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f9d07ae644
Summary: Fixes https://github.com/pytorch/xla/issues/3490. Updates `torch::lazy::BackendDevice` with changes below: 1. Remove the no-op string constructor. 2. Update default ordinal to `-1`. 3. Add a `is_valid` function to check if `ordinal` is valid/non-default (`ordinal >= 0`). Pull Request resolved: https://github.com/pytorch/pytorch/pull/76264 Reviewed By: mrshenli Differential Revision: D35860266 Pulled By: alanwaketan fbshipit-source-id: 554ebe16a0683d37b00270c4f35163bf690bfe28 (cherry picked from commit b941d10e8545dfecfb34e4d5c24a29a1cc49bc4b)
78 lines
2.1 KiB
C++
78 lines
2.1 KiB
C++
#include <torch/csrc/lazy/backend/backend_device.h>
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#include <c10/core/Device.h>
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#include <c10/util/Exception.h>
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#include <c10/util/StringUtil.h>
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#include <torch/csrc/lazy/core/tensor.h>
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#include <torch/csrc/lazy/backend/backend_interface.h>
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namespace torch {
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namespace lazy {
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// TODO(alanwaketan): Use the backend API to get the default device type.
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BackendDevice::BackendDevice()
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: type_(std::make_shared<BackendDeviceType>()), ordinal_(-1) {}
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BackendDevice::BackendDevice(std::shared_ptr<BackendDeviceType>&& type, int64_t ordinal)
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: type_(std::move(type)), ordinal_(ordinal) {}
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int8_t BackendDevice::type() const {
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TORCH_INTERNAL_ASSERT(type_);
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return type_->type;
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}
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std::string BackendDevice::toString() const {
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TORCH_INTERNAL_ASSERT(type_);
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std::string str = type_->toString();
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if (has_index()) {
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str.append(std::to_string(ordinal_));
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}
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return str;
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}
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int BackendDevice::compare(const BackendDevice& rhs) const {
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if (type() != rhs.type()) {
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return type() < rhs.type() ? -1 : +1;
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}
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return ordinal_ < rhs.ordinal_ ? -1 : (ordinal_ > rhs.ordinal_ ? +1 : 0);
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}
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std::ostream& operator<<(std::ostream& os, const BackendDevice& device) {
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os << device.toString();
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return os;
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}
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BackendDevice atenDeviceToBackendDevice(const c10::Device& device) {
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TORCH_CHECK(device.type() == at::kLazy, device);
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int64_t ordinal = device.has_index() ? device.index() : -1;
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return BackendDevice(getBackend()->GetDefaultDeviceType(), ordinal);
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}
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// TODO(whc) refactor this: we need to support non 1 on 1 mapping for torch/XLA.
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c10::Device backendDeviceToAtenDevice(const BackendDevice& device) {
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return c10::Device(at::kLazy, device.ordinal());
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}
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c10::optional<BackendDevice> GetBackendDevice(const at::TensorList tensors) {
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for (auto& tensor: tensors) {
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if (auto lt = TryGetLtcTensor(tensor)) {
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return lt->GetDevice();
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}
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}
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return c10::nullopt;
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}
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c10::optional<BackendDevice> GetBackendDevice(const at::Tensor& tensor) {
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if (auto lt = TryGetLtcTensor(tensor)) {
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return lt->GetDevice();
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}
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return c10::nullopt;
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}
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c10::optional<BackendDevice> GetBackendDevice() {
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return c10::nullopt;
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}
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} // namespace lazy
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} // namespace torch
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