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520771d7b3
# 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. Pull Request resolved: https://github.com/pytorch/pytorch/pull/118846 Approved by: https://github.com/malfet
43 lines
1.2 KiB
C++
43 lines
1.2 KiB
C++
#include <torch/csrc/utils/device_lazy_init.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/python_headers.h>
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#include <torch/csrc/utils/object_ptr.h>
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#include <iostream>
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namespace torch::utils {
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namespace {
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std::array<bool, at::COMPILE_TIME_MAX_DEVICE_TYPES> is_initialized{};
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} // anonymous namespace
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void device_lazy_init(at::DeviceType device_type) {
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pybind11::gil_scoped_acquire g;
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// Protected by the GIL. We don't use call_once because under ASAN it
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// has a buggy implementation that deadlocks if an instance throws an
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// exception. In any case, call_once isn't necessary, because we
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// have taken a lock.
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if (is_initialized[static_cast<int>(device_type)]) {
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return;
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}
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std::string module_name = "torch." + at::DeviceTypeName(device_type, true);
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auto module = THPObjectPtr(PyImport_ImportModule(module_name.c_str()));
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if (!module) {
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throw python_error();
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}
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auto res = THPObjectPtr(PyObject_CallMethod(module.get(), "_lazy_init", ""));
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if (!res) {
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throw python_error();
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}
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is_initialized[static_cast<int>(device_type)] = true;
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}
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void set_requires_device_init(at::DeviceType device_type, bool value) {
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is_initialized[static_cast<int>(device_type)] = !value;
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}
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} // namespace torch::utils
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