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https://github.com/pytorch/pytorch.git
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6284d2a82b
Summary: This is a first step towards creating context manager that errors out on synchronizing calls. Pull Request resolved: https://github.com/pytorch/pytorch/pull/61889 Reviewed By: albanD Differential Revision: D29805280 Pulled By: ngimel fbshipit-source-id: b66400fbe0941b7daa51e6b30abe27b9cccd4e8a
60 lines
1.9 KiB
C++
60 lines
1.9 KiB
C++
#pragma once
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// This header provides C++ wrappers around commonly used CUDA API functions.
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// The benefit of using C++ here is that we can raise an exception in the
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// event of an error, rather than explicitly pass around error codes. This
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// leads to more natural APIs.
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//
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// The naming convention used here matches the naming convention of torch.cuda
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#include <c10/core/Device.h>
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#include <c10/cuda/CUDAException.h>
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#include <c10/cuda/CUDAMacros.h>
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#ifdef __HIP_PLATFORM_HCC__
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#include <hip/hip_version.h>
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#endif
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#include <cuda_runtime_api.h>
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namespace c10 {
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namespace cuda {
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// NB: In the past, we were inconsistent about whether or not this reported
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// an error if there were driver problems are not. Based on experience
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// interacting with users, it seems that people basically ~never want this
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// function to fail; it should just return zero if things are not working.
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// Oblige them.
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// It still might log a warning for user first time it's invoked
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C10_CUDA_API DeviceIndex device_count() noexcept;
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// Version of device_count that throws is no devices are detected
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C10_CUDA_API DeviceIndex device_count_ensure_non_zero();
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C10_CUDA_API DeviceIndex current_device();
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C10_CUDA_API void set_device(DeviceIndex device);
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C10_CUDA_API void device_synchronize();
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// the subsequent functions are defined in the header because for performance
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// reasons we want them to be inline
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C10_CUDA_API void __inline__ memcpy_and_sync(
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void* dst,
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void* src,
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int64_t nbytes,
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cudaMemcpyKind kind,
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cudaStream_t stream) {
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#if defined(HIP_VERSION) && (HIP_VERSION >= 301)
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C10_CUDA_CHECK(hipMemcpyWithStream(dst, src, nbytes, kind, stream));
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#else
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C10_CUDA_CHECK(cudaMemcpyAsync(dst, src, nbytes, kind, stream));
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C10_CUDA_CHECK(cudaStreamSynchronize(stream));
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#endif
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}
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C10_CUDA_API void __inline__ stream_synchronize(cudaStream_t stream) {
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C10_CUDA_CHECK(cudaStreamSynchronize(stream));
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}
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} // namespace cuda
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} // namespace c10
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