#pragma once
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#include <c10/core/Allocator.h>
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#include <ATen/core/Generator.h>
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#include <c10/util/Exception.h>
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#include <c10/util/Optional.h>
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#include <c10/util/Registry.h>
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#include <cstddef>
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#include <functional>
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#include <memory>
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// Forward-declares THCState
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struct THCState;
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// Forward-declares at::cuda::NVRTC
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namespace at { namespace cuda {
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struct NVRTC;
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}} // at::cuda
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namespace at {
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class Context;
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}
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// NB: Class must live in `at` due to limitations of Registry.h.
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namespace at {
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constexpr const char* CUDA_HELP =
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"PyTorch splits its backend into two shared libraries: a CPU library "
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"and a CUDA library; this error has occurred because you are trying "
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"to use some CUDA functionality, but the CUDA library has not been "
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"loaded by the dynamic linker for some reason. The CUDA library MUST "
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"be loaded, EVEN IF you don't directly use any symbols from the CUDA library! "
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"One common culprit is a lack of -Wl,--no-as-needed in your link arguments; many "
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"dynamic linkers will delete dynamic library dependencies if you don't "
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"depend on any of their symbols. You can check if this has occurred by "
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"using ldd on your binary to see if there is a dependency on *_cuda.so "
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"library.";
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// The CUDAHooksInterface is an omnibus interface for any CUDA functionality
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// which we may want to call into from CPU code (and thus must be dynamically
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// dispatched, to allow for separate compilation of CUDA code). How do I
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// decide if a function should live in this class? There are two tests:
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//
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// 1. Does the *implementation* of this function require linking against
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// CUDA libraries?
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//
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// 2. Is this function *called* from non-CUDA ATen code?
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//
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// (2) should filter out many ostensible use-cases, since many times a CUDA
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// function provided by ATen is only really ever used by actual CUDA code.
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//
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// TODO: Consider putting the stub definitions in another class, so that one
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// never forgets to implement each virtual function in the real implementation
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// in CUDAHooks. This probably doesn't buy us much though.
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struct CAFFE2_API CUDAHooksInterface {
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// This should never actually be implemented, but it is used to
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// squelch -Werror=non-virtual-dtor
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virtual ~CUDAHooksInterface() {}
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// Initialize THCState and, transitively, the CUDA state
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virtual std::unique_ptr<THCState, void (*)(THCState*)> initCUDA() const {
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TORCH_CHECK(false, "Cannot initialize CUDA without ATen_cuda library. ", CUDA_HELP);
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}
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virtual Generator* getDefaultCUDAGenerator(DeviceIndex device_index = -1) const {
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TORCH_CHECK(false, "Cannot get default CUDA generator without ATen_cuda library. ", CUDA_HELP);
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}
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virtual Device getDeviceFromPtr(void* data) const {
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TORCH_CHECK(false, "Cannot get device of pointer on CUDA without ATen_cuda library. ", CUDA_HELP);
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}
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virtual bool isPinnedPtr(void* data) const {
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return false;
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}
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virtual bool hasCUDA() const {
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return false;
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}
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virtual bool hasMAGMA() const {
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return false;
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}
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virtual bool hasCuDNN() const {
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return false;
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}
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virtual const at::cuda::NVRTC& nvrtc() const {
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TORCH_CHECK(false, "NVRTC requires CUDA. ", CUDA_HELP);
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}
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virtual int64_t current_device() const {
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return -1;
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}
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virtual bool hasPrimaryContext(int64_t device_index) const {
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TORCH_CHECK(false, "Cannot call hasPrimaryContext(", device_index, ") without ATen_cuda library. ", CUDA_HELP);
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}
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virtual c10::optional<int64_t> getDevceIndexWithPrimaryContext() const {
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return c10::nullopt;
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}
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virtual Allocator* getPinnedMemoryAllocator() const {
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TORCH_CHECK(false, "Pinned memory requires CUDA. ", CUDA_HELP);
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}
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virtual bool compiledWithCuDNN() const {
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return false;
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}
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virtual bool compiledWithMIOpen() const {
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return false;
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}
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virtual bool supportsDilatedConvolutionWithCuDNN() const {
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return false;
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}
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virtual bool supportsDepthwiseConvolutionWithCuDNN() const {
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return false;
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}
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virtual long versionCuDNN() const {
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TORCH_CHECK(false, "Cannot query cuDNN version without ATen_cuda library. ", CUDA_HELP);
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}
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virtual std::string showConfig() const {
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TORCH_CHECK(false, "Cannot query detailed CUDA version without ATen_cuda library. ", CUDA_HELP);
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}
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virtual double batchnormMinEpsilonCuDNN() const {
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TORCH_CHECK(false,
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"Cannot query batchnormMinEpsilonCuDNN() without ATen_cuda library. ", CUDA_HELP);
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}
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virtual int64_t cuFFTGetPlanCacheMaxSize(int64_t device_index) const {
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TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP);
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}
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virtual void cuFFTSetPlanCacheMaxSize(int64_t device_index, int64_t max_size) const {
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TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP);
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}
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virtual int64_t cuFFTGetPlanCacheSize(int64_t device_index) const {
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TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP);
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}
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virtual void cuFFTClearPlanCache(int64_t device_index) const {
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TORCH_CHECK(false, "Cannot access cuFFT plan cache without ATen_cuda library. ", CUDA_HELP);
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}
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virtual int getNumGPUs() const {
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return 0;
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}
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};
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// NB: dummy argument to suppress "ISO C++11 requires at least one argument
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// for the "..." in a variadic macro"
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struct CAFFE2_API CUDAHooksArgs {};
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C10_DECLARE_REGISTRY(CUDAHooksRegistry, CUDAHooksInterface, CUDAHooksArgs);
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#define REGISTER_CUDA_HOOKS(clsname) \
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C10_REGISTER_CLASS(CUDAHooksRegistry, clsname, clsname)
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namespace detail {
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CAFFE2_API const CUDAHooksInterface& getCUDAHooks();
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} // namespace detail
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} // namespace at
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