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05fa05cbae
Follows #127727 Pull Request resolved: https://github.com/pytorch/pytorch/pull/127764 Approved by: https://github.com/Skylion007
51 lines
1.7 KiB
C++
51 lines
1.7 KiB
C++
#pragma once
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#include <c10/core/TensorOptions.h>
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// device_lazy_init() is always compiled, even for CPU-only builds.
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namespace torch::utils {
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/**
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* This mechanism of lazy initialization is designed for each device backend.
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* Currently, CUDA and XPU follow this design. This function `device_lazy_init`
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* MUST be called before you attempt to access any Type(CUDA or XPU) object
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* from ATen, in any way. It guarantees that the device runtime status is lazily
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* initialized when the first runtime API is requested.
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*
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* Here are some common ways that a device object may be retrieved:
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* - You call getNonVariableType or getNonVariableTypeOpt
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* - You call toBackend() on a Type
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*
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* It's important to do this correctly, because if you forget to add it you'll
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* get an oblique error message seems like "Cannot initialize CUDA without
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* ATen_cuda library" or "Cannot initialize XPU without ATen_xpu library" if you
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* try to use CUDA or XPU functionality from a CPU-only build, which is not good
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* UX.
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*/
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void device_lazy_init(at::DeviceType device_type);
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void set_requires_device_init(at::DeviceType device_type, bool value);
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inline void maybe_initialize_device(at::Device& device) {
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// Add more devices here to enable lazy initialization.
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if (device.is_cuda() || device.is_xpu() || device.is_privateuseone()) {
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device_lazy_init(device.type());
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}
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}
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inline void maybe_initialize_device(std::optional<at::Device>& device) {
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if (!device.has_value()) {
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return;
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}
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maybe_initialize_device(device.value());
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}
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inline void maybe_initialize_device(const at::TensorOptions& options) {
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auto device = options.device();
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maybe_initialize_device(device);
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}
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bool is_device_initialized(at::DeviceType device_type);
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} // namespace torch::utils
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