
int device;
AT_CUDA_CHECK(cudaGetDevice(&device));
// Thread local PoolWindows are lazily-initialized
// to avoid initialization issues that caused hangs on Windows.
// See: https://github.com/pytorch/pytorch/pull/22405
// This thread local unique_ptrs will be destroyed when the thread terminates,
// releasing its reserved handles back to the pool.
static auto pool = std::make_shared<CudnnPoolType>();
thread_local std::unique_ptr<CudnnPoolType::PoolWindow> myPoolWindow(
pool->newPoolWindow());
auto handle = myPoolWindow->reserve(device);
AT_CUDNN_CHECK(cudnnSetStream(handle, c10::cuda::getCurrentCUDAStream()));
return handle;
}
}} // namespace at::native