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**Problem & Solution:** Assume we have something like: ``` x = some_op(...) x0 = x[0] do_something_with_and_is_last_use_of(x0) do_a_bunch_of_other_things() x1 = x[1] ``` In this case, the memory associated with `x0` cannot be released until `x1 = x[1]`. Since `x1 = x[1]` does not use additional memory, it would be beneficial to move and `x1 = x[1]` and all such `getitem` operations to be immediately after `x = some_op(...)` such as ``` x = some_op(...) x0 = x[0] x1 = x[1] do_something_with_and_is_last_use_of(x0) do_a_bunch_of_other_things() ``` **Results:** For instance, for the `res2net101_26w_4s` model in pytorch benchmark, when running with `aot_eager` backend and with `activation_memory_budget=0.4`, the peak memory are * baseline: 7.73GiB * with the chage: 6.45GiB As a sanity check, for the same setting with `inductor` backend, the peak memory is not regressed. cc and credit to @ShatianWang for noticing this issue. Pull Request resolved: https://github.com/pytorch/pytorch/pull/155809 Approved by: https://github.com/fmassa, https://github.com/bdhirsh |
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| .. | ||
| distributed/ddp | ||
| dynamo | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| gpt_fast | ||
| inductor_backends | ||
| inference | ||
| instruction_counts | ||
| nested | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| transformer | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
PyTorch Benchmarks
This folder contains scripts that produce reproducible timings of various PyTorch features.
It also provides mechanisms to compare PyTorch with other frameworks.
Setup environment
Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:
# Install torchvision. It comes with the pytorch stable release binary
pip3 install torch torchvision
# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python setup.py build develop
# Check the pytorch installation version
python -c "import torch; print(torch.__version__)"
Benchmark List
Please refer to each subfolder to discover each benchmark suite. Links are provided where descriptions exist: