pytorch/caffe2/operators/copy_op.cu

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#include "caffe2/core/context_gpu.h"
#include "caffe2/operators/copy_op.h"
namespace caffe2 {
template <>
class CopyOnDeviceLikeOp<CUDAContext, CUDAContext, CUDAContext>
: public Operator<CUDAContext> {
public:
template <class... Args>
explicit CopyOnDeviceLikeOp(Args&&... args)
: Operator<CUDAContext>(std::forward<Args>(args)...) {}
USE_OPERATOR_FUNCTIONS(CUDAContext);
bool RunOnDevice() override {
auto& input = Input(0);
auto* output = OperatorBase::Output<Tensor>(0, CUDA);
CUDAContext context(GetGPUIDForPointer(Input(1).raw_data()));
output->ResizeLike(input);
context.template CopyItems<CUDAContext, CUDAContext>(
input.meta(),
input.numel(),
input.raw_data(),
output->raw_mutable_data(input.meta()));
return true;
}
};
// From CPU, copy it to whatever the current context
REGISTER_CUDA_OPERATOR(
CopyFromCPUInput,
CopyOp<CUDAContext, CUDAContext, CPUContext>);
// CopyGPUToCPU and CopyCPUToGPU should both be carried out in a cuda context,
// since gpu code will be involved.
REGISTER_CUDA_OPERATOR(
CopyGPUToCPU,
CopyOp<CUDAContext, CPUContext, CUDAContext>);
REGISTER_CUDA_OPERATOR(
CopyCPUToGPU,
CopyOp<CUDAContext, CUDAContext, CPUContext>);
// If we only specify Copy, we assume that it is a gpu to gpu copy - maybe
// involving different GPUs.
REGISTER_CUDA_OPERATOR(Copy, CopyOp<CUDAContext, CUDAContext, CUDAContext>);
REGISTER_CUDA_OPERATOR(
CopyOnDeviceLike,
CopyOnDeviceLikeOp<CUDAContext, CUDAContext, CUDAContext>);
} // namespace caffe2
using CopyGPUToCPU_CUDA = caffe2::
CopyOp<caffe2::CUDAContext, caffe2::CPUContext, caffe2::CUDAContext>;
using CopyCPUToGPU_CUDA = caffe2::
CopyOp<caffe2::CUDAContext, caffe2::CUDAContext, caffe2::CPUContext>;
C10_EXPORT_CAFFE2_OP_TO_C10_CUDA(CopyGPUToCPU, CopyGPUToCPU_CUDA);
C10_EXPORT_CAFFE2_OP_TO_C10_CPU_KERNEL_ONLY(CopyCPUToGPU, CopyCPUToGPU_CUDA);