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c6b180a316
Fixes #127908 ## Description Created docs to document the torch.cuda.cudart function to solve the issue #127908. I tried to stick to the [guidelines to document a function](https://github.com/pytorch/pytorch/wiki/Docstring-Guidelines#documenting-a-function) but I was not sure if there is a consensus on how to handle the docs of a function that calls an internal function. So I went ahead and tried what the function will raise, etc. from the user endpoint and documented it (i.e. I am giving what actually _lazy_init() will raise). Updated PR from #128298 since I made quite a big mistake in my branch. I apologize for the newbie mistake. ### Summary of Changes - Added docs for torch.cuda.cudart - Added the cudart function in the autosummary of docs/source/cuda.rst ## Checklist - [X] The issue that is being fixed is referred in the description - [X] Only one issue is addressed in this pull request - [X] Labels from the issue that this PR is fixing are added to this pull request - [X] No unnecesary issues are included into this pull request Pull Request resolved: https://github.com/pytorch/pytorch/pull/128741 Approved by: https://github.com/msaroufim
192 lines
4.1 KiB
ReStructuredText
192 lines
4.1 KiB
ReStructuredText
torch.cuda
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===================================
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.. automodule:: torch.cuda
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.. currentmodule:: torch.cuda
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.. autosummary::
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:toctree: generated
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:nosignatures:
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StreamContext
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can_device_access_peer
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current_blas_handle
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current_device
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current_stream
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cudart
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default_stream
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device
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device_count
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device_of
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get_arch_list
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get_device_capability
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get_device_name
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get_device_properties
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get_gencode_flags
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get_sync_debug_mode
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init
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ipc_collect
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is_available
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is_initialized
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memory_usage
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set_device
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set_stream
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set_sync_debug_mode
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stream
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synchronize
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utilization
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temperature
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power_draw
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clock_rate
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OutOfMemoryError
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Random Number Generator
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-------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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get_rng_state
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get_rng_state_all
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set_rng_state
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set_rng_state_all
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manual_seed
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manual_seed_all
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seed
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seed_all
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initial_seed
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Communication collectives
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-------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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comm.broadcast
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comm.broadcast_coalesced
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comm.reduce_add
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comm.scatter
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comm.gather
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Streams and events
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------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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Stream
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ExternalStream
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Event
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Graphs (beta)
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-------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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is_current_stream_capturing
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graph_pool_handle
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CUDAGraph
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graph
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make_graphed_callables
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.. _cuda-memory-management-api:
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Memory management
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-----------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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empty_cache
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list_gpu_processes
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mem_get_info
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memory_stats
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memory_summary
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memory_snapshot
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memory_allocated
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max_memory_allocated
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reset_max_memory_allocated
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memory_reserved
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max_memory_reserved
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set_per_process_memory_fraction
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memory_cached
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max_memory_cached
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reset_max_memory_cached
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reset_peak_memory_stats
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caching_allocator_alloc
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caching_allocator_delete
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get_allocator_backend
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CUDAPluggableAllocator
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change_current_allocator
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.. FIXME The following doesn't seem to exist. Is it supposed to?
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https://github.com/pytorch/pytorch/issues/27785
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.. autofunction:: reset_max_memory_reserved
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NVIDIA Tools Extension (NVTX)
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-----------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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nvtx.mark
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nvtx.range_push
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nvtx.range_pop
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nvtx.range
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Jiterator (beta)
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-----------------------------
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.. autosummary::
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:toctree: generated
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:nosignatures:
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jiterator._create_jit_fn
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jiterator._create_multi_output_jit_fn
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TunableOp
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---------
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Some operations could be implemented using more than one library or more than
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one technique. For example, a GEMM could be implemented for CUDA or ROCm using
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either the cublas/cublasLt libraries or hipblas/hipblasLt libraries,
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respectively. How does one know which implementation is the fastest and should
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be chosen? That's what TunableOp provides. Certain operators have been
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implemented using multiple strategies as Tunable Operators. At runtime, all
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strategies are profiled and the fastest is selected for all subsequent
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operations.
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See the :doc:`documentation <cuda.tunable>` for information on how to use it.
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.. toctree::
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:hidden:
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cuda.tunable
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Stream Sanitizer (prototype)
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----------------------------
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CUDA Sanitizer is a prototype tool for detecting synchronization errors between streams in PyTorch.
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See the :doc:`documentation <cuda._sanitizer>` for information on how to use it.
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.. toctree::
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:hidden:
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cuda._sanitizer
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.. This module needs to be documented. Adding here in the meantime
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.. for tracking purposes
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.. py:module:: torch.cuda.comm
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.. py:module:: torch.cuda.error
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.. py:module:: torch.cuda.graphs
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.. py:module:: torch.cuda.jiterator
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.. py:module:: torch.cuda.memory
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.. py:module:: torch.cuda.nccl
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.. py:module:: torch.cuda.nvtx
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.. py:module:: torch.cuda.profiler
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.. py:module:: torch.cuda.random
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.. py:module:: torch.cuda.sparse
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.. py:module:: torch.cuda.streams
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