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pytorch/benchmarks
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Bin Wen 6900aacf54 [fbcode] Fix operator_benchmark with jit mode (#67382) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67382

two simple updates:

* fix running benchmark with --use_jit. Previously will fail with error

  torch.jit.frontend.UnsupportedNodeError: import statements aren't supported:
  File "/proc/self/fd/3/bmm_test.py", line 9
  def __invoke_main():
    import ctypes
    ~~~~~~ <--- HERE
    import ctypes.util
    import errno

* add matmul to bmm benchmark as D31837588

Test Plan:
buck run mode/opt caffe2/benchmarks/operator_benchmark/pt:bmm_test --  --forward_only=True --mkl_num_threads=1 --omp_num_threads=1
 --use_jit=True

Reviewed By: ShijunK

Differential Revision: D31960528

fbshipit-source-id: 84b892934149784d1b8a0f90b0233cc2f1cf1f5f
2021-10-28 08:48:10 -07:00
..
cpp Revert D30652629: use irange for loops 2021-10-15 15:23:10 -07:00
distributed Remove .data from benchmarks and tensorboard (#65389) 2021-09-22 11:16:59 -07:00
fastrnns Remove .data from benchmarks and tensorboard (#65389) 2021-09-22 11:16:59 -07:00
framework_overhead_benchmark
functional_autograd_benchmark Prefer mT and mH over transpose(-2, -1) and transpose(-2, -1).conj() (#64181) 2021-10-18 13:02:25 -07:00
instruction_counts
operator_benchmark [fbcode] Fix operator_benchmark with jit mode (#67382) 2021-10-28 08:48:10 -07:00
overrides_benchmark Use classmethods for overrides (#64841) 2021-09-17 08:32:49 -07:00
profiler_benchmark
record_function_benchmark
serialization
sparse
static_runtime [SR] NNC out variant for aten::where (#67255) 2021-10-28 06:48:22 -07:00
tensorexpr
compare-fastrnn-results.py
compare.sh
README.md
upload_scribe.py Fix benchmark's import module and remove its usage of tools.stats.scribe (#61808) 2021-07-19 09:45:05 -07:00

README.md

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
conda install pytorch torchvision -c pytorch

# 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