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https://github.com/zebrajr/pytorch.git
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fc0135ca11
Summary: This backs out D60320595 which itself turned off FakeTensor caching when a SymInt was present. There has been a lot of dynamic shape fixes done this year and tests pass so I'm assuming some of that work fixed what was breaking previously. Test Plan: Reran the tests listed in T196779132 and they pass. ## Perf ### Instruction Counter Benchmark: - 26% win on add_loop_eager_dynamic - 13% win on add_loop_inductor_dynamic_gpu ### Perf Dashboard Compilation Latency wins across the board but especially strong on the dynamic tests (like cudagraphs_dynamic) - for example MobileBertForMaskedLM went from 66s -> 50s. Differential Revision: [D75467694](https://our.internmc.facebook.com/intern/diff/D75467694) Pull Request resolved: https://github.com/pytorch/pytorch/pull/152662 Approved by: https://github.com/anijain2305 |
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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: