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Summary: Working towards https://docs.google.com/document/d/10yx2-4gs0gTMOimVS403MnoAWkqitS8TUHX73PN8EjE/edit?pli=1# This PR: - Ensure that all the submodules are listed in a rst file (that ensure they are considered by the coverage tool) - Remove some long deprecated code that just error out on import - Remove the allow list altogether to ensure nothing gets added back there Pull Request resolved: https://github.com/pytorch/pytorch/pull/73983 Reviewed By: anjali411 Differential Revision: D34787908 Pulled By: albanD fbshipit-source-id: 163ce61e133b12b2f2e1cbe374f979e3d6858db7 (cherry picked from commit c9edfead7a01dc45bfc24eaf7220d2a84ab1f62e)
60 lines
2.7 KiB
ReStructuredText
60 lines
2.7 KiB
ReStructuredText
torch.utils.bottleneck
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======================
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.. automodule:: torch.utils.bottleneck
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.. currentmodule:: torch.utils.bottleneck
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`torch.utils.bottleneck` is a tool that can be used as an initial step for
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debugging bottlenecks in your program. It summarizes runs of your script with
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the Python profiler and PyTorch's autograd profiler.
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Run it on the command line with
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::
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python -m torch.utils.bottleneck /path/to/source/script.py [args]
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where [args] are any number of arguments to `script.py`, or run
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``python -m torch.utils.bottleneck -h`` for more usage instructions.
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.. warning::
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Because your script will be profiled, please ensure that it exits in a
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finite amount of time.
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.. warning::
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Due to the asynchronous nature of CUDA kernels, when running against
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CUDA code, the cProfile output and CPU-mode autograd profilers may
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not show correct timings: the reported CPU time reports the amount of time
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used to launch the kernels but does not include the time the kernel
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spent executing on a GPU unless the operation does a synchronize.
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Ops that do synchronize appear to be extremely expensive under regular
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CPU-mode profilers.
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In these case where timings are incorrect, the CUDA-mode autograd profiler
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may be helpful.
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.. note::
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To decide which (CPU-only-mode or CUDA-mode) autograd profiler output to
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look at, you should first check if your script is CPU-bound
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("CPU total time is much greater than CUDA total time").
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If it is CPU-bound, looking at the results of the CPU-mode autograd
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profiler will help. If on the other hand your script spends most of its
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time executing on the GPU, then it makes sense to start
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looking for responsible CUDA operators in the output of the CUDA-mode
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autograd profiler.
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Of course the reality is much more complicated and your script might not be
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in one of those two extremes depending on the part of the model you're
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evaluating. If the profiler outputs don't help, you could try looking at
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the result of :func:`torch.autograd.profiler.emit_nvtx()` with ``nvprof``.
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However, please take into account that the NVTX overhead is very high and
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often gives a heavily skewed timeline.
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.. warning::
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If you are profiling CUDA code, the first profiler that ``bottleneck`` runs
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(cProfile) will include the CUDA startup time (CUDA buffer allocation cost)
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in its time reporting. This should not matter if your bottlenecks result
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in code much slower than the CUDA startup time.
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For more complicated uses of the profilers (like in a multi-GPU case),
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please see https://docs.python.org/3/library/profile.html
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or :func:`torch.autograd.profiler.profile()` for more information.
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