This is a collection of open source benchmarks used to evaluate PyTorch performance.
torchbenchmark/models contains copies of popular or exemplary workloads which have been modified to
(a) expose a standardized API for benchmark drivers, (b) optionally, enable JIT,
(c) contain a miniature version of train/test data and a dependency install script.
The benchmark suite should be self contained in terms of dependencies, except for the torch products which are intended to be installed separately so different torch versions can be benchmarked.
We support python 3.7 and 3.8, and 3.8 is recommended. Currently, there are compatibility issues with 3.9+. Conda is optional but suggested. To switch to python 3.8 in conda:
# using your current conda environment:
conda install -y python=3.8
# or, using a new conda environment
conda create -n torchbenchmark python=3.8
conda activate torchbenchmark
# we depend on git lfs tool to store minimal input dataset such as images and annotations
# the total size of input dataset is ~20 MB
conda install -y git-lfs
If you are running Nvidia GPU tests, we support CUDA 10.2+, and CUDA 11.3 is recommended:
conda install -y -c pytorch magma-cuda113
Then install pytorch, torchtext, and torchvision using conda:
conda install -y pytorch torchtext torchvision cudatoolkit=11.3 -c pytorch-nightly
Or use pip: (but don't mix and match pip and conda for the torch family of libs! - see notes below)
pip install --pre torch torchvision torchtext -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html
Install the benchmark suite, which will recursively install dependencies for all the models. Currently, the repo is intended to be installed from the source tree.
git clone <benchmark>
cd <benchmark>
python install.py
Note that when building PyTorch from source, torchtext and torchvision must also be built from source to make sure the C APIs match.
See detailed instructions to install torchtext here, and torchvision here.
Make sure to enable CUDA (by FORCE_CUDA=1) if using CUDA.
Then,
git clone <benchmark>
cd <benchmark>
python install.py
- Setup steps require connectivity, make sure to enable a proxy if needed.
- See the CI scripts and their orchestration in config.yml for hints about how to replicate the CI environment if you have issues
- PyTorch versions before 1.6 are not compatible with all the models in torchbenchmark. See branch wconstab/compare_torch_versions for a set of models that worked back to torch 1.4.0.
- torch, torchtext, and torchvision must all be installed from the same build process. This means it isn't possible to mix conda torchtext with pip torch, or mix built-from-source torch with pip torchtext. It's important to match even the conda channel (nightly vs regular). This is due to the differences in the compilation process used by different packaging systems producing incompatible python binary extensions.
Various sources of noise, such as interrupts, context switches, clock frequency scaling, etc. can all conspire to make benchmark results variable. It's important to understand the level of noise in your setup before drawing conclusions from benchmark data. While any machine can in principle be tuned up, the steps and end-results vary with OS, kernel, drivers, and hardware. To this end, torchbenchmark picks a favorite machine type it can support well, and provides utilities for automated tuning on that machine. In the future, we may support more machine types and would be happy for contributions here.
The currently supported machine type is an AWS g4dn.metal instance using Amazon Linux. This is one of the subsets of AWS instance types that supports processor state control, with documented tuning guides for Amazon Linux. Most if not all of these steps should be possible on Ubuntu but haven't been automated yet.
To tune your g4dn.metal Amazon Linux machine, run
sudo `which python` torchbenchmark/util/machine_config.py --configure
When running pytest (see below), the machine_config script is invoked to assert a proper configuration and log config info into the output json. It is possible to --ignore_machine_config if running pytest without tuning is desired.
There are currently two top-level scripts for running the models.
test.py offers the simplest wrapper around the infrastructure for iterating through each model and installing and executing it.
test_bench.py is a pytest-benchmark script that leverages the same infrastructure but collects benchmark statistics and supports pytest filtering.
In each model repo, the assumption is that the user would already have all of the torch family of packages installed (torch, torchtext, torchvision, ...) but it installs the rest of the dependencies for the model.
python test.py will execute the APIs for each model, as a sanity check. For benchmarking, use test_bench.py. It is based on unittest, and supports filtering via CLI.
For instance, to run the BERT model on CPU for the train execution mode:
python test.py -k "test_BERT_pytorch_train_cpu"
The test name follows the following pattern:
"test_" + <model_name> + "_" + {"train" | "eval" } + "_" + {"cpu" | "cuda"}
pytest test_bench.py invokes the benchmark driver. See --help for a complete list of options.
Some useful options include:
--benchmark-autosave(or other save related flags) to get .json output-k <filter expression>standard pytest filtering--collect-onlyonly show what tests would run, useful to see what models there are or debug your filter expression--cpu_onlyif running on a local CPU machine and ignoring machine configuration checks
Sometimes you may want to just run train or eval on a particular model, e.g. for debugging or profiling. Rather than relying on main implementations inside each model, run.py provides a lightweight CLI for this purpose, building on top of the standard BenchmarkModel API.
python run.py <model> [-d {cpu,cuda}] [-m {eager,jit}] [-t {eval,train}] [--profile]
Note: <model> can be a full, exact name, or a partial string match.
-k "test_train[NAME-cuda-jit]"for a particular flavor of a particular model-k "(BERT and (not cuda) and (not jit))"for a more flexible approach to filtering
Currently, the models run on nightly pytorch builds and push data to Facebook's internal database. The Nightly CI publishes both V1 and V0 performance scores.
See Unidash (internal only)
See Adding Models.