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# Motivation According to [[RFC]A device-agnostic Python runtime API design for stream-based accelerators](https://github.com/pytorch/pytorch/issues/128403), this PR intends to introduce a device-agnostic runtime API design. I personally prefer the **Simple Version** APIs that no longer accept the device type as an input argument. It means we will leverage `getAccelerator` to fetch the current accelerator. And it is flexible to expand these APIs to handle multiple types of accelerator scenarios. The design does **NOT** break the previous design philosophies. I also believe that namespace torch.accelerator is better. It lets users know that the APIs they are calling are running on an accelerator rather than CPU. This is important. Meanwhile, we can follow a simple API design principle: 1. Device-agnostic APIs should be placed under the torch.accelerator namespace and not accept a device_type optional parameter. 2. Device-specific APIs should be placed under device-specific submodules. 3. APIS required by both CPU and accelerators should be placed under the torch namespace and accept a device_type optional parameter. Also, I list the pros and cons of **Simple Version** here: Pros: - `torch.accelerator.foo` will have the same input argument as `torch.xxx.foo`, bringing a better user experience; - more concise, facilitate the developer to write a device-agnostic code. Cons: - no obvious drawbacks. # Additional Context I list the new APIs here: ```python torch.accelerator.is_available() -> bool: torch.accelerator.current_accelerator() -> torch.device: torch.accelerator.device_count() -> int: torch.accelerator.current_device_idx() -> int: torch.accelerator.set_device_idx(device: Union[torch.device, str, int, None]) -> None: torch.accelerator.current_stream(device: Union[torch.device, str, int, None]) -> torch.Stream: torch.accelerator.set_stream(stream: torch.Stream) -> None: torch.accelerator.synchronize(device: Union[torch.device, str, int, None]) -> None: ``` According to the discussion with Alban, we decide to change the API name `set_device` to `set_device_idx` and `current_device` to `current_device_idx` for more explicit. And will submit other PR to support device and stream context manager. Pull Request resolved: https://github.com/pytorch/pytorch/pull/132204 Approved by: https://github.com/EikanWang, https://github.com/abhilash1910, https://github.com/gujinghui, https://github.com/albanD
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155 lines
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.. PyTorch documentation master file, created by
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sphinx-quickstart on Fri Dec 23 13:31:47 2016.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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:github_url: https://github.com/pytorch/pytorch
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PyTorch documentation
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===================================
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PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
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Features described in this documentation are classified by release status:
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*Stable:* These features will be maintained long-term and there should generally
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be no major performance limitations or gaps in documentation.
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We also expect to maintain backwards compatibility (although
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breaking changes can happen and notice will be given one release ahead
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of time).
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*Beta:* These features are tagged as Beta because the API may change based on
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user feedback, because the performance needs to improve, or because
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coverage across operators is not yet complete. For Beta features, we are
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committing to seeing the feature through to the Stable classification.
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We are not, however, committing to backwards compatibility.
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*Prototype:* These features are typically not available as part of
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binary distributions like PyPI or Conda, except sometimes behind run-time
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flags, and are at an early stage for feedback and testing.
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Community
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community/*
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.. toctree::
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:glob:
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:maxdepth: 1
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:caption: Developer Notes
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notes/*
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.. toctree::
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:maxdepth: 1
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:caption: Language Bindings
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cpp_index
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Javadoc <https://pytorch.org/javadoc/>
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torch::deploy <deploy>
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.. toctree::
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:glob:
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:maxdepth: 2
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:caption: Python API
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torch
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nn
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nn.functional
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tensors
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tensor_attributes
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tensor_view
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torch.amp <amp>
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torch.autograd <autograd>
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torch.library <library>
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accelerator
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cpu
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cuda
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torch.cuda.memory <torch_cuda_memory>
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mps
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xpu
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mtia
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meta
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torch.backends <backends>
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torch.export <export>
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torch.distributed <distributed>
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torch.distributed.tensor <distributed.tensor>
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torch.distributed.algorithms.join <distributed.algorithms.join>
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torch.distributed.elastic <distributed.elastic>
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torch.distributed.fsdp <fsdp>
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torch.distributed.tensor.parallel <distributed.tensor.parallel>
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torch.distributed.optim <distributed.optim>
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torch.distributed.pipelining <distributed.pipelining>
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torch.distributed.checkpoint <distributed.checkpoint>
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torch.distributions <distributions>
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torch.compiler <torch.compiler>
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torch.fft <fft>
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torch.func <func>
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futures
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fx
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fx.experimental
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torch.hub <hub>
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torch.jit <jit>
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torch.linalg <linalg>
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torch.monitor <monitor>
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torch.signal <signal>
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torch.special <special>
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torch.overrides
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torch.package <package>
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profiler
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nn.init
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nn.attention
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onnx
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optim
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complex_numbers
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ddp_comm_hooks
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quantization
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rpc
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torch.random <random>
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masked
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torch.nested <nested>
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size
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sparse
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storage
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torch.testing <testing>
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torch.utils <utils>
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torch.utils.benchmark <benchmark_utils>
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torch.utils.bottleneck <bottleneck>
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torch.utils.checkpoint <checkpoint>
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torch.utils.cpp_extension <cpp_extension>
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torch.utils.data <data>
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torch.utils.deterministic <deterministic>
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torch.utils.jit <jit_utils>
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torch.utils.dlpack <dlpack>
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torch.utils.mobile_optimizer <mobile_optimizer>
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torch.utils.model_zoo <model_zoo>
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torch.utils.tensorboard <tensorboard>
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torch.utils.module_tracker <module_tracker>
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type_info
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named_tensor
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name_inference
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torch.__config__ <config_mod>
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torch.__future__ <future_mod>
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logging
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torch_environment_variables
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.. toctree::
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:maxdepth: 1
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:caption: Libraries
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torchaudio <https://pytorch.org/audio/stable>
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TorchData <https://pytorch.org/data>
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TorchRec <https://pytorch.org/torchrec>
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TorchServe <https://pytorch.org/serve>
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torchtext <https://pytorch.org/text/stable>
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torchvision <https://pytorch.org/vision/stable>
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PyTorch on XLA Devices <https://pytorch.org/xla/>
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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