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949b6f685d
Enable -Werror on s390x Example of original issue on s390x: https://github.com/pytorch/pytorch/actions/runs/11014606340/job/30585632704 Most of warnings are not specific to s390x, but specific to gcc-13 or gcc-14. To test it on s390x an image with gcc-13 is needed. For s390x it's tested for new regressions on every merge due to trunk workflow. `-Wdangling-reference` produces either obviously false warnings or suspicious warnings, which on closer inspection look plausibly safe. `-Wredundant-move` with new gcc complains about `std::move(...)` disabling copy elision. But removing `std::move(...)` makes used clang versions complain about copying objects when they could be moved. For now also disable it. Pull Request resolved: https://github.com/pytorch/pytorch/pull/136527 Approved by: https://github.com/malfet
101 lines
2.9 KiB
C++
101 lines
2.9 KiB
C++
#pragma once
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#include <memory>
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#include <ostream>
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#include <string>
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#include <ATen/Tensor.h>
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#include <c10/macros/Export.h>
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#include <c10/util/Deprecated.h>
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#include <optional>
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namespace c10 {
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struct Device;
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}
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namespace torch::lazy {
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// Backend should extend it and define their own supported hardware types.
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struct TORCH_API BackendDeviceType {
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int8_t type{(int8_t)at::kCPU};
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// Note: previous default value was '0', which actually maps to at::kCPU, at
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// least now it is explicit, we may want to make default/undefined semantics
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// more clear though
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BackendDeviceType() : type((int8_t)at::kCPU) {}
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BackendDeviceType(int8_t type) : type(type) {}
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virtual ~BackendDeviceType() = default;
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virtual std::string toString() const {
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return "Unknown";
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}
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};
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class TORCH_API BackendDevice {
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public:
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// The default constructor will set both the device type and ordinal
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// to backend specific defaults.
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BackendDevice();
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BackendDevice(std::shared_ptr<BackendDeviceType>&& type, int64_t ordinal);
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int8_t type() const;
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int64_t ordinal() const {
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return ordinal_;
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}
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bool operator==(const BackendDevice& other) const {
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return compare(other) == 0;
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}
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bool operator!=(const BackendDevice& other) const {
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return compare(other) != 0;
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}
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bool operator<(const BackendDevice& rhs) const {
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return compare(rhs) < 0;
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}
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std::string toString() const;
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private:
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int compare(const BackendDevice& rhs) const;
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// Use shared_ptr instead of unique_ptr so that BackendDevice can be copied.
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std::shared_ptr<BackendDeviceType> type_;
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int64_t ordinal_;
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};
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TORCH_API std::ostream& operator<<(
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std::ostream& os,
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const BackendDevice& device);
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// Helpers for converting a c10::Device to BackendDevice and vice versa.
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TORCH_API BackendDevice atenDeviceToBackendDevice(const c10::Device& device);
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TORCH_API c10::Device backendDeviceToAtenDevice(const BackendDevice& device);
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// Tries to extract the backend device out of the lazy tensor. Returns nullopt
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// if the input is not a lazy tensor.
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TORCH_API std::optional<BackendDevice> GetBackendDevice(
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const at::ITensorListRef tensors);
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TORCH_API std::optional<BackendDevice> GetBackendDevice(
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const at::TensorList tensors);
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TORCH_API std::optional<BackendDevice> GetBackendDevice(
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const at::Tensor& tensor);
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TORCH_API std::optional<BackendDevice> GetBackendDevice(
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const std::optional<c10::Device>& device);
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// For variadic template.
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TORCH_API std::optional<BackendDevice> GetBackendDevice();
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C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Winfinite-recursion")
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template <typename T, typename... Args>
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std::optional<BackendDevice> GetBackendDevice(
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const T& tensor,
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const Args&... forward_tensors) {
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auto optional_device = GetBackendDevice(tensor);
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if (optional_device) {
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return optional_device;
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}
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return GetBackendDevice(forward_tensors...);
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}
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C10_DIAGNOSTIC_POP()
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} // namespace torch::lazy
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