mirror of
https://github.com/pytorch/pytorch.git
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4dce5b71a0
Modernize the development installation:
```bash
# python setup.py develop
python -m pip install --no-build-isolation -e .
# python setup.py install
python -m pip install --no-build-isolation .
```
Now, the `python setup.py develop` is a wrapper around `python -m pip install -e .` since `setuptools>=80.0`:
- pypa/setuptools#4955
`python setup.py install` is deprecated and will emit a warning during run. The warning will become an error on October 31, 2025.
- 9c4d383631/setuptools/command/install.py (L58-L67)
> ```python
> SetuptoolsDeprecationWarning.emit(
> "setup.py install is deprecated.",
> """
> Please avoid running ``setup.py`` directly.
> Instead, use pypa/build, pypa/installer or other
> standards-based tools.
> """,
> see_url="https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html",
> due_date=(2025, 10, 31),
> )
> ```
- pypa/setuptools#3849
Additional Resource:
- [Why you shouldn't invoke setup.py directly](https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156027
Approved by: https://github.com/ezyang
68 lines
2.5 KiB
Markdown
68 lines
2.5 KiB
Markdown
# Benchmarking tool for the autograd API
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This folder contain a set of self-contained scripts that allows you to benchmark autograd with different common models.
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It is designed to run the benchmark before and after your change and will generate a table to share on the PR.
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To do so, you can use `functional_autograd_benchmark.py` to run the benchmarks before your change (using as output `before.txt`) and after your change (using as output `after.txt`).
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You can then use `compare.py` to get a markdown table comparing the two runs.
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The default arguments of `functional_autograd_benchmark.py` should be used in general. You can change them though to force a given device or force running even the (very) slow settings.
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### Sample usage
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```bash
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# Make sure you compile pytorch in release mode and with the same flags before/after
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export DEBUG=0
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# When running on CPU, it might be required to limit the number of cores to avoid oversubscription
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export OMP_NUM_THREADS=10
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# Compile pytorch with the base revision
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git checkout main
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python -m pip install --no-build-isolation -v -e .
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# Install dependencies:
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# Scipy is required by detr
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pip install scipy
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# Run the benchmark for the base
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# This will use the GPU if available.
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pushd benchmarks/functional_autograd_benchmark
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python functional_autograd_benchmark.py --output before.txt
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# Compile pytorch with your change
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popd
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git checkout your_feature_branch
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python -m pip install --no-build-isolation -v -e .
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# Run the benchmark for the new version
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pushd benchmarks/functional_autograd_benchmark
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python functional_autograd_benchmark.py --output after.txt
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# Get the markdown table that you can paste in your github PR
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python compare.py
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popd
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```
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### Files in this folder:
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- `functional_autograd_benchmark.py` is the main entry point to run the benchmark.
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- `compare.py` is the entry point to run the comparison script that generates a markdown table.
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- `torchaudio_models.py` and `torchvision_models.py` contains code extracted from torchaudio and torchvision to be able to run the models without having a specific version of these libraries installed.
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- `ppl_models.py`, `vision_models.py` and `audio_text_models.py` contain all the getter functions used for the benchmark.
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### Benchmarking against `functorch`
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```bash
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# Install stable functorch:
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pip install functorch
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# or install from source:
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pip install git+https://github.com/pytorch/functorch
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# Run the benchmark for the base
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# This will use the GPU if available.
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pushd benchmarks/functional_autograd_benchmark
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python functional_autograd_benchmark.py --output bench-with-functorch.txt
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```
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