pytorch/benchmarks/functional_autograd_benchmark

Benchmarking tool for the autograd API

This folder contain a set of self-contained scripts that allows you to benchmark autograd with different common models. It is designed to run the benchmark before and after your change and will generate a table to share on the PR.

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). You can then use compare.py to get a markdown table comparing the two runs.

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.

Sample usage

# Make sure you compile pytorch in release mode and with the same flags before/after
export DEBUG=0
# When running on CPU, it might be required to limit the number of cores to avoid oversubscription
export OMP_NUM_THREADS=10

# Compile pytorch with the base revision
git checkout main
python -m pip install --no-build-isolation -v -e .

# Install dependencies:
# Scipy is required by detr
pip install scipy

# Run the benchmark for the base
# This will use the GPU if available.
pushd benchmarks/functional_autograd_benchmark
python functional_autograd_benchmark.py --output before.txt

# Compile pytorch with your change
popd
git checkout your_feature_branch
python -m pip install --no-build-isolation -v -e .

# Run the benchmark for the new version
pushd benchmarks/functional_autograd_benchmark
python functional_autograd_benchmark.py --output after.txt

# Get the markdown table that you can paste in your github PR
python compare.py

popd

Files in this folder:

• functional_autograd_benchmark.py is the main entry point to run the benchmark.
• compare.py is the entry point to run the comparison script that generates a markdown table.
• 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.
• ppl_models.py, vision_models.py and audio_text_models.py contain all the getter functions used for the benchmark.

Benchmarking against functorch

# Install stable functorch:
pip install functorch
# or install from source:
pip install git+https://github.com/pytorch/functorch

# Run the benchmark for the base
# This will use the GPU if available.
pushd benchmarks/functional_autograd_benchmark
python functional_autograd_benchmark.py --output bench-with-functorch.txt