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005c5694eb
Summary: This diff refactors the code for the "torch.mtia.memory_stats" API to maintain the same file hierarchy as its CUDA counterpart: - All device memory APIs are now located under ".../mtia/memory.py". - Device memory APIs can be accessed using either "torch.mtia.XYZ" or "torch.mtia.memory.XYZ". Test Plan: Passed a local unit test: `buck run //mtia/host_runtime/torch_mtia/tests:test_torch_mtia_api` ``` Ran 14 tests in 16.657s OK I1127 11:06:06.505201 2133030 afg_bindings.cpp:943] afg-aten::mul.out-dtype_Float-bBtLGD6Y executable has been unloaded I1127 11:06:06.506654 2133030 afg_bindings.cpp:943] afg-add-dtype_Float-fa37JncC executable has been unloaded W1127 11:06:08.731138 2133030 HazptrDomain.h:148] Tagged objects remain. This may indicate a higher-level leak of object(s) that use hazptr_obj_cohort. ``` Differential Revision: D66549179 Pull Request resolved: https://github.com/pytorch/pytorch/pull/141723 Approved by: https://github.com/nautsimon
157 lines
4.2 KiB
ReStructuredText
157 lines
4.2 KiB
ReStructuredText
.. 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: 1
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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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mtia.memory
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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.fsdp.fully_shard <distributed.fsdp.fully_shard>
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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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