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https://github.com/zebrajr/pytorch.git
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5c46600f84
# Motivation This PR intends to extend `cuda_lazy_init` to `device_lazy_init` which is a device-agnostic API that can support any backend. And change `maybe_initialize_cuda` to `maybe_initialize_device` to support lazy initialization for CUDA while maintaining scalability. # Design We maintain a flag for each backend to manage the lazy initialization state separately. # Additional Context No need more UTs. This is a reland PR, the original PR is [refactor lazy init to device-agnostic](https://github.com/pytorch/pytorch/pull/118846). This is a common PR, and does not trigger xpu ciflow. Differential Revision: [D53478332](https://our.internmc.facebook.com/intern/diff/D53478332) Pull Request resolved: https://github.com/pytorch/pytorch/pull/119248 Approved by: https://github.com/EikanWang, https://github.com/gujinghui, https://github.com/jgong5, https://github.com/atalman
82 lines
2.0 KiB
C++
82 lines
2.0 KiB
C++
#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
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// ${generated_comment}
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#include "torch/csrc/Device.h"
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#include "torch/csrc/DynamicTypes.h"
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#include "torch/csrc/Exceptions.h"
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#include "torch/csrc/autograd/python_nested_functions.h"
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#include "torch/csrc/autograd/generated/python_return_types.h"
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#include "torch/csrc/autograd/python_variable.h"
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#include "torch/csrc/autograd/utils/wrap_outputs.h"
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#include "torch/csrc/autograd/utils/python_arg_parsing.h"
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#include "torch/csrc/autograd/generated/variable_factories.h"
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#include "torch/csrc/utils/out_types.h"
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#include "torch/csrc/utils/pycfunction_helpers.h"
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#include "torch/csrc/utils/python_arg_parser.h"
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#include "torch/csrc/utils/structseq.h"
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#include "torch/csrc/utils/device_lazy_init.h"
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#ifndef AT_PER_OPERATOR_HEADERS
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#include <ATen/Functions.h>
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#else
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$ops_headers
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#endif
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using at::Tensor;
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using at::Device;
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using at::Layout;
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using at::Scalar;
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using at::ScalarType;
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using at::Backend;
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using at::OptionalDeviceGuard;
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using at::DeviceGuard;
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using at::TensorOptions;
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using at::IntArrayRef;
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using at::OptionalIntArrayRef;
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using at::Generator;
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using at::TensorList;
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using at::Dimname;
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using at::DimnameList;
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using namespace torch::autograd::utils;
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namespace torch::autograd {
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// generated forward declarations start here
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${py_forwards}
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static PyMethodDef nested_functions[] = {
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{NULL, NULL, 0, NULL},
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${py_method_defs}
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{NULL}
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};
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static PyObject* THPNestedVariableFunctionsModule = NULL;
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void initNestedFunctions(PyObject* module) {
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nested_functions[0] = get_nested_functions_manual()[0];
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static struct PyModuleDef def = {
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PyModuleDef_HEAD_INIT,
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"torch._C._nested",
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NULL,
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-1,
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nested_functions
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};
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PyObject* nested = PyModule_Create(&def);
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THPNestedVariableFunctionsModule = nested;
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if (!nested) {
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throw python_error();
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}
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// steals a reference to nested
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if (PyModule_AddObject(module, "_nested", nested) != 0) {
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throw python_error();
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}
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}
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// generated methods start here
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${py_methods}
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} // namespace torch::autograd
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